UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Eco-evolutionary perspective on life-history traits with special emphasis on seed dormancy and its genetic… Liu, Yang 2016

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2016_november_liu_yang.pdf [ 8.86MB ]
Metadata
JSON: 24-1.0314387.json
JSON-LD: 24-1.0314387-ld.json
RDF/XML (Pretty): 24-1.0314387-rdf.xml
RDF/JSON: 24-1.0314387-rdf.json
Turtle: 24-1.0314387-turtle.txt
N-Triples: 24-1.0314387-rdf-ntriples.txt
Original Record: 24-1.0314387-source.json
Full Text
24-1.0314387-fulltext.txt
Citation
24-1.0314387.ris

Full Text

  ECO-EVOLUTIONARY PERSPECTIVE ON LIFE-HISTORY TRAITS WITH SPECIAL EMPHASIS ON SEED DORMANCY AND ITS GENETIC BASIS OF ADAPTATION IN CONIFERS   by  Yang LIU  A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in The Faculty of Graduate and Postdoctoral Studies  (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  September 2016 © Yang Liu, 2016  ii  Abstract Life-history traits, known as fitness components, are related to the timing and success of development, reproduction, and senescence throughout the life cycle. Selection in variable environments may favor plants to defer germination until suitable conditions occur. Seed dormancy is an innate constraint on germination timing and prevents germination during periods that are ephemerally favorable. The timing of seed germination is the earliest life-history trait that is expressed and sets the context for the traits that follow. As such, seed dormancy may be construed as an adaptation for survival during bad seasons and can exert cascading selective pressures on subsequent life stages. Seed size is another important life-history trait linking the ecology of reproduction and seedling establishment with that of vegetative growth. As the two traits are, at modulations, regulated by hormone signaling cascades, evolve under correlated selective pressures, and exhibit co-varying phenotypes, this dissertation intended to elucidate their eco-evolutionary dynamics and possible genetic basis of adaptation. From an eco-evolutionary perspective, I demonstrated that dynamic climatic variables rather than constant geographic variables are the true environmental driving forces in seed dormancy and size variations in Pinus contorta Dougl. Evapotranspiration and precipitation in the plant-to-seed transition are the most critical climatic variables for seed dormancy and size variations, respectively. Unlike random temperature fluctuations between generations, wide temperature shifts considerably alter population structures and accelerate life-history evolution. Regarding the genetic basis of adaptation, environmental cues trigger different seed-set programming in Picea glauca and Arabidopsis by employing lineage-specific and deeply conserved microRNAs at different expression levels, respectively, to entrain phenotypical variations, such as dormancy intensities. Our findings additionally point to auxin as a key player that likely works in conjunction with the ABA and GA signal pathways previously investigated in mechanisms underpinning the seed-to-plant transition by chilling in Picea glauca seeds. This dissertation increases our understanding of plant evolution and persistence in the context of climate change and provides fundamental insight for understanding how microRNAs are at play in seed-set  iii  programs to regulate phenotypes, how winter chilling contributes to the timing of phenology, and how conifer life histories may develop under new climate scenarios.    iv  Résumé Les traits d’histoire de vie ou traits de vie, composantes de la fitness (valeur adaptative) via leurs effets sur la fécondité et la survie, sont liés au timing et au succès du développement, de la reproduction, et de la sénescence tout au long du cycle de vie. La sélection dans l’environnement variable peut favoriser la plante en remettant à plus tard la germination des graines jusqu’à ce que les conditions environnementales deviennent favorables. La dormance des graines est une contrainte innée sur le timing de germination, empêchant celle-ci pendant une période éphémèrement favorable. Le timing de germination des graines est un des traits de vie importants parce qu’il s’exprime très tôt dans le cycle de vie et influence fortement les traits suivants. De ce fait, la dormance des graines peut être interprétée comme une adaptation par la survie pendant de mauvaises saisons et exercer des pressions sélectives en cascade sur les étapes de la vie ultérieures. La taille des graines est un autre trait de vie important, reliant l’écologie de la reproduction et de la mise en place des semis à celle de la pousse végétative. Ces deux traits de vie sont étroitement façonnés par les mêmes voies de signalisation hormonale et covarient de manière héritable en évoluant sous des pressions sélectives associées. Cette thèse vise à élucider l’éco-évolution de ces deux traits conjointement et leurs bases génétiques possibles de l’adaptation. Sur le point de vue éco-évolutive, j’ai démontré que certaines variables climatiques dynamiques plutôt que les variables géographiques constantes sont les véritables forces motrices de l’environnement sur la dormance des graines et leurs variations de taille. Pendant la transition de la plante à la graine, l’évapotranspiration et les précipitations sont les variables climatiques les plus critiques pour la dormance des graines et la variation de taille, respectivement. Contrairement à la fluctuation aléatoire de température entre les générations, de larges changements permanents de température peuvent considérablement modifier la structure des populations et accélérer l’évolution de l’histoire de vie. Sur la base génétique de l’adaptation, les indices environnementaux déclenchent des programmes de développement de la graine différents de conifères et Arabidopsis en générant de petits ARNs spécifiques à la lignée et profondément conservés à différents niveaux d’expression, respectivement. Ce déclenchement mène aux variations  v  phénotypiques, y compris à la variabilité dans la dormance des graines. De plus, mes résultats indiquent que, dans les mécanismes sous-jacents pendant la transition de la graine à la plante due à l’exposition au froid humide chez les conifères, l’auxine en tant qu’acteur clé fonctionne probablement en conjonction avec les voies de signalisation de l’ABA et des gibbérellines précédemment étudiées. Cette thèse contribue à augmenter notre compréhension de l’évolution de la plante et sa persistance dans le contexte du changement climatique. Elle accroît nos connaissances fondamentales pour mieux comprendre comment de petits ARNs façonnent le programme du développement de la graine afin de réguler les phénotypes, comment le froid humide pendant l’hivernage contribue au timing de la phénologie, et enfin comment les histoires de vie peuvent se développer chez les conifères dans de nouveaux scénarios climatiques.    vi  Preface I conceived of the research questions and objectives with altruistic aid of my program supervisor, project advisors, collaborators, and my supervisory committee members. I coordinated my projects, conducted the primary research, carried out data analyses and interpretations, and drafted the manuscripts. Sections of this dissertation have been published or accepted in peer-reviewed journals, listed below: ■ Chapter 2: Liu Y and El-Kassaby YA (2014) Germination timing plasticity in conifers associated with temperature based ecological variations during seed maturation. Seed Sci. Res. 25: 29-45 ■ Chapter 3: Liu Y, Wang T, and El-Kassaby YA (2016) Contributions of dynamic environmental signals during life-cycle transitions to early life-history traits in lodgepole pine (Pinus contorta Dougl.). Biogeosciences 13: 2945-2958 ■ Chapter 4: Liu Y*, Barot S, El-Kassaby YA, and Loeuille N (2016) Impact of temperature shifts on the joint evolution of seed dormancy and size. Ecol. Evol. ■ Chapter 5: Liu Y, Müller K, El-Kassaby YA, and Kermode A (2015) Role of hormone flux and signaling in the transition from dormancy to germination in response to temperature cues in white spruce (Picea glauca) seeds. BMC Plant Biol. 15:292 Liu Y, Kermode A, and El-Kassaby YA (2013) The role of moist-chilling and thermo-priming on the germination characteristics of white spruce (Picea glauca [Moench] Voss) seed. Seed Sci. Technol. 41:321-335 ■ Chapter 6: Liu Y and El-Kassaby YA (2016) Regulatory programs at seed set overridden by microRNAs control phenotypical variations in Picea glauca and Arabidopsis thaliana. (under review) Liu Y* and El-Kassaby YA* (2016) Landscape of fluid sets of hairpin-derived 24-nt small RNAs at seed set uncovers special epigenetic features in Picea glauca. Genome Biol. Evol. Liu Y and El-Kassaby YA (2016) Regulatory cross-talk between microRNAs and hormone signalling cascades controls phenotypical variations: A case study in seed dormancy modulations during seed set of Arabidopsis thaliana. (under review) (corresponding author: *)  The author, Yang LIU University of British Columbia   vii  Table of Contents  Abstract ......................................................................................................................................................... ii Résumé ......................................................................................................................................................... iv Preface ......................................................................................................................................................... vi Table of Contents ........................................................................................................................................ vii List of Tables ................................................................................................................................................ x List of Figures ............................................................................................................................................. xii List of Abbreviations .................................................................................................................................. xv Acknowledgements .................................................................................................................................... xvi Dedication ................................................................................................................................................. xvii 1 Introduction ........................................................................................................................................... 1 1.1 Eco-evolution and (epi)genetic basis of adaptation ...................................................................... 1 1.2 Research objectives ....................................................................................................................... 4 1.3 Dissertation overview ................................................................................................................... 4 2 Timing of seed germination is correlated with environments at seed set ............................................. 7 2.1 Introduction ................................................................................................................................... 7 2.3 Materials and methods ................................................................................................................ 10 2.3.1 Seed and climate data .......................................................................................................... 10 2.3.2 Germination assay and evaluation ...................................................................................... 11 2.3.3 Experimental design and statistical analyses ...................................................................... 13 2.4 Results ......................................................................................................................................... 16 2.4.1 Phenotypic plasticity and seed germination timing............................................................. 16 2.4.2 Heat sums and germination timing ..................................................................................... 19 2.4.3 Climate and germination timing.......................................................................................... 28 2.5 Discussion ................................................................................................................................... 29 2.5.1 Phenotypical plasticity and environmental uncertainties .................................................... 30 2.5.2 Climate change and plasticity ............................................................................................. 32 3 Contribution of environmental signals to life-history traits at life-cycle transitions .......................... 34 3.1 Introduction ................................................................................................................................. 34 3.2 Methods....................................................................................................................................... 37 3.2.1 Population and climate data ................................................................................................ 37 3.2.2 Data analysis and visualization ........................................................................................... 39  viii  3.3 Results ......................................................................................................................................... 41 3.3.1 Life-history traits explained by climate .............................................................................. 41 3.3.2 Adaptive plasticity and bet-hedging.................................................................................... 53 3.4 Discussion ................................................................................................................................... 54 3.4.1 Environmental conditions in the plant-to-seed transition ................................................... 54 3.4.2 Temperature signals in winter-chilling ............................................................................... 57 3.4.3 Germination cues in the seed-to-plant transition ................................................................ 58 3.4.4 Seed size and environmental conditions during seed ripening ........................................... 59 4 Impact of temperature shifts on the joint evolution of seed dormancy and size ................................. 61 4.1 Introduction ................................................................................................................................. 61 4.2 The model ................................................................................................................................... 64 4.2.1 Description of the ecological model ................................................................................... 64 4.2.2 Investigations of eco-evolutionary dynamics ...................................................................... 67 4.2.3 Simulations of deterministic and stochastic environmental changes .................................. 69 4.3 Results ......................................................................................................................................... 70 4.3.1 Ecological dynamics ........................................................................................................... 70 4.3.2 Evolution of seed dormancy ............................................................................................... 70 4.3.3 Evolution of seed size ......................................................................................................... 71 4.3.4 Coevolution of seed dormancy and size .............................................................................. 72 4.3.5 Effects of stochasticity ........................................................................................................ 79 4.4 Discussion ................................................................................................................................... 79 4.4.1 Temperature shifts and life-history evolution ..................................................................... 79 4.4.2 Impact of joint and independent evolution .......................................................................... 81 4.4.3 Population structures ........................................................................................................... 83 5 Hormone flux and signaling during the seed-to-plant transition ......................................................... 85 5.1 Introduction ................................................................................................................................. 85 5.2 Materials and methods ................................................................................................................ 88 5.2.1 Population selection ............................................................................................................ 88 5.2.2 Sampling from dry seeds to germinants .............................................................................. 90 5.2.3 RNA and protein extraction for expression analyses .......................................................... 94 5.3 Results ......................................................................................................................................... 95 5.3.1 Selection of one population most responsive to external stimuli ........................................ 95 5.3.2 Profiles of germination and marker gene .......................................................................... 102 5.3.3 Hormone changes during chilling treatment and germination .......................................... 104 5.3.4 Hormone changes after dormancy decay .......................................................................... 115  ix  5.4 Discussion ................................................................................................................................. 116 5.4.1 Plant hormones co-ordinately respond to temperature cues .............................................. 116 5.4.2 Winter chilling under new climate scenarios and its effects on conifer life histories ....... 119 6 microRNA production at the plant-to-seed transition ....................................................................... 121 6.1 Introduction ............................................................................................................................... 121 6.2 Materials and methods .............................................................................................................. 129 6.2.1 Sampling strategy at seed set ............................................................................................ 129 6.2.2 RNA isolation, library construction, and sRNA sequencing ............................................ 129 6.2.3 Small RNA dataset analysis .............................................................................................. 131 6.2.4 Statistical analysis ............................................................................................................. 132 6.2.5 Evolutionary analysis ........................................................................................................ 133 6.2.6 Gene expression analysis .................................................................................................. 133 6.3 Results ....................................................................................................................................... 135 6.3.1 sRNA transcriptome profiling throughout seed set ........................................................... 135 6.3.2 Spatiotemporal comparison of miRNA families and MIR genes ...................................... 135 6.3.3 Expression pattern of selected genes and miRNA explained by environments ................ 139 6.4 Discussion ................................................................................................................................. 158 6.4.1 Landscape of miRNA expression at seed set within and between species ....................... 159 6.4.2 Impact of environments on seed set programs .................................................................. 160 7 Conclusions ....................................................................................................................................... 163 7.1 Research novelties .................................................................................................................... 163 7.2 Eco-evolutionary perspectives on seed dormancy and size ...................................................... 164 7.3 Genetic and epigenetic basis of adaptation on seed dormancy ................................................. 166 7.4 Perspectives ............................................................................................................................... 168 7.4.1 Eco-evolutionary perspective on life-history traits in climate change .............................. 168 7.4.2 Seed dormancy and germination ....................................................................................... 169 References ................................................................................................................................................. 171 Appendices ................................................................................................................................................ 211 Appendix A: Adaptive dynamics .......................................................................................................... 211 Appendix B: Supplementary information ............................................................................................. 214     x  List of Tables Table 1.1 Key components in each research chapter .................................................................................... 6 Table 2.1 A list of important climatic variables .......................................................................................... 11 Table 2.2 MANOVA for the seven germination parameters ...................................................................... 21 Table 2.3 Expected Mean Squares (EMS) and variance components for each germination parameter ..... 21 Table 2.4 AMMI analysis of variance for timing of seed germination in three conifer species ................. 21 Table 2.5 Canonical correlation analysis between individual variables with their own and with the opposite set of variables ............................................................................................................................................ 22 Table 3.1 Canonical correlation analysis between individual variables and their own and opposite set of variables ...................................................................................................................................................... 43 Table 3.2 Multi- and uni-variate analyses for life-history traits ................................................................. 44 Table 3.3 Parameter estimates and statistical tests for the climatic variables-based hierarchical models regarding seed dormancy and weight .......................................................................................................... 45 Table 3.4 Parameter estimates and statistical tests for the geographic variables-based hierarchical models regarding seed dormancy and seed weight ................................................................................................. 45 Table 4.1 Variable/parameter symbols and values used in simulations ...................................................... 67 Table 5.1 Description of genes and primer pairs used for qPCR ................................................................ 92 Table 5.2 ANOVA for the germination full model ..................................................................................... 98 Table 5.3 Reduced ANOVA model after the removal of the control treatment (no moist-chilling-no priming) .................................................................................................................................................................... 98 Table 5.4 Average and range of the germination parameters across treatments for five white spruce seed lots ............................................................................................................................................................. 100 Table 6.1 Key genes related to seed dormancy and germination .............................................................. 126 Table 6.2 Description of genes and primer pairs used for qPCR .............................................................. 134 Table 6.3 Summary of a cohort of key and conserved miRNA populations involved in development, phase transitions, and beyond in plants ............................................................................................................... 141 Table 6.4 Identification of communal and conserved isoform miRNAs between Arabidopsis and P. glauca during seed set........................................................................................................................................... 144 Table 6.5 Identification of unique and conserved miRNAs in Arabidopsis ecotype Col compared with Cvi and studied P. glauca population P4 compared with P1 to 3 during seed set ........................................... 146 Table B.1 Description of seedlots of interior spruce, lodgepole pine, and western hemlock and their International Seed Testing Association (ISTA) seed test result. ............................................................... 216 Table B.2 Errors from ecosystem zone for seed dormancy and weight models ....................................... 217  xi  Table B.3 Putative homologs of three genes used for phylogeny analyses .............................................. 218 Table B.4 Sequencing statistics ................................................................................................................ 222 Table B.5 Comparative summary of spruce miRNA reads in this study and previous reports................. 223 Table B.6 Identified conserved miRNAs at seed set in Arabidopsis ........................................................ 236 Table B.7 Identified miRNAs expressed in more than 14 libraries in P. glauca ...................................... 237    xii  List of Figures Figure 1.1 A flow diagram of this dissertation ............................................................................................. 5 Figure 2.1 Locations of the 15 seed lots marked on the map of ecosystem zones in BC, Canada (left) and ClimateBC (right) ....................................................................................................................................... 10 Figure 2.2 Schematic representation of the cumulative germination curve for parameters used to characterize timing of seed germination ..................................................................................................... 13 Figure 2.3 G×E interaction involving five seed sources for each of three conifer species ......................... 23 Figure 2.4 The Genotype main effect and G×E Interactions (GGE) biplot for environment centered analysis of timing of seed germination of three conifer species ............................................................................... 24 Figure 2.5 Dormancy index (DI), difference of germination speed (DGS), and difference of germination capacity (DGC) of the 15 seed lots of the three studied species ................................................................... 25 Figure 2.6 Germination parameters (AUC, GS, and GC) profiling for non-manipulated environment (control) across 15 western hemlock, logepole pine, and “interior” spruce seed lots ................................................ 26 Figure 2.7 Monthly cumulative heat sums for seed lots of “interior” spruce (a), lodgepole pine (b) and western hemlock (c), and for the three species (d)...................................................................................... 27 Figure 2.8 Correlation circle of PCA based on climatic data estimated by ClimateWNA ......................... 29 Figure 2.9 A proposed model explaining regulation via molecular mechanisms and environmental cues during transitions of the plant life cycle ...................................................................................................... 31 Figure 3.1 Geographical distribution of the 83 populations (green triangles) on the map regarding ecosystem zones (A) and MAT value (B) of British Columbia, Canada. .................................................................... 38 Figure 3.2 The distribution of the samples based on mean annual temperature (MAT) against annual heat moisture index (AHM) ................................................................................................................................ 38 Figure 3.3 The 15 most correlated climatic variables with seed dormancy (A) and size (B) after partial least squares (PLS) regression and PCA in red (C) for 194 climatic variables ................................................... 46 Figure 3.4 Seed dormancy (DI, a) and weight (SW, b) distribution for the 83 populations labeled on the map of British Columbia, Canada ....................................................................................................................... 47 Figure 3.5 Seed dormancy (DI) prediction in the 2050s ............................................................................. 48 Figure 3.6 Linear relationship between 1,000-seed weight and ‘predicted’ 1000-seed weight using 83 populations .................................................................................................................................................. 49 Figure 3.7 Map of predicted seed dormancy using climatic model for the reference period and the 2050s in lodgepole pine across B.C. .......................................................................................................................... 50 Figure 3.8 Studies of phenotypic plasticity ................................................................................................. 51 Figure 3.9 Studies of bet-hedging strategy ................................................................................................. 52  xiii  Figure 3.10 Important environment stages and intrinsic mechanisms in the life cycle of lodgepole pine for life-history traits .......................................................................................................................................... 55 Figure 3.11 The amount of changes for DD_0_winter, DD5_spring, and DD5 in the 2050s relative to present ......................................................................................................................................................... 58 Figure 4.1 Life cycle of seed-adult stages ................................................................................................... 65 Figure 4.2 Ecological equilibria for a spectrum of seed dormancy (α, A-C) and of seed size (γ, D-F) when the alternative trait is fixed ......................................................................................................................... 73 Figure 4.3 Pairwise invasibility plots (PIPs) (A) and evolutionary dynamics (B) ...................................... 74 Figure 4.4 Evolutionary end points and corresponding number of populations for the independent evolution of seed dormancy (A-B) and size (C-D) after numerical simulations of 5.0×107 and 1.0×108 steps.......... 75 Figure 4.5 Eco-evolutionary dynamics of each trait (seed dormancy [A] and size [B]) in opt or temperature shifts and robustness analysis ..................................................................................................................... 76 Figure 4.6 Evolutionary end points and corresponding number of populations for the joint evolution of seed dormancy and size after a numerical simulation of 5.0×107 and 1.0×108 steps .......................................... 77 Figure 4.7 Fecundity without considering density-dependent competition as a function of seed size (A) and trade-off of plant total reproductive investment and seed dispersal-related survival (B) ........................... 78 Figure 5.1 Schematic representation of sampling to determine germination of white spruce seeds in different germination conditions. ............................................................................................................................... 91 Figure 5.2 Effects of moist-chilling and priming on germination ............................................................ 101 Figure 5.3 Dormancy index (DI) of the five white spruce seed lots ......................................................... 102 Figure 5.4 Effect of moist chilling on the germination performance of white spruce seeds..................... 104 Figure 5.5  Changes in ABA, ABA metabolites, and ABA signaling components during the transition from dormancy to germination of white spruce seeds ....................................................................................... 109 Figure 5.6 Changes in GAs and GA signaling components during the transition from dormancy to germination of white spruce seeds ............................................................................................................ 111 Figure 5.7 Changes in IAA, IAA conjugates, and auxin-related gene expression during the transition from dormancy to germination of white spruce seeds ....................................................................................... 113 Figure 5.8 Profiles of cytokinins and their metabolites in seeds of white spruce (as determined by UPLC/ESI-MS/MS) during moist-chilling at 3°C (0, 10, and 21 d), and during germination (6, 24 and 80 h) and seedling growth (9 d) ..................................................................................................................... 114 Figure 5.9 The results of principle component analysis applied to the expression of all the genes used in previous qPCR analysis in ABA and GA pathways over three different germination conditions ............ 115 Figure 6.1 Interaction network of miRNAs and phytohormone signaling cascades involved in seed dormancy and germination ....................................................................................................................... 124  xiv  Figure 6.2 Illustration of sampling strategy during seed set of Picea glauca and Arabidopsis thaliana .. 130 Figure 6.3 sRNAs annotation and/ or distribution in the mapped reads of all Picea glauca and Arabidopsis thaliana libraries ....................................................................................................................................... 147 Figure 6.4 Temporal expression patterns of top conserved miRNAs in P. glauca (A) and Arabidopsis (B) .................................................................................................................................................................. 149 Figure 6.5 Analyses of miRNA reads in Arabidopsis ............................................................................... 151 Figure 6.7 Analyses of miRNA reads in P. glauca ................................................................................... 153 Figure 6.8 Nucleotide frequency in mature miRNA sequences (A) and at each mature miRNA position (B), and the number of repeat modules per “conserved” MIR gene (C) and across MIR genes (D) in P. glauca and Arabidopsis ........................................................................................................................................ 154 Figure 6.9 Phylogenetic tree of homologs for four genes (DOG1, ABI3, ARF10, and ARF16) in gymnosperms and model angiosperms ..................................................................................................... 155 Figure 6.10 Nucleotide alignment of putative pgARF10 between P. glauca (BT119832.1) and Cycas rumphii (FN433183.1) .............................................................................................................................. 156 Figure 6.11 The hairpin structures of possible pgl-miR160s by computational prediction ...................... 157 Figure 6.12 Triplot diagram for RDA between relative expression of miRNA-gene combinations (red lines) and environments (blue arrows) ................................................................................................................ 158 Figure 6.13 Comparison of features of MIR genes, mature miRNAs, and their predicted RNA targets at seed set in P. glauca and Arabidopsis ............................................................................................................... 161 Figure 7.1 Known germination stimulants derived from combustion of plant material ........................... 170 Figure A.1 Configurations of PIPs ............................................................................................................ 213 Figure B.1 Overview of conserved miRNAs expression patterns across seed set phases of four populations in P. glauca (A) and two ecotypes (Cvi and Col) in Arabidopsis (B) ...................................................... 215    xv  List of Abbreviations ABA abscisic acid ABI3 abscisic acid insensitive 3 AHM annual heat moisture index AMMI additive main effect and multiplicative interaction ANOVA analysis of variance ARF10/16 auxin response factor 10/16 AUC area under germination curve CCA canonical correlation analysis CSS continuously stable strategy DD_0_summer summer degree-days below 0°C DI dormancy index DOG1 delay-of-germination 1 Eref07 and _summer July and summer Hargreaves reference evaporation ESS evolutionarily stable strategy GAs gibberellins GC germination capacity GGE biplot genotype main effect and G×E interactions biplot GLM general linear model GS germination speed MANOVA multivariate analysis of variance MAT mean annual temperature miRNA microRNA PCA principle component analysis 4-PHF four-parameter Hill function PIPs pairwise invasibility plots PLS partial least squares PPT07 and 10 July and October precipitation (mm) q(RT-)PCR quantitative (RT-)PCR Tmax07 July maximum mean temperature (°C)  Note: see page 11 and 125 for more abbreviations for specific terms on climatic variables and gene names, respectively.   xvi  Acknowledgements A haul of five-year PhD journey is like a flowing river: small at first, narrowly contained within its banks, and then rushing passionately past boulders and over waterfalls.  On the occasion of graduation, I’d like to thank my program supervisor, Dr. Yousry A. El-Kassaby for his enormous trusts in my expedition and unrelenting encouragements especially in my ebbs. I’d like to thank my project advisors, Drs. Nicolas Loeuille (UPMC), Sébastien Barot (UPMC), and Allison Kermode (SFU) for devoting great efforts to my projects. Equally, I’d like to thank my supervisory committee members, Drs. Lacey Samuels, Robert Guy, Allison Kermode, and Yousry A. El-Kassaby for directing me on the track of becoming a biologist. I am cordially thankful to UBC for sheltering many excellent faculty members. My academic progress cannot be attained without illuminations from their lectures and/or personal communications with them. I here warmly thank Drs. V. LeMay, S. Otto, T. Wang, H. Curat, R. Hamelin, J. Richardson, T. Kozak, L. Rieseberg, K. Adams, M. Hirst, S. Godfrey, C. Rouget, R. Miller, M. O’Hagan, K. Ritland, J. Whitton, and W. Maddison. I also thank WestGrid Glacier High Performance Computing Consortium for providing the platform of executing my simulations in batch and the funding from Mitacs-Sorbonne Universités to me and the Johnson’s Family Forest Biotechnology Endowment and the National Science and Engineering Research Council of Canada Discovery and Industrial Research Chair to Dr. El-Kassaby. Also, I extend my gratitude to lab mates and departmental staff for their kindness and the convivial environment they create: Ben, Omar, Omnia, Jaroslav, Ilga, Susan, Blaise, and Frances in El-Kassaby’s group; Kerstin, Reza, Ying, and Ryan in Kermode’s group; Marta, Thibaud, Julien, Clémentine, Kejun, Floriane, and Thomas in iEES de Paris; Norman, Natasha, Rosemarie, and Andrea in the Department of Forest and Conservation Sciences. Additionally, many thanks to a couple of graduates and professionals for their generous help of all kinds: Anna (U of M), Raphaël (Forestry), Diane and Denise (BCGSC). Finally, I am deeply grateful to my family, relatives, and intimate friends for their firm backup over the years!  xvii  Dedication        To my dearest mother and numerous people who have supported me!            Organisms determine what aspects of the part of the physical world is relevant to them and they construct out of these relevant bits and pieces a world of interactions.  ―  Richard C. Lewontin   Introduction 1  1 Introduction 1.1 Eco-evolution and (epi)genetic basis of adaptation The conifers (Coniferales or Pinophyta) are one of four living groups of cone-bearing gymnosperms (Gymnospermae; the other three taxa: cycads, Ginkgo, and gnetophytes), which together with extant flowering plants or angiosperms (Angiospermae or Magnoliophyta) make up the seed plants. In the 480-million-year history of land plants, ancestral gymnosperms and conifers originated in the middle Devonian (middle Paleozoic era, c. 385 million years ago (MYA)) and Jurassic period (early-middle Mesozoic era, 251-160 MYA), respectively, and angiosperms appeared in the early Cretaceous period (late Mesozoic era, c. 140 MYA) and began dominating the earth after the late Cretaceous period (c. 100 MYA) (Scheckler 2001; Schneider et al. 2004). Evolutionarily, extant conifers are twice as old as angiosperms. Charles Darwin described the obscure origin of angiosperms as a “perplexing phenomenon” and an “abominable mystery” (Crepet 2000). Two prominent differences between gymnosperms and angiosperms are their contrasting reproductive development and the development of water-conducting xylem cells.  The conifers cover vast tracts in the Northern hemisphere and shape many temperate and boreal ecosystems. To date, there are 635 recognized conifer species, comprising approximately two thirds of all gymnosperm species (Farjon and Page 1999) and representing around 70 genera, including the ecologically and economically important ones, such as Pinus, Abies, Larix, and Picea. By contrast, tremendous and more recent adaptive radiation (Kenrick 1999) has resulted in 250,000 angiosperm species (Kuzoff and Gasser 2000). Despite their relatively small numbers, many conifer species are widely distributed and have broad ecological amplitude, for instance, the Scots pine (Pinus sylvestris L.) is found from W. Europe to Asia at elevations ranging from sea level to 2,440 m altitude (Castro et al. 2004). There are some conifers are only dispersed in regions with similar environmental conditions, for example, the Chihuahua spruce (Picea chihuahuana Martinez) is confined to western mountain tops in Mexico (Ledig et al. 1997). Our intuition may mislead us into a notion that conifers have a broad plasticity and such a notion triggers a thought-provoking question: are we fully convinced to put forth alarming concerns about the impact of Introduction 2  climate change in conifers even including those that are distributed in a variety of habitats? Indeed, our planet enters a phase of anthropogenic climate change of unprecedented speed and magnitude. Projected change in global mean temperature over the next century are equivalent in magnitude to the change over 5,000 years following the Last Glacial Maximum, but are expected to occur 50 times faster (IPCC 2007). Globally, in such a context, species have responded by tracking the environment for which they are best suited through local adaptation, range shift, range reduction, or a combination of these (Walther et al. 2002; Parmesan and Yohe 2003; Cleland et al. 2007; Breshears et al. 2008). The rate of required change, however, may outpace the ability to respond, and species and even populations may become locally extinct after specific tipping points are reached. At the receding edge of species distributions in particular, future climate is likely to surpass adaptive capacity in many cases, resulting in extirpations (Davis and Shaw 2001). Natural selection may not result in efficient adaptation, as selection pressures are multi-directional, involving traits that may be inversely associated at the molecular levels (Jump and Peñuelas 2005). Natural selection may also lag behind the fast pace of climate change, possibly leading to genotypes that are maladapted to new environments (Jump et al. 2006). Knowledge about traits that vary with ecological niches should therefore help predict how these traits may evolve under climate change. Life-history traits, known as fitness components due to their predictable monotonic relationship with fitness, are related to the timing and success of development, reproduction, and senescence throughout the life cycle (Calow 1998). Environmental settings have appreciable influences on life history and in the life cycle, the timing of life-history traits (e.g., flowering, seed setting, seed size, seed number, seed germination constraint by seed dormancy depth (i.e., delayed onset of germination), etc.) are co-variated and thus coevolved. For example, life cycles with early flowering, small seeds, deep dormancy and slow germination are associated with habitats exposed to high temperature, low rainfall and high radiation (Vidigal et al. 2016); variations in seed dormancy and seed size often have a concomitant effect (reviewed by Baskin and Baskin (1998)) and are correlated in a negative manner (Thompson and Grime 1979; Grime et al. 1981; Rees 1996; Kiviniemi 2001; Larios et al. 2014; Vidigal et al. 2016); climate change is accelerating plant developmental transitions in temperate environments and advanced flower timing Introduction 3  increases dormancy intensities (Debieu et al. 2013; Springthorpe and Penfield 2015; Vidigal et al. 2016); early germination increases seed fecundity due to prolonged vegetative growth and nutrient accumulation but may also bring about high seedling mortality (Vidigal et al. 2016); and there exists a negative correlation between seed dormancy and longevity (Nguyen et al. 2012). In this dissertation, I mainly focus on two life-history traits; that is, seed dormancy and seed size. Seeds are the time capsules of life and seed dormancy is the intrinsic mechanism to lock that state. It temporarily blocks germination through adaptation to the prevailing environments so that germination is timed to avoid unfavorable environmental conditions for subsequent plant establishment and growth and thus sets the context for the traits that follow (Donohue et al. 2010). Notably, induction of primary dormancy was greatly influenced by the effect of maternal environments on embryo/endosperm (Schmitt et al. 1992; Donohue 2009; Postma and Ågren 2015) and/or on seed coat properties (MacGregor et al. 2015). Such effects can be passed down for generations (Galloway and Etterson 2007; Lu et al. 2016) and have been observed even in long-lived perennials, such as conifers (Stoehr et al. 1998).. Moist-chilling can, on the one hand, terminate dormancy, while under some conditions, extended chilling elicits non-dormant seeds into the dormancy cycling (i.e., secondary dormancy) (Penfield and Springthorpe 2012). To date, molecular underpinnings of seed dormancy emphasize the role of plant hormones (e.g., ABA, gibberellins, and auxin) and signaling (Finch-Savage and Leubner-Metzger 2006; Bewley et al. 2012). An increasing number of findings show that model plants (angiosperms) and conifers share some conserved mechanisms in the regulation of seed dormancy and germination (Forbis et al. 2002; Linkies et al. 2010; Hauser et al. 2011; Germain et al. 2012). This implies the possibility of studying how environmental cues modulate seed dormancy phenotypes (i.e., positive or negative selection) at molecular levels in conifers. Another important life-history trait is seed size. Seed size control is also regulated by hormone signaling cascades at embryogenesis (Hu et al. 2008; Footitt et al. 2011) with substantial environmental influences, such as temperature in the two preceding years. Empirical evidence favors the notion that seed production during mast years (i.e., good-seed years) is tightly related to high temperature in the previous spring and summer, late spring frost and summer rainfall of the last two years (Y.A. El-kassaby, per. comm.) Introduction 4  and the difference in temperature from one growing season to the next was modelled to predict the occurrence of mast years (Schauber et al. 2002; Smaill et al. 2011; Krebs et al. 2012). Considering yearly climatic variability, Kelly et al. (2013) developed a model based on temperature differentials over multiple seasons to predict seed yield and this model was further validated by Pearse et al. (2014). The robustness of these models emanated from the hypothesized correlation between seed size and environments and, in turn, lends support to the crucial role of climate in seed size modulations. Environmental cues are important selective forces in life-history evolution and the crucial stages for ecotype selection are at plant-to-seed and seed-to-plant transitions and when germinants sprout into seedlings (Chapters 2-4). Classic genetics and epigenetics approaches make up fundamental parts of studying adaptive evolution. In response to environmental signals, dormancy decay (i.e., seed-to-plant transition) is controlled by hormone flux and signaling (genotype effects) (Chapter 5). The environmental conditions during embryogenesis (i.e., plant-to-seed transition) shape epitypes and the epigenetic imprinting is mitotically propagated and thus these conditions entrain phenotypic variations throughout the whole life history (Chapter 6).  1.2 Research objectives This dissertation centres on two life-history traits (i.e., seed dormancy and size) in long-lived species (conifers) and intends to articulate two overarching questions: (1) What are prevailing environmental cues that comprise key selective pressures, and how do they affect adaptive evolution under climate change? (2) What is the genetic and epigenetic basis of adaptation in the studied phenotype? 1.3 Dissertation overview To attain these objectives, I divide my investigation into five blocks, which correspond to the five research chapters in this dissertation. Figure 1.1 conceptually depicts the flow of the dissertation and Table 1.1 summarizes the key components of each research chapter. Introduction 5     Figure 1.1 A flow diagram of this dissertation   Introduction 6  Table 1.1 Key components in each research chapter Chapter Research hypothesis Motivation Approach Predicted outcome 2 environmental settings at seed set affect seed dormancy intensities and germination performance.    • elucidate the association between germination timing and climate during seed set; • demonstrate germination performance in different germination niches; • predict the evolutionary direction of seed dormancy under climate change. 15 coniferous seed lots from three species; five manipulated and one non-manipulated conditions for germination; MANOVA for all germination parameters and ANOVA for each parameter; G×E interaction analysis using AMMI visualized by GGE biplot; PCA for core climatic variables and CCA for those climatic variables and germination timing. • strong and positive correlation between climate (e.g., temperature, precipitation) at seed set and germination timing; • conifer seed dormancy may deepen in climate change. 3 seed dormancy and size variations among lodgepole pine (Pli) seed lots across British Columbia (B.C.), Canada are correlated with climate at seed set. • discern the causes and consequences for the evolution of seed dormancy and size; • uncover critical climate variables for seed dormancy and size variations; • project Pli seed dormancy as climate changes. 83 Pli seed lots across B.C.; seed germination manipulated with or without moist-chilling; PLS analyses to refine environmental variables best explaining the germination performance (compared with PCA); likewise, MANOVA and CCA; broad-sense heritability (H2) based on dormancy index. • we’re able to report true driving forces in seed dormancy and size variations; • Pli seed dormancy may increase in B.C. 4 as the evolution of seed dormancy and size involves correlated selective pressures, the two traits may coevolve. • explore evolutionary trajectories of the two traits in scenarios of deterministic and stochastic temperature variations; • test the correlation between seed dormancy and size; • reveal ecological dynamics when temperature shifts or fluctuates.  adaptive dynamics (see Appendix A) • temperature shifts may or may not select for dormancy, which relies on which of the two antagonistic mechanisms dominate in evolution; • temperature shifts may be less conducive to the evolution of seed size; • negative correlation between the two traits; • ecological structures alter as affected by different evolutionary trajectories. 5 molecular mechanisms governing dormancy release by moist-chilling involve hormone flux and signaling in conifers (gymnosperms) as reported in model plants (angiosperms).  • decipher the hormone-based mechanisms that underpin dormancy alleviation and germination in response to temperature signaling (i.e., moist-chilling and transfer to germination conditions) in conifer. selection of one conifer population; transcriptomic profiling of putative genes in three phytohormone (ABA, GA and auxin) pathways; western blotting for selected proteins; hormone quantification. • the dormancy-to-germination transition is correlated with changes in hormone conjugations, signaling components, and their potential interactions in signaling cascades. 6 in response to external cues (e.g., temperature, phenology), seed set programs overridden by small RNAs control seed dormancy variation.  • describe the relative expression pattern of conserved microRNA populations; • characterize the feature of novel miRNA emergences; • unveil the extent to which environments account for the expression pattern of key genes and miRNAs involved in seed dormancy. small RNA sequencing across seed set in populations of white spruce and Arabidopsis; bioinformatics (statistical and evolutionary analyses) employed to analyze the sequencing data; validation by qPCR. • environmental cues trigger seed set to employ different sets of small RNAs at different expression levels to modulate dormancy variation.   Timing of seed germination is correlated with environments at seed set 7  2 Timing of seed germination is correlated with environments at seed set  2.1 Introduction The two most important transitions in the plant life cycle are reproduction (from plant to seed) and timing of seed germination or decay of seed dormancy (from seed to plant). Timing of seed germination is the earliest plant life-history attribute and sets the context for subsequent traits associated with fitness, thus influencing fecundity and survival (Donohue et al. 2010). In the seed-to-plant chronology, the environment of parental plants can exert a carry-over effect on the offspring beyond one generation (Kendall and Penfield 2012) and this is known  as environmental pre-conditioning (Rowe 1964). For example, seed germination variation is associated with cone-crop years in Picea glauca (Caron et al. 1993); germination strategy of Mediterranean pines follows a typical Mediterranean pattern, that is, early accomplishment of seed germination during rainy seasons facilitating seedling development during mild winters and following springs prior to harsh and water-stressed summers (Skordilis and Thanos 1995), thus indicating an association between seed dormancy and provenance. Of those environmental factors conducive to facultative levels of seed dormancy, temperature is quite critical (Morley 1958; Koller 1962; Bewley et al. 2012), which strongly mediates short photoperiods for dormancy response in woody species (Svendsen et al. 2007; Granhus et al. 2009; Kalcsits et al. 2009a). Moreover, growth cessation, bud set, and bud dormancy (note that dormancy in buds is similar in molecular mechanisms to that in seeds) are sequential and intricately connected processes in the annual cycle of plants and these processes can be accelerated by temperature in both coniferous and deciduous woody plant species (Kalcsits et al. 2009a; Tanino et al. 2010). One of the most important temperature parameters affecting life-history traits is heat sum (i.e., monthly or seasonal degree-hours/days above the 5°C threshold) (Van Dijk and Hautekèete 2007) and a model which emphasizes heat sum accumulation in combination with night length was developed, highlighting the role of temperature rather than photoperiod in favouring of the growth of Pinus sylvestris, Picea abies, and Betula pendula (Hänninen et al. 1990). Furthermore, influence of the circadian Timing of seed germination is correlated with environments at seed set 8  temperature is that the day temperature only affects the rate of dormancy development while the night temperature influences most other parameters including the depth of seed dormancy (Kalcsits et al. 2009b). In comparison with temperature, the ecological significance of the precipitation effect seems minor as most conclusions on the positive correlation between water availability/air humidity and seed dormancy are mainly drawn from studies of annual plants under controlled conditions (Steadman et al. 2004; Hoyle et al. 2008). The complex mechanism controlling the coupling of dormancy variation to temperature has just begun to be unravelled. In Norway spruce (Picea abies), environmental conditions such as temperature during reproduction can substantially affect progeny fitness (Johnsen et al. 2005; Kvaalen and Johnsen 2008; Granhus et al. 2009). More notably, the temperature during post-meiotic megagametogenesis (embryogenesis) and seed maturation could shift the growth cycle programme of embryos and give rise to significant and long-lasting phenotypic changes in the progeny (Skrøppa et al. 2007). Besides epigenetic mechanisms, genetic variation is also responsible for the timing of seed germination and its evolutionary consequences (Baskin and Baskin 1998; Donohue et al. 2010). The expression of DOG1 gene which governs seed dormancy variation in nature is influenced by seed maturation temperature (Chiang et al. 2011; Kendall et al. 2011) and the expression of DORMANCY-ASSOCIATED MADS-BOX (DAM) genes which are related to bud endodormancy are also induced by temperature (Horvath et al. 2010). In addition, germination phenology and dormancy induced by warm temperature are genetically associated at specific chromosomal regions as candidate loci were identified in Arabidopsis using quantitative trait locus analysis (QTLs) (Huang et al. 2010). Seed dormancy variation is also mediated by the receptiveness to dormancy-breaking factors or germination triggers (Black et al. 2006). The most conspicuous environmental changes affecting perennials are those associated with seasonality (Bradshaw 1965) with emphasis on temperature as the main cue for the timing of seed germination. Such environmental conditions can be described as the germination niche, part of the larger regeneration niche (Grubb 1977; Fenner and Thompson 2005). Moreover, phenotypic plasticity could operate on the expression of traits within particular environments and on the shape of the Timing of seed germination is correlated with environments at seed set 9  reaction norm (i.e., a set of phenotypes produced by a genotype over a range of environments) (Schlichting and Pigliucci 1998; Pigliucci 2001). By definition, phenotypic plasticity corresponds to the ability of an organism to react to an environmental input with a change in form, state, movement, or rate of activity (West-Eberhard 2003). It is an inherent mechanism with environmental influence, as the products of certain genes responsible for phenotypic plasticity are constitutively sensitive to environmental cues, such as temperature and/or light (Pigliucci 2001; Pigliucci and Murren 2003). Relatively, molecular mechanisms underpinning phenotypic plasticity are particularly understood for plant phytochromes correlated with light regime and/or light spectral quality (Schmitt and Wulff 1993; Andersson and Shaw 1994; Smith 1995). In this study, we selected three conifer species representing the interior (lodgepole pine (Pinus contorta var. latifolia) and “interior” spruce (Picea glauca (Moench) Voss × Picea engelmannii Parry ex. Engelm.) and coastal (western hemlock (Tsuga heterophylla (Raf.) Sarg.)) regions in British Columbia (BC), Canada, to test the hypothesis: patterns of timing of seed germination are correlated with environmental conditions during seed development. The influence of phenotypic plasticity on seed germination timing was also characterized during seedling emergence. Seed treatments consisted of manipulated conditions (stratification (i.e., moist-chilling), thermo-priming (15 or 20°C) and their combinations) and non-manipulation (control) to retain natural seed dormancy. The environmental conditions applied in this experiment were intended to simulate natural germination ecology (i.e., moist-chilling during winter) and the possible increase in temperature and early arrival of spring anticipated under climate change (IPCC 2007). To our knowledge, the timing of seed germination in association with climate parameters at seed development has not been studied in gymnosperms. The results obtained from this study are expected to shed light on future timing of seed germination under on-going climate change.   Timing of seed germination is correlated with environments at seed set 10  2.3 Materials and methods 2.3.1 Seed and climate data Seed material and climatic variables during seed maturation  We used five seed lots for each of the three studied species (lodgepole pine, “interior” spruce, and western hemlock), and their geographic locations, ecosystem zones, and attributes are provided in Table B.1 (Appendix B) and Figure 2.1. Since the used seeds were stored at -20°C under strictly controlled humidity and oxygen levels, we assumed that this storing condition did not affect either seed dormancy or timing of germination. Monthly and seasonal cumulative heat sums and additional 193 climate variables for each seed lot’s habitat were estimated using ClimateWNA (the average climatic data for the period between 1961-1990) (Wang et al. 2006a; Wang et al. 2012). A summary for climatic variables is given in Table 2.1.  Figure 2.1 Locations of the 15 seed lots marked on the map of ecosystem zones in BC, Canada (left) and ClimateBC (right) Note: lodgepole pine seed lot #: 3679, 4939, 8435, 40428, and 42255; “interior” spruce seed lot #: 33356, 35707, 37842, 39450, and 45353; and western hemlock seed lot #: 3439, 4094, 35571, 39235, and 53002. (Reprinted with permission of source: http://www.for.gov.bc.ca/hfd/library/documents/treebook/biogeo/biogeo.htm) Timing of seed germination is correlated with environments at seed set 11  Table 2.1 A list of important climatic variables Abbreviations Full names Eref06, 07, 08, 09, _summer, and _autumn June, July, August, September, summer, and autumn Hargreaves reference evaporation (caculated from temperature and solar radiation) DD_0_summer summer degree-days below 0°C PPT06, 07, 09, 10, and _autumn June, July, September, October, and autumn precipitation (mm) Tmax07 and _summer July and summer maximum mean temperatures (°C) PAS04, 05, and _spring April, May, and spring precipitation as snow (mm) DD18_04, _06, _07, 09, _10, _spring, and _autumn April, June, July, September, October, spring, and autumn degree-days above 18°C DD18 annual degree-days above 18°C DD5_07, 09, and _autumn July, September, and autumn degree-days above 5°C Tmin 04, 10, 11, _spring and _autumn April, October, November, spring and autumn minimum mean temperatures (°C) CMD09 and _autumn September and autumn Hargreaves climatic moisture deficit (calculated by Eref and precipitation) NFFD10 October number of frost-free days MAT mean aunnal temperature (°C) MAP mean annual precipitation (mm) AHM annual heat-moisture index (MAT+10)/(MAP/1000)) Tave09, 10, _spring, and _autumn September, October, spring and autumn mean temperature (°C)  2.3.2 Germination assay and evaluation Germination assay Each seed lot was represented by four replications of 75 seeds each in each treatment. All assays were conducted in a moisture-controlled germination chamber (moisture content (MC) ≈ 97%), using plastic transparent germination boxes (4.5×4.5×1.5cm) containing cellulose wadding (Kimpack®) and filter paper saturated with 50ml of distilled water. Replicates were randomly allocated on trays in the germination chamber with alternating temperatures of 30/20°C (light/dark) under 8-hour photoperiod provided by fluorescent illumination (≈ 13.5 μmol∙m-2s-1). Germination count was conducted over a 21-day period following standard International Seed Testing Association rules (i.e., recommended optimum germination conditions for the seed of a particular species) (ISTA 1999). Germinants were counted daily throughout the germination test and germinated seeds were removed. Seeds were counted as germinated if the radicle Timing of seed germination is correlated with environments at seed set 12  emerged to four times the seed length. The number of empty and dead seeds was determined in the end by cutting test and the total number of seed used in each replication was adjusted accordingly. The germination manipulated environments consisted of 21-day stratification followed by 3-day thermo-priming at 20°C (E1), 21-day stratification followed by 3-day thermo-priming at 15°C (E2), stratification (E3), 3-day thermo-priming at 20°C (E4), 3-day thermo-priming at 15°C (E5), and no manipulation (control, E6). Seed germination timing evaluation Germination parameters were estimated from cumulative germination curves fitting a mathematical function known as the four-parameter Hill function (4-PHF) specifically developed to gauge germination timing and seed dormancy (El-Kassaby et al. 2008) as follows: Y = Y0 + 𝑎𝑋𝑏𝑐𝑏+ 𝑋𝑏  where Y is the cumulative germination percentage at time X, Y0 is initial germination percentage tested immediately after seed release, a is the final germination percentage equivalent to germination capacity (GC), b is a mathematical parameter controlling the shape and steepness of the curve (the larger the b value, the steeper the rise toward a), and c is the time required to achieve 50% germination of the total germinated seed which denotes germination speed. We estimated the area under the germination curve (AUC) expressing the germination course, while the area between the non-manipulated and manipulated germination curves was used to quantify the degree of dormancy (Dormancy index: DI) (Fig. 2.2). This mathematical function also allowed the estimation of time at maximum germination rate (TMGR), lag time before germination onset (lag), and the duration between lag and c (Dlag-50). Parameters c, TMGR, Dlag-50, and lag have the same unit (day) and b, c, TMGR, and Dlag-50 are means to characterize germination speed (GS). Generally, c and TMGR are very close but c is more straightforward and more widely used. As such, c is most typical to represent germination speed and is used henceforth to represent GS. Comprehensively, three core germination terms, namely, AUC, GS, and GC were used to characterize germination curves and gauge the timing of germination. eqn 1 Timing of seed germination is correlated with environments at seed set 13  2.3.3 Experimental design and statistical analyses Experimental design  A modified approach to testing the evolution of plasticity ˗ resurrection ˗ was used, which allows for comparisons among plant genotypes from different environments, stored as seeds, and grown simultaneously under controlled conditions (Franks et al. 2008; Franks et al. 2014). A nested-factorial experiment implemented in a completely randomized design was used following the additive linear model: )()()( ijkmikjjkkijiijkm TCSTTCSy   where, μ is the overall mean, Si is the effect of the ith species (i = 1 to 3, fixed effect), Cj(i) is the effect of the jth seed lot nested within species (j = 1 to 5, random effect), Tk is the effect of the kth treatment (k = 1 to 6, fixed effect, STik is the interaction between ith species and kth treatment, TCkj(i) is the interaction between jth seed lot within ith species and kth treatment, and εm(ijk) is the residual term (m = 1 to 4). Stratification (moist-chilling) involved exposing the seed to moisture (MC ≈ 100%) under 2°C for 21 days prior to germination (ISTA 1999) while thermo-priming involved exposing fully soaked seed to moisture (MC = 100%) and relatively high temperature (15°C and 20°C) in darkness (Liu et al. 2013b). Time course (d)Germination %GSGClag(a)AUCTime course (d)DGSDIGermination %DGCAUG(b) Figure 2.2 Schematic representation of the cumulative germination curve for parameters used to characterize timing of seed germination Note: AUC: area under germination curve; DI (dormancy index): the area between germination curves of no treatment and any treatment; DGS: the diffrence in the germination speed of two treatments, where GS is expressed by the number of days to reach 50% of final germination between manipulated and eqn 2 Timing of seed germination is correlated with environments at seed set 14  control environments; DGC: the difference in the germination capacity (GC) or the final germination percentage; and lag: the lag time before germination onset. Statistical analysis Multivariate and univariate analyses of germination timing To examine how germination timing varied with different species and treatments (manipulated germination environments), the germination parameters were analysed individually (univariate: ANOVA) and collectively (multivariate: MANOVA), with the aid of the generalized linear model (GLM) procedure in SAS® (vers. 9.3; SAS Institute Inc., Cary, NC) (Manly 2005; Tabachnick and Fidell 2012). A total of 17 out of 360 observations did not converge using the 4-PHF and were excluded from the analyses (El-Kassaby et al. 2008). To meet ANOVA and MANOVA assumptions (i.e., normal distribution), the logarithmic transformation was applied to all germination parameters. Statistical significance was set at P < 0.05 and the corrected alpha level for each significance test of ANOVA was 0.00714 (0.05/7 tests). Genotype × Environment (G×E) interaction G×E analyses were conducted using the Additive Main Effect and Multiplicative Interaction (AMMI) (Gauch 1992) and visualized using the Genotype main effect and G×E Interactions biplot (GGE biplot) (Yan and Kang 2003). The original untransformed data were used for this analysis. AMMI is a unified approach that fits the additive main effects of genotypes and environments by the usual analysis of variance and then describes the non-additive parts by principle component analysis (PCA) fitted to the AMMI model as follows: 𝑌𝑔𝑒  =  𝜇 + 𝛼𝑔 + 𝛽𝑒 + ∑ 𝜆𝑛Υ𝑔𝑛𝛿𝑒𝑛𝑁𝑛=1 + 𝜃𝑔𝑒 + εger  where, 𝑌𝑔𝑒  is specific seed lot’s germination timing (genotype: g) in environment (non-manipulated or manipulated: e), 𝜇 is the grand mean, 𝛼𝑔 are the genotype mean deviations (genotype mean minus the grand mean), 𝛽𝑒 are the environment mean deviations, 𝜆𝑛 is the eigenvalue of PCA axis n, Υ𝑔𝑛 and 𝛿𝑒𝑛 are the genotype and environment PCA scores for PCA axis n, N is the number of PCA axes retained in the model, eqn 3 Timing of seed germination is correlated with environments at seed set 15  𝜃𝑔𝑒  is the residual, and εger  is the random error (the difference between the 𝑌𝑔𝑒  mean and the single observation for replicate r). In the ANOVA of a completely randomized block design for the G×E analysis, the following model was applied, ijkjkijjiijkl BGEEGY   where, μ is the overall mean, Gi is the effect of the ith “genotype”, Ej is the effect of the jth environment, GEij is the interaction of ith genotype with jth environment, Bjk is the effect of the kth replication in the jth environment, and εijk is the random error. To facilitate the interpretation of the AMMI analysis, the GGE biplot approach was applied using GLM and interactive matrix language (IML) procedures in SAS ((Yan 2001; Kang 2003; Yan and Tinker 2006), which is constructed by plotting the first principle component scores of genotypes (seed lots) and the environments (non-manipulated or manipulated) against their respective scores for the second principle component. In the biplot, the angles between the environment or genotype vectors proximately correspond to the correlation coefficients among the environments or genotypes. The cosine of the angle between two vectors approximates the correlation between them and the length of the vectors is proportional to the standard deviation within respective environments or genotypes. Virtually, an ideal environment has the longest vector of all test environments (most discriminating and informative) and is closely located on the abscissa (most representative); while above average performance of a genotype in an environment is that the angle between its vector and the environment’s vector is less than 90°. If an environment line drawn from the graph origin (0, 0) cuts the line between the genotypes at a 90° angle, this indicates that the genotypes would have the same performance of the concerned trait(s) in that environment; alternatively, if an environment line is skewed to one side, this indicates that the closer genotype would give a better performance in that environment. To rank the genotypes as per their performance in an environment, a line is drawn passing through the biplot origin and that environment and along it is the ranking of the genotypes’ performance. The presence of close associations within a single mega-environment represents the same eqn 4 Timing of seed germination is correlated with environments at seed set 16  genotype information and could be streamlined to fewer test environments. In summary, the biplot could well reveal the performance of genotypes under different environments and the most representative environment(s). Canonical correlation analysis between timing of seed germination and climate To test correlations between timing of seed germination and ecological habitats, canonical correlation analysis (CCA) was conducted, which deals with the relationship between two sets of variables or between two pairs of vectors. Parameters gauging ecological habitats (i.e., climate variables) were retrieved from ClimateWNA (see above). Due to the large number of climate data, principle component analysis (PCA) was firstly performed, to retain only the most intrinsically correlated climate variables. By using appropriate cut-offs for principle components (PCs), the representative parameters were selected. The threshold for significant test of canonical correlation is set at P < 0.05. Statistical analyses were conducted using SAS®. 2.4 Results  2.4.1 Phenotypic plasticity and seed germination timing Phenotypic plasticity of timing of seed germination  Phenotypic plasticity of seed germination timing was revealed by the multivariate and univariate statistical analyses (Table 2.2 and 2.3). The four multivariate analyses revealed that environmental conditions exerted significant differences on germination timing (Table 2.2). The univariate analysis supported the multivariate results and all sources of variation (species, seed lots, and treatments) were significant across the studied seven germination parameters (Table 2.3). With the exception of GC and b, species accounted for more than 70% of total variance for the remaining five germination parameters (AUC, GS, TMGR, Dlag-50, and lag), suggesting that species have contrasting germination timing as indicated by their varied responses to the different manipulations (treatments) (given species as random effect, data not shown). It should be noted that the two interaction terms (ST and TC) were significant, indicating that species and seed lots within species showed different response to the treatments applied, hence indicative of differences in phenotypic plasticity (Table 2.3).  Timing of seed germination is correlated with environments at seed set 17  G×E analysis unravels the phenotypic plasticity of timing of seed germination  The significant interactions of species and seed lots within species with their corresponding environments were further investigated graphically to illustrate the phenotypic plasticity of timing of seed germination (Fig. 2.3). Compared with lodgepole pine (AUC: 38.34-77.96; GS: 3.59-8.01; and GC: 63.67-100.00) and “interior” spruce (AUC: 36.19-74.64; GS: 4.23-10.31; and GC: 72.16-99.06), western hemlock (AUC: 5.00-63.79; GS: 6.16-18.51; and GC: 31.05-97.00) showed greater plasticity as quantified by the differences between the highest and lowest responses across germination environments (Fig. 2.3). Furthermore, compared with non-manipulated environment (E6 in Figure 2.3), thermo-priming at 15°C (E5) was detrimental to GC in western hemlock (seedots #3439, #35571, #39235, and #53002) and “interior” spruce (seed lot #37842); however both AUC and GS showed corresponding improvement. This is presumably attributable to supraoptimal high temperature that gives rise to thermoinhibition (Toh et al. 2008). In pursuit of further analysis of the G × E interactions, the AMMI analysis of variance was performed, showing that the variations in germination parameters can be well explained by the data (R2 =0.985, 0.878, and 0.992cca for AUC, GS, and GC, respectively) and that differences between genotypes (i.e., seed lots) accounted for large portion of the total variation SS (67, 60, and 31% for GC, AUC, and GS, respectively) (Table 2.4). Moreover, the environment (treatments) captured between 25 and 37% of the total variation, leaving a small portion of the variation in the G×E interaction (3, 6, and 22% for AUC, GC, and GS, respectively), indicating that G×E greatly influenced germination speed (GS). The sources of variation of replications in their environments and residual accounted for only 1.0, 0.8, and 12.2% for AUC, GC, and GS, respectively, supporting the model’s goodness of fit. The GGE biplot of the AMMI analysis showed that Factor 1 (PC1) was dominantly more important than Factor 2 (PC2) for seed lot evaluation, as indicated by the 68.7 vs. 17.7, 85.8 vs. 9.6, 75.5 vs. 17.7% GGE variation explained by the two axes for AUC, GS, and GC, respectively (Fig. 2.4). As such, the biplot explained 86.4, 95.5, and 93.2% of the total variation relative to genotype (i.e., seed lots) plus G×E for AUC, GS, and GC, respectively, and thus the goodness of fit of the biplots was quite good across the three main germination parameters. Figure 2.4 also suggested that the manipulated environments could be Timing of seed germination is correlated with environments at seed set 18  partitioned into two clusters or mega-environments corresponding to E1, E2, and, E3 (all include stratification) and E4, E5, and, E6 (do not include stratification) based on the wide obtuse angles (i.e., strong negative correlation), implying strong crossover effect (i.e., genotypes changed ranking from environment to environment). E1 (stratification + thermo-priming at 20°C) and E6 (non-manipulation) were the most discriminative as well as representative environments in their respective clusters (Fig. 2.4). The five genotypes of western hemlock had better AUC and GC performance (or improvement) than those of lodgepole pine and “interior” spruce in E1 and GS in E6. By contrast, lodgepole pine and “interior” spruce were superior to western hemlock in GS in E1 (Fig. 2.4). The genotype performance was intertwined among the three species in terms of AUC and GS in E6 and lodgepole pine and “interior” spruce genotypes had similar performance in both E1 and E6 (Fig. 2.4). When different genotypes show similar seedling emergence under the same manipulated germination environment, this is indicative of phenotypic plasticity of timing of seed germination. Based on the results of GGE biplot (Fig. 2.4), different genotypes occupied the same position and this is attributable to phenotypic plasticity of timing of seed germination, genotypes and their interactions with germination environments. It is interesting to note that interior and coastal seed lots contributed dissimilarly to germination timing. Although different species were represented by seed lots originating from the same ecosystem zone (i.e., similar habitat) [#33356 (spruce), #4939 (pine), and #53002 (hemlock)], [#35707 (spruce) and #39235 (hemlock)], and [#45353 (spruce) and #35571 (hemlock)], they produced contrasting germination timing behaviour (Table B.1 and Fig. 2.1), indicating that species account for major differences as supported by their high percentage of total germination variation (Table 2.3). These results indicate that genetics plays an important role in phenotypic plasticity of germination timing, which is in line with what was found by Fernández-Pascual et al. (2013), and their genetic effects on the phenotypic plasticity between interior and coastal species are more pronounced than those within interior or coastal species. It is noteworthy to mention that western hemlock seed lots were sampled from its coastal and interior ranges (diversity of habitat) while lodgepole pine and “interior” spruce were sampled from their respective predictable habitat. Timing of seed germination is correlated with environments at seed set 19  2.4.2 Heat sums and germination timing Heat sums during seed maturation correlated with timing of seed germination  We selected three environments for subsequent analysis, E1 and E6 as representative of the two most discriminative environments (see above) and E3 (stratification) as it is the standard treatment for conifer seed utilization (ISTA 1999) (Fig. 2.4). Dormancy index (DI) comparison among the studied three species indicated that “interior” spruce benefited the least from stratification (black column: difference between stratification and non-manipulated environments) (Fig. 2.5). However, the combined effect of stratification followed by thermo-priming at 20°C (black + white columns) was favourable for western hemlock as it exhibited the greatest increase across its five seed lots while lodgepole pine and “interior” spruce displayed a similar increase that is not as substantial as that observed for western hemlock (Fig. 2.5). Difference of germination speed (DGS) mirrored DI results (Fig. 2.5), suggesting that temperature was a pivotal germination cue for western hemlock; however, thermo-priming was of little beneficial effect, though not detrimental, in heightening germination capacity (DGC) (Fig. 2.5). Overall, western hemlock had substantially lower AUC (8.43 ± 0.86 vs. 45.84 ± 3.74 (lodgepole pine) and 46.55 ± 1.67 (“interior” spruce)) and GC (58.60 ± 17.85 vs. 75.05 ± 6.13 (lodgepole pine) and 87.35 ± 2.55 (“interior” spruce)); however, this trend was reversed for GS (18.13 ± 0.87 vs. 7.84 ± 0.15 (lodgepole pine) and 9.45 ± 0.19 (“interior” spruce)), indicating poor but faster germination under non-manipulated environment (Fig. 2.6). Since temperature was the main factor used to manipulate germination environment, we estimated each seed lot’s native environment heat sums (Fig. 2.7). Generally, heat sums of western hemlock were higher than “interior” spruce followed by lodgepole pine, reflecting its habitat (low elevation) (Fig. 2.7d). This record was positively associated with the increment of DI and DGS when seeds were manipulated with thermo-priming after stratification (Fig. 2.5 and Fig. 2.7). Judging by individual seed lot, seed lot #53002 and #35571 had the highest and lowest heat sums in western hemlock, respectively, which corresponded to the highest and lowest increment in terms of DI and DGS when seeds underwent thermo-priming after stratification (Fig. 2.5 and Fig. 2.7). Lodgepole pine and “interior” spruce had similar heat sums across seed Timing of seed germination is correlated with environments at seed set 20  lots, among which seed lot #37842 had the highest heat sums from April to July (data not shown) and correspondingly had the highest increment of DI and DGS when seeds were given thermo-priming after stratification (Fig. 2.5 and Fig. 2.7).  Timing of seed germination is correlated with environments at seed set 21  Table 2.2 MANOVA for the seven germination parameters MANOVA test† F Value Num DF†† Den DF†† Pr > F Wilks' Lambda 351.35 12 576 <.0001 Pillai's Trace 63.62 12 578 <.0001 Hotelling-Lawley Trace 1380.12 12 444.92 <.0001 Roy's Greatest Root 2767.80 6 289 <.0001 † null hypothesis for the four statistic tests (using different algorithms) is the centroids of the seven germination variables are equal across the six environments; †† Num DF and Den DF represent numerator and denominator degrees of freedom, respectively.  Table 2.3 Expected Mean Squares (EMS) and variance components for each germination parameter SOV† df EMS  Parameters†† (% explained variance)  AUC GS GC TMGR Dlag-50 lag b Si††† 2 120𝜑𝑠 + 24σ𝑐2 + σ𝑒2 -** -** -** -** -** -** -** Cj(i) 12 24σ𝑐2 + σ𝑒2 52.16** 60.12** 19.23** 65.23** 18.38** 31.23** 12.75** Tk††† 5 60φ𝑡+20σ𝑠𝑡2  + 4σ𝑡𝑐2  + σ𝑒2 -** -** -** -** -** -** -** STik††† 10 20𝜑𝑠𝑡 + 4σ𝑡𝑐2  + σ𝑒2 -** -** -** -** -** -** -** TCkj(i) 20 4σ𝑡𝑐2  + σ𝑒2 16.44** 11.00** 20.56** 6.97 24.81** 4.14 17.27 corrected εm(ijk) 293 σ𝑒2 31.40 28.88 60.21 27.80 56.81 64.63 69.98 †S, species, C: seed lot/species, T: treatments, ST: species × treatments interaction, TC: seed lot/species × treatments interaction, and ε: residual term †† see text for germination parameters explanation; †††S, T and ST no variance components or percent of total variation were estimated for the fixed effect; **, P < 0.05; -, no percent of total variation estimated for the fixed effect.  Table 2.4 AMMI analysis of variance for timing of seed germination in three conifer species SOV df Parameters† AUC GS GC SS % of total SS  R2 SS % of total SS R2 SS % of total SS R2 Environment (E) 5 44904.0** 33.32 0.985 26969.8** 37.14 0.878 1305.9** 24.52 0.992 Rep (Environment) 3 27.1 0.02 84.3 0.12 0.9 0.02 Genotype (G) 14 80947.9** 60.07 22361.9** 30.79 3554.2** 66.74 G × E 70 4696.5** 3.49 16333.2** 22.49 309.7** 5.82 Residual 250 1975.9 1.47 8891.7 12.24 43.4 0.81 corrected total†† 342 134754.6  72617.8  5325.3  †AUC, area under the germination curve, GS: germination speed, and GC: germination capacity; †† due to 17 of total 360 observations being rendered meaningless using our method to converge; **, P < 0.05. Timing of seed germination is correlated with environments at seed set 22  Table 2.5 Canonical correlation analysis between individual variables with their own and with the opposite set of variables Variables Timing of SG† Ecology Germination parameters†† AUC 0.8206 0.6867 GS -0.9013 -0.7540 GC 0.3684 0.3083 b 0.3617 0.3027 Dlag-50 -0.8983 -0.7520 TMGR -0.8855 -0.7410 lag -0.7767 -0.6500 Climate parameters††† Elevation 0.7230 0.8639 MAT -0.6666 -0.7970 DD_18 0.6691 0.7994 Tave_at -0.6377 -0.7620 DD5_at -0.6038 -0.7220 DD_18_at 0.6361 0.7601 DD_18_09 0.6101 0.7290 PAS04 -0.0432 -0.052 PAS05 0.0892 0.1065 Eref06 -0.1109 -0.1330 † Timing of SG denotes timing of seed germination. †† See text for each germination parameter description. ††† Climate parameters abbreviation: MAT, mean annual temperature (°C); DD_18, degree-days below 18°C (heating degree-days); Tave-at, autum (Sep.-Nov.) mean temperature (°C); DD5_at, autumn degree-days above 5°C; DD_18_at, autumn degree-days below 18°C; DD_18_09, September degree-days below 18°C; PAS04, April precipitation as snow; PAS05, May precipitation as snow; and Eref06, June Hargreaves reference evaporation.Canonical variate % explained variance by Their own The opposite Timing of SG 49.85% 34.92% Ecology 66.49% 46.57% A B Timing of seed germination is correlated with environments at seed set 23   Figure 2.3 G×E interaction involving five seed sources for each of three conifer species Note: western hemlock: Hw, lodgepole pine: Pli, and “interior” spruce: Sx; and six manipulated environments (E1-E6, see text for description) measured by three germination parameters (area under germination curve: AUC, germination speed: GS, and germination capacity: GC). Timing of seed germination is correlated with environments at seed set 24  Factor 1 (68.71 %)-1.0 -0.5 0.0 0.5 1.0Factor 2 (17.72 %)-1.0-0.50.00.51.0H5AUCH4H1H2H3P5P4P2P1P3S4S2S5S1S3E2E5E6E1E3E4 GSFactor 1 (85.83 %)-1.0 -0.5 0.0 0.5 1.0Factor 2 (9.68 %)-1.0-0.50.00.51.0H2H1H3H4H5P4P3P2P1P5S5S3S1S4S2E4E1E3E5E6E2Factor 1 (75.50 %)-1.0 -0.5 0.0 0.5 1.0Factor 2 (17.74 %)-1.0-0.50.00.51.0H2GCH4H3H1H5P5P4 P2P1P3S3S5 S2S1 S4E6E2E3E1E4E5 Figure 2.4 The Genotype main effect and G×E Interactions (GGE) biplot for environment centered analysis of timing of seed germination of three conifer species Note: area under germination curve: AUC, germination speed: GS, and germination capacity: GC (data not transformed); H1-H5, P1-P5, and S1-S5 represent five seed lots for western hemlock, lodgepole pine, and “interior” spruce, respectively. Timing of seed germination is correlated with environments at seed set 25  333560 10 20 30 40 50 6035707378423945045353367949398435404284225534394094355713923553002Interior spruceLodgepole pineWestern hemlockDI  -14 -12 -10 -8 -6 -4 -2 03335635707378423945045353367949398435404284225534394094355713923553002Interior spruceLodgepole pineWestern hemlockDGS0 10 20 30 40 50 603335635707378423945045353367949398435404284225534394094355713923553002Interior spruceLodgepole pineWestern hemlockDGC  Figure 2.5 Dormancy index (DI), difference of germination speed (DGS), and difference of germination capacity (DGC) of the 15 seed lots of the three studied species  Note: Black column represents difference between non-manipulated (control) and stratification environments and the entire horizontal column (black plus white) represents difference between stratification followed by thermo-priming at 20°C and non-manipulated environments; and the white column represents the improvement or decline observed after stratification followed by thermo-priming at 20°C. Data represent the average of four replicates of each environment. Timing of seed germination is correlated with environments at seed set 26  No manipulation environment020406080100GS (day)GC (%)AUC (area)   Figure 2.6 Germination parameters (AUC, GS, and GC) profiling for non-manipulated environment (control) across 15 western hemlock, logepole pine, and “interior” spruce seed lots Note: area under germination curve: AUC, germination speed: GS, and germination capacity: GC  3439 4094 35571 39235 53002 3679 4939 8435 40428 42255 33356 35707 37842 39450 45353 Western hemlock Lodgepole pine “interior” spruce Timing of seed germination is correlated with environments at seed set 27   Figure 2.7 Monthly cumulative heat sums for seed lots of “interior” spruce (a), lodgepole pine (b) and western hemlock (c), and for the three species (d)Timing of seed germination is correlated with environments at seed set 28  2.4.3 Climate and germination timing Timing of seed germination strongly correlated with habitat ecology during life-history transition Since patterns of timing of seed germination were associated with heat sums during seed development, correlation analysis of germination timing and respective ecological habitat was performed. Firstly, a PCA on 193 climatic variables showed that PC1 and PC2 accounted for 56 and 25% of the total variations, respectively, and were temperature- and precipitation-related components, respectively (Fig. 2.8). Using cut off ± 0.98 and ± 0.80 for PC1 and PC2, respectively, nine variables were selected; namely, MAT, DD_18, Tave_at, DD5_at, DD_18_at, DD_18_09, PAS04, PAS05, and Eref06 (see Fig. 2.8 for variables explanation). In the canonical correlation analysis (CCA), only one (CCA1) out of the total seven canonical variates was significant across all tests (not shown). The loadings and cross loadings indicated that five out of seven variables regarding timing of seed germination (SG), namely, AUC, GS, Dlag-50, TMGR, and lag, were strongly correlated with their own canonical variates (i.e., timing of SG, >0.80 or <-0.80) as well as with the opposite canonical variates (i.e., Ecology, >0.65 or <-0.65) (Table 2.5A). Likewise, the geographical variable (i.e., elevation) and all temperature-related variables were strongly or moderately correlated with their own canonical variates (i.e., Ecology, >0.70 or <-0.70) and the opposite canonical variates (i.e., timing of SG, >0.60 or <-0.60), respectively (Table 2.5A). However, all the precipitation-related variables were neither correlated with their own canonical variates nor with the opposite canonical variates (range: -0.2 to 0.2) (Table 2.5A). Based on covariate matrices for ‘timing of SG’ canonical variate, 49.85 and 34.92% of variance was explained by the canonical variates for the same group of variables and for the opposite group of variables, respectively; for ‘Ecology’ canonical variate, 66.49 and 46.57% of variance was explained by the canonical variates for the same group of variables and for the opposite group of variables, respectively (Table 2.5B). This suggested that timing of seed germination was strongly correlated with their habitat ecology. Timing of seed germination is correlated with environments at seed set 29  PAS04    PAS05PC1 (55.56%)-1.0 -0.5 0.0 0.5 1.0PC2 (25.06%)-1.0-0.50.00.51.0Erf06MATTave_atDD5_atDD_18DD_18_atDD_18_09 Figure 2.8 Correlation circle of PCA based on climatic data estimated by ClimateWNA Note: Parameters in red circles emblematically represent the variables selected for canonical correlation analysis. See Table 2.5 for the full name of the climate parameters abbreviations. 2.5 Discussion  All evolutionary change involves changes in development (Stearns and Hoekstra 2000), and development is intrinsically plastic such that the whole life process is possible (Behera and Nanjundiah 1995). Unlike other variables, timing is directional and completely asymmetric: early events can influence later ones but not vice versa. Therefore, studying timing of seed germination in combination with seed development is of apparent importance (Finch-Savage and Leubner-Metzger 2006; Bewley et al. 2012). In the present study, we demonstrated a strong correlation between timing of seed germination and seed developmental environment, particularly, temperature-based climatic variables, such as, heat sums. We also elucidated the phenotypic plasticity of timing of seed germination in five manipulated germination environments using two representative environmental regimes (i.e., non-manipulated (control) and manipulated through 21-day stratification at 2°C followed by 3-day thermo-priming at 20°C) gauged by three representative parameters (i.e., AUC, GS, and GC). We therefore reinforced that timing of seed germination is correlated with Timing of seed germination is correlated with environments at seed set 30  temperature-based environmental conditions during seed development, though affected by the phenotypic plasticity during seedling emergence. 2.5.1 Phenotypical plasticity and environmental uncertainties Recent years have seen mounting interest in studying heritability and phenotypic plasticity with respect to the evolution of life-history traits, including seed dormancy (Schlichting and Pigliucci 1998; Pigliucci and Murren 2003; West-Eberhard 2003; de Jong 2005). As early as the 1940s, Waddington (1942) proposed that an environmentally stimulated phenotype (i.e., plasticity) could eventually become converted into a fixed response to prevalent environmental conditions (assimilation) through continued selection, which is currently termed as genetic assimilation (Pigliucci and Murren 2003). Most organisms have evolved a certain degree of phenotypic plasticity of a variety of traits, which can lower the deleterious effects of novel environments, so that they exhibit greater stability with respect to components of fitness over a broad range of environments, and thus promote survival (West-Eberhard 2003; Bell 2008), as demonstrated in the classical study of Clausen and Hiesey (1960) on Achillea. A history of phenotypic plasticity could increase the rate of adaptation in a new environment; however, the magnitude of this change hinges on the strength of selection in the original environment (Fierst 2011). Global and regional fluctuations in climate and ecology could produce a prolonged, though intermittent, evolutionary trend in an intermittently expressed correlated set of traits (West-Eberhard 2003). As a result, environmental fluctuations preclude optimal adaptation to any single environment (Meyers and Bull 2002) and plastic genotype-by-environment interactions may result in a release of heritable variation in a novel environment (Hermisson and Wagner 2004; Fierst 2011), and may ultimately shape the response to selection in the new environment (Price et al. 2003). Empirical studies revealed a variety of mechanisms to cope with ecological uncertainties (Meyers and Bull 2002). Bet hedging, first proposed by Cohen (1966) favoured a non-genetic probabilistic germination strategy. It explains delayed germination of seeds of an annual plant in a variable environment, and with seeds that germinate stochastically, a parent plant can be "assured" that at least some of its progeny will survive (Bulmer 1994). Moreover, Bull (1987) raised the hypothesis that early germination is risky to survival, differentially among years, but early viable Timing of seed germination is correlated with environments at seed set 31  germinators have much higher fecundity than late germinators. Epigenetic regulation is thought to be evolutionarily useful for responding to fluctuating environments over a relatively short span (e.g., on the order of a single life cycle) (Jablonka and Lamb 1998), and the dynamics of epigenetic regulation during phase transitions in yellow cedar (Chamaecyparis nootkatensis D. Don) and Arabidopsis have been described (Müller et al. 2012). Possibly, epigenetic mechanisms underlie bet-hedging in the timing of seed germination.  Figure 2.9 A proposed model explaining regulation via molecular mechanisms and environmental cues during transitions of the plant life cycle Notwithstanding the importance of phenotypic plasticity and bet-hedging strategies in the face of variable and sometimes unpredictable conditions, many traits remain remarkably stable, which is termed as robustness (Waddington 1957). The advantage of robustness is that it allows the individual to develop fundamental traits, independent of fluctuations in the environment (Debat and David 2001). Such robustness is based on the theory of canalization; i.e., the property of a developmental process, of being to some extent modifiable, but to some extent resistant to modification (Waddington 1942; 1961). However, hidden genetic variance could be released due to a major mutation or environmental stress regardless of whether the genotype is canalized or not (Hermisson and Wagner 2004). Timing of seed germination is correlated with environments at seed set 32  Taken together, we summarized possible strategies involved in the transitions of the plant life cycle (Fig. 2.9). After fertilization, seeds begin the plant-to-seed transition. During the seed developmental phase, factors in the seed’s developmental environment such as temperature plays a crucial role, as it can affect the imprinting or memory of gene expression via genetic and epigenetic mechanisms and determines subsequent life-history traits. In the seed-to-plant transition, seeds can germinate in response to appropriate germination triggers by executing the previously “memorized” mechanisms. Besides, other strategies including phenotypic plasticity, bet-hedging and robustness are also involved in this transition. 2.5.2 Climate change and plasticity Global warming has negative impacts on forest disturbances by altering the frequency, intensity, duration and timing of insect and pathogen outbreaks, exotic species, invasions, fire, and drought (Dale et al. 2001). Shifts in annual timing of life-history events are a common response of populations to climate change (Forrest and Miller-Rushing 2010; Visser et al. 2010). The relative importance of temperature regulation of the dormancy cycle is anticipated to profoundly increase with current climate change and phenotypic plasticity is a primary means by which plants cope with global change scenarios (Matesanz et al. 2010; Nicotra et al. 2010). In the context of global climate change, trees could migrate, through seed and pollen, to more suitable habitat, and thus maintain their ‘bioclimatic envelops’ (Kremer et al. 2012). However, a single pollen grain only carries half the number of alleles compared with a seed and only seeds can establish a new population in a new habitat. On the other hand, documented wind-driven effective seed dispersal of forest trees is only confined to a few kilometers distance (around two orders of magnitude shorter than effective pollen dispersal) (Kremer et al. 2012). Consequently, sessile and long-lived organisms such as trees are likely to confront climate change in their original habitats and this challenge cannot be met by genetic changes (Bradshaw 1965). Furthermore, genetic variation in germination phenology is clinal, associated with a climate gradient characterized by increasing temperature in summer and rainfall in winter (Montesinos-Navarro et al. 2012). With shortening of winter and increasing length of the growing season (Robeson 2004; Schwartz et al. 2006), seeds may remain partially dormant in spring and need an extended time to germinate. Depending on whether cold stratification is minimally met or exceeded at present, shorter Timing of seed germination is correlated with environments at seed set 33  winters may delay or advance germination, respectively (Walck et al. 1997). In North America, the number of chilling days has remained sufficient for vegetation dormancy decay north of 40°N (BC latitude: north of 54°N) resulting in an advanced green-up with earlier springs (Zhang et al. 2007) (note that dormancy in buds and seeds is controlled by similar molecular mechanisms). This indicates that though seeds in natural habitats remain un-germinated until spring, early spring warm-up accelerates germination, as shortened winters have not yet affected dormancy decay. Predictably, unimodal heat sum curves in Figure 2.7 will shift to the left (i.e., reaching peaks earlier) and their shoulders will be wider due to milder temperature in the future, and thus dormancy will tend to increase owing to the positive correlation of climatic variables (i.e., milder winter in the future) and dormancy variations we have explored. In addition, variation in the geographic distribution, climate conditions, habitat preferences, and life cycle of species can affect the favourable period of seedling establishment (Vranckx and Vandelook 2012; Carta et al. 2014). Current warming in the north has proven to improve tree survival and growth, and increase sexual reproduction, pollen production, and mature filled seed production in disparate parts of species range (Rehfeldt et al. 2002; Reich and Oleksyn 2008; Alberto et al. 2013). Investigation of phenological effect on ecosystem productivity across temperate forest types and between spring and autumn seasons also shows that an extended growing season can increase net productivity despite increased carbon loss at high temperatures (Richardson et al. 2010). Taken together, the changing climate in the north will probably result in improved germination, growth, and establishment. The timing of germination and its evolution in the field involve many environmental and genetic facets including complex environment cues. Our study highlighted that though the phenotypic plasticity influences timing of seed germination under manipulated germination conditions, seed germination timing remains highly associated with temperature-based environments during seed development in conifers. This study provided insight into the germination niche as the vulnerability of seed germination timing to seed developmental environments and made possible the prediction of timing of seed germination based on current climate change. Contribution of environmental signals to life-history traits at life-cycle transitions 34  3 Contribution of environmental signals to life-history traits at life-cycle transitions 3.1 Introduction Climate change has already altered the timing of major life-history transitions, such as seed germination timing (from seed to plant). Timing is directional and completely asymmetric and the timing of seed germination is the earliest life-history trait that is expressed and sets the context for the traits that follow (Donohue et al. 2010). It is controlled by the level of seed dormancy; whereby dormant seeds await germination cues for dormancy release (Baskin and Baskin 1998; Finch-Savage and Leubner-Metzger 2006). Moreover, early developmental stages of plants are more sensitive to environmental perturbations than adult stages and represent a major bottleneck to regeneration from seeds (Johnsen and Skrøppa 1996; Hedhly et al. 2009). At the core of plant regeneration, temperature and water availability (or precipitation) are critical drivers for a plant’s distribution (Woodward and Williams 1987). Hence, climate-changed plant regeneration will be affected in both temperature- and moisture-controlled ecosystems (Walter and Breckle 2002) and, consequently, a lot of plant regeneration climate research has been directed at tundra and boreal forest, and treeline ecotones (reviewed by Walck et al. (2011)). Seed size is another important life-history trait and subject to changing environmental settings. The palaeontological record suggests that seed size remained small across all plants until the Cretaceous period (124 MYA). Seed size began to increase after the Cretaceous-Tertiary transition (65 MYA). One of the most popular explanations is that climate change gives rise to seed size variation, which took place during that period (Eriksson et al. 2000). On the ecological timescale, empirical studies lend support to the impact of environment on seed size variation. Since the early 1950s, effects of environmental stimuli, such as temperature and photoperiod on seed size and weight have been noted. Chenopodium polyspermum L. seeds from mother plants grown in long days have lower germination frequency and thicker seed coats when compared to seeds from short days (Pourrat and Jacques 1975). Large seed size with more provisions stored for seedlings may be favorable in variable environments (Venable and Brown 1988). Seed plants have a general trend of increasing embryo to seed ratio (E:S) in morphological seed dormancy and the shift in E:S Contribution of environmental signals to life-history traits at life-cycle transitions 35  is likely a heterochronic change, having vital implications to life history of seed plants (Forbis et al. 2002; Friis et al. 2015). A priori, the environment plays a crucial role in life-history traits in general and seed size in particular. Adaptive phenotypic plasticity underpins rapid phenological shifts in response to climate change and evolves when cues reliably predict fitness consequences of life-history decision (Simons 2014). This was evident by the performance of seedlings produced by central European trees growing in central Norway as they expressed phenology similar to that of their adjacent ecotype and were exceedingly different from those produced at their original habitat (Skrøppa et al. 2010). Differences in adaptive traits between populations are inconsistent with the Mendelian genetic framework and probably modulated by epigenetic mechanisms (Yakovlev et al. 2012). Germination-cuing under favorable conditions is similar within species; however, these conditions do not persist throughout the seasons. Likewise, populations growing in different ecological niches may have different degrees of exposure to unfavorable environments. As such, the prevention of germination of some seeds even under favorable conditions would be of significance. When environments fluctuate unpredictably, a “bet-hedging” strategy (Slatkin 1974) is expected to spread germination over time to reduce the risk of outright extinction. This results in the evolution of traits that maximizes the geometric-mean fitness by reducing fitness variance over generations (Gillespie 1977). To date, much of the evidence for bet-hedging remains restricted to plants with simple life-histories (e.g., annuals) (Childs et al. 2010). A continuously changing environment is constantly selecting for new adapted genotypes resulting in greater genetic diversity (Jump et al. 2009). Adaptive evolution characterized by the genetic architecture allows population persistence in the long term (Lande and Shannon 1996). However, sustained directional selection due to climate change could potentially eliminate a proportion of the genetic variation needed for continued adaptation. To date, it remains unclear whether adaptive evolution can keep pace with climate change (Etterson and Shaw 2001). Life-history strategy for long-lived organisms is influenced primarily by survival (Adler et al. 2014). Evergreen coniferous forests in the Pacific Northwest are unique among the Northern Hemisphere Contribution of environmental signals to life-history traits at life-cycle transitions 36  temperate forests in their species composition and high productivity (Waring and Franklin 1979). Our study species, lodgepole pine (Pinus contorta Dougl.), is an aggressive pioneer species distributed over wide geographic and ecologic ranges across British Columbia, Canada and is therefore expected to display a wide spectrum of dormancy variation owing to adaptation to diversified local habitats (Plomion et al. 2007). Cone and seed production in lodgepole pine is not as cyclic as in many other conifers. Cone drop soon after seed maturation is commonly observed in coastal areas but lodgepole pine growing in the interior tends to be serotinous, indicating that mature cones do not drop or open to release seeds unless exposed to fluctuating high temperature during a prolonged hot summer and low autumn temperatures or due to fire or insect damage (Fowells 1965; Owens et al. 1981; 1982). Most serotinous cones take several years to open and seeds are released in large quantities; consequently, any seed collection made from a single tree consists of a mixture of different seed-crop years. Before the cones eventually open, seeds are wrapped and sealed by scales and sticky resin vesicles without exposure to any germination cues (such as moisture, oxygen, etc.); thus, we assumed that seed dormancy in lodgepole pine is least affected over its natural storage period1. Seed dormancy is an intrinsic attribute affecting regeneration dynamics and seed size is one of the vital determinants for the evolution of seed dormancy. The objectives of this study are to evaluate the contributions of local environmental effects during life-cycle transitions to seed dormancy and size variations of lodgepole pine populations across British Columbia, Canada and to predict how life-history traits evolve locally under ongoing climate change. We assume that genetic effects are constant (i.e., anchored at equilibrium levels) across study periods and our models emphasize the dimension of environmental effects. Studies of seed dormancy and size allow us to investigate the relationship between                                                       1 In addition, although seed ageing affects seed performance, we assume that seed dormancy phenotype of our studied conifer species is least affected during the after-collection storage, because conifer seed tests show that most conifer species exhibit slow deterioration rates bounded within the range of -0.05 to -0.50% germination per year and only three species have deterioration rates greater than 1% per year (western redcedar (Thuja plicata Donn ex D. Don): -1.23%, western hemlock (Tsuga heterophylla , (Raf.) Sarg.): -1.13%, and red fir (Abies procera Rehder): -1.02%) (D. Kolotelo, Ministry of Forestry, Lands and Natural Resources, pers. comm.). Contribution of environmental signals to life-history traits at life-cycle transitions 37  these two life-history traits and their relationship with environments in life-cycle transitions. This study can also contribute to providing the missing empirical evidence of bet-hedging strategy in long-lived perennials. 3.2 Methods 3.2.1 Population and climate data Plant materials, and current and future climate data The 83 lodgepole pine seed lots used were representative of 83 different populations 2  covering 22 ecosystem zones. The seed lots are distributed throughout the species’ natural range across British Columbia (B.C.), Canada, encompassing coastal area and interior regions with a spatial grid over a latitudinal range from 49 to 60°N and longitudinal range from 115 to 132°W, which consist of tundra, boreal and temperate forests, and treeline ecotone ecosystems (Fig. 3.1). The studied populations were selected primarily based on two important climate variables; namely, mean annual temperature (MAT) and annual heat moisture index (AHM) (Fig. 3.2) (Wang et al. 2006b) and geographic variables, including longitude, latitude, and elevation were also considered. Climate data (197 climatic variables) of the 83 studied sites for the reference normal period 1961-1990 were generated using ClimateWNA version 4.85 (a software package used for regional climate predictions using historical weather station data and global circulation models) (Wang et al. 2012). The same 197 climatic variables for the future period between 2041 and 2070 (or 2050s) were also projected using ClimateWNA. The future climate data were downscaled to point locations using a delta approach (Wang et al. 2012). We used three representative concentration pathways (RCP) 2.6, 4.5 and 8.5 from the CGCM4 model output of the phase 5 of the Coupled Model Intercomparison Project (CMIP5), generated by the United States National Center for Atmospheric Research. The CCSM4 model output was included in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) (Stocker et al.                                                       2 A seed lot consists of the yearly collection of seeds from a particular location and elevation. A lot comprises the unit of our sampling and can represent the feature of a population in one patch. We therefore used population to define selected seed lots.    Contribution of environmental signals to life-history traits at life-cycle transitions 38  2013). Moreover, CCSM4 is close to the average of an array of models in both temperature and precipitation increases in the study areas (B.C.). In the prediction of seed dormancy indicators across B.C. for the reference period and the 2050s, climate data were generated for each pixel at the spatial resolution of 800 × 800 m using ClimateWNA. The full list of climatic variables is given in Table 2.1 and the four most relevant variables for the present study were Eref07 and _summer (July and summer Hargreaves reference evaporation estimated based on temperature and solar radiation), DD_0_summer (summer degree-days below 0°C), PPT07 and 10 (July and October precipitation (mm)), and Tmax07 (July maximum mean temperature (°C)).  Figure 3.1 Geographical distribution of the 83 populations (green triangles) on the map regarding ecosystem zones (A) and MAT value (B) of British Columbia, Canada. MAT (°C)-4 -2 0 2 4 6 8 10AHM010203040    Figure 3.2 The distribution of the samples based on mean annual temperature (MAT) against annual heat moisture index (AHM) Contribution of environmental signals to life-history traits at life-cycle transitions 39  3.2.2 Data analysis3 and visualization To investigate which climatic variables can best explain the observed patterns of seed dormancy and size variations, partial least squares (PLS) analyses were conducted using SAS® (vers. 9.3; SAS Institute Inc., Cary, NC) (Crossa et al. 2013). The goal of PLS regression is to analyze multivariate response based on a large set of explanatory variables (i.e., climatic variables). This technique combines features from principle component analysis (PCA) and multiple regression (Abdi 2007; Carrascal et al. 2009). PLS regression is particularly suitable when the matrix of predictors has more variables than observations, and when there is multicollinearity among N values. Using the machine-learning algorithm (i.e., PLS), important climate variables can be identified through a process of model optimization and there is no need to explain the contribution of climate variables in the initial input data set. In the PLS biplot, the angles between the environment vectors approximately correspond to the correlation coefficients among the environments. The cosine of the angle between two vectors approximates the correlation between them, and the length of the vectors is proportional to the standard deviation within respective environments or genotypes (Yan 2001; Yan and Tinker 2006). Virtually, an ideal environment has the longest vector of all test environments (most discriminating and informative) and is closely located on the abscissa (most representative) (Liu and El-Kassaby 2015). As a comparison, PCA for explanatory variables and canonical correlation analysis (CCA) dealing with both explanatory and response variables were also performed (Liu and El-Kassaby 2015). To examine how seed dormancy and size varied in ecosystem zones, the two variables were analyzed collectively using multivariate analyses (MANOVA) with the aid of general linear model (GLM) procedure in SAS (Manly 2005; Tabachnick and Fidell 2012). To investigate how the most correlated climatic and geographic variables affect life-history traits, hierarchical models were established using PROC MIXED in SAS with errors split into population and ecosystem zones but having unequal variances at the population                                                       3 Long-lived perennials have high within-population variability (Gst = 0.084, (Hamrick JL, et al. 1992. New For. 42:95-124)), however, broad-sense heritability for conifer seed size and germination is 0.36 and 0.89, respectively (El-Kassaby, Y.A. et al. 1992. Silvae Genetica 41:48-54; Chaisurisri, K. et al. 1992. Silvae Genetica 41:348-355). This indicates that it is possible to assume the genetic variability within populations for the two phenotypes as residuals. Contribution of environmental signals to life-history traits at life-cycle transitions 40  level (Raudenbush and Bryk 2001; West et al. 2007). The most correlated climatic variables through the PLS analysis were used and the model was expressed as follows: (life-history trait)ij = (β0 + εj) + βk × Pkij + βl × Tlij + εij where i and j represent two levels, namely, ith population within jth ecosystem zone; Pkij and Tlij represent the kth precipitation- and lth temperature-based variable in ith population within jth ecosystem zone, respectively; εj and εij represent errors from the ecosystem zone and population level, respectively. Intercept (β0 + εj) and coefficients (βk and βl (k, l = 1, 2, 3, …)) were estimated using our experiment data. Analogously, fitting models using longitude, elevation, and latitude as independent variables were established. Using the foregoing climatic model for the reference period and the 2050s via CCSM4 RCP 4.5, DI values were estimated for each pixel across BC at the resolution of 800 × 800 m and mapped across B.C. for visualization. To investigate the phenotypic plasticity of timing of seed germination, Pearson’s product-moment correlation was calculated. The mean phenotypic plasticity of timing of seed germination is the magnitude of the average response of the population genotypes to specific environments and calculated as a population’s mean AUC in the chilling manipulation minus its corresponding value in the control (i.e., DI). To investigate the range of possible plastic response to dormancy and to determine its genetic contribution to the phenotypic variance, the following linear model was used: DIij = μ + Gi + Ej + GEij + ε where DIij is dormancy index of ith genotype in jth environment, μ is the overall mean, Gi is the effect of the ith genotype, Ej is the effect of the jth environment, GEij is the interaction of ith genotype with jth environment, and ε is the random error. The range of phenotypic plasticity is interpreted as G×E variation (VG×E) and the extent of genetic control of a trait was calculated by broad-sense heritability (H2 = VG/(VG + VG×E + Vε)). To evaluate bet-hedge strategy in seed germination, two potential traits were tested, i.e., germination capacity and time to germination. Specifically, germination capacity was the subject of Cohen’s classic bet-hedging model (Cohen 1966), in which dormancy is expected to evolve in proportion to the probability of encountering a “bad” year, which corresponds to no chilling manipulation in this study. eqn 5 eqn 6 Contribution of environmental signals to life-history traits at life-cycle transitions 41  Time to germination in a “bad” or “good” year (i.e., no chilling vs. chilling) represents within-season variation, which has been shown to be advantageous when germination success is unpredictable within season (Simons 2009). The standard deviation (STD) for these two traits in a “good” and “bad” year was also calculated to measure the amount of variation across studied populations. 3.3 Results 3.3.1 Life-history traits explained by climate Life-history traits strongly correlated with climatic variables in the plant-to-seed transition Partial least squares (PLS) analyses for seed dormancy and weight indicated that the first and second components accounted for 15 and 13%, and 18 and 9% of the total variation, respectively (Fig. 3.3). The 15 most correlated climatic variables with respect to life-history traits were correlated with environmental factors related to temperature (Fig. 3.3A and B). This indicates that temperature plays a major role in the development of life-history traits (Liu and El-Kassaby 2015). By contrast, the PCA analysis showed several temperature-based variables which were intrinsically correlated (Fig. 3.3C). In addition, the PLS analyses classified the 83 population habitats into 21 and 20 major categories for seed dormancy and weight, respectively (not shown). The first canonical correlation analysis (CCA) was significant across all statistical tests (Table 3.1A). Seed dormancy and weight had moderate (0.66) and very strong (0.90) correlations with climate, moderate (0.59) and high (0.74) correlations with life-history traits (i.e., combination of seed dormancy and weight), and moderate (0.53-0.55) correlation with geographic variables (Table 3.1B). This implies that the climatic variables were more important than the geographic variables in variations of life-history traits. Based on respective covariate matrices for “life-history traits”, “climatic ecology”, and “geographic ecology” canonical variates, 62.35 and 47.11%, 62.85 and 45.06%, 14.80 and 6.82% of variance were explained by their corresponding canonical variates for the same group of variables and the other group of variables (Table 3.1C). Furthermore, multivariate analyses showed that ecosystem zones were significant Contribution of environmental signals to life-history traits at life-cycle transitions 42  for seed dormancy and weight, and had moderate correlation (R2 = 0.55-0.56, P < 0.0001) with life-history traits (Table 3.2). In the hierarchical model for seed dormancy using climatic variables, the climatic variables Eref07, Eref_summer, and DD_0_summer were significant (Table 3.3A). This indicates that summer moisture and temperature, the period corresponding to the plant-to-seed transition, played an important role in the development of seed dormancy. The intercept including error from ecosystem zones was not significant (Table 3.3A). For seed weight, variables PPT07 and PPT10, Tmax07, and intercept were significant (Table 3.3B), indicating that July temperature and precipitation and October precipitation were important to seed weight. Ecosystem zones, representing ecological boundaries in geography, had a greater impact on the variation of seed weight than that of seed dormancy. In the geographic variable-based hierarchical model for seed dormancy, only longitude and intercept were significant (Table 3.4A and Fig. 3.4A). For seed weight, longitude, elevation and intercept were significant (Table 3.4B and Fig. 3.4B), indicating that longitude was an important geographic factor in life-history traits and geographic factors had greater influence on the variation of seed weight than seed dormancy. Considering all these results collectively, life-history traits were significantly influenced by precipitation as well as temperature in the plant-to-seed chronology, and roughly distributed in a longitude pattern. Prediction of life-history traits in response to climate change Using current climate data, seed dormancy predicted had a moderate linear relationship (R2 = 0.47, P < 0.0001) with that observed (Fig. 3.5), which was used for the correction of the climate scenario predicted in the 2050s. Seed dormancy predictions using three greenhouse gas emission scenarios (RCP2.6, 4.5, and 8.5) showed that DI in the 2050s would increase (Fig. 3.5). In general, the spatial pattern of DI across the entire province showed that the seed dormancy in the south of B.C. would pronouncedly increase and the territory previously not suitable for pines to establish (grey area in 1970s) is expected to shrink in the 2050s, which are responses to climate change (Fig. 3.7). In addition, seed weight “predicted” had a low-moderate linear relationship (R2 = 0.34, P < 0.0001) with observed values (Fig. 3.6).   Contribution of environmental signals to life-history traits at life-cycle transitions 43  Table 3.1 Canonical correlation analysis between individual variables and their own and opposite set of variables A)  Test criteria Canonical function Likelihood ratio Approximate F-value Num DF† Den DF† Pr > F†† 1 0.1371 2.89 60 102 <0.0001 2 0.5776 1.31 29 52 0.1943 † Num DF and Den DF represent numerator and denominator degrees of freedom, respectively. †† Hypothesis is, H0: none of the canonical functions is significant; Ha: at least one of the canonical functions is significant (P < 0.05). B) Variables Life-history traits Climatic ecology† Geographic ecology†† Life-history traits Seed weight 0.7429 0.9021 0.5481 DI 0.5909 0.6582 0.5327 Note:  †Based on the PLS analysis, 27 most correlated climatic variables for seed dormancy (DI) and weight were used as Climatic Ecology (three climatic variables were strongly correlated with both seed dormancy and weight). ††Geographic Ecology consists of latitude, longitude, and elevation.  C)  Canonical variate % explained variance by Their own The opposite Life-history traits 62.35% 47.11% Climatic Ecology 62.85% 45.06% Geographic Ecology 14.80%  6.82%   Contribution of environmental signals to life-history traits at life-cycle transitions 44  Table 3.2 Multi- and uni-variate analyses for life-history traits (A) MANOVA test criteria; (B) MANOVA table reporting the correlation of seed dormancy (DI) and weight with ecosystems; (C) ANOVA table reporting significant effect of the genotype on phenotypic variance (i.e., DI) and associating broad-sense heritability (H2).  A) Test criteria MANOVA tests† F Value Num DF†† Den DF†† Pr > F Wilks' Lambda 3.30 42 120 <0.0001 Pillai's Trace 3.27 42 122 <0.0001 Hotelling-Lawley Trace 3.34 42 105.24 <0.0001 Roy's Greatest Root 4.53 21 61 <0.0001 † null hypothesis for the statistic tests is the centroids of life-history traits are equal across the 22 ecosystem zones (P < 0.05). †† Num DF and Den DF represent numerator and denominator degrees of freedom, respectively. B) SOV Parameters  DI Seed weight DF Mean squares F-value R2 Mean squares F-value R2 Ecosystem zones 21 130.3132 3.50** 0.55 0.3011 3.63** 0.56 corrected error 61 37.1821  0.0829  SOV, source of variation; DF, degrees of freedom; EMS, expected mean squares. ** P < 0.025 (= 0.05/2 dependent variables). C) SOV DF EMS Mean squares F-value Variance components H2 Genotype (G) 82 σε2 + 8σG2  346.8691 67.01** 54.02% 0.54 Environment (E) 1 σε2 + 4σG×E2  + 332φE  89393.0625 17268.20** -¶ G × E 82 σε2 + 4σG×E2  129.8935 25.09** 39.43% Error (ε) 498 σε2 5.1767   6.55% ¶ no variance components or percent of total variation were estimated for the fixed effect (E). ** P < 0.05.  Contribution of environmental signals to life-history traits at life-cycle transitions 45  Table 3.3 Parameter estimates and statistical tests for the climatic variables-based hierarchical models regarding seed dormancy and weight A) Seed dormancy model  Effect Estimate Standard Error DF Statistic P Intercept¶ (β0) 2.4819 7.9418 21 t = 0.31 0.7577 Eref07 (β1) 2.7076 0.8932 58 F = 9.19 0.0036 Eref_summer (β2) -0.9284 0.3354 58 F = 7.66 0.0076 DD_0_summer (β3) 6.5170 1.8480 58 F = 9.06 0.0039 ¶ residual from the ecosystem zone (ɛj) is integrated into “intercept” (Appendix B, Table B.2). B) Seed weight model Effect Estimate Standard Error DF Statistic P Intercept¶ (β0) 1.3290 0.3799 21 t = 3.50 0.0021 PPT07 (β1) 0.0046 0.0019 58 F = 5.66 0.0207 PPT10 (β2) -0.0014 0.0007 58 F = 4.68 0.0346 Tmax07 (β3) 0.0721 0.0150 58    F = 23.11 <0.0001 ¶ residual from the ecosystem zone (ɛj) is integrated into “intercept” (Table B.2).  Table 3.4 Parameter estimates and statistical tests for the geographic variables-based hierarchical models regarding seed dormancy and seed weight (A) Seed dormancy model Effect Estimate Standard Error DF Statistic P Intercept¶ (β0) 146.32 32.3917 21 t = 4.52 0.0002 Longitude (β1’) 0.9794 0.3150 58 F = 9.66 0.0029 Elevation (β2’) 0.0029 0.0020 58 F = 1.98 0.1643 Latitude (β3’) -0.1070 0.3808 58 F = 0.08 0.7797 Note: the same model as described in the text but using geographic variables as independent variables, (life-history trait)ij = (β0’ + εj) + β1’× Longij + β2’×Elevij + β3’×Latij + εij where i and j represent two levels, i.e., ith population in jth ecosystem zone; Longij, Elevij and Latij represent the longitude, elevation, latitude for ith population within jth ecosystem zone; εj and εij represent errors from ecosystem zone and population, respectively. Intercept (β0’+ εj) and coefficients (β1’, β2’ and β3’) were estimated using our experiment data. (B) Seed weight model Effect Estimate Standard Error DF Statistic P Intercept¶ (β0) 10.8374 1.6076 21 t = 6.74 <.0001 Longitude (β1’) 0.0615 0.0127 59 F = 23.32 <.0001 Elevation (β2’) -0.0003 0.0001 59 F = 11.43 0.0013 Note: assuming that β3’ is zero in the model. ¶ residual from ecosystem zones (ɛj) is integrated into ‘intercept’ (data not shown).   Contribution of environmental signals to life-history traits at life-cycle transitions 46   Figure 3.3 The 15 most correlated climatic variables with seed dormancy (A) and size (B) after partial least squares (PLS) regression and PCA in red (C) for 194 climatic variables Note: three variables (Eref07 and _summer, and PPT10) were highly correlated with both seed dormancy and size; fonts in black and grey in panels (A) and (B) represent the temperature- and precipitation-based climatic variables, respectively; see Table 2.1 for full names of abbreviated variables. Contribution of environmental signals to life-history traits at life-cycle transitions 47     Figure 3.4 Seed dormancy (DI, a) and weight (SW, b) distribution for the 83 populations labeled on the map of British Columbia, Canada    Contribution of environmental signals to life-history traits at life-cycle transitions 48   Figure 3.5 Seed dormancy (DI) prediction in the 2050s   Contribution of environmental signals to life-history traits at life-cycle transitions 49  (CONTINUED)  (A) Linear relationship between predicted DI and DI used for the correction of predicted seed dormancy; (B) Corrected seed dormancy prediction using representative concentration pathways (RCP) 2.6, 4.5 and 8.5, respectively. Note: model for seed dormancy prediction, DI = (2.4819 + ecosystem zone) + 2.7079 × Eref07 - 0.9284 × Eref_summer + 6.6170 × DD_0_summer [refer to Table B.2 for intercept adjustment in each ecosystem zone]; the population was ranked as per its current DI in ascending order and 95% confidence interval (CI) was plotted. 2.0 2.5 3.0 3.5 4.0 4.52.02.53.03.54.04.5R2 = 0.34p < 0.0001Seed weight  measuredSeed weight ''Predicted''  Figure 3.6 Linear relationship between 1,000-seed weight and ‘predicted’ 1000-seed weight using 83 populations  Contribution of environmental signals to life-history traits at life-cycle transitions 50     Figure 3.7 Map of predicted seed dormancy using climatic model for the reference period and the 2050s in lodgepole pine across B.C. Note: DIs (i.e., dormancy indexes) are classified into five categories and represented by different colors on the map. The higher the value, the more dormant the seeds. Contribution of environmental signals to life-history traits at life-cycle transitions 51   Figure 3.8 Studies of phenotypic plasticity (A) Reaction norms for AUC with or without moist-chilling treatment across the 83 populations; (B) Relationship between phenotypic plasticity and AUC with or without moist-chilling treatment. Best fit lines based on linear models are provided. Contribution of environmental signals to life-history traits at life-cycle transitions 52   Figure 3.9 Studies of bet-hedging strategy Bet-hedge dormancy involving germination capacity and time to germination (left) and their STD (right) in response to “good-year” and “bad-year” treatments. Note: the population was ranked as per its current DI in ascending order.  Contribution of environmental signals to life-history traits at life-cycle transitions 53  3.3.2 Adaptive plasticity and bet-hedging Correlated response in plasticity to timing of seed germination after “winter-chilling” Among the 83 lodgepole pine populations, variance of seed dormancy was significantly explained by genotype and environment (Table 3.2C). Estimate of broad-sense heritability (H2) was 0.54 corresponding with the observed variance component for genotypes (Table 3.2C), indicating that genetic components have a moderate influence on seed dormancy variation. In response to significant G×E interaction (Table 3.2C), a plot of the reaction norms of AUC showed a range shift, nonparallelism, and crossing between moist-chilling and control (Fig. 3.8), where G×E accounted for 39% of the variance and was attributed to crossing of reaction norms (Table 3.2C), indicating that environments changed the adaptive values of the life-history trait. AUC and phenotypic plasticity under chilling treatment had a moderate correlation (R2 = 0.53, P < 0.0001) (Fig. 3.8), indicating that populations that germinated after chilling treatment were more plastic. However, when no treatment prior to germination was applied, AUC and plasticity yielded a weak and not statistically significant correlation (R2 = 0.04, P = 0.3479) (Fig. 3.8). Therefore, germination after the chilling treatment was able to increase the magnitude of phenotypic plasticity, which was a response to subsequent predictable environments. Expression of bet-hedge under manipulated environmental uncertainty Relative to “bad-year” simulation, “good-year” resulted in higher and more uniform germination capacity and shorter time to germination across the 83 populations (Fig. 3.9). The standard deviation (STD) of germination capacity and time to germination were evenly distributed on the two sides of respective average line for both good- and bad-year across populations (Fig. 3.9) and “good-year” had lower average STD germination capacity and STD time to germination (Fig. 3.9). This indicates that, although bet-hedge is a risk strategy for unpredictable environments, “good-year” can lower the risk by means of allowing higher germination capacity and shorter time to germination across populations. Contribution of environmental signals to life-history traits at life-cycle transitions 54  3.4 Discussion Climate change is accelerating plant life-cycle transitions in coordination with the seasons. In life-cycle transitions, the environment plays a critical role in the development of life-history traits and in response to environmental stimuli, intrinsic mechanisms of genetics, epigenetics, phenotypic plasticity, bet-hedge strategy, and adaptive evolution take effect (Fig. 3.10). In this study, we found that seed dormancy and size were most correlated with evapotranspiration, precipitation and maximum mean temperature during the plant-to-seed transition, respectively, using 83 lodgepole pine populations across British Columbia (B.C.), Canada. We predicted that the range of seed dormancy variation would increase across B.C. in the 2050s. Moreover, winter-chilling can increase the magnitude of life-history plasticity (predictable factor) and lower the bet-hedge strategy (unpredictable factor). However, future climate may bring about insufficient winter-chilling required to decay seed dormancy, thus resulting in adverse consequences for the timing of phenology and the growth and establishment in lodgepole pine. This study allowed us to gain insights in the role of dynamic environments in shaping life-history characteristics. 3.4.1 Environmental conditions in the plant-to-seed transition In the plant-to-seed chronology, the sexual reproduction of plants is vulnerable to climate change as influenced by the maternal environment (Schmitt et al. 1992; Donohue 2009; Hedhly et al. 2009); temperature is involved in both genetically based and environmentally induced parental effects (Lacey 1996). Global warming has resulted in an upward shift in species optimum elevation and latitude (Parmesan and Yohe 2003; Lenoir et al. 2008; Chen et al. 2011), an observation similar to that reported by Parmesan (2006) as she pointed out that the best places to seek potential changes in species ranges is at their altitudinal and latitudinal limits (Parmesan 2006). This suggested that climatic variables are the real causes of species’ responses to environmental signals. Compared with the last 3 decades, the Pacific Northwest is expected to warm about 0.8-2.9°C by mid-century (Leung et al. 2004; Duffy et al. 2006; Mote et al. 2008), and 1.5-5.4°C by the end of the century (Mote et al. 2008). Warming in this region will be probably greater in summer (3.9°C on average) than in winter (2.7°C) (Mote et al. 2008). Contribution of environmental signals to life-history traits at life-cycle transitions 55   Figure 3.10 Important environment stages and intrinsic mechanisms in the life cycle of lodgepole pine for life-history traits Note: *, seed dispersal may not occur in the following season and it may take years until the cone drops and seeds release in nature; the reproduction cycle of (lodgepole) pine undergoes 3 years; that is, seed/pollen cone initiation (August, Semptember, October)→ dormant (November, December, January, February, March)→ pollen development (April, May)→ pollination (June)→ female gametophyte development (July, August)→ dormant (September, October, November, December, January, February, March, April)→ female gametophyte development (May, June). In the hierarchical model for seed dormancy, the climatic variable, summer degree-days below 0°C (DD_0_summer) was not equal to zero only for 16 out of the total 83 study populations and was projected to be zero for 80 populations in the 2050s. We performed approximation by removing DD_0_summer from the model and the result of seed dormancy projection could be interpreted as the following: given the current-future difference in July Hargreaves reference evaporation (Eref) is more than one third higher than that in summer Eref (Jun.-Aug. period), seed dormancy will increase in the 2050s, and if such a difference Seed dispersal*ChillingGermination(offspring)WinterReproduction(seed parent)Fertilization & Seed developmenttemperaturesignalGermination cuesEnvironmental conditionsGrowth & establishmentFemale gametophyte developmentPollinationSeed/pollen-cone development2nd year 1st year3rd year springsummersummer[seed soil bank]Contribution of environmental signals to life-history traits at life-cycle transitions 56  is less than one third higher, seed dormancy will diminish. These results also suggest that the allocation of summer Eref in July (i.e., seed development period) was critical in the development of seed dormancy. The ecosystem zones were significant in analyses of life-history traits using MANOVA (Table 3.2B) but not significant for seed dormancy and weight models (Appendix B, Table B.2). This indicates that dynamic climatic variables are true driving effects on modulating life-history traits. On the other hand, gene flow as a single pollen grain carries half the number of alleles and may yield effective distance spanning from a few centimeters to thousands of kilometres (Nathan et al. 2008). The “abundant center” model, which deals with the spatial distribution for populations across species’ ranges and its evolutionary potential, also has implications (Etterson and Shaw 2001; Parmesan 2006; Volis et al. 2014); that is, compared with core populations, locally adapted peripheral populations had lower adaptive potential and were outperformed in the novel environment. Ecosystem zones like other geographic variables are not individual units to classify life-history traits. A number of ecologically and evolutionary relevant genes have been identified in Arabidopsis and some annuals, such as FLC (FLOWERING LOCUS C), SCR/SP11 (S-LOCUS CYSTEINE-RICH PROTEIN/ S-LOCUS PROTEIN 11) (Amasino 2010; Shimizu et al. 2011), as they provide genetic diversity in adaptive evolution. Moreover, both seed dormancy (genes in ABA and GA signaling circuit) and flowering (such as FLC) were modulated by epigenetic mechanisms (Bossdorf et al. 2008; Chinnusamy et al. 2008; Müller et al. 2012). This indicates that epigenetic changes play a significant role in evolution and ecology and the environment signals acting on genes by epigenetic modification were crucial for life-history traits. In recent years, much progress has been made in uncovering genes operating on different seed compartments; i.e., embryo, endosperm, and seed coat, which modulate seed development (Le et al. 2010). Processes that regulate seed size and development are coordinated across several morphologically distinct sub-regions (Belmonte et al. 2013) and the complex cross-talk and integration of signals from different components of the seed together determine its final size (Garcia et al. 2005). Contribution of environmental signals to life-history traits at life-cycle transitions 57  3.4.2 Temperature signals in winter-chilling Winter dormancy is an important adaptive strategy as it prevents plants from flushing during short warm periods in the winter. Winter chilling is an important environmental signal for plant life histories, which accelerates flowering through vernalization in winter annuals and alleviates both bud and seed dormancy, allowing the onset of growth in spring (Penfield 2008; Penfield and Springthorpe 2012). Seeds take advantage of environmental temperature as a key signal to coordinate timing of seed germination, allowing plants to synchronize their life histories with the seasons. Low temperature can promote dormancy at the inception of seed maturation, but promotes dormancy alleviation in mature seeds after imbibition. It is therefore assumed that chilling plays a dual role in regulating dormancy (Batlla and Benech-Arnold 2010). With substantial climate warming (>3°C), chilling may be insufficient in many woody perennials, such as poplar, western hemlock, and Sitka spruce,  resulting in delayed bud burst and poor growth (Cannell and Smith 1986; Murray et al. 1989; Morin et al. 2009). Climate warming in some habitats, however, may extend the chilling period (Guy 2014), which may also put timed dormancy decay in jeopardy (Finch-Savage et al. 2007; Penfield and Springthorpe 2012). Based on the three different RCP scenarios, we predicted that winter-chilling days (DD_0_winter) would on average decrease by 24% across the 83 study habitats in the 2050s relative to present (Fig. 3.11, ranges also provided), which may lead to insufficient dormancy alleviation through winter-chilling. However, spring and annual heat sums (DD5_spring and DD5) would increase by 95 and 49%, respectively, in the 2050s (Fig. 3.11), resulting in earlier springs and advanced vegetative green-ups and an increase in growing season length (Robeson 2004; Schwartz et al. 2006). In the 2050s, inadequate winter-chilling may delay germination and an extended germination span leads to adverse conditions during dry summers. As such, future climate will change the timing of conifer phenology and may give rise to adverse consequences. In terms of molecular mechanisms in alleviation of seed dormancy via winter-chilling, several studies demonstrated that moist-chilling involves changes in levels of ABA, GAs, and auxin, and transcripts in respective signaling cascades, GA3 OXIDASE 1, for example (Ali-Rachedi et al. 2004; Yamauchi et al. 2004; Liu et al. 2013a; Liu et al. 2015).  Contribution of environmental signals to life-history traits at life-cycle transitions 58   Figure 3.11 The amount of changes for DD_0_winter, DD5_spring, and DD5 in the 2050s relative to present  Note: The population was ranked as per its current DI in ascending order. Each data point is the average of predictions using three scenarios (RCP2.6, 4.5, and 8.5). Bars indicate the SEM. Ranges across populations under different scenarios are provided in a table format. 3.4.3 Germination cues in the seed-to-plant transition Changes in climate alter patterns of phenology and thus multiple life-history traits. In response to climate change, phenotypic plasticity associated with life-history traits (Pigliucci 2001; Chevin et al. 2010; Franks et al. 2014; Liu and El-Kassaby 2015) and genetically based trait responses (Bradshaw and Holzapfel 2001; 2008; Thompson et al. 2013) were well documented. In the long run, only the species that can respond by phenotypic plasticity and/or genetically based local adaptation can persist (Jump and Peñuelas 2005). However, the evolutionary response to climate change may be attenuated due to constraints causing a time Contribution of environmental signals to life-history traits at life-cycle transitions 59  lag between the environmental change and an observed evolutionary response (Etterson and Shaw 2001; Davis et al. 2005). In tree populations, the extent of the constraints will hinge on phenotypic variation, strength of selection, fecundity, interspecific competition, and biotic interaction (Aitken et al. 2008). Differences in plasticity exist among populations, but plasticity presumed to be adaptive may often be neutral or maladaptive (Caruso et al. 2006). An optimal balance between adaptive and non-adaptive (bet-hedging) plasticity may exist and possibly vary among populations. Moreover, germination characters are expected to exhibit phenotypic plasticity to environmental variables experienced not only by seed following dispersal but also by seed parents prior to dispersal (Schmitt et al. 1992). It is noteworthy that seed dormancy and germination is a quantitative trait that interacts with environment factors (Bentsink et al. 2007). Some QTLs associated with germination phenology in Arabidopsis can attain allele frequencies approaching fixation within a single generation even though they started with frequencies below 50% (Huang et al. 2010), indicating strong directional selection. Seed dormancy in Arabidopsis was associated with a cohort of genes controlled by seasonally distinct hormone-signaling pathways in the seed soil bank, such as DOG1 (DELAY OF GERMINATION 1), MFT (MOTHER OF FLOWERING TIMING), DELLAs (repressors of germination potential and GA signaling), and PIFs (PHYTOCHROME INTERACTING FACTORS) (Footitt et al. 2011; Footitt et al. 2014). In addition, photoperiod can affect seed dormancy and germination and phytochromes were the most investigated photoreceptors. Phytochromes are temperature- and light-dependent in association with GA pathway via the bHLH transcription factor SPATULA (SPT) (Heschel et al. 2007). SPT is a light-stable repressor of seed germination and mediates the germination response to temperature through temperature-sensitive changes in its transcription (Penfield et al. 2005). 3.4.4 Seed size and environmental conditions during seed ripening Although the 1000-seed weight from individual mother trees of Norway spruce (Picea abies (L.) Karst.) produced in the same stand and year largely varies (Skrøppa and Tho 1990), climatic conditions in a specific year during seed maturation have significant influence on seed size variation (Skrøppa et al. 2007). Furthermore, the size of white spruce (Picea glauca (Moench) Voss) cone crops was predicted by climate Contribution of environmental signals to life-history traits at life-cycle transitions 60  variables two years prior to the seed production and this projection model explained 54% of the variation in cone crops (Krebs et al. 2012). Hence, environmental signals at conifer seed maturation substantially contribute to their seed size variation. In summary, we reinforced the importance of climatic signals during seed set to the formation of early life-history traits (i.e., seed dormancy and size). Variations of life-history traits may be acquired through signal transduction cascades and/or gene/protein imprinting triggered by specific environmental variables, such as evapotranspiration and precipitation. The period of winter chilling exerts a pronounced influence on the range of life-history plasticity and the variation of bet-hedge strategy when the life-history trait expressed (i.e., seed emergence). In response to previous memory during seed development and different genetic architectures that every individual harbors, germination behaviour (e.g., timing of seed germination) varies even given the same optimal germination cues. Impact of temperature shifts on the joint evolution of seed dormancy and size 61  4 Impact of temperature shifts on the joint evolution of seed dormancy and size 4.1 Introduction Selection in variable environments may favor plants to synchronize seed dispersal with environmental conditions allowing germination or defer germination until suitable conditions occur (Freas and Kemp 1983). Seed dormancy is an innate constraint on germination timing under conditions that would otherwise promote germination in non-dormant seeds (Simpson 1990) and prevents germination during periods that are ephemerally favorable (Bewley 1997). Timing of seed germination is the earliest trait in plant life history, allowing plants to regulate when and where they grow. It affects the evolution of other life-history traits that follow in the life cycle, such as fecundity and survival (Hamilton 1966). As such, seed dormancy may be construed as an adaptation for survival during bad seasons and can exert cascading selective pressures on subsequent life stages. Plants bear seeds with a spectrum of dormancy intensities (Baskin and Baskin 1998) and distribute their offspring across time, hedging their bets against unpredictable environments (Venable 2007; Poisot et al. 2011). This increases the likelihood that some seeds will survive regardless of environmental variations. Seed dormancy variability among individuals is associated with environmental heterogeneity (Angevine and Chabot 1979) and heterogeneous environments may select for bet-hedging strategies, as population growth is an inherently multiplicative process that is very sensitive to occasional extreme values (Dempster 1955). Cohen (1966) indicated that low germination probabilities can be expected in harsh environments as individuals can germinate in improved conditions and decrease their average mortality. However, Ellner (1985) predicted that increasing the frequency of favorable years may also lead to lower germination rates due to increased density-dependent effects imposed by competitive interactions (Ellner 1985a; 1985b). In contrast to periodic fluctuations of good and bad seasons among years, climate change increases the probability of bad seasons for initially locally adapted phenotypes, as environments continuously move away from past optimums. Predictably, air temperatures will increase by 0.8-1.0°C in 2050s and by 2-4°C Impact of temperature shifts on the joint evolution of seed dormancy and size 62  in 2100s (IPCC 2007). Such a warming is expected to reduce seedling emergence (Hoyle et al. 2013; Cochrane et al. 2015). On the other hand, the evolution of seed dormancy is favored by high seed persistence in the soil seed bank to alleviate the cost of delayed germination (Childs et al. 2010). Both Cohen and Ellner’s models suggested that an increase in seed survivorship selects a low seed germination (Cohen 1966; Ellner 1985a; 1985b). Climate change engenders long-term exposure to high soil temperatures, which may reduce seed survival, thus selecting for lower levels of seed dormancy (Ooi et al. 2009). Taken together, climate change may increase seed numbers in life cycle and decrease dormancy levels due to increased seed mortality. Seed size is another crucial life-history trait that links the ecology of reproduction and seedling establishment with that of vegetative growth. Seed size commonly varies over five to six orders of magnitude among coexisting plant species (Leishman et al. 2000). Seed size is closely correlated with changes in plant form and vegetative type, followed by dispersal syndrome and net primary productivity (Moles et al. 2005; Moles et al. 2007). Effects of temperature on seed size are not consistent, as both increased (Murray et al. 2004; Liu et al. 2016) or reduced (Hovenden et al. 2008) seed sizes have been documented. Production of dimorphic or heteromorphic seeds by a single plant allows plants to decrease temporal variance in offspring success through bet-hedging (Venable et al. 1987). The diversity of seed size may be maintained by tolerance-fecundity trade-offs (i.e., more tolerant (fecund) species gain more (less) stressful regeneration sites, respectively) (Muller-Landau 2010). The role of differential seed size in promoting species coexistence has been stressed by previous theoretical studies (Geritz 1995; Rees and Westoby 1997; Geritz et al. 1999). Large seed size confers direct advantages to many fitness-related plant characteristics, including recruitment and survivorship (Mcginley et al. 1987; Moles and Westoby 2004), and establishment (Leishman et al. 2000; Moles and Westoby 2004) because large seeds accumulate copious nourishing substances for germination and have better tolerance in face of disturbances (e.g., abiotic stresses) (Geritz et al. 1999; Westoby et al. 2002). On the other hand, for a given reproductive investment, seed size is negatively correlated with seed number (Harper et al. 1970; McGinley and Charnov Impact of temperature shifts on the joint evolution of seed dormancy and size 63  1988; Jakobsson and Eriksson 2000) and large seeds are less dispersible due to their great size (Salisbury 1975). Although the evolution of seed dormancy and size was modelled separately, variation in seed size (morphology) often has a concomitant effect on seed dormancy (reviewed by (Baskin and Baskin 1998)). Lines of genetic evidence underpin that during development, physiological seed dormancy and seed size are regulated by phytohormone signaling pathways, which have opposite effects on seed dormancy and size (Hu et al. 2008; Footitt et al. 2011), thus suggesting that they evolve in a coordinated manner. Also, some common selective pressures are likely to affect seed dormancy and size simultaneously, such as light, water availability or potential, and intraspecific competition (Baskin and Baskin 1998; Larios et al. 2014). Owing to environmental pressures (e.g., frost, drought), species that produce light seeds are more likely to possess some type of seed dormancy (morphological, physiological, physical, morphophysiological, or physiophysical) (Venable and Brown 1988; Rees 1993) and a negative relationship between seed dormancy and size was documented in many cases, though this pattern is not universal (Thompson and Grime 1979; Grime et al. 1981; Rees 1996; Kiviniemi 2001; Larios et al. 2014; Vidigal et al. 2016). These inconsistencies may be explained by an incomplete consideration of other co-varying factors (e.g., dispersal, fire, predation) (Rees 1996) or by phylogenetic constraints (Willis et al. 2014). Additionally, germination of large-seeded species is strongly facilitated by temperature fluctuations, ensuring germination after deep burial or in litter layers (Ghersa et al. 1992; Pearson et al. 2002; Xia et al. 2016). In this chapter, we model and parameterize a stage-structured population to study the impact of changing temperatures on the joint and independent evolution of seed dormancy and size. Altering temperature leads to an enlarged mismatch of a species’ eco-evolutionary trajectory in its actual living habitat and the environment to which it is best suited. We incorporate the impact of temperature on germination success. Under evolutionary forces driven by interplays between environments and life-history traits, we aim to investigate: 1. the effects of temperature shifts on the evolution of seed dormancy and size. Global change, by producing increasingly frequent bad years, should select for dormancy. However, when germination success is Impact of temperature shifts on the joint evolution of seed dormancy and size 64  negatively affected, the number of seeds may increase in the soil seed bank, thus increasing mortality through density-dependent effects. We here investigate which of the two antagonistic mechanisms dominate in the evolution of seed dormancy. Moreover, we expect that temperature shifts will be less conducive to the evolution of seed size as fecundity benefits of reduced seed size can offset survival costs in the context of environmental change, so that temperature shift does not change the overall balance of benefits and costs. 2. whether evolutionary dynamics differ when we allow for a joint evolution of the two traits (scenarios subject to coevolution). As per empirical observations, we expect the joint evolution to yield a negative correlation between the two traits (Thompson and Grime 1979; Grime et al. 1981; Rees 1996; Kiviniemi 2001; Larios et al. 2014; Vidigal et al. 2016). 3. effects of the evolution on the ecological structure at the population level (relative abundance of seeds and adults). We expect that, (1) decreases in seed dormancy will increase the number of adults relative to seeds, because the probability of germination increases (note: constant adult survival assumed) while seed survival decreases; and (2) changes on seed size will not significantly alter the population structure, because seed size affects seed survival and fecundity in opposite ways. Nonetheless, maladaptation caused by temperature shifts or fluctuations interacts with evolution and may have a great impact on the population structure. We expect that in temperature shifts, the number of seeds relative to adults will increase, thus leading to more balanced population structure, while the total population density will shrink due to the altered environment. Because the probability of germination greatly decreases particularly at wide temperature shifts, resulting in less adults, while elevated fecundity due to relaxed adult density-dependent competition (and predictably smaller seeds, if seed size evolves or coevolves with seed dormancy) results in more seeds. 4.2 The model 4.2.1 Description of the ecological model We model the dynamics of a two-stage population (seeds and adults) under the assumption that density-dependent competition affects seed survival, germination, and adult fecundity, using Ricker functions Impact of temperature shifts on the joint evolution of seed dormancy and size 65  (Ricker 1954). We assume that temperature constrains germination, as seedling is the most fragile phase and the temperature for seed emergence is important in plant life histories. The local dynamics in seed, S, and adult, A, populations for a given morph j are described by the following recursion equations in matrix form: (𝑆𝑗[𝑡 + 1]𝐴𝑗[𝑡 + 1]) = 𝑇𝑗 (𝑆𝑗[𝑡]𝐴𝑗[𝑡]) =  (𝑉S𝑗 𝑌𝑗𝐺𝑗 𝑉A𝑗) (𝑆𝑗[𝑡]𝐴𝑗[𝑡]) where Tj is transitional matrix; VSj, Yj, Gj, and VAj represent seed survival, fecundity (yield), germination, and adult survival, respectively. We assume that seed dormancy α affects seed survival VSj and germination Gj, while seed size γ affects seed survival VSj and fecundity Yj. Figure 4.1 delineates the life cycle of seed-adult and Table 4.2 summarizes the model’s variables and parameters. Note that seed size is equivalent to seed mass thereafter.  Figure 4.1 Life cycle of seed-adult stages Note: α denotes the probability of dormant seeds. 𝑉S𝑗 = 𝛼𝑗 × 𝜃 × 𝑒− ∑ 𝑎𝑗𝑘𝑆𝑘𝑁𝑘=1 = 𝛼𝑗 ×𝑝𝛾𝑗 + 𝑢𝑞𝛾𝑗 + 𝑣× 𝑒− ∑ 𝑎𝑗𝑘𝑆𝑘𝑁𝑘=1  For a given morph i, from eqn 8, αj is the basic probability of surviving while dormant in the soil seed bank from one time step to another. This basic mortality is modulated by the effects of seed size, which is incorporated in the second term of the equation. The function we use is monotonically increasing with seed size, given the parameter constraints listed for p, q, u, and v in Table 4.2. Hence, we assume that larger eqn 8  eqn 7 Impact of temperature shifts on the joint evolution of seed dormancy and size 66  seeds survive better (Geritz et al. 1999; Westoby et al. 2002). Finally, the probability of survival is reduced by seed density-dependent effects (e.g., due to competition or to seed predator attraction), which is modelled by the third term of eqn 8. Note that because Vsj is a probability, it is necessary that its maximum αj*p/q is below one. 𝑌𝑗 =𝜔𝛾𝑗× 𝑒− ∑ 𝑏𝑗𝑘𝐴𝑘𝑁𝑘=1 × [1 − 𝑑𝛽(𝛾𝑗)] =  𝜔𝛾𝑗× 𝑒− ∑ 𝑏𝑗𝑘𝐴𝑘𝑁𝑘=1 × (1 − 𝐵𝛾𝑗) From eqn 9, fecundity Yj, is constrained by ω, the total reproductive investment of the plant, which is distributed among seeds given seed size γj (Harper et al. 1970; McGinley and Charnov 1988; Jakobsson and Eriksson 2000). Fecundity is adult density-dependent, as reflected by the second term of the equation. The third term of the equation depicts the probability that seeds are retained locally, which increases with seed size (Salisbury 1975). 𝐺𝑗 = (1 − 𝛼𝑗) × 𝑒− ∑ (1−𝛼𝑟𝑗𝑘)𝑐𝑗𝑘𝑆𝑘𝑁𝑘=1 × 𝑒[− 12(𝑇𝑜𝑝𝑡−𝑇 𝑥𝜂 )2] From eqn 10, 1- αj is the probability of germination. Success of germination is reduced by juvenile seedling density-dependent competition, embodied by the second term of the equation. We assume that seed germination hinges on the difference between the optimal germination temperature Topt, and the actual local temperature in the patch Tx. Note that the function with respect to the temperature difference is monotonically decreasing so that germination probabilities are reduced when temperature differs more from the optimum. This relationship is modulated by the third term of eqn 10. 𝑉A𝑗 = 𝑉A0  The eqn 11 represents the probability of adult survival, VAj. As we are simply interested in how seed traits evolve in response to germination constraints and do not account for how adult survival influences evolution, we assume constant VAj.   eqn 9  eqn 10  eqn 11  Impact of temperature shifts on the joint evolution of seed dormancy and size 67  Table 4.1 Variable/parameter symbols and values used in simulations Symbol Variables/parameters Value/range Note/unit α seed dormancy [0.01, 0.99] probability, (0, 1);  αr: resident α, αm: mutant α, α: either resident or mutant α γ seed size (mass) * [0.01, ∞) weight unit; γr: resident γ, γm: mutant γ, γ: either resident or mutant γ  a b c intensity of density dependent competition 0.001 0.002 0.003 per individual B dispersal-related probability of mortality when seed size is one 3/5; (0,1) dimensionless dβ(γ) dispersal (β)-related mortality probability (0, 1) dimensionless N the total number of morph types [1, ∞) in numbers; morph types range from 1st to kth p q u  v shape parameters for the function describing how germination depends on seed size 0.8 1 0.1 0.4 arbitrary, p and q are dimensionless, and the unit of u and v is (weight unit)-1; since surviving and dormant seed is a probability, p/q, u/v ∈ (0, 1); since seed size positively correlated with seed survival, we assume p/q > u/v. VA0 adult survival probability 0.93; (0, 1] the basic value assumes perennial species t generation (simulation) time 1.0×108 number of generations T patch temperature Topt - 25°C Tx - temperature in a local patch (Topt ±1.5 or 3°C) °C θ seed size-related survival probability in the soil seed bank (0.25, 0.80) dimensionless ω investment in reproduction  10 weight unit η niche width 3; (0, ∞) °C *we define that large seeds are those that can contribute to higher than 70% of seed survival rate in the soil seed bank. 4.2.2 Investigations of eco-evolutionary dynamics As the stage-structured model involves complex non-linear functions of phenotypical traits, analytical investigation is not possible. We therefore rely on extensive simulations and graphical analyses to Impact of temperature shifts on the joint evolution of seed dormancy and size 68  understand evolutionary dynamics. Overall, three scenarios were considered: (i) evolution of seed dormancy α, (ii) evolution of seed size γ, and (iii) joint evolution of the two traits.  (i) Evolution of seed dormancy We investigate the adaptive dynamics of seed dormancy using pairwise invasibility plots (hereafter PIPs). These plots display the relative fitness of rare mutants within resident populations, thereby allowing assessments of evolutionary dynamics (Dieckmann and Law 1996; Geritz et al. 1998) and characterizing evolutionary singularities (i.e., points at which the fitness gradient vanishes (Geritz et al. 1998)). Analyses of PIPs assume that (1) the resident population is at stable equilibrium; (2) reproduction is clonal; and (3) the mutant population is rare. To overcome these restrictive hypotheses (Dieckmann and Law 1996; Geritz et al. 1998), we undertake extensive numerical simulations to construct evolutionary trajectories of seed dormancy over time. To build PIPs, we set a spectrum of residents of seed dormancy phenotype whose trait values vary from 0.01 to 0.99 with an interval of 0.01 (i.e., 99 discrete traits). The ecological equilibria of seed, S*, and adult, A*, for those traits are accordingly calculated. We then test the possibility of invasion of each resident phenotype by rare mutants. Possibility of invasion is evaluated by the long-term growth rate of the population of mutant seeds and adults when rare. The leading eigenvalue (λL) of the transitional matrix Tj in eqn 7 is used to approximate the long-term growth rate. By definition, a successful invader has a λL strictly superior to one. Computations are carried out on Mathematica 10.3 (Wolfram Research Inc. 2015). While PIPs graphically illustrate configurations of evolutionary singularities, they implicitly assume a separation of evolutionary and ecological time scales, as the resident population has to reach the equilibrium before a new mutation occurs. Many empirical observations however suggest that evolution may be as fast as ecological dynamics (Hairston et al. 2005). To relax this limitation, we employ numerical simulations of seed dormancy, in which mutants are introduced with a given probability at each time step, even if the resident population is not at equilibrium. The extent to which ecological and evolutionary time scales overlap may be directly manipulated via altering the probability of mutations. We simulate a span of 1.0×108 time steps and initial resident trait values (i.e., αr and γr) are both 0.5 while initial population size Impact of temperature shifts on the joint evolution of seed dormancy and size 69  for seeds and adults are both 5. In each time step, phenotypical trait α, can randomly mutate. Mutation takes place at a fixed probability (baseline: 10-8) and affects a single seed of a resident population. The value for mutants αm, is randomly drawn from a Uniform distribution centered on the parent trait α, with an amplitude bounded between -0.04 and +0.04, and the initial mutant population is 5.0×10-6 and 0 for seeds and adults, respectively. Populations of seeds and adults are respectively checked every 100 steps and very small populations (< 5.0×10-8) are supposedly extinct and removed from the simulation. Each set of simulations of the eco-evolutionary dynamics is carried out on R 3.1.2 (R core team, 2014) and replicated for 20 times. (ii) Evolution of seed size Likewise, we rely on PIPs and numerical simulations to investigate the evolution of seed size. Procedures are identical to the scenario (i), except that seed dormancy α is fixed and mutations occur on seed size γ. (iii) Coevolution of seed dormancy and size PIPs cannot be applied to the context of coevolution, so we only rely on numerical simulations to understand the scenario. The simulation algorithm is similar with that of the scenarios (i) and (ii). The only difference was that in each time step, either phenotypical trait α or γ, can randomly mutate with an equal probability (i.e., half baseline mutation rate relative to the scenario (i) and (ii)). 4.2.3 Simulations of deterministic and stochastic environmental changes Temperature, as a crucial environmental factor, was manipulated to evaluate the effects of environmental change on eco-evolutionary dynamics. Three deterministic and two stochastic situations were simulated. Scenario I was set as the local patch temperature equal to the optimal germination temperature (Tx=Topt=25°C). We consider temperature shifts of 1.5°C (scenario II) or 3°C (scenario III) in local patches. As we use symmetric Gaussian functions, such shifts equivalently mimic warmer or colder situations relative to Topt. In addition to these fixed and deterministic shifts on temperature, we also study scenarios in which random fluctuations occur. To simulate environmental uncertainties, we use white noise with mean optimal temperature of 25°C across years but variance within 1.5°C (Scenario IV) or 3°C (scenario V). Impact of temperature shifts on the joint evolution of seed dormancy and size 70  4.3 Results 4.3.1 Ecological dynamics We first focus on the ecological dynamics without considering evolution. In Figure 4.2, we illustrate how the population structure changes with seed dormancy and size. Overall, given a seed size γ, levels of seed dormancy α have substantial influence on the state of ecological dynamics reflected by equilibrium densities (i.e., the number of seeds and adults) (Fig. 4.2A-C). In general, given our parameter options, we get unbalanced populations and adults are more than seeds (Fig. 4.2A-C). Symmetrically varying temperature around the opt (optimum) by 1.5 or 3°C, however, results in fewer adults accompanied by more seeds, and 3°C enables such a change in a higher amplitude than 1.5°C (Fig. 4.2A, B, or C). The smaller seed dormancy α, the larger the imbalance on the population structure (i.e., comparison of adult and seed densities) (Fig. 4.2A, B, or C). Patterns are similar regardless of the seed size γ chosen, indicative of robustness of ecological dynamics in response to changes on α (Fig. 4.2 A, B, or C). By contrast, when seed dormancy α is fixed at 0.5, variation in seed size has minor influence on the population structure (Fig. 4.2E). When temperature shifted by 1.5 or 3°C, the number of adults declines and that of seeds increases and the higher temperature deviation (i.e., 3°C) again results in the most balanced ecological structure (Fig. 4.2E). This pattern is again consistent and robust, as it is observed at different levels of seed dormancy (compare Fig. 4.2E with 4.2D or F). 4.3.2 Evolution of seed dormancy The pairwise invasibility plots (PIPs) show that seed dormancy is always counter-selected under the assumption of our model (Fig. 4.3A, black PIP). PIPs are corroborated by simulations, also showing that seed dormancy α is selected against (Fig. 4.3B, black curves). As expected, evolutionary dynamics are progressively faster when starting at high dormancy (α=0.8) than at low dormancy (α=0.2 and 0.1) (Fig. 4.3B). Given the observed results in Figure 4.2A-C, where we now report the direction of evolutionary changes (red arrows), evolution, via pushing seed dormancy toward smaller values, generally decreases the balance between adult and seed densities. While seed dormancy α is always counter-selected, the speed of Impact of temperature shifts on the joint evolution of seed dormancy and size 71  dormancy evolution for different scenarios is compared by showing evolutionary states after 5.0×107 and 1.0×108 steps (empty and filled circles, respectively), given the probability of dormancy starting at 0.5 (Fig. 4.2B). We note that evolutionary speed changes depending on temperature shifts with important consequences for the ecological structure (Fig. 4.2B). At the opt where species are locally adapted, seed dormancy evolves more slowly than in maladapted environments (opt ± 1.5 or 3°C), indicating that the speed of evolution is the fastest in non-opt environments where seed dormancy is more counter-selected (Fig. 4.2B and 4.4A). Such a relationship remains when simulation steps are extended to 1.0×108 times (Fig. 4.2B and 4.4A). Whilst the evolution of dormancy generally makes the population structure more unbalanced, at opt ± 3°C where species are largely not adapted, evolution first increases then decreases the amount of adults while eventually increases the amount of seeds, resulting in a balanced distribution of the two stages and decreased total populations (Fig. 4.2B and 4.4B). These results are observed, regardless of the values at which γ is fixed (Fig. 4.2A and C). Due to these differences in evolutionary speed, for a given simulation time, evolved dormancy α is lower in opt ± 1.5°C than in opt and lowest in opt ± 3°C and there are no qualitative changes on such a pattern when sets of parameter values are randomly tweaked up- or down-ward (Fig. 4.5A). This indicates that shifts in the environment suppress seed dormancy to lower values in the case of our model. 4.3.3 Evolution of seed size PIPs show that, in consideration of the evolution of seed size γ, only one singularity exists in each case. This singularity is convergent (i.e., given a resident strategy, mutant strategies closer to the singularity are favored) and not invisible (i.e., when the singularity is reached, no mutant can invade), thus a continuously stable strategy (CSS) (Eshel 1983; Christiansen 1991) (Fig. 4.3A, grey PIPs). As a result, the evolution of seed size eventually leads to this point (Fig. 4.3B, grey curves). High seed dormancy increases the selected seed size value (compare the three grey PIPs in Fig. 4.3A). The observed evolutionary dynamics are consistent with the analysis of PIPs (Fig. 4.3B, grey curves). Based on the pattern analyzed in Figure 4.2D-F on evolutionary directions and equilibria, the evolution of seed size only has minor effects on the structure of the population at equilibrium. Given low Impact of temperature shifts on the joint evolution of seed dormancy and size 72  dormancy α (< 0.3), small seed sizes γ are inclined to be counter-selected (Fig. 4.2D and 4.3A). Analogously, the speed of seed size evolution for different scenarios is compared using its evolutionary endpoints after numerical simulations for 5.0×107 and 1.0×108 steps. Evolution of γ is close to CSS points before 5.0×107 steps (Fig. 4.2E). Evolved seed size is higher for well adapted phenotypes, compared with phenotypes experiencing temperature shifts of 1.5 or 3°C (Fig. 4.2E). Evolution of seed size γ ends in fixation at CSS points for more repeated simulations of 1.0×108 steps (Fig. 4.4C). Temperature shifts have distinct influence on ecological structures in the evolution of seed size. More precisely, as temperature shifted by 1.5 or 3°C, the number of seeds increases while that of adults decreases but adults always outnumber seeds (due to low seed persistence assumed) and the total population declines (Fig. 4.2E and 4.4D). We finally note that the foregone results are robust, given different values of seed dormancy α (Fig. 4.2D and F, also see Fig. 4.5 for other robustness tests). 4.3.4 Coevolution of seed dormancy and size Should seed dormancy and size jointly evolve, selected seed size γ and dormancy α gradually decline (Fig. 4.6). This may be easily explained by the two previous scenarios on “independent” evolution; as seed dormancy is always counter-selected and evolved seed size becomes lower when the value of seed dormancy decreases (Fig. 4.3), coevolution simply leads to ever decreasing values for the two traits (Fig. 4.6A, B). Compared with the independent evolution, seed dormancy and size evolve almost at the same speed (compare empty circles in Fig. 4.6A, B and filled circles in Fig. 4.4A, C keeping in mind that the effective mutation rate is half in coevolution relative to independent evolution). Corresponding ecological dynamics (Fig. 4.6C) are consistent with trends observed given the ecological equilibrium status (Fig. 4.2). Specifically, adults outnumber seeds in opt, temperature shift by 1.5°C, while seeds exceed adults in temperature shift by 3°C, which resembles the ecological structure in the evolution of seed dormancy only (Fig. 4.6C and 4.2A, B). This indicates that the evolution of dormancy has substantial influence on the evolution of the two traits and ecological systems. Impact of temperature shifts on the joint evolution of seed dormancy and size 73   Figure 4.2 Ecological equilibria for a spectrum of seed dormancy (α, A-C) and of seed size (γ, D-F) when the alternative trait is fixed Note: the red arrows indicate the evolutionary direction; filled red circles ( ) represent different evolutionary equilibria in respective conditions, while open blue circles ( ) represent the values of evolved trait after simulations of 5.0×107 steps (the fixed trait values are shown in each panel and the initial values of evolved trait (α or γ) are 0.5; seeds (black curves) and adults (green curves) are shown in pairs distinguished by alphabet numbers; that is, the same number means the number of seeds and adults in the same simulation condition.  Impact of temperature shifts on the joint evolution of seed dormancy and size 74   Figure 4.3 Pairwise invasibility plots (PIPs) (A) and evolutionary dynamics (B) Note: For A, the evolved morphs of seed dormancy and size are discriminated in black and grey, respectively; shades in black and grey depict leading eigenvalues (λL) larger than one (marked by + sign; otherwise, marked by - sign), thus possibly invaded by mutants and its edges in cross shape represent λL equals to 1; ES and CS represent evolutionary stability and convergent stability, respectively; For B, one convergent and non-invasible singularity exists for seed size, marked in red dashed line and termed continuously stable strategy (CSS, which corresponds to an evolutionarily stable equilibrium where no evolutionary dynamics exist); no ESS (i.e., non-invasible singular strategies) exists for seed dormancy and it evolves towards zero, marked in red dashed line.  Impact of temperature shifts on the joint evolution of seed dormancy and size 75   Figure 4.4 Evolutionary end points and corresponding number of populations for the independent evolution of seed dormancy (A-B) and size (C-D) after numerical simulations of 5.0×107 and 1.0×108 steps Note: the error bar for each end points or the number of populations (i.e., seeds or adults) is calculated by 20 replicates for each set of simulations; sd represents standard deviation (i.e., temperature variation); the values of fixed trait and the initial evolved trait (α or γ) are 0.5. Impact of temperature shifts on the joint evolution of seed dormancy and size 76   Figure 4.5 Eco-evolutionary dynamics of each trait (seed dormancy [A] and size [B]) in opt or temperature shifts and robustness analysis Note: simulations were run using different parameters values and the initial α and γ were 0.5 for both; the following parameter values (seed and adult survival) were replaced for corresponding ones in the main text: 1, B=4/5; 2, p=0.85; 3, VA0=0.94; 4, p=0.75; 5, VA0=0.9; 6, B=2/5, and three different values of α or γ were also implemented in the model (the one used in this study was highlighted in red); due to no ESS for seed dormancy, the end point of α was used for comparison after simulations of 5.0×107 steps and the evolution of γ almost can get to its CSS, if it exists, after 5.0×107 steps.  Impact of temperature shifts on the joint evolution of seed dormancy and size 77   Figure 4.6 Evolutionary end points and corresponding number of populations for the joint evolution of seed dormancy and size after a numerical simulation of 5.0×107 and 1.0×108 steps Note: the error bar for each end points or the number of populations (i.e., seeds or adults) is calculated by 20 replicates for each set of simulations; sd represents standard deviation (i.e., temperature variation); the initial matrices (α, γ) and (seeds, adults) are (0.5, 0.5) and (5, 5), respectively. Impact of temperature shifts on the joint evolution of seed dormancy and size 78   Figure 4.7 Fecundity without considering density-dependent competition as a function of seed size (A) and trade-off of plant total reproductive investment and seed dispersal-related survival (B) Note: red arrows indicate the evolutionary direction.    Impact of temperature shifts on the joint evolution of seed dormancy and size 79  4.3.5 Effects of stochasticity As the simulation time extended (i.e., 1.0×108 steps), increased temperature variation (i.e., within 3°C) ends up in higher end points of α or γ relative to their counterparts in non-varying (opt) scenarios (Fig. 4.4A and C). This indicates that the evolved trait (α or γ) has a palpable effect on bet-hedging (i.e., reduced speed in conter-selection) especially for the elevated amplitude of temperature fluctuations and in the long term. Furthermore, temperature variation alters the ecological structure, where the relative frequency of adults and seeds becomes more balanced with higher amplitude of temperature variation (Fig. 4.4B, D). In the scenarios of joint evolution, selected seed sizes are always higher in increased temperature variation (i.e., within 3°C) than in opt (Fig. 4.6B). However, evolved seed dormancy phenotypes are on average lower in temperature variation than in opt (Fig. 4.6A). This suggests that seed size evolves more slowly than dormancy under environmental uncertainties when compared with the scenario in opt. Moreover, coevolution alters the ecological system such that adults and seeds become more balanced with higher degree of temperature variation (Fig. 4.6C). 4.4 Discussion While pieces of previous work deal with the evolution of either seed dormancy or size (e.g., Cohen (1966); Venable and Brown (1988)), we here investigate existing feedbacks between the two traits as well as their joint evolution. Also, we explicitly tackle effects of climate change in this evolutionary context rather than solely focusing on fixed environmental settings. Besides evolved traits, we also elucidate how population structures alter over time given different evolutionary strategies for species that are thermally adapted or non-adapted. This study is therefore able to advance our understanding of eco-evolutionary feedbacks that shape and maintain biodiversity in the context of climate changes. 4.4.1 Temperature shifts and life-history evolution Regardless of whether seed dormancy α and size γ jointly or independently evolve, selection gives rise to faster evolution when species are not locally adapted (i.e., temperature shifts by 1.5 or 3°C) (Fig. 4.4A, C and 3.18A, B). Climate change could directly select for higher levels of seed dormancy through increasing Impact of temperature shifts on the joint evolution of seed dormancy and size 80  the probability of bad years. However, our results do not follow this expectation, as temperature shifts in fact select for lower levels of dormancy (Fig. 4.4C and 4.6C). A possible explanation relies on variations in density dependent effects. In our model, seed germination is directly affected by the competition among seedlings and while temperature shifts indeed worsen the environment (thereby selecting for more dormancy), it also globally decreases the number of seeds, thus relaxing the competition at germination stage. This indirect effect creates positive effects for more germination and thus less dormancy. These implications that global change directly and indirectly affects the selection of dormancy levels are in line with empirical and experimental evidence. Soil temperature has been shown to largely impact the synchronization of seed germination in the soil seed bank (reviewed by Finch-Savage and Leubner-Metzger (2006)). Consistent with our results, increased temperature or decreased elevation that is ascribed to elevated temperature as well as decreased precipitation and soil moisture, promotes dormancy loss (Ooi et al. 2012; Zhou and Bao 2014). Note, however, that germination processes do not simply depend on temperature effects. For example, moist-chilling is a common dormancy-breaking stimulus for imbibed mature dormant seeds in natural stands, while under some conditions, extended chilling can result in secondary dormancy (i.e., non-dormant seeds fail to germinate due to reentering dormant state by unfavorable cues for germination) (Penfield and Springthorpe 2012). In such conditions, temperature shifts may increase the time to germination due to insufficient dormancy decay or re-induction to dormancy. This mechanism is important in the plant life cycle and can be easily included in future versions of the model, for instance by modifying the environmental constraints in the Gj equation (see the Model section), to incorporate moisture effects in addition to temperature effects, as well as the possibility of secondary dormancy. Temperature and other selective pressures pertaining to temperature also affect seed size evolution (Vidigal et al. 2016). Our results imply an overall decrease in seed sizes with temperature shifts. In concordance with this, increasing temperature during growth has been shown to reduce nutrient and water availability, which in turn lower seed size (Wulff 1986). Low elevation with higher temperature has also Impact of temperature shifts on the joint evolution of seed dormancy and size 81  been suggested to lead to smaller seeds (Zhou and Bao 2014; Vidigal et al. 2016). More generally speaking, small seeds are superior colonizers and large seeds are superior competitors. Equally important is the stochasticity of temperature to the evolution of seed dormancy and size. With wide temperature variation (e.g., 3°C relative to 1.5°C) between generations, species undergo high variance in the fitness and thus bet-hedging effects give rise to low germination fractions and /or large seed size (better provisioning to survive harsh settings) (Fig. 4.4A, C and 4.6B). This result is evidenced by previous theoretical investigations (Ellner 1985a; 1985b; Gremer and Venable 2014). 4.4.2 Impact of joint and independent evolution If seed dormancy α and size γ jointly evolve, both are counter-selected in our model (Fig. 4.6A, B). In their independent evolution, seed dormancy and size evolve as fast as in coevolution (Fig. 4.6A, B and Fig. 4.4A, C); seed size gets to a selective strategy while dormancy is selected against (Fig. 4.3 and 4.4A, C). These indicate that in the coevolution scenarios, the evolution of seed size dampens but does not alter the counter-selection of seed dormancy and eventually seed dormancy and size are selected against. Long-lived species buffered from temporal variation in the environment often exhibit less dormancy (Venable and Brown 1988; Rees 1994). Nonetheless, seed dormancy is not an all-or-nothing trait. Contrary to what is observed in our result, environmental uncertainty (Cohen 1966; Bulmer 1984) and/or competition (such as, density dependence) in fluctuating environments (Ellner 1987) have been shown to favour seed dormancy. The potential agent of selection, high precipitation or a low amount with substantial fluctuation between generations, selects for dormancy (Volis and Bohrer 2013). Also, a negative correlation between seed dormancy and size is generally observed (Thompson and Grime 1979; Grime et al. 1981; Rees 1996; Kiviniemi 2001; Larios et al. 2014; Vidigal et al. 2016), such that when dormancy evolves to a small value, seed size should evolve toward a large value. We do not observe selection of increased dormancy, nor do we get a negative correlation between seed size and seed dormancy. This inconsistency between our results and empirical observations may rest on the fact that in our model, populations reach stable equilibrium densities (compared with Gremer and Venable (2014)), such that deep dormancy cannot be selected as a bet-hedging strategy to reduce mortality due to density-dependent mortality or to direct Impact of temperature shifts on the joint evolution of seed dormancy and size 82  variations in the environment. In fact, when no selective forces impose on dormancy, dormancy turns into a supplementary source of mortality. Moreover, the counter-selection of seed dormancy due to extra costs is imposed on seed survival in our model. Deep seed dormancy may be selected for given high seed survival rate in the soil seed bank (Cohen 1966; Venable and Brown 1988; Gremer and Venable 2014) and/or a decreased density-dependent effect (Ellner 1985a; 1985b; Gremer and Venable 2014). Additionally, we found that when secondary dormancy is incorporated into the model (i.e., a portion of non-dormant and non-germinable seeds becomes dormant and goes into the soil seed bank), the model may select for certain levels of dormancy (results of another modified model not detailed here). These results indicate that low seed persistence invariably selects against seed dormancy, and increasing seed persistence may alter dormancy evolution in stable systems and thus the correlation between seed dormancy and size. In our model, considering long-lived species exposed to density dependent effects, we see that joint evolution results in low dormancy and small seed size. When dormancy decreases, germination increases at the cost of seed persistence and the adult population does not significantly change if the seed population can be maintained. In our model smaller seeds allow higher fecundity (Fig. 4.7) for a given total reproductive investment (Fig. 4.7). Increasing fecundity through smaller seeds sustains the seed population while decreasing seed persistence. This represents one evolutionary scenario leading towards quick germination and smaller seed size at the cost of seed persistence. In nature, the soil seed bank is more associated with annuals than perennials, which is supported by comparative studies (Rees 1993; 1996; Thompson et al. 1998). Dormancy can evolve differently in perennials, as there are other sources of variabilities, such as fire (Liu et al. 2016). This also suggests that evolutionary forces do not necessarily favor large seed size to increase seed persistence in the soil seed bank. It is worth noting that, if large seeds are selected for, seed dormancy is not likely to be always selected against, in the sense that seed persistence has high benefits. In perennial plants, the combination of traits (high fecundity, small seeds, low seed persistence, and low dormancy) we observe corresponds to some species in nature (e.g., aspen (Populus tremula L.) and fireweed (Chamerion angustifolium (L.) Holub)). As such, our model leads to strategies akin to the life Impact of temperature shifts on the joint evolution of seed dormancy and size 83  history of some opportunistic species, which are more effective in exploiting ephemeral ecological opportunities. Aspen, for example, has high seed production capacity (1,000-1,500 seeds/catkin and as many as 40,000 catkins/tree). Seeds of aspen are very small and light (~0.06-0.17 g/thousand-grains), which helps dispersal over long distances, and its germinability after maturation is usually fast and high (70-95%), but its viability decreases after dispersal and this corresponds to the transient seed banks of aspen (Thompson et al. 1997). 4.4.3 Population structures In our model, even when species are well adapted, adults exceed seeds in numbers (Fig. 4.2, 4.4 and 4.6) due to high and increased seed mortality as evolution proceeds. Temperature shift robustly leads to more balanced population structures (Fig. 4.2), while evolution increases the imbalance in population structures particularly in opt or slight temperature shift (i.e., 1.5°C) (Fig. 4.2). Considering the interaction of evolution and temperature shifts or variation, the population structure becomes more balanced, while the total population decreases (Fig. 4.4B, D and 4.6C). Temperature shifts impose extra costs on germination and these determine whether the seed-adult system can be sustained in the unbalanced structure (i.e., compared with the constant adaptive scenario). Apparently, temperature shifts causing maladaptation do not increase population density (i.e., total number of seeds and adults). As the population structure becomes more balanced in temperature shifts, there must exist critical points (i.e., combinations of temperature and evolved trait(s)) at which seed and adult density are equal, evidenced by contrasting ecological dynamics when temperature shifts by 1.5 or 3°C (Fig. 4.2, 4.4B, D, and 4.6C). In interaction with evolved traits, high temperature shifts (e.g., 3°C) largely affect population structure, which is facilitated when the evolution of seed dormancy is allowed. The evolution of dormancy has indeed a large influence on the population structure and in conjunction with temperature shifts, results in seed numbers superior to adults. This process is attained mainly through altering fecundity and germination. The probability of germination greatly decreases at high temperature shifts, resulting in fewer adults (note that constant adult survival is assumed), while elevated fecundity due to decreased adult density-dependent competition (and predictably smaller seeds, if seed size evolves or coevolves with seed Impact of temperature shifts on the joint evolution of seed dormancy and size 84  dormancy), resulting in more seeds (note that seed survival deteriorates due to counter-selected seed dormancy). Our results illustrate that climate change not only has direct impacts on population structures (as already observed (Walther et al. 2002; Clark et al. 2014)), but also shows how evolutionary trajectories may exacerbate these changes. Moreover, the soil seed bank can help balance population dynamics by spreading risk and allow population recovery after disturbance (Grime 1989), while global warming leads to decreased seed persistence (Ooi et al. 2009; Childs et al. 2010). In response to temperature shifts or variations of high magnitude, significant changes in ecological structure (e.g., from an unbalanced to a more balanced state) occur in stable systems, indicating that life history changes significantly and may gradually lack the power of resilience (no trace of resistance and recovery in simulation results), thus becoming more vulnerable to collapse.Hormone flux and signaling during the seed-to-plant transition 85  5 Hormone flux and signaling during the seed-to-plant transition 5.1 Introduction Conifers are ecologically and economically important plants, and coniferous forests cover vast tracts in the northern hemisphere. White spruce (Picea glauca) is a keystone species of boreal forests in the North American taiga. In Canada, over 100 million white spruce seedlings are out-planted yearly for regeneration (Bousquet et al. 2007). However, our understanding of molecular mechanisms underlying the dormancy and germination of white spruce seeds and of conifers in general remains quite limited. As the white spruce genome was the first to be sequenced and assembled amongst conifer species in 2013, interest in investigating aspects of the molecular mechanisms underlying key developmental and physiological processes is mounting (Rigault et al. 2011; Birol et al. 2013; Nystedt et al. 2013).  Moist-chilling is a common dormancy-breaking stimulus for conifer seeds both in natural stands and under laboratory conditions. Specific requirements can vary enormously amongst different conifer species, as well as amongst different clones and seed lots of a given species (Baskin and Baskin 1998; Bewley et al. 2012). For white spruce, the typical moist-chilling requirement under laboratory conditions is approximately 21 days.  During seed maturation, exposure of seeds on the parent plant to low temperatures can influence the depth of primary dormancy of the mature seeds. In the imbibed mature dormant seed, dormancy alleviation is often promoted by exposure to chilling. It is therefore assumed that chilling plays a dual role in regulating dormancy (Batlla and Benech-Arnold 2010). In addition, under some conditions, extended chilling can result in secondary dormancy (Finch-Savage et al. 2007; Penfield and Springthorpe 2012). Mechanisms that underlie the beneficial effects of moist-chilling on dormancy alleviation undoubtedly involve plant hormones - with abscisic acid (ABA) and gibberellins (GAs) receiving the most attention, alone and within the context of their interplay or crosstalk with other hormones such as auxins, cytokinins, and ethylene (Kucera et al. 2005; Finkelstein et al. 2008; Anderson et al. 2012; Graeber et al. 2012; Linkies and Leubner-Metzger 2012; Liu et al. 2014).  Hormone flux and signaling during the seed-to-plant transition 86  Although evolutionarily independent from the other seed-bearing plants since 260 million years ago (Schneider et al. 2004), the seeds of conifers exhibit conserved mechanisms regulating their dormancy and germination with seeds of angiosperms, including those mediated by ABA (Forbis et al. 2002; Linkies et al. 2010; Hauser et al. 2011). Several studies demonstrate that moist-chilling invokes changes in the levels of, and sensitivity to, ABA and GAs in conifer seeds (Ogawa et al. 2003; Ali-Rachedi et al. 2004; Yamauchi et al. 2004). ABA levels are reduced during moist-chilling-induced dormancy termination of yellow-cypress (Chamaecyparis nootkatensis D. Don) and Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) seeds (Corbineau et al. 2002; Schmitz et al. 2002). ABA levels of western white pine (Pinus monticola Douglas ex D. Don) seeds also decline significantly during moist-chilling, and this decline is associated with an increase in germination capacity (Feurtado et al. 2004). It is noteworthy that if dormancy-breaking conditions are not met, seeds maintain high ABA levels; and dormancy imposition and maintenance require ABA biosynthesis (Feurtado et al. 2007). For western white pine seeds, it is the ratio of ABA biosynthesis to catabolism that appears to be the key factor that determines the capacity for dormancy maintenance versus germination.  GAs have a positive effect on dormancy alleviation and germination of conifer seeds (Feurtado et al. 2007); likewise, dormancy alleviation of moist-chilled Arabidopsis seeds depends on the expression of GA3 oxidase 1 of the GA biosynthesis pathway (Ogawa et al. 2003; Yamauchi et al. 2004). In hazel (Corylus avellana L.), moist-chilling has a pronounced effect on the capacity of the seeds for GA biosynthesis, although active GA production does not take place until the seeds are placed in germination conditions (Williams et al. 1974). In the ABA signaling cascade of Arabidopsis, concerted actions of four transcription factors, i.e. ABSCISIC ACID INSENSITIVE 3 (ABI3), FUSCA 3 (FUS3), LEAFY COTYLEDON 1 (LEC1), and LEAFY COTYLEDON 2 (LEC2), mediate various seed maturation processes and some of these factors also participate in the transition from dormancy to germination (Holdsworth et al. 2008; Nambara et al. 2010). Orthologs of ABI3, encoding a structurally conserved transcription factor have been isolated from angiosperm and gymnosperm (conifer) species, and they act as central regulators of seed development and dormancy (Romanel et al. 2009; Graeber et al. 2012). A member of the ABI3/VP1 family cloned from Hormone flux and signaling during the seed-to-plant transition 87  yellow-cypress is positively associated with dormancy maintenance (Zeng et al. 2003). Through yeast two-hybrid analyses, a yellow-cypress ABI3 Interacting protein (CnAIP2) that functions as a negative regulator of ABI3 was recently identified (Zeng et al. 2013); note that this protein is different from the Arabidopsis E3 ubiquitin ligase, AIP2 (Zhang et al. 2005). CnAIP2, like CnABI3, acts as a central gatekeeper of important plant life cycle transitions including the seed dormancy-to-germination transition (Zeng et al. 2013).  GAs also modulate plant growth and development and can act antagonistically to ABA in the control of both seed dormancy and germination (Finkelstein et al. 2008; Sun 2008). Notably, regulation of seed germination via light and temperature is correlated with GA metabolism and signaling in many species (Yamauchi et al. 2004; Holdsworth et al. 2008; Seo et al. 2009; Graeber et al. 2012). Exogenous application of GAs to western white pine seeds initiates a decrease in ABA levels in dormant seeds by changing ABA homeostasis; i.e., by promoting ABA catabolism or transport over ABA biosynthesis (Feurtado et al. 2007).  The hormone auxin (principally indole-3-acetic acid [IAA]) regulates many aspects of plant growth and development. Amide-linked conjugates of IAA synthesized during seed development (Bialek and Cohen 1989; Ljung et al. 2002) can serve as a source of free IAA during seed germination (Bialek et al. 1992; Rampey et al. 2004). Several lines of evidence implicate a role for auxins in seed dormancy maintenance in Arabidopsis (Brady et al. 2003; Ramaih et al. 2003; Liu et al. 2007); auxin-mediated seed dormancy maintenance depends on ABI3 and this inhibitory effect can be nullified by moist-chilling (Liu et al. 2013b). The hub of the auxin signaling pathway is the TRANSPORT INHIBITOR RESPONSE1 (TIR1)/AUXIN SIGNALING F-BOX (AFB) protein signaling system (Chapman and Estelle 2009; Vanneste and Friml 2009; Calderón Villalobos et al. 2012). In this work, we studied one white spruce (Picea glauca) population from British Columbia, Canada, to elucidate the hormone-based mechanisms that underpin dormancy alleviation and germination in response to temperature signaling (i.e., moist chilling and transfer to germination conditions). This research will help provide insights into how winter chilling contributes to the timing of phenology, and how conifer life histories may develop under new climate scenarios. Hormone flux and signaling during the seed-to-plant transition 88  5.2 Materials and methods 5.2.1 Population selection Seed material Five seed lots of white spruce (Picea glauca) representing five different seed planning zones (SPZ) in British Columbia, Canada were used and all exhibited similar average standard germination percentages (Appendix B, Table B.1). Seed lots from natural stands are required to pass two tests for registration and reforestation use on Crown land. These are: purity (> 97%) and moisture content on a fresh weight basis (4.9 to 9.9%) (Kolotelo et al. 2001). Seeds were maintained at -18°C (moisture content between 4.9 and 9.9%). Just prior to use, the seeds were transferred to vials (75 per vial) and stored at 4°C. Seed treatments, controls, and germination conditions The experimental design was a completely randomised design implemented in a three-way factorial (5×2×3 levels) with five white spruce seed lots (random effect), with or without moist-chilling treatment (fixed effect), and without priming or with thermo-priming at 15 or 20°C (fixed effect). Moist-chilling was conducted as per ISTA (1999) rules; for white spruce seeds, this involves soaking the seeds for 24 hours at room temperature, surface-drying the seeds and then conducting 21 days of moist-chilling at 2 °C. The control was comprised of non-moist-chilled seeds. Thermo-priming treatments were conducted at 15 or 20°C for three days in the dark; the control was comprised of non-thermo-primed seeds. A final control was comprised of seeds that were neither subjected to moist-chilling nor thermo-priming. Each treatment combination consisted of four replications of a 75-seed sample. Treatments were conducted in a manner that allowed for germination assays to begin on the same day.  Seeds were imbibed for 24 hours in distilled water prior to moist-chilling. The soaked seeds were then placed in clear plastic germination boxes (4.5×4.5×1.5cm) lined with moistened cellulose wadding (Kimpack®) and filter paper, and were chilled for 21 days at 2°C under high humidity conditions and without supplemental light (Bonner and Karrfalt 2008). After moist-chilling, replicates destined for thermo-priming were transferred to a light-proof and temperature-set chamber at 15 or 20°C for three days. Hormone flux and signaling during the seed-to-plant transition 89  Germination was conducted in an incubator with alternating temperatures of 30/20 °C (light/dark) with 8 hours of fluorescent illumination (approximately 13.5 μmol m-2s-1) per day. Germination was conducted over a 28-day period following standard ISTA conditions (ISTA 1999). Germinants were counted daily throughout the germination test. Seeds were counted as germinated if the radicle emerged to four times the seed length (approx. 4 mm for white spruce seeds) and showed gravitropic curvature, as this is the standard in the forest industry (Kolotelo et al. 2001). The numbers of empty and diseased seeds were determined at the end of the test by cutting open non-germinated seeds. Germination parameters The four-parameter Hill function (4-PHF) (El-Kassaby et al. 2008) was used to quantify the germination of each seed lot (5) - treatment (6) - replication (4) combination (N = 120). Parameters estimated were: a, the maximum cumulative germination percentage equivalent to germination capacity (GC); b, a mathematical parameter controlling the shape and steepness of the curve (the larger the b value, the steeper the rise toward a); c, the time required to achieve 50% germination, equivalent to R50 (germination speed (Thomson and El-Kassaby 1993)); TMGR, the time of maximum germination rate; lag, the time of germination onset; Dlag-50, the duration between lag and c; and DI, dormancy index, the area between the germination curves of control and any pre-treatment. Data analyses The effect of seed lot, moist-chilling, thermo-priming, and their interactions on 4-PHF parameters was investigated using the GLM procedure in SAS® vers. 9.1.3 (SAS Institute 1999). The germination parameters were subjected to analyses of variance using the following additive linear model as follows:   where, μ is the overall mean, Sj is the effect of the jth seed moist-chilling treatment (j = 1 to 2, fixed effect), Pk is the effect of the kth thermo-priming treatment (k = 1 to 3, fixed effect), Cl is the effect of the lth seed lot (l = 1 to 5, random effect), SPjk is the interaction between seed jth moist-chilling and kth thermo-priming treatment combination, SCjl is the interaction between jth moist-chilling and lth seed lot, PCkl is the )( jklijklkljljklkjijkl SPCPCSCSPCPSy   eqn 12  Hormone flux and signaling during the seed-to-plant transition 90  interaction between kth thermo-priming and lth seed lot, SPCjkl is the three-way interaction among jth moist-chilling, kth priming and lth seed lot; and εi(jkl) is the residual term (i = 1 to 4) (see Table 5.2 for sources of variation (SOV), degrees-of-freedom (df) and component of variance (EMS). Additionally, we utilised a reduced model that represents a subset of the above-mentioned full model. This was needed to conduct a simplified analysis that isolated the moist-chilled treatment. The threshold for statistical significance was always set at P < 0.05. 5.2.2 Sampling from dry seeds to germinants Seed materials, germination testing, and seed sampling One white spruce population from British Columbia, Canada (located at 54°26'N, 121°44'W, 850 m elevation), was selected for this study based on cumulative germination performance after the standard 21-day moist-chilling treatment (see Section 5.2.1) (Liu et al. 2013b). For germination characterization, seeds were first moist-chilled in clear plastic germination boxes (Hoffman) lined with moistened cellulose wadding and filter paper, and moistened with 50 mL of sterile water for 21 days at 3°C in a dark environment. The boxes containing seeds were then transferred into germination conditions (30/20°C, 8-h-photoperiod and 70% relative humidity). Light was provided by fluorescent illumination at approximately 13.5 μmol·m-2s-1. Standard germination was conducted over a 21-day span following the International Seed Testing Association standards (ISTA 1999). As controls for the transfer to the germination-promoting conditions (30/20°C and light), seeds were transferred to constant darkness at 30/20°C, or were kept in moist-chilling conditions (constant 3°C) with an 8-h photoperiod. Germination assays, scoring, and quantification were performed as previously described (Liu et al. 2013b). Seed sampling for molecular and biochemical analyses was conducted on three biological replicates and included times during moist-chilling (0, 10 and 21 d) and after transfer to germination or control conditions (6 h, 24 h, 80 h, and 9 d) (Fig. 5.1). For seeds that had been maintained in darkness, the sampling was also conducted in darkness. Samples comprising the three replicates were collected and immediately frozen in liquid N2 and stored at -80°C.   Hormone flux and signaling during the seed-to-plant transition 91  Reference gene selection and gene query using BLASTN Three genes were chosen and used as internal controls: CO220221 (peroxisomal targeting signal receptor), CO206996 (hypothetical protein), and AY639585 (ubiquitin conjugating enzyme 1, UBC1); these were selected due to their constitutive expression during developmental transitions as determined by published microarray profiling (Friedmann et al. 2007; Palovaara and Hakman 2008). A subset of genes specifying proteins mediating the committed steps of ABA, GA and auxin biosynthesis/catabolism or signaling pathways (Fig. 5.5-5.7 B), were used to query the spruce EST database (PlantGDB) and the white spruce whole genome data (NCBI) using BLASTN. Primers (Table 5.1) were designed with the primer3 tool online (Rozen and Skaletsky 2000).  Figure 5.1 Schematic representation of sampling to determine germination of white spruce seeds in different germination conditions. Note: In standard germination conditions (30/20°C and an 8-h photoperiod), seed coat rupture and radicle protrusion was observed at 80 h in the majority of the population. The stage at which the radicle had emerged to four times of the seed length was reached on d 9. Hormone flux and signaling during the seed-to-plant transition 92  Table 5.1 Description of genes and primer pairs used for qPCR Gene abbreviation GeneBank accession number Arabidopsis homolog accession Arabidopsis locus description BLASTN score (bits) E-value Identities Primer pairs Expected size (bp) Peroxisomal targeting signal receptor CO220221   (Friedmann et al. 2007) 5’- ATGCCTATCTGAAATGGACAC 3’- ACTGTCTATGTTTGGCAGCAC 149 Hypothetical protein CO206996   (Friedmann et al. 2007) 5’- GTCGTGTGGATTGTCTCTGC 3’- ATGTATTCGAAGAGGAGGAATG 202 UBC1 AY639585 NM_105097 Ubiquitin-conjugating enzyme 1 962 0.00 100% (477/477) 5’- GGAACAGTGGAGTCCTGCTT 3’- CCTTGCGGTGGACTCATATT 148 EMB32 (LEA)* DQ120067  Dehydrin LEA (Dong and Dunstan 1996; Xia and Kermode 1999) 5’- GAGAACGGTGTTCTGGATGA 3’- CAGCGGTATCCCTGATGTTA 166 AAO3** BT103270 NM_128273 Abscisic aldehyde oxidase 3 64 6.00E-08 86% (59/68) 5’- TTCTAGCAGCATCGGTTCAC 3’- CTGCAAATAGCGCTCAACAT 158 †CYP707A4** DR569557 NM_112814 ABA 8'-hydroxylase 52 9.00E-05 80% (89/110) 5’- CGAACTGGCAAAGCTACAAA 3’- ATCCGAGAGGCATGATGATT 182 SnRK2.2** EX436780 NM_114910 Sucrose nonfermenting 1(Snf1)-related protein kinase 2.2 202 3.00E-50 80% (419/522) 5’- CGTGACTTGAAGCTGGAAAA 3’- CCACAGGACCATACATCTGC 197 †††ABI3* BT102260 AJ131113 ABA insensitive 3 321 1.00E-85 87% (288/330) 5’- ACGTTGGCAATCTAGGAAGG 3’- CGCCAGTATTTTCAAGCAGA 186 ††††AIP2* BT118051 AY268951 ABI3 interacting protein 2 339 3.00E-91 80% (590/732) 5’- ATGTGAAGCCCCTTTCATTC 3’- AGAAGCGCCGATAAACTTTG 172 PgKS GU144565  Ent-kaurene synthase (Keeling et al. 2010; Niu et al. 2014) 5’- ACATGGAAAATGCAGAACCA 3’- CTCTCTTGCAGCCTTGAATG 174 PgCPS ES262766  Ent-copalyl diphosphate synthase (Keeling et al. 2010; Niu et al. 2014) 5’- CTTGGTATCGCCCGATATTT 3’- GTGCGAACGAAGAAGTCTGA 153 GA20ox1** DR575289 NM_118674 Gibberellin 20 oxidase 1 54 2.00E-05 89% (42/47) 5’- GAGAAATAACGCCCACGAAT 3’- GGCCATAGATTTGCCCTAAA 196 BME3** DR570635 NM_115338 Blue micropylar end 3 52 9.00E-05 83% (65/78) 5’- GTATTCGGGCAGAAGCCTAC 3’- GGAATCTGAACCCCTGAAGA 199 SPY** EX309351 NM_111987  Spindly 105 2.00E-20 78% (396/377) 5’- AGACTCGTTGGCAGATCCTT 3’- TGAAGCTTCCAAAGGTGATG 158 EXP2** BT104733 NM_120611 Expansin A2 60 2.00E-07 91% (42/46) 5’- GGCAAAGCAACTCCTACCTC 3’- CATCTGCATTCGAGCTGTCT 170 SPT** EX439688 NM_119857 SPATULA 68 1.00E-09 80% (118/146) 5’- CGCAAGAAGATTCTGGTGAA 3’- GGTACTCGATTGCTTCGTCA 204    Hormone flux and signaling during the seed-to-plant transition 93   (CONTINUED) Gene abbreviation GeneBank accession number Arabidopsis homolog accession Arabidopsis locus description BLASTN score (bits) E-value Identities Primer pairs Expected size (bp) ASA1/2** BT105301 NM_001203302 Anthranilate synthase component I-1/2 150 4E-31 71% (198/280) 5’- CCTATGTTCCTGGGATGCTT 3’- CGAAAAGACTGTCGGATTCA 153 ASB1** BT110778 AY099834 Anthranilate synthase beta subunit 1 404 2.00E-100 74% (429/576) 5’- AAATTCAGCCATTCCCAAAG 3’- GCTCGGGTTTACCATGTTCT 163 TSA1** BT112370 NM_115321 Tryptophan synthase alpha chain 1 200 7.00E-45 69% (329/478) 5’- CCTATGTTCCTGGGATGCTT  3’- CGAAAAGACTGTCGGATTCA 153 TSB1** BT109977 NM_124862 Tryptophan synthase beta subunit 1 802 0.00E+00 77% (721/934) 5’- GAGGTGGTTCAAATGCAATG 3’- TGCCCGTCTTCATCTTGTAG 184 AAO1** BT103270 NM_180718 Aldehyde oxidase 1 222 1.00E-50 72% (282/393) 5’- TTCTAGCAGCATCGGTTCAC 3’- CTGCAAATAGCGCTCAACAT 158 AMI1** BT113550 NM_100769 Amidase 1 206 3.00E-46 70% (305/435) 5’- CAGTGGCAAAAGGCTATCAA 3’- CAGTCCCCCTTCTCAAATGT 187 IAR3** BT106976 NM_104055 IAA-alanine resistant 3 (IAA-amino acid hydrolase) 332 1.00E-80 68% (598/873) 5’- GAGGGAGCATTGGAAAATGT 3’- TTGAATGTTGTGGGATTGCT 166 ILL1/2** BT106591 NM_125049(8) IAA-leucine resistant (ILR)-like 1/2 (IAA-amino acid hydrolase) 338 3.00E-82 70% (504/723) 5’- ATTGATTGCCTTCCAACACA 3’- TCTCCCAGACGAGTGTCAAG 154 PIN1-like DR565243.1 FJ031883 Pin formed 1-like (Hakman et al. 2009) 5’- TCTGGCATTCGCTTTAACTC 3’- ATACACCCACCCGAAAATCT 179 CUC-like BT102493.1 HM638414 Cup-shaped cotyledon-like (Larsson et al. 2012) 5’- ACCATGTCCAGCAACCTCCT 3’- TATGGAGCTGGGCCTGATTT 108 CUL1** BT115619 NM_001203732 cullin 1, a component of SCF ubiquitin ligase complexes 846 0.00E+00 76% (800/1051) 5’- CCCTTGCATGTGCAAAATAC 3’- CCTTGTCCACATCCTCAATG 171 TIR1** BT107385 NM_116163 Transport inhibitor response1 276 3.00E-65 68% (554/819) 5’- AAATGCAGCAAATGAACAGC 3’- TTGCAGAGAATGTTGCCTTC 187 AFB3** BT110362 NM_101152 auxin signaling F-box 3 130 1.00E-25 66% (328/500) 5’- TGATTGGTTGAGCTGCTTTC 3’- TTGTGGGGCTCTCAACATAA 190 Auxin/IAA* EX428820 AY289601 auxin-induced protein 2 (auxin/IAA2) (Goldfarb et al. 2003) 5’- GAAGTCATGGACTCCACCAG 3’- CTGACTAGGAGATGCCGAAA 156 ARF4 CO256727  Auxin responsive factor 4 (Friedmann et al. 2007) 5’- ATTGCCCCGTTAAGTCTAATG 3’- CCTTTTCCCCTGATTGTTGAG 171 * the gene has been annotated in other conifer species and the homolog accession number is from conifers instead of from Arabidopsis;  ** the gene has only been annotated in Arabidopsis and related references have been displayed in the space of five to seven columns; † with reference to (Kushiro et al. 2004; Saito et al. 2004); †† with reference to (Klimaszewska et al. 2010); ††† with reference to (Zeng et al. 2003); †††† with reference to (Zhang et al. 2005; Zeng et al. 2013). Hormone flux and signaling during the seed-to-plant transition 94  5.2.3 RNA and protein extraction for expression analyses RNA isolation, quantitative (q)RT-PCR and principle component analysis RNA was isolated from seeds as previously outlined (Müller et al. 2012). Two µg of RNA was reverse-transcribed into cDNA using the EasyScript PlusTM kit (abmGood) with oligo-dT primers. First-strand cDNA synthesis products were diluted fivefold, and one µl of cDNA was used to carry out semi-quantitative RT-PCR for a primer specificity check. Quantitative RT-PCR (qRT-PCR) analyses were run with three biological replicates per sample in 15-µl reaction volumes in an ABI7900HT machine (Applied Biosystems) using the PerfeCTa® SYBR® Green SuperMix with ROX (Quanta Biosciences). The reaction mixture consisted of 1.0 μl fivefold diluted cDNA, 7.5 µl supermix and 1.0 μl of each primer (10 μmol·L-1). The reaction procedure was 5 min at 95°C, 45 cycles of 15s at 95°C and 60 s at 59°C. Dissociation curves were generated at the end of each qRT-PCR to validate the amplification of only one product. Efficiency calculation and normalization were performed using real-time PCR Miner (www.miner.ewindup.info/) (Zhao and Fernald 2005) and data quality was confirmed through internal controls and no-template-controls, and by comparing the repeatability across replicates. An average expression value for each gene at each time point was generated from the normalized data. Principle component analysis (PCA) was performed using SAS® (vers. 9.3; SAS Institute Inc., Cary, NC) based on the expression patterns of all genes in different germination conditions at 6 h, 24 h, and 80 h as described in the text. Western blot analysis Protein extracts were generated by grinding the seed materials in protein extraction buffer (50 mM Tris pH 8.0, 150 mM NaCl,1% Triton X-100,and 100 g·ml-1 phenylmethylsulfonyl fluoride) and protein concentration was determined by measuring OD750 with the aid of photometer. Protein extracts (30 μg total soluble protein) were separated by 10% SDS-PAGE and transferred onto Amersham Hybond-P (PVDF) membranes using wet electro-blotting. The blots were blocked overnight at 3°C using 5% (w/v) non-fat dry milk and 0.1% (v/v) Tween-20 (PBST) followed by three washes (15 min each) with PBST. Blots were Hormone flux and signaling during the seed-to-plant transition 95  incubated with the anti-pgKS (ent-kaurene synthase) antibody (1:500 dilution) for 1 h at room temperature (provided by T-P. Sun’s lab). After three washes with PBST (15 min each) the membrane was incubated with the anti-rat HRP (horseradish peroxidase) antibody (1:40,000 dilution) for 1 h at room temperature. After three washes with PBST (15 min each) the membrane was drained and placed within wrap film containing 2 ml Supersignal West Pico solution and the membrane exposed to light. Chemiluminescent images were captured by a CCD camera system (Fujifilm LAS 4000). Plant hormone quantification by HPLC-ESI-MS/MS Methods for quantification of multiple hormones and metabolites, including ABA and its metabolites (cis-ABA, trans-ABA, ABA-GE, PA, DPA, 7’OH-ABA, and neoPA), gibberellins (GA53 and GA34), auxins/auxin conjugates (IAA, IAA-Asp, and IAA-Glu), and cytokinins (iPR, cis-ZR, and cis-ZOG) followed those previously described (Chiwocha et al. 2003; Chiwocha et al. 2005). Briefly, lyophilized seed samples were ground and a mixture of all internal standards was added to duplicate homogenized seed samples (~50 mg each), and extraction performed using acidic isopropanol. Samples were reconstituted and purified by solid phase extraction (SPE) with Sep-Pac C18 cartridges (Waters, Mississauga, ON, Canada). Subsequently, samples were injected onto an ACQUITY UPLC® HSS C18 SB column (2.1×100mm, 1.8µl) with an in-line filter and separated by a gradient elution of water containing 0.02% formic acid against an increasing percentage of a mixture of acetonitrile and methanol (50:50, v/v). The analysis utilized the Multiple Reaction Monitoring function of the MassLynx v4.1 (Waters Inc) control software. The quality control samples and internal standard and solvent negative controls were prepared and analyzed along with samples. 5.3 Results 5.3.1 Selection of one population most responsive to external stimuli We first examined the effects of the various treatments on the germination characteristics of the five different seed lots (Fig. 5.2). The best treatment for seed lot 37842 was the 20°C-thermo-priming combined with moist-chilling.  This treatment was substantially superior to that of the standard treatment (i.e., moist-Hormone flux and signaling during the seed-to-plant transition 96  chilling alone) (Fig. 5.2; seed lot 37842). In the absence of moist chilling, a 20°C thermo-priming treatment (as compared to the no-thermo-priming control) stimulated relatively prompt germination of non-moist-chilled seeds, noticeably affecting both the ‘lag’ parameter and the total germination. Likewise, the other seed lots showed a significant benefit of the thermo-priming following moist-chilling (Fig. 5.2), and again the thermo-priming at 20°C yielded better germination than the thermo-priming at 15°C.  Without exception, the five seed lots yielded higher estimates of DI following the combined treatment of moist-chilling and three days of 20°C thermo-priming compared with standard moist-chilling alone (note: a high DI value means greater differences between treatments, indicating an improved germination pattern) (Fig. 5.3). Seven out of the 12 first-order interactions involving moist-chilling (moist-chilling × priming and moist-chilling × seed lots) as a source of variation were significant, indicating that perhaps the inclusion of the control treatment (no moist-chilling and no priming) in the analyses may have polarised the results of the germination parameter analyses (Table 5.2). This in turn may have obscured the true effects of the treatments. To overcome this, we repeated the previous analyses for the moist-chilled and non-moist-chilled seeds separately (Table 5.3). Unlike the 3-way analyses, the differences between the two priming treatments were consistently significant across all germination parameters (Table 5.3B). Thus priming at 20°C (compared with 15°C) produced a similar germination capacity (a), but led to a steeper rise toward maximum germination (b), faster germination speed (c), shorter time to reach the maximum germination rate (TMGR), shorter germination lag (lag) and a shorter duration between the time at germination onset and time to reach 50% germination (Dlag-50) (Table 5.3B). The results also confirmed the added benefits of the combined moist-chilling-priming treatment. Additionally, among the five seed lots studied, there were significant differences for four out of the six germination parameters (Table 5.3B). The first-order interaction between priming and seed lot was not significant for four out of six germination parameters, thus justifying the abbreviated analysis (Table 5.3). Additionally, the two significant first-order interactions between priming and seed lots were related to promptness and evenness of germination (lag and Dlag-50) Hormone flux and signaling during the seed-to-plant transition 97  rather than to the germination rate (Table 5.3B), indicating that priming was effective in optimising maximum germination rates. The summary statistics of germination parameters provided a generalised depiction of the various treatments on germination behaviour (Table 5.4). While results from the control treatment (no moist-chilling and no priming) represent the baseline performance of the seed lots, they could not be used as a benchmark for comparison because the standard seed pre-treatment commonly applied prior to seedling production required moist-chilling for 21 days at 2°C. Thus we use this treatment as the benchmark for improved germination patterns for seedling production. Generally, all germination parameters of both the combined treatments of priming and moist-chilling were better than that of moist-chilling alone. Furthermore, 3-days priming at 20°C was consistently better than priming at 15°C for the same duration (Table 5.4). Additionally, the same trend is observed when priming alone (15 or 20°C) was compared with the control treatment. Hormone flux and signaling during the seed-to-plant transition 98  Table 5.2 ANOVA for the germination full model  (A) Expected Mean Squares for the ANOVA model used to analyse the germination parameters using five interior spruce seed lots and (B) Mean square, F- and P-values for the various sources of variation across the studied germination parameters of five interior spruce seed lots (see text for germination parameters explanation). SOV df EMS Moist-chilling (M) 1 60φm + 12σms2 + σe2 Priming (P) 2 40φp + 8σps2 +  σe2 Seed lot (S) 4 24σs2 +  σe2 M × P 2 20σmp2 +4σmps2 +  σe2 M × S 4 12σms2 +  σe2 P × S 8 8σps2 +  σe2 M × P × S 8 4σmps2 +  σe2 Residual 90 σe2  SOV a b c TMGR Lag Dlag-50 MS F p MS F p MS F p MS F p MS F p MS F p Moist-chilling (M) 2014 9.94 .0344 225.8 94.03 .0006 162.8 230.20 .0002 139.6 162.86 .0001 25.5 217.53 .0001 59.4 71.73 .0011 Priming (P) 5.8 0.53 .6103 142.4 46.12 .0001 41.1 222.58 .0001 46.1 220.84 .0001 47.4 135.54 .0001 0.3 0.72 .5136 Seed lot (S) 592.0 48.5 .0001 33.5 16.57 .0001 11.9 161.98 .0001 12.4 175.81 .0001 4.4 22.89 .0001 3.1 15.43 .0001 M × P 4.3 0.21 .8123 5.3 1.60 .2612 2.5 9.54 .0076 1.8 6.62 .0201 0.4 1.99 .1991 3.3 10.56 .0057 M × S 202.6 16.60 .0001 2.4 1.19 .3220 0.7 9.61 .0001 0.9 12.15 .0001 0.1 0.62 .6527 0.8 4.16 .0039 P × S 11.1 0.91 .5148 3.1 1.53 .1592 0.2 2.51 .0164 0.2 2.96 .0055 0.3 1.83 .0807 0.5 2.38 .0225 M × P × S 20.0 1.64 .1257 3.3 1.65 .1217 0.3 3.51 .0014 0.3 3.83 .0006 0.2 1.13 .3504 0.3 1.59 .1401 Residual 12.2   2.0   0.1   0.1   0.2   0.2   A B Hormone flux and signaling during the seed-to-plant transition 99  Table 5.3 Reduced ANOVA model after the removal of the control treatment (no moist-chilling-no priming) (A) Expected Mean Squares for the ANOVA model used to analyse the germination parameters using five interior spruce seed lots and (B) Mean square, F-value and P-vlaue for the various sources of variation across the studied germination parameters of five interior spruce seed lots (see text for germination parameters explanation). SOV df EMS Priming (P) 2 20φp + 4σps2 +σe2 Seed lot (S) 4 12σs2 + σe2 P× S 8 4σps2 +σe2 Residual 45 σe2  SOV a b c TMGR Lag Dlag-50 MS F p MS F p MS F p MS F p MS F p MS F p Priming (P) 3.8 0.67 .5396 99.4 17.45 .0012 31.8 469.55 .0001 32.9 583.28 .0001 23.7 58.92 .0001 1.4 2.76 .1228 Seed lot (S) 78.9 14.42 .0001 11.9 3.77 .0099 4.1 102.61 .0001 4.1 101.7 .0001 1.9 12.6 .0001 1.5 10.16 .0001 P× S 5.6 1.03 .4275 5.7 1.80 .1021 0.1 1.71 .1224 0.1 1.41 .2204 0.4 2.59 .0204 0.5 3.34 .0044 Residual 5.5   3.2   0.04   0.04   0.2   0.2   A B Hormone flux and signaling during the seed-to-plant transition 100  Table 5.4 Average and range of the germination parameters across treatments for five white spruce seed lots  (none: as control, no moist-chilling-no priming, Mc: moist-chilling, P20°C: priming at 20°C for three days, and P15°C: priming at 15°C for three days). No. Treatment Parameters in average (range) a b c TMGR Lag DLag-50 1 none 87.3 (76.8-98.3) 9.8 (8.1-13.8) 9.4 (8.3-10.4) 9.2 (8.1-10.3) 5.6 (5.1-6.7) 3.9 (2.9-4.9) 2 M.c. 95.2 (91.1-99.9) 13.4 (10.3-18.6) 7.5 (6.6-8.5) 7.4 (6.5-8.3) 4.8 (3.6-5.7) 2.7 (1.9-4.2) 3 M.c. + P20ºC 94.5 (85.7-99.4) 9.0 (6.0-13.4) 5.0 (4.0-5.7) 4.9 (3.8-5.6) 2.6 (1.6-3.6) 2.4 (1.9-3.3) 4 M.c. + P15ºC 95.2 (88.9-99.9) 10.7 (8.2-13.4) 6.6 (5.9-7.6) 6.5 (5.8-7.5) 3.7 (2.5-4.7) 2.9 (2.0-3.9) 5 P20ºC 86.7 (71.1-97.9) 6.7 (4.6-10.9) 7.9 (6.5-9.4) 7.5 (5.9-9.0) 3.4 (2.4-5.2) 4.5 (3.6-5.9) 6 P15ºC 86.2 (66.2-97.7) 8.3 (5.9-11.6) 8.8 (7.7-10.5) 8.5 (7.4-10.1) 4.8 (3.9-6.0) 4.0 (3.1-5.5) Note: a, the maximum cumulative germination percentage; b, a mathematical parameter controlling the shape and steepness of the curve; c, the time required to achieve 50% germination; TMGR, the time of maximum germination rate; lag, the time of germination onset; and Dlag-50, the duration between lag and c.Hormone flux and signaling during the seed-to-plant transition 101    Figure 5.2 Effects of moist-chilling and priming on germination (Mc: moist-chilling, P20°C: priming at 20°C for three days, McP20°C: moist-chilling followed by 20°C priming for three days, P15°C: priming at 15°C for three days, McP15°C: moist-chilling followed by 15°C priming for 3 days, CK: control (no treatment).  Seed lot numbers are indicated on the bottom right-hand side of each graph. Hormone flux and signaling during the seed-to-plant transition 102   Figure 5.3 Dormancy index (DI) of the five white spruce seed lots  (Mc: moist-chilling, McP: moist-chilling followed by 20°C priming for three days, CK: control (no treatment). The data for DI calculation are based on the average of four replicates of each treatment. 5.3.2 Profiles of germination and marker gene Germination profiles of white spruce seeds under different germination conditions The mature seeds of white spruce have a relatively shallow dormancy level, and can germinate even without moist chilling. However, exposure of seeds to moist chilling led to faster and more uniform germination (Fig. 5.4A). To investigate the effect of light on dormancy alleviation and germination, white spruce seeds were placed under different conditions following exposure to 21 days of moist chilling. The fastest and most homogenous germination occurred when seeds were subjected to light (an 8-h photoperiod) and a 30/20 °C temperature regime (standard germination conditions), compared to when they were kept in darkness (Fig. 5.4A). After the 21-day moist-chilling, subsequent seed germination under the combined conditions of 30/20 °C and an 8-h photoperiod was more successful than in constant darkness but with the same 30/20 °C temperature cycle. This was the case based on most germination parameters: dormancy index (i.e., area between germination curves of no- treatment and any treatment; 15.54 ± 2.12 vs. 7.56 ± 0.97), germination Seedlot (#)33356 35707 37842 39450 45353DI05101520253035Mc   v.s. CKMcP v.s. CKHormone flux and signaling during the seed-to-plant transition 103  speed (i.e. the time required for 50% germination, 8d vs. 10d), and lag time to germination (6 d vs. 7 d) (Fig. 5.4A).  Germination capacities were similar for the two treatments at the end of the 21-day study period (96% vs. 94%) (Fig. 5.4A). Seeds subjected to a control treatment - maintaining them at 3 °C, but exposing them to an 8-h photoperiod - were unable to germinate (Fig. 5.4A). Regardless of the light conditions after transfer to germination temperatures (8-h photoperiod or constant darkness), germination of the population of seeds that had been subjected to moist chilling was faster and more synchronous than for the populations of seeds that had not received moist-chilling; and 21-day chilling was more beneficial than the 10-day chilling treatment (Fig. 5.4A). This indicates that moist-chilling has a significant effect on dormancy alleviation, and that light cues following exposure of seeds to germination temperatures facilitate germination. Expression of the gene EMB32, encoding a Late Embryogenesis Abundant (LEA) protein  Dormancy status was also investigated by monitoring the expression of the ABA-regulated gene EMB32, a member of the Late Embryogenesis Abundant (LEA) group. Dormancy maintenance bears some similarities to the late maturation program (Arc et al. 2012), and EMB32 and the other LEAs have a role in ensuring seed survival in the desiccated/dormant state. As such, EMB32 can be used as a dormancy marker (Williamson et al. 1985). Indeed, during moist-chilling of white spruce seeds, the expression of this LEA gene was maintained at a high level, but this expression decreased very quickly when seeds were transferred to germination conditions, even as soon as six hours under standard germination conditions (Fig. 5.4B, 30 °C). Thus, the dormancy to germination transition began promptly when the seeds were exposed to light upon transfer to germination temperatures. Hormone flux and signaling during the seed-to-plant transition 104   Figure 5.4 Effect of moist chilling on the germination performance of white spruce seeds (A). Germination of white spruce seeds under the conditions represented in (A) and without moist chilling. Data points are means ± SE of four dishes of 100 seeds each. While biologists define the completion of germination as radicle emergence, ‘germination’ percentage in the forest industry is based on the number of seeds that reach the stage when the radicle has emerged to four times the seed length (approximately 4 mm for white spruce). In B, we used this latter definition. (B). Transcript dynamics of the dormancy marker, EMB32 during moist-chilling (0, 10, 21 d), germination (6 h, 24 h, 80 h) and seedling growth (9 d) (black bars). At the 6 h time-point, transcript levels were determined under three conditions: in seeds after their transfer to standard germination conditions (30/20°C and 8-h photoperiod) (black bar), in seeds maintained in darkness at 30 °C (dark grey bars), and in seeds maintained in darkness at 20 °C (light grey bars). Relative expression levels as determined by RT-qPCR are shown. Each data point is the average of three biological replicates. Bars indicate the SEM. Note: one asterisk (*) indicates that the gene has been annotated in gymnosperms but not in white spruce. 5.3.3 Hormone changes during chilling treatment and germination Dynamic changes in plant hormone pathways in response to temperature cues during moist-chilling and seed germination To investigate hormone metabolism and signaling during dormancy alleviation and germination, hormone levels and transcription of genes specifying the protein mediators of hormone metabolism and signaling were determined at the various sampling stages (Fig. 5.1).   Hormone flux and signaling during the seed-to-plant transition 105  ABA metabolism and signaling  Biologically active cis-S(+)-ABA did not substantially change in abundance during moist-chilling itself, but decreased during subsequent germination of previously chilled white spruce seeds (Fig. 5.5A). The so-called “trans-ABA” is in fact a product of isomerization of natural ABA under UV light, and this did not change during the transition to germination. Generally the bioactive ABA levels were much higher than those of the ABA catabolites. From the changes in ABA metabolites it was apparent that the main ABA metabolism pathway in white spruce seeds is through 8’-hydroxylation (resulting in phaseic acid (PA), which is further reduced to dihydrophaseic acid (DPA)). Nonetheless, secondary catabolism pathways such as 7’ and 9’ hydroxylation (resulting in 7’hydroxy ABA and neo-PA) as well as conjugation (resulting in ABA-GE) were also represented. The various catabolites, especially PA and the 7’OH-ABA increased during moist chilling, as well as during germination of moist-chilled seeds (Fig. 5.5A).  Transcript abundance of ABI3 was markedly up-regulated during the first 10 d of moist chilling, but declined to a barely detectable level at 21 d (Fig. 5.5C). SnRK2.2 transcripts exhibited a similar expression pattern during the moist chilling phase (Fig. 2C). Thus, while absolute ABA levels remained constant, transcription of genes for ABA signaling components, and thereby sensitivity to ABA, started to decline during the latter part of the moist chilling phase (Fig. 5.5 B, C). Moreover, transcripts of a putative ortholog of a negative regulator of ABI3, CnAIP2 (Zhang et al. 2005; Zeng et al. 2013), steadily accumulated during moist-chilling and remained high during early germination (6 h). ABI3 transcripts, were high at the mid-point during moist chilling, then declined precipitously during late moist chilling and early germination, but increased during the later stages of germination (24 h and 80 h) (Fig. 5.5C). Transcripts encoding AAO3 (ABA biosynthesis enzyme) underwent few changes during moist chilling, but increased dramatically during early germination under standard conditions, followed by a decline; those for CYP707A4 (encoding ABA 8’ hydroxylase) were not detectable (Fig. 5.5C). Similar to AAO3, transcripts encoding CYP707A4 and SnRK2.2 showed a significant up-regulation during the early stages when seeds were first transferred to standard germination conditions at 6 h, with transcripts declining at the later stages (Fig. 5.5C). An actual decline in bioactive ABA was not evident until 24 h of germination Hormone flux and signaling during the seed-to-plant transition 106  (Fig 5.5A).  At the seedling stage (9 d), transcripts for all of the monitored genes involved in ABA metabolism and signaling decreased to a very low level (Fig. 5.5C). GA metabolism and signaling  Of the 14 GAs that were quantified in white spruce seeds (i.e., GA1, 3, 4, 7, 8, 9, 19, 20, 24, 29, 34, 44, 51, and 53), only GA53 and GA34 were present at detectable levels. GA53 is an early precursor in the 13-hydroxylation pathway (GA53→GA44→GA19→GA20(→GA29)→GA1→GA8) and leads to the formation of bioactive GA1 and its inactive degradation product GA8; GA34 is an inactive catabolite of biologically active GA4 in the non-hydroxylation biosynthetic pathway (GA12→GA15→GA24→GA9(→GA51)→GA4→GA34). The presence of intermediates from both biosynthesis routes suggests that both GA metabolic pathways are active in white spruce seeds during dormancy alleviation and germination. Moreover, the presence of GA34 suggests that GA4 must have been produced at earlier stages. GA53 of the early 13-hydroxylation pathway conducive to the formation of bioactive GA1 increased steadily during germination under standard conditions after seeds had received moist chilling. During moist-chilling itself, GA53 and GA34 were maintained at steady-state levels, with GA53 present at ~5-fold higher levels than GA34 (Fig. 5.6A). GA34 increased most substantially at 9 d (i.e. during seedling growth) (Fig. 5.6A). Most of the GA-related genes that we monitored (those encoding mediators of GA- biosynthesis, signaling, or action; Fig. 5.6B) were expressed at low levels during moist-chilling (Fig. 5.6C). (Note that all reference genes were expressed during these times). The expression of SPT (SPATULA), encoding a mediator of ABA- and GA- signaling cross talk, decreased to a low level at 10 d of moist chilling but exhibited a 14-fold increase at 21 d (Fig. 5.6C).  We also investigated transcript abundance of genes known to be positively regulated by GA as indirect indicators of the presence of active GA. The GA-regulated cell wall-modifying gene, expansin 2 (EXP2), exhibited a 15-fold up-regulation within 6 h after transfer of moist chilled seeds to standard germination conditions (Fig. 5.6C). The expression of other GA-related genes was also substantially increased during early germination before radicle protrusion; moderate expression occurred between 24-80 h, while at the seedling stage, the expression of all of the monitored genes was low or virtually undetectable Hormone flux and signaling during the seed-to-plant transition 107  (Fig. 5.6C). This is indicative of the presence of active GA during completion of germination and during very early seedling growth (seedling emergence).  Ent-pgKS protein levels were increased during the first 10 d of moist chilling, with a decline during the latter period of moist chilling (Fig. 5.6D). Upon transfer of moist-chilled seeds to germination conditions, the levels increased by 6 h (coincident with increased transcript levels; Fig. 5.6C). The most pronounced ent-pgKS protein levels were evident in seeds at 80 h under standard germination conditions; however, the control treatments indicated that either changing the light conditions or exposing seeds to germination temperatures were sufficient to trigger the increased levels of this protein (Fig. 5.6D).   Auxin metabolism and signaling  Active IAA was almost at constant levels throughout the moist chilling period and during germination (Fig. 4A). IAA conjugates (IAA-Asp and IAA-Glu) strikingly increased over 20-fold during the 21 d of moist-chilling (Fig. 5.7A). These conjugated IAAs declined markedly during the first 6 h in standard germination conditions; later seedling growth was accompanied by an increase in both active and conjugated IAA (Fig. 5.7A). In the auxin pathway (Fig. 5.7B, C), the expression of auxin biosynthesis genes (ASA1/2, ASB1, TSA1, TSB1, and AAO1) was highest at 10 d of moist chilling, then declined at the later stages of moist chilling (21 d). Interestingly, AMI1, another auxin biosynthesis gene in a parallel pathway with AAO1, exhibited lowest expression at 10 d of moist chilling (Fig. 5.7C). This suggests that auxin was actively synthesized during moist-chilling and mediators of the two pathways that synthesize auxin were separately activated at early and late moist-chilling. Likewise, ASA1/2, ASB1, TSA1, TSB1, and AAO1 exhibited high expression levels at 6 h and 80 h, while AMI1 had a constant low expression level during germination. In Arabidopsis, there exists a third auxin biosynthesis pathway via YUC (Won et al. 2011); no homolog to the Arabidopsis YUC gene was found in white spruce after an extensive database search, and this auxin biosynthesis route may not exist in the seeds of this conifer species. The expression of IAR3 and ILL1/2, which specify enzymes that convert conjugated IAA to active IAA, as well as expression of genes for the auxin transporters PIN1-like and CUC-like was significantly up- and then down- regulated in association Hormone flux and signaling during the seed-to-plant transition 108  with the transcript regulation of the biosynthesis genes of the AAO1 pathway during moist chilling (Fig. 5.7C). In seeds placed under standard germination conditions, the genes for the auxin transporters exhibited a pattern of heightened transcript abundance during germination, and lowered expression during seedling growth (Fig. 5.7C).  Auxin signaling primarily depends on the TIR1/AFB auxin receptor (TAAR), Aux/IAA, and ARF4. The expression of TIR1, AFB3 and Aux/IAA was significantly up and then down regulated during moist-chilling and that of ARF4 appeared to follow the same pattern but at a lower absolute level (Fig. 5.7C). At 6 h in germination conditions, the expression of TIR1, AFB3, Aux/IAA, and ARF4 significantly increased but only TIR1 and AFB3 continued to increase at 24h. At the seedling stage, ARF4 along with TIR1, AFB3, and Aux/IAA was expressed at a fairly low level (Fig. 5.7C). Likewise, transcript for CUL1, a component of SCF ubiquitin ligase complexes, was substantially produced during 80 h in germination but not during seedling growth (Fig. 5.7C). Hormone flux and signaling during the seed-to-plant transition 109   Figure 5.5  Changes in ABA, ABA metabolites, and ABA signaling components during the transition from dormancy to germination of white spruce seeds (A). Profiles of ABA and its metabolites as determined by UPLC/ESI-MS/MS during moist-chilling at 3°C (0, 10, and 21 days) and during germination (6, 24 and 80 h) and seedling growth (9 d). Each data point is the average of two biological replicates.  Hormone flux and signaling during the seed-to-plant transition 110  (CONTINUED) (B). Schematic of ABA biosynthesis, signaling, and catabolism. (C). Transcript levels of ABA metabolism and signaling genes and selected downstream targets during moist-chilling (0, 10, 21 d), germination (6 h, 24 h, 80 h) and seedling growth (9 d) (black bars). Also shown are previously chilled seeds placed in two control treatments - 6 h germination conditions under darkness at 30°C (dark grey bar) or 20°C (light grey bar). Each data point is the average of three biological replicates.  Bars indicate the SEM. Note: two superscript asterisks (**) indicate that the gene is only annotated in angiosperms; one asterisk (*) indicates that the gene has been annotated in gymnosperms but not in white spruce; no asterisk indicates that the gene has been annotated in white spruce.   Hormone flux and signaling during the seed-to-plant transition 111   Figure 5.6 Changes in GAs and GA signaling components during the transition from dormancy to germination of white spruce seeds  (A). Profiles of the GA precursor GA53 and the metabolite GA34 as determined by UPLC/ESI-MS/MS during moist-chilling at 3°C (0, 10, and 21 days), and during germination (6, 24 and 80 h) and seedling growth (9 d). Each data point is the average of two biological replicates. No active GAs were detected in our analysis. (B). Schematic characterization of key genes and their interplays in GA signaling cascades. Connections represent positive (arrow) and negative (block) regulation. (C). Transcript levels of GA metabolism genes and selected downstream targets during moist-chilling (0, 10, 21 d), germination (6 h, 24 h, 80 h) and seedling growth (9 d) (black bars).  Also shown are previously chilled seeds placed in two control treatments - 6 h germination conditions under darkness at either 30°C (dark grey bar) or 20°C (light grey bar).  Each data point is the average of three biological replicates. Bars indicate the SEM. Note: see Figure 5.5 note for asterisks. (D). Ent-pgKS protein levels during moist-chilling, Hormone flux and signaling during the seed-to-plant transition 112  germination, and growth of white spruce seeds. Immunoblots show 30 µg of total protein extract per lane. Blots were probed with anti-KS antibody and anti-tubulin as a loading control.  Hormone flux and signaling during the seed-to-plant transition 113   Figure 5.7 Changes in IAA, IAA conjugates, and auxin-related gene expression during the transition from dormancy to germination of white spruce seeds  Hormone flux and signaling during the seed-to-plant transition 114   (CONTINUED) (A). IAA and IAA conjugates in seeds as determined by UPLC/ESI-MS/MS during moist-chilling at 3°C (0, 10, and 21 d) and during germination (6, 24 and 80 h) and seedling growth (9 d). Each data point is the average of two biological replicates.  (B). Schematic characterization of key genes and their interplays in auxin signaling cascade. Connections represent positive (arrow) regulation.  (C). Transcript levels of auxin metabolism genes, auxin signaling genes and selected downstream targets during moist-chilling (0, 10, 21 d), germination (6 h, 24 h, 80 h) and seedling growth (9 d) (black bars). Also shown are previously chilled seeds placed in two control treatments - 6 h germination conditions under darkness at either 30°C (dark grey bar) or 20°C (light grey bar). Each data point is the average of three biological replicates. Bars indicate the SEM. Notes: 1) see Figure 5.5 note for asterisk in D and E, and the horizontal line in E; 2) no other ARF homologs (such as ARF16) and GH3 (converting active IAA to IAA-aa) homologs were found in white spruce by BLASTN. Cytokinins biosynthesis precursors0d 10d 21d 6h 24h 80h 9dConcentration (ng/g DW)01020304050cis-ZR iPR Catabolism product of cytokinins0d 10d 21d 6h 24h 80h 9dConcentration (ng/g DW)01020304050cis-ZOG  Figure 5.8 Profiles of cytokinins and their metabolites in seeds of white spruce (as determined by UPLC/ESI-MS/MS) during moist-chilling at 3°C (0, 10, and 21 d), and during germination (6, 24 and 80 h) and seedling growth (9 d) Each data point is the average of two biological replicates. cis-ZR, cis-Zeatin riboside; iPR, Isopentenyladenine roboside; cis-ZOG, cis-Zeatin-O-glucoside.Hormone flux and signaling during the seed-to-plant transition 115  5.3.4 Hormone changes after dormancy decay Dynamic changes of hormone signaling pathways after dormancy termination during germination and radicle protrusion  To separate the contributions of optimal germination temperature and light signaling to germination completion (i.e., radicle protrusion), two additional germination conditions in place of the standard conditions were used after seeds had received 21 days of moist chilling. As controls, seeds were not exposed to light (i.e., kept in darkness) but were exposed to either an optimal germination temperature (30/20 °C) (Fig. 5.1) or a non-optimal germination temperature (constant 20 °C) (not shown in Fig. 5.1). Transferring seeds to standard (i.e. optimal) germination conditions led to greater fold transcript changes than transferring seeds to the same temperature regime but keeping them in darkness. Transferring seeds to 20°C in darkness further reduced transcript induction (Figs. 5.5C, 5.6C, and 5.7C). This effect was particularly obvious for the studied genes of the GA pathway. S80PC1 (68.96%)0.70 0.75 0.80 0.85 0.90 0.95PC2 (27.08%)-1.0-0.50.00.51.0S06S24 D80L24 L80D06D24L06 Figure 5.9 The results of principle component analysis applied to the expression of all the genes used in previous qPCR analysis in ABA and GA pathways over three different germination conditions S06/S24/S80, D06/D24/D80, and L06/L24/L80 represent standard, darkness, and low temperature (3°C) germination conditions corresponding to D/E/F, H/I/J, and K/L/M in Figure 5.1, respectively.  Hormone flux and signaling during the seed-to-plant transition 116  PCA analysis for all studied genes in different germination conditions was conducted (Fig. 5.9). Gene expression variations (68.96 and 27.08%) were explained by PC1 and PC2, respectively, and the PCA grouped the samples into five clusters (Fig. 5.9). Based on PCA analysis, we found that: 1) germination initiation (6 h) and radicle protrusion (80 h) under standard germination conditions (30/20 °C and 8-h photoperiod) were associated with similar gene expression patterns. The same was true of 6 h and 24 h in darkness with a 30/20 °C temperature cycle and of 24h and 80h in constant low temperature (3°C) with an 8-h photoperiod; 2) gene expression patterns at 80 h in constant darkness were similar to those at 24 h with an 8-h photoperiod; 3) six h in low temperature was associated with unique gene expression patterns. Therefore, seeds in constant darkness with temperature cycles displayed a similar expression pattern but were delayed in time, compared with those seeds placed under both optimal germination temperature and photoperiod cycles, Conversely, seeds in constant low temperature with an 8-h photoperiod exhibited different gene expression patterns at 6 h, and at 24 and 80 h, despite not completing germination (visible radicle protrusion) (Fig. 5.9). Taken together, temperature and light jointly promoted germination mediated by ABA, GA, and auxin pathways. 5.4 Discussion 5.4.1 Plant hormones co-ordinately respond to temperature cues Moist-chilling is associated with changes in hormone flux IAA biosynthesis was active during moist-chilling (Fig. 5.7C), but active IAA levels were maintained at constant levels, while conjugated IAA-Asp and IAA-Glu steadily and significantly increased (Fig. 5.7A). Conjugated IAAs are regarded as storage compounds, which, in seeds, are either stored to be activated by de-conjugation and serve in early seedling growth, or are used for an entry route into subsequent catabolism (Leyser 2006). IAA conjugated to amino acids such as aspartate and glutamate may be largely degraded (Ludwig-Müller 2011). Although the function of IAA conjugates and the genes that regulate their formation is scarcely investigated, the large amount of IAA conjugates that accumulated during moist-chilling likely Hormone flux and signaling during the seed-to-plant transition 117  has biological significance. More information is required concerning the cellular distribution of the different auxin forms as well as their relative dependence on specific transport mechanisms (Zažímalová et al. 2010).  The PIN family proteins and the recently discovered PIN-LIKES are important as IAA efflux carriers in IAA transport between the cytosol and the endoplasmic reticulum (Zažímalová et al. 2010; Barbez et al. 2012). We observed that, at 10 d of moist-chilling, and during subsequent germination, transcripts of PIN1-like and CUC-like were markedly up-regulated (Fig. 5.7C) while active IAA remained relatively constant (Fig. 5.7A). Auxin-induced cell expansion connected to the acidification of the cell wall, is thought to invoke an increase in the activity of the wall loosening proteins, expansins (Hager 2003), which can disrupt the non-covalent bonds that form between cellulose and hemicellulose in the wall and thus promote cell expansion (Cosgrove et al. 2002). Despite no substantial overall increase in IAA during germination, IAA may nonetheless be redistributed within seed tissues to active areas of cell expansion due to the action of various transporters (Fig. 5.7C). Polar transport sets up auxin gradients in specific cell types, and such gradients can provide developmental cues during key processes including embryogenesis and root development (Friml et al. 2003; Blilou et al. 2005). The auxin response not only depends on auxin levels and locations, but also on the specificity and strength of the TIR1-Aux/IAA and Aux/IAA-ARF interactions (Calderón Villalobos et al. 2012). The decreased availability of TIR1 could lead to increased levels of free Aux/IAAs, which would combine with ARF4, thereby eventually decreasing free ARF4 levels. Hence, it is possible that prior to 10 d of moist chilling, Aux/IAA is predominantly combined with TIR1/AFB3 rather than with ARF4, and that the free ARF4 contributes to the increase of ABI3 transcripts at 10 d (Fig. 5.7C and 5.5C), because ARFs may bind to putative auxin response elements (AuxREs) of the ABI3 gene promoter (Liu et al. 2013a). Conversely, after 10 d, ARF4 may be more likely to interact with Aux/IAA (Calderón Villalobos et al. 2012), thus lowering free ARF4 and contributing to the decreasing expression of ABI3 at 21 d (Fig. 5.7C and 5.5C). These changing interactions between components of the ABA and auxin signaling pathways may promote dormancy alleviation (Liu et al. 2013a). Our analyses were confined to monitoring transcript levels and so we can only speculate as to changes at the level of the proteins that mediate auxin and ABA action. Changes Hormone flux and signaling during the seed-to-plant transition 118  in transcript levels for the ABI3 antagonist – CnAIP2 – may also be relevant here as the CnAIP2 promoter is exquisitely regulated by auxin. Active ABA did not decrease during moist-chilling itself, but did decrease substantially during subsequent germination (Fig. 5.5A), at a time when GA53 of the early 13-hydroxylation pathway conducive to the formation of bioactive GA1, increased steadily (Fig. 5.6A and C). Thus an increased GA/ABA ratio, was clearly associated with germination of white spruce seeds (Finch-Savage and Leubner-Metzger 2006). We did not detect any bioactive cytokinin in our samples (Fig. 5.8). Cytokinin and auxin have long been known to interact antagonistically, and the past five years have seen significant advances in our understanding of the extensive crosstalk between cytokinin and various other hormones, particularly auxin (reviewed by El-Showk et al. (2013)). In our study, none of bioactive free base cytokinins (zeatin, dihydrozeatin, and isopentenyladenine) was detected during moist chilling. However, the biosynthesis precursors cis-zeatin roboside (cis-ZR) and isopentenyladenine riboside (iPR) were markedly increased, while the catabolism product cis-zeatin-O-glucoside (cis-ZOG) was detected only at very low levels during moist-chilling (Fig. 5.8). This may indicate that a small amount of cis-zeatin was transiently produced as a result of moist chilling. SPT (SPATULA) is thought to be involved in both ABA and GA signaling cross talk and may drive two antagonist roles in mature seeds of Arabidopsis ― ‘dormancy-promoting’ and ‘dormancy-repressing’ -- depending on the ecotype background (Penfield et al. 2005; Vaistij et al. 2013). In white spruce seeds, SPT expression decreased to a low level at 10 d of moist chilling but exhibited a 14-fold increase at 21 d (Fig. 5.6C). Its role in white spruce dormancy alleviation and germination remains to be determined. Germination conditions When seeds were transferred to germination conditions, we observed remarkably strong changes in expression of our monitored genes typically by only 6 h. Transcripts encoding the GA-regulated cell wall-modifying protein expansin 2 (EXP2) were 15-fold up-regulated, indicating a strong up-regulation in GA signaling (Fig. 5.6C). ABA declined especially after 24 h (Fig. 5.5A). ABI3 was expressed at low levels at Hormone flux and signaling during the seed-to-plant transition 119  6 h but was up regulated at 24 h, perhaps relevant to a ‘stress sensing’ function at this critical stage (Fig. 5.5C). At the radicle protrusion time-point (80 h), transcripts of genes specifying auxin biosynthesis enzymes (ASA1/2, ASB1, TSA1, and AAO1), or proteins mediating conversion to active IAA (IAR3 and ILL1/2), and signaling (Aux/IAA) were substantially produced, and these pathways may have acted in concert with those of the GA and ABA signaling pathways (Fig. 5.7C). Thus auxin likely also plays a pivotal role in germination of white spruce seeds.  Plants have evolved a battery of photoreceptors to sense ambient light and transduction of light signals (Moglich et al. 2010). In the control of seed dormancy and germination, phytochromes represent the most investigated photoreceptors. Phytochromes are temperature- and light-dependent in association with the GA pathway via SPT (Heschel et al. 2007). The expression of SPT significantly decreased at 6 h and the transcript levels were almost the same as those in the seeds exposed to light or kept in darkness. However, the seeds placed in 30°C had a lower level of SPT transcripts than seeds placed in 20°C (Fig. 5.6C). In white spruce, as in Arabidopsis, SPT may be a light-stable repressor of seed germination and may play a role in the germination response to temperature through temperature-sensitive changes in its transcription (Penfield et al. 2005).  5.4.2 Winter chilling under new climate scenarios and its effects on conifer life histories Winter chilling is an important signal for regulating plant life histories; chilling leads to a competence for flowering through vernalization in winter annuals, and alleviates both bud and seed dormancy, allowing the onset of growth in the spring (Penfield 2008; Penfield and Springthorpe 2012). It is noteworthy that climate change may ultimately result in winter shortening and an increase in the growing season length (Robeson 2004; Schwartz et al. 2006). In North America, the number of winter chilling days has become insufficient for bud dormancy break from 40°N southward as climate changes, leading to delayed vegetation green-up, but it has remained sufficient from 40°N northwards as earlier springs lead to an advanced green-up onset (Zhang et al. 2007). A similar geographic pattern as observed in budburst may also occur for germination in temperate regions and two possible scenarios exist depending on whether moist-chilling requirements are minimally met; namely, fast and prompt germination leading to greater recruitment (adequate chilling) Hormone flux and signaling during the seed-to-plant transition 120  or an extended germination span leading to adverse conditions during dry summers (inadequate chilling) (see Fig. 5.4A). Thus shorter winters may delay or advance germination (Walck et al. 1997). On the other hand, the range of spruce trees and other conifers cover large climatic gradients while their subpopulation can be adapted to their local environments (Aitken et al. 2008; Mimura and Aitken 2010). These populations may draw on alternative molecular solutions to respond to local environmental conditions (Prunier et al. 2011; Prunier et al. 2012). Presumably, variations in gene expression contribute to phenotypic diversity including dormancy variation and, therefore sustain the adaptability of conifer populations (Verta et al. 2013). As such, our results of gene expression during moist-chilling may help predict future seed recruitments in response to climate change. Finally, it is important to note that the seeds of certain other conifer species (yellow cypress, western white pine and white bark pine) exhibit much deeper dormancy at maturity than white spruce seeds. These seeds require several months of moist chilling to alleviate their dormancy, and may well be more substantially impacted by climate change, as the extended cold period is so critical for their ability to germinate.   microRNA production at the plant-to-seed transition 121  6 microRNA production at the plant-to-seed transition 6.1 Introduction Since their genesis some 420 MYA, vascular plants have evolved elaborate gene regulatory networks (GRNs) to control functional gene expression, while at transcriptional and post-transcriptional levels, microRNAs (miRNAs) are well-established players involved in various developmental programs (Dugas and Bartel 2004; Sparks et al. 2013) and plasticity (Rubio-Somoza and Weigel 2011). In response to environmental cues, GRNs can be overridden via instigating the biogenesis of different miRNA populations. In major land plant lineages, only a couple of distinct families of deeply conserved miRNAs are evolutionarily ancient and stable (Zhang et al. 2006; Axtell et al. 2007; Axtell and Bowman 2008). Large diverse sets of lineage-specific miRNAs often exceed the conserved miRNAs (Lindow and Krogh 2005; Cuperus et al. 2011). Presumably, large numbers of MIR genes are expanded and unraveled (Fattash et al. 2007; Nozawa et al. 2012), or young MIR genes are spawned, weakly expressed, and eventually frequently lost (Fahlgren et al. 2007). This indicates that the repertoire of MIR genes has undergone dynamic selection. From the phylogenetic perspective, the distribution of miRNA families seems to be approximately proportional to the antiquity of the evolutionary lineages (Taylor et al. 2014). Novel miRNAs may adjust the variance of expression levels of their target genes to maintain the stability of transcription networks and after selection ― they reset mean gene expression to improve fitness of specific phenotypes (Wu et al. 2009a). Co-option of ancient and young miRNA families to conduct new functions is important in the evolution of new phenotypes (Taylor et al. 2014) or phenotypic variations. The conifers (Pinophyta or Coniferales) are masters of adaptation due to their long endurance over periods of climatic sways (Jaramillo-Correa et al. 2004; Anderson et al. 2006; Tollefsrud et al. 2008) and wide geographic distribution (Wang and Ran 2014). These features trigger an interesting question of how the role of conifer miRNAs is at play to contribute to adaptive plasticity in adaptation to diverse habitats. In Picea abies and Pinus taeda, lines of evidence have shown that environmental conditions at seed set can substantially affect progeny performance (Johnsen et al. 2005; Kvaalen and Johnsen 2008) and this process microRNA production at the plant-to-seed transition 122  is mediated by miRNAs (Oh et al. 2008; Yakovlev et al. 2010). Recently, it was reported that environmental cues (e.g., temperature, CO2) alter the expression of miRNAs (e.g., miR156, -157, -160, -164, and -172) to affect Arabidopsis development and growth (May et al. 2013). It is therefore of interest to investigate how miRNAs are manipulated in number and/or type to coordinate adaptation to different environmental stimuli at spatiotemporal scales. In this study, we chose populations within two spermatophytes with contrasting developmental time span (i.e., Picea glauca and Arabidopsis thaliana) to comparatively investigate the landscape of miRNA emergence and its correlation with phenotypical variation at seed set. As of May 2016, 494 unique miRNA entities in spruce were documented, of which 155 are known on miRBase and 21 are deeply conserved miRNA families in plants (Källman et al. 2013; Xia et al. 2015). Although extant conifers (Pennsylvanian, 318-299 Myr ago) are evolutionarily twice as old as angiosperms (early Cretaceous, 146 Myr ago) (Schneider et al. 2004), most embryogenesis-related genes in Arabidopsis have homologs, with high congruity, in conifers (Cairney and Pullman 2007), such as in Pinus taeda (83%) (Cairney et al. 2006) and Larix kaempferi (78%) (Zhang et al. 2012). Transcript profiling of the zygotic embryo in P. pinaster is highly correlated with that in Arabidopsis (Xiang et al. 2011; de Vega-Bartol et al. 2013). These similarities indicate that epigenetic modifications at (post-)transcriptional levels may greatly contribute to different seed set programming between conifers and Arabidopsis. The phenotype we focused on is seed dormancy (i.e., innate constraint on seed germination under conditions that would otherwise promote germination in non-dormant seeds). As post-zygotic quiescence may be the starting point of the evolution of seed dormancy (Mapes et al. 1989), dormancy modulation is assumed to be the consequence of finely tuned programs at seed set. We therefore hypothesize that selected populations can represent different modes of reproductive development that are regulated by miRNAs and exert cascading effects on ensuing phenotypes, including seed dormancy. We focus on miRNA-mRNA nodes responsible for seed dormancy formation (summerized in Figure 6.1) (Liu and El-Kassaby 2016) and three key conserved genes (i.e., ABA INSENSITIVE 3 (ABI3), AUXIN RESPONSE FACTOR 10/16 (ARF10/16), and DELAY OF GERMINATION 1 (DOG1)). ABI3 is a conserved gene at embryogenesis microRNA production at the plant-to-seed transition 123  (Fischerova et al. 2008) and, in association with ARF10/16, regulate seed dormancy (Liu et al. 2013a), while DOG1 is involved in dormancy cycling as a response to seasonal environmental signals (Vidigal et al. 2016). Through this study, we aimed to explore an overarching question of how miRNAs imprint seed set programs and thus affect phenotypic variation in populations of P. glauca and Arabidopsis. To address this question, this study comparatively asked, throughout seed set phases among populations within and between species: (1) the relative expression pattern of deeply conserved miRNA populations; (2) features of highly abundant miRNAs, module elements making up their precursors, and the classification of their target genes; (3) characteristics of novel miRNA emergences; and (4) the extent to which environments (i.e., temperature and phenology) account for the expression pattern of key genes and miRNAs involved in seed dormancy during seed set?  microRNA production at the plant-to-seed transition 124   Figure 6.1 Interaction network of miRNAs and phytohormone signaling cascades involved in seed dormancy and germination Note: all novel MiRs are not validated by experiments; the question mark (?) indicates that novelMiRs are only detected in some seed set phase(s) and their expressions are transient and low; The sequences for novelMiR_1~9 are aaaggatcaaccaaagtgaagaca, ggtccaagcccaacatcagaggat, aagaaaattacagactcggaaaga, ctccagatttgcaataagggctt, tcatctccatccaaatgatccaat, agattatgttcaatctttgactac, catctattggtcaaatattatttt, aaactaaccagcatttcggatatc, tagtagatgtgtatgctttgaca, respectively. microRNA production at the plant-to-seed transition 125  Abbreviations: 1) ABA pathway – ABA, abscisic acid; ZEP, ZEAXANTHIN EPOXIDASE; NCED, 9-CIS-EPOXYCAROTENOID DIOXYGENASE; AAO3, ABSCISIC ACID ALDEHYDE OXIDASE 3; ABA-GE, ABSCISIC ACID GLUCOSE ESTER; ABA8ox, ABA 8’-HYDROXYLASE; PA, PHASEIC ACID; DPA, DIHYDROPHASEIC ACID; PYR/PYL/RCAR, PYRBACTIN RESISTANCE 1-LIKE/REGULATORY COMPONENT OF ABA RECEPTOR; PP2C, PROTEIN PHOSPHATASE 2C; ABI1/2/3/4/5, ABA INSENSITIVE 1/2/3/4/5; SnRK2, SUCROSE NONFERMENTING 1 (SNF1)- RELATED PROTEIN KINASE 2; SPT, SPATULA; MYB96, MYB DOMAIN PROTEIN 96; AIP2, ABI3-INTERACTING PROTEIN 2; LEC1/2, LEAFY COTYLEDON 1/2; FUS3, FUSCA 3; PKS5, SOS2-LIKE PROTEIN KINASE 5; DOG1, DELAY OF GERMINATION 1; DEP, DESPIERTO; SUVH4/5, SU(VAR)3-9 HOMOLOG 4/5; LDL1/2, LYSINESPECIFIC DEMETHYLASE LIKE 1; WRKY41, WRKY 41; RAF 10/11, MAP3K genes (MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE); KEG, KEEP ON GOING; XERICO, XERICO. 2) GA pathway – GA, GIBBERELLIN; GA20ox, GIBBERELLIN 20 OXIDASES; GA3ox, GIBBERELLIN 3 OXIDASES; GA2ox, GIBBERELLIN 2 OXIDASES; PIL5, PHYTOCHROME INTERACTING FACTOR 3-LIKE 5; BME3, BLUE MICROPYLAR END 3; GAMYB, GAMYB PROTEIN; DELLA, DELLA PROTEIN; RGL2, RGA-LIKE 2; SLN1, SLENDER BARLEY 1 (a DELLA protein); XTH, XYLOGLUCAN ENDOTRANSGLUCOSYLASE/ HYDROLASE; EXP, EXPANSIN; PME, PECTIN METHYLESTERASE. 3) Auxin pathway – IAA, INDOLE-3-ACETIC ACID; ASA1/2, ANTHRANILATE SYNTHASE COMPONENT I-1/2; ASB1, ANTHRANILATE SYNTHASE BETA SUBUNIT 1; TSA1, TRYPTOPHAN SYNTHASE ALPHA CHAIN 1; TSB1, TRYPTOPHAN SYNTHASE BETA SUBUNIT 1; TAA1, TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS 1; YUC, YUCCA; AAO1, ALDEHYDE OXIDASE 1; AMI1, AMIDASE 1; CUC, CUP-SHAPED COTYLEDON; PIN, PIN FORMED; TCP, TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR; CUL1, CULLIN 1; ASK1, ARABIDOPSIS SERINE/THREONINE KINASE 1; RBX, REGULATOR OF CULLINS; TIR1, TRANSPORT INHIBITOR RESPONSE 1; AFBs, AUXIN SIGNALING F-BOXs; AUX/IAA, AUXIN-INDUCED PROTEIN 2 (AUXIN/IAA); ARFs, AUXIN RESPONSIVE FACTORs.  Note: see Table 6.1 for gene description.microRNA production at the plant-to-seed transition 126  Table 6.1 Key genes related to seed dormancy and germination Gene name Full name Accession no. (locus) Dormancy (mutants*) Attribute & gene product function References ABA signal pathway ABA1 ABSCISIC ACID-DEFICENT MUTANT 1 AT5G67030 ↓ ABA biosynthesis, Zeaxanthin epoxidase (Koornneef et al. 1982; Ishitani et al. 1997; Niyogi et al. 1998; Xiong et al. 2001) NCED5 9-CIS-EPOXYCAROTENOID DIOXYGENASE 5 AT1G30100 ↓ ABA biosynthesis gene (Frey et al. 2012) CYP707A1/2 CYTOCHROME P450, FAMILY 707, SUBFAMILY A, POLYPEPTIDE 1/2 AT4G19230 AT2G29090 ↑ ABA catabolic gene, ABA 8'-hydroxylase activity, negatively regulated by ABI4 (Millar et al. 2006; Okamoto et al. 2006; Matakiadis et al. 2009) ABI1/2 ABA INSENSITIVE 1/2 AT4G26080 AT5G57050 ↓ Ser/ Thr protein phosphatases 2C (PP2C); dominantly Negative regulation of ABA (Koornneef et al. 1984; Leung et al. 1997; Ma 2009; Park et al. 2009) HONSU HONSU (KOREAN FOR ABNORMAL DROWSINESS) AT1G07430 ↑ PP2C protein, interacts with PYR1/RCAR11 (Kim et al. 2013) RDO5 REDUCED DORMANCY 5 AT4G11040 ↑ PP2C phosphatase, ABA sensitivity (Xiang et al. 2014) ABI3 ABA INSENSITIVE 3 AT3G24650 ↓ VP1/ ABI3-type B3-domain transcription factor (TF); positive regulation of ABA and germination repression (Koornneef et al. 1984; Giraudat et al. 1992; Finkelstein 1994) ABI4 ABA INSENSITIVE 4 AT2G40220 ↓ AP2 domain-containing/ EREBP TF; positive regulation of ABA and germination repression (Finkelstein et al. 1998; Shu et al. 2013) ABI5 ABA INSENSITIVE 5 AT2G36270 - bZIP TF; positive regulation of ABA and germination repression (Finkelstein and Lynch 2000; Brocard-Gifford et al. 2003; Finkelstein et al. 2008) LEC1 LEAFY COTYLEDON 1 AT1G21970 ↓ CCAAT-box binding, HAP3 homolog (Meinke et al. 1994; Lotan et al. 1998; Huang et al. 2015) LEC2 LEAFY COTYLEDON 2 AT1G28300 ↓ B3 domain, regulator of seed maturation (Meinke et al. 1994) microRNA production at the plant-to-seed transition 127  Gene name Full name Accession no. (locus) Dormancy (mutants*) Attribute & gene product function References FUS3 FUSCA 3 AT3G26790 ↓ highly conserved RY motif [CATGCA(TG)]; together with LEC1 positively regulating ABI3 (during late embryogenesis) (Meinke et al. 1994; Nambara et al. 2000) WRKY41 WRKY 41 AT4G11070 ↓ directly enhances ABI3 transcription (Ding et al. 2014b) RAF10/11 MAP3K genes (MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE) AT5G49470 AT1G67890 ↓ directly enhances ABI3 transcription (Lee et al. 2015c) DEP DESPIERTO AT1G70910 ↓ promotes ABI3 transcription (Barrero et al. 2010) MYB96 MYB BOMAIN PROTEIN 96 AT5G62470 ↓ positive regulator of ABI4 (Lee et al. 2015a; Lee et al. 2015b) CHO1 CHOTTO 1 AT5G57390 ↓ AP2 domain-containing TF; regulates nutritional responses downstream of ABI4 (Yamagishi et al. 2009; Yano et al. 2009) BIN2 BRASSINOSTEROID-INSENSITIVE 2 AT4G18710 - interacts with ABI5 via phosphorylation and stabilization of ABI5 (Hu and Yu 2014) PKS5 SOS2-LIKE PROTEIN KINASE 5 (other names: CIPK11 or SnRK3.22) AT2G30360 - phosphorylates ABI5 at Ser-42 (Xie et al. 2010; Zhou et al. 2015) GA signal pathway GA1/2 GIBBERELLIN-DEFICENT MUTANT 1/2 - ↑ negative regulator of GA response (Koornneef and Karssen 1994; Lee et al. 2002) GA2oxs GIBBERELLIN 2-OXIDASES AT1G30040, etc. ↓ GA-deactivating gene (Yamauchi et al. 2007) DDF1 DWAUF AND DELAYED FLOWERING 1 AT1G12610 ↓ AP2 domain-containing TF; promotes GA2ox7 (Magome et al. 2008) RGL2/SPY RGA-LIKE 2/ SPINDLY AT3G03450 AT3G11540 ↑ DELLA-type GRAS TF/ O-GlcNAc; GA signaling (Jacobsen and Olszewski 1993; Lee et al. 2002) MYB33 MYB DOMAIN PROTEIN 33 AT5G06100 ↓ GAMYB-like protein (Daszkowska-Golec et al. 2013; Li et al. 2014) microRNA production at the plant-to-seed transition 128  Gene name Full name Accession no. (locus) Dormancy (mutants*) Attribute & gene product function References TCP14/15 TEOSINTE BRANCHED1/ CYCLOIDEA/ PROLIFERATING CELL FACTOR 14/15 AT3G47620, AT1G69690 ↑ DELLAs restrict cell cycle progression by repressing their activities (Resentini et al. 2015) Auxin signal pathway ARF10/16 AUXIN RESPONSE FACTOR 10/16 AT2G28350 AT4G30080 ↓ directly enhances ABI3 transcription (Liu et al. 2007; Liu et al. 2013a) Cross-talks SPT SPATULA AT4G36930   (Belmonte et al. 2013; Vaistij et al. 2013) SUVH4/5 SU(VAR)3-9 HOMOLOG 4/5 AT5G13960 AT2G35160 ↑ repress ABI3 and DOG1 transcription (Zheng et al. 2012) Others DOG1 DELAY OF GERMINATION 1 AT5G45830 ↑ a quantitative trait locus involving in seed dormancy control (Nakabayashi et al. 2012; Graeber et al. 2014) LDL1/2 LYSINESPECIFIC DEMETHYLASE LIKE 1 AT1G62830 AT3G13682 ↑ negatively regulates DOG1 and repress dormancy (Zhao et al. 2015) ATHB13 HOMEODOMAIN LEUCINE ZIPPER 1 TF AT1G69780 - Expresses during the seed-to-seedling transition, negaltive regulator of early root growth (Silva et al. 2016) * include four mutant classes: loss-of-function or deficiency, overproduction, insensitivity, and hypersensitivity or constitutive response. They either alter in hormone biosynthesis (the first two) or in hormone response/ signal transduction (the last two).microRNA production at the plant-to-seed transition 129  6.2 Materials and methods 6.2.1 Sampling strategy at seed set Plant material, growing conditions, and sample collection White spruce (Picea glauca) is a keystone species of boreal forests in the North American taiga and its seed and pollen cones develop on the same tree and are diclinous (i.e., unisexual). According to station records, four populations of P. glauca (P 1~4), characterized by different pollination timing and seed set (developmental) duration, were chosen and 20 developing cones for each population were collected at early, middle, and late developmental stages for a total of four time points at the Kalamalka Research Station seed orchards (50°-51°37'N, 119°16'-120°29'W), British Columbia, Canada (Fig. 6.2). The difference of the length of early and late pollination season is ~10 days (Fashler and El-Kassaby 1987; El-Kassaby and Davidson 1991) and we therefore assume that developing seeds at early and late fertilization are subject to similar phenology in the same phase. Likewise, as per contrasting seed set durations, we selected two wild strains of the model organism Arabidopsis thaliana, Cvi-0 and Col-0, originating from the Cape Verde Islands and Columbia (Missouri, USA), respectively. Moreover, the two Arabidopsis ecotypes exhibit contrasting dormancy intensities  (Koornneef et al. 2000; Ali-Rachedi et al. 2004). They were cultivated in 2-in planting trays containing soil mixed with slow-release fertilizer 14-14-14 in a growth chamber with 16/8h day/night photoperiod, PPFD (photosynthetic photon flux density) of 250 µmol·m2·s-1, and constant temperature of 25°C. Individual flowers were tagged on the day of flowering and developing seeds were sampled manually every day for 9 consecutive days after pollination (DAP) (Fig. 6.2). Five more sampling time points (10-14 DAP) were performed for Cvi due to its slow seed set (Fig. 6.2). After dissection from white spruce cones or Arabidopsis inflorescences, developing seeds were immediately frozen in liquid nitrogen and stored at -80°C until further use. 6.2.2 RNA isolation, library construction, and sRNA sequencing Total RNAs were extracted and divided into two aliquots (~15 µg each) from developing seed samples of P. glauca and Arabidopsis using PureLink Plant RNA Reagent (Ambion) according to the manufacturer’s instructions. The intergrity and quantity of the RNAs were assessed on a BioAnalyser 2100 (Agilent microRNA production at the plant-to-seed transition 130  Technologies) and a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific). To enrich sRNAs (i.e., small RNAs), an aliquot of total RNA underwent polyA selection using a Miltenyi MultiMACS mRNA isolation kit (130-092-519) following the manufacturer’s protocol, and the flow through portion was used for plate-based miRNA construction. Constructed libaries were pooled by phylum; that is, 15 P. glauca and 25 Arabidopsis samples were pooled seperately, and both lanes were 31 base SET lanes. Sequencing (Illumina HiSeqTM 2500) was implemented using one short SET indexed lane per pool (BC Cancer Agency Genome Sciences Centre, Vancouver, Canada).  Figure 6.2 Illustration of sampling strategy during seed set of Picea glauca and Arabidopsis thaliana Note: Different color of square cells represents different populations and ecotypes of P. glauca and Arabidopsis, respectively, where a white square cell with a cross represents a corresponding population or ecotype that has not yet been fertilized or has already matured; Populations of P. glauca are exposed to similar phenology during seed set in the seed orchard and ecotypes of Arabidopsis are cultivated in a growth chamber with the same growth conditions in periodic cycles;  Ticks (√) are sampling time points; microRNA production at the plant-to-seed transition 131  A dashed line in the horizontal direction roughly divides seed development into two phases: morphogenesis (up) and maturation (down); *: after the ‘mature’ phase, five to eight days are usually followed to finish post maturation and seed dormancy arrest. 6.2.3 Small RNA dataset analysis The sequence data were partitioned into individual libraries based on the index read sequences, and the reads underwent an initial QC assessment (Chu et al. 2015). After being preprocessed to clean reads by trimming adapters and barcode sequences using an internal matching algorithm (BC Cancer Agency), the raw sequencing data (in bam format) were parsed into sam, fastq, fasta, and txt formats under Linux in a command-line environment for subsequent use. The sRNA toolbox was used to profile sRNAs and size distribution, perform miRNA analysis using miRNAs for Arabidopsis or a high confidence set of miRNAs from miRBase for P. glauca, and their consensus differential expression (Rueda et al. 2015). miRNAs in sRNA sequencing libraries were computationally predicted against the P. glauca genome assemblies (PG29 v3, 20Gb divided into 30Mb per file) (Warren et al. 2015) using miRPlant (An et al. 2014), and their mRNA targets were predicted using transcripts without miRNA genes on psRNATarget (Dai and Zhao 2011). After genome-wide identification of sRNAs, the unique sRNA sequences, counts, and precursors were archived and compared with previously identified spruce sRNAs (Källman et al. 2013; Xia et al. 2015). We focused on the conserved and novel miRNAs of high strengh of prediction (score ≥ 0) in Arabidopsis, while in P. glauca, only on miRNAs that were detected in at least 14 of 15 libraries across four populations. As most conifer genes are not annotated, we employed reciprocal BLAST to identify homologs. Specifically, miRNA-targeted genes in P. glauca were retrieved via a BLASTN search against the Arabidopsis genome on EnsemblPlants (http://plants.ensembl.org) and then putative genes in Arabidopsis were searched via BLASTN against the P. glauca genome. Homologs were identified only if the same pair of sequences were found. To annotate target mRNA functions, the top predicted target gene for each miRNA of interest was aligned against the Gene Ontology (GO) protein database for GO term classification and KEGG pathway enrichment (Ashburner et al. 2000; Kanehisa and Goto 2000). microRNA production at the plant-to-seed transition 132  6.2.4 Statistical analysis Exploratory data analysis (Martinez et al. 2011) was used to get a sense of the conserved and differentially expressed miRNAs in P. glauca and Arabidopsis and to delineate the results in bivariate plots with histograms along the diagnal (normality not required), which provided the frequency distribution of each variable, and scatterplot matrices using LOESS procedure in smoothing fitted curves were produced for each panel, which summarized the relationship between the variables (Carr et al. 1987). Heat maps using log2 of count per million graphically depicted the most conserved and differentially expressed miRNAs at seed set across populations/ecotypes, whereby cluster analyses were performed for seed set phases and key miRNAs within phylum using hierachical clustering. The numbers of conserved and novel miRNAs were separately summarzied by phase in Arabidopsis or by population in P. glauca using VennDiagram (Chen and Boutros 2011). To feature the expression pattern of novel miRNAs and their relationship with seed set phases in populations of P. glauca or Arabidopsis ecotypes, principle component analysis (PCA) on the correlation matrix (correlations are the covariances of standardized variables) was implemented. Variables for PCA included population/ecotype, developmental phase, mature miRs (chromosome, miR type and loci used instead in Arabidopsis), and their length and relative expression (number of a specific miR read relative to total reads in libraries). To visualize their correlations, a PCA biplot was generated using the scaling 2 approach (the angles between descriptors reflect their correlations) (similar intepretation as GGE or PLS biplot in our previous studies (Liu and El-Kassaby 2015; Liu et al. 2016)). To extract expression structures of genotypes (represented by miRNA and mRNA relative expression) that can be explained by environments (temperature at seed set and phenology represented by developmental phase and pattern), redundacy analysis (RDA) that combines multivariate regression with PCA of dependent variables was conducted (Legendre and Gallagher 2001) and visualized by an ordination triplot with both response and explanatory variables in the same coordinate using scaling 2. All the above analyses were carried out under The R Project for Statistical Computing (R 3.2.2). microRNA production at the plant-to-seed transition 133  6.2.5 Evolutionary analysis As repetitive DNA sequences (e.g., transposable elements, minisatellites, and palindromic sequences) are evolutionarily conserved and have significant functional importances, MIR genes were analyzed by repeat DNA modules using ModuleOrganizer (Tempel and Talla 2014). Based on our previous assumption regarding the correlation between seed set patterns and seed dormancy variation, we concentrated on miRNA-mRNA nodes and key conserved genes (i.e., ARF10/16, ABI3, and DOG1) responsible for seed dormancy modulation. To investigate those genes of interest annotated in other conifers or Arabidopsis but not yet documented in P. glauca, BLASTN analysis was performed using the P. glauca PlantGDB Putative Unique Transcripts (PUTs) database on ConGenIE (http://congenie.org/). These genes are detailed in Appendix B, Table B.3. Multiple alignments of transcripts were conducted using ClustalW 2.0 (Larkin et al. 2007). To uncover mRNAs targeted by communally conserved miRNAs between phyla, tBLASTN was executed through Arabidopsis proteins with reference to conserved domains/motifs against translated (six frames) nucleotide database and a phylogram was constructed using the maximum likelihood algorithm with 1,000 rounds of bootstrapping, both at default settings, within the MEGA v6.0 software package (Tamura et al. 2013). 6.2.6 Gene expression analysis With high confidence, genes targeted by conserved miRNAs of interest were experimentally validated using quantitative RT-PCR (qRT-PCR) assay as follows. Two µg of the other aliquot of total RNAs was reverse-transcribed into cDNA using the EasyScript PlusTM kit (abmGood) with oligo-dT primers following the manufacturer’s instructions and first-strand cDNA synthesis products were diluted fivefold as qRT-PCR templates. qRT-PCR was run in 15µl reaction volumes on an ABI StepOnePlusTM machine (Life Technologies) using the PerfeCTa® SYBR® Green SuperMix with ROX (Quanta Biosciences). The reaction components and procedure were carried out as previously described (Liu et al. 2015). Three technical replicates of each of three biological replicates were used. Reference genes were used as previously descibed (Czechowski et al. 2005; Liu et al. 2015). Primer pairs for qPCR amplification were listed in Table 6.2.microRNA production at the plant-to-seed transition 134  Table 6.2 Description of genes and primer pairs used for qPCR   Gene abbreviation GeneBank accession or PUT number homolog accession Species Average bit score E-value (lowest) average identity (similarity) Primer pairs Amplicon size (bp) AtACT2 At3g18780 or NM_112764  Arabidopsis thaliana    5'- GCTGACCGTATGAGCAAAGA 3' - ATCTGCTGGAATGTGCTGAG 137 AtABI3 AT3G24650  Arabidopsis thaliana    5' - CTTGAAGCAAAGCGACGTGG 3' - TGTCTTACTTTAACCCCTCGTAT 300 AtARF10 AT2G28350  Arabidopsis thaliana    5' - GTGGACAAGCGTTTGAGGTT 3' - GGCGGAGACAGTACCCATAA 179 AtARF16 AT4G30080  Arabidopsis thaliana    5' - ACCTCCTCCTCCTCCTCCAT 3' - ATGTTTGCGGTATCCGTTGA 216 AtDOG1 AT5G45830  Arabidopsis thaliana    5'-GAGCTGATCTTGCTCACCGATGTAG 3'-CCGCCACCACCTGAAGATTCGTAG 205 hypothetical protein CO206996  P. glauca    5’- GTCGTGTGGATTGTCTCTGC 3’- ATGTATTCGAAGAGGAGGAATG 202 pgABI3 BT102260 AJ131113 P. glauca    5' - ACGTTGGCAATCTAGGAAGG 3' - CGCCAGTATTTTCAAGCAGA 186  (the following two genes obtained via tBLASTN using corresponding protein in Arabidopsis) putative pgARF10/16 PUT-175a-Picea_glauca-8273 NM_119154 P. glauca 224.17 (224.17) 1.43e-66 (1.43e-66) 63.07 % (73.30 %) 5' - CTCTTGGCATCTCTGGACAA 3' - GGCATCTTCGTCTCATCTCA 168          putative pgDOG1 PUT-175a-Picea_glauca-35006 NM_123951 P. glauca 131.72 (131.72) 1.05e-35 (1.05e-35) 33.01 % (55.34 %) 5' - GCGCTTATTGTGGAGACAGA 3' - GGAAGCAGTAGCCTGGAGTC 183 microRNA production at the plant-to-seed transition 135  6.3 Results 6.3.1 sRNA transcriptome profiling throughout seed set After quality filtering, Illumina deep sequencing generated 15.1M reads, on average in 15 Picea glauca libraries (Appendix B, Table B.4). By contrast, there were on average 10.3M reads in 25 Arabidopsis thaliana libraries (Table B.4). This indicates that the net production of sRNAs (i.e., small RNAs) was generally greater in P. glauca than in Arabidopsis. Mapping of the quality-filtered reads to corresponding genomes with predictable hairpin RNA secondary structures yielded an average of 3.33M (24 ± 6%) and 7.33M (84.3 ± 5%) reads in P. glauca and Arabidopsis libraries, respectively (Fig.6.3 and Table B.4). This suggests that miRNA reads may be more abundant in Arabidopsis than in P. glauca at seed set. Compared with previous reports (Källman et al. 2013; Xia et al. 2015), we identifed 44 of 155 known miRNAs (i.e., reads deposited in miRBase) and 42 of 339 novel miRNAs (Table B.5). Moreover, we detected another 1233 unique miRNAs, in which 23% (i.e., 283 reads) were 24-nt long (Table B.5). This observation in developing seeds is different from that in buds (~1%) (Källman et al. 2013), which confirms that 24-nt long sRNAs are specific to reproductive tissues (Nystedt et al. 2013). In the archived miRNA reads under Arabidopsis species on miRBase, the known miRNAs among quality-filtered sRNA reads were on average 0.3% (0.05M) and 10% (0.65M) in P. glauca populations and Arabidopsis ecotypes over time, respectively (Table B.4 and Fig. 6.3), while 3.5% spruce miRNA reads (i.e., 44/1275) were documented on miRBase throughout species (Table B.5). This indicates that an appreciable amount of non-conserved miRNAs was produced at seed set in P. glauca and their production mechanism may differ from Arabidopsis due to long miRNAs that are highly expressed and specific to the seed set period. In addition, sRNA reads in Arabidopsis classified into r/t/sn/snoRNAs accounted for 27.3, 1.3, 4.4, and 0.1% of the total reads, respectively (Fig. 6.3). 6.3.2 Spatiotemporal comparison of miRNA families and MIR genes Temporal expression patterns of deeply conserved miRNA families A cohort of key and conserved miRNA populations involved in plant development and phase transitions were summarized in Table 6.3. The top 40 conserved and differentially expressed miRNAs were microRNA production at the plant-to-seed transition 136  respectively selected from P. glauca and Arabidopsis libraries and showcased in bivariate plots (Appendix B, Fig. B.1). In each panel, histograms along the diagonal were distributed in a right-skewed manner and scatter plots for P. glauca and Arabidopsis generally showed a monotonic and linear relationship, respectively (Fig. B.1). This indicates that the expression of conserved miRNAs is intrinsically correlated among different phases of seed set and populations/ecotypes. The expression of the top 20 conserved miRNAs was used to create a heat map and perform cluster analyses for seed set phases and expression patterns across time (Fig. 6.4). In P. glauca, the seed set phases were divided into two major groups, A1 and A2, primarily including populations of late (P 4-1~4) and early (P 1-1~3) maturation, respectively (Fig. 6.4A). Interestingly, phases in the population of low medium maturation (P 2) were more closely clustered with those in late maturation (P 4), i.e., Group A1, while those of high medium maturation (P 3) were grouped in the cluster of early maturation (P 1), i.e., Group A2 (Fig. 6.4A). Likewise, the seed set phases of the two Arabidopsis ecotypes were clustered into five groups, B1~5 (Fig. 6.4B). The expression pattern of miRNAs for Cvi ecotype at flowering (Cvi_0) was prominently different from the other developmental phases, i.e., Group B1 (Fig. 6.4B). Col at flowering (Col_0) and Cvi_1, Col_1~4 and Cvi_2~3, and Col_9 and Cvi_4~14 were grouped together, respectively, which corresponded to Group B2, B4, and B5 (Fig. 6.4B). This indicates that Col and Cvi are subject to miRNA regulation with the same pattern for a relatively long period at early and late seed set, respectively. Group B3 only contained Col_5~8, indicating that, at the middle-to-late phases, the regulation by miRNAs is different and notable in Col (Fig. 6.4B). In general, the expression of top conserved miRNAs was strikingly lower in P. glauca than in Arabidopsis across seed set (comparing the colour ranges in Fig. 6.4A and B). As per their expression mode, miRNAs were clustered into three major groups, i.e., high (C1 and D1), medium (C2 and D2) and low (C3 and D3) expression in P. glauca and Arabidopsis, respectively (Fig. 6.4). The miR166, miR167 and -396, and miR394, -408 and -390 were at high, medium and low level in both P. glauca and Arabidopsis, respectively (Fig. 6.4). The expression of miR159 was at medium level in Arabidopsis (Group D2) but at low level in P. glauca (Group C3) (Fig. 6.4). Moreover, some conserved miRNAs were communally microRNA production at the plant-to-seed transition 137  identified in P. glauca and Arabidopsis and are engaged in auxin and GA signaling pathways, including miR159, -160 and -167 (Fig. 6.4 and Table 6.4). In addition, there were conserved miRNAs uniquely detected in the Col ecotype at seed set or in the population of late maturation (P 4) in P. glauca (Table 6.5). Conserved and novel miRNAs in Arabidopsis In Arabidopsis, the number of novel miRNA reads was almost ten times as many as conserved ones (Fig. 6.5A). Conserved miRNAs were expressed in almost all developmental phases at seed set, while novel miRNAs were differently produced in phases as well as ecotypes (Fig. 6.5A and B). There were 91 conserved miRNAs identified in all libraries (Table B.6), in which 85 were expressed across all seed set phases (Fig. 6.5B). 705 novel miRNA reads in a total of 5749 reads were detected in both early (Cvi_0~7 and Col_0~4) and late (Cvi_8~14 and Col_5~9) maturation in the two ecotypes (Fig. 6.5B), in which 44 novel miRNAs were expressed across all phases in two ecotypes. Based on novel miRNAs, PCA was performed and as per the PCA biplot, the proportion of variance explained by the first two axes was only 41.2% (Fig. 6.5C), so our interpretation of the first pair of axes partially extracts relevant features of the whole dataset. As Cvi has longer span of seed set than Col (Fig. 6.2), the developmental phase was highly correlated with ecotype while attributes of miRs (length, type, loci, and chr) were correlated with each other (Fig. 6.5C). The pattern of miR expression was also highly correlated with attributes of miRs, particularly in positive correlation with loci (Fig. 6.5C). This indicates that novel miR expression may have a locus bias. The correlation of developmental phase with miR attributes and expression was nearly orthogonal (Fig. 6.5C), indicating a close to zero correlation. Since novel miRNAs frequently emerged and decayed, target genes of only conserved miRNAs were annotated using Gene Ontology (GO) classification. GO classification showed that miRNAs were mostly involved in the regulation of genes related to metabolic and cellular processes at seed set in Cvi and Col (Fig. 6.5D). Unique miRNA reads in populations of P. glauca In P. glauca, considerable miRNAs were generated across seed set phases in populations (Fig. 6.6A). The number of miRNAs detected in all four populations (1,318 reads) was as many as that of unique miRNAs in different populations (1,200 reads on average) (Fig. 6.6B). This observation is analogous to the frequent microRNA production at the plant-to-seed transition 138  emergence and decay for novel miRNAs in Arabidopsis (Fig. 6.5B, (Fahlgren et al. 2007)). As per the PCA biplot for all identified miRNAs, the proportion of variance explained by the first two axes was 56.9% (Fig. 6.6C). In the ordination plane, the graph demonstrated that developmental phase and population, and attributes of miRs (mature miRs and length) were highly correlated, respectively (Fig. 6.6C). The expression of miRs had high correlation with developmental phases and population, but low correlation with miR attributes (Fig. 6.6C). 107 miRNAs were identified in at least 14 of 15 libraries across populations, in which more than 100 copies were detected in at least one library (Table B.7). In addition, GO classification for target genes of the 107 miRNAs showed that miRNAs were mostly involved in the regulation of genes related with metabolic and cellular processes, which was in line with observations in Arabidopsis (Fig. 6.5D). Comparison of MIR gene architecture between P. glauca and Arabidopsis We used mature miRNAs (curated in Table B.5 and B.6) to perform the nucleotide frequency analysis. There was statistically more G nucleotide in mature miRNA sequences in P. glauca (0.26 ± 0.10) than in Arabidopsis (0.22 ± 0.09) (Fig. 6.7A), while T more frequently occurred in Arabidopsis (0.31 ± 0.11) than in P. glauca (0.29 ± 0.09) (Fig. 6.7A). Moreover, the 10th position of mature miRNAs was most probably a G in P. glauca but least probably in Arabidopsis (Fig. 6.7B), and the 3’ end of the mature miRNAs (19-24 nt) had a high frequency of G in P. glauca (average probability = 0.36) than in Arabidopsis (prob = 0.21) (Fig. 6.7B). MIR genes usually originate from non-protein-coding fragments. DNA repeat modules are evolutionarily conserved and using MIR genes for conserved miRNAs in Arabidopsis (Table B.6) and enriched miRNAs throughout seed set in P. glauca (Table B.7), we found that there were significantly more repeat modules per MIR gene in Arabidopsis (7.4 ± 2.2) than in P. glauca (4.1 ± 2.1) (Fig. 6.7C). 134 repeat modules were identical between P. glauca and Arabidopsis, while 83 repeats were unique in Arabidopsis (Fig. 6.7D). The total number of repeat modules in Arabidopsis (656) was significantly higher than in P. glauca (323) (Fig. 6.7D). These differences indicate that their MIR genes are subject to divergent evolution, in the sense that transposons, short repeats and other insertions seem targets of differential demethylation microRNA production at the plant-to-seed transition 139  (Gehring et al. 2009), allowing changes in the expression pattern of imprinted genes (Pignatta et al. 2014). Two possibilities may result in the MIR gene evolution: pre-existing MIR genes evolve and update their functions or new MIR genes emerge to acquire new functions. 6.3.3 Expression pattern of selected genes and miRNA explained by environments We chose seed dormancy phenotype to investigate how environments affect the expression of conserved miRNAs targeting genes related to seed dormancy and also that of key genes responsible for the phenotype across phyla, thus collectively manipulating phenotypical variation. Gene phylogeny for ARF10/16 showed that gymnosperm and model angiosperm species were separated into different clades (except Picea) having higher sequence similarity with angiosperms (Fig. 6.8). This indicates that ARF10/16 is ancient and evolutionarily conserved within the plant kingdom. Conserved domain analysis showed that putative ARF10 in P. glauca harbours an Aux_IAA super family domain (Fig. 6.9). The pivotal activation function of ARF proteins is conferred by their four-domain architecture, including DNA binding region (a B3 and an ARF domain) and protein dimerization motifs (Ulmasov et al. 1999; Tiwari et al. 2003). Loss of the canonical four-domain structure has promoted functional shifts within the ARF family by disrupting either dimerization or DNA-binding capacities (Finet et al. 2013). As such, the putative ARF10 in P. glauca may not correspond to its counterpart in Arabidopsis or the essential Aux_IAA domain is sufficient to function in P. glauca. No homolog hits for ABI3 and DOG1 were obtained in some species (Appendix B, Table B.3) and the phylogeny showed that they might undergo substantial selection (Fig. 6.8). miR160 targets ARF10 and its hairpin structure was computationally predicted (Fig. 6.10). In the RDA triplot, the percentage of accumulated constrained eigenvalues showed that the first axis explained 43.8% variance (Fig. 6.11), indicating that the major trends have been modelled by RDA. In addition to species and dev_phase, developmental temperature played an important role in the dispersion of developmental phases along the first axis and had high correlation with miR160 and ARF10/16 (Fig. 6.11). As transcripts of ARF10/16 are targeted by miR160 (Liu et al. 2013a), the expression patterns of ARF10/16 and miR160 were highly positively correlated with each other but negatively correlated with that of DOG1 (Fig. 6.11). In addition, the projections of the same developmental phases on the first axis were overlaid across populations in P. microRNA production at the plant-to-seed transition 140  glauca (Fig. 6.11). This suggests that the same phase between populations has more similarities in gene expression pattern than different phases within populations.   microRNA production at the plant-to-seed transition 141  Table 6.3 Summary of a cohort of key and conserved miRNA populations involved in development, phase transitions, and beyond in plants miRNA family Function Studied species Literature miR156 • target SPL (SQUAMOSA PROMOTOR-BINDING PROTEIN-LIKE); • cambrial meristem during dormancy-release; • control flowering and timing of sensitivity in vernalization; • vegetative phase changes promoted by sugar and post-germination stages (highly abundant in seedlings and decrease during the juvenile-to-adult transition) poplar (Populus), maize (Zea mays), Arabidopsis thaliana, oilseed rape (Brassica napus), rice (Oryza sativa), safflower (Carthamus tinctorius), cotton (Gossypium hirsutum), and other annual herbaceous and perennial trees (Chuck et al. 2007a; Wang et al. 2009; Wu et al. 2009b; Martin et al. 2010; Nodine and Bartel 2010; Wang et al. 2011a; Körbes et al. 2012; Cao et al. 2013; Gu et al. 2013; Huang et al. 2013; Yang et al. 2013; Ding et al. 2014a; Peng et al. 2014; Xie et al. 2015) miR158 seed development and maturation Brassica napus (Huang et al. 2013) miR159 • target GAMYB genes in GA pathway and also control ABA via MYB factors; • flowering time; • seed development and maturation; • seed germination Arabidopsis thaliana, oilseed rape (Brassica napus), Gloxinia (Sinningia speciosa) (Achard et al. 2004; Tsuji et al. 2006; Allen et al. 2007; Reyes and Chua 2007; Körbes et al. 2012; Huang et al. 2013; Li et al. 2013) miR160 • target ARFs; • cambrial meristem during dormancy-release; • endosperm development; • seed germination and post-germination; • root cap formation poplar (Populus), maize (Zea mays), Arabidopsis thaliana  (Mallory et al. 2005; Wang et al. 2005; Liu et al. 2007; Gu et al. 2013; Ding et al. 2014a) miR163 • target PXMT1 (S-ADENOSYLMETHIONINE-DEPENDENT CARBOXYL METHYLTRANSFERASE); • promote seed germination and primary root elongation. Arabidopsis thaliana (Chung et al. 2016) miR164 • target CUC (CUP-SHAPED COTYLEDON); • seed (endosperm) development maize (Zea mays), rice (Oryza sativa), cotton (Gossypium hirsutum), (Sieber et al. 2007; Gu et al. 2013; Peng et al. 2014; Xie et al. 2015) miR166 • vascular tissue development in trees; • seed development and maturation program through targeting PHB/ PHV (type III HD-ZIP transcription factors), which binds the promoter of a master regulator of seed maturation, LEC2 (LEAFY COTYLEDON2) poplar (Populus), oilseed rape (Brassica napus), pine (Pinus taeda), safflower (Carthamus tinctorius) (Ko et al. 2006; Oh et al. 2008; Du et al. 2011; Robischon et al. 2011; Kang et al. 2012; Körbes et al. 2012; Tang et al. 2012; Cao et al. 2013) microRNA production at the plant-to-seed transition 142  miRNA family Function Studied species Literature miR167 • target ARFs; • tissue- and stage-specific modulation in zygotic embryo and gametophyte; • female and male reproduction; • endosperm development; • cultured cells Arabidopsis thaliana, pine (Pinus taeda), maize (Zea mays), rice (Oryza sativa), oilseed rape (Brassica napus), safflower (Carthamus tinctorius) (Wu et al. 2006; Yang et al. 2006; Oh et al. 2008; Kang et al. 2012; Körbes et al. 2012; Cao et al. 2013; Gu et al. 2013; Peng et al. 2014) miR168 seed development and metabolism maize (Zea mays), safflower (Carthamus tinctorius) (Cao et al. 2013; Gu et al. 2013) miR169 • differential miRNA expression patterns between active and dormant bud; • seed (endosperm) development maize (Zea mays), tea (Camellia sinensis), cotton (Gossypium hirsutum), (Kang et al. 2012; Gu et al. 2013; Jeyaraj et al. 2014; Xie et al. 2015) miR171 • flowering; • cell differentiation; • auxiliary bud formation; • accumulation in the reproductive organs Arabidopsis thaliana, Barley (Hordeum vulgare) (Llave et al. 2002; Reinhart et al. 2002; Parizotto et al. 2004; Curaba et al. 2013) miR172 • target APETALA2-like transcription factors (TOE1/2/3, AP2, SMZ, SNZ) • cambrial meristem during dormancy-release; • control flowering and timing of sensitivity in vernalization; • vegetative phase changes and post-germination stages (opposite pattern with miR156); • epidermal infection Arabidopsis thaliana, poplar (Populus), maize (Zea mays), Lotus japonicas, and other annual herbaceous and perennial trees (Aukerman and Sakai 2003; Chen 2004; Lauter et al. 2005; Chuck et al. 2007a; Chuck et al. 2007b; Jung et al. 2007; Zhao et al. 2007; Wu et al. 2009b; Martin et al. 2010; Wang et al. 2011a; Huang et al. 2013; Ding et al. 2014a; Holt et al. 2015) miR319 • target transcription factors of the TCP family involving multiple biological pathways; • petal growth and development; • seedling development and embryo patterning; • leaf morphogenesis maize (Zea mays), Arabidopsis thaliana (Nag et al. 2009; Kang et al. 2012; Schommer et al. 2012) miR390 • target TAS3 non-coding RNA; OSK (STRESS-RESPONSIVE LEUCINE-RICH REPEAT RECEPTOR-LIKE KINASE); • vegetative development; • Cadmium (Cd) stress tolerance rice (Oryza sativa), maize (Zea mays), Arabidopsis thaliana (Marin et al. 2010; Ding et al. 2016) miR393 • endosperm/ovule dev; • auxin-related development of leaves; • target mRNAs encoding auxin receptors and cyclin-like F-box; • soybean nodulation maize (Zea mays), Arabidopsis thaliana, cotton (Gossypium hirsutum), soybean (Glycine max) (Zhang et al. 2009; Si-Ammour et al. 2011; Xia et al. 2012; Gu et al. 2013; Xie et al. 2015; Yan et al. 2015) microRNA production at the plant-to-seed transition 143  miRNA family Function Studied species Literature miR394 target cyclin-like F-box maize (Zea mays) (Zhang et al. 2009) miR395 • target APS (ATP SULFURYLASE) • mir395b/c/f and a/d/e act as a positive or negative regulator of seed germination under stress, respectively Arabidopsis thaliana (Kim et al. 2010) miR396 • target GRF (GROWTH REGULATING TRANSCRIPTION FACTOR) family; • cell division and differentiation during leaf development; • pistil/ flower and fruit development; • senescence Arabidopsis thaliana, tomato (Lycopersicon) (Jones-Rhoades and Bartel 2004; Wang et al. 2011b; Jeong and Green 2013; Liang et al. 2014; Thatcher et al. 2015; Cao et al. 2016) miR397 developmental stages of grain development rice (Oryza sativa) (Peng et al. 2014) miR408 • target UCC2, PLANTACYANIN, CUPREDOXIN, and LAC3; • differential miRNA expression patterns between active and dormant bud; • leaf senescence regulated; • positive role in stress resistance tea (Camellia sinensis), wheat (Triticum aestivum) (Feng et al. 2013; Jeyaraj et al. 2014; Thatcher et al. 2015) miR414 differential miRNA expression patterns between active and dormant bud tea (Camellia sinensis) (Jeyaraj et al. 2014) miR444 mediate plant tolerance to the N-starvation stress wheat (Triticum aestivum L.) (Gao et al. 2016) miR447 • target 2PGK (2-PHOSPHOGLYCERATE KINASE); • involvement in phytic acid metabolism and senescence Arabidopsis thaliana (Allen et al. 2005; Thatcher et al. 2015) miR528 endosperm development maize (Zea mays) (Gu et al. 2013) miR529 evolutionarily related to miRNA156 but lost in some lineages eudicots (Morea et al. 2016) miR782 differential miRNA expression patterns between active and dormant bud tea (Camellia sinensis) (Jeyaraj et al. 2014) miR824 seed development oilseed rape (Brassica napus) (Körbes et al. 2012) miR828 ovule specific  cotton (Gossypium hirsutum) (Xie et al. 2015) miR1861 developmental stages of grain development rice (Oryza sativa) (Peng et al. 2014) miR1867 developmental stages of grain development rice (Oryza sativa) (Peng et al. 2014)   microRNA production at the plant-to-seed transition 144  Table 6.4 Identification of communal and conserved isoform miRNAs between Arabidopsis and P. glauca during seed set Name1 Predicted target2 Alignment Target gene function3 E4 UPE5 miR159b-3p AT1G18080.1 miRNA     21 UUCUCGAGGGAAGUUAGGUUU 1                   :::::::::::::::::::::        Target  1575 AAGAGCUCCCUUCAAUCCAAA 1595   GA and flowering pathways 0 22 BT123375 miRNA     21 UUCUCGAGGGAAGUUAGGUUU 1                   :.::::::::::::::::::         Target   410 AGGAGCUCCCUUCAAUCCAAU 430    - 1.5 12 miR160c-5p AT1G77850 AT2G28350 AT4G30080 miRNA     21 ACCGUAUGUCCCUCGGUCCGU 1                   :::::::::::::::::::::        Target    21 UGGCAUACAGGGAGCCAGGCA 41     AUXIN RESPONSE FACTOR (ARF) 10, 16 and 17 0 17 BT119832 miRNA     20 CCGUAUGUCCCUCGGUCCGU 1                   :::::.::::::::::::::        Target   501 GGCAUGCAGGGAGCCAGGCA 520    putative ARF 10/16/17 (NCBI No. FN433183) in Cycas rumphii 0.8 19 miR163 AT1G66720.1 miRNA     24 UAGCUUCAAGGUUCAGGAGAAGUU 1                   ::::::::::::::::::::::::        Target   356 AUCGAAGUUCCAAGUCCUCUUCAA 379    methylation 0 13 BT112171 miRNA     22 GCUUCAAGGUUCAGGAGAAGUU 1                   :: :::::::::: .:::::::        Target   332 CGCAGUUCCAAGUAUUCUUCAA 353    - 2.5 15 miR166b-5p AT4G14713.1 miRNA     21 GGAACUUGGUCUGUUGUAAGG 1                   .::::::..: ::::::::::        Target  1764 UCUUGAAUUAAACAACAUUCC 1784   cell proliferation 2 13 EF677221  miRNA     21 GGAACUUGGUCUGUUGUAAGG 1                   :::::::.::::::: ::::         Target   469 CCUUGAAUCAGACAAGAUUCG 489    - 3 15 miR166g AT2G34710 miRNA     20 CCCUUACUUCGGACCAGGCU 1                    ::.:::::::::::::::.        Target  1340 UGGGAUGAAGCCUGGUCCGG 1359   HOMEOBOX PROTEIN 14, associated with development 2 21 HQ391915 miRNA     20 CCCUUACUUCGGACCAGGCU 1                    ::.:::::::::::::::.        Target     8 CGGGAUGAAGCCUGGUCCGG 27     homeodomain leucine zipper protein 2 16 miR167a-5p AT1G30330 AT5G37020 miRNA     21 AUCUAGUACGACCGUCGAAGU 1                   :::::::::::::::::::::        Target  1284 UAGAUCAUGCUGGCAGCUUCA 1304   AUXIN RESPONSE FACTOR 6 and 8 0 24 FJ469921 miRNA     20 UCUAGUACGACCGUCGAAGU 1                   .:::..:::::::::.:::         Target   392 GGAUUGUGCUGGCAGUUUCU 411    R2R3-MYB transcription factor 3 18 miR171a-3p AT3G60630.1 miRNA     21 CUAUAACCGCGCCGAGUUAGU 1                   :::::::::::::::::::::        Target  2862 GAUAUUGGCGCGGCUCAAUCA 2882   cell differentiation and division 0 14 BT102743 miRNA     21 CUAUAACCGCGCCGAGUUAGU 1                   :::::::::::::::::::::        Target   284 GAUAUUGGCGCGGCUCAAUCA 304    - 0 17 miR319b AT4G23710.1  miRNA    21 UCCCUCGAGGGAAGUCAGGUU 1                   :::::::::::::::::::::        Target  1262 AGGGAGCUCCCUUCAGUCCAA 1282   proton transport 0 15 BT110042.1 miRNA     20 CCCUCGAGGGAAGUCAGGUU 1                    :::::::::::::::::::        Target    83 UGGAGCUCCCUUCAGUCCAA 102    - 1 22 miR390b-5p AT5G03650.1 miRNA     21 CCGCGAUAGGGAGGACUCGAA 1                   ::::.:::::.::.:::::::        Target   410 GGCGUUAUCCUUCUUGAGCUU 430    starch branching enzyme 1.5 21 EX354481 miRNA     21 CCGCGAUAGGGAGGACUCGAA 1                   ::::.:::::.::.:::::::        Target   137 GGCGUUAUCCUUCUUGAGCUU 157    - 1.5 17 miR394b-5p AT1G27350 miRNA     20 CCUCCACCUGUCUUACGGUU 1                   :::::: :::::::::::::        Target  2592 GGAGGUUGACAGAAUGCCAA 2611   ribosome associated membrane protein 1 15 BT112917 miRNA     20 CCUCCACCUGUCUUACGGUU 1                   :::::: :::::::::::::        Target   830 GGAGGUAGACAGAAUGCCAA 849    - 1 20   microRNA production at the plant-to-seed transition 145  Name1 Predicted target2 Alignment Target gene function3 E4 UPE5 miR396-5p AT1G53910 miRNA     20 UCAAGUUCUUUCGACACCUU 1                   ::::::::::::::::::::        Target   526 AGUUCAAGAAAGCUGUGGAA 545    ethylene response factor 0 14 BT102125 miRNA     21 UUCAAGUUCUUUCGACACCUU 1                   :::::::::::::: ::::::        Target   164 AAGUUCAAGAAAGCCGUGGAA 184    - 1.5 34 miR408-3p AT2G02860 miRNA     20 GGUCCCUUCUCCGUCACGUA 1                   : ::::::::::::::::::        Target   687 CAAGGGAAGAGGCAGUGCAU 706    SUCROSE TRANSPORTER 3 1 23 BT103532 miRNA     20 GGUCCCUUCUCCGUCACGUA 1                   :::::::.::::::::::.         Target   102 CCAGGGAGGAGGCAGUGCGA 121    - 2 25 miR824-5p AT3G57230 miRNA     21 AGGGAAGAGUGUUUACCAGAU 1                   ::.::::::::::::::::::        Target  2516 UCUCUUCUCACAAAUGGUCUA 2536   MADS-box transcription factor 0.5 15 BT112142  miRNA     21 AGGGAAGAGUGUUUACCAGAU 1                   ::.: :::::.:::::::::         Target   266 UCUCCUCUCAUAAAUGGUCUC 286    - 3 13 1 nomenclature of miR: (organism)miRnx - precursor arm and/ or .y, where n, a sequential number representing family of miR; x, lettered suffixes representing family member (i.e., closely related mature sequences); -5p or -3p denote 5' or 3' arm of the precursor; .y, integer denoting occurrence of more than one mature sequence from the same precursor; 2 for each miRNA, shown is the most confidently predicted in Arabidopsis (above) and P. glauca (below); 3 refer to GO enrichment analysis, TAIR10 and NCBI; 4 expectation, stringent threshold [0-0.2] gives lower false positive prediction; 5 maximum energy to unpair the target site, small value (range: [0, 100]) is better.  microRNA production at the plant-to-seed transition 146  Table 6.5 Identification of unique and conserved miRNAs in Arabidopsis ecotype Col compared with Cvi and studied P. glauca population P4 compared with P1 to 3 during seed set Name Predicted target Alignment Target gene function E UPE Arabidopsis thaliana ath-miR158b AT3G10740.1 miRNA     20 ACGAAACAGAUGUAAACCCC 1                   :::::::::::::::::::         Target  3354 UGCUUUGUCUACAUUUGGGA 3373   xylan metabolism (cell wall modification) 1 19 ath-miR447c-5p AT4G03440.1 miRNA     21 AAAUGAGCUGUAACAUUCCCC 1                   :::::::::::::::::::::        Target  2443 UUUACUCGACAUUGUAAGGGG 2463   ankyrin repeat family protein¶ (protein-protein interaction) 0 18 ath-miR774a AT3G19890.1 miRNA     21 CUACCGGUAUACCCAUUGGUU 1                   ::::::::::::::::::::         Target    41 GAUGGCCAUAUGGGUAACCAC 61     F-box protein 1 19 ath-miR776 AT1G08760.1 miRNA     21 UUGUAGUUAUCUUCUGAAUCU 1                   ::.:::: :::::::::::::        Target   660 AAUAUCAUUAGAAGACUUAGA 680    unknown 1.5 7 ath-miR779.1 AT2G22500.1 miRNA     21 UACUCGUCGUUGUAUCGUCUU 1                   :::::::::::::::::::::        Target  2399 AUGAGCAGCAACAUAGCAGAA 2419   mitochondrial dicarboxylate carriers (proton transport) 0 12 ath-miR832-3p CP002687 miRNA     21 GAACGAACCUAACCCUUAGUU 1                   :::::::::::::::::::::        Target  1110 CUUGCUUGGAUUGGGAAUCAA 1130   intergenic, chr. 4 0 13 ath-miR833a-5p CP002687 miRNA     22 UGAUCUGGCUCAUGUUGUUUGU 1                   :.:.:.::::::: ::::::::        Target  2016 AUUGGGCCGAGUAGAACAAACA 2037   Intergenic, chr. 4 centromere region 2 16 ath-miR843 AT3G13840.1 miRNA     21 AGGUUACUUCGAGCUGGAUUU 1                   :.:::::::::::::::::::        Target   159 UUCAAUGAAGCUCGACCUAAA 179    GRAS family transcription factor (regulation of transcription) 0.5 18 ath-miR860 CP002684 miRNA     21 UAUGUAUCAGGUUAGAUAACU 1                   :::.:::::.::::.::: ::        Target  2580 AUAUAUAGUUCAAUUUAUAGA 2600   intergenic, chr. 1 3 14 ath-miR864-5p AT3G11080.1 miRNA     21 AAACUUCAGUUAGUAUGGACU 1                   ::::::::::.:..:..:::.        Target  2800 UUUGAAGUCAGUUGUGUCUGG 2820   Receptor-like protein 35, signal transduction 3 16 ath-miR1886.2 AT2G37160.1 miRNA     21 GGUUAGUUUCUAAAGUAGAGU 1                   :::::::::::::::::::::        Target  2420 CCAAUCAAAGAUUUCAUCUCA 2440   transducin/WD40 repeat-like superfamily protein 0 30 ath-miR3440b-5p† AT5G08490.1 miRNA     21 CUUCACCUACCCGGUUCUUUU 1                   ::::::::::::: :::.::         Target    73 GAAGUGGAUGGGCAAAGGAAU 93     response to ABA 2.5 23 ath-miR5026 CP002688 miRNA     21 UGCACAGUGCUAGAAUACUCA 1                   :::::::::::::::::::::        Target  1960 ACGUGUCACGAUCUUAUGAGU 1980   intergenic, chr. 5 0 15 ath-miR5644 AT5G41620.1 miRNA     20 AUGGCAAUAGGCGUUGGGUG 1                   ::::::::::::::::::::        Target  1158 UACCGUUAUCCGCAACCCAC 1177   cell morphogenesis 0 17 ath-miR8169 AT3G24340.1 miRNA     21 AGACACUCACUGAGACAGAUA 1                   :::::::::::::::::::::        Target  2015 UCUGUGAGUGACUCUGUCUAU 2035   chromatin remodeling 40 0 15 ath-miR8171 AT5G56380.1 miRNA     21 AGGAUGGUGACCGGGUGGAUA 1                   :::::::::::::::::::::        Target  2038 UCCUACCACUGGCCCACCUAU 2058   F-box protein 0 21 Picea glauca pgl-miR157c-5p BT105462 miRNA     21 CACGAGAGAUAGAAGACAGUU 1                   ::::::::: :::::::::::        Target   279 GUGCUCUCUCUCUUCUGUCAA 299    male and female cone development in Pinus (PtSPL1, KJ711108) 1 23 pgl-miR157d BT119207 miRNA     20 CACGAGAGAUAGAAGACAGU 1                   ::::::::: ::::::::::        Target   152 GUGCUCUCUCUCUUCUGUCA 171    - (mRNA seq) 1 18 Note: see Table 6.4 for header notation. † comPARE predicts that its validated target is AT3G01460, which is involved in embryo development ending in dormancy;¶ ANK gene cluster is consistent with a tandem gene duplication and birth-and-death process.microRNA production at the plant-to-seed transition 147    Figure 6.3 sRNAs annotation and/ or distribution in the mapped reads of all Picea glauca and Arabidopsis thaliana libraries Note: small RNAs mainly include: transfer RNAs (tRNAs), ribosomal RNAs (rRNAs), microRNAs (miRNAs), small interfering RNAs (siRNAs), small nuclear ribonucleic RNAs (snRNAs), small nucleolar RNAs (snoRNAs), long noncoding RNAs (lncRNAs), Piwi interacting RNAs (piRNAs), and repeat associated RNAs (asiRNAs).   microRNA production at the plant-to-seed transition 148  A)     microRNA production at the plant-to-seed transition 149  B)   Figure 6.4 Temporal expression patterns of top conserved miRNAs in P. glauca (A) and Arabidopsis (B) Note: Hierarchical clustering of the 20 most abundant conserved miRNAs and seed developmental phases;  The color indicates the relative expression level: grey/yellow- low, red/white- medium and blue- high;  Filled black circles mark miRNA communally identified in P. glauca and Arabidopsis and empty black circles mark closely related miRNA (i.e., in the same miR family) identified in both species; The clusters are divided into several major “clades”, labeled by A, B, C, and D; Dashed and curly lines represent “at” and “to”, respectively (e.g., “P 1-1~3” means Population 1 at time point 1 to 3; P 1~3 means Population 1 to 3).  microRNA production at the plant-to-seed transition 150  microRNA production at the plant-to-seed transition 151   Figure 6.5 Analyses of miRNA reads in Arabidopsis (A) Statistics of unique read numbers in stacked and compiled libraries for conserved and novel miRNAs; (B) Identification of the unique read number in conserved and novel miRNAs at early (Cvi_0~7 and Col_0~4) and late (Cvi_8~14 and Col_5~9) maturation; (C) PCA biplot of conserved and constantly expressed novel miRNAs; (D) GO classification as per biological process for genes targeted by miRNAs of high strength of prediction. Note: dev_phase: developmental phase, chr: chromosome; microRNA production at the plant-to-seed transition 152  (CONTINUED) Gene category and GO code: apoptotic process (GO:0006915), response to stimulus (GO:0050896), developmental process (GO:0032502), cellular process (GO:0009987), metabolic process (GO:0008152), biological regulation (GO:0065007), cellular component organization or biogenesis (GO:0071840), localization (GO:0051179).    microRNA production at the plant-to-seed transition 153   Figure 6.6 Analyses of miRNA reads in P. glauca (A) Statistics of unique miRNA read numbers in stacked and compiled libraries by populations; (B) Identification of the unique read number in populations; (C) PCA biplot of all miRNAs indentified; (D) GO classification as per biological process for genes targeted by miRNAs of high strength of prediction. Note: pop: population, dev_phase: developmental phase.  microRNA production at the plant-to-seed transition 154   Figure 6.7 Nucleotide frequency in mature miRNA sequences (A) and at each mature miRNA position (B), and the number of repeat modules per “conserved” MIR gene (C) and across MIR genes (D) in P. glauca and Arabidopsis Note: one or three asterisks represent that the mean between two groups is significant (P ≤ 0.05) or highly significant (P < 0.001), respectively, using student t-test; The average length of analyzed mature miRNAs is 21.7 ± 1.36nt and 21.6 ± 1.23nt in P. glauca and Arabidopsis, respectively.  microRNA production at the plant-to-seed transition 155   Figure 6.8 Phylogenetic tree of homologs for four genes (DOG1, ABI3, ARF10, and ARF16) in gymnosperms and model angiosperms Note: as per tBLASTN, DOG1 has high sequence similarity with TGA6/2 (i.e., basic leucine zipper TF involved in the activation of SA-responsive genes). microRNA production at the plant-to-seed transition 156     Figure 6.9 Nucleotide alignment of putative pgARF10 between P. glauca (BT119832.1) and Cycas rumphii (FN433183.1)    microRNA production at the plant-to-seed transition 157     Figure 6.10 The hairpin structures of possible pgl-miR160s by computational prediction   microRNA production at the plant-to-seed transition 158   Figure 6.11 Triplot diagram for RDA between relative expression of miRNA-gene combinations (red lines) and environments (blue arrows) 6.4 Discussion Since the first report on plant miRNAs in 2002 (Reinhart et al. 2002), substantial advances have been gained on our understanding of miRNA essential regulatory modules, chiefly comprising phase transition, organ development as well as developmental timing, and stress responses. In regulation of nodes in GRNs (e.g., transcription factors), miRNAs constitute important components in the evolution of organismal complexity. To date, evolutionary analysis on miRNAs and MIR genes is almost comprehensive and profound throughout species in phylogeny (Nozawa et al. 2012), but missing representative species in a subgroup of gymnosperms – conifers, occupying important taxonomic positions. Although recently there have been sporadic reports on conifer small RNAs (Källman et al. 2013; Xia et al. 2015), no small RNA sequencing centres on its reproductive periods, during which the small RNAs of 24-nt size class are specifically and microRNA production at the plant-to-seed transition 159  uniquely generated (Nystedt et al. 2013). This implies a different landscape of small RNAs at conifer seed set and thus a supplemental characterization of sRNAs in conifers necessitates this study. In quest of molecular underpinnings of today’s diversity of life, we rely on comparative studies of deeply conserved, known and novel miRNAs in two spermatophytes, Picea glauca and Arabidopsis thaliana, to unravel the evolutionary mechanism of miRNA families and MIR genes and the influence of environmental clues on the expression of miRNA-gene pathways. 6.4.1 Landscape of miRNA expression at seed set within and between species Of 37 miRNA families that are deeply conserved in plant development throughout the plant kingdom (Willmann and Poethig 2007), we identified 12 miRNA families at seed set between phyla (Table 6.2). Some are conserved across spermatophytes (i.e., miR163, miR394, and miR396), tracheophytes (i.e., miR159), and embryophytes (i.e., miR160, miR166, miR167, miR171, miR319, miR390, and miR408). The ubiquitously conserved miRNAs had significantly differential expression abundance between P. glauca and Arabidopsis and between populations within species (Fig. 6.4), and this observation is correlated to genome evolution (Hodgins et al. 2016). These mature miRNA sequences have a well-conserved consensus with minor variation at the 5´ or 3´ end between P. glauca and Arabidopsis (Table 6.2), and their targets may not always be conserved partially due to no homologs found in P. glauca (Table 6.2). This indicates that the cognate miRNA-target pairs may be acquired prior to the split of gymnosperms and angiosperms but have undergone selection. Conserved miRNAs in Arabidopsis (Table B.6) were enriched and expressed across seed set (Fig. 6.5B), while abundant miRNAs expressed throughout seed set in P. glauca did not belong to miRNA families documented on miRBase (Table B.7). Nonetheless, their target genes were classified into the same GO categories (Figs. 6.5 and 6.6 D). Moreover, miRNA-target complementarity in plants is extensive and stringent (Rhoades et al. 2002), suggestive of coevolution of miRNAs and their targets. Taken together, miRNA-target systems undergo evolution in adaptation to genome evolution while the function of their target genes is universal in support of similar molecular mechanisms and signaling cascades at seed set across phyla. microRNA production at the plant-to-seed transition 160  In general, ancient miRNAs are more highly and broadly expressed than younger miRNAs (Fahlgren et al. 2007); while newly emerged miRNAs may be used as substrates for natural selection and form specific miRNA circuitry with GRNs, whereby speciation occurs (Chen and Rajewsky 2007). As requirements for a functional MIR gene are less demanding than a protein-coding gene, MIR gene can easily evolve from various sources of unstructured transcripts, such as gene duplication, intergenic regions, transposable elements (Nozawa et al. 2012). MIR genes for conserved miRNAs in Arabidopsis contained more DNA repeat modules than those for enriched miRNAs in P. glauca (Fig. 6.7). This is in line with their genome evolution, as genetic divergence is suppressed in conifers evidenced by few whole genome duplications (Leitch and Leitch 2012; Li et al. 2015). On the other hand, although members of plant MIR gene families are often highly similar, the same gene family varies in size and genomic structure between species, indicating dosage effects and spatiotemporal differences in gene regulation (Li and Mao 2007). Through constraining variance or mean gene expression, miRNAs render phenotypic traits evolvable as well as heritable (Wagner and Altenberg 1996) and after selection, these miRNAs may improve fitness of phenotypes (Wu et al. 2009a). In regulation of target genes with similar functions, different species may rely on variants of the same MIR gene families or produce distinct miRNAs through changes in miRNA precursor sequences and binding sites. We detected some miRNAs solely in one population/ecotype, which were conserved in miRNA families on miRBase (Table 6.3). As per their target gene function, these genes are not key components in GRN and they primarily target repeats, intergenic sequences, and genes in cell communication and signaling (Table 6.3). This suggests that altering mean miRNA expression to regulate its target gene expression is subject to fine-tuning in cooperation with key genes. 6.4.2 Impact of environments on seed set programs The phenology or temporal control of the life cycle provides adaptive strategies to avoid adverse consequences in harsh environments at seed set and seedling establishment (Krämer 2015). In the life cycle, temperature is of utmost importance in seed set, germination, and seedling stages due to epigenetic imprinting and their fragile state (Liu et al. 2016). At seed set, temperature signals are critical selective pressures and have a strong influence on life history traits, such as timing of seed setting and seed dormancy microRNA production at the plant-to-seed transition 161  depth (Springthorpe and Penfield 2015; Vidigal et al. 2016). Specifically, the temperature-mediated control of flowering has evolved to constrain the maternal environments for setting seeds to a specific temperature window, thus yielding seeds with dormancy variation. Maternal environments (e.g., temperature) have a persistent and transgenerational effect on the expression pattern of regulatory molecules in GRNs (e.g., DOG1 (Chiang et al. 2013)) and on the biogenesis of versatile miRNAs in life history. The latter was confirmed by miRNAs (i.e., miR160) targeting key genes (i.e., ABI3 and ARF10/16) that modulate timing of seed set and the dormancy state (Huo et al. 2016). In this study, the chosen populations of P. glauca and ecotypes of Arabidopsis are characterized by different fertilization time-points, contrasting seed set durations and ensuing phenotypical variation (i.e., dormancy intensities) (Fig. 6.2). We tested the impact of thermal imprinting and phenology at seed set on seed dormancy modulation through a selected miRNA-gene pair (i.e., miR160-ARF10/16) and key genes (i.e., ABI3 and DOG1) (Fig. 6.11). Our result reinforces the notion that phenotypic variations are manipulated by the interaction of environmental signals and genotypes with an emphasis on miRNAs as well as genes.   Figure 6.12 Comparison of features of MIR genes, mature miRNAs, and their predicted RNA targets at seed set in P. glauca and Arabidopsis Note: description in the boxes indicates communal features in conifer and Arabidopsis; f(G): frequency of G ribonucleotide. In this study, we emphasized that the programs for phenotypical variations are formulated at the seed set period, which paves the way for plant adaptation and the evolution of traits expressed in the adult microRNA production at the plant-to-seed transition 162  stage. We demonstrated that phenotypical variations are overridden by diversified miRNA repertoires among populations within and between species (summarized in Fig. 6.12), as evidenced by 1) the expression pattern of deeply conserved miRNAs reflects shared seed set programs in plants; 2) different abundances and types of miRNAs at seed set contribute to adaptive plasticity and phenotypical variation; and 3) environments have significant influences on the expression of key genes and miRNAs that are involved in the modulation of the studied phenotype in both species. This study has opened new perspectives for understanding the evolutionary mechanism of miRNAs in association with MIR genes, miRNA targets and genome evolution. Notwithstanding deeply conserved miRNA families play an important role across a plurality of plants, few studies concretely articulate biological mechanisms and significances of novel miRNAs that are frequently born and die in large quantities. In the future, we will strive to isolate species-specific sets of non-conserved miRNAs by clades and compare their MIR genes in loci and investigate whether those MIR genes evolve in sync with genome evolution between species within spermatophyte and how environments contribute to adaptive variations at molecular levels (e.g., epigenetic imprinting, genetic variant, etc.).Conclusions 163  7 Conclusions 7.1 Research novelties This dissertation asked whether global climate change significantly impacts eco-evolutionary dynamics of life-history traits (i.e., seed dormancy and size) in long-lived species like conifers. I tackled this primary question using cross-disciplinary approaches, which bridge ecology, evolution, modeling, genetics, and epigenetics. My work goes beyond other pieces of previous work in this theme on several grounds.  Studies of seed dormancy and size allow us to understand the relationship between these two life-history traits and their correlation with environments in life-cycle transitions (Chapter 2 and 3). My dissertation helps obtain a complete understanding of passive plasticity (i.e., inevitable change in phenotype due to resource limitations; Chapter 2 and 3) and ontogenetic plasticity (i.e., active developmental response; Chapter 6) (Pigliucci and Hayden 2001; Wright and Macconnaughay 2002) on seed dormancy modulation. My study additionally provides us with clues to the missing empirical evidence of bet-hedging strategy in long life-history plants (Chapter 3).  An innovative approach was to use the machine-learning algorithm (i.e., partial least squares regression, Chapter 3) to select important climatic variables for studied traits. Important climatic variables can be identified through a process of model optimization without explaining the contribution of climatic variables in the initial input data set (i.e., PCA). This methodology is particularly useful when the matrix of predictors has more variables than observations and when there is multicollinearity among N values.  In theoretically modeling seed dormancy and germination (Chapter 4), I investigated the impact of each trait on the evolution of the other as well as their joint evolution. I explicitly tackled effects of climate change on the evolution of these traits rather than solely focusing on fixed environmental settings. Besides evolved traits, I also elucidated how population structures change along evolutionary trajectories, so that eco-evolutionary feedback loops are explicitly considered. This Conclusions 164  study increases our understanding of plant evolution and persistence in the context of climate changes.  Molecular mechanisms underpinning seed dormancy have been extensively documented in model plants. I comparatively uncovered similar molecular mechanisms accounting for dormancy alleviation in conifers (Chapter 5). This research helps provide insight into how winter chilling contributes to the timing of phenology, and how conifer life histories may develop under new climate scenarios.  Populations chosen from two species (spruce and Arabidopsis) representing two spermatophytes have different modes of reproductive development, which are hypothetically regulated by sets of microRNA populations and exert cascading effects on ensuing phenotypes, including seed dormancy. I deciphered how microRNA entities, in response to environment cues, alter seed set programs and thus contribute to adaptive plasticity and account for phenotypic plasticity within and between species (Chapter 6). In bioinformatics, a crucial step is the prediction of MIR genes for each miRNA through scanning across the whole spruce genome (20 Gb). Additionally, extremely scant information about epigenetic mechanism and significance is available for conifers with intimidating genome size (200 × Arabidopsis genome). I supplemented the missing link – how conifers produce diversified microRNAs at seed set. This study provides fundamental insight for understanding evolutionary mechanisms of microRNAs, MIR genes, and genome evolution. 7.2 Eco-evolutionary perspectives on seed dormancy and size In the context of global warming, seeds play an important role as the main vehicle for plant migration (Parolo and Rossi 2008), regeneration (Walck et al. 2011), and persistence (Ooi et al. 2012). Ecological (climatic and geographic) variation in early life-history transitions is a vital determinant of the adaptive evolution of timing of seed germination. Chapter 2 aimed to investigate the correlation between timing of seed germination and environmental signals during seed development. I examined seed germination timing in conifers using 15 seed lots of lodgepole pine, “interior” spruce, and western hemlock collected from Conclusions 165  natural stands in British Columbia (B.C.), Canada, under manipulated (stratification, thermo-priming (15 or 20°C) and their combinations) and non-manipulated (control) conditions. Timing of seed germination showed strong and positive correlation with the temperature-based environmental signals during seed development. This pattern persisted across species and seed lots within species, substantiating the historic importance of environmental conditions during seed development and maturation to life-history traits. Moreover, the strategy of phenotypic plasticity affecting timing of seed germination was observed across the applied germination treatments. These results provide insight into the germination niche as affected by global warming, indicating that conifer seed dormancy in B.C. (north of 54°N) tends to increase and the changes associated with early spring warm-up are expected to accelerate seedling emergence, as shortened winters would have minimal effect on dormancy decay. Environmental signals are important triggers in the life-cycle transitions and play a crucial role in life-history evolution. Yet very little is known about the leading ecological factors contributing to the variations of life-history traits in perennial plants. Chapter 3 explored both the causes and consequences for the evolution of life-history traits (i.e., seed dormancy and size) in lodgepole pine (Pinus contorta Dougl.) across British Columbia (B.C.), Canada. I selected 83 lodgepole pine populations covering 22 ecosystem zones of B.C. and through their geographic coordinates, 197 climatic variables were generated accordingly for the reference (1961-1990) and future (2041-2070) periods. I found that dynamic climatic variables rather than constant geographic variables are the true environmental driving forces in seed dormancy and size variations and thus provide reliable predictors in response to global climate change. Evapotranspiration and precipitation in the plant-to-seed chronology are the most critical climate variables for seed dormancy and size variations, respectively. Hence, I predicted that levels of seed dormancy in lodgepole pine would increase across large tracts of B.C. in the 2050s. Winter-chilling is able to increase the magnitude of life-history plasticity and lower the bet-hedge strategy in the seed-to-plant transition; however, if winter-chilling is insufficient in some regions of B.C. in the 2050s, seed germination may be delayed, while unfavorable conditions during dry summers may result in adverse consequences to the survival of seedlings owing to Conclusions 166  extended germination span. These findings provide useful information to studies related to assessments of seed transfer and tree adaptation. Seed dormancy and size are two important life-history traits that interplay as adaptation to varying environmental settings. As evolution of both traits involves correlated selective pressures, it is of keen interest to comparatively investigate the evolution of the two traits jointly as well as independently. Chapter 4 explored evolutionary trajectories of seed dormancy and size using adaptive dynamics in scenarios of deterministic or stochastic temperature variations. Ecological dynamics usually result in unbalanced population structures and temperature shifts or fluctuations of high magnitude give rise to more balanced ecological structures. When only seed dormancy evolves, it is counter-selected and temperature shifts hasten this evolution. Evolution of seed size results in the fixation of a given strategy and evolved seed size decreases when seed dormancy is lowered. When coevolution is allowed, evolutionary variations are reduced while the speed of evolution becomes faster given temperature shifts. Such coevolution scenarios systematically result in reduced seed dormancy and size and similar unbalanced population structures. I discussed how this may be linked to the system stability. Dormancy is counter-selected because population dynamics lead to stable equilibrium, while small seeds are selected as a bet-hedging strategy allowing the production of more seeds. Our results suggest that unlike random temperature variation between generations, temperature shifts with high magnitude can considerably alter population structures and accelerate life-history evolution. 7.3 Genetic and epigenetic basis of adaptation on seed dormancy After illumination of how climate change may impact eco-evolutionary dynamics, another critical outstanding question of our time is the extent to which adaptive evolution at molecular levels will ameliorate the detrimental effects of contemporary climate change. A central thrust of means to address such a knotty issue is a focus on genetics and epigenetics to study adaptive process. I demonstrated in Chapter 5 that, in addition to classic ABA and GA mechanisms, auxin appears to be actively involved in dormancy termination and germination of white spruce seeds. I hypothesized that auxin signalling plays a role in these Conclusions 167  processes partly by interacting with ABA signaling. This is in accordance with recent findings regarding the crosstalk of auxin and ABA in the regulation of seed dormancy in angiosperms (Liu et al. 2013a). Auxin has a dominant role in plant morphogenesis and is an inescapable player in many developmental processes and a central component of crosstalk networks. Our findings now point to auxin as a key player that likely works in conjunction with the ABA and GA signal pathways previously investigated in mechanisms underlying dormancy alleviation by chilling in conifer seeds. Our study also yields insights into the speed with which imbibed seeds can adjust their transcription to environmental conditions, as demonstrated when seeds were transferred from moist chilling to germination conditions. After only six hours in light at higher temperatures, significant changes in transcript abundance were observed. MicroRNAs (miRNAs), 21-25 ribonucleotide sequences, carry genetic signals and are instrumental in the evolution of organismal complexity through diversifying miRNA repertoires. The evolution of miRNA families and MIR genes has been extensively characterized in plants but the conifer clade is incomplete because of its giga-genome. In Chapter 6, I outlined the landscape of miRNAs respectively by conserved families and abundances in two major spermatophytes represented by populations of Picea glauca and Arabidopsis thaliana. In both species, the pattern of relative expressions of conserved miRNAs was highly correlated with seed set phases. In contrast to consistent features in Arabidopsis, the most abundant miRNAs in P. glauca were not the deeply conserved ones in plants, implying that spruce (conifer) relies on lineage-specific miRNAs to program seed set. Comparing the architecture of MIR sequences producing abundant miRNAs, I found Arabidopsis contained significantly more repeat modules than P. glauca, suggesting that their MIR genes have undergone divergent evolution. In both species, environments can largely account for the expression of key genes and miRNAs involved in phenotype modulations. In conclusion, environmental cues trigger conifer and Arabidopsis seed set to employ lineage-specific and deeply conserved miRNAs at different expression levels, respectively, to regulate phenotypical variations. Conclusions 168  7.4 Perspectives 7.4.1 Eco-evolutionary perspective on life-history traits in climate change Seed dormancy is an intrinsic attribute affecting regeneration dynamics and seed size acts as one of the vital determinants for the evolution of seed dormancy. While the goal of the present models is to better understand their covariation in isolation, an important perspective is to account for explicit spatial aspects. These spatial aspects are especially important in the global change context, as temperature shifts depend on latitude and altitude gradients and species dispersal to higher altitudes and latitudes is thought to be a major constraint to their future survival. Also, spatial context influences gene flows and evolutionary dynamics with again important consequences for species competition and survival (Norberg et al. 2012). The two traits I studied here are intrinsically related to seed dispersal such that a spatially explicit context should modify our results. While this study uncovered seed dormancy as a means to disperse in time, seed dispersal is another important means to dispersal in space and also a risk-spreading strategy (Cohen and Levin 1987; Buoro and Carlson 2014). They may evolve as phenotypic plasticity (e.g., bet-hedging) (Slatkin 1974; Philippi and Seger 1989; Gomez-Mestre and Jovani 2013) to reduce parent-offspring conflict, kin competition, and local extinction (Ellner 1986; Vitalis et al. 2013; Gremer and Venable 2014), thus promoting adaptation, stability and persistence (Kovach-Orr and Fussmann 2013). Consequently, selection acts on trade-offs in temporal and spatial dispersal and eventually maximizes fitness (Buoro and Carlson 2014). The study in Chapters 2-4 is therefore a springboard toward more integrative scenarios aiming to better forecast the evolution of life-history traits in temporally and spatially variable environments. As a broader extension, a set of related life history traits in different stages of the life cycle can be combined to study how environments affect life history, as briefly introduced in Chapter 1. Such a research expansion is meaningful, because some adverse consequences aroused by climate change for one trait may be cancelled out by other traits in later life-cycle stages. On the other hand, multiple life-history traits are genetically linked to the same quantitative trait loci (i.e., QTL) (Alonso-Blanco et al. 1999) or environment-specific QTLs (Haselhorst et al. 2011) and regulated by communal mechanisms that control their crosstalk with environments, such as flowering locus and ambient temperature (Chen et al. 2014; Verhage et al. Conclusions 169  2014). These molecular mechanisms, in turn, constrain an organism’s living habitats, plasticity, and distribution, for instance, the flowering time diversity is associated with cis-regulatory variation (Rosas et al. 2014), and flowering time loci restrict potential range size and niche breadth (Banta et al. 2012). Furthermore, eco-evolutionary studies can be confluent with molecular biology. The last decade has witnessed emerging studies on molecular and genetic mechanisms underpinning adaptive evolution and underlying plasticity relevant to climate change. Plastic molecular responses to environmental signals can occur in many ways (e.g., genetics, epigenetics, and genomics), while adaptive plasticity through epigenetic modifications is of keen interest (Herman et al. 2014). Epigenetic stability or reversibility may evolve through changes in coding or regulatory DNA regions (Donohue 2014). Epigenetic stability may evolve through changes in the DNA sequence of genes involved in the regulation of methylation and histone modifications. If we manage to identify those loci (e.g., QTL analysis, genome-wide association studies (GWAS)) and observe differences among natural variants from different ecological contexts, we may infer the genetic and environmental basis of variation in epigenetic stability and potential costs of epigenetic reversibility. Identification of loci associated with natural variation in epigenetic stability would fuel genetic and ecological studies of adaptation. For example, temperature-dependent differential transcriptomes at embryogenesis canalize the expression of genes involving chromatin modifications, thus mediating plasticity (Yakovlev et al. 2014). 7.4.2 Seed dormancy and germination As of June 2016, a couple of dormancy genes have been reported, including DOG1 (DELAY OF GERMINATION1) (Huang et al. 2010; Graeber et al. 2014; Footitt et al. 2015), RDO5 (REDUCED DORMANCY5) (Xiang et al. 2014), and the qSD locus in barley and rice (Gu et al. 2008; Sato et al. 2016). To advance this domain, we need to understand more about the basis of how seeds function during development, germination, dormancy, and storage to anticipate how they will respond to changes in the environment. Our profound knowledge on seeds can, in turn, serve seed industries. For example, prompt and synchronized germination is important in container nurseries to preserve genetic diversity that is strongly affected by thinning and culling processes, and to save greenhouse heating. Recent reports show Conclusions 170  that smoke-derived chemicals promote germination and germination stimulants have been identified, such as karrikin and cyanide (Fig. 7.1) (Flematti et al. 2004; Flematti et al. 2011; Nelson et al. 2012; Guo et al. 2013). These chemicals are promising to be applied to break seed dormancy. Moreover, the fact that fire is an inherent feature of the existence of land plants may entrain another interesting research topic: how the evolution of plants is associated with the occurrence of wildfire.  Figure 7.1 Known germination stimulants derived from combustion of plant material Note: Substances in blue boxes are known to stimulate seed germination, here depicted by blue arrows. NOx represents NO or NO2 and could theoretically be derived by combustion or by microbial activity in the soil. Oxidation of NH4+ or NOx to NO2− (nitrite) or NO3− (nitrate) can occur by microbial nitrification. Chemicals would normally be eluted into the soil by rain. Other factors such as light and temperature may also regulate seed germination; reprinted with permission of Nelson et al. (2012). In summary, this dissertation, which explored environmental effects on the eco-evolutionary dynamics of seed dormancy and size and their genetic basis of adaptation, can be considered as a stepping stone toward a better understanding of how inherently correlated life-history traits in the life cycle cooperatively control life history and the extent to which anthropogenic climate change impacts adaptation and life-history evolution.  171  References Abdi H. 2007. Partial least square regression Thousand Oaks, CA/London/New Dehli: Sage Publications, Inc. p. 740-744. Achard P, Herr A, Baulcombe DC, Harberd NP. 2004. Modulation of floral development by a gibberellin-regulated microRNA. Development 131:3357-3365. Adler PB, Salguero-Gómez R, Compagnoni A, Hsu JS, Ray-Mukherjee J, Mbeau-Ache C, Franco M. 2014. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. USA 111:740-745. Aitken SN, Yeaman S, Holliday JA, Wang TL, Curtis-McLane S. 2008. Adaptation, migration or extirpation: Climate change outcomes for tree populations. Evol. Appl. 1:95-111. Alberto FJ, Aitken SN, Alía R, González-Martínez SC, Hänninen H, Kremer A, Lefèvre F, Lenormand T, Yeaman S, Whetten R et al. . 2013. Potential for evolutionary responses to climate change - Evidence from tree populations. Glob. Change Biol. 19:1645-1661. Ali-Rachedi S, Bouinot D, Wagner MH, Bonnet M, Sotta B, Grappin P, Jullien M. 2004. Changes in endogenous abscisic acid levels during dormancy release and maintenance of mature seeds: studies with the Cape Verde Islands ecotype, the dormant model of Arabidopsis thaliana. Planta 219:479-488. Allen E, Xie ZX, Gustafson AM, Carrington JC. 2005. microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 121:207-221. Allen RS, Li J, Stahle MI, Dubroue A, Gubler F, Millar AA. 2007. Genetic analysis reveals functional redundancy and the major target genes of the Arabidopsis miR159 family. Proc. Natl. Acad. Sci. USA 104:16371-16376. Alonso-Blanco C, Blankestijn-de Vries H, Hanhart CJ, Koornneef M. 1999. Natural allelic variation at seed size loci in relation to other life history traits of Arabidopsis thaliana. Proc. Natl. Acad. Sci. USA 96:4710-4717. Amasino R. 2010. Seasonal and developmental timing of flowering. Plant J. 61:1001-1013. An J, Lai J, Sajjanhar A, Lehman ML, Nelson CC. 2014. miRPlant: An integrated tool for identification of plant miRNA from RNA sequencing data. BMC Bioinformatics 15:275. Anderson JV, Dogramaci M, Horvath DP, Foley ME, Chao WS, Suttle JC, Thimmapuram J, Hernandez AG, Ali S, Mikel MA. 2012. Auxin and ABA act as central regulators of developmental networks associated with paradormancy in Canada thistle (Cirsium arvense). Funct. Integr. Genomics 12:515-531.  172  Anderson LL, Hu FS, Nelson DM, Petit RJ, Paige KN. 2006. Ice-age endurance: DNA evidence of a white spruce refugium in Alaska. Proc. Natl. Acad. Sci. USA 103:12447-12450. Andersson S, Shaw RG. 1994. Phenotypic plasticity in Crepis tectorum (Asteraceae): Genetic correlations across light regimens. Heredity 72:113-125. Angevine MW, Chabot BF. 1979. Seed germination syndromes in higher plants. New York, NY, USA: Columbia University Press. p. 188-206. Arc E, Chiban K, Grappin P, Jullien M, Godin B, Cueff G, Valot B, Balliau T, Job D, Rajjou L. 2012. Cold stratification and exogenous nitrates entail similar functional proteome adjustments during Arabidopsis seed dormancy release. J. Proteome Res. 11:5418-5432. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al. . 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25:25-29. Aukerman MJ, Sakai H. 2003. Regulation of flowering time and floral organ identity by a microRNA and its APETALA2-like target genes. Plant Cell 15:2730-2741. Axtell MJ, Bowman JL. 2008. Evolution of plant microRNAs and their targets. Trends Plant Sci. 13:343-349. Axtell MJ, Snyder JA, Bartel DP. 2007. Common functions for diverse small RNAs of land plants. Plant Cell 19:1750-1769. Banta JA, Ehrenreich IM, Gerard S, Chou L, Wilczek A, Schmitt J, Kover PX, Purugganan MD. 2012. Climate envelope modelling reveals intraspecific relationships among flowering phenology, niche breadth and potential range size in Arabidopsis thaliana. Ecol. Lett. 15:769-777. Barbez E, Kubeš M, Rolčík J, Beziat C, Pĕnčík A, Wang B, Rosquete MR, Zhu J, Dobrev PI, Lee Y et al. . 2012. A novel putative auxin carrier family regulates intracellular auxin homeostasis in plants. Nature 485:119-122. Barrero JM, Millar AA, Griffiths J, Czechowski T, Scheible WR, Udvardi M, Reid JB, Ross JJ, Jacobsen JV, Gubler F. 2010. Gene expression profiling identifies two regulatory genes controlling dormancy and ABA sensitivity in Arabidopsis seeds. Plant J. 61:611-622. Baskin CC, Baskin MJ. 1998. Seeds: Ecology, biogeography, and evolution of dormancy and germination San Diego, California: Academic Press. Batlla D, Benech-Arnold RL. 2010. Predicting changes in dormancy level in natural seed soil banks. Plant Mol. Biol. 73:3-13.  173  Behera N, Nanjundiah V. 1995. An investigation into the role of phenotypic plasticity in evolution. J. Theor. Biol. 172:225-234. Bell G. 2008. Selection: The mechanism of evolution (2nd ed.). New York, USA: Oxford University Press. Belmonte MF, Kirkbride RC, Stone SL, Pelletier JM, Bui AQ, Yeung EC, Hashimoto M, Fei J, Harada CM, Munoz MD et al. . 2013. Comprehensive developmental profiles of gene activity in regions and subregions of the Arabidopsis seed. Proc. Natl. Acad. Sci. USA 110:E435-E444. Bentsink L, Soppe W, Koornneef M. 2007. Genetic aspects of seed dormancy. Oxford, UK: Blackwell publishing Ltd p. 113-132. Bewley JD. 1997. Seed germination and dormancy. Plant Cell 9:1055-1066. Bewley JD, Bradford KJ, Hilhorst HWM, Nonogaki H. Seeds: Physiology of development, germination, and dormancy (3rd ed.) [Internet]. eBook: Springer; 2012.  Bialek K, Cohen JD. 1989. Free and conjugated indole-3-acetic Acid in developing bean seeds. Plant Physiol. 91:775-779. Bialek K, Michalczuk L, Cohen JD. 1992. Auxin biosynthesis during seed germination in Phaseolus vulgaris. Plant Physiol. 100:509-517. Birol I, Raymond A, Jackman SD, Pleasance S, Coope R, Taylor GA, Saint Yuen MM, Keeling CI, Brand D, Vandervalk BP et al. . 2013. Assembling the 20 Gb white spruce (Picea glauca) genome from whole-genome shotgun sequencing data. Bioinformatics 29:1492-1497. Black M, Bewley JD, Halmer P. 2006. The encyclopedia of seeds. Wallingford, UK: CABI Publishing. Blilou I, Xu J, Wildwater M, Willemsen V, Paponov I, Friml J, Heidstra R, Aida M, Palme K, Scheres B. 2005. The PIN auxin efflux facilitator network controls growth and patterning in Arabidopsis roots. Nature 433:39-44. Bonner FT, Karrfalt RP. 2008. The woody plant seed manual. Washington D.C., USA: USDA Forest Service. p. 727. Bossdorf O, Richards CL, Pigliucci M. 2008. Epigenetics for ecologists. Ecol. Lett. 11:106-115. Bousquet J, Isabel N, Pelgas B, Cottrell J, Rungis D, Ritland K. 2007. Spruce. Forest Trees:93-114. Bradshaw AD. 1965. Evolutionary significance of phenotypic plasticity in plants. Adv. Genet. 13:115-155. Bradshaw WE, Holzapfel CM. 2001. Genetic shift in photoperiodic response correlated with global warming. Proc. Natl. Acad. Sci. USA 98:14509-14511.  174  Bradshaw WE, Holzapfel CM. 2008. Genetic response to rapid climate change: It's seasonal timing that matters. Mol. Ecol. 17:157-166. Brady SM, Sarkar SF, Bonetta D, McCourt P. 2003. The ABSCISIC ACID INSENSITIVE 3 (ABI3) gene is modulated by farnesylation and is involved in auxin signaling and lateral root development in Arabidopsis. Plant J. 34:67-75. Breshears DD, Huxman TE, Adams HD, Zou CB, Davison JE. 2008. Vegetation synchronously leans upslope as climate warms. Proc. Natl. Acad. Sci. USA 105:11591-11592. Brocard-Gifford IM, Lynch TJ, Finkelstein RR. 2003. Regulatory networks in seeds integrating developmental, abscisic acid, sugar, and light signaling. Plant Physiol. 131:78-92. Bull JJ. 1987. Evolution of phenotypic variance. Evolution 41:303-315. Bulmer M. 1994. Theoretical evolutionary ecology. Sunderland, MA: Sinauer Associates. Bulmer MG. 1984. Delayed germination of seeds: Cohen's model revisited. Theor. Popul. Biol. 26:367-377. Buoro M, Carlson SM. 2014. Life-history syndromes: Integrating dispersal through space and time. Ecol. Lett. 17:756-767. Cairney J, Pullman GS. 2007. The cellular and molecular biology of conifer embryogenesis. New Phytol. 176:511-536. Cairney J, Zheng L, Cowels A, Hsiao J, Zismann V, Liu J, Ouyang S, Thibaud-Nissen F, Hamilton J, Childs K et al. . 2006. Expressed sequence tags from loblolly pine embryos reveal similarities with angiosperm embryogenesis. Plant Mol. Biol. 62:485-501. Calderón Villalobos LI, Lee S, De Oliveira C, Ivetac A, Brandt W, Armitage L, Sheard LB, Tan X, Parry G, Mao H et al. . 2012. A combinatorial TIR1/AFB-Aux/IAA co-receptor system for differential sensing of auxin. Nat. Chem. Biol. 8:477-485. Calow P. 1998. The encyclopedia of ecology and environmental management. New York: Blackwell Science. Cannell MGR, Smith RI. 1986. Climatic Warming, spring budburst and frost damage on trees. J. Appl. Ecol. 23:177-191. Cao D, Wang J, Ju Z, Liu Q, Li S, Tian H, Fu D, Zhu H, Luo Y, Zhu B. 2016. Regulations on growth and development in tomato cotyledon, flower and fruit via destruction of miR396 with short tandem target mimic Plant Sci. 247:1-12.  175  Cao S, Zhu QH, Shen W, Jiao X, Zhao X, Wang MB, Liu L, Singh SP, Liu Q. 2013. Comparative profiling of miRNA expression in developing seeds of high linoleic and high oleic safflower (Carthamus tinctorius L.) plants. Front. Plant Sci. 4:489. Caron GE, Wang BSP, Schooley HO. 1993. Variation in Picea glauca seed germination associated with the year of cone collection. Can. J. Forest Res. 23:1306-1313. Carr DB, Littlefield RJ, Nicholson WL, Littlefield JS. 1987. Scatterplot matrix techniques for large N. J. Am. Stat. Assoc. 82:424-436. Carrascal LM, Galvan I, Gordo O. 2009. Partial least squares regression as an alternative to current regression methods used in ecology. Oikos 118:681-690. Carta A, Probert R, Moretti M, Peruzzi L, Bedini G. 2014. Seed dormancy and germination in three Crocus ser. Verni species (Iridaceae): Implications for evolution of dormancy within the genus. Plant Biol. 16:1065-1074. Caruso CM, Maherali H, Sherrard M. 2006. Plasticity of physiology in Lobelia: Testing for adaptation and constraint. Evolution 60:980-990. Castro J, Zamora R, Hódar JA, Gómez JM. 2004. Seedling establishment of a boreal tree species (Pinus sylvestris) at its southernmost distribution limit: Consequences of being in a marginal Mediterranean habitat. J. Ecol. 92:266-277. Chapman EJ, Estelle M. 2009. Mechanism of auxin-regulated gene expression in plants. Annu. Rev. Genet. 43:265-285. Chen H, Boutros PC. 2011. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12:35. Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD. 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333:1024-1026. Chen K, Rajewsky N. 2007. The evolution of gene regulation by transcription factors and microRNAs. Nat. Rev. Genet. 8:93-103. Chen M, MacGregor DR, Dave A, Florance H, Moore K, Paszkiewicz K, Smirnoff N, Graham IA, Penfield S. 2014. Maternal temperature history activates Flowering Locus T in fruits to control progeny dormancy according to time of year. Proc. Natl. Acad. Sci. USA 111:18787-18792. Chen XM. 2004. A microRNA as a translational repressor of APETALA2 in Arabidopsis flower development. Science 303:2022-2025. Chevin LM, Lande R, Mace GM. 2010. Adaptation, plasticity, and extinction in a changing environment: Towards a predictive theory. PLoS Biol. 8:e1000357.  176  Chiang GC, Barua D, Dittmar E, Kramer EM, de Casas RR, Donohue K. 2013. Pleiotropy in the wild: The dormancy gene DOG1 exerts cascading control on life cycles. Evolution 67:883-893. Chiang GCK, Bartsch M, Barua D, Nakabayashi K, Debieu M, Kronholm I, Koornneef M, Soppe WJJ, Donohue K, de Meaux J. 2011. DOG1 expression is predicted by the seed-maturation environment and contributes to geographical variation in germination in Arabidopsis thaliana. Mol. Ecol. 20:3336-3349. Childs DZ, Metcalf CJE, Rees M. 2010. Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. P. Roy. Soc. B-Biol. Sci. 277:3055-3064. Chinnusamy V, Gong ZZ, Zhu JK. 2008. Abscisic acid-mediated epigenetic processes in plant development and stress responses. J. Integr. Plant Biol. 50:1187-1195. Chiwocha SDS, Abrams SR, Ambrose SJ, Cutler AJ, Loewen M, Ross ARS, Kermode AR. 2003. A method for profiling classes of plant hormones and their metabolites using liquid chromatography-electrospray ionization tandem mass spectrometry: An analysis of hormone regulation of thermodormancy of lettuce (Lactuca sativa L.) seeds. Plant J. 35:405-417. Chiwocha SDS, Cutler AJ, Abrams SR, Ambrose SJ, Yang J, Ross ARS, Kermode AR. 2005. The etr1-2 mutation in Arabidopsis thaliana affects the abscisic acid, auxin, cytokinin and gibberellin metabolic pathways during maintenance of seed dormancy, moist-chilling and germination. Plant J. 42:35-48. Christiansen FB. 1991. On conditions for evolutionary stability for a continuously varying character. Am. Nat. 138:37-50. Chu A, Robertson G, Brooks D, Mungall AJ, Birol I, Coope R, Ma Y, Jones S, Marra MA. 2015. Large-scale profiling of microRNAs for The Cancer Genome Atlas. Nucleic Acids Res. 44:e3. Chuck G, Cigan AM, Saeteurn K, Hake S. 2007a. The heterochronic maize mutant Corngrass1 results from overexpression of a tandem microRNA. Nat. Genet. 39:544-549. Chuck G, Meeley R, Irish E, Sakai H, Hake S. 2007b. The maize tasselseed4 microRNA controls sex determination and meristem cell fate by targeting Tasselseed6/indeterminate spikelet1. Nat. Genet. 39:1517-1521. Chung PJ, Park BS, Wang H, Liu J, Jang IC, Chua NH. 2016. Light-inducible MiR163 targets PXMT1 transcripts to promote seed germination and primary root elongation in Arabidopsis. Plant Physiol. 170:1772-1782. Clark LV, Brummer JE, Glowacka K, Hall MC, Heo K, Peng J, Yamada T, Yoo JH, Yu CY, Zhao H et al. . 2014. A footprint of past climate change on the diversity and population structure of Miscanthus sinensis. Ann. Bot. 114:97-107.  177  Clausen J, Hiesey WM. 1960. The balance between coherence and variation in evolution. Proc. Natl. Acad. Sci. USA 46:494-506. Cleland EE, Chuine I, Menzel A, Mooney HA, Schwartz MD. 2007. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22:357-365. Cochrane A, Holye G, Yates C, Wood J, Nicotra A. 2015. Climate warming delays and decreases seedling emergence in a Mediterranean ecosystem. Oikos 124:150-160. Cohen D. 1966. Optimizing reproduction in a randomly varying environment. J. Theor. Biol. 12:119-129. Cohen D, Levin SA. 1987. The interaction between dispersal and dormancy strategies in varying and heterogeneous environments. In: Teramoto E, Yamaguti M, editors. Mathematical Topics in Population Biology, Morphogenesis and Neurosciences. Heidelberg: Springer-Verlag. p. 110-122. Corbineau F, Bianco J, Garello G, Come D. 2002. Breakage of Pseudotsuga menziesii seed dormancy by cold treatment as related to changes in seed ABA sensitivity and ABA levels. Physiol. Plant. 114:313-319. Cosgrove DJ, Li LC, Cho HT, Hoffmann-Benning S, Moore RC, Blecker D. 2002. The growing world of expansins. Plant Cell Physiol. 43:1436-1444. Crepet WL. 2000. Progress in understanding angiosperm history, success, and relationships: Darwin's abominably "perplexing phenomenon". Proc. Natl. Acad. Sci. USA 97:12939-12941. Crossa J, Vargas M, Cossani CM, Alvarado G, Burgueño J, Mathews KL, Reynolds MP. 2013. Evaluation and interpretation of interactions. Agron. J. 107:736-747. Cuperus JT, Fahlgren N, Carrington JC. 2011. Evolution and functional diversification of MIRNA genes. Plant Cell 23:431-442. Curaba J, Talbot M, Li Z, Helliwell C. 2013. Over-expression of microRNA171 affects phase transitions and floral meristem determinancy in barley. BMC Plant Biol. 13:6. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. 2005. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 139:5-17. Dai X, Zhao PX. 2011. psRNATarget: A plant small RNA target analysis server. Nucleic Acids Res. 39:W155-W159. Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Hanson PJ, Irland LC, Lugo AE, Peterson CJ et al. . 2001. Climate change and forest disturbances. Bioscience 51:723-734.  178  Daszkowska-Golec A, Wojnar W, Rosikiewicz M, Szarejko I, Maluszynski M, Szweykowska-Kulinska Z, Jarmolowski A. 2013. Arabidopsis suppressor mutant of abh1 shows a new face of the already known players: ABH1 (CBP80) and ABI4-in response to ABA and abiotic stresses during seed germination. Plant Mol. Biol. 81:189-209. Davis MB, Shaw RG. 2001. Range shifts and adaptive responses to Quaternary climate change. Science 292:673-679. Davis MB, Shaw RG, Etterson JR. 2005. Evolutionary responses to changing climate. Ecology 86:1704-1714. de Jong G. 2005. Evolution of phenotypic plasticity: Patterns of plasticity and the emergence of ecotypes. New Phytol. 166:101-117. de Vega-Bartol JJ, Simões M, Lorenz WW, Rodrigues AS, Alba R, Dean JF, Miguel CM. 2013. Transcriptomic analysis highlights epigenetic and transcriptional regulation during zygotic embryo development of Pinus pinaster. BMC Plant Biol. 13:123. Debat V, David P. 2001. Mapping phenotypes: Canalization, plasticity and developmental stability. Trends Ecol. Evol. 16:555-561. Debieu M, Tang C, Stich B, Sikosek T, Effgen S, Josephs E, Schmitt J, Nordborg M, Koornneef M, de Meaux J. 2013. Co-variation between seed dormancy, growth rate and flowering time changes with latitude in Arabidopsis thaliana. PloS One 8:e61075. Dempster ER. 1955. Maintenance of genetic heterogeneity. Cold Spring Harbor Symp. Quant. Biol. 20:25-32. Dieckmann U, Doebeli M. 1999. On the origin of species by sympatric speciation. Nature 400:354-357. Dieckmann U, Law R. 1996. The dynamical theory of coevolution: A derivation from stochastic ecological processes. J. Math. Biol. 34:579-612. Ding Q, Zeng J, He XQ. 2014a. Deep sequencing on a genome-wide scale reveals diverse stage-specific microRNAs in cambium during dormancy-release induced by chilling in poplar. BMC Plant Biol. 14:267. Ding YF, Ye YY, Jiang ZH, Wang Y, Zhu C. 2016. MicroRNA390 is involved in cadmium tolerance and accumulation in rice. Front. Plant Sci. 7:235. Ding ZJ, Yan JY, Li GX, Wu ZC, Zhang SQ, Zheng SJ. 2014b. WRKY41 controls Arabidopsis seed dormancy via direct regulation of ABI3 transcript levels not downstream of ABA. Plant J. 79:810-823.  179  Dong JZ, Dunstan DI. 1996. Expression of abundant mRNAs during somatic embryogenesis of white spruce [Picea glauca (Moench) Voss]. Planta 199:459-466. Donohue K. 2009. Completing the cycle: maternal effects as the missing link in plant life histories. Philos. T. R. Soc. B. 364:1059-1074. Donohue K. 2014. The epigenetics of adaptation: Focusing on epigenetic stability as an evolving trait. Evolution 68:617-619. Donohue K, de Casas RR, Burghardt L, Kovach K, Willis CG. 2010. Germination, postgermination adaptation, and species ecological ranges. Annu. Rev. Ecol. Evol. Syst. 41:293-319. Du J, Miura E, Robischon M, Martinez C, Groover A. 2011. The Populus Class III HD ZIP transcription factor POPCORONA affects cell differentiation during secondary growth of woody stems. PloS One 6:e17458. Duffy PB, Arritt RW, Coquard J, Gutowski W, Han J, Iorio J, Kim J, Leung LR, Roads J, Zeledon E. 2006. Simulations of present and future climates in the western United States with four nested regional climate models. J. Clim. 19:873-895. Dugas DV, Bartel B. 2004. MicroRNA regulation of gene expression in plants. Curr. Opin. Plant Biol. 7:512-520. El-Kassaby YA, Davidson R. 1991. Impact of pollination environment manipulation on the apparent outcrossing rate in a Douglas-fir seed orchard. Heredity 66:55-59. El-Kassaby YA, Moss I, Kolotelo D, Stoehr M. 2008. Seed germination: Mathematical representation and parameters extraction. For. Sci. 54:220-227. El-Showk S, Ruonala R, Helariutta Y. 2013. Crossing paths: Cytokinin signalling and crosstalk. Development 140:1373-1383. Ellner S. 1985a. ESS germination strategies in randomly varying environments. I. Logistic-type models. Theor. Popul. Biol. 28:50-79. Ellner S. 1985b. ESS germination strategies in randomly varying environments. II. Reciprocal yield-law models. Theor. Popul. Biol. 28:80-116. Ellner S. 1986. Germination dimorphisms and parent offspring conflict in seed germination. J. Theor. Biol. 123:173-185. Ellner S. 1987. Competition and dormancy: A reanalysis and review. Am. Nat. 130:798-803. Eriksson O, Friis EM, Löfgren P. 2000. Seed size, fruit size, and dispersal systems in angiosperms from the Early Cretaceous to the Late Tertiary. Am. Nat. 156:47-58.  180  Eshel I. 1983. Evolutionary and continuous stability. J. Theor. Biol. 103:99-111. Etterson JR, Shaw RG. 2001. Constraint to adaptive evolution in response to global warming. Science 294:151-154. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL et al. . 2007. High-throughput sequencing of Arabidopsis microRNAs: Evidence for frequent birth and death of MIRNA genes. PloS One 2:e219. Farjon A, Page C. 1999. Conifers: Status survey and conservation action plan. IUCN/SSC Conifer Specialist Group. IUCN, Gland, Switzerland and Cambridge, UK. p. 121 Fashler AMK, El-Kassaby YA. 1987. Effect of water spray cooling treatment on reproductive phenology in a Douglas-fir seed orchard. Silvae Genet. 36:245-249. Fattash I, Voss B, Reski R, Hess WR, Frank W. 2007. Evidence for the rapid expansion of microRNA-mediated regulation in early land plant evolution. BMC Plant Biol. 7:13. Feng H, Zhang Q, Wang Q, Wang X, Liu J, Li M, Huang L, Kang Z. 2013. Target of tae-miR408, a chemocyanin-like protein gene (TaCLP1), plays positive roles in wheat response to high-salinity, heavy cupric stress and stripe rust. Plant Mol. Biol. 83:433-443. Fenner M, Thompson K. 2005. The ecology of seeds. Cambridge, UK: Cambridge University Press. Fernández-Pascual E, Jiménez-Alfaro B, Caujapé-Castells J, Jaén-Molina R, Díaz TE. 2013. A local dormancy cline is related to the seed maturation environment, population genetic composition and climate. Ann. Bot. 112:937-945. Feurtado JA, Ambrose SJ, Cutler AJ, Ross ARS, Abrams SR, Kermode AR. 2004. Dormancy termination of western white pine (Pinus monticola Dougl. Ex D. Don) seeds is associated with changes in abscisic acid metabolism. Planta 218:630-639. Feurtado JA, Yang J, Ambrose SJ, Cutler AJ, Abrams SR, Kermode AR. 2007. Disrupting abscisic acid homeostasis in western white pine (Pinus monticola Dougl. Ex D. Don) seeds induces dormancy termination and changes in abscisic acid catabolites. J. Plant Growth Regul. 26:46-54. Fierst JL. 2011. A history of phenotypic plasticity accelerates adaptation to a new environment. J. Evol. Biol. 24:1992-2001. Finch-Savage WE, Cadman CSC, Toorop PE, Lynn JR, Hilhorst HWM. 2007. Seed dormancy release in Arabidopsis Cvi by dry after-ripening, low temperature, nitrate and light shows common quantitative patterns of gene expression directed by environmentally specific sensing. Plant J. 51:60-78.  181  Finch-Savage WE, Leubner-Metzger G. 2006. Seed dormancy and the control of germination. New Phytol. 171:501-523. Finet C, Berne-Dedieu A, Scutt CP, Marlétaz F. 2013. Evolution of the ARF gene family in land plants: Old domains, new tricks. Mol. Biol. Evol. 30:45-56. Finkelstein R. 1994. Mutations at two new Arabidopsis ABA response loci are similar to the abi3 mutations. Plant J. 5:765-771. Finkelstein R, Reeves W, Ariizumi T, Steber C. 2008. Molecular aspects of seed dormancy. Annu. Rev. Plant Biol. 59:387-415. Finkelstein RR, Lynch TJ. 2000. The Arabidopsis abscisic acid response gene ABI5 encodes a basic leucine zipper transcription factor. Plant Cell 12:599-609. Finkelstein RR, Wang ML, Lynch TJ, Rao S, Goodman HM. 1998. The Arabidopsis abscisic acid response locus ABI4 encodes an APETALA 2 domain protein. Plant Cell 10:1043-1054. Fischerova L, Fischer L, Vondráková Z, Vágner M. 2008. Expression of the gene encoding transcription factor PaVP1 differs in Picea abies embryogenic lines depending on their ability to develop somatic embryos. Plant Cell Rep. 27:435-441. Flematti GR, Ghisalberti EL, Dixon KW, Trengove RD. 2004. A compound from smoke that promotes seed germination. Science 305:977. Flematti GR, Merritt DJ, Piggott MJ, Trengove RD, Smith SM, Dixon KW, Ghisalberti EL. 2011. Burning vegetation produces cyanohydrins that liberate cyanide and stimulate seed germination. Nature Commun. 2:360. Footitt S, Clay HA, Dent K, Finch-Savage WE. 2014. Environment sensing in spring-dispersed seeds of a winter annual Arabidopsis influences the regulation of dormancy to align germination potential with seasonal changes. New Phytol. 202:929-939. Footitt S, Douterelo-Soler I, Clay H, Finch-Savage WE. 2011. Dormancy cycling in Arabidopsis seeds is controlled by seasonally distinct hormone-signaling pathways. Proc. Natl. Acad. Sci. USA 108:20236-20241. Footitt S, Müller K, Kermode AR, Finch-Savage WE. 2015. Seed dormancy cycling in Arabidopsis: Chromatin remodelling and regulation of DOG1 in response to seasonal environmental signals. Plant J. 81:413-425. Forbis TA, Floyd SK, de Queiroz A. 2002. The evolution of embryo size in angiosperms and other seed plants: Implications for the evolution of seed dormancy. Evolution 56:2112-2125.  182  Forrest J, Miller-Rushing AJ. 2010. Toward a synthetic understanding of the role of phenology in ecology and evolution. Philos. T. R. Soc. B. 365:3101-3112. Fowells HA. 1965. Silvics of forest trees of the United States. Washington D.C.: Agric. Handb. US Dep. Agric. Franks SJ, Avise JC, Bradshaw WE, Conner JK, Etterson JR, Mazer SJ, Shaw RG, Weis AE. 2008. The resurrection initiative: Storing ancestral genotypes to capture evolution in action. Bioscience 58:870-873. Franks SJ, Weber JJ, Aitken SN. 2014. Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol. Appl. 7:123-139. Freas KM, Kemp PR. 1983. Some relationships between environmental reliability and seed dormancy in desert annual plants. J. Ecol. 71:211-217. Frey A, Effroy D, Lefebvre V, Seo M, Perreau F, Berger A, Sechet J, To A, North HM, Marion-Poll A. 2012. Epoxycarotenoid cleavage by NCED5 fine-tunes ABA accumulation and affects seed dormancy and drought tolerance with other NCED family members. Plant J. 70:501-512. Friedmann M, Ralph SG, Aeschliman D, Zhuang J, Ritland K, Ellis BE, Bohlmann J, Douglas CJ. 2007. Microarray gene expression profiling of developmental transitions in Sitka spruce (Picea sitchensis) apical shoots. J. Exp. Bot. 58:593-614. Friis EM, Crane PR, Pedersen KR, Stampanoni M, Marone F. 2015. Exceptional preservation of tiny embryos documents seed dormancy in early angiosperms. Nature 528:551-554. Friml J, Vieten A, Sauer M, Weijers D, Schwarz H, Hamann T, Offringa R, Jürgens G. 2003. Efflux-dependent auxin gradients establish the apical-basal axis of Arabidopsis. Nature 426:147-153. Galloway LF, Etterson JR. 2007. Transgenerational plasticity is adaptive in the wild. Science 318:1134-1136. Gao S, Guo C, Zhang Y, Zhang F, Du X, Gu J, Xiao K. 2016. Wheat microRNA member TaMIR444a is nitrogen deprivation-responsive and involves plant adaptation to the nitrogen-starvation stress. Plant Mol. Biol. Rep. 34:931. Garcia D, Fitz Gerald JN, Berger F. 2005. Maternal control of integument cell elongation and zygotic control of endosperm growth are coordinated to determine seed size in Arabidopsis. Plant Cell 17:52-60. Gauch HG. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Amsterdam: Elsevier.  183  Gehring M, Bubb KL, Henikoff S. 2009. Extensive demethylation of repetitive elements during seed development underlies gene imprinting. Science 324:1447-1451. Geritz SA, van der Meijden E, Metz JA. 1999. Evolutionary dynamics of seed size and seedling competitive ability. Theor. Popul. Biol. 55:324-343. Geritz SAH. 1995. Evolutionarily stable seed polymorphism and small-scale spatial variation in seedling density. Am. Nat. 146:685-707. Geritz SAH, Kisdi É, Meszéna G, Metz JAJ. 1998. Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol. Ecol. 12:35-57. Germain H, Lachance D, Pelletier G, Fossdal CG, Solheim H, Seguin A. 2012. The expression pattern of the Picea glauca Defensin 1 promoter is maintained in Arabidopsis thaliana, indicating the conservation of signalling pathways between angiosperms and gymnosperms. J. Exp. Bot. 63:785-795. Ghersa CM, Arnold RLB, Martinezghersa MA. 1992. The role of fluctuating temperatures in germination and establishment of Sorghum halepense - Regulation of germination at increasing depths. Funct. Ecol. 6:460-468. Gillespie J. 1977. Natural selection for variances in offspring numbers: A new evolutionary priciple. Am. Nat. 111:1010-1014. Giraudat J, Hauge BM, Valon C, Smalle J, Parcy F, Goodman HM. 1992. Isolation of the Arabidopsis ABI3 gene by positional cloning. Plant Cell 4:1251-1261. Goldfarb B, Lanz-Garcia C, Lian ZG, Whetten R. 2003. Aux/IAA gene family is conserved in the gymnosperm, loblolly pine (Pinus taeda). Tree Physiol. 23:1181-1192. Gomez-Mestre I, Jovani R. 2013. A heuristic model on the role of plasticity in adaptive evolution: plasticity increases adaptation, population viability and genetic variation. P. Roy. Soc. B-Biol. Sci. 280: 20131869. Graeber K, Linkies A, Steinbrecher T, Mummenhoff K, Tarkowská D, Turečková V, Ignatz M, Sperber K, Voegele A, de Jong H et al. . 2014. DELAY OF GERMINATION 1 mediates a conserved coat-dormancy mechanism for the temperature- and gibberellin-dependent control of seed germination. Proc. Natl. Acad. Sci. USA 111:E3571-E3580. Graeber K, Nakabayashi K, Miatton E, Leubner-Metzger G, Soppe WJ. 2012. Molecular mechanisms of seed dormancy. Plant Cell Environ. 35:1769-1786. Granhus A, Fløistad IS, Søgaard G. 2009. Bud burst timing in Picea abies seedlings as affected by temperature during dormancy induction and mild spells during chilling. Tree Physiol. 29:497-503.  184  Gremer JR, Venable DL. 2014. Bet hedging in desert winter annual plants: Optimal germination strategies in a variable environment. Ecol. Lett. 17:380-387. Grime J. 1989. Seed banks in ecological perspective. San Diego: Academic. p. 15-22. Grime JP, Mason G, Curtis AV, Rodman J, Band SR, Mowforth MAG, Neal AM, Shaw S. 1981. A Comparative study of germination characteristics in a local flora. J. Ecol. 69:1017-1059. Grubb PJ. 1977. The maintenance of species-richness in plant communities: The importance of the regeneration niche. Biol. Rev. 52:107-145. Gu XY, Turnipseed EB, Foley ME. 2008. The qSD12 locus controls offspring tissue-imposed seed dormancy in rice. Genetics 179:2263-2273. Gu Y, Liu Y, Zhang J, Liu H, Hu Y, Du H, Li Y, Chen J, Wei B, Huang Y. 2013. Identification and characterization of microRNAs in the developing maize endosperm. Genomics 102:472-478. Guo Y, Zheng Z, La Clair JJ, Chory J, Noel JP. 2013. Smoke-derived karrikin perception by the alpha/beta-hydrolase KAI2 from Arabidopsis. Proc. Natl. Acad. Sci. USA 110:8284-8289. Guy RD. 2014. The early bud gets to warm. New Phytol. 202:7-9. Hager A. 2003. Role of the plasma membrane H+-ATPase in auxin-induced elongation growth: Historical and new aspects. J. Plant Res. 116:483-505. Hairston NG, Ellner SP, Geber MA, Yoshida T, Fox JA. 2005. Rapid evolution and the convergence of ecological and evolutionary time. Ecol. Lett. 8:1114-1127. Hakman I, Hallberg H, Palovaara J. 2009. The polar auxin transport inhibitor NPA impairs embryo morphology and increases the expression of an auxin efflux facilitator protein PIN during Picea abies somatic embryo development. Tree Physiol. 29:483-496. Hamilton WD. 1966. The moulding of senescence by natural selection. J. Theor. Biol. 12:12-45. Hänninen H, Häkkinen R, Hari P, Koski V. 1990. Timing of growth cessation in relation to climatic adaptation of northern woody plants. Tree Physiol. 6:29-39. Harper JL, Lovell PH, Moore KG. 1970. The shapes and sizes of seeds. Annu. Rev. Ecol. Syst. 1:327–356. Haselhorst MSH, Edwards CE, Rubin MJ, Weinig C. 2011. Genetic architecture of life history traits and environment-specific trade-offs. Mol. Ecol. 20:4042-4058. Hauser F, Waadt R, Schroeder JI. 2011. Evolution of abscisic acid synthesis and signaling mechanisms. Curr. Biol. 21:R346-R355.  185  Hedhly A, Hormaza JI, Herrero M. 2009. Global warming and sexual plant reproduction. Trends Plant Sci. 14:30-36. Herman JJ, Spencer HG, Donohue K, Sultan SE. 2014. How stable 'should' epigenetic modifications be? Insights from adaptive plasticity and bet hedging. Evolution 68:632-643. Hermisson J, Wagner GP. 2004. The population genetic theory of hidden variation and genetic robustness. Genetics 168:2271-2284. Heschel MS, Selby J, Butler C, Whitelam GC, Sharrock RA, Donohue K. 2007. A new role for phytochromes in temperature-dependent germination. New Phytol. 174:735-741. Hodgins KA, Yeaman S, Nurkowski KA, Rieseberg LH, Aitken SN. 2016. Expression divergence is correlated with sequence evolution but not positive selection in conifers. Mol. Biol. Evol. 33:1502-1516. Holdsworth MJ, Bentsink L, Soppe WJJ. 2008. Molecular networks regulating Arabidopsis seed maturation, after-ripening, dormancy and germination. New Phytol. 179:33-54. Holt DB, Gupta V, Meyer D, Abel NB, Andersen SU, Stougaard J, Markmann K. 2015. micro RNA 172 (miR172) signals epidermal infection and is expressed in cells primed for bacterial invasion in Lotus japonicus roots and nodules. New Phytol. 208:241-256. Horvath DP, Sung S, Kim D, Chao W, Anderson J. 2010. Characterization, expression and function of DORMANCY ASSOCIATED MADS-BOX genes from leafy spurge. Plant Mol. Biol. 73:169-179. Hovenden MJ, Wills KE, Chaplin RE, Vander Schoor JK, Williams AL, Osanai YUI, Newton PCD. 2008. Warming and elevated CO2 affect the relationship between seed mass, germinability and seedling growth in Austrodanthonia caespitosa, a dominant Australian grass. Glob. Change Biol. 14:1633-1641. Hoyle GL, Steadman KJ, Daws MI, Adkins SW. 2008. Pre- and post-harvest influences on seed dormancy status of an Australian Goodeniaceae species, Goodenia fascicularis. Ann. Bot. 102:93-101. Hoyle GL, Venn SE, Steadman KJ, Good RB, McAuliffe EJ, Williams ER, Nicotra AB. 2013. Soil warming increases plant species richness but decreases germination from the alpine soil seed bank. Glob. Change Biol. 19:1549-1561. Hu J, Mitchum MG, Barnaby N, Ayele BT, Ogawa M, Nam E, Lai WC, Hanada A, Alonso JM, Ecker JR et al. . 2008. Potential sites of bioactive gibberellin production during reproductive growth in Arabidopsis. Plant Cell 20:320-336. Hu Y, Yu D. 2014. BRASSINOSTEROID INSENSITIVE2 interacts with ABSCISIC ACID INSENSITIVE5 to mediate the antagonism of brassinosteroids to abscisic acid during seed germination in Arabidopsis. Plant Cell 26:4394-4408.  186  Huang DQ, Koh C, Feurtado JA, Tsang EWT, Cutler AJ. 2013. MicroRNAs and their putative targets in Brassica napus seed maturation. BMC Genomics 14:140. Huang M, Hu Y, Liu X, Li Y, Hou X. 2015. Arabidopsis LEAFY COTYLEDON1 mediates postembryonic development via interacting with PHYTOCHROME-INTERACTING FACTOR4. Plant Cell 27:3099-3111. Huang XQ, Schmitt J, Dorn L, Griffith C, Effgen S, Takao S, Koornneef M, Donohue K. 2010. The earliest stages of adaptation in an experimental plant population: Strong selection on QTLs for seed dormancy. Mol. Ecol. 19:1335-1351. Huo H, Wei S, Bradford KJ. 2016. DELAY OF GERMINATION1 (DOG1) regulates both seed dormancy and flowering time through microRNA pathways. Proc. Natl. Acad. Sci. USA 113:E2199-E2206. IPCC. 2007. Climate change 2007: The physical science basis. Cambridge, UK. Ishitani M, Xiong L, Stevenson B, Zhu JK. 1997. Genetic analysis of osmotic and cold stress signal transduction in Arabidopsis: Interactions and convergence of abscisic acid-dependent and abscisic acid-independent pathways. Plant Cell 9:1935-1949. ISTA. 1999. International rules for seed testing. Seed Sci. Technol. 27:50-52. Jablonka E, Lamb MJ. 1998. Epigenetic inheritance in evolution. J. Evol. Biol. 11:159-183. Jacobsen SE, Olszewski NE. 1993. Mutations at the spindly locus of Arabidopsis alter gibberellin signal-transduction. Plant Cell 5:887-896. Jakobsson A, Eriksson O. 2000. A comparative study of seed number, seed size, seedling size and recruitment in grassland plants. Oikos 88:494–502. Jaramillo-Correa JP, Beaulieu J, Bousquet J. 2004. Variation in mitochondrial DNA reveals multiple distant glacial refugia in black spruce (Picea mariana), a transcontinental North American conifer. Mol. Ecol. 13:2735-2747. Jeong DH, Green PJ. 2013. The role of rice microRNAs in abiotic stress responses. J. Plant Biol. 56:187-197. Jeyaraj A, Chandran V, Gajjeraman P. 2014. Differential expression of microRNAs in dormant bud of tea [Camellia sinensis (L.) O. Kuntze]. Plant Cell Rep. 33:1053-1069. Johnsen Ø, Fossdal CG, Nagy N, Molmann J, Daehlen OG, Skrøppa T. 2005. Climatic adaptation in Picea abies progenies is affected by the temperature during zygotic embryogenesis and seed maturation. Plant, Cell Environ. 28:1090-1102.  187  Johnsen Ø, Skrøppa T. 1996. Adaptive properties of Picea abies progenies are influenced by environmental signals during sexual reproduction. Euphytica 92:67-71. Jones-Rhoades MW, Bartel DP. 2004. Computational identification of plant MicroRNAs and their targets, including a stress-induced miRNA. Mol. Cell 14:787-799. Jump AS, Hunt JM, Martinez-Izquierdo JA, Peñuelas J. 2006. Natural selection and climate change: Temperature-linked spatial and temporal trends in gene frequency in Fagus sylvatica. Mol. Ecol. 15:3469-3480. Jump AS, Marchant R, Peñuelas J. 2009. Environmental change and the option value of genetic diversity. Trends Plant Sci. 14:51-58. Jump AS, Peñuelas J. 2005. Running to stand still: Adaptation and the response of plants to rapid climate change. Ecol. Lett. 8:1010-1020. Jung JH, Seo YH, Seo PJ, Reyes JL, Yun J, Chua NH, Park CM. 2007. The GIGANTEA-regulated microRNA172 mediates photoperiodic flowering independent of CONSTANS in Arabidopsis. Plant Cell 19:2736-2748. Kalcsits L, Silim S, Tanino K. 2009a. The influence of temperature on dormancy induction and plant survival in woody plants. London: CABI International. Kalcsits LA, Silim S, Tanino K. 2009b. Warm temperature accelerates short photoperiod-induced growth cessation and dormancy induction in hybrid poplar (Populus x spp.). Trees-Struct. Funct. 23:971-979. Källman T, Chen J, Gyllenstrand N, Lagercrantz U. 2013. A significant fraction of 21-nucleotide small RNA originates from phased degradation of resistance genes in several perennial species. Plant Physiol. 162:741-754. Kanehisa M, Goto S. 2000. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27-30. Kang M, Zhao Q, Zhu D, Yu J. 2012. Characterization of microRNAs expression during maize seed development. BMC Genomics 13:360. Kang MS. 2003. Handbook of formulas and software for plant geneticists and breeders. Binghamton, NY: Food Products Press. Keeling CI, Dullat HK, Yuen M, Ralph SG, Jancsik S, Bohlmann J. 2010. Identification and functional characterization of monofunctional ent-copalyl diphosphate and ent-kaurene synthases in white spruce reveal different patterns for diterpene synthase evolution for primary and secondary metabolism in gymnosperms. Plant Physiol. 152:1197-1208.  188  Kelly D, Geldenhuis A, James A, Penelope Holland E, Plank MJ, Brockie RE, Cowan PE, Harper GA, Lee WG, Maitland MJ et al. . 2013. Of mast and mean: Differential-temperature cue makes mast seeding insensitive to climate change. Ecol. Lett. 16:90-98. Kendall S, Penfield S. 2012. Maternal and zygotic temperature signalling in the control of seed dormancy and germination. Seed Sci. Res. 22:S23-S29. Kendall SL, Hellwege A, Marriot P, Whalley C, Graham IA, Penfield S. 2011. Induction of dormancy in Arabidopsis summer annuals requires parallel regulation of DOG1 and hormone metabolism by low temperature and CBF Transcription Factors. Plant Cell 23:2568-2580. Kenrick P. 1999. Botany - The family tree flowers. Nature 402:358-359. Kim JY, Lee HJ, Jung HJ, Maruyama K, Suzuki N, Kang H. 2010. Overexpression of microRNA395c or 395e affects differently the seed germination of Arabidopsis thaliana under stress conditions. Planta 232:1447-1454. Kim W, Lee Y, Park J, Lee N, Choi G. 2013. HONSU, a protein phosphatase 2C, regulates seed dormancy by inhibiting ABA signaling in Arabidopsis. Plant Cell Physiol. 54:555-572. Kiviniemi K. 2001. Evolution of recruitment features in plants: A comparative study of species in the Rosaceae. Oikos 94:250-262. Klimaszewska K, Pelletier G, Overton C, Stewart D, Rutledge RG. 2010. Hormonally regulated overexpression of Arabidopsis WUS and conifer LEC1 (CHAP3A) in transgenic white spruce: implications for somatic embryo development and somatic seedling growth. Plant Cell Rep. 29:723-734. Ko JH, Prassinos C, Han KH. 2006. Developmental and seasonal expression of PtaHB1, a Populus gene encoding a class III HD-Zip protein, is closely associated with secondary growth and inversely correlated with the level of microRNA (miR166). New Phytol. 169:469-478. Koller D. 1962. Preconditioning of germination in lettuce at time of fruit ripening. Am. J. Bot. 49:841-844. Kolotelo D, Van Steenis E, Peterson M, Trotter D, and Dennis J. 2001. Seed handling guidebook. Victoria, BC: British Columbia, Tree Improvement Branch. Koornneef M, Alonso-Blanco C, Bentsink L, Blankestijn-de Vries H, Debeajon I, Hanhart CJ, Léonkloosterziel KM, Peeters T, Raz V. 2000. The genetics of seed dormancy in Arabidopsis thaliana. Wallingford, UK: CAB International. p. 365-373. Koornneef M, Jorna ML, Derswan DLCB, Karssen CM. 1982. The isolation of abscisic acid (ABA) deficient mutants by selection of induced revertants in non-germinating gibberellin sensitive lines of Arabidopsis thaliana (L.) Heynh. Theor. Appl. Genet. 61:385-393.  189  Koornneef M, Karssen CM. 1994. Seed dormancy and germination. New York: Cold Spring Harbor Laboratory Press. Koornneef M, Reuling G, Karssen CM. 1984. The isolation and characterization of abscisic-acid Insensitive mutants of Arabidopsis thaliana. Physiol. Plant. 61:377-383. Körbes AP, Machado RD, Guzman F, Almerão MP, de Oliveira LFV, Loss-Morais G, Turchetto-Zolet AC, Cagliari A, Maraschin FD, Margis-Pinheiro M et al. . 2012. Identifying conserved and novel microRNAs in developing seeds of Brassica napus using deep sequencing. PloS One 7:e50663. Kovach-Orr C, Fussmann GF. 2013. Evolutionary and plastic rescue in multitrophic model communities. Philos. T. R. Soc. B. 368:20120084. Krämer U. 2015. Planting molecular functions in an ecological context with Arabidopsis thaliana. eLIFE 4:e06100. Krebs CJ, LaMontagne JM, Kenney AJ, Boutin S. 2012. Climatic determinants of white spruce cone crops in the boreal forest of southwestern Yukon. Botany 90:113-119. Kremer A, Ronce O, Robledo-Arnuncio JJ, Guillaume F, Bohrer G, Nathan R, Bridle JR, Gomulkiewicz R, Klein EK, Ritland K et al. . 2012. Long-distance gene flow and adaptation of forest trees to rapid climate change. Ecol. Lett. 15:378-392. Kucera B, Cohn MA, Leubner-Metzger G. 2005. Plant hormone interactions during seed dormancy release and germination. Seed Sci. Res. 15:281-307. Kushiro T, Okamoto M, Nakabayashi K, Yamagishi K, Kitamura S, Asami T, Hirai N, Koshiba T, Kamiya Y, Nambara E. 2004. The Arabidopsis cytochrome P450 CYP707A encodes ABA 8′ -hydroxylases: key enzymes in ABA catabolism. EMBO J. 23:1647-1656. Kuzoff RK, Gasser CS. 2000. Recent progress in reconstructing angiosperm phylogeny. Trends Plant Sci. 5:330-336. Kvaalen H, Johnsen Ø. 2008. Timing of bud set in Picea abies is regulated by a memory of temperature during zygotic and somatic embryogenesis. New Phytol. 177:49-59. Lacey EP. 1996. Parental effects in Plantago lanceolata L. I. A growth chamber experiment to examine pre- and postzygotic temperature effects. Evolution 50:865–878. Lande R, Shannon S. 1996. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 50:434–437. Larios E, Búrquez A, Becerra JX, Venable DL. 2014. Natural selection on seed size through the life cycle of a desert annual plant. Ecology 95:3213-3220.  190  Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R et al. . 2007. Clustal W and Clustal X version 2.0. Bioinformatics 23:2947-2948. Larsson E, Sundstrom JF, Sitbon F, von Arnold S. 2012. Expression of PaNAC01, a Picea abies CUP-SHAPED COTYLEDON orthologue, is regulated by polar auxin transport and associated with differentiation of the shoot apical meristem and formation of separated cotyledons. Ann. Bot. 110:923-934. Lauter N, Kampani A, Carlson S, Goebel M, Moose SP. 2005. microRNA172 down-regulates glossy15 to promote vegetative phase change in maize. Proc. Natl. Acad. Sci. USA 102:9412-9417. Le BH, Cheng C, Bui AQ, Wagmaister JA, Henry KF, Pelletier J, Kwong L, Belmonte M, Kirkbride R, Horvath S et al. . 2010. Global analysis of gene activity during Arabidopsis seed development and identification of seed-specific transcription factors. Proc. Natl. Acad. Sci. USA 107:8063-8070. Ledig FT, Jacob-Cervantes V, Hodgskiss PD, Eguiluz-Piedra T. 1997. Recent evolution and divergence among populations of a rare Mexican endemic, Chihuahua spruce, following Holocene climatic warming. Evolution 51:1815-1827. Lee HG, Lee K, Seo PJ. 2015a. The Arabidopsis MYB96 transcription factor plays a role in seed dormancy. Plant Mol. Biol. 87:371-381. Lee K, Lee HG, Yoon S, Kim HU, Seo PJ. 2015b. The Arabidopsis MYB96 transcription factor is a positive regulator of ABSCISIC ACID-INSENSITIVE4 in the control of seed germination. Plant Physiol. 168:677-689. Lee S, Cheng H, King KE, Wang W, He Y, Hussain A, Lo J, Harberd NP, Peng J. 2002. Gibberellin regulates Arabidopsis seed germination via RGL2, a GAI/RGA-like gene whose expression is up-regulated following imbibition. Genes Dev. 16:646-658. Lee SJ, Lee MH, Kim JI, Kim SY. 2015c. Arabidopsis putative MAP kinase kinase kinases Raf10 and Raf11 are positive regulators of seed dormancy and ABA response. Plant Cell Physiol. 56:84-97. Legendre P, Gallagher ED. 2001. Ecologically meaningful transformations for ordination of species data. Oecologia 129:271-280. Leishman MR, Wright IJ, Moles AT, Westoby M. 2000. The evolutionary ecology of seed size. New York: Oxford University Press. Leitch AR, Leitch IJ. 2012. Ecological and genetic factors linked to contrasting genome dynamics in seed plants. New Phytol. 194:629-646. Lenoir J, Gegout JC, Marquet PA, de Ruffray P, Brisse H. 2008. A significant upward shift in plant species optimum elevation during the 20th century. Science 320:1768-1771.  191  Leung J, Mertlot S, Giraudat J. 1997. The Arabidopsis ABSCISIC ACID-INSENSITIVE2 (ABI2) and ABI1 genes encode hormologous protein phosphatases 2C involved in abscisic acid signal transduction. Plant Cell 9:759-771. Leung LR, Qian Y, Bian XD, Washington WM, Han JG, Roads JO. 2004. Mid-century ensemble regional climate change scenarios for the western United States. Clim. Change 62:75-113. Leyser O. 2006. Dynamic integration of auxin transport and signalling. Curr. Biol. 16:R424-R433. Li AL, Mao L. 2007. Evolution of plant microRNA gene families. Cell Res. 17:212-218. Li JY, Reichel M, Millar AA. 2014. Determinants beyond both complementarity and cleavage govern microR159 efficacy in Arabidopsis. PLoS Genet. 10:e1004232. Li X, Bian H, Song D, Ma S, Han N, Wang J, Zhu M. 2013. Flowering time control in ornamental gloxinia (Sinningia speciosa) by manipulation of miR159 expression. Ann. Bot. 111:791-799. Li Z, Baniaga AE, Sessa EB, Scascitelli M, Graham SW, Rieseberg LH, Barker MS. 2015. Early genome duplications in conifers and other seed plants. Sci. Adv. 1:e1501084. Liang G, He H, Li Y, Wang F, Yu DQ. 2014. Molecular mechanism of microRNA396 mediating pistil development in Arabidopsis. Plant Physiol. 164:249-258. Lindow M, Krogh A. 2005. Computational evidence for hundreds of non-conserved plant microRNAs. BMC Genomics 6:119. Linkies A, Graeber K, Knight C, Leubner-Metzger G. 2010. The evolution of seeds. New Phytol. 186:817-831. Linkies A, Leubner-Metzger G. 2012. Beyond gibberellins and abscisic acid: How ethylene and jasmonates control seed germination. Plant Cell Rep. 31:253-270. Liu PP, Montgomery TA, Fahlgren N, Kasschau KD, Nonogaki H, Carrington JC. 2007. Repression of AUXIN RESPONSE FACTOR10 by microRNA160 is critical for seed germination and post-germination stages. Plant J. 52:133-146. Liu XD, Zhang H, Zhao Y, Feng ZY, Li Q, Yang HQ, Luan S, Li JM, He ZH. 2013a. Auxin controls seed dormancy through stimulation of abscisic acid signaling by inducing ARF-mediated ABI3 activation in Arabidopsis. Proc. Natl. Acad. Sci. USA 110:15485-15490. Liu Y, El-Kassaby Y. 2015. Timing of seed germination correlated with temperature-based environmental conditions during seed development in conifers. Seed Sci. Res. 25:29-45.  192  Liu Y, El-Kassaby YA. 2016. Regulatory cross-talk between microRNAs and hormone signalling cascades controls phenotypical variations: A case study in seed dormancy modulations during seed set of Arabidopsis thaliana. Planta. Liu Y, Fang J, Xu F, Chu J, Yan C, Schläppi MR, Wang Y, Chu C. 2014. Expression patterns of ABA and GA metabolism genes and hormone levels during rice seed development and imbibition: A comparison of dormant and non-dormant rice cultivars. J. Genet. Genomics 41:327-338. Liu Y, Kermode AR, El-Kassaby YA. 2013b. The role of moist-chilling and thermo-priming on the germination characteristics of white spruce (Picea glauca) seed. Seed Sci. Technol. 41:321-335. Liu Y, Müller K, El-Kassaby YA, Kermode AR. 2015. Changes in hormone flux and signaling in white spruce (Picea glauca) seeds during the transition from dormancy to germination in response to temperature cues. BMC Plant Biol. 15:292. Liu Y, Wang T, El-Kassaby YA. 2016. Contributions of dynamic environmental signals during life-cycle transitions to early life-history traits in lodgepole pine (Pinus contorta Dougl.). Biogeosciences 13:2945-2958. Ljung K, Hull AK, Kowalczyk M, Marchant A, Celenza J, Cohen JD, Sandberg G. 2002. Biosynthesis, conjugation, catabolism and homeostasis of indole-3-acetic acid in Arabidopsis thaliana. Plant Mol. Biol. 49:249-272. Llave C, Xie ZX, Kasschau KD, Carrington JC. 2002. Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis miRNA. Science 297:2053-2056. Lotan T, Ohto M, Yee K, West M, Lo R, Kwong R, Yamagishi K, Fischer R, Goldberg R, Harada J. 1998. Arabidopsis LEAFY COTYLEDON1 is sufficient to induce embryo development in vegetative cells. Cell 93:1195-1205. Lu JJ, Tan DY, Baskin CC, Baskin JM. 2016. Effects of germination season on life history traits and on transgenerational plasticity in seed dormancy in a cold desert annual. Sci. Rep. 6:25076. Ludwig-Müller J. 2011. Auxin conjugates: Their role for plant development and in the evolution of land plants. J. Exp. Bot. 62:1757-1773. Ma Y. 2009. Regulators of PP2C phosphatase activity function as abscisic acid sensors. Science 324:1264-1268. MacGregor DR, Kendall SL, Florance H, Fedi F, Moore K, Paszkiewicz K, Smirnoff N, Penfield S. 2015. Seed production temperature regulation of primary dormancy occurs through control of seed coat phenylpropanoid metabolism. New Phytol. 205:642-652. Magome H, Yamaguchi S, Hanada A, Kamiya Y, Oda K. 2008. The DDF1 transcriptional activator upregulates expression of a gibberellin-deactivating gene, GA2ox7, under high-salinity stress in Arabidopsis. Plant J. 56:613-626.  193  Mallory AC, Bartel DP, Bartel B. 2005. MicroRNA-directed regulation of Arabidopsis AUXIN RESPONSE FACTOR17 is essential for proper development and modulates expression of early auxin response genes. Plant Cell 17:1360-1375. Manly BFJ. 2005. Multivariate statistical methods: A primer (3rd ed.). Boca Raton, Florida: Chapman and Hall/CRC. Mapes G, Rothwell GW, Haworth MT. 1989. Evolution of seed dormancy. Nature 337:645-646. Marin E, Jouannet V, Herz A, Lokerse AS, Weijers D, Vaucheret H, Nussaume L, Crespi MD, Maizel A. 2010. miR390, Arabidopsis TAS3 tasiRNAs, and their AUXIN RESPONSE FACTOR targets define an autoregulatory network quantitatively regulating lateral root growth. Plant Cell 22:1104-1117. Martin RC, Asahina M, Liu PP, Kristof JR, Coppersmith JL, Pluskota WE, Bassel GW, Goloviznina NA, Nguyen TT, Martínez-Andújar C et al. . 2010. The microRNA156 and microRNA172 gene regulation cascades at post-germinative stages in Arabidopsis. Seed Sci. Res. 20:79-87. Martinez WL, Martinez AR, Solka JL. 2011. Exploratory data analysis with MATLAB (2nd ed.). Boca Raton, Florida: CRC Press. Matakiadis T, Alboresi A, Jikumaru Y, Tatematsu K, Pichon O, Renou JP, Kamiya Y, Nambara E, Truong HN. 2009. The Arabidopsis abscisic acid catabolic gene CYP707A2 plays a key role in nitrate control of seed dormancy. Plant Physiol. 149:949-960. Matesanz S, Gianoli E, Valladares F. 2010. Global change and the evolution of phenotypic plasticity in plants. Ann. N. Y. Acad. Sci. 1206:35-55. May P, Liao W, Wu Y, Shuai B, McCombie WR, Zhang MQ, Liu QA. 2013. The effects of carbon dioxide and temperature on microRNA expression in Arabidopsis development. Nature Commun. 4:2145. Maynard Smith J. 1982. Evolution and the theory of games. Cambridge: Cambridge University Press. McGinley MA, Charnov EL. 1988. Multiple resources and the optimal balance between size and number of offspring. Evol. Ecol. 2:77-84. Mcginley MA, Temme DH, Geber MA. 1987. Parental investment in offspring in variable environments - Theoretical and empirical considerations. Am. Nat. 130:370-398. Meinke DW, Franzmann LH, Nickle TC, Yeung EC. 1994. Leafy cotyledon mutants of Arabidopsis. Plant Cell 6:1049-1064. Metz JAJ, Nisbet RM, Geritz SAH. 1992. How should we define fitness for general ecological scenarios? Trends Ecol. Evol. 7:198-202.  194  Meyers LA, Bull JJ. 2002. Fighting change with change: Adaptive variation in an uncertain world. Trends Ecol. Evol. 17:551-557. Millar AA, Jacobsen JV, Ross JJ, A. HC, Poole AT, Scofield G, Reid JB, Gubler F. 2006. Seed dormancy and ABA metabolism in Arabidopsis and barley: The role of ABA 8'-hydroxylase. Plant J. 45:942-954. Mimura M, Aitken SN. 2010. Local adaptation at the range peripheries of Sitka spruce. J. Evol. Biol. 23:249-258. Moglich A, Yang X, Ayers RA, Moffat K. 2010. Structure and function of plant photoreceptors. Annu. Rev. Plant Biol. 61:21-47. Moles AT, Ackerly DD, Tweddle JC, Dickie JB, Smith R, Leishman MR, Mayfield MM, Pitman A, Wood JT, Westoby M. 2007. Global patterns in seed size. Global Ecol. Biogeogr. 16:109-116. Moles AT, Ackerly DD, Webb CO, Tweddle JC, Dickie JB, Pitman AJ, Westoby M. 2005. Factors that shape seed mass evolution. Proc. Natl. Acad. Sci. USA 102:10540-10544. Moles AT, Westoby M. 2004. Seedling survival and seed size: A synthesis of the literature. J. Ecol. 92:372-383. Montesinos-Navarro A, Picó FX, Tonsor SJ. 2012. Clinal variation in seed traits influencing life cycle timing in Arabidopsis Thaliana. Evolution 66:3417-3431. Morea EG, da Silva EM, GF ES, Valente GT, Barrera Rojas CH, Vincentz M, Nogueira FT. 2016. Functional and evolutionary analyses of the miR156 and miR529 families in land plants. BMC Plant Biol. 16:40. Morin X, Lechowicz MJ, Augspurger C, O' Keefe J, Viner D, Chuine I. 2009. Leaf phenology in 22 North American tree species during the 21st century. Glob. Change Biol. 15:961-975. Morley F. 1958. The inheritance and ecological significance of seed dormancy in subterranean clover (Trifolium subterraneum L.). Aust. J. Biol. Sci. 11:261-274. Mote P, Salathé E, Dulière V, Jump E. 2008. Scenarios of future climate for the Pacific Northwest. Seattle, WA: University of Washington. Muller-Landau HC. 2010. The tolerance-fecundity trade-off and the maintenance of diversity in seed size. Proc. Natl. Acad. Sci. USA 107:4242-4247. Müller K, Bouyer D, Schnittger A, Kermode AR. 2012. Evolutionarily conserved histone methylation dynamics during seed life-cycle transitions. PloS One 7:e51532.  195  Murray BR, Brown AHD, Dickman CR, Crowther MS. 2004. Geographical gradients in seed mass in relation to climate. J. Biogeogr. 31:379-388. Murray MB, Cannell MGR, Smith RI. 1989. Date of budburst of 15 tree species in Britain following climatic warming. J. Appl. Ecol. 26:693-700. Nag A, King S, Jack T. 2009. miR319a targeting of TCP4 is critical for petal growth and development in Arabidopsis. Proc. Natl. Acad. Sci. USA 106:22534-22539. Nakabayashi K, Bartsch M, Xiang Y, Miatton E, Pellengahr S, Yano R, Seo M, Soppe WJ. 2012. The time required for dormancy release in Arabidopsis is determined by DELAY OF GERMINATION1 protein levels in freshly harvested seeds. Plant Cell 24:2826-2838. Nambara E, Hayama R, Tsuchiya Y, Nishimura M, Kawaide H, Kamiya Y, Naito S. 2000. The role of ABI3 and FUS3 loci in Arabidopsis thaliana on phase transition from late embryo development to germination. Dev. Biol. 220:412-423. Nambara E, Okamoto M, Tatematsu K, Yano R, Seo M, Kamiya Y. 2010. Abscisic acid and the control of seed dormancy and germination. Seed Sci. Res. 20:55-67. Nathan R, Schurr FM, Spiegel O, Steinitz O, Trakhtenbrot A, Tsoar A. 2008. Mechanisms of long-distance seed dispersal. Trends Ecol. Evol. 23:638-647. Nelson DC, Flematti GR, Ghisalberti EL, Dixon KW, Smith SM. 2012. Regulation of seed germination and seedling growth by chemical signals from burning vegetation. Annu. Rev. Plant Biol. 63:107-130. Nguyen TP, Keizer P, van Eeuwijk F, Smeekens S, Bentsink L. 2012. Natural variation for seed longevity and seed dormancy are negatively correlated in Arabidopsis. Plant Physiol. 160:2083-2092. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F et al. . 2010. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15:684-692. Niu S, Yuan L, Zhang Y, Chen X, Li W. 2014. Isolation and expression profiles of gibberellin metabolism genes in developing male and female cones of Pinus tabuliformis. Funct. Integr. Genomics 14:697-705. Niyogi KK, Grossman AR, Bjorkman O. 1998. Arabidopsis mutants define a central role for the xanthophyll cycle in the regulation of photosynthetic energy conversion. Plant Cell 10:1121-1134. Nodine MD, Bartel DP. 2010. MicroRNAs prevent precocious gene expression and enable pattern formation during plant embryogenesis. Genes Dev. 24:2678-2692. Norberg J, Urban MC, Vellend M, Klausmeier CA, Loeuille N. 2012. Eco-evolutionary responses of biodiversity to climate change. Nat. Clim. Change 2:747-751.  196  Nozawa M, Miura S, Nei M. 2012. Origins and evolution of MicroRNA genes in plant species. Genome Biol. Evol. 4:230-239. Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YC, Scofield DG, Vezzi F, Delhomme N, Giacomello S, Alexeyenko A et al. . 2013. The Norway spruce genome sequence and conifer genome evolution. Nature 497:579-584. Ogawa M, Hanada A, Yamauchi Y, Kuwalhara A, Kamiya Y, Yamaguchi S. 2003. Gibberellin biosynthesis and response during Arabidopsis seed germination. Plant Cell 15:1591-1604. Oh TJ, Wartell RM, Cairney J, Pullman GS. 2008. Evidence for stage-specific modulation of specific microRNAs (miRNAs) and miRNA processing components in zygotic embryo and female gametophyte of loblolly pine (Pinus taeda). New Phytol. 179:67-80. Okamoto M, Kuwahara A, Seo M, Kushiro T, Asami T, Hirai N, Kamiya Y, Koshiba T, Nambara E. 2006. CYP707A1 and CYP707A2, which encode abscisic acid 8'-hydroxylases, are indispensable for proper control of seed dormancy and germination in Arabidopsis. Plant Physiol. 141:97-107. Ooi MKJ, Auld TD, Denham AJ. 2009. Climate change and bet-hedging: Interactions between increased soil temperatures and seed bank persistence. Glob. Change Biol. 15:2375-2386. Ooi MKJ, Auld TD, Denham AJ. 2012. Projected soil temperature increase and seed dormancy response along an altitudinal gradient: Implications for seed bank persistence under climate change. Plant Soil 353:289-303. Owens JN, Simpson SJ, Molder M. 1981. Sexual reproduction of Pinus Contorta. I. Pollen development, the pollination mechanism, and early ovule development. Can J. Bot. 59:1828-1843. Owens JN, Simpson SJ, Molder M. 1982. Sexual reproduction of Pinus Contorta. II. Postdormancy ovule, embryo, and seed development. Can J. Bot. 60:2071-2083. Palovaara J, Hakman I. 2008. Conifer WOX-related homeodomain transcription factors, developmental consideration and expression dynamic of WOX2 during Picea abies somatic embryogenesis. Plant Mol. Biol. 66:533-549. Parizotto EA, Dunoyer P, Rahm N, Himber C, Voinnet O. 2004. In vivo investigation of the transcription, processing, endonucleolytic activity, and functional relevance of the spatial distribution of a plant miRNA. Genes Dev. 18:2237-2242. Park SY, Fung P, Nishimura N, Jensen DR, Fujii H, Zhao Y, Lumba S, Santiago J, Rodrigues A, Chow TFF et al. . 2009. Abscisic acid inhibits type 2C protein phosphatases via the PYR/PYL family of START proteins. Science 324:1068-1071. Parmesan C. 2006. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37:637-669.  197  Parmesan C, Yohe G. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37-42. Parolo G, Rossi G. 2008. Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl. Ecol. 9:100-107. Pearse IS, Koenig WD, Knops JMH. 2014. Cues versus proximate drivers: Testing the mechanism behind masting behavior. Oikos 123:179-184. Pearson TRH, Burslem DFRP, Mullins CE, Dalling JW. 2002. Germination ecology of neotropical pioneers: Interacting effects of environmental conditions and seed size. Ecology 83:2798-2807. Penfield S. 2008. Temperature perception and signal transduction in plants. New Phytol. 179:615-628. Penfield S, Josse EM, Kannangara R, Gilday AD, Halliday KJ, Graham IA. 2005. Cold and light control seed germination through the bHLH transcription factor SPATULA. Curr. Biol. 15:1998-2006. Penfield S, Springthorpe V. 2012. Understanding chilling responses in Arabidopsis seeds and their contribution to life history. Philos. T. Roy. Soc. B 367:291-297. Peng T, Sun H, Qiao M, Zhao Y, Du Y, Zhang J, Li J, Tang G, Zhao Q. 2014. Differentially expressed microRNA cohorts in seed development may contribute to poor grain filling of inferior spikelets in rice. BMC Plant Biol. 14:196. Philippi T, Seger J. 1989. Hedging one's evolutionary bets, revisited. Trends Ecol. Evol. 4:41-44. Pigliucci M. 2001. Phenotypic plasticity: Beyond nature and nurture. Maryland: Johns Hopkins University Press. Pigliucci M, Hayden K. 2001. Phenotypic plasticity is the major determinant of changes in phenotypic integration in Arabidopsis. New Phytol. 152:419-430. Pigliucci M, Murren CJ. 2003. Perspective: Genetic assimilation and a possible evolutionary paradox: Can macroevolution sometimes be so fast as to pass us by? Evolution 57:1455-1464. Pignatta D, Erdmann RM, Scheer E, Picard CL, Bell GW, Gehring M. 2014. Natural epigenetic polymorphisms lead to intraspecific variation in Arabidopsis gene imprinting. eLIFE 3:e03198. Plomion C, Chagné D, Pot D, Kumar S, Wilcox PL, Burdon RD, Prat D, Peterson DG, Paiva J, Chaumeil P et al. . 2007. Pines. In: Kole C, editor. Forest Trees. Genome mapping and molecular breeding in plants. Berlin: Springer. p. 29-92. Poisot T, Bever JD, Nemri A, Thrall PH, Hochberg ME. 2011. A conceptual framework for the evolution of ecological specialisation. Ecol. Lett. 14:841-851.  198  Postma FM, Ågren J. 2015. Maternal environment affects the genetic basis of seed dormancy in Arabidopsis thaliana. Mol. Ecol. 24:785-797. Pourrat Y, Jacques R. 1975. The influence of photoperiodic conditions received by the mother plant on morphological and physiological characteristics of Chenopodium polyspermum L. seeds. Plant Sci. Lett. 4:273–279. Price TD, Qvarnstrom A, Irwin DE. 2003. The role of phenotypic plasticity in driving genetic evolution. P. Roy. Soc. B-Biol. Sci. 270:1433-1440. Prunier J, Gérardi S, Laroche J, Beaulieu J, Bousquet J. 2012. Parallel and lineage-specific molecular adaptation to climate in boreal black spruce. Mol. Ecol. 21:4270-4286. Prunier J, Laroche J, Beaulieu J, Bousquet J. 2011. Scanning the genome for gene SNPs related to climate adaptation and estimating selection at the molecular level in boreal black spruce. Mol. Ecol. 20:1702-1716. Ramaih S, Guedira M, Paulsen GM. 2003. Relationship of indoleacetic acid and tryptophan to dormancy and preharvest sprouting of wheat. Funct. Plant Biol. 30:939-945. Rampey RA, LeClere S, Kowalczyk M, Ljung K, Sandberg G, Bartel B. 2004. A family of auxin-conjugate hydrolases that contributes to free indole-3-acetic acid levels during Arabidopsis germination. Plant Physiol. 135:978-988. Raudenbush SW, Bryk AS. 2001. Hierarchical linear models: Applications and data analysis methods (2nd ed.). CA: SAGE Publications, Inc. Rees M. 1993. Trade-offs among dispersal strategies in British plants. Nature 366:150-152. Rees M. 1994. Delayed germination of seeds: A look at the effects of adult longevity, the timing of reproduction, and population age/stage structure. Am. Nat. 144:43-64. Rees M. 1996. Evolutionary ecology of seed dormancy and seed size. Philos. T. Roy. Soc. B 351:1299-1308. Rees M, Westoby M. 1997. Game-theoretical evolution of seed mass in multi-species ecological models. Oikos 78:116-126. Rehfeldt GE, Tchebakova NM, Parfenova YI, Wykoff WR, Kuzmina NA, Milyutin LI. 2002. Intraspecific responses to climate in Pinus sylvestris. Glob. Change Biol. 8:912-929. Reich PB, Oleksyn J. 2008. Climate warming will reduce growth and survival of Scots pine except in the far north. Ecol. Lett. 11:588-597.  199  Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP. 2002. MicroRNAs in plants. Genes Dev. 16:1616-1626. Resentini F, Felipo-Benavent A, Colombo L, Blázquez MA, Alabadí D, Masiero S. 2015. TCP14 and TCP15 mediate the promotion of seed germination by gibberellins in Arabidopsis thaliana. Mol. Plant 8:482-485. Reyes JL, Chua NH. 2007. ABA induction of miR159 controls transcript levels of two MYB factors during Arabidopsis seed germination. Plant J. 49:592-606. Rhoades MW, Reinhart BJ, Lim LP, Burge CB, Bartel B, Bartel DP. 2002. Prediction of plant microRNA targets. Cell 110:513-520. Richardson AD, Black TA, Ciais P, Delbart N, Friedl MA, Gobron N, Hollinger DY, Kutsch WL, Longdoz B, Luyssaert S et al. . 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. T. R. Soc. B. 365:3227-3246. Ricker WE. 1954. Stock and recruitment. J. Fish Res. Board Can. 11: 559–623. Rigault P, Boyle B, Lepage P, Cooke JEK, Bousquet J, MacKay JJ. 2011. A white spruce gene catalog for conifer genome analyses. Plant Physiol. 157:14-28. Robeson SM. 2004. Trends in time-varying percentiles of daily minimum and maximum temperature over North America. Geophys. Res. Lett. 31:L04203. Robischon M, Du J, Miura E, Groover A. 2011. The Populus class III HD ZIP, popREVOLUTA, influences cambium initiation and patterning of woody stems. Plant Physiol. 155:1214-1225. Romanel EAC, Schrago CG, Counago RM, Russo CAM, Alves-Ferreira M. 2009. Evolution of the B3 DNA binding superfamily: New insights into REM family gene diversification. PloS One 4:e5791. Rosas U, Mei Y, Xie Q, Banta JA, Zhou RW, Seufferheld G, Gerard S, Chou L, Bhambhra N, Parks JD et al. . 2014. Variation in Arabidopsis flowering time associated with cis-regulatory variation in CONSTANS. Nature Commun. 5:3651. Rowe JS. 1964. Environmental preconditioning with special reference to forestry. Ecology 45:399-403. Rozen S, Skaletsky H. 2000. Primer3 on the WWW for general users and for biologist programmers. Methods Mol. Biol. 132:365-386. Rubio-Somoza I, Weigel D. 2011. MicroRNA networks and developmental plasticity in plants. Trends Plant Sci. 16:258-264.  200  Rueda A, Barturen G, Lebrón R, Gómez-Martín C, Alganza A, Oliver JL, Hackenberg M. 2015. sRNAtoolbox: An integrated collection of small RNA research tools. Nucleic Acids Res. 43:W467-W473. Saito S, Hirai N, Matsumoto C, Ohigashi H, Ohta D, Sakata K, Mizutani M. 2004. Arabidopsis CYP707As encode (+)-abscisic acid 8'-hydroxylase, a key enzyme in the oxidative catabolism of abscisic acid. Plant Physiol. 134:1439-1449. Salisbury E. 1975. The survival value of modes of dispersal. P. Roy. Soc. B-Biol. Sci. 188:183-188. SAS Institute. 1999. SAS/STAT user's guide (Ver. 8). Cary, NC.: SAS institute, Inc. Sato K, Yamane M, Yamaji N, Kanamori H, Tagiri A, Schwerdt JG, Fincher GB, Matsumoto T, Takeda K, Komatsuda T. 2016. Alanine aminotransferase controls seed dormancy in barley. Nature Commun. 7:11625. Schauber EM, Kelly D, Turchin P, Simon C, Lee WG, Allen RB, Payton IJ, Wilson PR, Cowan PE, Brockie RE. 2002. Masting by eighteen New Zealand plant species: The role of temperature as a synchronizing cue. Ecology 83:1214-1225. Scheckler SE. 2001. Afforestation - the first forests. in Palaeobiology II. Malden, MA, USA.: Blackwell Science Ltd. Schlichting CD, Pigliucci M. 1998. Phenotypic evolution: A reaction norm perspective. Sunderland, MA: Sinauer Associates. Schmitt J, Niles J, Wulff R. 1992. Norms of reaction of seed traits to maternal environments in Plantago lanceolata. Am. Nat. 139:451-466. Schmitt J, Wulff RD. 1993. Light spectral quality, phytochrome and plant competition. Trends Ecol. Evol. 8:47-51. Schmitz N, Abrams SR, Kermode AR. 2002. Changes in ABA turnover and sensitivity that accompany dormancy termination of yellow-cedar (Chamaecyparis nootkatensis) seeds. J. Exp. Bot. 53:89-101. Schneider H, Schuettpelz E, Pryer KM, Cranfill R, Magallon S, Lupia R. 2004. Ferns diversified in the shadow of angiosperms. Nature 428:553-557. Schommer C, Bresso EG, Spinelli SV, Palatnik JF. 2012. Role of microRNA miR319 in plant development. In: Sunkar R, editor. MicroRNAs in plant development and stress responses. Berlin: Springer. Schwartz MD, Ahas R, Aasa A. 2006. Onset of spring starting earlier across the Northern Hemisphere. Glob. Change Biol. 12:343-351.  201  Seo M, Nambara E, Choi G, Yamaguchi S. 2009. Interaction of light and hormone signals in germinating seeds. Plant Mol. Biol. 69:463-472. Shimizu KK, Kudoh H, Kobayashi MJ. 2011. Plant sexual reproduction during climate change: Gene function in natura studied by ecological and evolutionary systems biology. Ann. Bot. 108:777-787. Shu K, Zhang H, Wang S, Chen M, Wu Y, Tang S, Liu C, Feng Y, Cao X, Xie Q. 2013. ABI4 regulates primary seed dormancy by regulating the biogenesis of abscisic acid and gibberellins in arabidopsis. PLoS Genet. 9:e1003577. Si-Ammour A, Windels D, Arn-Bouldoires E, Kutter C, Ailhas J, Meins F, Jr., Vazquez F. 2011. miR393 and secondary siRNAs regulate expression of the TIR1/AFB2 auxin receptor clade and auxin-related development of Arabidopsis leaves. Plant Physiol. 157:683-691. Sieber P, Wellmer F, Gheyselinck J, Riechmann JL, Meyerowitz EM. 2007. Redundancy and specialization among plant microRNAs: Role of the MIR164 family in developmental robustness. Development 134:1051-1060. Silva AT, Ribone PA, Chan RL, Ligterink W, Hilhorst HW. 2016. A predictive co-expression network identifies novel genes controlling the seed-to-seedling phase transition in Arabidopsis thaliana. Plant Physiol. 170:2218-2231. Simons AM. 2009. Fluctuating natural selection accounts for the evolution of diversification bet hedging. P. Roy. Soc. B-Biol. Sci. 276:1987-1992. Simons AM. 2014. Playing smart vs. playing safe: The joint expression of phenotypic plasticity and potential bet hedging across and within thermal environments. J. Evol. Biol. 27:1047-1056. Simpson G. 1990. Seed dormancy in grasses. Cambridge, UK: Cambridge University Press. Skordilis A, Thanos CA. 1995. Seed stratification and germination strategy in the Mediterranean pines Pinus brutia and Pinus halepensis. Seed Sci. Res. 5:151-160. Skrøppa T, Kohmann K, Johnsen Ø, Steffenrem A, Edvardsen ØM. 2007. Field performance and early test results of offspring from two Norway spruce seed orchards containing clones transferred to warmer climates. Can. J. Forest Res. 37:515-522. Skrøppa T, Tho T. 1990. Diallel crosses in Norway spruce. I. Variation in seed yield and seed weight. Scand. J. For. Res. 5:355-367. Skrøppa T, Tollefsrud MM, Sperisen C, Johnsen Ø. 2010. Rapid change in adaptive performance from one generation to the next in Picea abies - Central European trees in a Nordic environment. Tree Genet. Genom. 6:93-99. Slatkin M. 1974. Hedging ones evolutionary bets. Nature 250:704-705.  202  Smaill SJ, Clinton PW, Allen RB, Davis MR. 2011. Climate cues and resources interact to determine seed production by a masting species. J. Ecol. 99:870-877. Smith H. 1995. Physiological and ecological function within the phytochrome family. Annu. Rev. Plant Physiol. Plant Mol. Biol. 46:289-315. Sparks E, Wachsman G, Benfey PN. 2013. Spatiotemporal signalling in plant development. Nat. Rev. Genet. 14:631-644. Springthorpe V, Penfield S. 2015. Flowering time and seed dormancy control use external coincidence to generate life history strategy. eLIFE 4:e05557. Steadman KJ, Ellery AJ, Chapman R, Moore A, Turner NC. 2004. Maturation temperature and rainfall influence seed dormancy characteristics of annual ryegrass (Lolium rigidum). Aust. J. Agric. Res. 55:1047-1057. Stearns SC, Hoekstra RF. 2000. Evolution: An introduction. Oxford, UK: Oxford University Press. Stocker TF, Qin D, Plattner G-K, Alexander LV, Allen SK, Bindoff NL, Bréon F-M, Church JA, Cubasch U, Emori S et al. . 2013. Technical summary. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors. Climate change 2013: The physical science basis. Contribution of working group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. p. 33-115. Stoehr MU, L'Hirondelle SJ, Binder WD, Webber JE. 1998. Parental environment aftereffects on germination, growth, and adaptive traits in selected white spruce families. Can. J. Forest Res. 28:418-426. Sun TP. 2008. Gibberellin metabolism, perception and signaling pathways in Arabidopsis. Arabidopsis Book 6:e0103. Svendsen E, Wilen R, Stevenson R, Liu RS, Tanino KK. 2007. A molecular marker associated with low-temperature induction of dormancy in red osier dogwood (Cornus sericea). Tree Physiol. 27:385-397. Tabachnick BG, Fidell LS. 2012. Using multivariate statistics (6th ed.). Boston: Pearson Education. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. 2013. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30:2725-2729. Tang X, Bian S, Tang M, Lu Q, Li S, Liu X, Tian G, Nguyen V, Tsang EW, Wang A et al. . 2012. MicroRNA-mediated repression of the seed maturation program during vegetative development in Arabidopsis. PLoS Genet. 8:e1003091.  203  Tanino KK, Kalcsits L, Silim S, Kendall E, Gray GR. 2010. Temperature-driven plasticity in growth cessation and dormancy development in deciduous woody plants: A working hypothesis suggesting how molecular and cellular function is affected by temperature during dormancy induction. Plant Mol. Biol. 73:49-65. Taylor RS, Tarver JE, Hiscock SJ, Donoghue PC. 2014. Evolutionary history of plant microRNAs. Trends Plant Sci. 19:175-182. Tempel S, Talla E. 2014. Visual ModuleOrganizer: A graphical interface for the detection and comparative analysis of repeat DNA modules. Mobil. DNA 5:9. Thatcher SR, Burd S, Wright C, Lers A, Green PJ. 2015. Differential expression of miRNAs and their target genes in senescing leaves and siliques: Insights from deep sequencing of small RNAs and cleaved target RNAs. Plant, Cell Environ. 38:188-200. Thompson J, Charpentier A, Bouguet G, Charmasson F, Roset S, Buatois B, Vernet P, Gouyon PH. 2013. Evolution of a genetic polymorphism with climate change in a Mediterranean landscape. Proc. Natl. Acad. Sci. USA 110:2893-2897. Thompson K, Bakker JP, Bekker RM. 1997. The soil seed banks of North West Europe: Methodology, density and longevity. Cambridge: Cambridge University Press. Thompson K, Bakker JP, Bekker RM, Hodgson JG. 1998. Ecological correlates of seed persistence in soil in the north-west European flora. J. Ecol. 86:163-169. Thompson K, Grime JP. 1979. Seasonal variation in the seed banks of herbaceous species in ten contrasting habitats. J. Ecol. 67:893-921. Thomson AJ, El-Kassaby YA. 1993. Interpretation of seed germination parameters. New For. 7:123-132. Tiwari SB, Hagen G, Guilfoyle T. 2003. The roles of auxin response factor domains in auxin-responsive transcription. Plant Cell 15:533-543. Toh S, Imamura A, Watanabe A, Nakabayashi K, Okamoto M, Jikumaru Y, Hanada A, Aso Y, Ishiyama K, Tamura N et al. . 2008. High temperature-induced abscisic acid biosynthesis and its role in the inhibition of gibberellin action in Arabidopsis seeds. Plant Physiol. 146:1368-1385. Tollefsrud MM, Kissling R, Gugerli F, Johnsen Ø, Skrøppa T, Cheddadi R, Van der Knaap WO, Latalowa M, Terhürne-Berson R, Litt T et al. . 2008. Genetic consequences of glacial survival and postglacial colonization in Norway spruce: Combined analysis of mitochondrial DNA and fossil pollen. Mol. Ecol. 17:4134-4150. Tsuji H, Aya K, Ueguchi-Tanaka M, Shimada Y, Nakazono M, Watanabe R, Nishizawa NK, Gomi K, Shimada A, Kitano H et al. . 2006. GAMYB controls different sets of genes and is differentially regulated by microRNA in aleurone cells and anthers. Plant J. 47:427-444.  204  Ulmasov T, Hagen G, Guilfoyle TJ. 1999. Dimerization and DNA binding of auxin response factors. Plant J. 19:309-319. Vaistij FE, Gan YB, Penfield S, Gilday AD, Dave A, He ZS, Josse EM, Choi G, Halliday KJ, Graham IA. 2013. Differential control of seed primary dormancy in Arabidopsis ecotypes by the transcription factor SPATULA. Proc. Natl. Acad. Sci. USA 110:10866-10871. Van Dijk H, Hautekèete N. 2007. Long day plants and the response to global warming: Rapid evolutionary change in day length sensitivity is possible in wild beet. J. Evol. Biol. 20:349-357. Vanneste S, Friml J. 2009. Auxin: A trigger for change in plant development. Cell 136:1005-1016. Venable DL. 2007. Bet hedging in a guild of desert annuals. Ecology 88:1086-1090. Venable DL, Brown JS. 1988. The selective interactions of dispersal, dormancy, and seed size as adaptations for reducing risk in variable environments. Am. Nat. 131:360-384. Venable DL, Búrquez A, Corral G, Morales E, Espinosa F. 1987. The ecology of seed heteromorphism in Heterosperma pinnatum in central Mexico. Ecology 68:65-76. Verhage L, Angenent GC, Immink RG. 2014. Research on floral timing by ambient temperature comes into blossom. Trends Plant Sci. 19:583-591. Verta JP, Landry CR, MacKay JJ. 2013. Are long-lived trees poised for evolutionary change? Single locus effects in the evolution of gene expression networks in spruce. Mol. Ecol. 22:2369-2379. Vidigal DS, Marques AC, Willems LA, Buijs G, Méndez-Vigo B, Hilhorst HW, Bentsink L, Picó FX, Alonso-Blanco C. 2016. Altitudinal and climatic associations of seed dormancy and flowering traits evidence adaptation of annual life cycle timing in Arabidopsis thaliana. Plant, Cell Environ. 39:1737-1748 Visser ME, Caro SP, van Oers K, Schaper SV, Helm B. 2010. Phenology, seasonal timing and circannual rhythms: Towards a unified framework. Philos. T. R. Soc. B. 365:3113-3127. Vitalis R, Rousset F, Kobayashi Y, Olivieri I, Gandon S. 2013. The joint evolution of dispersal and dormancy in a metapopulation with local extinctions and kin competition. Evolution 67:1676-1691. Volis S, Bohrer G. 2013. Joint evolution of seed traits along an aridity gradient: Seed size and dormancy are not two substitutable evolutionary traits in temporally heterogeneous environment. New Phytol. 197:655-667. Volis S, Ormanbekova D, Yermekbayev K, Song M, Shulgina I. 2014. Introduction beyond a species range: A relationship between population origin, adaptive potential and plant performance. Heredity 113:268-276.  205  Vranckx G, Vandelook F. 2012. A season- and gap-detection mechanism regulates seed germination of two temperate forest pioneers. Plant Biol. 14:481-490. Waddington CH. 1942. Canalization of development and the inheritance of acquired characters. Nature 150:563-565. Waddington CH. 1957. The strategy of the genes: A discussion of some aspects of theoretical biology. London: Allen & Unwin. Waddington CH. 1961. Genetic assimilation. Adv. Genet. 10:257-293. Wagner GP, Altenberg L. 1996. Perspective: Complex adaptations and the evolution of evolvability. Evolution 50:967-976. Walck JL, Baskin JM, Baskin CC. 1997. A comparative study of the seed germination biology of a narrow endemic and two geographically-widespread species of Solidago (Asteraceae) .1. Germination phenology and effect of cold stratification on germination. Seed Sci. Res. 7:47-58. Walck JL, Hidayati SN, Dixon KW, Thompson K, Poschlod P. 2011. Climate change and plant regeneration from seed. Glob. Change Biol. 17:2145-2161. Walter H, Breckle SW. 2002. Walter's vegetation of the earth: The ecological systems of the geo-biosphere (4th ed.). Berlin: Springer  Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJ, Fromentin JM, Hoegh-Guldberg O, Bairlein F. 2002. Ecological responses to recent climate change. Nature 416:389-395. Wang JW, Czech B, Weigel D. 2009. miR156-regulated SPL transcription factors define an endogenous flowering pathway in Arabidopsis thaliana. Cell 138:738-749. Wang JW, Park MY, Wang LJ, Koo Y, Chen XY, Weigel D, Poethig RS. 2011a. miRNA control of vegetative phase change in trees. PLoS Genet. 7:e1002012. Wang JW, Wang LJ, Mao YB, Cai WJ, Xue HW, Chen XY. 2005. Control of root cap formation by MicroRNA-targeted auxin response factors in Arabidopsis. Plant Cell 17:2204-2216. Wang L, Gu X, Xu D, Wang W, Wang H, Zeng M, Chang Z, Huang H, Cui X. 2011b. miR396-targeted AtGRF transcription factors are required for coordination of cell division and differentiation during leaf development in Arabidopsis. J. Exp. Bot. 62:761-773. Wang T, Hamann A, Spittlehouse DL, Aitken SN. 2006a. Development of scale-free climate data for western Canada for use in resource management. Int. J. Climatol. 26:383-397. Wang T, Hamann A, Yanchuk A, O'Neill GA, Aitken SN. 2006b. Use of response functions in selecting lodgepole pine populations for future climates. Glob. Change Biol. 12:2404-2416.  206  Wang TL, Hamann A, Spittlehouse DL, Murdock TQ. 2012. ClimateWNA - High-resolution spatial climate data for western North America. J. Appl. Meteorol. Clim. 51:16-29. Wang XQ, Ran JH. 2014. Evolution and biogeography of gymnosperms. Mol. Phylogen. Evol. 75:24-40. Waring RH, Franklin JF. 1979. Evergreen coniferous forests of the pacific northwest. Science 204:1380-1386. Warren RL, Keeling CI, Yuen MM, Raymond A, Taylor GA, Vandervalk BP, Mohamadi H, Paulino D, Chiu R, Jackman SD et al. . 2015. Improved white spruce (Picea glauca) genome assemblies and annotation of large gene families of conifer terpenoid and phenolic defense metabolism. Plant J. 83:189-212. West-Eberhard MJ. 2003. Developmental plasticity and evolution. Oxford, UK: Oxford University Press. West BT, Welch KB, T. GA. 2007. Linear mixed models: A practical guide using statistical software. New York: Chapman and Hall/CRC. Westoby M, Falster D, Moles A, Vesk P, Wright I. 2002. Plant ecological strategies: Some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33:125-159. Williams P, Bradbeer J, Gaskin P, MacMillan J. 1974. Studies in seed dormancy VIII. The Identification and Determination of Gibberellins A1 and A9 in Seeds of Corylus avellana L. Planta 117:101-108. Williamson JD, Quatrano RS, Cuming AC. 1985. Em polypeptide and its messenger RNA levels are modulated by abscisic acid during embryogenesis in wheat. Eur. J. Biochem. 152:501-507. Willis CG, Baskin CC, Baskin JM, Auld JR, Venable DL, Cavender-Bares J, Donohue K, Rubio de Casas R, Working Group NG. 2014. The evolution of seed dormancy: Environmental cues, evolutionary hubs, and diversification of the seed plants. New Phytol. 203:300-309. Willmann MR, Poethig RS. 2007. Conservation and evolution of miRNA regulatory programs in plant development. Curr. Opin. Plant Biol. 10:503-511. Won C, Shen XL, Mashiguchi K, Zheng ZY, Dai XH, Cheng YF, Kasahara H, Kamiya Y, Chory J, Zhao YD. 2011. Conversion of tryptophan to indole-3-acetic acid by TRYPTOPHAN AMINOTRANSFERASES OF ARABIDOPSIS and YUCCAs in Arabidopsis. Proc. Natl. Acad. Sci. USA 108:18518-18523. Woodward FI, Williams BG. 1987. Climate and plant distribution at global and local scales. Vegetatio 69:189-197. Wright SD, Macconnaughay KD. 2002. Interpreting phenotypic plasticity: The importance of ontogeny. Plant Species Biol. 17:119-131.  207  Wu CI, Shen Y, Tang T. 2009a. Evolution under canalization and the dual roles of microRNAs: A hypothesis. Genome Res. 19:734-743. Wu G, Park MY, Conway SR, Wang JW, Weigel D, Poethig RS. 2009b. The sequential action of miR156 and miR172 regulates developmental timing in Arabidopsis. Cell 138:750-759. Wu MF, Tian Q, Reed JW. 2006. Arabidopsis microRNA167 controls patterns of ARF6 and ARF8 expression, and regulates both female and male reproduction. Development 133:4211-4218. Wulff RD. 1986. seed size variation in desmodium paniculatum: I. Factors affecting seed size J. Ecol. 74:87-97. Xia JH, Kermode AR. 1999. Analyses to determine the role of embryo immaturity in dormancy maintenance of yellow cedar (Chamaecyparis nootkatensis) seeds: Synthesis and accumulation of storage proteins and proteins implicated in desiccation tolerance. J. Exp. Bot. 50:107-118. Xia K, Wang R, Ou X, Fang Z, Tian C, Duan J, Wang Y, Zhang M. 2012. OsTIR1 and OsAFB2 downregulation via OsmiR393 overexpression leads to more tillers, early flowering and less tolerance to salt and drought in rice. PloS One 7:e30039. Xia Q, Ando M, Seiwa K. 2016. Interaction of seed size with light quality and temperature regimes as germination cues in 10 temperate pioneer tree species. Funct. Ecol. 30:866–874. Xia R, Xu J, Arikit S, Meyers BC. 2015. Extensive families of miRNAs and PHAS loci in Norway spruce demonstrate the origins of complex phasiRNA networks in seed plants. Mol. Biol. Evol. 32:2905-2918. Xiang D, Venglat P, Tibiche C, Yang H, Risseeuw E, Cao Y, Babic V, Cloutier M, Keller W, Wang E et al. . 2011. Genome-wide analysis reveals gene expression and metabolic network dynamics during embryo development in Arabidopsis. Plant Physiol. 156:346-356. Xiang Y, Nakabayashi K, Ding J, He F, Bentsink L, Soppe WJJ. 2014. REDUCED DORMANCY5 encodes a protein phosphatase 2C that is required for seed dormancy in Arabidopsis. Plant Cell 26:4362-4375. Xie C, Zhou X, Deng X, Guo Y. 2010. PKS5, a SNF1-related kinase, interacts with and phosphorylates NPR1, and modulates expression of WRKY38 and WRKY62. J. Genet. Genomics 37:359-369. Xie F, Jones DC, Wang Q, Sun R, Zhang B. 2015. Small RNA sequencing identifies miRNA roles in ovule and fibre development. Plant Biotechnol. J. 13:355-369. Xiong L, Lee B, Ishitani M, Lee H, Zhang C, Zhu JK. 2001. FIERY1 encoding an inositol polyphosphate 1-phosphatase is a negative regulator of abscisic acid and stress signaling in Arabidopsis. Genes Dev. 15:1971-1984.  208  Yakovlev I, Fossdal CG, Skrøppa T, Olsen JE, Jahren AH, Johnsen Ø. 2012. An adaptive epigenetic memory in conifers with important implications for seed production. Seed Sci. Res. 22:63-76. Yakovlev IA, Fossdal CG, Johnsen Ø. 2010. MicroRNAs, the epigenetic memory and climatic adaptation in Norway spruce. New Phytol. 187:1154-1169. Yakovlev IA, Lee Y, Rotter B, Olsen JE, Skrøppa T, Johnsen O, Fossdal CG. 2014. Temperature-dependent differential transcriptomes during formation of an epigenetic memory in Norway spruce embryogenesis. Tree Genet. Genom. 10:355-366. Yamagishi K, Tatematsu K, Yano R, Preston J, Kitamura S, Takahashi H, McCourt P, Kamiya Y, Nambara E. 2009. CHOTTO1, a double AP2 domain protein of Arabidopsis thaliana, regulates germination and seedling growth under excess supply of glucose and nitrate. Plant Cell Physiol. 50:330-340. Yamauchi Y, Ogawa M, Kuwahara A, Hanada A, Kamiya Y, Yamaguchi S. 2004. Activation of Gibberellin biosynthesis and response pathways by low temperature during imbibition of Arabidopsis thaliana seeds. Plant Cell 16:367-378. Yamauchi Y, Takeda-Kamiya N, Hanada A, Ogawa M, Kuwahara A, Seo M, Kamiya Y, Yamaguchi S. 2007. Contribution of gibberellin deactivation by AtGA2ox2 to the suppression of germination of dark-imbibed Arabidopsis thaliana seeds. Plant Cell Physiol. 48:555-561. Yan W, Kang MS. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. Boca Raton, Florida: CRC Press. Yan W, Tinker NA. 2006. Biplot analysis of multi-environment trial data: Principles and applications. Can. J. Plant Sci. 86:623-645. Yan WK. 2001. GGEbiplot - A windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron. J. 93:1111-1118. Yan Z, Hossain MS, Arikit S, Valdés-López O, Zhai JX, Wang J, Libault M, Ji TM, Qiu LJ, Meyers BC et al. . 2015. Identification of microRNAs and their mRNA targets during soybean nodule development: Functional analysis of the role of miR393j-3p in soybean nodulation. New Phytol. 207:748-759. Yang JH, Han SJ, Yoon EK, Lee WS. 2006. Evidence of an auxin signal pathway, microRNA167-ARF8-GH3, and its response to exogenous auxin in cultured rice cells. Nucleic Acids Res. 34:1892-1899. Yang L, Xu M, Koo Y, He J, Poethig RS. 2013. Sugar promotes vegetative phase change in Arabidopsis thaliana by repressing the expression of MIR156A and MIR156C. eLIFE 2:e00260. Yano R, Kanno Y, Jikumaru Y, Nakabayashi K, Kamiya Y, Nambara E. 2009. CHOTTO1, a putative double APETALA2 repeat transcription factor, is involved in abscisic acid-mediated repression of gibberellin biosynthesis during seed germination in Arabidopsis. Plant Physiol. 151:641-654.  209  Zažímalová E, Murphy AS, Yang H, Hoyerová K, Hošek P. 2010. Auxin transporters - Why so many? Cold Spring Harb. Perspect. Biol. 2:a001552. Zeng Y, Raimondi N, Kermode AR. 2003. Role of an ABI3 homologue in dormancy maintenance of yellow cedar seeds and in the activation of storage protein and Em gene promoters. Plant Mol. Biol. 51:39-49. Zeng Y, Zhao T, Kermode AR. 2013. A conifer ABI3-interacting protein plays important roles during key transitions of the plant life cycle. Plant Physiol. 161:179-195. Zhang BH, Pan XP, Cannon CH, Cobb GP, Anderson TA. 2006. Conservation and divergence of plant microRNA genes. Plant J. 46:243-259. Zhang L, Chia JM, Kumari S, Stein JC, Liu Z, Narechania A, Maher CA, Guill K, McMullen MD, Ware D. 2009. A genome-wide characterization of microRNA genes in maize. PLoS Genet. 5:e1000716. Zhang XR, Garreton V, Chua NH. 2005. The AIP2 E3 ligase acts as a novel negative regulator of ABA signaling by promoting ABI3 degradation. Genes Dev. 19:1532-1543. Zhang XY, Tarpley D, Sullivan JT. 2007. Diverse responses of vegetation phenology to a warming climate. Geophys. Res. Lett. 34:L19405. Zhang Y, Zhang S, Han S, Li X, Qi L. 2012. Transcriptome profiling and in silico analysis of somatic embryos in Japanese larch (Larix leptolepis). Plant Cell Rep. 31:1637-1657. Zhao L, Kim Y, Dinh TT, Chen X. 2007. miR172 regulates stem cell fate and defines the inner boundary of APETALA3 and PISTILLATA expression domain in Arabidopsis floral meristems. Plant J. 51:840-849. Zhao M, Yang S, Liu X, Wu K. 2015. Arabidopsis histone demethylases LDL1 and LDL2 control primary seed dormancy by regulating DELAY OF GERMINATION 1 and ABA signaling-related genes. Front. Plant Sci. 6:159. Zhao S, Fernald RD. 2005. Comprehensive algorithm for quantitative real-time polymerase chain reaction. J. Comput. Biol. 12:1047-1064. Zheng J, Chen F, Wang Z, Cao H, Li X, Deng X, Soppe WJ, Li Y, Liu Y. 2012. A novel role for histone methyltransferase KYP/SUVH4 in the control of Arabidopsis primary seed dormancy. New Phytol. 193:605-616. Zhou X, Hao H, Zhang Y, Bai Y, Zhu W, Qin Y, Yuan F, Zhao F, Wang M, Hu J et al. . 2015. PKS5/CIPK11, a SnRK3-type protein kinase, is important for ABA responses in Arabidopsis through phosphorylation of ABI5. Plant Physiol. 168:659-676.  210  Zhou Z, Bao W. 2014. Changes in seed dormancy of Rosa multibracteata Hemsl. & E. H. Wilson with increasing elevation in an arid valley in the eastern Tibetan Plateau. Ecol. Res. 29:693-700.            Face à la roche, le ruisseau l’emporte toujours, non pas par la force, mais par la persévérance. (In the confrontation between the stream and the rock, the stream always wins, not through strength but by perseverance.)  ― Henry Jackson Brown, auteur américain   211  Appendices Appendix A: Adaptive dynamics The framework of adaptive dynamics is used to study the evolutionary dynamics of a trait and depict the ecological consequences for the community (Metz et al. 1992; Dieckmann and Law 1996; Geritz et al. 1998).  This methodology is established on the interaction between ecology and evolution and represents ecological models in an evolutionary perspective. It focuses on the effect of the ecological feedback loop and evolutionary success hinges on the positivity of the long-term growth rate of a mutant in the environmental conditions set by the current resident. Invasive fitness The notion of adaptive dynamics was created by Metz et al. (1992). The ecological and evolutionary time-scales are separated. A mutant on one studied trait occurs in a population (i.e., resident), which is at an ecological equilibrium. The mutant’s fitness is determined by the ecological state of the system. The invasive fitness corresponds to the growth rate of a new rare mutant with a trait very close to the resident trait in the ecological community set by a resident population. A positive fitness indicates that the mutant can invade the resident population and become the new resident, while a negative fitness indicates that the mutant cannot invade the resident and will go extinct. Canonical equation The evolutionary dynamics of a trait, analytically derived by Dieckmann and Law (1996), are described using the following equation: 𝑑𝑠𝑑𝑡= 𝐶𝜇𝜎2𝑃0(𝑠) (𝜕𝜔𝑃𝑚𝑢𝑡(𝑠𝑚𝑢𝑡 , 𝑠)𝜕𝑠𝑚𝑢𝑡)𝑠𝑚𝑢𝑡→𝑠 where C is a scaling parameter, μ represents the mutation rate per individual, σ2 is the variance of phenotypic effects associated with mutations and P0(s) is the density of the plant population at the ecological equilibrium for a trait s. The last term of the equation is the fitness gradient associated with a resident trait s. when this fitness gradient is positive, trait s will increase and when it is negative, trait s will decrease. eqn 13   212  Singular strategy and stability When the fitness gradient in eqn 13 vanishes, corresponding traits are at evolutionary equilibria, termed as singular strategies, s* (Geritz et al. 1998). Singular strategies can be classified as per two stability criteria: invasibility and convergence. A singular strategy is non-invasible given no neighbour mutant can invade such a strategy (i.e., the definition of an ESS, (Maynard Smith 1982)). A singular strategy is convergent if, in the vicinity of s*, only mutants with trait values closer to the singular strategy can invade (Eshel 1983; Christiansen 1991). Convergence and invasibility criteria can be investigated by differentiating fitness functions twice with traits s and smut (Geritz et al. 1998). In light of these two criteria, we can get four properties of singular strategies: (1) continuously stable strategy (CSS): a non-invasible and convergent strategy, this type of singular strategy corresponds to an eco-evolutionary stable equilibrium, where the evolutionary dynamics end; (2) invasible repellor: an invasible and non-convergent strategy; any population can invade this point and the nearby population evolves away; (3) Garden-of-Eden: a non-invasible and non-convergent strategy; is not an attractor (organisms do not evolve toward it); it has little practical interest; (4) evolutionary branching point: an invasible and convergent strategy; attractor, that is, organisms evolve toward such a point, but once there, mutant strategies can invade and results in disruptive selection, and polymorphism (diversification) arises (Dieckmann and Doebeli 1999). Pairwise invasibility plots (PIPs) PIPs are a useful tool to visualize adaptive dynamics of an evolved trait (Geritz et al. 1998). We here show four possible generic local configurations, which correspond to the four types of singular strategies.   213   Figure A.1 Configurations of PIPs Note: grey-shaded and white areas represent positive and negative fitness, respectively. In the grey regions, the mutant may invade and replace the resident, while in the white regions, the mutant will disappear. Black arrows indicate evolutionary directions; and filled and empty circles represent stable and unstable ESS, respectively. 214  Appendix B: Supplementary information A)     215  B)  Figure B.1 Overview of conserved miRNAs expression patterns across seed set phases of four populations in P. glauca (A) and two ecotypes (Cvi and Col) in Arabidopsis (B)    216  Table B.1 Description of seedlots1 of interior spruce, lodgepole pine, and western hemlock and their International Seed Testing Association (ISTA) seed test result2. Seedlot code Species Seed planning zones3 Elevation (m) Year collected Germination% Moisture content % 3679 Pli TOD (50°56ʼN 122°50ʼW) 1320 1978 93 5.1 4939 Pli WK  (49°7ʼN 118°22ʼW) 1450 1987 94 6.7 8435 Pli CT (52°3ʼN 121°5ʼW) 1300 1988 95 6.2 40428 Pli CHL (52°51ʼN 123°38ʼW) 1100 1996 96 7.3 42255 Pli TOA (50°27’ʼN 120°3ʼ31’’W) 1445 2005 96 6.5 33356 Sx WK (50°15’N 118°10ʼW) 1190 1991 96 7.0 35707 Sx MIC (51°2ʼN 118°48ʼW ) 1200 1991 97 7.0 37842 Sx MGR (54°26ʼN 121°44ʼW) 850 1992 95 8.0 39450 Sx CP (55°3ʼN 125°2ʼW) 875 1994 95 7.6 45353 Sx SM (54°39ʼN 128°45ʼW) 800 1996 97 7.0 3439 Hw NST (55°30ʼN 128°57ʼW) 150 1978 92 6.7 4094 Hw M (48°59ʼN 124°25ʼW) 850 1979 94 6.9 35571 Hw SM (54°35ʼN 128°5ʼW) 750 1992 97 6.1 39235 Hw MIC (51°2ʼN 118°16ʼW) 900 1993 93 6.8 53002 Hw WK (50°8’30’ʼN 117°58ʼ32’’W) 610 2008 96 6.9 Note: western hemlock: Hw, lodgepole pine: Pli, and “interior” spruce: Sx; 1 Data collected in this experiment. 2 Seed tests carried out by the Tree Seed Centre (Tree Improvement Branch, Ministry of Forests, Lands and Natural Resource Operations) as per International Seed Testing Association rules. 3 The Seed Planning Zones in BC are abbreviated TOD, WK, CT, CHL, TOA, MIC, MGR, CP, SM, NST, and M; these denote Tompson Okanagan Dry, West Kootenay, Cariboo Transition, Chilcotin, Tompson Okanagan Arid, Mica, McGregor, Central Plateau, Submaritime, Nass Skeena Transition, and Maritime zones, respectively.    217  Table B.2 Errors from ecosystem zone for seed dormancy and weight models  Seed dormancy model Seed weight model Ecosystem zone DF Estimate STD  Pred t Value Pr > |t| Estimate STD Pred t Value Pr > |t| BB 58 -0.8391 1.5939 -0.53 0.6006 0.09572 0.0974 0.98 0.3300 BLK 58 -1.1614 1.6123 -0.72 0.4742 -0.04007 0.1055 -0.38 0.7060 CHL 58 0.1542 1.7931 0.09 0.9318 -0.1539 0.1218 -1.26 0.2110 CP 58 1.8555 1.669 1.11 0.2708 -0.0866 0.1099 -0.79 0.4340 CT 58 -0.6866 1.8897 -0.36 0.7177 0.1130 0.1515 0.75 0.4590 DK 58 0.0068 1.7932 0.00 0.9970 -0.1033 0.1319 -0.78 0.4370 EK 58 1.8960 1.7285 1.10 0.2772 0.05822 0.0989 0.59 0.5590 FIN 58 0.2597 1.641 0.16 0.8748 -0.1450 0.1054 -1.38 0.1740 FN 58 0.2124 1.811 0.12 0.9070 0.1040 0.1377 0.76 0.4530 GL 58 0.6919 1.8805 0.37 0.7143 0.0824 0.1525 0.54 0.5910 HH 58 0.5785 1.5921 0.36 0.7177 0.1606 0.1103 1.46 0.1510 M 58 -1.0011 1.7276 -0.58 0.5645 0.1579 0.1440 1.10 0.2780 MGR 58 0.4678 1.8017 0.26 0.7961 -0.2729 0.1316 -2.07 0.0430 MRB 58 -1.2299 1.8959 -0.65 0.5191 -0.0010 0.1509 -0.01 0.9950 NCH 58 -0.3918 1.6630 -0.24 0.8146 -0.0329 0.1127 -0.29 0.7720 NST 58 -2.1080 1.6615 -1.27 0.2096 -0.2655 0.1111 -2.39 0.0200 QL 58 0.0288 1.8147 0.02 0.9874 -0.0751 0.1338 -0.56 0.5770 SA 58 -0.7419 1.6473 -0.45 0.6541 -0.0279 0.1064 -0.26 0.7940 SM 58 -0.4658 1.6725 -0.28 0.7816 -0.1020 0.1200 -0.85 0.3990 TOA 58 0.9769 1.6045 0.61 0.5450 0.2323 0.0996 2.33 0.0230 TOD 58 1.4143 1.6246 0.87 0.3876 0.2303 0.0984 2.34 0.0230 WK 58 0.0826 1.7959 0.05 0.9635 0.0716 0.1203 0.60 0.5540 The ecosystem zones in BC are abbreviated BB, BLK, CHL, CP, CT, DK, EK, FIN, FN, GL, HH, M, MGR, MRB, NCH, NST, QL, SA, SM, TOA, TOD and WK; these denote Big Bar, Bulkley, Chilcotin, Central Plateau, Cariboo Transition, Dease Klappan, East Kootenay, Finlay, Ft. Nelson, Georgia Lowlands, Hudson Hope, Maritime zones, McGregor, Mt. Robson, Nechako, Nass Skeena Transition, Quesnel Lakes, Shuswap Adams, Submaritime, Tompson Okanagan Arid, Tompson Okanagan Dry, and West Kootenay, respectively.    218  Table B.3 Putative homologs of three genes used for phylogeny analyses      Species ABA INSENSITIVE 3 (ABI3) (gymnosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Average bit score (top) Average e-value (lowest) Average Identity (similarity) Picea abies PUT-175a-Picea_abies-7049  161.96 (232.65) 4.56e-20 (1.05e-65) 65.30 % (77.92 %) Picea glauca PUT-175a-Picea_glauca-41040  233.42 (233.42) 1.01e-67 (1.01e-67) 90.60 % (96.58 %) Picea sitchensis PUT-183a-Picea_sitchensis-30211  54.68 (54.68) 5.41e-8 (5.41e-8) 39.33 % (53.93 %) Cryptomeria japonica PgdbCjaponica_21159  154.07 (154.07) 8.48e-42 (8.48e-42) 65.71 % (80.95 %) Cycas rumphii      Ginkgo biloba PgdbGbiloba_5148  101.68 (101.68) 3.25e-24 (3.25e-24) 60.00 % (81.43 %) Gnetum gnemon      Picea engelmannii × Picea glauca PgdbPengPgla_6162  42.74 (42.74) 1.31e-4 (1.31e-4) 35.06 % (50.65 %) Pinus banksiana      Pinus contorta      Pinus pinaster PgdbPpinaster_14745  143.28 (143.28) 4.94e-39 (4.94e-39) 50.75 % (70.15 %) Pinus sylvestris      Pinus taeda PgdbPtadea_12535  90.51 (90.51) 5.27e-19 (5.27e-19) 37.04 % (57.04 %) (angiosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Score Expect Identities Amborella trichopoda   XM_006852058.1 254 bits(650) 8E-74 142/235 (60%) Arabidopsis thaliana  NM_113376    Populus trichocarpa   XM_002303052 502 bits(1293) 1E-166 359/698 (51%) Zea mays viviparous1 (vp1) NM_001112070 241 bits(616) 3E-67 136/254 (54%) Oryza sativa  NM_001051697 222 bits(565) 4E-60 128/249 (51%) Brassica napus  NM_001315761.1 902 bits(2331) 0 574/715 (80%)  219     Species AUXIN RESPONSE FACTOR 10 (ARF10) (gymnosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Average bit score (top) Average e-value (lowest) Average Identity (similarity) Picea abies UCPabies_isotig13254  445.66 (445.66) 1.09e-149 (1.09e-149) 58.91 % (68.48 %) Picea glauca PUT-175a-Picea_glauca-8273  224.17 (224.17) 1.43e-66 (1.43e-66) 63.07 % (73.30 %) Picea sitchensis PUT-183a-Picea_sitchensis-12003  217.62 (217.62) 1.98e-63 (1.98e-63) 45.81 % (66.08 %) Cryptomeria japonica PgdbCjaponica_5096  169.86 (169.86) 4.62e-48 (4.62e-48) 36.86 % (50.73 %) Cycas rumphii PgdbCrumphii_4452  208.38 (208.38) 2.84e-60 (2.84e-60) 49.10 % (63.96 %) Ginkgo biloba PgdbGbiloba_7362  62.00 (62.00) 1.86e-11 (1.86e-11) 33.33 % (50.00 %) Gnetum gnemon PgdbGgnemon_356399  208.38 (208.38) 2.20e-61 (2.20e-61) 37.09 % (47.80 %) Picea engelmannii × Picea glauca PgdbPengPgla_13293  104.76 (104.76) 6.96e-25 (6.96e-25) 58.33 % (75.00 %) Pinus banksiana PgdbPbanksiana_7142  106.88 (113.62) 7.98e-23 (5.95e-27) 52.91 % (68.87 %) Pinus contorta PgdbPcontorta_99  110.73 (115.16) 9.59e-25 (2.19e-27) 44.04 % (56.79 % Pinus pinaster PgdbPpinaster_1853  148.67 (148.67) 6.53e-41 (6.53e-41) 34.23 % (44.23 %) Pinus sylvestris PgdbPsylvestris_2073779  101.29 (101.29) 2.15e-25 (2.15e-25) 60.00 % (74.29 %) Pinus taeda PgdbPtadea_34119  172.94 (172.94) 5.88e-49 (5.88e-49) 37.60 % (48.80 %) (angiosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Score Expect Identities Amborella trichopoda  LOC18995790 XM_006846723 657 bits(1696) 0 383/737 (52%) Arabidopsis thaliana  NM_128394.4    Populus trichocarpa   XM_006384847.1 775 bits(2002) 0 444/748 (59%) Zea mays  JX428504.1 648 bits(1672) 0 376/701 (54%) Oryza sativa  AB071299.1 695 bits(1794) 0 395/721 (55%) Brassica napus  XM_013836968.1 1102 bits(2849) 0 615/714 (86%)  220      Species ARF16/ 17 (gymnosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Average bit score (top) Average e-value (lowest) Average Identity (similarity) Picea abies UCPabies_isotig13254  467.62 (467.62) 1.74e-158 (1.74e-158) 60.81 % (72.77 %) Picea glauca PUT-175a-Picea_glauca-36625  348.98 (348.98) 3.50e-114 (3.50e-114) 59.30 % (73.33 %) Picea sitchensis PUT-183a-Picea_sitchensis-12003  230.34 (230.34) 2.79e-68 (2.79e-68) 36.97 % (53.22 %) Cryptomeria japonica PgdbCjaponica_5096  193.74 (193.74) 1.30e-56 (1.30e-56) 42.59 % (57.79 %) Cycas rumphii PgdbCrumphii_4452  212.23 (212.23) 8.75e-62 (8.75e-62) 37.94 % (52.06 %) Ginkgo biloba PgdbGbiloba_7362  62.39 (62.39) 1.59e-11 (1.59e-11) 33.63 % (48.67 %) Gnetum gnemon PgdbGgnemon_356399  232.26 (232.26) 2.19e-70 (2.19e-70) 38.98 % (52.82 %) Picea engelmannii × Picea glauca PgdbPengPgla_13293  103.99 (103.99) 1.07e-24 (1.07e-24) 53.01 % (80.72 %) Pinus banksiana PgdbPbanksiana_7142  114.97 (127.49) 1.11e-23 (1.28e-31) 51.85 % (75.65 %) Pinus contorta PgdbPcontorta_99  108.42 (112.08) 3.04e-24 (2.29e-26) 51.88 % (74.62 %) Pinus pinaster PgdbPpinaster_4516  154.84 (154.84) 7.25e-44 (7.25e-44) 55.15 % (66.06 %) Pinus sylvestris PgdbPsylvestris_2073779  100.91 (100.91) 2.27e-25 (2.27e-25) 60.00 % (72.86 %) Pinus taeda PgdbPtadea_34119  179.10 (179.10) 3.44e-51 (3.44e-51) 41.53 % (56.45 %) (angiosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Score Expect Identities Amborella trichopoda   XM_006846723 652 bits(1681) 0 373/720 (52%) Arabidopsis thaliana  NM_119154  0  Populus trichocarpa   XM_002316334 801 bits(2070) 0 431/686 (63%) Zea mays  XM_008645286.1 699 bits(1804) 0 383/719 (53%) Oryza sativa  AB071299  733 bits(1891) 0 398/701 (57%) Brassica napus  XM_013852053 1234 bits(3193) 0 614/668 (92%)  221  Species DELAY OF GERMINATION 1 (DOG1) (gymnosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Average bit score (top) Average e-value (lowest) Average Identity (similarity) Picea abies      Picea glauca PUT-175a-Picea_glauca-35006  131.72 (131.72) 1.05e-35 (1.05e-35) 33.01 % (55.34 %) Picea sitchensis      Cryptomeria japonica PgdbCjaponica_17047  38.51 (38.51) 2.71e-4 (2.71e-4) 38.00 % (58.00 %) Cycas rumphii      Ginkgo biloba      Gnetum gnemon      Picea engelmannii × Picea glauca      Pinus banksiana PgdbPbanksiana_5706  43.90 (43.90) 2.27e-5 (2.27e-5) 25.41 % (39.46 %) Pinus contorta      Pinus pinaster      Pinus sylvestris      Pinus taeda PgdbPtadea_32109  52.76 (52.76) 1.72e-7 (1.72e-7) 25.10 % (40.17 % (angiosperm) Hit ID (respective PlantGDB or UC Davis PUTs database) Acession No. in NCBI Score Expect Identities Amborella trichopoda   XM_006830201 139 bits(349) 3.00E-37 85/252 (34%) Arabidopsis thaliana  NM_123951    Populus trichocarpa   XM_006380338 110 bits(276) 6.00E-27 63/211 (30%) Zea mays  XM_008675465.1 63.9 bits(154) 2.00E-10 62/243 (26%) Oryza sativa  XM_015766197 90.5 bits(223) 1.00E-18 66/235 (28%) Brassica napus  KM373223.1 207 bits(526) 2.00E-62 102/129 (79%)  222  Table B.4 Sequencing statistics Library Raw reads Reads in analysis (quality filtered) Reads mapped the genome with predictable hairpin structure Reads in miRBase Useful reads/ quality filtered reads (%) Reads in miRBase under Arabidopsis/ quality filtered reads (%) Cvi_0 2,929,874 2,270,806 1,736,648 677,640 76.4772 29.8414 Cvi_1 8,024,042 6,790,650 5,479,440 792,875 80.6910 11.6760 Cvi_2 8,931,756 7,764,480 6,521,475 928,658 83.9911 11.9603 Cvi_3 10,340,203 8,912,047 7,186,010 1,547,148 80.6325 17.3602 Cvi_4 12,233,016 10,622,705 8,383,036 2,095,759 78.9162 19.7291 Cvi_5 10,572,318 8,982,592 7,305,717 1,721,227 81.3320 19.1618 Cvi_6 10,567,151 8,841,677 7,149,633 1,967,579 80.8629 22.2535 Cvi_7 9,982,635 8,151,177 6,526,097 1,767,267 80.0632 21.6811 Cvi_8 9,932,935 7,995,440 6,338,556 1,866,071 79.2771 23.3392 Cvi_9 9,969,729 8,002,190 6,335,602 2,142,067 79.1733 26.7685 Cvi_10 9,791,092 7,969,760 6,486,277 1,660,487 81.3861 20.8348 Cvi_11 11,886,424 9,783,636 7,865,333 2,069,369 80.3927 21.1513 Cvi_12 12,750,931 10,860,887 9,323,179 1,633,421 85.8418 15.0395 Cvi_13 8,941,585 7,133,966 5,679,383 1,565,806 79.6105 21.9486 Cvi_14 8,390,383 6,756,272 5,448,264 1,538,045 80.6401 22.7647 Col_0 11,021,420 9,395,721 8,635,139 1,796,109 91.9050 19.1162 Col_1 11,625,558 10,023,838 9,257,778 1,721,021 92.3576 17.1693 Col_2 11,331,773 9,836,290 9,226,074 1,915,333 93.7963 19.4721 Col_3 7,985,273 6,890,571 6,185,226 945,721 89.7636 13.7249 Col_4 9,094,377 7,787,215 7,145,216 1,336,870 91.7557 17.1675 Col_5 8,848,382 7,625,017 6,797,881 1,093,779 89.1523 14.3446 Col_6 13,371,880 11,539,042 10,277,747 1,997,994 89.0693 17.3151 Col_7 12,356,989 10,468,643 9,432,629 1,941,235 90.1036 18.5433 Col_8 11,222,065 9,451,169 8,141,818 1,160,209 86.1461 12.2758 Col_9 14,867,503 12,185,128 10,380,548 1,628,708 85.1903 13.3664        P1_1 1,194,345 1,180,797 383,094 1,136 32.4437 0.0962 P1_2 6,913,567 6,835,591 2,032,178 10,940 29.7294 0.1600 P1_3 10,998,502 10,875,747 2,452,669 42,067 22.5517 0.3868 P2_1 26,007,430 25,715,712 6,050,650 107,404 23.5290 0.4177 P2_2 25,590,236 25,304,443 5,649,160 108,969 22.3248 0.4306 P2_3 17,194,089 17,001,245 4,980,120 51,376 29.2927 0.3022 P2_4 17,681,943 17,482,721 4,853,528 58,868 27.7619 0.3367 P3_1 27,451,473 27,143,217 4,493,203 83,881 16.5537 0.3090 P3_2 58,190 57,549 10,719 56 18.6259 0.0973 P3_3 1,140,704 1,127,778 382,674 1,355 33.9317 0.1201 P3_4 12,937,225 12,792,447 3,271,072 21,732 25.5703 0.1699 P4_1 27,777,485 27,465,316 4,708,343 93,015 17.1429 0.3387 P4_2 21,786,005 21,542,455 3,685,105 64,888 17.1062 0.3012 P4_3 16,591,205 16,405,257 3,267,506 68,879 19.9174 0.4199 P4_4 13,276,103 13,127,259 3,754,278 37,457 28.5991 0.2853    223  Table B.5 Comparative summary of spruce miRNA reads in this study and previous reports   (1) Known miRNAs    mature_miR count reported by  Xia et al. 2015 reported by Källman et al. 2013 AAGCTCAGGAGGGATAGCGCC 21 NA MIR390 CAGCCCTTCTGCTATCCACAAC 22 pab-miR946a-3p MIR946 CCAGCCCTTCTGCTATCCACAT 22 pab-miR946c NA CGAAGGGTCGGACTTGTTTAGC 22 pab-miR3701c-5p NA CGCCAAAGGAGAGTTGCCCTG 21 NA MIR399 TAAACAATGCCCACCCTTCATC 22 pab-miR3701b MIR3701 TAAGCCAAGGCAGAGTTGCAAG 22 pab-miR3698b NA TACAACGGCCAGGCGACATTG 21 pab-miR3695a NA TACCACTGAAATTATTGTTCGA 22 NA MIR1313 TATCGGAATCTGTTACTGTTTC 22 pab-miR947 MIR947 TCACATCTGGGCCACGATGGTT 22 pab-miR950a-5p NA TCACGTCAGGGCCACGATGGTT 22 pab-miR950b MIR950 TCAGAGTTTTGCCAGTTCCGCC 22 NA MIR1311 TCAGGAGCTGCTCGTAGGTGA 21 pab-miR3693b NA TCGATAAGCCTCTGCATCCAG 21 NA MIR162_2 TCGGACCAGGCTTCATTCCCC 21 NA MIR166 TCTTCCCAACCCCTCCCATGCC 22 pab-miR482o NA TCTTCCCTAAACCTCCCAAACC 22 pab-miR482k NA TCTTCCCTACTCCTCCCATTCC 22 pab-miR482a MIR482 TCTTTCCACTTCTACCCATTTC 22 pab-miR482e NA TCTTTCCTACTCCTCCCATTCC 22 pab-miR482c NA TCTTTTCAGTTCTACCCATTTC 22 pab-miR482g NA TGAACAATGCCCACCCTTC