Science, Faculty of
Botany, Department of
DSpace
UBCV
Pascual Robles, Mariana Alicia
Doctor of Philosophy - PhD
University of British Columbia
Speciation is the process by which barriers to gene flow evolve between previous interbreeding populations. In this dissertation, I explore the earliest stages of speciation process, using the silverleaf sunflower (Helianthus argophyllus) as an experimental system. The species occurs on the coastal plain of southern Texas and is comprised of two ecotypes that differ in flowering time due to a presence/absence polymorphism for a major flowering time gene, FLOWERING LOCUS T (HaFT1). Asynchrony in flowering time can facilitate divergence and speciation. Therefore, I mapped the distribution of the HaFT1 polymorphism and explored its the effects on phenology and other traits. I found that HaFT1 was rare on the mainland, but both haplotypes were abundant in the barrier islands. The polymorphism at HaFT1 was closely correlated with variation in flowering time, plant height, seed size, and habitat. The rarity of HaFT1 is most likely due to high rates of immigration of the late flowering ecotype to the islands, combined with anthropogenic disturbance, which may ease establishment. I also asked if differences in flowering phenology cause assortative mating, thereby reducing gene flow and increasing reproductive isolation (RI). Parentage analyses revealed that RI was strong early and late in the flowering season when only one of the ecotypes was flowering, but weak during the middle of the season, when both ecotypes were flowering. A genomic scan based on re-sequencing data further revealed that even in the face of high gene flow in the present, assortative mating has had a genomic-wide impact, particularly in a region of low recombination where HaFT1 is found. Lastly, I took advantage of Hurricane Harvey, which hit my H. argophyllus sites, to ask what factors might aid survival of an extreme flooding event. I found that individuals that were shorter were more likely to survive. In the year following Hurricane Harvey, I observed massive establishment of H. argophyllus at sites where it was not present the previous year, suggesting the importance of seed banks in adaptation of the species to extreme climate events. Overall, H. argophyllus appears to be both resistant and resilient to extreme climatic events.
THE EFFECTS OF A FLOWERING LOCUS T POLYMORPHISM ON PHENOLOGY, REPRODUCTIVE ISOLATION AND HURRICANE SURVIVAL IN THE SILVERLEAF SUNFLOWER by Mariana Alicia Pascual Robles H.B.Sc., Universidad Autónoma del Estad de Morelos, 2014 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Botany) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) July 2023 © Mariana Alicia Pascual Robles, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: The effects of a FLOWERING LOCUS T polymorphism on phenology, reproductive isolation and hurricane survival in the silverleaf sunflower submitted by Mariana Alicia Pascual Robles in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Botany Examining Committee: Loren H. Rieseberg, Professor, Botany, UBC Supervisor Quentin Cronk, Professor, Botany, UBC Supervisory Committee Member Sean Graham, Professor, Botany, UBC Supervisory Committee Member Jeannette Whitton, Associate Professor, Botany, UBC University Examiner Sally Aitken, Professor, Forest and Conservation Sciences, UBC University Examiner iii Abstract Speciation is the process by which barriers to gene flow evolve between previous interbreeding populations. In this dissertation, I explore the earliest stages of speciation process, using the silverleaf sunflower (Helianthus argophyllus) as an experimental system. The species occurs on the coastal plain of southern Texas and is composed of two ecotypes that differ in flowering time due to a presence/absence polymorphism for a major flowering time gene, Flowering Locus T (HaFT1). Asynchrony in flowering time can facilitate divergence and speciation. Therefore, I mapped the distribution of the HaFT1 polymorphism and explored its effects on phenology and other traits. I found that HaFT1 was rare on the mainland, but both haplotypes were abundant in the barrier islands. The polymorphism at HaFT1 was closely correlated with variation in flowering time, plant height, seed size, and habitat. The rarity of HaFT1 is most likely due to high rates of immigration of the late flowering ecotype to the islands, combined with anthropogenic disturbance, which may ease establishment. I also addressed whether if differences in flowering phenology cause assortative mating, thereby reducing gene flow and increasing reproductive isolation (RI). Parentage analyses revealed that RI was strong early and late in the flowering season when only one of the ecotypes was flowering, but weak during the middle of the season, when both ecotypes were flowering. A genomic scan based on re-sequencing data further revealed that even in the face of high gene flow in the present, assortative mating has had a genomic-wide impact, particularly in a region of low recombination where HaFT1 is found. Lastly, I took advantage of Hurricane Harvey, which hit my H. argophyllus sites, to ask what factors might aid survival in an extreme flooding event. I found that individuals that were shorter were more likely to survive. In the year following Hurricane Harvey, I observed massive establishment of H. argophyllus at sites where it was not present the previous year, suggesting iv the importance of seed banks in adaptation of the species to extreme climate events. Overall, H. argophyllus appears to be both resistant and resilient to extreme climatic events. v Lay Summary How are new species born? It is challenging to study the birth of a species. In many cases, so much time has passed that we can no longer know how it all started; all we can see is what makes a species different. The wild silverleaf sunflowers of the Texas coast are at the earliest stage of speciation, providing a rare snapshot of how the process begins. Additionally, through an accidental encounter with Hurricane Harvey, this study shows how some species are likely to benefit from the increases in extreme climate events that are forecast under climate change. Moreover, it demonstrates that some species are strong enough to withstand and quickly recover from devastating storms. This information may help us breed crops that are resilient to extreme environments, as well as better guard the coastal environments that protect three quarters of the world’s population from hurricanes and other storms. vi Preface This thesis builds on the doctoral thesis of Brook M. Moyers at the Rieseberg lab. Loren Rieseberg and I designed the research program. I led all of the sampling carried out in natural populations, assisted by Winnie Cheung and Oscar Dorado Ruiz. Marco Todesco designed the primers used for genotyping. I carried out most of the lab work and all of the data analyses. The last steps of lab work were done by Helia Jafari under my supervision. I wrote the thesis. Loren Rieseberg and my committee (Quentin Cronk and Sean Graham) provided advice and feedback throughout this project. vii Table of Contents Abstract ..................................................................................................................................... iii Lay Summary .............................................................................................................................. v Preface ...................................................................................................................................... vi Table of Contents ..................................................................................................................... vii List of Tables .............................................................................................................................. ix List of Figures ............................................................................................................................ ix List of Abbreviations ................................................................................................................. xii Acknowledgements ................................................................................................................. xiii Dedication ................................................................................................................................ xv Chapter 1: Introduction .............................................................................................................. 1 1.1 Ecological divergence............................................................................................... 1 1.2 Geography and land use in region of study .............................................................. 5 1.3 Study species ........................................................................................................... 7 Chapter 2: A fine-scale geographic mosaic of phenotypical and phenological differences caused by presence/absence polymorphism in a FLOWERING LOCUS T homolog ................... 11 2.1 Introduction........................................................................................................... 11 2.1 Methods ................................................................................................................ 16 2.3 Results ................................................................................................................... 23 2.4 Discussion .............................................................................................................. 38 2.5 Conclusions............................................................................................................ 43 Chapter 3: Assortative mating between two silver-leaf sunflower ecotypes depends on genotype, environment and time............................................................................................. 45 3.1. Introduction........................................................................................................... 45 3.2. Methods ................................................................................................................ 49 3.3. Results ................................................................................................................... 53 3.4. Discussion .............................................................................................................. 66 3.5. Conclusions............................................................................................................ 73 viii Chapter 4: Hurricane Harvey and the perks of being a wild sunflower .................................... 75 4.1 Introduction........................................................................................................... 75 4.2. Methods ................................................................................................................ 82 4.3 Results ................................................................................................................... 85 4.4. Discussion .............................................................................................................. 98 4.5. Conclusions.......................................................................................................... 105 Chapter 5: Conclusions ........................................................................................................... 106 5.1 Introduction......................................................................................................... 106 5.2 Divergence, local adaptation and gene flow......................................................... 107 5.3 Low recombination .............................................................................................. 112 5.4 Extreme events and seed banks ........................................................................... 113 5.5 Limitations and future studies ............................................................................. 114 References .............................................................................................................................. 116 Appendix A ............................................................................................................................. 135 Appendix B ............................................................................................................................. 139 ix List of Tables Table 2.1. Primer information ................................................................................................... 20 Table 2.2. Weather stations ...................................................................................................... 20 Table 2.3. Population genotyping information .......................................................................... 25 Table 2.4. Number of individuals at a reproductive phase by month, region and genotype ...... 32 Table 3.1. Summary of the total number of seedlings genotyped by sampling population and region of origin ......................................................................................................................... 53 Table 3.2. Comparison of genotype proportions between offspring and parental individuals ... 54 Table 3.3. Deviations from Hardy-Weinberg expectations in offspring populations .................. 55 Table 3.4. Genotypes of the seedlings (offspring) included in the reproductive isolation analyses by each locality ......................................................................................................................... 58 Table 3.5. Reproductive isolation by month, region and genotype ............................................ 59 Table 3.6. Fst differentiation in different genomic regions between HaFT1 genotypes and between regions ....................................................................................................................... 62 Table 4.1. Average height by HaFT1 genotype of H. argophyllus before Hurricane Harvey in different barrier island populations ........................................................................................... 89 Table 4.2. Surviving H. argophyllus genotypes per island population ........................................ 91 Table 4.3. Frequency of HaFT1 in H. argophyllus across years by population ............................ 95 Table 4.4. Genotype frequency changes of HaFT1 in H. argophyllus populations across years .. 96 Table 4.5. Mean seed weight in grams of groups of ten seeds of Helianthus argophyllus by region, month and year ............................................................................................................. 97 x List of Figures Figure 1.1.Distribution of the species and ecotypes of the silverleaf sunflower.......................... 9 Figure 2.1. Classification of flowering stages ............................................................................. 18 Figure 2.2. Distribution and frequency (f) of the flowering gene HaFT1. ................................... 26 Figure 2.3. Genotype frequencies in the two studied regions. .................................................. 27 Figure 2.4. Flowering stage proportions in each region by month. ............................................ 28 Figure 2.5. Height at the beginning of flowering by region. ....................................................... 30 Figure 2.6. Association of flowering stage with genotype by month and the interaction of region, genotype and month. .................................................................................................... 31 Figure 2.7. Average seed weight (g) produced by each genotype. ............................................. 33 Figure 2.8. Mean seed weight (g) by flowering stage by month ................................................ 35 Figure 2.9. Proportion of individuals at different life stages months and regions for each ecotype. .................................................................................................................................... 36 Figure 2.10. Mean monthly precipitation (in) in each region . ................................................... 37 Figure 3.1. Frequency of HaFT1 in seedings by region and time of seed collection. ................... 56 Figure 3.2. Frequency of HaFT1 in seedlings depending on their position on a plant. ................ 57 Figure 3.3. Mean reproductive isolation ................................................................................... 61 Figure 3.4. Genome-wide frequency distribution of Fst values for individual SNPs.................... 63 Figure 3.5. Frequency distribution of Fst values for SNPs on chromosome six. .......................... 64 Figure 3.6. Fst scan across chromosome 06. ............................................................................. 65 Figure 4.1. Satellite image of Hurricane Harvey at 3:15 p.m. on August 25 2017. ...................... 79 xi Figure 4.2. Image of the sampled population of H. argophyllus in Port Aransas before and after Hurricane Harvey. ..................................................................................................................... 85 Figure 4.3. Image of a silverleaf sunflower killed by Hurricane Harvey next to a survivor. ......... 86 Figure 4.4. Survival of H. argophyllus by flowering stage considering all populations. ............... 87 Figure 4.5. Height and survival of H. argophyllus considering all populations. ........................... 88 Figure 4.6. Mosaic plot showing the proportion of H. argophyllus survivors per genotype in the barrier islands. .......................................................................................................................... 90 Figure 4.7. Proportion of surviving individuals of H. argophyllus for each barrier island population, distance to a body of water (km) and genotype. .................................................... 93 Figure 4.8. Genotype frequencies in the same populations before and after the hurricane. ..... 94 Figure 4.9. Seed weight by region, month and year. ................................................................. 96 Figure 5.1. Records of Helianthus argophyllus in Texas. .......................................................... 109 xii List of Abbreviations DNA = Deoxyribonucleic Acid Fst = Fixation index; the variance of allele frequencies between populations; a measure of population differentiation GxE = Genotype by environment interaction PPM = Plant preservative mixture RI = Reproductive isolation U.S.A. = United States of America xiii Acknowledgements I first want to acknowledge the University professors who taught me the wonders of field work, plants, evolution, conservation and hard work: Dra. Rosa Cerros Tlatilpa, Dr. Dulce Arias and Dr. Oscar Dorado. Then, of course, I offer my enduring gratitude to Dr. Loren Rieseberg for inviting me to work on his lab –particularly after I mocked the study of cultivated plants and then tried to make him eat insects and corn fungus. Thank you for the support and patience, but more importantly thank you for still believing in me even when I had lost all hope. Thanks to my committee for improving the contents of this thesis in so many ways but mostly for being so nice always. Jeannette, thank you for coming to my rescue! Thank you Winnie, for being who you are. You are a ray of sunshine. Thank you to the hundred Rieseburglars that I have met during the 50 years of my passing through the lab. Thank you Julia, Emily, Kat [no, the other one, no, not that one, yes, that one], Kaichi, Pauline, Eric, Fernando, Mojtaba, Natalia, Armando and Kristin, and the other 88 I am forgetting right now. Special thanks to Marco Todesco, for being so nice and so patient with someone who never had a clue of what she was doing and asked the same questions for half a century. And of course, thank you Brook, for jing bao and everything else. A los chicos del departamento tresveinticinco that beared with me for years; you are the best group of people I could have crossed paths with. Kristin (aka the bunny in the moon), Maddie (aka the coolest girl in town), Julian (aka Mr. Nature), Liboria mai lov (aka the pun queen), Manny (aka the floral fashionista), and Amy (aka Miss. Invasion and Maui’s mom), I love you. Gaby you are a ray of sunshine. Angie, Taylor and Marysol, and the rest of the Latinamerican gals in the BRC and beyond thank you for all the dancing and for laughing at my jokes. Stillian, xiv Pierre, Kenny, Sam, Indre, Devyani, Michaela, Tomoko, Ana, and anyone who ever shared a dance floor, a beach or a beer with me; you all injected me with life and joy, and even if you were only a brief part of my life I will remember you always. Here, I should probably add Alice Liu who kept me on track and saved my [**] so many times. Gracias flaquito chulo por mudarte a 5 mil kilómetros de todo lo que conocías para estar conmigo otra vez y para siempre. A mi mamá, papá y hermano por ponerme en este camino y hacer la vida mucho más ligera y demasiadas cosas más. Finally, the Anti-acknowledgement award goes without a doubt to the mental health system at UBC and the rest of British Columbia. Everyone involved made my life way more horrible and complicated than it already was through my PhD. This thesis exists despite all of you. I am still alive despite all of you. My revenge is to be happy. xv Dedication Too many people come to mind. My parents. My dad taught me to love science since I was very little. My feminist mom always had big dreams for me and made sure I was prepared. A mi flaquito for keeping my pieces together and reminding me what really matters in life. <3 To Kristin. I miss you. And definitely Brook Moyers Without whom this thesis would not exist. To all my friends. I absolutely adore all of you. You are the best part of life. A special dedication to all those fighting or surviving a storm, know that “The strength of a tree comes not from growing thicker in the good years, but from staying alive during the bad, dry times” – Western Shoshone elder (Harney, 1995) 1 Chapter 1: Introduction 1.1 Ecological divergence Speciation is often characterized by the gradual accumulation of reproductive barriers, resulting in “a continuum of reproductive isolation” in nature (Stankowski & Ravinet, 2021). Such barriers can occur at different life stages and can be determined by internal or external factors. It is the contribution, timing, and interaction of isolating barriers that determines the level of reproductive isolation (Sobel & Chen, 2014; Stankowski & Ravinet, 2021). Closely related species rarely show complete reproductive isolation (for several examples see Lowry et al., 2008a; Sobel & Chen, 2014). This could be because divergence is recent or because different evolutionary forces and/or the environment have hampered divergence. Speciation is not a fixed ultimate step in a progression of divergence, but rather the process itself (Clausen, 1951; Hendry et al., 2007). Usually, populations characterized by low levels of reproductive isolation and ecological differentiation, and/or with ongoing gene flow, are considered to be at early stages of divergence (Lowry, 2012; Stankowski & Ravinet, 2021). However, neither gene flow nor reproductive isolation are constant, particularly in highly variable environments (in both time and space). Also, the divergence of plant lineages appears to be more strongly impacted by fine-scale environmental heterogeneity compared to more vagile animal species (Anacker & Strauss, 2014). At the same time, when environmental variation is unpredictable and/or occurs at a small spatial scale, polymorphisms can be maintained within populations (Friedman et al., 2019). The importance of the environment in the formation of new plant lineages has been known for over a century. In Western science, variation within species in relationship to the environment, and the ecotype concept, were well established by the work of botanists such as Göte 2 Turesson*, William Hesey, David Keck and Jens Clausen (reviewed in Lowry, 2012; Núñez-Farfán & Schlichting, 2015). Their reciprocal transplant and common garden experiments showed that phenotypic variation was not randomly distributed nor merely a plastic response to the environmental conditions where different plant populations grew. Ecotypes are genetically distinct populations that are adapted to specific environmental conditions. They are often viewed as the intermediate phase between panmixia and speciation (Clausen, 1951; Grant, 1981; Lowry, 2012). Because reproductive cycles are tightly linked to the environment, the process of local adapation can disrupt gene flow patterns among populations (e.g., Antonovics, 1968; McNeilly & Antonovics, 1968). The more variance in reproductive timing, the higher the potential for non-random mating (Weis et al., 2014) and, hence, for divergence. Assortative mating, spatial structure, and ecological selection can individually, or in combination, produce clusters of similar individuals that might represent ecotypes or species (Caetano et al., 2020). However, it should be kept in mind that reproductive isolation typically is not directly selected for, but most often appears to be a by-product of divergent selection (Schluter, 2001). My favorite example involves the Howea palms of Lord Howe island, Howea belmoreana and H. forsteriana (Savolainen et al., 2006; Papadopulos et al., 2013; Hipperson et al., 2016; Osborne et al., 2019), where edaphic adaptation led to divergence in flowering time. In some cases, time can be a larger limiting factor for mating than space (Hendry & Day, 2005). However, assortative mating due to reproductive asynchrony does not necessarily prevent the merger of populations. Shifts in flowering time, for example, typically lead to incomplete reproductive isolation (for several examples see Lowry et al., 2008a; Sobel & Chen, 2014). Still, closely related species can coexist for millions of years despite ongoing hybridization (e.g., Hipperson et al., 2016; Nomura et al., 2022; although see Todesco et al. 2016). This is because * Fun fact: In December 2022 the term “ecotype” turned 100 years old (Turesson, 1922). 3 phenology often represents just one of several reproductive barriers isolating taxa (Grant, 1949, 1981; Levin, 1971; Coyne & Orr, 2004; Lowry et al., 2008a; Widmer et al., 2009; Baack et al., 2015; Christie et al., 2022). A crucial element preventing gene flow between taxa is the relationship between plant fitness and the environment, including selection against maladapted immigrants and hybrids (e.g., Irwin, 2020; Levin, 2009; Martin & Willis, 2007; Melo et al. 2014; Ostevik et al. 2016). While immigrant inviability has frequently been documented in reciprocal transplant experiments (reviewed in Lowry et al. 2008a and Christie et al., 2022), direct evidence of extrinsic selection against plant hybrids is less common in such studies (although see Melo et al., 2014; Ostevik et al., 2016), apparently because heterosis can mask the effects of environmental selection in early generation hybrids (Baack et al., 2015). Environmental selection can also lower mating opportunities between groups that occupy different environments/niches (Schluter, 2009), which has been referred to by botanists as ecogeographic isolation (Stebbins, 1950). A positive association between ecological or habitat divergence and reproductive isolation has been found in many taxa (e.g., Funk et al., 2006; Karrenberg et al., 2019; Lowry et al., 2008b). Ecological divergence appears to be one of the first and strongest reproductive barriers to arise during the speciation process in plants (Christie & Strauss, 2018), and as noted above, can act both before and after fertilization (Christie et al., 2022). However, as ecological speciation is based on extrinsic factors, it is dependent on the stability or heterogeneity of the environment, and therefore could be transient (Seehausen et al., 2008). There are many examples of the breakdown of reproductive barriers in disturbed habitats, particularly when disturbance has anthropogenic causes, where the resulting ecological instability, changes in natural ranges, and habitat modification facilitate gene flow between taxa (e.g., Abbott et al., 2003; Anderson, 1948; Anderson & Stebbins, 1954; Heiser et al., 1979; Todesco et al., 2016; Wiegand, 1935). Likewise, extreme climatic events can drastically modify the dynamics of natural populations 4 (Grant et al., 2017), including the selective environment that previously prevented populations from merging. However, at the beginning of the speciation process, between parapatric or sympatric taxa, some level of gene flow is expected to occur. A recent explosion in research about divergence under gene flow has uncovered the tight interconnection between ecological speciation and genomic architecture/recombination patterns. Numerous examples of genomic regions of reduced recombination that harbor alleles causing reproductive isolation through ecological adaptation have been discovered across a wide range of eukaryotes, e.g., seagrass flies (Mérot et al., 2021), fungi (Hartmann et al., 2020), butterflies (Merrill et al., 2019), birds (Küpper et al., 2015; Sanchez-Donoso et al., 2022), flowering plants (Lowry & Willis, 2010; Twyford & Friedman, 2015; Wang et al., 2020; Huang et al., 2020), fish (Jones et al., 2012; Berg et al., 2016; Thorstensen et al., 2022), honeybees (Christmas et al., 2019), ascidians (Satou et al., 2021), and mice (Hager et al., 2022). Such regions, sometimes called “supergenes”, have been found to affect a diverse array of traits, including habitat preference and/or life history traits (Lowry & Willis, 2010; Twyford & Friedman, 2015; Christmas et al., 2019; Huang et al., 2020; Todesco et al., 2020), social structure (Pracana et al., 2017; P. Cohen & Privman, 2020), mating system (Li et al., 2016), ornamentation and mating behaviour (Küpper et al., 2015), migratory routes (Lundberg et al., 2017), and mimicry (Clarke & Sheppard, 1960, 1972; Iijima et al., 2018), to mention a few examples. In this thesis, I examine an early stage in the speciation process in two ecotypes (early versus late flowering) of the silverleaf sunflower (Helianthus argophyllus) from the coastal plain of southern Texas. The two ecotypes differ in flowering time and other phenotypic traits that map to a genomic region of low recombination, which contains presence/absence variation (PAV) for a homolog of a key flowering gene, FLOWERING LOCUS T (HaFT1). I first explore the fine-scale distribution of allochrony caused by HaFT1 in the early and late flowering ecotypes of H. argophyllus (Chapter 2). Then, I estimate the contribution of HaFT1 to reproductive isolation 5 between the ecotypes, through reproductive allochrony (Chapter 3). Finally, I explore the consequences of an extreme storm that ravaged the Texas coast, where the two ecotypes are found (Chapter 4). 1.2 Geography and land use in region of study This study was mainly conducted on Mustang Island and the North section of Padre Island, which are barrier islands that are near the Corpus Christi Bay in Texas, U.S.A. Additional sampling localities were from the adjacent mainland, near Sarita in Kenedy County, and at the Welder Wildlife Refuge in Sinton, San Patricio County, Texas. Barrier islands form from the deposition of sand by wind and waves. Such islands tend to be long and narrow and form rapidly under favorable conditions (Morton et al., 2004). The Gulf of Mexico barrier island system, which is the longest in the world, is estimated to be ca. 2,000 years old (Garrison et al., 2010). Mustang and Padre Islands together are 200 km long, up to 3 km wide, and range from 10 to 15 m in elevation (Garrison et al., 2010). The weather is warm and humid, with relative humidity near 100% in summer (Morton et al., 2004). Precipitation and humidity decline with distance from the coast, and vegetation is susceptible to summer droughts (Morton et al., 2004). Mainland sites where H. argophyllus is found are open and characterized by sandy soil, but abundant sand is lacking (even near shoreline), so sand dunes similar to those on the barrier islands do not form (Morton et al., 2004). Common plants around the sand dunes in the barrier islands where H. argophyllus grows include: Ipomoea imperati (beach morning glory; Convolvulaceae), Croton punctatus (Gulf croton Euphorbiaceae), Ipomoea pres-carpae (bay-hops; Convolvulaceae), Spartina patens (saltmeadow cordgrass; Poaceae), Uniola paniculata (sea oats; Poaceae), Cakile sp. (searocket; Brassicaceae), Amaranthus greggii (amaranth; Amaranthaceae), and Tidestromia lanuginosa (wolly tidestromia; Amaranthaceae) (pers. obs.). In the mainland H. argophyllus co-occurs with 6 Quercus sp. (oak; Fagaceae), Opuntia sp.(cactus; Cactaceae), Cladium mariscoides (smooth sawgrass; Cyperaceae), Chamaescrista fasciculata (partridge pea; Fabaceae), and Centrosema virginianum (spurred butterfly pea; Fabaceae) (pers.obs). In the populations further inland the silverleaf sunflower was found along with Phlox glabrifolia (Rio Grande Phlox; Polemoniaceae), Opuntia sp.(cactus; Cactaceae) and near Prosopis glandulosa (mezquite; Fabaceae). This region has been populated by the Karankawa tribe for at least 4,500 years (Geneser, 2011). The Karankawa territory overlaps with much of the native range of the silverleaf sunflower, particularly in the coastal region between Corpus Christi and Galveston Bay (Lipscomb, 1976; Geneser, 2011; Seiter, 2017). Depending on food availability, this nomadic tribe used to migrate between the mainland and the barrier islands in the Gulf of Mexico (Lipscomb, 1976; Seiter, 2017). Whether disturbance by the Karankawa affected the distribution and abundance of H. argophyllus is not clear. Currently, this species is mostly found in anthropogenically disturbed habitats (Foster, 2020). After the colonization by European settlers and until 1950, the Mustang and Padre Islands were used for grazing (Lipscomb, 1976; Nueces County, 2012). In fact, Mustang Island got its name from the thousands of horses that roamed freely on the island. Padre Island was once described as being “green as a garden” (Price & Gunter, 1943 as cited in White et al., 1978). A combination of overgrazing and severe droughts in the late 1800s left large portions of the island filled with “barren sand and comparable to a desert” (White et al., 1978). Later, it was reported that in 1942 part of North Padre Island was barren of vegetation as a result of severe salt-water flooding (Price & Gunter, 1943 as cited in White et al., 1978). Over the next half century, vegetation on dunes and grass flats across both islands nearly doubled, from 29 km2 in 1938 to 54 km2 in 1974 (White et al., 1978). 7 1.3 Study species Helianthus argophyllus is an angiosperm from the family Asteraceae. It is an annual herb that can grow 1–5 m in height (Heiser et al., 1969). The species is commonly known as the silverleaf sunflower because of the vast amount of trichomes on its epidermis that give a tomentose or floccose appearance and a velvet-like texture, particularly to its stem and leaves. Helianthus argophyllus is native to the southeastern Texas coastal plain, U.S.A. However, it can be found as an introduced or naturalized species elsewhere in the Eastern U.S.A., including North Carolina and Florida, where it also inhabits coastal areas. In the U.S.A., its distribution is ~280 km2 (Khoury et al., 2020; Fig. 1.1). There are also reports of H. argophyllus occurrences in other regions of the world, such as Fiji, Mozambique, South Africa, Australia, Japan and Argentina (National Species List, n.d.; Heiser et al., 1969; Smith, 1991; Toshikazu & Tetsuro, 2004; South African National Biodiversity Institute, 2020), where it appears to be naturalized (or at least in the process of becoming naturalized). The silverleaf sunflower is an obligate outcrosser without any known specialized pollinators, and can naturally breed with its sister species, H. annuus, although hybrids are partially sterile (Heiser, 1951). These species diverged 1.5–1.7 million years ago (Mason, 2018). Helianthus annuus is the progenitor of cultivated sunflower, so H. argophyllus has frequently been tapped for valuable agronomic traits, including resistance to several fungal diseases (Besnard et al., 1997; Qi et al., 2016) and drought tolerance (Hussain et al., 2019). 1.3.1 H. argophyllus ecotypes Despite its limited geographic distribution and an obligately outcrossing mating system, recent studies have found large-scale differences in flowering time among individuals (Moyers & Rieseberg, 2016; Todesco et al., 2020) of the silverleaf sunflower. Although it breeds 8 synchronously throughout much of its native range on the coastal plain of Texas, flowering can be bimodal for a small number of populations along barrier islands in the Gulf of Mexico. There are two flowering syndromes that correlate with other plant traits and habitat, forming two ecotypes. The late-flowering ecotype is characterized by low branching and greater height, apparently because vegetative growth is favoured until they begin flowering in the fall; this ecotype is largely found in drier mainland habitats of Texas, where there is selection for a later flowering optimum (Moyers, 2015; Moyers & Rieseberg, 2016). Nonetheless, they also inhabit barrier islands in the Gulf of Mexico, where selection appears to favor early flowering (Moyers & Rieseberg, 2016). In contrast, the early-flowering ecotype is characterized by individuals that begin flowering in mid-summer, are relatively short, and are highly branched (Moyers, 2015; Moyers & Rieseberg, 2016). These latter are almost exclusively found on two barrier islands in the Gulf of Mexico. Both ecotypes are found mixed within the same populations. 9 A) Direct records of the presence of the silverleaf sunflower in Texas; the purple dots are records from genebanks or botanical gardens, and the blue dots are records from herbaria. The red arrows mark the locations of the sampling sites for this thesis and the letters represent the names of each site. SN=Sinton, RP= Rockport PA= Port Aransas, MA= Mustang-Aransas, MB= Mustang Bridge, PB= Padre Beach, WP= Water Park, RS= Riviera-Sarita. B) The median spatial results obtained by Khoury et al., 2020 for modelling the distribution of Helianthus argophyllus. The shades of orange show the probability of occurrence of the species. C) The late and the early- flowering ecotype in a common garden in Vancouver, BC, 2011.Images A and B were directly taken from the Github repository of Khoury et al., 2020 at: https://dcarver1.github.io/cwrUSA_maps/?p=173. Image C was taken from Moyers (2015). SN RP AM MB, PB and WP PA RS A) B) C) Late-flowering ecotype Early-flowering ecotype 115 100 85 35 30 25 20 30 28 26 -99 -97 - 95 - 93 -99 -97 - 95 - 93 Figure 1.1.Distribution of the species and ecotypes of the silverleaf sunflower. 10 A recent investigation has shown that these flowering time differences are controlled by a ~10 Mb region of low recombination, which is polymorphic for the presence/absence of a FLOWERING LOCUS T homolog, HaFT1 (Todesco et al., 2020). Early-flowering plants can be homozygous or heterozygous for the presence of HaFT1, while late-flowering plants are homozygous for its absence, which shows that the deletion behaves as a recessive allele. Furthermore, the genomic region containing HaFT1 introgressed into H. argophyllus from H. annuus, its sister species, within the past ~100,000 years (Todesco et al., 2020). This latter makes the early-flowering ecotype of H. argophyllus an excellent example of adaptive introgression (Suarez-Gonzalez et al., 2018). Thus, H. argophyllus offers a useful experimental system for studying the interplay between assortative mating and recombination suppression in local adaptation and speciation in the presence of gene flow, which is the main focus of my thesis. 11 Chapter 2: A fine-scale geographic mosaic of phenotypical and phenological differences caused by presence/absence polymorphism in a FLOWERING LOCUS T homolog 2.1 Introduction The beginning of flowering is one of the most decisive life history stages in angiosperms (Rathcke & Lacey, 1985), marking the transition directing resources from vegetative growth to sexual reproduction. Its timing is therefore an essential component for plant fitness. Flowering at an optimal time is critical for maximizing nutrient and pollinator availability (Parra-Tabla & Vargas, 2004; Sandring & Agren, 2009), while avoiding herbivores (Pilson, 2000), pathogens and various abiotic stresses (Franks et al., 2007; Kenney et al., 2014; Jordan et al., 2015; Kazan & Lyons, 2016). This may not only impact seed set, but in addition the amount of nutrients available to seeds for germination and survival to the next generation (Augspurger, 1983). Differences in flowering time can also have implications for reproductive isolation between biological groups; they can lead to or prevent assortative mating and change the genetic composition of the next generation, which can affect ecosystem functioning (McIntosh, 2002). Moreover, plants flowering asynchronously from the rest of the population may miss the opportunity to reproduce at all if no other plants are sexually mature (Schmitt, 1983). This is especially critical for annual species that cannot self-fertilize. Given the importance of flowering time, it is perhaps unsurprising that it is frequently reported to be under selection, and that the optimal timing of initiation of flowering is affected by a myriad of often interacting factors (Rathcke & Lacey, 1985; Forrest & Miller-Rushing, 2010). These include flowering-inducing genes (Kardailsky et al., 1999), population dynamics (Internicola et al., 2006), life-history strategy and plant size (Mitchell-Olds, 1996; Callahan & Pigliucci, 2002; Du & Qi, 2010), as well as abiotic factors such as photoperiod, temperature, 12 precipitation/moisture and their interactions (Goodwin, 1944; Cereceda et al., 2000; Callahan & Pigliucci, 2002; Blackman et al., 2011; Andrés & Coupland, 2012; Henry et al., 2014; Matthews & Mazer, 2016; Liu & Zhang, 2020). Plants of the same species growing in contrasting environments often have different flowering phenologies (Clausen et al., 1941; McNeilly & Antonovics, 1968; Antonovics, 2006; Hall & Willis, 2006; Levin, 2009; Moyers & Rieseberg, 2016; Ostevik et al., 2016; Sianta & Kay, 2021), and the flowering season frequently varies spatially and temporally (Clausen et al., 1941; Gordo & Sanz, 2010; Williams et al., 2021). However, there is not always an optimal flowering peak in every environment, and differences in flowering time can result from reproductive character displacement (Waser, 1983), which reduces mating with similar individuals competing for resources, deters maladaptive hybridization, or averts swamping from close relatives in sympatry (Ellstrand et al., 2010; Spriggs et al., 2019). Finally, flowering time can be affected by stochastic forces such as drift and contingency (Devaux & Lande, 2008), and demographic factors such as population density (Schmitt et al., 1987) . Regardless of why they occur, differences in flowering are a key factor in driving and reinforcing ecological races (Clausen et al., 1940, 1948), species boundaries (Osborne et al., 2019) and species radiations (Spriggs et al., 2019). When flowering time is under divergent selection, it can act as a ‘magic trait’ (magic traits are defined as those that contribute to non-random mating and are subject to divergent selection) (Gavrilets, 2004; Servedio et al. 2011), permitting the evolution of reproductive isolation in sympatry. However, this requires selection to be strong enough to overcome the effects of migration and drift; a higher migration rate will inhibit population differentiation (Lenormand 2002; Yeaman & Otto 2011). Genetic exchange between groups of organisms under divergent selection can result in the transfer of maladaptive alleles, as well as disruption of adaptive allelic combinations (Slatkin, 1987), unless the alleles under selection are inside a genomic region with low recombination. Indeed, reproduction-related genes under divergent selection have been reported to be inside 13 genomic structural variants, such as chromosomal inversions (Lowry & Willis, 2010; Twyford & Friedman, 2015; Merrill et al., 2019; Todesco et al., 2020; Mérot et al., 2021). These variants can halt the homogenizing effects of gene flow in a genomic region by impeding recombination in heterozygotes (Butlin, 2005; Dobzhansky & Sturtevant, 1938; Kirkpatrick & Barton, 2006; Hoffmann & Rieseberg, 2008; Kirkpatrick, 2010). This can have extensive impacts on ecological and evolutionary dynamics in affected populations, playing a key role in local adaptation (Dobzhansky, 1970), adaptive divergence, ecotype formation and ecological speciation in the presence of gene flow (Lowry and Willis, 2010; Charlesworth, 2016; Tuttle et al., 2016, Todesco et al., 2020). Evidence of the role that chromosomal rearrangements play in local adaptation and ecotype formation mainly comes from linking these polymorphisms to environmental clines and functional traits (Hoffmann & Rieseberg, 2008; Lowry &Willis, 2010; Oneal et al., 2014; Twyford & Friedman, 2015; Todesco et al., 2020). In Erythranthe (formerly called Mimulus), genomic modifications that reduce recombination have resulted in differences in life history and reproductive strategies that can co-exist despite ongoing gene flow (Twyford & Friedman, 2015). In prairie sunflowers (Helianthus petiolaris), reciprocal transplant experiments of dune and non-dune ecotypes have directly linked inversions affecting flowering time and seed size with habitat-specific selection, allowing identification of the life history stage(s) at which selection acts (Goebl et al., 2022). The silverleaf sunflower, Helianthus argophyllus, provides another example in which a region of low recombination is associated with adaptive divergence and assortative mating. Despite its limited geographic distribution and an obligately outcrossing mating system, recent studies have found large-scale differences in flowering time among individuals (Moyers & Rieseberg, 2016; Todesco et al., 2020). The species breeds synchronously throughout much of its native range on the coastal plain of Texas, but flowering can be bimodal for a small number of populations along the Gulf of Mexico. There are two flowering syndromes that correlate with 14 other plant traits and habitats, forming two ecotypes. The late-flowering ecotype is characterized by low branching and greater height, apparently because vegetative growth is favored until they begin flowering in the fall; this ecotype is largely restricted to drier mainland regions of Texas, where there is selection for a later flowering optimum (Moyers, 2015; Moyers & Rieseberg, 2016). Nonetheless, they also inhabit barrier islands in the Gulf of Mexico, where selection appears to favor early flowering (Moyers & Rieseberg, 2016). The early-flowering ecotype is characterized by individuals that begin flowering in mid-summer, are relatively short in height, are highly branched, and are almost exclusively found in two barrier islands in the Gulf of Mexico (Moyers, 2015; Moyers & Rieseberg, 2016). A recent investigation has shown that these flowering time differences are controlled by a ~10 Mb non-recombining region, which is polymorphic for the presence/absence of a FLOWERING LOCUS T homolog, HaFT1 (Todesco et al., 2020). Presence/absence polymorphisms describe genomic regions that are present in one individual, but absent in another individual within a species (Springer et al., 2009; Takahashi-Kariyazono et al., 2020). Early-flowering plants can be homozygous or heterozygous for the presence of HaFT1, while late-flowering plants are homozygous for its absence, which shows that the deletion behaves as a recessive allele (Todesco et al., 2020). Further, this genomic region introgressed into H. argophyllus from H. annuus, its sister species, within the past ~100,000 years (Todesco et al., 2020). Common garden experiments suggest that the flowering time of H. argophyllus is highly heritable (Todesco et al., 2020). However, there was minimal environmental variation in the common garden, so I wondered if this observation would hold in natural populations in different habitats/environments (the mainland and the barrier islands). Flowering time often affects fitness-related traits in plants such as height and seed mass (e.g. Leishman et al., 1995; Bolmgren & Cowan, 2008; Du & Qi, 2010), but whether and how genotypic variation at HaFT1 affects these traits in natural populations of H. argophyllus is unknown. Furthermore, the association of a non-recombining region with different ecological and reproductive traits makes this species a case study of putative ecological divergence in the presence of gene flow. 15 In this chapter I examined the fine-scale geographic distribution of the HaFT1 presence/absence polymorphism in H. argophyllus and its possible correlations with phenotypic variation, phenology and variation in temperature and precipitation. I asked the following specific questions: (1) Do the effects of HaFT1 on flowering phenology vary across environments? If there is a genotype by environment interaction, for example, then genotypes might produce different flowering phenologies from one habitat to another (e.g., mainland vs island); (2) Does the dominance of HaFT1 on flowering phenology extend to fitness-related traits that are likely to be affected by flowering time differences such as plant height and seed size? Alternatively, are heterozygotes at HaFT1 intermediate for such traits, or overdominant? (3) Given the large differences in flowering time observed in common gardens, how much is HaFT1 disrupting random mating in natural populations? In populations where flowering time differences are contributing to assortative mating, I would expect to find an excess of homozygotes relative to Hardy-Weinberg expectations. This would suggest that random mating is being disturbed by asynchronous phenologies, although I recognize that other demographic processes could also lead to an excess of homozygotes, such as drift, inbreeding and underdominance. Height and seed mass have been commonly used as surrogates of plant fitness (e.g., Westoby et al., 2002). It seems likely that if HaFT1 affects the beginning of flowering it will also impact plant height and seed mass due to changes in resource allocation (e.g. Augspurger, 1983; Bolmgren & Cowan, 2008; Du & Qi, 2010). Seed mass has been shown to positively affect seedling emergence and survival (Ahmed et al., 2019), especially in changing environments (Westoby et al., 2002), such as sand dunes (Maun 1994, 1998; Zhang, 1996; Ostevik et al., 2016). Therefore, the measurement of such traits would potentially allow me to infer differences in fitness between the genotypes of HaFT1 across habitats. Considering that previous work (Todesco et al., 2020) indicates a dominant effect of HaFT1 on flowering, I expect 16 to find no differences in seed size or height between plants that are homozygous or heterozygous for the presence of HaFT1. On the other hand, seed size and plant height are considered functional traits that correlate with a plant’s ability to thrive in disturbed environments, like coastal sand dunes (Brunbjerg et al., 2014). Shorter plants and larger seeds are usually found in areas with high disturbance (Westoby, 1998; Maun, 2009). Therefore, I expect to find such traits more often in early flowering plants, which carry the HaFT1 gene, than in later flowering plants, which lack a copy of HaFT1. 2.1 Methods 2.1.1 Sampling Field work was performed in three trips each in 2017 and 2018 (July, September and November). I collected leaf tissue from populations where HaFT1 was expected to be segregating; five were located on the barrier islands and two on the mainland – one of which was not found until September 2018. Two additional further inland populations corresponding to the late-flowering ecotype, where there was no previous indication of HaFT1 being polymorphic, were collected for comparative purposes. I selected the populations based on the coordinates of previous studies (Moyers, 2015; Todesco et al, 2020). At least 30 individuals were sampled from each of the 9 populations. In total, I sampled 551 plants at the five localities in the barrier islands and 290 from the four mainland populations. I collected plants in each population by setting an imaginary line across the apparent limits of each population, and walking along one edge of the line, collecting plants to the left every 2-2.5 m. For plants growing in sand dunes, I attempted to sample from the dune crest, leeward slope and backdunes, although this was not always possible. I also determined distance to the sea or the nearest large body of water (lagoon) based on collection of coordinates. Besides, I recorded the phenological stage (see flowering stages below) and the height of each plant. Finally, I 17 placed young leaf tissue and mature seed heads in coin envelopes and mesh bags, respectively, and immediately placed them on silica to dry. Each plant was tagged in the first visit and the same individuals were revisited for posterior sampling. Once in the lab, I randomly selected ten seeds that did not show evidence of deformities or insect predation (i.e. presence of holes). I weighed seeds in groups of ten on a precision balance for 501 individuals (356 from the island and 145 from the mainland), of which 155 were collected early in the season (July and September) and 346 were from late in the season (November). 2.1.2 Flowering stages Each plant was measured for height from soil to the apical bud that marks the beginning of flowering. I only included height measurements when the first flower bud appeared (stage 1; Fig. 2.1) in our analyses (see below), although height measurements were taken at other stages to keep track of development. I also noted when the first bud appeared on a lateral branch. In addition, I scored individual plants for flowering stage based on a metric developed here (Fig. 2.1). 18 Figure 2.1. Classification of flowering stages Stage 0. Plants in vegetative stage with no sign of flower bud formation. Stage1. Flower buds forming, but not yet opening. Stage 2. First flowers open; these typically are located on the main apical meristem or the two most apical branches, though they can occur on lower lateral branches as shown in the illustration. Stage 3. Peak flowering; flowers and buds can be seen on most branches of the plant. Stage 4. Earliest flowers begin fruit and seed production. Stage 5. Senescence has begun; most branches are brown and have seeds and fruits. Stage 0 1 2 3 4 5 Flower buds Flower head Seed head Senesced leaf Live branch Senesced branch 19 2.1.3 Genotyping I extracted DNA following a modified 96-well plate version of the 3% CTAB protocol of Doyle & Doyle (1987) using dried leaves collected in the field and from fresh-frozen 3–4 weeks old seedlings germinated in Petri dishes. All DNA was eluted in 10 mM Tris-HCl (pH 8.0). Plants were genotyped using the Cleaved-Amplified Polymorphic Sequence (CAPS) protocol developed for genotyping the HaFT1 region by Todesco et al. (2020). All individuals were genotyped with primer pair A, which is closest to HaFT1 (Table 2.1; Appendix A), and have 100% association with HaFT1 when compared to sequence data (Todesco et al. 2020). A subset of 171 samples were genotyped using primer pair B (Table 2.1; Appendix A) to test the robustness of the assay. The HaFT1 genotypes predicted by the two assays were nearly identical (see Results). Because primer pair A is closer to HaFT1, I assume that it correctly predicts the HaFT1 genotype, and that differences between the two assays are due to rare recombination events. Lastly, for 68 mother plants it was not possible to extract DNA or the DNA was too poor in quality; I inferred their genotypes based on the genotypes of a minimum of four progeny. The early-flowering ecotype plants, are homozygous or heterozygous for the presence of HaFT1, and are represented in figures as black (1/1) or orange (1/0), respectively. Throughout this thesis, whenever I refer to the early-flowering ecotype I will be talking jointly about HaFT1 genotypes 1/1 and 1/0. The late-flowering ecotype plants, are homozygous for the absence of HaFT1 (0/0) and are represented in pink in corresponding figures. 2.1.4 Environmental data I obtained daily summaries of temperature and precipitation from seven weather stations located near the sampling sites with data from 1980–2021 from the National Oceanic and Atmospheric Administration (NOAA) website (Table 2.2). 20 Table 2.1. Primer and enzyme information for genotyping for HaFT1 Primer pair Chromosome Position SNP Forward primer Reverse primer PCR product (bp) Restriction enzyme Digestion product (bp) 1/1 0/0 1/1 0/0 A Ha412HOChr06 139806448 C T CAGAAATGGGCTGCTCTAGA TGTAGCCAATCCACACATTTTCA 295 EcoRI 295 192+103 B Ha412HOChr06 140142429 G T TCAGTATATGTTTGAGGTGGCA CCACACTGGCAGCTGTATCT 430 BsaI 242+182 430 Table 2.2. Weather stations near sampling sites Network ID Latitude Longitude Name Start of record End of record GHCND:USC00416739 27.4466 -97.2968 Padre Island National Seashore 2005-04-01 2019-12-09 GHCND:USC00417170 27.81391 -97.075 Port Aransas 1986-01-01 2021-09-24 GHCND:USC00417704 28.0183 -97.0561 Rockport 1959-01-01 2021-09-25 GHCND:USC00418081 27.2169 -97.6955 Sarita 7 E 1899-05-01 2017-09-30 GHCND:USC00418354 28.10106 -97.56895 Sinton 1921-09-01 2021-08-31 GHCND:USW00012924 27.7742 -97.5122 Corpus Christi International Airport 1946-08-01 2021-09-25 GHCND:USW00012926 27.68333 -97.28333 Corpus Christi NAS, 1945-03-01 2021-09-23 GHCND:USW00012972 28.08361 -97.04639 Rockport Aransas CO Airport 1996-07-01 2021-09-24 GHCND:US1TXAR0008 27.972687 -97.134913 Aransas Pass 6.1 NNW 2012-05-20 2017-08-24 21 2.1.5 Statistical analyses I calculated all frequencies and proportions of HaFT1 by locality (sampled population) and region (island or mainland). I aggregated data for 2017 and 2018 as they presented very similar patterns. I estimated the fixation index by population as: F= (1-(observed heterozygosity/ expected heterozygosity)) Hardy-Weinberg equilibrium was measured by population and region, using HWExact or HWFisher tests, depending on test assumptions, from the R package “HardyWeinberg” (Graffelman, 2015). Deviation of genotype frequencies from Hardy-Weinberg expectations were tested by sampling site using Fisher’s tests, through the “fisher.test” function of the stats R package (R Core Team, 2020). The effects of HaFT1 genotype, month, region, sampling locality and their interactions (fixed effects) on the initiation of flowering were evaluated with generalized linear mixed effects models following a binomial distribution (for this I only considered flowering stages 0 and 1), where sampling locality was also considered as a random effect, using the “glmer” function of the R package lme4 (Bates et al., 2015). Model comparison was done with the ANOVA function using a Chi-square test as implemented by the same R package, and model selection was performed according to these results and the lowest AIC score. For these models I excluded further inland populations of Riveira-Sarita and Sinton, as there was no variation in genotype or flowering phenology. In addition, I only considered the months of July and September as there was only one individual that began flowering in November. Once the most relevant fixed factors were established, based on the model selection, I compared model results when HaFT1 genotypes were predicted by the different primer primer pairs (pair A versus pair B). 22 To examine the effects of HaFT1 genotype throughout all flowering stages I grouped data by month and region and performed Fisher’s tests with the “fisher.test” function of the stats package (R Core Team, 2020), as well as Fisher post hoc tests using the “pairwise.fisher.test” function with a Bonferroni correction for multiple testing from the package reporttools (Rufibach, 2009). I used two different approaches to analyze variation in plant height at the onset of flowering. First, I examined height by region, in which height at flowering among genotypes was compared using a Kruskal-Wallis test due to the non-normal distribution of the data. The test was implemented with the function “kruskal.test” from the R stats package (version 3.6.2; R Core Team, 2020), and posthoc analyses were performed using the “pairwise.wilcox.test” function from the same R package. Second, I employed a linear mixed model, where sampling site was the random factor and genotype, region and month were fixed factors. A Shapiro-Wilk test indicated that the distribution of height was not normal. However, visual inspection of a normal quantile plot (also know as “q-q plot”) and the empirical and theoretical density obtained using the “descdist” and the “fitdist” functions of the fitdistrplus R package (version 1.1; Delignette-Muller & Dutang, 2015), showed that the observed data was not very different from a Gaussian distribution. Hence, a linear model was appropriate for this analysis. The fixed factors were tested in different combinations and with additive and multiplicative interactions. The best model was chosen according to AIC values. Differences in seed weight were also analyzed in two ways. In the first analysis, seed weight was compared between regions and months, and between genotypes within those groupings through ANOVAs, and by using appropriate posthoc Tukey tests. For this I used the “aov” function of R software package stats (version 3.6.2; R Core Team, 2020). All data was tested for normality using the “shapiro.test” function from the previously referenced R package. Seed weight was not normally distributed when divided into regions (island vs mainland), so a square 23 root transformation was employed for data from both regions to improve normality and proceed with ANOVA and Tukey tests. For the second analysis, seed weight was analyzed in a linear mixed model framwork, using the “lmer” function from the R package lme4 (Bates, 2015). Sampling site was considered as a random variable and the following factors as fixed effects: mother plant’s genotype, region, sampling site, month, flowering stage, natural logarithm of the mother plant’s distance to the sea and the natural logarithm of the mother plant’s distance to a body of water. The best model was selected based on AIC scores. Finally, in order to explore whether there are climatological differences between the mainland and the barrier island regions that might help explain the different distribution of ecotypes, I analyzed climatological data obtained from various weather stations in the region (Table 2.2). I excluded three localities due to insufficient data: Aransas Pass 6.1 nnw, Corpus Christi International Airport and Rockport Aransas CO Airport. Similarly, most of the climatic variables included in the data set, such as snow and wind speed, had insufficent data (at least 55% of values not available). Hence, I only report on variation in precipitation and temperature. This data was averaged per month of each year. Since their distribution was not normal, difference between regions was analyzed with a Wilcoxon rank sum test, using the function “wilcox.test” from the “stats” package of R (version 3.6.2, R Core Team, 2020). 2.3 Results I genotyped 762 individuals, 171 of which I genotyped using two primer pairs. Both primer pairs predicted the same genotype for 168 individuals (98.2%). While it is not possible to rule out genotyping error for the remaining 1.8% of individuals, I suspect that these are a consequence 24 of recombination. Although this is a low recombination region, one recombination event has been observed previously between these primer pairs by Todesco et al. (2020). Of 762 individuals genotyped, only 41 are homozygous for the presence of HaFT1. The overall frequency of HaFT1 in the region of study is 0.17, and the gene is about two times more frequent in the island populations (0.22) than in mainland populations (0.1) (Fig. 2.2 and Fig. 2.3). This latter value is strongly influenced by one population with a frequency of 0.41; the remaining mainland populations have a frequency of HaFT1 of zero or less than 0.01. Neither region, mainland or island, is predicted to be in HW equilibrium, both due to a deficiency of heterozygotes, although this was not the case for all sampling sites. On the barrier islands, three out of five populations were in Hardy Weinberg equilibrium. But the Port Aransas population (the northernmost population on the barrier islands) had a deficit of heterozygotes, and the Padre Beach population showed an excess of heterozygotes. 25 Table 2.3. Population information: mean coordinates, frequency of HaFT1 and its genotypes Population Region Mean latitude Mean longitude Genotype Total HaFT1 Frequency Fixation index Significant deviation from HW* 1/1 1/0 0/0 Aransas Mustang island 27.69941 -97.1543 6 31 55 92 0.23 0.06 no Mustang Bridge island 27.61806 -97.2163 3 21 57 81 0.17 007 no Padre Beach island 27.57912 -97.222 0 50 81 131 0.19 -0.23 yes Port Aransas island 27.83098 -97.0523 26 40 46 112 0.41 0.27 yes water park island 27.60968 -97.2225 0 20 69 89 0.11 -0.13 no Flour Bluff mainland 27.6463 -97.2832 6 20 13 39 0.41 -0.6 no Riviera Sarita mainland 27.27565 -97.8038 0 0 56 56 0 Na no Rockport mainland 27.99654 -97.1117 0 1 112 113 0.004 -0.004 no Sinton mainland 28.11539 -97.3871 0 0 49 49 0 Na no SUM 41 183 538 762 * Deviations of genotype frequencies from Hardy Weinberg expectations were tested through Fisher tests; α= 0.05 26 HaFT1 is not fixed in any sampled population. The Port Aransas population exhibited the largest fixation index (0.27). Four populations (two in the island habitat) had a negative fixation index, indicative of an excess of heterozygotes. However, the deviation from Hardy Weinberg expectations was only significant at Padre Beach, the southernmost population on the island; no individuals homozygous for the presence of HaFT1 were found over the two years of sampling, despite 61% of the population being heterozygotes. f=0 f<0.005 f=0.41 f=0.23 f=0.41 f=0.17 f=0.11 f=0.19 f=0 genotype n=150 n=100 n=50 1/1 1/0 0/0 Figure 2.2. Distribution and frequency (f) of the flowering gene HaFT1. 27 HaFT1 has a highly restricted geographic distribution within H. argophyllus; it is only present within 15 km from the Gulf of Mexico and between 28 and 27.5 degrees of latitude (Fig. 2.2). The mainland is predominantly populated by the homozygotes for the absence of HaFT1 (0/0) that comprise close to 90% of the sampled individuals in that habitat. This genotype is also predominant on the island, where it represents nearly 60% of the sampled individuals (Fig. 2.3). A higher proportion of more advanced stages of reproductive phenology was recorded at the barrier island localities in comparison to those on the mainland throughout all the sampling months (Fig. 2.4). For example, in July, while on the mainland 97% of plants were still in vegetative phase, 58% of plants from island populations had started their reproductive phase. Figure 2.3. Genotype frequencies in the two studied regions. 28 Significant differences in flowering stage were observed between regions for each month (Fisher tests; July: p<0.005; September: p<0.005; November: p<0.005). When using generalized linear mixed models to test the effect of genotype on the initation of flowering, the model with the best fit based on AIC criteria included an interaction between genotype and month as fixed effects (AIC= 516.7). Similarly, when comparing models between the two primer pairs, primer pair A always had the best model fit, supporting its use for predicting HaFT1 genotypes; the best model with primer B had an AIC= 530.02. mainland island mainland island mainland island region Figure 2.4. Flowering stage proportions in each region by month. 29 The first inflorescence usually develops at the plant apex (except when the apical meristem is damaged), so the beginning of flowering has an effect on the height of the plant. The best linear mixed model for height included genotype and region as fixed effects and sampling location as a random effect, with an AIC of 2870.8, which contrasts, for example, with the full model that included genotype, region and month as fixed effects and that had an AIC of 2880.7. The second best model included an interaction between genotype and month (AIC= 2871.1) and the third best model only included the HaFT1 genotype (AIC= 2877.5) Analyses of height within each region revealed a striking pattern. In coastal populations, plants homozyous for HaFT1 flowered at the shortest height, but they were the tallest genotype on the island (see Fig.2.5). Specifically, on the mainland, plants homozyogous for the presence of HaFT1 began to bloom, on average, at 88.00 cm (sd= 26.88), heterozygotes at 136.18 cm (sd= 33.64) and plants homozygous for the absence of HaFT1 at 181.07 cm (sd= 70.96). In contrast on the island, homozygotes for HaFT1 flowered on average at 201.81 cm (sd= 77.60), heterozygotes at 165.80 cm (sd= 75.20), and homozygotes for the absence allele at 141.08 cm (sd= 63.55). These differences were significant both in the island and the mainland, according to Kruskal- Wallis tests (p<0.005 and p= 0.01, respectively). Posthoc analyses showed that in the island genotypes 1/1 and 1/0 did not differ in height, but that these two genotypes differed significantly from genotype 0/0 (Fig. 2.5). In the mainland, the two homozygous genotypes (1/1 versus 0/0) differed in height, and heterozygotes were intermediate (Fig. 2.5). 30 Letters correspond to results of Kruskal-Wallis post-hoc tests within each region. *p<0.07 The effect of HaFT1 was also evident at later flowering stages. The absence homozygote (0/0) was by far the most common genotype and, as expected, it was later maturing and, thereofre, associated with less advanced reproductive stages, while the early-flowering ecotype (genotypes 1/1 and 1/0) tended to develop earlier and be associated with more advanced flowering phases each month, as shown in figures 2.6A and 2.6B. However, I only detected differences in flowering stages between genotypes in the first two months of sampling, but not the third one (Fisher tests July: p<0.005, September: p<0.05, November: p= 0.08; Fig. 2.6A). When comparing this data within each region, differences appear earlier (in July) on the island than on the mainland (in September). However, by November no differences between genotypes in flowering stages were seen on the island, as they were finished flowering, but differences can still be detected on the mainland (Fig. 2.6B). Figure 2.5. Height at the beginning of flowering by region. a ab b* a a b genotype mainland island 31 Lowercase letters indicate significant differences from pairwise Fisher’s tests; where no letters are shown there was either not enough data or different genotypes did not have significant differences in their flowering stages. * p<0.07 Figure 2.6. Association of flowering stage with genotype by month (A) and the interaction of region, genotype and month (B). ab b a a ab * b* A B a a b mainland island 32 Table 2.4. Number of individuals at a reproductive phase (flowering stages 1 to 4; Fig.2.1) by month, region and genotype Month Region Genotype Sampled plants Individuals at a reproductive phase July mainland 1/1 0 0 July mainland 1/0 1 0 July mainland 0/0 118 4 July island 1/1 26 23 July island 1/0 84 65 July island 0/0 197 91 Sept mainland 1/1 6 5 Sept mainland 1/0 20 20 Sept mainland 0/0 56 47 Sept island 1/1 13 13 Sept island 1/0 56 55 Sept island 0/0 98 97 Nov mainland 1/1 1 1 Nov mainland 1/0 10 10 Nov mainland 0/0 97 97 Nov island 1/1 15 15 Nov island 1/0 88 88 Nov island 0/0 151 151 33 Lowercase letters indicate results from Tukey tests; those in black above the boxplots show differences between months (A) and between regions averaged across months (B); letters in red below boxplots show results from Tukey tests between genotypes within each group, month or regions. HaFT1 genotype, region and month also affected seed weight (Fig. 2.7). Of the 501 individuals for which I obtained seed weight, four were at flowering stage one, 57 at stage two, 72 at stage three, 76 at stage four, 290 at stage five, and two were cases in which the flowering stage was not recorded. Among these, 18 had a 1/1 genotype, 131 were heterozygotes and 227 were 0/0 homozygotes (126 individuals could not be genotyped). The genotypes had different seed masses (ANOVA, F= 10.76, p<0.005; post hoc Tukey tests were all p<0.005). Homozygotes for the presence of the HaFT1 gene had the heaviest seeds (mean= 0.071, sd= 0.016), followed by heterozygotes (mean= 0.057, sd= 0.017). Homozygotes for the absence of HaFT1 yielded the lightest seeds (mean= 0.051, sd= 0.19). a b a b c a ab b a a a a ab b a ab b Figure 2.7. Average seed weight (g) produced by each genotype. 34 A similar pattern was observed when comparing seed weight by month (Fig. 2.7A), in which plants sampled in earlier months tended to set heavier seeds (July: mean= 0.075, sd= 0.032; September: mean= 0.060, sd= 0.020; November: mean= 0.050, sd= 0.016). An ANOVA (F= 13.696, p<0.005) and a posthoc Tukey analysis revealed that there was a significant difference between the mass of seeds sampled in all three months (July versus September, p= 0.027; July vs November, p<0.005; September vs November, p<0.005; Fig. 2.7A), as well as between regions (F= 4.8, p= 0.03) (Fig. 2.7B). Seeds were heavier in the barrier islands (mean= 0.055 grams, sd= 0.02) than in the mainland (mean= 0.050, sd= 0.016). These results are backed up by linear mixed models (for model description see methods). The best two models included genotype and month as additive or multiplicative interactions (AIC= -2156 and AIC= -2155.4, respectively). This indicates that genotype affects seed weight and suggests a possible relationship with differential use of resources depending on the time of the reproductive season. The third best model included an interaction between flowering stage and month (AIC= -2150.2). This is similar to the previous models in that genotype is a major determinant of flowering stage. Likewise, I observed that the timing of flowering (and likely fertilization) had an effect on seed weight. The heaviest seeds (0.085-0.090 grams per seed) were a product of plants that were recorded in July as mostly producing seeds (in flowering stage 4) or at their peak of flowering (stages two and three) (Fig. 2.8A). Meanwhile, seeds from plants that in July were just beginning to bloom (flowering stage 1) had a mean mass of slightly above 0.03 grams (Fig. 2.8A). In contrast, the seeds coming from plants that were scored in September and November show the opposite trend: plants at more advanced stages in phenology had lighter seeds (Fig. 2.8A). Using the same data to compare seed weight between ecotypes resulted in a clear trend, in which the early-flowering ecotype plants produced heavier seeds than those plants from the late-flowering ecotype, particularly at the beginning of the flowering season (July) even between plants that began to bloom at the same time (Fig.2.8B). 35 (A). Same data segregated by ecotype (B). seed weight (g) A) B) Figure 2.8. Mean seed weight (g) by flowering stage by month 36 In either habitat, the early-flowering ecotype reached its peak of flowering earlier than the late-flowering ecotype, which is reflected as a higher proportion of individuals at a peak of flowering (blue arrow; see two left panels of Fig. 2.10). I also observed that the flowering phenology occurred at a faster rate within the mainland than in the barrier islands. On the mainland (top two panels of Fig. 2.9), most plants of both ecotypes did not begin budding until September, such that eight weeks later, in November, the majority of plants were predominantly finishing their life cycle and producing seeds. Meanwhile, in the barrier islands (bottom two panels of Fig. 2.9), while all plants were done flowering in November, many had begun flowering even before July. Some flowering stages were grouped for better visualization: vegetative= flowering stage 0; flower buds= flowering stage one; flowering peak= stages two and three; mostly seeds= stages four and five. Blue arrow marks the flowering peak. Finally, there were significant differences between mainland and island in monthly average precipitation (p<0.001; Fig. 2.10). This is most apparent in August and September; the mainland receives more precipitation than the island in August, but less than the island in September. In addition, there are significant differences in minimum, observed, and maximum temperature between these regions (all were p<0.001), with more extreme temperatures always found on the mainland. Early-flowering ecotype Late-flowering ecotype mainland island Figure 2.9. Proportion of individuals at different life stages months and regions for each ecotype. 37 Estimated from daily data from 01/01/1981 to 30/08/2021 from weather stations near the sampled populations. mainland island Figure 2.10. Mean monthly precipitation (in) in each region. 38 2.4 Discussion Here I fine-mapped the distribution of an HaFT1 presence/absence polymorphism in silverleaf sunflower (H. argophyllus) on the southern coast of Texas. I found that the presence allele was largely restricted to the barrier islands in the Gulf of Mexico, although the frequency of HaFT1 was not homogeneous among localities and instead followed a patchy distribution. However, HaFT1 was also detected in a small number of individuals (mainly from one population) on the mainland. As expected, the polymorphism at HaFT1 was closely correlated with variation in flowering time, plant height, seed size and several features of the environment, including distance from the sea, and to some degree with precipitation and temperature. These results corroborate findings of a genome wide association study of the species (Todesco et al., 2020) and further suggest that the harsher mainland climate likely constrains the spread of HaFT1. Previous work, including reciprocal transplants, Qst/Fst comparisons and quantifications of survival and reproduction in the field, indicate that early flowering is favored in the barrier islands, but not inland (Moyers, 2015; Moyers & Rieseberg, 2016). Thus, multiple lines of evidence imply that this polymorphism is likely maintained, in part, by a balance between selection favoring HaFT1 on the barrier islands and dispersal of the late-flowering allele from mainland populations to the islands (but see below). Analyses of the timing of flowering and seed production offers additional insights into why HaFT1 is favored on the barrier islands. I found that phenology differs not just between genotypes, but also between habitats, suggestive of GxE interactions. The reproductive phase in the mainland habitat, particularly for the 0/0 homozyogtes, occurs over approximately seven weeks. In contrast, on the barrier islands, the reproductive season is two to three times longer, with the early-flowering ecotype having a much longer reproductive phase than the late-flowering ecotype plants at either location (Fig. 2.9). 39 I suspect that the differences in these reproductive windows partially explain differences in seed mass between the genotypes. Individuals that were at the peak of their flowering or seed producing phase (stages 2, 3 and 4) early in the season, in July, produced seeds that were three times heavier than seeds from plants that just begun flowering that month (Fig. 2.8A). Moreover, seeds collected in November had lower mass than both previous sampled months (Tukey, nov-July p= 0.003, nov-sept p<0.005; Fig. 2.8). In addition, seed mass has two main peaks (Fig. 2.8A), each of which occurs two months after a precipitation peak (Fig. 2.10), suggesting a connection with the bimodal precipitation regime of southern Texas. Nonetheless, seed weight is also dependant on the mother plant’s genotype, with 1/1 homozygotes producing the heaviest seeds and heterozygotes producing intermediate seed mass in both regions (Fig. 2.7). Given that ecotype is determined by the genotype for HaFT1, they are expected to differ in seed weight as well, which can be seen in Figure 2.8B. Larger seeds have been associated with dune sunflower ecotypes in H. neglectus and H. petiolaris (Andrew et al., 2013; Ostevik et al., 2016), and it has been hypothesized that increased seed size boosts emergence rates of buried seeds and affects burial depth (Maun, 1994, 1998; Zhang, 1996; Ostevik et al., 2016). A larger seed mass may also be beneficial for plants that are adapted to a rare environment by impeding dispersal to nearby habitats to which the plants are maladapted; in the case of the early-flowering ecotype it might prevent them from dispersing into the sea or lagoon waters. In Cakile edentula (Brassicaceae) plants collected from dunes in Ontario, Canada, larger seed also size correlated with faster development and earlier flowering (Zhang, 1996). Given multiple lines of evidence that early flowering is favored on the barrier islands, it is surprising that the frequency of the HaFT1 is so low, with a frequency of 0.23 on the barrier island and just 0.17 in the study area overall. A likely contributing factor (noted above) is the effect of dispersal of late-flowering alleles from the mainland. However, the Barrier Islands are isolated from the mainland by water, which could serve as an impediment to pollen dispersal via insects, and to seed dispersal via mammals. While some dispersal is likely to occur, it seems unlikely to be high enough to account for the frequency of HaFT1. 40 I also considered the possibility of heterozygote advantage, which could limit increases of HaFT1 in the population. Such an advantage (if it exists) does not appear to come from heterosis. In 2016 I followed the growth of 350 seedlings during their first month of life, and there were no differences between genotypes in terms of height, number of branches or leaves, or in the size of the largest leaf. Also, heterozygotes phenotyped in the present study were found to display intermediate values for seed mass (Fig. 2.7) and height (Fig. 2.5) at flowering (although the effect is not significantly different from the 1/1 homozygotes). Given that HaFT1 is dominant with respect to flowering time, this may suggest that some of the phenotypic effects associated with the HaFT1 polymorphism are due to linkage rather than pleiotropy. Furthermore, for a previous experiment (unpublished data) I measured pollen viability among genotypes, but did not detect significant differences between homozygous and heterozygous genotypes. In sum, I found no evidence of heterozygote advantage, although I note that these data are not a direct measurement of fitness, and further evaluation of survival and reproductive output in natural settings is required. It is also possible that there has not been time for the the HaFT1 allele to spread to high frequency. The introgression of the allele from H. annuus appears to have occurred very recently (Todesco et al., 2020). In addition, the Mustang/Padre Island barrier-chain is estimated to be ~2,000 years old (Garrison et al, 2010), and several sites have been subject to intense anthropological disturbance for the past 200 years (see below). Finally, the strength and direction of selection on flowering time may vary temporally or be affected human activities. The occurrence of silverleaf sunflower has always been associated with disturbed sites (Heiser, 1951). Recently, a study about coastal flora in Texas determined that H. argophyllus has a 95% chance of being found in a location of high human disturbance (Foster, 2020). Even apparently natural sites often have a history of anthropogenic interventions. For example, dune management plans included the use of Christmas trees to restore sand dunes in South Padre Island after hurricane Allen (Ehrlich, 2018). Further, around populations with the lowest frequency of the 1/1 genotype, foredune ridges were established 41 after a land-use change from livestock-only to allow urbanization and recreation between 1950 and 1971 (Nueces County, 2012). A herd of 1,200 cows used to inhabit the region in the Northern part of Padre Island (Harris, 1965 as cited in Dahl et al., 1975). In the late 1800s and early 1900s, north Padre Island was described as a barren desert due to a combination of overgrazing and severe drought (Price & Gunter, 1943 as cited in White et al., 1978). This same area was reported to have no grass in 1941 after a severe salt-water flooding event (Price & Gunter, 1943 as cited in White et al., 1978). While the effects of such activities on the distribution of the silverleaf sunflower genotypes are unclear, they serve as examples of ongoing human modifications of the landscape that could potentially lead to changes in genotype frequencies. For instance, recreational activities might boost rates of dispersal between regions, which would increase the prevalence of the late-flowering ecotype on the island, despite selection against late blooming. A second puzzle is the mixed evidence for assortative mating. Plants from either mainland or island were not at HW equilibrium due a lack of heterozygotes. This is expected because differences in flowering time between genotypic classes should lead to assortative mating. However, the picture is more complex when I analyze this by sampled locality. While I had expected homozygotes to be over-represented in all populations, instead, two populations on the barrier islands had fixation indexes around zero due to the absence or low representation of the 1/1 genotype. Nearly two thirds of the 1/1 homozygotes genotyped for the entire area were found in a single population, Port Aransas. This was the only population statistically different from Hardy Weinberg equilibrium; it had a dearth of heterozygotes. Other processes can also result in a deficit of heterozygotes, such as inbreeding, lower heterozygote fitness (underdominance) or population substructure. These processes seem unlikely in Port Aransas because H. argophyllus is an obligate outcrosser and previous research (Moyers, 2015; Moyers & Rieseberg, 2016) failed to detect genetic substructuring within wild populations of H. argophyllus. Furthermore, measurements on height and seed weight indicate that heterozygotes are intermediate, or at least as fit as one of the parents, in both regions. 42 Therefore, considering considering the effect of HaFT1 on phenology, I deem asynchronous reproduction as the most plausible explanation for excess homozygosity, although I recognize that other explanations cannot be ruled out by this data set. In the next chapter, I analyze whether reproductive asynchrony associated with each ecotype is affecting gene flow. In contrast to Port Aransas, the population with the highest frequency of HaFT1 in the mainland (Flour Bluff; Table 2.3), has a fixation index near zero (although negative, which indicates some excess of heterozygotes), and it follows Hardy Weinberg expectations. It is puzzling why assortative mating is seen in one population rather than both. However, genotype frequencies at these sites are likely to be affected by pollen flow from nearby unsampled populations, and the mainland population is more likely to have immigration of the 0 allele than Port Aransas given the dominance of the 0/0 genotype on the mainland. Gene flow by pollen has been detected up to 1 km from the source population in H. annuus (Arias & Rieseberg, 1994), so it offers a plausible explanation for the variation in levels of assortative mating reported here. In addition, selective pressures experienced by the different genotypes likely vary both locally and regionally and could also contribute to the observed pattern. It is noteworthy that flowering time and partially pleiotrophic traits such as height and seed size are not the only traits affected by the region of low recombination where HaFT1 is found. Leaf carbon and nitrogen content, the ratio between them, and leaf color also map to this genomic region (Todesco et al., 2020). Because the latter traits were measured on leaves harvested prior to the initation of flowering, I suspect they are under the control of linked genes rather than HaFT1. High C:N ratios have been documented in the dune sunflower, H. anomalus, and are thought to reflect adaptation to low nutrient soils (Donovan et al., 2009). Island dunes are nutrient poor as well (Todesco et al. 2020), so a similar explanation may hold for H. argophyllus. Reduced recombination in the HaFT1 region may act to hold this suite of locally adapted traits together, similar to the role suggested for inversions (Kirkpatrick & Barton, 2006). Interestingly, the frequency, distribution and length of the chromosomal region that contains HaFT1 are consistent with theoretical expectations for the establishment and spread of inversions that 43 contribute to local adaptation in the presence of gene flow (Connallon & Olito, 2022). These authors modeled probabilities for the fixation of chromosomal inversions depending on their length, mode and strength of selection. They found that inversions maintained by local adaptation tend to be large in terms of length, and to remain polymorphic in the local populations where they are favored. While the HaFT1 region does not appear to be an inversion, it does share key characteristics with inversions, including an apparent lack of recombination and high levels of sequence divergence between haplotypes (Todesco et al., 2020). 2.5 Conclusions HaFT1 is a relatively rare gene in the silverleaf sunflower, restricted to populations within 15 km of the Gulf of Mexico in southern Texas. Its distribution, however, does not follow a perfect sigmodial nor linear cline with respect to the sea and seems to be distributed in a more mosaic fashion. I know that the habitat only recently became suitable for sunflower colonization (Nueces County, 2012), that there are some traits (e.g., larger seed mass) that could hamper migration to the mainland, and that different selection regimes on flowering between the barrier islands and the mainland might be keeping HaFT1 from colonizing the mainland. However, that does not explain why HaFT1 has an intermediate frequency within the habitat where there is evidence for early flowering being preferentially selected (Moyers, 2015). It is possible that an excess of gametes from the late-flowering ecotype could be driving this trend. However, this explanation is incomplete. It seems plausible that the patchy distribution of HaFT1 is the result of different selection-migration regimes taking place at each sampled locality, possibly related to recent human activities. Future studies that estimate gene migration rates and examine locality-specific variation in selection pressures should be a priority. Further, I found an interaction between environment, HaFT1 and flowering phenology. In fact, all measured traits exhibit a similar GxE. While the two ecotypes differ significantly in flowering, 44 I also found substantial overlap in breeding season both within and between regions, in part because of the extended flowering period of the island ectoype. Although heterosis is commonly reported in crosses between plant ecotypes or species, I found no evidence for heterozygote advantage in silverleaf sunflower. Instead, heterozygotes were either intermediate between the parentals for fitness correlates such as plant height and seed size, or statistically indistinguishable from the non-local homozygote. This implies that heterozygotes are probably weakly selected against in both habitats, but no more so than the non-local parent. While selection against hybrids can lead to reinforcement or limit the blending of species (Butlin, 1987; Kirkpatrick, 2000; Spriggs et al., 2019; Irwin, 2020), this seems unlikely to occur through further divergence in flowering time, because the flowering time window itself appears to under strong selection (Moyers, 2016). On the other hand, conspecific pollen precedence is known to have arisen between dune and non-dune ecotypes of the prairie sunflower (Ostevik et al. 2016). Thus, a gamete preference experiment to test for such a mechanism in these silverleaf sunflower ecotypes is warranted. Overall, given the presence of divergent selection on flowering time, but implied continued immigration of 0 alleles or 0/0 homozygotes from the mainland, it seems unlikely that HaFT1 will be either fixed or lost in the barrier islands in the near future. Finally, it is of upmost importance to directly measure fitness of each of the genotypes in nature since it has been documented that low hybrid fitness (in this case heterozygote fitness) can lead to the extinction of one or both parental forms (Todesco et al., 2016; Irwin & Schluter, 2021). 45 Chapter 3: Assortative mating between two silver-leaf sunflower ecotypes depends on genotype, environment and time 3.1. Introduction The evolution of reproductive isolation (RI) is widely considered to be a critical step in the process of species formation (e.g. Mayr, 1942; Coyne & Orr, 2004). The degree of RI between two populations can be a decisive factor in the delimitation of lineages (e.g., Stankowski & Ravinet, 2021). Hence, the rise, fall and maintenance of reproductive barriers — defined here as genetically-based barriers to reproduction — are central aspects of evolutionary studies. Evolutionary biologists have long recognized that gene flow represents a key impediment to speciation (Romanes, 1886; Dobzhansky, 1941; Mayr, 1942). Divergent selection may promote the build-up of associations between alleles favoured in a specific habitat and those underlying other reproductive barriers, whereas gene flow and genetic recombination can break up these allelic combinations (Felsenstein, 1981; Slatkin, 1987; Trickett & Butlin, 1994), and bring maladaptive alleles into a population (Antonovics et al., 1971; Kirkpatrick & Barton, 2006; Lopez et al., 2008, but see Abbott et al., 2013). Thus, speciation is thought to occur most readily when there is no gene flow between diverging lineages, such as when populations are separated by a geographic barrier (i.e., allopatric speciation). There is less agreement regarding the likelihood of speciation when there is gene flow, although theoretical models indicate that the conditions favoring parapatric speciation (when diverging populations are spatially separated, but contiguous) are much broader than those for sympatric speciation, where there are no physical barriers separating the diverging lineages (reviewed in Coyne & Orr, 2004). When disruptive selection is weak or non-existent, even a small amount of gene flow—as low as one migrant per generation—is enough to hamper divergence (Wright, 1931; Antonovics, 1968; Mallet et al., 2009). However, ecotypes experiencing opposing selective pressures can diverge even when gene flow levels are high or do not differ from random mating (Millicent & Thoday, 1960; Caisse & Antonovics, 1978; Felsenstein, 1981). The key differences between 46 these scenarios are the strength of selection and the genetic architecture of adaptive variation and other forms of RI (Felsenstein, 1981). Strong disruptive selection will be effective in eliminating hybrids, while tightly linked genes have less chances of being fragmented by recombination, as would be the case for regions of low recombination (Ortiz-Barrientos et al., 2016; Huang & Rieseberg, 2020). Thus, divergence with gene flow is facilitated when traits that are subject to disruptive selection have a genetic basis that cannot be broken easily by recombination (e.g., a single gene or multiple genes housed within a chromosomal rearrangement) (Rieseberg, 2001; Noor et al., 2001; Ortiz-Barrientos et al., 2016; Huang & Rieseberg, 2020; Todesco et al., 2020). The likelihood of speciation with gene flow is further enhanced if disruptively selected traits also cause RI. Such traits are also known as “magic traits” (Gavrilets, 2004; Bolnick & Fitzpatrick, 2007; Servedio et al., 2011). For instance, adaptation to spatially separated habitats could reduce the chance that differentially adapted individuals meet, leading to assortative mating. RI in this situation can be viewed as a pleiotropic by-product of adaptive divergence (Hall & Willis, 2006; Schluter, 2009). The reduction of gene flow between adaptive groups, or heterospecific matings, will depend on the level of assortative mating (i.e., full separation vs. permeable or plastic assortative mating) (Fox, 2003; Devaux & Lande, 2008; Weis et al., 2014). Chromosomal rearrangements and other recombination suppressors can act as magic traits by supressing recombination between loci under disruptive selection and those causing assortative mating or other forms of RI (Twyford & Friedman, 2015; Ortiz-Barrientos et al., 2016). One of the most discernible magic traits is allochrony (Servedio et al., 2011), which refers to differences in phenology or the timing of life cycles, and is considered to be a key driver of divergence (Coyne & Orr, 2004; Hendry & Day, 2005; Taylor & Friesen, 2017). A temporal division of reproduction, even without selection, reduces interbreeding between individuals with different phenologies (Devaux & Lande, 2008). In combination with disruptive selection, allochrony appears to be a common mechanism for local adaptation, speciation (Bolnick & 47 Fitzpatrick, 2007; Taylor & Friesen, 2017), and species maintenance in the presence of gene flow (Hopkins, 2013; Juri & Premoli, 2021). In angiosperms, alterations of flowering time have long been known to play a fundamental role in the emergence and maintenance of ecotypes (Clausen et al., 1940, 1941, 1948), species (Osborne et al., 2019; Nomura et al., 2022), and even species radiations (Spriggs et al., 2019). Neighboring populations in a heterogeneous environment frequently show diverging flowering times (Clausen et al., 1941; Levin, 1971, 2009; Antonovics, 2006; Savolainen et al., 2006; Hall & Willis, 2006; Moyers & Rieseberg, 2016; Ostevik et al., 2016; Friedman et al., 2019; Sianta & Kay, 2021), and flowering time differences can act as an important barrier to gene flow between adjacent populations. Nonetheless, observations of variation in flowering phenology are insufficient for inferring whether phenology affects speciation, as there are many other potential barriers to reproduction including geography, pollinator behavior, pollen tube reception, allelic incompatibilities, hybrid infertility, and immigrant inviability, to mention just a few examples (Grant, 1949; Levin, 1971; Coyne & Orr, 2004; Lowry, Modliszewski, et al., 2008; Widmer et al., 2009; Baack et al., 2015; Christie et al., 2022). Also, the relevance of these barriers to RI can vary widely depending on the system (Lowry, Modliszewski, et al., 2008; Coughlan & Matute, 2020). It usually takes many barriers to complete isolation (Lowry, Modliszewski, et al., 2008; Ostevik et al., 2016; Coughlan & Matute, 2020; Kunerth et al., 2022). However, my goal in this study is to infer the effect of reproductive phenology on mating patterns between the genotypes that make up the early and the late-flowering ecotypes of the silver-leaf sunflower, Helianthus argophyllus. To do this, I make direct estimates of RI between groups reproducing at different times. I previously demonstrated that there are differences in both the initiation of flowering and its duration between ecotypes of H. argophyllus (Todesco et al., 2020; Chapter 2). As described in more detail in Chapter 2, much of this variation is due to a presence/absence polymorphism of a major flowering-time gene, HaFT1; the late flowering ecotype is homozygous for the absence 48 of the HaFT1 gene, whereas the early flowering ecotype is either homozygous or heterozygous for HaFT1. However, flowering time was also found to be affected by environmental variation, and by genotype by environment (GxE) interactions (see Chapter 2). Here I determine the impact of flowering time variation on RI between these genotypes by examining the parentage of progeny from plants that were sampled in in the previous chapter. My main interest was to answer the following questions: (1) Do differences in phenology result in assortative mating for flowering time? (2) If so, how does the strength of assortative mating vary within and between genotyes –and their corresponding ecotypes– across time and space? (3) How are differences in flowering time reflected in the genomic landscape of divergence? These questions are relevant because strong assortative mating will reduce gene flow between ecotypes, thereby accelerating evolutionary divergence and speciation. In the case of H. argophyllus, we expect to detect higher assortative mating at the extremes of the flowering season when each ecotype dominates its respective flowering landscape, and a reduction in RI in the middle of the season when both ecotypes are flowering. For example, the genotypes 1/1 and 1/0, which make up the early flowering ecotype, should be more frequent in offspring early in the season, when those genotypes make up most of the flowering individuals in a population. Therefore, HaFT1 should have a higher frequency in offspring produced at the beginning of the reproductive season, when gametes with at least one copy of it are more abundant, and decrease by the end of the season when more homozygotes for the absence of HaFT1 (0/0) are flowering. Finally, given that divergence with gene flow is expected to be facilitated by reduced recombination (Huang & Rieseberg, 2020), I also predict that there will be differences in patterns of divergence across the genome. As mentioned earlier, divergent selection will tend to increase differentiation in selected regions, while gene flow will tend to homogenize regions that are neutral or only weakly selected (Wu, 2001; Wu & Ting, 2004; Nosil et al., 2009; Seehausen et al., 2014; Ostevik et al., 2016; Todesco et al., 2020). Hence, there should be less divergence in areas of the genome where gene flow and recombination are high and more in 49 genomic regions that are under divergent selection and that are not as strongly influenced by gene flow and recombination (Pinho & Hey, 2010). 3.2. Methods 3.2.1. Sampling The sampling scheme is the same as that described in Chapter 2, except that for offspring collection I also noted the branch from which each seedling came from. The shoot apex and the closest two or four branches (depending on the plant height and structure) were considered to be apical branches; all others were considered to be lateral branches. 3.2.2. Genotyping The mother plants that are included here were selected depending on offspring availability, germination and survival. It was not possible to recover healthy and mature achenes from all individuals sampled in the field, but I aimed to genotype the offspring of at least 15 mother plants per population. For RI estimates, seven of the nine populations analyzed in Chapter 2 were included in this chapter (see Table 3.1) because I am mainly interested in the segregation of HaFT1 and its effects on RI between the different genotypes within populations. The mainland populations Riviera-Sarita and Sinton were excluded because the frequency of HaFT1 is zero, and would therefore provide no information on RI caused by phenology as a product of HaFT1. For each mother plant, 5-10 achenes were soaked on filter paper in Petri dishes for 48 hours in a solution of 1% of PPM (Plant Cell Technologies, Washington, D.C., U.S.A.), a broad-spectrum biocide/fungicide, to minimize fungal contamination. Achenes were subsequently scarified by cutting on the wide end of the seed coat, which was removed. They were kept moist with 1% PPM. 50 Achenes were kept in the dark until they germinated (which happened 3-5 days after scarification). Seedlings were grown in the Petri dishes until the first pair of true leaves developed. After this, each seedling was placed in a well of a 96-well plate, quick frozen with liquid Nitrogen, and stored at -80° C until DNA was extracted. The DNA extraction and genotyping were done using the same methods as in Todesco et al. (2020) and Chapter 2. Genotype frequencies were compared between mother plants and seedlings per sampling locality through Fisher or chi-square tests, according to assumptions. 3.2.3. HaFT1 frequency I calculated the frequency of HaFT1 in the offspring populations from all seedlings that were genotyped. Likewise, I calculated the frequency of HaFT1 in the mother plants using all genotyped parental plants. I assessed deviations of genotype frequencies from Hardy-Weinberg expectations using a chi-square test or a Fisher’s exact test (for small sample sizes), with the R package “Hardy-Weinberg” and the functions HWChisq or HWExact, respectively (Graffelman, 2015). 3.2.4. Paternity analyses Based on the HaFT1 genotype of each seedling and its maternal parent, I inferred the likely flowering genotype of the pollen parent. I estimated the probability of each pollen parent genotype based on the genotype frequencies of co-flowering plants in the population. A mother plant homozygous for the presence of HaFT1 (1/1) can produce 1/1 or 1/0 seedlings. For 1/1 seedlings, we know that one copy of the HaFT1 allele came from the mother plant, whereas the other copy came from either a 1/1 or 1/0 pollen parent, the probability of which was calculated from the frequency of 1/1 and 1/0 genotypes flowering at the same time at that site. This meant the exclusion of genotypes that were at a vegetative or budding stage at the 51 time of fruit collection; all other stages were considered (for flowering-stage classification see Chapter 2). Likewise, for 1/0 seedlings, the presence allele must come from the maternal parent, but the absence allele came from either a 1/0 or 0/0 paternal parent. Thus, the probability of the paternal donor genotype was calculated based on the frequency of co-flowering 1/0 and 0/0 genotypes. The same logic was applied to seedlings from heterozygote mothers. Note that this approach assumes that variation in paternity is solely due to flowering time variation and masks other factors that might lead to differences in paternity. However, it also offers a way of isolating the effects of flowering time on assortative mating and reproductive isolation (see below). To compare the proportion of seedlings (p) with at least one copy of HaFT1, I calculated the confidence intervals for each proportion by month and region using the following formula, where z equals 1.96 and n is the sample size: ! ± # ∗ √!(1 − !)* 3.2.5. Reproductive isolation To estimate reproductive isolation, I used the following linear equation from Sobel and Chen (2014, their online appendix D), following considerations from Martin & Willis (2007) for unequal abundance of gametes. Here, H represents heterospecific (i.e., between genotype) crosses and C conspecific (i.e., within genotype) crosses: RI = 1 − 2 (observed H/expected H)(observed C/expected C) + (observed H/expected H) An RI of 1 is indicative of complete reproductive isolation, meaning that were no successful crosses between flowering time genotypes. An RI of 0 indicates random mating between genotypes, whereas an RI of -1 (which is biologically unlikely) indicates extreme disassortative mating, in which no within-genotype crosses were successful. I estimated RI for each mother 52 plant at each time point (July, September and November), whenever viable achenes were available. For estimating the expected proportion of each type of cross I used the total number of individuals of each genotype for each population. In this way, I was able to compare the difference between the observed frequency of each type of cross, considering the proportion of different genotypes flowering at each population and time point (as a result of flowering divergence), with that expected without such differences (when considering all individuals sampled in a population). After estimating RI for each maternal parent, I calculated the mean for each genotype by region and month. Finally, I estimated 95% confidence intervals of RI, by bootstrap resampling with 10,000 replicates using the function boot.ci from the “boot” R package. 3.2.6. Genomic patterns of divergence between genotypes and regions To determine whether reductions in gene flow resulting from reproductive asynchrony affected patterns of genomic differentiation, I estimated genome-wide Fst between homozygote genotypes and between individuals from the different geographic regions (island vs. mainland), regardless of their genotype, using the SNP data from Todesco et al. (2020). This data set included 4,676,707 SNPs across the entire genome, and 208,468 variants across chromosome 06. Variants are relatively evenly distributed across the genome, except in regions with transposable elements, since those were excluded from SNP calling by Todesco et al. (2020). Fst was calculated with the “vcftools” software (Danecek et al., 2011) using the function weir-fst-pop. I used a Kolmogorov-Smirnov test, with the R function ks.test of the dgof package (Arnold & Emerson, 2011) to test for differences in the distribution and frequency of the Fst values. To test for differences in Fst values between homozygote genotypes and those between 53 geographic regions, I performed a Kruskal Wallis test using the “kruskal.test” function from the R software package stats (version 3.6.2; R Core Team, 2020). 3.3. Results A total of 2498 seedlings were genotyped in the lab, of which 1890 were from island populations and 608 from the mainland (Table 3.1); these offspring came from 339 mother plants. The seedling genotypes were used for the estimation of HaFT1 frequencies in the offspring and for comparison of genotype frequencies with those of parental populations (Table 3.1). Table 3.1. Summary of the total number of seedlings genotyped by sampling population and region of origin, including the number of mother plants they came from, and the HaFT1 frequency of seedlings and mother plants Sampling site Region Mother plants Genotyped seedlings Seedling HaFT1 frequency* HaFT1 frequency in mother plants 1/1 1/0 0/0 Aransas Mustang island 49 49 112 244 0.26 (+) 0.23 Mustang Bridge island 14 7 26 35 0.30 (+) 0.17 Padre Beach island 57 34 161 303 0.23 (+) 0.19 Port Aransas island 57 80 152 207 0.35 (-) 0.41 water park island 50 15 106 359 0.14 (+) 0.12 Flour Bluff mainland 24 7 18 78 0.15 (-) 0.41 Riviera Sarita** mainland 30 0 0 176 0 0 Rockport mainland 28 0 2 155 0.006 (+) 0.004 Sinton** mainland 30 0 0 172 0 0 SUM 339 192 577 1729 *(-) or (+) indicates if the frequency of HaFT1 in seedling populations is lower or higher, respectively, than that of mother plants. All numbers are rounded to the second decimal, except in Rockport where they are rounded to the third decimal. **Populations not included in estimation of reproductive isolation. In all but one population in the barrier islands the frequency of HaFT1 was higher in the seedlings than in mother plants (Table 3.1). In fact, the frequency of HaFT1 in the islands is quite variable among populations, ranging from -0.35 to 0.30. In general, the differences among 54 generations are small, with the exception of Flour Bluff, the one population in the mainland with a high frequency of HaFT1, which had a reduction of 15% in the frequency of HaFT1 in the seedling generation. In terms of genotypes, most populations have statistically equal frequencies between the parental and offspring generations, except for three populations: Mustang Bridge and Padre Beach in the islands, and Flour Bluff in the mainland. Each one shows different frequency changes, although both of the island populations had an increase of the 1/1 genotype and the mainland population an increase of the 0/0 genotype (Table 3.2). Overall, barrier island populations have a lower frequency of heterozygotes than was found in parental populations (Table 3.2). Many populations also show a decrease in the frequency of one of the homozygous genotypes (Table 3.2). Table 3.2. Comparison of genotype proportions between offspring and parental individuals Sampling site Region Offspring - Parentals++ Significant difference in genotype frequencies between generations? 1/1 1/0 0/0 Aransas Mustang island 0.06 -0.06 0.00 no Mustang Bridge island 0.07 0.12 -0.19 yes Padre Beach island 0.07 -0.06 -0.01 yes Port Aransas island -0.05 -0.01 0.06 no water park island 0.03 -0.01 -0.02 no AVERAGE 0.03 0.00 -0.03 Flour Bluff mainland -0.09 -0.34 0.42 yes Riviera Sarita** mainland 0.00 0.00 0.00 no Rockport mainland 0.00 0.004 0.00 no Sinton** mainland 0.00 0.00 0.00 no AVERAGE -0.02 -0.08 0.11 ++Offspring vs parentals show the reduction or increase of frequency of each genotype in the offspring generation in comparison with that of the parentals. Differences between genotype frequencies were calculated through Fisher or chi-square tests, according to assumptions with α= 0.05. Average shows mean differences between offspring and parentals by genotype for each region. Underlining indicates negative numbers. **Populations not included in estimation of reproductive isolation. 55 According to Hardy-Weinberg expectations, calculated from the seedlings’ genotypes’ there was a dearth of heterozygous offspring in all populations where HaFT1 is segregating, except for Rockport in the mainland, which showed a decrease of 1/1 homozygotes. In fact, in five of the seven populations where HaFT1 is segregating, seedling genotype frequencies deviated from Hardy-Weinberg expectations, although the significance of this was marginal for Padre Beach (Table 3.3). Table 3.3. Deviations from Hardy-Weinberg expectations in offspring populations Sampling site Region Observed- Expected Is seedling population in Hardy-Weinberg equilibrium? Early-flowering ecotype Late- flowering ecotype 1/1 1/0 0/0 Aransas Mustang island 0.054 -0.108 0.054 no Mustang Bridge island 0.016 -0.033 0.016 yes Padre Beach island 0.015 -0.031 0.015 no* Port Aransas island 0.056 -0.112 0.056 no Water park island 0.011 -0.022 0.011 no Flour Bluff mainland 0.044 -0.088 0.044 no Riviera Sarita** mainland 0 0 0 yes Rockport mainland -4.057 × 10-5 8.113 × 10-5 -4.067 × 10-5 yes Sinton** mainland 0 0 0 yes * indicates value of p= 0.057. Underlining indicates negative numbers. **Populations not included in estimation of reproductive isolation. The frequency of HaFT1 in the offspring varied according to the environment and time of seed collection. In seedlings from mainland populations, the frequency of HaFT1 is lower than 0.1 all year, and is least frequent in seeds sampled at the end of the reproductive season. In contrast, in the islands, HaFT1 is at its highest frequency in July (0.5) and becomes less common in offspring through the season, reaching 0.28 in seeds sampled in September and 0.17 in those from November (Fig. 3.1). These averages are maintained when only considering populations 56 used for RI analyses; there is only a slight shift in November in the mainland, when HaFT1 frequency is 0.07 rather than the 0.02 shown in Figure 3.1. This graph includes seedlings from all populations, not just those considered for the RI analyses. When taking into account the branch from which each achene came from, I found that overall apical branches tend to have a higher frequency of HaFT1 (Fig. 3.2) in their offspring than lateral ones, although significant differences were found only in September (X2= 4.5, p= 0.034). mainland island Figure 3.1. Frequency of HaFT1 in seedings by region and time of seed collection. 57 Figure 3.2. Frequency of HaFT1 in seedlings depending on their position on a plant. The vertical lines represent confidence intervals of the proportion. Of the 339 genotyped mother plants, 60 belonged to populations where HaFT1 is not present and were excluded from the RI analyses. Additionally, 19 mother plants were removed due to uncertain seedling genotypes. Therefore, the RI analyses included seedlings from 260 mother plants, of which only 12 had less than 3 seedlings genotyped. Overall, seedlings came from 11 mother plants homozygous for the presence of HaFT1 (which represents ~26% of the 1/1 homozygotes sampled in this two-year study), 115 heterozygous mother plants and 134 homozygotes for the absence of HaFT1 (Table 3.4). A total of 1,991 seedlings were used for the RI analysis: 192 were homozygous for the presence of HaFT1, 524 were heterozygous (82% produced by the early-flowering ecotype) and 1,275 were homozygous for the absence of HaFT1 (36% produced by the early-flowering ecotype plants; Table 3.4). 58 Table 3.4. Genotypes of the seedlings (offspring) included in the reproductive isolation analyses by each locality I first estimated RI per mother plant, and then obtained means and standard deviations for each genotype, month and region (Figure 3.3; Table 3.5). Sampling site Region Genotype 1/1 1/0 0/0 total Aransas Mustang island 49 104 237 390 Mustang Bridge island 7 23 35 65 Padre Beach island 34 157 295 486 Port Aransas island 80 141 195 416 Water park island 15 79 280 374 Flour Bluff mainland 7 18 78 103 Rockport mainland 0 2 155 157 SUM 192 524 1275 1991 59 Table 3.5. Reproductive isolation by month, region and genotype Month Region Genotype Moms† Total seedlings Mean RI Sd RI‡ Max RI§ Min RI** 95% CI†† July island 1/1 2 12 1.00 0.00 1 1 na July island 1/0 8 46 0.61 0.66 1 -1 (-0.12, 0.87 ) September island 1/1 9 50 0.67 0.28 1 0.31 ( 0.49, 0.84 ) September island 1/0 84 550 0.40 0.20 1 0.1 ( 0.36, 0.44 ) September island 0/0 58 355 -0.38 0.21 -0.1 -0.91 (-0.43, -0.32 ) November island 1/1 2 18 0.38 0.19 0.51 0.24 ( 0.24, 0.37 ) November island 1/0 64 353 0.22 0.31 1 -0.21 ( 0.14, 0.29 ) November island 0/0 64 347 -0.11 0.26 0.21 -0.59 (-0.17, -0.05 ) September mainland 1/1 1 3 1.00 na 1 1 na September mainland 1/0 7 32 0.04 0.00 0.04 0.04 na September mainland 0/0 3 14 -0.04 0.00 -0.04 -0.04 na November mainland 1/0 7 22 -0.09 0.13 0.08 -0.24 (-0.21, -0.03 ) November mainland 0/0 35 189 0.82 0.36 1 -0.08 ( 0.68, 0.92 ) † Moms=number of mother plants ‡ Sd RI=standard deviation of RI § Max RI=maximum value of RI of a mother plant ** Min RI=minimum value of RI for a mother plant ** 95% CI=95% confidence intervals.// na=no variation or too few samples to calculate confidence intervals (sample sizes of >30 recommended for estimating confidence intervals). Underlined numbers are negative. 60 The temporal patterns of RI also differ between regions. There were too few samples of the 1/1 genotype from the mainland to discern any pattern. However, it is clear that in this region the 1/0 genotype —although close to random mating (RI ranges -0.24 to 0.08)— is less reproductively isolated by the end of the season in November (mean RI= -0.08), while the opposite is true for homozygotes for the absence of HaFT1 (Table 3.5, Fig. 3.3). The mating patterns of the 0/0 genotype seem to be random at the beginning of the season (mean RI of -0.04 in September) and approach total RI by the end (mean RI of 0.82 in November). It is important to remember that two mainland populations were excluded from the RI calculations as HaFT1 was not present in any of the seedlings analyzed (Table 3.4; Riviera Sarita and Sinton populations). Temporal patterns of RI variation are partially reversed on the island. The 1/1 genotype began the reproductive season completely isolated (RI= 1 in July) and reduced its RI by the end of the season (mean RI in November= 0.38), although no individual plant RI values was below 0.24 (Table3.5). The 1/0 genotype behaved similarly but with lower RI values (mean RI in July= 0.61, mean RI in September= 0.40, and mean RI in November= 0.22; Table 3.5, Fig. 3.3). In contrast, even when RI also increased towards the end of the reproductive season for the 0/0 genotype as in the mainland region, its mean RI values did not exceed those expected by random mating in the islands (mean RI in September= -0.38, and mean RI in November= -0.11; Table 3.5, Fig. 3.3). 61 estimated from paternity analyses of seedling genotypes collected at each different month in the two regions. The size of the circle represents the number of seedlings from which the mean was obtained. The left panel contains information from the mainland and the right panel from the barrier islands. Finally, genome-wide estimates of Fst show that there is more differentiation between the homozygotes of each ecotype than there is among individuals of different regions (Fig. 3.4, Table 3.6). Furthermore, the distribution of the genomic Fst values is different for comparisons between homozygous ecotype plants versus comparisons between individuals that inhabit different regions (Kolmogorov-Smirnov test, D= 0.37, p<0.005). The same result was obtained when only comparing chromosome 06, where HaFT1 is found (Kolmogorov-Smirnov test, D= 0.35, p<0.005). When comparing homozygotes there are several variants with Fst values of 1.0 (Table 3.6; Figs. 3.5 and 3.6, lower panels), whereas the maximum value of Fst in chromosome 06 in the between regions comparison is only 0.5 (Table 3.6; Figs. 3.5 and 3.6, upper panels). mainland island Figure 3.3. Mean reproductive isolation 62 Table 3.6. Fst differentiation in different genomic regions between HaFT1 genotypes and between regions Comparison Region Mean Sd Min Max between regions genomic 0.029 0.052 0.000 0.673 between homozygotes genomic 0.035 0.096 0.000 1.000 between regions chromosome 06 0.049 0.097 0.000 0.620 between homozygotes chromosome 06 0.086 0.224 0.000 1.000 63 (number of SNPs on y-axis; frequency of Fst values on x-axis). Upper panel: between regions; Lower panel: between individuals homozygous for the presence or absence of HaFT1. Inner panels show zoomed images with Fst values from 0.3 to 1. Figure 3.4. Genome-wide frequency distribution of Fst values for individual SNPs 64 It includes the low recombination region that contains the HaFT1 locus. Upper panel: between regions; Lower panel: between individuals homozygous for the presence or absence of HaFT1. Inner panels show zoomed images with Fst values from 0.3 to 1. Figure 3.5. Frequency distribution of Fst values for SNPs on chromosome six. 65 Upper panel shows the differentiation between geographic regions. Lower panel shows differentiation between homozygotes at HaFT1. The region of low recombination where HaFT1 is found is marked in blue brackets. The black arrow marks the location of HaFT1. Figure 3.6. Fst scan across chromosome 06. 66 3.4. Discussion Speciation is often considered to result from the gradual accumulation of reproductive barriers, leading to “a continuum of reproductive isolation” (Stankowski & Ravinet, 2021). Such barriers can occur at different life stages and can be determined by internal or external factors; it is the contribution of each isolating barrier and their interactions that determines the total level of reproductive isolation (RI) (Sobel & Chen, 2014; Stankowski & Ravinet, 2021; Kunerth et al., 2022). Many closely related species show incomplete RI ( for several examples see Lowry et al., 2008; Sobel & Chen, 2014; Christie et al., 2022), which can persist even after millions of years of divergence (e.g. Hipperson et al., 2016). At the same time, disparities in flowering time seem to be common between closely related taxa that inhabit different environments in the absence of geographical barriers (e.g. Clausen et al., 1941; Antonovics, 2006; Hall & Willis, 2006; Levin, 2009; Twyford & Friedman, 2015; Moyers & Rieseberg, 2016; Ostevik et al., 2016; Sianta & Kay, 2021). Nonetheless, interspecific flowering divergence is rarely tracked across the entire flowering season in natural populations, and even fewer studies have directly measured how variation in flowering time divergence over time affects the strength of reproductive isolation. The results from this chapter represent a unique data set by considering gene flow and reproductive isolation in natural populations at different time points of the year and at a spatially fine-scale. 3.4.1. Changes in HaFT1 between generations In most populations, changes between generations in genotype proportions are small (on average 0.06). Differences are more common in barrier island than in mainland populations. Almost all island populations showed a reduction in heterozygotes in seedlings in comparison with the parental population (Table 3.2), which likely represents a signal of assortative mating due to differences in phenology. Although there are other processes that could also result in heterozygote deficits (e.g. drift, inbreeding and underdominance), as I discussed in the previous chapter, these seem unlikely to explain the reduced heterozygosity in the studied populations 67 because of large population sizes, an obligate outcrossing mating system and evidence that heterozygotes are at least as fit as one of the homozygotes in both habitats. Although seedlings most commonly showed a reduction in heterozygosity relative to parental populations on the island, Padre Beach is the only island site where the deficit of heterozygotes resulted in a significant difference in genotype proportions between generations. Padre Beach had no 1/1 individuals in either 2017 nor 2018. Yet, this genotype represented 7% of the offspring germinated in the lab (Table 3.1). This population had the highest proportion of heterozygous individuals among island populations, and I was able to find achenes as early as July, when only heterozygotes were flowering. Hence, changes in genotypic frequencies most likely resulted from preferential mating among heterozygous genotypes due to flowering time differences. Two other populations also showed significant differences in genotypic proportions between generations: Flour Buff in the mainland, and Mustang Bridge in the barrier islands, each one showing a different trend (Table 3.2). In Flour Buff the difference is driven by a substantial increase (> 40%) in the proportion of 0/0 genotypes in offspring compared to mother plants (Table 3.2). This population is in the mainland, where HaFT1 is three times less abundant than in the islands. In both September and November, there were fewer seedlings with the 1/1 and 1/0 genotypes than would be expected considering the frequency of genotypes flowering at the time in the field. Again, I view assortative mating is the most parsimonious explanation for the difference in genotypic proportions. Meanwhile, Mustang Bridge experienced a reduction in the frequency of the 0/0 genotype by nearly 20% in the offspring (Table 3.2). However, achenes collected in November, when the genotype for the absence of HaFT1 is more common, had low germination rates, and so the 0/0 genotype is probably under-represented for this reason. 68 In addition to the reduction in heterozygosity at HaFT1, I found that seedlings had a higher proportion of the 1/1 genotype than the parental generation in most populations (Tables 3.1 and 3.2). This may suggest that negative selection could be acting on germination or establishment of the 1/1 genotype in natural populations. This result need not be contrary to evidence of selection favoring early flowering in the barrier islands (Moyers & Rieseberg, 2016), because selection can act in opposite directions at different life stages of an organism (Schluter et al., 1991; Goebl et al., 2022). There are many factors that could interfere with the establishment of the 1/1 homozygotes. As I showed in Chapter 2, achenes homozygous for the presence of HaFT1 have a larger mass, which may make them more attractive to predators. It has been shown that in wild sunflowers that pre-dispersal fruit predation by vertebrates is higher for larger achenes (Alexander et al., 2001). However pre-dispersal fruit predation is already accounted for in our data, so a post-dispersal explanation for differences in the frequency of HaFT1 1/1 homozygotes between generations is needed. Higher post-dispersal predation has also been observed in sunflowers that flower early (Dechaine et al., 2010) and those with large seeds (Abramsky, 1983). Larger seeds have also been observed to be eaten first in other species (Alexander et al., 2001; Janzen, 1969), although Dechaine (2010) showed that the actual preference is for higher oil content rather than size. Furthermore, sunflowers experience high post-dispersal predation by vertebrates during the winter (Robel & Slade, 1965; Alexander et al., 2001). A non-mutually exclusive explanation for differences in frequency of the 1/1 genotype between generations is that genotypes of HaFT1 differ in germination or establishment. This seems unlikely, because in six years of work with H. argophyllus, I have not observed any sort of delay in germination; viable seeds germinate as soon as they get enough moisture. This observation has been confirmed by Dr. B. Moyers (University of Massachusetts, Boston, U.S.A., pers. com.), who also did her doctoral dissertation on H. argophyllus. On the other hand, if there were differences in seedling establishment, we would expect an opposite pattern, as larger sunflower seeds, at least in H. annuus, typically have higher rates of germination and 69 establishment (Ahmed et al., 2019). Hence, I think it is more likely that the reduction of 1/1 homozygotes between generations is because of differential post-dispersal predation, although appropriate testing for causality is needed. Post-dispersal predation may also contribute to the abundance of heterozygotes, despite assortative mating between homozygous genotypes. 3.4.2. Variation in HaFT1 gene and genotype frequencies within seedling populations Within seedling populations, I found that most populations where HaFT1 is segregating deviated from Hardy-Weinberg expectations (Table 3.3); they all had a deficit of heterozygotes, with the exception of Rockport (Table 3.3). In contrast, in populations where HaFT1 is missing (Riviera Sarita and Sinton, not included in RI estimates) or at low frequency (Rockport, p= 0.004), there were minimal differences from expected genotype proportions. Overall, this is consistent with our observations that assortative mating is mainly caused by the action of the HaFT1 allele on flowering time. The frequency of HaFT1 in the seedling populations becomes rarer over the course of the flowering season in both mainland and island populations (Fig. 3.1). The effects of timing of collection and differences in flowering on the frequency of HaFT1 are also noticeable when sampling different branches of the same individual. Sunflowers have monopodial growth, and the first flower tends to appear on the main apex (Chapter 2). This leads to HaFT1 being more frequent in the apical branches, particularly for the early-flowering ecotype (Fig. 3.2). It would be relevant for researchers to consider this factor when sampling H. argophyllus populations in the barrier islands in the future. Despite this, gene frequencies reported here are similar to those previously reported for the same populations and month analyzed in the present project (Todesco et al., 2020). In Todesco el al. (2020), all material was collected in November 2015, and the frequency of HaFT1 in the mainland and island of 0.01 (n= 50) and 0.22 (n= 49), respectively, is similar to that reported here (i.e., 0.025, n= 540, in the mainland, and 0.17, n= 735, in the island). Thus, between 2015 and 2018 the frequency of HaFT1 in both regions in November seems to be relatively stable. 70 3.4.3. The effects of flowering time divergence on reproductive isolation The high proportion of heterozygotes in all populations where HaFT1 is segregating is evidence of a lack of strong reproductive barriers between individuals that differ in HaFT1 alleles and the surrounding low recombining region. One of the most convincing cases of sympatric speciation in plants that does not involve polyploidization or shifts in mating system concerns the Howea palms of Lord Howe island in the South Pacific, Howea belmoreana and H. forsteriana (Savolainen et al., 2006; Papadopulos et al., 2013; Hipperson et al., 2016; Osborne et al., 2019). This species pair diverged approximately 3.3 million years ago as a result of adaptation to different soils, which appears to have led to divergence in flowering time. While thought to be one of the main reproductive barriers between the species (Savolainen et al., 2006; Osborne et al., 2019), they overlap in flowering for over half (6-8 weeks) of their 8 to 15 week flowering window (Hipperson et al., 2016). This results in an RI‡‡ estimate of just 0.04. Indeed, hybrids represent half of the seedlings sampled in the wild (Hipperson et al., 2016). In contrast, RI§§ is 0.53-0.72 at the juvenile stage, when hybrids are only 20% of individuals (Hipperson et al., 2016), and at the adult phase, hybrids only represent 0.05% of the population (Hipperson et al., 2016). Thus, species differentiation is mainly due to post-zygotic ecological selection rather than divergence in flowering time. Similarly, a study on RI on the prairie sunflower, Helianthus petiolaris, found that there is almost full flowering overlap between sunflower ecotypes, and RI is mostly due to ecological factors (Ostevik et al., 2016). Nonetheless, in this species, ecotype delimitation is clear among dune and non-dune habitats, unlike the silver-leaf sunflower in which both ecotypes are mixed in all populations sampled within 15 km from the Gulf of Mexico. ‡‡ Estimated from supporting information provided by Hipperson et al., (2016), using the formula from Sobel & Chen (2014): RI = 1 +, where “obs H” and “exp H” are the observed and expected heterospecific crosses, which correspond to hybrids, and “obs C” and “exp C” are the observed and expected conspecific crosses, which correspond to the crosses within parental species. Observed proportions were obtained from the sampled juvenile and seedling populations, and expected proportions were calculated from the adult individuals sampled at different sites. §§ Ibid 71 Asynchrony of flowering in H. argophyllus is not great enough to avoid gene flow between ecotypes, and heterozygotes are nearly as common in offspring as in the parental population, with a slight reduction in the former (Table 3.2). While ecological selection favours the early-flowering ecotype on the barrier islands (Moyers, 2015), it does not appear to be strong enough to eliminate the late-flowering ecotype or hybrids. Thus, it seems that the early-flowering ecotype is unlikely to become fixed in the barrier islands in the near future. The direct effects of flowering phenology on rates of gene flow or RI are rarely quantified in natural populations (Roumet et al., 2015), even though low gene flow could be a crucial component of RI, especially in populations without geographical barriers (Hendry & Day, 2005; Taylor & Friesen, 2017; Osborne et al., 2019). There is a wide variety of results in previous studies. For instance, in a meta-analysis at different life stages from a variety of plant systems (Lowry, Modliszewski, et al., 2008), RI from estimated phenology, only considering the amount of overlap, from 20 taxa ranged from 0.01 to 0.96 (mean 0.4), while RI for hybrid seed set ranged from -0.7 to 1 (mean 0.4). Nonetheless, many of those studies relied on experimental crosses and did not assess hybrid formation in the wild. In a study of gene flow between cultivated and weedy sunflowers, H. annuus, differences in phenology were responsible for differential hybridization rates throughout the flowering season, which went from 30%, when flowering overlapped to 0%, when it did not (Roumet et al., 2015). In the silver-leaf sunflower, reproductive allochrony also results in different patterns of RI between genotypes, region and time (Fig. 3.3, Table 3.5). RI patterns are opposite between ecotypes (Fig. 3.3). In the mainland the 0/0 genotype has higher levels of RI than in the island (Fig. 3.3, Table 3.5) and it reached it highest values at the end of the season in both regions, when it produces more homospecific crosses. The earlier blooming period in the island resulted in genotypes 1/1 and 1/0 exhibiting higher RI than the 0/0 genotype all season. The early-flowering ecotype (genotypes 1/1 and 1/0) had its highest RI at the beginning of the reproductive season (Fig. 3.3, Table 3.5), and approximated random mating by the end of it, when the bulk of flowering for the late-flowering ecotype occurs. I do not have enough data to 72 know the trend for the 1/1 genotype in the mainland, but I suspect that it is similar to heterozygotes because HaFT1 is dominant to the null allele. 3.4.4. Genomic patterns of divergence The silver-leaf sunflower is seemingly at an early stage of divergence, given the sympatry between ecotypes in the barrier islands and a minor portion of the mainland, the substantial overlap in flowering times (Chapter 2), and the low RI values for the 0/0 genotype in the islands. The latter is the result of extensive heterospecific matings. However, RI values only reflect the extent of genetic exchange occurring during the two years of sampling, whereas patterns of genomic divergence offer a means for assessing historical patterns of gene flow. Therefore, I also calculated levels of genomic divergence between selected genotypes and populations of H. argophyllus, with the goal of estimating the relative importance of two of the main factors impeding gene flow in this system: phenology and geography. For the former, I determined patterns of genomic divergence between the 0/0 and 1/1 genotypes at HaFT1, which differ in flowering time. For the latter, I determined patterns of genomic divergence between mainland and island populations, which are isolated by 5 km of water. If flowering time differences are a stronger barrier to gene flow than geographical distance, genomic divergence should be higher between HaFT1 genotypes than between geographical regions. This was what I found, except that the high Fst values between the HaFT1 genotypes are largely restricted to chromosome 6, especially to the region of low recombination where HaFT1 is found. For the rest of the genome, geography may have had a slightly larger effect than flowering time in limiting historical gene flow. At the same time, the overall low levels of differentiation across the genome are consistent with my RI estimates, suggesting that these ecotypes are at an early stage of divergence with extensive gene flow (Lowry, 2012; Choi et al., 2020; Stankowski & Ravinet, 2021). 73 Assortative mating has been shown to be ineffective in preventing the blending of two populations when selection against hybrids is weak (e.g., Irwin, 2020). In the case of H. argophyllus, heterozygotes exhibit intermediate values for fitness correlates such as plant height and seed mass (Chapter 2). It is the region of low recombination in chromosome 6 where HaFT1 is found (Todesco et al., 2020) that has allowed the formation of a new ecotype despite ongoing gene flow with the more common and wide-spread form, the late-flowering ecotype (0/0 genotype). This is reflected in the Fst patterns observed, where much greater differentiation is found on chromosome 6 than the rest of the genome (Table 3.6 and Fig. 3.4). This fits the expectation that not all all genomic regions have the same opportunities for gene exchange and recombination (Wu & Ting, 2004), such as alleles that are a result of habitat-mediated adaptation and ecological reproductive isolation. While the role of chromosomal differences in ecotype formation has long been recognized (e.g., Clausen, 1951), there is increasing evidence from different biological systems that alleles causing ecologically-based reproductive isolation are often found within regions of low recombination, allowing co-existence of divergent morphs (Feder et al., 2003; Twyford & Friedman, 2015; Koch et al., 2021; Irwin & Schluter, 2021; Sanchez-Donoso et al., 2022). 3.5. Conclusions Differences in genotype frequencies between the parental and and offspring generation are strongest for the 1/1 HaFT1 genotype; I reason that this might be due to post-dispersal negative selection, possibly by granivory. This could help to explain why heterozygotes remain abundant despite assortative mating between the homozygous genotypes. Assortative mating is tightly linked to the presence of HaFT1 in a population, which affects whether a seedling population deviates from Hardy-Weinberg expectations. Further, it affects levels of reproductive isolation. HaFT1 in the seedling populations became rarer over the flowering season in both mainland and island populations and this changed the patterns of RI across regions and genotypes. For the two genotypes that make up the early-flowering ecotype (1/1 and 1/0), levels of RI are higher in the island regions and reach a maximum at the 74 beginning of the mating season; RI decreases as time progresses. The opposite pattern is true for the late-flowering ecotype, for which RI is higher in the mainland and reaches its maximum on both regions at the end of the season. The genomic patterns of differentiation (Fst) show that reproductive asynchrony has resulted in lower levels of gene flow and recombination in the genomic region where HaFT1 is found (Fig. 3.6), while it has not prevented gene exchange in the rest of the genome. This leads me to conclude that the ecotypes of H. argophyllus are at early stages of divergence, where differences in flowering phenology are not strong enough to prevent gene flow between ecotypes. Local adaptation in combination with assortative mating are powerful ingredients for speciation (Gavrilets, 2004), but in H. argophyllus neither seem to be strong enough to prevent pervasive gene flow. 75 Chapter 4: Hurricane Harvey and the perks of being a wild sunflower 4.1 Introduction Coastal sandy environments are constantly changing, shaped by geology, wind and wave exposure, vegetation (Stallins & Parker, 2008), and more recently by humans. These environments are extremely stressful to plants (Grant et al., 2017) because of exposure to salt spray, drought, high solar radiation, and nutrient deficiency, as well as disturbance by waves, high tides, wind, and storms. As a consequence, coastal environments are both dynamic and prone to degradation and destruction (Moreno-Casasola et al., 2008); and are good examples of so-called stochastic environments (Clark, 1986). Incessant disturbance and change generate microhabitats that require specialized phenotypes to survive them (Barbour & DeJong, 1977; Lowry, Rockwood, et al., 2008; Brunbjerg et al., 2012; Pardini et al., 2015). Plants in coastal dunes not only adapt to the changing conditions, they also modify the dunes both above and below ground and contribute to their formation and maintenance by, for example, retaining sediments and forming new soil (Feagin et al., 2015). The presence of vegetation on coastal environments is also well known to ameliorate the effects of strong winds and storms (i.e. White et al., 1978; Miller et al., 2010; Provoost et al., 2011; Feagin et al., 2015). Also, storms can present opportunities for early successional dune species to colonize newly opened spaces (Lugo, 2008; Zimmerman et al., 2014; Feagin et al., 2015; Hogan et al., 2018). Disturbances can be short or long term, and can act synergistically (Feagin et al., 2015). Short disturbances such as flooding are intense and rapid, while long-term disturbances include processes such as sea-level rise and the formation of barrier islands (Feagin et al., 2015). Large-scale disturbances can reveal information about the natural world that can be overlooked by regular field observations in stable environments or controlled experiments (Cooper-Ellis et al., 1999; Griffith et al., 2008; Lescak et al., 2015). Large-scale disturbances alter the eco-evolutionary dynamics of ecosystems, and their consequences are linked to their scope and scale (Grant et al., 2017), with highly variable impacts on different organisms. For example, 76 mangroves near Port Aransas, a year after Hurricane Harvey in Texas (2017), suffered from reduced soil nutrient availability and increased root burial (Kuhn et al., 2021). In contrast, hurricanes can have positive effects for some organisms. For example, after Hurricane George hit the Dominican Republic in 1998, the Hispaniolan parrot, Amazona ventralis, increased its distribution from 84 ha to 1690 ha (White et al., 2005). Similarly, the previously rare bitter switchgrass, Panicum amarum, became abundant in the coastal habitats of New York, U.S.A., after Hurricanes Irene and Sandy in 2011 (Cheplick, 2017). Large-scale mortality from extreme weather events can result in population bottlenecks and/or strong selection. Estimates of recovery vary depending on the organism and population under study, habitat, successional stage, vegetation type and cover, and even land-use history (Lomascolo & Mitchell, 2001; Thompson et al., 2002, 2007; Lugo, 2008; Armitage et al., 2020; Kuhn et al., 2021). The effects of extreme weather events can be temporary (a matter of days, e.g., Lugo, 2008; Biggs et al., 2018) or last several years (Lugo, 2008). However, given their unanticipated nature, many studies of extreme events lack appropriate data for testing different explanatory hypotheses for the patterns observed in natural populations. Furthermore, results can be contradictory. For example, a positive correlation between toepad size and hurricane activity over the last 70 years was reported for Caribbean populations of Anolis lizards, suggesting that their evolutionary trajectory has been affected by large storms (Donihue et al., 2020). However, another study of Anolis after Hurricane Maria (2017) revealed size reductions in all morphological traits measured, including toepad size (the authors attribute such change to differential age mortality), although most phenotypic traits returned to pre-hurricane values 11 months after the storm (Avilés-Rodríguez et al., 2021). Nonetheless, some generalizations are possible. Several studies that have focused on plant communities in hurricane-prone regions have found that many of these plants have traits that allow them to withstand strong winds and/or the plants share several primary succession life-history traits that allow them to quickly colonize newly open habitats (Lugo, 2008; Zimmerman et al., 2014; Feagin et al., 2015; Hogan et al., 2018). There is also evidence for trade-offs 77 between resistance (i.e., amount of damage received, number of individuals that survive) and resilience (i.e., ability and speed to return to normal state) to disturbance (Lugo, 2008; Zimmerman et al., 2014; Hogan et al., 2018; Patrick et al., 2020). 4.1.2. Storms in Texas The first record made by Europeans of a major storm hitting Texas was in 1527 (by some accounts, in 1528) near Galveston Island (~85 km from Houston and ~300 km north of Port Aransas), when a storm sunk several Spanish ships (Johnson, 1995; Roth, 2010; Hyser, 2021). Nearly 400 years later, the deadliest natural disaster in the history of the U.S.A. struck again near Galveston in 1900 (Roth, 2010). The so-called Great Galveston Storm had wind gusts over 200 km/hr and a storm surge of nearly 5 meters (Roth, 2010; National Park Service (U.S.A.), 2019; City of Galveston 1900 Storm Committee, 2020). The Texas coast experiences an average of around 0.8 hurricanes per year (Morton & Pieper, 1977; Roth, 2010). Based on Roth’s account of hurricanes in the Texas coast (Roth, 2010), I calculate that the sampling sites of Helianthus argophyllus (Asteraceae) have encountered at least 35 hurricanes in the 20th century, experiencing substantial wind gusts, flash floods, record breaking rainfall, storm surges and even tornadoes. For example, several of the sites were strongly impacted by Hurricane Carla, which in 1961, flooded 88% of Mustang Island (U.S. Army Corps of Engineers, 1962) and eroded 15 m of dunes in Port Aransas (Morton & Pieper, 1977). Together, Hurricanes Carla (1961) and Beulah (1967) contributed to a substantial retreat of the vegetation line (Morton & Pieper, 1977); in Port Aransas, dune vegetation retreated up to 60 m and in north Padre Island around 30 m (Morton & Pieper, 1977). The largest retreats were associated with localized dune blowouts (the removal of parts of a dune by the wind). Yet, despite their major catastrophic consequences, such storms may, in some cases, be beneficial for the environment by relieving periods of drought (Roth, 2010); it is estimated that without them east Texas summers would have 10% less rainfall (Roth, 2010). 78 The 2017 Atlantic hurricane season was among the most intense in recent history (National Weather Service (U.S.A.), 2017, 2022; Blake & Zelinsky, 2018). On August 25th 2017, Hurricane Harvey hit the southern coast of Texas (Fig. 4.1), making landfall near Rockport, where wind gusts were up to 230 km/hr. It was the first major hurricane to make landfall in this region since Celia in 1970, and the first Category 4 hurricane to make landfall in Texas since Carla in 1961 (National Weather Service (U.S.A.), 2017). The maximum storm surge was nearly 4 meters. However, the main (and devastating) effect of this hurricane was caused by high rainfall, because the storm stalled for several days around Rockport (National Weather Service (U.S.A.), 2017, 2022; Blake & Zelinsky, 2018). Some areas received over a meter of rain in less than 48 hours (National Weather Service (U.S.A.), 2017, 2022; Blake & Zelinsky, 2018). The highest recorded rainfall from Harvey was 1.6 m, making it the largest tropical cyclone rainfall event in the colonial history of the continental United States; the previous record of 1.2 m was also in Texas, resulting from Tropical Storm Amelia in 1978 (Blake & Zelinsky, 2018). 79 Figure 4.1. Satellite image of Hurricane Harvey at 3:15 p.m. on August 25 2017. Image generated by the Geostationary Operational Environmental Satellite 13 (GOES-13) from the National Oceanic and Atmospheric Administration (NOAA) of the USA. Retrieved May 26, 2022, from https://earthobservatory.nasa.gov/images/90822/hurricane-harvey-approaches-texas The changing nature of events like hurricanes and storms makes studying their repercussions on natural populations a matter of luck (but see Grant et al., 2017 and Pruitt et al., 2019). Indeed, Gutschick and BassiriRad (2003) write “Stochasticity challenges researchers as much as it challenges organisms to respond adaptively”. Scientists have estimated that global warming made Hurricane Harvey’s precipitation 8-18% more intense (Van Oldenborgh et al., 2017). Measuring the aftermath of extreme weather events provides a rare opportunity to obtain knowledge that can be used to understand the role of large disturbances on the evolutionary dynamics of natural populations, as well as potentially to adapt to and mitigate the effects of climate change on coastal habitats (Duarte et al., 2013). This is particularly relevant considering that, tropical storms, hurricanes and heavy rains are the most common drivers for human 80 involuntary migration and displacement (Pörtner et al., 2022); this is an alarming fact, since an increase in intensity and amount of rainfall derived from tropical hurricanes is expected due to anthropogenic-caused global warming (Knutson et al., 2021; Knutson, 2022) and that three-quarters of the human population live within 50 km from a coast (Small & Nicholls, 2003). In addition, when focal species are crop wild-relatives, such as H. argophyllus, such information can aid in the design of extreme-weather resistant crops. In this chapter I describe the impact of Hurricane Harvey on H. argophyllus, which is a primary successional species that appears to be well-adapted to the disturbance regime of coastal sand dunes in the Gulf of Mexico. Two months before Hurricane Harvey ravaged the coast of Texas, I sampled 200 individuals of H. argophyllus in the coastal dunes of five sites in the barrier islands: Mustang and Padre Island, and one population from the mainland: Rockport. The original study plan for these populations was to characterize their phenology, its link to the presence of a major flowering-time gene, HaFT1, and its impact on assortative mating throughout the reproductive season. For that purpose, I marked each plant with a metal tag, recorded its coordinates, height and reproductive stage. After the storm, I returned to the populations to see if I could find enough individuals alive to allow me to continue my studies of phenology and reproductive isolation. Indeed, enough individuals survived that I was able to continue tracking phenology. However, I also took advantage of this unique situation to examine the response of the species to Hurricane Harvey and to evaluate possible factors contributing to differential survivorship such as distance to water, plant height, and flowering time. Because mortality was primarily caused by flooding, I expected that populations closest to a body of water to be most negatively affected. Also, in many species, waterlogging is known to have larger negative impacts on plants in the reproductive than vegetative phase (Orchard & Jessop, 1984; Wen et al., 2002; Grassini et al., 2007). This leads to the prediction that tall, early flowering plants, which typically are homozygous or heterozygous for the presence of HaFT1, might be more susceptible to flooding than later flowering plants that lack the HaFT1 gene. Waterlogging is also expected to 81 reduce components of fecundity (Orchard & Jessop, 1984; Grassini et al., 2007; Yasumoto et al., 2011), which I measured here through analyses of seed weight in surviving plants. More broadly this data set allowed me to test the expected trade-off between resistance and resilience. Species that have high resistance to large disturbances such as hurricanes are thought to recover more slowly (have low resilience) than species with low resistance, and vice versa (Patrick, 2020). Whether this prediction holds for an annual plant that shows preference for disturbed sites, like H. argophyllus (Foster, 2020) is unclear. It is plausible, for example, that this species relies on seed banks to recover from extreme disturbances, and that extreme events may trigger the expansion of populations rather than their demise. Considering all the above, I asked the following questions: Is there evidence of a quick population recovery via seed banks after hurricane Harvey? Is there evidence of a trade-off between resistance and resilience in the silverleaf sunflower populations? Did the storm affect all populations and individuals equally? Or were populations closer to water less likely to survive? Did any of the life history traits and underlying genotypic variation at the HaFT1 locus affect survivorship? If so, is this reflected in the frequency of HaFT1 the following year? Did plants that survived hurricane Harvey have decreased seed mass in comparison to the following year? 82 4.2. Methods 4.2.1. Sampling In July 2017 I sampled and marked 200 individuals with metal tags, to enable tracking over the remainder of the flowering season. One hundred and seventy-eight of these plants were successfully genotyped for the presence or absence of HaFT1, which underlies a major difference in flowering time between early and late-flowering ecotypes of H. argophyllus (Todesco et al. 2020; Chapter 2). Of the genotyped plants, 149 are from five localities in the barrier islands (Port Aransas, Aransas Mustang, Mustang Bridge, Water Park, and Padre Beach) and 29 from one site in the mainland (Rockport). Locality information, sampling methodology, and protocols for genotyping for HaFT1 are described in Chapters 2 and 3. I went back in September 2017 to evaluate the damage from Hurricane Harvey and re-sampled as many surviving individuals as feasible until I reached at least 30 individuals per locality, to continue the phenology observations. I genotyped a total of 93 new individuals from the five populations in the barrier islands. The sole population sampled in the mainland was fully destroyed, as was everything around Rockport, but I was able to find 15 plants in that region later in November that year; I successfully genotyped 11 of these plants. In total, I genotyped 282 individuals in 2017. In July 2018 I sampled and genotyped 189 individuals from the same localities (159 from island populations). Therefore, a total of 471 individuals were genotyped for the analyses presented here. For all genotyped plants, I measured their height and categorized their flowering stage as described in Chapters 2 and 3. Finally, whenever available, I also collected seeds and measured their average weight in groups of 10 using an analytical balance. For comparison, I took the same data from the same populations in the following year. 83 4.2.2. Distance to water For each plant tagged in July 2017 I obtained GPS coordinates and estimated its distance to a body of water (i.e. the Laguna Madre lagoon or the sea) through ArcMap using the North America Datum 1983 with 5 m resolution as a reference. 4.2.3. Statistical analyses I first compared the genotype frequencies between the plants sampled before the hurricane against those that I later re-sampled. For this, I used a Fisher’s exact test though the function “fisher.test” of the stats package (R Core Team, 2020). I then compared genotype frequencies, height, flowering stage and location (and interactions) for those plants that survived vs. those that did not. I subsequently repeated this analysis by region (mainland vs. island) and by location. To compare genotype frequencies and differences in flowering stages, I also performed a Fisher’s exact test, and if necessary I then performed Fisher post hoc tests using the “pairwise.fisher.test” function with a Bonferroni correction for multiple testing, using the package reporttools (Rufibach, 2009). To compare heights between surviving groups (categorized as “yes” vs. “no”), given that height is not normally distributed (Shapiro test p<0.05), I used a Wilcoxon signed-rank test. Further, to compare height between genotypes at different life stages and time points I performed Kruskall-Wallis tests, using the function “Kruskal.test” of the stats package (R Core Team, 2020) and post-hoc tests with the “pairwise.wilcox.test” fuction of the same package. In addition, I estimated 95% confidence intervals for the height by bootstrap resampling with 10,000 replicates using the function boot.ci from the “boot” R package (Davison & Hinkley, 1997; Canty & Ripley, 2022). To study if differences in survival correlate with flowering stage at the time of the hurricane, I ran a Chi-square test using the function “chisq.test” from the stats package (version 3.6.2) (R 84 Core Team, 2020) comparing survival between each of the flowering stage categories recorded at the time of sampling, in July 2017. I then integrated each explanatory variable (genotype, distance to water, height and flowering stage) individually, additively, and with interactions, in a generalized linear model, with survival as a binomial response variable, using the function glm from the “stats” package (version 3.6.2) (R Core Team, 2020). The Akaike information criterion was used to identify the best supported model. I also ran linear models in which I considered the proportion of individuals surviving per population as the response variable and mean distance to a body of water, mean height and/or genotype as explanatory variables, using the lm function from the “stats” package (version 3.6.2) (R Core Team, 2020). Finally, I compared seed weight between years using a Kruskall-Walis test, due to the non-normality of the data. However, because 1/1 homozygous seeds are heaviest early in the season (see Chapters 2 and 3) and likely more frequent when Harvey hit (and hence more likely to be lost), I used another Kruskall-Walis test to compare the seed weight of offspring produced in September and November of each year. 85 4.3 Results Hurricane Harvey killed 83% of the H. argophyllus plants tagged in July (Figs. 4.2, 4.3). Figure 4.2. Image of the sampled population of H. argophyllus in Port Aransas before and after Hurricane Harvey. Upper picture shows the population in July 2017 and lower picture shows the same population in September 2017; only one branch of an individual survived, which produced fruits later that November. Arrow points at landscape marker for comparison. 86 The arrow points at a metal tag, indicating that this plant was sampled in July. Picture taken at the Aransas Mustang site, where only two sampled plants survived. Figure 4.3. Image of a silverleaf sunflower killed by Hurricane Harvey next to a survivor. 87 According to the phenology analysis, there was no relationship between flowering stage and survival (χ2= 0.07, p= 0.8) (Fig. 4.4). About the same percentages of individuals that were either beginning to bloom or at vegetative stage in July survived the storm (Fig. 4.4). A similar result is obtained if I only consider the island individuals (Fisher’s test, p= 0.16). 19% 81% 83% 17% Figure 4.4. Survival of H. argophyllus by flowering stage considering all populations. 88 There was a clear pattern regarding height and survival. The surviving plants were shorter than non-surviving plants (Wilcoxon test, W= 2497.5, p= 0.03). On average, survivors were 108.44 cm tall (sd= 59.25), while non-survivors were 138.16 cm tall (sd= 66.97) on July 2017 (Fig. 4.5). The same trend occurred when only considering the island individuals, where survivors where on average 108.64 cm (sd= 59.25) and plants that died where 137.4 cm (68.44) (Wilcoxon test, W= 1917.5, p= 0.047). The dot indicates the average value and the bars the 95% confidence interval. Considering that height can be correlated with HaFT1 genotype (see Chapters 2 and 3), I analyzed the genotypes of the plants tagged in July. Because the HaFT1 allele was rare in the mainland population, Rockport, and there were few survivors, I only considered island populations for testing survivorship differences between genotypes among and within populations. Figure 4.5. Height and survival of H. argophyllus considering all populations. 89 In the islands, 14% of the plants that did not survive had the 1/1 genotype, 29% were heterozygotes and 57% were homozygous for the absence of HaFT1. Within populations, height varied greatly (Table 4.1). Overall, homozygotes for the presence of HaFT1 measured before Harvey’s impact were taller than the other two genotypes. All three genotypes were on average taller at Port Aransas (Table 4.1). Table 4.1. Average height by HaFT1 genotype of H. argophyllus before Hurricane Harvey in different barrier island populations Population Genotype Mean height (cm) Sd n Aransas Mustang 1/1 89.00 21.21 3 1/0 111.75 58.47 8 0/0 120.2 52.89 16 Mustang Bridge 1/1 191.5 62.93 2 1/0 104.25 45.77 5 0/0 123.58 26.11 16 Padre Beach 1/0 79.00 39.60 10 0/0 87.46 46.25 31 Port Aransas 1/1 224.4 49.77 12 1/0 196.5 94.85 10 0/0 177.25 68.38 8 Water Park 1/0 106.25 70.23 8 0/0 102.20 55.73 20 Sd= Standard deviation When comparing survival rates between genotypes, the proportion of survivors for each genotype were: 1/17 (5.9%) homozygotes for the presence of HaFT1; 7/41 (17.1%) heterozygotes; and 24/91 (26.3%) homozygotes for the absence of HaFT1 (Table 4.2), but these proportions were not significantly different (p= 0.13), probably because of modest sample sizes. Likewise, no significant differences in survival between genotypes were detected within populations (Table 4.2, Fig. 4.6). 90 Bar widths indicate sample size. Figure 4.6. Mosaic plot showing the proportion of H. argophyllus survivors per genotype in the barrier islands. 91 Table 4.2. Surviving H. argophyllus genotypes per island population * Mean height (standard deviation) in cm For the generalized linear models I only considered data from the barrier islands as no survivors were found on the mainland in September; although I found a few in November, these were at a different locality. Also, HaFT1 is nearly absent in that region, so comparisons between genotypes was not possible. In the models for barrier island plants, I included as explanatory variables, in different combinations: height, genotype and distance to a body of water. The model with the best fit, where survival was the response variable, included distance to a body of water plus the effect of plant height in July as explanatory variables (AIC= 109.03). Under this model, distance to water had a p-value of 1.35e-06, but height was not a significant factor (p= 0.23). The second best model (AIC= 109.95) included the interaction between distance to water and height, and again only distance to water was significant (p= 0.02). The third best model included the interaction between distance to water and height, plus the effects of genotype (AIC= 117.06); in this case, data were filtered to only include individuals that were genotyped, and the interaction was significant in this model (p<0.005). Population Survived Height (sd)* n Proportion of genotypes 1/1 1/0 0/0 Fisher’s test p-value Aransas-Mustang no 115.6 (55.6) 24 0.13 0.33 0.54 0.67 yes 110.7 (15.3) 3 0 0 1.00 Mustang Bridge no 129.5 (46.3) 17 0.12 0.29 0.59 0.23 yes 121.5 (30) 6 0 0 1.00 Padre Beach no 86.3 (44.2) 37 0 0.22 0.78 0.25 yes na 4 0 0.50 0.50 Port Aransas no 199.6 (73.4) 29 0.38 0.34 0.28 1 yes 262 (na) 1 1.00 0 0 Water Park no 115.9 (51.5) 10 0 0.3 0.7 1 yes 96.4 (62.9) 18 0 0.28 0.72 TOTAL no 138.2 (67) 117 0.14 0.29 0.58 0.13 yes 108.4 (59.2) 32 0.03 0.22 0.75 92 Neither of the linear models in which I attempted to explain the proportion of individuals surviving per population by either the mean distance to a body of water (F= 7.86, R2= 0.72, p= 0.07) or the mean height (F= 0.48, R2= 0.14, p= 0.54) was significant. However, when I filtered the data to only consider individuals that were genotyped, the proportion of survivors for each population was now positively related to distance to a body of water (F= 4.95, R2= 0.31, p= 0.048; Fig. 4.7 left panel), which explained 31% of the variation in survival among island populations. When further partitioning the filtered data by genotype another pattern emerged: genotype was a significant factor explaining differences in the proportion of individuals surviving within each population, but only when a linear model included a multiplicative interaction between genotype and distance to a body of water (F= 4.14, R2= 0.44, p= 0.04). Homozygotes for the absence of HaFT1 had higher survivorship at greater distances to a body of water (p= 0.01). Heterozygotes showed a similar trend, but this was not significant (p= 0.10), while survivorship of homozygotes for the presence of HaFT1 was unaffected by distance to water (p= 0.91) (Fig. 4.7, right panel). 93 The left panel shows for the five barrier island populations the proportion of individuals that were successfully genotyped for HaFT1 and that survived hurricane Harvey. The right panel shows the proportion of survivors per genotype per population. In order to complete the phenology observations and estimates of reproductive isolation, I tagged additional surviving individuals in September 2017, two weeks after the storm, in the same populations as in July. I genotyped these additional individuals for HaFT1 and used them along with the few survivors to provide more robust estimates of genotype frequencies after Harvey (Figure 4.8); these represent a total of 217 individuals. Genotype frequencies differ when comparing plant populations before and after the hurricane in island populations, whether I include the surviving genotypes from July in the post-hurricane frequencies (Fisher’s test, p<0.005) or not (Fisher’s test, p<0.005). When looking at each sampling site the results varied but only one population showed a significant difference: Padre Beach, the southernmost population displayed a large increase in 1/0 genotype (Fisher’s test, p<0.005). Among island populations, homozygotes for the presence of HaFT1 went from representing 11% of the barrier island populations to only 4%; heterozygotes increased from 28% to 46% and homozygotes for the absence of HaFT1 went from 61% to 50%. genotype proportion of survivors distance to water (km) distance to water (km) p-value= 0.048 R2= 0.31 p-value= 0.04 R2= 0.44 Figure 4.7. Proportion of surviving individuals of H. argophyllus for each barrier island population, distance to a body of water (km) and genotype. 94 “f” indicates the frequency of HaFT1. Population names are in the left side panel, in latitudinal order: RP= Rockport. PA= Port Aransas. AM= Aransas Mustang. MB= Mustang bridge. WP= Water Park. PB= Padre Beach. * marks a significant difference in genotype frequencies before and after the hurricane (p<0.05). n=50 n=10 n=25 f=0.22 f=0 f=0.44 f=0.20 f=0 f=0.02 f=0.57 f=0.45 f=0.17 f=0.26 f=0.12 f=0.36* f=0.20 f=0.09 f=0.14 f=0.18 f=0.15 f=0.03 genotype RP PA11 AM MB WP PB 0/0 1/0 1/1 Figure 4.8. Genotype frequencies in the same populations before and after the hurricane. 95 Overall, HaFT1 slightly increased in frequency in island populations from 0.25 before the storm to 0.27 afterwards. This is higher than the frequency of HaFT1 among the surviving plants tagged in July (f= 0.14) because most of the tagged survivors were concentrated in Water Park, a population which had a low frequency of HaFT1 to begin with (also f= 0.14) and lacked the 1/1 genotype. When comparing between years (Table 4.3), island populations from July 2017 do not differ from those from July 2018 (χ2= 2.59, p= 0.27). The overall frequency of HaFT1 went from 0.25 in July 2017 to 0.20 in July 2018. Collectively, island populations saw an average reduction of 5% of the 1/1 genotype and the same increase in 0/0 genotypes (Table 4.4). Island populations had an average reduction of 4% in the frequency of HaFT1 between July 2017 and July 2018. The highest reductions were observed in Port Aransas and Water Park (Table 4.3). In the latter population HaFT1 nearly disappeared in the July 2018 samplings (frequency 0.03); in addition, it had no 1/1 genotypes in either of the sampled years (Table 4.4). In Rockport, the only mainland population sampled before the hurricane, HaFT1 went from 0.02 in 2017 to 0 in 2018, but the genotype proportions were not statistically different (Fisher’s test, p= 0.49) (Table 4.3). No island population had different genotype frequencies between samples collected in July of each year (p>0.05) (Table 4.4). Table 4.3. Frequency of HaFT1 in H. argophyllus across years by population Population Region HaFT1 freq. 2017* HaFT1 freq. 2018* Aransas-Mustang island 0.26 0.22 Mustang Bridge island 0.20 0.20 Padre beach island 0.12 0.15 Port Aransas island 0.57 0.44 Water Park island 0.14 0.03 Rockport mainland 0.02 0 *HaFT1 freq. 2017 and HaFT1 freq. 2018 only include estimates obtained from plants sampled in July. 96 Table 4.4. Genotype frequency changes of HaFT1 in H. argophyllus populations across years Population Region 2018 – 2017* Are genotype frequencies different between years? 1/1 1/0 0/0 Aransas-Mustang island -0.08 0.08 -0.01 no Mustang Bridge island -0.09 0.18 -0.10 no Padre Beach island 0 0.05 -0.05 no Port Aransas island -0.07 -0.11 0.18 no Water Park island 0 -0.22 0.22 no AVERAGE -0.05 0 0.05 no Rockport mainland 0 -0.03 0.03 no *Differences between genotype frequencies were calculated as July 2018 values – July 2017 values; significance of change was tested through Fisher’s exact tests with α= 0.05. Seed weight represents average weight based on groups of 10 seeds mainland island Seed weight (g) Figure 4.9. Seed weight by region, month and year. 97 In general, seeds were lighter in 2017 than they were in 2018 (Fig. 4.9, Table 4.5), for both island (Kruskal-Wallis χ2= 48.07, p<0.001) and mainland populations (Kruskal-Wallis χ2= 26.92, p<0.001). Groups of ten seeds from island populations in September 2017 weighed 0.055 g on average (sd= 0.013), while those from September 2018 weighted 0.066 g (sd= 0.015), and were significantly heavier (Kruskal-Wallis χ2= 14.10, p<0.001). In November 2017, seeds weighed 0.047 g on average (sd= 0.016), while those of November 2018 were significantly heavier (Kruskal-Wallis χ2= 37.15, p<0.001), averaging 0. 059 g (sd= 0. 015) per ten seeds (Fig. 4.9, Table 4.5). For the mainland, I could only compare seeds between years for November as none were sampled in September 2017; again, seeds were lighter for the year Harvey hit the region (Kruskal-Wallis χ2= 17.24, p<0.001), with a mean mass of 0.045 g for every 10 seeds (sd= 0.016) compared to 0.057 g (sd= 0.015) for 2018 (Fig. 4.9, Table 4.5). Table 4.5. Mean seed weight in grams of groups of ten seeds of Helianthus argophyllus by region, month and year Year Region Month Mean Sd Number of mother plants 2017 island September 0.055 0.013 113 2018 island September 0.066 0.015 29 2017 island November 0.047 0.016 130 2018 island November 0.059 0.015 70 2018 mainland September 0.066 0.012 11 2017 mainland November 0.045 0.016 70 2018 mainland November 0.057 0.015 29 98 4.4. Discussion Inhabiting coastal dunes comes with great challenges for plants, from ever-changing topography and environmental stressors such as extreme climatic conditions. Dune floras appear to be assembled through a combination of regional diversification and local environmental filtering along a sea-inland environmental gradient (Brunbjerg et al., 2014). Because of the harsh environment, coastal dune floras typically are species poor, with disturbance adaptation being a common theme (Brunbjerg et al., 2012; Pardini et al., 2015). Extreme weather events are considered to affect the eco-evolutionary trajectories of organisms over geological time-scales (Gutschick & BassiriRad, 2003; Grant et al., 2017). Mass mortality caused by a weather event can drastically reduce population size and genetic variation in a population. However, survivorship may have a strong stochastic component, reducing the link with genotypic variation. Documented changes in allelic frequencies following such events are rare (Grant et al., 2017; Pruitt et al., 2019). Hurricanes are one of the main destructive forces for coastal habitats and can be an important selective force on the organisms that inhabit coastal dunes. For example, even long lived-perennial species, such as palms, have been shown to have different levels of adaptation and resistance to strong winds, depending on the frequency of hurricanes locally (Griffith et al., 2008). Two weeks after Hurricane Irene (27 Aug 2011), there was a reduction in density of several annual herbaceous species growing on open beaches at Staten Island, New York, U.S.A. (Cheplick, 2017). This was followed by a rapid rebound for some populations in the same year (Cheplick, 2017). While I did not measure plant density, high mortality rates (83%) can be used as a proxy. I observed fewer individuals of H. argophyllus at all sampling sites after Hurricane Harvey hit the Texas coast in 2017. However, the silverleaf sunflower, like many annual sunflowers, is a weedy, early-successional species that thrives along roadsides, waste areas and disturbed soils; in southern coastal Texas it dominates disturbed foredunes. After Harvey, enough individuals survived (Fig. 4.3) that it was not difficult to re-sample each population until I reached at least 30 individuals in September 2017. Moreover, I noted an increase in H. argophyllus populations in 2018. For example, I detected a population of at least 5,000 99 individuals (pers. obs.) in coastal prairies about 600 m from the coast near Texas state highway no. 361, which I did not see in 2017. In addition, another large population (around 1,500 individuals) appeared near Rockport in 2018 close to the intersection of Texas state road no. 35 and Texas state highway no. 188, in a wide traffic island where large volumes of debris from the surrounding areas were dumped by trucks after the storm (pers. obs.). This appears to be a case of demographic recovery after a hurricane-induced bottleneck, which is not uncommon in flooding-adapted plants (Blom et al., 1990; Blom & Voesenek, 1996). In the sand dune vegetation around the sampling sites of H. argophyllus, this species is the largest plant and is one of the few (if not the only one) with lignified stems. Wind gusts of up to 230 km/hr likely damaged these plants during the hurricane. Nonetheless, I suspect that flooding due to rainfall and/or the storm surge was the main cause of damage. Hurricane surge or storm surge is an abnormal rise of sea water level during a storm (National Oceanic and Atmospheric Administration U.S.A. website). Such surges are considered to be the most destructive consequence of storms on the Texas coast (Bodine, 1969), along with flooding (Roth, 2010). Different water depths have been show to induce different physiological responses (at least in willows) which persist after flooding (Mozo et al., 2021). In the case of the silverleaf sunflower, all dead plants showed signs of drowning, exhibiting moist, blackened, and partially rotten stems and branches (Fig. 4.10). Moreover, most plants were pointing towards the sea (Fig. 4.10), which appears to be the result of waters receding back to the sea after flooding events. I suspect that the variation in survival rates among populations (Table 4.2) relates to the hurricane path and associated differences in floodwater depth. The Port Aransas population was hit first by the storm, and only one plant survived there (Fig. 4.10). Harvey passed through Port Aransas and then stalled for four days on Rockport, where there were no silverleaf sunflower survivors. 100 Figure 4.10. Dead H. argophyllus plants after the hurricane pointing towards the Gulf of Mexico, to the left of the photograph. Image taken at the Aransas-Mustang site. Although the often large effective population size of H. argophyllus likely minimizes the impact of drift on species-wide allele frequencies, the high percentage of mortality among the sampled individuals in July reveals a bottleneck that appears to have affected populations (and genotypes) differentially (Table 4.2, Fig. 4.8). First, while heterozygosity tends to be insensitive to bottlenecks (Allendorf, 1986), between years there was a reduction in heterozygosity in two sampled populations (Table 4.4): Port Aransas and Water Park suffered a reduction in heterozygosity of 10–20%; although these differences are not significant. These two populations have nothing in common: they are located at opposite ends of the barrier islands, one North and one South; the former had the highest frequency of HaFT1 throughout the sampling season and the largest number of 1/1 genotypes; the latter is on the lower end of HaFT1 frequency and had no 1/1 genotypes in any year. In addition, Port Aransas had the lowest number of survivors while the Water Park had 101 the highest (Table 4.2). Yet, they both had the largest reduction of HaFT1 from one year to the next (Table 4.3). I am unclear as to what mechanisms would be acting differentially in these localities, in comparison with those nearby in the barrier islands. Second, bottlenecks can lead to the loss of rare alleles. In 2018, HaFT1 was not present in any individual sampled in the mainland site (Rockport), whereas in 2017 it had a frequency of 0.02. Rare alleles are the most likely to be lost after a stochastic event like a hurricane. However, despite the overall decline, the overall large number of plants in the island region was sufficient to retain the HaFT1 allele. In terms of survival, when analyzed independently from other factors, the HaFT1 genotype is not significantly associated with survivorship in the islands (Fisher test, p= 0.42; Fig. 4.6). This is reflected in the fact that island adult plant populations do not show a change in genotype frequencies from 2017 to 2018, although, there was a slight increase in the frequency of the 0/0 genotype in that region (Table 4.4). If HaFT1 had a strong effect on fitness in the barrier islands, Hurricane Harvey could have had a subtle maladaptive effect in the populations Port Aransas and Water Park, where it had its largest decrease. However, this might have been counter-balanced by the new populations that arose after disturbance. For example, in Port Aransas HaFT1 was still at a medium frequency (0.44) in July 2018, but the frequency of 1/1 genotypes went from 40% of the population in July 2017 to 33% in July 2018, and this could be relevant given that Port Aransas is the population with the highest frequency of HaFT1 and 1/1 genotypes in the area. Hurricane Harvey affected the weight of achenes, which were heavier in 2018 than in 2017 (Fig. 4.3). A reduction in offspring output was also detected after Hurricane Katrina in Spartina alterniflora (Biber & Caldwell, 2008). However, there are not many other species to compare with, likely due to the serendipitous nature of such studies. Nevertheless, it is known that fruit and seed production of many plant species is affected when abiotic stresses occur during the flowering season (Hedhly, 2011; Borghi et al., 2019), although most of this research focuses on 102 crop productivity (Barnabás et al., 2008). In cultivated sunflower, for example, short-term waterlogging (1–6 days) at the reproductive and grain-filling stages reduces oil content and yield (Orchard & Jessop, 1984; Grassini et al., 2007; Yasumoto et al., 2011). One trait that differentiated survivors from non-survivors was plant height. Survivors were significantly shorter than the plants that died (Fig. 4.5). I am unsure of the mechanisms for the apparent resistance differential, but it might relate to plants being at slightly different life stages at the time the hurricane impacted the populations. Plants that were budding in July were on average taller than those still in vegetative stage, as shown in Appendix Table B.1. The developmental age of plants can affect their responses to stresses (Rankenberg et al., 2021). Unfortunately, age-dependant responses of plants to abiotic stresses are not as well studied as responses to biotic stressors (e.g., Rankenberg et al., 2021). However, there is evidence of larger impacts on plant development when abiotic stresses occur at the reproductive stage. For example, chilling temperatures in rice at anthesis result in sterile flowers (Wen et al., 2002). Moreover, it has been shown that waterlogging in sunflower cultivars has larger physiological impacts when it occurs after anthesis than on vegetative stages (Orchard & Jessop, 1984; Grassini et al., 2007). One proposed explanation is that roots stop growing after anthesis (Sadras et al., 1989; Grassini et al., 2007) and waterlogging exacerbates this condition (Grassini et al., 2007). Flooding reduces plant respiration and photosynthesis processes (Bailey-Serres & Voesenek, 2008), and one response of sunflower cultivars and other plants to flooding is root aeration through adventitious root formation (Wample & Reid, 1978; Bailey-Serres et al., 2012). However, adventitious root formation has not been linked to increased growth rate or survival in sunflowers (Gao et al., 2019; Wample & Reid, 1978). I did not notice adventitious roots in the silverleaf populations (although, I was also not looking for them). The observation of differences in survival due to height differs from what has been reported from greenhouse experiments involving cultivated sunflowers (Gao et al., 2019). However, in the greenhouse study, only the roots of juvenile plants were covered with 103 water, whereas entire plants were inundated by Hurricane Harvey, and taller plants were likely at the reproductive stage (see above). Another critical aspect of survival here is the distance to the nearest body of water. This makes sense given that the storm surge was the most damaging effect of Hurricane Harvey, with depth of inundation reaching up to 1.6 m in some places (National Weather Service (U.S.A.), 2017, 2022; Blake & Zelinsky, 2018). The proportion of surviving individuals in the barrier island populations was positively and linearly correlated with distance to water (p= 0.04), and explained 32% of the variation in survival. This relationship was stronger for homozygotes for the absence of HaFT1 than for heterozygotes, but no relationship was seen for homozygotes for the presence of HaFT1, perhaps because of the small sample size (Fig. 4.9). Overall, the best model to explain variance in survival did not include genotype or flowering time, but did include distance to a body of water plus the effect of plant height in July (AIC= 109.03). In Chapters 2 and 3, I demonstrated that plant height is correlated with flowering time and genotype. Here I found similar correlations: the 1/1 homozygotes were 201.9 cm (sd= 65.7) tall and 94% were budding; heterozygotes were 133.9 cm (sd= 81.5) and 74% were budding, and 0/0 homozygotes were 116 cm (sd= 55.9) and 45% were budding. I also know that 1/1 homozygotes survived five times less than the 0/0 homozygotes and 2.8 times less than the heterozygotes, although these differences were not statistically significant. However, the number of surviving plants was small, and it might be that this study lacks sufficient power to detect significant effects of genotype and flowering time on survivorship. Helianthus argophyllus appears to be well-adapted to the transient disturbances that characterize coastal sand dunes (Kruckeberg & Rabinowitz, 2003). Like most other plant species inhabiting coastal dune ecosystems (Pardini et al., 2015), the silverleaf sunflower is only found in highly disturbed areas (Foster, 2020). In 2018 I observed large populations at sites where it was not present in 2017. It is likely that sporadic large disturbances help maintain populations of H. argophyllus. Studies of its sister species, H. annuus, have found that without disturbance 104 local populations become extinct (Cummings & Alexander, 2002) because few seedlings emerge in the absence of disturbance (Moody-Weis & Alexander, 2007). I suspect that Hurricane Harvey reduced competition from grasses and other coastal dune vegetation, thereby generating favourable conditions for the establishment or expansion of H. argophyllus populations, most likely from seed banks. While the role of seed banks in plant population dynamics is poorly studied, it is generally accepted that disturbance promotes establishment of opportunistic species (e.g., Baskin & Baskin, 2014). Furthermore, there is evidence that in wild populations of H. annuus, seed banks can contribute up to nearly a quarter of the emerged seedlings on a given year; although persistence in the soil is variable, with 2–8% of seeds buried in a given year surviving 2–4 years (Alexander & Schrag, 2003). Seed dormancy is a well-known strategy, particularly in annual plants, as it optimizes reproduction/survival in stochastic environments (D. Cohen, 1966), meaning environments where year-to-year variation is so unpredictable that it is risky to have all seeds germinate at once. I also think it is likely that H. argophyllus populations from the barrier islands are adapted to flooding conditions. In 2020, I ran an experiment in which I tested the germination capability of silverleaf sunflower achenes collected from the barrier islands and the mainland. Under flooding conditions, individuals that came from island populations germinated at higher percentages (mean= 83.3%, sd= 15.35) than those from the mainland (mean= 69.6%, sd= 23.22). There was no difference in the germination rate between the regions under control conditions. However, there was a large variation in germination rate among individuals. Regardless of the origin of the new populations of H. argophyllus, their presence, along with the fact that I was able to re-sample enough individuals in September to continue with the assortative mating research, is seemingly at odds with a report on the effects of Hurricane Harvey on different coastal environments, which found a negative correlation between resilience and resistance to the storm (Patrick et al., 2020). This trade-off has been reported in other systems after large disturbances (Lugo, 2008; Zimmerman et al., 2014; Hogan et al., 2018). In short, elements of the ecosystem that exhibited the largest changes (those that are 105 less resistant) due to Hurricane Harvey returned to pre-storm levels more quickly (they are more resilient) (Patrick et al., 2020), whereas sedentary biological components (e.g., mangrove forests) that were more resistant to disturbance (higher survival rate), took longer to recover in comparison with motile biological components (e.g., fish and shrimp) (Patrick et al., 2020). No annual plants or coastal dune organisms were included in their analysis, but given my observations, I believe that the silverleaf sunflower is both resistant and resilient to large storms, making it a valuable component of coastal dune conservation and restoration. 4.5. Conclusions Silverleaf sunflower populations were impacted by Hurricane Harvey. This weather event was likely responsible for the reduced frequency of 1/1 genotypes and lighter seed mass observed in 2017, as well as the loss of HaFT1 in one of the mainland populations in the following year. Survival was associated with height, perhaps due to differences in the impact of flooding in plants in different developmental stages. Another crucial factor determining survival was distance to the nearest body of water, putatively because individuals closer to one were more likely to drown. However, these impacts are probably transient, as Hurricane Harvey appeared to promote massive germination and establishment via seeds from the seed bank at specific sites, likely over-whelming the small changes in HaFT1 frequencies that were initially caused by the hurricane. I recommend that future studies focus on the mechanisms that underlie the resistance and resilience to large disturbances of this species. The silverleaf sunflower should also be included in efforts to conserve and restore coastal sand dunes in Southern Texas, as well as in breeding programs that aim to increase resilience of the cultivated sunflower to extreme climate events. 106 Chapter 5: Conclusions 5.1 Introduction My thesis improves our understanding of the mechanisms that favour or prevent divergence under pervasive gene flow between ecologically differentiated populations. It adds support to the premise that genomic regions experiencing low recombination can facilitate the co-existence of divergent morphs, by preventing breakage of allelic combinations favoured in different environments (Feder et al., 2003; Koch et al., 2021; Sanchez-Donoso et al., 2022; Twyford & Friedman, 2015). My thesis emphasizes that reproductive isolation (RI) due to differences in phenology (allochrony) can change depending on genotype, habitat and time point in the reproductive season; the latter in particular is often neglected in studies of RI. Furthermore, my research reinforces previous suggestions that details of plant ecology, including a rapidly changing environment and differences in range size, can have a considerable impact on RI, particularly early on in the process of divergence. Overall, my results support the premise that ecological divergence plays a crucial role early in the speciation process (Christie & Strauss, 2018; Irwin, 2020). Assortative mating as a by-product of such divergence has had a genome-wide impact on differentiation between the ecotypes of silverleaf sunflower, despite flowering time being a porous reproductive barrier. This is most evident for the region of low recombination on chromosome 6 (where differences in flowering time are encoded), in which minimal gene flow occurs. Finally, my dissertation provides evidence of the capacity of sessile natural populations for both resistance and resilience to extreme weather events, which is unexpected according to previous research and reviews (Patrick et al., 2020). Below, I provide an overview of findings and final considerations for the results obtained in each chapter, as well as limitations and possible areas for future research. 107 5.2 Divergence, local adaptation and gene flow In Wright’s continent-island model, a locally advantageous allele can be maintained despite being opposed by gene flow, if its selection coefficient is larger than the migration/gene flow rate (Wright, 1931). One way for this to occur is through strong ecologically based divergent selection for traits that occupy different resources in time or space (Schluter, 2001; Hendry & Day, 2005). This effect can be enhanced when the trait under selection has a simple genetic architecture and affects RI (Gavrilets, 2004; Bolnick & Fitzpatrick, 2007; Servedio et al., 2011). Another non-mutually exclusive way in which divergence can be favored is to have a genomic structure that allows for locally favoured allelic combinations to be inherited together despite high levels of gene flow (Noor et al., 2001; Kirkpatrick & Barton, 2006; Twyford & Friedman, 2015; Ortiz-Barrientos et al., 2016; Huang & Rieseberg, 2020). The H. argophyllus flowering ecotypes have several of these qualities. For example, there is evidence for partial niche separation in this species, both spatially by habitat, and temporally by differences in flowering time driven by divergent selection (Moyers, 2015; Moyers & Rieseberg, 2016). Furthermore, reproduction in H. argophyllus is determined primarily by a single flowering time locus, HaFT1, which is inside a region of low recombination that controls other ecologically important traits (Todesco et al., 2020). Additionally, low levels of genomic differentiation in this species (Chapter 3) point to it being at an early stage of divergence (Lowry, 2012; Stankowski & Ravinet, 2021). At first glance, the silverleaf sunflower seems like an ideal candidate for ecological speciation, but there are several limitations for its divergence, which I outline below. 5.2.1 Asymmetric immigrant inviability vs asymmetric immigration This species appears to be in selection-migration balance. In general, immigrant inviability has been found to be one of the most important reproductive barriers between ecotypes (Christie et al., 2022), but in H. argophyllus it is not strong enough to prevent the late-flowering ecotype from successfully establishing on the islands (Chapter 2), because it is found in almost all localities sampled to date. In contrast, the early-flowering ecotype is rare on the mainland 108 (Chapter 2), which potentially suggests that immigrant inviability is an asymmetric reproductive barrier in H. argophyllus. This interpretation is consistent with results from other studies, which have found that immigrant inviability is among the most asymmetric reproductive barriers (i.e. Ostevik et al., 2016; Christie et al., 2022). However, a reciprocal transplant experiment found that while local genotypes were favoured at island sites, no fitness differences between locals and non-locals were seen at mainland sites, possibly because of an exceptional drought in 2011 (Moyers, 2015). This pattern of local adaptation is the opposite of that suggested by the silverleaf sunflower ecotype distributions (Chapter 2), perhaps indicating that the ecotype distribution results more from asymmetric immigration than asymmetric immigrant inviability. Asymmetric immigration in this species most likely stems from disparities in gamete abundance. The most recent area of occurrence estimated for H. argophyllus (Khoury et al., 2020) is about 280 km2, of which only a minor portion corresponds to the barrier islands (Fig. 5.1). Thus, the range of the early-flowering ecotype represents a small fraction of that of the late-flowering ecotype which so far has been found across all of the former’s distribution. This pattern of range asymmetry between sister taxa is common in Helianthus species (Kantar et al., 2015), and is suggestive of budding speciation, which is when a small, peripheral population/species derives from a larger ranged progenitor population/species that remains largely unchanged (Grant, 1981; Gottlieb, 2004; Crawford, 2010; Anacker & Strauss, 2014). Unequal gamete abundance can erode local adaptation of range edge populations (García-Ramos & Kirkpatrick, 1997; Lenormand, 2002; Yeaman & Otto, 2011). In H. argophyllus, high gene flow from the mainland likely hampers the increase of the early-flowering allele in the islands. 109 The purple dots are records from genebanks or botanical gardens, and the blue dots are records from herbaria. The red oval and arrow mark the small region where the early-flowering ecotype has been recorded in Texas. Image taken and adjusted from the Github repository of Khoury et al. (2020): https://dcarver1.github.io/cwrUSA_maps/?p=173. While asymmetric immigration may explain the rarity of HaFT1, I suspect that differential disturbance of island versus mainland sites likely also contributes to its rarity. In contrast to the mainland, the barrier islands face the elements on two fronts: their ocean and lagoon sides. This makes them more susceptible to storms, wind and wave action, resulting in habitats that are commonly transient (Miller et al., 2010; Moreno-Casasola et al., 2008). In addition, tourism and housing development is higher around the sampling sites in the barrier islands than those from the mainland (pers. obs.). Early-flowering ecotype 30 28 26 -99 -97 - 95 30 20 100 90 110 Figure 5.1. Records of Helianthus argophyllus in Texas. 110 Consistent with this hypothesis, the population site with the highest frequency of HaFT1 in previous research (Moyers, 2015; Moyers & Rieseberg, 2016; Todesco et al., 2020) is located inside the Padre Island National Seashore, which is managed by the United States National Park Service. This population was not included in my thesis due to logistical and safety issues. According to the website of the Park, this region is the largest stretch of undeveloped barrier island in the world (https://www.nps.gov/pais/index.htm). Moreover, the population with the highest frequency of HaFT1 included in my thesis is the one at Port Aransas, where around 2015 the silverleaf population had been “free of human management for at least ten years” (personal communication with Dr. Brook Moyers, University of Massachusetts, Boston, U.S.A.). In contrast, the populations studied here in which homozygotes for the presence of HaFT1 were not found in either of the sampling years are located at sites that have been drastically impacted by humans in since the 1800s, particularly through overgrazing by livestock, the making of artificial water channels, the creation of artificial lands for human use (Harris, 1965 as cited in Dahl et al., 1975; White et al., 1978), and tourism (McAtee & Drawe, 1980). These observations suggest that naturally disturbed habitats on the barrier islands are either more resistant to establishment of the late-flowering ecotype than sites with anthropogenic disturbance, or that they are more likely to receive mainland (late-flowering) immigrants, or both. I therefore hypothesize that anthropogenic disturbance has favoured the establishment of late-flowering immigrants while affecting the spread of HaFT1. 5.2.2 Plasticity in phenology The flowering times of the ecotypes of H. argophyllus overlap extensively, confining assortative mating to the extremes of the flowering season, and likely impeding ecotype divergence. The overlap is partly due to the strong GxE interaction seen for reproductive phenology, which implies that this is a plastic trait (Bradshaw, 1965). For example, 0/0 genotypes flower earlier at island sites than mainland sites, whereas 1/1 genotypes flower later at mainland sites than at island sites (Chapter 2). 111 Three classes of photoperiod responses have been identified in wild and cultivated H. annuus: day neutral, short-day response, and long-day response (Blackman, 2013). The latter two responses also appear to be found in H. argophyllus, with the early-flowering ecotype responding to long days and the late-flowering ecotype responding to short days. There are also parallels between the species in terms of the genetic architecture of flowering-time responses. In H. annuus, the largest flowering time QTL between wild and cultivated accessions is caused by a frameshift mutation in HaFT1, which shifts flowering time later under long days (Blackman et al., 2010). Likewise, in H. argophyllus, a deletion of HaFT1 in the late-flowering ecotype results in a delay in flowering under long days (Todesco et al. 2020). However, the silverleaf-sunflower ecotypes are under the same photoperiodic regime in nature, and so differences in photoperiod responses cannot account for habitat-related shifts in flowering time among individuals with the same HaFT1 genotype. On the other hand, there is evidence that water availability affects flowering time in this species (Moyers, 2016), which offers a potential explanation for this variation. Changes in flowering time when populations inhabit different or stressful environments is common in plants (e.g., Clausen, 1951; Savolainen et al., 2006; Jordan et al., 2015; Sianta & Kay, 2021) and are expected in heterogenous and changing environments (e.g., Franks et al., 2007; Blackman, 2017; Friedman et al., 2019; Williams et al., 2021), such as the coastal dunes (Moreno-Casasola et al., 2008; Stallins & Parker, 2008). Water deficits can either delay or prompt flowering (Jordan et al., 2015). The former has been reported in H. argophyllus growing in the barrier islands (Moyers, 2016). In years of exceptional heat and drought (e.g., in 2011), a negative correlation is seen between height and flowering time, with the latter having unimodal distribution towards late flowering (Moyers, 2016). In contrast, in cool and wet years (e.g., in 2015) the relationship is positive and clearly bimodal (with early and late flowering morphs; Moyers, 2016). Thus, dry years appear to decrease the window of opportunity for assortative mating between the two silverleaf sunflower ecotypes. Warmer temperatures and less precipitation are projected for Texas in the current century (Nielsen-Gammon et al., 2020), which I predict will impede further evolutionary divergence in this species. 112 5.2.3 Pulling it all together It would seem then that numerous factors are acting against the fixation of HaFT1 in the barrier islands. Flowering is plastic and reproductive phenologies greatly overlap even in “good years.” In addition, range distribution asymmetries and anthropogenic disturbance seems to aid in the establishment of the late-flowering ecotype. Yet, HaFT1 is still widely prevalent in the barrier islands, perhaps suggesting that the selection coefficient for early flowering in this region may be large enough under conditions of regular water availability and low anthropogenic disturbance to overcome these obstacles to its persistence. Lastly, as we lack information on the demographic history of the species, I cannot discard the possibility that HaFT1 was once fixed in the barrier islands and that I am observing the final steps of its extinction in H. argophyllus. Long-term observations of these populations could address this possibility. 5.3 Low recombination In the silverleaf sunflower, neither local adaptation nor assortative mating are strong enough to prevent pervasive gene flow when/where the two ecotypes meet; this is reflected in the relatively low levels of RI (Chapter 3) and by the low values of genomic Fst (mean genomic Fst between homozygotes is 0.035; Chapter 3). However, recombination suppression around HaFT1 extends its effects, thereby maintaining a suite of phenotypic and physiological differences between the ecotypes (Chapter 3). This adds to increasing evidence pointing to the important role of regions of low recombination in local adaptation and ecotype formation (Hoffmann & Rieseberg, 2008; Lowry, Rockwood, et al., 2008; Oneal et al., 2014; Ortiz, 2015; Twyford & Friedman, 2015; Todesco et al., 2020). 113 5.4 Extreme events and seed banks Vegetation, geomorphology and weather interact in coastal sandy habitats. Many of the plants growing in such environments are able to withstand and survive (resistance) different kinds of stresses, disturbances and the microhabitats that result from them (Barbour & DeJong, 1977; Lowry, Rockwood, et al., 2008; Brunbjerg et al., 2012; Pardini et al., 2015). Many species also share early-successional strategies that allow them to colonize the constantly newly-formed habitats (Lugo, 2008; Zimmerman et al., 2014; Feagin et al., 2015; Hogan et al., 2018), which can allow their populations to recover from large disturbances (resilience). The silverleaf sunflower mostly grows in locations of high disturbance (Foster, 2020). Although, I speculated above that there is an uncertain future for HaFT1 and the early-flowering ecotype in the barrier islands, I have also found evidence of how an intense storm recently prompted demographic growth of H. argophyllus (Chapter 4). Contrary to the presumption of trade-offs between resistance and resilience from major disturbances (Lugo, 2008; Zimmerman et al., 2014; Hogan et al., 2018; Patrick et al., 2020), the silverleaf sunflower shows signs of both resistance (i.e., several individuals survived and managed to reproduce) and resilience (i.e., the next year populations were at regular sizes and new populations had emerged) to large storms, such as Hurricane Harvey in 2017. This species therefore represents a fundamental component for coastal protection in Southern Texas, and perhaps even in Florida, where some early-flowering populations have become naturalized. Moreover, because of its close relationship with an important crop species, cultivated sunflower (H. annuus), H. argophyllus potentially contains an important reservoir of adaptations that could be used to breed sunflowers that are resistant to abiotic stresses and disturbance. There is already evidence that storms are increasing in frequency and intensity with climate change. For example, in 2020, for the first time in history, the Atlantic hurricane season used all of its 21 available names and therefore had to resort to the Greek alphabet to name the last six storms of the season (National Oceanic and Atmospheric Administration, 2020). My thesis provides a first look at likely vegetation changes under climate change in a coastal species, and I 114 found that the increased strength and frequency of extreme events favours species such as H. argophyllus that require disturbance for their existence (Foster, 2020) (Chapter 4). Moreover, Chapter 4 highlights two often neglected topics: 1) differential impacts of abiotic stress on plants at different developmental stages; and 2) the importance of seed banks to demographic, ecological and evolutionary processes in plants. Seed banks can help a species recover its population size after a bottleneck event. Population size is a critical indicator of extinction probability after a disturbance (or repeated disturbances); larger population sizes delay extinction (Mangel & Tier, 1994). Additionally, if a population is already adapted to its environment, the reduction in fitness caused by an extreme event that lasted only one generation would be transient (Lyberger et al., 2021). Alternatively, if extreme events are common, seed banks could impede adaptation by bringing back to the population alleles that were purged after such events Hurricane Harvey might have affected individuals of the silverleaf sunflower differentially according to their developmental age, with shorter, pre-reproductive plants apparently exhibiting greater survivorship than taller, early flowering plants. However, these genotypic effects might have only been transient. The sprouting of large new populations one year after Hurricane Harvey points to the relevance of including often overlooked seed banks in studies of population ecology, evolution and conservation, particularly when focusing on vegetation regeneration and population persistence. 5.5 Limitations and future studies One limitation of this research is that I did not identify the particular selection pressures driving divergent adaptation, and I did not quantify the contributions of other reproductive barriers that might be acting through the life-time of silverleaf sunflower individuals as have other studies (e.g., Ostevik et al., 2016; Ramsey et al., 2003). Hence, a follow-up study that explores various other forms of RI would provide more understanding of RI between the silverleaf sunflower ecotypes. 115 Additionally, it would be important to directly measure fitness of each of the genotypic classes at the HaFT locus in nature, because differences in parental and hybrid fitness contribute to immigrant inviability and extrinsic postzygotic isolation, respectively. In addition, the fitness relationship between parental lineages and their hybrids is key to predicting the outcomes of hybridization (Todesco et al., 2016; Irwin & Schluter, 2021), and would also be useful to explore. Furthermore, the strength and direction of selection likely varies between years due to the heterogenous and changing nature of the dune environment where H. argophyllus grows. Thus, long-term observations will be key to understanding this species. Given the plasticity in flowering in the silverleaf sunflower and the constant changes in the coastal dune environment, it is critical to measure both fitness and levels of gene flow between ecotypes in years of extreme drought or other extreme weather conditions. And to better comprehend how the environment affects gene flow and fitness, it would be informative to know if trait correlations (including GxE effects on flowering) are restricted to environments with a drastically changing water regime like the Texas coast. Likewise, it would be useful to know how environmental variation affects the expression of HaFT1 in H. argophyllus and whether the effects are similar in H. annuus, the species from which the gene was originally derived from. 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Forest Ecology and Managment, 332, 64–74. 135 Appendix A Table A.1 Comparison of genotypes for primer pair A and B. population sample ID primer pair A primer pair B population sample ID primer pair A primer pair B Aransas Mustang 7_964 1/1 1/1 Port Aransas 7_1011 1/1 1/1 Aransas Mustang 7_967 1/1 1/1 Port Aransas 7_1013 1/1 1/1 Aransas Mustang 7_972 1/1 1/1 Port Aransas 7_1015 1/1 1/1 Aransas Mustang 8_316 1/1 1/1 Port Aransas 7_1017 1/1 1/1 Aransas Mustang 8_335 1/1 1/1 Port Aransas 7_84 1/1 1/1 Aransas Mustang 8_91 1/1 na Port Aransas 7_85 1/1 1/1 Flour Bluff 8_412 1/1 1/0 Port Aransas 7_89 1/1 na Flour Bluff 8_428 1/1 1/1 Port Aransas 7_97 1/1 1/1 Flour Bluff 8_440 1/1 1/1 Port Aransas 7_992 1/1 1/1 Flour Bluff 8_442 1/1 1/1 Port Aransas 7_996 1/1 1/1 Flour Bluff 8_446 1/1 1/1 Port Aransas 7_997 1/1 1/1 Flour Bluff 8_448 1/1 1/1 Port Aransas 8_124 1/1 1/1 Mustang Bridge 7_937 1/1 1/1 Port Aransas 8_128 1/1 1/1 Mustang Bridge 7_940 1/1 1/1 Port Aransas 8_129 1/1 1/1 Mustang Bridge 8_309 1/1 na Port Aransas 8_130 1/1 1/1 Port Aransas 7_100 1/1 1/1 Port Aransas 8_131 1/1 1/1 Port Aransas 7_1005 1/1 1/1 12 Port Aransas 8_134 1/1 1/0 Port Aransas 7_1006 1/1 1/1 Port Aransas 8_142 1/1 1/1 Port Aransas 7_1007 1/1 1/1 Port Aransas 8_147 1/1 1/0 Port Aransas 7_1008 1/1 1/1 Port Aransas 8_359 1/1 1/1 136 population sample ID primer pair A primer pair B population sample ID primer pair A primer pair B Aransas Mustang 7_969 1/0 1/0 Padre Beach 8_283 1/0 1/0 Aransas Mustang 7_979 1/0 1/0 Padre Beach 8_289 1/0 1/0 Aransas Mustang 8_100 1/0 1/0 Padre Beach 8_32 1/0 1/0 Aransas Mustang 8_102 1/0 1/0 Padre Beach 8_33 1/0 1/0 Aransas Mustang 8_103 1/0 na Padre Beach 8_45 1/0 1/0 Aransas Mustang 8_105 1/0 1/0 Padre Beach 8_50 1/0 1/0 Aransas Mustang 8_320 1/0 1/0 Padre Beach 8_55 1/0 1/0 Aransas Mustang 8_325 1/0 1/0 Padre Beach 8_59 1/0 1/0 Aransas Mustang 8_92 1/0 1/0 Padre Beach 8_60 1/0 1/0 Aransas Mustang 8_96 1/0 1/0 Port Aransas 7_1010 1/0 1/0 Flour Bluff 8_414 1/0 1/0 Port Aransas 7_1012 1/0 1/0 Flour Bluff 8_417 1/0 1/0 Port Aransas 7_328 1/0 1/0 Flour Bluff 8_418 1/0 1/0 Port Aransas 7_83 1/0 1/0 Flour Bluff 8_419 1/0 1/0 Port Aransas 7_88 1/0 1/0 Flour Bluff 8_423 1/0 1/0 Port Aransas 7_90 1/0 1/0 Flour Bluff 8_439 1/0 na Port Aransas 7_91 1/0 1/0 Mustang Bridge 7_55 1/0 1/0 Port Aransas 7_92 1/0 1/0 Mustang Bridge 7_935 1/0 1/0 Port Aransas 7_96 1/0 1/0 Mustang Bridge 7_938 1/0 1/0 Port Aransas 7_993 1/0 1/0 Mustang Bridge 7_943 1/0 1/0 Port Aransas 8_125 1/0 1/0 Mustang Bridge 8_311 1/0 1/0 Port Aransas 8_132 1/0 1/0 Mustang Bridge 8_71 1/0 na Port Aransas 8_338 1/0 1/0 Mustang Bridge 8_78 1/0 na Port Aransas 8_342 1/0 1/0 Mustang Bridge 8_81 1/0 1/0 Port Aransas 8_343 1/0 1/0 Padre Beach 7_1061 1/0 1/0 Port Aransas 8_354 1/0 1/0 137 population sample ID primer pair A primer pair B population sample ID primer pair A primer pair B Padre Beach 7_1072 1/0 1/0 Port Aransas 8_355 1/0 1/0 Padre Beach 7_1075 1/0 1/0 Port Aransas 8_357 1/0 1/0 Padre Beach 7_927 1/0 1/0 water park 7_23 1/0 1/0 Padre Beach 7_930 1/0 1/0 water park 8_17 1/0 1/0 Padre Beach 8_245 1/0 1/0 water park 8_267 1/0 1/0 Padre Beach 8_254 1/0 1/0 water park 8_29 1/0 na Padre Beach 8_281 1/0 na population plant ID primer pair A primer pair B population plant ID primer pair A primer pair B Aransas Mustang 8_104 0/0 0/0 Rockport 8_187 0/0 0/0 Aransas Mustang 8_110 0/0 0/0 Rockport 8_190 0/0 0/0 Mustang Bridge 7_41 0/0 0/0 Rockport 8_201 0/0 na Mustang Bridge 7_45 0/0 0/0 Rockport 8_208 0/0 0/0 Mustang Bridge 7_54 0/0 0/0 Rockport 8_209 0/0 na Mustang Bridge 7_931 0/0 0/0 Rockport 8_364 0/0 0/0 Mustang Bridge 8_307 0/0 0/0 Rockport 8_371 0/0 0/0 Mustang Bridge 8_76 0/0 0/0 Rockport 8_376 0/0 0/0 Mustang Bridge 8_83 0/0 0/0 Rockport 8_382 0/0 0/0 Padre Beach 7_1053 0/0 0/0 Rockport 8_386 0/0 0/0 Padre Beach 7_1054 0/0 na Rockport 8_390 0/0 na Padre Beach 7_1056 0/0 0/0 Rockport 8_397 0/0 0/0 Padre Beach 7_1062 0/0 0/0 Rockport 8_405 0/0 0/0 Padre Beach 8_252 0/0 0/0 Sinton 7_362 0/0 0/0 138 population plant ID primer pair A primer pair B population plant ID primer pair A primer pair B Padre Beach 8_37 0/0 0/0 Sinton 7_369 0/0 0/0 Padre Beach 8_42 0/0 0/0 Sinton 8_151 0/0 0/0 Padre Beach 8_46 0/0 0/0 Sinton 8_153 0/0 0/0 Padre Beach 8_47 0/0 0/0 Sinton 8_164 0/0 0/0 Padre Beach 8_48 0/0 0/0 Sinton 8_177 0/0 na Padre Beach 8_49 0/0 0/0 water park 7_1093 0/0 0/0 Port Aransas 7_93 0/0 0/0 water park 7_1098 0/0 0/0 Port Aransas 7_95 0/0 0/0 water park 7_1103 0/0 0/0 Figure A.1. Location of each of the two primer pairs used to genotype all individuals. IE=early-flowering ecotype, ME=late-flowering ecotype. Figure modified from Todesco et al (2020). ME IE Primer pair A Primer pair B HaFT1 139 Appendix B Table B.1. Mean height from H. argophyllus plants at different life stages in July 2017 Population Life stage Mean height (cm)* Sd Aransas-Mustang vegetative 101.5 28.9 budding 125.6 64.1 Mustang Bridge vegetative 120.3 35.1 budding 137.1 49.5 Padre Beach vegetative 77.2 26.2 budding 104.6 68.4 Port Aransas vegetative 176.4 59.4 budding 207.0 75.5 Water Park vegetative 72.5 44.9 budding 130.1 57.6 AVERAGE vegetative 100.7 48.2 budding 156.3 75.6
Thesis/Dissertation
eng
Botany
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rieseberg, Loren H.
The effects of a FLOWERING LOCUS T polymorphism on phenology, reproductive isolation and hurricane survival in the silverleaf sunflower
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