THE GENETIC STRUCTURE OF SPECIES’ GEOGRAPHIC RANGES: AN EVALUATION
USING THE COSTAL DUNE ENDEMIC CAMISSONIOPSIS CHEIRANTHIFOLIA
By
Adriana López Villalobos
A thesis submitted to the Department of Biology
in conformity with the requirements for the Degree of Doctor of Philosophy
Queen’s University
Kingston, Ontario Canada
June 2017
Copyright © Adriana López-Villalobos, 2017
ABSTRACT
The development of molecular techniques has spurred thousands of population genetic studies on a wide variety of plant and animal species. Particularly important, but still relatively rare, are studies that properly test for geographic variation in genetic structure across species’ ranges. This thesis investigates the effects of population density, mating system variation, distance between populations and hybridization on the genetic diversity, differentiation and structure across the range of Camissoniopsis cheiranthifolia (Onagraceae). By combining a transplant experiment with microsatellites, I also provide an empirical test of one of the most poorly resolved questions in evolutionary biology: Why do species exhibit limits to their distributions?
I developed 24 species-specific nuclear microsatellites loci (nSSR) and used 13 of these and six variable chloroplast microsatellites (cpSSR) to investigate the genetic consequences of the transition from outcrossing to selfing in C. cheiranthifolia. As predicted, small-flowered, selfing populations had lower nSSR diversity (but not cpSSR) than large-flowered, outcrossing populations but they were not more differentiated. The reduction in diversity was greater than the expected from selfing alone, but could not be accounted for by indirect effects of selfing on population density. Five parapatric nSSR clusters and three groups of cpSSR haplotypes usually
(but not always) differed in mating system, suggesting that selfing may often initiate ecogeographic isolation. However, lineage-wide diversity failed to support that selection for reproductive assurance initiated selfing in this species.
I also investigated if hybridization with C. bistorta has influenced the genetic structure of
C. cheiranthifolia in areas of closest parapatry. I confirmed that closet parapatry was associated with extensive genetic continuity between self-incompatible C. cheiranthifolia and C. bistorta
ii and these were indistinguishable genetically within the broader context of geographic variation in
C. cheiranthifolia, suggesting that C. bistorta could be an inland ecotype of C. cheiranthifolia
Finally, fitness increased toward and beyond the northern range limit of C. cheiranthifolia but edge individuals were not best suited to conditions beyond the range. Despite strong genetic differentiation and very low, nondirectional gene flow, there was no evidence of local adaptation, except at the coarsest spatial scale, suggesting range limitation via constraints to dispersal.
iii Co-Authorship
My thesis has been prepared in the manuscript format as outlined in the guidelines provided by
The School of Graduate Studies. All chapters are co-authored by Dr. Christopher G. Eckert, who
contributed financially and intellectually to the design, development and editing of the thesis.
Karen E. Samis collaborated in two of the manuscripts presented in this thesis. In chapter one she
constructed the genetic library used to develop the microsatellite markers used in the first and
subsequent chapters. In chapter 5 she conducted the transplant experiment, collected and
analyzed these data and contributed at all stages of the manuscript preparation.
PUBLICATIONS ARISING FROM AND INCLUDED IN THIS THESIS
Lopez-Villalobos A., Samis, K. E. and Eckert, C.G. 2014. Microsatellite primers for
Camissoniopsis cheiranthifolia (Onagraceae) and cross-amplification in closely related
species. Applications in Plant Sciences 109: 599–611.
López-Villalobos A. and Eckert, C.G. (submitted 16 Nov. 2016). Consequences of multiple
mating system shifts for population and range-wide genetic structure in a coastal dune plant.
Molecular Ecology.
López-Villalobos A. and Eckert, C.G. (in prep.). Does hybridization contribute to range-wide
population genetic structure in a coastal dune plant? For submission to American Journal
of Botany.
Samis, K.E., A. López-Villalobos, and C.G. Eckert. 2016. Strong genetic differentiation but not
local adaptation toward the range limit of a coastal dune plant. Evolution 70: 2520–2536.
iv OTHER PUBLICATIONS ARISING FROM, BUT NOT INCLUDED IN THIS THESIS:
Dick, C.A., J.A. Herman, R.E. O’Dell, A. Lopez-Villalobos, C. Eckert, and J.B. Whittall. 2013.
Cryptic genetic subdivision in the San Benito evening primrose (Camissonia benitensis).
Conservation Genetics 15: 165–175.
Viengkone J., López-Villalobos A., & Eckert, C.G. (in prep.). A test of whether local genetic
stocks yield better restored populations in a Pacific coastal dune endemic plant.
v ACKNOWLEDGEMENTS
First and foremost, I would like to thank my supervisor Chris Eckert for his time and effort spent reading and providing feedback on my chapters. I want to acknowledge his patience throughout this long journey for both of us. Thank you as well for helping me improve my thinking, research and writing skills.
I also want to thank each one of my committee members Drs. Vicki Friesen, Steve
Lougheed, and Ryan Danby for their availability, support and their valuable comments to this project. Special thanks to Karen Samis for all the information provided, the guidance and support,
I received, especially at the beginning of my PhD. Thank you for being such a great team member and collaborator.
I thank the CONACYT (The Mexican National Council for Science and Technology) for funding this doctoral degree at Queen's University and the Natural Sciences and Engineering
Council of Canada (NSERC) for the Discovery Grants to CGE who contributed to the expenses of this research.
The work collated in this thesis is the product of between two and three-month field seasons and at least three years of lab work worth of data. It involved thousands of DNA extractions, agarose gels, PCR reactions, fragment sequenced, tons of frustration but lots of amazing students without whom this would not have been possible. I am very grateful to my field crew: Lindsey Button, Natalie Morril, Karen Samis and Anna Hargreves. To my image analyses team: Han Tao, Carlie Smith, Amanda Yuan and Moumita Kramar. And the best lab crew ever:
Benjamin Shiff, Kyle Millar, Jon Vienkone and Meera Manghani. I would like to extend a special recognition to Zhengxin Sun for all his hard work in producing my thousands of PCR
vi fragments, for his guidance, his help troubleshooting and the encouragement I received from him throughout the entire phase of my lab work.
I want to thank many good-natured people that marked my PhD with their generosity and friendship in one way or another during my field trips. Mary Alice Kesler, Sula Vanderplank, Eli
Wehncke, Javier “el Twinky”, Alicia Cardoso, Rodrigo Beas, Sara Teck, Julio Lorda, Dave
Hubbard and Jenny Dougan for providing me with a place to land in different parts along the coast of Baja California and California. Jon Rebman, Mike Simpson, Jose Delgadillo and Sula for their advice id-ing the difficult camissonias during field collections, for showing me the beauty of the Baja California Flora and for allowing me to use the herbarium facilities at
SDNHM and Rancho Santa Ana Botanical Garden.
Many thanks go to all the past and present lab mates at the Eckert lab. For our science and non-science conversations, for their support and invaluable companionship during this time.
Thanks to friends and colleagues at the Biology Department for those special times. Special thanks to go to Maggie Barkowska for helping me with coding complicated R functions, for encouraging me to talk out loud my results and help me brainstorm ideas, for the support and friendship I recieved in this last stage of my PhD.
Further, I would like to thank everybody close to me outside of the Biology Department, the support and community at the BRC, my family and friends here in Kingston, in Portugal and back in the Mexico. Lulú, Rubén y Alex gracias por no tirar la toalla, por su amor y apoyo incondicional en todo momento. No hay palabras para expresar mi infinito agradecimiento. Ao
Nuno Ramalho por tudo isso e muito mais. Obrigada por todo o teu apoio agora e até sempre.
vii TABLE OF CONTENTS
ABSTRACT ...... ii CO-AUTHORSHIP ...... iv Publications arising from and included in this thesis: ...... iv Publications arising from, but not included in this thesis: ...... v ACKNOWLEDGEMENTS ...... vi TABLE OF CONTENTS ...... viii LIST OF TABLES ...... xii LIST OF FIGURES ...... xv LIST OF ABREVIATIONS...... xix CHAPTER 1. GENERAL INTRODUCTION ...... 1 THEORETICAL FRAMEWORK ...... 3 Factors that influence the genetic diversity and structure within and among populations ...... 3 Theoretical assumptions about genetic structure across species’ geographic ranges ...... 5 Other factors that affect genetic diversity and structure across species ranges ...... 7 THESIS OBJECTIVES AND DESCRIPTION OF THE CHAPTERS ...... 9 LITERATURE CITED ...... 13 CHAPTER 2. MICROSATELLITE PRIMERS FOR CAMISSONIOPSIS CHEIRANTHIFOLIA (ONAGRACEAE) AND CROSS-AMPLIFICATION IN RELATED SPECIES ...... 23 ABSTRACT ...... 23 INTRODUCTION ...... 23 METHODS AND RESULTS ...... 24 CONCLUSIONS...... 27 LITERATURE CITED ...... 28 CHAPTER 3. CONSEQUENCES OF MULTIPLE MATING-SYSTEM SHIFTS FOR POPULATION AND RANGE-WIDE GENETIC STRUCTURE IN A COASTAL DUNE PLANT ...... 37 ABSTRACT ...... 37 INTRODUCTION ...... 38
viii METHODS ...... 43 Study system, sampling strategy and population size ...... 43 Nuclear and chloroplast microsatellite genotyping ...... 44 Analyses of population density and within-population genetic diversity ...... 45 Large-scale genetic structure across the species’ range ...... 46 Effect of mating system on population genetic differentiation ...... 48 RESULTS ...... 49 Population density and within-population genetic diversity...... 49 Range-wide genetic structure ...... 51 Diversity within genetic clusters ...... 52 Pairwise population genetic differentiation ...... 53 Phenotypically mixed populations ...... 53 DISCUSSION ...... 54 Selfing reduces nuclear but not chloroplast genetic diversity within populations ...... 56 Selfing does not increase population differentiation ...... 57 Genetic subdivision is often but not always associated with mating system shifts ...... 57 Was selection for reproductive assurance involved in the shift to higher selfing across Pt. Conception? ...... 58 Was higher selfing at the southern range limit spurred by long-distance dispersal and adaptation to drier habitat? ...... 59 Why is the loss of self-incompatibility associated with differentiation in southern California? ...... 59 Why no divergence associated with the transition to selfing on the Channel Islands? ...... 60 Reproductive isolation in phenotypically mixed populations ...... 62 LITERATURE CITED ...... 63 CHAPTER 4. DOES HYBRIDIZATION CONTRIBUTE TO RANGE-WIDE POPULATION GENETIC STRUCTURE IN A COASTAL DUNE PLANT? ...... 88 ABSTRACT ...... 88 INTRODUCTION ...... 89 MATERIALS AND METHODS ...... 95 Range proximity based on herbarium records ...... 95
ix Sampling, DNA isolation and genotyping ...... 96 Geographic variation in allele sharing ...... 97 Range-wide population genetic structure ...... 97 Isolation-by-distance ...... 99 RESULTS ...... 99 Range proximity based on herbarium records ...... 99 Geographic variation in population genetic diversity and interspecific allele sharing ...... 100 Range-wide population genetic structure ...... 101 Isolation-by-distance ...... 102 DISCUSSION ...... 102 Genetic continuity among LF-SI C. cheiranthifolia and C. bistorta populations...... 103 Is C. bistorta an inland ecotype of C. cheiranthifolia? ...... 106 LITERATURE CITED...... 110 CHAPTER 5. STRONG GENETIC DIFFERENTIATION BUT NO LOCAL ADAPTATION TOWARDS THE RANGE LIMIT OF A COASTAL DUNE PLANT.... 131 ABSTRACT ...... 131 INTRODUCTION ...... 131 MATERIALS AND METHODS ...... 136 Transplant experiment design ...... 136 Statistical analysis of local adaptation ...... 138 Population genetic analyses ...... 140 RESULTS ...... 142 Transplant experiment...... 142 Population genetic diversity and structure...... 145 DISCUSSION ...... 147 Weak local adaptation...... 149 Challenges to theoretical conceptions of range limits...... 153 Broader implications ...... 153 LITERATURE CITED...... 156 CHAPTER 6: GENERAL DISCUSSION AND FUTURE RESEARCH ...... 181
x Chapter 2. Microsatellite primers for Camissoniopsis cheiranthifolia (Onagraceae) and cross- amplification in related species ...... 181 Chapter 3. Consequences of multiple mating-system shifts for population and range-wide genetic structure in a coastal dune plant ...... 183 Chapter 4. Does hybridization contributes to range-wide population genetic structure in a coastal dune plant? ...... 188 Chapter 5. Strong genetic differentiation but not local adaptation towards the range limit of a coastal dune plant ...... 192 LITERATURE CITED ...... 195 APPENDICES SUPPORTING INFORTMATION FOR EACH CHAPTER ...... 201 APPENDIX 2. Sampling information, population codes and mating type of individuals used in this study...... 201 APPENDIX 3. Supplementary information for Chapter 3. Genetic consequences of a mating system shift ...... 206 APPENDIX 4.1. Supplementary information for Chapter 4. Population information and herbarium records ...... 219 APPENDIX 4.2. Range-Wide Bayesian analysis of genetic structure using INSTRUCT ... 223 APPENDIX 4.3. DAPC analysis to visualize genetic differentiation among INSTRUCT clusters ...... 228 APPENDIX 5.1. R script for ASTER analysis of variation in survival and reproduction in transplanted populations of Camissoniopsis ...... 232 APPENDIX 5.2. Effect of Variation in initial seedling size on analyses of lifetime fruit production ...... 232 APPENDIX 5.3. Continued population viability beyond the northern range limit ...... 238 APPENDIX 5.4. Life-history variation among planting sites ...... 241 APPENDIX 5.5. Life-history variation among source populations planted beyond the northern range limit ...... 244 APPENDIX 5.6. Life table analysis of variation in demographic parameters among planting sites and source Populations ……...... 246 APPENDIX 5.7. Heterogeneity in the partitioning of phenotypic variation in fitness across transplant sites ...... 248
xi APPENDIX 6.1. Analyses of variation at nine chloroplast microsatellite loci in C. cheiranthifolia and C. bistorta ...... 251
xii LIST OF TABLES
CHAPTER 2. Table 2.1. Characteristics of 24 microsatellite primers pairs developed for Camissoniopsis cheiranthifolia (Onagraceae) ...... 30 Table 2.2. Estimation of population genetic parameters for 21 microsatellite loci in four Camissoniopsis cheiranthifolia and one C. bistorta population ...... 33 Table 2.3. Cross-amplification and alleles sizes of 24 microsatellite primers pairs in Camissoniopsis cheiranthifolia and other nine related species ...... 35 CHAPTER 3. Table 3.1. Effect of mating system variation and plant density on two measures of genetic diversity at 13 microsatellite loci ...... 78 Table 3.2. Lineage-wide genetic diversity parameters for each of the INSTRUCT clusters detected across the geographic range of Camissoniopsis cheiranthifolia ...... 79 Table 3.3. Association between pairwise genetic differentiation within the five INSTRUCT clusters, geographic distance and harmonic mean density in Camissoniopsis cheiranthifolia ...... 80 CHAPTER 4. Table 4.1. Analysis of molecular variance (AMOVA) of 25 large-flowered, self-incompatible (LFSI) populations of both C. cheiranthifolia and C. bistorta...... 124 CHAPTER 5. Table 5.1. Transplant sites and source populations of Camissoniopsis cheiranthifolia used in this study ...... 169 Table 5.2. ASTER analysis of variation in lifetime fruit production of Camissoniopsis cheiranthifolia among source populations and planting ...... 171 Table 5.3 Measures of genetic diversity at 13 microsatellite loci for six populations of Camissoniopsis cheiranthifolia assigned to three geographically distinct clusters (South, Mid, North) by INSTRUCT analysis ...... 172 Table 5.4. Contemporary proportional gene flow between population genetic clusters of Camissoniopsis cheiranthifolia estimated using BAYESASS ...... 173 APPENDICES — SUPPLEMENTARY INFORMTATION FOR EACH CHAPTER
xiii APPENDIX A2 Table A2.1. Sampling information, population codes and mating type of individuals used in this study ...... 201 APPENDICES A3 Table A3.1. Location and sampling effort for the Camissoniopsis cheiranthifolia populations used in this study ...... 209 Table A3.2. Summary of PCR optimization of chloroplast microsatellite (cpSSR) primers and initial screening of Camissoniopsis cheiranthifolia populations ...... 212 Table A3.3. Characteristics of and primers for the six chloroplast microsatellite loci (cpSSR) used in this study ...... 214 Table A3.4. Chloroplast SSR haplotypes detected in 159 individuals from 32 Camissoniopsis cheiranthifolia populations ...... 215 Table A3.5. Consistency in assignment between prior INSTRUCT and posterior DAPC groups...... 216 Table A3.6. Multilocus population genetic parameters based on nuclear (nSSR) and chloroplast (cpSSR) microsatellites for each population of Camissoniopsis cheiranthifolia sampled ...... 217 APPENDIX A4 Table A.4.1. Location of large-flowered self-incompatible Camissoniopsis cheiranthifolia subsp. suffruticosa and C. bistorta populations sampled ...... 219 Table A.4.2. Institutions and number of herbarium records used in this study ...... 221 APPENDICES A5 Table A5.1. Unconditional mean (± SE) lifetime fruit production calculated from the ASTER model for the reciprocal analysis ...... 233 Table A5.2. Unconditional mean (± SE) lifetime fruit production calculated from ASTER model for the large-scale analysis ...... 234 Table A5.3. Estimated mutation-scale effective population size (diagonal) and gene flow (off- diagonal) between population genetic clusters of Camissoniopsis cheiranthifolia estimated from three replicate runs of migrate-n ...... 235 Table A5.4. Life history variation among Camissoniopsis cheiranthifolia plants transplanted at five sites towards the northern range limit and beyond...... 243
xiv Table A5.5. Variation in life-history among Camissoniopsis cheiranthifolia from eight source populations planted beyond the northern range limit ...... 245 Table A5.6. Partitioning of variance in lifetime fruit production among source populations and maternal families within source populations of Camissoniopsis cheiranthifolia planted at three sites towards and beyond the northern range limitt ...... 250 APPENDIX A6 Table A6.1. Summary of PCR optimization of cpSSR primers and initial screening of Camissoniopsis cheiranthifolia and Camissoniopsis bistorta populations ...... 253 Table A6.2. Characteristics of nine chloroplast microsatellite loci tested in Camissoniopsis cheiranthifolia and C. bistorta populations ...... 254 Table A6.3. Haplotypes found in 265 individuals from 51 populations at nine microsatellite loci; 32 of C. cheiranthifolia and 19 of C. bistorta ...... 255
xv LIST OF FIGURES
CHAPTER 3.
Fig.3.1. Geographical variation in plant density, expected heterozygosity He, and rarefied
number of alleles per locus (Ar) among 38 populations of Camissoniopsis cheiranthifolia across the species’ geographic range ...... 81 Fig.3. 2 Correlations among within-population genetic diversity at 13 nSSR loci, population density and two indicators of the mating system ...... 82 Fig.3.3. Range-wide genetic structure at 13 nSSR loci in Camissoniopsis cheiranthifolia inferred by INSTRUCT Bayesian clustering ...... 83 Fig.3.4. Geographic distribution and genetic distance among12 cpSSR haplotypes detected in Camissoniopsis cheiranthifolia ...... 84
Fig.3.5. Regression of genetic distance (G'ST) on geographic distance between pairs of populations and on log-transformed harmonic mean density of population pairs within each INSTRUCT cluster ...... 85 Fig.3.6. Genetic structure among Camissoniopsis cheiranthifolia populations around Pt. Conception and the relation between floral phenotypes and the cluster ancestry within the three phenotypically mixed populations...... 86
Fig.3.7. Observed and expected decline in within-population genetic diversity (He) with increased self-fertilization (s) for 12 populations of Camissoniopsis cheiranthifolia ...... 87 CHAPTER 4. Fig.4.1. Species’ geographic distributions and probability of hybridization based on Euclidean distances to a C. bistorta herbarium record ...... 125 Fig.4.2. Genetic diversity and inbreeding coefficients among 32 populations of C. cheiranthifolia from five different genetic clusters and 18 populations of C. bistorta ...... 126 Fig.4.3. Number of private and shared alleles per locus in relation to sample size ...... 127 Fig.4.4. Range-wide genetic structure of 50 populations of Camissoniopsis cheiranthifolia and C. bistorta at 12 nSSR microsatellite loci using INSTRUCT ...... 128 Fig.4.5. INSTRUCT and DAPC analyses of 583 individuals sampled from 25 populations of LF-SI C. cheiranthifolia and C. bistorta ...... 129
xvi Fig.4.6. Analysis of isolation-by-distance among 25 LF-SI populations of C. cheiranthifolia and C. bistorta...... 130 CHAPTER 5. Fig.5.1. Geographic locations and ecological distance among source populations and planting sites in a transplant experiment up to and beyond the northern range limit of Camissoniopsis cheiranthifolia...... 174 Fig.5.2. Hierarchical life history stages used in the ASTER analysis of variation in survival and reproduction ...... 176 Fig.5.3. Variation in lifetime fruit production of Camissoniopsis cheiranthifolia among source populations and transplant sites ...... 177 Fig.5.4. The association between relative fitness of transplanted C. cheiranthifolia at a planting site and the geographic distance or climatic distance of their source population from that site in the large-scale analysis...... 178 Fig.5.5. Population genetic structure towards the northern range limit of Camissoniopsis cheiranthifolia ...... 179 Fig.5.6. Isolation-by-distance shown by a positive correlation between pairwise genetic
distance (measured as FST) and geographic distance between populations of Camissoniopsis cheiranthifolia ...... 180 APPENDICES — SUPPLEMENTARY INFORMTATION FOR EACH CHAPTER APPENDICES A3 Fig.A3.1. Parameters used to validate the number of clusters (K) as a function of K from independent runs in INSTRUCT for each value of K from 1 to 10 ...... 206 Fig.A3.2. Differentiation among INSTRUCT clusters along the first four discriminant functions of the DAPC analysis ...... 207 Fig.A3.3. Isolation-by-distance among 38 populations of Camissoniopsis cheiranthifolia across the species' geographic range ...... 208 APPENDICES A4 Fig.A4.1. Geographic coordinates of C. bistorta herbarium records ...... 222 Fig.A4.2. Evaluation of the number of clusters. ∆K and Deviance information criterion “DIC” values from two independent INSTRUCT analyses ...... 225
xvii Fig.A4.3. Cluster solutions from K 2 to 8 for a range-wide INSTRUCT analysis of 56 populations of C. cheiranthifolia and C. bistorta at 12 nuclear microsatellite loci ...... 226 Fig.A4.4. Distribution of individuals within each posterior discriminant function of the DAPC analysis based on prior INSTURCT clusters ...... 227 Fig.A.4.5. DAPC analysis using the K-means clustering algorithm on 50 populations of C. cheiranthifolia and C. bistorta ...... 231 APPENDICES A5 Fig.A5.1. The optimal number genetic clusters (K) in a Bayesian clustering analysis performed using INSTRUCT as evaluated by the deviance information criterion (DIC) and the ∆K as a function of K clusters (Evanno et al. 2005) ...... 236 Fig.A5.2. The association between relative fitness of transplants at a planting site and the distance of their source population from that site in the reciprocal experiment ...... 237 Fig.A5.3. Recruitment of Camissoniopsis cheiranthifolia at 16 experimental blocks beyond the species’ northern range limit ...... 239 Fig.A5.4. Negative correlation between the inbreeding coefficient of adult plants in six source populations of Camissoniopsis cheiranthifolia and the lifetime fruit production of transplants from those source populations averaged across two planting sites within the geographic range (W133 and E0) ...... 240 Fig.A5.5. Variation in lifetime fitness measured as per-capita per-year fruit production (r) of Camissoniopsis cheiranthifolia among source populations and transplant sites ...... 247 APPENDICES A6 Fig.A6.1. Sequence alignment for three to four individuals including C. cheiranthifolia and C. bistorta at each of three cpSSR loci ...... 256 Fig.A6.2. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations genotyped at 9 cpSSR microsatellite loci, and the geographic distribution and frequency of each of the 20 haplotypes detected ...... 257 Fig.A6.3. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 7 cpSSR microsatellite loci. Loci CCMP2 and CSSR7 removed ...... 258 Fig.A6.4. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 8 cpSSR microsatellite loci. Locus CCMP2 removed ...... 259
xviii Fig.A6.5. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 8 cpSSR microsatellite loci. Locus CCMP2 removed ...... 260
xix List of Abbreviations
ACM – Abundant center distribution model CW – Corolla width HSA – Home site advantage IBD – Isolation-by-distance LF – Large-flowered SF – Small-flowered SC– Self-compatibility or self-compatible SI – Self-incompatibility or self-incompatible LF-SI – Large-flowered self-incompatible LF-SC – Large-flowered self-compatible SF-SC – Small-flowered self-compatible BAJA – Baja California México NPC – North of Point Conception NRL – Northern range limit Pt. Conception – Point Conception SRL – Southern range limit nSSR – Nuclear microsatellite markers cpSSR – Chloroplast microsatellites markers
xx CHAPTER 1. GENERAL INTRODUCTION
Understanding the patterns and processes associated with geographical variation in population genetic structure across species’ ranges is a major goal in evolutionary biology. Theres is increased interest in this topic, largely motivated by conservation concerns, given the accelerated modification of the landscape, loss of biodiversity, climate change and the spread of invasive species. The level of genetic diversity within and among populations should reflect a species’ potential to respond to environmental challenges (pathogens, climatic fluctuations, environmental variation, etc.) and its capacity for adaptation. Therefore, from the theoretical point of view, understanding the geographic distribution of genetic diversity and differentiation allows a deeper understanding of the species’ demographic and evolutionary history. From a practical perspective, such knowledge, can increase our ability as scientist and conservation biologists to better predict species’ responses to environmental changes, biological invasions and possible pest outbreaks, as well as it can be used to inform management strategies for small, endangered and/or geographically marginal populations.
The accelerated pace at which genetic data and computational power are becoming available has spurred rapid advances in the field, allowing us to answer questions that were, until recently, beyond reach (Andrews et al. 2013). However, one of the current challenges is to be able integrate these genetic (or genomic) data with information about species’ ecology, distribution to connect patterns with processes. This approach requires an integrated concept of what constitutes a species’ geographic range and which aspects of species’ biology and ecology predict genetic diversity, differentiation and structure at different spatial scales. The work I present in this thesis addresses these issues.
1 Using a herbaceous short-lived perennial plant endemic to the Pacific coastal dunes of
Baja California (México), California (USA) and southern Oregon (USA), I evaluate the importance of geographic variation in the mating system, potential for hybridization with a sister taxon, and tested theoretical predictions about why species exhibit stable range limits. This system allowed me to approach the question of what factors influence the geographic variation in genetic structure across species’ ranges in a way that not many other studies have (Eckert et al.
2008; Sexton et al. 2009). Camissoniopsis cheiranthifolia (Hornemann ex Sprengel) W. L.
Wagner & Hoch, comb. nov. (Onagraceae) is an ideal system because it exhibits substantial contemporary variation in mating system, demography and population isolation within its near- one-dimensional geographic distribution (Samis & Eckert 2007; 2009; Dart et al. 2012). The species occurs on coastal dune habitat, which is more or less continuous, quantifiable and readily accessed, making it easier to sample the entire range and test range limits theory (Samis & Eckert
2007; 2009). Ongoing taxonomic work on the genus (Raven 1969; Wagner et al. 2007) has provided crucial information about geographic variation in floral morphology that reflects variation in the mating system and life history.
This species shows transitions in the mating system from outcrossing to selfing, with populations ranging from large-flowered self-incompatible to small-flowered, self-compatible and highly selfing towards both range limits (Raven 1969; Dart et al. 2012). The pattern of floral and mating system variation is described in more detail in Chapters 2 and 3 based on the work by
Dart et al. (2012). The hypotheses and predictions that I evaluate in each of my chapters are based on genetic data obtained from species-specific nuclear microsatellite nSSR (Chapter 2) and universal chloroplast microsatellites cpSSR (Weising & Gardner 1999; Chung & Staub 2003). I evaluate the joint effect of contemporary population density, spatial isolation and variation in the
2 mating system as potential predictors of genetic diversity and differentiation from 38 populations using 13 of these nSSRs and six cpSSRs (Chapter 3). To investigate the potential effects of hybridization on range-wide population genetic structure, I also studied a closely related taxon, the putative sister species, Camissoniopsis bistorta (Torr. & A. Gray) W.L. Wagner & Hoch
(Onagraceae) that occurs parapatrically with C. cheiranthifolia along the coast of southern
California and northern Baja California, Mexico. C. bistorta is self-incompatible and highly outcrossing and hybridization between the two congeners has been hypothesized by Raven
(1969) and as a result of geographic surveys during fieldwork by our research group (Dart et al.
2012 and this thesis). I sampled 18 populations from across the range of C. bistorta and performed range-wide analyses of genetic diversity, structure and differentiation using 12 nSSR loci for both species (Chapter 4). Chapter 5 is a collaboration with Dr. Karen E. Samis that has been published in Evolution. In it, we used data collected as part of my thesis and combined it with previous field transplant experiments and fitness data to test for reductions in genetic diversity and asymmetries in gene flow towards the northern range limit of C. cheiranthifolia.
THEORETICAL FRAMEWORK
Factors that influence genetic diversity and structure within and among populations
The genetic diversity within populations and the genetic differentiation among them should be dictated by the interplay of genetic drift, gene flow and natural selection, which in turn are influenced by the demographic processes, the spatial distribution and environmental heterogeneity among populations. Population size and the degree to which size fluctuates through time determine the rate at which variation is lost through genetic drift. Size and spatial separation among populations influence the direction and strength of gene flow and thus the replenishment
3 of the genetic variation. Environmental variation and the amount of phenotypic variation will ultimately influence the patterns of natural selection.
The processes that structure genetic variation among populations can be roughly classified as historical versus contemporary. Historical processes are no longer operating but have had a lasting effect on current genetic structure. Vicariance events, such as range disjunctions caused by the formation of mountain ranges, peninsulas, seaways or islands shaped the distribution of genetic linages within species (e.g. Riddle et al. 2000; Bernardi et al. 2003;
Kropf et al. 2006; Garrick et al. 2009; Poulakakis et al. 2012). In temperate regions, range fragmentation caused by glaciations and sequential founder events during post-glacial expansion have also strongly affected the current geographic distribution of genetic lineages, especially those in the Northern Hemisphere (Pamilo and Savolainen 1999; Hewitt 2000; 2004; Petit et al.
2003; Petit et al. 2005). Contemporary factors such as population demography or impediments to present-day gene flow continue to shape genetic structure of species ranges (Eckert et al. 2008).
One prediction that is commonly tested is that census population size should be correlated with the levels of genetic diversity contained within populations, which in turn should reflect the effective size of a population, Ne. Under equilibrium, genetic diversity (at neutral or weakly selected sites) should be the product of Ne and the mutation rate per generation. Ne will approximate the number of individuals contributing to reproduction if all individuals within a population have an equal chance to reproduce and census size (N) remains constant overtime.
The expectation then is that, all else being equal, larger populations should maintain higher levels of genetic diversity than small populations because they will experience fewer fluctuations in allele frequencies. However, in reality, natural populations often fluctuate in size through time and individuals vary in their reproductive success, explaining why Ne values are often far lower than the census numbers of breeding individuals in a species (Frankham 1995; Charlesworth
4 2009). While studies on a wide range of plant and animal species have often demonstrated a positive association between genetic diversity and measures of contemporary population census size, this is not always the case (reviewed in Ellstrand and Elam 1993; Frankham 1995; 1996;
Oostermeijer et al. 2003; Leimu et al. 2006; Bazin et al. 2006). Other factors such as species’ life history traits can also influence genetic diversity by modifying population demographic processes changing the relationship between N and Ne. Although intuitive, the prediction that life history traits are important determinants of genetic diversity has yet to be evaluated across most taxa by empirical data (Leffler et al. 2014). Moreover, the influence of some life history traits on genetic diversity might be expected to be different between distinct genomes (e.g. nuclear, mitochondrial and/or chloroplast) (Graustein et al. 2002; Bazin et al. 2006; Aguinagalde et al.
2005; Duminil et al. 2007; Nabholz et al. 2008).
Theoretical assumptions about genetic structure across species’ geographic ranges
Many empirical studies that have characterized within-species range-wide patterns of genetic structure, have been motivated by the theoretical expectation that the distribution of individuals covaries geographically with the abiotic and biotic factors that influence the survival and reproduction of individuals, and hence population growth (Brown 1984). In an ideal world, one with relatively smooth ecological gradients, the highest abundance of individuals should occur where conditions are most suitable for survival and reproduction, and decline steadily in all directions away from this point (Hengeveld and Haeck 1982, Brown et al. 1995). This Abundant
Centre Model (ACM), predicts that the center of a species’ range should coincide with the point where a species achieves its highest abundance and individual fitness, with populations becoming progressively smaller, less dense and more spatially isolated towards the range limits (Brussard
1984; Lawton 1993; Vucetich and Waite 2003; Sexton et al. 2009). Populations located at the
5 edge of the environmental gradient might experience greater demographic stochasticity, which may increase the risk of local extinction, contributing to range limits (Angert 2009; Gerst et al.
2011).
The basic implications of this “Abundant Centre Model” (ACM) in relation to patterns of neutral genetic diversity are a reduction in two key parameters: effective population size (Ne) and gene flow (m) towards range limits. This in turn, should cause a decline in within-population diversity and an increase in between-population differentiation from the range center to the margins. The reduction in genetic diversity in small, isolated, marginal populations will be enhanced if they experience higher demographic instability leading to more cycles of extinction and recolonization with associated population bottlenecks and founder events. The decline in diversity towards range limits may ultimately inhibit adaptation to conditions beyond the limits
(Hoffman and Blows 1994; Hoffman and Parson 1997; Gaston 2003; Blows and Hoffman 2005).
However, a reduction in within-population diversity is not the only explanation as to why species do not continue to expand beyond their range margins. If individuals disperse randomly in space, small, relatively unproductive marginal populations may receive a higher proportion of immigrants from larger, more productive, central populations than from other marginal populations. This asymmetrical gene flow may thwart local adaptation to extreme environments at range margins thereby inhibiting range expansion. This antagonism between gene flow and selection, in contrast to the ‘drift hypothesis’ posed earlier, may enhance genetic diversity within and reduce differentiation among marginal populations (Barton 2001).
The ACM underlies all of the expectations concerning how differences in population demography and isolation associated with geographic position (central vs. peripheral) should influence the patterns of gene flow and the genetic diversity of populations. However, empirical evaluations of the demographic predictions of ACM accumulated over the last 20 years have
6 challenged this widely accepted biogeographical model (e.g. Gaines and Sagarin 2002; Gaston
2003; Sagarin et al. 2006; Sexton et al. 2009). For instance, smaller, less abundant and/or less productive populations towards the range edge are not always found (e.g. Samis and Eckert
2007). Eckert et al. (2008) reviewed tests for geographic differences in genetic diversity and differentiation based on the geographic predictions of the ACM. This study found support for the reduction in genetic diversity and increase in genetic differentiation towards range margins (65% of studies detected the pattern); however, very few studies properly tested these predictions, putting in doubt the generality of this model. For instance, many studies simply oversampled geographic central populations and fewer populations at the margins, due to a lack of continuous sampling throughout the range (but see Sagarin and Gaines 2002). Many examined only the northern range edge of species in the Northern Hemisphere, where glaciations and post-glacial colonization likely reduced diversity and increased differentiation at the northern edge, due to range fragmentation and founder effect, rather than contemporary demography and distribution.
Moreover, even when populations across the range were included, studies rarely incorporated data on population size or geographic isolation to evaluate whether genetic diversity covaries with contemporary geographic variation in population demography and distribution, as predicted by the ACM (Vucetich and Waite 2003; Eckert et al 2008). These issues weaken our ability to connect patterns with processes, and given the discussion above, it is perhaps not surprising that the few attempts to review empirical work on the demographic and genetic predictions of the
ACM, especially for differences in genetic variation and its partitioning between central and peripheral populations, have produced mixed results.
Other factors that affect genetic diversity and structure across species’ ranges
7 But what else can cause deviations from the basic demographic model? Mating system and seed dispersal directly influence the amount and partitioning of genetic variation within and among plant populations (Glemin et al. 2006; Duminil et al. 2007). In hermaphroditic, self-compatible plants, a transition to higher levels of selfing will reduce pollen dispersal and consequently may reduce both Ne and m (Duminil et al. 2009). Transitions from outcrossing to selfing have occurred many times independently in flowering plants (Barrett 1996). In species where the transition has been confirmed, outcrossing populations often occur at the center of the range and selfing populations towards the range margins (Rick and Tanksley 1981; Rick 1986; Holtsford and Ellestrand 1989; Busch 2005). This pattern might arise if range edges are associated with ephemeral or marginal habitats where self-fertilization evolves in smaller and more isolated populations, as a result of selfing allowing reproductive assurance to aliviate outcross pollen limitation (Baker 1955; Stebbins 1957; Pannell and Dorken 2006; Pannel et al. 2015). These differences in the ecology of selfing and outcrossing populations will create a pattern that mimics the genetic expectations of the ACM, making it difficult to disentangle the relative contribution of variation in mating system, population size, spatial isolation and geographic location (i.e. peripherality), unless these parameters are quantified and explicitly included in the analysis.
Hybridization between closely related species in some parts of the range but not others might also alter the genetic expectations of the ACM. Very often closely related species meet at the margins of their distributions resulting in parapatric ranges, opening opportunities for gene flow between heterospecific populations. Hybridization at the range limit of one of the species
(but not necessarily the other) can increase genetic variation in marginal populations via introgression of new alleles or by increasing census population size, thus buffering marginal populations from the loss of genetic diversity (Hamilton and Miller 2016). Higher variation in marginal populations might facilitate adaptation to range limits and allow range expansion (Grant
8 and Grant 2008; Pfenning et al. 2016). The extent of introgressive gene flow between taxa depends on the dispersal ability and relative fitness of parental species compared to their hybrids, which is likely influenced by ecological factors (Barton and Gale 1993; Arnold and Hodges
1995; Rieseberg 1995; Arnold 1997). Thus, hybridization may occur in some areas of a species’ range more than others, thereby contributing to geographic variation in genetic differentiation.
However, the role that hybridization plays in shaping the genetic structure of species’ ranges remains relatively unstudied (but see Sweigart and Willis 2003).
THESIS OBJECTIVES AND DESCRIPTION OF THE CHAPTERS
The main goal of my thesis is to understand range-wide patterns of neutral genetic diversity and population structure at different spatial scales, while analyzing the contribution of mating system variation, contemporary demography, spatial isolation and hybridization. The work that I present in this thesis represents one of the most complete range-wide population genetic studies with nuclear microsatellites polymorphisms; it covers most of the methodological gaps found in many of the studies reviewed by Eckert et al. (2008) and attempts to connect patterns with processes by incorporating data on variation in the mating system and contemporary population size. In summary, this thesis addresses two of the most enduring and poorly resolved questions in evolutionary biology: 1) What predicts genetic diversity and structure across species’ ranges? and 2) Why do species exhibit limits to their geographic distributions? By understanding the relative contribution of contemporary population demography, spatial isolation among populations, mating system variation, potential for hybridization and deeper evolutionary history, I also uncover several unexpected aspects of my two study systems.
9 Understanding the processes that underlie species distributions has been a long-standing goal for evolutionary biologists; however, for many systems the lack of genetic tools has been a hindrance to tests of the theoretical hypotheses. Late in the 1990s DNA microsatellites became one of the preferred tools in molecular ecology because of their high allelic diversity, codominant expression and high reproducibility compared to other available tools at the time (Powell et al.
1996; Hancock 1999; Ellegren 2004). Microsatellites are short sequence motifs, tandemly repeated through the nuclear, chloroplast and, in some species, the mitochondrial genome of eukaryotes. They exhibit amongst the highest mutation rates of any DNA markers (Dallas 1992;
Di Renzo et al. 1994). Polymorphisms derive primarily from variability in the number of repeats
(allele size differences), commonly due to slipped-strand mispairing during DNA replication, resulting in either the gain or loss of a single repeat unit (the stepwise mutation model SMM;
Kimura and Ohta 1978) or multiple repeats simultaneously (infinite alleles model IAM; Kimura and Crow 1964). Size homoplasy is perhaps the most important limitation of microsatellites, but this is often resolved by developing species-specific loci. In Chapter 2 I present the development and first assessment of performance of 24 microsatellite markers for C. cheiranthifolia from an enriched genomic library. All 24 primers were tested in four populations of C. cheiranthifolia, one population of its closest sister taxon C. bistorta and eight other species. I showed that these markers are highly polymorphic and exhibit high allelic diversity and levels of heterozygosity consistent with variation in the mating system among populations.
In Chapter 3, I evaluate the hypothesis that a shift to higher selfing rates influences population size and consequently how genetic variation is distributed within and among populations. Theoretically, the evolution of higher self-fertilization will result in both genetic and ecological consequences for populations within species at multiple spatial scales. Few studies have empirically evaluated the joint effects of selfing and population size on within-population
10 diversity, among-population differentiation, as well as large-scale coincidence between mating system shifts and population genetic structure. I analyzed all these factors across the geographic range of Camissoniopsis cheiranthifolia. I also tested whether selfing populations showed higher variance in genetic diversity and population density, as would be expected if selfing populations experience more and stronger bottlenecks and/or recurrent founder events. In addition to large- scale genetic structure, I also evaluated genetic structure at a much finer spatial scale in three populations that consist of large and small-flowered, self-compatible, outcrossing and selfing individuals. Many of the predictions tested in this chapter use mating system estimates from a previous study by Dart et al. (2012), which described the patterns of geographic variation in floral traits, fruit production and mating system across the range of C. cheiranthifolia. Chapter 3 was submitted to Molecular Ecology (November 2016) and is currently undergoing revisions for resubmission.
In Chapter 4, I tested for evidence of hybridization between C. cheiranthifolia and C. bistorta across both species’ ranges. Because gene flow is more likely to occur between populations that are closer together than further apart, the first step was to estimate range overlap between the species. I gathered herbarium records and estimated Euclidean distances between specimens of both species with unique geographic coordinates. I then compared the Euclidean distances from each C. cheiranthifolia records to the closest C. bistorta and compared distances grouped by locations where populations differ in their mating systems. I tested the prediction that gene flow should be higher among self-incompatible populations of C. cheiranthifolia and C. bistorta, which should exhibit the closest parapatry than with any other group of self-compatible populations of C. cheiranthifolia. This is one of the few attempts in the plant literature to quantitatively estimate the degree of parapatry across the range of two species while assessing the potential for hybridization with molecular markers. I also analyzed range-wide population
11 genetic structure with Bayesian analyses and compared levels of genetic diversity among genetic linages. In a set of more focused analyses among 25 outcrossing self-incompatible populations of both species, I evaluated assignment probabilities to genetic clusters between C. cheiranthifolia and C. bistorta and compared patterns of isolation-by-distance within and between species.
Given that populations within species should be more similar to one another than to populations between species, I predicted that, if C. bistorta and C cheiranthifolia are reproductively isolated, then pairwise differentiation between heterospecific populations should be much higher than between conspecific populations and should not vary with geographic distance. Alternatively, hybridization between C. bistorta and C. cheiranthifolia will decrease differentiation between heterospecific populations and result in isolation-by-distance between them.
In my final data chapter (Chapter 5), I present a test of range limit theory. This work was done in collaboration with Karen Samis and Chris Eckert. It combines population genetic analysis of microsatellite polymorphisms with a transplant experiment within the range, at the range edge of, and 60 km beyond the northern range of C. cheiranthifolia. This study is motivated by previous data suggesting a decrease in patch size and habitat occupancy, but an increase in mean fitness toward the northern range limit of the species (Samis and Eckert 2009).
We proposed that rather than limits being imposed by niche limitation, C. cheiranthifolia might be dispersal limited in a metapopulation. We hypothesized that the northern range limit of C. cheiranthifolia occurs on an ecological gradient that increases fitness, thereby reducing patch extinction toward the range edge, but also decreasing dispersal. As a result, the probability of recolonization and metapopulation persistence declines toward the limit. Evidence for this hypothesis should be consistent with the following predictions: (1) High population viability combined with reduced gene flow allows local adaptation of edge populations. (2) Edge populations will perform better than interior when planted beyond the range limit. (3) Within-
12 population genetic diversity declines toward the limit. Finally, with limited gene flow among populations (4) genetic differentiation at neutral loci will be high, and (5) there will be no tendency for asymmetric gene flow from interior to edge populations. This work has been recently published in Evolution and was done in collaboration with. Karen Samis, who performed the transplant experiments and wrote these sections of the manuscript. I collected, analyzed and wrote the sections pertaining to the evolutionary predictions (genetic data) of this study. Eckert analyzed the ecological data and participated at every step of the process and greatly contributed to and improved the final versions of this manuscript.
Finally, in Chapter 6, I summarize the main findings of this thesis and discuss how this work contributes to our understanding of the factors that cause geographic variation in population genetic structure and diversity and the evolutionary ecology of species’ geographic ranges. I also provide future research directions using these two coastal dune endemics based on the main results of my thesis.
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22 CHAPTER 2. Microsatellite primers for Camissoniopsis cheiranthifolia (Onagraceae) and
cross-amplification in related species
ABSTRACT
• Premise of the study: We developed 24 nuclear microsatellite primers from an enriched genomic library for the Pacific coastal dune endemic Camissoniopsis cheiranthifolia to study the consequences of mating system differentiation, the genetics of species’ range limits, and hybridization with its closest sister taxon, C. bistorta.
• Methods and Results: Twenty-four primer pairs were developed and characterized in four populations of C. cheiranthifolia and one population of C. bistorta. We also tested eight additional taxa for cross-amplification. The average number of alleles per locus per species was
4.3 and 6.0, respectively. The number of loci that amplified and were variable within the eight related taxa ranged from six to 17.
• Conclusions: These markers will be useful in studying mating system evolution, the genetic structure of species’ ranges, hybridization, and the provenance of material used for habitat restoration in C. cheiranthifolia, C. bistorta, and related species.
INTRODUCTION
Camissoniopsis cheiranthifolia (Hornem. ex Spreng.) W.L. Wagner & Hoch (Onagraceae) is a diploid, bee-pollinated, short-lived perennial endemic to the Pacific coastal dunes of Baja
California, California and Oregon (Raven, 1969; Wagner et al., 2007). Being restricted to coastal dunes, it is continuously distributed along a near-linear, easily-accessed geographic range, providing opportunities for studying the ecology and evolution of geographic range limits (Samis and Eckert 2007, 2009). This species also exhibits striking variation in floral traits and the
23 relative importance of outcrossing vs. self-fertilization providing opportunities to investigate the evolution of mating systems (Eckert et al., 2006; Button et al., 2012). Dart et al. (2012) showed that populations in southern California are large-flowered (LF), predominantly outcrossing and either largely self-incompatible (SI) or self-compatible (SC). Populations in Baja California towards the southern range limit, on the Channel Islands off California and north of Point
Conception, California to the northern range limit in southern Oregon are small-flowered (SF),
SC and predominantly selfing. The proportion of seeds outcrossed estimated at the population level from the segregation of allozyme polymorphism in progeny arrays ranged 0.0–1.0 and correlated positively with flower size. Lineages within Camissoniopsis and closely related
Eulobus and Camissonia appear to have undergone speciation via polyploidization involving hybridization (Raven, 1969; Wagner et al., 2007). In Camissoniopsis, five of 14 species are polyploid, predominantly selfing, and were likely derived through hybridization. Camissoniopsis cheiranthifolia and C. bistorta (Torrey & A. Gray) P. H. Raven are the only two species that include outcrossing populations. Throughout the genus, species’ ranges frequently overlap, and ongoing hybridization may be maintaining high morphological variation within and low differentiation among species. We developed microsatellite markers for C. cheiranthifolia that would cross-amplify in related taxa to better investigate mating system evolution, the genetic structure of geographic ranges and the ecology and genetics of hybridization.
METHODS AND RESULTS
A microsatellite-enriched genomic library was developed following Glenn and Schable (2005) and Hamilton et al. (1999). Using silica dried leaf tissue from one plant from each of two populations (Appendix 1; Table A2.1) total DNA was isolated using cetyltrimethylammonium bromide (CTAB) extraction (Doyle and Doyle, 1990). We digested 5 mg of pooled DNA at
24 37 °C overnight with Alu I + Hae III + Rsa I restriction enzymes. Digested DNA was dephosphorylated using 0.01 U calf intestinal alkaline phosphatase per pmol ends of DNA at
50ºC for 1 h, purified using an equal volume of 25:24:1 phenol:choloroform:isoamyl alcohol, precipitated using 2.5 volumes of cold 100% ethanol and 3 M NaOAc, and then resuspended in
TE buffer (10 mM Tris, pH 8.0, 1 mM EDTA). DNA quality and size was evaluated on 1.5% agarose gels (fragments ranged from 200–1000 bp).
Digested DNA was ligated to 1 mM SNX of double-stranded linkers using T4 DNA ligase (Invitrogen, Burlington, Ontario, Canada) and 20 U Xmn I (New England Biolabs, Whitby,
Ontario, Canada) overnight at 16ºC. Linker ligation was tested using PCR amplification with
SNX forward primer (5′- ctaaggccttgctagcagaagc -3′) in a reaction with 1x buffer, 2.0 mM MgCl2,
150 mM dNTPs (Roche Diagnostics, Laval, Quebec, Canada), 0.5 mM primer, 1 mg/ml BSA and
1 U Taq polymerase (reagents from Invitrogen except dNTPs).
Linker-ligated DNA was hybridized to 3’ biotinylated (AC)13, and (AG)13 probes for 4 h at 70ºC after 10 min at 95ºC. Enriched DNA was captured using streptavidin beads
(DynaBeads® M-280 Streptavidin, Invitrogen) and verified with PCR as above. About 20 ng/ml of amplified DNA was used in transformation with the TOPO TA cloning kit (Invitrogen) and grown on LB plates with 50 ng/ml ampicillin. About 350 colonies were screened for microsatellites using fluorescent DIG probes (Roche Diagnostics). For positive clones, insert sizes were estimated with PCR using M13 primers and verified on 1% agarose gels. DNA was extracted from 115 positive clones with appropriate insert sizes and PCR products were sequenced at Genome Quebec (McGill University, Montreal, Quebec, Canada) or Robarts
(University of Western Ontario, London, Ontario, Canada). Ninety-three of these clones contained a total of 90 unique microsatellite regions. Primer pairs were designed for the 32 clones that had both linkers, suitable flanking region at both ends and a minimum of eight repeats.
25 We used Primer3web 4.0.0 (Koressaar and Remm. 2007; Untergrasser et al., 2012) and Amplifix
1.5.4 (http://crn2m.univ-mrs.fr/AmplifX) to design primer pairs optimized to contain 18–22 bases, 40–60% GC content, 50–60°C melting temperature and yield 100–350 bp PCR products.
The forward primer of each pair was labeled with a D4 red labeled M13 tail (5′-
CACGACGTTGTAAAACGA -3′) (Sigma-Aldrich, Oakville, Ontario, Canada). The number and the identity of samples used for an initial testing of each pair varied. We used 1–7 DNA samples from 5–42 (mean = 30.6) populations covering the entire geographic range of C. cheiranthifolia and 1–6 DNA samples from 3–12 (mean = 9.7) populations of C. bistorta (Appendix 1; Table
A2.1). Each sample was genotyped twice in single-locus 5 µl PCR reactions containing 0.5 µl
DNA template [10 µg/µl], 2.5 µl Multiplex PCR Master Mix (Qiagen, Toronto, Ontario, Canada),
0.1 µl of each forward and reverse primers [10 µM], 1.1 µl of M13taq [1 µM] (Sigma-Aldrich),
0.2 µl Q-Solution and 0.5 µl sterile double distilled water. PCR involved 15 min denaturation at
94°C, followed by 35 cycles of 20 s at 94°C, 30 s at 55°C or 57°C, and 40 s at 72°C, with 10 min final extension at 72°C. PCR product was diluted with double distilled water to a final volume of
15 µl and fragments were sized using a GenomeLab GeXP with the CEQ 8000 Genetic Analysis
System version 9.0 (Beckman Coulter, Mississauga, Ontario).
Of 32 primer pairs, 24 yielded variable fragments of expected size and two of these amplify within two other loci (Table 2.1). For these two loci (A31b and C135b) a second primer pair (A31c and C135c) was re-designed to improve consistency of amplification in some C. cheiranthifolia but mainly in C. bistorta populations. For each locus, we estimated the number of alleles (A), observed (Ho) and expected (He) heterozygosities in one LF-SI, one LF-SC, two SF-
SC populations (southern and northern parts of the range) and one LF-SI C. bistorta using
GenAlEx 6.5 (Peakall and Smouse, 2012). We did not test for deviations from Hardy-Weinberg
26 because all populations of C. cheiranthifolia, including LF-SI populations, can exhibit some self- fertilization (Dart et al., 2012), so that Ho < He in many cases reported below.
Within populations, A ranged from 1–12 across loci (mean = 4.3) and was highest in the
LF-SI populations compared to the LF-SC and the two SF-SC populations (Table 2.2). Using only 13 loci for which the same individuals were genotyped, we detected 130 alleles total, of which 56 were found only in C. cheiranthifolia (mean ± 1 SE = 4.30 ± 0.49 private alleles per locus) and 10 only in C. bistorta (0.77 ± 0.26 private alleles per locus) suggesting that these markers could be useful to detect hybridization between these species, although a broader sample is required to determined which are diagnostic. Ho and He were highly variable but predictable based on the mating system, as both were highest in the two LF-SI populations, lower in mixed mating LF-SC and lower still in the two SF-SC populations (Table 2.2), thereby verifying the potential of these markers for studying the genetic consequences of mating system differentiation.
Although cross-amplification often failed in samples from the eight related taxa, there were many loci at which amplification was successful (Appendix 1, Table 2.3). Of the 24 loci developed for
C. cheiranthifolia, 17 were tested in Camissoniopsis micrantha (Spreng.) W. L. Wagner & Hoch,
C. lewisii (P.H. Raven) W.L. Wagner & Hoch, Eulobus angelorum (S. Watson) W. L. Wagner &
Hoch, E. californicus Nuttall ex Torr. & A. Gray and E. crassifolius (Greene) W. L. Wagner &
Hoch, and successful amplification occurred for 17, 15, 9 and 9 loci, respectively. Dick et al
(2013) tested 16 of these 24 loci in the serpentine endemic Camissonia benetensis P. H. Raven, and its two widespread congeners C. strigulosa (Fischer & C. Meyer) P. H. Raven and C. contorta (Douglas) P. H. Raven and found six variable loci, which they used to quantified patterns of genetic diversity.
CONCLUSIONS
27 All 24 microsatellite loci were variable in C. cheiranthifolia and C. bistorta and a number of them also amplified in the eight closely related taxa providing opportunities to test a broad range of ecological and evolutionary questions within-species and across taxa. These markers will facilitate our ongoing studies of mating system evolution and geographic range limits in C. cheiranthifolia as well as the genetic and ecological consequences of hybridization between C. cheiranthifolia and C. bistorta. The high frequency of cross-amplification in related taxa provides opportunities for comparative studies investigating the genetic consequences of variation in life history and mating system, and ongoing hybridization between this morphologically and ecologically variable group.
LITERATURE CITED
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between floral traits and the mating system in Camissoniopsis cheiranthifolia (Onagraceae):
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28 Eckert C. G., K. E. Samis & S. R. Dart. 2006. Reproductive assurance and the evolution of
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29 Table 2.1. Characteristics of 24 microsatellite primers pairs developed for Camissoniopsis cheiranthifolia (Onagraceae). Repeat motifs (with number of repeats in the original sequence), annealing temperature (Ta), range of amplified fragment sizes (including
M13 tag) and GenBank accession number.
Locus Primer sequence (5’ – 3’) Repeat motif Ta Fragment size Multi- GenBank (°C) range (bp)a Plexedc accession F: TCCTGTTGTTGTCCTTTCTT A18 (TC) 55 212–225 single Pr032165043 R: CCTCGTACAAGGACATGG 28
F: GAAGCCCTTCAGAGGTTAAT A31bb (TC) 55 220–242 single Pr032165044 R: TAACCTCCTGGTCTTTCAGA 10
F: TGCTAGCAGAAGCCCTTCAG A31cb (TC) 55 174–211 single Pr032165045 R: GTGCCTGACCTATGATGTCG 10
F: CCTGAAAAATGGAAATTGTGC B11 (GA) 55 120–152 2plex1 Pr032165046 R: TTCACAGGACAGGACTGGAC 9
F: CACATTCCTTCACATTTGGT B34 (TC) 57 237–253 2plex2 Pr032165047 R: CTTCAAAGGACAACCCTTTT 10
F: TCCTAACCATGCCGACTCGT B59 (TC) 57 121–179 2plex5 Pr032165048 R: ACAGCAACTTCCCTGCAATCA 25
F: AATCCGAACGCTAACCACAG C110 (GA) 57 194–210 2plex2 Pr032165049 R: TCAACCTCGAATCCAAGTCC 8
30 F: TTTACTGTCTTTGGTGTCTG C133 (GA) 55 121–157 2plex4 Pr032165050 R: GGCTGCTGAGGAGAAGAT 14
F: ACAGTGGTGGTTTCAATTTC C135bb (TC) 57 131–149 3plex Pr032165051 R: CAAAGAGCGAAGAAGAAGAA 12
F: CCGCCTTCATCTGTACTCCA C135cb (TC) 57 219–255 single Pr032165052 R: AGTGTATTGGCGATTTCAGG 12
F: CCTGGTGCTACTCCTATGTAT C18 (GA) 57 173–211 single Pr032165053 R: GCCTTTCCTTATTGCAATCGT 15
F: GAAAAAGGAGTTGGTGCAG C19 (TC) 57 222–316 3plex Pr032165054 R: CAAAGAGAAATGTGGCAAAC 14
F: TCTCTTCTTCCTTTCCTCCT C32 (GA) 55 189–217 single Pr032165055 R: CCTGAAATCCAGTGATCATA 14
F: CCTGAAATCCAGTGATCATA C42 (TC) 55 243–255 single Pr032165056 R: GCATAGGATACTGTGGGGTA 14
F: GACGGGCAATAGAGTTTACA C49 (TG) 57 196–214 3plex Pr032165057 R: TATAGACTGCCGGCTTTAAC 12
F: AAGGAGAGGACAGGCTGTTG C55 (GA) 57 123–155 single Pr032165058 R: GCAGATCACATACCTCTGCTT 14
F: TGCTTATAAGTGATGATGCCT (GA) (GT) C66 9 1 57 209–247 single Pr032165059 R: CTGGTCCAAATTCCTCTGGT (GA)3
F: GAAGTACGAGATGCAGAACG C67 (TC) 57 233–257 2plex3 Pr032165060 R: GCATACCTCAGAACGCTTAG 15
31 F: TGAAATCATGCACCGGACTA C89 R: (TA)5(GA)8 57 196–212 2plex5 Pr032165061 AAAGGATTCTTGTGAAGGAATGA F: CCATGCATTATTTCCAACTC D17 (TC) 57 215–249 single Pr032165062 R: TCCTCTCACTTCGTGTTTTC 24
F: CTTTTCAAAGGTGGGAGCAA E19b (TC) 57 205–247 single Pr032165063 R: GCCTGCAAATAATGCCATGT 24
F: CATTGCTGTGCTTCTGTTC E30 (TC) 55 180–218 2plex1 Pr032165064 R: CTCTACTTGTGGCTGTGGAT 17
F: TGTCTCCTTCCTGTGTGTGG E42 (GA) 55 179–197 2plex4 Pr032165065 R: AAAATCCTCCATCCCCTGTC 10
F: GATATGGCTTACAATGCAACG E70 (TC) 57 128–144 2plex3 Pr032165066 R: GTGAAGCAGTGAACCAAGCA 15
Notes: a Range of fragment sizes including the M13 tag [5' CACGACGTTGTAAAACGA 3'] attached to the forward primer. b For two loci (A31b and C135b) we developed two additional primer pairs A31c and C135c (see text for details). c For genotyping, we used single primer pair reactions for 11 loci, one triplex reaction (loci C135b+C49+C19) and five duplex reactions (B11+E30, C110+B34, E70+C67, C133+E42 and B59+C89) adjusting the number of cycles in the PCR program for
B59+C89 to 32 (Table 2.1).
32 Table 2.2. Estimation of population genetic parameters for 21 microsatellite loci in four Camissoniopsis cheiranthifolia populations representing each geographic region and mating type, plus one population of the sister species C. bistorta. For each population, the population code (Appendix 1), the mating type and geographic region are indicated. (LF-SI = large-flowered self-incompatible, LF-SC
= large-flowered self-compatible and SF-SC small-flowered self-compatible).
C. cheiranthifolia C. bistorta
LF-SI (CBF) LF-SC (CCO) Southern SF-SC (BES) Northern SF-SC (CMC) LF-SI (CCU)
Locus n A Ho He n A Ho He n A Ho He n A Ho He n A Ho He
A18 8 5 0.37 0.77 12 3 0.33 0.50 15 4 0.47 0.54 16 2 0.06 0.18 10 5 0.33 0.64
A31b 25 6 0.52 0.68 13 4 0.54 0.69 14 4 0.36 0.72 23 5 0.17 0.65 21 5 0.19 0.26
B11 29 7 0.65 0.70 30 4 0.41 0.56 37 2 0.03 0.13 42 5 0.17 0.20 21 7 0.57 0.79
B34 29 5 0.41 0.63 30 4 0.30 0.52 37 2 0.00 0.23 42 5 0.17 0.41 21 4 0.57 0.65
B59 29 12 0.55 0.87 30 7 0.20 0.82 37 8 0.11 0.67 42 7 0.05 0.64 21 8 0.57 0.81
C110 29 4 0.35 0.39 30 2 0.00 0.06 37 3 0.05 0.24 42 3 0.05 0.05 21 4 0.33 0.49
C133 29 6 0.79 0.74 30 4 0.13 0.13 7 4 0.03 0.13 42 3 0.02 0.05 21 4 0.81 0.67
C135b 29 7 0.48 0.68 30 2 0.32 0.43 37 4 0.05 0.27 42 4 0.12 0.16 21 6 0.47 0.77
C135c 13 10 0.31 0.83 10 6 0.27 0.74 10 2 0.00 0.24 19 4 0.05 0.25 19 9 0.68 0.79
C19 29 6 0.58 0.76 30 3 0.32 0.52 37 6 0.05 0.20 42 4 0.07 0.46 21 7 0.33 0.71
C32 11 5 0.27 0.56 13 6 0.46 0.68 8 2 0.13 0.12 9 2 0.11 0.45 9 4 0.22 0.50
C42 8 4 0.38 0.49 10 3 0.30 0.54 11 2 0.09 0.43 5 2 0.00 0.32 18 9 0.61 0.68
33 C49 29 4 0.59 0.61 30 2 0.23 0.45 37 5 0.08 0.15 42 3 0.05 0.05 21 8 0.52 0.73
C55 15 5 0.33 0.79 6 3 0.17 0.62 6 2 0.00 0.28 13 4 0.15 0.68 9 7 0.33 0.83
C67 29 5 0.43 0.46 30 5 0.40 0.61 37 3 0.03 0.08 42 5 0.08 0.38 21 5 0.62 0.72
C89 29 4 0.35 0.65 30 6 0.23 0.53 37 3 0.05 0.52 42 3 0.02 0.51 21 3 0.38 0.56
D17 9 4 0.50 0.65 10 7 0.40 0.87 10 1 0.00 0.00 11 2 0.36 0.46 11 7 0.54 0.75
E19b 23 12 0.70 0.86 15 3 0.47 0.52 21 6 0.10 0.67 19 2 0.00 0.15 12 7 0.42 0.73
E30 29 7 0.59 0.62 30 4 0.40 0.69 37 4 0.05 0.08 42 4 0.10 0.22 21 5 0.43 0.56
E42 29 6 0.34 0.36 30 2 0.00 0.06 37 2 0.08 0.21 42 3 0.00 0.18 21 6 0.62 0.75
E70 29 7 0.52 0.81 30 4 0.60 0.63 37 4 0.22 0.20 42 3 0.11 0.12 21 7 0.52 0.56
Mean 23.29 6.24 A 0.48 A 0.66 A 22.8 4.00B 0.31 B 0.53 B 26.0 3.48 B 0.09C 0.29 C 31.5 3.57 B 0.09C 0.31C 18.2 6.05 A 0.48A 0.66 A
Notes: n = number of individuals screened/locus/population; A = number of alleles; Ho and He = observed and expected heterozygosity, respectively. Different upper case subscript letters beside mean values in each parameter measured represent significant (P < 0.01) differences between populations after paired one-tailed t-test comparisons.
For an additional three loci the number of individuals within each population was low and data were collected from >1 population within each mating type, and estimates calculated from the pooled sample of individuals within species. These data are provided here:
A31c: C. cheiranthifolia n = 15, A = 8, Ho = 0.31, He = 0.78, C. bistorta n = 7, A = 4, Ho = 0.40, He = 0.76; C18: C. cheiranthifolia n
= 23, A = 12, Ho = 0.26, He = 0.84, C. bistorta n = 8, A = 7, Ho = 0.5, He = 0.83; C66: C. cheiranthifolia n = 18, A = 9, Ho = 0.22, He
= 0.88, C. bistorta n = 8, A = 8, Ho = 0.86, He = 0.86.
34 Table 2.3. Cross-amplification and alleles sizes of 24 microsatellite primers pairs developed for Camissioniopsis cheiranthifolia and screened in C. bistorta, C. micrantha, C. lewisii, Eulobus crassifolius, E. californicus, E. angelorum, Camissonia benetensis, C. strigulosa and C. contorta.
C. bistorta C. micrantha C. lewisii E. crassifolius E. angelorum E. californicus C. benetensis C. contorta C. strigulosa
Locus (80,12) (14,3) (2,1) (14, 3) (6,1) (5,1) (213, 19) (42, 2) (62, 3) A18 162–203 214 152–162 Failed Failed Failed Failed Failed Failed 171–192a 171–192a 171–192a A31bb 225–237 203–236 nt nt nt nt 180–202a 180–202a 180-202a A31c 177–201 nt nt nt nt nt nt nt nt B11 122–148 145 133 131–143 131–135 131–135 Failed Failed Failed B34 239–251 247–249 249 245–255 245–249 Failed 183–236 183–236 183–236 B59 123–157 121–143 127–147 121–167 Failed Failed nt nt nt C110 192–204 192–202 202 188–202 194–204 194–202 Failed Failed Failed C133 121–149 142–158 142 142–149 141–145 142–148 Failed Failed Failed C135b 131–177 139–143 142 Failed Failed Failed Failed Failed Failed C135c 218–274 nt nt nt nt nt nt nt nt C18 177–201 nt nt nt nt nt nt nt nt C19 221–255 226–236 232 221–247 Failed Failed Failed Failed Failed C32 187–209 216 221 172–216 Failed Failed Failed Failed Failed C42 235–273 235–245 Failed Failed 206–246 235–245 166–174 166–174 166–174 C49 191–231 198–202 196 196–208 196–208 195–208 Failed Failed Failed C55 235–273 nt nt nt nt nt nt nt nt
C66 209–247 nt nt nt nt nt nt nt nt
C67 233–257 237–249 173–241 Failed Failed 235–245 209–219 209–219 209–219 165–204a C89 194–222 200 202 298–326 314–334 nt nt nt 314–332a E19b 205–247 nt nt nt nt nt nt nt nt E30 179–214 188–198 192–194 192–282 181-247 213–235 177–187 177–187 177–187
35 E42 179–202 180–192 191 180–188 Failed Failed Failed Failed Failed E70 124–144 126–136 133–145 122–144 129-139 133–143 103–119 103–119 103–119 D17 219–249 nt Failed Failed Failed Failed Failed Failed Failed
Notes: Total numbers of individuals from the number of populations sampled are indicated in parentheses.
Amplification failures (Failed) and loci that not tested in some species (nt) are indicated.
Data for the three species of Camissonia are from Dick et al. (2013). aThese primers amplified two non-overlapping variable regions in the species indicated, so two fragment ranges are provided.
36 CHAPTER 3. Consequences of multiple mating-system shifts for population and range-wide genetic structure in a coastal dune plant
ABSTRACT
Evolutionary transitions from outcrossing to selfing can strongly affect the genetic diversity and structure of species at multiple spatial scales. We investigated the genetic consequences of mating system shifts in the North American, Pacific coast dune endemic plant Camissoniopsis cheiranthifolia (Onagraceae) by assaying variation at 13 nuclear (n) and six chloroplast (cp) microsatellite (SSR) loci for 38 populations across the species range. As predicted from the expected reduction of effective population size (Ne) caused by selfing, small-flowered, predominantly selfing (SF) populations had much lower nSSR diversity (but not cpSSR) than large flowered, predominantly outcrossing (LF) populations. The reduction of nSSR diversity was greater than expected from the effects of selfing on Ne alone, but could not be accounted for by indirect effects of selfing on population density. Although selfing should reduce gene flow,
SF populations were not more genetically differentiated than LF populations. We detected five clusters of nSSR genotypes and three groups of cpSSR haplotypes across the species range consisting of parapatric groups of populations that usually (but not always) differed in mating system, suggesting that selfing may often initiate ecogeographic isolation. However, lineage- wide genetic variation was not lower for selfing clusters, failing to support the hypothesis that selection for reproductive assurance spurred the evolution of selfing in this species. Within three populations where LF and SF plants coexist, we detected genetic differentiation among diverged floral phenotypes suggesting that reproductive isolation (probably postzygotic) may help maintain the striking mating system differentiation observed across the range of this species.
37 INTRODUCTION
Evolutionary shifts from outcrossing to self-fertilization have occurred repeatedly in many angiosperm groups and may have profound genetic consequences at multiple spatial scales: from genotype frequencies within populations, through large-scale patterns of genetic subdivision to the rate and extent of diversification among species (Stebbins 1974; Barrett 2002). At the population level, complete selfing reduces effective population size (Ne) by one-half compared to outcrossing (Pollak 1987; Nordborg 2000) hastening the loss of genetic variation via drift
(Charlesworth & Wright 2001). Multilocus homozygosity of inbred populations reduces opportunities for recombination thereby slowing the decay in linkage disequilibrium
(Charlesworth et al. 1993) further enhancing the loss of genetic diversity via linked selection
(selective sweeps and background selection, Barrett et al. 2014). Because selfing limits opportunities for outcross siring it also reduces gene flow via pollen (Ingvarsson 2002). As a result, selfing taxa should exhibit lower genetic diversity within and higher differentiation among populations than outcrossed taxa.
In addition to the effects on Ne and gene flow, selfing may also affect population demography in ways that can influence the amount and structuring of genetic variation. Severe founder effect should be more frequent for selfers because selfing allows populations to be founded by very small numbers of individuals (Baker 1955; Pannell et al. 2015). Similarly, selfing populations may be more likely to recover from extreme demographic bottlenecks or persist at chronically small size whereas outcrossing populations would go extinct via Allee effect under similar conditions (Cheptou 2004; Morgan et al. 2005; Dornier et al. 2008). Small population size or a history of dramatic fluctuations in population size will strongly reduce long- term Ne (Pannell & Dorken 2006). On the other hand, selfing might buffer population growth
38 from variation in pollination service (Morgan et al. 2005) and speed recovery from population crashes, thereby offsetting the other effects of selfing on Ne. The net result is that selfing populations may vary more in Ne and genetic diversity than outcrossing populations (Schoen &
Brown 1991).
Species where floral traits affecting the mating system vary among and even within populations provide excellent opportunities for investigating the genetic consequences of selfing because confounding differences in ecology and life history that may also affect genetic structure are less likely than in between-species comparisons. Studies on these variable species generally support the prediction that selfing populations are less diverse but more differentiated than outcrossing populations at allozyme loci (reviewed in Hamrick & Godt 1990), microsatellite loci
(e.g. Awadalla & Ritland 1997; Tedder et al. 2015) and DNA sequences (e.g. Liu et al. 1998,
1999; Pérusse & Schoen 2004; Busch 2005; Busch et al. 2011; Ness et al. 2010; Pettengill et al.
2016). However, for the few studies where estimates of the selfing rates are available, the reduction in diversity is greater than expected from the direct effect of selfing on Ne alone, suggesting indirect effects through altered population demography (Barrett et al. 2014).
Contemporary population census size correlates with genetic diversity (and hence Ne) in a wide range of plants (reviewed in Ellstrand & Elam 1993; Frankham 1996; Oostermeijer et al. 2003;
Leimu et al. 2006), however the joint effect of population size and the mating system on genetic diversity and differentiation has rarely been investigated (Barrett & Husband 1990; Mable &
Adam 2007; Ness et al. 2010; Willi & Määttänen 2011).
In addition to population-level effects, shifts in the mating system may initiate large-scale genetic subdivision and ultimately lead to reproductive isolation of nascent lineages (Coyne &
Orr 2004; Martin & Willis 2007). Although a shift to higher selfing may genetically isolate a population from its outcrossing progenitor, selfing is not generally viewed as a reproductive
39 isolating mechanism per se because it reduces gene flow among selfing populations as much as it does between selfing and outcrossing populations (Coyne & Orr 2004). Nevertheless, a shift to higher selfing can trigger significant changes to reproductive and population ecology that lead to genetic and eco-geographic isolation (e.g. Busch 2005; Runquist et al. 2014). For instance, the loss of self-incompatibility (SI) is sometimes associated with pre-zygotic crossing barriers between SI and derived self-compatible (SC) populations (Lewis & Crowe 1958). Changes in floral morphology, color, scent and phenology that often evolve with a shift to selfing (Sicard &
Lenhard 2011; Doubleday et al. 2013) may also lead to isolation via pollinators (e.g. Runquist et al. 2014).
Selfing might also facilitate ecogeographic isolation and ultimately large-scale genetic subdivision. For instance, reproductive assurance may enable colonization of ecologically and geographically marginal habitats. Selfing can further enhance adaptation to these new habitats by exposing new favorable recessive mutations to selection (Charlesworth 1992) and reducing gene flow into marginal populations from larger, more productive populations in core habitat
(Kirkpatrick & Barton 1997). This is supported by widespread observations that populations with the capacity for selfing disproportionately occur on islands, at the peripheries of species’ ranges and the leading edge of expanding range boundaries (Pannell et al. 2015). However, whether mating system shifts are associated with ecogeographic structuring of species ranges has rarely been investigated, and the available evidence is mixed (Ness et al. 2010; Busch et al. 2011;
Pettengil & Moeller 2012a,b; but see Foxe et al. 2010).
In this study, we investigate the joint effects of selfing and population size on genetic diversity within and differentiation among populations, as well as the coincidence between mating system shifts and range-wide population genetic structure in the coastal dune endemic
Camissoniopsis cheiranthifolia (Hornemann ex Sprengel) W. L. Wagner & Hoch, comb. nov.
40 (Onagraceae). This species exhibits striking geographical variation in floral traits and, consequently, the relative importance of outcrossing vs. self-fertilization (Eckert et al. 2006; Dart et al. 2012) along its narrow, near 1-dimensional coastal distribution from Northern Baja
California (México) to southern Oregon (USA) (Raven 1969; Wagner et al. 2007). Taxonomic work by Raven (1969) followed by detailed surveys of floral morphology and mating system by
Dart et al. (2012) revealed geographic divergence in floral traits suggesting several potentially independent transitions in the mating system. Populations in San Diego County and just south of the USA-México border are large-flowered (LF, mean corolla width [cw] = 29.6 mm), self- incompatible (LF-SI) and predominantly outcrossing (mean proportion seed self-fertilized [s] =
0.18, range of s = 0.01–0.38). Populations just to the north in Orange, Los Angeles, Ventura and
Santa Barbara Counties are LF (cw = 27.7 mm) but self-compatible (LF-SC) and somewhat more selfing (s = 0.36, range = 0.11–0.53). On the near coastal Channel Islands and north of Pt.
Conception all the way to the species northern range limit in southern Oregon populations are small-flowered (SF, cw = 16.3 mm), self-compatible (SF-SC) and predominantly selfing (s =
0.76, range = 0.43–0.99). SF, predominantly selfing populations also occur at the southern range limit in Baja California (cw = 17.2 mm, s = 0.84).
This parapatric pattern of mating system variation suggests at least three or four independent transitions from outcrossing to higher selfing in C. cheiranthifolia and thus opportunities for ecogeographic isolation between lineages with divergent mating systems. In addition to including populations that are distinctly LF or SF, C. cheiranthifolia includes at least three populations located north of Pt. Conception exhibiting bimodal variation in floral morphology, suggesting two distinct phenotypes: large flowered (cw > 20 mm) and small flowered (≤ 20 mm; Dart et al. 2012; Dart & Eckert 2013, 2015; see also Raven 1969, pp. 258,
259, 266). These phenotypes are as diverged in terms of floral traits as phenotypically pure LF
41 and SF populations on opposite sides of Pt. Conception (Dart et al. 2012). These populations allow investigation of reproductive isolation of diverged phenotypes in sympatry.
Here, we use analyses of variation at nuclear and chloroplast microsatellite loci plus measures of contemporary population census size, flower size and mating system parameters to address the following predictions:
(1) A shift to higher selfing will influence the population size and consequently how genetic variation is distributed within and among populations. Specifically: (a) Population size will be lower and more variable in predominantly selfing than predominantly outcrossing populations.
(b) Within-population genetic diversity will correlate negatively with selfing and positively with population size. (c) Population size and hence within-population diversity will vary more among selfing than among outcrossing populations. (d) Genetic differentiation among populations will vary positively with selfing and the distance between populations, and negatively with mean population size.
(2) Shifts in the mating system will coincide with major genetic subdivisions across the species range.
(3) Shifts to higher selfing will be associated with reduced lineage-wide genetic diversity if selfing in C. cheiranthifolia evolved via selection for reproductive assurance (Schoen et al. 1996) as suggested by Raven (1969) and Linsley et al. (1973).
(4) The genetic consequences of selfing (predictions 1–3) will be most pronounced when a mating-system shift is associated with colonization of islands, because island colonization may involve severe founder effects and island populations are separated from one another by inhospitable habitat (ocean).
42 (5) Phenotypically mixed populations of C. cheiranthifolia represent secondary contact by diverged lineages that are reproductively isolated in sympatry, which will be indicated by parallel divergence in floral morphology and microsatellite loci within these populations.
METHODS
Study system, sampling strategy and population size – Camissoniopsis cheiranthifolia is a diploid, bee-pollinated plant endemic to the Pacific coastal dunes of Baja California (México),
California and southern Oregon (USA) (Raven 1969; Samis & Eckert, 2007; Wagner et al. 2007).
Fruits contain ~ 40 tiny seeds (~ 0.15 mg/seed) that lack dispersal structures and likely move with blowing sand. Seeds germinate during winter rains, and established plants are typically annual (Samis & Eckert 2009).
We studied 38 populations across the entire species’ geographic distribution representing the range of variation in flower size, self-compatibility and the mating system, including seven large-flowered, self-incompatible (LF-SI), five large-flowered, self-compatible (LF-SC), 23 small-flowered, self-compatible (SF-SC) and three phenotypically mixed (Mixed-SC) populations (Table A3.1). Populations were classified as LF vs. SF if mean corolla width was >
20 mm and < 20 mm, respectively, based on measurements made by Dart et al. (2012), but LF and SF populations also differ in herkogamy (spatial separation between receptive stigmas and dehiscing anthers within flowers), floral longevity, display size and autofertility. We classified individuals within Mixed-SC populations using the same criterion. Only populations from San
Diego County, California and just south of the USA-México border were SI (Dart et al. 2012).
One SF-SC population from each of three Channel Islands (Santa Rosa, Santa Cruz, San Nicolas
Islands) was sampled in 2003 by Dart et al. (2012), and we sampled the remaining 35 during
2009 and 2010. A population was defined as a group of plants separated from other such groups
43 by at least 1 km, usually much more. The amount of available dune habitat varies widely across the range with some populations distributed across large dune complexes whereas others are restricted to small dune pockets and beaches. Because the size of the area sampled can influence estimates of genetic diversity and differentiation, we randomly sampled 19–42 plants from within a standardized 300–500 m2 plot within each population and preserved the tissue in silica.
Population size is the product of density and population area. We estimated local plant density by counting the reproductive plants (with ≥ 1 bud, flower or fruit) within 2–3 evenly spaced, 2m- wide transects running parallel to the ocean within the sample plot at each of 35 mainland populations, including 31 sampled in 2009, 32 in 2010 and 28 in both years (Table A3.1).
Population area proved difficult to reliably estimate because C. cheiranthifolia are very patchily distributed in the dunes we sampled (Samis & Eckert 2007), hence we used density to estimate population size.
Nuclear and chloroplast microsatellite genotyping – Following guidelines that a sample of 20–
30 individuals assayed for 10–20 polymorphic nuclear microsatellites (nSSR) yields reliable estimates of most population genetic parameters (Hale et al. 2012), we genotyped ~ 25 individuals/population (mean/population = 25.97, total n = 987, Table A3.1) for 13 polymorphic nSSR loci (B11, B34, B59, C19, C49, C67, C89, C110, C133, C135b, E30, E70, E42) following
López-Villalobos et al. (2014). Ten percent of samples were assayed twice to assess repeatability.
Fragment sizes were binned using the MsatAllele package (version 1.04, Alberto 2009) for the R statistical computing environment (used for all analyses unless otherwise stated, version 3.1.2, R
Development Core Team 2015).
We assayed variation at chloroplast microsatellites (cpSSR, Provan et al. 2001) using six conserved primer pairs (Weising & Gardner 1999; Chung & Staub 2003) for ~ 4 individuals from
44 each of 32 populations, of which 30 were also assayed for nSSR variation (total n = 159; Tables
A3.1, A3.2, A3.3). Sequencing of cpSSR PCR products from three individuals with divergent fragment size at each locus confirmed that allele size variation was due to variation in the number of repeats in the targeted microsatellites.
Analyses of population density and within-population genetic diversity (prediction 1) – Of the
28 populations sampled in 2009 and 2010 (TableA3.1), plant density correlated strongly between years (Pearson r = +0.80, n = 28, P < 0.001) and did not differ between years (2009: mean ± 1
2 SE = 0.38 ± 0.06 plants/m , range = 0.02–1.28; 2010: 0.42 ± 0.07, 0.02–2.25; paired t = 0.23, df
= 61, P = 0.81). The amount of among-population variation in density also did not differ between years (Levene’s test F1,61 = 0.006, P = 0.98). We therefore used 2009 data to represent populations sampled in both years.
Two-sample t-tests evaluated whether density differed between phenotypically pure LF and SF populations (excluding the three Mixed-SC populations) and between SI vs. SC LF populations. Levene’s test centered on the median implemented by the leveneTest command in the car R package (version 2.5, Fox & Weisberg 2011) tested whether density was more variable among SF than among LF populations. Using all phenotypically pure populations, we calculated
Pearson correlations to test whether density covaried with two continuous predictors of the mating system: (i) mean corolla width (23 populations, data from Dart & Eckert [2015]), which correlates strongly with the proportion of seeds outcrossed (tm, r = + 0.71, P = 0.001, Dart et al.
2012) and even more strongly with the inbreeding coefficient (r = – 0.79, P < 0.00001, A. López-
Villalobos & C.G. Eckert unpublished data) and (ii) proportion of seeds outcrossed (tm) estimated using progeny array analysis for 13 populations by Dart et al. (2012). Density was log10- transformed to ensure statistical assumptions were met.
45 Within-population diversity was measured as allelic richness (Ar) rarified to smallest sample size (n = 19) and expected heterozygosity (He) estimated using the R package diveRsity
(version 1.9.73, Keenan et al. 2013). We evaluated the joint influence of density and mating system on Ar and He using a linear model with density and an indicator of the mating system and their interaction as potential predictors. We used three indicators of the mating system (as above): categorical flower size (LF, SF), mean corolla width, and tm. Linear models were fit using the lm function in R and the significance of predictors assessed using likelihood ratio tests.
The different combinations of alleles across six chloroplast microsatellites were considered unique haplotypes (Table A3.4). Within-population rarefied haplotypic diversity (Rh) was estimated using the software RAREFAC (Petit et al. 1998).
Large-scale genetic structure across the species’ range (predictions 2–5) – Bayesian clustering of nuclear SSR genotypes implemented by the software INSTRUCT (Gao et al. 2007) inferred the assignment of individuals into genetic clusters. INSTRUCT is similar to STRUCTURE
(Pritchard et al. 2000) but does not assume random mating, making it suitable for inbreeding populations (Gao et al. 2007, 2011). We ran simulations using a model that infers population structure and population selfing rates (based on heterozygote deficiency) from 1–10 clusters using 250,000 burn-in chains and 500,000 MCMC iterations for 10 independent replicates. We increased memory but used default settings otherwise. The Gelman-Rubin statistic was calculated to assure convergence between chains, and the most likely number of clusters was determined using the ΔK method (Evanno et al. 2005; Fig. A3.1). The outputs of the 10 repeated independent runs at the best K were aligned using CLUMPP (version 1.1.1; Jakobsson and
Rosenberg 2007). Analysis of molecular variance (AMOVA) implemented in the poppr R package (version 2.0.2; Kamvar et al. 2014) partitioned genetic variation among INSTRUCT
46 clusters and populations within clusters following Excoffier et al. (1992) with significance of each variance component evaluated using randomization tests in the ade4 R package (version
2.7.2, Dray & Dufour 2007).
Discriminant analysis of principal components (DAPC) using the adegenet R package
(version 1.4-2, Jombart 2008; Jombart et al. 2010) visualized genetic differentiation among individuals placed into each INSTRUCT cluster based on their highest ancestry coefficient
(Table A3.5) and consistency between prior (INSTRUCT) and posterior group assignment
(DAPC) was investigated. The number of PCs retained was evaluated using the cross-validation procedure.
To test prediction 5, we more closely examined the genetic structure of the three Mixed-
SC populations by including them in an INSTRUCT analysis with only island populations, LF-
SC mainland populations south of Pt. Conception and SF-SC populations to the north. Within each mixed population, we classified individuals as LF or SF based on corolla width measured as described above when leaf tissue was collected and tested for differentiation in individual
INSTRUCT ancestry coefficients between LF and SF phenotypes using a linear model with population, floral phenotype and their interaction as potential predictors.
We detected very limited cpSSR variation within populations (Table A3.6); hence large- scale genetic structure was examined by graphing the geographic distribution of distinct haplotypes. Relatedness among haplotypes was examined by constructing a median-joining network using NETWORK (version 4.6.1.2, www.fluxus-engineering.com), where one-bp length change was given a weight of one and the branches in the network correspond to the minimum number of mutational differences between haplotypes (Bandelt et al. 1999).
To test prediction 3, we evaluated changes in genetic diversity associated with deeper genetic subdivision (potentially involving shifts in the mating system) by comparing Ar, He, the
47 rarefied number of private alleles (Pr, estimated using ADZE, Szpiech et al. 2008), and rarefied cpSSR haplotype richness (Rh, estimated using RAREFAC) between INSTRUCT clusters.
Effect of mating system on population genetic differentiation (predictions 1d and 4) – We tested the prediction that selfing increases population genetic differentiation by comparing pairwise differentiation in allele frequencies at nuclear microsatellite loci among INSTRUCT clusters (that varied in mating system) while statistically controlling for the geographic distance among populations and local density, a major component of population census size. Pairwise differentiation was measured as G'ST (Hedrick 2005) estimated using the R package DiveRsity because it is less sensitive than FST (Weir and Cockerham 1984) to the amount of genetic variation within populations, which may vary with mating system (Hedrick 2005; Meirmans and
Hedrick 2011). Geographic distance between populations was estimated as the great circle surface distance calculated using the R package geosphere (version 1.3-11, https://cran.rproject.org/web/packages/dismo/). The density of the populations in each pair was estimated as the harmonic mean density because population divergence via drift is best predicted by harmonic rather than the arithmetic mean population size (Hedrick 2000). Island populations were excluded from this analysis because density was not measured and these populations are separated from each other and from mainland populations primarily by open ocean.
To test prediction 1d, we fit variation in pairwise G'ST to a linear model including
INSTRUCT cluster (reflecting mating system and deeper historical genetic subdivision), pairwise geographic distance, harmonic mean density and their interactions as potential predictors. The range of pairwise geographic distances varied substantially among clusters, so we only included pairwise population comparisons within the range of most pairwise comparisons in other clusters
(≤ 275 km). Because pairwise G'ST values were not statistically independent, we tested for
48 significance of each predictor using randomization (Manly 2006, 106 permutations per analysis).
Significant interactions between cluster and both geographic distance and density were examined further by testing for the effects of distance, and density on G'ST within each cluster. Interactions between distance and density were not analyzed further because they were never close to significant. The direct effects of distance and density on G'ST while statistically controlling the effect of the other variable were displayed graphically by calculating the standardized partial residuals and adding these to mean G'ST.
To test prediction 4, we tested whether mean pairwise G'ST was higher between islands than between mainland populations or between island and mainland populations, while controlling for geographic distance as above.
RESULTS
Population density and within-population genetic diversity – Plant density, our proxy of population size, varied almost 100-fold among the 35 populations surveyed (range = 0.02–1.28 plants/m2; mean ± 1 SE = 0.36 ± 0.064 plants/m2) but did not differ between the 12 LF (0.52 ±
0.066) and 20 SF populations (0.30 ± 0.048; Fig. 1; t = 1.25, df = 30, P = 0.22) or between the 7
SI (0.45 ± 0.15) and 5 SC LF populations (0.63 ± 0.16; t = 0.99, df = 10, P = 0.34). Density did not covary with either continuous predictor of the mating system (corolla width: r = +0.30, n =
23, P = 0.16; proportion seeds outcrossed [tm]: r = +0.19, n = 13, P = 0.53). The variance in density did not differ between LF (SD = 0.37) and SF (0.27) populations (Levene’s test F1,30 =
1.17, P = 0.29) or between SI (0.39) and SC (0.37) LF populations (F1,10 = 0.85, P = 0.38).
We detected 178 alleles across 13 nuclear, microsatellite loci (mean ± 1 SD = 13.69 ±
5.46 alleles/locus; range = 9–29). Both rarefied allelic richness (Ar) and expected heterozygosity
(He), decreased with increased selfing but were not influenced by density nor was there an
49 interaction between mating system and density (Figs. 3.1 and 3.2, Table 3.1). Ar ranged 1.74–
5.15, averaged 3.04 ± 0.15, was higher for 12 LF (3.81 ± 0.11) than 23 SF populations (2.60 ±
0.076) and for 7 LF-SI (4.50 ± 0.26) than 5 LF-SC populations (2.83 ± 0.085; t = 5.21, df = 10, P
= 0.00040). He ranged 0.10–0.64, averaged 0.34 ± 0.024 and was also higher for LF (0.48 ±
0.033) than SF populations (0.27 ± 0.022) and for LF-SI (0.56 ± 0.030) than LF-SC populations
(0.37 ± 0.010; t = 5.00, df = 10, P = 0.00053). Selfing also correlated negatively with the proportion of polymorphic loci, which ranged from 0.54–1.00 (mean ± 1 SE = 0.875 ± 0.031; analysis not shown).
In contrast to the negative correlation between within-population genetic diversity and selfing across all populations, He and Ar were higher for the more highly selfing SF-SC populations on the Channel Islands (He ± 1 SE = 0.443 ± 0.055, Ar = 3.65 ± 0.29) than the more outcrossing LF-SC populations on the adjacent mainland (He = 0.370 ± 0.01, Ar = 2.80 ± 0.08) though only the later was significant (t-tests, df = 6: He P = 0.13, Ar P = 0.016).
Contrary to expectations, Ar varied more among LF (SD = 1.00) than among SF (0.63) populations (Levene's test: F1,33 = 5.57, P = 0.024), and more among LF-SI (0.69) than LF-SC
(0.19) populations (F1,10 = 5.82, P = 0.036). Among-population variation in He did not differ between LF (SD = 0.11) and SF populations (0.10; F1,33 = 1.24, P = 0.27) or between LF-SI
(0.080) and LF-SC populations (0.022; F1,10 = 4.056, P = 0.072).
Across six cpSSR loci we detected 18 length variants with 2–4 alleles per locus (mean ±
1 SD = 3.00 ± 0.33) resulting in 12 distinct haplotypes across the 159 individuals sampled from
32 populations (Table A3.4). One haplotype (HAP7) was detected in 45.2% of the individuals assayed, while the other 11 occurred at much lower frequencies (range = 1.2–13.2%). Haplotype diversity was not higher within LF than SF populations, as we detected only one haplotype in all
50 but three populations all of which were SF-SC, one of which was on the Channel Islands (Fig.
3.4).
Range-wide genetic structure –INSTRUCT analysis revealed five genetic clusters (Figs. 3.3,
A3.1) corresponding to geographically adjacent groups of populations that, as predicted, usually differed in flower size and/or self-incompatibility; from south to north: (1) all six SF-SC populations from Baja California (hereafter SF-SC BAJA), (2) all seven LF-SI populations (LF-
SI), (3) all LF-SC populations plus the three Channel Island SF-SC populations and several individuals from the Mixed-SC populations north of Pt. Conception (LF-SC & I), (4) eight SF-
SC populations from north of Pt. Conception to Wilder Ranch just north of Monterrey Bay, plus the remaining individuals from Mixed-SC populations (SF-SC NPC), (5) all seven SF-SC populations from Point Reyes National Park to the northern range limit (SF-SC NRL). AMOVA partitioned 24.2% of the molecular variance among clusters (P < 0.01), 30.7% (P < 0.01) among populations within clusters and 45.10% (P < 0.01) among individuals within populations.
The first discriminant axis from DAPC analysis clearly separated populations in the SF-
SC BAJA cluster from the other four INSTRUCT clusters, which were relatively undifferentiated
(Fig. 3.3, Fig. A3.2). Discriminant axis 2 separated the other four clusters but not by geographical proximity. Genotypes from LF-SI were, on average, closer to those from SF-SC
NRL populations towards the northern range limit than geographically adjacent LF-SC & I genotypes, while LF-SC & I genotypes were intermediate between the two northerly clusters of
SF-SC genotypes. The two LF clusters were in closer proximity to each other along discriminant axis 3 and together, were closer to genotypes from SF-SC NPC (Fig. 3.2).
Analysis of cpSSR haplotypes yielded a less resolved pattern of range-wide genetic structure (Fig.3.4). The median-joining network was star-like with a very common central
51 haplotype restricted to populations south of Pt. Conception (HAP7, Table A3.4). This haplotype is likely ancestral because, in addition to being the most common haplotype in C. cheiranthifolia, it is a common haplotype in a range-wide survey of its sister taxon C. bistorta and this is the only haplotype shared between species (A. López-Villalobos & C. G. Eckert unpublished data). HAP7 was found in 15 of the 17 populations south of Pt. Conception, including all three Channel Island populations, all but one LF-SC population, all LF-SI populations and all but one Baja SF-SC populations. It was also fixed in all but one of these populations (Channel Island population
CSR1C). One LF-SC population (CDW2C) and one SF-SC population in Baja California
(BBR1C) each contained a different derived haplotype closely related to HAP7. The remaining eight derived haplotypes were found exclusively in SF-SC populations north of Pt. Conception: five in populations from just north of Pt. Conception to San Francisco Bay and three (more distantly related to HAP7) in populations north towards the range limit.
Diversity within genetic clusters – As a group, LF-SI genotypes exhibited higher cluster-wide nSSR diversity than all other population groups. There was little or no overlap in 95% confidence intervals for Ar and He between LF-SI and all four groups of SF population (Baja,
Channel Islands, NPC, NRL) and even mainland LF-SC (Table 3.2). In contrast, neither Ar or He were higher for mainland LF-SC than any group of SF-SC, including the Channel Island populations from within the same genetic cluster. Mainland LF-SC populations also did not contain more private alleles than island populations.
Rarefied cpSSR haplotype diversity (Rh, Fig. 3.4, Table 3.2) was not higher for LF than
SF lineages. Rh for LF-SC genotypes was on par with SF-SC in Baja California and the Channel
Islands and lower than SF-SC north of Pt. Conception. LF-SI genotypes exhibited lowest Rh, as all expressed widespread HAP7.
52
Pairwise population genetic differentiation –Mean pairwise genetic differentiation between populations ranged 0.09–0.99 for G'ST and 0.10–0.89 for FST and averaged (± 1 SE) 0.80 ± 0.003 and 0.51 ± 0.006, respectively. We detected significant isolation-by-distance (IBD) for G'ST across the whole species’ range (Fig. A3.3, Mantel test r = +0.53, Pperm < 0.0001) and for the more restricted sample of population pairs in the comparison among clusters (Fig. 3.5, Table
A3.3; Pperm ~ 0.0019). However, G'ST did not vary among clusters that differed in mating system
(Pperm ~ 0.080) nor did it correlate negatively overall with harmonic mean density (Pperm ~ 0.13).
The effect of both distance and density on G'ST varied among clusters (Pperm ~ 0.010 and Pperm ~
0.050, respectively) but no other interaction was significant (both Pperm > 0.45). Within clusters, we detected significant IBD for mainland LF-SC and SF-SC NPC and near significant IBD for
Baja SF-SC. Pairwise G'ST varied with density only among SF-SC NRL populations but the correlation was unexpectedly positive not negative.
While controlling for geographic distance, pairwise G'ST was higher between island SF-
SC and adjacent mainland LF-SC populations (mean = 0.540) than between populations on either the islands or the mainland (Pperm ~ 0.0018), but island populations were not more differentiated from each other (0.334) than mainland populations were (0.368, Pperm = 0.51).
Phenotypically mixed populations –Range-wide INSTRUCT analysis (Fig. 3.3) as well as a more focused analysis including only LF-SC & I and SF-SC NPC clusters (Fig. 3.6) indicated that Mixed-SC genotypes were admixed between LF-SC & I and SF-SC NPC clusters. The percentage of admixed individuals varied among populations (c2 = 118.1, simulated P < 0.00001) and was zero in all five LF populations and three of seven SF populations, < 5% in another three
SF populations, somewhat higher for the three islands populations (3.4%, 8.6%, 13.8%), and
53 much higher for one SF population (CSA1C = 22.7%) and all mixed populations (CGN1C =
58.1%, CMN1C = 18.2%, CSP1C = 16.0%). The average LF cluster ancestry varied among populations north of Pt. Conception (F9,233 = 19.4, P < 0.00001), and was < 0.10 for all SF populations, somewhat higher for mixed CSP1C (0.125) and significantly higher for mixed
CGN1C (0.455) and CMN1C (0.556, Tukey contrasts P < 0.0001).
Fitting a linear model to individual LF & I ancestry values within mixed populations revealed variation among populations (F2,83 = 13.1, P < 0.0001; Tukey contrasts: CGN1 = CMN1
> CSP) and that LF phenotypes had high higher values than SF phenotypes (mean ± 1 SE: LF =
0.814 ± 0.036, SF = 0.144 ± 0.028; F1,83 = 228.1, P < 0.00001). The difference between phenotypes varied among populations (interaction F2,83 = 3.3, P = 0.042), though LF & I ancestry of LF phenotypes was consistently higher and very rarely overlapped with SF phenotypes (Fig.
3.6).
In contrast, analysis of cpSSR variation (Fig. 3.4) did not reveal admixture between mixed populations and LF-SC populations south of Pt. Conception. We assayed two of three mixed populations (total n = 2 LF and 7 SF individuals). CGN1C contained one derived haplotype and CSP1C two different derived haplotypes, none of which were detected south of Pt.
Conception.
DISCUSSION
Selfing reduces nuclear but not chloroplast genetic diversity within populations –Consistent with prediction 1b, nSSR diversity was lower within predominantly selfing than more outcrossing populations of C. cheiranthifolia, matching similar findings from other species exhibiting mating system variation among populations (Charlesworth & Yang 1998; Liu et al.
1998, 1999; Wright et al. 2002; Mable et al. 2005; Mable & Adam 2007; Ness et al. 2010,
54 Pettengill et al. 2016). However, nuclear diversity was reduced more than expected from the effect of selfing on effective population size (Ne) alone (Pollak 1987). The slope of He regressed on the proportion of progeny selfed among populations of C. cheiranthifolia was –0.79 ± 0.072
(1 SE), significantly lower than the expected 50% reduction (slope = –0.50, Fig. 3.7). Three other studies have also yielded greater than 50% reductions in genetic diversity (reviewed in Busch &
Delph 2012), also suggesting a contribution of other genetic or demographic processes associated with selfing (Barrett et al. 2014).
A transition to selfing caused by selection for reproductive assurance may be initiated by or at least coincident with an episode of small population size (Schoen et al. 1996), and such a bottleneck may reduce lineage-wide diversity and hence the diversity of individual populations
(e.g. Savolainen et al. 2000; Ramos-Onsis et al. 2004; Foxe et al. 2009; Guo et al. 2009;
Pettengill & Moeller 2012). In fact, reproductive assurance was suggested as the primary selective force responsible for transitions to higher selfing in C. cheiranthifolia by Raven (1969) and Linsley et al. (1973). However, our results are mixed: Highly outcrossing LF-SI populations exhibited higher cluster-wide diversity than all three predominantly selfing population clusters.
The same was not true for predominantly outcrossing LF-SC populations, which had estimated diversity on par with the selfing population clusters and even the SF-SC Channel Island populations. The lack of difference between mainland LF-SC and island SF-SC populations is particularly surprizing given that their grouping in the same cluster suggests that island population were likely derived from mainland LF-SC through long-distance dispersal (with possible founder effect). Lineage-wide bottlenecks are not consistently associated with transitions to selfing in C. cheiranthifolia.
Smaller size or demographic fluctuations of individual selfing populations occurring after the transition to selfing may reduce within-population diversity without reducing lineage-wide
55 diversity, but few studies have tested this hypothesis (but see Schoen & Brown 1991; Mable &
Adam 2007; Ness et al. 2010; Willi & Määttänen 2011). Our analyses of density do not suggest that selfing populations are smaller or more temporally variable in size than more outcrossing populations in C. cheiranthifolia. Density did not correlate with any predictor of the mating system or any measure of within-population genetic diversity. While we do not have long-term demographic records for the populations we studied, substituting space for time provided no evidence of greater temporal variation in selfing populations as neither density nor genetic diversity varied more among SF than among LF populations.
Because self-fertilization preserves linkage disequilibrium, neutral diversity may be strongly reduced by selfing if genome-wide purifying selection against deleterious alleles reduces diversity at linked loci (Barrett et al. 2014). However, this requires high levels of selfing (> 90%,
Barrett et al. 2014), higher than typically observed in SF populations of C. cheiranthifolia (mean
= 80% Dart et al. 2012). Diversity may also be reduced by selective sweeps of beneficial mutations, which potentially occur as individual populations become locally adapted. Selfing can additionally hasten this process by exposing beneficial recessive mutations to selection
(Charlesworth 1992). However reciprocal transplant experiments involving populations towards the northern range limit (the NRL cluster) have failed to detect local adaptation of individual populations, even over the scale of hundreds of km (Samis et al. 2016).
Selfing does not increase population differentiation – By reducing Ne and gene flow
(Charlesworth & Pannell 2001; Glémin et al. 2006), selfing should increase genetic differentiation among populations, but this was not the case in C. cheiranthifolia. Statistically controlling for geographic distance, population size and range-wide genetic subdivision, we detected no difference in pairwise G'ST between population clusters that differed in mating
56 system. Instead pairwise differentiation among populations within INSTRUCT clusters was consistently strong (mean G'ST = 0.59), even among outcrossing LF-SI populations (0.53), suggesting very little gene flow among populations of C. cheiranthifolia (see also Samis et al.
2016). We also did not find stronger isolation-by-distance (IBD) among LF than SF populations.
This result is at odds with the very few studies that have found significant IBD among outcrossing but not selfing populations (St Onge et al. 2011; Pettengil et al. 2016). It is possible that in C. cheiranthifolia, sporadic outcrossing detected in SF populations by Dart et al. (2012) and Dart and Eckert (2013) provides gene flow among populations sufficient to generate a pattern of IBD. Yet gene flow must still be low enough to maintain the reduced diversity we observed within SF compared to LF populations.
Genetic subdivision is often but not always associated with mating system shifts – Analyses of nSSR variation detected five parapatric clusters of populations. Considering Raven’s (1969) proposal that C. cheiranthifolia and C. bistorta differentiated from a SI ancestor similar to LF-SI
C. cheiranthifolia in southern California and then spread southward and northward in a stepping stone fashion along the coast, our nSSR results along with data on floral variation (Dart et al.
2012) suggest strong genetic subdivision associated with three transitions to higher selfing: (i)
LF-SI in San Diego Co. and adjacent México SF-SC in Baja California; (ii) LF-SI LF-SC to the north in Orange, Los Angeles, Ventura and Santa Barbara Co.; (iii) LF-SC south of Pt.
Conception SF-SC north of Pt. Conception. In contrast, we did not detect genetic divergence between mainland LF-SC and Channel Island SF-SC populations. We also detected one genetic subdivision that was not associated with a change in the mating system: between SF-SC populations south vs. north of San Francisco Bay, an important phylogeographic break in the genetic structure of a few marine (Dawson et al. 2001; Kelly & Palumbi 2010) and other
57 terrestrial taxa (Matocq 2002; Chatzimanolis & Caterino 2008; Kuchta et al. 2009; Reilly et al.
2015) including the co-occurring dune plant Abronia umbellata (Doubleday et al. 2013; S.U.
Greer, S.I. Wright & C.G. Eckert unpublished data). Our cpSSR results suggest differentiation associated with the LF SF shift across Pt. Conception and also differentiation of SF-SC populations at San Francisco Bay, but not subdivision associated with shifts south of Pt.
Conception (LF-SI LF-SC or LF-SI SF-SC in Baja). Below we discuss each of the mating system transitions in C. cheiranthifolia in light of our genetic results.
Was selection for reproductive assurance involved in the shift to higher selfing across Pt.
Conception? – Raven (1969) and Linsley et al. (1973) hypothesized that the colonization of habitat north of Pt. Conception was associated with selection for reproductive assurance because these dunes are exposed to stronger winds and heavier fog that impede foraging by bees that outcross C. cheiranthifolia. Consistent with this, Dart & Eckert (2015) showed that seed set in
LF-SC populations south of Pt. Conception is pollen limited and that selfing does alleviate pollen limitation in SF-SC populations. Pollen limitation in outcrossing populations may have set the stage for selection of reproductive assurance during colonization of less hospitable northern dunes. Reduced population size during selection for reproductive assurance may be expected to reduce the genetic diversity of derived SF-SC lineage north of Pt. Conception (Schoen et al.
1996; Busch & Delph 2012; but see Barrett et al. 2014) but diversity was not lower in SF-SC than LF-SC clusters. Although gene flow from outcrossing populations after a shift to selfing may erase the genetic signature of a population bottleneck (Barrett et al. 2014), strong divergence between LF-SC and SF-SC population at nSSR and cpSSR loci suggests that gene flow across Pt. Conception is very low. While a definitive experimental test of the reproductive
58 assurance hypothesis for C cheiranthifolia has yet to be performed, the available ecological and genetic evidence is equivocal at best.
Was higher selfing at the southern range limit spurred by long-distance dispersal and adaptation to drier habitat? – Pronounced subdivision in nSSR allele frequencies occurred between SF-SC populations in Baja California and all other C. cheiranthifolia populations. Given their proximity along the DAPC axes and the observation that they shared the dominant, likely ancestral cpDNA haplotype, SF-SC BAJA populations probably diverged from LF-SI populations. Strong divergence in allele frequencies plus reduced diversity at nSSR loci and few private alleles or cpSSR haplotypes are consistent with relatively recent divergence of SF-SC populations through long-distance dispersal. SF-SC populations of C. cheiranthifolia in Baja
California are largely restricted to the San Quintin Valley, an area of relatively mesic habitat surrounded by desert (Vanderplank 2011), which isolates them from LF-SI populations north of
Punta Banda (Fig. 3.3). Subsequent selection for rapid reproduction under reduced and more seasonal precipitation characteristic of this region (Dettinger et al. 1998; Caso et al. 2007) may account for the diminutive stature, early flowering and higher selfing (Guerrant 1989) of SF-SC
BAJA populations compared to LF populations further north (A. López-Villalobos & C.G.
Eckert unpublished data).
Why is the loss of self-incompatibility associated with differentiation in southern California? –
Although differentiation between LF-SI in San Diego Co. and somewhat more selfing LF-SC populations to the north supports our prediction 2, the high genetic differentiation is surprising given their close parapatry and lack of any obvious barrier to gene flow. Although SC populations outcross less, there is very little difference in floral morphology (Dart et al. 2012) or
59 pollinator community (Linsley et al. 1973) that might lead to pre-zygotic isolation. Ecological variation across this region is mild (Cooper 1967; Axelrod 1978; Samis & Eckert 2007) except for a southward decline in rainfall along the Mediterranean aridity gradient that characterizes coastal California (Dallman 1998). The SI populations we studied did experience lower average rainfall than SC populations to the north (LF-SI: mean 258 mm/yr, range among LF-SI populations =220 – 296 mm/yr; LF-SC: mean = 371, range = 294 – 429; http://en.climate- data.org/info/sources/; http://www.wrcc.dri.edu/) and this may have led to adaptive differentiation in traits affecting gene flow (e.g. Kay 2006; Martin & Willis 2007; Lowry et al.
2008). For instance, SI populations initiate flowering earlier than SC populations in a common greenhouse environment. However, flowering phenologies broadly overlap in the field, providing plenty of opportunity for gene flow via pollen (A. López-Villalobos & C.G. Eckert unpublished data). Partial pre-zygotic isolation often arises between SI and derived SC lineages (“unilateral incompatibility”, Lewis & Crowe 1958) where SI genotypes can only cross as pollen donors to
SC genotypes. However preliminary investigation in C. cheiranthifolia did not detect post- pollination crossing barriers (A. López-Villalobos & C.G. Eckert unpublished data). In the absence of any compelling evidence for reproductive isolation between LF-SI and LF-SC populations, we are investigating the possibility that hybridization between LF-SI and the SI sister C. bistorta (Raven 1969) may contribute to the genetic distinctness of SI C. cheiranthifolia
(Chapter 4).
Why no divergence associated with the transition to selfing on the Channel Islands? – The association between island colonization and the evolution of selfing has long been pinned to selection for reproductive assurance during the initial stages of island population establishment after long-distance dispersal (Baker 1955; Barrett 1996; Pannell et al. 2015). It is these cases
60 where a shift to selfing should be strongly associated with reduced genetic diversity and differentiation from mainland populations. Such is not the case in C. cheiranthifolia. Although
Channel Island populations are highly selfing (Dart et al. 2012), within-population and aggregate genetic diversity was unexpectedly high, and differentiation from mainland LF-SC populations was weak. Moreover, distance-dependent differentiation among island populations was not higher than among adjacent mainland populations (see also Su et al. 2010). Although our results generally contrast with work on island populations (Frankham 1997; Franks 2010), some studies have either failed to detect reduced diversity (Su et al. 2010; Désamoré et al. 2012; Garcia-
Verdugo et al. 2013) or found even higher diversity in island than mainland populations of the same species (Chiang et al. 2006; Fernández-Mazuecos & Vargas 2011; Désamoré et al. 2012;
Caterino et al. 2015).
Ongoing gene flow with mainland populations might have increased diversity and reduced differentiation of Channel Island C. cheiranthifolia populations. Although there is no evidence for quaternary land bridges between these islands and adjacent mainland, some of the
Channel Islands might have been joined during the Last Glacial Maximum when the sea levels were > 100 m lower and separation from the mainland might have narrowed to ~ 6 km (Junger &
Johnson 1980; Vedder & Howell 1980). Human-mediated gene flow is also likely, as humans have strongly impacted the landscape and biota of the Channel Islands over the last 13,000 yrs, and have transported plants and animals to and between islands, especially over the last 200 yrs
(Wenner & Johnson 1980; Junak et al. 1995; Hofman et al. 2015). Although there are no records of humans moving C. cheiranthifolia specifically, Raven (1969) and subsequent population surveys (K. E. Samis & C. G. Eckert unpublished data) discovered LF phenotypes on San
Nicolas Island that have persisted for > 100 yrs and could potentially have originated via long- distance dispersal from the mainland (Raven 1969, p. 259).
61
Reproductive isolation in phenotypically mixed populations – Divergence in mating system often occurs in parapatry (Busch 2005; Runquist & Moeller 2014), setting the stage for local adaptation to different environments and consequently ecological isolation between nascent lineages (Oneal et al. 2014; Runquist & Moeller 2014). The long-term maintenance of divergence may depend upon how quickly reproductive isolation evolves and the extent to which it prevents introgression upon secondary contact. Phenotypically mixed populations of C. cheiranthifolia provide an opportunity to infer the extent of reproductive isolation. The high admixture between INSTRUCT clusters based on nSSR variation in these mixed populations plus the close association between floral morphology and cluster ancestry suggest that these mixed populations represent secondary contact between lineages that likely diverged on opposite sides of Pt. Conception. How long these lineages have been in secondary contact is unknown.
The observation that none of the individuals assayed for cpSSR variation in these populations possessed the dominant haplotype found south of Pt. Conception suggests that there has been time for introgression of the LF lineage into the dominant SF lineage. Moreover, that large- flowered phenotypes were observed in the Morro Bay region during surveys in the early 1960s by Raven (1969) indicate that at least some of these populations have been remained phenotypically mixed for ≥ 50 generations suggesting barrier(s) to further introgression.
The mechanism of reproductive isolation between diverged phenotypes in mixed populations is not obvious. Flowering phenologies overlap broadly within populations (Dart and
Eckert 2015) and although they are more highly selfing than LF phenotypes, SF phenotypes outcross 10-20% of seed and sporadically much more (Dart et al. 2012; Dart & Eckert 2013) suggesting plenty of opportunity for mating between morphs. Preliminary crosses between plants from phenotypically pure LF and SF populations from either side Pt. Conception yielded SC,
62 morphologically intermediate and fully fertile F1s with normal meiosis (Raven 1969). Pollen viability assayed by cotton blue staining was lower among F1 with an SF maternal parent (mean stainability = 65%, n = 5 F1 progeny) than an LF maternal parent (87%, n = 3; analysis of
Raven’s data by 2-sample t-test: t = 3.36, df = 6, P = 0.015), suggesting statistically significant but mild post-zygotic barriers to introgression. There are also no clues as to the ecological factors that allow persistence of outcrossing LF phenotypes in these otherwise selfing SF populations.
Variation among populations in traits affecting the mating system is commonly reported within flowering plant species (Jain 1976), but the maintenance within populations of distinct morphs diverged in mating system is unlikely in theory and rarely reported in nature (Goodwillie et al.
2005). These mixed populations of C. cheiranthifolia offer unusual opportunities to investigate the evolutionary causes and consequences of mating system polymorphism.
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77 Table 3.1. Effect of mating system variation and plant density on two measures of genetic diversity at 13 microsatellite loci, rarefied allelic richness (Ar) and expected heterozygosity (He), among populations of Camissoniopsis cheiranthifolia across the species’ geographic range. Significant results are shown in bold
Response Mating system indicator Mating system log10(Density) M x D
Ar Flower size (LF/SF) F1, 29 = 25.79, P < 0.0001 F1, 29 = 0.17, P = 0.68 F1, 28 = 0.02, P = 0.88
Corolla width F1, 18 = 10.44, P = 0.0046 F1, 18 = 2.14, P = 0.16 F1, 17 = 3.04, P = 0.099
Ppn outcrossed (tm) F1, 8 = 8.25, P = 0.021 F1, 8 = 1.49, P = 0.26 F1, 7 = 0.39, P = 0.55
He Flower size (LF/SF) F1, 29 = 43.29, P < 0.0001 F1, 29 = 0.053, P = 0.82 F1, 28 = 0.018, P = 0.89
Corolla width F1, 18 = 14.73, P = 0.0012 F1, 18 = 0.47, P = 0.50 F 1,17 = 1.50, P = 0.24
Ppn outcrossed (tm) F1, 8 = 43.13, P = 0.00017 F1,8 = 1.19, P = 0.31 F1, 7 = 0.43, P = 0.53
Three indicators of the mating system were used: categorical flower size (measured for 32 populations), mean corolla width (21 populations) and the proportion of seeds outcrossed estimated using progeny array analysis (tm, measured for 11 populations by Dart et al. 2012). Cells include F ratios and P values from likelihood ratio tests. Phenotypically mixed populations were not included in this analysis.
78
Table 3.2. Lineage-wide genetic diversity parameters for each of the INSTRUCT clusters detected across the geographic range of
Camissoniopsis cheiranthifolia
INSTRUCT Sub grouping n n indiv Ar He Pr Rh clustera pops SF-SC BAJA 6 160 5.82 (4.81 – 6.82) 0.45 (0.32 – 0.58) 0.45 (0.11– 0.79) 1.00 LF-SI 7 179 9.69 (7.94 – 11.94) 0.74 (0.70– 0.78) 1.72 (1.24 – 2.20) 0.00 LF-SC & I 8 221 7.49 (5.34 – 9.64) 0.57 (0.46 – 0.67) 0.68 (-0.02 – 1.37) 1.80
Mainland LF 5 128 5.44 (4.04 – 6.83) 0.50 (0.40 – 0.60) 1.37 (0.82 – 1.92) 1.00 Island SF 3 93 6.43 (4.64 – 8.23) 0.53 (0.42 – 0.65) 2.37 (1.23 – 3.51) 0.98 SF-SC NPC SF only 7 154 6.29 (4.58 – 8.00) 0.45 (0.30 – 0.59) 0.27 (-0.04 – 0.58) 3.76 SF-SC NRL 7 184 6.55 (5.13 – 7.96) 0.46 (0.32 – 0.60) 0.40 (0.15 – 0.64) 1.99 aCluster names are as in Fig. 3.3. n pops = number of populations within each cluster, n indiv = number of individual genotypes assigned to that cluster, Ar = rarefied allelic richness, He = expected heterozygosity, Pr = rarefied number of private alleles per locus, and Rh = rarefied cpSSR haplotype richness. 95% confidence limits (if estimable) are in brackets. The LF-SC & I cluster contains SF
(island) and LF (mainland) populations; therefore parameters were also estimated for SF and LF populations separately, and Pr and Rh are for a comparison of these two groups specifically. Number of plants assayed for cpSSR: SF-SC BAJA = 23, LF-SI = 20, LF-SC &
I = 42, SF-SC NPC = 37, SF-SC NRL = 37. For SF and LF individuals within the LF-SC & I cluster: SF = 25 and LF = 17.
Phenotypically mixed populations were not included in this analysis.
79 Table 3.3. Association between pairwise genetic differentiation among populations (G'ST) within the five INSTRUCT clusters and geographic distance between populations and harmonic mean density of the two populations in Camissoniopsis cheiranthifolia
Mean ± 1SD Partial standardized β (Pperm)
Cluster Pairwise G'ST Pairwise distance Harmonic mean Distance Density (km) density (plants/m2)
SF-SC Baja 0.530 ± 0.171 52.30 ± 31.10 0.219 ± 0.142 +0.668 (0.068) +0.353 (0.31)
LF-SI 0.526 ± 0.155 109.42 ± 65.59 0.293 ± 0.258 –0.048 (0.83) –0.421 (0.070)
LF-SC (mainland) 0.368 ± 0.158 60.94 ± 41.88 0.510 ± 0.258 +0.778 (0.019) –0.113 (0.67)
SF-SC NPC 0.510 ± 0.096 125.82 ± 92.05 0.227 ± 0.131 +0.564 (0.012) –0.064 (0.75)
SF-SC NRL 0.473 ± 0.154 135.41 ± 65.39 0.177 ± 0.044 +0.646 (0.020) +0.484 (0.044)
Cluster means (± 1 SD) for the response variable (G'ST) and the two predictors are presented along with partial standardized regression coefficients (β) from a multiple regression model with both predictors. Significance (Pperm) was determined by permutation.
Phenotypically mixed populations were not included in this analysis.
80 0 LF−SI ● LF−SC ● SF−SC Mixed−SC 0.5 A 0 )
2 ● ● 0.0 ● ● ● ● ● ● ●● −0.5 ● ● ● ● ● ● ● ● ● ● ● ● ● −1.0 ● (Density, plants/m (Density, ● 10 −1.5 log
−2.0
B 0.6 ●
● ● ● 0.4 ● e ●● ● ●● H ● ● ●● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ●
0.0
C 5.0
● 4.0 ●
r ● ● A ● ● 3.0 ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● 2.0 ●● ● ● ● ●
1.0 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Latitude (°N)
Fig 3.1. Geographical variation in (A) plant density, (B) expected heterozygosity He, and (C) rarefied number of alleles per locus (Ar) among 38 populations of Camissoniopsis cheiranthifolia across the species’ geographic range. Closed triangles are large-flowered self-incompatible populations (LF-SI), closed circles large-flowered, self-compatible populations (LF-SC), open circles small-flowered, self-compatible populations (SF-SC) closed squares self-compatible, phenotypically mixed populations (Mixed-SC). The dashed vertical lines indicate the location of Punta Banda (31.6°N latitude) and Point Conception (34.5°) where the transitions from LF to SF occur. The SF-SC populations within these boundaries are on three Channel Islands (San Nicolas, Santa Cruz, Santa Rosa)
81 0 LF−SI ● LF−SC ● SF−SC 0.7 A B C 0.6 0 ● 0.5 ●● ● 0.4 ● ● ● e ● ● ● ● ● ● ● ● ● ● ● ● ● ●● H 0.3 ● ● ● ● ●● ● ●● ● ● ●●● ● ●● ● ● ●● ● 0.2 ●● ● ● ● ● ● ● 0.1 ● r = +0.58 ● r = +0.85 ● ● r = +0.21 P = 0.003 P = 0.0005 P = 0.127 0.0 n = 24 n = 12 n = 32 D E F 5.0
● 4.0 ● ● r ● ● A ● ● ● ● ● ● 3.0 ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● 2.0 ● r = +0.46 ● ●● ● r = +0.50 ● ● r = +0.16 P = 0.023 P = 0.052 P = 0.388 1.0 n = 24 n = 12 n = 32 10 15 20 25 30 35 0 0.2 0.4 0.6 0.8 1−2.0 −1.5 −1.0 −0.5 0.0 0.5 2 Corolla width (mm) Proportion seeds outcrossed log10(Density, plants/m )
Fig 3.2. Correlations among within-population genetic diversity at 13 nSSR loci, population density and two indicators of the mating system: population mean corolla width and the proportion of seeds outcrossed (tm, data from Dart et al. 2012) among populations of Camissoniopsis cheiranthifolia. Within-population genetic diversity was measured as expected heterozygosity (He, panels A, B, C) and rarefied allelic richness (Ar, panels D, E, F). Populations characterized by different flower size and self-incompatibility are distinguished by different symbols as in Fig. 1. Pearson correlation coefficients (r) with associated P-values and sample sizes (n) are in the bottom right corner of each panel. The three phenotypically mixed populations (Mixed-SC) and the three Channel Island populations are not included in this analysis, as density was not measured for thes
82
Fig 3.3. Range-wide genetic structure at 13 nSSR loci in Camissoniopsis cheiranthifolia inferred by INSTRUCT Bayesian clustering (panels A, B) and discriminant analysis of principal components DAPC summarizing differentiation in allele frequencies (C). (A) INSTRUCT plot of 987 individuals sampled from 38 populations. Populations are ordered by latitude (bottom to top = south to north) along the vertical axis and are divided by thin horizontal lines. Population information is given in table A3.2. The five colors represent estimated ancestry proportion to each of K = 5 genetic clusters. (B) Geographic variation in INSTRUCT cluster ancestry. Populations are represented by pie charts showing the mean ancestry in each of the five clusters as in A. Clusters closely coincide with population groups distinguished by flower size, self-incompatibility and geographic location: SF-SC populations in Baja California, Mexico (yellow), LF-SI populations in San Diego County, California (dark blue), LF- SC populations from southern California plus SF-SC populations from the Channel Islands (red), SF-SC populations from North of Point Conception (NPC) to Point Reyes National Park (light blue), and SF-SC populations from Point Reyes to the northern range limit (NRL) in southern Oregon (green). Phenotypically mixed populations are plotted off the coast with arrows indicating their mainland locations. (C) Relative genetic distance estimated using DAPC between individual genotypes in the five INSTRUCT clusters (indicated by colors as in A and B). Each point represents an individual multilocus genotype on the first two discriminant axes (DAs), and circles around each cluster are inertia ellipses enclosing 67% of the genotypes within each cluster. The inset shows the same genotypes plotted on the first and third DAs. See supporting information – Appendix (Fig. A3.2) for additional analyses.
83 A B HAP1 Oregon
N HAP8 HAP11
HAP7 HAP10 California HAP5 mv4 HAP6
HAP9 HAP4
HAP12 mv1
Point Conception
Channel Islands
HAP2 Punta Banda San Martin Island Mexico
scale appro HAP3
Fig 3.4. Geographic distribution of (A) and genetic distance among (B) 12 cpSSR haplotypes detected among 159 individuals from 32 populations in Camissoniopsis cheiranthifolia. (A) Populations are represented by pie charts showing the frequency of haplotypes within each indicated by different colors. (B) Median-joining network summarizing the genetic distance among haplotypes, with the size of each circle proportional to frequency of the haplotype (Table A.34). Small filled black circles labeled as 'mv' indicate undetected haplotypes. Hatch marks on the lines joining haplotypes indicate one base pair change in microsatellite length.
84 0 ● SF−SC BAJA LF−SI LF−SC ● SF−SC NPC ● SF−SC NRL 1.0
T ● S 0.8 0 ● ● G' ● ● ● ● ● e ● ● ● ● s ● ● ● i ● ● ● 0.6 ● ● ● ● ● ● ● w ● ● ● r ● ● ● ● ● i ● ●●● ●● ● ● ● ● ● ●● ● ●● a ● ● ● ● ● ●● ● ● ● ● ● ● p ●● ● ● ● ● ●● ●● ● ● ● ●
d ● 0.4 ● ● ● ●● ●
e ● t ● ● ●
s ● ● ● u ● j
d 0.2 ●
A ●
0.0 0 50 100 150 200 250 300 −1.50 −1.25 −1.00 −0.75 −0.50 −0.25 0.00 Pairwise distance (km) log10(Harmonic mean density)
Fig 3.5. Regression of genetic distance (measured as G'ST) on geographic distance between pairs of populations (left panel) and on log-transformed harmonic mean density of population pairs (right panel) within each INSTRUCT cluster. The y-axis values are standardized partial residuals from a multiple regression involving both predictors added to mean G'ST. INSTRUCT clusters are color coded as in Fig. 3.3. Pairwise comparisons within the two most northern clusters SF-SC NPC and NRL where restricted to populations within 275 km of each other, a similar range of distances found between populations from the other three clusters. Solid lines indicate significant partial regressions, dashed lines non-significant. The LF-SC cluster includes only mainland populations, and phenotypically mixed populations were not included in SF-SC NPC.
85
Fig 3.6. Genetic structure among Camissoniopsis cheiranthifolia populations around Pt. Conception (A) and the relation between floral phenotypes and the cluster ancestry within the three phenotypically mixed populations (B). The upper panel shows an INSTRUCT analysis involving the 18 populations from the parapatric LF-SC & I and SF-SC NPC clusters (see Fig. 3.3). The cluster depicted in turquoise is referred to as “large-flowered” and the cluster in red as “small-flowered”. The lower panel shows the differentiation in large-flowered cluster ancestry between large- and small-flowered phenotypes within mixed populations. r2 values are from a linear model with floral phenotype as a predictor of ancestry.
86
Fig 3.7. Observed (dashed line) and expected (solid line) decline in within-population genetic diversity (He) with increased self-fertilization (s) for 12 populations of Camissoniopsis cheiranthifolia. The expected decline includes only the effect of selfing on effective population size (slope = –0.5). The observed line is a linear regression forced through an intercept of 1 (r2 = 0.92, F1,11 = 119.9, P < 0.00001) with slope (± 1 SE) = –0.79 ± 0.072, which is significantly lower than –0.5 (1-sample t-test: t = –4.01, df = 11, P = 0.0020).
87 Chapter 4. Does hybridization contribute to range-wide population genetic structure in a coastal dune plant?
ABSTRACT
Premise of the study: Interspecific hybridization can alter the genetic structure of a species’ geographic range, especially when there is geographic variation in the degree of sympatry/parapatry with close relatives. However this hypothesis has rarely been evaluated. The
Pacific coastal dune endemic C. cheiranthifolia exhibits strong genetic structure across its distribution with some genetic subdivisions concordant with shifts in the mating system from outcrossing to higher levels of self-fertilization, but others not. For instance, large-flowered, self- incompatible (LF-SI) populations are genetically differentiated from closely parapatric, large- flowered, self-compatible (LF-SC) populations despite little difference in mating system and habitat affinities. The pattern of geographic variation in growth form and leaf traits suggests that
LF-SI C. cheiranthifolia may have diverged from LF-SC populations via hybridization with the predominantly inland SI sister species C. bistorta.
Methods: We combined an analysis of variation in spatial proximity between the species based on 1506 herbarium specimens, with population analyses of genetic structure based on 1209 genotypes at 12 nuclear microsatellite loci from 32 C. cheiranthifolia and 18 C. bistorta populations covering their entire geographic ranges.
Key results: Consistent with our prediction, LF-SI C. cheiranthifolia is more closely parapatric with C. bistorta than any other group of C. cheiranthfolia populations, including LF-SC populations. Closer parapatry was associated with extensive genetic continuity between LF-SI C. cheiranthifolia and C. bistorta. In fact, the SI populations of both species were indistinguishable
88 genetically within the broader context of geographic variation in C. cheiranthifolia, suggesting that C. bistorta could be considered an inland ecotype of C. cheiranthifolia.
INTRODUCTION
The distribution of genetic diversity across species’ geographic ranges should be dictated by the interplay of genetic drift, gene flow and selection, which in turn should be influenced by how the pattern of environmental variation influences the fitness of individuals, population demographic processes and the spatial distribution of populations. In theory, when a nascent species colonizes a geographical gradient of environmental conditions, it should become most abundant where individual survival, reproduction and hence population growth are highest, and increasingly less abundant as conditions depart from this optimum (Hengeveld and Haeck, 1982; Brown, 1984).
As a result, a species is expected to achieve the highest abundance at the center of its range, with populations becoming progressively smaller and more spatially isolated towards the range limits
(Brussard, 1984; Lawton, 1993; Vucetich and Waite, 2003). This Abundant Center Model
(“ACM”) leads to testable predictions concerning the genetic structure of species ranges (Soulé,
1973; Antonovics, 1976; Brussard, 1984; Hoffman and Blows, 1994; Lesica and Allendorf,
1995; Barton, 2001; Eckert et al., 2008). At the most basic level, the ACM predicts that effective population size (Ne) and gene flow decline from range center to range edge. As a result, peripheral populations should contain less diversity and be more differentiated from one another than geographically central populations (Eckert et al., 2008). Peripheral populations might also experience more frequent bottlenecks and founder events associated with more vigorous extinction and recolonization dynamics, further reducing diversity and increasing differentiation.
Ultimately, feedbacks between population demography and genetic structure may restrict
89 adaptation to marginal conditions thereby yielding the range limit (Hoffman and Blows, 1994;
Hoffman and Parson, 1997; Gaston, 2003; Blows and Hoffman, 2005).
Empirical studies often find lower diversity and increased differentiation among peripheral compared to central populations (Eckert et al., 2008), yet the ACM – upon which these predictions are based – is less frequently supported (Sagarin and Gaines, 2002; Sexton et al.,
2009), suggesting a mismatch between demographic causes and genetic consequences. However, a clear connection between pattern and process is undermined by a relative scarcity of studies that sample species across their entire geographic range and incorporate historical and contemporary variation in other factors known to strongly affect population genetic variation
(Eckert et al., 2008). For instance, the mating system and dispersal capacity strongly influence genetic structure, especially in plants (Jarne, 1995; Hamrick and Godt, 1996; Duminil et al.,
2007) and may often vary across geographic distributions (Hargreaves and Eckert 2014), yet these important life history traits are rarely measured in conjunction with geographic surveys of genetic variation (Chapter 3). Hybridization with closely-related sympatric or parapatric taxa may also vary across species’ distributions, potentially influencing genetic diversity and possibly the adaptive potential of populations at the range edge (Pfennig et al., 2016); however hybridization is rarely considered in studies investigating the genetic structure of species ranges
(Arnold, 1997).
Closely related species may exhibit range overlap in particular regions of their geographic ranges, often towards range edges (Hewitt, 1996). Geographically peripheral zones of hybridization (Barton and Hewitt, 1985) may exacerbate or oppose the expected reduction in genetic diversity at range edges (Rhymer and Simberloff, 1996; Arnold, 1997; Vilà et al., 2000;
Pfennig et al., 2016). For instance, hybridization might result in "gene swamping" thereby thwarting local adaptation of edge populations (Abbott, 1992; Ellstrand and Elam, 1993; Rhymer
90 and Simberloff, 1996; Kirkpatrick and Barton, 1997). Alternatively, hybridization can introduce genetic variation into edge populations leading to genetic rescue and perhaps increased adaptive potential in the face of small population size and greater spatial isolation (Arnold, 1997; Ellstrand and Schierenbeck, 2000; Whitney et al., 2010; Hedrick, 2013; Carlson et al., 2014; Pfennig et al.,
2016). However, despite an extensive body of literature on hybridization, hybrid zones, and speciation via hybridization (reviewed in: Rieseberg and Carney, 1998; Seehausen, 2004; Mallet,
2007; Soltis and Soltis, 2009; Yakimowski and Rieseberg, 2014), few empirical studies have evaluated the contributions of hybridization to range-wide patterns of genetic diversity and differentiation by quantifying range-wide genetic structure in closely parapatric or sympatric congeners (Londo and Schaal, 2007; Gross et al., 2007; Ortego et al., 2014).
In plants, the mating system might influence the permeability of reproductive isolation between species and hence the rate of hybridization (Coyne and Orr 2004). As a result, geographic variation in the mating system might influence the geographic pattern of hybridization. There are many instances, for example, of plant species in which peripheral populations have evolved higher levels of self-fertilization (Jain, 1976; Lloyd, 1980; Eckert et al.,
2006) and, consequently, restricted opportunities for hybridization. Reduced flower size, pollen production and reliance on animal pollen vectors typical of selfing populations might reduce interspecific pollen movement and fertilization. Evolutionary shifts in the mating system may also lead to post-pollination crossing barriers between species. For example, the loss of self- incompatibility (SI) is sometimes associated with the inability of derived self-compatible (SC) populations to cross as males with SI individuals (Lewis and Crowe, 1958; de Nettancourt, 2001).
All this suggests that hybridization will be most common in regions where populations of congeners are predominantly outcrossing and even where outcrossing is promoted by similar floral mechanisms. As argued above, interspecific fertilization is more likely when both taxa are
91 SI than when one or the other is SC. Moreover, SI may facilitate introgression when there is an asymmetry in abundance between taxa (Brennan et al., 2002). Small population size may lead to a loss of S-allele diversity and consequent cross-incompatibility (Reinartz and Les, 1994).
Because alleles at the self-incompatibility locus often predate species divergence, they might be propelled across species barriers after even infrequent interspecific hybridization (De Nettancourt,
2001) because hybrid individuals bearing novel S-alleles may enjoy disproportionately high male fitness in small populations. For example, lower genetic diversity and seed set has been observed in small populations of Rorippa spp. (Brassicaceae), presumably with low S-allele genotype diversity, compared to populations in contact zones with SI congeners, possibly suggesting that hybridization restores fertility by increasing S-allele diversity (Bleeker, 2007). However, studies that document geographic variation in the mating system and its association with interspecific hybridization are rare (Sweigart and Willis, 2003; Goodwillie and Ness, 2013; Brandvain et al.,
2014; Brys et al., 2014).
Camissoniopsis cheiranthifolia (Hornemann ex Sprengel) W.L. Wagner & Hoch
(Onagraceae) is a short-lived perennial, near-annual plant endemic to the Pacific coastal dunes of
North America. Across its linear and near one-dimensional geographic range, the species meets some of the demographic assumptions of the ACM, such as increased spatial distance between neighboring occupied sites towards the southern range limit (but not the northern) and greater seed production in central vs. peripheral populations (see Samis and Eckert, 2007), but it also exhibits large-scale geographic variation in genetic structure associated with repeated mating system shifts (Lopez-Villalobos and Eckert submitted). Analysis of nuclear microsatellite polymorphisms revealed five parapatric clusters of genotypes, three of which are associated with transitions to higher selfing and associated changes in floral morphology and self- incompatibility: (i) Large-flowered and self-incompatible populations (“LF-SI”) in San Diego
92 Co. and adjacent México differentiated from small-flowered and self-compatible populations
(“SF-SC”) in Baja California; (ii) Large-flowered and self-compatible populations (“LF-SC”) south of Pt. Conception clustered separately from SF-SC north of Pt. Conception. We have also detected instances of strong genetic differentiation, despite a continuous distribution of populations and little difference in mating system or floral morphology; (iii) LF-SI were highly differentiated from LF-SC populations in Orange, Los Angeles, Ventura and Santa Barbara
Counties. Given that most of the genetic subdivisions across the range of C. cheiranthifolia are associated spatially with shifts in the mating system, the minor change in mating system and very close parapatry between LF-SC and LF-SI populations, as well as a lack of an expected pattern of isolation-by-distance among LF-SI populations lead us to hypothesize that hybridization with a sister taxon might be contributing to the genetic differentiation in this region.
Closely related Camissoniopsis cheiranthifolia and C. bistorta (Torr. & A. Gray) W.L.
Wagner & Hoch (Onagraceae) exhibit parapatric distributions. Camissoniopsis cheiranthifolia is endemic to Pacific coastal dunes of western North America, from northern Baja California,
Mexico to southern Oregon, United States, whereas C. bistorta is distributed inland, parallel to the southern half of C. cheiranthifolia’s range; from south of Pt. Conception California to northern Baja California; however, the degree of parapatry between them has never been quantified. Camissoniopsis bistorta flowers from March to June and completely overlaps with the longer flowering period of C. cheiranthifolia (January to August), suggesting little phenological isolation. Both taxa may share bee pollinator species from Andrena, Diandrena and
Onagrandrena (Raven, 1969 pp. 261, 270; Linsley et al., 1973). Preliminary crosses among few individuals from a limited number of SI populations of both species (~ 10 from each of 3 to 6 populations of each species) also suggested that seeds are readily produced in both directions
(Raven 1969; Lopez-Villalobos and Eckert unpublished data). Raven (1969) observed
93 phenotypic intermediates (“hybrids” Raven, 1969 pp. 263) that were somewhat perennial but otherwise resembled C. bistorta on the sandy riverbeds near San Diego, a habitat that seems intermediate between the coastal dunes inhabited by C. cheiranthifolia and the firmer inland soils where C. bistorta occurs. Camissoniopsis bistorta herbarium records (~0.05%), collected from
San Diego and northern Baja California, Mexico, have been annotated as C. cheiranthifolia x C. bistorta hybrids based on Raven’s observations and later taxonomic revisions of specimens
(Table A4.2), suggesting weak crossing barriers between species.
Hybridization with C. bistorta might have contributed to genetic differentiation between LF-
SI and closely parapatric LF-SC populations of C. cheiranthifolia. Moreover, hybridization between SI populations of the two taxa may have been facilitated by introgression of self- incompatibility alleles because hybrid individuals bearing novel S alleles would enjoy enhanced male fitness in situations of low S allele diversity in recipient populations. This would account for preferential hybridization between SI populations and explain consequent differentiation between LF-SI and LF-SC C. cheiranthifolia. In this study we investigated the hypothesis that hybridization between C. cheiranthifolia and C. bistorta has contributed to the genetic differentiation between closely parapatric LF-SI and LF-SC populations of C. cheiranthifolia.
Here we test the following predictions of this hypothesis:
(1) Large-flowered, self-incompatible (LF-SI) populations of C. bistorta and C. cheiranthifolia
are more geographically proximate than LF-SC populations to the north or SF-SC
populations to the south.
(2) Populations of C. bistorta share more alleles exclusively with LF-SI populations than with
LF-SC or SF-SC populations of C. cheiranthifolia.
94 (3) Genetic differentiation for neutral polymorphisms should be weaker between C. bistorta
and LF-SI C. cheiranthifolia than between C. bistorta and any other group of C.
cheiranthifolia populations.
(4) Within-species pairwise differentiation between populations should increase with
geographic distance (isolation-by-distance). If C. bistorta and C cheiranthifolia are
reproductively isolated, then pairwise differentiation between heterospecific populations
should be much higher than between conspecific populations and should not vary with
geographic distance. Alternatively, hybridization between C. bistorta and C.
cheiranthifolia will decrease differentiation between heterospecific populations and result
in isolation-by-distance between them.
MATERIALS AND METHODS
Range proximity based on herbarium records – To test prediction 1, we estimated geographical variation in degree of parapatry and thus opportunity for hybridization between C. cheiranthifolia and C. bistorta, based on geographic proximity between herbarium records. A total of 2398 herbarium records for C. cheiranthifolia (n = 727 spp. cheiranthifolia and n = 586 spp. suffruticosa) and 1085 for C. bistorta with geographic coordinates were retrieved from 37 different institutions (Table A4.2). Some locations were represented by multiple accessions so we selected only the 1631 locations with unique records (n = 448 C. cheiranthifolia spp. cheiranthifolia, 367 C. cheiranthifolia spp. suffruticosa, 816 C. bistorta). One hundred and seventeen C. cheiranthifolia spp. cheiranthifolia (36.34%) and six from C. cheiranthifolia spp. suffruticosa (1.65%) were collected on the Channel Islands and two from San Martin Island
(0.55%). We excluded these island records from the analyses because no C. bistorta occurred on any of the islands off the Coast of California and Baja California. We obtained 1506 mainland
95 records with a unique combination of species, latitude and longitude: 330 C. cheiranthifolia spp. cheiranthifolia (21.88%), 359 C. cheiranthifolia spp. suffruticosa (23.80%) and 815 C. bistorta
(54.11%). One C. bistorta record was found ~142 km away from the majority of observations close to the northern range limit. This location might represent a very small or a rare population; therefore opportunities for gene flow with C. cheiranthifolia might be insignificant (Fig. A4.1) and we excluded it from the analyses. The Euclidean geographic distance (km) between every C. cheiranthifolia record to its closest C. bistorta record was estimated with custom R scripts available upon request. These distances were then plotted against the latitude of each C. cheiranthifolia record. Variation in proximity to C. bistorta was compared among the different genetic clusters of C. cheiranthifolia identified by López-Villalobos and Eckert (submitted) using
1-way analysis of variance (ANOVA) with Tukey HSD contrasts in the R statistical computing environment (used for all analyses unless otherwise stated, version 3.1.2, R Development Core
Team 2017).
Sampling, DNA isolation and genotyping – Lopez-Villalobos and Eckert (submitted; Chapter 3) sampled 987 individuals from 38 populations of C. cheiranthifolia (mean/population = 23.97) distributed across the entire species’ geographic range, and genotyped them at 13 nuclear microsatellite markers. Of these we used the data from 32 mainland populations
(mean/population = 25.15; n = 805) and added 18 populations of C. bistorta. Populations of C. bistorta were sampled across the entire species' geographic range during May and July of 2010 using locations from herbarium specimens collected from 1990–2010. At each C. bistorta population, we collected leaf tissue from 6–34 randomly chosen plants separated by at least 2 m
(mean = 22.38/population, total n = 404; Table A4.1) following Lopez-Villalobos and Eckert
(submitted; Chapter 3). Each C. bistorta plant was genotyped at 13 nuclear microsatellite loci
96 (Table A3.2) following Lopez-Villalobos et al. (2014), however one locus (C89) could not be resolved for 0.83% samples and > 14% in two particular populations, hence this locus was excluded from analysis (although its inclusion dos not alter the conclusions made below; data not shown). Ten percent of samples were assayed twice to ensure repeatability. Fragment sizes were binned using the MsatAllele R package (version 1.04, Alberto, 2009).
Geographic variation in allele sharing – To test prediction 2, we compared the per-locus number of private alleles within and the number of alleles exclusively shared between C. bistorta and the five genetic clusters of C. cheiranthifolia populations across all possible sample sizes up to the minimum sample size (n = 128) using ADZE (Szpiech et al., 2008). We also compared within-population allelic richness (Ar) rarified to smallest sample size (n = 6), expected heterozygosity (He) and the inbreeding coefficient (F, estimated using the R package diveRsity, version 1.9.73, Keenan et al., 2013) between C. bistorta and each cluster of C. cheiranthifolia populations to test the expectation that self-incompatibility and thus higher outcrossing would be associated with higher within-population Ar and He and lower F in C. bistorta and LF-SI C. cheiranthifolia than more highly selfing populations of C. cheiranthifolia. Population estimates of Ar, He and F were contrasted among the six groups of populations using 1-way ANOVA with
Tukey HSD contrasts.
Range-wide population genetic structure – To test prediction 3, we analyzed genetic structure of all 32 C. cheiranthifolia and 18 C. bistorta populations together using Bayesian clustering of nuclear SSR genotypes implemented in the software INSTRUCT (Gao et al., 2007). INSTRUCT uses a model, similar to the widely used STRUCTURE (Pritchard et al., 2000), but it does not assume random mating, making it suitable for partially self-fertilizing populations of C.
97 cheiranthifolia (Gao et al., 2007; Gao et al., 2011). We ran simulations using a model that infers population structure from 1–15 clusters using 250 000 burn-in chains and 500 000 MCMC iterations for 15 independent replicates using increased memory but using default settings for all other parameters. The most likely number of clusters was determined using the ΔK method
(Evanno et al., 2005). The outputs of the 15 repeated independent runs at the most likely K were aligned using CLUMPP (version 1.1.1; Jakobsson and Rosenberg, 2007) and plotted using
DISTRUCT (version 1.1; Rosenberg, 2004). We also used the K-means algorithm (Legendre and
Legendre, 1998) as an alternative clustering method but this yielded very similar results so we only report these in Appendix 4.3 and Fig. A4.5. To visualize differentiation between clusters identified by INSTRUCT, we performed a discriminant analysis of principal components
(DAPC) implemented in the R package Adegenet (version 1.4-2; Jombart, 2008; Jombart et al.,
2010). Each individual was assigned to one INSTRUCT cluster based on its highest ancestry coefficient and DAPC was performed using 90 principal components (95.3% of the variance) according to a cross-validation procedure implemented in Adegenet.
Range-wide INSTRUCT analysis revealed that LF-SI C. cheiranthifolia clustered with C. bistorta genotypes, hence we performed additional INSTRUCT and DAPC analyses (as above) involving only these genotypes. This analysis detected only two genetic clusters (Fig. A3.2). To evaluate the extent to which these clusters corresponded to species, we performed two analyses.
First, we compared the partitioning of microsatellite variation between species (and populations within species) to that among clusters (and populations within clusters) following Excoffier et al.
(1992) as implemented in the R package poppr (version 2.0.2; Kamvar et al., 2014) with significance of each hierarchical variance component evaluated using randomization tests in ade4
(version 2.7.2; Dray and Dufour, 2007). Second, we compared ancestry coefficients to one of the clusters between C. cheiranthifolia and C. bistorta with a 2-sample t-test.
98
Isolation-by-distance – To test prediction 4, we fit variation in pairwise population differentiation (measured as FST, Weir and Cockerham, 1984, estimated using DiveRsity) to a linear model with geographic distance (estimated as the great circle surface distance calculated using the R package geosphere, version 1.3-11, https://cran.rproject.org/web/packages/dismo/), population pair type (between C. bistorta populations, between C. cheiranthifolia populations, between heterospecific populations) and the interaction between these terms as fixed effects.
Because pairwise population FST estimates are not independent from one another, we tested the significance of each predictor using permutation tests following Manly (2006) with 10 000 permutations per test.
RESULTS
Range proximity based on herbarium records – Consistent with Samis and Eckert (2007) the herbarium records indicated that C. cheiranthifolia regularly occurs from Coos Bay in Oregon,
USA to Bahia del Rosario, Baja California, Mexico and only one record was found beyond the current northern range limit (see also Raven, 1969). Euclidean distance between each C. cheiranthifolia location and the closest record of C. bistorta location ranged 0.0–1074.9 km and averaged (mean ± SD) 229.8 ± 310.2 km. Log10-transformed distance to C. bistorta varied significantly among groups of C. cheiranthifolia records classified by population genetic cluster
2 (r = 0.83, F4,684 = 811.7, P < 0.0001). As expected, distances were much higher for the three groups of small-flowered C. cheiranthifolia specimens collected largely beyond the range limits of C. bistorta (Fig. 4.1). As predicted, distance to a C. bistorta record was also significantly shorter for C. cheiranthifolia specimens falling within the range of LF-SI populations than those within the range of LF-SC populations (Fig. 4.1B).
99
Geographic variation in population genetic diversity and interspecific allele sharing –
We detected 272 alleles at 12 loci among the 1209 individuals sampled from 50 populations of both species across their geographic ranges (range = 13–35 alleles/locus; mean ± 1 SD = 22.67 ±
7.94 alleles/locus). As expected from variation in the relative importance of self-fertilization vs. outcrossing among clusters of C. cheiranthifolia populations (López Villalobos and Eckert, submitted), both measures of within-population genetic variation, expected heterozygosity (He,
Fig. 4.2A) and the rarified number of alleles per locus (Ar, Fig. 4.2B) were much higher for LF-
SI C. cheiranthifolia and SI C. bistorta populations than groups of LF-SC and SF-SC C. cheiranthifolia populations. Stronger SI in C. bistorta than LF-SI C. cheiranthifolia detected by
Dart et al. (2012) was associated with higher Ar but not He. Accordingly, estimates of the inbreeding coefficient (F) were lowest for C bistorta and LF-SI C. cheiranthifolia somewhat higher for LF-SC C. cheiranthifolia and highest for SF-SC C. cheiranthifolia (Fig. 3.2C).
In all, 265 alleles were detected in C. bistorta (22.66 ± 7.93; range = 12–33), 261 in C. cheiranthifolia (13.42 ± 4.46; range = 9 – 24), and of these, 111 alleles where private to C. bistorta and only seven to C. cheiranthifolia. Controlling for sample size, we detected the highest number of private alleles per locus in C. bistorta (Fig. 4.3A, mean ± 1SE = 4.32 ± 0.77 alleles/locus, 95% CI = 2.81–5.82) with no evidence of overlap in 95% confidence intervals with any C. cheiranthifolia group across all samples sizes (n = 2–128; Fig. 4.3A). Within C. cheiranthifolia, LF-SI populations, as a group, exhibited higher private alleles (0.62 ± 0.24 95%
CI 0.13 – 1.11Fig.4.3A) than groups of SC populations, which together did not differ from one another.
LF-SI populations of C. cheiranthifolia exclusively shared significantly more alleles per locus with C. bistorta than with any other group of C. cheiranthifolia populations (mean ± 1SE =
100 1.59 ± 0.22; 95% CI = 1.16 – 2.02; Fig. 4.3B). SF-SC populations from Baja California exhibited the second highest degree of exclusive allele sharing with C. bistorta (0.47 ± 0.19; 95% CI 0.09–
0.84), despite the fact that populations of C. bistorta are geographically closer to LF-SC C. cheiranthifolia populations, which exclusively shared few alleles with C. bistorta (0.05 ± 0.024;
95% CI 0.003–0.09).
Range-wide population genetic structure – INSTRUCT analyses across all 50 populations of both species revealed six genetic clusters (Fig. 4.4) based on the highest delta-K (Fig. A4.2).
Four of these six clusters are consistent with previously identified genetic clusters in C. cheiranthifolia (López-Villalobos and Eckert, submitted; Chapter 3); LF-SC populations south of
Pt. Conception and three clusters of SF-SC populations: two north of Pt. Conception: from Pt.
Conception to San Francisco (NPC), and from San Francisco to the northern range limit in southern Oregon (NRL), and one in Baja California to the southern range limit (SF-SC BAJA).
The other two genetic clusters contained a mixture of LF-SI C. cheiranthifolia and C. bistorta genotypes.
DAPC implemented with each individual assigned to one of the six INSTRUCT clusters based on its highest ancestry coefficient (Fig. 4.4C), revealed almost complete overlap between samples from the two clusters including those from LF-SI C. cheiranthifolia and C. bistorta populations, but substantial differentiation between these two overlapping clusters and the other four clusters of C. cheiranthifolia genotypes, especially SF-SC genotypes from Baja California
(BAJA) and from just north of Pt. Conception (NPC). Considering additional axes of the DAPC did not show strong separation between the two clusters involving LF-SI C. bistorta and C. cheiranthifolia genotypes but it revealed in stronger differentiation among the other clusters of C. cheiranthifolia genotypes (Fig. A4.5). K-means clustering identified five genetic clusters but the
101 posterior assignment was largely consistent with the four clusters of SC C. cheiranthifolia and another including both C. bistorta and LF-SI C. cheiranthifolia populations (Fig. A4.6).
INSTRUCT analyses including only LF-SI C. cheiranthifolia and C. bistorta genotypes revealed two clusters (K = 2, Fig. 4.2) differentiated somewhat by latitude of origin but including genotypes of both species (Fig. 4.5). Partitioning of molecular variance between species was lower and not significant between INSTRUCT clusters, however, cluster only explained 5% of the total variance (Table 4.1). In both analyses, most variation was partitioned among populations (22-24%) and among individuals within populations (73-75%).
Mean ancestry coefficient to the southerly cluster was higher for C. cheiranthifolia genotypes (mean ±1 SE = 0.78 ± 0.01) than C. bistorta genotypes (0.53 ± 0.02; t = 6.7, df = 580,
P < 0.0001).
Isolation-by-distance – Including only LF-SI C. cheiranthifolia and C. bistorta, pairwise genetic differentiation among populations (FST) ranged 0.006-0.446, averaged (± 1 SE) 0.209 ± 0.004 and increased with geographic distance (Fig. 4.5, r = +0.42, Pperm < 0.0001). Pairwise divergence was somewhat higher between C. cheiranthifolia populations (mean ± 1 SE = 0.241 ± 0.018, range = 0.085–0.379) than between C. bistorta populations (0.190 ± 0.005, 0.006–0.400).
However, differentiation between heterospecific populations (0.23 ± 0.006, 0.46–0.45) was not higher than between C. cheiranthifolia populations (Fig. 4.6B). The correlation between genetic and geographic distance did not differ among the three types of population comparisons (Pperm ~
0.683).
DISCUSSION
102 Based on mutual self-incompatibility and previous reports of intermediate phenotypes (Raven,
1969; Dart et al., 2012; Lopez-Villalobos and Eckert, submitted), we predicted that LF-SI populations of C. cheiranthifolia should be in closest parapatry with C. bistorta and that hybridization may have contributed to their genetic distinctness from parapatric LF-SC and SF-
SC populations. Although studies of hybridization in plants commonly recognize the importance of spatial proximity (Comes and Abbott, 1999; Ellstrand et al., 1999; Abbott et al., 2013;
Geraldes et al., 2014; Ahrens and James, 2015), only studies in animals have quantified degree of parapatry/sympatry from historical records and related this to genetic measures of hybridization between taxa (e.g. Rosser et al., 2015). Consistent with our prediction, analysis of spatial isolation based on herbarium records suggests that parapatry is closer between C. bistorta and
LF-SI C. cheiranthifolia than LF-SC and SF-SC populations in southern California and Baja
California. The closer proximity of C. bistorta and LF-SI C. cheiranthifolia matches our evidence of strong genetic similarity between them discussed below. The genetic similarity between LF-SI C. cheiranthifolia and C. bistorta was much greater than anticipated, not distance-dependent and not likely to be a product of ongoing hybridization. In fact, LF-SI C. cheiranthifolia and C. bistorta were indistinguishable genetically within the broader context of range-wide differentiation in C. cheiranthifolia.
Genetic continuity among LF-SI C. cheiranthifolia and C. bistorta populations. – All analyses indicated strong genetic continuity between C. bistorta and LF-SI C. cheiranthifolia.
INSTRUCT analyses clustered these two groups of genotypes together, exclusive of all other C. cheiranthifolia genotypes, and C. bistorta shared more alleles with LF-SI C. cheiranthifolia than with LF-SC or nearby SF-SC C. cheiranthifolia in Baja California (Fig 3B). Moreover
INSTRUCT analysis of just C. bistorta and LF-SI C. cheiranthifolia failed to detect species-
103 specific clustering but instead, showed a north-to-south pattern of geographic differentiation regardless of species. This pattern of genetic structure and differentiation between species contrasts to those observed between other pairs of closely related taxa in that, despite gene flow between them, populations are differentiated by their species identity (Vallejo-Marin and Lye,
2013; Noutsos et al., 2014; Li et al., 2014).
Isolation-by-distance analysis of C. bistorta and LF-SI C. cheiranthifolia populations revealed a positive and significant correlation between geographic distance and genetic distance
(FST), indicating that gene flow across their entire distribution is largely a function of geographic distance (Wright, 1943; Kimura and Weiss, 1964) and suggests that populations have probably existed in this region long enough to achieve equilibrium between drift and migration (dispersal).
The scatter of pairwise FST comparisons showed high variance across most pairwise comparisons
< 250 km, although this is especially pronounced at short distances, suggesting that drift might be relatively important at the local scale, even where gene flow should have the strongest homogenizing effect reducing genetic variability among populations (Hutchinson and Templeton,
1999).
Evidence of reproductive isolation (RI) between closely related species is often inferred from greater pairwise FST between populations of different species than between populations of the same species accounting for the geographic distance between populations (Albaladejo and
Aparicio, 2007; Cavender-Bares and Pahlich, 2009; Burge et al., 2013; Noutsos et al., 2014).
Such is not the case for C. bistorta and LF-SI C. cheiranthifolia. Although most high pairwise
FST values over relatively short distances involved between-species comparisons, we did not find that FST between populations of different species were higher than between populations of the same species, similar to results from INSTRUCT and allele sharing analyses discussed above.
Higher pairwise FST between C. cheiranthifolia populations than C. bistorta populations can
104 probably be explained by the difference in distribution of appropriate habitat. While, C. cheiranthifolia has a one-dimensional distribution along the coast, and each population has only one potential neighbour to the north and to the south likely conforming to a stepping-stone model of gene flow (Kimura and Weiss, 1964), each C. bistorta population has a larger number of potential neighbours across this species’ 2-dimensional inland distribution conforming more closely to a classic Island model of gene flow (Wright, 1943). Comes and Abbott (1999) also found stronger genetic differentiation among coastal populations of Sencio glaucus compared to its inland sister taxon S. vernalis.
Camissoniopsis bistorta and C. cheiranthifolia occur in distinct habitats with a largely parapatric distribution suggesting eco-geographic isolation. However, along the Coast of San
Diego County and northern Baja California, the two habitats come very close to one another and appear to intergrade in some places. Raven (1969) pointed to a series of populations composed of intermediate phenotypes between the typical C. cheiranthifolia and C. bistorta, concluding that hybridization was probably very common in this region. Aside from ecogeographic isolation, both species are primarily outcrossing, overlap in flowering time and likely share similar pollinators, hence other pre-pollination barriers are liable to be weak. Post-pollination pre- zygotic isolation might also be unlikely as both species are self-incompatible, and therefore unilateral self-incompatibility should not interfere with cross-fertilization (de Nettancourt, 2001;
Baek et al., 2014) and the two species are readily crossed in both directions under glasshouse conditions. Information on post-zygotic isolation between these species is scant. Raven (1969) crossed an LF-SI C. cheiranthifolia (from Mission Bay) with a C. bistorta (Juan Capistrano,
Orange Co.), obtaining seeds in both directions but only those sired by C. bistorta with C. cheiranthifolia maternal plant successfully germinated. However, the pollen fertility of these F1
105 progeny (assayed using pollen stainability with blue lactophenol) was only slightly reduced compared to parental plants (Raven, 1969 p. 263-264).
Genetic continuity between C. bistorta and LF-SI C. cheiranthifolia may be the result of extensive introgression following secondary contact of allopatrically diverged lineages or incomplete differentiation of lineages despite adaptation to different habitats (Stankowski et al.,
2015). On the whole, our results clearly favor the latter hypothesis: (1) significant IBD ocurred across the range of both species, (2) average FST between species was not higher than within species, (3) most alleles were shared between species with no obvious differences in the pattern of private alleles in parapatric vs. allopatric populations, (4) genetic admixture between taxa was not particularly high in parapatric coastal populations and (5) coastal and inland populations are contained in both INSTRUCT genetic clusters, regardless of species designation. The results of our study are similar to those from Shepherd and Raymond (2010), who found higher average pairwise FST within than between E. pilularis and the more geographically restricted E. pyrocarpa. AMOVA also showed little molecular variance (~2%) between taxa. Because E. pilularis and E. pyrocarpa exhibit morphological differences that are likely associated with water use efficiency in distinct environments, in situ adaptive divergence of E. pyrocarpa from E. pilularis might have occurred recently. The authors concluded that the lack of genetic differentiation between species indicates that E. pyrocarpa may be better viewed as an “ecotype” of E. pilularis.
Is C. bistorta an inland ecotype of C. cheiranthifolia?
Ecological adaptation is a key component in models of divergence with gene flow (Sobel et al.,
2010; Nosil, 2012). There are many examples of plant species that show obvious morphological and ecological differences associated with adaptation to different environments (Linhart and
106 Grant, 1996; Kawecki and Ebert, 2004; Lexer and Fay, 2005; Schemske and Bierzychudek,
2007), even in the presence of extensive gene flow (e.g. Cooper et al., 2010; Huang et al. 2014;
Lowry et al. 2008). Adaptive differentiation in coastal vs. inland habitats has occurred repeatedly in plants, as evidenced by Turesson (1922a; 1922b; 1925) pioneering experimental analyses of local adaptation and many studies since (Jordan, 1992; Hall and Willis 2006; Abbott and Comes,
2007; Lowry et al., 2008; Lowry et al., 2009). McNeilly and Antonovics (1968) proposed a model for parapatric speciation via ecotypic divergence involving four stages: 1) a population occupies a new habitat and exhibits no breeding barriers and no divergence in adaptive characters; 2) the population exhibits genetically based divergence in adaptive characters but still no breeding barriers, at which point can be considered an ecotype; 3) the distribution of ecotypes becomes discontinuous from adjacent parental material and partial mating barriers are developed, genetic divergence can be evident, but populations are still not fully reproductive isolated; 4) the two populations are independent from each other and reproductive isolation and speciation has been achieved. Based on the results of this study C. bistorta and LF-SI C. cheiranthifolia might represent an example of inland and coastal ecotypes, respectively, at the very early stages of divergence, probably somewhere between stage 2 and 3.
In contrast to most species within Camissoniopsis, Camissoniopsis cheiranthifolia and C. bistorta have generally been recognized as distinct taxa, as they are the only two taxa that contain large-flowered outcrossing populations. Although there has not been a quantitative comparison of life-history and morphology between species, they are broadly distinguished from each other in the taxonomic literature by their distribution (coastal vs. coastal and inland) and the fact that C. cheiranthifolia is considered a perennial or short-lived perennial and C. bistorta an annual (rarely a short-lived perennial only close to the coast). In general, C. cheiranthifolia populations in southern California tend to be more prostrate and sometimes ascending, whereas C. bistorta is
107 decumbent to ascending. Leaves of C. cheiranthifolia tend to be narrowly ovate to obovate and minutely serrate, whereas those of C. bistorta are lance-shaped to narrowly arrowhead-shaped, sometimes toothed, and thinner. Seed capsules in C. bistorta are usually straight or slightly wavy and contain small seeds (0.9-1mm), whereas capsules of C. cheiranthifolia are strongly coiled and contain larger seeds (1.2-1.3 mm; Raven, 1969; Baldwin and Goldman, 2012). Earlier taxonomic treatments of C. bistorta distinguished inland and coastal varieties that broadly intergrade but that differ in fruit shape (capsule). The inland form has relatively slender, long and straight capsules and the coastal populations have thicker, shorter, usually coiled capsules, more similar to those in coastal C. cheiranthifolia (Raven, 1969 pp. 271).
Some of this morphological variation between species might be the result of plastic responses to different environmental conditions; however it likely represents genetic, possibly adaptive differentiation in response to selection in contrasting habitats. Camissoniopsis bistorta spans a wider range of habitats and plant communities than the sand dune endemic C. cheiranthifolia. From sea level and up to 2000 ft. in elevation (exceptionally 8000 ft.), this species is often associated with Coastal Strand, Coastal Sage Scrub, Chaparral and Southern Oak
Woodland (Raven, 1969), and hence the abiotic and biotic environment is probably more diverse than the one experienced by C. cheiranthifolia. Yet, genetic clusters of genotypes that included C. bistorta and LF-SI C. cheiranthifolia are not markedly differentiated from other clusters of C. cheiranthifolia populations at the broader scale (Fig. 4.4C). In fact, C. cheiranthifolia populations north of Point Conception and south of Punta Banda are more strongly differentiated from both LF-SI clusters, further supporting the idea that inland C. bistorta might represent an ecotype of C. cheiranthifolia that has relatively recently adapted to inland habitats in southern
California and adjacent Baja California. Surveys of morphological variation across the range of
SI populations are needed to identify traits under potential selection to coastal vs. inland habitats.
108 Of particular interest will be physiological traits, such as those related to salt (e.g. Lowry et al.,
2009) and drought tolerance (Lowry et al., 2008), the selection of which likely differs between coastal and inland habitats.
In some cases, differentiation at neutral markers has been found to be less than expected based on ecotypic morphological and life history divergence. In the Mimulus guttatus species complex, for example, little differentiation at nuclear loci scattered across the genome has been found between coastal and inland ecotypes (that also differ in annual vs. perennial life history)
(Sweigart and Willis, 2003; Modliszewski and Willis, 2012; Brandvain et al., 2014; Oneal et al.,
2014; but see Lowry et al., 2008). However, differentiation at quantitative trait loci (QTLs) associated with important adaptive traits is strong (Lowry and Willis, 2010; Oneal et al., 2014).
Similar to our study these examples indicate little genetic structure at neutral markers between putative coastal and inland ecotypes (but see Lowry et al., 2008). One possible explanation is that divergence between LF-SI coastal and inland ecotypes is evident only for adaptive traits.
Parapatric populations that experience gene flow and strong selection face an antagonistic relationship between the divergent effects of selection and the homogenizing effects of gene flow and recombination (Barton and de Cara, 2009; Barton, 2010). However, divergence in the presence of gene flow can be facilitated by the genetic architecture of adaptive traits (Smadja and
Butlin, 2011; Yeaman and Whitlock, 2011). For instance, pleiotropy and chromosomal inversions can shield genes under strong selection for adaptation to a particular environment, preventing their breakup and favoring divergence (e.g. Twyford and Freedman, 2015). In
Mimulus guttatus, a large chromosome inversion polymorphism has been shown to contribute to local adaptation, reproductive isolation and life history divergence between annual and perennial populations of the yellow monkeyflower (Lowry and Willis, 2010). This polymorphism not only shows stronger genetic differentiation between coastal and inland genotypes than at neutral genes,
109 but also is associated with climatic differences between the ecotypes (Oneal et al., 2014). Several species in the related genus Oneothera are well-known for intraspecific variation in chromosome structure (Holsinger and Ellstrand, 1984; Levin, 2002; Rauwolf et al., 2008; Golczyk et al., 2014).
Although there is no evidence of chromosomal variation within C. cheiranthifolia, variation in chromosome structure has not been investigated extensively, leaving it possible that chromosomal arrangements may protect adaptive differentiation between SI coastal and inland populations in spite of gene flow.
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123 Table 4.1. Analysis of molecular variance (AMOVA) of 25 large-flowered self-incompatible (LFSI) populations of both C. cheiranthifolia and C. bistorta. Two analyses were performed: AMOVA 1 compares the variance between species, populations within species and individuals within populations. AMOVA 2 compares variance between genetic clusters, populations within clusters and individuals within populations. The degrees of freedom (d.f.), the percentage of total variance, F statistics (Phi) and P values obtained from randomization tests with 999 permutations are presented.
Between species Between clusters
Source of % Total % Total d.f. P-value d.f. P-value Variation variance variance
Between species or 1 1.07 0.08 1 4.73 0.001 clusters
Among populations within species or 23 23.78 0.001 33 21.87 0.001 clusters
Among individuals 557 75.14 0.001 547 73.40 0.001 within populations
Total 581 100.00 581 100.00
124 44 A N B N
C. cheiranthifolia cheiranthifolia C. cheiranthifolia suffruticosa C. bistorta 35
34 33
34 x C. cheiranthifolia C. bistorta
a C D
C. bistorta C. bistorta & c MEX-USA SFSC C. cheiranthifolia
34
Figure 4.1. Species’ geographic distributions and probability of hybridization based on Euclidean distances to a C. bistorta herbarium record. A) Distribution of C. cheiranthifolia spp. cheiranthifolia north of Pt. Conception (gray circles, n = 329), C. cheiranthifolia spp. suffruticosa south of Pt. Conception (red circles, n = 359) and C. bistorta (blue triangles, n = 815) based on herbarium specimens. B) Close up of the distribution of C. bistorta and C. cheiranthifolia spp. suffruticosa (red circles) only. C) Distribution of pairwise Euclidean distances from each C. cheiranthifolia to its closest C. bistorta herbarium location across the range of both species with landmarks as in B. D) Group comparisons by whether records where found in the geographic region of SFSC (small-flowered self-compatible) populations in Baja California, Mexico, LFSI (large-flowered self-incompatible) populations, LFSC (large-flowered self-compatible) populations, SFSC from north of Pt. Conception to the San Francisco bay (NPC) and SFSC populations north of the San Francisco bay to the northern range limit (NRL) of C. cheiranthifolia. Different letters represent significantly different comparisons after Tukey HSD contrasts. The y-axis in (D) is log10 + 1 transformed. Green dashed, magenta dashed and black dotted lines represent the putative range limits of C. bistorta, Punta Banda (31.6°N transition from LFSI to SFSC), the transition from SI to SC and the border between MEX and USA, respectively.
125 0.8 A a
a 0.6
H e b bc 0.4 c c
0.2
B a 5
4 b
A 3 r c c c c 2
1
1.0 C a a 0.8 ad
0.6 b bd F is 0.4 c
0.2
0.0 SF BAJA BIST LFSI LFSC SF NPC SF NRL
Figure 4.2. Genetic diversity and inbreeding coefficients (Fis) among 32 populations of C. cheiranthifolia from five different genetic clusters (nSSR) and 18 populations of C. bistorta. A) Expected heterozygosity (He), B) allelic richness (Ar) and C) inbreeding coefficients (Fis). Group labels and different colors are: small-flowered, self-compatible populations from Baja California (SF BAJA, yellow, npops = 6), self-incompatible populations from C. bistorta (BIST npops = 18), large-flowered self-incompatible populations of C. cheiranthifolia (LFSI, blue, npops = 7), large- flowered self-compatible populations (LFSC, white, npops = 5), small-flowered self-compatible populations North of Point Conception (SF NPC, light blue, npops = 7) and small-flowered self- compatible populations north of the San Francisco Bay to the northern range limit of C. cheiranthifolia (SF NRL, dark green, npops = 7). Within each boxplot, the horizontal line represents the median, boxes extend to the 25th and 75th percentiles, whiskers extend to the most extreme points or 1.5 interquartile ranges from the median, whichever is less.
126 ● ● ●
0 SFSC BAJA BIST LFSI LFSC SFSC NPC SFSC NRL
5.0 A 0
4.0
3.0
2.0
1.0
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2 16 30 44 58 72 86 100 114 128 Sample size
Figure 4.3. Number of private and shared alleles per locus in relation to sample size. A) Mean number of private alleles per locus for each of the five genetic clusters of C. cheiranthifolia and all C. bistorta populations together. B) Uniquely shared alleles between C. bistorta and each of the five clusters of C. cheiranthifolia. Population groups represented by colours as follows: C. bistorta magenta, LFSI blue, LFSC red, SF Baja yellow, SF NPC light blue, SF NRL green. The lowest sample size across groups is n = 128 (therefore rarefaction n = 128), which corresponds to the number of individuals in the LFSC group. Error bars are standard errors at each sample size estimated by the program ADZE.
127
Figure 4.4. Range-wide genetic structure of 50 populations of Camissoniopsis cheiranthifolia and C. bistorta at 12 nSSR microsatellite loci using INSTRUCT. A) Bar plot of ancestry probabilities to each of six genetic clusters of 1209 individuals from 18 C. bistorta (nind = 404) and 32 C. cheiranthifolia (nind = 805) populations spanning the geographic distribution of both species. B) Geographic distribution C. cheiranthifolia (circles) and C. bistorta (triangle) populations and their mean population ancestry coefficients. Inset shows the approximate location of each C. bistorta population sampled and their average ancestry. C) Discriminant analysis of principal components (DAPC) showing the differentiation among six clusters. Each point is an individual that was assigned to one of the six clusters based on its highest ancestry coefficient. Colors are as in Fig. 4.2 but the blue and magenta clusters both have individuals from LF-SI populations of C. cheiranthifolia and C. bistorta. Different symbols in (C) also represent individuals from different clusters as in (A). Crosses are individuals classified as C. bistorta and circles around points are inertia ellipses enclosing 67% of the samples within each cluster.
128
Figure 4.5. INSTRUCT and DAPC analyses of 583 individuals sampled from 25 populations of C. bistorta (nind = 404; npops = 18) and large-flowered, self-incompatible C. cheiranthifolia (nind = 179; npops = 7). A) INSTRUCT bar plot of ancestry coefficients for each genotype for each of K = 2 clusters. B) Distribution of populations sampled and their mean ancestry to each cluster. C) Discriminant analysis of principal components (DAPC) with individuals assigned to each INSTRUCT cluster based on their highest ancestry coefficients. Triangles and population codes ending in “B” indicate C. bistorta and circles and codes ending in “C” indicate C. cheiranthifolia. Gray triangles represent C. bistorta coastal populations, black triangles are C. bistorta inland populations and black circles represent the LFSI C. cheiranthifolia populations sampled.
129 0.5 A
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Figure 4.6. Analysis of isolation-by-distance among 25 LF-SI populations of C. cheiranthifolia and C. bistorta. A) Association between pairwise genetic differentiation (FST) and geographic distance (km) between populations within C. cheiranthifolia (unfilled circles), within C. bistorta (triangles) and between species (gray stars). B) Comparisons between residual pairwise FST controlling for geographic distance using 10,000 permutations. Different letters represent significant differences among groups. Within each boxplot, the horizontal line represents the median, boxes extend to the 25th and 75th percentiles, whiskers extend to the most extreme points or 1.5 interquartile ranges from the median, whichever is less.
130 CHAPTER 5. Strong genetic differentiation but not local adaptation towards the range limit of a coastal dune plant
ABSTRACT
All species have limited geographic distributions; but the ecological and evolutionary mechanisms causing range limits are largely unknown. That many species’ geographic range limits are coincident with niche limits suggests limited evolutionary potential of marginal populations to adapt to conditions experienced beyond the range. We provide a test of range limit theory by combining population genetic analysis of microsatellite polymorphisms with a transplant experiment within, at the edge of, and 60 km beyond the northern range of a coastal dune plant. Contrary to expectations, lifetime fitness increased towards the range limit with highest fitness achieved by most populations at and beyond the range edge. Genetic differentiation among populations was strong, with very low, non-directional gene flow suggesting range limitation via constraints to dispersal. In contrast, however, local adaptation was negligible, and a distance-dependent decline in fitness only occurred for those populations furthest from home when planted beyond the range limit. These results challenge a commonly held assumption that stable range limits match niche limits, but also raise questions about the unique value of peripheral populations in expanding species’ geographical ranges.
INTRODUCTION
Species are thought to expand their geographic ranges through local adaptation. Natural selection at the range edge should favor traits that enhance survival and reproduction in marginal environments. The resulting increase in frequency of alleles enhancing such traits allows the
131 species to spread outwards (Levin 2000). However it appears that, for almost all species, range expansion along relatively continuous ecological gradients ultimately stalls, giving rise to distributional limits. At some point, local adaptation seems inadequate to ensure long-term population persistence.
Reciprocal transplant experiments directly test for local adaptation, which is manifested as the superior performance of genotypes planted at home compared to those transplanted from other sites (the “home site advantage” [HSA], Kawecki and Ebert 2004). However, reciprocal transplants have rarely been used to quantify local adaptation towards species’ range limits.
Arrontes (1993) and Griffith and Watson (2006) found that peripheral populations were better adapted to conditions at the range edge than populations from the range interior suggesting some degree of local adaptation to variation in habitat along an ecological gradient across the edge. In contrast, neither Angert and Schemske (2005) nor Geber and Eckhart (2005) detected superior performance of edge populations, suggesting a failure of local adaptation at the range edge.
Moreover, the prediction that edge populations are best suited to enable expansion beyond current range limits in the event of climate change (Hargreaves et al. 2014) has infrequently been tested using transplant experiments and is generally not supported (Geber and Eckhart 2005;
Angert and Schemske 2005; Volis et al. 2014; but see Stanton-Geddes et al. 2012; Stevens and
Emery 2015). In addition to reflecting an incomplete understanding of the evolutionary dynamics of species’ ranges, the lack of empirical evidence prevents sound decisions as to the conservation value of peripheral populations (Lesica and Allendorf 1995; Bunnell et al. 2004; Hampe and Petit
2005; Gibson et al. 2009; Hardie and Hutchings 2010) and how assisted migration might be used to avoid species extinction during climate change (Hoegh-Guldberg 2009; Vitt et al. 2010).
132 Several mechanisms might cause adaptation to fail, depending on the genetic constraints that compromise fitness and how population demography varies towards the range limit (Antonovics
1976; Hoffman and Blows 1994; Bridle and Vines 2007). Reduced genetic variation in edge populations may be due to the rarity of required genetic variants (“genostasis”, Bradshaw 1991) or, as predicted by the abundance center model (ACM), genetic drift may reduce genetic diversity in small, range edge populations (Lawton 1993; Gaston 2003; Vucetich and Waite
2003; Eckert et al. 2008; Hardie and Hutchings 2010; ACM = abundance declines with habitat quality from the range center to edge, Brown 1984). Dispersal from large central populations to small edge populations can lead to adaptation being thwarted by asymmetric gene flow (“gene swamping”, Kirkpatrick and Barton 1997; Barton 2001; Bridle et al. 2009; Paul et al. 2011), which in the extreme may maintain peripheral populations in sink habitats. Although each explanation invokes different mechanisms, they are not mutually exclusive and all assume that the range limit is a spatial manifestation of the species’ niche limit: that the limit occurs on an ecological gradient affecting fitness such that fitness of individuals migrating beyond the limit is insufficient for population persistence (Sexton et al. 2009). The drift and gene swamping hypotheses further assume that population demography across the range generally corresponds to the ACM (Brown 1984). However, the ACM is not well supported (Sagarin and Gaines 2002,
Sagarin et al. 2006; Sexton et al. 2009; Abeli et al. 2013), especially by range-wide demographic surveys (e.g. Samis and Eckert 2007), and alternative models have recently been considered
(Abeli et al. 2013; Martínez-Meyer et al. 2013; Duncan et al. 2015). The correspondence between geographical range limits and niche limits, although rarely tested, is probably much weaker than expected (Hargreaves et al. 2014).
133 Seemingly stable range limits can also be imposed by dispersal constraints caused by limited dispersal capacity of the species in question (e.g. Stevens and Emery 2015) or when gradients in habitat heterogeneity affect dispersal between available habitat patches (Holt and Keitt 2000;
Holt et al. 2005; Kokko and López-Sepulcre 2006). Increasing spatial isolation between patches or inhospitability of the habitat matrix between patches may reduce the recolonization rate of vacant patches, thereby driving a species’ metapopulation towards collapse. Populations transplanted to patches beyond the limit may enjoy high fitness in the years of an experiment, but the species remains absent from available patches because recolonization via dispersal is not frequent enough to support the species on the landscape (Carter and Prince 1988).
Reciprocal transplant experiments in species with long-term range limits can help distinguish among these different explanations (Hargreaves et al. 2014). However, using transplant data alone to evaluate the processes potentially responsible for fitness variation across the range limit can be challenging. For example, a significant decline in fitness across the limit without variation among source populations (i.e., no source population x planting site interaction) may be caused by genostasis, drift or gene swamping. These can be distinguished by combining transplant data with population-genetic analysis of neutral polymorphisms (Moeller et al. 2011; Gould et al.
2013, Volis et al. 2015). A decline in within-population genetic diversity towards the range limit would be consistent with drift, whereas low differentiation in neutral markers among source populations would be consistent with gene swamping, and strong differentiation among populations would favour genostasis. As another example, an increase in mean fitness towards a range limit leaves dispersal limitation as a likely explanation, but genetic signatures of ongoing range expansion, such as a decline in diversity towards the limit, would suggest a dispersal lag. In
134 contrast, evidence of local adaptation and significant neutral differentiation would be more consistent with reduced recolonization leading to metapopulation collapse.
Although renewed interest in the mechanisms causing species’ range limits has lead to a growing body of evidence from reciprocal transplant experiments (Hargreaves et al. 2014) and population genetic analysis of neutral markers (Eckert et al. 2008), the potential synergy between these two approaches has very rarely been exploited (Moeller et al. 2011; Paul et al. 2011; Gould et al. 2013; Stanton-Geddes et al. 2012; Volis et al. 2015). Here we combine data from transplant experiments and microsatellite markers to investigate the extent of local adaptation towards the northern range limit of a short-lived, herbaceous plant endemic to the Pacific coastal dunes of western North America, Camissoniopsis cheiranthifolia (Hornemann ex Sprengel) W.L. Wagner
& Hoch (Onagraceae). This species is very common in coastal dune habitat within its historically stable range limits from Baja California, Mexico to Oregon, USA, but range-wide population survey data show only a very loose fit to the ACM (Samis and Eckert 2007). Moreover, previous work, including our own, indicate range limits are not coincident with abrupt shifts in plant community composition or climate (Fig. 1, and Samis and Eckert 2009), geomorphology, or edaphic factors in this region (Cooper 1958; Breckon and Barbour 1974; Barbour et al. 1976).
Previously, Samis and Eckert (2009) reported that lifetime fitness increased towards and beyond the range limit for C. cheiranthifolia transplanted into four sites distributed across the northern-most 185 km of species’ range plus one site 60 km beyond the limit. Combined with observations that patch size and occupancy declined towards the limit (Samis and Eckert 2009;
K.E. Samis and C.G. Eckert unpublished data) these data suggest that dispersal limitation in a metapopulation, not niche limitation may explain the northern limit (Samis and Eckert 2009).
Together, these results lead to the following hypothesis: The northern range limit of C.
135 cheiranthifolia occurs on an ecological gradient that increases fitness thereby reducing patch extinction towards the range edge, but also decreases dispersal. As a result, the probability of patch recolonization and ultimately metapopulation persistence decline towards the limit. Here we test the following key predictions of this hypothesis: (1) High population viability combined with reduced gene flow allows local adaptation of edge populations. (2) Edge populations will perform better than interior when planted beyond the range limit. (3) Within-population genetic diversity declines towards the limit. Finally, with limited gene flow among populations (4) genetic differentiation at neutral loci will be high, and (5) there will be no tendency for asymmetric gene flow from interior to edge populations.
MATERIALS AND METHODS
Transplant experiment design
We used eight source populations from the northern half of the species’ range and five planting sites located within and beyond the species’ northern range limit (Fig.5 1). Plants in all study populations were self-compatible, relatively small-flowered, autogamous and highly self- fertilizing (Dart et al. 2012). The small seeds (~ 0.15 mg/seed, ~ 40 seeds/fruit) lack dispersal structures and are passively dispersed with blowing sand. Seeds germinate during winter rains, and established plants appear to live for 1–3 yrs, although most are annual (K.E. Samis and C.G.
Eckert, unpublished data).
Given that the spatial scale at which the genetic and ecological factors imposing the range limit are difficult to ascertain in any species, we designed our experiment to assess fitness variation at two spatial scales (Table 5.1, Fig. 5.1). The “reciprocal experiment” included seedlings from four naturally occurring source populations located across the final 132 km of the
136 species’ northern range in Oregon, USA. In this experiment, populations E2, W36, W102 and
W132 (E = edge, W = within range, number = km from range edge; note Samis and Eckert
(2009) used place names for the same populations) were reciprocally transplanted into sites E0
(the range edge), W36, W102 and W133 (where site:source pairs, E0:E2 and W133:W132, are different patches of occupied habitat and plants in the same dune complexes). The “large-scale experiment” included four more source populations from further south in northern California,
USA (W176, W290, W441 and W611; Fig. 5.1) transplanted along with the four northern source populations at the most southern transplant site (W133), at the range edge (E0), and at one site 60 km beyond the range limit (B60). Experimental details are provided in Samis and Eckert (2009) and briefly summarized below.
Four to six (median = 5) seedlings per 24 maternal families per source population were randomly allocated to 16 blocks at each of sites W133, E0, B60 (~7 or 8 seedlings/source population/block; total n = 8 sources × 24 families × 5 replicates = 960 plants × 3 sites) and eight blocks at sites W102 and W36 (15 seedlings/source population/block; total n = 4 sources × 24 families × 5 replicates = 480 plants × 2 sites). A total of 3782 seedlings (seeds would have been too difficult to track in dune habitat) were planted in April and monitored throughout their lives, over three reproductive seasons. For each transplant, survival and fruit production were recorded at the end of the first, second and third growing seasons (see Samis and Eckert 2009). Lifetime fruit production reliably measures lifetime fitness because fruit number per plant correlates strongly with seeds produced per plant (mean r ~ +0.95, n = 1502 plants, Samis 2007).
Although we used open-pollinated seed and thus fitness could be influenced by non-genetic, maternal environment effects, these are unlikely to have contributed much to fitness variation among source populations (Samis 2007). Mean maternal family seed mass varied among source
137 populations (range = 0.12–0.17 mg/seed; 1-way ANOVA P < 0.0001), but did not correlate with latitude or any component of experimental transplant fitness at any of the five planting sites
(Samis 2007). Similarly, the vigor of maternal plants, as reflected by their size, did not correlate with latitude (r = –0.30, P = 0.11, n = 29 populations; reanalysis of data from Samis and Eckert
2007) or the mean performance of source populations in the experiments reported below. Meta- analyses of transplant experiments have failed to show that attempts to eliminate maternal effects influence estimates of local adaptation (Hereford 2009; C.G. Eckert, unpublished data). Although we did not quantify local adaptation at germination and emergence, we failed to detect variation in these early life history stages across planting sites for plants growing from seeds produced by transplanted individuals, suggesting that conditions for recruitment and early selective pressures are unlikely to vary across our planting sites (see Samis and Eckert 2009 for empirical results
[Fig. 5.5] and detailed discussion).
Statistical analysis of local adaptation
Data for lifetime fruit production were strongly zero-inflated as 35% of plants died without reproducing. Accordingly, we performed all analyses using ASTER models (reaster() command, version 0.8-27; Shaw et al. 2008; Geyer et al. 2007, 2013) for the R statistical computing environment (version 3.2, R Core Development Team 2015). We modeled survival and flowering as Bernoulli variables (0 or 1) and seasonal fruit productions as a zero-truncated Poisson variables (see life-cycle graph in Fig.5.2). We tested the fixed effects of source population, planting site and their interaction on the composite of these fitness components using likelihood ratio tests. Block nested within planting site was modeled as a random effect. ASTER models based on the 3-year life cycle graph (Fig. 5.2) failed to converge due to the small numbers of plants surviving to and reproducing in year 3 (only 4 of 1881, and 15 of 2834 plants reproduced
138 in year 3 in the reciprocal and large-scale analyses, respectively). Hence, we merged fruit production in years 2 and 3 (F2 and F3 in Fig. 5.2), which worked because all individuals reproducing in year 3 also reproduced in years 1 and 2 (S1 = 1 and S2 = 1 for all S3 = 1). For graphical display, we calculated unconditional expected values of lifetime fruit production and its standard error from fixed effects models (C. Geyer, personal communication). Upon discovery of a significant site by source interaction, we analyzed variation among source populations within each site and performed pairwise comparisons among source populations using ASTER while holding the overall type I error rate at 5% using sequential Bonferroni (Holm 1979).
We also evaluated local adaptation by testing whether at any given site the mean fitness of a source population correlated negatively with distance between the planting site and its home site. With lifetime fruit production estimated as above, we fit fixed-effects linear models with site as a categorical predictor, distance (km and climatic, separately) from source site as a continuous predictor and their interaction. Because, the performance of a source at one site was not statistically independent of its performance at another, we tested the significance of these three parameters using permutation tests with 10,000 randomizations of the data (Manly 2006). We present approximate P-values as they varied slightly between randomization runs.
To determine the climatic distance among sites and sources, we extracted yearly mean climate for each location (11 observations of nine variables: air temperature, diurnal temperature range, frost free days, precipitation, wet days, relative humidity, percent sunshine [all CRU CL
2.0], potential evapotranspiration [Malmstrom PET], and soil moisture [CpcSoilMoisture]) for the 30-year period from 1971-2000 from each data source using FetchClimate (Microsoft
Research Cambridge; http://research.microsoft.com/en-us/projects/fetchclimate). We calculated the Euclidean distance among sites and sources in each experiment using the dist function in R
139 based on the first five principal components calculated using the correlation matrix of climate variables in the R package FactoMineR (version 1.32, Lê et al. 2008).
Seedlings were prepared for transplanting in the greenhouse and planted into experimental plots one site at a time, hence there was unavoidable variation in initial seedling size among sites.
However, within-site correlations between size at planting and lifetime fruit production were inconsistent and usually non-significant. ASTER models including initial size as a covariate yielded near identical results as those presented below (Supporting Information 2).
Population genetic analyses
In 2009, we sampled six of the eight source populations used in the transplant experiment
(logistic constraints prevented sampling of the other two). At all sites, we sampled ≥ 500 m from transplant plots to avoid transplant experiment descendants. Each sample was genotyped for 13 polymorphic microsatellite loci following Lopez-Villalobos et al. (2014). We quantified within- population genetic diversity using several complementary measures: total number of alleles detected (A), total number of private alleles (Ap), rarefied number of alleles per locus (Ar) using
1000 bootstrap samples of the smallest sample size (n = 20), observed (Ho) and expected heterozygosities (He) and the inbreeding coefficient (F) estimated using the R package DiveRsity
(version 1.9.73, Keenan et al. 2013). We tested whether these parameters declined towards the range edge or were correlated with mean lifetime fitness among source populations (averaged across the two common planting sites within the range or at the site beyond the range) by computing Pearson correlations.
Several complementary analyses evaluated the amount and pattern of genetic differentiation among populations. First, we assigned individuals into genetically differentiated clusters with the Bayesian method implemented in INSTRUCT (Gao et al. 2007), which uses a
140 model similar to STRUCTURE (Pritchard et al. 2000) but allows inbreeding (Gao et al. 2007,
2011) that predominates in populations of C. cheiranthifolia studied here (Dart et al. 2012). We ran simulations using a model that infers population structure with admixture from 1–15 clusters,
500,000 MCMC iterations with 250,000 burn-in steps and 10 thinning intervals for 10 independent replicates. Memory was increased, but otherwise we used defaults for all other parameters. The Gelman-Rubin statistic indicated convergence between chains. The most likely number of clusters (K) was determined using the delta-K (ΔK) method of Evanno et al. (2005) and by examining deviance information criterion values (Gao et al. 2007, 2011; Fig. A5.1). The outputs of repeated independent runs with the optimal K were aligned using CLUMPP (version
1.1.1, Jakobsson and Rosenberg 2007). We partitioned genetic variation among INSTRUCT clusters and populations within clusters with hierarchical analysis of molecular variance
(AMOVA, Excoffier et al. 1992) using the R package poppr (version 1.1.4, Kamvar et al. 2014) and tested for significance using randomization tests in ade4 (version 1.6.2, Dray and Dufour
2007). Differentiation among INSTRUCT clusters was visualized with principal component analysis (PCA) based on covariance among allele frequencies using GenoDive (version 2.0b23,
Meirmans and Van Tienderen 2004). Using the R package ecodist (version 1.2.9, Goslee and
Urban 2007), we quantified isolation-by-distance among populations by correlating pairwise genetic distances (measured as FST estimated using DiveRsity) and pairwise geographic distance
(measured as the great circle surface distance) between populations, and tested for significance using a Mantel test with 10,000 permutations. We used FST to measure differentiation among populations as it was influenced to a lesser extent by variation in allelic diversity than alternative measures (e.g. G’ST, Jost’s D, Meirmans and Hedrick 2011).
We used Bayesian assignment implemented in BAYESASS (version 3.0, Wilson and
Rannala 2003) to estimate proportional gene flow (m) over the last several generations between
141 INSTRUCT clusters. Each run involved 107 steps with a burn-in of 106 steps, a sampling interval of 100 steps and mixing parameters of 0.1 for m, 0.3 for the inbreeding coefficient (F) and 0.3 for allele frequencies. Convergence was evaluated using Tracer (version 1.6.0, Rambaut et al. 2014) to examine trace files and compare convergence and resulting posterior distributions from three independent runs with different random number seeds. Bayesian deviance was calculated for each run following Meirmans (2014).
Longer-term gene flow between INSTRUCT clusters was evaluated using Bayesian
MCMC coalescent analyses in migrate-n (version 3.2.6, Beerli 2006, 2009), which estimated mutation-scaled effective population size (Qj = 4Neµ) of each cluster and the mutation-scaled
immigration rates between clusters (Mij = mij/µ, where mij is the proportion of recipient cluster j consisting of immigrants from source cluster i). We used uniform priors for both Q
(0,10) and Mij (0, 1000) divided into 1,500 bins, the Brownian motion approximation of the step-wise mutation model for microsatellites and a constant mutation rate across loci. Each run consisted of four replicate long MCMC chains of 5 x 106 steps minus a burn-in of 10,000 steps with a sampling increment of 100 steps. To facilitate exploration of parameter space, we used static heating with temperatures of 1, 1.5, 3 and 1000. Results were combined from the four
replicate chains. Qj and Mij were estimated as the mode of the posterior distribution with lower and upper 95% confidence limits estimated as the 2.5- and 97.5-percentiles of the posterior distribution, respectively. Reliability of parameter estimates was evaluated by comparing results from three independent runs.
RESULTS
Transplant experiment
142 Within the first growing season, 79% of all 3782 plants survived and 51% reproduced (Fig. 5.2).
Over three years, 35% of plants did not reproduce at all, 39% reproduced in only the 1st season,
14% in only the 2nd season, 12% in the 1st and 2nd seasons, 0.37% in the 2nd and 3rd seasons, and
0.13% in all three seasons. After the 3rd season, 20 plants were alive but all had died by two years later.
ASTER analysis (Table 5.2) revealed that for both the reciprocal and large-scale analyses, lifetime fruit production (“fitness” hereafter) varied among planting sites, source populations, and that these two effects interacted. Although the pattern of fitness variation among sites differed somewhat among source populations, fitness consistently increased towards the range limit in both analyses (Fig. 5.3). In the reciprocal analysis, highest fitness occurred at the two northerly sites, which did not differ from each other (mean lifetime fruit production ± SE: E0 = 8.37 ± 0.35 fruits, W36 = 8.69 ± 0.36; pairwise contrast P = 0.17), and declined significantly to W102 (5.02
± 0.265) and again to the most southerly site (W133 = 3.48 ± 0.22; all other pairwise P <
0.0001). The large-scale analysis also revealed a more than 2-fold increase in fitness from site
W133 (3.11 ± 0.18) to E0 (8.74 ± 0.31) for all eight source populations (P < 0.0001). Mean fitness increased substantially again from the range edge to beyond the limit (B60 = 14.56 ± 0.36;
P < 0.0001). However, this increase was evident only among the five most northerly source populations (E2–W176), not the three more southerly California populations (W290–W611).
The fitness of source populations relative to each other varied significantly among sites in both analyses (Table 5.2) but there was no evidence of local adaptation. Instead of HSA, the reciprocal analysis revealed either consistent differences between sources that did not vary among sites or idiosyncratic site-specific differences between sources (Fig. 5.3A). For instance, population W36 consistently outperformed W132, and the pairwise contrast between these
143 populations was significant at all sites within the range (Table A5.1) as well as at B60 beyond the range (Table A5.2). Pairwise differences between other Oregon sources were either inconsistent among sites or not significant (Table A5.1). The large-scale analysis revealed some evidence for local adaptation, but it was inconsistent and only evident at the coarsest spatial scale. Within the two clusters of populations beyond the limit (B60; Fig. 5.3B) there was no tendency for superior performance by the more northerly sources (Table A5.2). At the range edge (E0) there was a notable lack of differentiation in fitness among source populations (Figs. 5.3 and 5.4). Significant variation at this site was due to particularly low performance by W132 (Table A5.2). When this population was removed from analysis, variation among the remaining sources was far from significant (LR = 4.86, df = 6, P = 0.56). There was significant variation among sources at the most southerly site, but again no tendency for superior performance by local sources.
Under local adaptation, the relative fitness of transplants at a site should correlate negatively with the geographic and/or climatic distance of their source population from that site Figs. 5.1,
5.4). In the large-scale analysis, geographic and climatic distance correlated strongly (r = +0.95,
P < 0.0001, n = 24). Fitness varied among sites (permutation P < 0.00001) and declined with distance (geographic P ~ 0.0020, climatic P ~ 0.0057), but there was significant heterogeneity in the effect of distance among sites (geographic P < 0.0001, climatic P ~ 0.0016). Analyzing sites separately, distance was only significant for B60 (geographic: P ~ 0.0032, E0 P ~ 0.80, W133 P
~ 0.73; climatic: P ~ 0.0047, E0 P ~ 0.76, W133 P ~ 0.85). Excluding B60, distance, and the interaction with site were non-significant (all P > 0.60). At B60, the distance effect was not linear, and resulted from a contrast between high fitness of the five most northerly sources vs. lower fitness of the three southerly sources (Fig. 5.4). Similar analysis for the reciprocal analysis (Fig.
144 A5.2) did not reveal an effect of distance (geographic P ~ 0.90; climatic P ~ 0.76) or any interaction between site and distance (geographic P ~ 0.15; climatic P ~ 0.32).
Finally, because we did not transplant seeds our experiment may have missed differential early mortality, which may be important in dune habitat (Maun 2004). However, recruitment
(until year 5) from experimental plants at B60 was strong enough to maintain positive population growth beyond the range edge (Supporting Information 3, Fig. A5.3).
Population genetic diversity and structure
Rarefied allelic richness (Ar) and expected heterozygosity (He) were lowest for edge population
E2, and there was a consistent but non-significant tendency for He to increase with distance (km) from the range edge (Table 5.3). However, neither measure correlated with transplant fitness among source populations within or beyond the range. Estimates of the inbreeding coefficient (F) were uniformly high (all F > 0.64), as expected for predominantly selfing populations (Dart et al.
2012), but did not correlate with geographic position. F correlated negatively with transplant fitness within the range (Fig. A5.4) and at both within range sites considered separately (W133 r
= –0.46; E0 r = –0.76), but not at B60 (Table 5.3).
Populations were generally strongly differentiated (overall FST = 0.491, 95% confidence interval = 0.456–0.526, range of pairwise FST = 0.067–0.51). INSTRUCT analysis revealed three genetic clusters of two geographically adjacent populations each (Fig. A5.5, Fig. A5.1): W611 grouped with W290 (southern cluster), W176 with W132 (mid) and W36 with E2 (northern).
AMOVA partitioned genetic variance into 26.2% among clusters, 23.7% between populations within clusters, 34.8% among individuals within populations and 15.3% within individuals (all
145 components significant, P < 0.00001). The first three axes of the PCA accounted for 41.9%,
30.6% and 18% of genetic variance, respectively. PC1 clearly separated north from the other two clusters and revealed considerable overlap between W290 of the southern cluster and W176 of the mid cluster. PC2 distinguished mid from the other two clusters, which overlapped. PC3 separated component populations of southern and mid clusters, but not northern. Clusters and populations within clusters were arranged in latitudinal order only along PC1. Isolation-by- distance among populations was significant (Fig. 5.6, r = +0.44, P = 0.028), however the very low pairwise FST between E2 and W36 (FST = 0.067, mean between others = 0.34, range = 0.24–
0.51) contributed substantially to this correlation. With this removed, isolation-by-distance was weaker (r = +0.31) and not significant (P = 0.14).
Analysis of trace files indicated that independent BAYESASS runs converged to near- identical posterior distribution for all variables, with very similar Bayesian deviance (4305.849,
4306.488, 4306.938). Measures of contemporary gene flow between populations were all very low (< 1%) with no tendency for immigrants to disproportionately move from central to peripheral clusters (Table 5.4). The proportion of individuals classified as residents was very high
(> 98%) and did not differ between clusters.
Independent runs of migrate-n showed less convergence (Table A5.3). For instance, estimated mutation-scale effective population size (Q) was significantly higher for the southern than northern cluster (with mid intermediate) in one run but not in the other two, both of which still produced relatively high estimates of Q for southern but lowest estimates for mid. Moreover, the shape of the posterior distributions for Q was often irregular though roughly unimodal.
Posterior distributions for mutation-scaled gene flow (M) between clusters were all unimodal and near normal. Estimates were also largely concordant among runs (Kendall’s coefficient of
146 concordance = +0.75, c2 = 11.20, P = 0.047), but 95% confidence intervals were large and broadly overlapped zero. Again, there was no indication of higher gene flow towards edge (mean
M = 3.89, range = 3.00–4.33) than towards center (4.33, 3.00–5.67).
DISCUSSION
Experimental transplants combined with population genetic analysis yielded three main results that challenge our key predictions: Fitness increased towards and beyond the northern range limit of C. cheiranthifolia and there was no evidence that edge individuals were best suited to conditions beyond the range. And, despite strong differentiation of putatively-neutral microsatellite polymorphisms and indications of very limited gene flow among source populations there was no evidence of local adaptation, except at the coarsest spatial scale. Finally, although genetic variation did not decline significantly towards the range edge, diversity was lowest at the edge.
Geographic limits are generally viewed as a spatial expression of a species’ niche (Gaston
2003; Sexton et al. 2009), which predicts a decline in fitness of individuals transplanted towards the range limit and a further decline leading to unsustainable population growth beyond. In contrast, our results indicate a marked increase in fitness of experimental C. cheiranthifolia towards the limit and high fitness beyond for all eight populations originating 60-670 km away from the extralimital site. While broadly inconsistent with theoretical explanations for range limits, the pattern we observed is in line with results from the limited number of experimental transplants across geographical range limits. Reanalyzing data from Hargreaves et al. (2014) reveals 11 tests of geographic range limits (involving 10 taxa) where source populations were planted at both interior and edge sites, and in only five of these did fitness decline towards the
147 edge (Arrontes 1993; Jenkins and Hoffmann 1999; Battisti et al. 2005; Stanton-Geddes and
Anderson 2011). In three of the six tests where fitness increased from interior to edge (Prince
1976; Levin and Clay 1984; Levin and Schmidt 1985; Schulz and Bruelheide 1999; Crozier
2004), the relative increase was greater than what we observed in C. cheiranthifolia. In addition, of 33 tests reviewed by Hargreaves et al. (2014) where individuals were transplanted within and beyond geographical limits (almost always from interior source populations), fitness increased beyond the limit for 10. Several studies have also demonstrated long-term persistence of species planted beyond their range limits without matching within-range controls (reviewed in Gaston
2003; e.g. Woodward 1990; Van der Veken et al. 2007, 2012).
Our results suggest that, contrary to the abundant center model (Brown et al. 1995), habitat quality increases, not decreases towards the northern range edge in C. cheiranthifolia. However, it is difficult to identify the specific components of habitat quality that generated the observed increase. Samis and Eckert (2009) showed a systematic change in the species composition of the coastal dune plant community across the northern range margin that correlated strongly with fitness of transplanted C. cheiranthifolia. Although the abiotic variables underlying the geographical trend in plant community were not obvious, we can speculate that ecological factors play a role given that post hoc analyses of life history variation among the plants in this experiment indicate that the increase in fitness towards the range limit was primarily due to a steady decline in the proportion of plants dying before reproduction, which varied from 53% at site W133 to 16% at E0 (Supporting Information 4, Table A5.4). Much of this mortality was associated with plants withering, rather than obvious herbivory or disease. Given the importance of soil moisture for seedling survival in Mediterranean climate coastal dunes where growing seasons are dry (Dallman 1998; Maun 1994), increased moisture towards the limit (yearly mean soil moisture on latitude; r = +0.88, P < 0.0001) may explain increased survival.
148 High fitness beyond the range involved a more complex developmental response involving delayed reproduction, low pre-reproductive mortality (similar to at E0) and increased spring/summer and overwinter survival (Supporting Information 3 and 4). Beyond the range, plants also benefitted from a 2-fold increase in the probability of reproduction in more than one year (Table A5.4). Delayed growth and reproduction has been observed for other plant species transplanted beyond their range, but it most often reduces fitness (Gaston 2003), as exemplified by Mimulus cardinalis planted above its altitudinal range (Angert and Schemske 2005). However, this response is not universal (e.g. Prince and Carter 1985; Stevens and Emery 2015) and, in the case of C. cheiranthifolia, reproduction delayed until the second season was associated with increased lifetime fruit production (Table A5.4). Taken together, our results support the conclusions of Samis and Eckert (2009), who proposed that the conditions beyond the limit or at least C. cheiranthifolia’s response to those conditions are not a simple continuation of the same environmental or developmental trend leading up to the limit.
Although lifetime fruit production was highest beyond the range limit, delayed reproduction at that site reduced the effect of high net reproductive rate (Ro) on population rate of increase (r).
Life table analysis for each source population at each planting site (Supporting Information 6) revealed that while r did not increase beyond the limit (as observed for lifetime fruit production,
Fig. 5.3) it increased towards the range limit for all source populations, and was still higher at
B60 than at W133 for all but one population (Fig. A5.5).
Weak local adaptation
Although stable range limits are thought to arise from a failure to adapt to conditions beyond, experimental analyses of local adaptation towards range limits are rare and results are mixed
149 (Hargreaves et al. 2014). We found no evidence for local adaptation in the reciprocal analysis and, in the large-scale analysis, only weak evidence for geographical (or climatic) adaptation, and only at the largest possible spatial scale (the relatively low fitness of three southerly compared to five northerly sources at B60). Moreover, seven of eight sources had very similar performance at the edge site, and their relative fitness did not decrease with increasing geographical or climatic distance from home at any within-range site. The lack of local adaptation across such large spatial scales seems unusual. The home-site advantage (HSA) indicative of local adaptation often occurs (~70% of tests, Hereford 2009; Leimu and Fischer 2008) although strict reciprocity of
HSA occurs less frequently (< 50% Leimu and Fischer 2008). Our reciprocal analysis detected
HSA at only one site (but this particular resident population performed well at all sites) and reciprocal HSA in none of the six dyadic comparisons among sites and sources. However, existing reciprocal transplant studies may overestimate local adaptation, as many involve populations that were phenotypically (“ecotypically”) differentiated or were distributed across dramatic ecological gradients (e.g. Sambatti and Rice 2006, Ågren and Schemske 2012). In comparison, studies of range limits using reciprocal transplants are often blind to phenotypic or environmental variation among populations. Accordingly, several transplant studies have failed to detect significant local adaptation towards range edges (Prince and Carter 1985; Arrontes
1993; Angert and Schemske 2005; Geber and Eckhart 2005; but see Griffith and Watson 2006,
Vergeer and Kunin 2013, Volis et al. 2014, 2015).
Of three potential explanations for the absence of local adaptation across a northern range limit, none seems likely for C. cheiranthifolia. First, local adaptation requires that populations are distributed across an environmental gradient that affects fitness (e.g. Hereford 2009). Given the modifying effect of the ocean on the abiotic environment of coastal dunes, it is possible that our
150 planting sites occurred across a relatively gentle ecological gradient. However, differentiation in climate (Fig. 5.1) and dune plant community (Samis and Eckert 2009) among transplant sites and source populations, plus mean fitness of experimental C. cheiranthifolia across transplant sites clearly rejects this explanation and indicates substantial environmental differentiation to set the stage for local adaptation.
The evolutionary response of populations experiencing differential selection across a gradient may be opposed by gene flow (Lenormand 2002) thwarting local adaptation at the range limit
(Kirkpatrick and Barton 1997; Barton 2001; Bridle et al. 2009). However, our estimates of short- and long-term gene flow (albeit with wide CI) are very low, which is perhaps not surprising given that seeds of C. cheiranthifolia lack any obvious dispersal mechanisms, and populations in the northern half of the range are largely selfing (Dart et al. 2012). Moreover, source populations were strongly differentiated at microsatellite loci and exhibited significant genetic structure that correlated to some extent with geography. That being said, a mismatch between the pattern of population differentiation for neutral polymorphisms and traits influencing fitness is not unexpected (Reed and Frankham 2001, 2003). There are several examples where adaptive divergence has occurred despite high gene flow, suggesting local selection is strong enough to overwhelm gene flow (e.g. Hoekstra et al. 2004; Sambatti and Rice 2006; Fitzpatrick et al. 2015).
Our results are more paradoxical because populations have failed to locally adapt despite significant environmental variation among sites with low gene flow.
Finally, local adaptation may also fail if there is limited genetic variation for traits that improve performance. A lack of required genetic variants may be predicted in northern populations of C. cheiranthifolia where predominant self-fertilization, that occurs in all populations throughout the northern half of the species range, might reduce effective population
151 size thereby hastening the loss of variation via drift (Wright et al. 2008). Moreover, the reproductive assurance provided by selfing may allow populations to persist at small size or through bottlenecks (Lloyd 1980; Eckert et al. 2006) thereby exacerbating the loss of diversity.
However, there is little empirical support for the hypothesis that selfing constrains range expansion. Selfing taxa typically have larger, not smaller, geographic ranges than their outcrossing counterparts (Grossenbacher et al. 2015), and there is no evidence that they exhibit lower levels of local adaptation (Hereford 2010). In the case of C. cheiranthifolia, local density
(a major component of census population size) does not decline towards the northern limit
(Samis and Eckert 2007, 2009). However, long-term effective population size may decrease right at the range edge based on a reduction in within-population microsatellite allelic richness and expected heterozygosity in edge population E2. Although genetic diversity was expected to correlate positively with fitness (Reed and Frankham 2003), with both declining towards the range limit, neither diversity measure correlated with the fitness among populations in our study
(Table 5.3). Despite the obvious logistic challenges, analysis of quantitative genetic variation involving multiple populations under field conditions is required to better evaluate the adaptive potential of C. cheiranthifolia populations and whether it declines towards the range edge (e.g.
Colautti et al. 2010; Volis et al. 2014).
Although our results do not provide strong evidence of the often-predicted low genetic diversity in range-edge populations, we found some evidence that a combination of genetic drift and low gene flow may constrain population-level adaptation in C. cheiranthifolia. First, the inbreeding coefficient and fitness of experimental transplants correlated negatively among source populations when planted within the range (Fig. A5.4). This is likely due to the differential fixation of genetic load among predominantly selfing populations (Bataillon and Kirkpatrick
152 2000; Kirkpatrick and Jarne 2000), as crossing experiments have detected only negligible segregating load within northern C. cheiranthifolia populations (Dart and Eckert 2015). Second, we detected significant variation in fitness among source populations at some planting sites but this was manifested as a home-site advantage in only one instance. Instead, some populations
(especially W36) performed well at several sites. Such unilateral fitness advantages are common in reciprocal transplant experiments (Leimu and Fischer 2008) and likely arise from restrictions in the spread of favorable alleles. Local adaptation depends on the balance between spatial variation in selective pressures and gene flow. Too much gene flow overwhelms selection but low gene flow should enhance local adaptation by allowing the spread of advantageous alleles, especially in extreme environments (Gomulkiewicz et al. 1999; Alleaume-Benharira et al. 2006).
Third, supplementary analysis indicated that the amount of variation in lifetime fruit production among maternal families within source populations (a crude estimate of genetic variation for fitness within populations, Supporting Information 7) was very low across all three planting sites in the large-scale analysis, accounting for < 3% of total phenotypic variation in fitness (Table
A5.6). Although this increased slightly from W133 (~ 0%) to E0 (1.5%) and B60 (2.3%), it was not significant at any site. The hypothesis that low gene flow constrains genetic capacity for local adaptation in northern C. cheiranthifolia could be supported by transgressive segregation for fitness among progeny from between-population crosses (Johansen-Morris and Latta 2006).
Challenges to theoretical conceptions of range limits
All explanations for long-term species’ range limits variously focus on low genetic diversity and antagonistic gene flow, but all fundamentally assume that range limits are coincident with niche limits such that fitness declines towards the range edge. Along with previous results from Samis
153 and Eckert (2009) this study rejects that assumption, suggesting a role for dispersal limitation
(e.g. Carter and Prince 1981, 1988: Holt and Keitt 2000; Holt et al. 2005; Bahn et al. 2006,
Stevens and Emery 2015). Observed declines in patch size and occupancy towards the northern limit of C. cheiranthifolia (Samis and Eckert 2009) led us to hypothesize that this limit might have arisen from reduced between-patch dispersal. Our results support some of the predictions of this hypothesis, but not others. As expected, we found strong neutral genetic differentiation among populations indicative of limited dispersal. We also failed to find directional gene flow from core to edge populations (see also Dixon et al. 2013) characteristic of gene-swamping
(Bridle and Vines 2007). However, we did not observe a strong reduction in genetic diversity towards the range edge, nor did we detect local adaptation, or evidence that edge populations performed best beyond the range. Results from other studies are mixed. Gene flow was asymmetric in Mimulus cardinalis, but not strong enough to forestall adaptation at the range edge
(Angert and Schemske 2005; Paul et al. 2011). In contrast, asymmetrical gene flow (Moeller et al.
2011) might potentially be involved in the lack of adaptation at the range edge of Clarkia xantiana ssp. xanitana (Geber et al. 2005). As discussed above, it is possible that some restrictions on genetic diversity within populations, as reported for C. xantiana xantiana (Moeller et al. 2011), may hamper local adaptation in C. cheiranthifolia. However, high fitness at E0 and
B60 indicates that this apparent failure of adaptation is very unlikely to have contributed to the range limit. Further evaluation of whether the northern limit in C. cheiranthifolia is caused by dispersal limitation will require an integrated approach. Theoretical modeling could explore the possibility that altered metapopulation dynamics can impose a range limit despite an increase in the quality of habitat patches towards the limit. Additional transplants and population genetic analysis at finer spatial scales may reveal a role for patch dynamics.
154 Broader implications
The extent of local adaptation towards range margins impinges on the conservation value of
geographically peripheral populations, ecological restoration and assisted migration to
mitigate diversity loss during rapid anthropogenic climate change (Lawton 1993; Hunter and
Hutchinson 1994; Lesica and Allendorf 1995; Vucetich and Waite 2003; Bunnell et al. 2004).
In Canada, for instance, two-thirds of the 199 plants species protected under the federal
Species At Risk Act (SARA, Bill C-5), occur in peripheral populations at their northern range
limit in southern Canada but are much more abundant and widely distributed to the south in
the USA (Yakimowski and Eckert 2007; Gibson et al. 2009; R. Jackiw and C.G. Eckert,
unpublished data). Adaptation of peripheral populations becomes important when considering
how seed sources should be selected for ecological restoration. The apparent ubiquity of local
adaptation has led to the popularity of the “local is best” approach, and experimental
transplants have delineated seed transfer zones for key species (Havens et al. 2015).
Recognition of rapid climate change has sharpened the debate around these issues as
peripheral populations may be valuable if they are best poised to participate in range
expansion and/or provide the best seed for assisted migration (Aitken and Whitlock 2013).
Peripheral populations of C. cheiranthifolia exhibited performance on par with more
central populations suggesting they are of high genetic quality. However they do not seem
better adapted to conditions at the northern edge than populations from hundreds of km further
south, despite being genetically isolated from one another along a significant environmental
gradient (see Lesica and Allendorf 1995). Moreover, there is little evidence that peripheral C.
cheiranthifolia populations will provide the best genotypes for either natural or human-
assisted dispersal beyond the northern limit. The five most northerly source populations did
155 perform better at site B60 than the three more southerly populations, but there was no
distance-dependent trend among five populations distributed across the last 200 km of the
range (Supporting Information 5, Table A5.5). In this regard, our results are in line with the
scant experimental evidence to date (Gauthier et al 1998; Geber and Eckhart 2005; Poll et al.
2009; reviewed in Hargreaves et al. 2014). Such experiments shed light on the need for
species-specific decisions in management of peripheral populations (Havens et al. 2015) and
an integrated, experimentally-informed framework to predict success at and beyond range
edges.
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168 Table 5.1. Transplant sites and source populations of Camissoniopsis cheiranthifolia used in this study.
n source Range position n n Site (Source) populations Location transplants plants code planted per Latitude (°N), Longitude (°W) per site assayed site Beyond the northern range edge (B)
South Jetty, Siuslaw River, OR B60 8 944 – 43.981, –124.139
At the range edge (E)
Horsfall Beach, Horsfall, OR E0 (E2) 8 960 (20) 43.452, –124.277
Within the range (W)
Bullard's Beach SP, OR W36 4 470 32 43.146, –124.415 Euchre Creek Dunes, OR W102 4 478 – 42.540, –124.398 Pistol River State Park, OR W133 (W132) 8 930 23 42.262, –124.402 Tolowa Dunes State Park, CA W176 – – 20 41.871, –124.211
169 Manila Dunes, Humboldt Bay, CA W290 – – 25 40.847, –124.174 Mackerricher State Park, CA W441 – – – 39.503, –123.784 South Beach, Point Reyes NS3, CA W611 – – 22 38.046, –122.988 The number in each site code indicates the great circle distance (in km) from the range edge planting site (E0). The four northerly source populations (E2, W36, W102, W132) were reciprocally transplanted at E0, W36, W102 and W133 plus B60. The four southerly source populations (W176, W290, W441, W611) were planted along with the four northern source populations at W133, E0 and B60. Source populations E2 (43.436ºN, 124.277ºW) and W132 (42.271ºN, 124.405ºW) were collected near but not at their home planting site (E0 and W133, respectively). Numbers of individuals planted into each site and the number from each source assayed for 13 microsatellite loci are indicated. The number of plants assayed (mean = 24/site) is adequate to obtain reliable estimates of most population genetic parameters (Hale et al. 2012).
170 Table 5.2. ASTER analysis of variation in lifetime fruit production of Camissoniopsis cheiranthifolia among source populations and planting sites.
Analysis Effect df Likelihood P-value ratio
Reciprocal analysis (Sources: E2, W36, W102, W132 Site 3 17.39 0.00058 Sites: E0, W36, W102, W133) Source 3 79.09 < 0.0001
Site × Source 9 37.00 < 0.0001
Large-scale analysis (Sources: E2, W36, Site 2 16.33 0.00028 W102, W132, W176, W290, W500, W611 Sites: B60, E0, W133) Source 7 176.18 < 0.0001
Site × Source 14 327.81 < 0.0001
The reciprocal analysis included four northern source populations reciprocally transplanted 0– 133 km from the range edge. The large-scale analysis included eight source populations planted at one site within the range (W133), one at the range edge (E0) and another beyond the range limit (B60). See Table 5.1 and Fig. 5.1 for details. Details of the ASTER analysis are in Fig. 5.2 and summary statistics are in Fig. 5.3. The significance of source, site and the interaction between the two were evaluated using likelihood-ratio tests. Significant variation among transplant blocks within sites was detected in all models (P < 0.0001).
171 Table 5.3. Measures of genetic diversity at 13 microsatellite loci for six populations of Camissoniopsis cheiranthifolia assigned to three geographically distinct clusters (South, Mid, North) by INSTRUCT analysis.
Population n A Ap Ar Ho He F Cluster E2 20 26 3 1.74 0.03 0.10 0.73
W36 32 50 8 2.88 0.09 0.25 0.64
North 52 54 13 3.37 0.07 0.21 0.68
W132 23 38 7 2.50 0.07 0.31 0.78
W176 20 27 4 1.86 0.05 0.19 0.74
Mid 43 50 11 3.33 0.06 0.33 0.82
W290 25 42 10 2.54 0.08 0.22 0.67
W611 22 39 8 2.70 0.12 0.36 0.68
South 47 60 21 3.88 0.10 0.39 0.75
Correlations
Distance to range edge (km) +0.38 +0.75† +0.71† -0.27
Within-range fitness +0.21 +0.07 -0.36 -0.83*
Beyond-range fitness -0.32 -0.57 -0.52 +0.16
Parameters: n = plants assayed, A = number of alleles detected in a sample, Ap = number of private alleles, Ar = bootstrapped mean alleles per locus adjusted to n = 20, Ho = observed heterozygosity, He = expected heterozygosity, F = inbreeding coefficient. Among-population correlations between genetic parameters and distance to the range edge (km) and mean lifetime fruit production of transplants averaged across planting sites at W133 and E0 and lifetime fruit production of transplants at site B60 are reported below (* P < 0.05, † 0.05 < P < 0.10).
172 Table 5.4. Contemporary proportional gene flow between population genetic clusters of Camissoniopsis cheiranthifolia estimated using BAYESASS.
From (j) North Mid South In (i): (W36 + E2) (W132 + W176) (W290 + W611) North 0.9875 0.0064 0.0061 (0.9708 – 0.9997) (1.198e-8 – 0.0190) (1.134e-7 – 0.0180) Mid 0.0073 0.9851 0.0076 (5.379e-7 – 0.0215) (0.9653 – 0.9996) (3.969e-8 – 0.0226) South 0.0067 0.0067 0.9866 (1.247e-7 – 0.0197) (1.126e-8 – 0.0199) (0.9687 – 0.9997) Estimates are the proportion of individuals in population i that are derived from population j. Diagonal elements (i = j) are the proportion of residents in each population. Values above the diagonal represent gene flow towards the range edge; those below represent gene flow towards the range center. 95% confidence intervals are in brackets below each estimate.
173 A
B
N
B60 60 km beyond
E0 Range edge
W36
W102
45° W133 W176
Oregon W290
California
40°
W441
35°
W611 0 30 60 km
Mexico 30°
1.0 0.0 −1.0 −2.0 PC2 (8.7%) −7.0 −6.0 −5.0 −4.0 −3.0 −2.0 −1.0 0.0 1.0 2.0 3.0 PC1 (76.1%)
Figure 5.1. Geographic locations (A) and ecological distance (B) among source populations and planting sites in a transplant experiment up to and beyond the northern range limit of Camissoniopsis cheiranthifolia. A. Dotted lines mark the species’ geographic range limits, and the arrow indicates the approximate range center. Inset: Three transplant sites were located within the range up to 133 km from the range limit (W36, W102, W133), one was at the range edge (E0), and one site was 60 km beyond the limit (B60). Four more southerly source populations (W176, W290, W441, W611) were also planted at W133, E0 and B60. See Table 1 for details. All eight source populations were planted into 16 blocks at each of W133, E0 and
174 B60 (closed symbols; large-scale analysis), and all four northerly sources were planted into eight blocks at W102 and W36 (open circles; reciprocal analysis also including sites W133 and E0, and sources W132 and E2 which are not shown, but at this scale overlap with W133 and E0, respectively). B. Scores of each transplant site and source population (E2 and W132, in grey) along the first two dimensions of a PCA on the correlation matrix of yearly means for nine climate variables at each location (see text for details). Axes have been scaled to represent the % variation explained by each component. PC1 is positively correlated with latitude (r = +0.97, P < 0.0001, n = 11).
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Figure 5.2. Hierarchical life history stages used in the ASTER analysis of variation in survival and reproduction. Probabilities of survival to (S1, S2, S3) and flowering (R1, R2, R3) in each reproductive season were modeled as Bernoulli variables (0 or 1), and seasonal fruit productions (F1, F2, F3) were modeled as zero-truncated Poisson variables. Sums for each life-history variable for the reciprocal and large-scale analyses are presented in the inset table to the right. When variables S1, S2, S3, R1, R2, R3, F1, F2, F3 are numbered 1 – 9, respectively, the ASTER variable predecessor structure (see left inset) of this life cycle is 0, 1, 2, 1, 2, 3, 4, 5, 6, where 0 is the root (total seedlings planted). Because so few individuals survived to and reproduced in year 3, ASTER models would not converge and we merged fruit production in years 2 and 3 because all individuals that flowered in year 3 also flowered in years 1 and 2 (revised predecessor structure = 0, 1, 1, 2, 3, 4).
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Figure 5.3. Variation in lifetime fruit production of Camissoniopsis cheiranthifolia among source populations and transplant sites. (A) Four source populations reciprocally transplanted at four sites up to the northern range limit. (B) Eight source populations transplanted at three sites up to and beyond the range limit. Points are expected values derived from the ASTER models in Table 2, and error bars are 95% confidence intervals. The experimental design along with site and source codes are explained in Table 1 and Fig. 1.
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Figure 5.4. The association between relative fitness of transplanted Camissoniopsis cheiranthifolia at a planting site and the geographic distance (upper panel) or climatic distance (lower panel) of their source population from that site in the large-scale analysis. Climatic distance is displayed as the Euclidean distance between each site-source pair based on location scores along five principle components of nine yearly mean climate variables (see text for details). Different symbols are used to represent the three planting sites. Points are unconditional expectation of lifetime fruit production estimated using the ASTER model in Table 5.2.
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Figure 5.5. Population genetic structure towards the northern range limit of Camissoniopsis cheiranthifolia. The top panel presents the results of Bayesian clustering performed using INSTRUCT on six of the eight source populations used in the transplant experiment. The bottom two panels present a principal components analysis (PCA) based on microsatellite allele frequencies for these populations. Three geographically adjacent INSTRUCT clusters are represented by different colors (south = red, mid = blue, north = green). In the PCA, the percentage of genetic variance explained by each axis is indicated, points are individuals, and the source populations are distinguished by a combination of point shape and color (corresponding to the INSTRUCT plot).
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Figure 5.6. Isolation-by-distance shown by a positive correlation between pairwise genetic distance (measured as FST) and geographic distance between populations of Camissoniopsis cheiranthifolia (r = +0.44, P = 0.028). The asterisk marks the exceptionally low differentiation between populations W36 and E2. With this point removed, the correlation is weaker and not significant (r = +0.31, P = 0.14).
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CHAPTER 6 – GENERAL DISCUSSION AND FUTURE RESEARCH
Over the last two decades, the development of molecular techniques has spurred thousands of population genetic studies on a wide variety of plant and animal species. Particularly important, but still relatively rare, are studies that test for geographic variation in genetic diversity and structure across entire species’ ranges (reviewed in Eckert et al., 2008). My thesis contributes to this literature and addresses many of the methodological gaps identified (Eckert et al. 2008) that hinder testing predictions concerning geographic variation in genetic structure across species’ ranges. Collectively, the chapters that encompass my thesis contribute to the understanding of ecology and evolution of species’ geographic ranges (Gaston 2009; Sexton, 2009) by providing an evaluation of two fundamental questions: 1) What factors influence geographic variation in population genetic diversity and differentiation? and 2) Why do species exhibit evolutionarily stable limits to their geographic distributions? Each chapter (chapters 3 to 5) also provides a case study relevant in other fields of evolutionary biology, such as mating system evolution (Chapter
3), reproductive isolation and speciation (Chapter 3 and 4) and local adaptation (Chapter 5). By testing theoretical predictions in each of these fields, my thesis also uncovers several unexpected aspects of the evolutionary history of C. cheiranthifolia and C. bistorta. In the next section, I summarize the main findings from each chapter, highlighting the most novel and important contributions to the existing literature and identify questions that merit future study.
Chapter 2. Microsatellite primer for Camissoniopsis cheiranthifolia (Onagraceae) and cross-amplification in related species.
The development of 24 species-specific microsatellite loci for C. cheiranthifolia proved to be instrumental to addressing the evolutionary questions examined in this thesis and will likely
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continue to be a useful tool for future studies of the ecology and evolutionary history of C. cheiranthifolia and C. bistorta. In addition to being highly polymorphic in C. cheiranthifolia and
C. bistorta, some of them also amplified in eight other taxa (C. micrantha, C. lewisii, Eulobus crassifolius, E. californicus, E. angelorum, Camissonia benitensis, C. strigulosa and C. contorta) within and outside the genus Camissoniopsis, revealing a potential broader application. To highlight the relevance of these loci (aside from the studies presented in this thesis) for potential future studies, in restoration and conservation for instance, I will briefly summarize two studies
(not presented in this thesis) that have used some of these microsatellite loci. One of them is an undergraduate project that I designed and conducted in collaboration with Jon Viengkone and Dr.
Eckert and the other is a published study lead by Justen Whittall and his team at University of
Santa Clara in California (Dick et al. 2013).
In the study by Viengkone et al. (in prep.), we used 10 microsatellites (nSSR), flower size and fruit set to evaluate the effect of restoration in the coastal dune ecosystem where C. cheiranthifolia occurs. We tested whether 18 putatively restored populations of C. cheiranthifolia differed from 21 natural populations (n = 975 individuals total) in their levels of genetic diversity, structure and reproductive fitness. We investigated wheather restored populations exhibit shifts in the means and variances of floral traits, and if they show evidence of genetic admixture that might indicate that they were planted with nonlocal stock. We found that, for the most part, C. cheiranthifolia in restored sites did not exhibit reductions in fruit set or deviations from the natural regional flower size, and that levels of genetic diversity (measured as expected heterozygosity) did not differ between restored and natural populations. However, we detected high genetic admixture in three populations north of Point Conception that also exhibited greater floral sizes and significantly lower fruit set compared to natural sites from that region, suggesting
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that local genotypes were not used in restoration at those locations. This study is an example of how using these newly developed DNA markers, along with morphological data on reproductive effort and historical information about population locations can reveal past restoration practices.
This information could be used to inform future management and conservation decisions involving C. cheiranthifolia.
The study by Dick et al. (2013) demonstrates the use of these markers for conservation efforts. They investigated the genetic diversity of the serpentine endemic and federally threatened
Camissonia benitensis (Onagraceae) in two different serpentine habitats and compared it to its two widespread relatives with overlapping distributions, C. contorta (non-tolerant of serpentine soils) and C. strigulosa (found on both serpentine and non-serpentine soils). They also examined the potential for hybridization between C. benitensis and C. strigulosa to reveal potential threats for the conservation of this species. Using seven microsatellites, this study found that populations of C. benitensis are not differentiated according to habitat type or geography, suggesting a lack of ecotypic differentiation. Inbreeding coefficients were high and genetic diversity lower than the other two more widespread relatives. Moreover, they did not detect evidence of hybridization with C. strigulosa but they argue that the low genetic diversity might pose a threat to this species in the future. Management and conservation strategies should be aimed at maximizing genetic variation by including seeds from populations in the different genetic clusters.
In addition to the three manuscripts that I present in this thesis, these two studies represent a small example of the variety of opportunities that these microsatellites offer.
Chapter 3. Consequences of multiple mating-system shifts for population and range-wide genetic structure in a coastal dune plant.
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The work I present in this chapter contributes to three of the four principal reasons why the evolution of self-fertilization from outcrossing has attracted so much attention from earlier naturalists, taxonomists and evolutionary biologists, and continues to motivate the testing of theoretical predictions. According to Barrett et al. (2014), the shift to self-fertilization has become a model system in ecology and evolution for several reasons:
1) It has important genetic and genomic consequences (reviewed by Charlesworth &
Wright 2001) that can affect the genetic structure across species’ ranges (as described
in this chapter).
2) It is often associated with major changes in morphology, sex allocation and life-history
of species, thus contributing to ecological diversity and providing a valuable system
for the study of plant adaptation (Sicard and Lenhard 2011).
3) Selfing confers species the ability to reproduce under conditions of low mate
availability, providing them with the opportunity to establish colonies in new habitats
with few or a single individual following dispersal (Lloyd 1992). These ecological
scenarios are expected to result in important demographic consequences for
populations (Lloyd 1980).
4) Shifts to selfing can influence macroevolutionary processes affecting speciation,
extinction rates and overall lineage diversification (Igic and Bush 2013; Wright et al
2013).
In the following paragraphs I will be discussing four main results that are important contributions to these fields and will discuss some ideas for potential future research.
Consistent with the theoretical reductions of within-population diversity (point 1 above), and with the work on other species (e.g. Charlesworth & Yang 1998; Liu et al. 1998, 1999;
184
Wright et al. 2002; Mable et al. 2005; Mable & Adam 2007; Ness et al. 2010, Pettengill et al.
2016), we showed that SF-SC populations exhibit lower genetic diversity compared to LF outcrossing populations but contrary to expectations, genetic differentiation was not much greater for selfing than outcrossing populations (Hamrick & Godt, 1996; Duminil et al., 2007). Moreover, the overall reduction in diversity was greater than expected from the effect of selfing on effective population size (Ne) alone (Fig. 7; Pollak 1987), suggesting the contribution of other genetic or demographic processes associated with selfing (Busch & Delph 2012; Barrett et al., 2014).
However, lineage-wide levels of genetic diversity do not support the hypothesis that bottlenecks
(and hence reproductive assurance) are consistently associated with the transitions to selfing in C. cheiranthifolia as predicted if reproductive assurance (mentioned above in 3) was the primary mode by which selfing evolved in this species (Raven 1969; Linsley et al. 1973. Although populations representing all three categories of mating system (LF-SI, LF-SC and SF-SC) and were included in our analysis of the effect of selfing on genetic diversity (Fig. 3.7), one question that arises from this result is whether adding more populations to our analysis would produce a result more in line with the expected 50% reduction due to selfing alone.
The lack of support for the reproductive assurance hypothesis, in particular, challenges our interpretations of the mechanism behind the evolution of selfing in two cases; one associated with strong genetic structure between SF-SC and LF-SC populations north and south of Pt.
Conception and one with no strong genetic structure detected between LF-SC mainland and SF-
SC island populations (Fig.3.3). First, the evolution of selfing and the colonization north of Pt.
Conception has been hypothesized to be associated with differences in the ecological environment north and south of Pt. Conception (Linsley et al., 1973; Raven, 1969). The much stronger winds, cooler temperature and more regular fog during the flowering season compared to sites south of Pt. Conception would impede outcross pollination thereby selecting for self-
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fertilization. Despite evidence of much lower fruit set in large-flowered populations and pollen limitation in comparison to small-flowered populations (Dart et al., 2012; Dart and Eckert, 2015),
I did not observe a strong reduction in genetic diversity in SF-SC compared to LF-SC populations, as would be expected if selfing evolved via reproductive assurance, although, a thorough test to rule out this hypothesis has yet to be conducted.
The colonization of the Channel Islands from the mainland is an ideal scenario where one would expect that selfing evolved via reproductive assurance after long distance dispersal. Given that the capacity for uniparental reproduction (and thus selfing) and the establishment of colonies following long-distance dispersal (called “Baker’s law” after Stebbins 1957) has long been pinned to selection for reproductive assurance (Baker, 1955; Barrett, 1996; Pannell et al., 2015), the analyses on the three Channel island populations represent an interesting case study. First, we found no support for a bottleneck associated with the colonization of the islands (aggregate genetic diversity was not strongly reduced). And second, despite being separated by ocean we did not find support for the classic expectation that restricted gene flow and more pronounced genetic drift (given the expectation of smaller population sizes) should lead to less within-population genetic variation and higher among-population differentiation in island than mainland populations (see Barrett 1996; Frankham 1997; Franks 2010). Instead, we showed that within- population genetic diversity and diversity among the three island populations (aggregate genetic diversity) were unexpectedly high, despite high selfing rates in these SF-SC populations (Dart et al., 2012).
Linking the genetic (point 1) and ecological consequences (point 2) of the evolution of selfing, this study showed that at the largest geographic scale, shifts to higher levels of self-
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fertilization, associated changes in the morphology of the flowers and the ecology of the populations coincide with three of the four deep genetic subdivisions across the range of C. cheiranthifolia, suggesting that selfing may often initiate ecogeographic isolation (nSSR; Fig.3.3).
The most differentiated of these genetic clusters is in fact, also associated with the most complex transitions that involve the loss of self-incompatibility and the reduction in flower size by almost half of the average found among LF-SI populations (Dart et al. 2012). Moreover, these SF-SC populations in Baja California live in an ecological environment with more fluctuations in rainfall and temperature than that experienced by LF-SI populations, suggesting adaptation to a more desert-like compared to a more Mediterranean-like environment. Despite this, cpSSR data showed the same widely distributed haplotype among all populations south of Pt. Conception.
This last result suggests a common evolutionary origin with LF-SI populations, but leaves two questions open. 1) What are the factors that caused the strong reduction in gene flow and divergence between LF-SI and SF-SC? 2) What are the mechanisms behind the evolution of selfing in these populations?
The genetic break that separates C. cheirantifolia populations south of Punta Banda
(populations in San Diego Co., Tijuana and Ensenada) from those further south in San Quintín is very similar to what has been found for the unrelated, but ecologically similar species Abronia umbellata (Greer, S.I. Wright, S. & C.G. Eckert unpublished data). Despite a different evolutionary history, both species show very similar nuclear genetic breaks. However, in contrast to C. cheiranthifolia, populations of A. umbellata south of Punta Banda are presumably outcrossing and show high levels of nucleotide diversity (Greer, S.I. Wright & C.G. Eckert unpublished data), yet both species exhibit almost-identical clustering of populations in this region, suggesting that other factors, not only those associated with the transition to selfing, may be involved in reductions to gene flow. Vicariance events may explain this; a north gulf split of
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the Baja California Peninsula between Ensenada and Punta Banda coincides with lineage diversification in other taxa with similar distributions (Riddle et al. 2000). However, haplotype diversity does not change south of Punta Banda in SF-SC populations in San Quintín. A comparative study between C. cheirantifolia and A. umbellata with universal cpSSR and other chloroplast sequences might provide insights into the similarities and differences of the genetic differentiation at Punta Banda. What remains to be understood is the mechanism behind the evolution of selfing in this group and the convergent overall morphology and mating system with populations north of Point Conception. A reciprocal transplant experiments with plants from LF-
SC and SF-SC populations could test the hypothesis that adaptation has been involved in the evolution of reduced flower size and selfing in the dryer habitats in Baja California.
Chapter 4. Does hybridization contribute to range-wide population genetic structure in a coastal dune plant?
Closely related species with overlapping distributions that experience hybridizations may enjoy increased levels of genetic diversity, providing genetic rescue for small populations (Carlson et al., 2014; Pfennig et al., 2016), but can also result in “gene swamping", thwarting local adaptation at range edges (via Kirkpatrick and Barton, 1997). However, despite these important consequences of hybridization, it is rarely considered in studies investigating the genetic structure of species ranges (Arnold 1997). Previously (Chapter 3), we found genetic structure and differentiation between large-flowered and outcrossing populations (LF-SI and LF-SC), as well as a lack of an expected pattern of isolation-by-distance, despite no geographic discontinuities in their distributions or strong differences in the ecological environment. We hypothesized that hybridization with C. bistorta might have contributed to the genetic differentiation between LF-
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SI and closely parapatric LF-SC populations of C. cheiranthifolia. This would have been facilitated by closest geographic proximity and a self-incompatible system because hybrid individuals bearing novel S-alleles would enjoy enhanced male fitness if there was of low S- allele diversity in recipient populations, explaining consequent differentiation between LF-SI and
LF-SC C. cheiranthifolia.
In this chapter, I confirmed the closest parapatry between LF-SI C. cheiranthifolia and C. bistorta, but we did not detect evidence of hybridization. Large-scale analyses of genetic structure across both species’ ranges showed six genetic clusters: four of these identical to those previously found in Chapter 3 and two more clusters of LF-SI populations including both C. cheiranthifolia and C. bistorta. These were differentiated from nearby LF-SC populations to the north and SF-SC populations to the south in Baja California. Strong genetic divergence between
LF-SI (C. cheiranthifolia and C. bistorta) and SF-SC populations from Baja California south of
Ensenada is not surprising given that these populations are geographically disjunct and strongly differentiated in floral morphology and mating system. However, geographically adjacent and outcrossing LF-SC populations to the north were also genetically differentiated, although somewhat overlapping in the multivariate space. Several questions remain unanswered: Why are
LF-SC and LF-SI populations reproductively isolated? What are the factors preventing gene flow between LF-SC and LF-SI populations? If hybridization is not playing a role here, then what other aspects of the ecology or the landscape in this region maintain reproductive isolation between LF-SC and LF-SI populations?
One mechanism that prevents gene flow between recently derived SI and SC taxa is the presence of unilateral incompatibility (UI) that allow cross-compatibility between closely related
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species or populations involving pollen of SI individuals onto SC the stigmas but where the reciprocal cross fails (SI x SC rule; Lewis and Crowe 1958; de Nettancourt 2012). In C. cheiranthifolia this possibility seems unlikely given that preliminary data from reciprocal crosses between LF-SI and LF-SC populations of C. cheiranthifolia produced seed set in both directions, suggesting no post-pollination pre-zygotic isolation (López-Villalobos and Eckert unpublished).
However pollen tube growth, germination rates and fitness of the offspring have never been evaluated and these are required to test for post-pollination pre-zygotic isolation and better understand the mechanisms underlying apparent barriers to gene flow between SI and SC populations.
In contrast to the genetic differentiation between LF-SC and LF-SI populations of C. cheiranthifolia, genetic structure between LF-SI C. cheiranthifolia and C. bistorta only provided evidence of a latitudinal pattern, roughly dividing populations south and north of San Onofre
California, rather than by species or by habitat type (inland vs. coastal). We predicted that evidence of reproductive isolation should be consistent with higher between-species than within- species genetic differentiation. However, we did not find that FST was higher between-species than within populations of C. cheiranthifolia. This result, in addition to stronger genetic divergence in allele frequencies across the range of C. cheiranthifolia leads us to suggests that C. bistorta might represent an inland ecotype that arose form the coastal C. cheiranthifolia. In his revision of the genus Camissonia, Raven (1969) hypothesized that a self-incompatible ancestor very similar to the LF-SI populations of C. cheiranthifolia subsp. suffruticosa gave rise to the self-incompatible, annual C. bistorta and to the self-compatible perennial C. cheiranthifolia subsp. cheiranthifolia distributed north of Pt. Conception (Raven 1969 p. 161-162). Our results support the part of this hypothesis that suggests that C. cheiranthifolia might represent the
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ancestral species, and hence, that inland habitat is derived from coastal habitat. Our results do not agree, however, with the genetic divergence that leads to species delimitation, at least based on these 12 microsatellite loci. The first step to confirming our hypothesis that C. bistorta is an ecotype of C. cheiranthifolia requires characterizing morphological and physiological traits between LF-SI populations of both species, in both the field and glasshouse conditions (e.g.
Lowry et al. 2009). It has been shown in other studies of coastal and inland populations that adaptive differentiation at loci (QTLs) associated with important adaptive traits is sometimes stronger than neutral genetic variation (Lowry and Willis, 2010; Oneal et al., 2014). This approach can also be used to identify loci and alleles unique to each ecotype. If coastal and inland LF-SI populations are indeed ecologically differentiated, a transplant experiment to test for local adaptation would be the ultimate test to support our hypothesis of ecotypic differentiation. Other approaches to try to uncover further genetic differences could involve whole genome sequencing of coastal and inland individuals (e.g. greater number of loci, and the possibility to disentangle the contribution of neutral vs. adaptive traits).
Preliminary analyses using cpSSR (Appendix 6; FigA6.2-A6.5) showed more genetic structure between C. cheiranthifolia and C. bistorta but still did not show evidence that all populations of C. cheiranthifolia and C. bistorta are separated by their species identity. A group of six different haplotypes, strongly differentiated form the rest of the haplotypes in the network, were found among all the inland populations north of Escondido California (Fig. A6.2.). Six of these haplotypes were found in a single population and one in two populations. Five more haplotypes were found south of San Onofre California and one of these (haplotype 15) was widely distributed among both C. bistorta (both coastal and inland) and C. cheiranthifolia populations. Similar to the nSSR data, the cpSSR networks suggest a north to south
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differentiation between populations of C. bistorta and C. cheiranthifolia, but in this case, the southern group is the only one that shares haplotypes between species. These data should be taken with caution as sequencing revealed that fragment size variation at two loci (CCMP2 and
CSSR7) in two individuals (same population; CSP1B) of C. bistorta from the northern range limit is given by deletions in the flanking region (Fig. A6.1). Hence, I did not present these data in this study because the same variation that caused fragments to become shorter in C. cheiranthifolia does not have the same origin as the variation that causes fragments to become shorter in C. bistorta for the one individual sequenced. Sequencing more individuals exhibiting fragment size variation from other populations of C. bistorta should reveal the identity of the haplotype variation we observed in these preliminary analyses and provide insights into a deeper evolutionary history (i.e. seed dispersal) of these populations.
Chapter 5. Strong genetic differentiation but not local adaptation towards the range limit of a coastal dune plant
Explanations for the lack of local adaptation and hence the failure of a species to adapt to the conditions at the range limit, assume that range limits occur on an ecological gradient that affects fitness such that fitness of individuals migrating beyond the limit is insufficient for population persistence (Sexton et al., 2009). If range limits coincide with niche limits, then a lack of evolutionary potential in marginal populations is suggested. Some genetic predictions have been invoked to explain this pattern. Less genetic diversity in marginal populations (compared to those from the interior) can result from the lack of genetic variants (“Genostasis”, Bradswhaw, 1991) or stronger genetic drift, as predicted by the ACM (Brown, 1984; reviewed in Eckert et al., 2008).
In contrast, populations can maintain high genetic diversity, but adaptation to edge conditions
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may be thwarted by asymmetrical gene flow from large central populations (adapted to their home conditions) to small edge populations impeding adaptation (“gene swamping,” Kirkpatrick and Barton, 1997; Bridle et al., 2009). All these hypotheses predict a decline in fitness of individuals transplanted toward the range limit and a further decline leading to unsustainable population growth beyond the range. However, some studies have found that fitness increases at the edge and beyond. Furthermore, they all assume that the range limit is a spatial manifestation of the species’ niche limit and that population demography across the range corresponds to the
ACM, none of which are strongly supported by the available empirical literature (Hargreaves et al., 2014; Eckert, 2008). An alternative explanation is that stable range limits result from a limited dispersal capacity of the species in question (e.g., Stevens and Emery, 2015) or when gradients in habitat heterogeneity affect dispersal between available habitat patches (Holt et al.,
2005; Kokko and Lopez-Sepulcre, 2006).
Previously, Samis and Eckert (2009) suggested that dispersal limitation in a metapopulation and not niche limitation may explain the northern limit in C. cheiranthifolia
(Samis andEckert 2009). In chapter 5 we tested the hypothesis that the northern range limit of C. cheiranthifolia occurs on an ecological gradient that increases fitness, thereby reducing patch extinction toward the range edge, but also decreasing dispersal. As a result, the probability of patch recolonization and ultimately metapopulation persistence declines toward the limit. We confirmed that niche limits and range limits do not coincide in this species, and in fact, higher fitness of transplanted plants towards the edge and beyond indicate that habitat quality increases rather than decreases. However, some of our results challenged our predictions and the interpretations of the mechanisms behind the northern range limit in C. cheiranthifolia. For instance, despite evidence of significant climatic variation, strong genetic differentiation and very
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low estimates of short- and long-term gene flow (albeitwith wide CI), we did not find local adaptation in the reciprocal analysis and only weak evidence for geographical (or climatic) adaptation in the large-scale analysis. Furthermore, genetic diversity was expected to correlate positively with fitness (Reed and Frankham 2003), but neither expected heterozygocisty nor allelic richness correlated with the fitness among populations in this study, although genetic diversity drastically decreased right at the edge (E2) suggesting the importance of genetic drift.
The fact that our study did not meet one of the key assumptions for the explanation of long-term species’ range limits, that is, niche limits should coincide with species range limits, together with a lack of local adaptation, suggest an important role for dispersal limitation (e.g.,
Holt and Keitt, 2000; Holt et al., 2005; Bahn et al., 2006; Stevens and Emery, 2015). Additional transplants and population genetic analysis at finer spatial scales may reveal a role for patch dynamics and fine-scale population structure. Particularly interesting would be to compare the northern range limit with the southern range limit. What factors impose the southern range limit in Baja California? Are these similar to what we suggest for the northern range limit? Populations towards both range limits exhibit similar levels of genetic diversity (including the stronger reduction in the most northern and southern population) than populations used in this study
(Chapter 3), but: Is reduced genetic diversity just as a result of selfing on effective population size? Do populations in Baja California experience asymmetrical gene flow? Does genetic diversity correlate with fitness? Are populations towards the southern range limit locally adapted? In chapter 3 we hypothesized that populations towards the southern range limit, may have adapted to a much dryer (transition zone from Mediterranean climate to desert) environmental conditions and rapid maturation selected for the small size, small flowers and selfing in that region (Guerrant et al., 1989). This hypothesis predicts local adaptation in the
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southern range limit. Given that moisture is a limiting factor in the southern edge but was perhaps responsible for the higher survival of transplanted plants before reproduction (and thus higher fitness see Table A5.4), the southern range edge may impose a much harsher environment resulting in lower fitness and local adaptation. To answer these questions, perhaps a similar study combining transplant experiments combined with the genetic gathered for thesis can shed light into the mechanisms explaining the stability of range limits in C. cheiranthifolia.
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APPENDIX 2. Supplementary information for Chapter 2.
Table A2.1. Sampling information, population codes and mating type of individuals used in this study. Species: Camissoniopsis cheiranthifolia (ch), Camissoniopsis bistorta (bi), Camissoniopsis micrantha (mi), Camissoniopsis lewisii (le), Eulobus angelorum
(an), Eulobus crassifolius (cr), Eulobus californicus (ca).
Location Population Taxa Latitude Longitude Mating and Herbarium Country State n code sampled (°N) (°W) floral type accession no. Baja Mexico Guerrero Negro BGN cr 27.9556 -114.0670 5 LF-SI SD92680 California Transpeninsular Hwy BTH an 29.09152 114.15297 6 LF-SI SD144733 near Santa Ana Bocana del Rosario BBR ch 30.0478 -115.7863 7 SF-SC SD95717
Bocana del Rosario BBR cr 30.1691 -115.7973 5 LF-SI SD91289
El Socorro BES ch 30.3186 -115.8257 37 SF-SC SD52704
El Socorro BES le 30.3235 -115.8186 2 SF-SC SD11800
Bahia Santa Maria BBS cr 30.3973 -115.9051 4 LF-SI UCR41467
Bahia San Quinín BBQ ch 30.3801 -115.9904 5 SF-SC UCR38448
Bahia Falsa BBF ch 30.4558 -116.0342 5 SF-SC SD91177
La Chorera BCH ca 30.4782 -115.9929 5 LF-SI ASU0033348
San Martin Island BSM ch 30.48312 -116.1022 6 SF-SC SD77648
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Ejido Leandro Valle, Northwest of San BQW ch 30.7058 -116.0356 4 SF-SC SD91177 Quinín San Antonio del Mar BSA ch 31.1077 -116.3084 5 SF-SC SD124971 Punta Banda sand BPB bi 31.7258 -116.6481 6 LF-SI SD93875 spit Ensenada beaches BEB ch 31.8102 -116.6092 5 LF-SI SD64735 La Mission, scenic BMI bi 32.0946 -116.8811 5 LF-SI SD72998 Hwy Los Arenales BLA bi 32.2067 -116.9147 5 LF-SI SDSU3341 Paseo playas de BTB bi 32.5202 -117.1229 6 LF-SI SD101764 Tijuana Borderfields SP USA California CBF bi 32.5355 -117.1189 5 LF-SI SD181102 bluffs Borderfields SP sand CBF ch 32.5365 -117.1229 29 LF-SI SD83479 dunes
Silver Strand CSS mi 32.6385 -117.1425 5 LF-SI SD189780
Silver Strand CSS ch 32.6410 -117.1437 6 LF-SI SD38644
Willow Glen Dr. CWG bi 32.7568 -116.9011 6 LF-SI SD176653
Cuyamaca Street CCU bi 32.84763 -116.98145 21 LF-SI SDSU3338
El Monte CEM bi 32.8926 -116.8470 5 LF-SI SD3324
Torrey Pines SP CTP bi 32.9187 -117.2584 5 LF-SI SD181105
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Torrey Pines SP CTP ch 32.9290 -117.2591 6 LF-SI SD227356
Camp Pendleton CCP ch 33.2484 -117.4300 5 LF-SI SD203540 San Nicolas Island CSN3 ch 33.2655 -119.4972 4 SF-SC SD70471 (big dune) San Nicolas Island CSN2 ch 33.2572 -119.5617 3 LF-SC SBBG117416 (naval facility) San Nicolas Island CSN1 ch 33.2707 -119.5434 3 SF-SC SBBG33797 (canyon) San Onofre SP CSO ch 33.3808 -117.5770 5 LF-SI DS510009
San Onofre SP CSO bi 33.3964 -117.5898 5 LF-SC SD124489
Dana Point Preserve CDP ch 33.4607 -117.7155 5 LF-SI UCR203990
Dana Point Preserve CDP bi 33.46247 -117.7133 5 LF-SI UCR215311
Dockweiler SB CDW ch 33.9235 -118.4320 4 LF-SC SD38668 Santa Rosa - China CSR2 ch 33.9293 -120.1782 3 SF-SC SBBG36622 Camp Santa Rosa - Skunk CSR1 ch 33.9798 -119.9973 4 SF-SC POM171247 Point Santa Cruz - Sauce CSC2 ch 34.0108 -119.8829 5 SF-SC SD229734 Beach Santa Rosa - CSR3 ch 34.0241 -120.0700 5 SF-SC RSA132262 Carrington Point Santa Cruz -Fraser CSC1 ch 34.0571 -119.9220 4 SF-SC SBBG53934 point
Ormond Beach COR ch 34.1399 -119.1893 4 LF-SC UC57062
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Point Mugu SP *CPM ch 34.11447 119.1494 4 LF-SC SBBG95027
McGrath SB CMG ch 34.2246 -119.2592 6 LF-SC SBBG14459 San Buenaventura CBV ch 34.2679 -119.2783 5 LF-SC RSA44553 SB Santa Paula CSP bi 34.3558 -119.0369 6 LF-SI SBBG124315
Coal Oil Point *CCO ch 34.4083 -119.8793 30 LF-SC SD38666
Guadalupe Nipomo CGN3 ch 34.9504 -120.6535 7 SF-SC CAS297044
Guadalupe Nipomo CGN2 mi 35.0258 120.6331 5 SF-SC SD38675
Guadalupe Nipomo CGN2 ch 35.0287 -120.6323 6 SF-SC SDSU19557
Morro Bay Strand CMS ch 35.3986 -120.8669 6 SF-SC CAS690774
Point Lobos SP CPL ch 36.5171 -121.9512 5 SF-SC CAS323912
Salinas River CSA ch 36.7745 -121.7956 5 SF-SC UCD103530
Sun Set Beach SP CST ch 36.8766 -121.8252 5 SF-SC UC942887
Sun Set Beach SP CST mi 36.8782 -121.8262 4 SF-SC RSA187219
Wilder Ranch CWR ch 36.9541 122.0799 5 SF-SC POM38414
Point Reyes NP CPR2 ch 38.0461 -122.9879 7 SF-SC RSA119359
Manchester Beach CMC ch 38.9827 -123.7057 42 SF-SC CAS807342 SP
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Manilla Dunes CMA ch 40.8474 -124.1738 6 SF-SC HSC45467 Community Center Tolowa Dunes SP CTD ch 41.8705 -124.1738 5 SF-SC POM305910
Oregon Pistol River OPR ch 42.2709 -124.4049 5 SF-SC OSC62832
Bullards Beach SP OBU ch 43.1463 -124.4151 4 SF-SC CM485480
North Spit Overlook ONO ch 42.2709 124.4049 7 SF-SC WS316639 Notes: Mating types: SF-SC = small-flowered self-compatible, LF-SI = large-flowered self-incompatible, LF-SC = large-flowered self-compatible. Location: NP = National Park, SP = State park, SB = State beach. n = number of individuals assayed. Herbarium accession numbers from specimens collected at each of the sampling locations or nearby locations are provided for each population sampled. Herbaria codes: ASU = Arizona State University Tempe, CAS or DS =
California Academy of Sciences San Francisco, CM = Carnegie Museum of Natural History, HSC = Humboldt State University
Herbarium, OSC = Oregon State University, POM and RSA = Rancho Santa Ana Botanic Garden, SBBG = Santa Barbara Botanic
Garden Herbarium, SD = San Diego Natural History Museum, SDSU = San Diego State University San Diego, UC = University of
California Berkeley, UCD = University of California Davies, UCR = University of California Riverside, WS = Washington State
University.* One plant from each of these two populations was used for the construction of the genomic library.
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APPENDIX 3. Supplementary information for Chapter 3. Genetic consequences of mating system shifts.
1.10 A
1.08
1.06
Rubin Statistic 1.04 −
1.02 Gelman 1.00
B
80000
70000
60000 DIC values
50000
C 100
80
60 K
40
20
0 1 2 3 4 5 6 7 8 9 10 Number of clusters (K)
Fig A3.1 Three parameters used to validate the number of clusters (K) as a function of K from independent runs for each value of K from 1 to 10. (A) Gelman-Rubin statistic, (B) Deviance information criterion (DIC) and (C) ∆K. In panels A and B points are the estimated values for each K across the 10 replicates. For all the simulated runs, the reported diagnostic Gelman-Rubin statistic is < 1.10, indicating good convergence in term of both log-likelihood and estimated selfing rates. DIC values decreased from K = 1 to 10 but the rate of change slowed after K = 5. This value also coincides with a decrease in variance between subsequent values of K (curve starts plateauing and shows the lowest standard deviation) suggesting lower predictability in clustering solutions after K = 5. ΔK showed a strong mode at K = 5 indicating the highest level of structure, hence the K that best explains our data is five.
206
Fig A3.2 Differentiation among INSTRUCT clusters along the first four discriminant functions (DA1, DA2, DA3, DA4) of the DAPC analysis. Forty-five principal components were retained. Colours represent posterior assignment of DAPC based on INSTRUCT clusters as in Fig 3.3. DAPC is a two-step procedure that transforms allele frequency data into linear combinations of uncorrelated variables using principal component analyses (PCA) and then summarizes genetic variance by maximizing genetic differentiation between groups, while minimizing the within- group variation with a linear discriminant analysis (LDA). We performed this DAPC analysis by assigning each individual to one of the five INSTRUCT clusters based on its highest ancestry coefficient. The number of PCs to be retained was evaluated using the cross validation procedure and fixed to no more than one-third of the total number of PCs (Jombart et al. 2010).
207
● ● ●
0 SF−SC BAJA LF−SI LF−SC SF−SC NPC SF−SC NRL Among clusters
1.0
0
0.8 ● ●
T ● ● ● ● S ● ● ● ● ● ● ● ● ● ● ● ● ●● G' ● ● 0.6 ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● e ● ●●● ● ● ●●
s ● ● ●● ●
i ● ● ● ● ● ● ● ● ● ● ● ● w ● ● 0.4 ● ●● r ● ● ●
i ● a ●
p ● ● 0.2 r = +0.533 ● P perm< 0.0001 0.0 1.0
0.8
T ● ● S ● ● ● ● ●
F ● 0.6 ● ● ● ● ● ● e ● ● ● ● ● ● ● s ● ● ●● ● ● ●● ● i ● ● ●●● ● ● ● w ● ● ●● ● ● r 0.4 ●● ● ● ●● ● ● ● i ●● ●● ● ●●● ● ● a ● ●● ● ●● p ●●● ● 0.2 ● ● r = +0.534 ● P perm< 0.0001 0.0 0 160 320 480 640 800 960 1120 1280 1440 1600 Geographic distance (km)
Fig A3.3. Isolation-by-distance among 38 populations of Camissoniopsis cheiranthifolia across the species' geographic range. Pairwise genetic distance was measured as G'ST (top panel), which controls for variation in within-population genetic diversity, and FST (bottom panel), which does not. Pairwise comparison between populations within INSTRUCT clusters are denoted by filled symbols with contrasting colours as in Fig. 3. Comparisons between populations in different clusters are open diamonds. Correlation coefficients (r) and Pperm values are from Mantel tests with 10,000 permutations. Grey solid lines are the overall isolation-by-distance regression lines.
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Table A3.1 Location and sampling effort for the Camissoniopsis cheiranthifolia populations used in this study
Pop. Country State Location Latitude Longitude Mating n n Data code (°N) (°W) type nSSR cpSSR collected BBR1C MEX BCN Bocana del Rosario 30.0478 -115.7864 SF-SC 31 6 GN HC DE CW MS BES1C MEX BCN El Socorro 30.3187 -115.8257 SF-SC 37 5 GN HC DE CW MS BBQ2C MEX BCN Bahia San Quintin 30.3801 -115.9904 SF-SC 23 0 GN CW MS DE BBF1C MEX BCN Bahia Falsa 30.4558 -116.0342 SF-SC 0 4 HC
BSM1C MEX BCN San Martin Island 30.4832 -116.1022 SF-SC 23 4 GN HC DE
BQW2C MEX BCN San Quintin West 30.7058 -116.0356 SF-SC 20 4 GN HC DE CW MS BSA1C MEX BCN San Antonio del Mar 31.1077 -116.3084 SF-SC 26 0 GN DE
BEB2C MEX BCN Ensenada beaches 31.8102 -116.6092 LF-SI 27 5 GN HC DE
CBF1C U.S.A. CA Borderfields SP 32.5365 -117.1229 LF-SI 29 5 GN HC DE
CSS1C U.S.A. CA Silver Strand SP 32.6410 -117.1437 LF-SI 34 0 GN DE CW
CTP1C U.S.A. CA Torrey Pines SP 32.9291 -117.2591 LF-SI 23 3 GN HC DE MS CCP1C U.S.A. CA Camp Pendleton 33.24845 -117.4301 LF-SI 22 0 GN DE CW
CSN4C U.S.A. CA San Nicolas Island 33.26446 -119.53415 SF-SC 29 4 GN HC CW MS
209
CSO1C U.S.A. CA San Onofre 33.3807 -117.5771 LF-SI 19 4 GN HC DE MS CDP2C U.S.A. CA Dana Point preserve 33.4607 -117.7155 LF-SC 25 3 GN HC DE
CDW2C U.S.A. CA Dockweiler Beach 33.9235 -118.4320 LF-SC 24 4 GN HC DE CW MS CSR4C U.S.A. CA Santa Rosa Island 33.9778 -120.0818 SF-SC 29 11 GN HC CW MS CSC4C U.S.A. CA Santa Cruz Island 3403390 -119.9025 SF-SC 35 10 GN HC CW MS COR2C U.S.A. CA Ormond Beach 34.1385 -119.1845 LF-SC 25 4 GN HC DE CW MS CMG1C U.S.A. CA McGrath SB 34.2246 -119.2591 LF-SC 22 5 GN HC DE CW MS CBV1C U.S.A. CA San Buenaventura SB 34.2679 -119.2783 LF-SC 27 0 GN DE CW MS CCO1C U.S.A. CA Coal Oil Point 34.4083 -119.8793 LF-SC 30 4 GN HC DE CW CGN3C U.S.A. CA Guadalupe Nipomo 34.9503 -120.6535 SF-SC 23 5 GN HC DE CW CGN2C U.S.A. CA Guadalupe Nipomo 35.0286 -120.6322 SF-SC 22 4 GN HC DE CW CGN1C U.S.A. CA Guadalupe Nipomo 35.0335 -120.6238 Mixed- 31 4 GN CW SC CSP1C U.S.A. CA Morro Bay SP 35.3658 -120.8623 Mixed- 25 6 GN HC DE SC CW MS CMS1C U.S.A. CA Morro Bay strand SB 35.3986 -120.8669 SF-SC 21 0 GN DE CW MS CPL1C U.S.A. CA Point Lobos SP 36.5171 -121.9511 SF-SC 24 4 GN HC DE CW CAS1C U.S.A. CA Asilomar SP 36.6189 -121.9409 SF-SC 0 4 HC
210
CSA1C U.S.A. CA Salinas River SP 36.7745 -121.7956 SF-SC 22 0 GN DE CW
CMN1C U.S.A. CA Monterrey Bay east 36.6224 -121.8463 Mixed- 33 0 GC DE CW dunes. SC CST1C U.S.A. CA Sunset Beach SP 36.8767 -121.8252 SF-SC 22 5 GN HC DE CW MS CWR1C U.S.A. CA Wilder ranch 36.95411 -122.0798 SF-SC 20 5 GN HC DE CW CPR2C U.S.A. CA Point Reyes NP 38.0460 -122.9878 SF-SC 22 6 GN HC DE CW CMC1C U.S.A. CA Manchester Beach SP 38.9827 -123.7057 SF-SC 42 7 GN HC DE CW MS CMA1C U.S.A. CA Manilla community 40.8474 -124.1738 SF-SC 25 5 GN HC DE center CW MS CTD1C U.S.A. CA Tolowa Dunes SP 41.8705 -124.1738 SF-SC 20 6 GN HC DE CW OPR1C U.S.A. OR Pistol river 42.2708 -124.4049 SF-SC 23 5 GN HC DE CW OBU1C U.S.A. OR Bullard’s Beach SP 43.1463 -124.415 SF-SC 32 4 GN HC DE CW ONO1C U.S.A. OR North Spit overlook 42.2708 124.4049 SF-SC 20 4 GN HC DE CW MS
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Table A3.2. Summary of PCR optimization of chloroplast microsatellite (cpSSR) primers and initial screening of Camissoniopsis cheiranthifolia populations.
Parameter
Number of loci screened 33
No of polymorphic loci detected 6
Locus name according to reference CCMP2, CCMP10, CSSR7, CSSR8, CSSR12, CSSR23
Polymorphic gene regions in the cp TrnQ-TrnS{5' to trnS(GCU)] rpl2-rps19 intergenic, PsbC-TrnS, Ycf3, PsbB-PsbT, genome of Nicotiana tabaccum Rp12-TrnH
Populations tested 32 Mean, range individuals per 3.5 (1–7) population
Total volume 5 µl PCR reactions: 0.5 µl DNA template [10 µg/µl], 2.5 µl Initial PCR reaction volumes and Multiplex PCR Master Mixa , 0.1 µl of each forward and reverse primers [10 µM], concentrations 1.1 µl of M13taq [1 µM]b , 0.7 µl sterile double distilled water
15 min denaturation at 94°C, followed by 35 cycles of 45 s at 94°C, 50 s at 50°C, Thermo-cycling conditions and 55 s at 72°C, with 16 min final extension at 72°C
212
Duplex reactions: CCMP10+CCMP2; CSSR8+CSSR23 and CSSR12+ CSSR7 following the initial PCR reaction protocol but adjusting the double distilled water in the PCR from 0.7 µl in the single reactions to 0.5 µl. Final product was diluted Final PCR multiplex reactions with sterile double distilled water to a final volume of 15 µl and fragments were sized in a GenomeLab GeXP with the Beckman-Coulter GenomeLab DNA Size Standard Kit 400c
Previous assays of cpDNA sequence following Taberlet et al. (1991)[1] among 12 individuals sampled from across the species range detected no variation in 149 bp of TFcd (trnL intron) and 298 bp of TFef regions (trnL/F intergenic spacer; E. Austin, K.E. Samis & C.G. Eckert unpublished data). We screened 33 consensus universal cpSSR primers (Weising & Gardner 1999, Chung & Staub 2003) using 3–7 individuals (mean = 3.5) from each of 32 populations. Six primer pairs (CCMP2, CCMP10, CSSR7, CSSR8, CSSR12, CSSR23) that consistently amplified and exhibited variation were used to assay 159 individuals from a total of 30 populations assayed for nSSR variation plus two additional populations (BBF1C and CAS1C; Table A3.1). The forward primer of each pair included a D4 red labeled M13 tail (5′- CACGACGTTGTAAAACGA -3′, Sigma-Aldrich, Oakville, Ontario, Canada). Initial testing and final PCR conditions, amplification and fragment sizing procedure are in presented. Alleles were manually binned by size. In addition to fragment analyses, we sequenced the PCR products of 46 individuals (one individual of each allele at each locus in both directions; F and R primers) to determine the cause of cpSSR size variation and to evaluate the possibility of size homoplasy in geographically distant individuals sharing the same size fragment size. Complementary sequences for each individual were aligned first and all alleles for each locus compared and analyzed for differences in repeat type and size. Sequences alignment was performed using the Software MEGA 6.3 Molecular Evolutionary Genetics Analysis (MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets (Tamura et al. 2013)[2]. Although the motif of the microsatellite found in C. cheiranthifolia was often different from the one found in Nicotiana Tabacum or the other species tested in Weising & Gardner (1999) and Chung & Staub (2003), the allele sizes based on the fragment analyses were consistent with differences in the number of repeats in the microsatellite region. a PCR reactions were performed using Qiagen Multiplex Master Mix (Qiagen, Toronto, Ontario, Canada). b 18 bp M13Tag 5′- CACGACGTTGTAAAACGA -3′ (Sigma-Aldrich, Oakville, Ontario, Canada). c Scored with CEQ 8000 Genetic Analysis System (version 9.0, Beckman Coulter, Mississauga, Ontario, Canada). [1]Taberlet P, Gielly L, Pautou G, Bouvet J (1991) Universal primers for amplification of three non-coding regions of chloroplast DNA. Plant. Mol. Biol. 17, 1105–1109. [2]Tamura, K. et al. (2013) MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729.
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Table A3.3 Characteristics of and primers for the six chloroplast microsatellite loci (cpSSR) used in this study
a Locus Primer sequence (5’ – 3’) forward Repeat motif in Location Size in Ta Fragment Reference (F) primer, reverse (R) primer tobacco in tobaccoc (°C) size range tobaccob (bp)a 5' to trnS 189 Weising & F: GATCCCGGACGTAATCCTG CCMP2 (A) (GCU) 50 273–277 Gardner 1999 R: ATCGTACCGAGGGTTCGAAT 11 rpl2-rps19 103 Weising & F: TTTTTTTTTAGTGAACGTGTCA CCMP10 (T) intergenic 50 130–133 Gardner 1999 R: TTCGTCGDCGTAGTAAATAG 14 F: CGGGAAGGGCTCGKGCAG PsbC-TrnS 349 Chung & Staub CSSR7 R: (T)11 50 243–245 2003 GTTCGAATCCCTCTCTCTCCTTTT F: Ycf3 249 Chung & Staub CSSR8 TTGATCTTTACGGTGCTTCCTCTA (T)5C(T)17 50 344–346 2003 R: TCATTACGTGCGACTATCTCC F: PsbB-PsbT 249 Chung & Staub CSSR12 CCAAAAACTTGGAGATCCAACTA (A)8 50 283–284 2003 C R: Rp12- 217 Chung & Staub F: AYGGRGGTGGTGAAGGGAG CSSR23 TTCCATAGATTCGATCGTGGTTTA (A)14 TrnH 50 356–358 2003 TTR: TCAATTCCCGTCGTTCGCC CCATAGATTCGATCGTGGTTTA Repeat motifs are as the original Tobacco sequence, annealing temperature (Ta), range of amplified fragment sizes in C. cheiranthifolia (including 18 bp of M13 tag) and reference publication. a Names of consensus chloroplast primers according to Weising & Gardner (1999) and Chung & Staub (2003). b Names of chloroplast genes in Tobacco genome including the CCMP and CSSR (cpSSR) primer sequences. c Expected size predicted by the Tobacco genome. d Degenerate positions according to references are D (= A, T or G), K (= T or G), R (= A or G), and Y (= C or T).
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Table A3.4 Chloroplast SSR haplotypes detected in 159 individuals from 32 Camissoniopsis cheiranthifolia populations
Locus Haplotype CCMP2 CCMP10 CSSR7 CSSR8 CSSR12 CSSR23 Total Frequency
HAP1 277 130 345 284 244 356 7 0.044 HAP2 273 131 345 284 244 356 9 0.056 HAP3 273 131 346 284 244 356 21 0.132 HAP4 275 131 345 284 245 356 4 0.025 HAP5 276 131 344 283 244 356 6 0.037 HAP6 276 131 345 284 244 356 3 0.018 HAP7 276 131 345 284 244 356 72 0.452 HAP8 277 131 346 284 244 356 8 0.050 HAP9 276 132 345 284 244 357 16 0.100 HAP10 276 132 345 284 245 356 2 0.012 HAP11 277 132 345 284 245 356 4 0.025 HAP12 276 133 345 284 244 358 7 0.044
Locus names are the same as the original reference (Tables A3.2 and A3.3). Total = the number of plants with each haplotype.
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Table A3.5 Consistency in assignment between prior INSTRUCT and posterior DAPC groups
Prior groups from INSTRUCT analyses Posterior groups from DAPC INSTRUCT LF-SC LF-SI LF-SC & SF-SC SF-SC LF-SC LF-SI LF-SC & SF-SC SF-SC cluster Baja Islands NPC NRL Baja Islands NPC NRL SF-SC Baja 159 0 1 0 0 159 0 1 0 0 LF-SI 1 175 3 0 0 0 175 4 0 0 LF-SC 0 0 127 0 1 0 0 127 0 1 SF-SC Island 0 0 90 3 0 0 0 92 1 0 Mixed-SC 0 1 31 55 2 0 0 31 56 2 SF-SC NPC 0 2 2 149 1 0 1 3 149 1 SF-SC NRL 0 0 1 0 183 0 0 1 0 183 Total genotypes 160 178 255 207 187 159 176 259 206 187 Correct assignment by 159 170 240 193 182 DAPC to prior groups OAP % 99.4 97.2 97.6 97.6 100.0
Entries are the number of genotypes of each mating type and geographic region (rows). Columns on right side of the table are prior INSTRUCT clusters (highest ancestry coefficient to one cluster). Columns on left side of table are posterior assignment of genotypes by DAPC to each cluster. OAP = overall assignment probability expressed as a percent.
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Table A3.6 Multilocus population genetic parameters based on nuclear (nSSR) and chloroplast (cpSSR) microsatellites for each population of Camissoniopsis cheiranthifolia sampled
nSSR cpSSR Pop'n Mating n n A H N R Haplotype code type nSSR r e cpSSR h h BBR1C SF-SC 31 1.74 0.10 6 1 0.0 5
BES1C SF-SC 37 2.70 0.24 5 1 0.0 7 BBQ1C SF-SC 23 2.35 0.27 0 NA NA NA BBF1C SF-SC 0 NA NA 4 1 0.0 7 BSM1C SF-SC 23 2.62 0.27 4 1 0.0 7 BQW1C SF-SC 20 2.13 0.26 4 1 0.0 7 BSA1C SF-SC 26 2.74 0.29 0 NA NA NA BEB1C LF-SI 27 4.09 0.56 5 1 0.0 7 CBF1C LF-SI 29 5.15 0.65 5 1 0.0 7 CSS1C LF-SI 34 5.13 0.63 0 NA NA NA CTP1C LF-SI 23 5.11 0.63 3 1 0.0 7 CCP1C LF-SI 22 3.71 0.50 0 NA NA NA CSN4Ca SF-SC 29 3.30 0.45 4 1 0.0 7 CSO1C LF-SI 19 3.60 0.43 4 1 0.0 7 CDP1C LF-SI 25 4.71 0.56 3 1 0.0 7 CDW2C LF-SC 24 2.67 0.38 4 1 0.0 4 CSR4Ca SF-SC 29 3.4 0.35 11 2 0.65 3 & 7 CSC4Ca SF-SC 35 4.24 0.55 10 1 0.0 7 CSC1C SF-SC NA NA 4 1 0.0 7 CSC2C SF-SC NA NA 6 1 0.0 7 COR1C LF-SC 25 2.91 0.38 4 1 0.0 7 CMG1C LF-SC 22 2.64 0.41 5 1 0.0 7 CBV1C LF-SC 27 3.11 0.35 0 NA NA NA
217
CCO1C LF-SC 30 2.84 0.37 4 1 0.0 7 CGN3C SF-SC 23 1.89 0.13 5 1 0.0 9
CGN2C SF-SC 22 2.48 0.23 4 2 0.75 9 & 12 Mixed- CGN1C 31 4.03 0.6 4 1 0.0 12 SC Mixed- CSP1C 25 2.72 0.39 6 2 0.80 10 & 11 SC CMS1C SF-SC 21 2.84 0.28 0 NA NA NA CPL1C SF-SC 24 3.66 0.44 4 1 0.0 8 Mixed- CMN1C 33 3.37 0.48 0 NA NA NA SC CAS1C SF-SC 0 NA NA 4 1 0.0 8 CSA1C SF-SC 22 2.43 0.29 0 NA NA NA CST1C SF-SC 22 2.08 0.22 5 1 0.0 9 CWR1C SF-SC 20 2.05 0.20 5 1 0.0 9 CPR2C SF-SC 22 2.67 0.37 6 1 0.0 2 CMC1C SF-SC 42 3.01 0.26 7 1 0.0 1 CMA1C SF-SC 25 2.5 0.23 5 1 0.0 3 CTD1C SF-SC 20 1.85 0.19 6 2 0.9 2 & 3 OPR1C SF-SC 23 2.52 0.31 5 1 0.0 3 OBU1C SF-SC 32 2.88 0.25 4 1 0.0 3 ONO1C SF-SC 20 1.77 0.10 4 1 0.0 3
Populations are ordered by increasing latitude. Sample sizes (n) for nuclear microsatellites (nSSR) and chloroplast microsatellite (cpSSR) assays are presented along with estimates of rarefied allelic richness (Ar based on n = 19) and expected heterozygosity (He) for nSSR loci as well as number of cpSSR haplotypes (Nh), rarefied cpSSR haplotypic richness (Rh based on n = 3) and the identity of the cpSSR haplotype(s) detected (see Fig. 3.4 and Table A3.5). Mating types = large-flowered self-incompatible populations (LF-SI), large-flowered, self-compatible populations (LF-SC), small-flowered self-compatible populations (SF-SC) and phenotypically mixed populations (Mixed-SC).
218
APPENDIX 4.1. Supplementary information for Chapter 4. Population information and herbarium records.
Table A4.1. Location of large-flowered self-incompatible Camissoniopsis cheiranthifolia subsp. suffruticosa and C. bistorta populations sampled. Number of samples genotyped for each molecular marker and the type of data collected and used in this study for each population. The first letter of a population code (Pop. code) refers to California “C” or Baja California “B”, the second and third letters are initials of the locality name, the number refers to the number of collection within a site and the last letter stand for C. cheiranthifolia “C” or C. bistorta “B”. The codes for the data collected are as follows: GN = genotypic data from 12 nuclear microsatellites loci; GH = haplotypic data from 9 chloroplast microsatellites loci
Pop. Country State Location Spp. Latitude Longitude Coastal/I n n Data code (°N) (°W) nland nSSR cpSSR collected BEB2C MEX BCN Ensenada beaches C 31.8102 -116.6092 Coastal 27 5 GN GH
CBF1C U.S.A. CA Borderfields SP C 32.5365 -117.1229 Coastal 29 5 GN GH
CSS1C U.S.A. CA Silver Strand SP C 32.6410 -117.1437 Coastal 34 0 GN
CTP1C U.S.A. CA Torrey Pines SP C 32.9291 -117.2591 Coastal 24 3 GN GH
CCP1C U.S.A. CA Camp Pendleton C 33.24845 -117.4301 Coastal 22 0 GN
CSO1C U.S.A. CA San Onofre S 33.3807 -117.5771 Coastal 20 4 GN GH
CDP2C a U.S.A. CA Dana Point preserve S 33.4607 -117.7155 Coastal 25 3 GN HC BPB1B MEX BCN Punta Banda B 31.72579 -116.6482 Coastal 25 6 GN GH
BMI1B MEX BCN La Mision B 32.09459 -116.88116 Coastal 34 6 GN GH
BLA2B MEX BCN Los Arenales Ensenada B 32.20673 -116.91471 Coastal 22 6 GN GH
BTB1B MEX BCN Tijuana Beaches B 32.52023 -117.12291 Coastal 25 3 GN GH
219
CBF1B U.S.A. CA Borderfields SP B 32.53552 -117.11892 Coastal 22 5 GN GH
CRO1B U.S.A. CA Robb Field Park B 32.75383 -117.23795 Coastal 26 5 GN GH
CWG1B U.S.A. CA Willow Glen Dr. B 32.75682 -116.90113 Inland 21 5 GN GH
CCU1B U.S.A. CA Cuyamaca Street Field B 32.84763 -116.98145 Inland 21 6 GN HC
CEM1B U.S.A. CA El Monte Field B 32.89267 -116.84702 Inland 21 5 GN GH
CTP4B a U.S.A. CA Torrey Pines SP B 32.91877 -117.25849 SF-SC 30 8 GN GH
CRI1B U.S.A. CA Rincon Indian Reserve B 33.28914 -116.89005 Inland 20 7 GN GH
CPV1B U.S.A. CA Pauma Valley B 33.31356 -117.00305 Inland 21 6 GN GH
CPP1B U.S.A. CA Pelican Point B 33.34595 -117.51284 Coastal 25 5 GN GH
CSO1B U.S.A. CA San Onofre B 33.39637 -117.58981 Coastal 21 5 GN GH
CSM1B U.S.A. CA San Mateo Campground B 33.40754 -117.58596 Inland 27 6 GN GH
CDP4B a U.S.A. CA Dana Point Preserve B 33.46247 -117.71334 Coastal 30 8 GN GH
CGF1B U.S.A. CA Griffith Park. B 34.1201 -118.2867 Inland 0 5 GH
CSD1B U.S.A. CA Dripping spring SP B 34.26252 -116.9273 Inland 7 5 GN GH
CSP1B U.S.A. CA Santa Paula B 34.35587 -119.03694 Inland 7 7 GN GH
Notes: Population code, location and sample size of LF-SC and SF-SC populations of C. cheiranthifolia are presented in Table A2.1 of Lopez-Villalobos (2017). a subpopulations were pooled together for the population genetic analyses for nuclear microsatellites.
220 Table A4.2. Institutions and number of herbarium records used in this study. The abbreviation and full name of each institution, accessions with geographic coordinates and the total number of records from each are provided. Abbreviation Institution Total records A Harvard University Herbarium 1 ANS Academy of Natural Sciences 1 Herbario de la Universidad Autónoma de Baja BCMEX 6 California, Mexico. CAS, DS California Academy of Sciences 188 CDA California Department of Food and Agriculture 3 CHSC California State University, Chico 27 CLARK Riverside Metropolitan Museum 4 CM Carnegie Museum of Natural History 1 CSUSB California State University, San Bernardino 15 GH Gray Herbarium 46 GMDRC Granite Mountains Desert Research Center 1 HSC Humboldt State University 22 IRVC UC Irvine 6 JEPS, UC Jepson Herbarium and UC Berkeley 245 LA UC Los Angeles 23 NMNH Smithsonian National Museum of Natural History 2 NY New York Botanical Garden 58 California Polytechnic State University San Luis OBI 41 Obispo ORE University of Oregon 9 OSC Oregon State University 12 OSU Oregon State University 32 PGM Pacific Grove Museum of Natural History 3 POM, RSA Rancho Santa Ana and Pomona Botanical Gardens 543 SBBG Santa Barbara Botanic Garden 149 SD San Diego Natural History Museum 493 SDSU San Diego State University 84 SEINET Southwest Environmental Information Network 31 SFV California State University, Northridge 15 SJSU San Jose State University 4 UCD Davis Herbarium 20 UCR University of California, Riverside 234 UCSB UC Santa Barbara 58 UCSC UC Santa Cruz 5 VVC Victor Valley College 1 WILLU Willamete University 5 WSU Washington State University 5 WTU Burke Museum of Natural History 2 Total 2398
−116 ● ● ● ●● ● ● ● ●●●●●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● −117 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● ●●●●●● ●●●●● −118 ● ●●●●●● ● ●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●● −119 ●●●●●●●
Longitude ●●● ● −120
−121 ● 31 32 33 34 35 Latitude
Figure A4.1. Geographic coordinates of C. bistorta herbarium records. The most northern specimen is removed from the Euclidean distance analysis because it far away from most of the records.
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APPENDIX 4.2. Range-Wide Bayesian analysis of genetic structure using INSTRUCT.
To infer the number of genetic clusters using the Bayesian clustering software INSTRUCT we compared the two most common statistics: the deviance information criterion value (DIC) as suggested per the authors of INSTRUCT (Gao et al. 2011) and the widely used delta-K (ΔK) method of Evanno et al. (2005). The best number of clusters based on DIC values should coincide with either the lowest DIC or the value that first plateaus or continues increasing slightly but it should have the lowest variance between repetitions of the same K. The ΔK is used to infer the best number of clusters from the largest rate of change in the likelihood function with respect to K. This (ΔK) is calculated based on the second order rate of change of the likelihood function as ΔK = m([L’’K])/s[L(K)] and it should be highest at the “true” number of clusters K (Evanno et al. 2005). Whereas DIC is well suited for detecting the actual number of subpopulations and performs well under scenarios of weak population structure, the Evanno method finds the uppermost clustering level in a given data set, thus is adequate to capture the highest level of hierarchy in a data set.
DIC values decreased from K = 1 to 15 in both analyses (across both species’ ranges,
“large-scale” and among 25 LF-SI populations of both species “finer-scale”) but the rate of change slowed down at K = 6 for the “large-scale” analysis and after K = 8 in the “finer-scale” analysis. This value also coincides with the lowest variance and the decrease between subsequent values of K (curve starts plateauing and shows the lowest standard deviation) suggesting lower predictability in clustering solutions after K = 6 and K= 8 respectively for the “large-scale” and
“finer-scale” analyses (Fig. A4.6B and D). In the small-scale analysis (Fig. A4.6D), a change in
DIC values decreases after K = 8 but the curve flatten out at values > K = 12. For this analysis the lowest variance among chains is found at K = 2, 12, 13 and 14, suggesting one major group at the
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uppermost hierarchical level (K= 2) and finer structure with K = >12 clusters. Because we were interested in the uppermost level of hierarchical structure we used the ΔK ad-hoc statistic of
Evanno et al. (2005) for the analyses presented in this chapter and only explored lower hierarchical structure values for comparisons (see Figs. S7 and S8). The ΔK showed a strong mode at K = 6 for the “large-scale” and a K = 2 for the “finer-scale” analyses, consistent with the lowest variance among DIC values (Fig. A4.6 B & D), hence indicating the highest level of structure that best explains our two datasets (Fig.A4.5 A&C).
224
20 A 30 C
25 15 20 K 10 15
Delta 10 5 5
0 0
D 127000 B 55000
107000 50000 alues
45000
DIC v 87000
40000 67000 1357911 13 15 1357911 13 15 K (number of clusters)
Figure A4.2. ∆K (Evanno et al. 2005) and Deviance information criterion “DIC” (Gao et al. 2011) values from two independent INSTRUCT analyses. “Large-scale” analyses of 32 populations of C. cheiranthifolia (A and C) and 18 of C. bistorta (n = 50) and “small-scale” analysis of seven LF-SI populations of C. cheiranthifolia and 18 of C. bistorta (n = 25) (B and D). Delta K for the “large-scale” and “small-scale” analyses, respectively in panels A and B and average DIC across 10 replicates of each K tested for “large-scale” (panel C) and “small-scale” (panel D) analyses. INSTRUCT analyses were performed for values of K from 1 to 15 maximum number clusters. The point in red represents the best number of clusters based on Delta K and the one used for subsequent analyses. bistorta at 12 nuclear microsatellite loci. This analysis includes populations from the Channel Islands and Mixed-SC populations north of Pt. Conception (see Chapter 3) of C. cheiranthifolia. Population codes ending in “B” are C. bistorta and “C” are C. cheiranthifolia. Populations are ordered by latitude.
225 K = 2
K = 3 SF-SC BAJA LF-SI C. cheiranthifolia LF-SI C.bistorta
K = 4 SF-SC BAJA LF-SI C. cheiranthifolia LF-SI C.bistorta
K = 5 SF-SC BAJA LF-SI C. cheiranthifolia LF-SC SF-SC NRL LF-SI C.bistorta
K = 6 SF-SC BAJA LF-SC SF-SC NPC SF-SC NRL LF-SI C.bistorta
K = 7 LF-SC SF-SC NPC SF-SC NRL LF-SI C.bistorta
K = 8 CRI1B BMI1B CTP1C CPL1C CST1C CTP1B CBF1C CTD1C BLA2B BTB1B CBF1B BES1C CSS1C CSP1C CPV1B CPP1B CSP1B BSA1C BEB2C CCP1C CSN4C CDP2C CSR4C CSC4C CBV1C CSA1C CPR2C BPB1B CDP2B CSD1B BBR1C CSO1C OPR1C CCU1B CSO1B BBQ2C BSM1C COR1C CCO1C CGN3C CGN1C CGN2C CMS1C OBU1C CRO1B CEM1B CSM1B CMN1C CMC1C CMA1C ONO1C CMG1C CDW2C CWR1C BQW2C CWG1B C. cheiranthifolia populations C. bistorta populations
Figure A4.3. Cluster solutions from K 2 to 8 for a range-wide INSTRUCT analysis of 56 populations of C. cheiranthifolia and C. bistorta at 12 nuclear microsatellite loci. This analysis includes populations from the Channel Islands and Mixed-SC populations north of Pt. Conception (see Chapter 3) of C. cheiranthifolia. Population codes ending in “B” are C. bistorta and “C” are C. cheiranthifolia. Populations are ordered by latitude.
Figure A4.4. Distribution of individuals within each posterior discriminant function of the DAPC analysis based on prior INSTURCT clusters. Plots represents the genotypes along the A) first, B) second C) third and D) fourth discriminant functions. Colors represent the posterior assignment into DAPC clusters corresponding to individuals from large-flowered self- incompatible populations of C. bistorta and C. cheiranthifolia (dark blue southern cluster and magenta clusters in Fig 4; LF-SI), large-flowered self-compatible populations (red; LF-SC), small-flowered self-compatible populations from Baja California, Mexico (yellow; SF-SC BAJA), populations form North of Point Conception (NPC) and up to Manilla Dunes (light blue; SF-SC NPC) and the five most northern populations (green; SF-SC NRL).
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APPENDIX 4.3. DAPC analysis to visualize differentiation among instruct clusters
DAPC is a two-step procedure that transforms allele frequency data into linear combinations of uncorrelated variables using principal component analyses (PCA) and then summarizes genetic variance by maximizing genetic differentiation between groups, while minimizing the within- group variation with a linear discriminant analysis (LDA). We performed this DAPC analyses by assigning each individual to one of the six INSTRUCT cluster based on its highest ancestry coefficient. The number of PCs to be retained was evaluated using the cross validation procedure and was fixed to no more than one-third of the total number of PCs (Jombart et al 2010).
Two DAPC were conducted: the first one allowed us to visualize the extent of differentiation among INSTRUCT clusters in the multivariate space and the second one tested for genetic structure (clustering) using another method, the K-means algorithm implemented in the adegenet
R package. Results for the first DAPC analysis are presented in the main text.
DAPC analysis using the K-means clustering method
A discriminant analysis of principal components (DAPC) is a multivariate analysis that provides an alternative method to infer population genetic structure with genetic data. It is is designed to identify and describe clusters of genetically related individuals from multilocus data, which mostly includes cases where the number of alleles exceeds the number of individuals. DAPC is a two-step analysis that first transforms the data into uncorrelated variables using principal component analysis (PCA) and then applies a discriminant analysis (DA) to maximize the differentiation among predefined groups (clusters). DAPC also derives probabilities of membership for each individual to each predefined cluster, providing an “assignment measure” of how clear-cut or admixed the different clusters are. The number of principal components (PCs)
228
needs to be adjusted in order to determine the reduced space inside which observations would be best discriminated into the pre-defined groups in the DAPC analysis. For this step, we used the cross validation procedure and fixed the number of PCs to no more than one-third of the total number of PCs (Jombart et al 2010) to assure the stability of group membership (to avoid unstable assignment of individuals to clusters).
The K-means clustering algorithm allows the definition of the data into prior genetic clusters. This method estimates a BIC (Bayesian criterion information) value at each K tested and then the optimal K, is chosen based on the lowest BIC or some other criteria (Jombart et al 2010; see below). The lowest works best under some population models (stepping stone, hierarchical and isolation-by-distance population models) but often, BIC values continue to decrease after the optimal K has been reached. In such cases, not only one K is the “optimal”, but rather a range of
K around the area in the graph where there is a less steep decrease in BIC, similar to the DIC values from INSTRUCT. The option is then that, researches can explore these different K and make decisions based on the knowledge of the study system and the data. The find.clusters ( ) function also allows the implementation of five automated criteria to help users find values of K when there isn’t a clear elbow in the curve of summary statistics. We used the “diffNgroup” criterion (implemented in the find.clust function) that is based on differences between successive values of the summary statistic (in this case BIC) as BIC Ki = BIC Ki – BIC Ki + 1. Then the
Ward’s clustering method (Ward.D in R) splits these values (BIC Ki..n) and the K retained is the one before the first group is divided by a big difference in BIC. The graph of BIC values ordered from the minimum to the maximum K should be inspected as recommended by Jombart (2008).
Each genotype was identified by its population of origin and the number of K tested spanned from 1to 50, extending our analyses beyond the number of populations that might be expected
(all 200 PCs retained, n.start = 100 and of 107 iterations each).
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Using the K-means algorithm on the 1209 individuals genotyped, we found that BIC values continue decreasing up to K = 50, thus we do not observe an elbow in the curve showing a clear cut optimal value. However, the steepest rate of change between decreasing BIC values occurs from K = 1 to 5, and then values decrease at a lower rate (Figure S9). By applying the automated criterion (diffNgroup) to chose a K, BIC values (BIC Ki) are split into two groups at K = 5 (BIC
= 2168.88). The first group of BIC values occurs from K = 1 to 5 and the second from K = 6 to
50. In this second group, the difference in BIC values fluctuate after K = 10 suggesting no predictability in the number of groups that describes the data after this K = 10. The five clusters are composed of 578, 161, 126, 191 and 153 individuals, roughly matching populations from LF-
SI populations of both C. cheiranthifolia and C. bistorta, SF-SC from Baja California, LF-SC,
SF-SC north of Point Conception and SF-SC from the northern range limit (see Chapter 3 and main text). In contrast to our prior INSTRUCT group assignment (see main text), K-means assigned all LF-SI genotypes from both species to a single group. However, similar to the
INSTRUCT analysis the other clusters are composed of LF-SC, and three groups of SF-SC populations (Fig.S9). DAPC overall posterior assignment probability was 0.99 with this K-means grouping and each group was reassigned into DAPC clusters with >98% probability (LF-SI 0.993,
SF-SC Baja 0.994, LF-SC 0.984, SF-SC NPC 0.987, SF-SC NRL 0.985). This analysis confirms two things: On one hand, it confirms that within C. cheiranthifolia, there is strong genetic differentiation among populations with different mating systems across and on the other, further suggest overall weak genetic differentiation among populations of LF-SI plants from both species with these 12 microsatellite loci.
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Figure A4.5. DAPC analysis using the K-means clustering algorithm on 50 populations of C. cheiranthifolia and C. bistorta. A) Bayesian Criterion Information (BIC) values for each number of subpopulations tested (K) with the sequential K-means clustering algorithm implemented in the find.clusters ( ) function from the Adegenet package (Jombart et al 2010). Each point represents the BIC value for each K (from1 to 50). Red circle at point K = 5 represents the number of clusters chosen by the automated criterion “diffNgroup” in which differences between successive values of the summary statistics (BIC) are divided into two groups using a Ward's clustering method. The retained K is the one before the first group switch. B) DAPC scatter plot of K = 5 clusters, four of them corresponding with populations of C. cheiranthifolia (LF-SC, and 3 groups of SF-SC, see chapter 3) and one cluster of LF-SI individuals of both C. cheiranthifolia and C. bistorta. Genotypes within this groups are represented by different symbols and colors depicted in the legend. Individuals of C. bistorta are marked with a plus (+) symbol
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APPENDIX 5.1. R script for ASTER analysis of variation in survival and reproduction in transplanted populations of Camissoniopsis cheiranthifolia.
This script uses the “TransplantData.csv” file deposited at DRYAD. APPENDIX 5.2. Effect of Variation in Initial Seedling Size on Analyses of Lifetime Fruit
Production
Within a few days before each seedling was planted into experimental plots at the field sites we counted the number of nodes on the primary stem, the number of lateral stems, and measured overall seedling height (to 0.1 cm). Because these measures of size were significantly correlated
(r = 0.24 – 0.53, all P < 0.00001), we used the first principal component (PC1) derived from the correlation matrix as an overall measure of size. All three measures loaded positively on PC1
(nodes = +0.584, lateral stems = +0.642, height = +0.496), which accounted for 59.4% of the variation in these measures.
As expected, PC1 varied significantly among planting sites in both the reciprocal analysis
(F3,1871 = 74.1, P < 0.00001, r2 = 0.106) and particularly the large-scale analysis (F3,1871 =
2160.0, P < 0.00001, r2 = 0.654), where most seedlings planted at B60 were smaller than those planted at W133 or E0. PC1 was then used as a covariate in ASTER model analyses. In the reciprocal analysis, initial size only covaried with lifetime fruit production within planting site
W36 (likelihood ratio LR = 7.51, P = 0.0061; all other P > 0.14). However, the overall effect of initial size (PC1) was positive and significant (LR = 11.90, P = 0.00056, estimated β = +0.0146).
Including PC1 as a covariate in the ASTER model still revealed strongly significant effects of planting site, source population and an interaction between site and source (all P < 0.0001).In the large-scale analysis, size covaried with fitness only at B60 but the effect was negative not positive (LR = 18.58, P < 0.00001, β = –0.0746; all other P > 0.50), and the overall effect of PC1 when included in the ASTER model was not significant (LR = 1.22, P = 0.27).
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Table A5.1. Unconditional mean (± SE) lifetime fruit production calculated from the ASTER model for the reciprocal analysis (Table 5.2, Fig. 5.3A).
Site Source Mean SE PWC
W133 W36 4.79 0.51 a
E2 4.62 0.51 a
W132 2.96 0.43 b
W102 2.16 0.33 b
W102 W36 6.87 0.63 a
E2 5.75 0.57 a
W102 4.63 0.50 ab
W132 3.35 0.42 b
W36 W36 10.86 0.78 a
W102 8.36 0.69 ab
W132 7.56 0.68 b
E2 7.33 0.65 b
E0 W36 9.82 0.75 a
W102 8.94 0.72 a
E2 8.24 0.69 a
W132 5.95 0.58 b Results of pairwise comparisons between source populations within sites (PWC) are indicated by letters. Source populations not sharing a letter are significantly different.
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Table A5.2. Unconditional mean (± SE) lifetime fruit production calculated from ASTER model for the large-scale analysis (Table 5.2, Fig. 5.3B). Site Source Fruits SE PWC W133 W290 6.06 0.74 a E2 4.55 0.64 a
W36 4.55 0.63 a
W132 2.68 0.50 b
W102 2.06 0.38 bc
W611 1.93 0.37 bc
W441 1.89 0.36 bc
W176 1.21 0.27 c
E0 W36 10.24 0.93 a W176 9.64 0.91 a
W611 9.23 0.9 a
W102 9.13 0.89 a
W441 8.93 0.88 a
W290 8.68 0.87 a
E2 8.33 0.86 a
W132 5.78 0.72 b
B60 W36 21.13 0.96 a W176 19.22 1.01 a
E2 17.81 1.02 ab
W102 17.05 1.02 ab
W132 14.99 1.03 b
W290 9.39 0.91 c
W441 8.68 0.88 c
W611 8.15 0.86 c
Results of pairwise comparisons between source populations within sites (PWC) are indicated by letters. Source populations not sharing a letter are significantly different.
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Table A5.3. Estimated mutation-scale effective population size (diagonal) and gene flow (off- diagonal) between population genetic clusters of Camissoniopsis cheiranthifolia estimated from three replicate runs of migrate-n. From (i) North Mid South In (j): (W36 + E2) (W132 + W176) (W290 + W611) Run 1 North 0.5300 4.33 3.67 (0.2733 – 1.1067) (0.00 – 21.33) (0.00 – 20.67) Mid 5.67 1.5967 4.33 (0.00 – 22.00) (1.1933 – 1.9200) (0.00 – 20.67) South 5.00 4.33 2.4167 (0.00 – 21.33) (0.00 – 20.67) (1.6133 – 3.4267) Run 2 North 1.1367 4.33 4.33 (0.4933 – 1.4000) (0.00 – 20.67) (0.00 – 21.33) Mid 5.00 0.5433 3.00 (0.00 – 22.00) (0.2800 – 0.8000) (0.00 – 19.33) South 3.67 3.67 2.5633 (0.00 – 20.67) (0.00 – 20.00) (1.2200 – 2.8733) Run 3 North 1.0633 4.33 3.67 (0.7533 – 1.4133) (0.00 – 21.33) (0.00 – 20.00) Mid 4.33 0.8767 3.00 (0.00 – 21.33) (0.5867 – 1.1933) (0.00 – 20.00) South 4.33 3.00 1.4100 (0.00 – 21.33) (0.00 – 19.33) (1.0533 – 1.7733) Off-diagonal elements are mutation-scaled proportions of individuals in population j that are derived from population I (Mij). Diagonal elements (i = j) are the estimates of mutation-scale effective population size (Qj). Values above the diagonal represent gene flow towards the range edge; those below represent gene flow towards the range center. 95% confidence intervals are in brackets below each estimate.
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Figure A5.1. The optimal number genetic clusters (K) in a Bayesian clustering analysis performed using INSTRUCT as evaluated by (A) the deviance information criterion (DIC) and (B) the ∆K as a function of K clusters (Evanno et al. 2005). Point are means across 10 replicate chains. In A, the optimal K yields the lowest variance in DIC among chains and the shallowest change in DIC between the next lowest and next highest value of K (Gao et al. 2011). In B, the optimal represents the largest
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Figure A5.2. The association between fitness of transplants at a planting site and the geographic distance (upper panel) or climatic distance (lower panel) of their source population from that site in the reciprocal experiment. Climatic distance is displayed as the Euclidean distance between each site-source pair based on location scores along five principle components of nine yearly mean climate variables (see text for details). These two measures of distance correlated strongly (r = +0.95, P < 0.0001, n = 16). Different symbols are used to represent the four planting sites. Points are unconditional expectation of lifetime fruit production estimated using the ASTER model in Table 5.2. Linear models with permutation tests detected a significant effect on fitness of planting site (P < 0.005) but not either measure of distance (geographical P ~ 0.90; climatic P ~ 0.76) or any interaction between site and distance (geographical P ~ 0.15; climatic P ~ 0.32).
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APPENDIX 5.3. Supporting Information 3: Continued Population Viability Beyond The
Northern Range Limit.
In June 2007 and June 2009, we censused all plants occurring in or just adjacent to the 16 experimental blocks at site B60 beyond the range. We found plants in all blocks in both years and scored each as reproductive (possessing buds, flowers or fruits) or nonreproductive.
Experimentally planted individuals were not included in these counts. In 2007, there was a single experimental plant in each of three blocks and 12 in another, but all had died by the 2009 census.
Log10-transformed total plant number per block in 2007 correlated positively with the sum of the reproductive plants per block in the two preceeding seasons (r = +0.54, 1-tailed P =
0.015). Plant number in 2009 correlated with the number of reproductive plants in 2007 (r =
+0.75, 1-tailed P = 0.00044; Fig. S3).
For both total plants and reproductve plants only, there was a strong positive correlation between years (total: r = +0.68, P = 0.0034; reproductive: r = +0.81, P = 0.00015). Total plant number did not differ between years (means: 2007 = 218.7, 2009 =141.3; paired t-test on log10- transformed data: t = 1.75, df = 15, P = 0.10), but the number of reproductve plants per block increased almost 10-fold from 2007 to 2009 (means: 2007 = 10.6, 2009 =102.1; paired t-test on log10-transformed data: t = 10.73, df = 15, P < 0.00001). These results suggest that, at least over the short term, populations experimentally established 60 km beyond the species’ northern range limit were maintaining positive growth.
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Figure A5.3. Recruitment of Camissoniopsis cheiranthifolia at 16 experimental blocks beyond the species’ northern range limit. Points are the log10-transformed numbers of new recruits per block (top panel: all plants, bottom panel reproductive plants only) in June 2007 and June 2009. See details in Supporting Information 3.
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Figure A5.4. Negative correlation between the inbreeding coefficient of adult plants in six source populations of Camissoniopsis cheiranthifolia and the lifetime fruit production of transplants from those source populations averaged across two planting sites within the geographic range (W133 and E0). Error bars are ± 1 SE. Populations are identified by the code beside each point.
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APPENDIX 5.4. Supporting Information 4: Life-history Variation Among Planting Sites
Each of the 3782 plants was classified as to whether it died before reproducing (n = 1326), reproduced in only one season (n = 1983) vs. multiple seasons (n = 473) or for single-season plants, whether it reproduced in the first year (n = 1459) vs. the second year (n = 524). The causes of fitness variation among sites and sources can be explored by analyzing the frequencies of these life-history phenotypes. To eliminate a partial confound between planting site and source population (the four most southerly sources were only planted at three sites), we analyzed variation among planting sites (W133, W102, W36, E0, B60) using the four northerly source populations (W132, W102, W36, E2).
Lifetime fruit production of the different life history phenotypes usually differed significantly. Of course, plants dying before reproduction produced no fruit. At all sites, plants reproducing in more than one season produced 2-times to 4-times more fruits than those reproducing in a single season (Table A5.4). Of the plants that reproduced once, there was a tendency for plants reproducing in their second year to produce more fruits than those reproducing in their first year at all sites except W133. However, this was only significant at sites W102 and B60 (Table A5.4).
The proportion of plants failing to reproduce declined steadily towards the range limit and remained low at site B60 beyond the limit. Of the plants that reproduced, a relatively high proportion reproduced only once, averaging 82% at sites W133, W102 and W36, increasing to
92% at the range limit and then decreasing substantially to 62% beyond the range. Among plants that reproduced in only one season, there was a striking contrast between most reproducing in their first year within the range (73% at W102 to 99% at E0) to almost all (91%) reproducing in the second year beyond the range.
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Taken together, these results indicate that the increase in fitness towards the range limit is mostly due to improved survival to reproduction, whereas the relatively high fitness beyond the limit results from a combination of high survival to reproduction, a 2-fold increase in the proportion of plants reproducing in more than one season and, to a lesser extent, a strong tendency for semelparous individuals to reproduce in their second year.
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Table A5.4. Life history variation among Camissoniopsis cheiranthifolia plants transplanted at five sites towards the northern range limit and beyond. Of all plants Of reproductive plants (%) Of plants reproducing 1x (%) Site % nonrepro > 1x 1x 1st year 2nd year Total n W133 53.2a 19.3 80.7b 82.4c 17.6 453 (14.49 *** 5.75) (5.90 ns 5.03) W102 43.9b 15.3 84.7b 73.1c 26.9 478 (18.56 *** 7.23) (6.13 *** 10.23) W36 23.6c 19.5 80.5b 94.8b 5.2 470 (25.33 *** 8.00) (7.68 ns 13.80) E0 16.2c 7.7 92.3a 99.2a 0.8 480 (32.64 *** 8.10) (8.05 ns 14.33) B60 19.9cd 38.0 62.0c 8.5d 91.5 473 (37.62 *** 12.70) (5.40 *** 13.38)
GLM with binomial errors detected significant variation among sites in the proportion of plants failing to reproduce (χ2 = 228.2, df = 4, P < 0.00001), the proportion of reproductive plants reproducing only once (χ2= 116.0, df = 4, P < 0.00001) and the proportion of semelparous plants reproducing in the first as opposed to the second season (χ2= 743.2, df = 4, P < 0.00001), but not among sites for the other life history categories shown (both P > 0.25). Letter superscripts show results of Tukey multiple comparisons among sites within columns performed using the glht command in the MultComp R package (version 1.4-1). Values not sharing a letter are significantly different. Values in parentheses are the mean lifetime fruit production of each life- history class with results of a linear model comparing log10-transformed values indicated by *** (P < 0.0001) or ns (P > 0.10). See details in Supporting Information 4.
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Appendix 5.5. Supporting Information 5: Life-history Variation Among Source Populations Planted Beyond the Northern Range Limit
Each of the 944 plants transplanted into site B60 beyond the range was classified as either having reproduced or failed to reproduce. Reproductive plants were further classified as having reproduced in a single season or more than one season. Single-season plants were classified as having reproduced in their first or second year (Table A5.5, see also Supporting Information 4).
Proportion not reproducing varied among source populations, and was more than twice as high among the three southerly source populations (W611, W441, W290, mean = 42.9%) than the five northerly sources (W176, W132, W102, W36, E2, mean = 19.6%; Z = 7.53, P < 0.00001, linear contrast performed using glht in the MultComp R package version 1.4-1). Among-population variation in the proportion of plants reproducing only once was less pronounced although marginally significant. However, there were no significant pairwise comparisons nor a significant contrast between southerly (mean = 69.9%) and northerly populations (mean = 63.0%; Z = 1.74,
P = 0.082). The proportion of semelparous plants reproducing in the first year varied among populations, however none of the pairwise comparisons between source populations were significant. Although the frequency of first-year reproduction was much higher in two of the three southerly sources, the overall contrast between southerly (mean = 15.7%) and northerly sources (7.9%) was not significant (Z = 1.52, P = 0.13).
Taken together these results suggest that the better performance of the five northerly than the three southerly source populations when planted beyond the range was primarily due to better survival to reproduction.
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Table A5.5. Variation in life-history among Camissoniopsis cheiranthifolia from eight source populations planted beyond the northern range limit.
Of all plants Of repro plants Of 1x plants Source % non repro % repro 1x % 1st year only Total n W611 44.1a 74.2 22.4 118 W441 37.6ab 75.3 3.6 117 W290 47.1a 60.3 21.0 119 W176 18.8c 67.3 4.7 117 W132 15.5c 74.5 5.5 116 W102 23.5bc 59.3 9.2 119 W36 19.3c 56.3 7.4 119 E2 21.0bc 57.4 13.0 119 GLM with binomial errors detected significant among-source population variation in proportion of plants failing to reproduce (non repro; χ2 = 62.7, df = 7, P < 0.00001), the proportion of reproductive plants reproducing only once (repro 1x, χ2 = 17.8, df = 7, P = 0.013) and the proportion of semelparous plants reproducing in their first year only (χ2 = 19.1, df = 7, P = 0.0079). Letter superscripts indicate results of Tukey multiple comparisons among source populations performed using the glht command in the MultComp R package (version 1.4-1). Values not sharing a letter are significantly different. None of the comparisons for % repro 1x or % 1st year only were significant. See Supporting Information 5 for details.
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APPENDIX 5.6. Supporting Information 6: Life Table Analysis of Variation in
Demographic Parameters Among Planting Sites and Source Populations
For each source population planted at each site, we used year-specific measures of survival (lx) and fruit-production (mx) to calculate demographic parameters using standard life table analysis.
Net reproductive rate (as expected Ro = Σlxmx was equal to lifetime fruit production predicted by the ASTER model (Pearson correlation among 32 source and site combinations, r = +1.0).
Generation time (T = Σxlxmx) varied 1.02 – 1.92 and was highest at B60 (T > 1.5 for all eight source populations vs. T < 1.6 elsewhere). Across all source x site combinations T correlated positively with Ro (r = + 0.64, P < 0.0001). As a result, the intrinsic rate of increase (r = ln(Ro)/T) measured as fruits produced per individual per year correlated only moderately with Ro
(r = +0.59, P = 0.00034). Consequently, the markedly higher fruit production beyond the northern range limit did not translate into higher per-capita, per year fruit production, although r was typically higher at B60 than at site W133. For other sites, the pattern of variation in r was very similar to that in lifetime fruit production (compare Fig. 3 with Fig. S5).
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Figure A5.5. Variation in lifetime fitness measured as per-capita per-year fruit production (r) of Camissoniopsis cheiranthifolia among source populations and transplant sites. (A) Four source populations reciprocally transplanted at four sites up to the northern range limit. (B) Eight source populations transplanted at three sites up to and beyond the range limit. Points are measures of r based on life table analysis of each source population at each planting site (see Supporting Information 6).
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APPENDIX 5.7. Supporting Information 7: Heterogeneity in the Partitioning of Phenotypic
Variation in Fitness Across Transplant Sites.
The amount of phenotypic variation among families can provide an upper estimate of the genetic variation within populations (Falconer and Mackay 1996). Our transplant experiment included replicate individuals from known maternal families but it was not optimally designed to provide information about the amount of phenotypic and genetic variation within populations.
Experimental transplants were distributed across 16 spatial blocks at each of sites W133, E0 and
B60. Although all source populations were equally represented in each block at each site, each maternal seed family within any given source population was only represented in five of 16 blocks. Because there was a partial confound between maternal family and block at each site, variance among maternal families does not perfectly represent the upper limit of genetic variation for fitness. Accordingly, we used restricted maximum likelihood (REML) implemented in the lmer function in the lme4 package (version 1.1-12, http://lme4.r-forge.r-project.org/) for the R statistical environment (version 3.3.0) to fit a random-effects, partially-nested linear model to variation in lifetime fruit production at each planting site with source population, maternal family nested within source population and block as effects. Because the distribution of lifetime fruit production was zero-inflated and could not be transformed to meet assumptions, we tested the significance of the source population and maternal family variance components using the parametric bootstrap approach following Faraway (2006, p. 160).
Most of the variation in lifetime fruit production was distributed among blocks and among replicate individuals within maternal families. Variance among source populations was significant at W133 and B60 but not at E0. Variance among maternal families within source
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populations was negligible at W133 and increased somewhat towards the range limit and beyond, but only neared significance at B60.
Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to quantitative Genetics. Longman,
Essex.
Faraway, J. J. 2006. Extending the Linear Model with R. Chapman & Hall/CRC, Boca Raton.
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Table A5.6. Partitioning of variance in lifetime fruit production among source populations and maternal families within source populations of Camissoniopsis cheiranthifolia planted at three sites towards and beyond the northern range limit.
Planting site Variance % of total Bootstrap P Variance component variance
Site W133 Among source populations 2.543 4.92 < 0.001
Among families within sources 0.000 0.00 1.000 Block 4.062 7.86 Residual 45.072 87.22
Total 51.677 100.00
Site E0
Among source populations 0.522 0.35 0.185 Among families within sources 2.274 1.53 0.220 Block 6.068 4.07
Residual 140.166 94.05 Total 149.030 100.00
Site B60 Among source populations 23.760 4.74 < 0.001 Among families within sources 11.350 2.27 0.057
Block 176.530 35.25 Residual 289.140 57.74 Total 500.780 100.00
Variance components were estimated by fitting a partially-nested, random-effects model to individual lifetime fruit production using restricted maximum likelihood. Significance of variance among source populations and maternal families nested within source populations was evaluated using parametric bootstrap analysis.
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APPENDIX 6.1 —Analyses of variation at nine chloroplast microsatellite loci in C. cheiranthifolia and C. bistorta
In addition to nSSR, nine chloroplast microsatellites (cpSSR; Provan et al. 2001) from conserved primer pairs developed by Weising and Gardner (1999) and Chung and Staub (2003) resulted polymorphic and were assayed for variation in 2 to 7 (mean 5.05) individuals from each of 19 populations of C. bistorta (n = 106) and 32 C. cheiranthifolia (n = 159; total n = 265) populations previously used in López-Villalobos and Eckert (submitted; chapter 3). The different combinations of alleles across nine loci gave 20 haplotypes across both species (Table A6.3).
One of the main assumptions of using chloroplast microsatellites is that variation in fragment sizes is given by mutations within the microsatellite. We confirmed this in C. cheiranthifolia by sequencing several PCR fragments from different individuals across the range and showed that variation was due to differences in the number of repeats within the microsatellite (see Chapter 3).
In C. bistorta, however, sequencing reveled that fragment size variation at two loci in two individuals from the northern range limit is given by deletions in the flanking region (Fig. A6.1).
Hence, we do not present analyses of cpSSR in this study because the same variation that caused fragments to become shorter in C. cheiranthifolia has not the same origin as the variation that causes fragments to become shorter in C. bistorta. Analyses with these data are presented in the in Figures A6.1 to A6.5.
Previous assays of cpDNA sequence following Taberlet et al. (1991) among 12 individuals sampled from across the range of C. cheiranthifolia detected no variation in 149 bp of
TFcd (trnL intron) and 298 bp of TFef regions (trnL/F intergenic spacer; E. Austin, K. E. Samis and C. G. Eckert, unpublished data). We screened 33 consensus universal cpSSR primers
(Weising and Gardner 1999, Chung and Staub 2003) using 3–7 individuals (mean = 3.5) from
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each of 53 populations of both, C. cheiranthifolia and C. bistorta. From this first assessment we detected six primer pairs (CCMP10, CCMP2, CSSR7, CSSR8, CSSR12, CSSR23) that consistently amplified and exhibited variation in C. cheiranthifolia and nine primers pairs that had variable fragments in C. bistorta (CCMP10, CCMP2, CSSR7, CSSR8, CSSR12, CSSR17,
CSSR20, CSSR21, CSSR23). We assayed 265 individuals from a total of 50 populations of both species; 30 C. cheiranthifolia and 20 C. bistorta that were also assayed for nSSR variation. Three additional populations were also included, two of C. cheiranthifolia (BBF1C and CAS1C see chapter 3) and one of C. bistorta (CGF1B from Los Angeles Co., inland). The forward primer of each pair included a D4 red labeled M13 tail (5′- CACGACGTTGTAAAACGA -3′, Sigma-
Aldrich, Oakville, Ontario, Canada). Initial testing and final PCR conditions, amplification and fragment sizing procedure are described in Table A6.1.
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Table A6.1. Summary of PCR optimization of cpSSR primers and initial screening of Camissoniopsis cheiranthifolia and C. bistorta populations. Parameter C. cheiranthifolia & C. bistorta
No. Polymorphic loci 9 detected Locus name according to CCMP2, CCMP10, CSSR7, CSSR8, CSSR12, CSSR17, reference CSSR20, CSSR21, CSSR23
Polymorphic gene regions 5' to trnS, rpl2-rps19 intergenic, PsbC-TrnS, Ycf3, PsbB- in the chloroplast genome PsbT, Rrn5-TrnR, NdhD-PsaC, TrnR-Rrn5, Rp12-TrnH of Nicotiana tabaccum
Populations tested 53 (32 C. cheiranthifolia and 21 C. bistorta) Mean, range individuals Mean = 5.0 ± SD 1.54 (range = 2–7) per population Total volume 5 µl PCR reactions: 0.5 µl DNA template [10 Initial PCR reaction µg/µl], 2.5 µl Multiplex PCR Master Mixa , 0.1 µl of each volumes and forward and reverse primers [10 µM], 1.1 µl of M13taq [1 concentrations µM]b , 0.7 µl sterile double distilled water 15 min denaturation at 94°C, followed by 35 cycles of 45 s Thermo-cycling conditions at 94°C, 50 s at 55°C, and 55 s at 72°C, with 16 min final extension at 72°C Duplex and triplex reactions: CCMP10+CCMP2; CSSR8+CSSR17+CSSR23; CSSR12+ CSSR7 and CSSR20+CSSR21 following the initial PCR reaction protocol but adjusting the double distilled water in the PCR Final PCR multiplex from 0.7 µl in the single reactions to 0.5 µl. Final product reactions was diluted with sterile double distilled water to a final volume of 15 µl and fragments were sized in a GenomeLab GeXP with the Beckman-Coulter GenomeLab DNA Size Standard Kit 400c Notes: a PCR reactions were performed using Qiagen Multiplex Master Mix (Qiagen, Toronto, Ontario, Canada). b 18 bp M13Tag 5′- CACGACGTTGTAAAACGA -3′, Sigma-Aldrich, Oakville, Ontario, Ca c scored with CEQ 8000 Genetic Analysis System (version 9.0, Beckman Coulter, Mississauga, Ontario, Canada).
253 Table A6.2. Characteristics of nine chloroplast microsatellite loci used in to genotype 265 individuals from 51 populations C. cheiranthifolia and C. bistorta. Repeat motifs are as the original Tobacco sequence, annealing temperature (Ta), range of amplified fragment sizes in C. cheiranthifolia and C. bistorta (including 18 bp of M13 tag) and reference publication. a Locus Primer sequence (5’ – 3’) (F) forward (R) reverse Repeat motif Location in Size in Ta Fragment size primer in Tobacco Tobaccob Tobaccoc (°C) range (bp)d F: GATCCCGGACGTAATCCTG 5' to trnS 189 CCMP2 (A) 50 250–282 R: ATCGTACCGAGGGTTCGAAT 11 (GCU) F: TTTTTTTTTAGTGAACGTGTCA rpl2-rps19 103 CCMP10 (T) 50 129–133 R: TTCGTCGDCGTAGTAAATAG 14 intergenic F: CGGGAAGGGCTCGKGCAG PsbC-TrnS 349 CSSR7 (T)11 50 337–346 R: GTTCGAATCCCTCTCTCTCCTTTT F: TTGATCTTTACGGTGCTTCCTCTA Ycf3 249 CSSR8 (T) C(T) 50 283–288 R: TCATTACGTGCGACTATCTCC 5 17 F: CCAAAAACTTGGAGATCCAACTAC PsbB-PsbT 249
CSSR12 R: TTCCATAGATTCGATCGTGGTTTATTC (A)8 50 240–245 CATAGATTCGATCGTGGTTTA F: CACACCAATCCATCCCGAACT Rrn5-TrnR 236 CSSR17 (A) 50 247–248 R: GGTGCGTTCCGRGGTGTGA 13 F: CCGCARATATTGGAAAAACWACAA NdhD-PsaC 311 CSSR20 (A) 50 349-356 R: GCTAARCAAATWGCTTCTGCTCC 8 F: CCACCCCGTCTCSACTGGATCT TrnR-Rrn5 280 CSSR21 (T)13 50 292–293 R: AAAAATAGCTCGACGCCAGGAT F: AYGGRGGTGGTGAAGGGAG Rp12-TrnH 217 CSSR23 (A) 50 353–358 R: TCAATTCCCGTCGTTCGCC 14 aNames of consensus chloroplast primers according to Weising & Gardner (1999) and Chung & Staub (2003). bNames of chloroplast genes in Tobacco genome where consensus chloroplast CCMP and CSSR (cpSSR) primer sequences are. cExpected size according to the Tobacco genome. dObserved fragment range in C.cheiranthifolia and C. bistorta eDegenerate positions according to references are D (= A T or G) K (= T or G) R (= A or G) and Y (= C or T).
Table A6.3. Haplotypes found in 265 individuals from 51 populations at nine microsatellite loci; 32 of C. cheiranthifolia and 19 of C. bistorta. Locus names are the same as the original reference (Table A6.1). Total refers to the number of plants with each haplotype. Haplotype CCMP10 CCMP2 CSSR12 CSSR7 CSSR8 CSSR17 CSSR20 CSSR21 CSSR23 Total Frequency HAP1 129 252 241 339 284 247 355 292 353 7 0.0264 HAP2 129 252 241 341 288 247 355 292 353 5 0.0189 HAP3 129 252 241 341 288 247 356 292 353 4 0.0151 HAP4 129 253 241 339 285 247 355 292 353 2 0.0075 HAP5 129 254 241 341 288 247 355 292 353 4 0.0151 HAP6 129 250 241 337 287 247 355 293 353 5 0.0189 HAP7 130 276 244 345 284 248 349 293 356 17 0.0642 HAP8 130 277 244 345 284 248 349 293 356 7 0.0264 HAP9 131 131 273 244 345 284 248 349 293 9 0.0340 HAP10 131 273 244 346 284 248 349 293 356 21 0.0792 HAP11 131 275 245 345 284 248 349 293 356 4 0.0151 HAP12 131 276 243 345 284 248 349 293 356 5 0.0189 HAP13 131 131 276 244 344 284 248 349 293 8 0.0302 HAP14 131 277 244 345 284 248 349 293 356 3 0.0113 HAP15 131 131 276 244 345 284 248 349 293 127 0.4792 HAP16 131 277 244 346 284 248 349 293 356 8 0.0302 HAP17 132 276 244 345 284 248 349 293 132 16 0.0604 HAP18 132 276 245 345 284 248 349 293 132 2 0.0075 HAP19 132 277 244 345 284 248 349 293 132 4 0.0151 HAP20 133 276 244 345 284 248 349 293 133 7 0.0264
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Locus CCMP2 Individual CSP1BA3, species C. bistorta from the northern range limit. Fragment size scored: 252 Individual ONO2CN14, species C. cheiranthifolia from the northern range limit. Fragment size scored: 273 Individual CMC1CK32, species C. cheiranthifolia from the northern part of the range. Fragment size scored: 277
Locus CSSR7 Individual CSP1BA1, species C. bistorta from the northern range limit. Fragment size scored: 339 Individual BBR1CN2, species C. cheiranthifolia from the southern range limit. Fragment size scored: 344 Individual ONO1CL7, species C. cheiranthifolia from the northern range limit. Fragment size scored: 346
Locus CSSR12 Individual CSP1BA1, species C. bistorta from the northern range limit. Fragment size scored: 241 Individual ONO1CL7, species C. cheiranthifolia from the northern range limit. Fragment size scored: 244 Individual CDW2CN5, species C. cheiranthifolia LF-SC from Los Angeles Co. Fragment size scored: 245 Individual CSP1CA31, species C. cheiranthifolia SF-SC from just north of Pt. Conception. Fragment size scored: 245
Figure A6.1. Sequence alignment for three to four individuals at each of three cpSSR loci. CCMP2 and CSSR7 show mutations in the flanking region for the genotypes from the C. bistorta CSP1B population and CSSR12 is shown only for comparisons of a locus that only shows variation within the microsatellite. Different colors represent different nucleotides and gaps represent missing bases. Sequences shown in this figure only represents a portion of the full alignment for each locus.
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Hap21 Hap20
Oregon N
Hap13 scale appro x 1:12,000,000 Hap14 Hap12 0 200 400 km
Hap11 C. cheiranthifolia pops.
Main haplotype #15 California
Hap10
Pt. Conception
Escondido
C. bistorta Baja sampled pops. 34
33 Escondido 32 Punta Banda
scale appro
0 100 31
Hap3 Hap2 Hap4
C. bistorta inland haplotypes
Hap1
Figure A6.2. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations genotyped at 9 cpSSR loci, and the geographic distribution and frequency of each of the 20 haplotypes detected. The size each haplotype is proportional to its frequency (Table A6.3). Haplotypes are color-coded representing those found in: both species = blue (mainly distributed south of Point Conception, small-flowered C. cheiranthifolia found among populations from north of Pt. Conception and up to the San Francisco Bay = light purple, from San Francisco to the northern range limit = orange, a population from the Channel Island = gray, the only population of large-flowered self-compatible C. cheiranthifolia that did not show the most common haplotype = brown and several multi-colored haplotypes representing different haplotypes detected in C. bistorta. Inset shows the sampling location and the frequency of each haplotype in each C. bistorta population sampled. Landmarks are mentioned in the text.
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Figure A6.3. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 7 cpSSR microsatellite loci (CCMP10, CSSR8, CSSR12, CSSR17, CSSR20, CSSR21). Two loci (CCMP2 and CSSR7) were removed for showing evidence of mutations in the flanking in one C. bistorta individual (from the most northern part of the species’ range), violating assumptions when constructing a network with C. cheiranthifolia (see Fig.S5). The size of each circle is proportional to the frequency of each haplotype. The connections between haplotype 1 and median vector 3 (located between hap 7 and hap 6) separates the two main groups of haplotypes by 14 mutations. The branch between haplotype 11 and 13 is separated by 2 mutations. The rest of the branches in the network are separated by only one mutation (represented by the gray letters with the name of each locus). Haplotypes are color-coded representing those found in: both species = blue, only small-flowered C. cheiranthifolia = pink, only C. bistorta = green and the only large-flowered self-compatible C. cheiranthifolia population (CDW2C) that did not show the most common haplotype = dark red.
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Figure A6.4. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 8 cpSSR microsatellite loci (CCMP10, CSSR7, CSSR8, CSSR12, CSSR17, CSSR20, CSSR21). Compared to the previous network, only locus CCMP2 was removed. The size of each circle in the network (representing each of 15 haplotypes) is proportional to its frequency. Connections between haplotype 13 and 16, and between haplotype 5 and median vector 8 are separated by 2 mutations. The branch connecting the two main linages is separated by four median vectors (mv6, mv7, mv8, mv9) connecting haplotypes 6, 7 and 8 mv3 and 14 mutations along the branch. The rest of the branches in the network are separated by only one mutation. Haplotypes are color-coded representing those found in: both species = blue, only small-flowered C. cheiranthifolia = pink, green = only C. bistorta and dark red= the only large-flowered self-compatible C. cheiranthifolia population (CDW2C) that did not show the most common haplotype.
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Figure A6.5. Median-Joining haplotype network of 32 C. cheiranthifolia and 19 C. bistorta populations at 8 cpSSR microsatellite loci (CCMP10, CCMP2, CSSR8, CSSR12, CSSR17, CSSR20, CSSR21). Compared to Fig. A6.2 and the previous network, only locus CSSR7 was removed. The size of the circles representing each of the 18 haplotypes is proportional to its frequency. Connections between haplotype 13 and 16, and between haplotype 5 and median vector 8 are separated by 2 mutations. The branch connecting the two main linages is separated by 14 mutations. Connections between haplotype 15 and 18, and between haplotype 6 and median vector 15 are separated by 2 mutations. The branch connecting the two main linages is separated by three median vectors (mv22, mv26 and mv12) and14 mutations. The rest of the branches in the network are separated by only one mutation. Haplotypes are color-coded representing those found in: both species = Blue, only small-flowered C. cheiranthifolia = Pink, only C. bistorta = green and the only large-flowered self-compatible C. cheiranthifolia population that did not show the most common haplotype = dark red.
260 B