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Uvic Thesis Template Population genomics of a timberline conifer, subalpine larch (Larix lyallii Parl.) by Marie Vance M.Sc., University of Neuchâtel, 2011 B.Sc., University of Victoria, 2009 A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY in the Department of Biology Marie Vance, 2019 University of Victoria All rights reserved. This dissertation may not be reproduced in whole or in part, by photocopy or other means, without the permission of the author. ii Supervisory Committee Population genomics of a timberline conifer, subalpine larch (Larix lyallii Parl.) by Marie Vance M.Sc., University of Neuchâtel, 2011 B.Sc., University of Victoria, 2009 Supervisory Committee Dr. Patrick von Aderkas, Department of Biology Co-supervisor Dr. Barbara Hawkins, Department of Biology Co-Supervisor Dr. Gerry Allen, Department of Biology Departmental Member Dr. Brian Starzomski, School of Environmental Studies Outside Member iii Abstract Supervisory Committee Dr. Patrick von Aderkas, Department of Biology Co-supervisor Dr. Barbara Hawkins, Department of Biology Co-Supervisor Dr. Gerry Allen, Department of Biology Departmental Member Dr. Brian Starzomski, School of Environmental Studies Outside Member Subalpine larch (Larix lyallii Parl.) has a narrow ecological niche at timberline in the Cascade Range and the Rocky Mountains of western North America. Demographic factors, including a long generation time (average 500 years) and a late arrival at sexual maturity (100-200 years), make it unlikely that this species will be able to adapt to predicted climate change. A better understanding of genetic structure and genetic diversity is necessary in order to effectively manage this species for future generations. Foliage from 62 populations of subalpine larch was collected in order to elucidate the range-wide population genomics of the species. DNA was extracted and a next- generation sequencing method, restriction site associated DNA sequencing (RAD-seq), was used to generate genome-wide single nucleotide polymorphism (SNP) marker data. Three genetically differentiated clusters were identified via principal components analysis, a discriminant analysis of principal components and Bayesian STRUCTURE analysis: the Cascade Range, the southern Rocky Mountains and the northern Rocky Mountains. A monophyletic group in the central Rocky Mountains was also identified in a dendrogram of genetic distance but this group had weak bootstrap support (49%), meaning genetic differentiation depends on relatively few genetic variants. Genetically differentiated groups should be prioritized for future management and conservation efforts. Negative values of Tajima’s D and preferred demographic scenarios generated by coalescent simulations indicated that 15 populations all have a recent history of expansion. Genetic diversity within these populations was found to be moderate (HO = 0.15 – 0.20), inbreeding coefficients were found to be high (FIS = 0.15 – 0.25) and genetic differentiation among populations was found to be high (average FST = 0.18). iv These results indicated that fragmentation driven by Holocene warming may have resulted in reduced effective population sizes. Smaller populations experience stronger genetic drift and an increased likelihood of inbreeding, which may hinder an adaptive response to natural selection. Still, parameter estimates for preferred demographic scenarios suggested a minimum effective population size of around 20,000 individuals, which is not considered small by most conservationists. A final study of 18 populations found local adaptation to cold temperature in the northern portion of the species range. In all seasons, populations from the northern Rocky Mountains had significantly higher cold tolerance than populations from the central Canadian Rocky Mountains and the northern Cascades. Winter cold tolerance showed strong clines associated with the frost-free period and degree days below zero. These two climate variables explained 65% of the explainable variance in phenotype when redundancy analysis models were conditioned on geography. Seven SNPs were identified that explained a significant portion of the variance in winter cold tolerance. Range-wide, additional SNPs were identified as FST outliers and/or as significantly correlated with environmental gradients, even after correcting for neutral genetic structure. Together, the results of this work indicate that dispersal, neutral evolutionary processes and natural selection have all played important roles in shaping patterns of genetic variation across the natural range of subalpine larch. All of these factors should be considered during the development of management and conservation strategies for this high-elevation conifer species. v Table of Contents Supervisory Committee ...................................................................................................... ii Abstract .............................................................................................................................. iii Table of Contents ................................................................................................................ v List of Tables .................................................................................................................... vii List of Figures .................................................................................................................... ix Acknowledgments............................................................................................................. xii Dedication ........................................................................................................................ xiv CHAPTER 1: INTRODUCTION ....................................................................................... 1 CHAPTER 2: GENETIC STRUCTURE .......................................................................... 13 Introduction ................................................................................................................... 13 Methods......................................................................................................................... 17 Sampling ................................................................................................................... 17 Molecular Techniques ............................................................................................... 24 Bioinformatics........................................................................................................... 29 Genotyping ................................................................................................................ 33 Data Analysis ............................................................................................................ 37 Results ........................................................................................................................... 41 Genotyping ................................................................................................................ 41 Data Analysis ............................................................................................................ 46 Discussion ..................................................................................................................... 59 CHAPTER 3: POPULATION GENOMICS .................................................................... 66 Introduction ................................................................................................................... 66 Methods......................................................................................................................... 70 Sample Collection ..................................................................................................... 70 Sequencing ................................................................................................................ 70 Bioinformatics........................................................................................................... 74 Gentoyping ................................................................................................................ 78 Data Analysis ............................................................................................................ 82 Results ........................................................................................................................... 90 RAD-seq Data ........................................................................................................... 90 Genetic Structure ...................................................................................................... 90 Heterozygosity .......................................................................................................... 92 Inbreeding Coefficients ............................................................................................. 96 Tajima’s D ................................................................................................................ 97 Genetic Differentiation ............................................................................................. 99 Demographic History .............................................................................................. 102 Discussion ................................................................................................................... 106 Genetic Structure ...................................................................................................
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