Science, Faculty of
Botany, Department of
DSpace
UBCV
Soto Gomez, Marybel
Doctor of Philosophy - PhD
University of British Columbia
The sister orders Pandanales and Dioscoreales are the last two major clades of monocots that remain to be examined in depth using phylogenomic approaches. Previous studies produced basic phylogenetic outlines for both orders, but left multiple higher-order relationships unresolved, including precise placements of non-photosynthetic mycoheterotrophic lineages. Species-level relationships in the Dioscorea yams, the largest Dioscoreales genus, are also poorly known despite the economic importance of this clade. Here I develop and apply phylogenomic methods to reconstruct the evolutionary histories of Pandanales, Dioscoreales and Dioscorea, using the resulting frameworks to address various phylogenetic and evolutionary hypotheses in each group. I employed a bi-organellar phylogenomic approach to resolve higher-order relationships in Pandanales, with mostly strong support. Mitogenomes are generally slowly evolving, but nevertheless permit inference of well-supported relationships. They also exhibit sporadic order-wide rate accelerations that are decoupled from plastome rate variation. I estimated clade ages to address several biogeographic hypotheses, and uncovered plastome structural variants that may define individual families. In Dioscoreales, I used plastid phylogenomic data and morphology to infer higher-order relationships. The molecular analyses resolve higher-order relationships, but are complicated by extreme substitution rate elevation observed in several mycoheterotrophic lineages. Nevertheless, my analyses confirm the non-monophyly of Burmanniaceae and Dioscoreaceae as currently circumscribed in angiosperm classification schemes. An updated morphological data set supports the local phylogenetic placement of several mycoheterotrophic genera for which plastid data are lacking, and ancestral-state reconstructions predict morphological synapomorphies relevant to the revision of family-level classification schemes. For phylogenomic studies of Dioscorea, I developed a customized bait panel comprising 260 low-to-single-copy nuclear genes. I assessed the utility of the panel with a pilot taxon sampling, supplemented with transcriptome data, that comprises representatives of all major Dioscorea clades, including multiple crop yams. The panel enables resolution of phylogenetic relationships at both deep and recent scales in Dioscorea, mostly with strong support, and provides insights into relationships between crop plants and putative crop wild relatives. Overall, my dissertation offers new insights into a broad diversity of evolutionary questions, made possible by resolution here of recalcitrant phylogenetic relationships in Pandanales, Dioscoreales and the Dioscorea yams.
PHYLOGENOMIC STUDIES OF THE MONOCOT SISTER ORDERS PANDANALES AND DIOSCOREALES by Marybel Soto Gomez B.Sc., The University of British Columbia, 2007 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2020 © Marybel Soto Gomez, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Phylogenomic studies of the monocot sister orders Pandanales and Dioscoreales in partial fulfillment of the requirements for submitted by Marybel Soto Gomez the degree of Doctor of Philosophy in Botany Examining Committee: Sean W. Graham, BotanySupervisor Jeannette Whitton, BotanySupervisory Committee Member University ExaminerUniversity Examiner Additional Supervisory Committee Members: Mary Berbee, BotanySupervisory Committee Member Loren Rieseberg, BotanySupervisory Committee Member Naomi Fast, BotanyDolph Schluter, Zoologyiii Abstract The sister orders Pandanales and Dioscoreales are the last two major clades of monocots that remain to be examined in depth using phylogenomic approaches. Previous studies produced basic phylogenetic outlines for both orders, but left multiple higher-order relationships unresolved, including precise placements of non-photosynthetic mycoheterotrophic lineages. Species-level relationships in the Dioscorea yams, the largest Dioscoreales genus, are also poorly known despite the economic importance of this clade. Here I develop and apply phylogenomic methods to reconstruct the evolutionary histories of Pandanales, Dioscoreales and Dioscorea, using the resulting frameworks to address various phylogenetic and evolutionary hypotheses in each group. I employed a bi-organellar phylogenomic approach to resolve higher-order relationships in Pandanales, with mostly strong support. Mitogenomes are generally slowly evolving, but nevertheless permit inference of well-supported relationships. They also exhibit sporadic order-wide rate accelerations that are decoupled from plastome rate variation. I estimated clade ages to address several biogeographic hypotheses, and uncovered plastome structural variants that may define individual families. In Dioscoreales, I used plastid phylogenomic data and morphology to infer higher-order relationships. The molecular analyses resolve higher-order relationships, but are complicated by extreme substitution rate elevation observed in several mycoheterotrophic lineages. Nevertheless, my analyses confirm the non-monophyly of Burmanniaceae and Dioscoreaceae as currently circumscribed in angiosperm classification schemes. An updated morphological data set supports the local phylogenetic placement of several mycoheterotrophic genera for which plastid data are lacking, and ancestral-state reconstructions predict morphological synapomorphies relevant to the revision of family-level classification schemes. For phylogenomic studies of Dioscorea, I developed a customized bait panel comprising 260 low-to-single-copy nuclear genes. I assessed the utility of the panel with a pilot taxon sampling, supplemented with transcriptome data, that comprises representatives of all major Dioscorea clades, including multiple crop yams. The panel enables resolution of phylogenetic relationships at both deep and recent scales in Dioscorea, mostly with strong support, and provides insights into relationships between crop plants and putative crop wild relatives. Overall, my dissertation offers new insights into a broad diversity of evolutionary iv questions, made possible by resolution here of recalcitrant phylogenetic relationships in Pandanales, Dioscoreales and the Dioscorea yams. v Lay Summary The plant orders Pandanales and Dioscoreales encompass a spectacular diversity of growth forms across lineages whose evolutionary relationships have remained largely uncertain. Both orders include highly modified non-photosynthetic species that derive nutrition from fungi instead of sunlight. The order Dioscoreales additionally comprises the Dioscorea yams, a staple tuber crop that feeds millions globally. My study contributes to an improved understanding of the evolution of Pandanales, Dioscoreales and the Dioscorea yams. I develop and employ genomic methods to infer well-resolved evolutionary trees for each group, and use these as frameworks for exploring various biological questions. For Pandanales, I address hypotheses concerning the historical geography and molecular evolution of the order. I explore the use of molecular and morphological data to place rare, non-photosynthetic plants in the evolutionary tree of Dioscoreales. For Dioscorea, I present methods that will help uncover wild yam species of potential use in crop yam improvement efforts. vi Preface A version of Chapter 2 has been published as: Soto Gomez, M., Lin, Q., da Silva Leal, E., Gallaher, T., Scherberich, D., Mennes, C.B., Smith, S.Y., Graham, S.W. 2020. A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns. Cladistics 1–24. I generated most of the new organellar sequence data, retrieved plastid gene sets and carried out all analyses except mitochondrial-based phylogenetic analyses; the latter were conducted by Q. Lin using mitochondrial gene sets that he retrieved. I also led manuscript writing. The study was conceived and co-written with S.W. Graham. Eduardo da Silva Leal, T. Gallaher, D. Scherberich and S.Y. Smith provided plant material; C.B. Mennes contributed data for molecular dating analyses; T. Gallaher helped with editing. Data for several taxa were also used in a publication that is not included here: Lam, V.K.Y., Soto Gomez, M., Graham, S.W. 2015. The highly reduced plastome of mycoheterotrophic Sciaphila (Triuridaceae) is colinear with its green relatives and is under strong purifying selection. Genome Biology and Evolution 7, 2220–2236. For Chapter 3, I generated most of the new molecular sequence data, with contributions from Q. Lin, N. Klimpert, V.K.Y. Lam, S. Jo, K.-J. Kim and J. Viruel. I collaborated with P.J. Rudall to obtain the morphological data set, which was updated and expanded from a previous publication. I conducted all analyses and also led the writing. The study was conceived and co-written with S.W. Graham and P.J. Rudall. A version of Chapter 4 has been published as: Soto Gomez, M., Pokorny, L., Kantar, M.B., Forest, F., Leitch, I.J., Gravendeel, B., Wilkin, P., Graham, S.W., Viruel, J. 2019. A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae). Applications in Plant Sciences 7: e11254. I designed the target enrichment panel and conducted the experimental work with J. Viruel. I performed all phylogenetic analyses; J. Viruel conducted comparisons of the newly-designed panel to other available panels. I also led manuscript writing. The chapter was co-written with S.W. Graham and J. Viruel. The study was conceived with F. Forest, B. Gravendeel, P. Wilkin, S.W. Graham and J. Viruel. Lisa Pokorny advised on methods and analyses. All coauthors helped with editing. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................ xi List of Figures .............................................................................................................................. xii Acknowledgements ......................................................................................................................xv Dedication .................................................................................................................................. xvii Chapter 1: Introduction ................................................................................................................1 1.1 Early systematic studies of monocots ................................................................................ 1 1.2 Phylogenomic studies of monocots ................................................................................... 3 1.3 Phylogeny of the orders Pandanales and Dioscoreales, and the genus Dioscorea ............ 4 1.4 Overview of the thesis and major goals ............................................................................. 6 Chapter 2: A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns .....................................................................8 2.1 Summary ............................................................................................................................ 8 2.2 Introduction ........................................................................................................................ 9 2.2.1 Higher-order relationships in Pandanales ................................................................. 10 2.2.2 Divergence time and molecular rate estimation........................................................ 11 2.2.3 Genome structural evolution ..................................................................................... 12 2.2.4 Study goals ................................................................................................................ 13 2.3 Materials and methods ..................................................................................................... 13 2.3.1 Taxon sampling and library preparation ................................................................... 13 2.3.2 Contig assembly, organellar gene annotation and plastome reconstruction ............. 14 2.3.3 Sequence alignment and data matrix construction.................................................... 15 2.3.4 Phylogenetic inference .............................................................................................. 16 2.3.5 Divergence time and substitution rate estimation ..................................................... 18 2.4 Results .............................................................................................................................. 21 viii 2.4.1 Higher-order relationships in Pandanales ................................................................. 21 2.4.2 Plastid genome structural evolution in Pandanales ................................................... 23 2.4.3 Divergence times and substitution rate shifts in Pandanales and other monocots .... 24 2.4.4 Divergence time estimates excluding mycoheterotrophic taxa ................................ 26 2.5 Discussion ........................................................................................................................ 27 2.5.1 Organellar data produce robust family-level relationships in Pandanales ................ 27 2.5.2 Organellar data sets strongly resolve genus-level relationships in Pandanales ........ 28 2.5.3 Fluctuating inverted repeat boundaries and gene loss in Pandanales plastomes ...... 30 2.5.4 Organellar age estimates and consequences for biogeographic hypotheses ............. 31 2.5.5 Age estimates in the presence of rapidly evolving mycoheterotrophic taxa ............ 33 2.5.6 Rate accelerations and decelerations in Pandanales organellar genomes ................. 34 Chapter 3: Can we reliably place fully mycoheterotrophic Dioscoreales lineages using plastid genome data and morphology? ......................................................................................48 3.1 Summary .......................................................................................................................... 48 3.2 Introduction ...................................................................................................................... 49 3.3 Materials and methods ..................................................................................................... 52 3.3.1 Taxon sampling and library preparation for molecular matrices .............................. 52 3.3.2 Contig assembly and gene annotation....................................................................... 53 3.3.3 Sequence alignment and molecular matrix construction .......................................... 54 3.3.4 Molecular phylogenetic analyses .............................................................................. 54 3.3.5 Constraint tests of alternative phylogenetic hypotheses ........................................... 56 3.3.6 Taxon sampling for morphological analyses ............................................................ 58 3.3.7 Morphological matrix scoring................................................................................... 58 3.3.8 Morphology-based phylogenetic analyses ................................................................ 59 3.3.9 Construction and phylogenetic analyses of a combined molecular-morphological matrix ................................................................................................................................... 60 3.3.10 Reconstruction of morphological character evolution .............................................. 60 3.4 Results .............................................................................................................................. 61 3.4.1 Higher-order relationships in Dioscoreales based on molecular data ....................... 61 3.4.2 Local placements of fully mycoheterotrophic taxa using morphological data ......... 63 ix 3.4.3 Combined molecular and morphological analysis .................................................... 65 3.4.4 Morphological ancestral-state reconstructions ......................................................... 66 3.5 Discussion ........................................................................................................................ 67 3.5.1 Plastid phylogenomics and higher-order relationships in Dioscoreales ................... 67 3.5.2 Phylogenomic inferences that include highly rate-elevated heterotrophic taxa ....... 70 3.5.3 Placing fully mycoheterotrophic lineages of Dioscoreales based on morphology ... 73 3.5.4 Afrothismiaceae, a proposed new family in Dioscoreales ........................................ 76 3.5.5 Conclusion ................................................................................................................ 78 Chapter 4: A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae) ...................................................89 4.1 Summary .......................................................................................................................... 89 4.2 Introduction ...................................................................................................................... 89 4.3 Materials and methods ..................................................................................................... 92 4.3.1 Bait design strategy to retrieve low-to-single-copy nuclear genes from Dioscorea and relatives .......................................................................................................................... 92 4.3.2 Taxon sampling for phylogenetic analysis ............................................................... 94 4.3.3 Library preparation and target enrichment ............................................................... 94 4.3.4 Transcriptome sequencing ........................................................................................ 95 4.3.5 Sequence processing, assembly and alignment ........................................................ 95 4.3.6 Detection and filtering of potential paralogs ............................................................ 96 4.3.7 Phylogenomic inference............................................................................................ 97 4.4 Results .............................................................................................................................. 98 4.4.1 Gene retrieval using target enrichment methods vs. transcriptome-based data ........ 98 4.4.2 Phylogenetic relationships inferred for the pilot taxon set of Dioscorea species ... 100 4.5 Discussion ...................................................................................................................... 101 4.5.1 Capture efficiency across phylogenetic scale in Dioscorea: lessons from a pilot sampling .............................................................................................................................. 102 4.5.2 Custom baits resolved lower- and higher-order relationships in a pilot taxon sampling .............................................................................................................................. 104 x 4.5.3 Characterization of loci targeted by the custom Dioscorea-specific enrichment panel ................................................................................................................................. 106 Chapter 5: Conclusion ...............................................................................................................116 5.1 Major findings and possible limitations ......................................................................... 116 5.1.1 Chapter 2. A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns ...................................................... 116 5.1.2 Chapter 3. Can we reliably place fully mycoheterotrophic Dioscoreales lineages using plastid genome data or morphology? ........................................................................ 118 5.1.3 Chapter 4. A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae) ............................................ 121 5.2 Some future directions ................................................................................................... 123 References ...................................................................................................................................125 Appendices ..................................................................................................................................148 Appendix A Supplementary tables and figures for Chapter 2 ................................................ 148 Appendix B Supplementary data, tables and figures for Chapter 3 ........................................ 225 Sub-Appendix B.1 Updated morphological characters for Dioscoreales ........................... 225 Appendix C Supplementary methods, tables and figures for Chapter 4 ................................. 305 Sub-Appendix C.1 Supplementary methods for handling potential paralogs ..................... 305 xi List of Tables Table 2.1 Pandanales age estimates inferred here and in previous studies ................................... 36 Table 3.1 Nutritional status and data type retrieved for Dioscoreales genera .............................. 80 Table 4.1 Gene capture efficiency using Dioscorea target enrichment panel ............................ 109 Table 4.2 Target gene retrieval from transcriptome-based samples ........................................... 111 Table A.1 Specimen and herbarium information for Chapter 2 ................................................. 148 Table A.2 GenBank accessions for newly sequenced taxa in Chapter 2 .................................... 150 Table A.3 Data partitioning schemes for Chapter 2 ................................................................... 153 Table A.4 Fossil calibrations for molecular dating and substitution rate analyses ..................... 170 Table A.5 Summary of bootstrap support for Pandanales across phylogenetic analyses ........... 171 Table A.6 Characteristics of Pandanales plastomes ................................................................... 173 Table A.7 Monocot age estimates inferred here and in previous studies ................................... 174 Table B.1 Specimen and herbarium information for Chapter 3 .................................................. 233 Table B.2 Status of plastid genes in mycoheterotrophic Dioscoreales ....................................... 234 Table B.3 Data partitioning schemes for Chapter 3 .................................................................... 235 Table B.4 Updated morphological matrix for Dioscoreales ....................................................... 259 Table B.5 Summary of bootstrap support for Dioscoreales across phylogenetic analyses ........ 260 Table C.1 Name and predicted function of genes targeted by Dioscorea gene panel ................ 307 Table C.2 Specimen and herbarium information for Chapter 4 .................................................. 323 Table C.3 Summary statistics of genes retrieved by Dioscorea gene panel ............................... 324 Table C.4 Data partitioning schemes for Chapter 4 .................................................................... 335 Table C.5 Length of coding and non-coding sequences retrieved by Dioscorea gene panel ..... 338 Table C.6 Comparison of Dioscorea-specific and Angiosperms-353 gene panels .................... 346 Table C.7 Comparison of Dioscorea-specific and Angisoperm v.1 gene panels ....................... 348 xii List of Figures Figure 2.1 Partitioned ML and parsimony analysis of Pandanales using combined organellar DNA data ...................................................................................................................................... 38 Figure 2.2 Partitioned ML analysis of Pandanales using plastid and mitochondrial DNA data .. 40 Figure 2.3 Parsimony analysis of Pandanales using plastid and mitochondrial DNA data .......... 42 Figure 2.4 Pandanales clade ages inferred from plastid and mitochondrial data .......................... 44 Figure 2.5 Substitution rates in Pandanales inferred from plastid and mitochondrial data .......... 46 Figure 3.1 Partitioned ML and parsimony analysis of Dioscoreales using plastid DNA data ..... 81 Figure 3.2 Relationships and branch lengths of mycoheterotrophic Dioscoreales ....................... 83 Figure 3.3 Constrained ML and parsimony analysis of Dioscoreales using morphology ............ 85 Figure 3.4 Morphological synapomorphies of Dioscoreales on constrained ML topology ......... 87 Figure 4.1 Recovered proportion of targets using Dioscorea gene panel ................................... 112 Figure 4.2 Coalescent-based analysis of Dioscorea .................................................................. 114 Figure A.1 Parsimony analysis of Pandanales using combined organellar DNA data ............... 177 Figure A.2 Parsimony analysis of 95-taxon matrix using plastid DNA data ............................. 179 Figure A.3 Parsimony analysis of monocots using mitochondrial DNA data ............................ 181 Figure A.4 Partitioned ML analysis of Pandanales using combined organellar DNA data ....... 183 Figure A.5 Unpartitioned ML analysis of 95-taxon matrix using plastid DNA data ................. 185 Figure A.6 Partitioned ML analysis of 95-taxon matrix using plastid DNA data ...................... 187 Figure A.7 Partitioned ML analysis of 96-taxon matrix using plastid DNA data ...................... 189 Figure A.8 Partitioned ML analysis of 94-taxon matrix using plastid DNA data ...................... 191 Figure A.9 Partitioned ML analysis of Pandanales using 83-gene plastid DNA data set .......... 193 Figure A.10 Unpartitioned ML analysis of 95-taxon matrix using plastid amino-acid data ...... 195 Figure A.11 Partitioned ML analysis of 95-taxon matrix using plastid amino-acid data ........... 197 Figure A.12 Unpartitioned ML analysis of monocots using mitochondrial DNA data .............. 199 Figure A.13 Partitioned ML analysis of monocots using mitochondrial DNA data .................. 201 Figure A.14 Unpartitioned ML analysis of monocots using mitochondrial amino-acid data .... 203 Figure A.15 Partitioned ML analysis of monocots using mitochondrial amino-acid data ......... 205 Figure A.16 Circular plastome map of Acanthochlamys bracteata (Velloziaceae) ................... 207 xiii Figure A.17 Circular plastome map of Benstonea copelandii (Pandanaceae) ............................ 209 Figure A.18 Circular plastome map of Croomia japonica (Stemonaceae) ................................ 211 Figure A.19 Comparison of plastome inverted repeat boundaries in Pandanales ...................... 213 Figure A.20 Monocot clade ages inferred from plastid data ...................................................... 215 Figure A.21 Monocot clade ages inferred from mitochondrial data ........................................... 217 Figure A.22 Substitution rates in monocots inferred from plastid data ...................................... 219 Figure A.23 Substitution rates in monocots inferred from mitochondrial data ......................... 221 Figure A.24 Monocot clade ages from re-analyzed matrix excluding mycoheterotrophs ......... 223 Figure B.1 Parsimony analysis of core taxon set using plastid DNA data ................................ 262 Figure B.2 Parsimony analysis of 98-taxon set using plastid DNA data .................................... 264 Figure B.3 Unpartitioned ML analysis of core taxon set using plastid DNA data ..................... 266 Figure B.4 Partitioned ML analysis of core taxon set using plastid DNA data .......................... 268 Figure B.5 Unpartitioned ML analysis of core taxon set using plastid amino-acid data ............ 269 Figure B.6 Partitioned ML analysis of core taxon set using plastid amino-acid data ................ 272 Figure B.7 Partitioned ML analysis of Stenomeris 90-taxon set using plastid DNA data.......... 274 Figure B.8 Partitioned ML analysis of Burmanniaceae 91-taxon set using plastid DNA data .. 276 Figure B.9 Partitioned ML analysis of Thismiaceae 91-taxon set using plastid DNA data ...... 278 Figure B.10 Partitioned ML analysis of Afrothismia 93-taxon set using plastid DNA data ...... 280 Figure B.11 Partitioned ML analysis of Afrothismia 90-taxon set using plastid DNA data ...... 282 Figure B.12 Partitioned ML analysis of 98-taxon set using plastid DNA data .......................... 284 Figure B.13 Unconstrained parsimony analysis of morphology ................................................ 286 Figure B.14 Unconstrained ML analysis of morphology ........................................................... 288 Figure B.15 Constrained parsimony analysis of morphology adding taxa simultaneously ........ 290 Figure B.16 Constrained ML analysis of morphology adding taxa simultaneously................... 292 Figure B.17 Summary of constrained ML and parsimony analyses of morphology adding taxa individually ................................................................................................................................. 294 Figure B.18 Parsimony analysis of combined molecular-morphological data ........................... 296 Figure B.19 Partitioned ML analysis of combined molecular-morphological data.................... 298 Figure B.20 Morphological synapomorphies of Dioscoreales on unconstrained parsimony topology ...................................................................................................................................... 300 xiv Figure B.21 Longitudinal section of Tacca leontopetaloides (Taccaceae) flower ..................... 302 Figure B.22 SEM micrograph of Stenomeris borneensis (Dioscoreaceae) flower bud .............. 303 Figure B.23 Longitudinal section of Tacca leontopetaloides (Taccaceae) gynoecium .............. 304 Figure C.1 Partitioned ML analysis of Dioscorea ...................................................................... 350 Figure C.2 Unpartitioned ML analysis of Dioscorea ................................................................. 352 Figure C.3 Coalescent-based analysis of Dioscorea excluding potential paralogs .................... 354 xv Acknowledgements I would like to begin by acknowledging that the land on which I completed my dissertation is the unceded territory of the Coast Salish Peoples, including the territories of the xʷməθkwəy̓əm (Musqueam), Skwxwú7mesh (Squamish), and Səl̓ílwətaʔ/Selilwitulh (Tsleil-Waututh) Nations. I am grateful for the opportunity to be immersed in a stimulating academic and social environment during my studies at UBC, and also for the various institutional initiatives aimed at professional and personal development, especially the BRITE program. I thank my supervisor Dr. Sean Graham for taking a chance on me, an agricultural scientist with a love of plants but no experience in evolutionary approaches, and for continually supporting my diverse interests. Beyond massively increasing my understanding of plant systematics and evolution under his supervision, he helped me grow as a scientist, leading by example on conducting thoughtful and rigorous research that is carefully communicated. I also thank my committee, Drs. Jeannette Whitton, Loren Rieseberg and Mary Berbee, whose perspectives and guidance at a scientific and human level were instrumental for completing my thesis. My collaborators, who provided expertise, data and materials were crucial for completing my thesis, especially Drs. Juan Viruel, Paula Rudall, Michael Kantar, Vincent Merckx, and the Monocot Assembling the Tree of Life (MonAToL) team. I also acknowledge the various institutions that provided me with sampling material from their living and natural history collections: the Missouri Botanical Garden, the Munich Botanical Garden, the Botanischer Garten Bonn, the Lyon Botanical Garden, and the Royal Botanic Gardens, Kew. A shared love of science has produced lasting connections and stimulating conversations with past and present members of the Graham lab. Special thanks to Vivienne Lam for her mentorship and friendship, Greg Ross for lab training (and both of them for awesome science trips), and to Wesley Gerelle and Nate Klimpert for always being happy to help with anything computer related. Being a Teaching Assistant was a prominent part of my graduate experience, and I thank the instructors with whom I worked for their mentorship in teaching, especially Shona Ellis. I also thank my dear friends inside and outside the department for their all-around support, especially Mike (to whom I owe so much), Adriana, Ailene, Ale, Anna, Chris, Daniel, Freek, Harriet, Javier, Jenny, KB, Ludo, Steph, Stilian, Taisha. xvi I am forever grateful for the love and support of my parents, who worked tirelessly to give my siblings and me the educational opportunities that opened our minds to the world. Thank you for always nurturing our curiosities, you are my role models. Thank you to my siblings for lifelong love, support, complicity and laughs; I have nothing but admiration for you two. I also thank my grandparents, especially papá Rubén, whose drive to pursue an education changed the course of the lives of his descendants. I am grateful to Sam for continued love, support, understanding, and for always reminding me of the bright (still ray) days that are to come. Finally, I thank the various sources of funding that enabled my research: UBC (Four Year Fellowship, affiliated fellowships [George Bror Boving Memorial Prize in Botany, Simons Foundation Doctoral Scholarship], and teaching assistantships), Natural Sciences and Engineering Research Council (NSERC) Canada Graduate Scholarships, Dr. Michael B. Kantar (University of Hawai’i, Manoa), and the Royal Botanic Gardens, Kew (Bentham-Moxon Trust). xvii Dedication To my parents, who gave me both guidance and freedom to pursue my dreams.1 Chapter 1: Introduction Phylogenetic trees underpin studies in multiple disciplines of biology, providing an evolutionary framework for characterizing and communicating relationships among taxa and the genes that they house. Understanding the evolutionary history of organisms represents useful context for investigations of their genetics, morphology, chemical composition, ecology and physiology, and also provides focal points for taxon selection for a diversity of comparative studies. Such ‘tree thinking’ (e.g., Baum and Smith, 2013) has been important since the early history of evolutionary thought. For example, the only image included in the groundbreaking book On the Origin of Species by Means of Natural Selection by Charles Darwin (1859) was a tree-like diagram with connecting branches representing common ancestry. Tree thinking is at the core of my study, which focuses on elucidating the uncertain phylogenetic history of the monocot sister orders Pandanales and Dioscoreales, and of the largest genus in the latter order, the Dioscorea yams. 1.1 Early systematic studies of monocots Over the last three decades, molecular systematists have been refining the phylogenetic outline of the monocots, one of the most species-rich, ecologically dominant and economically important lineages of land plants (Givnish et al., 2018). With ~84,600 species (Nic Lughadha et al., 2016) in ~77 families (APG, 2016) and 12 orders (Givnish et al., 2018), monocots are distributed in most terrestrial, most freshwater, and some marine habitats. They exhibit an extraordinary diversity of vegetative and floral forms, and encompass crop species that underpin much of the human diet (Ulijaszek et al., 2012; Givnish et al., 2018). Monocots also include the vast majority (~90%) of fully mycoheterotrophic (FM) taxa that have lost the ability to photosynthesize, and instead derive nutrients directly from soil-fungal partners, and indirectly from the autotrophic plants associated with the fungi (Leake, 1994; Merckx et al., 2013b). This richness of FM taxa is likely attributed to the herbaceous habit and fibrous (star-like) root systems that characterize monocots, which in turn facilitate colonization by fungi (Imhof, 2010). An improved understanding of the monocot ‘Tree of Life’ provides a powerful opportunity for characterizing the patterns of homology versus convergent evolution that underlie the diverse 2 morphologies, nutritional modes and distributions observed in this group. A robust evolutionary framework of monocots also provides context for understanding plant-human interactions, including investigations of parallel or unique footprints of crop domestication in closely and distantly related plant lineages. Early molecular systematic studies of monocots that were based on one or a few genes from across the three plant genomes (e.g., Chase et al., 1993, 1995, 2000, 2006; Duvall et al., 1993a,b; Davis et al., 2004; Givnish et al., 2005; Graham et al., 2006; Saarela et al., 2007), broadly validated many aspects of monocot relationships inferred from morphology-based cladistic analyses (Givnish et al., 2010), especially those of Dahlgren and colleagues (Dahlgren and Clifford, 1982; Dahlgren and Rasmussen, 1983; Dahlgren et al., 1985). In addition, few-gene studies have helped to further refine relationships that remained recalcitrant to morphology-based analyses, and revealed multiple surprises, including: (i) the placement of Acorus (Acorales) as the sister group of all other monocots (Duvall et al., 1993), as opposed to lineages considered to be ‘primitive’; these included taxa with petiolate leaves, broad laminas and reticulate venation that are now associated with Dioscoreales and Pandanales (Huber, 1969; Dahlgren et al., 1985), or taxa with free or only partially fused styles and carpels now associated with Alismatales, Dioscoreales and Petrosaviales (Thorne, 1992); (ii) the finding that the aquatic family Hydatellaceae, formerly placed in Poales (e.g., Hamann, 1976), is not a monocot but instead represents one of the deepest splits in flowering plant evolution (Saarela et al., 2007); (iii) the dismantling of Melanthiales, which were thought to represent ‘core stock’ from which monocots arose (Ambrose, 1980; Dahlgren et al., 1985; Thorne, 1992; Tamura, 1998), and redistribution of component taxa into orders that represent some of the deepest splits in monocots: Alismatales, Dioscoreales, Liliales and Petrosaviales (Chase et al., 1995, 2000, 2006; Zomlefer, 1999); and (iv) the dispersion of mycoheterotrophs that were previously thought to be close relatives across multiple different autotrophic lineages. For example, the mycoheterotrophs grouped in Orchidales by Cronquist (1981), likely based on achlorophylly and convergent morphological features related to loss of photosynthesis and adaptation to mycoheterotrophy (Leake, 1994), have since been relocated to Asparagales (Geosiris and Orchidaceae; APG, 1998; Chase et al., 2000, 2006; Goldblatt et al., 2008), Dioscoreales (Burmanniaceae and Thismiaceae; 3 Chase et al., 1995, 2000; Caddick et al., 2002a) and Liliales (Corsiaceae; Fay et al., 2006; Petersen et al., 2013; Mennes et al., 2015). 1.2 Phylogenomic studies of monocots The relatively recent application of massively parallel and high throughput DNA sequencing technologies for retrieving genome-scale data for phylogenetic investigations has further improved our understanding of evolutionary relationships in monocots. Phylogenomic studies based on full plastid genomes have been aimed at satisfactorily resolving higher-order relationships among and within the orders of monocots (Givnish et al., 2018), and built on earlier broad-scale studies using a few genes (e.g., Davis et al., 2004; Givnish et al., 2005; Graham et al., 2006). This included resolution of Dioscoreales and Pandanales as sister taxa, a major point of uncertainty in monocot phylogeny (e.g., Graham et al., 2006). Relationships within monocot orders have been addressed in plastid genome-based studies of Alismatales (Henriquez et al., 2014; Ross et al., 2016), Asparagales (Steele et al., 2012; Givnish et al., 2015), Liliales (Mennes et al., 2015; Givnish et al., 2016), Petrosaviales (Logacheva et al., 2014; Lam et al., 2018), and the commelinid orders Arecales, Commelinales, Dasypogonales, Poales and Zingiberales (Givnish et al., 2010; Barrett et al., 2013, 2016; Burke et al., 2014, 2016; Jones et al., 2014; Comer et al., 2015; Duvall et al., 2016; Wysocki et al., 2015; Givnish et al., 2018; Saarela et al., 2018). In this thesis, I focus attention on Pandanales and Dioscoreales, the last major monocot orders that remain to be studied in detail using phylogenomic data sets. Previous few-gene and/or morphological studies based on broad samplings of Pandanales (Caddick et al., 2002a; Rudall and Bateman, 2006) and Dioscoreales (Caddick et al., 2002a; Merckx et al., 2010), produced baseline phylogenetic outlines for both clades. These findings are mostly consistent with those of subsequent plastid phylogenomic studies that included a few taxa in each order (Lam et al., 2015, 2018; Givnish et al., 2018). However, multiple uncertainties still persist regarding family- and genus-relationships in Pandanales and Dioscoreales, including the precise placements of mycoheterotrophic taxa. Elevated substitution rates in heterotrophs across all three genomes (Bromham et al., 2013) can make these lineages challenging to include in molecular phylogenetic analyses, as they may be prone to long-branch artefacts (e.g., Nickrent et al., 2004; 4 Merckx et al., 2009a; Lam et al., 2015, 2018; Naumann et al., 2016). Moreover, there is substantial room for improving taxon sampling of mycoheterotrophs and autotrophs in phylogenomic studies of both orders. 1.3 Phylogeny of the orders Pandanales and Dioscoreales, and the genus Dioscorea Despite being relatively small orders, Pandanales and Dioscoreales both display an exceptionally diverse range of growth forms, which has hindered their recognition as major monocot clades and thus complicated inference of higher-order relationships among their component taxa. For example, the order Pandanales, with approximately 1300 species in ~34 genera and five families, includes trees, shrubs, woody and herbaceous climbers, and autotrophic and mycoheterotrophic herbs (Eriksson, 1994; Harling et al., 1998; Kao and Kubitzki, 1998; Stone et al., 1998; Kubitzki, 1998a,b,c; Rudall et al., 2005; Mabberley, 2008; Mello-Silva et al., 2011; Gallaher et al., 2015). Morphology-based cladistic inferences had aligned these disparate lineages with a very broad array of higher-order monocot taxa that are now recognized to belong to the orders Alismatales, Arecales, Dioscoreales and Poales (Dahlgren and Clifford, 1982; Dahlgren et al., 1985; Kubitzki, 1998; Maas-van de Kamer, 1995; Maas-van de Kamer and Weustenfeld, 1998). The modern five-family circumscription of Pandanales, first obtained in a three-gene plastid/nuclear study by Chase et al. (2000), was thus one of the early surprises of plant molecular systematics. Interfamily relationships in Pandanales were only recently settled in a plastid phylogenomic study that I co-authored, which included data for one third of the genera in the order (Lam et al., 2015). Genus-level relationships are relatively well understood in two families, Pandanaceae (Gallaher et al., 2015) and Stemonaceae (Lam et al., 2015), but remain unclear in the remaining three families, and molecular phylogenetic studies of Cyclanthaceae, the panama-hat family, have not yet been attempted. The order Dioscoreales includes ~865 species in ~22 genera, and an unsettled number of recognized families that ranges from three to six (Caddick et al., 2002a,b; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Lam et al., 2016, 2018; Givnish et al., 2018). Although a core set of autotrophs have remained undisputed elements of Dioscoreales, the current circumscription of the order was obtained relatively recently in a series of few-gene studies that added autotrophic Nartheciaceae, and mycoheterotrophic Burmanniaceae and Thismiaceae 5 (reviewed in Caddick et al., 2002a). A key phylogenetic problem in Dioscoreales concerns our understanding of relationships among and within the latter two mycoheterotrophic clades, which encompass more than half of the genera in the order and multiple independent switches to mycoheterotrophy across distantly related lineages (Merckx et al., 2013a,b). It can be difficult to obtain suitable material for the recovery of molecular data for many FM lineages, including most Thismiaceae, which are sparsely represented in collections, with many species presumed to be extinct (Merckx et al., 2009a; Merckx et al., 2013c). Even when molecular data are available, extreme rate elevation may mislead phylogenetic analyses (e.g., Lam et al., 2018). For these rare and highly rate-elevated mycoheterotrophs, morphology represents a potentially rich source of data for phylogenetic inference despite their often highly reduced and modified morphologies (e.g., Caddick et al., 2002a; Merckx and Smets, 2014; Shepeleva et al., 2020). Integrating across molecular and morphological data may therefore represent a viable strategy for resolving the placements of FM taxa in Dioscoreales, which have remained incompletely resolved to date. An additional systematic challenge in Dioscoreales concerns the Dioscorea yams, by far the most geographically widespread and largest of the genera in the order with ~625 species found in the tropics and subtropics (Wilkin et al., 2005; Govaerts et al., 2018). At least nine species of Dioscorea have been domesticated, forming a multi-species tuber crop complex that feeds millions globally (Mignouna et al., 2003; Lebot, 2009; Asiedu and Sartie, 2010; Bhattacharjee et al., 2011). Despite their economic importance, the evolutionary history of yams remains poorly characterized, in part due to very limited taxon sampling in previous studies (Wilkin et al., 2005; Couto et al., 2018; Viruel et al., 2018). Moreover, broad relationships in the genus have yet to be addressed using phylogenomic approaches, such as hybridization-based target enrichment (Mamanova et al., 2010), which can be used to retrieve hundreds of low-copy nuclear genes from degraded samples, including herbarium material (Lemmon and Lemmon, 2013). Recovering a phylogenetic framework of yams would be useful for characterizing the poorly understood origin and domestication history of most of the cultivated species (Malapa et al., 2005; Mukherjee and Bhat, 2013; Chaïr et al. 2016). Knowing the closest wild relatives of food-providing yams is important because the former species may represent reservoirs of useful genes for crop improvement (Harlan and de Wet, 1971; Dempewolf et al., 2014). 6 1.4 Overview of the thesis and major goals In Chapter 2, I perform phylogenomic analyses using gene sets from full plastid and mitochondrial genomes of all 28 photosynthetic genera in Pandanales and a representative of the mycoheterotrophic family Triuridaceae to resolve higher-order relationships in the order. This provides a basis for additional investigations concerning molecular rate evolution and biogeography in Pandanales, which to date have been addressed on a family basis but not for the order as a whole. I also test the sensitivity of molecular dating analyses to the inclusion of rate-elevated mycoheterotrophs, which has implications for interpreting previous age estimates in Pandanales and more broadly across monocots. I also characterize plastid genome structural evolution in the order, in search of rearrangements that may serve as synapomorphies defining photosynthetic families. In Chapter 3, I use a combination of plastid genome phylogenomics and an updated morphological data set to resolve higher-order relationships in Dioscoreales, considering all photosynthetic and mycoheterotrophic genera in the order. A major goal of this chapter is to generate evidence for updating the contentious family-level classification of Dioscoreales; this includes assessing whether all mycoheterotrophic lineages should be grouped in a single family (Burmanniaceae s.l., as in current angiosperm classification schemes; APG 2003, 2009, 2016), or divided into two or more families. I explore multiple ways of integrating sequence data and morphology for placing potentially problematic mycoheterotrophs in Dioscoreales phylogeny, particularly taxa with extreme substitution rate elevation and also recently extinct or poorly collected taxa for which we may never be able to obtain molecular data. The molecular-morphological approach that I developed an applied here should be useful for other heterotrophic plant lineages. I also perform ancestral-state reconstructions of morphology to identify potential synapomorphies that define major clades. In Chapter 4, I develop a nuclear target enrichment gene panel to enable phylogenomic studies of the Dioscorea yams (Dioscoreaceae). I assess the gene retrieval and phylogenetic performance of the panel using a pilot taxon sampling that comprises representatives of all major Dioscorea clades, including two yam crops and several potential crop wild relatives. This includes investigations on the effects of potentially paralogous genes on phylogenetic inference, and revisiting hypotheses about the closest wild relatives of D. alata, a major yam crop. I also 7 compare the characteristics of the Dioscorea-specific panel I developed to existing panels designed for angiosperm-wide studies to provide initial insights on whether these two variant approaches are complementary or redundant. Overall, I aim to establish a robust baseline for future studies that will expand taxonomic sampling in this large and economically important genus, using the methods developed here. 8 Chapter 2: A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns1 2.1 Summary I used a bi-organellar phylogenomic approach to address higher-order relationships in Pandanales, including the first molecular phylogenetic study of the panama-hat family, Cyclanthaceae. My genus-level study of plastid and mitochondrial gene sets includes a comprehensive sampling of photosynthetic lineages across the order, and provides a framework for investigating clade ages, biogeographic hypotheses and organellar molecular evolution. Using multiple inference methods and both organellar genomes, I recovered mostly congruent and strongly supported relationships within and between families, including the placement of fully mycoheterotrophic Triuridaceae. Cyclanthaceae and Pandanaceae plastomes have slow substitution rates, contributing to weakly supported plastid-based relationships in Cyclanthaceae. While generally slowly evolving, mitochondrial genomes exhibit sporadic rate elevation across the order. However, I infer well-supported relationships even for slower evolving mitochondrial lineages in Cyclanthaceae. Clade age estimates across photosynthetic lineages are largely consistent with previous studies, are well correlated between the two organellar genomes (with slightly younger inferences from mitochondrial data), and support several biogeographic hypotheses. I show that rapidly evolving non-photosynthetic lineages may bias age estimates upwards at neighbouring photosynthetic nodes, even using a relaxed clock model. Finally, I uncovered new genome structural variants in photosynthetic taxa at plastid inverted repeat boundaries that show promise as interfamilial phylogenetic markers. 1 This chapter was published as ‘Soto Gomez, M., Lin, Q., da Silva Leal, E., Gallaher, T., Scherberich, D., Mennes, C.B., Smith, S.Y., Graham, S.W. 2020. A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns. Cladistics 1–24.’ 9 2.2 Introduction The modern circumscription of the monocot order Pandanales (Angiosperm Phylogeny Group, APG, 2003, 2009, 2016) was an early surprise of plant molecular systematics, as its component families (Cyclanthaceae, Pandanaceae, Stemonaceae, Triuridaceae and Velloziaceae) had been aligned with a very diverse array of higher-order taxa in previous classification schemes (reviewed in Dahlgren and Clifford, 1982). The modern conception of the order was developed primarily using molecular systematic data sets comprising two plastid and one nuclear loci (e.g., Chase et al., 1993, 1995, 2000). These molecular systematic studies culminated in the finding that the non-photosynthetic family Triuridaceae belongs in Pandanales, based on analyses that incorporated sequence data of the nuclear 18S rDNA locus from Sciaphila, a genus of Triuridaceae, in a broad study of monocot relationships (Chase et al., 2000). The family Triuridaceae is a fully mycoheterotrophic clade that lacks photosynthetic genes such as rbcL and atpB (Lam et al., 2015; Petersen et al., 2018), two of the three loci that broadly underpin the APG classification systems. The assignment of Triuridaceae to Pandanales has since been supported in diverse studies using additional sources of data, including morphology, mitochondrial genes, a subset of non-photosynthetic plastid genes, and whole plastid genomes (Rudall and Bateman, 2006; Mennes et al., 2013; Lam et al., 2015, 2016, 2018; Givnish et al., 2018). Classification schemes based on early morphology-based cladistic inferences by Dahlgren and Rasmussen (1983) have been highly influential in monocot classification, but are also at odds with current schemes for Pandanales (e.g., APG, 2016). Dahlgren and Clifford (1982) and Dahlgren et al. (1985) also dispersed component families across multiple orders in their influential monographs on monocot classification. Specifically, they inferred that Cyclanthaceae and Pandanaceae are closely related to the palm order Arecales, Stemonaceae to the yam order Dioscoreales, Velloziaceae to bromeliads (now part of the grass order, Poales), and also tentatively aligned mycoheterotrophic Triuridaceae with taxa now recognized as Alismatales (Dahlgren and Clifford, 1982; Dahlgren et al., 1985). These discrepancies between morphological and molecular phylogenetic inferences may reflect the remarkable diversity of growth forms in the order, in addition to difficulties in interpreting reproductive structures in 10 higher-order comparisons. Pandanales are a relatively small order, with some 1300 species in ~34 genera, but the order’s growth forms include trees, shrubs, woody and herbaceous climbers, and photosynthetic and mycoheterotrophic herbs (Eriksson, 1994; Harling et al., 1998; Kao and Kubitzki, 1998; Kubitzki, 1998a,b,c; Stone et al., 1998; Rudall et al., 2005; Mabberley, 2008; Mello-Silva et al., 2011; Gallaher et al., 2015). Flowers of Cyclanthaceae and Pandanaceae are often densely aggregated and can be highly reduced, making them harder to compare to other monocot flowers (e.g., Dahlgren et al., 1985; Harling et al., 1998; Stone et al., 1998; Rudall and Bateman, 2006; Sajo et al., 2014), those of Stemonaceae deviate substantially from ‘canonical’ trimerous monocot flowers in being dimerous or pentamerous (Dahlgren et al., 1985; Rudall et al., 2005), and those of Triuridaceae appear to blur boundaries between flowers and inflorescences (e.g., Rudall and Bateman, 2006). 2.2.1 Higher-order relationships in Pandanales Relationships inferred among the five component families of Pandanales have been examined in pre-phylogenomic studies that employed various combinations of plastid loci (atpB, accD, clpP, matK, rbcL), mitochondrial regions (atpA, matR, nad1b-c intron), the nuclear 18S rDNA locus, and updated interpretations of morphology (Chase et al., 2000; Caddick et al., 2002a; Davis et al., 2004; Rudall and Bateman, 2006; Mennes et al., 2013; Lam et al., 2016). Setting aside Triuridaceae, multiple studies have recovered a close relationship between Cyclanthaceae and Pandanaceae, with Stemonaceae as their sister group, and Velloziaceae as the sister group of the other families in the order (Caddick et al., 2002a; Davis et al., 2004; Graham et al., 2006; Chase et al., 2006; Mennes et al., 2013; Lam et al., 2016). However, the mycoheterotrophic family Triuridaceae has drifted to different higher-order positions when included in phylogenetic analyses. For example, various molecular studies based on mitochondrial and nuclear markers recovered Triuridaceae as the sister group of Pandanaceae (Chase et al., 2000, Caddick et al., 2002a), the sister group of Velloziaceae (Davis et al., 2004), and the sister group of a clade comprising Stemonaceae, Cyclanthaceae and Pandanaceae (Davis et al., 2004; Mennes et al., 2013); a morphological analysis inferred it to be a lineage nested within Stemonaceae (Rudall and Bateman, 2006). Mennes et al. (2013) subsequently inferred strong support for the respective monophyly of Triuridaceae and Stemonaceae in phylogenetic estimates that included several 11 mitochondrial and nuclear genes, and a broad sampling of taxa in both families. More recently, plastid data have become available for Triuridaceae, which were once predicted to lack plastid genomes (Cronquist, 1988; Merckx et al., 2009a). Lam et al. (2016) retrieved up to three retained plastid genes (accD, clpP, matK) for four members of Sciaphila using Sanger sequencing, and placed Triuridaceae as the sister group of Cyclanthaceae and Pandanaceae in a monocot-wide phylogenetic analysis, although with very weak support for this arrangement. Plastid phylogenomic studies used all 18 non-photosynthetic protein-coding genes and four rDNA genes retained in the plastid genome of a representative of Triuridaceae (Sciaphila densiflora) and added those sequences to full gene sets representing the unreduced plastid genomes of photosynthetic monocots (Lam et al., 2015, 2018; Givnish et al. 2018). Parsimony analyses of these phylogenomic data either yielded weakly supported placements of Triuridaceae as the sister group of the Velloziaceae genus Xerophyta in a study that examined a subset of genera in Pandanales (Supplementary Fig. S7 in Lam et al., 2015), or inferred it to be part of a bizarre clade comprising other rapidly evolving mycoheterotrophic lineages belonging to multiple monocot orders (i.e., members of Burmanniaceae, Corsiaceae, Thismiaceae and Orchidaceae) in a broader angiosperm-wide analysis (Fig. 5 in Lam et al., 2018). The latter result conflicts substantially with our current understanding of monocot phylogeny, suggesting that both analyses were misled by long-branch artefacts concerning Triuridaceae (e.g., Felsenstein, 1978; Hendy and Penny, 1989). In contrast, likelihood analyses considering DNA, amino-acid and codon-based substitution models all placed Triuridaceae as the sister-group of Cyclanthaceae-Pandanaceae, with generally strong support (Lam et al., 2015, 2018; Givnish et al., 2018). However, plastid genome (plastome)-based studies have not yet sampled broadly across the photosynthetic genera in the order, and no molecular studies of relationships within Cyclanthaceae have been conducted to date. Moreover, a broad phylogenomic study of Pandanales that includes full mitochondrial gene sets has yet to be performed. 2.2.2 Divergence time and molecular rate estimation Substitutional rate shifts related to changes in life history have been postulated for clades that include a mixture of tree-like vs. herbaceous taxa (e.g., for the monocot orders Arecales vs. Poales, respectively; Bousquet et al., 1992; Gaut et al., 1996; Barrett et al., 2016; De la Torre et 12 al., 2017). This mix of growth forms is also found in Pandanales, but previous studies have not addressed in detail whether the order also exhibits similar rate shifts. Order-wide divergence times are also of interest as current age estimates for Pandanales have focused either on specific families (e.g., Pandanaceae: Gallaher et al., 2015; Velloziaceae: Mello-Silva et al., 2011, Alcantara et al., 2018; Triuridaceae: Mennes et al., 2013), or were part of broader monocot- or angiosperm-wide inferences (e.g., Janssen and Bremer, 2004; Bell et al., 2010; Hertweck et al., 2015; Magallón et al., 2015; Eguchi and Tamura, 2016, Foster et al., 2017, Givnish et al., 2018). The most thorough molecular dating analysis of Pandanales to date included multiple taxa of Triuridaceae, which are all fully mycoheterotrophic/non-photosynthetic (Mennes et al., 2013). Elevated evolutionary rates in all three genomic compartments of mycoheterotrophs (e.g., Merckx et al., 2009a; Lemaire et al., 2011; Lam et al., 2018) may make it more challenging to incorporate these taxa in molecular dating analyses, as postulated by Iles et al. (2015). However, this hypothesis, and (more broadly) order-wide age estimates using data derived from both organelles remain to be addressed in detail. 2.2.3 Genome structural evolution Plant mitochondrial genomes are challenging to assemble because of their rapid structural evolution and substantial structural variation within species, a contrast with their very slow evolution at the DNA substitutional level (reviewed in Gualberto and Newton, 2017). A substantial difference between mitochondrial and plastid genomes is that the latter are generally highly conserved structurally (e.g., Palmer, 1991; Raubeson and Jansen, 2005; Wicke et al., 2011). Rare plastome structural changes such as inversions, gene and intron loss, and shifting boundaries of the inverted repeat (IR) region, may therefore have utility as phylogenetic markers of major clades (e.g., Raubeson and Jansen, 1992; Kim and Jansen, 1994; Plunkett and Downie, 2000; Graham et al., 2000; McPherson et al., 2004; Wang et al., 2008; Davis and Soreng, 2010; Steele et al., 2012; Guisinger et al., 2010; Harris et al., 2013; Zhu et al., 2016). The increasing availability of full plastid genomes has further facilitated comparative studies on plastid genome structural evolution (e.g., Guisinger et al., 2011; Downie and Jansen, 2015; McKain et al., 2016). While comparative plastid genome evolution studies in Pandanales have been conducted within Stemonaceae (Lu et al., 2018), and for mycoheterotrophic Sciaphila densiflora and S. thaidanica 13 (Triuridaceae) relative to each other and to photosynthetic Carludovica palmata (Cyclanthaceae; Lam et al., 2015; Petersen et al., 2018), a comparative family-level study has yet to be performed across Pandanales as a whole. 2.2.4 Study goals I assembled organellar phylogenomic data sets comprising plastid and mitochondrial genes sets for all currently recognized photosynthetic genera in Pandanales. The non-photosynthetic family Triuridaceae was represented by one or two species from Sciaphila, the only genus in this family for which plastid genes have been recovered to date. I use these data sets to: (i) infer genus-level relationships of photosynthetic taxa in Pandanales, including the first molecular study of higher-order relationships in the panama-hat family Cyclanthaceae; (ii) investigate clade ages and consequences of these estimates for biogeographic hypotheses, and address possible biases in molecular dating analyses due to inclusion of substantially rate-elevated mycoheterotrophic taxa; (iii) characterize substitution rate shifts across photosynthetic members of Pandanales for both organellar genomes; and (iv) investigate the evolution of overall plastid genome structure by comparing several completed genomes for each family. 2.3 Materials and methods 2.3.1 Taxon sampling and library preparation I used ‘genome skims’ (Straub et al., 2012) to generate organellar genes sets for phylogenetic analyses by adding new plastid and mitochondrial data for 18 and 28 species, respectively, to published sequences in final matrices that included 28–29 Pandanales taxa for mitochondrial and plastid data sets, respectively (Tables A.1, A.2). All 26 currently recognized genera in the four photosynthetic families of Pandanales (Cyclanthaceae, Pandanaceae, Stemonaceae, Velloziaceae) are included, with the non-photosynthetic family Triuridaceae represented by a single genus, Sciaphila (one or two species). For plastid data I added newly sequenced species to a previously published matrix (Lam et al., 2018) that included data from 10 Pandanales taxa; I also retrieved sequences for Sciaphila thaidanica (Triuridaceae) from GenBank (MG757197.1), yielding a 96-taxon plastid matrix. This includes 85 taxa from all 12 monocot orders (sensu 14 Givnish et al., 2018), and 11 taxa representing outgroups from eudicots, magnoliids, and the orders Amborellales, Nymphaeales and Austrobaileyales. For the mitochondrial matrix, I generated data from 28 Pandanales taxa and seven additional monocots. The resulting 37-taxon matrix includes representatives from six monocot orders and a eudicot outgroup (Arabidopsis thaliana; GenBank accession NC_037304) (Tables A.1, A.2), but excludes Sciaphila thaidanica as mitochondrial data were not available for this taxon. Genome skims for Pandanales taxa were generated following approaches outlined in Lam et al. (2015). Briefly, I prepared genomic libraries using two additional library preparation kits (Bioo NEXTflex Rapid DNA Sequencing Kit, Bioo Scientific Corp., Austin, USA and NEBNext Ultra II DNA Library Prep Kit, New England Biolabs, Ipswich, USA) and size-selected all libraries using magnetic beads. I sequenced 17 libraries as 100-bp or 125-bp paired-end reads respectively on multiplexed lanes of HiSeq 2000 and HiSeq 2500 platforms (Illumina, San Diego, USA) that included 20–41 samples. I also sequenced a library for one taxon (Barbacenia involucrata) as 300-bp reads on a MiSeq platform using the v3 kit in a multiplexed lane that included four samples. The genome skims used to obtain mitochondrial sequences for additional monocot taxa were produced using methods outlined in Ross et al. (2015; Alismatales), Givnish et al. (2016; Liliales) and Lam et al. (2018; Asparagales, Petrosaviales). 2.3.2 Contig assembly, organellar gene annotation and plastome reconstruction I assembled full circularized plastome maps for Acanthochlamys bracteata (Velloziaceae), Benstonea copelandii (Pandanaceae) and Croomia japonica (Stemonaceae) following procedures described in Lam et al. (2015). I used Sanger sequencing to bridge gaps between contigs and to verify contig overlap, employing primers designed using Primer3 (Koressaar and Remm, 2007; Untergasser et al., 2012). I also retrieved the published complete plastome maps of five Stemonaceae taxa (Croomia japonica, C. heterosepala, C. pauciflora, Stemona japonica, S. mairei; Lu et al., 2018), one Cyclanthaceae taxon (Carludovica palmata; Lam et al., 2015) and two mycoheterotrophic Triuridaceae taxa (Sciaphila densiflora; Lam et al., 2015 and S. thaidanica; Petersen et al., 2018), and the sequences from the junction of the inverted repeat (IR) and large single copy (LSC) region of one Pandanaceae taxon (Pandanus odoratissimus; Wang et al., 2008). These allow for comparative examination of plastome structure for at least one 15 representative from each of the five families in Pandanales. I therefore considered a total of 11 entire plastomes and one set of IR-LSC junction sequences; note that the five Stemonaceae taxa represented by plastome maps were not included in organellar phylogenomic analyses, as mitochondrial data were not available for them, and all genera in the family were already represented for both organelles. I used approaches described in Lam et al. (2015) to assemble plastid contigs and retrieve plastid gene sets, with exceptions noted here. I selected contigs with at least 30× average coverage, and used Carludovica palmata (Pandanales; NC_026786.1) and Dioscorea elephantipes (Dioscoreales; NC_009601.1) as reference taxa for manually adjusting plastid gene start/stop codons and exon boundaries. I selected contigs with at least 10× average coverage for assembling mitochondrial contigs and retrieved mitochondrial genes using the blastn program from BLAST+ NCBI (version NCBI-BLAST-2.2.30+, Camacho et al., 2009), using Arabidopsis thaliana as a query. Reads from all newly sequenced taxa are available in the NCBI Sequence Read Archive (SRA; BioProject ID PRJNA613084). 2.3.3 Sequence alignment and data matrix construction I generated 82 individual plastid gene files, representing 78 protein-coding genes and four rDNA genes, each with 96 taxon terminals; mycoheterotrophic taxa Sciaphila thaidanica and S. densiflora were respectively represented by 14 and 18 protein-coding genes and four rDNA genes retained in their plastid genomes (Lam et al., 2015; Petersen et al., 2018). I retrieved plastid gene ycf1 for most photosynthetic taxa, but included this gene only in an additional Pandanales-focused matrix comprising 83 genes from 30 taxa (see below), as I had difficulties aligning it across the broader taxon set. For the mitochondrial data I generated 37 gene files, each representing a protein-coding gene and 37 taxon terminals. Missing genes (i.e., those lost in mycoheterotrophic Sciaphila and a few that were not recovered for individual photosynthetic taxa) were coded as missing data. I followed Lam et al. (2015) in constructing plastid and mitochondrial data matrices, with the following modifications: I generated initial DNA-based alignments of plastid genes using MAFFT v. 7.407 (Katoh et al., 2002), implemented in AliView v. 1.18.1 (Larsson, 2014), using the localpair flag to incorporate local pairwise alignment information and 1000 iterative refinement cycles (maxiterate 1000 flag). I then concatenated individual plastid gene files using SequenceMatrix v. 100.0 (Vaidya et al., 2011). For 16 mitochondrial genes, I conducted initial DNA-based alignments using the MUSCLE (Edgar, 2004a,b) online portal with default settings and concatenated individual gene files manually. I used AliView and Mesquite v. 3.04 (Maddison and Maddison, 2015) to perform alignment adjustments on separate plastid and mitochondrial matrices, respectively, using criteria laid out in Graham et al. (2000), Kelchner (2000), and Simmons and Ochoterena (2000). I staggered regions that were difficult to align globally, resulting in offset blocks of locally aligned sequences, which should minimize errors due to misalignment while retaining potentially informative variation (e.g., Steane et al., 1999; Graham et al., 2006; Bell et al., 2020). I verified open reading frames for all plastid protein-coding genes. I also maintained mitochondrial genes in reading frame, but the alignments included several more fragmented sequences and some internal stop codons (approximately two per species). The latter may reflect sites that undergo RNA editing (e.g., Gray, 2009), and/or that had a higher error rate in base-calling for mitochondrial data, reflecting the lower coverage sequencing that was possible for this genome (i.e., 10× vs. 30× for mitochondrial vs. plastid sequences, see above) (Harismendy et al., 2009). I also checked for matrix compilation errors as described in Lam et al. (2015). I obtained a 80,183-bp plastid matrix and a 36,807-bp mitochondrial matrix (for reference, these are derived from 68,668 bp and 21,143 bp of unaligned plastid and mitochondrial data in Acanthochlamys bracteata, respectively), and translated both into corresponding amino-acid matrices (24,839 and 12,269 residues, respectively). I also assembled a 119-gene combined organellar matrix (i.e., a plastid and mitochondrial data set) that included all sampled Pandanales (excluding S. thaidanica, for which only plastid data were available) and five outgroup taxa. All data sets are available at figshare (10.6084/m9.figshare.11498151). 2.3.4 Phylogenetic inference I analyzed data matrices using PAUP* v. 4.0a build 164 (Swofford, 2002) for parsimony analyses, and RAxML v7.4.2 (Stamatakis, 2006) under a graphical interface (Silvestro and Michalak, 2012) for likelihood analyses. For parsimony, I conducted separate analyses of the plastid data set (95-taxon version; see below for additional versions of this data set), the mitochondrial data set (37-taxon matrix) and a combined organellar data set (33-taxon matrix). In all three cases I represented Triuridaceae only by Sciaphila densiflora, and excluded Sciaphila 17 thaidanica (Triuridaceae); the latter is an extremely rapidly evolving species that was not available for the mitochondrial data set, but was included in one plastid-based likelihood analysis (see below). For each matrix, I ran heuristic searches using tree bisection and reconnection (TBR) branch swapping, considering 1000 random stepwise addition replicates, holding up to 100 trees at each step, and otherwise used default settings. For likelihood analyses, I considered unpartitioned and partitioned analyses of DNA and amino-acid data. I partitioned DNA alignments by gene and codon position (a ‘G x C’ scheme, e.g., Lam et al., 2015), with additional partitions included for each rDNA gene, starting with 238 initial partitions for the plastid DNA matrix, 111 for the mitochondrial DNA matrix and 349 for the combined organellar matrix. I started with 78 and 37 initial partitions for translated plastid and mitochondrial matrices, respectively, considering one partition per gene for the respective organellar genomes. I used PartitionFinder 2 (Lanfear et al., 2017) to combine partitions that did not have substantially different substitution models, limiting the latter to models implemented in RAxML, and employing the relaxed hierarchical clustering algorithm (r-cluster) and corrected Akaike Information Criterion (AICc) (see Table A.3 for final partitions and models). The best fit DNA substitution models were consistently GTR, GTR+G or GTR+I+G; I used the GTR+G model for all partitions in subsequent analyses, as the invariant sites parameter ‘I’ may be accommodated by the gamma parameter ‘G’ (Yang, 2006). I obtained a range of optimal amino-acid substitution models and applied them to corresponding final partitions (Table A.3). I estimated per-partition base frequencies and among- and within-site rate parameters independently, while linking branch length estimates across partitions. I also used PartitionFinder 2 to find optimal substitution models for unpartitioned DNA- and amino-acid (AA)-based matrices (Table A.3). I ran 12 likelihood analyses in total. Eight of these were based on one core version of each of the plastid and mitochondrial data sets (i.e., a 95-taxon plastid matrix and a 37-taxon mitochondrial matrix, see below); they correspond to partitioned plus unpartitioned analyses of both DNA and AA versions of each matrix, for a total of four analyses per organelle. For the plastid data I focused most efforts on the 95-taxon matrix as a common reference point for examining the effect of different likelihood approaches. Both core organellar matrices included a single mycoheterotrophic representative, Sciaphila densiflora. Three additional partitioned 18 DNA-based likelihood analyses of plastid data were performed to assess: (i) the effect of including both representatives of mycoheterotrophic Sciaphila (in an analysis of a 96-taxon plastid DNA matrix that includes S. densiflora and S. thaidanica; both are on long branches but the branch for the latter taxon was exceptionally long, see Results and Fig. A.7); (ii) the effect of excluding both long-branch Sciaphila taxa altogether (in an analysis of a 94-taxon plastid DNA matrix excluding S. densiflora and S. thaidanica); (iii) the effect of including ycf1, a highly variable plastid gene that I was able to align within Pandanales and two outgroups (in a DNA-based analysis of 83 plastid genes for 30 taxa that included one mycoheterotrophic representative, S. densiflora). Finally, I ran a combined DNA-based analysis of plastid and mitochondrial data; this was a partitioned analysis that included the 28 Pandanales taxa common to both organellar data sets and five outgroups. For each likelihood analysis, I conducted 20 independent searches for the best tree. I estimated branch support for parsimony and likelihood analyses using bootstrap analysis (Felsenstein, 1985). I considered poorly and strongly supported branches to have <70% or ≥95% bootstrap support, respectively (e.g., Soltis and Soltis, 2003). For parsimony analyses, I estimated branch support using 500 bootstrap replicates (Felsenstein, 1985), each with 100 random stepwise addition replicates and TBR branch swapping, and otherwise using default settings. For likelihood analyses I used 500 rapid bootstrap replicates, considering GTRGAMMA and PROTGAMMA approximations for the nucleotide and amino-acid substitution models, respectively; each bootstrap analysis used the same partitioning schemes as the searches for best trees. 2.3.5 Divergence time and substitution rate estimation I used the plastid and mitochondrial DNA-based data sets generated here to estimate divergence times and substitution rate shifts in Pandanales and related monocots using a Bayesian framework in BEAST v. 2.4.8 (Bouckaert et al., 2014). Each matrix excluded two long-branch taxa, Sciaphila densiflora and S. thaidanica, as their inclusion may distort age estimation (see below). The plastid matrix also excluded seven magnoliid, Amborellales, Nymphaeales and Austrobaileyales taxa, as their inclusion caused analyses to terminate prematurely; I otherwise included 87 and 36 taxa for plastid and mitochondrial data, respectively. I constructed a three-19 gene plastid matrix (accD, matK, rps3) and a 12-gene mitochondrial matrix (atp1, atp8, ccmC, ccmFc, ccmFn, cox1, matR, mttB, nad5, rpl2, rps3, rps4). Both data sets contain approximately 2000 parsimony informative sites for their sampled taxa; I did not include the full concatenated matrix in either case because previous studies have shown that adding genes tends not to significantly improve age estimates (e.g., Mulcahy et al., 2012; Foster et al., 2017); trial analyses using larger gene sets also proved impractical with available computational resources. I used a random local clock (RLC) model (Drummond and Suchard, 2010) to estimate divergence times and the number of molecular rate shifts. I ran plastid analyses using unpartitioned data, or with the data partitioned by codon position. The age and rate shift estimates were not substantially different across these two variant analyses, and the attempted partitioned analyses did not reach stationarity for several parameters (data not shown). I ran only unpartitioned analyses for the mitochondrial data. In both cases I constrained the full tree topology (e.g., Givnish et al., 2016, 2018), considering the trees resulting from partitioned DNA-based analyses of the 95-taxon plastid matrix and the partitioned DNA-based analyses of the 37-taxon mitochondrial matrix, after pruning mycoheterotrophic taxa from both respective trees and seven non-monocot taxa from the plastid tree. I used the bladj function in Phylocom (Webb et al., 2008) to obtain ultrametric plastid and mitochondrial input trees. Seven fossil calibrations were considered for the plastid data and two for the mitochondrial data (Table A.4), each applied with a conservative uniform prior distribution placing equal probability between the lower and upper age bounds. I set minimum fossil ages as the lower bounds of the uniform distribution, and set upper bounds to 151.8 Ma, the maximum age estimate for the angiosperm crown node in analyses by Silvestro et al. (2015). I used GTR + G with four gamma categories as the substitution model and employed an empirical estimate of base frequency, selecting the estimate clock.rate option, and using a birth-death branching model for the tree prior, a ‘birth diff’ rate of 0.1, an upper limit of 1.0 for the speciation and extinction rates, and a gamma distribution (α = 2, β = 0.5) for nucleotide exchange rates. I set the mean of the RLC model (ucldMean) as an exponential distribution with a mean of 0.33, leaving all other parameters as default, and used CIPRES (Miller et al., 2010) to run seven and 13 independent analyses using plastid and mitochondrial data, respectively. Each analysis was run for 200 million generations, sampling every 2,000 generations. 20 I also explored the consequences of conducting molecular dating analyses that include vs. exclude mycoheterotrophic taxa. The latter often have highly elevated rates of evolution in plastid, nuclear, and mitochondrial genes (e.g., Merckx et al., 2009a; Lam et al., 2018). Iles et al. (2015) hypothesized that such extremely long branches could have a distorting effect on age estimates. I assessed this by re-examining a published monocot-focused matrix of Pandanales by Mennes et al. (2013), which consists of two loci (one nuclear, one mitochondrial) and includes sequences for 30 mycoheterotrophic taxa in the orders Pandanales, Dioscoreales and Petrosaviales, in addition to 96 photosynthetic monocot taxa. I removed all mycoheterotrophs from the matrix used by Mennes et al. (2013), otherwise retaining their alignment, and ran analyses that replicated all of the conditions in their study. To parallel their methods, I used BEAST v.1.7.5 (Drummond et al., 2012) to run two independent analyses, each for 200 million generations, sampling every 10,000 generations, and used an uncorrelated lognormal relaxed clock (UCLN), GTR+I+G as the substitution model, a Yule branching model for the tree prior, and four fossil calibrations with lognormal prior distributions using the age of the fossil as the offset value (see Mennes et al., 2013, for complete methods). For all Bayesian analyses conducted here I assessed stationarity and convergence among independent analyses using Tracer v.1.7.1 (Rambaut et al., 2018). I used LogCombiner (v1.7.5 and v2.4.8; Drummond et al., 2012; Bouckaert et al., 2014) to combine independent analyses that converged (ignoring those that did not converge); this led to me combining five of seven analyses based on plastid data, nine of 13 based on mitochondrial data, and both analyses based on the version of the Mennes et al. (2013) matrix that excluded mycoheterotrophs. All resulting combined analyses reached stationarity with ESS values > 200 for all parameters. I summarized sampled trees from analyses that converged using TreeAnnotator (v1.7.5 and v2.4.8; Drummond et al., 2012; Bouckaert et al., 2014), discarding the first 10–25% of trees as burn-in, resampling at a lower frequency to obtain ~10,000 final trees when necessary, and with consensus ages representing the median of posterior distributions. 21 2.4 Results 2.4.1 Higher-order relationships in Pandanales Nearly all tree-wide relationships recovered in the three parsimony and 12 likelihood analyses of plastid, mitochondrial and combined organellar data sets are well supported by bootstrap analysis and are congruent with each other, ignoring taxon-sampling differences (Figs. 2.1–2.3, A.1–A.15; support values summarized in Table A.5). I use the topology from the partitioned likelihood analysis of the combined organellar data set (Fig. 2.1, left-hand tree, and Fig. A.4) as a general reference point for comparing results of other analyses here; inconsistent relationships relative to the combined organellar likelihood tree are highlighted in all tree figures. Most disagreements concern branches that were weakly supported in Cyclanthaceae in individual analyses (see below), in addition to a long-branch taxon (Sciaphila densiflora, Triuridaceae) that places differently in a single parsimony analysis of plastid data (Figs. 2.3, A.2). The monophyly of Pandanales and their sister-group status to Dioscoreales are inferred with moderate to strong bootstrap support across different analyses (Figs. 2.1–2.3, A.1–A.15, Table A.5). Setting aside Triuridaceae (for which I included only a single genus, Sciaphila), the remaining Pandanales families are nearly all strongly supported as monophyletic across analyses. The only exception is that Stemonaceae monophyly was strongly rejected in parsimony analysis of plastid data (Figs. 2.3, A.2), as Sciaphila (Triuridaceae) was inferred to be nested within Stemonaceae, as a well-supported sister-group of Pentastemona (labeled as branch ‘aa’ in Fig. 2.3, left-hand tree, and see Fig. A.2). This represents the only strong conflict between parsimony and likelihood analyses (i.e., cases where both conflicting arrangements are strongly supported). The unusual placement of Triuridaceae also represents a strong conflict between plastid and mitochondrial parsimony analyses: although its precise placement was poorly supported by the mitochondrial parsimony data, the latter analysis strongly rejects a sister-group relationship between Sciaphila (Triuridaceae) and Pentastemona (Stemonaceae), as Stemonaceae monophyly was strongly supported (Fig. 2.3, right-hand tree, and Fig. A.3). The mitochondrial parsimony placement of Triuridaceae as the sister-group of Cyclanthaceae and Pandanaceae is identical to its placement in all the remaining analyses: all likelihood analyses of mitochondrial data (Figs. 2.2, A.12–A.15; see branches ‘r’ and ‘q’ in Fig. 2.1 and Table A.5), parsimony and likelihood 22 analyses of combined organellar data (Figs. 2.1, A.1, A.4, Table A.5), and all likelihood analyses of plastid data (Figs. 2.2, A.5–A.11, Table A.5). Plastid-based likelihood analyses had the strongest support for the placement of Triuridaceae (90–99% bootstrap support for branch ‘q’; Figs. 2.2, A.5–A.11; Table A.5). Setting aside the unusual Stemonaceae-Triuridaceae clade in plastid-based parsimony analysis, relationships among the five Pandanales families were congruent across parsimony and likelihood analyses, and generally had moderate to strong support. Most genus-level relationships in Pandanales were consistent and strongly supported across all analyses performed here (Figs. 2.1–2.3, A.1–A.15, Table A.5). Considering the 26 internal branches in each rooted subtree that corresponds to Pandanales for analyses that included all sampled taxa except mycoheterotrophic S. thaidanica, 15–22 branches had strong support across likelihood analyses, 1–6 were moderately supported and 1–5 were poorly supported (Figs. 2.1, 2.2, A.4–A.15, Table A.5). In parsimony analyses there were 15–19 branches with strong support, 1–6 with moderate support and 1–4 with poor support (for the plastid-based parsimony tree I considered 27 branches to take account of the placement of Sciaphila within Stemonaceae in that analysis; Figs. 2.1, 2.3, A.1–A.3, Table A.5). Nearly all genus-level relationships in Pandanaceae and Stemonaceae were completely congruent across phylogenetic analyses, and most were strongly supported (Figs. 2.1–2.3, A.1–A.15, Table A.5). The sole difference in topology was the strongly supported placement of Sciaphila as the sister group of Pentastemona within Stemonaceae in plastid-based parsimony analyses (see above; Figs. 2.3, A.2). There were no topological differences within Pandanaceae across analyses, and all inferred relationships had strong bootstrap support. In Velloziaceae, genus-level relationships were congruent and moderately to strongly supported across analyses, except for the placement of Barbacenia: there was a moderately supported conflict for the placement of Barbacenia as either the sister group of Xerophyta (branch ‘x’ in Fig. 2.1, Table A.5; the highest bootstrap support for this relationship was 71% in the plastid-based likelihood analysis that included gene ycf1, Fig. A.9) or as the sister-group of all Velloziaceae genera except Acanthochlamys (see branch ‘bb’ in Figs. 2.3, A.3; this relationship had 71–82% bootstrap support in two mitochondrial-based analyses, Figs. 2.3, A.3, A.14, Table A.5). 23 I inferred strong support for most relationships in Cyclanthaceae in likelihood analysis of the combined organellar matrix; these were mostly congruent with the other phylogenetic analyses (cf. Figs. 2.1, A.4 vs. Figs 2.2, 2.3, A.1–A.3, A.5–A.15 and see Table A.5). All deviations from the topology obtained from likelihood analysis of the combined organellar matrix (Fig. 2.1, left-hand tree, and Fig. A.4) were poorly supported, except for the precise relationships among Dianthoveus, Evodianthus and Thoracocarpus: most analyses, including the combined organellar likelihood analysis, recovered a Dianthoveus-Evodianthus clade with poor to moderate support (clade ‘e’ in Fig. 2.1, Table A.5; see Figs. 2.2, 2.3, A.1, A.3, A.4, A.10–A.15), an arrangement that represents a moderately supported conflict with the plastid likelihood analysis that included gene ycf1, as the latter recovered an Evodianthus-Thoracocarpus clade with strong support (clade ‘cc’ in Fig. A.9, Table A.5). 2.4.2 Plastid genome structural evolution in Pandanales I compared the newly assembled complete circular plastomes for three Pandanales taxa here (Acanthochlamys bracteata, Velloziaceae, Fig. A.16, GenBank accession MN905940; Benstonea copelandii, Pandanaceae, Fig. A.17, GenBank accession MN905941; Croomia japonica, Stemonaceae, Fig. A.18, GenBank accession MN905942) to eight additional published plastomes for the order (for a total of 11 plastomes, nine from photosynthetic taxa and two from mycoheterotrophs; Table A.6). The nine plastomes from photosynthetic taxa are comparable in size, organization, gene content and gene order to those of other angiosperms (e.g., Hansen et al., 2007); their sizes range from 153,842 bp in Acanthochlamys to 158,545 bp in Carludovica. They are 91.7–91.9% bigger than S. thaidanica, the smallest of the two Triuridaceae plastomes (Table A.6). The total number of unique genes in the nine green plastomes ranges from 112–113, with 18–20 of these duplicated in the inverted repeat (IR) region (Table A.6). Counting IR duplications, there are 130–133 genes in the plastomes of photosynthetic taxa, with differences due both to gene loss and expansion/contraction of the IR at the junction with the large single copy (LSC) region (Fig. A.19). Compared to Stemonaceae plastomes, which have the highest gene content in my sampling, Sciaphila thaidanica has a genome that is ~15–18% as large (range considering total and unique genes); S. densiflora is ~21–25% as large, respectively (Table A.6). These differences reflect loss of photosynthesis-related and other genes, and to some degree the 24 loss of the IR in the Triuridaceae taxa (Petersen et al., 2018 for S. thaidanica; note that S. densiflora may have small cryptic repeats, Lam et al., 2015). I did not recover the two exons for the rps16 locus in the LSC of Acanthochlamys (Fig. A.16); BLAST-based searches of intergenic space between trnQ-UUG and trnK-UUU, the two genes that usually flank rps16, also did not recover these exons. Stemonaceae taxa considered here (Table A.6) have the largest IR (20 genes, including rps19 and rpl22; Figs. A.18, A.19C, Table A.6). In Acanthochlamys both rps19 and rpl22 are part of the LSC, and so the IR of this taxon contains 18 genes (Figs. A.16, A.19D, Table A.6). The IR of Carludovica has 19 genes; it includes rps19 but not rpl22 (Fig. A.19A, Table A.6). In Benstonea, the 5’-end of rps19 straddles the IRA junction and this gene portion is duplicated in the IRB; thus its IR encompasses 18 intact genes and one short partial reading frame (Figs. A.17, A.19B, Table A.6). Sciaphila thaidanica and S. densiflora respectively retain eight and nine of the 18–20 genes found in the IRs of the photosynthetic taxa sampled here; these genes are colinear across sampled photosynthetic and non-photosynthetic taxa, except for an inversion of the rps7 and 3’-rps12 gene block in S. densiflora (Fig. 3 in Lam et al., 2015). Outside the IR regions, there were additional inversions inferred in S. densiflora (rps18-accD gene block; Fig. 3 in Lam et al., 2015) and S. thaidanica (rps12-rps2 gene block; Fig. 2 in Petersen et al., 2018). Pandanales taxa were otherwise all fully colinear, except for a single inversion of the cemA-petA gene block in the two Stemona taxa (Lu et al., 2018). 2.4.3 Divergence times and substitution rate shifts in Pandanales and other monocots All 95% highest posterior density (HPD) intervals from analyses of the plastid and mitochondrial date sets overlapped for nodes of interest, but plastid data produced narrower intervals (mean interval length of 20 Ma) than mitochondrial data (mean of 31 Ma; Table 2.1, Figs. 2.4, A.20, A.21); note that the mycoheterotrophic family Triuridaceae was excluded from these analyses (see below for rationale). Median ages of major clades estimated from plastid and mitochondrial data were highly correlated (Pearson correlation coefficient, r = 0.97); ages based on the mitochondrial data are somewhat younger (slope, m = 0.94) by an average of 15 Ma (0–25 Ma, SD = 8 Ma) for the 13 nodes that overlap between the two data sets (Table 2.1, Figs. 2.4, A.20, 25 A.21). Outside Pandanales, the 95% HPD intervals for node ages overlapped across plastid and mitochondrial analyses (Table A.7). The estimated stem age of Pandanales (and thus the date of separation from its sister group, Dioscoreales) is 106 (98–117) Ma, and the order began diversifying into its constituent families (= order crown age) at 93 (85–104) Ma based on plastid data; mitochondrial age estimates for these two splits are more recent: 84 (70–109) and 71 (58–94) Ma, respectively (Table 2.1, Figs. 2.4, A.20, A.21). Plastid and mitochondrial data yielded similar estimates for the dates by which family-level splits in Pandanales were complete: 61 (53–71) and 56 (49–74) Ma, respectively. Stemonaceae have the oldest family-level crown age based on plastid data (65 [53–75] Ma), and Cyclanthaceae considering mitochondrial data (49 [47–54] Ma); Pandanaceae have the youngest family-level crown age based on both plastid (35 [23–51] Ma) and mitochondrial data (26 [20–35] Ma) (Table 2.1; Figs. 2.4, A.20, A.21). Bayesian analyses estimated 31 and 18 molecular rate shifts in the time-calibrated phylogenies inferred from plastid and mitochondrial data, respectively (note that these have different taxon samplings). Considering monocots, the plastid-based analyses yielded inferences of four rate shifts in Pandanales and 25 in the remaining monocot taxa (Figs. 2.5, A.22; full tree shown in the latter figure). The fastest overall plastid rates were observed in Zingiberaceae (3.2 x 10-3 substitutions per site per million years) and Poaceae (3.1 x 10-3 substitutions per site per million years). The slowest plastid rates were recovered in Arecaceae after the divergence of Calamus (0.1 x 10-4 substitutions per site per million years), and in Cyclanthaceae, Sararanga (Pandanaceae) and Japonolirion (Petrosaviaceae) (the latter all 0.2 x 10-4 substitutions per site per million years). In Pandanales, successive rate decelerations in plastid genes occurred in each of the common ancestors of Stemonaceae, Pandanaceae-Cyclanthaceae, Pandanaceae, Sararanga (Pandanaceae), and Cyclanthaceae, with the slowest overall rates in the latter family. Only one rate acceleration was inferred in Pandanales plastid data, after the divergence of Sararanga from all other Pandanaceae (Figs. 2.5, A.22). In analyses of mitochondrial data, 12 of the 18 estimated rate shifts occurred in Pandanales, where taxon sampling is heaviest, and the remaining six in other monocots (Figs. 2.5, A.23; full tree shown in the latter figure). The fastest overall mitochondrial rates were observed in Benstonea (Pandanaceae; 1 x 10-3 substitutions per site per million years), and in 26 Ludovia (Cyclanthaceae) and Stratiotes (Hydrocharitaceae) (the latter both 9 x 10-4 substitutions per site per million years). The slowest mitochondrial rate (1 x 10-4 substitutions per site per million years) was recovered for Tofieldia (Tofieldiaceae) and multiple Stemonaceae, Pandanaceae and Cyclanthaceae lineages (Figs. 2.5, A.23). In Pandanales, there were five rate decelerations and seven accelerations. Independent decelerations occurred once in Velloziaceae (in the Barbacenia terminal lineage), Stemonaceae (after the divergence of Pentastemona) and Pandanaceae, and three times in Cyclanthaceae (in the terminal lineages leading to Cyclanthus and Stelestylis, and the lineage leading to the common ancestor of all Cyclanthaceae excluding Cyclanthus). Accelerations occurred in each of the terminal lineages leading to Stichoneuron (Stemonaceae), and Benstonea (Pandanaceae), the lineage leading to the common ancestor of Pandanaceae-Cyclanthaceae, and four Cyclanthaceae taxa (terminal lineages leading to Asplundia, Dianthoveus, Ludovia, and the lineage leading to the common ancestor of a clade comprising Chorigyne, Ludovia, Sphaeradenia and Stelestylis) (Figs. 2.5, A.23). 2.4.4 Divergence time estimates excluding mycoheterotrophic taxa I re-analyzed a published dating analysis by Mennes et al. (2013) to explore the possible age biasing effect of very rapidly evolving taxa, by excluding mycoheterotrophs (in the orders Dioscoreales, Pandanales, Petrosaviales) and comparing nodes of interest in Pandanales and other monocots to the original results. There were multiple differences in median age estimates for nodes in the vicinity of where mycoheterotroph lineages were placed in the original study. Median age estimates from the original and modified Mennes et al. (2013) analyses were within the 95% HPD intervals of each other, except for the Pandanales crown node (= Velloziaceae stem node); this node was 62 (38–90) Ma with mycoheterotrophs excluded vs. 91 (69–110) Ma with them included (Table 2.1, cf. Fig. A.24 here vs. Fig. 4 in Mennes et al., 2013). The remaining two largest age differences across analyses were for the crown node of Dioscoreales (91 [70–110] Ma with mycoheterotrophs excluded vs. 101 [85–116] Ma with them included), and the crown node of Velloziaceae (31 [10–57] vs. 37 [9–75] Ma, respectively). These differences contrast with negligible age differences for the nine orders that did not include mycoheterotrophic taxa in the original matrix (0–2 Ma). 27 2.5 Discussion My study provides a comprehensive and well resolved picture of phylogenetic relationships at the family and genus level in the monocot order Pandanales using phylogenomic data from the plastid and mitochondrial genomes. These findings are well supported for most branches and are congruent across data sets and inference methods, with a key exception that appears to reflect a long-branch artefact involving the local placement of mycoheterotrophic Triuridaceae for one data set and analysis method (i.e., plastid data using parsimony). These new results provide a useful framework for thinking about substitution rate shifts, clade ages, biogeography and plastid genome structural evolution. 2.5.1 Organellar data produce robust family-level relationships in Pandanales The family-level topology I recovered across all likelihood analyses here (Figs. 2.1, 2.2, A.4–A.15) is congruent with the plastid phylogenomic studies of Lam et al. (2015) and Givnish et al. (2018), which also recovered strongly supported arrangements of the five families of Pandanales: (Velloziaceae, (Stemonaceae, (Triuridaceae, (Cyclanthaceae, Pandanaceae)))). Plastid rates appear to be much more elevated than mitochondrial rates in mycoheterotrophic Sciaphila (Triuridaceae) based on the substantially longer branches subtending this taxon in plastid-based likelihood analyses (cf. Fig. A.6 vs. A.13). These very long branches appear to underlie the unusual placement of Sciaphila in plastid-based parsimony analysis, as a strongly supported sister-group of Pentastemona within Stemonaceae here (labeled as branch ‘aa’ in Fig. 2.3 left-hand tree, Fig. A.2, Table A.5), or as the sister group of Xerophyta (Velloziaceae) in Lam et al. (2015, although with poor support in the latter study). In contrast, the mitochondrial-based parsimony analysis, and all likelihood analyses for both genomes considered individually or together, support Triuridaceae as the sister group of Cyclanthaceae-Pandanaceae. Considered together, the parsimony placement of Triuridaceae for the plastid data, but not for the less rapidly evolving mitochondrial data, likely reflects a long-branch artefact for the former genome, and so based on the current data at least, the placement of Triuridaceae in the order can be considered settled. 28 2.5.2 Organellar data sets strongly resolve genus-level relationships in Pandanales Multiple studies have focused on relationships within individual Pandanales families; these generally used comprehensive genus-level samplings of each family and surveyed one to several genes, morphological characters, or a combination of these, resulting in varying degrees of support for relationships. For example, the strongly supported topologies I recovered within Stemonaceae and Pandanaceae across most analyses here are consistent with previous studies that also reconstructed relationships in each family with strong support: for Stemonaceae, Caddick et al. (2002a) and Rudall et al. (2005) used combined molecular and morphological data, and Lam et al. (2015) used full plastome data; for Pandanaceae, Gallaher et al. (2015) used nuclear and combined nuclear-plastid data sets. Mennes et al. (2013) performed a detailed analysis of relationships in Triuridaceae. The representative taxon included here (Sciaphila) is classified under tribe Sciaphileae (Cheek, 2003). However, the tribe may not be monophyletic (Rudall and Bateman, 2006; Mennes et al., 2013). Additional mitochondrial and nuclear phylogenomic data would be useful to resolve these and other uncertain relationships in Triuridaceae. Relationships in the remaining two Pandanales families have also remained somewhat unclear. In Velloziaceae, I clarified the placements of Barbaceniopsis and Vellozia as a strongly supported clade in all analyses here; the placements of these two genera have varied and were poorly supported in previous studies (Menezes et al., 1993; Behnke et al., 2000, 2013; Salatino et al., 2001; Mello-Silva, 2005; Mello-Silva et al., 2011; Alcantara et al., 2018). The sole relationship in the family that remains uncertain in my analyses is that of Barbacenia, which I recovered as either the sister-group of Xerophyta (branch ‘x’ in Fig. 2.1, Table A.5, and see Figs. 2.2, A.4–A.9) or more commonly as the sister-group of Xerophyta plus Barbaceniopsis-Vellozia (branch ‘bb’ in Fig. 2.3, Table A.5, and see Figs. 2.1, 2.2, A.1–A.3, A.10–A.15), with poor to moderate support for each hypothesis. Only the latter arrangement has been recovered with strong support to date, in Bayesian analyses by Alcantara et al. (2018; posterior probability, PP, of 0.97) using five molecular markers. The relatively dense taxon sampling of Alcantara et al. (2018), comprising 150 of the ~310 species in Velloziaceae (Govaerts, 2019), may have contributed to their increased support for the placement of Barbacenia compared to my study and that of Mello-Silva et al. (2011). Here I used a much larger molecular data set, ranging from 29 37 to 119 organellar genes, focused on a representative genus-level sampling of the family consisting of six taxa; Mello-Silva et al. (2011) used the same five molecular markers as Alcantara et al. (2018) for a sampling of 47 Velloziaceae taxa but recovered Barbacenia as the sister group of Barbaceniopsis-Vellozia with poor support in parsimony analyses. Previous morphology-based studies produced a baseline phylogenetic outline for Cyclanthaceae, but multiple poorly supported branches persisted, and molecular studies of this group had not been performed until this study. Harling (1958) divided the family into subfamilies Cyclanthoideae (comprising Cyclanthus) and Carludovicoideae (comprising the remaining genera). The latter subfamily was further divided into the informal Sphaeradenia group (comprising Chorigyne-Stelestylis, Sphaeradenia, Ludovia) and Asplundia group (comprising the remaining genera in the subfamily). However, multiple relationships among and within these proposed groups remained unclear (Harling, 1958; Hammel and Wilder, 1989; Eriksson, 1989, 1994). My analyses confirm previously proposed hypotheses for the placement of Cyclanthus and for relationships within the Sphaeradenia group (Harling, 1958; Hammel and Wilder, 1989; Eriksson, 1989, 1994). The strongly supported sister-group relationship of Cyclanthus to all other Cyclanthaceae recovered here had been previously assumed by Harling (1958) and Eriksson (1994) as their studies did not include an outgroup; only Hammel and Wilder (1989) tested the latter placement in preliminary analyses using Freycinetia (Pandanaceae) as an outgroup, but they did not report on branch support. The topology for the Sphaeradenia group inferred here is consistent with the morphology-based hypotheses of Harling (1958), Hammel and Wilder (1989) and Eriksson (1994), although only the latter study included Chorigyne (described by Eriksson, 1989) and assessed branch support. My analyses also recovered four strongly supported arrangements in Cyclanthaceae that had not been previously proposed: an Evodianthus-Thoracocarpus clade, a Carludovica-Schultesiophytum clade, an Asplundia-Dicranopygium clade, and an arangement with Asplundia-Dicranopygium as the sister-group of all Cyclanthaceae except Carludovica-Schultesiophytum and Cyclanthus. Support for these newly proposed relationships was generally strong from analyses using combined organellar and mitochondrial data, except for the Evodianthus-Thoracocarpus clade, which was strongly supported only in plastid-based analyses that included the large and variable plastid locus ycf1 (Fig. A.9, Table A.5). Two portions of ycf1 are among 30 the most variable coding regions in land-plant plastomes (Dong et al., 2015), and this gene may therefore have contributed to improved support for the short branch subtending Evodianthus-Thoracocarpus relative to the other analyses here that excluded this gene. I found strong support for the non-monophyly of the Asplundia group, as Eriksson (1994) had suspected, as the Sphaeradenia group is nested within it. I thus propose maintaining the two-subfamily classification of Cyclanthaceae, but not the subdivision of the latter into smaller groups. 2.5.3 Fluctuating inverted repeat boundaries and gene loss in Pandanales plastomes The plastomes of photosynthetic land plants are highly conserved in structural organization, gene content, and gene order (e.g., Palmer, 1991; Raubeson and Jansen, 2005; Wicke et al., 2011). Large clusters of polycistronically transcribed genes impose a constraint on plastome organization, making large structural changes relatively rare, and therefore potentially useful as markers for clades (e.g., Downie and Palmer, 1992; Wang et al., 2008). Plastomes typically have a quadripartite structure with an inverted repeat (IR) region separating a small single-copy (SSC) region and a large single-copy (LSC) region (e.g., Kolodner and Tewari, 1979; Palmer, 1991). The IR of land plants varies somewhat in size as a result of expansions and contractions; these shifts are often lineage-specific and may have phylogenetic significance (Wang et al., 2008; Wicke et al., 2011). For example, changes in the location of the junction between the IR and single-copy regions have been used as synapomorphies at various phylogenetic scales in angiosperms, including at the genus level (e.g., the Berberis-Mahonia clade in Berberidaceae; Kim and Jansen, 1994) and subfamily level (e.g., Ehrhartoideae-Pooideae clade in Poaceae; Guisinger et al., 2010). The IR-LSC junction appears to have shifted dynamically in Pandanales, as its location varies across the four photosynthetic families (Fig. A.19). Junctions of the IR and LSC vary less within Pandanales families than between them, at least considering my sampling. For example, rpl22 terminates the IR in all six Stemonaceae plastomes examined here (Table A.6, Fig. A.19); in Pandanaceae, a partial rps19 copy terminates the IR in the two species examined here (note that one of them, Pandanus odoratissimus, is represented by IR-LSC junction sequences, not by a full plastome in Wang et al., 2008). Therefore, IR-LSC junctions likely act as genome structural synapomorphies for major clades in Pandanales, and additional complete plastomes should be surveyed to address this possibility. 31 Land-plant plastomes commonly encode 21 small and large ribosomal subunit proteins (rps and rpl genes) and loss of these genes from the plastome is rare in photosynthetic plants (Wicke et al., 2011). However, independent losses of rps16 have been documented for multiple plant lineages, including Acanthochlamys here (Table A.6, Fig. A.16), additional monocots (Dioscorea and some Asparagales: Hansen et al., 2007; Steele et al., 2012; McKain et al., 2016), eudicots (Populus and some Fabaceae: Doyle et al., 1995; Steane, 2005), gymnosperms (Pinus: Tsudzuki et al., 1992) and liverworts (Marchantia: Ohyama et al., 1986). The gene rps16 may be part of a group of plastid genes that are especially prone to being lost from the plastome following functional transfer to the nucleus (e.g., Martin et al., 1998; Ueda et al., 2008). Loss of rps16 may have occurred at least twice more in Velloziaceae, as I did not recover the 5’-exon from Barbaceniopsis or the 3’-exon from Vellozia. Additional full and completed plastomes would be useful to confirm the latter two cases, and to explore how labile the loss of rps16 is among closely related taxa in Pandanales. 2.5.4 Organellar age estimates and consequences for biogeographic hypotheses My finding that age estimates from plastid- and mitochondrial-based dating analyses were highly correlated and similar (mean difference per node = 13.09 Ma, SD = 7.54 Ma, Pearson correlation coefficient r = 0.97, slope m = 0.94; Tables 2.1, A.7), suggests that the relaxed clock models I used are robust to data sets under different substitution rate regimes when the latter are correctly accounted for. These findings are consistent with those of Magallón et al. (2013), who found that ages estimated from two variant data sets (consisting of four highly conserved plastid genes vs. one faster evolving plastid gene) were highly correlated despite an order of magnitude difference in the absolute rates of each data set. Variation in age estimates across data sets here may instead be more strongly influenced by differences in the number of fossil calibrations used (in turn affected by differences in taxon sampling); I respectively used six and two fossil calibrations for plastid- and mitochondrial-based analyses due to taxon sampling differences. For nodes of interest here, plastid and mitochondrial estimates were most similar in the vicinity of the two calibrated nodes common to the two organellar analyses (i.e., the monocot stem node and the Cyclanthus stem node): ages differed by only 4 Ma for the monocot stem node, by 5 Ma for the 32 Cyclanthaceae-Pandanaceae stem node, and were the same for the Cyclanthaceae crown node (Table 2.1, Figs. 2.4, A.20, A.21). Pandanales age estimates from plastid-based analyses here are most similar to the angiosperm-focused study of Magallón et al. (2015) considering also the monocot-wide study of Givnish et al. (2018), the original (and my re-analyzed) versions of Mennes et al. (2013), and family-focused studies on Pandanaceae (Gallaher et al., 2015) and Velloziaceae (Alcantara et al., 2018), summarized in Table 2.1. Estimates from mitochondrial-based analyses here are most similar to those of Magallón et al. (2015) and Givnish et al. (2018) (Table 2.1). I estimated younger ages for the stem and crown nodes of Pandanaceae and Velloziaceae compared to Gallaher et al. (2015) and Alcantara et al. (2018), respectively. Nevertheless, my estimates add support to the hypotheses presented in both of those studies that neither family follows a Gondwanan vicariance diversification pattern, as previously suggested by Callmander et al. (2003) for Pandanaceae, and by Mello-Silva et al. (2011) for Velloziaceae. For example, the diversification of Velloziaceae genera from Africa (Xerophyta) and South America (Barbacenia, Barbaceniopsis, Vellozia) began 11–16 Ma based on plastid data, 5–10 Ma based on mitochondrial analyses (data not shown) and 40–55 Ma in analyses by Alcantara et al. (2018), which are all well after the separation of West Gondwana into Africa and South America in the early Cretaceous (~105 Ma; McLoughlin, 2001). In Pandanaceae, the Madagascar endemic Martellidendron diverged from its common ancestor with Pandanus and Benstonea 7–21 Ma based on plastid analyses, 4–11 Ma based on mitochondrial analyses (data not shown), and 15–38 Ma in analyses by Gallaher et al. (2015), which are all well after the separation of Madagascar from East Gondwanan landmasses in the late Cretaceous (~65 Ma; McLoughlin, 2001). Members of Pandanaceae have a paleotropical distribution and the sister taxon of the family, Cyclanthaceae, is exclusively neotropical (Eriksson, 1994; Stone et al., 1998). These families diverged 53–71 Ma based on plastid data and 49–74 Ma based on mitochondrial data, well after the separation of Africa and South America (~105 Ma) (Table 2.1). Stem ages estimated here for Pandanaceae and Cyclanthaceae, in combination with fossils from modern-day Europe attributed to each family (Iles et al., 2015), may point to a Laurasian origin for both lineages, as previously considered by Gallaher et al. (2015). Stemonaceae are distributed in 33 tropical Asia and northern Australia, with one species (Croomia pauciflora) found in the southeastern United States (Rudall and Bateman, 2006). Croomia diverged from its sister taxon, Stichoneuron, 9–33 My based on plastid analyses and 18–35 My based on mitochondrial analyses (data not shown). North America separated from former Gondwanan landmasses 165–180 Ma (McLoughlin, 2001), and so the current distribution of Stemonaceae may be consistent with an Old World origin and subsequent dispersal to the New World. However, there is no known fossil record for Stemonaceae to further clarify the latter hypothesis (Collinson et al., 1993; Herendeen and Crane, 1995; Smith, 2013). My dating analyses excluded mycoheterotrophic Triuridaceae, but Mennes et al. (2013) attributed the pantropical distribution of the family largely to dispersal events that postdate major tectonic events. 2.5.5 Age estimates in the presence of rapidly evolving mycoheterotrophic taxa Elevated rates in the three genomic compartments of mycoheterotrophs (e.g., Merckx et al., 2009a; Lemaire et al., 2011; Lam et al., 2018) led Iles et al. (2015) to hypothesize that these taxa may be challenging to incorporate in molecular dating analyses. Here, I found that inclusion of fast-evolving mycoheterotrophs within a phylogenetic framework of slower-evolving photosynthetic relatives likely moderately biased ages upwards in the vicinity of the former taxa by 2–29 Ma. This finding is consistent with previous studies that have shown that relaxed clock methods, such as the uncorrelated lognormal relaxed clock (UCLN) model used here for reanalyzing the data set of Mennes et al. (2013), may perform poorly and mislead age estimation when there is substantial among-lineage rate variation (e.g., Dornburg et al., 2011; Beaulieu et al., 2015). For example, Beaulieu et al. (2015), found that substantial lineage-specific rate heterogeneity simulated near the angiosperm crown node produced ages that were ~70 Ma older than the fossil-based age that they used to fix that node, but biases towards older ages were localized in the vicinity of simulated rate shifts and did not have knock-on effects on ages for the rest of the phylogeny. I therefore recommend cautious interpretation of ages estimated in molecular dating analyses that include mycoheterotrophs, particularly for crown ages of the closest photosynthetic relatives. This may be particularly important when using plastid genes, as these may experience substantial relaxation or release of purifying selection after loss of photosynthesis in mycoheterotrophs (Graham et al., 2017), which may further exacerbate 34 lineage-specific rate heterogeneity. In contrast, mitochondrial and nuclear genes may be less prone to this effect, although even in these genomes rates can be moderately to substantially accelerated relative to photosynthetic relatives (e.g., see Fig. A.13 here; Merckx et al., 2009a; Lemaire et al., 2011). 2.5.6 Rate accelerations and decelerations in Pandanales organellar genomes Of the three plant genomes, the mitochondrial genome (mitogenome) usually has by far the lowest substitution rate, followed by the plastome and then the nuclear genome (Wolfe et al., 1987; Palmer and Herbon, 1988). Substitution rate regimes correlate with plant life history traits, and so rate shifts commonly occur in parallel across the three plant genomes (Eyre-Walker and Gaut, 1997; Drouin et al., 2008; Zhu et al., 2014). For example, molecular rates are consistently lower in woody trees, arborescent taxa and shrubs with relatively long generation times, compared to higher and more variable rates in herbaceous plants, which tend to have substantially shorter generation times; additional processes that may contribute to rate differences include plant height, population size, speciation rate and paternal inheritance in the case of organelles (Bousquet et al., 1992; Gaut et al., 1996; Laroche et al., 1997; Smith and Donoghue, 2008; Korall et al., 2010; Lanfear et al., 2013). More rarely, rate shifts may be decoupled across the plant genomes, as is the case here, where I detected mitochondrial rate accelerations in Benstonea (Pandanaceae) and multiple Cyclanthaceae taxa, alongside sequential plastid rate decelerations in the common ancestors of the Pandanaceae-Cyclanthaceae clade and Cyclanthaceae itself, where I observed faster mitochondrial than plastid rates for multiple taxa (Figs. 2.5, A.22, A.23). Significant mitochondrial-only rate elevations have been observed at synonymous substitution sites in a few angiosperm lineages (Palmer et al., 2000; Cho et al., 2004; Parkinson et al., 2005; Bakker et al., 2006; Mower et al., 2007; Sloan et al., 2008; Zhu et al., 2014; Skippington et al., 2015); here I did not partition rate estimates among synonymous and nonsynonymous sites. Life-history traits in Pandanaceae and Cyclanthaceae may contribute to the slow plastid rates estimated for each family. For example, Pandanaceae comprise arborescent taxa, shrubs and woody climbers (Stone et al., 1998) and Cyclanthaceae taxa are herbaceous, but can be relatively tall (e.g., up to 5 m in Carludovica palmata and Sphaeradenia gigantea; Harling et al., 1998). In contrast, I estimated relatively faster plastid rates for the 35 smaller and mostly herbaceous taxa in Stemonaceae and Velloziaceae (Kubitzki, 1998a,b,c). Life-history traits may underlie decoupling of organellar rates in some Pandanaceae and Cyclanthaceae taxa leading to the rate decelerations evident in plastid sequences here, but may be obscured in mitochondrial sequences by unknown processes or events, perhaps including cryptic pseudogenization, leading to rate accelerations in this genome (Figs. 2.5, A.22, A.23). Differences in rates of sequencing error could also contribute to among-lineage differences in rate estimates for organellar data, although this seems unlikely to have led to the many-fold differences observed across lineages here. The number of fully sequenced angiosperm mitogenomes remains limited (Havird et al., 2019), and as more become available, this should help clarify the extent and frequency of lineage-specific mitochondrial rate heterogeneity. 36 Table 2.1 Estimated ages for Pandanales inferred here and in previous studies. Three variant analyses performed here (columns 2–4) excluded mycoheterotrophic Triuridaceae and were based on (i) a subset of the plastid matrix comprising three genes, analyzed using a Bayesian random local clock (RLC) model; (ii) a subset of the mitochondrial matrix comprising 12 genes, analyzed using a RLC model and; (iii) a version of a matrix used in Mennes et al. (2013) with all mycoheterotrophs excluded here (analyzed with the Bayesian uncorrelated lognormal, UCLN, relaxed clock model as in the original study). Ages are shown for major Pandanales clades only (see Table A.7 for other lineages). Ages are median estimates here (or median or mean estimates as noted in previous studies); 95% HPD (highest posterior density) intervals of ages shown in brackets when available (n/a = not applicable; age not reported or unspecified due to taxon sampling differences). ______________________________________________________________________________________________________________________ Clade/node Plastid data Mitochondrial Mennes et al. Mennes et al. Magallón et al. Givnish et al. Other studies4, 5 (this study) data (2013) data, (2013)1 (2015)2 (2018)3 (this study) mycoheterotrophs excluded (analyzed here) ______________________________________________________________________________________________________________________ PANDANALES Stem 106 (98–117) 84 (70–109) 106 (90–121) 110 (96–123) 110 (97–122) 123 (120–126) 131 (93–175)4 Crown 93 (85–104) 71 (58–94) 62 (38–90) 91 (69–110) 72 (53–100) 93 (86–102) 101 (91–118)4 Velloziaceae Stem 93 (85–104) 71 (58–94) 62 (38–90) 91 (69–110) 72 (53–100) 93 (86–102) 101 (91–118)4 Crown 51 (44–58) 34 (27–47) 31 (10–57) 37 (9–75) 49 (24–76) n/a Stemonaceae Stem 81 (69–90) 64 (52–86) 43 (25–64) 46 (24–74) 54 (49–89) 76 (68–83) – Crown 65 (53–75) 46 (37–61) 27 (11–47) 27 (9–53) n/a 50 (44–56) – Triuridaceae Stem n/a n/a n/a 84 (62–103) n/a 75 (68–82) – Crown n/a n/a n/a 69 (50–90) n/a n/a – 37 ______________________________________________________________________________________________________________________ Clade/node Plastid data Mitochondrial Mennes et al. Mennes et al. Magallón et al. Givnish et al. Other studies4, 5 (this study) data (2013) data, (2013)1 (2015)2 (2018)3 (this study) mycoheterotrophs excluded (analyzed here) ______________________________________________________________________________________________________________________ Pandanaceae Stem 61 (53–71) 56 (49–74) 37 (22–56) 38 (20–64) n/a 44 (38–50) 85 (79–133)5 Crown 35 (23–51) 26 (20–35) n/a n/a n/a 30 (26–35) 65 (42–98)5 Cyclanthaceae Stem 61 (53–71) 56 (49–74) 37 (22–56) 38 (20–64) 54 (49–89) 44 (38–50) 85 (79–133)5 Crown 49 (47–55) 49 (47–54) 22 (10–36) 21 (9–37) n/a 18 (13–22) 45 (45–72)5 _________________________________________________________________________________________________________________________________ 1 Mennes et al. (2013): median ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 2 Magallón et al. (2015): median ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 3 Givnish et al. (2018): mean ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 4 Alcantara et al. (2018): ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 5 Gallaher et al. (2015): ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model and two fossil calibrations. Ages for the Cyclanthaceae-Pandanaceae stem node and Cyclanthaceae crown node represent the peak of the 95% HPD interval; the age for the Pandanaceae crown node is the median estimate. 38 Figure 2.1 Phylogenetic relationships in Pandanales inferred from combined analysis of 119 organellar genes (37 mitochondrial and 82 plastid genes in photosynthetic taxa; 35 mitochondrial and 22 plastid genes in mycoheterotrophic Sciaphila densiflora, Triuridaceae), using partitioned likelihood (left-hand) and parsimony (right-hand) analyses of DNA data (see text for analysis details). Thick lines indicate 100% bootstrap support; values <100% are shown above branches. A circled support value in the parsimony tree indicates a branch that conflicts with the likelihood tree, also indicated by crossed dotted lines between taxa. Branches (labeled ‘a-z’) are summarized for other analyses in Table A.5. The parsimony cladogram is one of ten shortest trees; an arrow indicates a Pandanales branch that collapses in the strict consensus tree (note that two additional branches collapse in outgroup taxa, Fig. A.1). Inset phylograms show full taxon samplings of each analysis (Figs. A.1, A.4); shaded portions represent Pandanales, scale bars indicate estimated substitutions per site or number of changes. 39 40 Figure 2.2 Phylogenetic relationships in Pandanales inferred from partitioned likelihood analyses of plastid and mitochondrial DNA data, each using a gene-by-codon partitioning scheme (see text for analysis details). The left-hand cladogram is from analysis of 82 plastid genes in photosynthetic taxa (22 in mycoheterotrophic Sciaphila densiflora, Triuridaceae), the right-hand cladogram from analysis of 37 mitochondrial genes (35 in S. densiflora). Thick lines indicate 100% bootstrap support; values <100% are shown at branches (values above vs. below branches in the plastid-based tree are with S. densiflora included or excluded, respectively; n/a = not applicable). Circled support values indicate branches that conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree); crossed lines indicate topological conflict between trees. Inset phylograms show full taxon samplings of each analysis (Figs. A.6, A.13); shaded portions represent Pandanales, scale bars indicate estimated substitutions per site. 41 42 Figure 2.3 Phylogenetic relationships in Pandanales inferred from parsimony analyses of plastid and mitochondrial DNA data (see text for analysis details). The left-hand cladogram shows relationships in the shortest tree from analysis of 82 plastid genes in photosynthetic taxa (22 in mycoheterotrophic Sciaphila densiflora, Triuridaceae), the right-hand cladogram shows one of two shortest trees from analysis of 37 mitochondrial genes (35 in S. densiflora; an arrow indicates a branch that collapses in the strict consensus tree). Thick lines indicate 100% bootstrap support; values <100% are shown above branches. Circled support values and labels indicate branches that conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree); crossed lines indicate topological conflict between trees. Two branches with moderate to strong support (labeled ‘aa’, ‘bb’) are summarized for other analyses in Table A.5. Inset phylograms show full taxon samplings of each analysis (Figs. A.2, A.3); shaded portions represent Pandanales, scale bars indicate estimated number of changes. 43 44 Figure 2.4 Clade ages of Pandanales estimated using a Bayesian random local clock (RLC) model. The tree topology and node heights (the latter representing median age estimates) are based on the three-gene plastid analysis (Fig. A.20); bars represent 95% highest posterior density (HPD) intervals for node ages, derived from analyses of the same three-gene plastid data set (lower bars in dark grey) and a 12-gene mitochondrial data set (upper bars in lighter grey). Plastid and mitochondrial age estimates outside Pandanales are respectively shown in Figs. A.20, A.21. Scale bar indicates time (millions of years, Ma). 45 46 Figure 2.5 Substitution rates in Pandanales inferred from a three-gene plastid data set (left-hand tree) and a 12-gene mitochondrial data set (right-hand tree) using a Bayesian random local clock (RLC) model. Branch colors (blue to red) summarize the range of branch rates, with rate shifts also indicated as substitutions per site per million years. Plastid and mitochondrial rates outside Pandanales are shown in Figs. A.22 and A.23, respectively. 47 48 Chapter 3: Can we reliably place fully mycoheterotrophic Dioscoreales lineages using plastid genome data and morphology? 3.1 Summary Integration of full mycoheterotrophs into the plant tree of life is often complicated by the morphological modifications and substitution rate accelerations that accompany loss of photosynthesis in these lineages and a shift to relying on soil fungi for nutrition. Here I explore the performance of plastid phylogenomic and morphological data sets for inferring local placements of mycoheterotrophic taxa in the monocot order Dioscoreales. My genus-level study includes a comprehensive sampling of both mycoheterotrophic and photosynthetic lineages in the order, including the first phylogenomic and morphological data for multiple genera. Parsimony and likelihood analyses of plastid data recover consistent placements for most surveyed mycoheterotrophs. Most branches in Dioscoreales are strongly supported, considering autotrophs and the least rapidly evolving mycoheterotrophs, which allows me to confirm that Burmanniaceae and Thismiaceae are distantly related, contrary to the current classification of the order. Local depression in support values is evident when substantially rate-elevated mycoheterotrophic taxa are included in analyses. More extreme rate elevation in mycoheterotrophic Afrothismia likely contributes to stronger long-branch artefacts in parsimony and likelihood analyses involving this taxon. Although generally weakly supported, constrained and unconstrained morphology-based analyses permit me to narrow down placements of mycoheterotrophs lacking molecular data, and also support a distant relationship between Thismiaceae and Burmanniaceae. Ancestral-state reconstructions recover multiple morphological synapomorphies useful for characterizing various among- and within-family clades. Finally, in anticipation of potentially substantial updates to the family-level classification of Dioscoreales, I propose the first taxonomic treatment of Afrothismia as Afrothismiaceae, a fully mycoheterotrophic monogeneric family in the order. 49 3.2 Introduction Phylogenomic data sets based on the plastid (chloroplast) genome have facilitated generally well-supported inferences of higher-order relationships within and among nearly all orders of monocots (Chapter 2; Givnish et al., 2010, 2015, 2016, 2018; Steele et al., 2012; Barrett et al., 2013, 2016; Henriquez et al., 2014; Logacheva et al., 2014; Comer et al., 2015; Mennes et al., 2015; Ross et al., 2015; Lam et al., 2018). However, phylogenomic approaches have not yet been attempted on a broad scale for the yam order Dioscoreales (although Givnish et al., 2018, and Lam et al., 2018, included a few exemplars in broader phylogenetic studies), and it remains as one of the last major monocot clades in need of detailed attention (e.g., Trias-Blasi et al., 2015). A major complicating factor in addressing higher-order relationships in Dioscoreales has been the large set of non-photosynthetic fully mycoheterotrophic (FM) lineages present in the order. Fully mycoheterotrophic plants obtain carbon from soil-fungal partners instead of through photosynthesis (Leake, 2004; Merckx et al., 2009b); as a consequence, their plastid genomes typically experience molecular rate elevation and extensive gene loss. This has been a general problem for including heterotrophs in broader phylogenetic analyses, because plastid data underlie the bulk of our knowledge of higher-order plant relationships. For example, two of the three loci that broadly underpin the Angiosperm Phylogeny Group (APG, 1998, 2003, 2009, 2016) classification systems, atpB and rbcL, are photosynthesis related genes missing from the plastid genomes of most heterotrophs (Graham et al., 2017; Lam et al., 2018; Wicke and Naumann, 2018). Plastid-based phylogenomic studies that include heterotrophic plants can employ other retained genes not related to photosynthesis (e.g., Delannoy et al., 2011; Logacheva et al., 2014; Givnish et al., 2015; Lam et al., 2015, 2018; Mennes et al., 2015; Feng et al., 2016). However, even when it is possible to assemble broadly sampled phylogenies (e.g., Lam et al., 2016, 2018), strongly elevated substitution rates in heterotrophic plants (e.g., Merckx et al., 2009a; Lemaire et al., 2011, Lam et al., 2018) may result in long-branch artefacts in phylogenetic inference (e.g., Nickrent et al., 2004; Merckx et al., 2009a; Lam et al., 2015, 2018; Naumann et al., 2016). This effect has sometimes been observed in rate-elevated autotrophic plants (e.g., Sanderson et al., 2000; Graham and Iles, 2009; Givnish et al., 2010). Coupled with the often large numbers of 50 missing genes in the plastid genomes of heterotrophs (e.g., Lam et al., 2018; Wicke and Naumann, 2018), these plants may prove to be especially challenging to include in phylogenetic inference (Burleigh et al., 2009; Wiens and Tiu, 2012). Despite this, Lam et al. (2018) demonstrated that all but the most rate-elevated heterotrophic lineages can be readily incorporated in plastid phylogenomic analyses, even when relatively few genes are retained per taxon. Approximately two thirds of the 22 genera that comprise the order Dioscoreales have yet to be included in phylogenomic analyses, with multiple heterotrophic and photosynthetic lineages unexamined to date. A broadly representative phylogenomic sampling would be useful for addressing higher-order relationships in the order, including the circumscription of families and their inter-relationships. Family delimitation in Dioscoreales remains unsettled, with three to six families recognized in previous studies (Caddick et al., 2002a,b; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Lam et al., 2016, 2018; Givnish et al., 2018). The lack of consensus on how many lineages deserve to be recognized at the family level results primarily from disagreement on whether the mycoheterotrophic lineages in the order belong to a single family (Burmanniaceae s.l.), as proposed in recent angiosperm-wide classification schemes (APG 2003, 2009, 2016), or to two distinct families, with Thismiaceae also recognized (as in Merckx et al., 2009a, 2010; Hunt et al., 2014; Merckx and Smets, 2014; Lam et al., 2016, 2018; Givnish et al., 2018; Shepeleva et al., 2020). There may even be a third mycoheterotrophic lineage, Afrothismia (currently placed in Thismiaceae), that merits recognition at the family level based on mitochondrial and nuclear evidence (Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Shepeleva et al., 2020). The latest APG classification (APG, 2016) recognized that the circumscription of Burmanniaceae s.l. is likely problematic and needs attention. Most of the autotrophic taxa in Dioscoreales have been placed in the strictly photosynthetic families Dioscoreaceae and Nartheciaceae in recent classification schemes (APG 2003, 2009, 2016; Burmanniaceae also includes some photosynthetic species in Burmannia). The composition of Nartheciaceae has been relatively stable in recent classifications (APG 2003, 2009, 2016; note that Isidrogalvia, included as a member of Nartheciaceae in Tamura et al., 2004, belongs in Alismatales; Azuma and Tobe, 2011), but relationships among the five genera 51 that comprise the family were mostly poorly supported in previous few-gene studies (Fuse et al., 2012; Zhao et al., 2012). The composition of Dioscoreaceae has fluctuated more, as Tacca and Trichopus are often excluded from that family, to instead be recognized as distinct monogeneric families (i.e., Taccaceae and Trichopodaceae as in APG, 1998; Huber, 1998b; Kubitzki, 1998d; Merckx et al., 2010; Merckx and Smets, 2014; Lam et al., 2016, 2018; Givnish et al., 2018). Additionally, the photosynthetic genus Stenomeris is placed in Dioscoreaceae in recent angiosperm classification schemes (APG 1998, 2003, 2009, 2016), but has also been considered its own family, Stenomeridaceae (reviewed in Caddick et al., 2002a). Stenomeris has an inconsistent local placement in the few phylogenetic studies that have included it (Caddick et al., 2002a; Wilkin et al., 2005; Merckx et al., 2009a, 2010; Hsu et al., 2013). A further complication for systematic studies in Dioscoreales is that some lineages, especially of FM taxa, are poorly collected and cannot yet be included in phylogenomic analysis due to limited material. In the case of full mycoheterotrophs, this often reflects rarity, high local endemism, and the fact that these inconspicuous plants tend to be overlooked outside the context of targeted surveys (Merckx et al., 2013c). For example, most Thismiaceae are known exclusively from the type collection, and many are presumed to be extinct (Merckx et al., 2009a). Although floral and vegetative traits of mycoheterotrophs can become substantially reduced and modified after loss of photosynthesis (Leake, 1994), they can still provide a rich source of information for phylogenetic inference and for ancestral-state reconstructions. Caddick et al. (2002a) compiled observations of various pollen (Caddick et al., 1998), floral (Caddick et al., 2000), seed coat (Bouman, 1995) and vegetative (Caddick et al., 2002a) characters into a morphological matrix for a comprehensive genus-level phylogenetic study of Dioscoreales that also included several genes. I re-examined and updated this morphological data set using recent literature and revised taxonomic concepts, also adding new data for Tiputinia, a recently described FM genus in Thismiaceae (Woodward et al., 2007). I used these data to attempt to place lineages in the Dioscoreales phylogeny, including those that currently entirely lack plastid genome data. I also performed ancestral-state reconstructions to uncover synapomorphies supporting families and other lineages in the order. Here I present the first broad plastid phylogenomic survey of Dioscoreales, and in tandem analyze an updated morphological data set, with the major goal of reconstructing genus- and 52 family-level relationships in the order. Specifically, I address (i) the viability of the current three-family APG (2016) classification scheme (comprising Burmanniaceae, Dioscoreaceae and Nartheciaceae), in particular whether all fully mycoheterotrophic taxa belong in Burmanniaceae; (ii) genus-level relationships within each family, including the placement of fully mycoheterotrophic lineages on potentially problematic long branches or known from morphology alone; and (iii) ancestral-state reconstructions of morphology considering the phylogenetic trees inferred here, which I use to survey for synapomorphies of lineages within the order. I also propose a new Dioscoreales family to accommodate the fully mycoheterotrophic lineage Afrothismia. 3.3 Materials and methods 3.3.1 Taxon sampling and library preparation for molecular matrices I used ‘genome skims’ (Straub et al., 2012) to generate new plastid gene sets for phylogenetic inference (i.e., the full complement of protein-coding genes and four rDNA genes) for 14 Dioscoreales species, and added these to published sequences representing 22 other taxa in the order, along with 62 additional monocots as outgroups (Table B.1). This sampling includes representatives of all 12 monocot orders (sensu Givnish et al., 2018). The 36 taxa sampled in Dioscoreales represent all nine strictly photosynthetic genera in the order and seven of 13 mycoheterotrophic genera for which I was able to retrieve sequence data. Six of the seven latter genera are fully mycoheterotrophic (FM); Burmannia includes both photosynthetic and FM taxa (Table 3.1). I was unable to obtain sequenceable DNA for photosynthetic Stenomeris (currently placed in Dioscoreaceae; APG 1998, 2003, 2009, 2016), despite numerous attempts using a range of herbarium and fixed material, and so I represented this genus in molecular analyses with three available plastid genes recovered from GenBank (Table B.1). Genome skims for all newly sequenced Dioscoreales taxa except Trichopus sempervirens were produced following approaches outlined in Lam et al. (2015). Briefly, I prepared genomic libraries using two additional library preparation kits (Bioo NEXTflex Rapid DNA Sequencing Kit, Bioo Scientific Corp., Austin, USA; NEBNext Ultra II DNA Library Prep Kit, New England Biolabs, Ipswich, USA). I sequenced these libraries as 100-bp, 125-bp or 300-bp paired-end reads respectively on 53 multiplexed lanes of HiSeq 2000, HiSeq 2500 or MiSeq platforms (Illumina, San Diego, USA) that included 4–45 taxa. Genome skims for T. sempervirens were obtained using the target enrichment gene panel I developed in Chapter 4, following approaches outlined in Viruel et al. (2019), and were sequenced as 150-bp paired-end reads in a multiplexed lane of a HiSeq X platform that included 96 taxa. 3.3.2 Contig assembly and gene annotation I used methods described in Lam et al. (2015) to assemble plastid contigs from genome skim data, and to retrieve sets of protein-coding and rDNA genes (after removing mitochondrial and nuclear contigs), with minor modifications noted here. I used CLC Genomics Workbench (v.6.5.1; CLC Bio, Aarhus, Denmark) to assemble contigs for most taxa. To assemble plastid contigs from Trichopus sempervirens, I used NOVOPlasty v. 2.7.2 (Dierckxsens et al., 2017), with the rbcL plastid gene sequence of Dioscorea elephantipes (GenBank accession NC_009601.1) as the seed input, and otherwise using default settings. All contigs assembled here had at least 30× average coverage regardless of assembly program. I retrieved plastid gene sets from T. sempervirens using GeSeq (Tillich et al., 2017) with default settings. For eight rate-elevated FM taxa I additionally ran pairwise BLASTP searches (Altschul et al., 1990) on individual genes to retain regions with an expect (e)-value threshold of 10, relative to homologous genes of photosynthetic taxa. I used genes from photosynthetic Tacca leontopetaloides as the query sequence for mycoheterotrophic Afrothismia, Haplothismia and Thismia, and photosynthetic Burmannia capitata as the query for mycoheterotrophic Apteria and Gymnosiphon. Most of the recovered protein-coding genes for Afrothismia hydra, A. korupensis and A. winkleri contain internal stop codons at 1–8 sites per gene, and were slightly to moderately truncated compared to other taxa (genes of A. gesnerioides were completely uninterrupted, and most were slightly to moderately truncated, or slightly longer compared to other monocots); one gene recovered for Thismia rodwayi also contains an internal stop codon (Table B.2). These internal stop codons may reflect undocumented RNA-edited sites (e.g., Takenaka et al., 2013; Bell et al., 2020) or instances of pseudogenization; I included all the Afrothismia and T. rodwayi sequences in phylogenetic analyses described below. 54 3.3.3 Sequence alignment and molecular matrix construction I added species that were newly sequenced here or retrieved from GenBank to a published matrix (Lam et al., 2018), yielding a final 98-taxon matrix. To do so, I generated 82 individual plastid gene files, representing 78 protein-coding genes and four rDNA genes, each with 98 taxon terminals. I generated DNA-based alignments for each gene, including manual adjustments, and prepared a concatenated matrix as described in Chapter 2 for plastid data in that study. To help with manual adjustments for aligning FM taxa, I additionally used the results from pairwise BLASTP searches described above. I followed Lam et al. (2015) to check for matrix compilation errors; none were found. Photosynthetic taxa are represented by all or most genes found in plastid genomes of photosynthetic angiosperms, except Stenomeris borneensis, for which I included the three available protein-coding genes (i.e., atpB, matK, rbcL; Table B.1). Fully mycoheterotrophic taxa are represented by 6–24 protein-coding genes and 2–4 rDNA genes, the reduced number likely reflecting gene loss (Table B.2). Missing genes (i.e., those lost in FM taxa or the few that were not recovered for photosynthetic taxa) were coded as missing data. The gene sets retrieved for each of the FM taxa are likely complete or nearly complete, based on preliminary circularized plastid genome assemblies (to be presented elsewhere). I retrieved the ycf1 locus for most photosynthetic taxa and the FM species Campylosiphon congestus, but did not include it in the final matrix due to difficulties aligning it across the full taxon set. The final DNA-based matrix is 80,390-bp long (derived from 67,156 bp of unaligned plastid sequence data in Dioscorea membranacea, for reference). I also translated this into an amino-acid-based matrix comprising 25,032 residues. 3.3.4 Molecular phylogenetic analyses I analyzed molecular data matrices using PAUP* 4.0a build 166 (Swofford, 2002) for parsimony analyses and RAxML-NG (Kozlov et al., 2019) for maximum likelihood analyses. For parsimony I analyzed two versions of the nucleotide-based matrix. I prepared a ‘core’ 89-taxon version excluding a photosynthetic Dioscoreaceae taxon (Stenomeris borneensis) with only three available genes, but including three of the least rapidly evolving FM taxa in Burmanniaceae (Burmannia itoana and Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). I selected these three taxa after assessing rate elevation through visual inspection of branch-55 length estimates from likelihood analyses here (see below). I focused most effort on this core 89-taxon set, using it as a reference point for examining the effect of potentially problematic taxa (i.e., either data-poor or on very long-branches) on phylogenetic inference. I also prepared an ‘all-inclusive’ 98-taxon version including all Dioscoreales taxa with available molecular data here (i.e., with photosynthetic Stenomeris and all 11 FM taxa included). For parsimony analysis of each matrix, I ran heuristic searches using tree bisection-reconnection (TBR) branch swapping, considering 1000 random addition replicates, holding up to 100 trees at each step, and otherwise used default settings. I estimated branch support using a bootstrap analysis (Felsenstein, 1985) consisting of 500 replicates, each with 100 random addition replicates, TBR branch swapping and otherwise default settings. For likelihood-based inference, I conducted unpartitioned and partitioned analyses of DNA and amino-acid data. I partitioned DNA alignments by both gene and codon position (a ‘G x C’ scheme, e.g., Lam et al., 2015) for the 78 protein-coding genes, with additional partitions for each of the four rDNA genes, resulting in 238 initial partitions. The amino-acid (AA) matrix had 78 initial partitions, corresponding to one partition per gene. I used the ModelFinder function (Kalyaanamoorthy et al., 2017) in IQ-TREE v. 1.6.12 (Nguyen et al., 2015) to combine partitions that did not have substantially different substitution models, employing the relaxed hierarchical clustering algorithm to examine the top 10% partition merging schemes (-rcluster 10), the corrected Akaike information criterion (AICc) to merge partitions, and considered only invariant (I) and gamma (G) rate heterogeneity across sites (-m TESTMERGEONLY). I obtained a range of optimal DNA and AA substitution models for the resulting partitioning schemes, which I used in subsequent likelihood analyses (Table B.3). I also used ModelFinder to find optimal substitution models for unpartitioned DNA- and AA-based matrices (Table B.3). I ran a total of ten variant likelihood analyses. Four of these were based on the core 89-taxon matrix (also analyzed using parsimony, see above) that excludes Stenomeris borneensis but includes three of the least rapidly evolving FM taxa in Burmanniaceae and Thismiaceae. I considered unpartitioned and partitioned analyses of both the DNA and AA versions of this matrix. I conducted six additional partitioned likelihood analyses of expanded DNA-based taxon sets to assess the placement of nine potentially problematic Dioscoreales taxa by adding: (i) a data-poor photosynthetic taxon, S. borneensis, to the core data set, resulting in a 90-taxon matrix; 56 (ii) two highly rate-elevated FM representatives of Burmanniaceae, Apteria aphylla and Gymnosiphon longistylus, to the core data set, resulting in a 91-taxon matrix; (iii) two highly rate-elevated FM representatives of Thismiaceae, Thismia tentaculata and T. rodwayi, to the core data set, resulting in a second 91-taxon matrix; (iv) four species of FM Afrothismia to the core data set, which are by far the most rate-elevated lineage that I surveyed (see below), resulting in a 93-taxon matrix; (v) the only sampled member of Afrothismia without sequence internal stop codons, A. gesnerioides, to the core data set, resulting in a second 90-taxon matrix; and (vi) all Dioscoreales taxa with available molecular data here, resulting in the all-inclusive 98-taxon matrix (also analyzed using parsimony, see above). All of these likelihood analyses included 40 independent searches for the best tree, each search starting from 20 parsimony-based and 20 random trees. I estimated branch support in likelihood analyses using bootstrap analysis (Felsenstein, 1985), and used the extended majority rule (MRE) bootstrap stopping test (Pattengale et al., 2010) every 50 bootstrap replicates. This test, which is implemented in RAxML-NG, consists of randomly splitting the set of replicates into two equal halves (with 1000 permutations per test), computing the majority rule consensus trees for each half, and using the weighted Robinson-Foulds (WRF) criterion to assess dissimilarity between the two resulting consensus trees. Bootstrapping stops when the relative WRF between sets is ≤1% for at least 990 permutations. Here this resulted in 100–1000 thorough-search bootstrap replicates across the ten variant likelihood analyses. Each bootstrap analysis used the same partitioning schemes and substitution models as the corresponding searches for best trees. For all bootstrap analyses performed here, I considered poorly and strongly supported branches to respectively have <70% and ≥95% support (e.g., Soltis and Soltis, 2003). 3.3.5 Constraint tests of alternative phylogenetic hypotheses I tested for five phylogenetic hypotheses of interest that were not recovered in optimal trees from analysis of the core molecular taxon set here (i.e., hypothesis i below), or from analysis of an expanded taxon set that included Afrothismia (i.e., hypotheses ii-v below). Hypothesis (i) considers a possible clade comprising all members of Burmanniaceae and Thismiaceae s.s. (excluding Afrothismia from consideration in this case, to remove the possible distorting effect of 57 its extreme rate elevation). This constraint would be consistent with the current circumscription of Burmanniaceae s.l. in recent angiosperm classifications (APG, 2003, 2009, 2016), ignoring Afrothismia. The remaining hypotheses consider (ii) a possible clade comprising Afrothismia and Dioscoreaceae, (iii) a possible clade comprising Afrothismia and Burmanniaceae, (iv) a possible clade comprising Afrothismia and Thismiaceae s.s., and (v) a possible clade comprising Afrothismia, Taccaceae and Thismiaceae s.s. Hypotheses ii-iv assess several different possible arrangements of Afrothismia in Dioscoreales that were either observed in other analyses here (i.e., hypotheses ii-iv, see below), or that are consistent with a possible circumscription of Thismiaceae s.l. to include Afrothismia (i.e., hypothesis iv; see Maas-van de Kamer, 1998), or with a possible placement uncovered in a subset of analyses in Merckx et al. (2009a, 2010) where Afrothismia was recovered as a close relative of Taccaceae and Thismiaceae s.s., but outside both families (i.e., hypothesis v). In all cases, I tested whether I had sufficient power to reject the trees recovered under constrained hypotheses versus unconstrained optimal trees for that taxon set. For tree searches using constrained branches, I excluded data-poor or long-branch FM taxa that were not relevant to the hypotheses. Thus, I employed the core 89-taxon set for test (i), and a modified version of the 93-taxon set including only two species of Afrothismia for tests (ii–v). I did this reduction on the latter matrix because three of the four Afrothismia species were nearly identical at the sequence level (see below). I made constraint trees for tests (i–iv) by fixing one branch to obtain two taxon partitions, one comprising the species of interest, and another comprising the remaining taxa. For test (v) I fixed two branches to simultaneously constrain two taxon partitions, one comprising Thismiaceae-Taccaceae, and the other comprising Afrothismia-Thismiaceae-Taccaceae (in this case effectively placing Afrothismia close to both families, but not allowing it inside either). I recovered the best likelihood trees that satisfied each of the five constraints using the –tree-constraint function in RAxML-NG with default settings. I then performed the Shimodaira-Hasegawa (SH) and approximately unbiased (AU) tests in CONSEL v.0.20 (Shimodaira and Hasegawa, 2001), using site-likelihoods from unpartitioned likelihood analyses, to address whether the fit of the data was significantly different for unconstrained vs. constrained trees. 58 3.3.6 Taxon sampling for morphological analyses I updated the taxon sampling in a previous morphological study (Caddick et al., 2002a) to include all 22 currently recognized genera in Dioscoreales. I added the genera Haplothismia and Tiputinia, and reduced each of four genera (Burmannia, Dioscorea, Tacca, Thismia) represented by more than one taxon in the original study (Caddick et al., 2002a) to composite genus-level placeholders. I removed four genera (Epipetrum, Nanarepenta, Rajania, Tamus) that have since been sunk into Dioscorea (Caddick et al., 2002b), one genus (Geomitra) that is considered a synonym of Thismia (Stone, 1980; Maas-van de Kamer, 1998; Merckx et al., 2013a), and one genus (Cymbocarpa) that is nested in Gymnosiphon with strong support in molecular phylogenies (Merckx et al., 2006, 2008a, and see Merckx et al., 2013a); I also considered these taxonomic updates when scoring each genus. In the final 24-taxon morphological matrix, 13 genera are represented by composite genus-level placeholders that were either reduced here (Burmannia, Dioscorea, Tacca, Thismia) or were scored at the genus level in the original study (Afrothismia, Aletris, Campylosiphon, Gymnosiphon, Hexapterella, Narthecium, Nietneria, Oxygyne, Stenomeris). Nine genera are represented by their full species diversity: eight of these are monotypic (Apteria, Dictyostega, Haplothismia, Lophiola, Marthella, Metanarthecium, Miersiella, Tiputinia) and one (Trichopus) has two accepted species sampled here for both morphological and molecular data (for accepted species in each genus see Govaerts, 2020; Govaerts et al., 2020; Merckx et al., 2013a). Japonolirion osense (Petrosaviaceae, Petrosaviales) was included as an outgroup taxon. 3.3.7 Morphological matrix scoring I updated the published morphological data set of Caddick et al. (2002a) to obtain a 35-character matrix consisting of discrete (binary or multistate) characters representing vegetative and reproductive structures of sporophyte plants (Sub-Appendix B.1, Table B.4). I used newly generated data, revised interpretations of the original data, and available literature to redefine and update existing characters, and also to add new characters (references in Sub-Appendix B.1). I retained 33 characters that were informative for Dioscoreales out of the 52 characters in the original matrix, and excluded most taxa outside Dioscoreales as being beyond the focus of this study. I scored morphological traits for the two newly added species, Haplothismia exannulata 59 and Tiputinia foetida, based on available literature (references in Sub-Appendix B.1). I combined two of the retained characters (characters 39, 40 in the original matrix) into one (now character 31), redefined nine characters (characters 1, 2, 9, 12, 14, 17, 18, 20, 26 in the updated matrix; Fig. B.21), and added three floral characters (characters 6, 8, 25; Figs. B.22, B.23); see Sub-Appendix B.1 and Table B.4. All but a few character states were the same across species that were combined to represent composite genus-level placeholders; however, states were re-scored as polymorphic in a few instances in which they differed among constituent species in the placeholder. States that could not be scored for individual taxa were coded as missing data. 3.3.8 Morphology-based phylogenetic analyses I conducted both unconstrained and constrained analyses of morphological data using parsimony- and likelihood-based approaches. I first used the 24-taxon morphological matrix to perform unconstrained searches for best trees and for bootstrap analysis. I then performed constrained analyses in which I used a topology derived from phylogenetic analyses of molecular data (see below) as a constraint for inferring morphology-based taxon placements. Unconstrained and constrained analyses of morphological data paralleled the approaches used in molecular analyses, but with several differences. I treated characters that are variable within individual lineages as polymorphic (i.e., ‘01’) for parsimony analysis. As likelihood analyses do not accommodate within-taxon polymorphisms, I recoded affected cells as missing data; this recoding rendered characters 4 and 30 constant, and so I excluded both characters from morphology-based likelihood analyses. I also used an MK substitution matrix for likelihood analyses, with acquisition bias correction to account for branch-length overestimation that may result from sampling only variable characters (Lewis, 2001), and considering up to three model states with equal rates (i.e., model MULTI3_MK+ASC_LEWIS). For the constrained parsimony and likelihood analyses of morphological data, I employed a 17-taxon tree derived from partitioned likelihood analyses of molecular data as a backbone constraint for morphology-based tree inference, in order to place seven Dioscoreales taxa missing from the tree constraint. Japonolirion osense was included as an outgroup taxon. All seven added taxa are fully mycoheterotrophic (FM); six of these have no available plastid data (Dictyostega, Hexapterella, Marthella, Miersiella, Oxygyne, Tiputinia), and one (Afrothismia) is 60 an extremely rate-elevated lineage with a highly uncertain placement based on the molecular analyses conducted here (see below). Each genus in the constraint tree is represented by one terminal (except Trichopus, represented by the two species that comprise the genus). The seven taxa missing from the constraint tree were first added simultaneously in parsimony and likelihood analyses. In a second series of analyses, each missing taxon was added individually in separate analyses, again using both parsimony and likelihood. For parsimony analyses, I enforced a backbone constraint in PAUP* and ran a heuristic search using the same settings as in the molecular analyses (see above). For likelihood analyses, I used the --tree-constraint function in RAxML-NG with default settings. I performed bootstrap analysis for both inference methods as for molecular data here, but for parsimony I limited initial trees to 100 per bootstrap replicate. 3.3.9 Construction and phylogenetic analyses of a combined molecular-morphological matrix I added the 24-taxon morphological matrix to the 98-taxon molecular matrix that includes all Dioscoreales taxa sampled for plastid data here. The final matrix of combined molecular and morphological data consists of 104 taxa: 16 of 22 Dioscoreales genera are represented by both data types, with the remaining six solely by morphological data (Table 3.1); all taxa outside Dioscoreales are represented solely by molecular data except the outgroup taxon Japonolirion osense for which both data types were scored. I analyzed the combined matrix using both parsimony and likelihood methods, following the same procedures as used for strictly molecular analyses, with exceptions noted. For parsimony-based heuristic searches, I considered a maximum of 100 trees per random addition replicate, holding one tree at each step. Partitioned likelihood analyses employed a gene-by-codon partitioning scheme for molecular data (Table B.3-I), and a single partition was used for the morphological data using the MULTI3_MK+ASC_LEWIS model. 3.3.10 Reconstruction of morphological character evolution I performed parsimony-based character state reconstructions using two topologies selected from phylogenetic analyses of morphological data that include placeholders for all Dioscoreales genera. As it was impractical to consider character state reconstructions for all optimal 61 parsimony trees, I arbitrarily selected one of 216 shortest trees from unconstrained parsimony analysis. I also reconstructed character states on the best tree from constrained likelihood analyses that included all taxa simultaneously. For each reconstruction analysis I used both ACCTRAN and DELTRAN optimizations in PAUP*, treating characters that are variable within taxa as polymorphic (i.e., ‘01’), and otherwise used default settings. 3.4 Results 3.4.1 Higher-order relationships in Dioscoreales based on molecular data Most tree-wide relationships recovered in the two parsimony and 10 likelihood analyses based on molecular data are well supported and consistent with each other (Figs. 3.1, B.1–B.12; support values for Dioscoreales summarized in Table B.5). I use the topology from partitioned DNA-based likelihood analysis of the core 89-taxon data set (Fig. 3.1, left-hand tree, and Fig. B.4) as a common reference point for comparison, and in particular for discussions of differences across the variant analyses of both the core taxon set (Figs. B.1, B.3, B.5, B.6) and expanded taxon sets. I included the latter to assess the placement of nine potentially problematic Dioscoreales taxa (i.e., the data-poor photosynthetic genus Stenomeris in Dioscoreaceae, and the eight most rate elevated FM species across Afrothismia, Burmanniaceae and Thismiaceae sampled here; Figs. 3.2, B.2, B.7–B.12). Three Dioscoreales branches in molecular analyses that conflict with the reference topology, indicated in the tree figures where they occur, are poorly to moderately supported (Fig. 3.1, right-hand tree, and Figs. B.1, B.2). Two of these involve Burmanniaceae branches in the vicinity of FM taxa when all these lineages are simultaneously included in parsimony analyses (i.e., the all-inclusive 98-taxon set; Fig. B.2). An additional conflict concerns the precise placement of photosynthetic Metanarthecium within Nartheciaceae: Metanarthecium is recovered either as the sister group of the rest of Nartheciaceae in parsimony analyses, with poor support (Fig. 3.1, right-hand tree, and Figs. B.1, B.2; Table B.5), or as the sister group of Aletris in all likelihood analyses, with moderate to strong support (branch ‘v’ in Fig. 3.1, left-hand tree, and Figs. B.3–B.12; Table B.5). Underlying relationships among and within the six Dioscoreales families (sensu Merckx et al., 2010; Lam et al., 2016) are nearly all strongly supported in the reference topology, and are 62 completely consistent with those inferred in most of the remaining analyses. All five parsimony and likelihood analyses of the core 89-taxon set recover the same basic reference topology for Dioscoreales (Figs. 3.1, B.1, B.3–B.6), except for the poorly supported Metanarthecium conflict noted above. Setting aside taxon sampling differences, the reference topology is also recovered in likelihood analyses of three variant matrices expanded beyond the core taxon set to include data-poor photosynthetic Stenomeris (90-taxon matrix, Fig. B.7), and the most highly rate-elevated FM taxa in Burmanniaceae (Apteria and Gymnosiphon; 91-taxon matrix, Figs. 3.2a, B.8), and Thismiaceae (Thismia rodwayi and T. tentaculata; 91-taxon matrix, Figs. 3.2b, B.9). Bootstrap support values for branches in likelihood analyses of these expanded data sets are mostly ≥95%, comparable to those in the reference analysis (Fig. 3.1, left-hand tree, and Fig. B.4), with a few exceptions in the expanded sets where inferred support is poor to moderate in the vicinity of added taxa (Figs. B.7, B.8), or more generally across Dioscoreales (Fig. B.9); see branches ‘a-z’ in Table B.5. Considering each of these expanded taxon sets individually, the analyses of a 90-taxon matrix that includes Stenomeris place this genus in a poorly supported clade with Dioscorea, the only other genus of Dioscoreaceae s.s. (Fig. B.7). Analyses of a 91-taxon matrix that includes Apteria and Gymnosiphon recover these two genera as a strongly supported clade in Burmanniaceae, in turn weakly supported as the sister group of Campylosiphon congestus; most of the remaining branches within the family have substantially less support with Apteria and Gymnosiphon included in analysis (Figs. 3.2a, B.8, and see branches ‘m,’ ‘o,’ and ‘p’ in Table B.5). Analyses of a 91-taxon matrix that includes two species of Thismia recover them as a strongly supported clade that is in turn weakly supported as the sister group of Haplothismia. In this analysis with both Thismia species included, support values show substantial drops for interfamily relationships and for the monophyly of Dioscoreales compared to the reference analysis (cf. Fig. 3.1 and Figs. 3.2b, B.9, and see branches ‘i,’ ‘k,’ ‘l,’ ‘r,’ and ‘z’ in Table B.5). The remaining analyses are based on the all-inclusive 98-taxon matrix (Figs. B.2, B.12), or on the 90-taxon or 93-taxon data sets that respectively include either one or four species of the extremely rate-elevated FM genus Afrothismia (Figs. B.10, B.11). The resulting trees all have nearly or exactly the underlying topology of the core 89-taxon reference analysis, considering only taxa in common across these sets (cf. Fig. 3.1 and Figs. B.2, B.10–B.12). However, species 63 added beyond the core set in these expanded analyses render some taxa non-monophyletic, with poor to moderate support for that in all cases. For analyses with the all-inclusive 98-taxon set, I infer unusual FM clades comprising members of multiple families, which include (i) Afrothismia and Burmanniaceae taxa in likelihood analyses (Fig. B.12), and (ii) Afrothismia, Burmanniaceae and Thismia (but not Haplothismia) in parsimony analyses (Fig. B.2); Thismiaceae s.s. (i.e., ignoring Afrothismia) are not monophyletic in either analysis as Thismia and Haplothismia are in disjunct positions (Figs. B.2, B.12). I can reject hypothesis (i) of a constrained clade comprising Burmanniaceae and Thismiaceae s.s. (P = 0.001 from AU and SH tests), as there is significant support for an unconstrained best likelihood tree in which these two lineages were recovered as distant relatives in Dioscoreales (Fig. 3.1, left-hand tree, and Fig. B.4); this analysis focused on the core set of 89 taxa, and thus excluded longer-branch Afrothismia, Burmanniaceae and Thismiaceae from consideration, to reduce possible distorting effects of highly rate-elevated taxa (Figs. 3.2, B.8–B.11). When I added Afrothismia to the core set and tested hypotheses for different placements within Dioscoreales compared to its unconstrained (but likely artefactual) placement as the sister group of Poaceae (Fig. B.10), I was unable to reject multiple possible alternative positions for the genus, using either AU or SH tests respectively, including: (ii) a possible position of Afrothismia within or as the sister group of Dioscoreaceae (P = 0.483 and P = 0.477; Fig. B.11), (iii) a possible position within or as the sister group of Burmanniaceae (P = 0.446 and P = 0.442; Figs. 3.2c, B.12), (iv) a possible position within or as the sister group of Thismiaceae s.s., represented by Haplothismia (P = 0.126 and P = 0.145; suggested by Fig. B.2), and (v) a possible arrangement with Afrothismia as the sister-group of a clade comprising Taccaceae and Thismiaceae s.s. (P = 0.15 and P = 0.155). 3.4.2 Local placements of fully mycoheterotrophic taxa using morphological data Overall relationships in Dioscoreales across the morphology-based searches, using both unconstrained parsimony and likelihood approaches, show notable differences compared to the molecular analyses (for example, compare Fig. 3.1 and Figs. B.13, B.14). Differences in morphology-based analyses include (i) the non-monophyly of Nartheciaceae; (ii) the non-monophyly of Dioscoreaceae (note the disjunct positions of Dioscorea vs. Stenomeris); and (iii) 64 various relationships among Dioscoreaceae, Taccaceae, Trichopodaceae and taxa in Thismiaceae (Figs. B.13, B.14). However, these conflicts with the molecular analyses were in general poorly supported, and in the case of parsimony analysis, were also unresolved in the strict consensus of optimal trees (see arrows in Fig. B.13). The monophyly of FM Thismiaceae s.l. (including Afrothismia) was recovered in all optimal parsimony trees (Fig. B.13), but not in the optimal likelihood tree, as photosynthetic Tacca and Stenomeris were interspersed among these FM taxa (Fig. B.14). Support for the monophyly or non-monophyly of Thismiaceae s.l., and for relationships within the family (in the parsimony tree; Fig. B.13) or the broader clade that contained Thismiaceae taxa (in the likelihood tree; Fig. B.14), were mostly poorly supported in both unconstrained morphology-based analyses. However, in no case were members of Thismiaceae found to be closely related to Burmanniaceae (Figs. B.13, B.14). The monophyly of Burmanniaceae (s.s., excluding Thismiaceae) is consistently recovered with moderate to strong support across unconstrained morphology-based and molecular analyses (for example, compare Fig. 3.1 and Figs. B.13, B.14). Within Burmanniaceae, Burmannia, Campylosiphon and Dictyostega are found in consistent positions across unconstrained morphology-based analyses, respectively as the successive sister groups of all remaining genera in the family except Hexapteralla (Figs. B.13, B.14); however, these arrangements all had poor bootstrap support. I also conducted constrained morphology-based parsimony and likelihood analyses, using a fixed topology to find optimal placements for seven FM taxa with unavailable or highly rate-elevated molecular data. Taxon placements using these constrained analyses are largely consistent with each other, whether the seven taxa are added simultaneously (Figs. 3.3, B.15, B.16), or in individual analyses with one added taxon per analysis (summarized in Fig. B.17). On the whole, the different constrained morphology-based analyses also agree with the unconstrained analyses (cf. Figs. 3.3, B.15–B.17 and Figs. B.13, B.14). For example, in Thismiaceae s.l., Afrothismia forms a clade with Thismia, and Oxygyne is in turn their sister-group, with support ranging from poor to moderate for the relevant branches in the constrained and unconstrained analyses including all taxa (Figs. 3.3, B.13–B.16). When added individually to constrained analyses, Afrothismia and Oxygyne are each the sister-group of Thismia with poor to moderate support for the respective clades (Figs. B.17a,b). The precise placement of Tiputinia in 65 Thismiaceae s.l. was less clear, as it was variously recovered with poor support as either the sister group of the remaining Thismiaceae s.l. taxa included in analyses (Fig. 3.3, left-hand tree, and Figs. B.16, B.17c), or in a polytomy with them (Fig. 3.3, right-hand tree, and Figs. B.13, B.15), or even in a clade with Haplothismia and photosynthetic Taccaceae (Fig. B.14). In Burmanniaceae, several relationships were also consistent across constrained and unconstrained analyses (cf. Figs. 3.3, B.15–B.17 and Figs. B.13, B.14). Hexapterella is consistently inferred to be the sister group of the rest of Burmanniaceae, followed successively by Burmannia, then Campylosiphon, and then Dictyostega, when these taxa are all included in analyses (Figs. 3.3, B.13–B.16). Miersiella consistently forms a clade with Gymnosiphon (Figs. 3.3, B.13–B.16, B.17e). The precise position of Marthella within Burmanniacae was more variable across analyses, although it was consistently inferred to be closely related to Apteria, Gymnosiphon and Miersiella (Figs. 3.3, B.13–B.16), or in a position consistent with this when added individually in constrained analysis (Fig. B.17d). 3.4.3 Combined molecular and morphological analysis Setting aside taxon sampling differences, there are no strongly supported topological conflicts (i.e., cases where both conflicting arrangements are strongly supported) among analyses based on molecular (Figs. 3.1, B.1–B.12) vs. morphological (Figs. 3.3, B.13–B.17) data; see also Table B.5. I therefore combined the full 98-taxon molecular matrix with available morphological data (Figs. B.18, B.19). Focusing only on the shared taxa across these diverse analyses, the broad relationships based on both parsimony and likelihood frameworks are congruent across analyses of strictly molecular (Figs. 3.1, B.1–B.12), strictly morphological (Figs. 3.3, B.13–B.17) and combined data (Figs. B.18, B.19); see also Table B.5. For likelihood analyses of the combined matrix, of the four Dioscoreales families comprising more than one genus, three (Dioscoreaceae s.s., Nartheciaceae and Thismiaceae s.s., the latter ignoring Afrothismia) are recovered as monophyletic and one (Burmanniaceae) is not monophyletic because Afrothismia is nested in it (Fig. B.19). These relationships were all poorly supported, except for the monophyly of Nartheciaceae. In contrast, in parsimony analyses only the family Nartheciaceae is recovered as monophyletic, with strong support for that clade; the remaining families are non-monophyletic with poor support (Fig. B.18). Setting aside the 66 placement of Afrothismia in likelihood analyses of the combined molecular-morphological matrix, the recovered family-level relationships are fully consistent with those based on partitioned likelihood analyses of the core 89-taxon set (Fig. 3.1, left-hand tree, and Fig. B.4), although with poor to moderate support in the combined former analysis (Fig. B.19). The backbone Dioscoreales topology from parsimony analyses of the combined molecular-morphological matrix (Fig. B.18) is poorly supported but similar to that from parsimony analyses of the 98-taxon molecular matrix (Fig. B.2). Both depict an eclectic and poorly supported mycoheterotrophic clade that includes all members of Burmanniaceae plus a nested subset of FM taxa from Thismiaceae s.l. (i.e., Afrothismia and all Thismiaceae except Haplothismia, see Fig. B.2, or except Haplothismia and Tiputinia, see Fig. B.18). Dioscoreaceae s.s. were also not monophyletic, as Stenomeris was not the sister-group of Dioscorea in either molecular or combined molecular-morphological parsimony analyses (Fig. B.2, B.18). Considering only taxa in common with the core molecular taxon set (e.g., Fig. 3.1), intrafamily relationships are generally consistent with those from molecular-morphological analyses (Figs. B.18, B.19). Local placements of FM taxa added in morphology-based analyses (Figs. 3.3, B.13–B.17) tend to differ compared to those from combined molecular-morphological parsimony and likelihood analyses, but are consistently poorly supported in both of the latter analyses (Figs. B.18, B.19). 3.4.4 Morphological ancestral-state reconstructions I examined parsimony-based character optimizations on two tree topologies, one inferred from unconstrained parsimony analysis (i.e., one of 216 shortest trees underlying Fig. B.13) and another from constrained likelihood analysis (i.e., the single best tree shown in Fig. 3.3, left-hand tree, and Fig. B.16). I focused on character transitions that represent common synapomorphies on both trees, and that were also reconstructed identically across ACCTRAN and DELTRAN optimization. These synapomorphies are summarized in Fig. 3.4; see Sub-Appendix B.1 for character descriptions, Table B.4 for taxon states, and Fig. B.20 for character optimizations on an optimal parsimony tree. Twenty of 35 morphological characters exhibited transitions satisfying the two conditions that I imposed (i.e., transitions shared across the two selected topologies and reconstructed 67 identically under ACCTRAN and DELTRAN optimization in each tree; Figs. 3.4, B.20). Nine of these transitions are useful for defining major inter- and intra-family clades that were recovered in phylogenetic analyses here. Specifically, Burmanniaceae are characterized by four floral synapomorphies: flowers with three stamens (character 9, convergent in the Thismiaceae s.s. genus Oxygyne), with anther thecae that both dehisce transversely (character 15) and are separated by a laterally expanded connective (character 13); the stigma lobes in the family also have a characteristic funnel shape (character 26). There are two diagnostic ovary characters for clades within Burmanniaceae: (i) a clade comprising Apteria, Gymnosiphon, Marthella and Miersiella is defined by a unilocular ovary (character 21) and (ii) a clade comprising these four taxa plus Dictyostega has parietal placentation (character 22). However, both states (in characters 21 and 22) are inferred to occur convergently in Tacca and Thismiaceae s.l. (Figs. 3.4, B.20). The presence of an annulus in the floral tube (character 8) defines a clade comprising Afrothismia, Oxygyne and Thismia. A clade comprising all members of Dioscoreales except Nartheciaceae and Burmanniaceae is characterized by the presence of raphide crystals in vegetative organs (character 3). A clade comprising all Dioscoreales except Nartheciaceae is defined by winged fruits (character 35). This state is inferred to have reversed independently in Thismia and also in Dioscorea, and can be considered as an autapomorphy for the corresponding placeholder taxa for these genera in Figs. 3.4, B.20 (or more accurately, polymorphic for the placeholder taxon, and possibly an apomorphy for a lineage within it). 3.5 Discussion 3.5.1 Plastid phylogenomics and higher-order relationships in Dioscoreales Only limited phylogenomic-scale data sets have been used to infer higher-order relationships in Dioscoreales to date, in analyses that included eight of 22 genera in the order. Plastid genome-based studies by Givnish et al. (2018) and Lam et al. (2018) sampled three photosynthetic genera (one from each of Dioscoreaceae s.s., Taccaceae and Nartheciaceae, but omitted Trichopodaceae), four fully mycoheterotrophic (FM) genera (one in Thismiaceae, the rest in Burmanniaceae), and one genus (Burmannia, Burmanniaceae) that includes both autotrophic and FM species. In addition, most of the sampled FM lineages have elevated substitution rates 68 (reflected in long branches), which can be problematic for phylogenetic inference if extreme (e.g., Lam et al., 2018). These phylogenomic inferences have been largely consistent with those recovered in earlier few-gene studies (Caddick et al., 2002a; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Lam et al., 2016), but multiple higher-order relationships remained unresolved. To address these outstanding issues in Dioscoreales phylogeny, my study substantially extends previous phylogenomic taxon samplings of the order for both photosynthetic and FM taxa. The expanded focus adds multiple photosynthetic lineages in Burmannia (Burmanniaceae), Dioscorea (the largest genus in Dioscoreaceae), Tacca (the sole genus of Taccaceae), Trichopodaceae (with both species included here) and Nartheciaceae (achieving a full genus-level sampling), plus several additional FM lineages in Thismiaceae s.l. (Haplothismia exannulata, Thismia rodwayi and four species of Afrothismia). Haplothismia was a fortuitous addition for Thismiaceae, as it represents an FM lineage that is relatively slowly evolving (e.g., Fig. 3.2b). I thus employed Haplothismia along with a core ‘slow’ set of taxa in analyses that also included more slowly evolving FM members of Burmanniaceae, and all sampled photosynthetic lineages in the order except for Stenomeris, represented here by only three available genes (e.g., Fig. 3.1). The main rationale for focusing initial analyses on this core taxon sampling was to develop a strongly supported phylogenetic baseline for iteratively adding data-poor and rate-elevated taxa. Additionally, with this approach I attempt to avoid possible long-branch artefacts (Felsenstein, 1978; Hendy and Penny, 1989) related to heterotrophic taxa, which may mislead phylogenetic inference and also depress support values when included (Neyland and Hennigan, 2003; Nickrent et al., 2004; Barkman et al., 2007; Merckx et al., 2009a; Su et al., 2015; Bellot et al., 2016; Lam et al., 2016, 2018; Naumann et al., 2016; Givnish et al., 2018). The underlying family-level relationships in Dioscoreales inferred from the core/slow set are strongly supported and consistent in terms of tree topology across a broad range of analyses here using parsimony and multiple likelihood approaches, and also various expanded taxon sets (Figs. 3.1, B.1, B.3–B.9; Table B.5). To the extent that taxon sampling overlaps, the interfamilial topology recovered here is congruent with previous studies using full plastid genomes (Givnish et al., 2018; Lam et al., 2018; neither of which included Trichopodaceae) or a few nuclear and mitochondrial genes (Merckx et al., 2009a, 2010; Merckx and Smets, 2014). I used the core taxon set to strongly reject recent angiosperm classification schemes that lump Thismiaceae s.l. 69 with Burmanniaceae (e.g., APG, 2003, 2009, 2016). Specifically, the distinctness of these two lineages is supported (i) by AU and SH tests, in which a constrained tree forcing members of Burmanniaceae and Thismiaceae s.s. together is significantly worse than the unconstrained optimal tree of the core set (see Results), and (ii) by the strongly supported intervening branches between these two lineages (e.g., Fig. 3.1). I instead recovered a sister-group relationship between FM Thismiaceae s.s. (ignoring Afrothismia) and photosynthetic Taccaceae with strong support (Fig. 3.1, left-hand tree). This inference was facilitated by including Haplothismia as a slower evolving FM representative of Thismiaceae s.s., likely avoiding more problematic longer branches in species of Thismia (Fig. 3.2b), which place weakly within Burmanniaceae in DNA-based parsimony analysis (Fig. B.2). This arrangement of Taccaceae and Thismiaceae s.s. confirms previous poorly supported inferences based on plastid data sets comprising fewer taxa (Givnish et al. 2018; Lam et al., 2018) or genes (Lam et al., 2016), and also poorly to strongly supported inferences from mitochondrial and nuclear data sets (Yokoyama et al., 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Shepeleva et al., 2020). I therefore recommend that this result be accommodated in future updates to angiosperm classification, by separating Burmanniaceae and Thismiaceae s.l. (whether or not the latter is recognized as its own family). Only a few higher-order relationships within families remain unclear based on phylogenetic inferences of the core set of taxa, or expanded cases that include small subsets of data-poor or long-branch FM taxa (Figs. 3.1, B.1, B.3–B.11). Among photosynthetic taxa, one uncertainty concerns the earliest splits in Nartheciaceae in parsimony analyses, specifically the relative positions of Aletris and Metanarthecium to each other and to a clade comprising the three other genera in the family (Figs. 3.1, right-hand tree, and Figs. B.1, B.2). Poor support for these relationships was also observed in previous few-gene studies of Nartheciaceae (Fuse et al., 2012; Zhao et al., 2012) and is likely associated with short deep branches in the family that may reflect rapid early diversification (e.g., Figs. B.1, B.4). A second uncertainty concerns the placement of Stenomeris, a key photosynthetic taxon for which phylogenomic data are currently lacking. The genus has been placed in Dioscoreaceae or in its own family, Stenomeridaceae (reviewed in Caddick et al., 2002a). Here, Stenomeris is inferred to be the sister group of Dioscorea (Dioscoreaceae) in likelihood analyses that include the three available plastid genes for this taxon, but with poor to moderate support (Fig. B.7, B.12), or alternatively in parsimony 70 analyses as the sister group of a clade comprising Dioscorea, Taccaceae, Trichopodaceae and elements of Thismiaceae, a finding with poor support (Fig. B.2). Its placement was also uncertain in previous phylogenetic studies based on a few genes or morphology (Caddick et al., 2002a; Wilkin et al., 2005; Hsu et al., 2013); note that a specimen of Stenomeris dioscoreifolia that was nested within Dioscorea with strong support in Merckx et al. (2009a, 2010) likely has contaminants from the latter genus or was mislabeled (V. Merckx, unpublished data). Future work should therefore prioritize DNA extraction using freshly collected material of Stenomeris for phylogenomic studies; in my experience this taxon is highly recalcitrant to extraction from herbarium material. 3.5.2 Phylogenomic inferences that include highly rate-elevated heterotrophic taxa Plastid phylogenomic approaches have proved useful for placing FM lineages in monocot phylogeny, despite the extremely long branches associated with high substitution rates in the plastid genome following loss of photosynthesis (Wicke et al., 2011; Barrett et al., 2014; Graham et al., 2017), and often extensive loss of plastid genes in some of these taxa. Here, only 9–28 genes were retrieved from FM members of Dioscoreales (Table B.2; for summaries of other FM monocots see Graham et al., 2017; Lam et al., 2018). However, extensive gene loss does not in itself appear to greatly interfere with phylogenetic inference in heterotrophic lineages. For example, previous studies inferred well-supported likelihood-based placements for FM lineages among broad samplings of autotrophic relatives, despite extensive gene losses in some of these taxa. These include: Burmanniaceae (Lam et al., 2018), Corsiaceae (Mennes et al., 2015; Bodin et al., 2016; Givnish et al., 2016; Lam et al., 2018), Geosiris (Iridaceae; Joyce et al., 2018; Lam et al., 2018), Petrosavia (Petrosaviaceae; Logacheva et al., 2014; Lam et al., 2018), Thismiaceae (Lam et al., 2018), Triuridaceae (Chapter 2; Lam et al., 2015, 2018), multiple Orchidaceae taxa (e.g., Aphyllorchis, Cephalanthera, Corallorhiza, Cymbidium, Cyrtosia, Danxiaorchis, Dipodium, Gastrodia, Hetaeria, Lecanorchis, Neottia, Rhizanthella, Risleya; Delannoy et al., 2011; Givnish et al., 2015; Feng et al., 2016; Lam et al., 2018; Kim et al., 2020; Li et al., 2020) and eudicot mycoheterotrophs in Ericaceae, Gentianaceae and Polygalaceae (Lam et al., 2018). When added to the core taxon set in small subsets, the most rate elevated FM lineages sampled here for Burmanniaceae and Thismiaceae s.s. (excluding Afrothismia) all place with 71 more slowly evolving relatives in their respective families (cf. Fig. 3.1 and Figs. B.8, B.9 for DNA-based partitioned likelihood analyses). For Burmanniaceae, these arrangements are generally congruent with inferences from previous studies (Merckx et al., 2006, 2008a; Givnish et al., 2018; Lam et al., 2018), and are consistent with multiple losses of photosynthesis in the family, as I recovered two photosynthetic lineages of Burmannia nested among FM lineages (Figs. 3.2b, B.8). Few-gene studies that include larger samplings of autotrophic Burmannia imply at least eight losses of photosynthesis in Burmanniaceae (Merckx et al., 2013b). A future priority should be to expand taxon sampling of autotrophs in phylogenomic analyses for the family, to further characterize the frequency of loss of photosynthesis in this lineage. Branch support here for the inferred local placements of long branch taxa within either Burmanniaceae or Thismiaceae s.s. was typically quite poor. One exception is the strongly supported Apteria-Gymnosiphon clade recovered here in a partitioned DNA-based likelihood analysis (Figs. 3.2b, B.8), which is at odds with a strongly supported Apteria-Burmannia clade inferred in a three-gene nuclear/mitochondrial parsimony analysis (Merckx et al., 2008a). In addition, branch support of neighboring branches tended to be substantially lower when the longest-branch FM taxa were included in parsimony or likelihood analyses here (cf. Figs. 3.1, B.4 and Figs. 3.2a,b, B.8, B.9). This disruptive effect has also been observed for other long-branch heterotrophs (Su et al., 2015; Bellot et al., 2016; Lam et al., 2016, 2018; Givnish et al., 2018), and possibly represents a mild long-branch artefact In general, likelihood analysis may be expected to be less prone to long-branch effects than parsimony (Felsenstein, 1981; Huelsenbeck, 1995; Swofford et al., 2001; for examples see Nickrent et al., 2004; Merckx et al., 2009a; Lam et al., 2015, 2016, 2018). However, it is clear that likelihood can also be misled by extremely elevated rates. For example, a distorting effect of long branches was observed within Orchidaceae for the FM genera Epipogium and Rhizanthella (Lam et al., 2018), within eudicots for the parasitic lineages Apodanthaceae and Cytinaceae (Nickrent et al., 2004) and Cynomorium (Barkman et al., 2007), and within magnoliids for the parasitic lineage Hydnora (Naumann et al., 2016). Afrothismia, the lineage with by far the most extreme rate elevation here (e.g., Fig. 3.2), provides an additional example of how difficult it can be to infer phylogenetic relationships reliably for extremely rapidly evolving heterotrophs. Variant parsimony and likelihood analyses here recovered Afrothismia in multiple highly 72 disjunct placements (Figs. 3.2c, B.2, B.10–B.12), none of which could be rejected by AU or SH tests, compared to its bizarre optimal placement close to Poaceae (Fig. B.10). The latter family is known to be one of the longest-branch photosynthetic lineages of angiosperms (Bousquet et al., 1992; De La Torre et al., 2017). Therefore, this placement for Afrothismia is likely also a long-branch artefact, as molecular and morphological evidence support the inclusion of this genus deeply nested within Dioscoreales (Merckx et al., 2010; Merckx and Smets, 2014, and see below). In the parsimony analysis that includes all the FM Dioscoreales lineages sampled for molecular data here, Afrothismia appears to ‘pull’ all long-branch taxa in Burmanniaceae and Thismiaceae s.s. into a nested clade in the former family (Fig. B.2). In a parallel study, a member of my lab group is currently exploring whether phylogenomic analyses based on full mitochondrial genomes, which have substantially lower rate elevations for Afrothismia (Lin, 2020), are less prone to these probable long-branch distortions. Could the plastid genes recovered from distantly related lineages of Afrothismia here represent ancient mitochondrial inserts instead of genuine plastid genes? This seems highly unlikely based on unpublished comparative data on substitutional rates of the full complement of mitochondrial genes of Afrothismia (Lin, 2020), and considering the recovery of preliminary circularized plastid genome assemblies for this lineage (N. Klimpert, Graham lab, unpublished data). First, mitochondrial genomes generally evolve very slowly at the DNA substitutional level (Palmer and Herbon, 1988), and indeed the mitochondrial genes recovered for Afrothismia appear to be only moderately rate elevated (Lin, 2020). The extremely fast rates observed here for Afrothismia plastid genes thus conflict with the idea that they are plastid inserts in the mitochondrial genome, which would be expected to evolve under the same low background mitochondrial mutation rate, even if non-functional there (Sloan and Wu, 2014). Second, mitochondrial genomes are known to evolve very rapidly at the structural level (reviewed in Gualberto and Newton, 2017); given the high frequency of mitochondrial genome rearrangements, large inserts of the kind that have been assembled for Afrothismia plastid genomes would generally not be expected to persist as contiguous sequences. Moreover, it would be surprising for these long plastid sequences to be retained as stable mitochondrial inserts across multiple distantly related species of Afrothismia, as observed here. Third, average read depth of assembled Afrothismia plastid genomes was 2–4× higher than that for contigs containing 73 mitochondrial genes. I also looked for evidence of mitochondrial inserts in the Afrothismia plastid genomes, and found none. Could the Afrothismia plastid genes I recovered represent nuclear inserts (e.g., Blanchard and Schmidt, 1995)? Although DNA transfer from plastids to the nucleus is ongoing, plastid inserts are generally fragmented, shuffled and eliminated from the nucleus relatively rapidly (Matsuo et al., 2005). It is therefore unlikely that a long contiguous set of plastid genes would persist in the nuclear genome of several distantly related lineages of Afrothismia. Indeed, ancient intact organellar inserts in the nuclear genome tend to be difficult to detect, due to their short length and degeneration from the original organellar sequence (e.g., Blanchard and Schmidt, 1995). Several other lineages of heterotrophic plants also display highly rate elevated plastid genes that are nonetheless retained cohesively as linked gene sets, consistent with them representing plastid genomes (e.g., Delannoy et al., 2011; Lam et al., 2015; Schelkunov et al., 2015; Lim et al., 2016). Thus, despite several unusual features of the Afrothismia genes recovered here (i.e., extreme rate elevation, internal stop codons, and truncation of individual genes in some lineages), they very likely represent genuine plastid genes with unusually high rate elevation. Demonstrating their genomic location definitively would require fully assembled nuclear and mitochondrial genomes to confirm. 3.5.3 Placing fully mycoheterotrophic lineages of Dioscoreales based on morphology Morphology-based studies in Dioscoreales have been useful for interpreting the evolution of the considerable (and often complex) trait diversity in the order (Caddick et al., 1998, 2000; Remizowa et al., 2008; Fuse et al., 2012; Zhao et al., 2012; Tobe et al., 2018; Shepeleva et al., 2020), for searching for synapomorphies for major clades (Caddick et al., 2002a; Merckx et al., 2008b; Zhao et al., 2012; Shepeleva et al., 2020) and for considering taxa of interest in a phylogenetic framework when molecular data are lacking (Caddick et al., 2002a; Merckx and Smets, 2014). I used morphological data in part to permit inclusion of all autotrophic and FM genera in Dioscoreales in analysis, including six FM genera for which plastid data are currently lacking. Two of these genera, Marthella and Miersiella (Burmanniaceae), lack molecular data altogether. Marthella comprises a single species, M. trinitatis, that is only known from Mount Tucuche in Trinidad, and is presumed to be extinct as it was last collected in 1898 (Maas et al., 1986; Merckx et al., 2013a). The remaining four genera (Dictyostega, Hexapterella, Oxygyne 74 and Tiputinia) have been included in few-gene analyses of mitochondrial and nuclear data (Merckx et al., 2006, 2008a; Yokoyama et al., 2008; Merckx and Smets, 2014; Shepeleva et al., 2020). I employed various unconstrained and constrained morphology-based parsimony and likelihood analyses to place FM taxa in Dioscoreales (Figs. 3.3, B.13–B.17). Constrained analyses in which FM taxa were added individually to a fixed topology yielded consistently stronger bootstrap support for the placements of added taxa (and fewer unresolved branches in the case of parsimony) than constrained analyses with all FM taxa added simultaneously and unconstrained analyses. However, the often weak support for overall relationships in these analyses likely reflects the far fewer informative sites in the 35-character morphological matrix than in the phylogenomic data sets. Nevertheless, morphology-based phylogenetic analyses here are useful for narrowing down the placements of FM Dioscoreales taxa lacking plastid data, and for confirming sequence-based inferences, such as the distant relationship between Burmanniaceae and Thismiaceae s.l.. I also examined ancestral-state reconstructions using parsimony to uncover morphological synapomorphies for different taxa. This was complicated by the large number of optimal trees, and ambiguity in some character reconstructions. I therefore focused on character transitions that (i) represent common synapomorphies across two morphology-based best trees, considering one from the constrained likelihood analysis (Fig. 3.4) and another from the unconstrained parsimony analysis (Fig. B.20); and (ii) were reconstructed identically across ACCTRAN and DELTRAN optimizations for each tree individually. Morphology-based phylogenetic analyses support the monophyly of Burmanniaceae s.s. (excluding Thismiaceae s.l.) inferred in sequence-based analyses here (for example, compare Fig. 3.1 vs. Figs. 3.3, 3.4, B.13–B.17, B.20) and elsewhere (Yokoyama et al., 2008; Merckx et al., 2009a, 2010; Givnish et al., 2018; Lam et al., 2018; Shepeleva et al., 2020). Four synapomorphies from ancestral-state reconstructions (flowers with three stamens, anther thecae that both dehisce transversely and are separated by a laterally expanded connective, and funnel-shaped stigma lobes; characters 9, 15, 13 and 26, respectively) also support the monophyly of Burmanniaceae s.s. The presence of raphide crystals (character 3) in all Dioscoreales except Burmanniaceae s.s. and Nartheciaceae, additionally supports the distant relationship of Burmanniaceae s.s. and Thismiaceae s.l. (including Afrothismia). Within Burmanniaceae, the 75 genus-level relationships recovered in morphological analyses here, and in previous studies based on a few genes and/or morphology, are generally congruent. However, genus-level relationships within this family have been recovered with poor to moderate support to date, and so multiple uncertainties persist (Caddick et al., 2002a; Merckx et al., 2006, 2008a; Yokoyama et al., 2008; Lam et al., 2016). Here, I consistently recovered a clade comprising Apteria, Gymnosiphon, Marthella and Miersiella (with moderate support in Fig. B.17d,e) and inferred a morphological synapomorphy for this arrangement (i.e., unilocular ovaries; character 21, Figs. 3.4, B.20). These findings are consistent with results in Caddick et al. (2002a), the only other study to consider the former two genera in a phylogenetic context to date, and the source of the morphological matrix that I updated. Uncertainties remain about precise relationships within Burmanniaceae when all genera in the family are considered. For example, Hexapterella is generally supported as the sister group of the rest of Burmanniaceae when included, with Burmannia as the next successive sister group (Figs. 3.3, B.13–B.17), although there is some variation in their positions among optimal parsimony trees in constrained and unconstrained analyses (Fig. 3.3, right-hand tree, and Fig. B.13). In contrast, these two genera are nested within the family in a three-gene nuclear/mitochondrial analysis, with moderate support (Merckx et al., 2008a). The latter arrangement is at odds with two synapomorphies identified here (unilocular ovaries with parietal placentation, respectively characters 21 and 22; Figs. 3.4, B.20) that exclude Burmannia and Hexapterella (which have trilocular ovaries with axile placentation) from more nested clades in Burmanniaceae. These two ovary characters do not appear to be highly labile within major clades in Dioscoreales based on my scoring (Figs. 3.4, B.20, Table B.4). In contrast to Burmanniaceae, the monophyly of Thismiaceae s.l. and s.s. (potentially either including or excluding Afrothismia), remains uncertain in most morphology-based analyses here (Figs. 3.3, B.13–B.17). The morphology-based placement of Oxygyne close to Thismia and Afrothismia, with poor to moderate support (Figs. 3.3, B.13–B.17), conflicts with the moderately supported placement of Oxygyne as the sister group of all other Thismiaceae s.s. in a three-gene nuclear/mitochondrial analysis by Shepeleva et al. (2020). Tiputinia and Haplothismia were recovered as a clade in previous sequence-based studies (Merckx and Smets, 2014; Shepeleva et al., 2020) with poor to moderate support; here they are variously recovered 76 among the first splits in Thismiaceae (Figs. 3.3, B.13, B.15, B.16) or as the sister group of photosynthetic Taccaceae (Fig. B.14), also with poor to moderate support. Morphology-based analyses here consistently recovered Afrothismia with some or all elements of Thismiaceae s.s., often as the sister-group of Thismia, but with poor to moderate support (Figs. 3.3, B.13–B.17). This arrangement differs from various sequence-based placements of Afrothismia here (although these were all poorly supported; see above and Figs. 3.2, B.2, B.10–B.12) and also in previous studies that placed Afrothismia outside Thismiaceae s.s., with strong support in some cases (Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Shepeleva et al., 2020). The clustering of Afrothismia with Thismiaceae s.s. in morphology-based analyses may reflect convergence in the reduced vegetative and floral structures of these FM lineages (Merckx et al., 2009a). There are also no synapomorphies uniting Afrothismia with Thismiaceae s.s. based on the criteria I applied (considering character transitions in common across two selected tree topologies, that were also reconstructed identically under ACCTRAN and DELTRAN optimization for each individual tree; Figs. 3.4, B.20). The morphology of Afrothismia should be assessed in more detail to explore its possible position as the sister group of Thismiaceae s.s. and photosynthetic Taccaceae (e.g., Merckx and Bidartondo, 2008; Merckx et al., 2009a; Merckx and Smets, 2014). Members of Afrothismia, Taccaceae and Thismiaceae s.s. are consistently placed together in a clade in all morphology-based analyses here, with bootstrap support ranging from poor to strong (Figs. 3.3, B.13–B.17). This arrangement is consistent with the mitochondrial-based findings of Merckx et al. (2009a) and Lin (2020). 3.5.4 Afrothismiaceae, a proposed new family in Dioscoreales The extremely high plastid substitution rates of Afrothismia and lack of clear morphological synapomorphies preclude the precise placement of this lineage in Dioscoreales using my plastid phylogenomic or morphological data sets. However, multiple previous studies have recovered Afrothismia outside Thismiaceae s.s., with strong support in some cases, using nuclear and/or mitochondrial sequences (Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Shepeleva et al., 2020). Genes from the latter two genomes appear to be substantially less rate-elevated than the plastid genome of Afrothismia (for example, compare 77 branch lengths and substitutions per site in Fig. 3.2c here vs. Fig. 4 in Merckx and Smets, 2014). Although the precise placement of Afrothismia in Dioscoreales remains unclear, it likely represents an additional origin of full mycoheterotrophy in the order (Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010). The family-level classification of Dioscoreales is clearly in need of additional attention, and the order could be redefined by recognizing multiple small families (e.g., as in APG, 1998) or larger redefined families, possibilities that I will explore in more detail elsewhere. However, in the former case, a distinct possibility is that Afrothismia could be recognized as the basis of its own family to accommodate its placement outside Thismiaceae s.s., as nested among photosynthetic lineages that in turn can be recognized as distinct families (Taccaceae and Trichopodaceae). I therefore propose the first taxonomic treatment of Afrothismia as Afrothismiaceae, a fully mycoheterotrophic monogeneric family in Dioscoreales comprising ~12 species (Merckx et al., 2013a): Afrothismiaceae Soto Gomez, Rudall & S.W. Graham, fam. nov. Type: Afrothismia (Engl.) Schltr., Bot. Jahrb. Syst. 38: 138 (1906). Included genera: Afrothismia. Characterized by cylindric rhizomes bearing clusters of root tubercles, the latter elongating into filiform root extensions (Maas-van de Kamer, 1998; Imhof, 1999; 2006); flowers zygomorphic (Maas-van de Kamer, 1998), with an urceolate two-chambered perianth tube separated into upper and lower compartments by a deeply inserted annulus; stamens inserted in lower part of perianth tube (below annulus), anthers adnate to stigma (Merckx et al., 2009a); all floral parts shed except placental column (Maas-van de Kamer, 1998); endemic to tropical Africa (Merckx et al., 2009a). Afrothismiaceae differs from Thismiaceae s.s. (redefined to exclude Afrothismia) in having thickened root tubercles (round, nodule-like projections that represent primary roots with an enlarged basal cortex; Imhof, 1999), whereas Thismiaceae s.s. have coralloid, vermiform (‘wormlike’), or tuberous roots (Maas-van de Kamer, 1998; Woodward et al., 2007). The root-shoot system of Afrothismiaceae underlies complex fungal colonization patterns that are not observed in Thismiaceae (Imhof et al., 2020). Flowers in Afrothismiaceae are zygomorphic, in contrast to actinomorphic flowers in Thismiaceae s.s. (with some exceptions in Thismia). In 78 terms of biogeography, Afrothismiaceae is endemic to tropical Africa, while Thismiaceae s.s. are notably absent from Africa, except for three species of Oxygyne found in Cameroon (Merckx et al., 2009a; Cheek et al., 2018). Afrothismiaceae differs from photosynthetic Taccaceae (the sister-group of Thismiaceae s.s.) in being fully mycoheterotrophic. 3.5.5 Conclusion My study addresses whether reliable placements can be inferred for fully mycoheterotrophic (FM) taxa in Dioscoreales using plastid phylogenomic and morphological data. The performance of plastid data on FM taxa is clearly impacted by rate elevation in several lineages, which can reduce support values (seen here when adding most FM lineages with elevated rates to a slower core taxon set), or can substantially disrupt phylogenetic inference (seen here when the extremely rate-elevated Afrothismia are included in analysis). However, the least rapidly evolving FM taxa in Burmanniaceae and Thismiaceae can be placed among their autotrophic relatives in consistent positions, with generally strong support in likelihood and parsimony analyses. Shorter branch FM taxa may therefore act as ‘anchors’ for the larger FM lineages they represent; here, these anchors appear to be useful for careful consideration of the placement of additional longer branch FM taxa, which are also generally recovered in consistent placements across various taxon addition experiments. My morphology-based analyses helped to elucidate the affinities of multiple unplaced FM taxa. These lineages were placed at the family level and sometimes more precisely, with mostly consistent arrangements across unconstrained and constrained analyses, but also generally with weak to moderate bootstrap support. Difficulties in inferring relative placements among FM taxa may in part be associated with the convergent evolution of a ‘mycoheterotrophic syndrome’ (e.g., Leake, 1994) that may lead to distantly related taxa grouping together on the basis of multiple homoplasious vegetative or floral traits that evolve repeatedly after photosynthesis loss (e.g., Merckx et al., 2013b). This is possibly the case here for Afrothismia and Thismiaceae s.s., which morphological characters place together in a clade, even though other sequence-based studies imply they are more distantly related (Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Shepeleva et al., 2020). Synapomorphies inferred from ancestral-state reconstructions represent an additional line of 79 evidence for confirming several phylogenetic hypotheses in Dioscoreales. These reconstructions of trait evolution and the morphological data set that I updated will be useful for exploring synapomorphies for the various family circumscription schemes that are possible in a revised classification of the order. Burmanniaceae and Thismiaceae should be recognized as separate clades. I expect that additional phylogenomic evidence from the nuclear and mitochondrial genomes will be useful for continuing to refine the placements of FM taxa in Dioscoreales. Surveying additional lineages in Burmanniaceae will also be useful for confirming the number of losses of photosynthesis in this family. 80 Table 3.1 Nutritional status and data type retrieved for the 22 genera that comprise Dioscoreales. Note that Burmannia (*) includes both photosynthetic and fully mycoheterotrophic species. ______________________________________________________________________________________ Genus Family Nutritional status Data type Photosynthetic Fully Molecular Morphological mycoheterotrophic ______________________________________________________________________________________ Apteria Burmanniaceae X X X Burmannia* Burmanniaceae X X X X Campylosiphon Burmanniaceae X X X Dictyostega Burmanniaceae X X Gymnosiphon Burmanniaceae X X X Hexapterella Burmanniaceae X X Marthella Burmanniaceae X X Miersiella Burmanniaceae X X Dioscorea Dioscoreaceae X X X Stenomeris Dioscoreaceae X X X Aletris Nartheciaceae X X X Lophiola Nartheciaceae X X X Metanarthecium Nartheciaceae X X X Narthecium Nartheciaceae X X X Nietneria Nartheciaceae X X X Tacca Taccaceae X X X Afrothismia Thismiaceae (?) X X X Haplothismia Thismiaceae X X X Oxygyne Thismiaceae X X Thismia Thismiaceae X X X Tiputinia Thismiaceae X X Trichopus Trichopodaceae X X X 81 Figure 3.1 Phylogenetic relationships in Dioscoreales inferred from analysis of 82 plastid genes (20–28 genes for fully mycoheterotrophic taxa, highlighted in grey) using partitioned likelihood (left-hand) and parsimony (right-hand) analyses of the core 89-taxon DNA data set. Thick lines indicate 100% bootstrap support; values <100% are shown above branches. Branches labeled ‘a-z’ (summarized in the text and Table B.5) are used as a common reference point for comparison across molecular analyses here; a circled support value in the parsimony tree conflicts with the likelihood tree (also shown by crossed solid lines). Upper phylograms depict full samplings of each analysis, with Dioscoreales shaded in grey (see Figs. B.1, B.4); scale bars indicate estimated substitutions per site (likelihood) or number of changes (parsimony). 82 83 Figure 3.2 Summary of phylogenetic relationships and branch lengths of fully mycoheterotrophic Dioscoreales taxa, inferred from DNA-based partitioned likelihood analyses of 82 plastid genes (9–28 genes for fully mycoheterotrophic, FM, taxa). Scales differ; bars to the right indicate estimated substitutions per site. Each panel consists of phylogram pairs including versus excluding highly rate-elevated full mycoheterotrophs. In panels (a) and (b) the top phylogram depicts the core 89-taxon set (see Fig. 3.1, left-hand tree, and Fig. B.4 for full taxon sampling); in panel (c) the top phylogram depicts the 93-taxon set that includes four species of Afrothismia (see Fig. B.10 for full taxon sampling). Relatively slowly evolving FM taxa present in the core taxon set (Fig. 3.1) are shown in red, as visual anchors to emphasize the rate elevation (corresponding to very long branches) of the FM lineages added in expanded taxon sets. Added taxa are shown in blue in each panel: (a) Apteria and Gymnosiphon, (b) two species of Thismia, and (c) four species of Afrothismia plus Apteria and Gymnosiphon. Note that Apteria and Gymnosiphon are common additions between the bottom phylograms in panels (a) and (c); see Figs. B.8, B.9, B.12 for full taxon samplings of the bottom phylograms in panels (a-c), respectively. Thick lines indicate 100% bootstrap support; values <100% are shown beside branches. 84 85 Figure 3.3 Morphology-based analyses to simultaneously place seven fully mycoheterotrophic Dioscoreales taxa on a molecular constraint tree using likelihood (left-hand tree) and parsimony (right-hand tree) methods (see Figs. B.15, B.16 for phylograms). The constraint tree is indicated with thick branches. Added taxa, noted with ‘*’, are lacking or have highly rate-elevated plastid data; all full mycoheterotrophs are highlighted in grey. The parsimony tree is one of 360 shortest trees (the closest to the likelihood tree based on the Robinson-Foulds metric); arrows indicate branches that collapse in the strict consensus tree. Four families that were recovered as monophyletic based on more than one sampled genus are indicated. Bootstrap values (given constrained trees) are shown above branches. 86 87 Figure 3.4 Morphological synapomorphies of Dioscoreales inferred using parsimony on a likelihood-based molecular tree constraint. Topological constraints are indicated with thickened lines and taxa added to the constraint tree using morphology (as in Fig. 3.3, left-hand tree) are noted with ‘†’; full mycoheterotrophs are highlighted in grey. Changes in numbered morphological characters (Sub-Appendix B.1) along a branch are indicated only for those with identical ACCTRAN and DELTRAN optimization; colours of vertical bars indicate states. Character states changing on identical branches on a tree inferred from an unconstrained morphology-based parsimony analysis (Fig. B.20) are indicated with ‘*’. 88 89 Chapter 4: A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae)1 4.1 Summary I developed a target enrichment panel for phylogenomic studies of Dioscorea, an economically important genus with incompletely resolved relationships. The bait panel comprises 260 low-to-single-copy nuclear genes targeted to work in Dioscorea, assessed here using a preliminary taxon sampling that includes both distantly and closely related taxa, including several yam crops and potential crop wild relatives. I applied coalescent-based and maximum likelihood phylogenomic inference approaches to the pilot taxon set, incorporating new and published transcriptome data from additional species. The custom panel retrieved ~94% of targets and >80% of full gene length from 88% and 68% of samples, respectively. It has minimal gene overlap with existing panels designed for angiosperm-wide studies and generally recovers longer and more variable targets. Pilot phylogenomic analyses consistently resolve most deep and recent relationships with strong support across analyses, and point to revised relationships between the crop species D. alata and candidate crop wild relatives. The customized panel I developed reliably retrieves targeted loci from Dioscorea, is informative for resolving relationships in denser samplings, and is suitable for refining our understanding of the independent origins of cultivated yam species; the panel likely has broader promise for phylogenomic studies across Dioscoreales. 4.2 Introduction The Dioscorea yams (Dioscoreaceae) are a genus of ~625 species (Govaerts et al., 2018) that have highly diverse underground storage organs. Around nine species with starchy, annually 1 This chapter was published as ‘Soto Gomez, M., Pokorny, L., Kantar, M.B., Forest, F., Leitch, I.J., Gravendeel, B., Wilkin, P., Graham, S.W., Viruel, J. 2019. A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae). Applications in Plant Sciences 7: e11254.’ 90 replaced tubers are cultivated on a large scale in ~50 countries (Lebot, 2009; Asiedu and Sartie, 2010; Bhattacharjee et al., 2011); two of these, D. alata and D. cayenensis, are a primary staple food for over 100 million people, predominantly in Western Africa (Mignouna et al., 2003; note that the cultivated form of D. cayenensis is sometimes referred to as D. cayenensis subsp. rotundata or as D. rotundata). An additional ~40–50 species are cultivated or wild-harvested as food (Martin and Degras, 1978). Yam lineages with perennial tubers and rhizomes have also been used as a main source of steroidal precursors (De Luca et al., 2012; Hua et al., 2017) and traditional medicines (Lebot, 2009). Our understanding of Dioscorea evolution has benefited greatly from previous studies using two to six plastid markers (Wilkin et al., 2005; Hsu et al., 2013; Maurin et al., 2016; Viruel et al., 2016; Couto et al., 2018) or a single nuclear gene (Viruel et al., 2018); these studies provide a baseline phylogenetic outline for the genus. However, relationships among most major clades and species remain highly uncertain, in part due to relatively limited taxon and gene sampling, coupled with low levels of molecular variation in sampled markers. For example, multiple poorly supported internal branches were associated with short branch lengths in Wilkin et al. (2005), who suggested that the two plastid markers they used were not sufficiently informative for inferring those relationships. The studies with the densest sampling to date included representatives of the approximately 10 major Dioscorea clades (Wilkin et al., 2005; Viruel et al., 2016, 2018; Couto et al., 2018), but only 8–22% of the species in the genus. Sampling of crops and possible crop wild relatives (CWRs) has also remained relatively limited (e.g., Terauchi et al., 1992; Malapa et al., 2005; Mignouna et al., 2005; Mukherjee and Bhat, 2013; Girma et al., 2014, 2016; Chaïr et al., 2016), and their inter-relationships have yet to be resolved (e.g., confirming the identity of CWRs of D. alata; Chaïr et al., 2016). Here I present and apply methodology to enable phylogenomic research in yams, based on hundreds of loci from the nuclear genome. Hybridization-based target enrichment (reviewed in Lemmon and Lemmon, 2013; McKain et al., 2018) is emerging as a useful reduced-representation method for plant phylogenomics, in part because it can be used to retrieve hundreds of low-to-single-copy nuclear (LSCN) genes for resolving both deep and shallow relationships that may otherwise remain recalcitrant to fewer or more conserved markers (e.g., Villaverde et al., 2018). The method is also suitable for lower-quality template DNAs, such as those extracted from herbarium 91 specimens (Lemmon and Lemmon, 2013; Hart et al., 2016; Léveillé-Bourret et al., 2018; McKain et al., 2018; Vatanparast et al., 2018; Villaverde et al., 2018). Such low-quality templates may exhibit reduced sequencing success using polymerase-chain reaction (PCR) and Sanger sequencing (e.g., Chau et al., 2018). This is particularly important for groups such as Dioscorea that are large and geographically widespread (Wilkin et al., 2005), with few species in wide cultivation. Genomic targets of hybridization-based enrichment approaches can be recovered using “universal” baits optimized across broad taxon sets (e.g., the Angiosperm v.1 kit of Buddenhagen et al., 2016; the Angiosperms-353 probe set of Johnson et al., 2019; the fern bait kit of Wolf et al., 2018) and using “taxon-specific” baits designed for a focus group, ranging in taxonomic scale from populations (e.g., Euphorbia balsamifera; Villaverde et al., 2018) to a variety of higher-level taxa (e.g., Inga, Nicholls et al., 2015; Asclepiadoideae, Weitemier et al., 2014; Asteraceae, Mandel et al., 2014). The increasing availability of public transcriptome data and pipelines for LSCN gene discovery and bait development that require relatively few genomic resources can enable clade-specific bait design, making phylogenomic research in groups of interest more attainable (see McKain et al., 2018 for details). Large samples of nuclear genes from different linkage groups retrieved by target enrichment can be used for species-tree inference, by assuming that any discordances among individual gene trees are due to incomplete lineage sorting (e.g., Mirarab et al., 2014). This may be particularly useful in rapid evolutionary radiations (Nicholls et al., 2015), and especially relevant for elucidating relationships in recent lineages such as Enantiophyllum, the largest and most economically important major clade of Dioscorea; this clade has ~80 species (Prain and Burkill, 1938) and a median estimated crown age of 19.4 Ma (Viruel et al., 2016). Various methods are suitable for phylogenomic inference. For example, individual gene trees can be reconciled by using approaches that account for coalescence differences to infer a species tree (e.g., Wickett et al., 2014; Nicholls et al., 2015). Major inconsistencies among gene trees may also occur due to hybridization/introgression and incorrect orthology vs. paralogy assignment (e.g., Maddison, 1997). When associated gene-flow levels are low, the former problem may be accommodated by methods that were designed to deal with incomplete lineage sorting (e.g., ASTRAL; Solís-Lemus et al., 2016). Target-enrichment methods may simplify orthology assignment when they focus on retrieving LSCN genes that tend to be repeatedly restored to 92 single-copy status (diploidization) after small- and large-scale duplication events. Although orthology of target loci is not guaranteed for different taxa, gene-tree comparisons or sequence similarity methods may additionally be used for cross-verification of orthology (e.g., Johnson et al., 2016). Here I used the relatively limited genomic resources currently available for Dioscorea and Dioscoreales, supplemented with new transcriptome data, to design custom baits for retrieving hundreds of LSCN genes. I applied this new Dioscorea-specific target enrichment panel to a pilot taxon set that is stratified to be representative of all major clades in the genus, and that includes some of the major crops and potential CWRs, in order to evaluate its gene recovery performance on both ingroup and outgroup Dioscoreales. I used the pilot taxon sampling to test the suitability of the customized enrichment panel for recovering different levels of relationship, including: (i) predicting currently elusive relationships among most major Dioscorea clades, and (ii) evaluating previously proposed hypotheses about wild relatives of a major yam crop, D. alata. 4.3 Materials and methods 4.3.1 Bait design strategy to retrieve low-to-single-copy nuclear genes from Dioscorea and relatives I identified putative LSCN genes as candidates for bait design using four Dioscoreales transcriptomes and two monocot reference genomes. I first obtained assembled transcriptomes of Dioscorea sp. (Sequence Read Archive [SRA] accession ERR364396, Wickett et al., 2014; note that this taxon is published as D. villosa but is likely to have been misidentified, see below) and Tacca chantrieri (J. Leebens-Mack, University of Georgia, unpublished data), and used Trinity v. 2.4.0 (Grabherr et al., 2011; Haas et al., 2013) with default settings to generate de novo transcriptome assemblies for D. alata (SRX652481; Wu et al., 2015) and D. composita (SRR1525775; Wang et al., 2015). I ran two separate analyses using MarkerMiner 1.0 (Chamala et al., 2015) with these four unfiltered transcriptome assemblies as input sequences, using either Oryza sativa (Poales; a default reference in MarkerMiner) or Xerophyta viscosa (Pandanales; as a manually added reference, Costa et al., 2017) to identify LSCN genes. Briefly, MarkerMiner identifies putative LSCN genes by first employing independent reciprocal BLAST (Altschul et 93 al., 1990, 1997) analyses between input transcripts and a user-specified reference proteome (Oryza and Xerophyta in my case) to obtain putative ortholog pairs. MarkerMiner then performs reciprocal BLAST analyses between the latter putative ortholog pairs and a set of user-specified LSCN gene references (Oryza and Xerophyta in my case) as a final filter to only retain putative ortholog pairs with putative low-to-single-copy status. MarkerMiner only reports the top scoring transcripts from the latter BLAST analyses as putatively orthologous LSCN genes; if additional hits are obtained, these are reported as “secondary transcripts” that may represent splice isoforms, putative paralogs, or partially assembled transcripts, all of which I did not consider for bait design (see Chamala et al., 2015 for a complete algorithm). I added Xerophyta as a reference taxon in MarkerMiner, using procedures outlined in Johnson (2017a) to create relevant reference files, one containing nucleotide sequences for all genes with introns masked as Ns, and another containing their translation into amino acids. I then used the amino-acid-based file to create a BLAST database using the makeblastdb application. I identified putative LSCN genes from the Xerophyta transcriptome (Costa et al., 2017) by using it as the input, with Oryza as the proteome and LSCN gene reference; MarkerMiner identified 555 putatively LSCN genes for the former taxon. For bait development, I selected a subset of 260 genes for which MarkerMiner produced top reciprocal BLAST hits to the three Dioscorea transcriptomes, and to the Tacca transcriptome when applicable. I then purchased custom baits designed and produced by Arbor Biosciences (Ann Arbor, Michigan, USA), based on selected LSCN genes, in the form of a myBaits enrichment kit comprising 20,000, 100-bp in-solution biotinylated baits with 3x tiling density. I submitted 1288 target coding sequences from 260 genes with a total length of 2,459,581 bp for bait panel manufacture; these constituted three or four different versions of the target loci, obtained from MarkerMiner analyses (bait sequences are available in the Dryad Digital Repository [https://doi.org/10.5061/dryad.4n2fk97]). The final bait panel consists of 17,120 baits for targeting 260 LSCN genes with lengths between 947–3440 bp (mean 1699 bp, excluding introns) in the Oryza and Xerophyta references, and the total length of targeted coding sequence is 441,717 bp (for individual gene annotations and lengths see Table C.1). I checked for target gene overlap between the Dioscorea-specific panel designed here and the universal kits of Buddenhagen et al. (2016; Angiosperm v.1 kit) and Johnson et al. (2018; Angiosperms-353 probe set) using Geneious R11 (www.geneious.com) and BLAST (Altschul et al., 1990, 1997). I 94 used the map-to-reference function in Geneious with sensitivity set to highest/slow and otherwise using default settings, and the BLAST application blastn with default settings; in both programs I used target gene sequences used for bait design for each of the universal kits as references and those from my kit as queries. 4.3.2 Taxon sampling for phylogenetic analysis I selected 25 Dioscoreales taxa to test the baits, focusing on Dioscorea. This pilot sample set consists of 24 species of Dioscorea and Trichopus zeylanicus (Table C.2). In order to assess the retrieved genes in the broader context of Dioscorea phylogeny, I added corresponding sequences from four Dioscorea leaf transcriptomes that I also generated (D. cochleariapiculata, D. communis, D. soso, D. sylvatica; Table C.2; see below for methodology), five additional published Dioscorea transcriptomes (D. alata, SRR3938623; D. nipponica, SRR5457049; D. cayenensis, SRR3938611; D. trifida, SRR3938635; D. zingiberensis, SRR1218151), and the four transcriptomes used for bait design (i.e., D. alata, D. composita, Dioscorea sp., and Tacca chantrieri). The resulting 38-taxon matrix represents all major clades of Dioscorea (see Viruel et al., 2016, 2018) and two outgroup taxa (Tacca chantrieri and Trichopus zeylanicus). 4.3.3 Library preparation and target enrichment I prepared whole-genome shotgun sequencing libraries for the 25 taxa used for target enrichment by using half-volume reactions of the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, USA). I used 200 ng of starting DNA and sheared it to 350-bp fragments with a Covaris ME220 sonicator (Covaris Inc., Woburn, USA) for library preparation. I size-selected libraries to obtain 300–400-bp fragments, which were then amplified with six PCR cycles using the NEBNext Dual Index Primers Set I (New England Biolabs) as amplification primers. I quantified libraries using Qubit (Thermo Fisher Scientific Inc., Waltham, USA) and verified fragment sizes using a TapeStation (Agilent Technologies, Santa Clara, USA). I enriched the 25 genomic libraries for target genes by grouping them with equal concentrations in four distinct pools (six or seven samples at equal concentrations per 1000-ng pool). I pooled samples based on phylogenetic proximity (i.e., more closely related taxa) and DNA concentrations of individual libraries, which ranged from 11 to 80 ng/μL (Table 4.1). I 95 used half-volume reactions of the target capture kit for each pool, and otherwise followed the manufacturer protocol to enrich each pool at 65°C for 16–20 h. I then multiplexed two pools per lane for sequencing on an Illumina MiSeq platform using the v2 Micro Kit to obtain 150-bp paired-end reads. 4.3.4 Transcriptome sequencing I obtained leaf transcriptomes from four Dioscorea species by collecting three young leaves from one individual per taxon, freezing them in liquid nitrogen within 15 minutes of harvesting. I shipped tissue on dry ice to Novogene Corporation Inc. (Sacramento, USA) for RNA extraction using the RNAprep Pure Plant Kit (Polysaccharides & Polyphenolics-rich; TIANGEN Biotech, Beijing, China), library preparation using the NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs), and sequencing. Samples were multiplex-sequenced in one Illumina HiSeq 4000 lane including a total of eight samples to obtain 150-bp paired-end reads. 4.3.5 Sequence processing, assembly and alignment I used FastQC v. 0.11.7 (Andrews, 2010) to assess the quality of Illumina raw reads from the 25 bait-enriched samples and for nine of the 13 transcriptome-based samples (i.e., those not used for bait design). I then used Trimmomatic v. 0.36 (Bolger et al., 2014) to remove low-quality reads and adapter sequences. I set a minimum Phred quality score of 20 and sequence length of 110 bp for bait-enriched samples. I applied the same Phred quality score for transcriptome samples, but used a 4-bp sliding window and a minimum sequence length of 40 bp. I used HybPiper v. 1.2 (Johnson et al., 2016) to assemble and extract LSCN targets from all 25 bait-enriched samples and from the nine previously unassembled transcriptomes. The reads_first.py script in HybPiper maps raw sequencing reads to the LSCN targets in a user-provided reference set. I then used the HybPiper script retrieve_sequences.py with the dna flag, which outputs a single sequence per gene that is selected using length, similarity, and depth of coverage criteria (see below for methodology to deal with potential paralogs). Thus, including output from enrichment- and transcriptome-based samples, HybPiper recovered up to 34 taxon terminals per file (the final files included sequences from the four additional transcriptomes used for bait design). I ran the HybPiper script intronerate.py on the 25 bait-enriched samples to recover introns and intergenic 96 sequences flanking targeted exons; I extracted both coding and non-coding regions as a single concatenated sequence for each target gene using the retrieve_sequences.py script with the supercontig flag. I used MAFFT v. 7.305b (Katoh et al., 2002) to align individual genes using an accuracy-oriented method that incorporated local pairwise alignment information and 1000 iterative refinement cycles with flags --localpair and --maxiterate 1000, and the --adjustdirectionaccurately flag to reverse-complement opposite strand sequences where necessary, and used trimAl v. 1.4.1 (Capella-Gutiérrez et al., 2009) to remove alignment columns where there were missing data for more than 20% of taxa. One gene was excluded from downstream analyses because it was too short after trimming (gene indicated in Table C.1). I visually inspected individual gene alignments in AliView v. 1.18.1 (Larsson, 2014) to identify potentially misaligned sequences. I also generated a gene tree for each target locus using FastTree v. 2.1 (Price et al., 2010) with the general time reversible (GTR) DNA substitution model and otherwise using default settings, and used a Python script (Johnson, 2017b) to detect branch length outliers for alignment modification (outliers defined as branches longer than 25% of the total tree length for ingroup taxa and longer than 75% for outgroup taxa). I deleted regions that were difficult to align at the start and end of genes, or flanking sequence gaps, and the final alignments included coding regions only. I visually identified alignment blocks that differed between DNA-based and transcriptome-based sequences; these may result from post-transcriptional modification processes, and so I deleted the divergent sections of transcriptome-based sequences. I also obtained summary statistics on gene alignments using AMAS (Borowiec, 2016), including length, missing data, and number of parsimony informative sites (Table C.3). 4.3.6 Detection and filtering of potential paralogs I detected 27 potential paralog instances; 26 of these through HybPiper paralog warnings and one by gene-tree visual inspection (indicated in Table C.1). In 22 of these cases, I selected one of the retrieved copies to be the most likely ortholog based on both highest percent identity to the reference gene and gene-tree topology concordance with our current understanding of Dioscoreales phylogeny (Wilkin et al., 2005; Viruel et al., 2016, 2018). However, I interpreted five additional genes as being the result of a single early gene duplication (including one gene not identified by HybPiper as a possible paralogy instance; gene 168), and so I separated each 97 into two paralogous genes, increasing the total number of recovered genes by five total (see Sub-Appendix C.1 for complete methods for filtering potential paralogs). Therefore, the final data set consisted of 264 LSCN genes, each with an individual gene alignment and associated gene tree (available in the Dryad Digital Repository). I concatenated these 264 genes into a 319,788-bp matrix using SequenceMatrix v. 100.0 (Vaidya et al., 2011). I also analyzed a partial data set that consisted of 232 LSCN genes by excluding the 32 genes with potential paralogy issues. 4.3.7 Phylogenomic inference I analyzed the recovered genes using coalescent-based and concatenated maximum likelihood methods; the latter is likely to result in misinferences if there are instances of incomplete lineage sorting (e.g., Maddison, 1997), but I included it as a point of reference for the coalescent analysis. I used ASTRAL-III (Zhang et al., 2018) with default settings for coalescent-based analyses on both the complete 264-gene data set and on a partial 232-gene data set that excluded genes with potential paralogs, and also for estimating branch support for the resulting species trees using both local posterior probabilities (LPP; Sayyari and Mirarab, 2016) and multilocus bootstrapping (MLB; Seo, 2008). Local posterior probabilities measure support for a quadripartition (the four clusters around each internal branch in a species tree) and are computed using the proportion of gene-tree quartets that agree or disagree with an internal branch. For MLB on the 264-gene (or 232-gene) set, I first inferred 100 pseudo-replicate trees from bootstrap analysis of individual genes using RAxML v. 8.2.11 (Stamatakis, 2014), with the GTRGAMMA DNA substitution model, in a pipeline developed by Ané (2018). ASTRAL then creates 100 replicate input data sets, each consisting of 264 (or 232) pseudo-replicate gene trees (one tree per gene, randomly sampled without replacement) for quartet analysis. The frequency of times that branches appeared across the 100 resulting “species” trees is then summarized with a greedy consensus method to estimate MLB support values for each branch in the actual species tree; the latter tree is inferred from quartet analysis of one best “species” tree per gene. For the concatenated likelihood analysis, I analyzed the 264-gene concatenated matrix unpartitioned and partitioned by gene. I used PartitionFinder 2 with the corrected Akaike Information Criterion (AICc) and the relaxed hierarchical clustering algorithm (r-cluster; Lanfear et al., 2016) to find the optimal DNA substitution model for the unpartitioned analysis, and to 98 identify and combine sets of genes that did not have substantially different models for the partitioned analysis. PartitionFinder 2 found GTR+I+G as the optimal model for unpartitioned analyses; for partitioned analyses the program retained 115 partitions, derived from 264 initial gene partitions, selecting GTR+I+G or GTR+G as the best substitution model in all cases, and I used the final data partitioning scheme (Table C.4) for phylogenetic inference. I did not attempt more complex methods, such as likelihood analyses that include codon partitions or that use amino-acid substitution models, as the sequences are all closely related (unpartitioned and partitioned likelihood analyses produced fully consistent results, and so I focused presentation of the results on the latter). I conducted likelihood analyses in RAxML using 20 independent searches for the best tree and estimated branch support using 500 bootstrap replicates (Felsenstein, 1985), applying the GTR+G approximation for all data partitions, as the gamma parameter (G) may adequately accommodate the invariant sites parameter (I) (Yang, 2006). I considered strongly supported branches to have at least 0.95 LPP and 90% bootstrap support, and poorly supported ones to have less than 0.90 LPP and 70% bootstrap support. 4.4 Results 4.4.1 Gene retrieval using target enrichment methods vs. transcriptome-based data On average, I obtained 922,847 raw reads per enriched sample, with values ranging from 258,300 (D. pohlii) to 2,830,650 (D. caucasica). Over 96.5% of recovered reads were retained for all enriched samples after removing low-quality bases and adapter sequences. I successfully recovered >243 (93.8%) of the 260 target genes from 22 of the 25 bait-enriched samples (note that gene recovery percentages here and below are based on the 260 genes for which baits were designed, including the gene that was later excluded due to its short length after quality trimming, and exclude the five additional genes obtained through paralog analysis; Table 4.1). Only three samples (outgroup taxon Trichopus zeylanicus; ingroup taxa D. minima and D. rockii) had lower gene recoveries, with 151 (58.1%), 157 (60.4%), and 198 (76.2%) genes, respectively (Table 4.1). Average enrichment efficiency, measured as the proportion of reads that HybPiper mapped to gene targets, was 31.6% (5.4–60.7%), and was above average (45.4%) for outgroup taxon T. zeylanicus. I recovered >80% of the full length of the reference target gene set 99 from 17 of the 25 enriched taxa (24.2–96.6%, average of 78.6%), and only three samples had <50% recovery (outgroup taxon T. zeylanicus, 24.2%; ingroup taxa D. minima, 38% and D. rockii, 45.5%). The recovered proportion of the full reference gene length varied by gene and by taxon, but I retrieved ≥75% of the sequence length for most genes (153–244 genes) and taxa (20 of 25 taxa; Fig. 4.1). Outgroup taxon T. zeylanicus and ingroup taxa D. minima, D. rockii, and D. prazeri were the only taxa for which recovery of ≥75% of the sequence length was possible for fewer than 100 genes (14, 53, 57, 87, respectively; Fig. 4.1). There were 86 genes for which I recovered sequences from all 25 bait-enriched taxa and 90 genes for which I recovered sequences from 24 taxa; there were only five genes for which the baits did not recover any sequences from more than 10 taxa (again, considering only the 260 genes for which baits were designed). After alignment quality trimming, I included an average of 276,920 bp (median 297,467 bp) per bait-enriched taxon in phylogenetic analyses (Table 4.1), which represents 62.7% of the full length (441,626 bp) of the reference target gene set. I recovered an average of 1390 bp of exon-flanking intron and intergenic sequences per locus (29–5108 bp, SD = 816 bp) from the 25 bait-enriched taxa, after alignment and quality trimming, excluding gaps (note that analyses here focus on coding regions; lengths of retrieved coding and non-coding sequences are summarized in Table C.5). I obtained more than 250 (96.5%) of the 260 target genes from the nine taxa represented by previously unassembled transcriptomes (i.e., not including the four transcriptomes used for bait design; Table 4.2). I recovered ≥75% of the full target length for most genes (242–255 genes) in all nine transcriptomes (Table 4.2). After alignment quality trimming, I included an average of 305,952 bp (median 307,352 bp) per transcriptome-based taxon in phylogenetic analyses, which represents 69.3% of the full length of the reference target gene set. Overall, individual gene alignments were on average 1204 bp long (144–3169 bp) and had 5.2% missing data (0–15.7%). The proportion of parsimony informative sites ranged from 14% to 38% (24% average; see Table C.3 for target gene summary statistics). The range of gene tree occupancy was 14–38 taxa, but most (213) gene trees included at least 35 of the 38 sampled taxa (Tables 4.1, 4.2, C.3). 100 4.4.2 Phylogenetic relationships inferred for the pilot taxon set of Dioscorea species Most inferred relationships for the pilot taxon sampling for Dioscorea were strongly supported and consistent across both coalescent-based (Fig. 4.2) and concatenated likelihood (Figs. C.1, C.2) analyses, with highly consistent LPP and MLB support for the former analysis. The quartet score for the coalescent-based tree, which is the proportion of gene-tree quartets also present in the species tree, was 87.5%. The monophyly of major Dioscorea clades, and the relationships among them, were consistent and most were strongly supported across analyses (Figs. 4.2, C.1, C.2). Only the major clade comprising the so-called “compound leaf” clade and its sister group D. trifida-D. cordata (see Fig. 4.2 for clade names and composition) was not strongly resolved: the entire clade was recovered across the two inference methods, but had low (0.81) support from ASTRAL LPP estimates (Fig. 4.2), and moderate support from ASTRAL MLB (89%; Fig. 4.2) and partitioned likelihood (88%; Fig. C.1) analyses. I also resolved most species-level relationships with strong support in coalescent-based and likelihood analyses. The placement of the two major yam crops, D. alata and D. cayenensis, was strongly resolved within the Enantiophyllum clade: D. alata formed a clade with D. calcicola, its previously proposed secondary gene pool CWR, and their sister group in turn was D. nummularia, a previously proposed primary gene pool CWR of D. alata (Vincent et al., 2013; see below). Dioscorea sp., published as D. villosa in Wickett et al. (2014), is the strongly supported sister group of D. sylvatica in the “Africa clade,” with nearly identical branch lengths in likelihood analyses for both species (Figs. C.1, C.2). However, D. villosa is actually part of the Stenophora clade (Viruel et al., 2016); the latter placement is supported in unpublished analyses of a taxonomically verified sample of this species using the bait set developed here (J. Viruel, Royal Botanic Gardens, Kew, unpublished data). A Dioscorea expert (P. Wilkin; Royal Botanic Gardens, Kew) assessed an image of the vouchered specimen (132728, ALTA; University of Alberta Vascular Plant Herbarium) for the Dioscorea sample (project sample code OCWZ) used in Wickett et al. (2014) to re-assess its identification. Despite the limited number of diagnostic characters available from the image, a D. sylvatica identification is convincing, and so transcriptome data for this taxon likely originated from a misidentified individual of that species. Interspecific relationships remained uncertain only in the Stenophora clade; D. rockii and D. membranacea formed a strongly supported sister group, but multiple species in this clade 101 had inconsistent relationships between the coalescent and concatenated analyses, suggesting episodes of incomplete lineage sorting for several closely related taxa in the clade. 4.5 Discussion Hybridization capture-based technologies are enabling the retrieval of hundreds of nuclear markers from diverse plant species for resolving previously intractable relationships (e.g., Sass et al., 2016; Wolf et al., 2018). Target enrichment using universal baits can have high enrichment success across broad taxonomic scales. For example, Buddenhagen et al. (2016) recovered >90% of targets from a 53-taxon angiosperm-wide sampling using their universal Angiosperm v.1 kit; Johnson et al. (2018) recovered on average 80% of targets from a 42-taxon angiosperm-wide sampling and found that target recovery was not influenced by the degree of relatedness between the enriched sample and taxa used to design their universal Angiosperms-353 probe set. However, taxon-specific kits may recover a higher proportion of gene targets and overall target length, due to high complementarity between baits and focus taxa (Folk et al., 2015). This is consistent with my findings, as here I recovered sequences from 99.6% of gene targets for D. calcicola (Table 4.1), and the Angiosperms-353 probe set recovered 76% for this taxon (Johnson et al., 2019). Furthermore, for a set of 23 genes in common to my study, the average recovery of the target length for D. calcicola was 95% (70–100%) using the Dioscorea-specific baits and 37% (0–90%) using the Angiosperms-353 probe set (see Table C.6 for information on individual genes). In future, it would be useful to sample additional taxa using the Dioscorea-specific and Angiosperms-353 kits for more refined comparisons. Efficient enrichment using universal markers requires that target loci are moderately conserved across the target group (Buddenhagen et al., 2016) and so may tend to be shorter and less variable compared to custom markers (Kadleck et al., 2017; Chau et al., 2018), if only target sequences (exons) are considered. In my study, the average target length of the Dioscorea-specific kit was 1699 bp. This is comparable to 1362 bp for a Heuchera kit (Folk et al., 2015), 1667 bp for a Leguminosae kit (Vatanparast et al., 2018), and ~1645 bp for a Euphorbia kit (Villaverde et al., 2018). However, average target length was shorter (820 bp) for Buddleja-specific locus sets compared to three different non-taxon-specific locus sets (1267 bp; Chau et 102 al., 2018). In contrast, the average target length for the universal Angiosperms-353 and Angiosperm v.1 kits was 738 bp and 343 bp, respectively. Average exon site variability (calculated from the number of sites in an alignment that show variation, including those that are parsimony informative and uninformative) of 42% from the Dioscorea-specific kit designed here (Table C.3) was higher than the average 14.6% variability reported for the Angisoperms-353 probe set for four selected genera (Linum, Neurachne, Oenothera, and Portulaca; Johnson et al., 2019), 18.5% from the Angiosperm v.1 kit for Cyperaceae (Léveillé-Bourret et al., 2018), and 30.6% from three different non-taxon-specific locus sets for Buddleja (Chau et al., 2018). However, target enrichment methods can also consider the so-called “splash zone” capture in regions flanking target exons, that consist of intronic and intergenic sequence (Weitemier et al., 2014; Johnson et al., 2016, 2019). These non-coding regions are expected to be much less constrained by purifying selection and so may be useful for shallower phylogenetic applications (Buddenhagen et al., 2016), including at the population level (Johnson et al., 2016, 2019), although alignment (homology assessment) may also be more difficult than with exon regions. Comparisons of the relative utility and ease of use of flanking regions from universal kits compared to the sort of taxon-specific exon targeting described here (I focused exclusively on coding regions) would be useful in future. A further potential benefit of custom bait design based on closer relatives of the target group is that it may help avoid targeting genes with lineage-specific duplications (Kadlec et al., 2017), while still being applicable across a fairly broad range of related taxa (Kadlec et al., 2017; Villaverde et al., 2018). 4.5.1 Capture efficiency across phylogenetic scale in Dioscorea: lessons from a pilot sampling Using transcriptomes from Dioscoreales genera Dioscorea and Tacca, and LSCN gene references from more distantly related Oryza and Xerophyta, I produced a versatile bait kit that successfully captures most target loci from the focus group (Dioscorea) and one other genus in the same order (Trichopus). The average capture efficiency here (i.e., the proportion of reads that HybPiper mapped to gene target references) of 31.6% (5.4–60.7%) is comparable to or lower than that from other custom baits (26% in Zingiberales, Sass et al., 2016; 48.6% in Euphorbia, 103 Villaverde et al., 2018; 54.7% in Heuchera, Folk et al., 2015; 59% in Oxalis, Schmickl et al., 2016). The average recovery of the total reference gene set length was relatively high here at 78.6% compared to other studies using custom baits (e.g., 33.4% in Folk et al., 2015; 59.4% in Sass et al., 2016; 73% in Villaverde et al., 2018), but varied across taxa (24.2–96.6%). Baits are expected to work well up to a 30% threshold of sequence divergence to targets (Johnson et al., 2019). Therefore, variability in enrichment success in my study was likely not fully due to insufficient affinity between samples and baits (including for Trichopus, as its uncorrected p-distance to sampled Dioscorea was 0–8%), but rather to differences in sample pooling strategy, starting DNA quality and quantity, library complexity, and genome size (Hart et al., 2016; Sass et al., 2016; Johnson et al., 2019). For example, I expect that lower gene recovery for Trichopus reported here (151 of 260 genes; Table 4.1) could have been improved by not pooling this outgroup taxon with Dioscorea taxa; baits were designed using Dioscorea transcriptomes and so a high proportion of them were likely sequestered by more similar ingroup samples (Villaverde et al., 2018). Dioscorea minima had similar gene recovery to outgroup Trichopus with 157 of 260 genes; the former ingroup taxon had the third lowest amount of starting DNA (1.88 ng/μL), which may have impacted enrichment success, especially if the quality of the available DNA was poor and the resulting genomic library had low complexity. Although I considered starting DNA amounts for the six- to seven-taxon pools on which I performed enrichments, samples with low amounts and/or poor quality DNA may benefit from even smaller pool sizes or individual enrichment. Additionally, genome size data are only available for D. alata, D. caucasica, D. cayenensis, D. communis, D. sylvatica, and D. zingiberensis among sampled taxa, and so I may have pooled taxa of varying ploidy levels and genome sizes; the latter are reported to range between 0.35 and 6.75 pg in Dioscorea (Bennett and Leitch, 2012; Zhang et al., 2013; Šmarda et al., 2014). Johnson et al. (2018) enriched samples with larger genomes in smaller pools, and this strategy may have contributed to their finding that genome size did not have a large impact on target recovery. Sample-pooling strategies should therefore consider amount and quality of starting DNA, phylogenetic distance to taxa upon which baits were designed (especially if baits are not universal), and ploidy level and genome size, when known. 104 4.5.2 Custom baits resolved lower- and higher-order relationships in a pilot taxon sampling My pilot study of Dioscorea was based on a relatively small but broadly representative set of taxa in order to assess how well the customized target enrichment panel may perform for broad samplings in the genus. I included taxa from all of the major Dioscorea lineages identified in previous studies (Viruel et al., 2016, 2018), as well as several groups of more closely related taxa, to assess whether I could recover different levels of relationships that remained unresolved or poorly supported in previous studies. The composition of major clades and their interrelationships mostly agreed with published findings (Wilkin et al., 2005; Hsu et al., 2013; Maurin et al., 2016; Viruel et al., 2016, 2018; Couto et al., 2018) but were consistently better supported across both coalescent-based and likelihood analyses here. The tree inferred by Viruel et al. (2018) from Bayesian analyses of a matrix consisting of one nuclear gene and five plastid markers recovered lineages from the Birmanica clade as being nested within the Enantiophyllum clade with strong support (posterior probability [PP] of 1); this arrangement was moderately supported in their likelihood analyses. Here I confirmed the monophyly of both the Enantiophyllum and Birmanica clades with strong support across all analyses (Figs. 4.2, C.1, C.2; see below for relationships to other major clades). The taxon sampling in my study has the highest species overlap with earlier studies by Wilkin et al. (2005), Viruel et al. (2016, 2018), and Couto et al. (2018). There was one strongly supported conflict concerning relationships among major clades between my study and that of Viruel et al. (2018), and two additional conflicts between my study and findings from Couto et al. (2018) considering Bayesian posterior probability values in their studies (support for these three branches from likelihood bootstrap analyses in those studies was moderate and low, respectively). The tree inferred from combined analysis of one nuclear gene and five plastid markers by Viruel et al. (2018) reconstructed the Mediterranean clade as the sister group of clade B (0.97 PP). However, in my study, clade B is the sister group of the Africa clade, with high support across all analyses. The tree inferred from two plastid genes by Couto et al. (2018) recovered the compound leaf clade as the sister group of all other taxa in clade B (0.99 PP), but here the latter is the sister group of the Birmanica clade with strong support across all analyses. Couto et al. (2018) also placed the Enantiophyllum clade as the sister group to a clade that 105 includes the Rajania, Birmanica, and Malagasy clades (0.96 PP), whereas in my study only the Malagasy clade is the strongly supported sister group of the Enantiophyllum clade across all analyses. Only one branch concerning major clade relationships was not consistently strongly resolved here: there was low ASTRAL LPP (0.81; Fig. 4.2) support, moderate ASTRAL MLB (89%; Fig. 4.2) support, and moderate to high likelihood (88–93%; Figs. C.1, C.2) support for the clade that comprises the compound leaf clade and its sister group D. trifida-D. cordata. The length of the branch subtending the entire compound leaf plus D. trifida-D. cordata clade was relatively short in the phylogeny produced by ASTRAL, in which branch lengths represent coalescent units that directly measure discordance in gene trees. Indeed, for this latter less strongly supported branch, quartet support for the species tree topology was 39% and support for the two alternative topologies was 24% and 36%. Gene-tree inference uncertainty and hybridization (including subsequent introgression and homoploid or polyploid speciation) are two additional possible sources of gene-tree and gene-species tree discordance that the methods employed here do not take into account. Gene-tree estimation error is also likely to occur with most biological data sets, but may be more challenging for multi-copy genes spanning a large number of species (Bayzid and Warnow, 2018), and so may be a more minor source of discordance in my pilot 38-taxon LSCN gene set. Hybridization is generally not well characterized for the lineages sampled here, except for the putative hybrid D. x monandra (D. cienegensis x D. glomerulata; Govaerts et al., 2018). However, natural hybridization has been documented between additional Dioscorea lineages (e.g., Martin and Cabanillas, 1963) and is relatively common in plants in general (Soltis and Soltis, 2009). Coalescent-based methods such as ASTRAL may incorrectly reconstruct species trees in the presence of gene flow, as incomplete lineage sorting is not the only possible source of gene-tree conflict (Solís-Lemus et al., 2016). However, when gene flow levels are low, ASTRAL can estimate the true species tree with increasing accuracy as more genes are added (Solís-Lemus et al., 2016). Interspecific gene flow can also be detected through gene-tree discordance (Galtier and Daubin, 2008); gene flow may not be widespread in my data set, given that nearly 90% of gene-tree quartets were also found in the species tree. Applying the Dioscorea-specific kit to a more expanded sampling of the genus that includes confirmed or suspected hybrids and potential parent species will be useful for testing how effective these 106 target loci are for detecting hybrids; this will require allele and homeolog phasing, a challenge that may be simplified by using longer-read sequencing platforms, such as MiSeq (Wolf et al., 2018). Resolution of species-level relationships using nuclear markers is of particular interest in Dioscorea because it includes multiple major and minor crop species, and the identities of the closest CWRs of most of these remain elusive, partly due to limited taxon sampling and insufficient variability of molecular markers, most of which have been from the plastid genome (Chaïr et al., 2005, 2016; Mukherjee and Bhat, 2013; Girma et al., 2016; but see Girma et al., 2014 for SNP analysis of Guinea yam and selected CWRs) or were not sequence-based (Terauchi et al., 1992; Malapa et al., 2005; Mignouna et al., 2005). Identifying CWRs is a significant endeavor for crop scientists (Dempewolf et al., 2014), as these taxa may represent a substantial pool of genetic diversity for traits that may be useful for future crop improvement and may be the most straightforward to cross in breeding experiments (Harlan and de Wet, 1971). I recovered D. calcicola, a previously proposed secondary gene pool CWR, as a strongly supported sister group of the major yam crop D. alata among sampled taxa, suggesting that it may be an even closer relative than its previously proposed primary gene pool CWR, D. nummularia. Primary gene pool CWRs are considered to be closely related (even conspecific) taxa that are fully interfertile with the crop taxon, whereas secondary gene pool CWRs represent more distantly related taxa that can be crossed to the crop taxon with more difficulty (Harlan and de Wet, 1971). These CWR groupings for Dioscorea were proposed in the Harlan and de Wet CWR inventory (Vincent et al., 2013) using genetic distances (Malapa et al., 2005) and phylogenetic relationships (Wilkin et al., 2005). Regardless of how CWR groupings are defined, my findings suggest that D. calcicola may have utility in future breeding programs for D. alata. 4.5.3 Characterization of loci targeted by the custom Dioscorea-specific enrichment panel The MarkerMiner pipeline I used for LSCN gene selection has previously been shown to select more conserved loci compared to two other commonly used methods, at least for LSCN gene discovery in Leguminosae (Vatanparast et al., 2018). However, my data set contained sufficient parsimony informative sites (average 24%) to strongly resolve most species-level relationships. 107 The customized panel designed here is currently being applied to >400 Dioscorea species, and preliminary phylogenomic analyses show that this approach can strongly resolve relationships using much denser sampling than that for this pilot taxon set (J. Viruel, Royal Botanic Gardens, Kew, unpublished data). The Dioscorea-specific nuclear gene targets may also be informative for resolving population-level relationships, such as those elucidated by Villaverde et al. (2018) for Euphorbia using a custom bait kit with an average of 8% parsimony informative sites for exons and 9% for exons plus introns. In Dioscorea, resolution at this shallow level may be particularly useful for characterizing variability of germplasm collections of wild and cultivated yam, a goal for improved management and use of genetic resources in this crop (Mignouna et al., 2005). Using non-coding regions (i.e., introns and intergenic regions), which I excluded here, may also be useful in this context. The MarkerMiner pipeline I implemented here recovered fewer loci that were flagged as potential paralogy instances by HybPiper, compared to two other methods used for LSCN gene identification in Leguminosae (Vatanparast et al., 2018). I detected potential paralogy, either through HybPiper warnings or by visual inspection of all generated gene alignments and trees, in 27 (10.4%) of the 260 genes for which baits were designed; orthologs were teased apart in most cases after inspection of gene trees. When I excluded 32 potentially paralogous genes (27 potential paralogy instances plus five genes that I derived from the latter after interpreting them as cases of early duplications, see Methods, Sub-Appendix C.1 and Table C.1), ASTRAL analysis of the resulting 232-gene data set (Fig. C.3) produced mostly highly supported relationships that were fully consistent with those from ASTRAL analysis of the full 264-gene data set (Fig. 4.2). This may be an underestimate of the amount of paralogy, but comparisons of analyses with and without these genes confirmed that phylogenomic inferences here are robust to inclusion vs. exclusion of the potential paralogs I recovered with the Dioscorea-specific kit (cf. Fig. 4.2 and Fig. C.3). Visual inspection of individual gene trees and alignments may therefore not be essential for species-tree inference. However, investigating loci flagged by HybPiper may be useful for the potential characterization and quantification of paralogy issues. Visual inspection of gene alignments and trees may be more critical when adding transcriptome data to target enrichment data, because post-transcriptional modifications may lead to inadvertent alignment of non-homologous regions in some genes across these two data types. I also note that 108 without visual inspection I may not have identified multiple instances where gene copies likely represented chimeras of orthologous and paralogous sequences. The versatile bait kit designed here recovered gene targets effectively from Dioscorea and one Dioscoreales outgroup taxon, enabling resolution of relationships at various taxonomic scales. This enrichment panel will be a useful tool for phylogenomic investigations of Dioscorea and allies. It may be used to develop an evolutionary framework for identifying the CWRs of yam crops, or to map traits of agricultural and evolutionary interest, and potentially investigate the importance of hybridization in the evolution of the genus. It may also be more broadly applicable in phylogenomic analyses across Dioscoreales. Whether universal angiosperm kits will displace the need for custom-designed taxon-focused kits remains to be thoroughly assessed. However, at minimum these two approaches should be complementary for groups of interest. The Dioscorea-specific kit has minimal overlap with kits designed for broader taxonomic studies in terms of target genes; it recovers a higher fraction of genes per those examined, and the recovered portions are longer and more variable for my group of interest than a universal kit allowed for a species of Dioscorea. Finally, analyses based on a pilot taxon sampling here predict that my enrichment panel will be suitable for phylogenomic analyses of a much denser taxonomic sampling in Dioscorea. 109 Table 4.1 Capture efficiency for the 25 taxa enriched using Dioscorea-specific baits, including DNA concentration of starting library (ng/μL) and designated enrichment pool. Species Starting library concentration (pool no.) Quality-filtered paired reads1 Enrichment efficiency (% reads on target) No. of assembled target genes (%)2 Recovered % of full target gene set length3 Gene tree occupancy (%)4 Total no. of bp retrieved by HybPiper No. of bp included in analyses Dioscorea antaly 78.6 (P4) 576,773 57.4 256 (98.5) 91.5 257 (97.3) 404,256 308,524 Dioscorea birmanica 40.6 (P3) 841,789 53.7 258 (99.2) 93.5 260 (98.5) 413,007 310,035 Dioscorea calcicola 51 (P4) 2,622,322 56.3 259 (99.6) 96.6 257 (97.3) 426,513 305,152 Dioscorea campestris 32.2 (P2) 1,125,117 16.8 257 (98.8) 83.8 259 (98.1) 369,975 301,896 Dioscorea caucasica 46.2 (P3) 2,829,147 47.1 253 (97.3) 81 250 (94.7) 357,693 288,868 Dioscorea communis 2 42.8 (P3) 791,550 56.8 258 (99.2) 92.1 258 (97.7) 406,539 307,100 Dioscorea cordata 57 (P4) 469,374 42.1 256 (98.5) 88.5 257 (97.3) 390,870 307,249 Dioscorea daunea 29.8 (P2) 893,672 12.5 252 (96.9) 78.9 252 (95.5) 348,579 292,679 Dioscorea glandulosa 11.1 (P1) 875,971 15.2 257 (98.8) 83.9 260 (98.5) 370,539 301,947 Dioscorea hombuka 16.6 (P1) 787,302 19.5 253 (97.3) 83.5 254 (96.2) 368,631 299,109 Dioscorea kituiensis 24.2 (P2) 990,417 8.9 252 (96.9) 68.6 246 (93.2) 302,829 253,766 Dioscorea mayottensis 37 (P2) 966,336 19.4 258 (99.2) 88.4 261 (98.9) 390,342 310,042 Dioscorea minima 16.1 (P1) 505,670 12.3 157 (60.4) 38 151 (57.2) 167,802 143,767 Dioscorea x monandra 24.2 (P2) 823,029 11.9 245 (94.2) 71.4 245 (92.8) 315,117 274,679 110 Species Starting library concentration (pool no.) Quality-filtered paired reads1 Enrichment efficiency (% reads on target) No. of assembled target genes (%)2 Recovered % of full target gene set length3 Gene tree occupancy (%)4 Total no. of bp retrieved by HybPiper No. of bp included in analyses Dioscorea nitens 13.6 (P1) 824,231 13.5 249 (95.8) 76.3 251 (95.1) 337,113 287,438 Dioscorea nummularia 77.2 (P4) 338,083 56.2 259 (99.6) 92.2 260 (98.5) 407,328 311,701 Dioscorea orthogoneura 23.2 (P1) 1,056,815 11.9 257 (98.8) 88.5 256 (97) 390,999 288,009 Dioscorea pentaphylla 80.4 (P4) 341,113 47.3 257 (98.8) 85.6 258 (97.7) 377,931 305,668 Dioscorea pohlii 74.6 (P3) 258,300 60.5 259 (99.6) 94.8 259 (98.1) 418,878 297,467 Dioscorea prazeri 29 (P2) 1,489,369 5.4 244 (93.8) 61.7 241 (91.3) 272,439 245398 Dioscorea rockii 24 (P1) 783,195 6.5 198 (76.2) 45.5 195 (73.9) 201,048 183,614 Dioscorea sansibarensis 39.4 (P3) 1,000,845 60.7 259 (99.6) 93.3 260 (98.5) 412,221 311,542 Dioscorea tentaculigera 11.8 (P1) 686,737 16.7 257 (98.8) 81.4 255 (96.6) 359,280 295,297 Trichopus zeylanicus 52.2 (P3) 389,519 45.4 151 (58.1) 24.2 149 (56.4) 107,082 97,020 1Number of reads produced after quality trimming using Trimmomatic (see text for details). 2Percentage calculated based on the 260 low-to-single-copy nuclear genes targeted using baits.3Percentage calculated from the total number of on-target nucleotides retrieved per sample, relative to the full length of the reference target gene set. 4Percentage calculated based on the 264 low-to-single-copy nuclear genes in the final data set. 111 Table 4.2 Target gene retrieval for nine taxa using transcriptome-based data (excluding four transcriptomes used for bait design). Species1 Quality-filtered paired reads2 Enrichment efficiency (% reads on target) No. of assembled target genes (%)3 Recovered % of full target gene set length4 Gene tree occupancy (%)5 Total no. of bp retrieved by HybPiper No. of bp included in analyses Dioscorea alata t2 35,322,722 1.8 254 (97.7) 95.3 254 (96.2) 420,909 306,180 Dioscorea cayenensis 59,713,857 1.8 257 (98.8) 95.5 257 (97.3) 421,914 308,395 Dioscorea cochleariapiculata 74,192,635 2.1 256 (98.5) 92.9 257 (97.3) 410,295 308,374 Dioscorea communis t1 61,649,891 2.2 257 (98.8) 91.9 258 (97.7) 405,759 307,352 Dioscorea nipponica 20,582,868 1.7 252 (96.9) 87.5 252 (95.5) 386,604 300,421 Dioscorea soso 60,869,766 2.3 258 (99.2) 94.3 258 (97.7) 416,262 309,712 Dioscorea sylvatica 56,326,258 2.3 258 (99.2) 95.1 258 (97.7) 420,039 309,733 Dioscorea trifida 32,346,683 1.7 254 (97.7) 92.5 255 (96.6) 408,300 305,784 Dioscorea zingiberensis 61,696,853 1.3 251 (96.5) 87.2 250 (94.7) 385,308 297,614 1 The “t” label for D. alata and D. communis indicates they are transcriptome-based sequences; both taxa are represented by an additional individual in phylogenomic analyses here, D. alata t1 (used for bait design and therefore not included in gene recovery analyses), and D. communis 2 (a DNA-based, target-enriched sample, see Table 4.1). 2 Number of reads produced after quality trimming using Trimmomatic (see text for details). 3 Percentage calculated based on the 260 low-to-single-copy nuclear genes targeted using baits. 4 Percentage calculated from the total number of on-target nucleotides retrieved per sample, relative to the full length of the reference target gene set. 5 Percentage calculated based on the 264 low-to-single-copy nuclear genes in the final data set. 112 Figure 4.1 Proportion of total reference gene length recovered per gene and taxon. Rows correspond to taxa, and columns correspond to a gene target. Target enrichment using the Dioscorea-specific baits designed here was performed on 25 taxa. Gene recovery for nine species with transcriptome-based data (not including the four transcriptomes used for bait design) is also shown here, see upper set of taxa in figure. Recovered proportions of the reference gene are indicated by shading. 113 114 Figure 4.2 Phylogenetic relationships in Dioscorea inferred from coalescent-based analyses of 264 genes recovered using target enrichment with the Dioscorea-specific baits designed here. Values next to branches are local posterior probabilities (LPP) and multilocus bootstrap support (MLB), respectively; thick branches have 1.0 LPP and 100% bootstrap support. Lineages in red are major crops; blue-labeled taxa (D. calcicola, D. nummularia) are previously identified crop wild relatives of the yam crops sampled here. Scale bar shows coalescent units for internal branches (not estimated by ASTRAL for terminal branches). 115 116 Chapter 5: Conclusion My study contributes to an improved understanding of the monocot ‘Tree of Life’ by addressing the particularly poorly characterized relationships within two orders, Pandanales and Dioscoreales, and within the largest genus in the latter order, the Dioscorea yams. Obtaining a clearer phylogenetic picture of these monocot lineages has allowed me to “fill these trees with life” (Endress et al., 2000) through exploration of multiple aspects of the biology of these plants, including their morphological and molecular evolution, plastid genome structural evolution, and their biogeography. Here, I discuss key findings, comment on possible caveats, and propose some potential future research directions that build on my thesis work. 5.1 Major findings and possible limitations 5.1.1 Chapter 2. A bi-organellar phylogenomic study of Pandanales: inference of higher-order relationships and unusual rate-variation patterns The robust genus-level phylogeny I inferred for Pandanales using plastid and mitochondrial phylogenomic data sets provides a framework for (i) probing molecular evolutionary processes in both organellar genomes, (ii) estimating clade ages and their consequences on biogeographic hypotheses, and (iii) identifying plastid genome (plastome) structural variants for use as possible synapomorphies for individual families. The plant mitochondrial genome (mitogenome) usually has by far the lowest substitution rate of the three plant genomes (Wolfe et al., 1987; Palmer and Herbon, 1988). It was therefore surprising to detect sporadic mitogenome rate accelerations that resulted in faster mitochondrial than plastid rates in some Pandanales taxa, although this has been observed in a few other lineages (Cho et al., 2004; Parkinson et al., 2005; Bakker et al., 2006; Mower et al., 2007; Sloan et al., 2008; Zhu et al., 2014; Skippington et al., 2015). Differences in plastid and mitochondrial substitutional rate regimes do not appear to affect estimates of order-wide divergence times, as the methods I used produce highly correlated clade ages across both organellar data sets. These divergence times allowed me to present a more cohesive picture of the historical geography of Pandanales, as previous studies in the order had focused on individual families (e.g., Pandanaceae: Gallaher et al., 2015; Velloziaceae: Mello-117 Silva et al., 2011, Alcantara et al., 2018; Triuridaceae: Mennes et al., 2013). For example, I added support to previously proposed hypotheses that Cyclanthaceae, Pandanaceae and Velloziaceae do not follow a Gondwanan vicariance diversification pattern (Gallaher et al., 2015; Alcantara et al., 2018). I also found evidence that the current distribution of Stemonaceae may reflect an Old World origin with subsequent dispersal to the New World. My methodological decision to omit fully mycoheterotrophic (FM) Triuridaceae from dating analyses is supported by the finding that inclusion of this rate-elevated lineage tends to bias local age estimates moderately upwards in analyses based on the widely used uncorrelated lognormal (UCLN) relaxed clock model (Drummond et al., 2006). The UCLN model was used in a previous study of Triuridaceae divergence times by Mennes et al. (2013). Using real and simulated data, Dornburg et al. (2011) showed that the less widely used random local clock (RLC) model (Drummond and Suchard, 2010) accommodates among-lineage rate heterogeneity better than the UCLN model. In the future, it would be useful to compare the performance of UCLN and RLC methods for phylogenies that include heterotrophic taxa, as these may represent some of the most challenging test cases for dating analyses of plants, due to noticeable rate elevation across all three genomes following loss of photosynthesis in these lineages (e.g., Chapter 3; Bromham et al., 2013; Lam et al., 2018). My findings therefore suggest that divergence times in any dating analysis that includes FM lineages (e.g., for Triuridaceae, Mennes et al., 2013 and Givnish et al., 2018) should be interpreted cautiously, particularly regarding the stem ages of FM lineages and their closest photosynthetic relatives. My study of Pandanales included plastome data for only one of approximately nine genera in mycoheterotrophic Triuridaceae, contrasting with the few-gene nuclear/mitochondrial study of Mennes et al. (2013) that surveyed five genera in the family. Efforts to generate plastid data from Kupea, another genus in Triuridaceae, were unsuccessful (V. Lam, Graham lab, unpublished data). A more complete picture of Pandanales clade ages, biogeography, and evolution will require a denser sampling of Triuridaceae in future phylogenomic studies using all three plant genomes, assuming that plastid genes can be retrieved from these FM lineages. Although the potential absence of plastomes is difficult to prove, it is thought to be a natural endpoint of genome degradation in heterotrophic plants (e.g., Graham et al., 2017), and may have occurred in the holoparasitic plant Rafflesia lagascae (Molina et al., 2014). To continue 118 characterizing molecular rates in Pandanales, additional investigations could address whether the mitochondrial rate accelerations that I detected affect a subset of genes or the whole mitogenome. In either case, the ratio of non-synonymous to synonymous substitutions (dN/dS) may provide insights into the modes and strength of natural selection acting on affected protein-coding genes. The implications of the detected organellar rate shifts on phylogenetic inferences in Pandanales remain somewhat unclear. For example, slow plastid rates in Cyclanthaceae likely contributed to weakly supported plastid-based relationships in this family, but mitochondrial-based inferences were well supported for this clade, even for its more slowly evolving members. The slowly fluctuating boundaries of the plastid inverted repeat (IR) regions in surveyed plastomes of Pandanales offer structural synapomorphies that may prove useful for delimiting each of the four photosynthetic families in the order. Confirming the suitability of these structural variants as family-level markers requires sampling of additional complete Pandanales plastomes beyond the one to six representatives per family that I included here. However, expanding this sampling should be straightforward to do in the future, as publicly available plastomes continue to accumulate quite rapidly (Tonti-Filippini et al., 2017), and computational methods for assembling circular genomes with accurately delimited inverted repeats (e.g., Dierckxsens et al., 2017) also continue to improve. 5.1.2 Chapter 3. Can we reliably place fully mycoheterotrophic Dioscoreales lineages using plastid genome data or morphology? I present the first broad plastid phylogenomic survey of Dioscoreales, in tandem with a re-investigation of morphology in the order. I used these data to clarify family- and genus-level relationships in Dioscoreales, including the highly uncertain placements of multiple FM taxa lacking phylogenomic data. I also identified morphological synapomorphies that are potentially useful for characterizing various among- and within-family clades. My study supports previous findings (e.g., Delannoy et al., 2011; Logacheva et al., 2014; Givnish et al., 2015; Lam et al., 2015, 2018; Mennes et al., 2015; Feng et al., 2016) that plastid phylogenomic approaches are generally useful for inferring well-supported placements of FM taxa in broadly sampled phylogenies, despite rate elevation and gene loss in these lineages. However, there may be limits to what can be accomplished for full mycoheterotrophs with very highly elevated substitution 119 rates (e.g., Lam et al., 2018), as demonstrated here for Afrothismia, an extremely rapidly evolving FM lineage in Dioscoreales. To investigate the placement of Afrothismia and other long-branch taxa in the order, I added different sets of these lineages to a baseline taxon sampling that included the least rapidly evolving full mycoheterotrophs in Burmanniaceae and Thismiaceae s.l., which appear to act as useful ‘anchors’ for faster evolving FM species in these two families. I found that branch support values were still often depressed upon addition of the more rapidly evolving FM lineages, a possible mild long-branch artefact. I also observed strong misinference when multiple sets of long-branch FM taxa were analyzed simultaneously. These lineages appeared to pull together (i.e., become attracted to each other) compared to their more distant placements when added individually. These probable instances of long-branch attraction occurred even using a likelihood framework, which is expected to be more resilient than parsimony to this problem (e.g., Felsenstein, 1981; Huelsenbeck, 1995; Swofford et al., 2001). Several FM taxa in Dioscoreales could not be sampled for phylogenomic data, including genera presumed to be extinct, such as the monotypic genus Miersiella (Maas et al., 1986; Merckx et al., 2013a). Nevertheless, I was able to score the morphology of some of these taxa, which permitted their integration into phylogenetic inferences here. Using a variety of constrained and unconstrained analyses of morphology, I narrowed down the placements of several FM genera. These had remained uncertain in the few studies that addressed their phylogenetic placement (Caddick et al., 2002a; Merckx et al., 2006; Merckx et al., 2008a; Merckx and Smets, 2014; Lam et al., 2016; Shepeleva et al., 2020). Ancestral-state reconstructions also produced several possible synapomorphies for characterizing individual FM clades. My overall findings should inform future changes to the rather contentious classification of Dioscoreales, which includes three families in the most recent schemes proposed by the Angiosperm Phylogeny Group (APG, 2003, 2009, 2016). In light of my results in Chapter 3 and other recent studies (e.g., Merckx et al., 2009a, 2010; Merckx and Smets, 2014; Lam et al., 2016, 2018; Givnish et al., 2018; Shepeleva et al., 2020), and despite residual uncertainties in placing several taxa (e.g., the autotrophic genus Stenomeris, and the FM genus Afrothismia), maintaining the status quo of the current three-family APG scheme is untenable. Although the broad circumscription of photosynthetic Nartheciaceae is unproblematic, my data confirm that the fully 120 mycoheterotrophic family Thismiaceae s.l. must be excluded from Burmanniaceae. The family Thismiaceae s.l. is in turn clearly nested in a large clade among highly distinctive photosynthetic lineages currently classified in Dioscoreaceae, including Dioscorea, Tacca and Trichopus. There are several options for re-ordering Dioscoreales classification to accommodate these findings, including (i) recognizing multiple small families, with photosynthetic Taccaceae and Trichopodaceae split from Dioscoreaceae, and Thismiaceae s.l. segregated from Burmanniaceae s.s; the status of Nartheciaceae would remain unchanged. This approach would result in an order with a minimum of six families; (ii) lumping all lineages outside Nartheciaceae and Burmanniaceae s.s. into one large and very diverse family (Dioscoreaceae has priority), resulting in a re-organized three-family classification; or (iii) recognizing two families outside Nartheciaceae and Burmanniaceae s.s., a more narrowly defined Dioscoreaceae (comprising the large genus Dioscorea and possibly Stenomeris), and an expanded Taccaceae that incorporates Thismiaceae s.l. and Trichopodaceae. The latter option would yield a four-family classification scheme for the order. Choosing among different family classification options will depend in part on resolution of the placement of photosynthetic Stenomeris and FM Afrothismia. I recovered Stenomeris as the sister group of Dioscorea based on limited phylogenetic data, with weakly conflicting placements in parsimony and likelihood analyses. The placement of Afrothismia here is highly variable and poorly supported across my molecular and morphological analyses. A redefined Dioscoreales classification scheme recognizing only three families (Burmanniaceae, Dioscoreaceae and Nartheciaceae) may be the least desirable option, as the resulting very broadly defined Dioscoreaceae would be extremely heterogeneous, with one or more FM lineages (Thismiaceae s.s. and perhaps Afrothismia) interspersed among diverse autotrophs (Dioscorea, Stenomeris, Tacca and Trichopus). The four-family scheme noted above also has FM taxa dispersed among autotrophs, in a redefined Taccaceae. There are few morphological characters supporting either the three-family or four-family schemes (Fig. 3.4). However, a slightly more narrowly defined Taccaceae that excludes Trichopodaceae, resulting in a five-family scheme, would have three unambiguous morphological synapomorphies: mature stamens reflexed downwards so that their apical extensions touch the stigma, unilocular ovaries, and parietal placentation (Fig. 3.4; characters 11, 21, 22, respectively). The most narrowly divided 121 classification option recognizing six families, would likely have to be further amended to accommodate the possibility that Afrothismia evolved full mycoheterotrophy separately from Thismiaceae s.s. as part of a clade that also includes autotrophic Tacca (e.g., Merckx and Bidartondo, 2008; Merckx et al., 2009a, 2010; Merckx and Smets, 2014). I therefore described the new family Afrothismiaceae in Chapter 3 to account for this possibility. A particular limitation of this study, contrasting with the one on Pandanales (Chapter 2), is the lack of a mitogenome-based data set to assess phylogenetic congruence across the two organellar genomes in Dioscoreales. Phylogenomic analyses based on full mitogenomes appear to have substantially shorter branches for FM taxa (for example, compare branch lengths of the FM genus Sciaphila in Fig. A.6 vs. Fig. A.13) and may therefore be less prone to long-branch distortions than their corresponding plastid genomes. This may be particularly useful for clarifying the placement of Afrothismia, an FM genus with extreme rate elevation that I recovered in highly unusual positions in plastid-based analyses, including as the unlikely sister group of the grass family Poaceae. Substantially slower mitochondrial rates in FM taxa may also be useful for uncovering long-branch artefacts in plastid analyses that are not readily evident, perhaps including local arrangements among closely related FM lineages (e.g., Apteria, Campylosiphon and Gymnosiphon in Burmanniaceae). A final caveat concerns my morphology-based analyses and the uncertainty in the topologies used for ancestral-state reconstructions, as accurate trees provide a necessary framework for identifying morphological synapomorphies. It would be useful to eventually update my various morphological analyses using a Dioscoreales phylogeny that clarifies the placement of Afrothismia and Stenomeris, and comprises more taxa in Burmanniaceae and Thismiaceae. This topology could then be used as a firmer basis for considering morphological synapomorphies inferred across parsimony, likelihood and Bayesian frameworks. Updated results from morphology should also help guide future taxonomic decisions on the family-level classification of the order. 5.1.3 Chapter 4. A customized nuclear target enrichment approach for developing a phylogenomic baseline for Dioscorea yams (Dioscoreaceae) I used a nuclear phylogenomic approach to address highly uncertain relationships in the Dioscorea yams, by far the largest genus in Dioscoreales and home of multiple economically 122 important species. This involved (i) developing a customized target enrichment panel comprising 260 low-to-single-copy nuclear genes, (ii) assessing the gene recovery performance of the panel across all major yam clades and an outgroup taxon, and (iii) testing the suitability of the panel for recovering different levels of relationships in Dioscorea. The resulting target enrichment panel is versatile, and permits effective gene target recovery across Dioscorea and an outgroup taxon (Trichopus, Dioscoreales). It also enables strongly supported inferences at deep and more recent scales in Dioscorea. The strongly supported placement of a wild species, D. calcicola, as a closer relative of the major yam crop D. alata than previously thought (Vincent et al., 2013), highlights the potential of the Dioscorea gene panel for fine-tuning the identification of yam crop wild relatives. More generally, my study and a series of recently published papers in which taxon-specific gene panels were developed for different groups (e.g., Folk et al., 2015; Sass et al., 2016; Schmickl et al., 2016; Kadlec et al., 2017; Chau et al., 2018; Vatanparast et al., 2018; Villaverde et al., 2018), illustrate how target enrichment approaches can be used to bring non-model plant lineages into a nuclear phylogenomic framework. However, it remains to be seen whether customized taxon-focused gene panels will be displaced by ‘universal’ panels designed for broader taxonomic studies (e.g., Angiosperms-353; Johnson et al., 2019) as the latter become more widely accessible, affordable, and mass produced. At minimum, these two approaches may be complementary, considering that my Dioscorea gene panel has very limited overlap in terms of target genes compared to two universal target enrichment panels, and it also recovers longer and more variable targets in the genus. Phylogenomic analyses in Chapter 4 focused exclusively on coding regions (i.e., gene targets) and recovered most relationships in Dioscorea with strong support. However, weaker support for a few relatively short branches might have been improved by considering the more variable intronic and intergenic regions that comprise the so-called “splash zone” that flanks gene targets. These non-coding regions, which are expected to be much less constrained by purifying selection, and thus more rapidly evolving and variable, may be useful for species- and even population-level inferences in Dioscorea, although alignments may also be more difficult than with exons. Phylogenetic resolution at the latter shallow level is particularly important in 123 Dioscorea for characterizing germplasm collections of wild and cultivated yam, an intended application of my gene panel. 5.2 Some future directions Future studies of Pandanales and Dioscoreales should aim to include phylogenomic sets for expanded samplings of full mycoheterotrophs, including additional genera of Burmanniaceae, Thismiaceae and Triuridaceae. In Burmanniaceae, including additional lineages of photosynthetic and FM Burmannia would be useful to address the number of losses of photosynthesis in this family, currently estimated to be at least eight (Merckx et al., 2013b). Obtaining sampling material for some of these lineages will likely remain a challenge; paucity of collections often reflects rarity and high local endemism, and difficulties in detecting these inconspicuous and ephemeral plants outside targeted surveys (Merckx et al., 2013c). A promising option for maximizing information retrieval from available collections of FM taxa may be the use of target enrichment methods (e.g., Chapter 4), which are suitable for generating phylogenomic data from lower-quality template DNAs, such as those extracted from herbarium specimens (Lemmon and Lemmon, 2013; Hart et al., 2016; Léveillé-Bourret et al., 2018; McKain et al., 2018; Vatanparast et al., 2018; Villaverde et al., 2018). An additional benefit of this approach is that it can generate phylogenomic data from the nucleus and both organellar genomes in a single reaction (e.g., Weitemier et al., 2014), which should be helpful for building on my phylogenomic studies of Pandanales and Dioscoreales here. An initial unpublished sampling of both orders (by J. Viruel at the Royal Botanic Gardens, Kew) using the target enrichment panel I developed in Chapter 4 generated data for several FM and autotrophic lineages in both orders; this research will continue to be addressed by other members of my lab group and collaborators at the Naturalis Biodiversity Center, Netherlands. The phylogeny of Dioscoreales I inferred in Chapter 4 represents a useful evolutionary framework to guide future taxon selection for comparative analyses. Two intriguing prospects include comparative transcriptomics across multiple independent transitions to full mycoheterotrophy in Dioscoreales, and across the highly heterogeneous underground storage organs observed in the order. In the former case, transcriptomes may be useful to investigate the 124 degree to which there are parallel evolutionary trajectories of photosynthesis-related genes across the plastid and nuclear genomes of a species, in terms of loss, retention and changes in their selective regime (e.g., Graham et al., 2017). These patterns could be compared across multiple independent instances of mycoheterotrophy in the order. Transcriptomes may also provide useful insights into the genes that underlie the formation of underground organs, which host fungi in FM taxa (Imhof, 2010), or store starch or secondary compounds in autotrophic taxa (Wilkin et al., 2005). For example, these data may shed light on (i) whether the vermiform to tuberous roots of some FM members of Burmanniaceae and Thismiaceae have a similar genetic basis as the thickened root tubercles that are characteristic of Afrothismia, and (ii) whether there are parallel genetic footprints across the tuberization processes in FM taxa and photosynthetic lineages such as Dioscorea and Tacca. Obtaining an improved understanding of the evolution of underground storage organs in Dioscorea in part motivated the development of the target enrichment panel presented in Chapter 4, as these structures are of both biological and economic interest. For example, tubers may be a key trait promoting diversification in Dioscorea (rhizomatous lineages tend to have fewer members and more restricted distributions) and also represent a globally important tuber crop (Wilkin et al., 2005). 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Journal of the Torrey Botanical Society 126, 58–62. 148 Appendices Appendix A Supplementary tables and figures for Chapter 2 Table A.1 Specimen source information for new data; herbarium abbreviations follow Thiers (continuously updated). ____________________________________________________________________________________________________________ Species1,2 Family Data type Voucher number [Collector number, herbarium] ____________________________________________________________________________________________________________ Pandanales Asplundia moritziana (Klotzsch) Harling Cyclanthaceae plastid, mitochondrial M.W. Chase 1236, no data Carludovica palmata Ruiz & Pav. Cyclanthaceae mitochondrial3 M.W. Chase 14836, K Chorigyne cylindrica R. Erikss. Cyclanthaceae plastid, mitochondrial S.Y. Smith 175, MICH Cyclanthus bipartitus Poit. ex A. Rich Cyclanthaceae mitochondrial3 M.W. Chase 1237, K Dianthoveus cremnophilus Hammel & G.J. Wilder Cyclanthaceae plastid, mitochondrial Hammel 16058, MO Dicranopygium microcephalum (Hook.f.) Harling Cyclanthaceae plastid, mitochondrial D. Scherberich 110608, G Evodianthus funifer (Poit.) Lindm. Cyclanthaceae plastid, mitochondrial S. Barrier 1825, K Ludovia lancifolia Brongn. Cyclanthaceae plastid, mitochondrial D. Scherberich 090621, W Schultesiophytum chorianthum Harling Cyclanthaceae plastid, mitochondrial D. Canal 717, JBB Sphaeradenia woodsonii Harling Cyclanthaceae plastid, mitochondrial S.Y. Smith 178, MICH Stelestylis surinamensis Harling Cyclanthaceae plastid, mitochondrial D. Scherberich 041129, G Thoracocarpus bissectus (Vell.) Harling Cyclanthaceae plastid, mitochondrial A. Fuentes & F. Torrico 5447, MO Benstonea copelandii Merr. Callm. & Buerki Pandanaceae plastid, mitochondrial C. Sherman & K. Bynum 303, MO Freycinetia banksii A. Cunn. Pandanaceae mitochondrial3 S.W. Graham 02-03-14, UBC Martellidendron hornei (Balf.f.) Callm. & Chassot Pandanaceae plastid, mitochondrial Gallaher 281, BISH, HAW Pandanus tectorius Parkinson ex Du Roi Pandanaceae plastid, mitochondrial Gallaher 443, BISH, HAW Sararanga sinuosa Hemsl. Pandanaceae mitochondrial3 Gallaher 461, BISH, HAW Croomia japonica Miq. Stemonaceae mitochondrial3 Rothwell & Stockey 43, ALTA Pentastemona sumatrana Steenis Stemonaceae mitochondrial3 B.G. Leiden 910375, K Stemona tuberosa Lour. Stemonaceae mitochondrial3 Rothwell & Stockey 46, ALTA Stichoneuron caudatum Ridl. Stemonaceae mitochondrial3 Rothwell & Stockey 45, ALTA Sciaphila densiflora Schltr. Triuridaceae mitochondrial3 Pillon Y. et al. 88, NOU, P Acanthochlamys bracteata P.C. Kao Velloziaceae plastid, mitochondrial P.C. Kao 1993, K Barbacenia involucrata L.B. Sm. Velloziaceae plastid, mitochondrial L.R. Lima et al. 254, K 149 ____________________________________________________________________________________________________________ Species1,2 Family Data type Voucher number [Collector number, herbarium] ____________________________________________________________________________________________________________ Barbaceniopsis castillonii (Hauman) Ibisch Velloziaceae plastid, mitochondrial C. & P. Ibisch 93.1248, 28.10.1993, BONN (BO-0-BONN-12974) Vellozia sp. Vand Velloziaceae plastid, mitochondrial Kubitzki & Feuerer 97-30, HBG Xerophyta elegans (Balf.) Baker Velloziaceae plastid, mitochondrial Rothwell & Stockey 48, ALTA Xerophyta retinervis Baker Velloziaceae mitochondrial3 B.G. Reeves 14, NBG Dioscoreales Dioscorea membranacea Pierre ex Prain & Burkill Dioscoreaceae mitochondrial M.W. Chase 21050, K Liliales Campynemanthe viridiflora Baill Campynemataceae mitochondrial3 Pillon et al. 24, NOU Asparagales Iris missouriensis Nutt. Iridaceae mitochondrial M.A. McPherson 000707-5a-7, ALTA Petrosaviales Japonolirion osense Nakai Petrosaviaceae mitochondrial1 M.W. Chase 3000, K Alismatales Butomus umbellatus L. Butomaceae mitochondrial3 Chase 6414, K Scheuchzeria palustris L. Scheuchzeriaceae mitochondrial3 D.W. Stevenson 23/2012, NY Stratiotes aloides L. Hydrocharitaceae mitochondrial3 J. Bogner s.n., ALTA Tofieldia coccinea Richardson Tofieldiaceae mitochondrial3 M.J. Waterway 2006-241, UBC ____________________________________________________________________________________________________________ 1 Additional plastid data: Sciaphila thaidanica K. Larsen (MG757197.1); Japonolirion osense (JQ068951-JQ069028). Additional mitochondrial data: Arabidopsis thaliana (L.) Heynh. (NC_037304). 2 See Lam et al. (2018) for source information for taxa not included in table (plastid data only). 3 Plastid data published in Lam et al. (2015), Ross et al. (2015) or Givnish et al. (2016). 150 Table A.2 GenBank accession numbers for newly sequenced plastid and mitochondrial gene sets (83 and 37 genes, respectively) and three fully circularized plastomes. __________________________________________________________ Gene1 GenBank accessions __________________________________________________________ Plastid accD MT047015-MT047030 atpA MT045997-MT046012 atpB MT047031-MT047045 atpE MT046013-MT046028 atpF MT046029-MT046043 atpH MT046044-MT046059 atpI MT046060-MT046075 ccsA MT046076-MT046091 cemA MT046092-MT046107 clpP MT046108-MT046123 infA MT046124-MT046139 matK MT046140-MT046154 ndhA MT046155-MT046170 ndhB MT046171-MT046186 ndhC MT046187-MT046201 ndhD MT046202-MT046216 ndhE MT046217-MT046230 ndhF MT047046-MT047061 ndhG MT046231-MT046246 ndhH MT046247-MT046262 ndhI MT046263-MT046278 ndhJ MT046279-MT046293 ndhK MT046294-MT046309 petA MT046310-MT046325 petB MT046326-MT046341 petD MT047062-MT047077 petG MT046342-MT046357 petL MT046358-MT046373 petN MT046374-MT046389 psaA MT046390-MT046405 psaB MT046406-MT046421 psaC MT047078-MT047093 psaI MT046422-MT046436 psaJ MT047094-MT047109 psbA MT047110-MT047124 psbB MT046437-MT046452 psbC MT046453-MT046468 psbD MT046469-MT046482 psbE MT046483-MT046498 psbF MT046499-MT046513 psbH MT046514-MT046529 psbI MT046530-MT046545 151 __________________________________________________________ Gene1 GenBank accessions _______________________________________________________________ psbJ MT046546-MT046561 psbK MT046562-MT046576 psbL MT046577-MT046592 psbM MT046593-MT046607 psbN MT046608-MT046623 psbT MT047125-MT047140 psbZ MT046624-MT046639 rbcL MT047141-MT047155 rpl14 MT046656-MT046671 rpl16 MT047156-MT047171 rpl2 MT046640-MT046655 rpl20 MT046672-MT046687 rpl22 MT047172-MT047186 rpl23 MT046688-MT046703 rpl32 MT047187-MT047201 rpl33 MT046704-MT046719 rpl36 MT046720-MT046735 rpoA MT046736-MT046751 rpoB MT047202-MT047216 rpoC1 MT046752-MT046767 rpoC2 MT046768-MT046782 rps11 MT046859-MT046874 rps12 MT046875-MT046890 rps14 MT046891-MT046906 rps15 MT046907-MT046922 rps16 MT046923-MT046934 rps18 MT046935-MT046950 rps19 MT047217-MT047232 rps2 MT046783-MT046798 rps3 MT046799-MT046814 rps4 MT046815-MT046826 rps7 MT046827-MT046842 rps8 MT046843-MT046858 ycf1 MT047249-MT047263 ycf2 MT047233-MT047248 ycf3 MT046951-MT046966 ycf4 MT046967-MT046982 rrn4.5 MT046983-MT046998 rrn5 MT046999-MT047014 rrn16 MT027519-MT027534 rrn23 MT027535-MT027550 Mitochondrial atp1 MT054850-MT054885 atp4 MT055331-MT055366 atp6 MT054306-MT054341 atp8 MT055392-MT055426 152 __________________________________________________________ Gene1 GenBank accessions __________________________________________________________ atp9 MT054797-MT054825 ccmB MT053886-MT053919 ccmFc MT054008-MT054042 ccmFc MT055098-MT055133 ccmFn MT055034-MT055069 cob MT054122-MT054157 cox1 MT054972-MT055006 cox2 MT053945-MT053980 cox3 MT054678-MT054711 matR MT054495-MT054530 mttB MT055559-MT055594 nad1 MT054431-MT054466 nad2 MN907133-MN907168 nad3 MT055447-MT055479 nad4 MT054557-MT054592 nad4L MT054620-MT054654 nad5 MT023139-MT023174 nad6 MT054185-MT054219 nad7 MT054368-MT054403 nad9 MT023265-MT023299 rpl10 MT055679-MT055692 rpl16 MT023300-MT023335 rpl2 MT055622-MT055652 rpl5 MT054068-MT054100 rps1 MT055268-MT055303 rps12 MT054739-MT054773 rps13 MT055162-MT055192 rps19 MT055211-MT055242 rps2 MT055506-MT055535 rps3 MT023203-MT023237 rps4 MT054247-MT054278 rps7 MT054914-MT054945 sdh4 MT055698-MT055731 ___________________________________________________________________________ 1 GenBank accessions for full circularized plastid genomes: Acanthochlamys bracteata (MN905940), Benstonea copelandii (MN905941), Croomia japonica (MN905942). 153 Table A.3 Optimal substitution models for unpartitioned analyses and for final partitioning schemes inferred using PartitionFinder 2 (see text). DNA analyses were partitioned using a gene-by-codon (‘G x C’) scheme; amino-acid (AA) analyses were partitioned by gene. (i) Plastid, (ii) mitochondrial and (iii) combined organellar. (i-A) Unpartitioned 95-taxon DNA matrix including a mycoheterotrophic taxon (Sciaphila densiflora); (i-B) G x C partitioning scheme for the same 95-taxon DNA matrix; (i-C) G x C partitioning scheme for a 96-taxon DNA matrix that includestwo mycoheterotrophic taxa (S. densiflora and S. thaidanica); (i-D) G x C partitioning scheme for a 94-taxon DNA matrix excluding both mycoheterotrophic taxa (S. densiflora and S. thaidanica); (i-E) G x C partitioning scheme for a 30-taxon Pandanales-focused DNA matrix that includes the plastid ycf1 locus; (i-F) Unpartitioned 95-taxon AA matrix; (i-G) Gene-based partitioning scheme for the same 95-taxon AA matrix; (ii-A) Unpartitioned DNA matrix; (ii-B) G x C partitioning scheme for DNA matrix; (ii-C) Unpartitioned AA matrix; (ii-D) Gene-based partitioning scheme for AA matrix; (iii-A) G x C partitioning scheme for a 33-taxon combined organellar DNA matrix. Genes are shown before the underscore and the ‘pos’ term after the underscore indicates the codon position (not applicable for rrn genes). _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ i-A)1 GTR + I + G -- i-B)1 GTR+I+G accD_pos1 2 GTR+I+G ccsA_pos1, rps15_pos1, accD_pos2, psbF_pos3 3 GTR+I+G accD_pos3, ycf4_pos3, cemA_pos3 4 GTR+I+G atpA_pos1 5 GTR+I+G atpB_pos2, atpA_pos2 6 GTR+I+G ndhG_pos3, rbcL_pos3, petA_pos3, psaC_pos3, psbI_pos3, atpA_pos3 7 GTR+I+G atpB_pos1, atpI_pos1, rpl36_pos2 8 GTR+I+G ndhJ_pos3, petB_pos3, atpB_pos3, psbB_pos3 9 GTR+G atpE_pos1, rpl14_pos1 10 GTR+I+G ndhI_pos1, rps18_pos2, atpE_pos2, rps19_pos2 11 GTR+I+G rpoA_pos3, rpl14_pos3, rps8_pos3, atpE_pos3, psbT_pos3 12 GTR+I+G atpF_pos1, rps16_pos1, ndhK_pos1 13 GTR+I+G atpF_pos2 14 GTR+I+G atpF_pos3, rpl20_pos3, rpoB_pos3, rpoC1_pos3, clpP_pos3, rpl33_pos3 154 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 15 GTR+I+G atpH_pos1, petG_pos1 16 GTR+I+G atpH_pos2, psbA_pos2 17 GTR+I+G atpH_pos3 18 GTR+I+G atpI_pos2 19 GTR+I+G rps18_pos3, rps14_pos3, psaI_pos3, rps4_pos3, atpI_pos3, psbM_pos3, psbK_pos3 20 GTR+I+G ccsA_pos2, ndhF_pos2 21 GTR+I+G ccsA_pos3 22 GTR+I+G cemA_pos1, rpoA_pos1, rpl33_pos1 23 GTR+G psbL_pos3, cemA_pos2 24 GTR+G clpP_pos1 25 GTR+I+G petA_pos2, clpP_pos2 26 GTR+G rps19_pos1, rps2_pos1, infA_pos1 27 GTR+G rps2_pos2, infA_pos2, rpl23_pos3, rps14_pos1 28 GTR+I+G infA_pos3, rpoC2_pos3, rps19_pos3 29 GTR+I+G matK_pos1 30 GTR+I+G petL_pos3, rpl22_pos2, matK_pos2 31 GTR+I+G matK_pos3, ndhE_pos3 32 GTR+I+G ndhA_pos1, ndhD_pos1 33 GTR+I+G ndhG_pos2, ndhD_pos2, ndhA_pos2 34 GTR+I+G ndhA_pos3 35 GTR+I+G ndhB_pos1, psbT_pos1, psbE_pos1 36 GTR+I+G ndhC_pos2, ndhB_pos2 37 GTR+G ndhB_pos3 38 GTR+I+G ndhC_pos1, petA_pos1 39 GTR+I+G ndhC_pos3 40 GTR+I+G rps11_pos3, rpl22_pos3, ndhD_pos3 41 GTR+I+G rpoB_pos1, ndhE_pos1 42 GTR+I+G psbZ_pos2, psbT_pos2, ndhE_pos2, psbM_pos2 43 GTR+I+G ndhF_pos1 44 GTR+I+G ndhF_pos3 45 GTR+G ndhG_pos1, rpl33_pos2 46 GTR+I+G rpoC1_pos1, ndhH_pos1 47 GTR+I+G ndhH_pos2 48 GTR+I+G petD_pos3, ndhH_pos3, rps3_pos3, rpl36_pos3, rpl32_pos3 49 GTR+I+G ndhJ_pos2, ndhI_pos2 50 GTR+I+G rps15_pos3, ndhI_pos3, rpl16_pos3 51 GTR+G ndhJ_pos1, rps14_pos2, psbN_pos1 52 GTR+I+G ndhK_pos2, rps4_pos2 53 GTR+I+G ndhK_pos3, psbH_pos3 54 GTR+I+G psbC_pos1, petB_pos1, psbA_pos1 55 GTR+I+G psbL_pos1, petB_pos2, psaA_pos2, psbB_pos2 56 GTR+I+G petD_pos1 57 GTR+I+G petD_pos2 155 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 58 GTR+I+G psbI_pos2, petG_pos2, psbE_pos2, psbL_pos2 59 GTR+I+G ycf3_pos3, petG_pos3, petN_pos3 60 GTR+I+G petL_pos1, rps12_pos3, rpoC2_pos2 61 GTR+I+G ycf3_pos2, petL_pos2, psbN_pos2, psbZ_pos1 62 GTR+G rpl23_pos2, rps7_pos2, petN_pos1, rpl14_pos2 63 GTR petN_pos2, psbI_pos1 64 GTR+I+G psaA_pos1 65 GTR+I+G psbD_pos3, psaA_pos3 66 GTR+I+G psaB_pos1 67 GTR+I+G psaB_pos2 68 GTR+I+G psaB_pos3 69 GTR+I+G psbB_pos1, psaC_pos1 70 GTR+G psaC_pos2 71 GTR+I+G psaI_pos1, rpl20_pos2, rps16_pos2 72 GTR+I+G psaI_pos2 73 GTR+I+G ycf4_pos2, psaJ_pos1 74 GTR+I+G psbK_pos2, psaJ_pos2 75 GTR+G rps16_pos3, psaJ_pos3 76 GTR+I+G psbA_pos3 77 GTR+I+G psbD_pos2, psbC_pos2 78 GTR+I+G rps2_pos3, psbC_pos3 79 GTR+I+G psbD_pos1 80 GTR+G psbE_pos3 81 GTR+I+G rpl2_pos1, rps12_pos1, psbF_pos1, ycf3_pos1, rps7_pos1 82 GTR+I+G psbF_pos2 83 GTR+I+G psbH_pos2, psbH_pos1 84 GTR+I+G rpl16_pos2, psbJ_pos1 85 GTR+I+G psbJ_pos2 86 GTR+G rps15_pos2, psbJ_pos3, psbZ_pos3 87 GTR+I+G psbK_pos1 88 GTR+G rrn4, psbM_pos1 89 GTR+G psbN_pos3 90 GTR+I+G rbcL_pos1 91 GTR+I+G rbcL_pos2 92 GTR+I+G rpl16_pos1 93 GTR+G rpl32_pos1, rpl20_pos1 94 GTR+G rpl22_pos1 95 GTR+G rpl23_pos1 96 GTR+I+G rps12_pos2, rpl2_pos2, rrn23, rrn5 97 GTR+G rpl2_pos3 98 GTR+G rpl32_pos2 99 GTR+G rps18_pos1, rpl36_pos1 100 GTR+I+G rps3_pos2, rpoA_pos2 101 GTR+I+G rpoB_pos2, rpoC1_pos2 156 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 102 GTR+I+G rps3_pos1, rpoC2_pos1 103 GTR+G rps8_pos1, rps11_pos1 104 GTR+I+G rps11_pos2, rps4_pos1 105 GTR+G rps7_pos3 106 GTR+I+G ycf4_pos1, rps8_pos2 107 GTR+G ycf2_pos1 108 GTR+I+G ycf2_pos2 109 GTR+G ycf2_pos3 110 GTR+I+G rrn16 i-C) 1 GTR+I+G accD_pos1 2 GTR+I+G psbF_pos3, accD_pos2, ccsA_pos1, rps15_pos1 3 GTR+I+G accD_pos3 4 GTR+I+G psbJ_pos1, atpA_pos1 5 GTR+I+G atpB_pos2, atpA_pos2 6 GTR+I+G psaC_pos3, psbI_pos3, atpA_pos3, ndhG_pos3, 7 GTR+I+G atpI_pos1, atpB_pos1 8 GTR+I+G petB_pos3, ndhJ_pos3, psbB_pos3, atpB_pos3 9 GTR+G rpl14_pos1, atpE_pos1 10 GTR+I+G atpE_pos2, ndhK_pos2, rps4_pos2 11 GTR+G infA_pos3, petA_pos3, atpE_pos3, psbT_pos3 12 GTR+I+G atpF_pos1, rps16_pos1, ndhK_pos1 13 GTR+I+G atpF_pos2 14 GTR+I+G rpl20_pos3, atpF_pos3, rpoB_pos3, rpoC1_pos3, rpl33_pos3, clpP_pos3 15 GTR+I+G petG_pos1, atpH_pos1 16 GTR+I+G psbA_pos2, atpH_pos2 17 GTR+I+G atpH_pos3 18 GTR+I+G atpI_pos2 19 GTR+I+G atpI_pos3, psbD_pos3, psaA_pos3 20 GTR+I+G ndhF_pos2, ccsA_pos2 21 GTR+I+G ccsA_pos3 22 GTR+I+G rpoC2_pos1, cemA_pos1, rpoA_pos1 23 GTR+I+G rps19_pos2, cemA_pos2 24 GTR+I+G cemA_pos3, ycf4_pos3 25 GTR+G clpP_pos1 26 GTR+I+G petA_pos2, clpP_pos2 27 GTR+G infA_pos1, rps2_pos1 28 GTR+G rps2_pos2, infA_pos2, rps14_pos1, rpl23_pos3 29 GTR+I+G matK_pos1 30 GTR+I+G petL_pos3, rpl22_pos2, matK_pos2 31 GTR+I+G ndhE_pos3, matK_pos3 32 GTR+I+G ndhD_pos1, ndhA_pos1 33 GTR+I+G ndhG_pos2, ndhA_pos2, ndhD_pos2 157 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 34 GTR+I+G ndhA_pos3 35 GTR+I+G ycf3_pos1, ndhB_pos1, psbT_pos1, psbE_pos1 36 GTR+I+G ndhB_pos2, ndhC_pos2 37 GTR+G ndhB_pos3 38 GTR+I+G petA_pos1, ndhC_pos1 39 GTR+I+G ndhC_pos3 40 GTR+I+G ndhD_pos3, rpl22_pos3 41 GTR+I+G ndhE_pos1, rpoB_pos1 42 GTR+I+G psbZ_pos2, psbT_pos2, psbM_pos2, ndhE_pos2 43 GTR+I+G ndhF_pos1 44 GTR+I+G ndhF_pos3 45 GTR+I+G psaI_pos1, rps16_pos2, rpl20_pos2, ndhG_pos1, rpl33_pos2 46 GTR+I+G ndhH_pos1, rpoC1_pos1 47 GTR+I+G ndhH_pos2 48 GTR+I+G petD_pos3, ndhH_pos3 49 GTR+G rps19_pos1, rps18_pos2, ndhI_pos1 50 GTR+I+G ndhI_pos2, ndhJ_pos2 51 GTR+G ndhI_pos3 52 GTR+G ndhJ_pos1, psbN_pos1, rps14_pos2 53 GTR+I+G ndhK_pos3, psbH_pos3 54 GTR+I+G psbM_pos1, psbA_pos1, petB_pos1 55 GTR+I+G psaA_pos2, psbB_pos2, petN_pos2, petB_pos2, psbL_pos1 56 GTR+I+G petD_pos1 57 GTR+I+G psbJ_pos2, psbF_pos2, psaJ_pos2, petD_pos2 58 GTR+I+G psbI_pos2, petG_pos2, psbE_pos2, psbL_pos2 59 GTR+I+G ycf3_pos3, petN_pos3, petG_pos3 60 GTR+G rpl36_pos1, petL_pos1, psbL_pos3 61 GTR+G petL_pos2, psbK_pos2 62 GTR+I+G rrn16, psbI_pos1, petN_pos1 63 GTR+I+G psaA_pos1 64 GTR+I+G psaB_pos1 65 GTR+I+G psaB_pos2 66 GTR+I+G psbC_pos3, psaB_pos3 67 GTR+I+G psbB_pos1, psaC_pos1 68 GTR+G psaC_pos2 69 GTR+I+G psaI_pos2 70 GTR+I+G rps14_pos3, psbK_pos3, psbM_pos3, rps18_pos3, psaI_pos3, rps4_pos3 71 GTR+I+G ycf4_pos2, psaJ_pos1 72 GTR+G psaJ_pos3, rps16_pos3 73 GTR+I+G psbA_pos3 74 GTR+I+G psbC_pos1 75 GTR+I+G psbD_pos2, psbC_pos2 158 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 76 GTR+I+G psbD_pos1 77 GTR+G psbE_pos3 78 GTR+I+G psbF_pos1, rpl36_pos2, rps7_pos1, rpl2_pos1, rps12_pos1 79 GTR+I+G psbH_pos1, psbH_pos2 80 GTR+G psbZ_pos3, psbJ_pos3 81 GTR+I+G psbK_pos1 82 GTR+I+G ycf3_pos2, psbZ_pos1, psbN_pos2 83 GTR+G psbN_pos3 84 GTR+I+G rbcL_pos1 85 GTR+I+G rbcL_pos2 86 GTR+G rpl23_pos2, rpl14_pos2, rps7_pos2 87 GTR+I+G rpoA_pos3, rpl14_pos3, rps8_pos3 88 GTR+I+G rpl16_pos1 89 GTR+I+G ycf4_pos1, rpl16_pos2 90 GTR+I+G rpl16_pos3 91 GTR+G rpl20_pos1, rpl32_pos1 92 GTR+G rpl22_pos1 93 GTR+G rpl23_pos1 94 GTR+I+G rrn23, rpl2_pos2, rps12_pos2 95 GTR+G rpl2_pos3 96 GTR+G rpl32_pos2 97 GTR+I+G rps15_pos3, rps3_pos3, rpl32_pos3, rpl36_pos3 98 GTR+G rpl33_pos1, rps3_pos1 99 GTR+I+G rps3_pos2, rpoA_pos2, rps12_pos3, rps15_pos2 100 GTR+I+G rpoB_pos2, rpoC1_pos2 101 GTR+I+G rpoC2_pos2, rps4_pos1 102 GTR+I+G rps19_pos3, rpoC2_pos3 103 GTR+G rps8_pos1, rps11_pos1 104 GTR+G rps11_pos2 105 GTR+I+G rps11_pos3 106 GTR+G rps18_pos1 107 GTR+I+G rps2_pos3 108 GTR+G rps7_pos3 109 GTR+I+G rps8_pos2 110 GTR+G ycf2_pos1 111 GTR+I+G ycf2_pos2 112 GTR+G ycf2_pos3 113 GTR+G rrn5, rrn4 i-D) 1 GTR+I+G accD_pos1 2 GTR+I+G psbF_pos3, accD_pos2 3 GTR+I+G accD_pos3 4 GTR+I+G atpA_pos1 159 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 5 GTR+I+G atpB_pos2, atpA_pos2 6 GTR+I+G psaC_pos3, psbI_pos3, atpA_pos3, rbcL_pos3, ndhG_pos3 7 GTR+I+G atpB_pos1, rpl36_pos2, atpI_pos1 8 GTR+I+G ndhJ_pos3, petB_pos3, atpB_pos3, psbB_pos3 9 GTR+G rpl14_pos1, atpE_pos1, rps19_pos1, infA_pos1, rps2_pos1 10 GTR+I+G rps14_pos1, rps4_pos1, rpl36_pos1, atpE_pos2, ndhI_pos1, rps18_pos2 11 GTR+I+G rpoC2_pos3, petA_pos3, rps19_pos3, atpE_pos3 12 GTR+I+G rps11_pos1, rps8_pos1, atpF_pos1, ndhK_pos1, rps16_pos1 13 GTR+I+G atpF_pos2 14 GTR+I+G rpl20_pos3, atpF_pos3, clpP_pos3, rpoB_pos3, rpoC1_pos3 15 GTR+I+G petG_pos1, atpH_pos1 16 GTR+I+G psbA_pos2, atpH_pos2 17 GTR+I+G atpH_pos3 18 GTR+I+G atpI_pos2 19 GTR+I+G psaI_pos3, atpI_pos3 20 GTR+I+G rps15_pos1, ccsA_pos1 21 GTR+I+G ndhF_pos2, ccsA_pos2 22 GTR+I+G ccsA_pos3 23 GTR+I+G cemA_pos1, rpl33_pos1, rpoA_pos1 24 GTR+I+G rps19_pos2, cemA_pos2 25 GTR+I+G ycf4_pos3, cemA_pos3 26 GTR+I+G clpP_pos1 27 GTR+I+G petA_pos2, clpP_pos2 28 GTR+G rpl2_pos3, infA_pos2 29 GTR+G infA_pos3, psbT_pos3, rps8_pos3, rpl14_pos3 30 GTR+I+G matK_pos1 31 GTR+I+G matK_pos2, rpl22_pos2 32 GTR+I+G ndhE_pos3, matK_pos3 33 GTR+I+G ndhA_pos1, ndhD_pos1 34 GTR+I+G ndhG_pos2, ndhD_pos2, ndhA_pos2 35 GTR+I+G ndhA_pos3 36 GTR+I+G psbT_pos1, psbE_pos1, rps12_pos2, ndhB_pos1 37 GTR+I+G ndhC_pos2, ndhB_pos2 38 GTR+G ndhB_pos3 39 GTR+I+G ndhC_pos1, ndhE_pos1, rpoB_pos1 40 GTR+I+G ndhC_pos3 41 GTR+I+G rpl22_pos3, ndhD_pos3 42 GTR+I+G psbZ_pos2, psbT_pos2, psbM_pos2, ndhE_pos2 43 GTR+I+G ndhF_pos1 44 GTR+I+G ndhF_pos3 160 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 45 GTR+G ndhG_pos1, rps16_pos2 46 GTR+I+G rpoC1_pos1, ndhH_pos1 47 GTR+I+G ndhH_pos2 48 GTR+I+G ndhH_pos3, petD_pos3 49 GTR+I+G ndhJ_pos2, ndhI_pos2 50 GTR+I+G ndhI_pos3, rpl16_pos3 51 GTR+G ndhJ_pos1, rps14_pos2, psbN_pos1 52 GTR+I+G ndhK_pos2, rps4_pos2 53 GTR+I+G ndhK_pos3, psbH_pos3 54 GTR+I+G petA_pos1 55 GTR+I+G psbC_pos1, petB_pos1, psbA_pos1 56 GTR+I+G psaB_pos2, psbB_pos2, psaA_pos2, petN_pos2, psbL_pos1, petB_pos2 57 GTR+I+G petD_pos1 58 GTR+I+G psbJ_pos2, psbF_pos2, petD_pos2, psaJ_pos2 59 GTR+I+G psbI_pos2, petG_pos2, psbE_pos2, psbL_pos2 60 GTR+I+G ycf3_pos3, petN_pos3, petG_pos3 61 GTR+I+G petL_pos1, psbL_pos3 62 GTR+G psbK_pos2, petL_pos2 63 GTR+G psbE_pos3, petL_pos3 64 GTR+G petN_pos1, psbI_pos1, rps7_pos2, rpl14_pos2 65 GTR+I+G psaA_pos1 66 GTR+I+G psbD_pos3, psaA_pos3 67 GTR+I+G psaB_pos1 68 GTR+I+G psaB_pos3 69 GTR+I+G psbB_pos1, psaC_pos1 70 GTR+G psaC_pos2 71 GTR+I+G rpl20_pos2, psaI_pos1 72 GTR+I+G psaI_pos2 73 GTR+I+G ycf4_pos2, psaJ_pos1 74 GTR+G rps16_pos3, psaJ_pos3 75 GTR+I+G psbA_pos3 76 GTR+I+G psbC_pos2, psbD_pos2 77 GTR+I+G rps2_pos3, psbC_pos3 78 GTR+I+G psbD_pos1 79 GTR+I+G rps12_pos1, rpl2_pos1, psbF_pos1, ycf3_pos1, rps7_pos1 80 GTR+I+G psbH_pos2, psbH_pos1 81 GTR+I+G psbJ_pos1, rpl16_pos2 82 GTR+G rps15_pos2, psbJ_pos3, psbZ_pos3 83 GTR+I+G psbK_pos1 84 GTR+I+G rps14_pos3, rps18_pos3, rps4_pos3, psbK_pos3, psbM_pos3 85 GTR+G rps7_pos3, psbM_pos1 86 GTR+I+G ycf3_pos2, psbN_pos2, psbZ_pos1 161 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 87 GTR+G psbN_pos3 88 GTR+I+G rbcL_pos1 89 GTR+I+G rbcL_pos2 90 GTR+I+G rpl16_pos1 91 GTR+I+G rpl22_pos1, rpl32_pos1, rpl20_pos1 92 GTR+G rpl23_pos1 93 GTR+I+G rpl23_pos2, rpl2_pos2, rrn4 94 GTR+G rpl23_pos3, rps2_pos2 95 GTR+G rpl32_pos2 96 GTR+I+G rps15_pos3, rps3_pos3, rpl32_pos3, rpl36_pos3 97 GTR+I+G rpl33_pos2, rpoA_pos2, rps3_pos2 98 GTR+I+G rpoA_pos3, rpl33_pos3 99 GTR+I+G rpoB_pos2, rpoC1_pos2 100 GTR+I+G rpoC2_pos1, rps3_pos1 101 GTR+I+G rpoC2_pos2, rps12_pos3 102 GTR+G rps11_pos2 103 GTR+G rps11_pos3 104 GTR+G rps18_pos1 105 GTR+I+G ycf4_pos1, rps8_pos2 106 GTR+G ycf2_pos1 107 GTR+I+G ycf2_pos2 108 GTR+G ycf2_pos3 109 GTR+I+G rrn16, rrn5 110 GTR+I+G rrn23 i-E) 1 GTR+I+G rpoC2_pos1, accD_pos1, cemA_pos1, petN_pos3 2 GTR+G accD_pos2, rpl32_pos1, rpl33_pos1, rps16_pos1, ycf4_pos1, rpoA_pos1 3 GTR+G petA_pos3, petB_pos3, accD_pos3, ndhJ_pos3, psaB_pos3, psbD_pos3, psaA_pos3, psbC_pos3 4 GTR+I+G atpB_pos1, atpA_pos1 5 GTR+I+G atpB_pos2, atpA_pos2 6 GTR+G atpH_pos3, atpA_pos3, atpF_pos3, ndhI_pos3, ndhG_pos3 7 GTR+G atpB_pos3, rps2_pos3 8 GTR+G rps2_pos1, rps3_pos1, atpE_pos1 9 GTR+G ycf2_pos1, rps7_pos2, rps12_pos2, atpE_pos2 10 GTR+G rpoC1_pos3, rps8_pos3, rpl20_pos3, infA_pos3, rpoA_pos3, atpI_pos3, ndhK_pos3, atpE_pos3, rps18_pos3, rps4_pos3, rpl14_pos3 11 GTR+I+G rps11_pos1, rpl16_pos1, atpF_pos1, rps15_pos1 12 GTR+I+G rpl22_pos2, rpoC2_pos2, atpF_pos2 13 GTR+I+G petN_pos1, atpH_pos1, psbD_pos1, psbC_pos1, psbE_pos1, psbA_pos1, petB_pos1, psbI_pos1 162 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 14 GTR+I+G atpH_pos2, psbA_pos2, psbC_pos2, psbD_pos2, psaA_pos2, psaB_pos2 15 GTR+I+G rpl36_pos1, rrn4, infA_pos1, ndhE_pos1, ycf3_pos1, rpl2_pos2, atpI_pos1, psaC_pos1 16 GTR+G psbJ_pos2, petL_pos2, psaJ_pos2, psbM_pos2, atpI_pos2 17 GTR+I+G ccsA_pos1, rpl20_pos1 18 GTR+G ccsA_pos2 19 GTR+G ycf1_pos3, petD_pos3, ccsA_pos3, ndhE_pos3 20 GTR+G cemA_pos2, petA_pos2, ycf3_pos2, rps19_pos2, rpl2_pos3, rps2_pos2 21 GTR+G matK_pos1, cemA_pos3, ycf4_pos3 22 GTR+I+G rpl16_pos2, petD_pos1, petA_pos1, clpP_pos1, psbN_pos1, rpoC1_pos1, ndhH_pos1, rpoB_pos1 23 GTR+G ycf2_pos2, clpP_pos2 24 GTR+G psbI_pos3, psbB_pos3, clpP_pos3, rbcL_pos3 25 GTR petG_pos2, psbE_pos2, infA_pos2, psbZ_pos1, petG_pos1, rpl36_pos2 26 GTR+G petL_pos3, psbJ_pos3, psbK_pos3, psbT_pos3, psaI_pos3, matK_pos2 27 GTR+G matK_pos3, ndhA_pos3 28 GTR+I+G ndhA_pos1, ndhG_pos1, rpl20_pos2, ndhD_pos1 29 GTR+G ndhA_pos2, ndhD_pos2, psaI_pos2, psbH_pos2, ndhG_pos2, psbK_pos2 30 GTR+I+G psbB_pos2, psaB_pos1, psaC_pos2, psbF_pos1, psbM_pos1, ndhB_pos1 31 GTR+I+G ndhB_pos2, petB_pos2, psbF_pos2, petN_pos2, psbZ_pos2 32 GTR rps7_pos3, ndhB_pos3, ndhE_pos2 33 GTR+G ycf4_pos2, psbH_pos1, ndhC_pos1 34 GTR+G psbL_pos1, psbT_pos1, ndhC_pos2, psbN_pos2, rpl23_pos2, psbT_pos2, psbI_pos2, psbL_pos2 35 GTR+G rps16_pos2, petG_pos3, ndhC_pos3, rps14_pos3 36 GTR+G ndhD_pos3, psaJ_pos3 37 GTR+G ndhF_pos1, rpl32_pos2 38 GTR+I+G psbK_pos1, ndhF_pos2 39 GTR+G rpl22_pos3, ndhF_pos3 40 GTR+I+G rpl23_pos3, rpl14_pos2, ndhH_pos2 41 GTR+G psaC_pos3, ndhH_pos3, rps3_pos3, rpl32_pos3, rps15_pos3 42 GTR+G rps8_pos2, ndhI_pos1, rpoC1_pos2, rps18_pos2 43 GTR+I+G ndhK_pos1, rps4_pos2, ndhI_pos2, ndhJ_pos2, rps3_pos2, rpoB_pos2 44 GTR+G ndhJ_pos1, rps11_pos2 45 GTR+G ndhK_pos2, rps4_pos1, rps14_pos1 163 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 46 GTR+I+G petD_pos2, psaJ_pos1 47 GTR+G rpl14_pos1, petL_pos1, rpl23_pos1, rps7_pos1, rps12_pos1, rpl2_pos1 48 GTR+I+G psbB_pos1, rrn5, psaA_pos1 49 GTR+G psbN_pos3, psaI_pos1, ycf3_pos3, psbE_pos3, rps12_pos3, rpl33_pos2 50 GTR+G psbA_pos3 51 GTR+G psbJ_pos1, rpl22_pos1, psbF_pos3 52 GTR+G psbH_pos3, rps19_pos3 53 GTR+G rps18_pos1, psbL_pos3, psbZ_pos3, rpoA_pos2, rps15_pos2 54 GTR+G rpoB_pos3, psbM_pos3 55 GTR+I+G rbcL_pos1 56 GTR+I+G rbcL_pos2 57 GTR+G rpl16_pos3, rpl36_pos3, rpl33_pos3 58 GTR+G rpoC2_pos3 59 GTR+G rps11_pos3, rps16_pos3 60 GTR+G rrn16, rps14_pos2, rrn23 61 GTR rps8_pos1, rps19_pos1 62 GTR+G ycf1_pos1 63 GTR+G ycf1_pos2 64 GTR+G ycf2_pos3 i-F) 1 HIVB+G -- i-G) 1 HIVW+G accD, ccsA 2 JTTDCMUT+I+G atpA 3 HIVB+I+G atpI, atpB 4 HIVB+G ycf4, atpE 5 HIVB+G rpl33, atpF, rpoC2 6 CPREV atpH 7 HIVB+G cemA 8 FLU+G clpP 9 JTT+G infA 10 HIVB+I+G matK 11 HIVW+I+G ndhA 12 MTMAM+I+G ndhB 13 HIVB+I+G ndhC, ndhJ 14 HIVB+I+G ndhD, psbK 15 MTMAM+G ndhE 16 HIVB+G ndhF 17 JTT+G ndhG 18 HIVB+I+G ndhH 164 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 19 HIVW+G rps18, ndhI 20 HIVW+G ndhK 21 FLU+G petA 22 JTT+I+G psaC, petB 23 MTMAM+G petD 24 MTMAM+G petG, psbL 25 HIVW+G petL, rpoA 26 MTZOA petN 27 CPREV+I+G psaA 28 HIVB+I+G psaB 29 HIVW+G psaI, rpl20 30 MTZOA+G psaJ 31 CPREV+I+G psbD, psbA 32 JTT+I+G psbB 33 CPREV+I+G psbF, psbC 34 FLU+I+G psbE, psbT 35 FLU+I+G psbH 36 CPREV psbI 37 MTMAM+I+G psbJ 38 HIVW+G psbM 39 MTMAM+G psbN 40 CPREV+G psbZ 41 LG+I+G rbcL 42 HIVW+I+G rpl14 43 CPREV+G rpl16 44 JTT+G rpl22 45 HIVW+G rpl23 46 HIVB+G rps7, rpl36, rpl2 47 HIVB+G rpl32 48 HIVB+G rpoB 49 HIVB+G rpoC1 50 JTT+G rps11 51 CPREV+G rps12 52 HIVW+G rps2, rps14 53 HIVW+G rps15, rps16 54 HIVW+G rps19, rps8 55 HIVB+G rps3 56 HIVW+I+G rps4 57 HIVW+G ycf2 58 HIVW+I+G ycf3 ii-A) 1 GTR+I+G -- 165 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ ii-B) 1 GTR+I+G nad5_pos1, nad9_pos2, nad2_pos1 2 GTR+I+G nad2_pos2, nad4_pos2, nad6_pos2, nad5_pos2 3 GTR+I+G nad1_pos3, nad4_pos3, nad2_pos3 4 GTR+I+G nad9_pos3, nad5_pos3, nad3_pos3 5 GTR+I+G cox2_pos1, cob_pos1, nad9_pos1, cox3_pos1 6 GTR+G atp4_pos1, rps19_pos1, rpl16_pos1, rps3_pos1 7 GTR+G rpl16_pos2, rps4_pos2, atp8_pos2 8 GTR+I+G nad4L_pos3, ccmC_pos3, atp4_pos3, rpl16_pos3 9 GTR+I+G rps2_pos2, ccmFn_pos2, ccmFc_pos1, rps3_pos2 10 GTR+G rpl2_pos3, rps3_pos3, rps4_pos3, rps19_pos3, sdh4_pos3 11 GTR+G atp4_pos2, ccmb_pos1 12 GTR+G ccmb_pos2, atp9_pos2 13 GTR+G ccmb_pos3, cox1_pos3, cob_pos3, cox3_pos3 14 GTR+I+G atp1_pos2, nad3_pos1, cox2_pos2 15 GTR+G cox2_pos3 16 GTR+G matR_pos2, ccmFc_pos2 17 GTR+G ccmFc_pos3 18 GTR+I+G atp8_pos1, rps2_pos1, rpl5_pos1 19 GTR+G ccmC_pos1, rpl5_pos2 20 GTR+G rpl5_pos3, sdh4_pos1 21 GTR+I+G cob_pos2, nad1_pos2 22 GTR+I+G nad6_pos1, atp9_pos1 23 GTR+I+G nad6_pos3, rps13_pos3 24 GTR+G rps4_pos1 25 GTR+I+G atp6_pos1, rpl10_pos2, rpl2_pos2, ccmFn_pos1 26 GTR+G atp6_pos2 27 GTR+I+G atp6_pos3 28 GTR+I+G nad7_pos1, nad1_pos1, nad4L_pos1, cox1_pos1 29 GTR+I+G nad7_pos2 30 GTR+I+G nad7_pos3 31 GTR+G matR_pos1 32 GTR+G matR_pos3 33 GTR+G nad4_pos1 34 GTR+I+G cox1_pos2, nad4L_pos2, cox3_pos2 35 GTR+G mttB_pos3, ccmFn_pos3 36 GTR+G rps12_pos1, rps12_pos2 37 GTR+I+G rps19_pos2, rps12_pos3 38 GTR+G atp9_pos3 39 GTR+I+G rps13_pos1, atp1_pos1 40 GTR+G atp1_pos3 41 GTR+G rps7_pos1, rpl2_pos1 42 GTR+I+G rps13_pos2, rps7_pos2 43 GTR+G rpl10_pos1, rps1_pos2, rps7_pos3 166 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 44 GTR+I+G ccmC_pos2 45 GTR+G rps1_pos1 46 GTR+G rps1_pos3 47 GTR+G atp8_pos3 48 GTR+G nad3_pos2 49 GTR+G rps2_pos3,rpl10_pos3 50 GTR+G mttB_pos1 51 GTR+G mttB_pos2 52 GTR sdh4_pos2 ii-C) 1 JTT+G -- ii-D) 1 CPREV+I+G nad4, nad2, nad5, cox3, cob, nad6 2 JTT+I+G nad9, atp1, cox2 3 JTT+G rpl16, matR 4 JTT+G rps3 5 JTT+G ccmFn, ccmb, ccmFc, atp4 6 JTTDCMUT+G rpl5 7 CPREV+G rps4, rps19 8 CPREV+G atp8, atp6 9 JTT+I+G nad7 10 CPREV+I+G nad1 11 MTMAM+G nad4L 12 HIVW+G rps12 13 MTMAM+G atp9 14 HIVB+G rps7 15 MTREV+I+G cox1 16 JTT+G ccmC 17 HIVB+G rps13 18 CPREV+G rps1 19 HIVW+G nad3 20 CPREV+G mttB, rps2 21 JTT+G rpl2, rpl10 22 CPREV sdh4 iii-A) 1 GTR+I+G nad1_pos1, psbI_pos1, cox1_pos1, nad2_pos1, nad4L_pos1 2 GTR+I+G ndhB_pos2, petB_pos2, psbZ_pos2, petN_pos2, psbJ_pos2, ndhC_pos2, nad2_pos2, nad1_pos2, psbF_pos2 3 GTR+G nad1_pos3, nad4_pos3, nad2_pos3 167 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 4 GTR+G nad9_pos2, nad5_pos1, nad9_pos1, cox2_pos1, atp6_pos1, cob_pos1 5 GTR+I+G nad5_pos2, atp9_pos2, cox1_pos2, cox3_pos2, ccmC_pos2, psaI_pos2, cox2_pos2 6 GTR+I+G nad3_pos3, nad5_pos3, cox2_pos3 7 GTR+I+G nad9_pos3, rpl10_pos3 8 GTR+I+G nad6_pos1, rpl16_pos1 9 GTR+G rps12_pos1, rps12_pos1, rps12_pos2, rps14_pos2, rpl16_pos2, rrn5 10 GTR+G rpl16_pos3, clpP_pos2, rps19_pos1, ndhI_pos1 11 GTR+I+G rps3_pos2, rps3_pos1, ccmFc_pos1 12 GTR+I+G rpl5_pos1, rps19_pos3, rps3_pos3 13 GTR+G ccmb_pos1, psbK_pos2, ndhG_pos2 14 GTR+G nad3_pos2, ccmb_pos2 15 GTR+I+G ccmb_pos3, cox1_pos3 16 GTR+G ccmFc_pos2, atp1_pos2, ccmC_pos1, rps12_pos2, matR_pos2 17 GTR+I+G ccmFc_pos3, rps13_pos3, nad6_pos3, atp8_pos2 18 GTR+G mttB_pos1, rpl5_pos2, mttB_pos2, psbN_pos2 19 GTR+G rpl2_pos3, rps7_pos3, rps1_pos2, rpl5_pos3 20 GTR+I+G cob_pos2, nad6_pos2, nad4_pos2 21 GTR+G cob_pos3, petL_pos2, cox3_pos3 22 GTR+G rps4_pos1, rps19_pos1, atp4_pos1, sdh4_pos1, sdh4_pos3 23 GTR+G rps4_pos2, rps4_pos1 24 GTR+G rps4_pos3 25 GTR+G atp6_pos2, atpI_pos2 26 GTR+I+G atp6_pos3 27 GTR+I+G nad7_pos1 28 GTR+I+G psaB_pos2, nad7_pos2, nad4_pos1, rpl23_pos2 29 GTR+I+G nad7_pos3 30 GTR+G matR_pos1, petA_pos1 31 GTR+G matR_pos3 32 GTR+I+G nad4L_pos2 33 GTR+I+G nad4L_pos3, ccsA_pos1, petN_pos3 34 GTR+G atpH_pos1, psbD_pos1, psbC_pos1, cox3_pos1 petN_pos1 35 GTR+I+G ccmFn_pos1, rpl2_pos2 36 GTR+I+G rps2_pos2, ccmFn_pos2, atp4_pos2 37 GTR+I+G atp4_pos3, ccmFn_pos3, mttB_pos3, ccmC_pos3 38 GTR+G psbF_pos3, rps12_pos3, psbZ_pos3, rps4_pos2 39 GTR+I+G atp9_pos1, atpA_pos1 40 GTR+G atp9_pos3 41 GTR+G rps13_pos1, atp1_pos1 42 GTR+G atp1_pos3 168 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 43 GTR+G rps7_pos1, rpl2_pos1 44 GTR+I+G rps13_pos2, rps7_pos2 45 GTR+I+G rps16_pos1, clpP_pos1, rpl16_pos2, rps11_pos2, psaJ_pos1, rps19_pos2, ycf4_pos1 46 GTR+G rps1_pos1 47 GTR+I+G rps1_pos3 48 GTR+I+G rps2_pos1, atp8_pos1 49 GTR+G atp8_pos3 50 GTR+G nad3_pos1, ycf2_pos3 51 GTR+G rps2_pos3 52 GTR+I+G rpl10_pos1, rpl10_pos2 53 GTR+G psbH_pos2, sdh4_pos2, psaJ_pos2, ccsA_pos2, psbJ_pos3 54 GTR+G rpl33_pos2, rpl33_pos1, rpl32_pos1, accD_pos2, rpl22_pos1, accD_pos1 55 GTR+G rps19_pos3, psbM_pos3, rpl33_pos3, accD_pos3 56 GTR+I+G atpA_pos2, atpB_pos2 57 GTR+G atpA_pos3, psbI_pos3, ndhG_pos3, psbB_pos3, rbcL_pos3 58 GTR+I+G atpB_pos1 59 GTR+G atpH_pos3, petA_pos3, atpB_pos3, rps2_pos3, rpl20_pos3, rpl14_pos3 60 GTR+G rps8_pos1, atpE_pos1, atpF_pos1 61 GTR+G ycf2_pos2, atpE_pos2 62 GTR+G ycf4_pos3, ndhJ_pos3, psbC_pos3, atpE_pos3, petB_pos3 63 GTR+I+G rps3_pos2, rpoC2_pos2, atpF_pos2 64 GTR+G rpoC2_pos3, atpF_pos3, ndhI_pos3 65 GTR+I+G psaA_pos2, psbE_pos2, psbA_pos2, psbB_pos2, atpH_pos2, psbC_pos2, psbD_pos2 66 GTR rrn4, rpl36_pos2, atpI_pos1 67 GTR+G psaB_pos3, psbD_pos3, psaA_pos3, atpI_pos3 68 GTR+G matK_pos3, ndhE_pos3, ccsA_pos3 69 GTR+G cemA_pos1, rps3_pos1, rpoA_pos1, rps15_pos1 70 GTR+G cemA_pos2, rps19_pos2, rps2_pos2 71 GTR+G rps14_pos3, rpoC1_pos3, cemA_pos3, rps18_pos3 72 GTR+I+G ndhH_pos3, psaC_pos3, clpP_pos3 73 GTR rpl2_pos3, rps2_pos1, infA_pos1 74 GTR psaC_pos1, psbZ_pos1, petG_pos1, infA_pos2 75 GTR+G infA_pos3, matK_pos1 76 GTR+G rpl32_pos2, psbT_pos3, matK_pos2 77 GTR+G ndhA_pos1, ndhD_pos1, ndhG_pos1 78 GTR+I+G ndhD_pos2, ndhA_pos2 169 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 79 GTR+G psaJ_pos3, rpl36_pos3, rps3_pos3, ndhA_pos3, rps15_pos3, petD_pos3 80 GTR psbT_pos1, psbL_pos1, psbM_pos1, psbE_pos1, psbF_pos1, psaC_pos2, ndhB_pos1 81 GTR ndhB_pos3, ndhE_pos2 82 GTR+G ycf4_pos2, ndhC_pos1 83 GTR+G ndhK_pos3, psbK_pos3, psaI_pos3, ndhC_pos3 84 GTR+G rpl22_pos3, ndhD_pos3 85 GTR+G ndhE_pos1, ndhK_pos1, rpoC1_pos1, ndhJ_pos1 86 GTR+G ndhF_pos1 87 GTR+I+G ndhF_pos2 88 GTR+G rps16_pos3, ndhF_pos3 89 GTR+I+G rpl14_pos1, rpoB_pos1, ndhH_pos1, petD_pos1, psbJ_pos1 90 GTR+I+G ndhH_pos2, petA_pos2, ndhI_pos2, rpl23_pos3 , ycf3_pos2, rpoC1_pos2, rpoB_pos2, ndhJ_pos2 91 GTR+G ndhK_pos2, rpl36_pos1, rps18_pos2 92 GTR+I+G rpl2_pos2, ycf3_pos1, psaB_pos1, psbA_pos1, petB_pos1 93 GTR+I+G petD_pos2 94 GTR psbM_pos2, petG_pos2, psbT_pos2, psbL_pos2, psbI_pos2 95 GTR petL_pos3, petG_pos3 96 GTR+G ycf2_pos1, rpl14_pos2, rps7_pos2, petL_pos1 97 GTR+I+G psbB_pos1, psaA_pos1 98 GTR+G rps16_pos2, ycf3_pos3, psaI_pos1, rps12_pos3, psbK_pos1, psbE_pos3 99 GTR+G psbA_pos3 100 GTR+G psbH_pos1, rps8_pos2, rps15_pos2, rpl22_pos2 101 GTR+G psbH_pos3, rpoA_pos3, rps8_pos3, rpoB_pos3, rps4_pos3 102 GTR+G psbL_pos3, rpoA_pos2 103 GTR+I+G psbN_pos1, rpoC2_pos1 104 GTR+G psbN_pos3 105 GTR+I+G rbcL_pos1 106 GTR+I+G rbcL_pos2 107 GTR+I+G rps11_pos1, rpl16_pos1 108 GTR+G rpl16_pos3, rpl32_pos3 109 GTR+I+G rpl20_pos1, rpl20_pos2 110 GTR rpl23_pos1, rpl2_pos1, rps7_pos1 111 GTR+G rps11_pos3 112 GTR+G rps14_pos1, rps18_pos1 113 GTR rps7_pos3 114 GTR+G rrn16, rrn23 170 Table A.4 Fossil calibrations for estimating divergence times and substitution rate shifts using a Bayesian random local clock (RLC) model. Each calibration was implemented using a uniform prior distribution, with minimum fossil ages as the lower bound and an upper bound of 151.8 My (the maximum age estimate for the angiosperm crown node in Silvestro et al., 2015). All calibrations were applied to plastid-based analyses; those indicated with an asterisk were applied to mitochondrial-based analyses. See Iles et al. (2015) for additional details on fossil taxa. ___________________________________________________________________________________________________________ Calibration Fossil Justification Justification Minimum Age (node)1 source2 age (Ma) justification ___________________________________________________________________________________________________________ Stem node Typha sp. Suite of inflorescence Grande (1984) 51.66 Radiometry Typha (A) characters Stem node Sabalites Suite of leaf Berry (1914) 83.6 Marine Coryphoideae (B) carolinensis characters biostratigraphy Stem node Cyclanthus Fruit Smith et al. 47 Radiometry & Cyclanthus* (C) messelensis apomorphies (2008) sedimentation rate Crown node Dioscorea Suite of leaf Pan et al. 27.23 Radiometry Dioscorea (D) wilkinii characters (2014) Stem node Limnobiophyllum Suite of vegetative Kvaček (1995), 66 Multiple Lemnoideae (E) scutatum characters & Stockey et al. stratigraphy controls phylogenetic analysis (1997) Stem node Liliacidites sp. A Phylogenetic analysis Doyle et al. 113 Biostratigraphy monocots* (F) of Doyle & of pollen characters (2008) Hickey (1976) ___________________________________________________________________________________________________________ 1Node labels indicate calibration placements in Figs. A.20, A.21. 2References for justification source listed in Iles et al. (2015). 171 Table A.5 Summary of bootstrap support for Pandanales relationships across 15 phylogenetic analyses conducted here (Figs. 2.1–2.3, A.1–A.15). Listed branches are moderately to strongly supported in at least one analysis (labels ‘a-z’ indicated in Fig. 2.1, ‘aa’ in Figs. 2.3, A.2 and ‘bb’ in Figs. 2.3, A.3). Abbreviations for analyses: ‘P’ = parsimony, ‘ML-u’ = unpartitioned likelihood analysis, ‘ML-p’ = partitioned likelihood analysis (using a gene-by-codon scheme for DNA data and a gene-based scheme for amino-acid data). Branches with <50% bootstrap support are shown as ‘–’; n/a = branch not available. ________________________________________________________________________________________________________________________________ Organellar Plastid Mitochondrial DNA DNA DNA DNA DNA amino amino DNA DNA DNA DNA DNA DNA amino amino ML-p P P ML-u ML-p ML-u ML-p ML-p ML-p ML-p P ML-u ML-p ML-u ML-p Branch 95 taxa 95 taxa 95 taxa 95 taxa 96 taxa 94 taxa 30 taxa ________________________________________________________________________________________________________________________________ a 98 86 – 62 76 54 56 71 75 69 88 96 93 44 41 b 100 100 100 100 100 100 100 100 100 100 99 98 99 90 85 c 99 97 46 53 – – – – – 59 94 97 95 45 46 d 99 96 44 46 – – – – – 47 95 98 97 41 45 e 52 44 – – – 31 36 – – – 82 77 78 76 78 f 99 99 75 90 96 97 98 95 96 97 93 87 90 85 79 g 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 h 100 100 98 99 100 99 98 99 99 100 100 100 100 100 100 i 100 100 100 100 100 54 100 100 100 100 100 100 100 100 100 j 98 89 – 39 38 – – 40 45 – 94 98 98 96 94 k 100 100 100 100 100 98 100 100 100 100 100 100 100 100 100 l 100 100 100 100 100 93 92 100 100 100 100 100 100 100 100 m 100 100 100 100 100 99 100 100 100 100 100 100 100 100 100 n 100 100 100 100 100 99 100 100 100 100 100 100 100 100 100 o 100 100 100 100 100 100 100 100 100 100 100 100 100 98 98 p 100 100 100 100 100 100 100 100 100 100 100 100 100 98 98 q 67 64 – 98 98 90 89 99 n/a 97 46 53 48 54 50 r 100 92 43 100 100 99 98 100 100 100 100 100 100 100 100 s 100 100 66 100 100 98 98 100 100 100 100 100 100 100 100 t 100 100 100 100 100 99 100 100 100 100 100 100 100 99 99 u 100 100 100 100 100 100 100 100 100 100 98 98 98 85 82 v 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 w 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 x 58 – – 34 35 – – 42 44 71 – – – – – 172 ________________________________________________________________________________________________________________________________ Organellar Plastid Mitochondrial DNA DNA DNA DNA DNA amino amino DNA DNA DNA DNA DNA DNA amino amino ML-p P P ML-u ML-p ML-u ML-p ML-p ML-p ML-p P ML-u ML-p ML-u ML-p Branch 95 taxa 95 taxa 95 taxa 95 taxa 96 taxa 94 taxa 30 taxa ________________________________________________________________________________________________________________________________ y 100 100 100 100 100 100 100 100 100 100 100 99 100 99 99 z 93 79 59 76 62 43 44 69 68 79 86 90 86 92 92 aa – – 100 – – – – – n/a – – – – – – bb – 66 – – – – – – – – 82 67 62 71 56 cc – – 67 – 62 – – 64 63 95 – – – – – 173 Table A.6 Characteristics of full circular plastomes representing the five Pandanales families. Gene counts only include those with intact reading frames; partial genes at IR boundaries are listed separately. Genes missing in Acanthochlamys and Sciaphila are with respect to the typical gene content of angiosperm plastomes. _________________________________________________________________________________________________________________________________ Species Length Total no. No. bp/genes No. unique No. unique No. unique Missing Partial genes (bp) genes duplicated protein-coding rDNA tRNA genes in IR2 in IR genes genes genes _________________________________________________________________________________________________________________________________ Cyclanthaceae Carludovica palmata 158,545 132 26,569/19 79 4 30 - ycf1 Pandanaceae Benstonea copelandii1 157,929 131 26,887/18 79 4 30 - ycf1, rps19 Stemonaceae Croomia heterosepala 154,407 133 27,210/20 79 4 30 - ycf1 Croomia japonica1 155,075 133 26,217/20 79 4 30 - ycf1 Croomia japonica 154,672 133 27,210/20 79 4 30 - ycf1 Croomia pauciflora 155,261 133 27,243/20 79 4 30 - ycf1 Stemona japonica 154,224 133 27,082/20 79 4 30 - ycf1 Stemona mairei 154,307 133 27,082/20 79 4 30 - ycf1 Triuridaceae Sciaphila densiflora 21,485 28 - 18 4 6 85 - Sciaphila thaidanica 12,780 20 - 14 4 2 93 - Velloziaceae Acanthochlamys bracteata1 153,842 130 27,015/18 78 4 30 1 (rps16) ycf1 _________________________________________________________________________________________________________________________________ 1 Plastomes generated for this study; additional plastomes were obtained from Lam et al. (2015; C. palmata, S. densiflora), Lu et al. (2018; C. heterosepala, C. japonica, C. pauciflora, S. japonica, S. mairei) and Petersen et al. (2018; S. thaidanica). 2 These are short partial gene copies in the IRB; a full copy of each gene is present in the IRA junction with the LSC. 174 Table A.7 Estimated monocot ages inferred here and in previous studies. Three variant analyses performed here (columns 2–4) excluded mycoheterotrophic Triuridaceae and were based on (i) a subset of the plastid matrix comprising three genes, analyzed using a Bayesian random local clock (RLC) model; (ii) a subset of the mitochondrial matrix comprising 12 genes, analyzed using a RLC model and (iii) a version of a matrix used in Mennes et al. (2013) with all mycoheterotrophs excluded here (analyzed with the Bayesian uncorrelated lognormal, UCLN, relaxed clock model as in the original study). Ages are median estimates here (or median or mean estimates as noted in previous studies); 95% HPD (highest posterior density) intervals of ages shown in brackets when available (n/a = not applicable; age not reported or unspecified due to taxon sampling). Ages for major Pandanales clades are summarized in Table 2.1. _________________________________________________________________________________________________________________________________ Clade/node Plastid data Mitochondrial Mennes et al. Mennes et al. Magallón et al. Givnish et al. Other studies (this study) data (2013) data, (2013)1 (2015)2 (2018)3 (this study) mycoheterotrophs excluded (this study) _________________________________________________________________________________________________________________________________ monocots Stem 150 (146–152) 146 (132–152) n/a n/a 136 (135–137) 136 (135–137) – Crown 148 (143–152) n/a 135 (134–138) 135 (134–138) 133 (132–135) 132 (131–134) – ACORALES Stem 148 (143–152) n/a 135 (134–138) 135 (134–138) 133 (132–135) 132 (131–134) – Crown n/a n/a 23 (6–51) 23 (4–52) n/a 0.2 (0.005–0.7) – ALISMATALES Stem 139 (131–146) 123 (106–150) 130 (116–137) 130 (116–137) 131 (129–133) 130 (129–132) – Crown n/a n/a 116 (95–133) 118 (90–134) 129 (127–131) 124 (121–126) – PETROSAVIALES Stem 118 (109–129) 105 (89–135) 120 (107–131) 122 (108–133) 124 (118–128) 127 (126–128) – Crown n/a n/a n/a4 41 (8–96) 98 (52–121) 67 (51–82) – DIOSCOREALES Stem 106 (98–117) 84 (70–109) 106 (90–121) 110 (96–123) 110 (97–122) 123 (120–126) – Crown 97 (88–108) n/a 91 (70–110) 101 (85–116) 95 (75–110) 119 (115–123) – 175 _________________________________________________________________________________________________________________________________ Clade/node Plastid data Mitochondrial Mennes et al. Mennes et al. Magallón et al. Givnish et al. Other studies (this study) data (2013) data, (2013)1 (2015)2 (2018)3 (this study) mycoheterotrophs excluded (this study) _________________________________________________________________________________________________________________________________ PANDANALES Stem 106 (98–117) 84 (70–109) 106 (90–121) 110 (96–123) 110 (97–122) 123 (120–126) 131 (93–175)5 Crown 93 (85–104) 71 (58–94) 62 (38–90) 91 (69–110) 72 (53–100) 93 (86–102) 101 (91–118)5 Velloziaceae Stem 93 (85–104) 71 (58–94) 62 (38–90) 91 (69–110) 72 (53–100) 93 (86–102) 101 (91–118)5 Crown 51 (44–58) 34 (27–47) 31 (10–57) 37 (9–75) 49 (24–76) n/a 74 (52–97)5 Stemonaceae Stem 81 (69–90) 64 (52–86) 43 (25–64) 46 (24–74) 54 (49–89) 76 (68–83) – Crown 65 (53–75) 46 (37–61) 27 (11–47) 27 (9–53) n/a 50 (44–56) – Triuridaceae Stem n/a n/a n/a 84 (62–103) n/a 75 (68–82) – Crown n/a n/a n/a 69 (50–90) n/a n/a – Pandanaceae Stem 61 (53–71) 56 (49–74) 37 (22–56) 38 (20–64) n/a 44 (38–50) 85 (79–133)6 Crown 35 (23–51) 26 (20–35) n/a n/a n/a 30 (26–35) 65 (42–98)6 Cyclanthaceae Stem 61 (53–71) 56 (49–74) 37 (22–56) 38 (20–64) 54 (49–89) 44 (38–50) 85 (79–133)6 Crown 49 (47–55) 49 (47–54) 22 (10–36) 21 (9–37) n/a 18 (13–22) 45 (45–72)6 LILIALES Stem 110 (102–121) 86 (70–111) 107 (97–119) 109 (97–121) 117 (110–123) 126 (124–127) – Crown 96 (87–106) n/a n/a n/a 97 (81–115) 111 (106–115) – ASPARAGALES Stem 109 (101–120) 86 (70–111) 104 (95–115) 104 (94–116) 115 (107–121) 126 (124–127) – Crown 104 (95–115) n/a 94 (93–96) 94 (93–96) 109 (99–118) 116 (111–121) – DASYPOGONALES Stem 100 (92–111) n/a 93 (84–104) 93 (84–105) 108 (101–117) 119 (114–124) – Crown 40 (26–57) n/a n/a n/a n/a 34 (28–38) – ARECALES Stem 100 (92–111) n/a 90 (90–93) 90 (90–93) 98 (84–112) 119 (114–124) – Crown 90 (86–95) n/a n/a n/a 49 (32–75) 85 (84–85) – 176 _________________________________________________________________________________________________________________________________ Clade/node Plastid data Mitochondrial Mennes et al. Mennes et al. Magallón et al. Givnish et al. Other studies (this study) data (2013) data, (2013)1 (2015)2 (2018)3 (this study) mycoheterotrophs excluded (this study) _________________________________________________________________________________________________________________________________ COMMELINALES Stem 71 (60–91) n/a 84 (83–85) 84 (83–85) 80 (77–97) 114 (110–117) – Crown 63 (45–84) n/a 74 (52–84) 72 (47–84) 69 (51–89) 110 (106–114) – ZINGIBERALES Stem 71 (60–91) n/a 84 (83–85) 84 (83–85) 80 (77–97) 114 (110–117) – Crown 34 (26–44) n/a 53 (33–74) 51 (30–75) 58 (42–76) 83 (83–84) – POALES Stem 101 (93–112) n/a 93 (84–104) 93 (84–105) 107 (100–116) 124 (122–125) – Crown 68 (54–84) n/a 87 (79–97) 87 (78–98) 101 (94–110) 120 (119–121) – ____________________________________________________________________________________________________________ 1 Mennes et al. (2013): median ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 2 Magallón et al. (2015): median ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 3 Givnish et al. (2018): mean ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 4 There was no Petrosaviales crown node in my re-analyses of the Mennes et al. (2013) data set as this order included only one photosynthetic and one mycoheterotrophic taxon in the original matrix. 5 Alcantara et al. (2018): ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model. 6 Gallaher et al. (2015): ages and 95% HPD intervals from analyses implemented in BEAST using a UCLN model and two fossil calibrations. The age for the Pandanaceae crown node represents the median estimate; the Cyclanthaceae-Pandanaceae stem node and Cyclanthaceae crown node were constrained by fossils and the ages for these nodes represent the peak of the 95% HPD interval. 177 Figure A.1 Pandanales phylogeny inferred from combined parsimony analysis of 119 organellar genes (37 mitochondrial and 82 plastid genes in photosynthetic taxa; 35 mitochondrial and 22 plastid genes in mycoheterotrophic Sciaphila densiflora, Triuridaceae) using DNA data (the right-hand tree in Fig. 2.1 depicts a subset of this taxon sampling). One of 10 shortest trees found for a Pandanales-focused matrix (excluding mycoheterotrophic Sciaphila thaidanica; arrows indicate branches that collapse in the strict consensus tree). Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the same combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated number of changes. 178 179 Figure A.2 Angiosperm phylogeny inferred from parsimony analysis of 82 plastid genes (22 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) using DNA data, showing the shortest tree found for a 95-taxon plastid matrix (excluding Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree); a strongly supported branch (labeled ‘aa’) is summarized for other analyses in Table A.5. Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated number of changes. The left-hand tree in Fig. 2.3 depicts a subset of this taxon sampling. 180 181 Figure A.3 Monocot phylogeny inferred from parsimony analysis of 37 mitochondrial genes (35 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) using DNA data, showing one of two shortest trees found (an arrow indicates a branch that collapses in the strict consensus tree). Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree); a moderately supported branch (labeled ‘bb’) is summarized for other analyses in Table A.5. Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated number of changes. The right-hand tree in Fig. 2.3 depicts a subset of this taxon sampling. 182 183 Figure A.4 Pandanales phylogeny inferred from combined DNA-based likelihood analysis of 119 organellar genes (37 mitochondrial and 82 plastid genes in photosynthetic taxa; 35 mitochondrial and 22 plastid genes in mycoheterotrophic Sciaphila densiflora, Triuridaceae), using a gene-by-codon partitioning scheme for a Pandanales-focused matrix (excluding mycoheterotrophic Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. The left-hand tree in Fig. 2.1 depicts a subset of this taxon sampling. 184 185 Figure A.5 Angiosperm phylogeny inferred from unpartitioned DNA-based likelihood analysis of 82 plastid genes (22 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) for a 95-taxon plastid matrix (excluding Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 186 187 Figure A.6 Angiosperm phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (22 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) using a gene-by-codon partitioning scheme for a 95-taxon plastid matrix (excluding Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. The left-hand tree in Fig. 2.2 depicts a subset of this taxon sampling. 188 189 Figure A.7 Angiosperm phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (22 and 18 respectively in mycoheterotrophic Sciaphila densiflora and S. thaidanica, Triuridaceae) using a gene-by-codon partitioning scheme for a 96-taxon plastid matrix. Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 190 191 Figure A.8 Angiosperm phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes using a gene-by-codon partitioning scheme for a 94-taxon plastid matrix (excluding mycoheterotrophic Sciaphila densiflora and S. thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 192 193 Figure A.9 Pandanales phylogeny inferred from partitioned DNA-based likelihood analysis of 83 plastid genes, including the ycf1 locus, using a gene-by-codon partitioning scheme for a Pandanales-focused matrix (excluding mycoheterotrophic Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree); a strongly supported branch (labeled ‘cc’) is summarized for other analyses in Table A.5. Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 194 195 Figure A.10 Angiosperm phylogeny inferred from unpartitioned amino-acid-based likelihood analysis of 78 plastid genes (18 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) for a 95-taxon plastid matrix (excluding mycoheterotrophic Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 196 197 Figure A.11 Angiosperm phylogeny inferred from partitioned amino-acid-based likelihood analysis of 78 plastid genes (18 in mycoheterotrophic Sciaphila densiflora, Triuridaceae) using a gene-based partitioning scheme for a 95-taxon plastid matrix (excluding mycoheterotrophic Sciaphila thaidanica, Triuridaceae). Inset cladogram shows relationships in Cyclanthaceae. Red branches conflict with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 198 199 Figure A.12 Monocot phylogeny inferred from unpartitioned DNA-based likelihood analysis of 37 mitochondrial genes. Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 200 201 Figure A.13 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 37 mitochondrial genes using a gene-by-codon partitioning scheme. Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. The right-hand tree in Fig. 2.2 depicts a subset of this taxon sampling. 202 203 Figure A.14 Monocot phylogeny inferred from unpartitioned amino-acid-based likelihood analysis of 37 mitochondrial genes. Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 204 205 Figure A.15 Monocot phylogeny inferred from partitioned amino-acid-based likelihood analysis of 37 mitochondrial genes using a gene-based partitioning scheme. Inset cladogram shows relationships in Cyclanthaceae. The red branch conflicts with the likelihood analysis of the combined organellar data (Fig. 2.1, left-hand tree). Thick lines indicate 100% bootstrap support; values <100% are shown at branches. Scale bar indicates estimated substitutions per site. 206 207 Figure A.16 Circular plastome map of Acanthochlamys bracteata (Velloziaceae). Genes inside the circle are transcribed clockwise and those outside counterclockwise. The gray circle indicates GC content (innermost gray circle shows a 50% threshold). Thicker lines indicate the inverted repeat (IR) copies. Genes with introns are indicated with ‘*’; a short partial copy of ycf1 in the IRB is indicated with ‘ψ’. The junctions between the IR copies and the large single copy (LSC) regions are depicted in Fig. A.19D. 208 rps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16SrRNAtrnI-GAU*trnA-UGC*23SrRNA4.5SrRNA5SrRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5SrRNA4.5SrRNA23SrRNAtrnA-UGC*trnI-GAU*16SrRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCrps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16SrRNAtrnI-GAU*trnA-UGC*23SrRNA4.5SrRNA5SrRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5SrRNA4.5SrRNA23SrRNAtrnA-UGC*trnI-GAU*16SrRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCD ψ (Velloziaceae)plastid genome153,842 bp209 Figure A.17 Circular plastome map of Benstonea copelandii (Pandanaceae). Genes inside the circle are transcribed clockwise and those outside counterclockwise. The gray circle shows GC content (innermost gray circle shows a 50% threshold). Thicker lines indicate the inverted repeat (IR) copies. Genes with introns are indicated with ‘*’; short partial copies of ycf1 and rps19 in the IRB are indicated with ‘ψ’. The junctions between the IR copies and the large single copy (LSC) regions are depicted in Fig. A.19B. 210 rps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16SrRNAtrnI-GAU*trnA-UGC*23SrRNA4.5SrRNA5SrRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5SrRNA4.5SrRNA23SrRNAtrnA-UGC*trnI-GAU*16SrRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCrps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16SrRNAtrnI-GAU*trnA-UGC*23SrRNA4.5SrRNA5SrRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5SrRNA4.5SrRNA23SrRNAtrnA-UGC*trnI-GAU*16SrRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCD (Pandanaceae)plastid genome157,929 bpBenstonea copelandiiψ 211 Figure A.18 Circular plastome map of Croomia japonica (Stemonaceae). Genes inside the circle are transcribed clockwise and those outside counterclockwise. The gray circle shows GC content (innermost gray circle shows a 50% threshold). Thicker lines indicate the inverted repeat (IR) copies. Genes with introns are indicated with ‘*’; a short partial copy of ycf1 in the IRB is indicated with ‘ψ’. The junctions between the IR copies and the large single copy (LSC) regions are depicted in Fig. A.19C. 212 rps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16S rRNAtrnI-GAU*trnA-UGC*23S rRNA4.5S rRNA5S rRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5S rRNA4.5S rRNA23S rRNAtrnA-UGC*trnI-GAU*16S rRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCrps19trnH-GUGrpl2*rpl23trnI-CAUycf2trnL-CAAndhB*rps73’rps12*trnV-GAC16S rRNAtrnI-GAU*trnA-UGC*23S rRNA4.5S rRNA5S rRNAtrnR-ACGtrnN-GUUycf1ndhFrpl32trnL-UAGccsAndhDpsaCndhEndhGndhIndhA*ndhHrps15ycf1trnN-GUUtrnR-ACG5S rRNA4.5S rRNA23S rRNAtrnA-UGC*trnI-GAU*16S rRNAtrnV-GAC3’rps12*rps7ndhB*trnL-CAAycf2trnI-CAUrpl23rpl2*trnH-GUGrps19rpl22rps3rpl16*rpl14rps8infArpl36rps11rpoApetD*petB*psbHpsbNpsbTpsbBclpP*5’rps12rpl20rps18rpl33 psaJtrnP-UGGtrnW-CCApetGpetLpsbEpsbFpsbLpsbJpetAcemAycf4psaIaccDrbcLatpBatpEtrnM-CAUndhCndhKndhJtrnF-GAAtrnL-UAA*trnT-UGUrps4trnS-GGAycf3*psaA psaBrps14trnfM-CAUtrnG-GCClhbAtrnS-UGApsbCpsbDtrnT-GGUtrnE-UUCtrnY-GUAtrnD-GUC psbMpetNtrnC-GCArpoBrpoC1*rpoC2rps2atpIatpHatpF*atpAtrnR-UCUtrnG-UCC*trnS-GCUpsbIpsbKtrnQ-UUGrps16*trnK-UUU*matKtrnK-UUU*psbACarludovica palmatachloroplast genome158,545 bpribosomal RNAstransfer RNAshypothetical chloroplast reading frames (ycf)other genesclpP, matKribosomal proteins (LSU)ribosomal proteins (SSU)RNA polymeraseRubisCO large subunitNADH dehydrogenaseATP synthasecytochrome b/f complexphotosystem IIphotosystem IIRAIRBSSCLSCD (Stemonaceae)plastid genome155,075 bp213 Figure A.19 Linearized plastomes from each of the five Pandanales families. Differences between plastomes from photosynthetic families Cyclanthaceae, Pandanaceae, Stemonaceae and Velloziaceae (A-D) imply shifting boundaries between the inverted repeat (IR) and large single-copy (LSC) regions, indicated by dotted lines. Homologous regions in the plastome of a non-photosynthetic Triuridaceae (E) are present as single copy, as the IR was likely lost for this taxon (see text). Asterisks (*) indicate genes with introns; scale bar shows plastome size (bp). Full plastomes are depicted in Figs. A.16–A.18 here (and Figs. 1, 2 in Lam et al., 2015). 214 215 Figure A.20 Clade ages of monocots estimated using a three-gene plastid data set and a Bayesian random local clock (RLC) model. Node heights represent median age estimates; gray bars the 95% highest posterior density (HPD) intervals for node ages. Labeled nodes indicate fossil calibrations placements (‘A–F’; Table A.4); scale bar shows time (millions of years, Ma). The tree in Fig. 2.4 depicts a subset of this taxon sampling. 216 217 Figure A.21 Clade ages of monocots estimated using a 12-gene mitochondrial data set and a Bayesian random local clock (RLC) model. Node heights represent median age estimates; gray bars the 95% highest posterior density (HPD) intervals for node ages. Labeled nodes indicate fossil calibration placements (‘C’, ‘F’; Table A.4); scale bar shows time (millions of years, Ma). Highest posterior density intervals within Pandanales are depicted in the tree in Fig. 2.4 (upper bars in lighter grey). 218 219 Figure A.22 Substitution rates in monocots inferred from a three-gene plastid data set using a Bayesian random local clock (RLC) model. Branch colors (blue to red) summarize the range of branch rates, with rate shifts also indicated as substitutions per site per million years. The left-hand tree in Fig. 2.5 depicts a subset of this taxon sampling. 220 221 Figure A.23 Substitution rates in monocots inferred from a 12-gene mitochondrial data set using a Bayesian random local clock (RLC) model. Branch colors (blue to red) summarize the range of branch rates, with rate shifts also indicated as substitutions per site per million years. The right-hand tree in Fig. 2.5 depicts a subset of this taxon sampling. 222 223 Figure A.24 Clade ages of monocots estimated from a version of the matrix used for dating analyses in Mennes et al. (2013) with all mycoheterotrophs excluded here (analyzed with the Bayesian uncorrelated lognormal, UCLN, relaxed clock model as in the original study). Node heights represent median age estimates; gray bars the 95% highest posterior density (HPD) intervals for node ages. Scale bar shows time (millions of years, Ma). 224 225 Appendix B Supplementary data, tables and figures for Chapter 3 Sub-Appendix B.1 Morphological characters for Dioscoreales taken from Caddick et al. (2002a; Appendix 1), with revisions and additions described below. I added data for two genera (Haplothismia and Tiputinia) and for three floral characters (6, 8, 25). Modified characters are indicated in bold font. Vegetative Characters 1. Roots: filiform (0); thickened (1).(Character 2 in Caddick et al., 2002a). I redefined this character to specifically describe root morphology rather than general underground organ morphology. Most Dioscoreales taxa scored here have filiform (thin, “threadlike”) roots: Burmanniaceae (Imhof et al., 2013); Dioscorea (Huber, 1998a); Nartheciaceae (Tamura, 1998); note that data are not available for Tacca, Trichopus and Stenomeris. In contrast to thin filiform roots, I define thickened roots to include the vermiform (“wormlike”) to tuberous roots that occur in Thismiaceae (Imhof et al., 2013; Woordward et al., 2007; Cheek et al., 2018) and some species of Burmannia (Imhof et al., 2013). Afrothismia, scored as “1”, have thickened root tubercles (round, nodule-like projections that represent primary roots with an enlarged basal cortex; Imhof, 1999); its tubercles may elongate into filiform root extensions (Imhof, 2006). This character was newly scored (as “1”) for Haplothismia (Airy Shaw, 1952; Sasidharan and Sujanapal, 2000) and Tiputinia (Woodward et al., 2007). 2. Leaf: linear, bifacial, lacking petiole (0); linear, unifacial, lacking petiole (1); petiolatewith expanded blade (2). (Character 34 in Caddick et al., 2002a). I redefined this character to better capture Dioscoreales-specific diversity of leaf morphology. Unifacial leaves are restricted to Nartheciaceae genera Lophiola, Narthecium and Nietneria (Tamura, 1998); petiolate leaves only occur in Dioscorea (Huber, 1998a), Stenomeris (Huber, 226 1998a) Tacca (Kubitzki, 1998d) and Trichopus (Huber, 1998b). Burmanniaceae and Thismiaceae, with leaves reduced to scales (or absent altogether), were scored as missing data. 3. Raphide crystals in vegetative organs: present (0); absent (1). (Character 45 in Caddick et al., 2002a). This character was newly scored (as “0”) for Tacca (Kubitzki, 1998d). 4. Styloid crystals in vegetative organs: absent (0); present (1). (Character 46 in Caddick et al., 2002a). Floral and Pollen Characters 5. Flowers: bisexual (0); unisexual (1). (Character 3 in Caddick et al., 2002a). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 6. Floral symmetry: actinomorphic (0); zygomorphic (1). (Newly added character). Most Dioscoreales have actinomorphic flowers (Nartheciaceae: Tamura, 1998; Remizowa et al., 2008; Burmanniaceae: Maas et al., 1986; Thismiaceae: Airy Shaw, 1952; Woodward et al., 2007; Cheek et al., 2018; Tacca: Kubitzki, 1998d; Dioscorea, Stenomeris and Trichopus: Burkill, 1951). Zygomorphic flowers are restricted to Afrothismia and some species of Thismia (Maas-van de Kamer, 1998). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 7. Expansion of hypanthium (the region between the levels of style and stamen insertion): not expanded (0); laterally expanded into an urceolate chamber (1). (Character 16 in Caddick et al., 2002a). 8. Annulus at mouth or deep inside floral tube: absent (0); present (1). 227 (Newly added character). Presence of an annulus is restricted to Afrothismia, Oxygyne and Thismia (Maas-van de Kamer, 1998). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Cheek et al., 2018). 9. Number of stamens (or staminodes in female flowers of Dioscorea): six (0); three (1). (Character 5 in Caddick et al., 2002a) I redefined character states to remove those not seen in Dioscoreales. The presence of three stamens is a synapomorphy for Burmanniaceae, but is also found in Oxygyne (this character was newly scored for the latter taxon; Cheek et al., 2018). Most Dioscorea species have six stamens in two whorls, and I scored it as “0” to reflect this, although the inner whorl may be replaced by three staminodia or entirely absent in a few species (Huber, 1998a). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 10. Stamen orientation at initiation: erect (0); reflexed downwards (1). (Character 11 in Caddick et al., 2002a). 11. Stamen orientation at maturity: erect (0); reflexed sharply downwards so that apical extension is in contact with stigma (1). (Character 12 in Caddick et al., 2002a). This character was newly scored (as “1”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 12. Stamen filaments: cylindrical, though sometimes very short or even adnate (0); short, flat and petaloid (1). (Character 15 in Caddick et al., 2002a). I redefined this character to describe filament shape rather than fusion. As previously scored, filament fusion occurs only in Thismia; Caddick (2000) also documented filament fusion in some species of Dioscorea, but fusion in the latter genus does not appear to be homologous to that in the former genus. The redefined character captures the distinct stamen morphology of Tacca 228 (Fig. 9, Caddick et al., 2000); note that data are not available for Haplothismia, Thismia, Tiputinia and Trichopus. 13. Anther thecae: adjacent to each other on connective (0); connective laterally expanded so that thecae are set apart on two wings or branches (1). (Character 14 in Caddick et al., 2002a). As originally scored, a laterally expanded connective was a synapomorphy of Burmanniaceae and Trichopus. However, I rescored T. sempervirens and T. zeylanicus as absent and ambiguous for this character, respectively, based on my own observations and following re-examination of the original data (Figs. 2.11, 2.12, 2.14. 2.15 in Caddick, 2000). This character was newly scored (as “0”) for Oxygyne (Cheek et al., 2018). 14. Single pronounced apical extension of anther connective beyond thecae: absent (0); present (1). (Character 13 in Caddick et al., 2002a). Apical extension of the anther connective occurs in Stenomeris, Tacca, Thismiaceae (data not available for Haplothismia, Oxygyne and Tiputinia) and Trichopus. Caddick et al. (2002a) described this character in terms of position of the thecae in relation to the connective apex, with thecae at the apex in the absence of an apical extension (e.g., Tacca) vs. thecae below the apex in the presence of an apical extension (e.g., Dictyostega). I rescored this character as present (“1”) for Tacca based on my own observations (Fig. B.21) and on the hypothesis of Caddick et al. (2000; Fig. 9e) that the irregular appendage above the thecae in this genus may be homologous with apical connective extensions found in other Dioscoreales taxa. Data for Dictyostega (Figs. 1c, 1d in Miers, 1841) are ambiguous, so I rescored this character state as missing (“?”) for this genus. 15. Anther thecal dehiscence: longitudinal (0); transverse (1). (Character 10 in Caddick et al., 2002a). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 229 16. Microsporogenesis: successive (0); simultaneous (1). (Character 26 in Caddick et al., 2002a). This character was newly scored (as “0”) for Aletris (Caddick et al., 1998). 17. Pollen aperture number: one (0); two or more (1). (Character 28 in Caddick et al., 2002a). I redefined this character to describe pollen aperture number rather than presence/absence, as most Dioscoreales have aperturate pollen except Apteria and Gymnosiphon (scored here as missing data, “?”; note that other Burmanniaceae and Thismiaceae genera that have no available data are also scored as “?”). I rescored character states for: Nartheciaceae (Merckx et al., 2008b); Burmanniaceae (Maas et al., 1986); Thismiaceae (Maas et al., 1986; Caddick et al., 1998); and Dioscorea, Japonolirion, Stenomeris, Tacca and Trichopus (Caddick et al., 1998). 18. Pollen aperture shape: sulcate (0); porate (1). (Character 29 in Caddick et al., 2002a). I redefined character states to capture Dioscoreales-specific aperture types not included in the original matrix (i.e., porate, “1”) and to remove those not seen in Dioscoreales. I rescored character states using the same sources as for character 17 (see above). 19. Pollen exine sculpturing: surface not spinulate (0); spinulate (1). (Character 30 in Caddick et al., 2002a). This character was newly scored (as “0”) for Nietneria (Merckx et al., 2008b). 20. Ovary position: superior or semi-inferior (0); inferior (1). (Character 4 in Caddick et al., 2002a). I redefined character state “0” to capture the superior or semi-inferior ovaries present in all Nartheciaceae genera except Nietneria (Rudall, 2002; Tobe et al., 2018). I rescored Nietneria (Tobe et al., 2018) and Apteria (Maas-van der Kamer, 1998) as “1”. This character was newly scored (as “1”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 230 21. Ovary locule number: trilocular (0); unilocular (1). (Character 20 in Caddick et al., 2002a). This character was newly scored (as “0”) for Lophiola (Tobe et al., 2018), and (as “1”) for both Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). 22. Ovary placentation: axile (0); parietal (1). (Character 25 in Caddick et al., 2002a). This character was newly scored (as “0”) for Lophiola and Metanarthecium (Tobe et al., 2018), and (as “1”) for Haplothismia (Airy Shaw, 1952), Oxygyne (Cheek et al., 2018) and Tiputinia (Woodward et al., 2007). 23. Number of ovules per ovary locule: numerous (three or more, not fixed) (0); few (one or two) (1). (Character 22 in Caddick et al., 2002a). This character was newly scored (as “0”) for Haplothismia (Airy Shaw, 1952) and Tiputinia (Woodward et al., 2007). Note that I switched the state labeling for this character compared to the original data set. 24. Septal nectaries: absent (0); present (1). (Character 23 in Caddick et al., 2002a). I rescored character states for Dioscorea (“0&1”, Caddick et al., 2000), Metanarthecium (“1”, Remizowa et al., 2008), Oxygyne (“?”) and Tacca (“0”, Rudall, 2002). This character was newly scored (as “0”) for Afrothismia, Haplothismia (Maas-van de Kamer, 1998; Merckx et al., 2013a) and Tiputinia (Woodward et al., 2007). 25. Style length: more or less as long as the floral tube (0); much shorter than floral tube and below the level of stamens (1). (Newly added character) 231 Short styles are characteristic of Stenomeris (Fig. B.22), Tacca (Fig. B.23), Thismiaceae (Maas-van der Kamer, 1998) and Trichopus (Huber, 1998b). This character was newly scored (as “1”) for Haplothismia (Maas van de Kamer, 1998) and Tiputinia (Woodward et al., 2007). 26. Stigma lobes: capitate or slightly lobed (0); funnel-shaped (1); laterally expanded (2). (Character 19 in Caddick et al., 2002a). I redefined character state “0” to describe stigma lobes as capitate or slightly lobed instead of acute (state “2” in the original data set). I rescored Stenomeris (as “0” based on Fig. 2.18 in Caddick, 2000) and Trichopus sempervirens (as “2” based on Fig. 11b,e in Caddick et al., 2000). This character was newly scored (as “0”) for Afrothismia (Franke, 2004; Cheek and Jannerup, 2005), Aletris, Lophiola and Nietneria (Tobe et al., 2018). Seed and Fruit Characters 27. Formation of a parietal layer in the developing ovule: crassinucellate (0); tenuinucellate (1). (Character 27 in Caddick et al., 2002a). 28. Endosperm cell walls: thin (0); thickened (1). (Character 36 in Caddick et al., 2002a). 29. Testa (outer integument of seed): 2 layered (0); 3 or more layered (1). (Character 37 in Caddick et al., 2002a). 30. Endotesta (inner layer of outer integument): not crystalline (0); crystalline (1). (Character 38 in Caddick et al., 2002a). 31. Distinct seed coat cuticles separating testa, tegmen and nucellus: absent (0); present (1). (Characters 39 and 40 in Caddick et al., 2002a). I combined characters 39 and 40 in the original data set as both described presence/absence of cuticles between different seed coat layers, and also had the same states for all scored taxa. 232 32. Seed ruminations: absent (0); present (1). (Character 41 in Caddick et al., 2002a). 33. Thickened endotesta: absent (0); present (1). (Character 43 in Caddick et al., 2002a). 34. Thickened exotegmen: absent (0); present (1). (Character 44 in Caddick et al., 2002a). 35. Fruit morphology: not winged (0); winged due to outgrowth of dorsal regions of each carpel (1). (Character 47 in Caddick et al., 2002a). This character was rescored for Thismia (“0&1”, Caddick, 2000) and Japonolirion (“0”, Remizowa et al., 2006). 233 Table B.1 Specimen source information; herbarium abbreviations follow Thiers (continuously updated). _________________________________________________________________________________________________________________________________ Species1 Family Voucher number [Collector number, herbarium] _________________________________________________________________________________________________________________________________ Dioscoreales Dioscorea membranacea Pierre ex Prain & Burkill Dioscoreaceae M.W. Chase 21050, K Aletris obovata Nash Nartheciaceae Iles & Norman 010, UBC Metanarthecium luteoviride Maxim. Nartheciaceae IV Taratenko s.n., MW Narthecium californicum Baker Nartheciaceae JM Saarela 19803, UBC Nietneria paniculata Steyerm. Nartheciaceae O. Hokche & P.J. Maas 849, U Tacca leontopetaloides (L.) Kuntze Taccaceae Wilkin 817, K Afrothismia gesnerioides H.Maas Thismiaceae (?) Sainge M. 2756, YA Afrothismia hydra Sainge & T.Franke Thismiaceae (?) Sainge 2625, YA Afrothismia korupensis Sainge & T.Franke Thismiaceae (?) Sainge M. 2637, YA Afrothismia winkleri (Engl.) Schltr. Thismiaceae (?) Sainge 2624, YA Haplothismia exannulata Airy Shaw Thismiaceae Sujanapal 30387, KFRI Thismia rodwayi F.Muell. Thismiaceae Merckx & Wapstra TAS4-5, L Trichopus sempervirens (H.Perrier) Caddick & Wilkin Trichopodaceae Ranaivojaona, R. 790, K Trichopus zeylanicus Gaertn. Trichopodaceae M.W. Chase, 16354, K _________________________________________________________________________________________________________________________________ 1 Additional sequences: Aletris fauriei (= A. foliata [Maxim.] Makino & Nemoto; NC_033412.1), A. spicata (Thunb.) Franch. (NC_033411.1), Burmannia coelestis D.Don (KT734618.1), B. disticha L. (NC_036661.1), Dioscorea collettii Hook.f. (NC_037717.1), D. polystachya Turcz. (NC_037716.1), D. rotundata (= D. cayenensis subsp. rotundata [Poir.] J.Miège; NC_024170.1), D. villosa L. (NC_034686.1), D. zingiberensis C.H.Wright (NC_027090.1), Metanarthecium luteoviride (NC_029214.1), Stenomeris borneensis (AF308018.1, AY973836.1, AF307475.1), Tacca leontopetaloides (NC_036658.1); see Lam et al. (2018) for the remaining taxa. 234 Table B.2 Status of protein-coding and rDNA plastid genes recovered from fully mycoheterotrophic Dioscoreales species for sequence-based phylogenetic analysis. Species are ordered vertically by number of genes retained, and genes horizontally by functional complex (gene nomenclature as in Wicke et al., 2011). Newly sequenced taxa are shown in blue. White box = gene present, black = gene lost (or possibly not retrieved from taxa with preliminary plastid genome assemblies), grey = genes containing internal stop codons. Ribosomal protein genes rDNA genesTaxon Family Nutritional status infArpl2rpl14rpl16rpl20rpl22rpl32rpl33rpl36rps2rps3rps4rps7rps8rps11rps12rps14rps15rps18rps19accDclpPmatKycf2rrn4.5rrn5rrn16rrn23protein-codinggenes (of 78inautotrophs)rDNA genes(of 4 inautotrophs)% of alignedgenescompared toautotrophsCampylosiphon congestus Burmanniaceae Full mycoheterotroph 24 4 34Burmannia itoana Burmanniaceae Full mycoheterotroph 21 4 30Gymnosiphon longistylus Burmanniaceae Full mycoheterotroph 19 4 28Haplothismia exannulata Thismiaceae Full mycoheterotroph 16 4 24Apteria ahylla Burmanniaceae Full mycoheterotroph 14 4 22Afrothismia korupensis Thismiacea (?) Full mycoheterotroph 9 2 13Afrothismia hydra Thismiacea (?) Full mycoheterotroph 9 2 13Afrothismia winkleri Thismiacea (?) Full mycoheterotroph 9 2 13Afrothismia gesnerioides Thismiacea (?) Full mycoheterotroph 8 2 12Thismia tentaculata Thismiaceae Full mycoheterotroph 7 3 12Thismia rodwayi Thismiaceae Full mycoheterotroph 6 3 11Ribosomal protein genes rDNA genesOtherOther235 Table B.3 Optimal substitution models for unpartitioned analyses and for final partitioning schemes inferred using the ModelFinder function in IQ-TREE (see text). DNA analyses were partitioned using a gene-by-codon (‘G x C’) scheme for protein-coding genes and individual partitions for rDNA genes; amino-acid (AA) analyses were partitioned by gene. (A) Unpartitioned DNA version of the core 89-taxon matrix; (B) G x C partitioning scheme for the same core DNA matrix; (C) unpartitioned AA version of the same core matrix; (D) partitioned AA version of the same core matrix using a gene-based partitioning scheme; (E) G x C partitioning scheme for a 90-taxon DNA matrix after adding three genes for photosynthetic Stenomeris to the core matrix; (F) G x C partitioning scheme for a 91-taxon DNA matrix after adding two long-branch fully mycoheterotrophic Burmanniaceae (Apteria, Gymnosiphon) to the core matrix; (G) G x C partitioning scheme for a second 91-taxon DNA matrix after adding two long-branch species of fully mycoheterotrophic Thismia (Thismiaceae) to the core matrix; (H) G x C partitioning scheme for a second 90-taxon DNA matrix after adding one of four sampled species of Afrothismia (A. gesnerioides) to the core matrix; (I) G x C partitioning scheme for a 93-taxon DNA matrix after adding all four sampled species of Afrothismia to the core matrix; (J) G x C partitioning scheme for an all-inclusive 98-taxon DNA matrix including all Dioscoreales taxa with available molecular data here. Genes are shown before the underscore and the ‘pos’ term after the underscore indicates the codon position (not applicable for rrn genes). _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ A) 1 GTR+F+I+G -- B) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+G ACCD_pos2, RPL22_pos2, RPS12_pos3 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1, PSBJ_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPOB_pos1 10 TVM+F+I+G ATPE_pos2, NDHK_pos2, RPOC1_pos2 11 GTR+F+I+G ATPE_pos3, RPL33_pos3, RPOB_pos3, RPOC1_pos3, YCF4_pos3 236 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 TVM+F+I+G ATPF_pos3, ATPH_pos3 15 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 16 F81+F ATPH_pos2 17 TVM+F+I+G ATPI_pos1, PSBF_pos1, YCF3_pos1 18 TVM+F+I+G ATPI_pos2, PETB_pos2, PSBI_pos2, PSBL_pos1 19 TVM+F+I+G ATPI_pos3 20 TVM+F+I+G CCSA_pos1, PSBF_pos3 21 GTR+F+I+G CCSA_pos2, NDHF_pos2 22 GTR+F+I+G CCSA_pos3, NDHF_pos3 23 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 24 TVM+F+G CEMA_pos2, PSBL_pos3, RPL23_pos3 25 TVM+F+G CEMA_pos3 26 TVMef+G CLPP_pos1 27 TVM+F+I+G CLPP_pos2, RPS2_pos2 28 GTR+F+I+G CLPP_pos3, RPS18_pos3, RPS2_pos3 29 TVM+F+G INFA_pos1, INFA_pos2, RPS14_pos1 30 TVM+F+I+G INFA_pos3, RPS19_pos3 31 GTR+F+I+G MATK_pos1 32 TVM+F+I+G MATK_pos2 33 TVM+F+I+G MATK_pos3 34 GTR+F+I+G NDHA_pos1, NDHD_pos1 35 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 36 TVM+F+I+G NDHA_pos3, NDHE_pos3 37 TIM3+F+I+G NDHB_pos1, RPL23_pos2, RPS12_pos2 38 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 39 TVM+F+G NDHB_pos3 40 TVM+F+I+G NDHC_pos1, NDHJ_pos1, PSBN_pos1, YCF4_pos2 41 GTR+F+I+G NDHC_pos3, PSBK_pos3 42 GTR+F+I+G NDHD_pos3 43 TVM+F+G NDHE_pos1, NDHI_pos1, RPL33_pos1, RPL36_pos2 44 GTR+F+G NDHE_pos2, PSBZ_pos2 45 TVM+F+I+G NDHF_pos1 46 GTR+F+G NDHG_pos1, PSBJ_pos3 47 GTR+F+I+G NDHG_pos3 48 GTR+F+I+G NDHH_pos1, RPOC1_pos1 49 TVM+F+I+G NDHH_pos2 50 GTR+F+I+G NDHH_pos3, PETD_pos3 51 TIM1+F+I+G NDHI_pos2, NDHJ_pos2 52 GTR+F+I+G NDHI_pos3 53 GTR+F+I+G NDHJ_pos3, PSAA_pos3, PSBM_pos3 54 TVM+F+I+G NDHK_pos3, PSBH_pos3 55 K81uf+F+I+G PETA_pos1, RPS16_pos1 56 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 237 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 57 TIM1+F+I+G PETB_pos1, PSBA_pos1 58 TVM+F+I+G PETB_pos3, PSBB_pos3 59 TVMef+I+G PETD_pos1 60 K81uf+F+I+G PETD_pos2 61 TVMef+G PETG_pos1, PETN_pos1, PSBE_pos1 62 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 63 TVM+F+I+G PETG_pos3, RPL20_pos2, YCF3_pos3 64 TN93+F+I+G PETL_pos1, RPS14_pos2 65 TVM+F+G PETL_pos2, PSAJ_pos1, PSBK_pos2, PSBZ_pos1 66 GTR+F+G PETL_pos3, PETN_pos3, PSAI_pos3, PSBE_pos3 67 HKY+F PETN_pos2 68 K81uf+F+I+G PSAA_pos1 69 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 70 TVM+F+I+G PSAB_pos1 71 GTR+F+I+G PSAB_pos3 72 TVM+F+I+G PSAC_pos1, PSBB_pos1 73 SYM+I PSAC_pos2 74 GTR+F+I+G PSAC_pos3, RBCL_pos3 75 TN93ef+G PSAI_pos1 76 HKY+F+G PSAI_pos2 77 SYM+G PSAJ_pos2 78 K81uf+F+G PSAJ_pos3 79 TIM3+F+I+G PSBA_pos2, PSBF_pos2 80 GTR+F+I+G PSBA_pos3 81 TVM+F+I+G PSBC_pos2, PSBD_pos2 82 GTR+F+I+G PSBC_pos3, PSBD_pos3 83 TIM3ef+I+G PSBH_pos1, PSBH_pos2 84 TPM3+F+G PSBI_pos1, PSBT_pos1, RPL23_pos1 85 HKY+F+I+G PSBJ_pos2 86 TIM1ef+I+G PSBK_pos1 87 TIM1+F+G PSBM_pos1, RPL36_pos1 88 TN93+F+G PSBN_pos3 89 TN93+F+I+G PSBT_pos2 90 GTR+F+G PSBT_pos3, RPS16_pos3 91 TIM1+F+I PSBZ_pos3 92 GTR+F+I+G RBCL_pos1 93 GTR+F+I+G RBCL_pos2 94 GTR+F+I+G RPL14_pos1 95 TN93+F+G RPL14_pos2, RPS7_pos2 96 TVM+F+I+G RPL14_pos3, RPS8_pos3 97 GTR+F+I+G RPL16_pos1 98 SYM+I+G RPL16_pos2 99 GTR+F+I+G RPL16_pos3 100 GTR+F+I+G RPL2_pos1, RPS12_pos1 101 SYM+I+G RPL2_pos2, RRN23 238 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 102 K81uf+F+G RPL2_pos3 103 K81uf+F+G RPL20_pos1 104 TVM+F+I+G RPL20_pos3, RPOC2_pos3 105 TVM+F+G RPL22_pos1, RPL32_pos1, RPS15_pos1 106 TVM+F+I+G RPL22_pos3, RPS11_pos3, RPS3_pos3 107 GTR+F+G RPL32_pos2 108 GTR+F+I+G RPL32_pos3 109 TVM+F+I+G RPL33_pos2, RPOA_pos2, RPOC2_pos2 110 TIM3+F+G RPL36_pos3 111 TVM+F+G RPOA_pos3 112 GTR+F+I+G RPOC2_pos1 113 TIM1+F+G RPS11_pos1, RPS8_pos1 114 TIM3ef+G RPS11_pos2 115 GTR+F+G RPS14_pos3 116 TVM+F+I+G RPS15_pos2 117 TVM+F+G RPS15_pos3 118 GTR+F+G RPS18_pos1 119 GTR+F+I+G RPS18_pos2, RPS19_pos2, RPS4_pos1, RPS4_pos2 120 K81uf+F+G RPS19_pos1, RPS7_pos1 121 GTR+F+G RPS2_pos1 122 GTR+F+G RPS3_pos1 123 GTR+F+I+G RPS3_pos2 124 GTR+F+G RPS4_pos3 125 GTR+F+G RPS7_pos3 126 GTR+F+G RPS8_pos2 127 TVM+F+G YCF2_pos1 128 TVM+F+G YCF2_pos2 129 TVM+F+G YCF2_pos3 130 SYM+I+G RRN16 131 K81+G RRN4, RRN5 C) 1 HIVB+F+I+G -- D) 1 HIVw+F ACCD 2 HIVw+F ATPA 3 HIVb+F ATPB, PSBT 4 HIVb ATPE, RPOC1 5 HIVb ATPF, RPL33 6 cpREV ATPH 7 HIVw+F ATPI, PSBF, PSBM, PSBN, YCF3 8 HIVw+F CCSA, RPL20, RPS15 9 HIVw+F CEMA, NDHG 10 FLU CLPP 239 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 11 JTT INFA, RPS11 12 HIVw+F MATK 13 HIVw+F NDHA, NDHI, NDHK 14 HIVw+F NDHB, PETG, PSBI, PSBL 15 HIVw+F NDHC, NDHJ 16 HIVb+F NDHD, PSBK 17 mtREV+F NDHE 18 HIVw+F NDHF, RPS3 19 HIVb NDHH 20 JTT-DCMut PETA 21 FLU+F PETB, PSBA, PSBB, PSBC, PSBD 22 HIVb PETD 23 HIVw+F PETL, RPOA, RPS19 24 mtZOA PETN 25 FLU+F PSAA, PSAB 26 JTT-DCMut PSAC 27 HIVw PSAI 28 cpREV PSAJ 29 FLU PSBE 30 FLU PSBH 31 mtREV PSBJ 32 mtZOA PSBZ 33 LG RBCL 34 HIVw RPL14 35 cpREV RPL16 36 HIVw+F RPL2 37 FLU RPL22 38 HIVw RPL23 39 JTT RPL32 40 HIVb+F RPL36, RPOB 41 HIVw+F RPOC2, RPS14 42 HIVb RPS12 43 HIVb RPS16 44 JTT+F RPS18, RPS4, RPS8 45 HIVw RPS2 46 cpREV RPS7 47 HIVw+F YCF2 48 HIVb+F YCF4 E) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+I+G ACCD_pos2, RPL32_pos1 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1 240 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPOB_pos1 10 GTR+F+I+G ATPE_pos2, NDHK_pos2 11 TVM+F+I+G ATPE_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+I+G ATPF_pos3, RPOB_pos3 15 TIM1+F+I+G ATPH_pos1 16 TPM3+F+I+G ATPH_pos2, PSBA_pos2, PSBF_pos2 17 TVM+F+I+G ATPH_pos3 18 TVM+F+I+G ATPI_pos1, PSBN_pos1 19 TVM+F+I+G ATPI_pos2, PETB_pos2 20 TVM+F+I+G ATPI_pos3, NDHK_pos3 21 TVM+F+I+G CCSA_pos1, PSBF_pos3 22 GTR+F+I+G CCSA_pos2, NDHF_pos2 23 TVM+F+I+G CCSA_pos3 24 TVM+F+G CEMA_pos1, YCF4_pos1 25 TVM+F+G CEMA_pos2, RPL23_pos3 26 TVM+F+I+G CEMA_pos3, PSBH_pos3 27 TVMef+G CLPP_pos1 28 TVM+F+I+G CLPP_pos2, RPS2_pos2 29 TVM+F+I+G CLPP_pos3, RPS18_pos3 30 TVM+F+G INFA_pos1, RPS15_pos2 31 TVM+F+G INFA_pos2, RPS14_pos1 32 TVM+F+I+G INFA_pos3, RPS19_pos3 33 GTR+F+G MATK_pos1 34 TVM+F+I+G MATK_pos2 35 TVM+F+I+G MATK_pos3 36 GTR+F+I+G NDHA_pos1, NDHD_pos1 37 TPM3uf+F+I+G NDHA_pos2, NDHG_pos2 38 TVM+F+I+G NDHA_pos3, NDHE_pos3 39 TIM3+F+I+G NDHB_pos1, YCF3_pos1 40 TN93+F+I+G NDHB_pos2, NDHC_pos2 41 TVM+F+G NDHB_pos3 42 TVM+F+G NDHC_pos1, NDHJ_pos1 43 GTR+F+I+G NDHC_pos3, PSBK_pos3 44 TVM+F+I+G NDHD_pos2, PSAJ_pos1 45 GTR+F+I+G NDHD_pos3 46 TVM+F+G NDHE_pos1, NDHI_pos1 47 GTR+F+I+G NDHE_pos2, PSBZ_pos2 48 TVM+F+I+G NDHF_pos1 49 GTR+F+I+G NDHF_pos3 241 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 50 GTR+F+G NDHG_pos1, PSBJ_pos3 51 GTR+F+I+G NDHG_pos3 52 GTR+F+I+G NDHH_pos1, RPOC1_pos1 53 TVM+F+I+G NDHH_pos2 54 TVM+F+I+G NDHH_pos3 55 TIM1+F+I+G NDHI_pos2, NDHJ_pos2 56 GTR+F+I+G NDHI_pos3 57 TVM+F+I+G NDHJ_pos3, PSBM_pos3 58 K81uf+F+I+G PETA_pos1, RPS16_pos1 59 TVM+F+I+G PETA_pos2, RPOB_pos2 60 TIM1+F+I+G PETB_pos1, PSBA_pos1 61 TVM+F+I+G PETB_pos3, PSBB_pos3 62 TVMef+I+G PETD_pos1 63 GTR+F+I+G PETD_pos2, PSAJ_pos2 64 GTR+F+G PETD_pos3, PSBT_pos3 65 TVMef+I PETG_pos1, PSBE_pos1 66 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 67 TVM+F+I+G PETG_pos3, YCF3_pos3 68 TN93+F+I+G PETL_pos1, RPS14_pos2 69 TPM3uf+F+G PETL_pos2, PSBZ_pos1 70 TPM3uf+F+I+G PETL_pos3, PETN_pos3 71 TIM2ef PETN_pos1 72 TPM3uf+F PETN_pos2, PSBI_pos2 73 K81uf+F+I+G PSAA_pos1 74 GTR+F+I+G PSAA_pos2, PSBB_pos2 75 GTR+F+I+G PSAA_pos3 76 TVM+F+I+G PSAB_pos1 77 TVM+F+I+G PSAB_pos2, PSBC_pos2, PSBD_pos2 78 GTR+F+I+G PSAB_pos3 79 TVM+F+I+G PSAC_pos1, PSBB_pos1 80 SYM+I PSAC_pos2 81 GTR+F+I+G PSAC_pos3, RBCL_pos3 82 TN93ef+G PSAI_pos1 83 HKY+F+G PSAI_pos2 84 TIM3+F+G PSAI_pos3, RPL22_pos2 85 K81uf+F+G PSAJ_pos3 86 GTR+F+I+G PSBA_pos3 87 GTR+F+I+G PSBC_pos1, PSBD_pos1 88 GTR+F+I+G PSBC_pos3, PSBD_pos3 89 TPM2uf+F+G PSBE_pos3, PSBN_pos3 90 K81+G PSBF_pos1, RPL23_pos2 91 TIM3ef+I+G PSBH_pos1, PSBH_pos2 92 TIM3ef PSBI_pos1, PSBT_pos1 93 GTR+F+G PSBJ_pos1, RPL36_pos2 94 HKY+F+I+G PSBJ_pos2 242 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 95 TIM1ef+I+G PSBK_pos1 96 TIM3+F+I+G PSBK_pos2 97 TIM2+F+I+G PSBL_pos1, PSBN_pos2 98 TVM+F+I+G PSBL_pos3, RPOA_pos2 99 TPM2uf+F+I+G PSBM_pos1, YCF3_pos2 100 TN93+F+I PSBM_pos2 101 TN93+F+I+G PSBT_pos2 102 TIM1+F+I PSBZ_pos3 103 GTR+F+I+G RBCL_pos1 104 GTR+F+I+G RBCL_pos2 105 GTR+F+I+G RPL14_pos1 106 TN93+F+G RPL14_pos2, RPS7_pos2 107 TVM+F+I+G RPL14_pos3, RPS8_pos3 108 GTR+F+I+G RPL16_pos1 109 SYM+I+G RPL16_pos2 110 GTR+F+I+G RPL16_pos3 111 GTR+F+I+G RPL2_pos1 112 SYM+I+G RPL2_pos2, RRN23 113 K81uf+F+G RPL2_pos3 114 K81uf+F+G RPL20_pos1 115 TIM1+F+G RPL20_pos2, RPS12_pos3 116 TVM+F+I+G RPL20_pos3 117 TVM+F+G RPL22_pos1, RPS15_pos1 118 TVM+F+I+G RPL22_pos3, RPS3_pos3 119 TPM3+F+G RPL23_pos1 120 GTR+F+G RPL32_pos2 121 GTR+F+I+G RPL32_pos3 122 GTR+F+G RPL33_pos1, RPOA_pos1 123 TVM+F+I+G RPL33_pos2, RPOC2_pos2 124 GTR+F+G RPL33_pos3, RPOA_pos3 125 TPM2uf+F+G RPL36_pos1 126 TIM3+F+G RPL36_pos3 127 TVM+F+I+G RPOC1_pos2, YCF4_pos2 128 TVM+F+I+G RPOC1_pos3 129 GTR+F+I+G RPOC2_pos1 130 TVM+F+I+G RPOC2_pos3 131 TIM1+F+G RPS11_pos1, RPS8_pos1 132 TIM3ef+G RPS11_pos2 133 TVM+F+G RPS11_pos3 134 K81uf+F+G RPS12_pos1 135 SYM+I+G RPS12_pos2, RRN16 136 GTR+F+G RPS14_pos3 137 TVM+F+G RPS15_pos3 138 TVM+F+G RPS16_pos3 139 GTR+F+G RPS18_pos1 243 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 140 TIM1+F+G RPS18_pos2, RPS4_pos1 141 GTR+F+G RPS19_pos1, RPS2_pos1 142 TVM+F+I+G RPS19_pos2, RPS4_pos2 143 GTR+F+G RPS2_pos3 144 GTR+F+G RPS3_pos1 145 GTR+F+I+G RPS3_pos2 146 GTR+F+G RPS4_pos3 147 K81uf+F+G RPS7_pos1 148 GTR+F+G RPS7_pos3 149 GTR+F+G RPS8_pos2 150 TVM+F+G YCF2_pos1 151 TVM+F+G YCF2_pos2 152 TVM+F+G YCF2_pos3 153 K81+G RRN4, RRN5 F) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+G ACCD_pos2 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1, PSBJ_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, NDHH_pos3, PETD_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPOB_pos1 10 TVM+F+I+G ATPE_pos2, NDHK_pos2, RPOC1_pos2 11 GTR+F+I+G ATPE_pos3, ATPF_pos3, RPOB_pos3, RPOC1_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 15 F81+F ATPH_pos2 16 TVM+F+I+G ATPH_pos3 17 TVM+F+I+G ATPI_pos1, PSBF_pos1, YCF3_pos1 18 TVM+F+I+G ATPI_pos2, PETB_pos2, PSBL_pos1 19 TVM+F+I+G ATPI_pos3, PSAI_pos3 20 TVM+F+I+G CCSA_pos1, PSBF_pos3 21 GTR+F+I+G CCSA_pos2, NDHF_pos2 22 GTR+F+I+G CCSA_pos3, NDHF_pos3 23 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 24 TVM+F+G CEMA_pos2, PSBL_pos3, RPL23_pos3 25 TVM+F+G CEMA_pos3 26 TVMef+G CLPP_pos1 27 TIM3+F+I+G CLPP_pos2 28 GTR+F+I+G CLPP_pos3, RPS14_pos3 244 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 29 TVM+F+G INFA_pos1, INFA_pos2, RPS14_pos1 30 TVM+F+I+G INFA_pos3, RPS19_pos3 31 GTR+F+I+G MATK_pos1 32 TVM+F+I+G MATK_pos2 33 TVM+F+I+G MATK_pos3 34 GTR+F+I+G NDHA_pos1, NDHD_pos1 35 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 36 TVM+F+I+G NDHA_pos3, NDHE_pos3 37 TIM3+F+I+G NDHB_pos1, PSBT_pos1, RPL23_pos2 38 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 39 TVM+F+G NDHB_pos3 40 TVM+F+I+G NDHC_pos1, NDHJ_pos1, PSBN_pos1, YCF4_pos2 41 GTR+F+I+G NDHC_pos3, PSBK_pos3 42 GTR+F+I+G NDHD_pos3 43 TVM+F+G NDHE_pos1, NDHI_pos1, RPL33_pos1, RPL36_pos2 44 GTR+F+I+G NDHE_pos2, PSBZ_pos2 45 TVM+F+I+G NDHF_pos1 46 GTR+F+G NDHG_pos1, PSBJ_pos3 47 GTR+F+I+G NDHG_pos3, RPL33_pos3, RPOA_pos3 48 GTR+F+I+G NDHH_pos1, RPOC1_pos1 49 TVM+F+I+G NDHH_pos2 50 K81uf+F+I+G NDHI_pos2, NDHJ_pos2, PSBM_pos1 51 GTR+F+I+G NDHI_pos3 52 TVM+F+I+G NDHJ_pos3, PETB_pos3, PSBM_pos3 53 TVM+F+I+G NDHK_pos3, PSBH_pos3 54 K81uf+F+I+G PETA_pos1, RPS16_pos1 55 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 56 TIM1+F+I+G PETB_pos1, PSBA_pos1 57 SYM+I+G PETD_pos1 58 K81uf+F+I+G PETD_pos2 59 TVMef+G PETG_pos1, PETN_pos1, PSBE_pos1, PSBI_pos1 60 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 61 TVM+F+I+G PETG_pos3, RPL20_pos2, YCF3_pos3 62 TN93+F+I+G PETL_pos1, RPS14_pos2 63 TVM+F+G PETL_pos2, PSAJ_pos1, PSBK_pos2, PSBZ_pos1 64 TPM2uf+F+G PETL_pos3, PSBE_pos3, PSBN_pos3 65 TPM3+F PETN_pos2, PSBI_pos2 66 GTR+F+I+G PETN_pos3, RPL22_pos2, RPS15_pos2, RPS3_pos2 67 K81uf+F+I+G PSAA_pos1 68 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 69 GTR+F+I+G PSAA_pos3, PSBC_pos3, PSBD_pos3 70 TVM+F+I+G PSAB_pos1 71 GTR+F+I+G PSAB_pos3 72 TVM+F+I+G PSAC_pos1, PSBB_pos1 73 SYM+I PSAC_pos2 245 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 74 GTR+F+I+G PSAC_pos3, RBCL_pos3 75 TN93ef+G PSAI_pos1 76 HKY+F+G PSAI_pos2 77 SYM+G PSAJ_pos2 78 K81uf+F+G PSAJ_pos3 79 TIM3+F+I+G PSBA_pos2, PSBF_pos2 80 GTR+F+I+G PSBA_pos3 81 TVM+F+I+G PSBB_pos3 82 TVM+F+I+G PSBC_pos2, PSBD_pos2 83 TIM3ef+I+G PSBH_pos1, PSBH_pos2 84 HKY+F+I+G PSBJ_pos2 85 TIM1ef+I+G PSBK_pos1 86 TN93+F+I+G PSBT_pos2 87 GTR+F+G PSBT_pos3, RPS16_pos3 88 TIM1+F+I PSBZ_pos3 89 GTR+F+I+G RBCL_pos1 90 GTR+F+I+G RBCL_pos2 91 GTR+F+I+G RPL14_pos1 92 GTR+F+I+G RPL14_pos2, RPL2_pos2, RPS12_pos2, RPS7_pos2 93 TVM+F+I+G RPL14_pos3 94 GTR+F+G RPL16_pos1 95 SYM+I+G RPL16_pos2 96 TVM+F+I+G RPL16_pos3, RPL22_pos3, RPS3_pos3, RPS8_pos3 97 GTR+F+G RPL2_pos1 98 K81uf+F+G RPL2_pos3 99 K81uf+F+G RPL20_pos1 100 GTR+F+I+G RPL20_pos3, RPOC2_pos3 101 TVM+F+G RPL22_pos1, RPL32_pos1, RPS15_pos1 102 TPM3+F+G RPL23_pos1 103 GTR+F+G RPL32_pos2 104 GTR+F+I+G RPL32_pos3 105 TVM+F+I+G RPL33_pos2, RPOA_pos2, RPOC2_pos2 106 TPM2uf+F+G RPL36_pos1 107 TIM3+F+G RPL36_pos3 108 GTR+F+I+G RPOC2_pos1 109 TVM+F+G RPS11_pos1 110 TIM3ef+G RPS11_pos2 111 TVM+F+G RPS11_pos3 112 TVM+F+G RPS12_pos1 113 K81uf+F+G RPS12_pos3 114 TVM+F+G RPS15_pos3 115 TVM+F+G RPS18_pos1 116 TVM+F+G RPS18_pos2, RPS4_pos1, RPS4_pos2 117 GTR+F+I+G RPS18_pos3 118 K81uf+F+G RPS19_pos1, RPS7_pos1 246 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 119 TPM3uf+F+G RPS19_pos2, RPS2_pos2 120 GTR+F+G RPS2_pos1 121 GTR+F+G RPS2_pos3 122 GTR+F+G RPS3_pos1, RPS8_pos1 123 GTR+F+G RPS4_pos3 124 GTR+F+G RPS7_pos3 125 GTR+F+G RPS8_pos2 126 TVM+F+G YCF2_pos1 127 TVM+F+G YCF2_pos2 128 TVM+F+G YCF2_pos3 129 SYM+I+G RRN16, RRN4 130 SYM+I+G RRN23 131 K80+G RRN5 G) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+I+G ACCD_pos2 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1, PSBJ_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, NDHH_pos3, PETD_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPOB_pos1 10 TVM+F+I+G ATPE_pos2, NDHK_pos2, RPOC1_pos2 11 GTR+F+I+G ATPE_pos3, ATPF_pos3, RPOB_pos3, RPOC1_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 15 F81+F ATPH_pos2 16 TVM+F+I+G ATPH_pos3 17 TVM+F+I+G ATPI_pos1, PSBF_pos1, YCF3_pos1 18 GTR+F+I+G ATPI_pos2, NDHE_pos2, PETB_pos2, PSBI_pos2, PSBL_pos1 19 TVM+F+I+G ATPI_pos3, NDHK_pos3, PSAI_pos3, PSBH_pos3 20 TVM+F+I+G CCSA_pos1, PSBF_pos3 21 GTR+F+I+G CCSA_pos2, NDHF_pos2 22 GTR+F+I+G CCSA_pos3, NDHF_pos3 23 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 24 TVM+F+G CEMA_pos2, PSBL_pos3, RPL23_pos3 25 TVM+F+G CEMA_pos3 26 TVMef+G CLPP_pos1 27 TIM3+F+I+G CLPP_pos2 28 GTR+F+I+G CLPP_pos3, RPS14_pos3 247 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 29 TVM+F+G INFA_pos1, INFA_pos2, RPS14_pos1 30 TVM+F+I+G INFA_pos3, RPL22_pos3, RPL32_pos3 31 GTR+F+I+G MATK_pos1 32 TVM+F+I+G MATK_pos2 33 TVM+F+I+G MATK_pos3 34 GTR+F+I+G NDHA_pos1, NDHD_pos1 35 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 36 TVM+F+I+G NDHA_pos3, NDHE_pos3 37 TIM3+F+I+G NDHB_pos1, PSBT_pos1, RPL23_pos2 38 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 39 TVM+F+G NDHB_pos3 40 TVM+F+I+G NDHC_pos1, NDHJ_pos1, PSBN_pos1, YCF4_pos2 41 GTR+F+I+G NDHC_pos3, PSBK_pos3 42 GTR+F+I+G NDHD_pos3 43 TVM+F+G NDHE_pos1, NDHI_pos1, RPL33_pos1, RPL36_pos2 44 TVM+F+I+G NDHF_pos1 45 GTR+F+G NDHG_pos1, PSBJ_pos3 46 GTR+F+I+G NDHG_pos3, RPL33_pos3, RPOA_pos3 47 GTR+F+I+G NDHH_pos1, RPOC1_pos1 48 TVM+F+I+G NDHH_pos2 49 K81uf+F+I+G NDHI_pos2, NDHJ_pos2, PSBM_pos1 50 GTR+F+I+G NDHI_pos3 51 TVM+F+I+G NDHJ_pos3, PETB_pos3, PSBM_pos3 52 K81uf+F+I+G PETA_pos1, RPS16_pos1 53 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 54 TIM1+F+I+G PETB_pos1, PSBA_pos1 55 TVMef+I+G PETD_pos1 56 K81uf+F+I+G PETD_pos2 57 TVMef+I PETG_pos1, PSBE_pos1, PSBI_pos1 58 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 59 TVM+F+I+G PETG_pos3, RPL20_pos2, YCF3_pos3 60 TN93+F+I+G PETL_pos1, PSAI_pos1, RPS14_pos2 61 GTR+F+G PETL_pos2, PSAJ_pos1, PSBZ_pos1 62 TPM2uf+F+G PETL_pos3, PSBE_pos3, PSBN_pos3 63 K81+G PETN_pos1, RRN4, RRN5 64 TIM3+F+G PETN_pos2, PSBZ_pos2 65 TIM3+F+I+G PETN_pos3, RPL22_pos2, RPS15_pos2 66 K81uf+F+I+G PSAA_pos1 67 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 68 GTR+F+I+G PSAA_pos3, PSBC_pos3, PSBD_pos3 69 TVM+F+I+G PSAB_pos1 70 GTR+F+I+G PSAB_pos3 71 TVM+F+I+G PSAC_pos1, PSBB_pos1 72 SYM+I PSAC_pos2 73 GTR+F+I+G PSAC_pos3, PSBB_pos3, RBCL_pos3 248 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 74 HKY+F+G PSAI_pos2 75 GTR+F+I+G PSAJ_pos2, PSBK_pos2 76 K81uf+F+G PSAJ_pos3 77 TIM3+F+I+G PSBA_pos2, PSBF_pos2 78 GTR+F+I+G PSBA_pos3 79 TVM+F+I+G PSBC_pos2, PSBD_pos2 80 TIM3ef+I+G PSBH_pos1, PSBH_pos2 81 HKY+F+I+G PSBJ_pos2 82 TIM1ef+I+G PSBK_pos1 83 TN93+F+I+G PSBT_pos2 84 GTR+F+G PSBT_pos3, RPS16_pos3 85 TIM1+F+I PSBZ_pos3 86 GTR+F+I+G RBCL_pos1 87 GTR+F+I+G RBCL_pos2 88 GTR+F+I+G RPL14_pos1 89 TIM1+F+G RPL14_pos2, RPS7_pos2 90 TVM+F+I+G RPL14_pos3, RPL36_pos3 91 GTR+F+G RPL16_pos1 92 SYM+G RPL16_pos2 93 GTR+F+I+G RPL16_pos3 94 GTR+F+G RPL2_pos1, RPL2_pos2 95 K81uf+F+I+G RPL2_pos3 96 TVM+F+G RPL20_pos1, RPL22_pos1, RPL32_pos1, RPS15_pos1 97 GTR+F+I+G RPL20_pos3, RPOC2_pos3 98 TPM3+F+G RPL23_pos1 99 GTR+F+G RPL32_pos2 100 TVM+F+I+G RPL33_pos2, RPOA_pos2, RPOC2_pos2 101 K81uf+F+G RPL36_pos1, RPS7_pos1 102 GTR+F+I+G RPOC2_pos1 103 TIM1+F+G RPS11_pos1, RPS8_pos1 104 TIM3ef+G RPS11_pos2 105 TVM+F+G RPS11_pos3 106 TVM+F+G RPS12_pos1 107 SYM+G RPS12_pos2, RRN16 108 K81uf+F+G RPS12_pos3 109 TVM+F+G RPS15_pos3 110 GTR+F+G RPS18_pos1 111 TIM1+F+G RPS18_pos2 112 GTR+F+I+G RPS18_pos3 113 K81uf+F+G RPS19_pos1 114 TIM3+F+G RPS19_pos2 115 GTR+F+G RPS19_pos3 116 GTR+F+G RPS2_pos1 117 TVM+F+G RPS2_pos2 118 GTR+F+G RPS2_pos3 249 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 119 GTR+F+G RPS3_pos1 120 GTR+F+I+G RPS3_pos2 121 TVM+F+I+G RPS3_pos3 122 TVM+F+G RPS4_pos1, RPS4_pos2 123 GTR+F+G RPS4_pos3 124 GTR+F+G RPS7_pos3 125 GTR+F+G RPS8_pos2 126 TVM+F+G RPS8_pos3 127 TVM+F+G YCF2_pos1 128 TVM+F+G YCF2_pos2 129 TVM+F+G YCF2_pos3 130 SYM+G RRN23 H) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+I+G ACCD_pos2 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, NDHH_pos3, PETD_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPL36_pos2, RPOB_pos1 10 GTR+F+I+G ATPE_pos2, NDHK_pos2 11 TVM+F+I+G ATPE_pos3, RPOC1_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1, PSBJ_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+I+G ATPF_pos3, CLPP_pos3, RPOB_pos3, RPS14_pos3 15 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 16 F81+F ATPH_pos2 17 TVM+F+I+G ATPH_pos3 18 TVM+F+I+G ATPI_pos1, PSBF_pos1, YCF3_pos1 19 GTR+F+I+G ATPI_pos2, NDHE_pos2, PETB_pos2, PSBI_pos2, PSBL_pos1 20 TVM+F+I+G ATPI_pos3, CEMA_pos3 21 TVM+F+I+G CCSA_pos1 22 GTR+F+I+G CCSA_pos2, NDHF_pos2 23 GTR+F+I+G CCSA_pos3, NDHF_pos3 24 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 25 TVM+F+G CEMA_pos2, PSBL_pos3 26 GTR+F+G CLPP_pos1 27 TVM+F+I+G CLPP_pos2, RPOC1_pos2, YCF4_pos2 28 TVM+F+G INFA_pos1, RPL33_pos1, RPS8_pos1 29 GTR+F+G INFA_pos2, NDHE_pos1, NDHI_pos1, RPS14_pos1, RPS2_pos1 250 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 30 TVM+F+I+G INFA_pos3, RPS15_pos3 31 GTR+F+I+G MATK_pos1 32 TVM+F+I+G MATK_pos2, PETL_pos3, PSAI_pos3 33 TVM+F+I+G MATK_pos3 34 GTR+F+I+G NDHA_pos1, NDHD_pos1 35 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 36 TVM+F+I+G NDHA_pos3, NDHE_pos3 37 TIM3+F+I+G NDHB_pos1, PSBT_pos1 38 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 39 TVM+F+G NDHB_pos3, RPL36_pos1 40 TVM+F+I+G NDHC_pos1, NDHJ_pos1, PSBN_pos1 41 GTR+F+I+G NDHC_pos3, PSBK_pos3 42 GTR+F+I+G NDHD_pos3 43 TVM+F+I+G NDHF_pos1 44 GTR+F+G NDHG_pos1, PSBJ_pos3 45 GTR+F+I+G NDHG_pos3, RPL33_pos3, RPOA_pos3 46 GTR+F+I+G NDHH_pos1, RPOC1_pos1 47 TVM+F+I+G NDHH_pos2 48 K81uf+F+I+G NDHI_pos2, NDHJ_pos2, PSBM_pos1 49 GTR+F+I+G NDHI_pos3 50 GTR+F+I+G NDHJ_pos3, PSAA_pos3, PSBM_pos3 51 TVM+F+I+G NDHK_pos3, PSBH_pos3 52 K81uf+F+I+G PETA_pos1, RPS16_pos1 53 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 54 TIM1+F+I+G PETB_pos1, PSBA_pos1 55 TVM+F+I+G PETB_pos3, PSBB_pos3 56 TVMef+I+G PETD_pos1 57 K81uf+F+I+G PETD_pos2 58 TVMef+G PETG_pos1, PETN_pos1, PSBE_pos1, PSBI_pos1, RRN5 59 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 60 TVM+F+I+G PETG_pos3, RPL20_pos2, YCF3_pos3 61 TIM2ef+G PETL_pos1 62 TVM+F+G PETL_pos2, PSAJ_pos1, PSBK_pos2, PSBZ_pos1 63 HKY+F PETN_pos2 64 TPM2uf+F+G PETN_pos3, PSBE_pos3, PSBN_pos3 65 K81uf+F+I+G PSAA_pos1 66 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 67 TVM+F+I+G PSAB_pos1 68 GTR+F+I+G PSAB_pos3 69 TVM+F+I+G PSAC_pos1, PSBB_pos1 70 SYM+I PSAC_pos2 71 GTR+F+I+G PSAC_pos3, RPL14_pos3, RPL36_pos3 72 TN93ef+G PSAI_pos1 73 HKY+F+G PSAI_pos2 251 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 74 SYM+G PSAJ_pos2 75 K81uf+F+G PSAJ_pos3 76 TIM3+F+I+G PSBA_pos2, PSBF_pos2 77 GTR+F+I+G PSBA_pos3 78 TVM+F+I+G PSBC_pos2, PSBD_pos2 79 GTR+F+I+G PSBC_pos3, PSBD_pos3 80 TIM3+F+G PSBF_pos3, PSBZ_pos3, RPL22_pos2 81 TIM3ef+I+G PSBH_pos1, PSBH_pos2 82 HKY+F+I+G PSBJ_pos2 83 TIM1ef+I+G PSBK_pos1 84 TN93+F+I+G PSBT_pos2 85 TVM+F+G PSBT_pos3, RPS8_pos3 86 TIM3+F+G PSBZ_pos2 87 GTR+F+I+G RBCL_pos1 88 GTR+F+I+G RBCL_pos2 89 GTR+F+I+G RBCL_pos3 90 GTR+F+G RPL14_pos1 91 TIM1+F+G RPL14_pos2, RPS7_pos2 92 GTR+F+I+G RPL16_pos1 93 SYM+I+G RPL16_pos2 94 GTR+F+I+G RPL16_pos3, RPL32_pos3 95 TIM2+F+G RPL2_pos1, RPL2_pos2 96 K81uf+F+G RPL2_pos3 97 GTR+F+G RPL20_pos1, RPL32_pos2 98 GTR+F+I+G RPL20_pos3, RPOC2_pos3 99 TVM+F+G RPL22_pos1, RPL32_pos1, RPS15_pos1 100 TVM+F+I+G RPL22_pos3 101 TPM3+F+G RPL23_pos1 102 TPM2uf+F+G RPL23_pos2, RRN4.5 103 GTR+F+G RPL23_pos3, RPS2_pos2, RPS7_pos3 104 TVM+F+I+G RPL33_pos2, RPOA_pos2, RPOC2_pos2 105 GTR+F+I+G RPOC2_pos1 106 SYM+G RPS11_pos1 107 TIM3ef+G RPS11_pos2 108 TVM+F+I+G RPS11_pos3, RPS3_pos3 109 TIM1+F+G RPS12_pos1 110 TIM1ef+G RPS12_pos2, RRN16 111 GTR+F+G RPS12_pos3 112 TN93+F+G RPS14_pos2 113 GTR+F+I+G RPS15_pos2, RPS8_pos2 114 TVM+F+G RPS16_pos3 115 GTR+F+G RPS18_pos1 116 TVM+F+G RPS18_pos2, RPS4_pos1, RPS4_pos2 117 GTR+F+G RPS18_pos3 118 K81uf+F+G RPS19_pos1 252 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 119 TPM3+F+G RPS19_pos2 120 GTR+F+G RPS19_pos3 121 GTR+F+I+G RPS2_pos3 122 GTR+F+G RPS3_pos1 123 GTR+F+I+G RPS3_pos2 124 GTR+F+G RPS4_pos3 125 K81uf+F+G RPS7_pos1 126 TVM+F+G YCF2_pos1 127 TVM+F+G YCF2_pos2 128 TVM+F+G YCF2_pos3 129 GTR+F+G RRN23 I) 1 TVM+F+I+G ACCD_pos1, RPL22_pos1 2 TVM+F+I+G ACCD_pos2, RPL20_pos1, RPL32_pos1, RPS15_pos1 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1, NDHH_pos1, PSBJ_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1, ATPI_pos1, PSBN_pos1 8 TVM+F+I+G ATPB_pos3, CLPP_pos3, PETA_pos3, RPS2_pos3 9 GTR+F+I+G ATPE_pos1, RPL36_pos2, RPOB_pos1 10 TVM+F+I+G ATPE_pos2, NDHK_pos2, RPOC1_pos2 11 GTR+F+I+G ATPE_pos3, RPL33_pos3, RPOB_pos3, RPOC1_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+G ATPF_pos3, RPS14_pos3, RPS18_pos3, RPS4_pos3 15 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 16 F81+F ATPH_pos2 17 TVM+F+I+G ATPH_pos3 18 TVM+F+I+G ATPI_pos2, PETB_pos2, PSBL_pos1 19 TVM+F+I+G ATPI_pos3 20 TVM+F+I+G CCSA_pos1 21 GTR+F+I+G CCSA_pos2, NDHF_pos2 22 GTR+F+I+G CCSA_pos3, NDHF_pos3 23 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 24 TVM+F+G CEMA_pos2 25 TVM+F+G CEMA_pos3 26 GTR+F+G CLPP_pos1 27 TPM3uf+F+I+G CLPP_pos2 28 TVM+F+G INFA_pos1, INFA_pos2, RPL2_pos3, RPS14_pos1 29 TVM+F+I+G INFA_pos3, RPS15_pos3 30 GTR+F+I+G MATK_pos1 31 TVM+F+I+G MATK_pos2 253 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 32 GTR+F+I+G MATK_pos3 33 GTR+F+I+G NDHA_pos1, NDHD_pos1 34 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 35 TVM+F+I+G NDHA_pos3, NDHE_pos3 36 TIM3+F+I+G NDHB_pos1, PSBT_pos1 37 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 38 TVM+F+G NDHB_pos3 39 TVM+F+I+G NDHC_pos1, NDHJ_pos1, YCF4_pos2 40 GTR+F+I+G NDHC_pos3, PSBK_pos3 41 GTR+F+I+G NDHD_pos3 42 GTR+F+G NDHE_pos1, RPS2_pos1 43 GTR+F+G NDHE_pos2, PSBZ_pos2 44 TVM+F+I+G NDHF_pos1 45 GTR+F+I+G NDHG_pos1, RPS12_pos3 46 GTR+F+I+G NDHG_pos3 47 TVM+F+I+G NDHH_pos2 48 GTR+F+I+G NDHH_pos3, PETD_pos3 49 TVM+F+G NDHI_pos1, RPS18_pos2 50 TIM1+F+I+G NDHI_pos2, NDHJ_pos2 51 TVM+F+G NDHI_pos3, RPS8_pos3 52 TVM+F+I+G NDHJ_pos3, NDHK_pos3, PSBH_pos3, PSBM_pos3 53 K81uf+F+I+G PETA_pos1, RPS16_pos1 54 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 55 TIM1+F+I+G PETB_pos1, PSBA_pos1 56 TVM+F+I+G PETB_pos3, PSBB_pos3 57 TVMef+I+G PETD_pos1 58 K81uf+F+I+G PETD_pos2 59 TVMef+G PETG_pos1, PETN_pos1, PSBE_pos1, PSBI_pos1, RPS12_pos2, RRN5 60 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 61 TVM+F+I+G PETG_pos3, RPL20_pos2, YCF3_pos3 62 TVM+F+I+G PETL_pos1, PSBL_pos3, RPOA_pos2 63 TVM+F+G PETL_pos2, PSAJ_pos1, PSBK_pos2, PSBZ_pos1 64 TPM3uf+F+G PETL_pos3, PSAI_pos3 65 TPM3+F PETN_pos2, PSBI_pos2 66 TPM3uf+F+G PETN_pos3, PSBF_pos3, RPL22_pos2 67 K81uf+F+I+G PSAA_pos1 68 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 69 GTR+F+I+G PSAA_pos3, PSBC_pos3, PSBD_pos3 70 TVM+F+I+G PSAB_pos1 71 GTR+F+I+G PSAB_pos3 72 TVM+F+I+G PSAC_pos1, PSBB_pos1 73 SYM+I PSAC_pos2 74 GTR+F+I+G PSAC_pos3, RPL14_pos3, RPL36_pos3 75 TN93ef+G PSAI_pos1 254 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 76 HKY+F+G PSAI_pos2 77 SYM+G PSAJ_pos2 78 K81uf+F+G PSAJ_pos3 79 TIM3+F+I+G PSBA_pos2, PSBF_pos2 80 GTR+F+I+G PSBA_pos3 81 TVM+F+I+G PSBC_pos2, PSBD_pos2 82 TPM2uf+F+G PSBE_pos3, PSBN_pos3 83 GTR+F+I+G PSBF_pos1, RPL2_pos2, YCF3_pos1, RRN23, RRN4 84 TIM3ef+I+G PSBH_pos1, PSBH_pos2 85 HKY+F+I+G PSBJ_pos2 86 TPM3uf+F+G PSBJ_pos3, PSBZ_pos3 87 TIM1ef+I+G PSBK_pos1 88 TIM1+F+G PSBM_pos1, RPL36_pos1 89 TN93+F+I+G PSBT_pos2 90 GTR+F+G PSBT_pos3, RPS19_pos3 91 GTR+F+I+G RBCL_pos1 92 GTR+F+I+G RBCL_pos2 93 GTR+F+I+G RBCL_pos3 94 GTR+F+G RPL14_pos1 95 TIM1+F+G RPL14_pos2, RPL23_pos2, RPS7_pos2 96 GTR+F+I+G RPL16_pos1 97 SYM+I+G RPL16_pos2 98 TVM+F+I+G RPL16_pos3 99 TVM+F+I+G RPL2_pos1, RPS12_pos1, RPS7_pos1 100 GTR+F+I+G RPL20_pos3, RPOC2_pos3 101 TVM+F+I+G RPL22_pos3 102 TPM3+F+G RPL23_pos1 103 TVM+F+G RPL23_pos3, RPS19_pos2, RPS2_pos2, RPS7_pos3 104 TIM3+F+G RPL32_pos2 105 TVM+F+I+G RPL32_pos3, RPS3_pos3 106 TIM1+F+G RPL33_pos1, RPS11_pos1, RPS8_pos1 107 TVM+F+I+G RPL33_pos2, RPOC2_pos2 108 TVM+F+G RPOA_pos3 109 TVM+F+I+G RPOC1_pos1, RPS4_pos1, RPS4_pos2 110 GTR+F+I+G RPOC2_pos1 111 TIM3ef+G RPS11_pos2 112 TVM+F+G RPS11_pos3 113 TN93+F+G RPS14_pos2 114 GTR+F+I+G RPS15_pos2, RPS3_pos2 115 TVM+F+G RPS16_pos3 116 GTR+F+G RPS18_pos1 117 TIM1+F+G RPS19_pos1 118 GTR+F+G RPS3_pos1 119 GTR+F+G RPS8_pos2 120 TVM+F+G YCF2_pos1 255 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 121 TVM+F+G YCF2_pos2 122 TVM+F+G YCF2_pos3 123 TIM1+F+I+G RRN16 J) 1 GTR+F+I+G ACCD_pos1 2 TVM+F+I+G ACCD_pos2 3 GTR+F+I+G ACCD_pos3 4 GTR+F+I+G ATPA_pos1, PSBJ_pos1 5 TVM+F+I+G ATPA_pos2, ATPB_pos2 6 TVM+F+I+G ATPA_pos3, NDHH_pos3, PETD_pos3, PSBI_pos3 7 GTR+F+I+G ATPB_pos1 8 TVM+F+G ATPB_pos3, PETA_pos3 9 GTR+F+I+G ATPE_pos1, RPOB_pos1 10 TVM+F+I+G ATPE_pos2, NDHK_pos2, RPOC1_pos2 11 GTR+F+I+G ATPE_pos3, ATPF_pos3, RPOB_pos3, RPOC1_pos3, YCF4_pos3 12 TVM+F+I+G ATPF_pos1, NDHK_pos1 13 HKY+F+I+G ATPF_pos2, RPS16_pos2 14 GTR+F+I+G ATPH_pos1, PSBC_pos1, PSBD_pos1 15 F81+F ATPH_pos2 16 TVM+F+I+G ATPH_pos3 17 TVM+F+I+G ATPI_pos1, PSBF_pos1, YCF3_pos1 18 GTR+F+I+G ATPI_pos2, NDHE_pos2, PETB_pos2, PSBI_pos2, PSBL_pos1 19 TVM+F+I+G ATPI_pos3, NDHK_pos3, PSAI_pos3, PSBH_pos3 20 TVM+F+I+G CCSA_pos1, PSBF_pos3 21 GTR+F+I+G CCSA_pos2, NDHF_pos2 22 GTR+F+I+G CCSA_pos3, NDHF_pos3 23 GTR+F+I+G CEMA_pos1, RPOA_pos1, YCF4_pos1 24 TVM+F+G CEMA_pos2, PSBL_pos3, RPL23_pos3 25 TVM+F+G CEMA_pos3 26 TVMef+G CLPP_pos1 27 GTR+F+I+G CLPP_pos2 28 GTR+F+I+G CLPP_pos3, RPS14_pos3 29 TVM+F+G INFA_pos1, INFA_pos2, RPS14_pos1 30 TVM+F+I+G INFA_pos3, RPL22_pos3, RPL32_pos3 31 GTR+F+G MATK_pos1 32 TVM+F+I+G MATK_pos2 33 TVM+F+I+G MATK_pos3 34 GTR+F+I+G NDHA_pos1, NDHD_pos1 35 TVM+F+I+G NDHA_pos2, NDHD_pos2, NDHG_pos2 36 TVM+F+I+G NDHA_pos3, NDHE_pos3 37 TIM3+F+I+G NDHB_pos1, PSBT_pos1, RPL23_pos2 38 TIM2+F+I+G NDHB_pos2, NDHC_pos2, PSBM_pos2, PSBN_pos2 256 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 39 TVM+F+G NDHB_pos3 40 TVM+F+I+G NDHC_pos1, NDHJ_pos1, PSBN_pos1, YCF4_pos2 41 GTR+F+I+G NDHC_pos3, PSBK_pos3 42 GTR+F+I+G NDHD_pos3 43 TVM+F+G NDHE_pos1, NDHI_pos1, RPL33_pos1, RPL36_pos2 44 TVM+F+I+G NDHF_pos1 45 GTR+F+G NDHG_pos1, PSBJ_pos3 46 GTR+F+I+G NDHG_pos3, RPL33_pos3, RPOA_pos3 47 GTR+F+I+G NDHH_pos1, RPOC1_pos1 48 TVM+F+I+G NDHH_pos2 49 K81uf+F+I+G NDHI_pos2, NDHJ_pos2, PSBM_pos1 50 GTR+F+I+G NDHI_pos3 51 TVM+F+I+G NDHJ_pos3, PETB_pos3, PSBM_pos3 52 K81uf+F+I+G PETA_pos1, RPS16_pos1 53 TVM+F+I+G PETA_pos2, RPOB_pos2, YCF3_pos2 54 TIM1+F+I+G PETB_pos1, PSBA_pos1 55 SYM+I+G PETD_pos1 56 K81uf+F+I+G PETD_pos2 57 TVMef+G PETG_pos1, PETN_pos1, PSBE_pos1, PSBI_pos1 58 TN93+F+I+G PETG_pos2, PSBE_pos2, PSBL_pos2 59 TVM+F+I+G PETG_pos3, PETN_pos3, RPL20_pos2, YCF3_pos3 60 TN93+F+I+G PETL_pos1, PSAI_pos1, RPS14_pos2 61 GTR+F+G PETL_pos2, PSAJ_pos1, PSBZ_pos1 62 TPM2uf+F+G PETL_pos3, PSBE_pos3, PSBN_pos3 63 TIM3+F+G PETN_pos2, PSBZ_pos2 64 K81uf+F+I+G PSAA_pos1 65 GTR+F+I+G PSAA_pos2, PSAB_pos2, PSBB_pos2 66 GTR+F+I+G PSAA_pos3, PSBC_pos3, PSBD_pos3 67 TVM+F+I+G PSAB_pos1 68 GTR+F+I+G PSAB_pos3 69 TVM+F+I+G PSAC_pos1, PSBB_pos1 70 SYM+I PSAC_pos2 71 GTR+F+I+G PSAC_pos3, RBCL_pos3 72 HKY+F+G PSAI_pos2 73 GTR+F+I+G PSAJ_pos2, PSBK_pos2 74 K81uf+F+G PSAJ_pos3 75 TIM3+F+I+G PSBA_pos2, PSBF_pos2 76 GTR+F+I+G PSBA_pos3 77 TVM+F+I+G PSBB_pos3 78 TVM+F+I+G PSBC_pos2, PSBD_pos2 79 TIM3ef+I+G PSBH_pos1, PSBH_pos2 80 HKY+F+I+G PSBJ_pos2 81 TIM1ef+I+G PSBK_pos1 82 TN93+F+I+G PSBT_pos2 83 GTR+F+G PSBT_pos3, RPS16_pos3 257 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 84 TIM1+F+I PSBZ_pos3 85 GTR+F+I+G RBCL_pos1 86 GTR+F+I+G RBCL_pos2 87 GTR+F+I+G RPL14_pos1 88 TN93+F+I+G RPL14_pos2 89 TVM+F+I+G RPL14_pos3 90 GTR+F+G RPL16_pos1 91 SYM+G RPL16_pos2 92 TVM+F+I+G RPL16_pos3, RPS11_pos3 93 GTR+F+G RPL2_pos1, RPL2_pos2 94 K81uf+F+I+G RPL2_pos3 95 TVM+F+G RPL20_pos1, RPL22_pos1, RPL32_pos1, RPS15_pos1 96 GTR+F+I+G RPL20_pos3, RPOC2_pos3 97 TN93+F+G RPL22_pos2 98 TPM3+F+G RPL23_pos1 99 GTR+F+G RPL32_pos2 100 TVM+F+I+G RPL33_pos2, RPOA_pos2, RPOC2_pos2 101 K81uf+F+G RPL36_pos1, RPS7_pos1 102 TIM3+F+G RPL36_pos3 103 GTR+F+I+G RPOC2_pos1 104 GTR+F+G RPS11_pos1, RPS18_pos2, RPS8_pos1 105 TIM3ef+G RPS11_pos2 106 TVM+F+G RPS12_pos1 107 K80+G RPS12_pos2 108 K81uf+F+G RPS12_pos3 109 TVM+F+I+G RPS15_pos2 110 TVM+F+G RPS15_pos3 111 K81uf+F+G RPS18_pos1 112 GTR+F+I+G RPS18_pos3 113 K81uf+F+G RPS19_pos1 114 GTR+F+G RPS19_pos2, RPS8_pos2 115 GTR+F+G RPS19_pos3 116 GTR+F+G RPS2_pos1 117 TPM3+F+G RPS2_pos2 118 GTR+F+G RPS2_pos3, RPS8_pos3 119 GTR+F+G RPS3_pos1 120 GTR+F+I+G RPS3_pos2 121 TVM+F+I+G RPS3_pos3 122 K81uf+F+G RPS4_pos1 123 TVM+F+G RPS4_pos2 124 GTR+F+G RPS4_pos3 125 K81uf+F+I RPS7_pos2, RRN4 126 GTR+F+G RPS7_pos3 127 TVM+F+G YCF2_pos1 128 TVM+F+G YCF2_pos2 258 _____________________________________________________________________________ No. of partitions Best model Partition subsets _____________________________________________________________________________ 129 TVM+F+G YCF2_pos3 130 SYM+G RRN16 131 SYM+G RRN23 132 K80+G RRN5 259 Table B.4 Morphological matrix for Dioscoreales taken from Caddick et al. (2002a), with revisions and additions. Revised characters and states are indicated in bold font. Newly added taxa, characters and states are indicated with ‘*’. Characters that are variable within individual lineages are coded with two states and missing data with “?”. Japonolirion (Petrosaviales) is included as an outgroup. ___________________________________________________________________________________________________ Characters ___________________________________________________________________________________________________ Taxon 1 2 3 4 5 6* 7 8* 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25* 26 27 28 29 30 31 32 33 34 35 _________________________________________________________________________________________________________________________ Aletris 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0* 0 0 0 0 0 0 0 1 0 0* 0 0 0 0 0 0 0 0 0 Lophiola 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 ? 0 0 0 0 0* 0* 0 0 0 0* ? 0 ? 0 ? 0 0 0 0 Metanarthecium 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 ? 0 0 0 0 0 0* 0 1 0 0 ? 0 ? 0 ? 0 0 0 0 Narthecium 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ? 0 0 0 0 0 0 Nietneria 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 ? 0 0 0* 1 0 0 0 0 0 0* ? 0 ? 0 ? 0 0 0 0 Apteria 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 0 ? ? 0 1 1 1 0 1 0 1 ? 1 0 0 1 0 ? ? 1 Burmannia 01 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 0 01 1 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 ? 1 Campylosiphon 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 1 ? 1 0 0 1 0 ? ? 1 Dictyostega 0 ? 1 0 0 0 1 0 1 0 0 0 1 ? 1 0 1 1 0 1 0 1 0 1 0 1 ? 1 0 ? ? 0 0 ? 1 Gymnosiphon 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 0 ? ? 0 1 1 1 0 1 0 1 1 1 0 0 1 0 ? ? 01 Hexapterella 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 ? 0 ? 0 1 0 0 0 1 0 1 ? 1 0 0 ? 0 ? ? 1 Marthella 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 ? ? ? ? 1 1 1 0 1 0 1 ? 1 0 0 1 0 ? ? 1 Miersiella 0 ? 1 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1 0 1 ? 1 0 0 ? 0 ? ? 0 Dioscorea 0 2 0 01 1 0 0 0 0 0 0 0 0 0 0 1 01 0 0 1 0 0 1 01 0 0 0 1 1 01 1 0 1 1 01 Stenomeris ? 2 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 0 1 0 1 Trichopus sempervirens ? 2 0 0 0 0 0 0 0 0 0 ? 0 1 0 ? 1 1 1 1 0 0 1 0 1 2 0 1 0 0 1 1 0 1 1 Trichopus zeylanicus ? 2 0 0 0 0 0 0 0 0 0 ? ? 1 0 ? 0 0 1 1 0 0 1 0 1 2 0 1 0 0 ? 1 0 1 1 Afrothismia 1 ? 0 0 0 1 1 1 0 1 1 0 0 1 0 ? 0 1 0 1 1 1 0 0* 1 0* ? ? ? ? ? 0 ? ? ? Tacca ? 2 0* ? 0 0 0 0 0 0 1 1 0 1 0 1 0 0 0 1 1 1 0 0 1 2 0 0 0 0 1 0 1 1 1 Haplothismia* 1 ? ? ? 0 0 ? 0 0 ? 1 ? ? ? 0 ? ? ? ? 1 1 1 0 0 1 ? ? ? ? ? ? ? ? ? ? Oxygyne 1 ? 0 0 0 0 1 1 1 1 1 0 0* ? 0 ? ? ? ? 1 1 1* 0 ? 1 0 ? ? ? ? ? 0 ? ? ? Tiputinia* 1 ? ? ? 0 0 ? 0 0 ? 1 ? ? ? 0 ? ? ? ? 1 1 1 0 0 1 ? ? ? ? ? ? ? ? ? ? Thismia 1 ? 0 0 0 01 1 1 0 1 1 ? 0 1 0 0 01 1 0 1 1 1 0 0 1 0 1 1 0 0 ? 0 0 ? 01 Japonolirion 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 ? ? 0 0 ? 0 0 0 0 260 Table B.5 Summary of bootstrap support for Dioscoreales relationships across 12 phylogenetic analyses of molecular data conducted here (Figs. 3.1, B.1–B.12). Labeled branches ‘a-z’ correspond to those from DNA-based partitioned likelihood analysis of the core 89-taxon matrix (Fig. 3.1, left-hand tree) as a general reference point for comparison across the remaining molecular analyses. Abbreviations: ‘AA’ = amino-acid data, ‘P’ = parsimony, ‘ML-u’ = unpartitioned likelihood analysis, ‘ML-p’ = partitioned likelihood analysis (using a gene-by-codon scheme for DNA and gene-based partitioning for amino-acid data). Bootstrap support values <5% are shown as ‘-’; ‘n/a’ indicates branches that are not available due to taxon sampling differences relative to the core 89-taxon matrix. _________________________________________________________________________________________________________________________ DNA DNA DNA DNA AA AA DNA DNA DNA DNA DNA DNA P P ML-u ML-p ML-u ML-p ML-p ML-p ML-p ML-p ML-p ML-p branch 89 taxa 98 taxa 89 taxa 89 taxa 89 taxa 89 taxa 90 taxa1 91 taxa2 91 taxa3 90taxa4 93taxa5 98taxa6 _________________________________________________________________________________________________________________________ a 73 73 65 66 60 59 65 65 63 63 62 65 b 100 100 100 100 96 99 100 100 100 100 100 100 c 100 100 100 100 100 100 100 100 100 100 100 100 d 100 100 100 100 100 100 100 100 100 96 97 100 e 100 100 100 100 100 100 100 100 100 n/a 91 99 f = Dioscoreaceae 100 100 100 100 100 100 100 100 100 n/a 90 99 g 100 100 100 100 100 100 100 100 100 100 100 100 h = Taccaceae 100 100 100 100 99 100 100 100 100 100 92 100 i 92 65 100 100 59 71 100 100 n/a 98 80 61 j = Trichopodaceae 100 100 100 100 100 100 100 100 100 95 95 100 k 71 43 99 96 43 63 79 97 n/a 87 69 n/a l 92 44 100 100 98 97 n/a 100 n/a n/a 62 n/a m 88 18 84 87 47 52 88 46 84 81 67 33 n 99 89 100 100 97 96 100 100 100 100 97 100 o 100 - 100 100 100 100 100 53 100 94 76 38 p 100 20 100 100 100 100 100 53 100 94 76 38 q = Burmanniaceae 100 n/a 100 100 100 100 100 n/a 100 94 75 n/a r 98 n/a 100 100 96 98 n/a n/a n/a n/a 37 n/a s 100 100 100 100 100 100 100 100 100 98 98 100 t 100 100 100 100 100 100 100 100 100 96 98 100 u 100 100 100 100 100 100 100 100 100 100 99 100 v 38 35 98 96 88 75 95 97 97 94 93 96 w 100 100 100 100 100 100 100 100 100 100 100 100 261 _________________________________________________________________________________________________________________________ DNA DNA DNA DNA AA AA DNA DNA DNA DNA DNA DNA P P ML-u ML-p ML-u ML-p ML-p ML-p ML-p ML-p ML-p ML-p branch 89 taxa 98 taxa 89 taxa 89 taxa 89 taxa 89 taxa 90 taxa1 91 taxa2 91 taxa3 90taxa4 93taxa5 98taxa6 _________________________________________________________________________________________________________________________ x 100 100 100 100 100 100 100 100 100 100 100 100 y = Nartheciaceae 100 100 100 100 100 100 100 100 100 96 95 100 z = Dioscoreales 98 n/a 100 100 99 100 n/a n/a n/a n/a 32 n/a __________________________________________________________________________________________________________________________ 1Includes three genes from photosynthetic Stenomeris borneensis (Dioscoreaceae). 2Includes two long-branch fully mycoheterotrophic Burmanniaceae taxa (Apteria aphylla, Gymnosiphon longistylus). 3Includes two long-branch fully mycoheterotrophic Thismiaceae taxa (Thismia rodwayi, T. tentaculata). 4Includes Afrothismia gesnerioides, one of four long-branch fully mycoheterotrophic species of Afrothismia sampled here. 5Includes all four long-branch fully mycoheterotrophic species of Afrothismia sampled here. 6Includes all Dioscoreales taxa with available molecular data here. 262 Figure B.1 Monocot phylogeny inferred from DNA-based parsimony analysis of 82 plastid genes (20–28 genes in fully mycoheterotrophic taxa; highlighted in grey), showing the shortest tree recovered for the core 89-taxon matrix that includes the shortest-branch fully mycoheterotrophic representatives of Burmanniaceae (Burmannia itoana, Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). Bootstrap support values are indicated beside branches if <100%. A circled support value indicates an internal branch that conflicts with the partitioned DNA-based likelihood analysis of the same core matrix (Fig. 3.1, left-hand tree). The scale bar indicates estimated number of changes. The right-hand tree in Fig. 3.1 depicts a subset of this taxon sampling. 263 264 Figure B.2 Monocot phylogeny inferred from DNA-based parsimony analysis of 82 plastid genes (9–28 genes in fully mycoheterotrophic taxa; highlighted in grey), showing the shortest tree recovered for an all-inclusive 98-taxon matrix that includes all Dioscoreales taxa with available plastid data. Taxa not included in the core 89-taxon matrix are shown in blue font. Bootstrap support values are indicated beside branches if <100%. Circled support values indicate internal branches that conflict with the partitioned DNA-based likelihood analysis of the core 89-taxon matrix (Fig. 3.1, left-hand tree). The scale bar indicates estimated number of changes. 265 266 Figure B.3 Monocot phylogeny inferred from unpartitioned DNA-based likelihood analysis of 82 plastid genes (20–28 genes in fully mycoheterotrophic taxa; highlighted in grey) for the core 89-taxon matrix that includes the shortest-branch fully mycoheterotrophic representatives of Burmanniaceae (Burmannia itoana, Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. 267 268 Figure B.4 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (20–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for the core 89-taxon matrix that includes the shortest-branch fully mycoheterotrophic representatives of Burmanniaceae (Burmannia itoana, Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. The left-hand tree in Fig. 3.1 depicts a subset of this taxon sampling. 269 270 Figure B.5 Monocot phylogeny inferred from unpartitioned amino-acid-based likelihood analysis of 78 plastid genes (16–24 in fully mycoheterotrophic taxa; highlighted in grey) for the core 89-taxon matrix that includes the shortest-branch fully mycoheterotrophic representatives of Burmanniaceae (Burmannia itoana, Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. 271 272 Figure B.6 Monocot phylogeny inferred from partitioned amino-acid-based likelihood analysis of 78 plastid genes (16–24 in fully mycoheterotrophic taxa; highlighted in grey) using a gene-based partitioning scheme for the core 89-taxon matrix that includes the shortest-branch fully mycoheterotrophic representatives of Burmanniaceae (Burmannia itoana, Campylosiphon congestus) and Thismiaceae (Haplothismia exannulata). Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. 273 274 Figure B.7 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (20–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for an expanded 90-taxon matrix after adding three genes of photosynthetic Stenomeris borneensis (Dioscoreaceae; shown in blue font) to the core 89-taxon matrix. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. 275 276 Figure B.8 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (18–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for an expanded 91-taxon matrix after adding two long-branch fully mycoheterotrophic Burmanniaceae (Apteria aphylla, Gymnosiphon longistylus; shown in blue font) to the core 89-taxon matrix. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. The bottom phylogram in Fig. 3.2a depicts a subset of this taxon sampling. 277 278 Figure B.9 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (9–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for a second expanded 91-taxon matrix after adding two long-branch fully mycoheterotrophic Thismiaceae (Thismia rodwayi, T. tentaculata; shown in blue font) to the core 89-taxon matrix. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. The bottom phylogram in Fig. 3.2b depicts a subset of this taxon sampling. 279 280 Figure B.10 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (10–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for an expanded 93-taxon matrix after adding four long-branch species of fully mycoheterotrophic Afrothismia (shown in blue font) to the core 89-taxon matrix, including sequences with internal stop codons for three of the added taxa. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. The top phylogram in Fig. 3.2c depicts a subset of this taxon sampling. 281 282 Figure B.11 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (10–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for a second expanded 90-taxon matrix after adding the sole sampled species of fully mycoheterotrophic Afrothismia with genes uninterrupted by internal stop codons (A. gesnerioides; shown in blue font) to the core 89-taxon matrix. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. 283 284 Figure B.12 Monocot phylogeny inferred from partitioned DNA-based likelihood analysis of 82 plastid genes (9–28 genes in fully mycoheterotrophic taxa; highlighted in grey) using a gene-by-codon partitioning scheme for an all-inclusive 98-taxon matrix that includes all Dioscoreales taxa with available plastid data. Taxa not included in the core 89-taxon molecular matrix are shown in blue font. Bootstrap support values are indicated beside branches if <100%. The scale bar indicates estimated substitutions per site. The bottom phylogram in Fig. 3.2c depicts a subset of this taxon sampling. 285 286 Figure B.13 Dioscoreales phylogeny inferred from unconstrained parsimony analysis of a 35-character morphological matrix, showing one of 216 shortest trees (the closest to the unconstrained likelihood tree for the same data set based on the Robinson-Foulds metric; see Fig. B.14); arrows indicate branches that collapse in the strict consensus tree. Fully mycoheterotrophic taxa are highlighted in grey; families recovered as monophyletic based on more than one sampled genus are noted. Bootstrap support values are indicated above branches; ‘-’ denotes values <5%. 287 288 Figure B.14 Dioscoreales phylogeny inferred from unconstrained likelihood analysis of a 35-character morphological matrix. Fully mycoheterotrophic taxa are highlighted in grey; two families are noted as reference points, one was recovered as monophyletic and the other as non-monophyletic based on more than one sampled genus. Bootstrap support values are indicated above branches. 289 290 Figure B.15 Morphology-based analyses to simultaneously place seven fully mycoheterotrophic Dioscoreales taxa on a molecular constraint tree using parsimony. Added taxa, noted with ‘*’, are lacking or have highly rate-elevated molecular data; all full mycoheterotrophs are highlighted in grey. This is one of 360 shortest trees (the closest to the likelihood tree for the same data set based on the Robinson-Foulds metric; see Fig. B.16); arrows indicate branches that collapse in the strict consensus tree. Four families that were recovered as monophyletic based on more than one sampled genus are indicated. Bootstrap values (given constrained trees) are shown above branches. The right-hand tree in Fig. 3.3 depicts a cladogram version of this tree. 291 292 Figure B.16 Morphology-based analyses to simultaneously place seven fully mycoheterotrophic Dioscoreales taxa on a molecular constraint tree using likelihood. Added taxa, noted with ‘*’, are lacking or have highly rate-elevated molecular data; all full mycoheterotrophs are highlighted in grey. Four families that were recovered as monophyletic based on more than one sampled genus are indicated. Bootstrap values (given constrained trees) are shown above branches. The left-hand tree in Fig. 3.3 depicts a cladogram version of this tree. 293 294 Figure B.17 Dioscoreales phylogenies showing optimal placements of seven full mycoheterotrophs, individually added to a molecular tree constraint using only morphological data in likelihood (left-hand trees in each panel) and parsimony (right-hand trees in each panel) analyses. Arrows in parsimony trees indicate branches that collapse in the strict consensus tree in cases where more than one shortest tree was found. Full mycoheterotrophs are highlighted in grey and blue font designates taxa added in each panel: (a) Afrothismia, (b) Oxygyne, (c) Tiputinia (the parsimony tree is one of five shortest trees), (d) Marthella (the parsimony tree is one of two shortest trees), (e) Miersiella (the parsimony tree is one of three shortest trees), (f) Dictyostega, and (g) Hexapterella (the parsimony tree is one of five shortest trees). Bootstrap support values are shown only for branches in the vicinity of added taxa. 295 296 Figure B.18 Monocot phylogeny inferred from parsimony analysis of a combined molecular-morphological matrix consisting of 82 plastid genes (9–28 for fully mycoheterotrophic taxa; highlighted in grey) and 35 morphological characters for 104 taxa represented by both data types (‘**’), or solely morphological data (‘*’), or solely molecular data. The phylogram is one of 245 shortest trees (the closest to the likelihood tree for the same data set based on the Robinson-Foulds metric; see Fig. B.19); arrows indicate branches that collapse in the strict consensus tree. The scale bar indicates estimated number of changes. 297 298 Figure B.19 Monocot phylogeny inferred from partitioned likelihood analysis of a combined molecular-morphological matrix consisting of 82 plastid genes (9–28 for fully mycoheterotrophic taxa; highlighted in grey) and 35 morphological characters for 104 taxa represented by both data types (**), or solely morphological data (*), or solely molecular data. The scale bar indicates estimated substitutions per site. 299 300 Figure B.20 Morphological synapomorphies of Dioscoreales inferred from parsimony-based character optimization onto an arbitrarily selected tree (of 216 shortest trees) from unconstrained parsimony analysis of a 35-character morphological matrix; arrows indicate branches that collapse in the strict consensus tree. Numbers above vertical bars indicate characters (listed in Sub-Appendix B.1) that change along a branch and colours indicate states, showing only characters recovered as synapomorphies from both ACCTRAN and DELTRAN optimizations. Asterisks below vertical bars indicate characters that were also recovered as synapomorphies when mapped onto the shortest tree from constrained likelihood analysis of morphological data using a molecular topological constraint (Fig. 3.4). Fully mycoheterotrophic taxa are highlighted in grey. 301 302 Figure B.21 Longitudinal section through a pre-anthetic anther of a Tacca leontopetaloides (Taccaceae) flower, showing the apical extension of the anther connective. 303 Figure B.22 SEM micrograph of dissected pre-anthetic flower bud of Stenomeris borneensis (Dioscoreaceae), showing short style and apical stamen extensions reaching downwards onto the stigma. 304 Figure B.23 Longitudinal section through the gynoecium of Tacca leontopetaloides (Taccaceae), showing short style above unilocular ovary containing ovules. 305 Appendix C Supplementary methods, tables and figures for Chapter 4 Sub-Appendix C.1 Supplementary methods for detecting and filtering potential paralogs. HybPiper produced paralog warnings for 26 loci, indicating that for one or more taxa it detected multiple contigs that spanned over 85% of the reference gene sequence (potential paralogs are indicated in Table C.1). For each of these instances, I inferred additional gene trees that used all of the identified gene copies per taxon, with potential paralogs retrieved using the paralog_retriever.py script in HybPiper. In 22 of these cases, I detected what I believe are ancient paralogs, recent paralogs, or allelic variants (nucleotide identity 26–97%); alignment inspection clearly pointed to nine of these gene copies as representing “chimeras” of orthologous and paralogous sequences that may result from PCR amplification of pre- and post-enriched libraries, Illumina sequencing, or retrieval artifacts in HybPiper. I filtered out all putative paralogous, allelic, and chimeric sequences to leave the single most similar copy (65–99% nucleotide identity), which I interpreted as the sequence most likely to be orthologous to other included taxa, and retained it for analysis. The resulting topologies (after alignment trimming; data not shown) were consistent with our current understanding of Dioscoreales phylogeny (Wilkin et al., 2005; Viruel et al., 2016, 2018). However, the topology of five additional gene trees was in one or more ways substantially divergent from our understanding of relationships in Dioscoreales, indicative of additional possible paralogy issues. One of these cases, gene 168, was not identified by HybPiper as a possible paralogy instance, but was instead picked up by visual inspection of the other 233 gene trees. For all five instances, it appears that HybPiper preferentially pulled out one of two copies per taxon, and so presumably HybPiper was unable to recover both copies during the initial analysis because one may have been too short or lacked sufficient read depth (M. G. Johnson, Texas Tech University, personal communication). I interpreted these five cases as being the result of a single early gene duplication and separated the resulting two distinct clades (also visible in alignments) into two paralogous genes (each with only a partial taxon set), increasing the total number of recovered genes by five total. I used the resulting two paralogs of each of these five genes as alternate user-provided low-to-single-copy nuclear gene references in HybPiper to search for these sequences in additional taxa. I recovered each gene copy from 20–30 taxa and ran the HybPiper script paralog_retriever.py on these. Again, I used sequence 306 similarity and tree-based methods to retain the copy most likely to be orthologous to other sampled taxa. 307 Table C.1 Name and predicted function of the 260 low-to-single-copy nuclear (LSCN) genes targeted by the Dioscorea-specific baits designed here. Locus name, gene product name and Gene Ontology (GO) classification1 are based on Oryza sativa2 (name starts with LOC_Os) or Xerophyta viscosa3 (name starts with Xvis) depending on which of the two taxa were used as LSCN gene references in MarkerMiner analyses (see text for details); numbers in brackets represent the corresponding gene codes used in this study, referred to in other appendices. Locus length was calculated as the average of three to four sequences (D. alata, D. composita, Dioscorea sp., and when applicable, Tacca chantrieri) representing each gene target for bait design (see text for details). Genes with potential paralogs identified using HybPiper or by gene-tree visual inspection are indicated with an asterisk (*). One gene excluded from phylogenetic analyses due to its short length after quality trimming is indicated with a double asterisk (**). Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os01g01410.1 (001) 1873 TKL_IRAK_C-LEC.1 - TKL_IRAK_C-LEC has homology to C-type lectin receptors Metabolic, cellular, protein modification LOC_Os01g01620.1 (002) 1375 kinase, pfkB family Metabolic (carbohydrate), cellular, catabolic LOC_Os01g03144.1 (003) 2280 white-brown complex homolog protein 7 Transport, cellular, catabolic, metabolic (nucleic acid) LOC_Os01g04200.1 (004) 1785 metallo-beta-lactamase family protein Metabolic LOC_Os01g07100.1 (005) 1198 expressed protein Uncharacterized LOC_Os01g08850.1 (006) 1268 expressed protein Uncharacterized LOC_Os01g09300.1 (007) 1212 oxidoreductase Biosynthetic, cellular, metabolic LOC_Os01g13190.1 (008) 1702 histidinol dehydrogenase, chloroplast precursor Multicellular organismal development, response to abiotic stimulus, metabolic LOC_Os01g15490.1 (009) 1819 phosphoadenosine phosphosulfate Biosynthetic, metabolic (protein), cellular LOC_Os01g15850.1 (010) 1430 expressed protein Uncharacterized LOC_Os01g21940.1 (011) 1740 WD domain, G-beta repeat domain containing protein metabolic (nucleic acid) 308 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os01g31610.1 (012) 1539 tyrosyl-tRNA synthetase Metabolic, translation, metabolic (nucleic acid), reproduction, development (embryo & post-embryonic) LOC_Os01g32660.1 (013) 1268 STE_MEK_ste7_MAP2K.2 - STE kinases include homologs to sterile 7, sterile 11 and sterile 20 from yeast Metabolic, cellular, protein modification LOC_Os01g35040.1 (014) 2052 ZOS1-09 - C2H2 zinc finger protein Metabolic (protein), catabolic, cellular LOC_Os01g35170.1 (015) 1030 tRNA methyltransferase Metabolic (nucleic acid) LOC_Os01g40620.1 (016) 1523 Uncharacterized ACR, COG1565 containing protein Uncharacterized LOC_Os01g43250.1 (017) 1082 WD domain, G-beta repeat domain containing protein n/a LOC_Os01g43630.1 (018) 2852 adaptin Transport, cellular LOC_Os01g45370.1 (019) 1287 radical SAM enzyme Metabolic (nucleic acid) LOC_Os01g48270.1 (020) 1626 AAA-type ATPase family protein Metabolic (protein & nucleic acid), catabolic LOC_Os01g48680.1 (021) 2458 two pore calcium channel protein 1 Transport, cellular, post-embryonic development, signal transduction, biosynthetic, metabolic (lipid) LOC_Os01g54020.1 (022) 1313 tRNA synthetase Metabolic (nucleic acid), biosynthetic, protein modification, flower development, translation LOC_Os01g54860.1 (023) 1465 enoyl-CoA hydratase/isomerase family protein Catabolic, cellular, metabolic (lipid), response to abiotic stimulus LOC_Os01g54910.1 (024) 2191 GTP-binding protein typA/bipA Catabolic, metabolic (nucleic acid), translation LOC_Os01g55300.1 (025) 2333 DNA-directed polymerase Response to stress & abiotic stimulus, metabolic (DNA), biosynthetic LOC_Os01g56150.1 (026) 1713 OsProCP1 - Putative Lysosomal Pro-x Carboxypeptidase homologue Metabolic (protein) LOC_Os01g56350.3 (027) 1971 PTAC12 Cellular component organization, metabolic (nucleic acid), biosynthetic LOC_Os01g58080.1 (028) 1425 membrane-associated salt-inducible protein n/a 309 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os01g58380.1 (029)* 1043 3-hydroxybutyryl-CoA dehydrogenase Metabolic (lipid), cellular LOC_Os01g58790.2 (030) 1496 GHMP kinases ATP-binding protein Metabolic, cellular LOC_Os01g63950.1 (031) 1196 expressed protein Uncharacterized LOC_Os01g64720.1 (032) 1101 expressed protein Uncharacterized LOC_Os01g68750.1 (033) 3440 adaptin Transport, cellular LOC_Os01g73680.1 (034) 1728 aspartyl aminopeptidase Metabolic (protein) LOC_Os02g01070.1 (035) 2359 FG-GAP repeat-containing protein Cellular LOC_Os02g02000.1 (036) 1664 cytochrome P450 Response to stress, cellular, metabolic (lipid) LOC_Os02g02570.1 (037) 1236 autophagy protein 5 Response to stress & abiotic stimulus & extracellular stimulus, transport, multicellular organismal development, catabolic LOC_Os02g02620.1 (038) 1758 sialyltransferase family domain containing protein Biosynthetic, protein modification, metabolic (carbohydrate) LOC_Os02g02860.1 (039) 1715 glutamyl-tRNA synthetase Cellular component organization, flower development, translation, metabolic (nucleic acid) LOC_Os02g03100.1 (040)* 1114 oxidoreductase, aldo/keto reductase family protein Metabolic LOC_Os02g06360.1 (041) 1156 uncharacterized protein CPn_0526/CP_0226/CPj0526/CpB0547 Metabolic (carbohydrate) LOC_Os02g13140.1 (042)** 1686 xaa-Pro dipeptidase Metabolic, cellular LOC_Os02g13950.1 (043) 1787 NHL repeat-containing protein Uncharacterized LOC_Os02g18450.1 (044)* 2414 GTP-binding protein typA/bipA Catabolic, metabolic (nucleic acid), translation LOC_Os02g30180.1 (045) 1496 ZOS2-07 - C2H2 zinc finger protein Uncharacterized LOC_Os02g33080.1 (046)* 1195 N-carbamoylputrescine amidase Metabolic, biosynthetic, cellular LOC_Os02g33860.1 (047) 1055 WD domain, G-beta repeat domain containing protein n/a 310 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os02g35500.1 (048) 1081 NAD binding domain of 6-phosphogluconate dehydrogenase containing protein Response to stress, metabolic LOC_Os02g36390.1 (049) 1171 nucleotide-sugar transporter family protein Transport, cellular LOC_Os02g39350.1 (050) 1916 eukaryotic translation initiation factor 2A Uncharacterized LOC_Os02g42520.2 (051) 1263 dehydrogenase Metabolic LOC_Os02g42540.1 (052) 967 expressed protein Uncharacterized LOC_Os02g42660.1 (053)* 1140 sphingolipid delta desaturase Biosynthetic, cellular, metabolic (lipid) LOC_Os02g44680.1 (054)* 1679 allantoin permease Transport, cellular LOC_Os02g44780.1 (055) 1236 polyprenyl synthetase Biosynthetic, cellular, metabolic (lipid) LOC_Os02g48460.1 (056) 1736 NAD dependent epimerase/dehydratase family protein Biosynthetic, metabolic (lipid) LOC_Os02g50100.1 (057) 1541 SET domain-containing protein Uncharacterized LOC_Os02g50360.1 (058) 2274 nicalin Metabolic (protein) LOC_Os02g52370.1 (059) 1618 armadillo/beta-catenin repeat family protein Uncharacterized LOC_Os02g52420.1 (060) 1245 peptidase, M24 family protein Protein modification, metabolic (protein) LOC_Os02g53500.1 (061) 1308 RFC5 - Putative clamp loader of PCNA, replication factor C subunit 5 Metabolic (nucleic acid), catabolic, response to stress LOC_Os02g54010.1 (062) 2314 lipase class 3 family protein Catabolic, metabolic (lipid) LOC_Os02g54980.1 (063) 1913 pheophorbide a oxygenase, chloroplast precursor Transport, cellular, metabolic LOC_Os02g54990.2 (064) 1496 TMS membrane protein/tumour differentially expressed protein n/a LOC_Os02g55000.1 (065) 1609 zinc finger CCCH-type with G patch domain-containing protein Uncharacterized LOC_Os02g55420.1 (066)* 1580 aminotransferase, classes I and II, domain containing protein Metabolic, response to stress & abiotic stimulus LOC_Os02g56910.1 (067) 2082 lecithine cholesterol acyltransferase Multicellular organismal development, catabolic, metabolic (lipid) LOC_Os02g56970.1 (068) 1342 uncharacterized protein yqjG Metabolic 311 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os02g57500.1 (069) 1324 expressed protein Uncharacterized LOC_Os02g57760.1 (070)* 1352 O-methyltransferase Metabolic, cellular LOC_Os02g58020.1 (071) 2510 ABC transporter, ATP-binding protein Transport LOC_Os02g58030.1 (072) 2559 leucine-rich repeat-containing protein 40 Signal transduction LOC_Os02g58590.1 (073) 1811 N-acetylglucosaminyltransferase I Biosynthetic, protein modification, metabolic (carbohydrate), response to stress & abiotic stimulus LOC_Os03g01970.1 (074) 2444 THO complex subunit 1 Metabolic (nucleic acid), cellular, epigenetic regulation of gene expression LOC_Os03g02640.1 (075) 1094 methylthioribose kinase Metabolic, cellular LOC_Os03g03650.1 (076) 1648 POLD2 - Putative DNA polymerase delta complex subunit Biosynthetic, metabolic (DNA) LOC_Os03g03710.1 (077) 2799 ribonuclease protein metabolic LOC_Os03g04940.1 (078) 1610 methyltransferase Uncharacterized LOC_Os03g05720.1 (079) 2959 WD domain, G-beta repeat domain containing protein n/a LOC_Os03g06880.1 (080) 1556 kinase, pfkB family Metabolic (carbohydrate), cellular, catabolic LOC_Os03g07800.1 (081) 1647 RNA recognition motif containing protein n/a LOC_Os03g07910.1 (082) 1227 nitrilase Metabolic LOC_Os03g08570.1 (083) 2068 amine oxidase, flavin-containing, domain containing protein Biosynthetic, cellular, metabolic (lipid) LOC_Os03g11120.1 (084) 1934 methionyl-tRNA synthetase Flower development, metabolic (nucleic acid), translation LOC_Os03g11670.1 (085) 2091 pentatricopeptide repeat domain containing protein Cellular component organization LOC_Os03g12460.1 (086) 2206 conserved oligomeric complex COG6 family protein Uncharacterized 312 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os03g12660.1 (087)* 1598 cytochrome P450 Cellular component organization, anatomical structure morphogenesis, cell growth, biosynthetic, metabolic (lipid), response to abiotic stimulus, cell differentiation, flower & multicellular organismal development LOC_Os03g16570.2 (088)* 1115 zinc finger, C3HC4 type domain containing protein Response to endogenous stimulus & stress & abiotic stimulus, signal transduction, protein modification LOC_Os03g16670.1 (089)* 1026 haloacid dehalogenase-like hydrolase family protein Metabolic LOC_Os03g17120.1 (090) 1435 arginine biosynthesis bifunctional protein argJ 1 Biosynthetic, cellular LOC_Os03g19650.3 (091) 1675 PPR2 Pollination, embryo & post-embryonic & multicellular organismal development, reproduction LOC_Os03g19690.1 (092) 2715 expressed protein Uncharacterized LOC_Os03g22570.1 (093) 2846 MIF4G domain containing protein Metabolic (nucleic acid), epigenetic regulation of gene expression, response to endogenous stimulus & abiotic stimulus, anatomical structure morphogenesis, post-embryonic & multicellular organismal development, reproduction, translation LOC_Os03g44150.1 (094) 1791 aminotransferase Biosynthetic, cellular, metabolic, response to stress & abiotic stimulus, catabolic LOC_Os03g49210.1 (095) 2085 BRCA1 C Terminus domain containing protein Uncharacterized LOC_Os03g53510.2 (096) 1717 WD domain, G-beta repeat domain containing protein Multicellular organismal development LOC_Os03g53530.1 (097) 1435 WD domain, G-beta repeat domain containing protein Metabolic (nucleic acid) LOC_Os03g55270.1 (098) 1069 TIP41 Uncharacterized LOC_Os03g55970.1 (099) 1866 uncharacterized protein ycf44 Cellular component organization LOC_Os03g58590.1 (100) 2368 expressed protein Uncharacterized 313 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os03g59640.1 (101) 2491 magnesium-chelatase subunit chlD, chloroplast precursor Metabolic, biosynthetic, cellular LOC_Os03g60460.1 (102) 1978 aminopeptidase Metabolic (protein & lipid), biosynthetic, cellular LOC_Os03g60910.1 (103) 2649 PPR repeat domain containing protein Cellular component organization, metabolic (nucleic acid), biosynthetic LOC_Os03g61560.1 (104) 2171 expressed protein n/a LOC_Os03g61920.1 (105) 1256 electron transfer flavoprotein subunit alpha, mitochondrial precursor n/a LOC_Os03g61950.1 (106) 1608 kelch motif family protein Uncharacterized LOC_Os04g01250.2 (107) 2270 amidase family protein Metabolic, response to stress & biotic stimulus LOC_Os04g02730.1 (108) 2160 tRNA-dihydrouridine synthase 3-like Metabolic (nucleic acid) LOC_Os04g10400.1 (109) 991 electron transfer flavoprotein subunit beta Cellular, catabolic LOC_Os04g14654.2 (110) 2135 Sec1 family transport protein Transport, cellular component organization LOC_Os04g15920.1 (111) 1310 dehydrogenase Biosynthetic, cellular, metabolic (secondary) LOC_Os04g25410.1 (112)* 1532 pentatricopeptide repeat domain containing protein Uncharacterized LOC_Os04g31390.1 (113) 3186 vacuolar protein sorting-associated protein 11 Transport, cellular component organization LOC_Os04g35480.1 (114) 1951 CDK5RAP1 Metabolic (nucleic acid) LOC_Os04g36062.1 (115) 1695 expressed protein Uncharacterized LOC_Os04g39170.1 (116) 1034 expressed protein Uncharacterized LOC_Os04g39210.1 (117)* 1324 agmatine deiminase Reproduction, embryonic & post-embryonic development, biosynthetic, cellular LOC_Os04g48820.1 (118) 1034 deoxyribonuclease tatD Uncharacterized LOC_Os04g50860.1 (119) 1356 expressed protein Uncharacterized LOC_Os04g52500.1 (120) 2030 lecithine cholesterol acyltransferase Metabolic (lipid) 314 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os04g53490.1 (121) 1162 OsCHL Chloroplastic lipocalin Response to stress LOC_Os05g02510.1 (122) 1996 beta-hexosaminidase precursor Metabolic (carbohydrate) LOC_Os05g05320.1 (123) 1567 PPR repeat domain containing protein n/a LOC_Os05g07632.1 (124) 1203 haloacid dehalogenase-like hydrolase family protein Metabolic LOC_Os05g24020.1 (125) 1527 emp24/gp25L/p24 family protein Uncharacterized LOC_Os05g28200.1 (126) 1419 prenyltransferase Metabolic, biosynthetic, cellular LOC_Os05g28500.1 (127) 1632 ubiquitin family protein n/a LOC_Os05g36910.1 (128) 1025 NC domain-containing protein Uncharacterized LOC_Os05g41010.1 (129) 1070 oxidoreductase Protein modification, metabolic LOC_Os05g43230.1 (130) 1425 oryzain alpha chain precursor Metabolic (protein) LOC_Os05g43470.1 (131) 1351 outer membrane protein, OMP85 family Biosynthetic, protein modification LOC_Os05g46330.1 (132) 1673 MYB family transcription factor Biosynthetic, metabolic (nucleic acid), protein modification LOC_Os05g47560.1 (133) 1817 serine/threonine-protein kinase SNT7, chloroplast precursor Metabolic, cellular, protein modification, generation of precursor metabolites & energy, photosynthesis LOC_Os05g47850.1 (134) 2924 chloroplastic group IIA intron splicing facilitator CRS1, chloroplast precursor Reproduction, embryo & post-embryonic development, response to endogenous stimulus LOC_Os05g48340.1 (135) 1837 proteasome-related Uncharacterized LOC_Os06g04460.1 (136) 1099 hypersensitive-induced response protein Biosynthetic, protein modification LOC_Os06g04530.1 (137) 1076 OsClp9 - Putative Clp protease homologue Metabolic, cellular component organization LOC_Os06g04820.1 (138) 1133 leucine carboxyl methyltransferase 1 Metabolic, cellular LOC_Os06g04910.1 (139) 1282 hydrolase, NUDIX family protein Metabolic LOC_Os06g07160.1 (140) 1239 expressed protein Transport, cellular component organization LOC_Os06g07550.1 (141) 1596 PPR repeat domain containing protein Uncharacterized LOC_Os06g08860.1 (142) 1301 transporter family protein Transport, cellular 315 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os06g13220.1 (143) 947 expressed protein n/a LOC_Os06g20320.1 (144) 1829 peptidyl-prolyl cis-trans isomerase, FKBP-type Metabolic (protein), cellular, protein modification, transport LOC_Os06g21600.1 (145) 1891 selenoprotein O Uncharacterized LOC_Os06g27970.1 (146) 1196 FKBP12-interacting protein of 37 kDa n/a LOC_Os06g34040.1 (147) 1493 DJ-1 family protein Uncharacterized LOC_Os06g44030.1 (148) 2124 WD domain, G-beta repeat domain containing protein Biosynthetic, metabolic (nucleic acid) LOC_Os06g44370.1 (149) 1293 WD domain, G-beta repeat domain containing protein Uncharacterized LOC_Os06g49740.1 (150) 1436 pre-mRNA-splicing factor syf2 Uncharacterized LOC_Os07g05880.1 (151)* 1631 OsFBK18 - F-box domain and kelch repeat containing protein Uncharacterized LOC_Os07g08970.1 (152) 2223 uncharacterized protein ycf45 Metabolic LOC_Os07g10490.1 (153) 2045 zeta-carotene desaturase, chloroplast/chromoplast precursor Biosynthetic, cellular, metabolic (lipid) LOC_Os07g12520.1 (154)* 2485 zinc ion binding protein Uncharacterized LOC_Os07g12530.1 (155) 1752 ABC1 family domain containing protein n/a LOC_Os07g12770.1 (156) 1481 transmembrane amino acid transporter protein Transport, response to biotic stimulus, cellular LOC_Os07g20150.1 (157) 1657 expressed protein Translation, metabolic, cellular LOC_Os07g26610.1 (158) 2179 phosphoglucomutase Metabolic (carbohydrate & nucleic acid), biosynthetic LOC_Os07g28940.1 (159) 2594 ATGRIP/GRIP Transport, cellular LOC_Os07g29360.1 (160) 3382 tetratricopeptide repeat domain containing protein Flower development, cellular component organization, protein modification, biosynthetic, metabolic (nucleic acid) LOC_Os07g31460.1 (161) 2472 peptide-Nasparagine amidase Metabolic LOC_Os07g36250.1 (162) 1439 peptide chain release factor protein Cellular component organization, metabolic (nucleic acid) LOC_Os07g40120.1 (163) 2341 pentatricopeptide Uncharacterized 316 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os07g40800.1 (164) 2208 PPR repeat domain containing protein Uncharacterized LOC_Os07g46410.1 (165) 1669 bifunctional thioredoxin reductase/thioredoxin Metabolic (carbohydrate), cellular homeostasis, biosynthetic, response to stress, catabolic LOC_Os07g47290.1 (166) 1710 xylose isomerase Metabolic (carbohydrate) LOC_Os07g48160.1 (167) 1476 alpha-galactosidase precursor Catabolic, cellular, metabolic (lipid & carbohydrate) LOC_Os07g49360.1 (168)* 1137 peroxidase precursor Response to stress & biotic stimulus, metabolic LOC_Os08g01320.1 (169) 1078 C3-BTB1 - Bric-a-Brac, Tramtrack, Broad Complex BTB domain with C3 subfamily conserved sequence Uncharacterized LOC_Os08g02120.1 (170)* 1193 kinase, pfkB family Biosynthetic, metabolic (carbohydrate), cellular, generation of precursor metabolites & energy LOC_Os08g02550.1 (171) 1373 proteasome subunit Cell cycle, protein modification, post-embryonic development, response to abiotic stimulus LOC_Os08g10450.1 (172) 1583 nucleoside transporter Transport, cellular LOC_Os08g23360.1 (173) 2074 signal recognition particle 68 kDa protein Uncharacterized LOC_Os08g28190.1 (174) 1296 actin Anatomical structure morphogenesis, cellular component organization, response to abiotic stimulus, cell differentiation, multicellular organismal development, cell growth LOC_Os08g29370.1 (175) 1435 peptidyl-prolyl cis-trans isomerase, chloroplast precursor Metabolic (protein), cellular, response to stress & biotic stimulus, generation of precursor metabolites & energy, photosynthesis, protein modification, cellular component organization LOC_Os08g32850.1 (176) 1834 methylcrotonoyl-CoA carboxylase beta chain, mitochondrial precursor Metabolic, cellular, catabolic LOC_Os08g35740.1 (177) 1450 12-oxophytodienoate reductase Response to stress & biotic stimulus, biosynthetic, cellular, metabolic (lipid) 317 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os08g38570.1 (178) 2603 WD domain, G-beta repeat domain containing protein Uncharacterized LOC_Os08g38700.1 (179)* 1276 C3-BTB2 - Bric-a-Brac, Tramtrack, Broad Complex BTB domain with C3 subfamily conserved sequence Uncharacterized LOC_Os08g42950.1 (180) 1073 haloacid dehalogenase-like hydrolase family protein Metabolic LOC_Os08g44330.1 (181) 2081 peptidylprolyl isomerase domain and WD repeat-containing protein 1 Anatomical structure morphogenesis, multicellular organismal & flower development, growth, protein modification, metabolic (protein), cellular component organization LOC_Os08g44530.1 (182) 2015 dihydroxy-acid dehydratase Multicellular organismal development, metabolic LOC_Os09g03620.1 (183) 1888 wall-associated receptor kinase-like 20 precursor Metabolic, cellular, protein modification LOC_Os09g07460.1 (184) 1102 kelch repeat protein Uncharacterized LOC_Os09g07660.2 (185) 2344 ELMO/CED-12 family protein n/a LOC_Os09g10820.1 (186)* 1236 oxidoreductase domain protein Metabolic LOC_Os09g16910.3 (187)* 1595 cysteine desulfurase 1, mitochondrial precursor Biosynthetic, cellular, metabolic LOC_Os09g19940.1 (188) 1814 cwfJ-like family protein Metabolic LOC_Os09g21760.1 (189) 1355 proteasome subunit Transport, cellular, response to endogenous stimulus, signal transduction, multicellular organismal development LOC_Os09g24530.1 (190) 1714 ribulose-1,5 bisphosphate carboxylase/oxygenase large subunit N-methyltransferase, chloroplast precursor Metabolic, cellular LOC_Os09g34970.1 (191) 1299 arsenical pump-driving ATPase Transport, cellular homeostasis LOC_Os09g39390.1 (192) 1452 oxidoreductase, aldo/keto reductase family protein Metabolic LOC_Os10g04620.1 (193) 1194 OsPOP20 - Putative Prolyl Oligopeptidase homologue Metabolic 318 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os10g06720.1 (194)* 1158 aldose 1-epimerase Metabolic (carbohydrate), cellular LOC_Os10g10990.1 (195) 1689 transcription initiation factor IIF, alpha subunit domain containing protein Metabolic (nucleic acid), biosynthetic LOC_Os10g32540.1 (196) 2867 SAP domain containing protein n/a LOC_Os10g32730.1 (197) 1448 haloacid dehalogenase-like hydrolase domain-containing protein 1A Metabolic, cellular, biosynthetic LOC_Os10g42630.1 (198)* 1214 expressed protein Uncharacterized LOC_Os11g19250.1 (199) 2658 AARP2CN domain containing protein Cellular LOC_Os11g34210.2 (200) 1843 aspartyl/glutamyl-tRNA amidotransferase subunit B Translation, metabolic (nucleic acid) LOC_Os11g34360.1 (201) 1688 lung seven transmembrane domain containing protein Uncharacterized LOC_Os11g36390.1 (202) 3251 RFC1 - Putative clamp loader of PCNA, replication factor C subunit 1 Metabolic (nucleic acid), response to endogenous stimulus & stress, cellular component organization, protein modification, catabolic, epigenetic regulation of gene expression, biosynthetic LOC_Os11g36420.1 (203) 1612 sialyltransferase family domain containing protein Biosynthetic, protein modification, metabolic (carbohydrate) LOC_Os11g38030.1 (204) 2098 CDK5RAP1 Metabolic (nucleic acid) LOC_Os11g39670.1 (205) 1405 seryl-tRNA synthetase Flower development, metabolic (nucleic acid), translation, cellular component organization LOC_Os11g41990.1 (206) 2381 armadillo/beta-catenin-like repeat family protein Uncharacterized LOC_Os11g43890.1 (207) 1090 WD domain, G-beta repeat domain containing protein Cellular component organization, protein modification, flower development LOC_Os11g45710.7 (208) 2151 Os11bglu36 SFR2 homologue Metabolic, response to stress & abiotic stimulus LOC_Os12g13390.1 (209) 1607 aspartyl aminopeptidase Metabolic (protein) LOC_Os12g18900.1 (210) 1431 aminotransferase Metabolic, cellular, biosynthetic, protein modification 319 Locus name (gene code) Locus length (bp) Gene product name GO classification LOC_Os12g29760.1 (211) 1101 oxidoreductase, aldo/keto reductase family protein Biosynthetic, cellular, metabolic LOC_Os12g36670.1 (212)* 1763 F-box/LRR-repeat protein 3 Uncharacterized LOC_Os12g39340.1 (213) 1506 glycosyl transferase, group 1 domain containing protein Metabolic (carbohydrate), response to stress & abiotic stimulus & endogenous stimulus, biosynthetic, protein modification, cellular LOC_Os12g40550.1 (214) 1612 peptidase Metabolic (protein), catabolic, cellular LOC_Os12g40830.1 (215) 1068 kinase, pfkB family Metabolic, cellular, biosynthetic, response to stress & abiotic stimulus, anatomical structure morphogenesis, cell differentiation, multicellular organismal development LOC_Os12g41780.2 (216) 1367 glycosyl transferase family 17 protein Metabolic (carbohydrate), biosynthetic, protein modification LOC_Os12g42870.1 (217) 1920 Mur ligase family protein Metabolic, cellular, reproduction, embryo & post-embryonic development, biosynthetic Xvis03_200370-PA (218) 1495 Similar to At5g39410 Probable mitochondrial saccharopine dehydrogenase-like oxidoreductase At5g39410 (Arabidopsis thaliana) Uncharacterized Xvis03_200541-PA (219) 2095 Similar to OST1A Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 1A (Oryza sativa subsp. japonica) Protein glycosylation Xvis03_200713-PA (220) 1628 Similar to OVA7 Serine--tRNA ligase, chloroplastic/mitochondrial (Arabidopsis thaliana) tRNA aminoacylation for protein translation Xvis03_202650-PA (221) 2182 Similar to At4g30825 Pentatricopeptide repeat-containing protein At4g30825, chloroplastic (Arabidopsis thaliana) Uncharacterized 320 Locus name (gene code) Locus length (bp) Gene product name GO classification Xvis03_202792-PA (222) 1358 Similar to thrA Bifunctional aspartokinase/homoserine dehydrogenase 1 (Serratia marcescens) Cellular, metabolic (amino acid), oxidation-reduction Xvis03_203301-PA (223) 1685 Protein of unknown function Uncharacterized Xvis03_203630-PA (224) 2117 Similar to ALDH22A1 Aldehyde dehydrogenase 22A1 (Arabidopsis thaliana) Uncharacterized Xvis03_205604-PA (225) 1659 Similar to ENT1 Equilibrative nucleotide transporter 1 (Arabidopsis thaliana) Nucleoside transmembrane transporter activity, transport Xvis03_205981-PA (226) 1745 Similar to SCY1 Preprotein translocase subunit SCY1, chloroplastic (Arabidopsis thaliana) Protein transport Xvis03_206876-PA (227) 1833 Similar to Os01g0184500 DEAD-box ATP-dependent RNA helicase 39 (Oryza sativa subsp. japonica) n/a Xvis03_207092-PA (228) 1784 Similar to FIS1 Probable aldehyde dehydrogenase (Linum usitatissimum) n/a Metabolic, oxidation-reduction Xvis03_208379-PA (229) 1214 Similar to FTA Protein farnesyltransferase/geranylgeranyltransferase type-1 subunit alpha (Pisum sativum) Protein prenylation Xvis03_210074-PA (230) 2410 Similar to HCF152 Pentatricopeptide repeat-containing protein At3g09650, chloroplastic (Arabidopsis thaliana) Uncharacterized Xvis03_210258-PA (231) 1133 Similar to At1g47056 F-box protein At1g47056 (Arabidopsis thaliana) Uncharacterized Xvis03_211553-PA (232) 1838 Similar to At5g10920 Argininosuccinate lyase, chloroplastic (Arabidopsis thaliana) n/a Xvis03_211598-PA (233) 1478 Similar to RPS1 30S ribosomal protein S1, chloroplastic (Spinacia oleracea) n/a Xvis03_211817-PA (234) 1079 Similar to SDR1 (+)-neomenthol dehydrogenase (Arabidopsis thaliana)" Metabolic Xvis03_212270-PA (235) 2113 Protein of unknown function Riboflavin biosynthetic, oxidation-reduction Xvis03_213427-PA (236) 1958 Similar to SYNO Asparagine--tRNA ligase, chloroplastic/mitochondrial (Arabidopsis thaliana) tRNA aminoacylation for protein translation 321 Locus name (gene code) Locus length (bp) Gene product name GO classification Xvis03_214553-PA (237) 1686 Similar to Pepd Xaa-Pro dipeptidase (Mus musculus) n/a Xvis03_214930-PA (238) 1222 Similar to GGB Geranylgeranyl transferase type-1 subunit beta (Arabidopsis thaliana) n/a Xvis03_215558-PA (239) 2176 Similar to At2g14440 Putative leucine-rich repeat receptor-like serine/threonine-protein kinase At2g14440 (Arabidopsis thaliana) Uncharacterized Xvis03_216139-PA (240) 1855 Similar to OVA6 Proline--tRNA ligase, chloroplastic/mitochondrial (Arabidopsis thaliana) Prolyl-tRNA aminoacylation, tRNA aminoacylation for protein translation Xvis03_216319-PA (241) 1668 Similar to PAO Pheophorbide a oxygenase, chloroplastic (Arabidopsis thaliana) Oxidation-reduction Xvis03_216507-PA (242)* 1589 Similar to At3g57050 Cystathionine beta-lyase, chloroplastic (Arabidopsis thaliana) n/a Xvis03_216679-PA (243) 2112 Similar to nt5c2 Cytosolic purine 5'-nucleotidase (Dictyostelium discoideum) Uncharacterized Xvis03_217097-PA (244) 1953 Protein of unknown function Uncharacterized Xvis03_217880-PA (245)* 1862 Similar to ANTR4 Probable anion transporter 4, chloroplastic (Arabidopsis thaliana) Transmembrane transport Xvis03_217921-PA (246) 1412 Similar to TDX TPR repeat-containing thioredoxin TDX (Arabidopsis thaliana) Cell redox homeostasis Xvis03_218940-PA (247) 1553 Similar to C02F5.7 Uncharacterized F-box/LRR-repeat protein C02F5.7 (Caenorhabditis elegans) Uncharacterized Xvis03_219053-PA (248) 959 Protein of unknown function Uncharacterized Xvis03_219094-PA (249) 1126 Similar to COG0212 5-formyltetrahydrofolate cyclo-ligase-like protein COG0212 (Arabidopsis thaliana) Uncharacterized Xvis03_219758-PA (250) 2248 Protein of unknown function Uncharacterized Xvis03_221065-PA (251) 1840 Similar to DEGP2 Protease Do-like 2, chloroplastic (Arabidopsis thaliana) n/a 322 Locus name (gene code) Locus length (bp) Gene product name GO classification Xvis03_222143-PA (252) 1239 Similar to At4g29120 Probable 3-hydroxyisobutyrate dehydrogenase-like 1, mitochondrial (Arabidopsis thaliana) Oxidation-reduction, pentose-phosphate shunt Xvis03_222215-PA (253)* 2136 Similar to MEE40 Pentatricopeptide repeat-containing protein At3g53700, chloroplastic (Arabidopsis thaliana) Uncharacterized Xvis03_223091-PA (254) 1315 Similar to At4g05090 Putative PAP-specific phosphatase, mitochondrial (Arabidopsis thaliana) Phosphatidylinositol phosphorylation Xvis03_223220-PA (255) 1709 Similar to GATA Glutamyl-tRNA(Gln) amidotransferase subunit A, chloroplastic/mitochondrial (Zea mays) n/a Xvis03_224494-PA (256) 2157 Similar to Fastkd3 FAST kinase domain-containing protein 3 (Mus musculus) Uncharacterized Xvis03_224867-PA (257) 2046 Similar to PUB13 U-box domain-containing protein 13 (Arabidopsis thaliana) Protein ubiquitination Xvis03_225344-PA (258) 1945 Similar to LCY1 Lycopene beta cyclase, chloroplastic/chromoplastic (Capsicum annuum) Carotenoid biosynthetic Xvis03_225506-PA (259) 2381 Similar to SPPA Serine protease SPPA, chloroplastic (Arabidopsis thaliana) Proteolysis Xvis03_225868-PA (260)* 1307 Similar to caa43 2-haloacrylate reductase (Burkholderia sp.) Oxidation-reduction 1 GO classification focuses on the biological processes associated with gene products; “n/a” represents no hits to a biological process. 2 http://rice.plantbiology.msu.edu/index.shtml 3 http://www.bioinformatics.nl/xerophyta/portal/ 323 Table C.2 Specimen source information for the 25 taxa enriched using Dioscorea-specific baits, and for the four newly generated transcriptomes (the latter are indicated with an asterisk)1. Species Voucher (collector number, herbarium) NCBI Sequence Read Archive (SRA) accession Wilkin et al. 1104, K SAMN11290802 Thapyai 409, BKF SAMN11290804 Wilkin 814, K SAMN11290794 Wood 9178, K SAMN11290814 no data, N/A (Kew living collection 1980-2270) SAMN11290818 Soto, M. & Viruel J. 002, K SAMN11290800 no data, K SAMN11290809 Soto, M. & Viruel J. Cit08, K SAMN11290810 F Axelrod 8407, UPR SAMN11290803 Thapyai 518, BKF SAMN11290805 JRI Wood et al., 18973, K SAMN11290812 Caddick et al. 329, K SAMN11290797 G Mwachala et al. 949a, EA SAMN11290808 Hladik 8507, P SAMN11290796 MW Chase 21050, K SAMN11290819 O Tellez 13086, no data SAMN11290817 Wood et al., 22679, no data SAMN11290813 Thapyai 452, BKF SAMN11290806 RJ Johns et al. 9824, K SAMN11290795 JRI Wood & E Guzman 17511, K SAMN11290816 MW Chase 21045, K SAMN11290801 LCS Assis & MK Ladeira 67, K SAMN11290815 Wilkin et al. 1075, K SAMN11290820 MW Chase 21052, K SAMN11290821 Hladik 6764, P SAMN11290799 Soto, M. & Viruel J. 001, K SAMN11290798 Soto, M. & Viruel J. 003, K SAMN11290807 Thapyai 436, BKF SAMN11290811 G. Lim 1A, BH SAMN11290823 Dioscorea Dioscorea antaly Dioscorea birmanica Dioscorea calcicola Dioscorea campestris Dioscorea caucasica Dioscorea cochleariapiculata* Dioscorea communis Dioscorea communis* Dioscorea cordata Dioscorea daunea Dioscorea glandulosa Dioscorea hombuka Dioscorea kituiensis Dioscorea mayottensis Dioscorea membranacea Dioscorea minima Dioscorea x monandra Dioscorea nitens Dioscorea nummularia Dioscorea orthogoneura Dioscorea pentaphylla Dioscorea pohlii Dioscorea prazeri Dioscorea rockii Dioscorea sansibarensis Dioscorea soso* Dioscorea sylvatica* Dioscorea tentaculigera Tacca Tacca chantrieri Trichopus Trichopus zeylanicus M Chase & M Fay 16354, K SAMN11290822 1 See main text for data access for the remaining taxa. Herbarium abbreviations follow Thiers (continuously updated). I obtained DNA for D. caucasica from the Kew DNA bank (ID: 35312); there is no voucher information available for it. 324 Table C.3 Gene summary statistics, obtained from analysis using AMAS (Borowiec, 2016), for each of the 264 low-to-single-copy nuclear targets retrieved using Dioscorea-specific baits; genes here represent 259 bait targets, excluding one gene removed after alignment quality trimming, plus five genes added after paralog filtering (see main text). Column '-' indicates alignment gaps or missing data; there were no unknown bases in alignments. For the three-digit gene code, see first column in Table C.1. Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 001 38 1581 60078 94 0.2 644 40.7 372 23.5 94 002 35 1047 36645 491 1.3 439 41.9 270 25.8 491 003 37 2007 74259 2726 3.7 832 41.5 491 24.5 2726 004 34 1216 41344 3158 7.6 585 48.1 353 29 3151 005 35 505 17675 2275 12.9 198 39.2 128 25.3 2275 006 36 995 35820 2127 5.9 413 41.5 247 24.8 2127 007 37 482 17834 1375 7.7 176 36.5 88 18.3 1375 008 37 781 28897 2162 7.5 379 48.5 221 28.3 2162 009 36 1008 36288 3639 10 422 41.9 258 25.6 3639 010 38 738 28044 646 2.3 358 48.5 201 27.2 646 011 38 1367 51946 5219 10 445 32.6 268 19.6 5219 012 36 1364 49104 1294 2.6 574 42.1 370 27.1 1294 013 37 918 33966 1133 3.3 286 31.2 159 17.3 1133 014 37 1804 66748 1405 2.1 822 45.6 472 26.2 1405 015 33 541 17853 1127 6.3 211 39 124 22.9 1127 016 37 1234 45658 2252 4.9 525 42.5 306 24.8 2252 017 37 867 32079 2369 7.4 411 47.4 199 23 2369 018 37 2567 94979 5988 6.3 924 36 545 21.2 5988 019 37 785 29045 991 3.4 297 37.8 152 19.4 990 020 35 829 29015 2113 7.3 279 33.7 178 21.5 2113 325 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 021 24 1556 37344 2391 6.4 588 37.8 293 18.8 2390 022 36 592 21312 2180 10.2 219 37 138 23.3 2180 023 35 991 34685 1239 3.6 404 40.8 245 24.7 1239 024 36 701 25236 2317 9.2 276 39.4 150 21.4 2317 025 36 1331 47916 1630 3.4 507 38.1 294 22.1 1630 026 36 1512 54432 2308 4.2 608 40.2 390 25.8 2308 027 36 1287 46332 1813 3.9 562 43.7 304 23.6 1813 028 38 1127 42826 191 0.4 561 49.8 359 31.9 191 029 35 729 25515 162 0.6 306 42 178 24.4 162 030 36 922 33192 2848 8.6 403 43.7 223 24.2 2848 031 36 816 29376 1429 4.9 431 52.8 232 28.4 1419 032 33 459 15147 835 5.5 217 47.3 110 24 826 033 37 3169 117253 7740 6.6 1269 40 741 23.4 7740 034 37 1341 49617 1544 3.1 471 35.1 264 19.7 1544 035 36 1396 50256 2798 5.6 539 38.6 367 26.3 2798 036 32 1422 45504 76 0.2 744 52.3 540 38 76 037 36 1110 39960 2226 5.6 540 48.6 301 27.1 2226 038 36 1344 48384 2338 4.8 572 42.6 333 24.8 2338 039 38 1470 55860 3516 6.3 609 41.4 321 21.8 3516 040 34 804 27336 594 2.2 285 35.4 161 20 594 041 36 417 15012 1013 6.7 158 37.9 103 24.7 1013 043 36 1451 52236 2471 4.7 567 39.1 330 22.7 2471 044 37 2091 77367 3732 4.8 744 35.6 408 19.5 3732 045 34 773 26282 627 2.4 292 37.8 188 24.3 627 046 36 780 28080 818 2.9 347 44.5 199 25.5 818 047 35 896 31360 1779 5.7 319 35.6 165 18.4 1779 048 37 901 33337 2307 6.9 397 44.1 218 24.2 2307 326 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 049 37 621 22977 1529 6.7 232 37.4 115 18.5 1529 050 37 1541 57017 1667 2.9 700 45.4 400 26 1667 051 36 972 34992 1268 3.6 407 41.9 253 26 1268 052 36 566 20376 1334 6.5 263 46.5 161 28.4 1334 053 35 836 29260 188 0.6 351 42 244 29.2 188 054 29 1564 45356 2382 5.3 677 43.3 419 26.8 2382 055 16 1022 16352 1188 7.3 342 33.5 226 22.1 1188 056 36 1513 54468 1218 2.2 633 41.8 369 24.4 1217 057 37 1105 40885 1891 4.6 505 45.7 278 25.2 1891 058 20 1345 26900 693 2.6 458 34.1 229 17 690 059 36 928 33408 2525 7.6 455 49 242 26.1 2525 060 37 882 32634 4282 13.1 452 51.2 218 24.7 4282 061 36 919 33084 1200 3.6 413 44.9 221 24 1200 062 37 1973 73001 6630 9.1 807 40.9 501 25.4 6630 063 35 1580 55300 1503 2.7 700 44.3 481 30.4 1502 064 35 1056 36960 2822 7.6 315 29.8 211 20 2822 065 36 1383 49788 950 1.9 647 46.8 357 25.8 950 066 37 1121 41477 1080 2.6 416 37.1 251 22.4 1080 067 37 1748 64676 3339 5.2 585 33.5 318 18.2 3339 068 30 1143 34290 1460 4.3 526 46 325 28.4 1460 069 35 970 33950 105 0.3 530 54.6 319 32.9 105 070 34 1093 37162 3193 8.6 485 44.4 295 27 3193 071 38 1356 51528 3352 6.5 569 42 364 26.8 3352 072 32 1203 38496 3612 9.4 517 43 244 20.3 3612 073 36 684 24624 1992 8.1 280 40.9 183 26.8 1983 074 33 197 6501 15 0.2 68 34.5 38 19.3 15 075 35 984 34440 1562 4.5 422 42.9 257 26.1 1562 327 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 076 37 1400 51800 4251 8.2 594 42.4 301 21.5 4250 077 38 2546 96748 7768 8 904 35.5 500 19.6 7767 078 35 1305 45675 2876 6.3 681 52.2 425 32.6 2876 079 36 2538 91368 5088 5.6 981 38.7 540 21.3 5088 080 35 1264 44240 1830 4.1 624 49.4 352 27.8 1830 081 37 1110 41070 1837 4.5 358 32.3 204 18.4 1837 082 37 824 30488 1857 6.1 406 49.3 248 30.1 1857 083 37 1812 67044 2166 3.2 788 43.5 407 22.5 2166 084 36 1545 55620 996 1.8 616 39.9 366 23.7 996 085 36 1668 60048 134 0.2 661 39.6 417 25 134 086 38 1798 68324 2225 3.3 880 48.9 488 27.1 2225 087 23 1399 32177 855 2.7 378 27 242 17.3 855 087b 28 1117 31276 1278 4.1 540 48.3 378 33.8 1278 088 33 859 28347 2902 10.2 344 40 181 21.1 2902 089 26 858 22308 808 3.6 410 47.8 228 26.6 808 089b 30 807 24210 2130 8.8 308 38.2 154 19.1 2130 090 37 921 34077 2716 8 385 41.8 251 27.3 2716 091 36 1525 54900 868 1.6 630 41.3 415 27.2 867 092 37 2214 81918 5642 6.9 782 35.3 442 20 5640 093 37 1566 57942 5509 9.5 598 38.2 386 24.6 5509 094 37 1119 41403 617 1.5 370 33.1 209 18.7 617 095 35 1805 63175 2897 4.6 709 39.3 417 23.1 2897 096 36 1401 50436 22 0 571 40.8 362 25.8 22 097 36 717 25812 2183 8.5 368 51.3 226 31.5 2183 098 36 732 26352 1963 7.4 267 36.5 170 23.2 1963 099 35 1696 59360 2725 4.6 706 41.6 440 25.9 2725 100 37 1453 53761 1889 3.5 513 35.3 276 19 1889 328 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 101 36 2253 81108 3816 4.7 855 37.9 511 22.7 3816 102 38 1617 61446 664 1.1 768 47.5 477 29.5 622 103 36 2246 80856 902 1.1 872 38.8 525 23.4 901 104 35 1629 57015 5506 9.7 749 46 451 27.7 5506 105 36 999 35964 3086 8.6 389 38.9 201 20.1 3086 106 37 566 20942 2228 10.6 236 41.7 129 22.8 2228 107 35 936 32760 1868 5.7 358 38.2 203 21.7 1868 108 38 1667 63346 1860 2.9 853 51.2 545 32.7 1854 109 35 763 26705 2069 7.7 290 38 154 20.2 2069 110 37 386 14282 192 1.3 145 37.6 85 22 189 111 37 1103 40811 2108 5.2 510 46.2 357 32.4 2108 112 35 1226 42910 44 0.1 514 41.9 295 24.1 44 113 36 2546 91656 448 0.5 887 34.8 494 19.4 448 114 37 1687 62419 1283 2.1 828 49.1 467 27.7 1283 115 37 839 31043 4879 15.7 298 35.5 150 17.9 4879 116 34 809 27506 890 3.2 316 39.1 189 23.4 890 117 37 886 32782 1927 5.9 453 51.1 293 33.1 1927 118 32 743 23776 1662 7 302 40.6 190 25.6 1662 119 36 1263 45468 2267 5 624 49.4 364 28.8 2266 120 35 1551 54285 4147 7.6 762 49.1 466 30 4147 121 37 691 25567 749 2.9 250 36.2 150 21.7 749 122 35 991 34685 2525 7.3 432 43.6 297 30 2525 123 35 1194 41790 3956 9.5 420 35.2 219 18.3 3956 124 36 785 28260 3364 11.9 313 39.9 165 21 3364 125 35 719 25165 2152 8.6 310 43.1 169 23.5 2147 126 32 677 21664 2213 10.2 183 27 105 15.5 2213 127 36 1470 52920 74 0.1 643 43.7 416 28.3 74 329 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 128 35 744 26040 820 3.1 331 44.5 231 31 820 129 37 791 29267 1672 5.7 357 45.1 224 28.3 1672 130 26 511 13286 689 5.2 251 49.1 148 29 689 131 36 925 33300 2461 7.4 360 38.9 214 23.1 2461 132 34 774 26316 3320 12.6 289 37.3 137 17.7 3312 133 37 1467 54279 3998 7.4 632 43.1 375 25.6 3998 134 37 2196 81252 4104 5.1 932 42.4 542 24.7 4104 135 35 1327 46445 5431 11.7 691 52.1 356 26.8 5431 136 35 668 23380 1636 7 271 40.6 144 21.6 1636 137 36 554 19944 456 2.3 172 31 92 16.6 456 138 35 774 27090 3857 14.2 312 40.3 151 19.5 3857 139 36 1167 42012 1002 2.4 530 45.4 325 27.8 1002 140 36 854 30744 2834 9.2 338 39.6 206 24.1 2834 141 36 1501 54036 1851 3.4 614 40.9 390 26 1851 142 34 1174 39916 2164 5.4 478 40.7 305 26 2156 143 30 257 7710 188 2.4 139 54.1 89 34.6 188 144 37 1317 48729 3130 6.4 507 38.5 264 20 3130 145 38 1492 56696 933 1.6 644 43.2 414 27.7 933 146 37 436 16132 735 4.6 214 49.1 108 24.8 735 147 36 1315 47340 1287 2.7 601 45.7 360 27.4 1287 148 37 942 34854 1905 5.5 379 40.2 200 21.2 1905 149 37 1054 38998 43 0.1 426 40.4 282 26.8 43 150 36 1126 40536 1624 4 489 43.4 302 26.8 1624 151 36 1328 47808 460 1 658 49.5 454 34.2 460 152 37 1868 69116 1956 2.8 765 41 474 25.4 1956 153 37 1609 59533 3776 6.3 574 35.7 353 21.9 3776 154 35 2109 73815 939 1.3 832 39.4 465 22 939 330 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 155 37 1421 52577 2807 5.3 586 41.2 316 22.2 2807 156 36 1026 36936 45 0.1 457 44.5 278 27.1 45 157 35 1099 38465 3400 8.8 393 35.8 228 20.7 3400 158 36 1054 37944 5973 15.7 489 46.4 264 25 5973 159 36 907 32652 2472 7.6 479 52.8 239 26.4 2472 160 37 2759 102083 6850 6.7 871 31.6 484 17.5 6850 161 37 1281 47397 1833 3.9 599 46.8 315 24.6 1833 162 37 1196 44252 2468 5.6 439 36.7 253 21.2 2468 163 37 1841 68117 631 0.9 797 43.3 510 27.7 631 164 36 1873 67428 4129 6.1 769 41.1 453 24.2 4129 165 37 1292 47804 3696 7.7 357 27.6 190 14.7 3696 166 35 1044 36540 2293 6.3 412 39.5 235 22.5 2293 167 32 946 30272 3542 11.7 425 44.9 226 23.9 3542 168 20 961 19220 818 4.3 305 31.7 203 21.1 818 168b 23 568 13064 171 1.3 234 41.2 163 28.7 171 169 38 708 26904 230 0.9 302 42.7 208 29.4 230 170 35 905 31675 1073 3.4 413 45.6 280 30.9 1073 171 38 831 31578 636 2 408 49.1 213 25.6 636 172 21 735 15435 0 0 302 41.1 141 19.2 0 173 35 906 31710 3617 11.4 374 41.3 237 26.2 3617 174 35 540 18900 963 5.1 150 27.8 77 14.3 963 175 38 1182 44916 1633 3.6 502 42.5 280 23.7 1630 176 38 1640 62320 4039 6.5 662 40.4 373 22.7 4038 177 29 842 24418 61 0.3 324 38.5 193 22.9 61 178 36 1925 69300 462 0.7 814 42.3 455 23.6 459 179 36 959 34524 2066 6 438 45.7 292 30.4 2066 180 37 865 32005 1569 4.9 350 40.5 191 22.1 1569 331 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 181 37 1826 67562 694 1 692 37.9 419 22.9 694 182 37 1789 66193 2190 3.3 765 42.8 463 25.9 2189 183 35 1610 56350 2849 5.1 686 42.6 423 26.3 2848 184 35 969 33915 180 0.5 484 49.9 331 34.2 180 185 36 1459 52524 4882 9.3 585 40.1 325 22.3 4882 186 38 1019 38722 3762 9.7 546 53.6 307 30.1 3762 187 36 1409 50724 524 1 519 36.8 341 24.2 524 188 37 1503 55611 1733 3.1 701 46.6 382 25.4 1732 189 35 919 32165 4864 15.1 385 41.9 229 24.9 4860 190 37 996 36852 1461 4 431 43.3 260 26.1 1461 191 36 840 30240 1570 5.2 329 39.2 160 19 1570 192 36 1218 43848 2623 6 478 39.2 288 23.6 2623 193 36 966 34776 559 1.6 455 47.1 260 26.9 559 194 28 1028 28784 736 2.6 443 43.1 241 23.4 736 194b 26 686 17836 78 0.4 271 39.5 108 15.7 78 195 33 457 15081 654 4.3 218 47.7 126 27.6 654 196 37 2390 88430 4273 4.8 998 41.8 562 23.5 4273 197 37 1189 43993 4149 9.4 497 41.8 267 22.5 4130 198 37 997 36889 1269 3.4 400 40.1 235 23.6 1269 199 34 2116 71944 7104 9.9 953 45 511 24.1 7103 200 36 1650 59400 3474 5.8 637 38.6 382 23.2 3473 201 38 1424 54112 1008 1.9 580 40.7 348 24.4 1006 202 37 1452 53724 6747 12.6 705 48.6 372 25.6 6746 203 37 1280 47360 2572 5.4 437 34.1 250 19.5 2572 204 36 1566 56376 241 0.4 576 36.8 346 22.1 241 205 28 234 6552 160 2.4 77 32.9 33 14.1 160 206 36 1713 61668 2674 4.3 706 41.2 408 23.8 2674 332 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 207 37 655 24235 636 2.6 293 44.7 189 28.9 636 208 37 1719 63603 2428 3.8 638 37.1 379 22 2428 209 36 1312 47232 2679 5.7 595 45.4 320 24.4 2679 210 37 1082 40034 2950 7.4 410 37.9 233 21.5 2950 211 37 974 36038 526 1.5 495 50.8 293 30.1 526 212 20 1624 32480 1612 5 783 48.2 569 35 1612 212b 27 654 17658 80 0.5 255 39 147 22.5 80 213 36 1247 44892 514 1.1 479 38.4 288 23.1 514 214 36 1085 39060 1950 5 498 45.9 284 26.2 1942 215 33 423 13959 1021 7.3 129 30.5 77 18.2 1021 216 36 1035 37260 80 0.2 470 45.4 287 27.7 80 217 35 1698 59430 3003 5.1 777 45.8 420 24.7 3003 218 36 1365 49140 1602 3.3 603 44.2 360 26.4 1602 219 36 1788 64368 689 1.1 733 41 426 23.8 689 220 32 1466 46912 6909 14.7 495 33.8 248 16.9 6909 221 36 1781 64116 3215 5 772 43.3 438 24.6 3215 222 35 836 29260 2764 9.4 356 42.6 183 21.9 2764 223 37 1134 41958 1964 4.7 511 45.1 314 27.7 1963 224 37 1874 69338 2480 3.6 639 34.1 396 21.1 2480 225 14 1269 17766 571 3.2 391 30.8 217 17.1 571 226 37 1265 46805 3177 6.8 457 36.1 256 20.2 3177 227 37 1731 64047 2250 3.5 824 47.6 560 32.4 2250 228 38 871 33098 882 2.7 389 44.7 212 24.3 881 229 34 770 26180 80 0.3 346 44.9 210 27.3 80 230 33 2079 68607 3831 5.6 903 43.4 572 27.5 3819 231 34 801 27234 2928 10.8 431 53.8 251 31.3 2928 232 37 1237 45769 1905 4.2 440 35.6 265 21.4 1905 333 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 233 37 1285 47545 2161 4.5 476 37 340 26.5 2161 234 28 557 15596 930 6 241 43.3 160 28.7 930 235 37 1288 47656 3491 7.3 561 43.6 300 23.3 3491 236 37 1264 46768 5719 12.2 488 38.6 266 21 5719 237 27 144 3888 131 3.4 42 29.2 25 17.4 131 238 33 458 15114 415 2.7 224 48.9 127 27.7 415 239 35 1509 52815 2207 4.2 565 37.4 348 23.1 2207 240 37 1315 48655 1966 4 390 29.7 236 17.9 1966 241 35 1485 51975 3606 6.9 565 38 355 23.9 3606 242 37 1395 51615 1907 3.7 794 56.9 523 37.5 1907 243 36 872 31392 4632 14.8 277 31.8 169 19.4 4632 244 24 460 11040 779 7.1 166 36.1 85 18.5 779 245 37 1452 53724 1392 2.6 611 42.1 367 25.3 1392 246 28 952 26656 1285 4.8 412 43.3 282 29.6 1285 247 37 1443 53391 2875 5.4 782 54.2 449 31.1 2875 248 34 750 25500 1437 5.6 345 46 208 27.7 1437 249 34 883 30022 2582 8.6 358 40.5 198 22.4 2582 250 37 2091 77367 4033 5.2 824 39.4 454 21.7 4033 251 35 812 28420 2874 10.1 356 43.8 231 28.4 2874 252 36 1090 39240 2280 5.8 526 48.3 322 29.5 2271 253 38 1809 68742 1590 2.3 858 47.4 574 31.7 1590 254 36 1073 38628 1431 3.7 489 45.6 284 26.5 1431 255 38 1461 55518 4797 8.6 661 45.2 390 26.7 4797 256 36 1849 66564 475 0.7 829 44.8 514 27.8 475 257 37 1635 60495 913 1.5 720 44 443 27.1 913 258 36 1684 60624 1042 1.7 677 40.2 428 25.4 1042 259 36 1081 38916 4823 12.4 465 43 296 27.4 4823 334 Gene code No. of taxa Alignment length No. of matrix cells Undetermined characters Missing % No. of variable sites % variable sites Parsimony informative sites % parsimony informative - 260 36 1017 36612 1467 4 475 46.7 274 26.9 1467 335 Table C.4 Data partitioning scheme inferred from 264 initial gene partitions using PartitionFinder 2 with the AICc criterion and relaxed hierarchical clustering algorithm. The 264 low-to-single-copy nuclear gene targets of the Dioscorea-specific baits designed here are shown using a three-digit number code (see first column in Table C.1). __________________________________________________________________ No. of Best DNA partitions substitution model Partition subsets __________________________________________________________________ 1 GTR+I+G 001 2 GTR+I+G 156, 002, 172 3 GTR+I+G 038, 003, 180, 079, 235 4 GTR+I+G 119, 004 5 GTR+I+G 259, 005 6 GTR+I+G 059, 006, 057, 030 7 GTR+I+G 044, 007, 092 8 GTR+I+G 008 9 GTR+I+G 131, 041, 229, 099, 009, 178 10 GTR+G 017, 010 11 GTR+I+G 011, 174, 094, 224 12 GTR+I+G 012 13 GTR+I+G 074, 205, 013 14 GTR+I+G 223, 014, 188, 171, 095, 107, 083 15 GTR+I+G 191, 124, 175, 015 16 GTR+I+G 016, 091 17 GTR+I+G 215, 018, 153 18 GTR+I+G 115, 123, 019, 113 19 GTR+I+G 020 20 GTR+I+G 021 21 GTR+I+G 022 22 GTR+I+G 228, 096, 023 23 GTR+I+G 024, 196 24 GTR+I+G 138, 154, 250, 025, 201 25 GTR+I+G 026, 075, 197, 089b, 058, 061 26 GTR+I+G 027, 134 27 GTR+I+G 028 28 GTR+I+G 098, 140, 048, 093, 084, 136, 029 29 GTR+G 031, 186 30 GTR+G 225, 032 31 GTR+I+G 033, 132 32 GTR+I+G 034, 243, 077, 181 33 GTR+I+G 035, 163, 085 34 GTR+I+G 036 35 GTR+I+G 037, 194b, 194 36 GTR+I+G 144, 244, 039 37 GTR+I+G 101, 040, 047, 157, 220 336 __________________________________________________________________ No. of Best DNA partitions substitution model Partition subsets __________________________________________________________________ 38 GTR+I+G 255, 133, 200, 109, 045, 043, 103 39 GTR+I+G 046, 051 40 GTR+I+G 049, 226 41 GTR+I+G 050, 086 42 GTR+I+G 052, 073, 173 43 GTR+I+G 241, 053 44 GTR+I+G 054 45 GTR+I+G 252, 055 46 GTR+I+G 056, 164, 189, 142, 249, 088 47 GTR+I+G 60 48 GTR+I+G 139, 227, 256, 062 49 GTR+I+G 063 50 GTR+I+G 064 51 GTR+I+G 127, 065, 248 52 GTR+I+G 148, 176, 232, 203, 110, 066 53 GTR+I+G 126, 081, 067 54 GTR+I+G 068 55 GTR+G 069 56 GTR+I+G 070, 147, 082 57 GTR+I+G 236, 166, 198, 071 58 GTR+G 072 59 GTR+I+G 206, 219, 076 60 GTR+I+G 143, 078 61 GTR+G 247, 080 62 GTR+I+G 087 63 GTR+G 087b 64 GTR+G 135, 089 65 GTR+I+G 090, 193 66 GTR+I+G 120, 158, 151, 117, 097 67 GTR+I+G 100 68 GTR+I+G 102 69 GTR+I+G 246, 104, 114 70 GTR+I+G 245, 106, 155, 105 71 GTR+I+G 238, 108, 254 72 GTR+I+G 111, 177 73 GTR+I+G 112 74 GTR+I+G 116 75 GTR+I+G 260, 218, 118, 211 76 GTR+I+G 208, 204, 121 77 GTR+I+G 122, 167 78 GTR+I+G 253, 125 79 GTR+I+G 184, 128 80 GTR+I+G 170, 129 81 GTR+G 130 82 GTR+I+G 165, 137 337 __________________________________________________________________ No. of Best DNA partitions substitution model Partition subsets __________________________________________________________________ 83 GTR+I+G 195, 141, 257 84 GTR+I+G 213, 145 85 GTR+G 146, 159 86 GTR+I+G 169, 149 87 GTR+G 150 88 GTR+I+G 152, 192, 182 89 GTR+I+G 160, 237 90 GTR+G 190, 161 91 GTR+I+G 162 92 GTR+I+G 168 93 GTR+I+G 168b 94 GTR+I+G 179 95 GTR+I+G 209, 183 96 GTR+I+G 185, 221 97 GTR+I+G 187 98 GTR+I+G 217, 199 99 GTR+G 202 100 GTR+I+G 207 101 GTR+I+G 210 102 GTR+I+G 212 103 GTR+G 212b 104 GTR+I+G 214 105 GTR+I+G 216 106 GTR+G 222 107 GTR+I+G 230 108 GTR+I+G 231 109 GTR+I+G 233 110 GTR+G 234 111 GTR+I+G 239 112 GTR+I+G 240 113 GTR+I+G 242 114 GTR+I+G 251 115 GTR+I+G 258 338 Table C.5 Average length of coding and non-coding sequences recovered from 25 bait-enriched taxa using the Dioscorea-specific bait kit. Coding sequence represents the 260 low-to-single-copy nuclear genes targeted by the kit; non-coding sequence comprises the off-target intron/intergenic regions flanking bait targets. Sequence lengths are averages from all taxa represented in alignments for each locus, after quality trimming and excluding gaps. For the three-digit gene code, see first column in Table C.1. Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 001 1578 2727 1150 002 1038 2191 1152 003 1925 3890 1966 004 1022 3327 2305 005 271 786 515 006 766 1812 1046 007 425 1530 1105 008 673 1348 676 009 494 2341 1848 010 713 1416 703 011 1127 3790 2663 012 1285 1552 267 013 842 2132 1290 014 1788 2566 778 015 485 1631 1146 016 1142 2752 1610 017 761 1888 1127 018 2287 5081 2794 019 749 2492 1742 020 682 2354 1671 021 864 3125 2262 022 449 1351 901 023 932 2258 1326 024 492 2029 1536 025 1211 4002 2790 026 1329 3100 1770 027 1209 3091 1881 028 1125 1577 453 029 711 1771 1060 339 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 030 641 1252 611 031 720 1589 869 032 372 1318 946 033 2869 6543 3674 034 1282 3063 1781 035 1246 3668 2422 036 1417 1834 416 037 1036 2990 1954 038 1236 3097 1861 039 1336 4095 2759 040 779 1893 1114 041 357 1034 677 042 235 624 389 043 1342 2873 1531 044 1938 5467 3529 045 745 1933 1188 046 745 1716 971 047 661 1759 1098 048 728 1990 1262 049 519 1098 579 050 1479 2831 1353 051 917 1873 956 052 546 1221 675 053 824 1230 407 054 1530 1669 139 055 356 721 365 056 1453 3212 1759 057 1039 2487 1447 058 977 2118 1141 059 565 2856 2291 060 397 1351 953 061 818 2048 1230 062 1725 3971 2246 063 1530 2274 745 064 528 1548 1020 065 1354 2249 895 066 1033 2143 1109 067 1613 3893 2280 340 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 068 1167 1511 344 069 960 1863 902 070 618 1210 591 071 1518 4399 2881 072 927 4007 3080 073 478 1366 887 074 424 1628 1205 075 911 2030 1118 076 1222 3593 2371 077 2036 5256 3220 078 1160 2252 1092 079 2071 4238 2167 080 1109 2317 1208 081 925 2621 1696 082 746 1508 762 083 1751 4591 2840 084 1496 3525 2029 085 1669 2199 530 086 1708 3484 1776 087 1337 2287 950 088 565 1610 1045 089 807 1458 651 090 818 2148 1330 091 1486 2034 548 092 1999 5908 3909 093 1118 2527 1409 094 1094 3774 2680 095 1614 3836 2223 096 1402 2271 870 097 595 1880 1284 098 578 1513 934 099 1611 2603 991 100 1374 4242 2868 101 1812 4890 3078 102 1580 2162 581 103 2205 3295 1090 104 1158 2712 1554 105 600 2399 1799 341 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 106 466 1373 907 107 945 2496 1552 108 1594 3626 2032 109 673 1732 1059 110 382 2981 2599 111 1020 1741 721 112 1223 1691 468 113 2533 4015 1482 114 1644 3672 2029 115 525 2573 2048 116 765 1747 982 117 762 1753 991 118 447 1428 981 119 851 1301 450 120 1134 3413 2279 121 661 1753 1092 122 836 2114 1278 123 700 1915 1215 124 570 1819 1249 125 622 1909 1287 126 506 1433 927 127 1470 2376 906 128 716 1018 302 129 678 1643 965 130 469 1121 652 131 739 1655 915 132 337 763 427 133 1160 3528 2368 134 1984 4184 2200 135 691 2983 2293 136 389 850 461 137 536 1506 970 138 330 2724 2393 139 1126 2077 951 140 521 1272 751 141 1418 2343 924 142 1077 2459 1382 143 255 810 555 342 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 144 1139 3562 2423 145 1415 2247 832 146 403 1159 756 147 1205 2354 1148 148 818 2432 1614 149 1054 1578 523 150 1055 2430 1375 151 1294 1624 330 152 1786 4312 2526 153 1366 3202 1836 154 2102 3359 1257 155 1245 2762 1517 156 1025 1622 597 157 669 1993 1323 158 914 3189 2276 159 792 3877 3084 160 2298 6654 4356 161 1205 2131 926 162 1144 3073 1929 163 1820 2307 487 164 1212 3161 1949 165 1153 2917 1764 166 811 2229 1418 167 680 2011 1331 168 697 881 184 169 715 1256 541 170 832 1415 583 171 797 2127 1331 172 735 1161 426 173 534 1657 1122 174 454 2189 1735 175 1117 3123 2006 176 1464 3091 1627 177 829 1285 456 178 1913 3849 1936 179 682 961 279 180 803 2117 1314 181 1798 4770 2973 343 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 182 1697 3647 1950 183 1470 3447 1977 184 949 1326 377 185 1175 4299 3124 186 692 2105 1413 187 1386 1757 370 188 1433 3108 1675 189 164 630 466 190 940 2072 1132 191 779 2168 1389 192 1152 2453 1301 193 927 2529 1601 194 889 1430 542 195 419 1548 1129 196 2237 5220 2983 197 1026 2007 980 198 937 2585 1648 199 1485 3867 2383 200 1327 2283 956 201 1435 2532 1097 202 941 6049 5108 203 1182 3290 2108 204 1536 2103 567 205 206 235 29 206 1497 3418 1921 207 605 1050 445 208 1623 3460 1837 209 1099 2449 1350 210 864 3067 2203 211 962 1828 866 212 1559 2272 714 213 1238 3177 1938 214 1023 3558 2536 215 380 956 576 216 1029 1508 479 217 1547 4124 2577 218 1309 1935 626 219 1746 3324 1578 344 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 220 815 1469 654 221 1748 3184 1437 222 531 1779 1248 223 1047 2593 1546 224 1770 4928 3158 225 825 1490 665 226 1149 2861 1712 227 1717 3717 2000 228 829 2235 1406 229 764 2268 1504 230 1612 1842 230 231 691 2110 1418 232 1166 2729 1563 233 1143 2309 1166 234 391 743 352 235 981 2339 1358 236 644 2796 2151 237 236 750 514 238 436 937 501 239 1295 2458 1162 240 1224 3344 2119 241 1303 3051 1748 242 1370 3202 1832 243 611 3312 2701 244 414 972 558 245 1398 2756 1358 246 860 1872 1012 247 1088 1463 374 248 692 1526 833 249 492 1489 996 250 2117 3898 1781 251 621 1841 1220 252 885 1151 266 253 1760 2737 976 254 979 2328 1349 255 855 2553 1698 256 1845 2290 445 257 1597 2581 984 345 Gene code Average length (bp) of recovered bait targets Average length (bp) of recovered bait targets plus non-coding flanking sequence Average length (bp) of recovered non-coding flanking sequence 258 1592 2081 489 259 696 2856 2160 260 956 1953 997 346 Table C.6 Comparison of target gene overlap and recovery between the Dioscorea-specific bait kit designed here and the Angiosperms-353 probe set (Johnson et al., 2019), estimated using Geneious and blastn (see text for details). Locus names for the Dioscorea kit are based on Oryza sativa1 (name starts with LOC_Os) or Xerophyta viscosa2 (name starts with Xvis) depending on which of the two taxa were used as low-to-single-copy nuclear gene references in MarkerMiner analyses (see text for details); numbers in brackets represent the corresponding gene codes used in this study (see first column in Table C.1). Note that genes 079 and 231 were only recovered as overlapping targets using blastn and Geneious, respectively. Gene recovery comparison is based on the number of nucleotides retrieved from D. calcicola by baits targeting homologous loci in both respective kits. Asterisks indicate loci that overlap with the Angiosperm v.1 kit (Buddenhagen et al., 2016), see Table C.7. Locus name (gene code), Dioscorea kit No. of recovered bp, Dioscorea kit3 % of gene length recovered, Dioscorea kit Locus name, Angiosperm-353 No. of recovered bp, Angiosperm-353 % of gene length recovered, Angiosperm-353 LOC_Os01g31610.1 (012) 1422 92 6051 933 87 LOC_Os02g01070.1 (035) 2406 100 5599 456 28 LOC_Os02g02570.1 (037) 1212 100 5699 360 52 LOC_Os02g02860.1 (039) 1833 100 6176 504 36 LOC_Os02g33080.1 (046) 194 100 6462 399 55 LOC_Os02g33860.1 (047) 1026 100 7029 135 17 LOC_Os02g50360.1 (058) 1626 72 5816 0 0 LOC_Os03g05720.1 (079)* 2538 86 6366 123 19 LOC_Os03g12460.1 (086)* 2190 100 6098 711 42 LOC_Os03g17120.1 (090) 1287 90 5366 303 26 LOC_Os03g44150.1 (094) 1830 100 6533 609 59 LOC_Os03g55970.1 (099)* 1809 100 6494 624 46 LOC_Os03g61560.1 (104) 2130 100 5339 345 20 347 _________________________________________________________________________________________________________________________________ 1 http://rice.plantbiology.msu.edu/index.shtml 2 http://www.bioinformatics.nl/xerophyta/portal/ 3 Number of recovered nucleotides may be larger than gene lengths reported in Table C.1 because the latter were calculated as averages considering the 3–4 Dioscoreales references for each gene. Locus name (gene code), Dioscorea kit No. of recovered bp, Dioscorea kit3 % of gene length recovered, Dioscorea kit Locus name, Angiosperm-353 No. of recovered bp, Angiosperm-353 % of gene length recovered, Angiosperm-353 LOC_Os04g53490.1 (121)* 819 70 7324 414 62 LOC_Os05g46330.1 (132) 3198 100 5634 150 15 LOC_Os06g20320.1 (144) 1842 100 5770 78 6 LOC_Os08g38570.1 (178) 2670 100 5426 1257 90 LOC_Os09g21760.1 (189) 1254 93 6050 0 0 LOC_Os11g43890.1 (207) 996 100 5859 528 71 LOC_Os12g18900.1 (210) 1317 92 5018 312 25 LOC_Os12g42870.1 (217)* 2010 100 5842 228 29 Xvis03_210258-PA (231) 1320 100 5822 0 0 Xvis03_211553-PA (232)* 1974 100 6450 735 65 348 Table C.7 Comparison of target gene overlap between the Dioscorea-specific bait kit designed here and the Angisoperm v.1 kit (Buddenhagen et al., 2016), estimated using Geneious and blastn (see text for details). Locus names for the Dioscorea kit are based on Oryza sativa1 (name starts with LOC_Os) or Xerophyta viscosa2 (name starts with Xvis) depending on which of the two taxa were used as low-to-single-copy nuclear gene references in MarkerMiner analyses (see text for details); numbers in brackets represent the corresponding gene codes used in this study (see first column in Table C.1). Note that multiple non-overlapping target loci from the Angiosperm v.1 kit may correspond to a single target locus from the Dioscorea-specific kit; gene 218 was only recovered as an overlapping target using Geneious. Asterisks indicate loci that overlap with the Angiosperm-353 kit (Johnson et al., 2019), see Table C.6. Locus name (gene code), Dioscorea kit Locus length (bp), Dioscorea kit Locus name(s), Angiosperm v.1 kit Locus length (bp), Angiosperm v.1 kit3 LOC_Os01g68750.1 (033) 3440 L45 606 LOC_Os02g39350.1 (050) 1946 L98, L99 190, 236 LOC_Os02g56910.1 (067) 2082 L13 154 LOC_Os02g57500.1 (069) 1324 L217 270 LOC_Os03g03710.1 (077) 2799 L140, L141 236, 257 LOC_Os03g05720.1 (079)* 2959 L253, L254 211, 1084 LOC_Os03g12460.1 (086)* 2206 L46 275 LOC_Os03g55970.1 (099)* 1866 L57, L58 207, 278 LOC_Os04g31390.1 (113) 3186 L136, L137, L138 177, 316, 333 LOC_Os04g53490.1 (121)* 1162 L278, L279 288, 271 LOC_Os05g28500.1 (127) 1632 L162 750 LOC_Os08g42950.1 (180) 1073 L377, L378 168, 218 LOC_Os10g32540.1 (196) 2867 L213, L214, L215, L216 219, 169, 446, 207 LOC_Os12g40550.1 (214) 1612 L445 243 LOC_Os12g42870.1 (217)* 1920 L506 181 Xvis03_200370-PA (218) 1495 L436 679 Xvis03_211553-PA (232)* 1838 L379, L380, L381 277, 282, 262 Xvis03_212270-PA (235) 2113 L274, L275, L276, L277 157, 312, 170, 224 Xvis03_216507-PA (242) 1589 L506 181 Xvis03_219758-PA (250) 2248 L199, L200, L201, L202, L203, L204 234, 199, 478, 167, 240, 326 Xvis03_223220-PA (255) 1709 L263 307 349 1http://rice.plantbiology.msu.edu/index.shtml 2http://www.bioinformatics.nl/xerophyta/portal/ 3Locus lengths respectively correspond to locus names in the previous column. 350 Figure C.1 Phylogenetic relationships in Dioscorea inferred from partitioned maximum likelihood analyses on a concatenated matrix of 264 genes recovered using target enrichment with the Dioscorea-specific baits designed here. The data matrix was partitioned by gene (genes with similar models and model parameters were pooled) and analyzed using corresponding DNA substitution models. Values next to branches are bootstrap support values; thick lines represent 100% bootstrap support. Lineages in red are major crops; blue labels indicate previously identified crop wild relatives. Scale bar indicates estimated substitutions per site. 351 0.04D. compositaD. alata t2D. calcicolaTrichopusD. mayottensisD. sosoD. dauneaD. cordataD. communis 2D. trifidaD. alata t1D. hombukaD. pohliiD. sp.D. caucasicaD. glandulosaD. antalyD. x monandraD. kituiensisD. orthogoneuraD. minimaD. cochleariapiculataD. pentaphyllaD. prazeriD. nummulariaD. birmanicaD. nitensD. tentaculigeraTaccaD. cayenensisD. sylvaticaD. rockiiD. membranaceaD. campestrisD. nipponicaD. sansibarensisD. communis t1D. zingiberensissubstitutions/siteEnantiophyllumMalagasyCompound Leaf‘Rajania’BirmanicaAfricaMediterraneanNew World IINew World IStenophoraCladeB8899957749352 Figure C.2 Phylogenetic relationships in Dioscorea inferred from unpartitioned maximum likelihood analyses on a concatenated matrix of 264 genes recovered using target enrichment with the Dioscorea-specific baits designed here. Values next to branches are bootstrap support values; thick lines represent 100% bootstrap support. Lineages in red are major crops; blue labels indicate previously identified crop wild relatives. Scale bar indicates estimated substitutions per site. 353 substitutions/siteD. campestrisD. kituiensisD. alata t2D. rockiiD. alata t1D. caucasicaD. nummulariaD. sansibarensisD. pentaphyllaD. calcicolaD. compositaD. hombukaD. trifidaD. dauneaD. cochleariapiculataD. mayottensisD. communis t1D. glandulosaD. birmanicaD. nipponicaD. minimaD. cayenensisD. sylvaticaD. nitensD. sosoD. orthogoneuraD. communis 2D. x monandraD. membranaceaD. prazeriD. cordataD. zingiberensisD. antalyD. tentaculigeraD. sp.D. pohliiTaccaTrichopus0.04EnantiophyllumMalagasyCompound Leaf‘Rajania’BirmanicaAfricaMediterraneanNew World IINew World IStenophoraCladeB939052354 Figure C.3 Phylogenetic relationships in Dioscorea inferred from coalescent-based analyses of a partial 232 gene set that excluded 32 potential paralogs. Genes were recovered using target enrichment with the Dioscorea-specific baits designed here. Values next to branches are local posterior probabilities (LPP) and multilocus bootstrap support (MLB), respectively; thick branches have 1.0 LPP and 100% bootstrap support. Lineages in red are major crops; blue labels indicate previously identified crop wild relatives. Scale bar shows coalescent units for internal branches (not estimated by ASTRAL for terminal branches). 355 3.0D. pohliiD. nipponicaD. zingiberensisD. cochleariapiculataD. communis 2Trichopus zeylanicusD. rockiiD. tentaculigeraD. dauneaD. trifidaD. campestrisD. compositaD. alata t1D. sosoD. calcicolaD. kituiensisD. prazeriD. hombukaD. sp.D. caucasicaD. communis t1D. nummulariaD. minimaD. x monandraD. orthogoneuraTacca chantrieriD. sylvaticaD. birmanicaD. cordataD. mayottensisD. sansibarensisD. pentaphyllaD. antalyD. glandulosaD. nitensD. cayenensisD. alata t2D. membranacea0.94/920.87/930.97/930.99/570.98/95coalescent unitsEnantiophyllumCladeBMalagasyCompound Leaf‘Rajania’BirmanicaAfricaMediterraneanNew World IINew World IStenophora
Thesis/Dissertation
eng
Botany
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Phylogenomic studies of the monocot sister orders Pandanales and Dioscoreales
Text