Science, Faculty of
Zoology, Department of
DSpace
UBCV
Askelson, Kenneth
Doctor of Philosophy - PhD
University of British Columbia
Understanding how selection, recombination, and introgression interact during the speciation process is a key goal in the field of evolution, and many questions remain unanswered. In my dissertation, I make use of a cryptic species complex of white-breasted nuthatches (Sitta carolinensis) to understand the landscape of genomic differentiation between these putative species and how recombination and introgression have shaped those patterns. I then extend my findings regarding recombination and differentiation in white-breasted nuthatches to examine a broad sampling of genomic differentiation in avian species pairs, to infer how chromosome length (and thus rates of recombination) have shaped genome-wide patterns of differentiation. Through my research, I found that white-breasted nuthatches are comprised of four differentiated populations that are on a continuum of differentiation and that ghost lineage introgression has been involved in shaping these patterns. I also found evidence for large inversion-like haploblocks occurring in white-breasted nuthatches. I then combined these differentiation patterns with recombination estimates to show that broad-scale recombination rates are negatively associated with differentiation. In my final chapter, I found that the relationship between differentiation and chromosome length varied among pairs, but the direction of this relationship was significantly negatively correlated with the age of divergence. This result points to large chromosomes with lower rates of recombination being the likely areas to have higher amounts of differentiation in younger species. My dissertation spans a wide research scope, from species-specific questions to broad questions about recombination and differentiation. It expands our knowledge of bird species by describing the genomic patterns of this cryptic species complex and how ghost lineage introgression has provided important variation. My research also advances the field by adding to the growing body of evidence that areas of low recombination are associated with regions of increased differentiation and that these landscapes of recombination likely shape genomic differentiation across bird species.
Introgression, recombination, and genomic differentiation in a cryptic species complex of North American passerines by Kenneth Askelson B.Sc., The University of Wyoming, 2016 M.Sc., The University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2023 © Kenneth Askelson, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Introgression, recombination, and genomic differentiation in a cryptic species complex of North American passerines submitted by Kenneth Askelson in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Zoology Examining Committee: Darren Irwin, Professor, Zoology, UBC Supervisor Dolph Schluter, Professor, Zoology, UBC Supervisory Committee Member Judith Mank, Professor, Zoology, UBC University Examiner Sally Aitken, Professor, Forest and Conservation Sciences, UBC University Examiner Additional Supervisory Committee Members: Michael Whitlock, Professor, Zoology, UBC Supervisory Committee Member Loren Rieseberg, Professor, Botany, UBC Supervisory Committee Member iii Abstract Understanding how selection, recombination, and introgression interact during the speciation process is a key goal in the field of evolution, and many questions remain unanswered. In my dissertation, I make use of a cryptic species complex of white-breasted nuthatches (Sitta carolinensis) to understand the landscape of genomic differentiation between these putative species and how recombination and introgression have shaped those patterns. I then extend my findings regarding recombination and differentiation in white-breasted nuthatches to examine a broad sampling of genomic differentiation in avian species pairs, to infer how chromosome length (and thus rates of recombination) have shaped genome-wide patterns of differentiation. Through my research, I found that white-breasted nuthatches are comprised of four differentiated populations that are on a continuum of differentiation and that ghost lineage introgression has been involved in shaping these patterns. I also found evidence for large inversion-like haploblocks occurring in white-breasted nuthatches. I then combined these differentiation patterns with recombination estimates to show that broad-scale recombination rates are negatively associated with differentiation. In my final chapter, I found that the relationship between differentiation and chromosome length varied among pairs, but the direction of this relationship was significantly negatively correlated with the age of divergence. This result points to large chromosomes with lower rates of recombination being the likely areas to have higher amounts of differentiation in younger species. My dissertation spans a wide research scope, from species-specific questions to broad questions about recombination and differentiation. It expands our knowledge of bird species by describing the genomic patterns of this cryptic species complex and how ghost lineage introgression has provided important variation. My research also advances the field by adding to iv the growing body of evidence that areas of low recombination are associated with regions of increased differentiation and that these landscapes of recombination likely shape genomic differentiation across bird species. v Lay Summary The focus of this dissertation is to better understand evolutionary processes and how a fundamental characteristic of genomes, recombination (the reshuffling of chromosome segments), contributes to those processes. In my research I ask questions that cover a range of topics such as whether there are multiple species of white-breasted nuthatch, and whether there is an association between chromosome size and differentiation among a wide sampling of bird species pairs. It is my goal to enrich our knowledge of speciation and how genomes diverge during that process. My results show that white-breasted nuthatches likely consist of multiple species, that their genomes harbor large polymorphic genomic blocks with reduced recombination that may be inversions, and that introgression with an unknown lineage has shaped differentiation. More broadly, my dissertation supports the view that variable recombination is a potent force in shaping differentiation in the genome and that its influence may be most important for young species pairs. vi Preface In all aspects of my dissertation, my supervisor Dr. Darren Irwin provided crucial advice, assistance, and support. In each chapter, I designed the research question, conducted field and lab work (DNA extraction and GBS [genotyping-by-sequencing] library preparation), analyzed the data, and wrote the results in the form of manuscripts. In Chapters 2-4, I collaborated with Dr. Garth Spellman from the Denver Museum of Nature and Science, who provided input and advice on various phases of these projects. A version of chapter 2 is published in Molecular Ecology, as: Askelson, K. K., Spellman, G. M., & Irwin, D. (2023). Genomic divergence and introgression between cryptic species of a widespread North American songbird. Molecular Ecology, 32, 6839–6853. https://doi.org/10.1111/mec.17169. For my dissertation, I also received samples loaned from the Burke Museum of Natural History and Culture and the Denver Museum of Nature and Science. The reference genome first featured in Chapter 2 was constructed by genome assembly team at Cantata Bio (formerly known as Dovetail genomics). In my final data chapter, I collaborated with Dr. Marcella Baiz who provided genomic data from the Toews Lab at Pennsylvania State University, advice on analyses, and manuscript editing. The work conducted in this dissertation was done under the following permits and ethical approvals: United States Geological Survey Bird Banding Permit No. 23691, Montana Fish Wildlife and Parks Scientific Collectors Permit No. 2019-063-W, Wyoming Game and Fish Department Scientific Research Permit No. 1222, Colorado Parks and Wildlife Scientific License No. 19TRb3276, Oregon Department of Fish and Wildlife Scientific Permit No. 038-20, and UBC Animal Care Certificate A17-0049. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................xv List of Figures ............................................................................................................................ xvii List of Abbreviations ............................................................................................................... xxiii Acknowledgments .................................................................................................................... xxiv Chapter 1: Introduction ................................................................................................................1 1.1 Speciation ........................................................................................................................ 1 1.2 Introgression ................................................................................................................... 4 1.3 Recombination ................................................................................................................ 5 1.3.1 Inversions .................................................................................................................... 7 1.4 Study organism ............................................................................................................... 8 1.5 The thesis ...................................................................................................................... 10 Chapter 2: Genomic divergence and introgression between cryptic species of a widespread North American songbird ...........................................................................................................14 2.1 Introduction ................................................................................................................... 14 2.2 Methods......................................................................................................................... 17 2.2.1 Genome Assembly .................................................................................................... 17 2.2.2 Field Methods ........................................................................................................... 18 viii 2.2.3 Lab Methods ............................................................................................................. 19 2.2.4 Data filtering ............................................................................................................. 20 2.2.5 Analysis of population structure ............................................................................... 22 2.2.6 Phylogeny estimation ................................................................................................ 23 2.2.7 Inference of introgression ......................................................................................... 24 2.3 Results ........................................................................................................................... 26 2.4 Discussion ..................................................................................................................... 35 Chapter 3: Polymorphic haplotype blocks present in a passerine ..........................................43 3.1 Introduction ................................................................................................................... 43 3.2 Methods......................................................................................................................... 47 3.2.1 Data processing ......................................................................................................... 47 3.2.2 Bioinformatic analyses .............................................................................................. 48 3.2.3 Comparisons of Phylogeny topology ........................................................................ 50 3.3 Results ........................................................................................................................... 51 3.4 Discussion ..................................................................................................................... 67 Chapter 4: Correlated landscapes of recombination and differentiation in an avian speciation continuum ...................................................................................................................73 4.1 Introduction ................................................................................................................... 73 4.2 Methods......................................................................................................................... 77 4.3 Results ........................................................................................................................... 83 4.4 Discussion ..................................................................................................................... 90 Chapter 5: Relationships between genomic differentiation, chromosome size, and divergence date in birds ...............................................................................................................95 ix 5.1 Introduction ................................................................................................................... 95 5.2 Methods......................................................................................................................... 98 5.3 Results ......................................................................................................................... 103 5.4 Discussion ................................................................................................................... 107 Chapter 6: Conclusion ...............................................................................................................112 6.1 Summary of key results .............................................................................................. 112 6.2 Unanswered questions ................................................................................................ 114 6.3 Broad implications ...................................................................................................... 115 References ...................................................................................................................................118 Appendices ..................................................................................................................................169 Appendix A Supplementary information for Chapter 2 ......................................................... 169 A.1 Genome assembly alignments of the white-breasted nuthatch genome to zebra finch (A) and the brown creeper (B) in Dgenies. Scaffolds in the white-breasted nuthatch genome are rows and chromosomes in zebra finch and brown creeper are columns. Lines and dots indicate how sequences are aligned to each other. ............................................................. 169 A.2 A) Admixture analysis with K=3 and B) K=5. This figure corresponds with the admixture analysis in figure 2.1. K=5 reveals additional structure in RS by grouping samples from Mexico. ......................................................................................................... 170 A.3 Cross-validation error (CV) across varying K in Admixture analysis. K=4 has lowest CV and is most supported. .................................................................................................. 171 A.4 Genotypes for 10 Eastern, 10 RS, and 2 putative F1 hybrids are shown. Individuals are color-coded according to population. Light purple and dark purple represent homozygote SNPs, the color is determined by the frequency of the SNP in the population. x Halflight purple and half dark purple are heterozygous SNPs. Only SNPs with FST of >0.9 are shown. ........................................................................................................................... 172 A.5 PCA of white-breasted nuthatch samples using PC 2 and 3. For formatting and analysis details, see figure 2.1 in the main text. PC 2 and 3 explain 12.6% and 7% of the variation in the data, respectively. ...................................................................................... 173 A.6 This figure corresponds with figure 2.5; see figure for formatting details. Genotypes across scaffolds 29 and 34 for a subset of individuals in each population are shown (A and C) next to corresponding PCA’s of SNPs from that region for all individuals ( and D). Genotypes with FST < or equal to 0.25 between RN and RS are excluded in the genotype by individual plots The scaffold 29 PCA is of 1,111 SNPs (B) and the scaffold 34 PCA is of 635 SNPs (D). ..................................................................................................................... 174 A.7 PCAs using only RS individuals for the regions of interest focused on in figure 2.5 and appendix A.6. A) PCA of scaffold 1 SNPs from 52 Mb – 58 Mb region (398 SNPs). B) PCA of scaffold 2 SNPs from 54 Mb – 59 Mb region (248 SNPs). C) PCA of scaffold 29 SNPs from 12 Mb – 14.1 Mb region (661 SNPs). D) PCA of scaffold 34 SNPs from 2 Mb – 4 Mb region (407 SNPs). Similar to figure 2.5 and appendix A.6, PCAs contain 3 clusters, a predominately RS cluster, a lower frequency cluster that is the RN haplotype, and a middle cluster of heterozygotes. In the case of the scaffold 34 region there is only a RS haplotype and heterozygotes. ............................................................................................................... 175 A.8 Log transformed nucleotide diversity from sliding windows across the genome for each population of white-breasted nuthatches. Each dot is a 5,000 sequenced bp window and is based on sequencing from 23 individuals. ............................................................... 176 Appendix B Supplementary information for Chapter 3 .......................................................... 177 xi B.1 Post-filtering SNP counts for scaffolds 1 Mb or greater. ........................................ 177 B.2 PCA’s of SNPs from Scaffolds 1-9. ....................................................................... 178 B.3 PCA’s of SNPs from scaffolds 10-19. .................................................................... 179 B.4 PCA’s of SNPs scaffolds 21-30. ............................................................................. 180 B.5 PCA’s of SNPs from scaffolds 31-39 ..................................................................... 181 B.6 PCA of Scaffold 22 SNPs across all white-breasted nuthatch samples after filtering. This PCA corresponds to figure 3.3d PCA but shows PC axes 1 and 2. ............................ 182 B.7 A) PCA of 9732 SNPs from Scaffold 20 across 356 white-breasted nuthatch samples. B) SNP loadings across the scaffolds, two high loading SNPs are marked in red. C) PCA excluding those two high loading SNPs seen in panel B. ..................................... 183 B.8 PCA of RN individuals (138 samples) using 4861 SNPs from scaffold 3. ............ 184 B.9 Genome-wide SNP phylogeny (A) compared to SNPS from the region of high LD in scaffold 2 (B) each phylogeny uses 1500 randomly selected SNPs. Sample sizes are as follows: 3 Red-breasted Nuthatches, 2 Pacific, 2 RN, 2 RS, and 2 Eastern. In the region speciic phylogeny (B) East hap 1 and East hap 2 contain only 1 individual each. ............. 185 B.10 Phylogenies of genome-wide SNPs (A) and Scaffold 20 region-specific SNPs from the area of elevated LD (B). Phylogeny details are the same as those in appendix B.9 except sample sizes of populations are larger. Non-eastern populations contain 3 individuals, and the Eastern population has 8, 4 of each haplotype. ............................................................. 186 Appendix C Supplementary information for Chapter 4 .......................................................... 187 C.1 PCA of 1,343,993 genome-wide SNPs with no missing data across 48 white-breasted nuthatches. PC 1 explains 30.2% of the variation in the data and PC 2 explains 18.5%.. ................................................................................................................................ 187 xii C.2 Recombination rate across scaffolds for RS (Rocky Mountain South). Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. ...................................................................... 188 C.3 Recombination rate across scaffolds for RN (Rocky Mountain North). Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffold 38 is dropped because it onl had enough SNPs to form one window. .................................................................................... 189 C.4 Recombination rate across scaffolds for the Pacific population. Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffolds 14, 15, and 38 are dropped because they only had enough SNPs to form one window. .............................................................. 190 C.5 Recombination rate across scaffolds for the Eastern white-breasted nuthatch population. Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. ......................... 191 C.6 Recombination rates on Scaffold 3 (Z chromosome) for each population (or putative species) of white-breasted nuthatch. The green dot highlights the region suspected to be the PAR and corresponds to the region identified in appendix C.7. ......................................... 192 C.7 A) Genome alignment between the white-breasted nuthatch and hooded crow genomes. The section of the hooded crow genome that corresponds to the pseudoautosomal region (PAR) and aligns to the white-breasted nuthatch is shown in the yellow-box. B) Read depth ratio between the mean depth of male vs female white-breasted nuthatches for 10,714 SNPs across the Z chromosome (scaffold 3). The red line represents non-overlapping sliding window of median depth using a 100 SNP window. C) This panel corresponds with xiii panel B but only shows 0 – 5 Mb position SNPs. The green line highlights the region (2,533,000 bp – 3,150,000 bp) of the suspected PAR. ....................................................... 193 C.8 Distribution of SNP counts for 1 Mb windows of FST between populations. For each comparison, the median SNP count is listed. ...................................................................... 194 C.9 Recombination rate in 1 Mb windows is compared to the weighted average FST between populations. This figure corresponds to main figure 4. In this figure, we show FST compared to the recombination landscape of the second population in the FST pair which was not used in figure 4. See figure 4.4 for information on dots and spline colors. ........... 195 Appendix D Supplementary information for Chapter 5 ......................................................... 196 D.1 Pearson correlation values between FST and chromosome size compared to divergence times in a dataset where duplicate species/populations have been removed. .. 196 D.2 Spearman rank correlation values for the relationship of FST and log chromosome length plotted against divergence date for the species pair using comparisons for all species pairs…. ................................................................................................................................ 197 D.3 Relationships between mean FST and log chromosome size for 27 avian species pairs only including scaffolds/chromosomes > 2 Mb. This corresponds to figure 1 in the main text, please see this figure for formatting details. ............................................................... 198 D.4 Pearson correlation values (excluding chromosomes/scaffolds that are 2 Mb or smaller) for FST and log chromosome size plotted against the divergence date of the pair. 199 D.5 Correlation and p- values between the association of FST /chromosome length and divergence date using the full data set. Row names indicate the input values (i.e., correlation coefficient for the relationship of FST and chromosome length) used to test the association with divergence date. This includes Pearson correlation coefficients with 2 Mb xiv chromosomes/scaffolds removed, Spearman rank correlation, and Spearman rank correlation with 2Mb chromosomes/scaffolds removed. Column names indicate the test used to examine the relationship between FST /chromosomes length and divergence date. 200 D.6 Spearman rank correlation values between FST and chromosome size compared to divergence times in a dataset where duplicate species/populations have been removed. .. 201 D.7 Pearson correlation values between FST and chromosome size with scaffolds/chromosomes < 2Mb excluded plotted against divergence age using the reduced portion of our dataset where species that appear more than once in comparisons are removed. .............................................................................................................................. 202 D.8 Correlation and p-values of the association between FST/chromosome length and divergence date retaining only 1 pair for populations that occur multiple times. For information regarding row and column names see appendix D.5. ..................................... 202 xv List of Tables Table 2.1. Genome-wide weighted average FST between populations. ........................................ 28 Table 4.1. Strengths of association (measured via R2 values) of recombination rate in 1 Mb windowed averages across the genome. R2 values with the same two populations (e.g., Pacific vs Pacific) are between replicate runs of ReLERNN for the same population data whereas all others are comparisons are between population recombination landscapes retained for further analysis........................................................................................................................................................ 85 Table 4.2. Strength of association between FST landscapes and recombination landscapes. In each row, windowed FST between a pair of populations is tested against a population recombination landscape using Spearman rank correlation (Cor column). Each “Cor” column refers to the Spearman rank correlation between FST of the population pair and the preceding “Recom. Group”. For example, the top row of the last “Cor” column is the Spearman rank correlation value between windowed FST of RS/RN and the Eastern population's recombination landscape. 88 Table 5.1. Summary of species comparisons used in study. When a single species is listed in “species names” column this indicates the comparison is between subspecies or putative species. An integer next to a single species name means multiple comparisons with different subspecies/putative species. S. townsendi / S. virens, Geothlypis tolmiei / G. philadelphia, and V. cyanoptera / V. chrysoptera original data citations are listed but FST per chromosome is derived from Irwin et al. (2018) and Wood et al. (2023) respectively. Sitta carolinensis comparisons 1-6 are with the following populations described in Chapter 2: 1) Rocky Mountain North and South; 2) Rocky South and Pacific; 3) Rocky North and Pacific; 4) Rocky South and East; 5) Rocky North and East; 6) East and Pacific. S. coronata is a comparison between S. coronata coronata and S. coronata auduboni. Z. leucophrys is a comparison between Z. leucophrys gambelii and Z. xvi leucophrys pugetensis. Phylloscopus trochiloides comparisons 1-3 are as follows: 1) Phylloscopus trochiloides trochiloides and Phylloscopus trochiloides plumbeitarsus; 2) Phylloscopus trochiloides trochiloides and Phylloscopus trochiloides viridanus; 3) Phylloscopus trochiloides plumbeitarsus and Phylloscopus trochiloides viridanus. Certhia americana is a comparison between Northern and Southern lineages. “Seq.” column is the sequencing strategy used, whether genotyping-by-sequencing (GBS; Elshire et al., 2011) or whole genome sequencing (WGS). In the “window size” column, “sequenced bp” indicates the FST window was determined by the number of sequenced sites. ............................................................................. 99 Table 5.2. Summary of Pearson correlation coefficients and p-values from linear regressions of FST and chromosome length. Divergence dates are included with the date source. All divergence dates are medians provided by TimeTree.org except for Sitta carolinensis, Setophaga coronata, Certhia americana, Phylloscopus trochiloides, Troglodytes species, Zonotrichia species, Vermivora species, and hummingbird species. Source refers to the study that reported the date of divergence. “mya” refers to the divergence date of the pair in millions of years. ...................... 105 xvii List of Figures Figure 2.1. a) PCA of 370,410 genome-wide SNPs from 360 white-breasted nuthatch samples across North America. PC axes 1 and 2 explain 14.7% and 12.6% of the variation, respectively. b) Geographic locations of samples. c) Admixture analysis of 370,410 SNPs and 360 white-breasted nuthatch individuals with K=4. Populations are color-coded based on population. RN and RS have the largest amount of mixed ancestry, with RN having the highest frequency of individuals with portions of Pacific ancestry. RS/RN intermediate (Int.) samples and Rocky Mountain/Eastern hybrids show mixed ancestry between their respective populations. D) Phylogeny of white-breasted nuthatch populations based on 3,000 randomly selected autosomal SNPs. Node dates are in millions of years and each node had a posterior probability of 1. Node bars reflect the 95% HPD interval. ............................................................................................... 27 Figure 2.2. Four ABBA-BABA tests using the Red-breasted Nuthatch as an outgroup. Each test shows a significant positive D statistic (unadjusted p-value). A positive D statistic indicates excess ABBA allele patterns. ........................................................................................................ 30 Figure 2.3. A) FST between RN and RS is shown for SNPs across the genome. Scaffolds 14, 15, 38, and 39 are not included as they did not contain enough SNPs for Dsuite windows. SNPs with NA or negative values for FST are omitted along with SNPs from scaffolds less than 1 Mb leaving 210,858 SNPs. Dotted vertical lines indicated scaffold boundaries and plotted in scaffold order (i.e., scaffold 1, 2, 3 etc.). The green line represents fd in 100 SNP sliding windows. In panels B-E, we show a few scaffolds in detail as examples. In these examples, we highlight in red the region used to construct phylogenies. ........................................................................................... 31 Figure 2.4. Phylogenies of the four selected regions identified from FST and fd scans. In each phylogeny, RS is older than all other white-breasted nuthatch populations. A) Scaffold 1 52 – 58 xviii Mb region (1253 SNPs). B) Scaffold 2 54-59 region (615 SNPs). C) Scaffold 29 12 – 14.1 Mb (1232 SNPs). D) Scaffold 34 2-4 Mb region (644 SNPs). Node indicates the age in millions of years and node bars indicate the 95% HPD of the node height. Across all phylogenies posterior probabilities for all nodes > 0.97. ................................................................................................. 33 Figure 2.5. Genotype by individuals plot (A and C) of genomic regions selected for further analysis with corresponding PCA’s (B and D). Each row is an individual and each column is a SNP. Individuals are color-coded on the sides to denote populations based on genome-wide SNPs. Genotype colors are determined by the reference allele. Dark purple is homozygous reference, light purple is homozygous alternate, and light/dark purple is heterozygous. Only SNPs with FST > 0.25 between RN and RS are shown in the genotype by individual plots to reduce complexity. PCA of scaffold 1 region (B) is of 741 SNPs and the PCA of the scaffold 2 region (D) is of 530 SNPs. ............................................................................................................ 34 Figure 3.1. Diagram of species tree and haplotype trees. Below each tree is the resulting phylogeny of haplotypes across populations. A) A tree example for standing variation explaining the presence of haplotypes across populations. B) A scenario of introgression explaining the presence of haplotypes in both populations. ................................................................................. 47 Figure 3.2. MDS values for PCA windows across the genome for each population of white-breasted nuthatches. Each dot represents a 50 SNP PCA. To the right of each plot, we show an approximate range of the population across North America based on sampling from Chapter 2. Colored bars denote scaffolds where deviation in MDS scores was found. Orange is scaffold 2, green scaffold 3, and pink scaffold 22. ......................................................................................... 52 Figure 3.3. PCA’s of individual scaffolds that show 3-cluster patterns consistent with two major haplotype groups. A) Scaffold 2 using only Eastern individuals (23) and 10,400 SNPs. B) xix Scaffold 11 PCA with 356 white-breasted nuthatch samples and 3099 SNPs. C) Scaffold 20 PCA with 356 white-breasted nuthatch samples and 9370 SNPs. PC axes 1 and 2 are shown in the supplementary materials. D) Scaffold 22 PCA using 9336 SNPs. PC 2 and 3 are shown as the best separate the three putative inversion clusters, PC 1 and 2 are shown in the supplementary materials. Colored stars highlight putative inversion type; black is homozygote hap 1, grey is het, and gold is hap 2. ................................................................................................................... 54 Figure 3.4. SNP loadings of PC axes corresponding to PCAs in figure 3. SNP loadings are plotted along scaffolds and shown in absolute values (SNP loadings can be negative and positive, but the absolute value in both cases indicates the strength of their contribution to the PC axis). Purple lines highlight areas with elevated SNP loadings. ............................................................. 56 Figure 3.5. LD (R2) between SNPs of Scaffolds 2, 11, and 20. Left most plots represent LD of SNPs with only individuals of “Hap1” of that scaffold, the middle plots show LD of SNPs for all individuals, and the right plots show LD of only “Hap2” individuals. SNP counts represent all the SNPs used by plink to calculate R2, but only R2 between SNPs greater than 0.7 is shown to reduce plot complexity. A) Eastern specific scaffold 2 LD of 3836 SNPs for 19 individuals of Hap1 and LD of 5209 SNPs for all 23 Eastern individuals. B) LD patterns for the scaffold 11 haplotype found in the Pacific and Rocky Mountain populations. 708 SNPs from 89 Rocky Mountain individuals of Hap1, LD of 1342 SNPs across all Rocky Mountain individuals, and LD of 611 SNPs from 64 Hap2 Rocky Mountain individuals. C) LD of 973 SNPs for 14 Hap1 Eastern individuals for scaffold 20 and LD of 1409 SNPs across all 23 Eastern individuals. ..... 58 Figure 3.6. A) Linkage disequilibrium of 4268 SNPs across all individuals in the Rocky Mountain populations for scaffold 22. B) linkage disequilibrium of 3724 SNPs across only hap 1 individuals in the Rocky Mountain populations for scaffold 22. SNP counts represent all the xx SNPs used by plink to calculate R2 but only R2 between SNPs greater than 0.7 is shown to reduce plot complexity. C) PCA of 984 SNPs from the 18.5-20 Mb end of scaffold 22. ............ 60 Figure 3.7. Genotype by individual plots of the four regions identified by lostruct and PCA. In each panel we show genotypes (columns) plotted across individuals (rows). Colors next to rows denote the population of the individuals and correspond to population colors used in previous figures. The number of individuals shown are reduced, and SNPs were filtered based on FST > 0.5 between haplotypes also to reduce complexity. In regions where the putative polymorphism appeared across multiple populations, FST was calculated between haplotypes in the Rocky Mountain populations. In the case of scaffold 2, Haplotype 2 only contains 1 individual so FST based SNP filtering is done between Hap1 and Hets. Blue and red represent homozygous SNPs and purple indicates heterozygous SNPs. The reference genome used in our study is a Pacific individual so in plots containing all Eastern individuals (A and C) colors of homozygous SNPs are determined by frequency within the hap groups. In panels with individuals from all populations (B and D) homozygous SNP color is determined by the reference allele. A) genotypes of 23 Eastern individuals for scaffold 2. B) Genotypes across 139 white-breasted nuthatches for scaffold 11. C) Genotypes of 23 Eastern individuals for scaffold 20. D) Scaffold 22 genotypes across 89 white-breasted nuthatches. ...................................................................... 62 Figure 3.8. A) Phylogeny of 1500 SNPs randomly selected across the genome. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), Eastern (2), Pacific (4), RN (4), and RS (4). B) Phylogeny of 514 SNPs selected from within the scaffold 11 haplotype. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), East hap 1 (2), Pacific hap 1 (2), Pacific hap 2 (2), RN hap 1 (2), RN hap 2 (2), RS hap 1 (2), and RS hap 2 (2). The same samples are used in both A and B. Bars on nodes represent the 95% HPD. ......... 64 xxi Figure 3.9. A) Phylogeny of 1500 SNPs randomly selected from across the genome. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), Pacific (3), RS (6), RN (4), East (6)). B) Phylogeny using 1500 SNPs randomly selected from within the scaffold 22 haplotype. Sample sizes are listed in parentheses next to populations: Euro (1), Red-breasted Nuthatch (3), Pacific hap 1 (3), RS hap 1 (3), RS hap 2 (3), RN hap 1 (3), RN hap 2 (1), East hap 1 (3), and East hap 2 (3). The same samples are used in both A and B. Bars on nodes represent the 95% HPD. ................................................................................................................ 66 Figure 4.1. Median recombination rate (cross over/base pair) for each scaffold plotted against the length of the scaffold. Scaffolds largely correspond to whole chromosomes, so as expected, the per-base recombination rate increases in smaller chromosomes. ................................................. 84 Figure 4.2. 1 Mb windowed averages of recombination rates (cross-over per bp per generation) are plotted between populations. A linear regression is fitted to the comparison (red). Columns indicate which population is on the x-axis and rows indicate which population is on the y-axis. Figure was made using code derived from Chang (2018). ........................................................... 86 Figure 4.3. For 999 1Mb genomic widows, we show FST between putative species pairs across the genome from largest to smallest scaffolds. Continuous genome position is for plotting purposes only. Beneath each FST comparison, we plot the average recombination rate (cross over/base pair) for that window. Line colors correspond to population recombination rates; RS (teal), RN (dark blue), Pacific (dark green), Eastern (light blue). This plot was generated from code adapted from Roelfs (2019). ................................................................................................. 87 Figure 4.4. Pairwise windowed FST between populations is plotted against the windowed recombination rate in one of the FST populations. The other population’s recombination rate is shown in appendix C.9. Orange is a cubic spline fit to all windows. Green, purple, and yellow are xxii cubit splines fit to windows from chromosomes > 40 Mb, 40-20 Mb, and < 20 Mb, respectively. Each dot represents a 1 Mb window. ............................................................................................ 89 Figure 4.5. Pairwise weighted average FST in 1 Mb windows plotted against the recombination rate of a population not occurring in the FST comparison. For further information regarding dots and cubic splines see figure 4.4. ................................................................................................... 90 Figure 5.1. Mean FST per log chromosome size across 27 species comparisons of birds. Certhia americana, Troglodytes sp., and hummingbird comparisons are not shown as original the manuscripts displayed their relationships. .................................................................................. 104 Figure 5.2. Pearson correlation coefficient (r) between FST and log chromosome length plotted against divergence date of the species in millions of years. A linear regression is fitted through the points with a 95% confidence band in grey. Species comparisons are colored by genus, and non-passerine birds are shown with a diamond symbol. ............................................................ 106 . xxiii List of Abbreviations GBS – Genotyping-by-Sequencing Kb – Kilobase LD – Linkage Disequilibrium Mb – Megabase MQ – Mapping quality PAR – Pseudoautosomal region PC – Principal Component PCA – Principal Component Analysis SNP – Single nucleotide polymorphism WGS – Whole genome sequencing xxiv Acknowledgments For supporting me through this research, I would like to thank the University of British Columbia (UBC) and the UBC Zoology department. I also thank the financial support I have received in completing my doctorate: UBC international doctoral fellowship, Werner and Hildegard Hesse Research Award in Ornithology, Colorado Field Ornithologist Research Grant, American Ornithological Society Student Research Award, Leona and John Breckenridge Memorial Scholarship in Zoology, and the Irwin Lab NSERC Discovery Grant. I am forever grateful to my advisor, Dr. Darren Irwin for supervising me through this research. Darren has served as my scientific advisor for over 7 years (including my MSc), and I could not be more thankful to have been his student. It is not often a professor has to navigate his lab through a global pandemic, but somehow, he did. I will miss our weekly meetings discussing science and life and generally being a member of the Irwin lab. I would also like to thank all the members of the Irwin lab who have helped me throughout the years and been such a joy to call my lab mates: Silu Wang, Maddie Ore, Libby Natola, Rashika Ranasinghe, Armando Geraldes, Caleigh Charlebois, Claudie Pageau, Finola Fogarty, and Quinn McCallum. Beyond my immediate lab group, others played invaluable roles in my research. Ildiko Szabo in the Beaty Biodiversity Museum knows anyone and everyone there is to know in the world of birds and would stop at nothing to aid me in any request I asked her over the years. It is hard to imagine how some of my work would have been accomplished without her, and for that, I am extremely grateful. Garth Spellman, who is the curator of Ornithology at the Denver Museum of Nature and Science, acted as a collaborator for many of the chapters in this dissertation. Our first meeting xxv was essentially just me asking if I could work on his system, to which he did not hesitate to say yes. Since then, he has been a constant source of support and helped my research progress in so many important ways. Which included contributing research funds towards the sequencing data produced in the fourth chapter of my dissertation. I am unbelievably lucky to have had the opportunity to work with such a fantastic scientist and thankful to him for allowing me to continue the research on white-breasted nuthatches. Dr. Sunita Sinha and Dr. David Levy-Booth of the UBC Sequencing and Bioinformatics consortium provided very helpful support and advice in generating my whole-genome sequencing data, and I thank them for their help. My committee members Dolph Schulter, Loren Rieseberg, and Mike Whitlock were also essential to the success of my research. They provided countless rounds of feedback in committee meetings and on drafts of my dissertation that improved them beyond measure, for which I am thankful. They are also simply three wonderful people, and I am thankful I had the opportunity to interact with them regularly. My entire academic journey would not have been possible without the unwavering love and support from my family. It was a huge step to move from my small mountain town in Colorado halfway across the continent and to a new country. Without my family it would have been infinitely harder, and they are one of the primary reasons I ever made it to graduate school. Finally, I want to thank my beloved fiancée, Sophie Nataros. She saw me through all the highs and lows that come with doing a PhD. She was a constant beacon of joy and happiness that kept my spirits high when I felt like I was stuck on a problem with no solution. Toward the end of my degree, she asked if she could be listed as a co-author on my dissertation with the title of xxvi “grand master of snacks”. When I told her this was not possible, she was quite displeased, so I figured this was the next best thing. 1 Chapter 1: Introduction Why and how do organisms change over time? This question is largely the central foundation for the field of evolution and was the question that famously interested Darwin and Wallace (Wallace, 1858; Darwin, 1859). Our understanding of evolution has advanced significantly since this period of time, and broadly we know that populations change over time through the interactions of selection (Darwin, 1859) and genetic drift (Wright, 1931) acting on genetic variation within populations. A highly important step was the unification of Gregor Mendel's (1865) genetic work with Darwinian evolution to form the Modern Synthesis (Huxley, 1942). This presented the opportunity for the field to expand, showing how mutations (the term first coined by Hugo De Vries [De Vries, 1901]) and their influence on traits (Morgan, 1911) can accumulate over time (Zuckerkandl & Pauling, 1965) providing the important genetic variation for which selection can act on resulting in evolution. Now, in the era of genomics (Goodwin et al., 2016), we have again significantly expanded our power to investigate different evolutionary processes that occur in organisms beyond the imagination of those who established the field. One such process of particular interest is that of speciation, where a subset of individuals in a previously panmictic population become reproductively isolated from the rest of the population whether through isolation over generations or other various mechanisms. 1.1 Speciation Defining species and thus speciation is a contentious topic with many proposed species concepts (reviewed by Coyne & Orr, 2004). Frequently, species are defined based on differences in physical appearance, but with the growing accessibility of genomic data, there is an appreciation for populations with deep levels of genomic differentiation that are otherwise 2 morphologically similar (Toews & Irwin, 2008), sometimes referred to as cryptic species (Bickford et al., 2007). Cryptic species have large implications for estimates of biodiversity on Earth, and genomics can help categorize populations that may have only slight differences in appearance. In the first major part of this dissertation, I ask whether a currently described species is comprised of several species. Broadly, it is viewed that reproductive isolation is an important characteristic surrounding speciation (Coyne & Orr, 2004) and a central tenet for the concept favored by Ernst Mayr: the “biological species concept” (Dobzhansky, 1937; Mayr, 1942). Under this concept and its focus of reproductive isolation, we can define different species as those that have reduced reproductive output when interbreeding or when different species do produce progeny, they have reduced fitness (i.e., low survival or fecundity). The outcome of reproductive isolation is reduced gene flow between populations, but the mechanisms underlying it are expansive and can vary widely among organisms (Coyne & Orr, 2004). Speciation, and the result of reproductive isolation, can occur through a variety of geographic contexts. The first is allopatric speciation (Mayr, 1942) which is the geographic separation of populations promoting their isolation. These geographic barriers can arise either through dispersal (individuals moving and colonizing a new area) or vicariance (barrier forming and splitting a population), both of which reduce gene flow between populations. Allopatric speciation is thought to have been involved in the formation of many species (Coyne & Orr, 2004). Another process is sympatric speciation (Poulton, 1903; Maynard Smith, 1966), when individuals become reproductively isolated while in a panmictic population. Many models have been proposed that could result in sympatric speciation but it is generally difficult to show empirically (reviewed by Bolnick & Fitzpatrick, 2007; Foote, 2018) and may not be a common 3 mechanism for which new species form. Another mechanism for speciation is parapatric speciation (Fisher, 1930), sometimes referred to as speciation with gene flow (Nosil, 2008). In this model, populations diverge while still having gene flow occur between populations. This may occur more frequently than sympatric speciation (Nosil, 2008). Importantly, speciation models are not mutually exclusive, and populations can become isolated for a period of time and then come back into contact (Kuehne et al., 2007). The factors that contribute to reproductive isolation can be broadly categorized into pre- and postzygotic isolation (Mayr, 1942, 1963). Prezygotic isolation reflects any barrier to the formation of a zygote between two organisms. This can be the result of factors such as signaling (Qvarnström et al., 2006; Danner et al., 2011; Vortman et al., 2013) and gametic incompatibility (Alipaz et al., 2001; Rawson et al., 2003). Postzygotic isolation refers to barriers to gene flow between populations that occur after zygote formation. These can range from severe barriers where the results are lethal (Cattani & Presgraves, 2009) to modest barriers where fitness is moderately reduced in offspring (Hatfield & Schluter, 1999; Ramsey et al., 2003; Taylor et al., 2009; Suzuki & Nachman, 2015). An important contributor to what influences the severity of postzygotic isolation is the number of genetic incompatibilities that have accumulated between organisms, otherwise known as Dobzhansky-Muller incompatibilities (Dobzhansky, 1936; Muller, 1942; Orr & Turelli, 2001). Recently, theoretical work has argued that partial prezygotic isolation may not be as effective at isolating species as partial postzygotic isolation (Irwin, 2020). Since speciation can be thought of as a continuum, diverging populations may come into contact and hybridize (Barton & Hewitt, 1985). During hybridization, sufficient reproductive isolation can prevent the collapse of these populations (Taylor et al., 2006; Behm et al., 2010; 4 Vonlanthen et al., 2012; Kearns et al., 2018) into panmixia but incomplete reproductive isolation can allow additional hybrid generations and backcrossing to occur. Through this kind of hybridization, genomic regions of one population may move into another population. 1.2 Introgression Introgression is when, through hybridization, genetic material is transferred from one distinct group of organisms to another (Anderson & Hubricht, 1938; Aguillon et al., 2022). Generally, introgression can occur if there are opportunities for gene flow and reproductive isolation between populations is incomplete. Introgression may also promote rapid adaptation. Sometimes known as “comet alleles” (Staubach et al., 2012), advantageous genomic regions can enter into a population and sweep to fixation. Some recent examples have highlighted dramatic examples of adaptive introgression (Pardo-Diaz et al., 2012; Oziolor et al., 2019; Stolle et al., 2022). For example, Oziolor et al. (2019) documented adaptive introgression between species of killifish being responsible for adaptation to polluted environments. Similar to adaptive introgression, there is a growing appreciation for the legacies of introgression from extinct populations (Szöllősi et al., 2013). Sometimes referred to as “ghost lineage introgression”, this occurs when past hybridization results in extant populations containing alleles of a now-extinct population (Ottenburghs, 2020). Ghost introgression can lead to discordant phylogenies between different areas of the genome and can bias tests for introgression (Tricou et al., 2022), and uncovering it can provide important insight into the evolution of populations. Currently, it is unclear how widespread ghost lineage introgression may be in extant lineages, but it has been documented in some cases within birds (Zhang et al., 5 2019), mammals (Kuhlwilm et al., 2019; Sheng et al., 2019; Rocha et al., 2022), and fish (Frei et al., 2022). As a second question in my first data chapter, I asked if introgression had occurred between putative cryptic species of birds and if genomic areas of introgression overlapped with observed patterns of differentiation between species. If introgression was identified between populations, I also wanted to identify its source. To do this, I constructed phylogenies based on genomic regions with signals of introgression to infer the source of introgression. 1.3 Recombination While mutations and introgression are important sources of genetic variation for populations, recombination can create new combinations of existing variation. Recombination takes place through two processes. The first is through independent assortment (Mendel, 1865) of chromosomes during meiosis. The second is through crossing-over during meiosis where chromosomes align and exchange genetic material (Creighton & McClintock, 1931). In this dissertation “recombination” refers to crossing-over between chromosomes. Recombination varies both within and between chromosomes, in some clades recombination is higher near the ends of chromosomes and in smaller chromosomes (Kong et al., 2002; Jensen-Seaman et al., 2004; Groenen et al., 2009; Barton et al., 2008; Backström et al., 2010; Peñalba et al., 2020). Because recombination involves the repair of double-strand breaks, it also may be a mutagenic force within the genome (Arbeithuber et al., 2015). Recombination is one of the primary forces that influence the frequency at which alleles at different loci are associated with one another, otherwise known as linkage disequilibrium (LD) (Lewontin & Kojima, 1960). Linkage disequilibrium, first described by Haldane et al. (1915), is 6 an important relationship among loci to which much population genetic theory has been dedicated (Slatkin, 2008). LD is important because it can reflect selective (Ohashi et al., 2004) and demographic histories (Slatkin, 1994). LD can also help provide a general explanation for why recombination evolves and is favored because, in the absence of recombination, LD can affect the efficiency of selection (Hill & Robertson, 1966; Felsenstein, 1974; Keightley & Otto, 2006). Recombination and its relationship with LD have important implications for speciation. Often areas of increased genomic differentiation between species have reduced recombination (Renaut et al., 2013; Burri et al., 2015; Wang et al., 2016; Wang et al., 2022). If there is gene flow between populations, recombination can facilitate the transfer and persistence of adaptive or neutral loci in the background of the new population, otherwise, they will likely be purged due to selection against deleterious alleles (Schumer et al., 2018). This means that the landscape of recombination can act as an important barrier to maintaining species by linking together adapted loci that are under selection between two populations. In the fourth chapter of my dissertation, I ask how much recombination landscapes change between populations and if areas of low recombination are associated with areas of elevated differentiation. While low recombination areas have co-occurred in areas of differentiation (Geraldes et al., 2011; Burri et al., 2015; Turbek et al., 2021), more comparative studies are needed to fully understand their contribution to genome-wide patterns. Given the challenges in mapping recombination across the genome, comparative studies of recombination differences between populations or species have been limited to few species, at least within birds (Backström et al., 2010; Singhal et al., 2015; Kawakami et al., 2017). In this dissertation I fill 7 this gap by comparing landscapes of recombination between populations and examining relationships between recombination and differentiation. 1.3.1 Inversions One group of low recombination regions that have been of theoretical and empirical interest are chromosomal inversions (Rieseberg, 2001; Noor et al., 2001; Kirkpatrick & Barton, 2006; Kirubakaran et al., 2016; Huang et al., 2020; Sanchez-Donoso et al., 2022; Lundberg et al., 2023). Inversions occur when sequences of DNA flip in orientation and can range in size from a few kilobases (Mercuri et al., 2022) to large, with inversions sometimes comprising over 100 megabases in length (Tuttle et al., 2016; Todesco et al., 2020; Sanchez-Donoso et al., 2022). Inversions are of interest to the study of speciation and local adaptation because inversions effectively suppress recombination in heterozygous individuals (Sturtevant, 1921) linking together loci within the inversion. While inversions do suppress recombination (Rönspies et al., 2022), they may also appear in regions of low recombination and thus be unrelated to recombination suppression in a given region of the genome (Sun et al., 2017). Due to this effective linkage of loci, inversions have been theorized to be important genomic features that promote local adaptation and speciation (Noor et al., 2001; Rieseberg, 2001; Kirkpatrick & Barton, 2006). In empirical studies, inversions have been implicated in adaptation and reproductive isolation in plants (Lowry & Willis, 2010; Todesco et al., 2020), mosquitos (Ayala et al., 2013), fish (Kirubakaran et al., 2016), and birds (Lundberg et al., 2023). Recent studies have suggested that inversions may be common in populations (Hooper & Price, 2017; Harringmeyer & Hoekstra, 2022). In my third data chapter using genomic data I ask 8 if patterns of variation consistent with inversions can be detected. I then ask whether polymorphisms found across populations are due to recent introgression or standing variation. 1.4 Study organism The research within this dissertation will focus on questions that investigate speciation, introgression, and recombination in birds. Birds are useful for studying evolution because they tend to have compact and conserved genome structure (Ellegren, 2010) and numerous hybrid zones in which questions surrounding reproductive isolation and introgression can be asked (Baldassarre et al., 2014; Delmore & Irwin, 2014; Taylor et al., 2014; Wang et al., 2019; Scordato et al., 2020; Natola et al., 2022). An excellent system to investigate cryptic speciation, introgression, recombination, and inversions are white-breasted nuthatches. White-breasted nuthatches are of interest because they are comprised of four highly divergent populations across North America that have been described using mitochondrial and a few nuclear loci (Spellman & Klicka, 2007; Walstrom et al., 2012). These populations are of interest because they are very similar morphologically (Mlodinow, 2014) and various subspecies have been described and debated based on morphology, but none were proposed to be separate species (Grinnell, 1926; Aldrich, 1944; Grinnell & Miller, 1944; Hawbecker, 1948; Wood, 1992). With white-breasted nuthatches being considered a single species but showing strong genetic differences, they may be an ideal candidate to investigate cryptic speciation. Of these four populations described based on genetic data, little is known about how reproductively isolated they are and how diverged they may be across the genome. There are differences in vocalizations between populations that suggest there is at least some prezygotic isolation between groups (Pandolfino & Pieplow, 2015). Evidence 9 presented by Walstrom et al. (2012) suggests these four populations vary in their times to a recent common ancestor ranging from recently split populations to much older divergence times. This continuum provides an excellent structure to conduct tests of introgression between different populations, search for inversions, and compare recombination landscapes. If these populations are in the process of speciation and thus accumulating reproductive isolation, the white-breasted nuthatch system could provide an opportunity to study recently formed hybrid zones. Based on sampling from Spellman & Klicka (2007) and Walstrom et al. (2012) these populations may come into contact in California, the front range of Colorado, and the border of Wyoming and Colorado. Where these populations come into contact could be areas where hybridization is taking place. While white-breasted nuthatches are compelling from an evolutionary perspective, they also have interesting ecological and behavioral characteristics. They are broadly distributed across North America and are considered year-round residents. Recent work has shown that irruptive migrations (nonregular migrations) may occur in white-breasted nuthatches (Dunn et al., 2022) similar to that of red-breasted nuthatches (Koenig & Knops, 2001; Dunn, 2019). This species is known for its feeding and caching habits, where it stores seeds in bark (Petit et al., 1989) and faces head down along the trunks of trees . The white-breasted nuthatch also exhibits an interesting suite of behaviors. As year-round residents, pair bonding can occur in the form of songs and displays as early as January, and these bonds are continually maintained (Kilham, 1972). To defend nests, white-breasted nuthatch also uses a plumage display to ward off potential predators (Kilham, 1968). It is also suspected that white-breasted nuthatches may employ chemicals from beetles to defend nests by rubbing them around the entrance (Kilham, 1968, 1971). 10 While the ecology and behavioral traits of white-breasted nuthatches are beyond the scope of this dissertation, they are an important connection to questions surrounding their evolution. How speciation and selection have shaped the genomes of these birds is at least in part driven by selection on the interaction of traits and behaviors in the environments in which nuthatches are situated. The results of this dissertation will not explicitly connect genotypes to phenotypes but instead investigate broad evolutionary interactions, such as the relationships between recombination and landscapes of differentiation. 1.5 The thesis Using the white-breasted nuthatch system in conjunction with genomic sequencing, in this dissertation I address several questions about speciation, introgression, differentiation, and recombination. In Chapter 2, I ask if white-breasted nuthatches comprise multiple species and if introgression has played a role in shaping patterns of genomic differentiation using FST, a measure of relative genetic differentiation between two populations (Wright, 1951; Weir & Cockerham, 1984; Cruickshank & Hahn, 2014). To answer these questions, I collected a large genetic sample of white-breasted nuthatches from the field and museum collections and used genotyping-by-sequencing (Elshire et al., 2011) to sequence loci from across the genome. Using overall levels of differentiation, frequency of hybrids, ABBA-BABA tests (Green et al., 2010; Durand et al., 2011), and phylogenies I determine whether white-breasted nuthatches are best described as multiple species and if areas of differentiation overlap with areas of introgression (if introgression has occurred). In chapter 3, I make use of my previously described genomic data to search for inversion-like patterns of genomic variation within populations of white-breasted nuthatches. In this 11 chapter, I utilize approaches based on PCA (principal component analysis) (Irwin et al., 2016; Li & Ralph, 2019) to identify genomic regions that exhibit a three-cluster pattern within a population which is commonly seen in inversions when using PCAs of SNPs (single nucleotide polymorphism) (Lundberg et al., 2017; Huang et al., 2020). Each cluster represents inversion genotypes: homozygous, heterozygous, and homozygous alternate. From the areas identified, some appear to be shared in multiple populations, so I then used SNP-based phylogenies to determine if these shared polymorphisms are the result of standing ancestral variation or due to gene flow and introgression between populations. In chapter 4, I incorporate whole-genome sequencing from a subset of white-breasted nuthatches. I ask to what extent recombination landscapes are correlated among populations of white-breasted nuthatches and if recombination rates are associated with patterns of differentiation. To estimate recombination, I used ReLERNN (Adrion et al., 2020), a machine-learning method that determines recombination rates from population genomic data. I predicted recombination would be highly correlated among populations due to conserved genome structure in birds (Ellegren, 2010). Alternatively, correlation of recombination landscapes may decline with phylogenetic distance because the recombination landscapes themselves have evolved. For recombination and differentiation, I hypothesized that high differentiation regions would be concentrated in areas of low recombination because linked selection (both background and positive) would have a wider influence in low recombination areas due to linkage (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993). As an alternative hypothesis, high-differentiation regions may be located in areas of high recombination because increased recombination could more efficiently bring together selected loci (Hill & Robertson, 1966). 12 For my final chapter, I broadened my scope and examined genomic patterns of differentiation from 32 species pairs of birds using published genomic datasets (Alcaide et al., 2014; Porter, 2015; Toews, Taylor, et al., 2016; Kenyon et al., 2017; Henderson & Brelsford, 2020; Baiz et al., 2021; Manthey et al., 2021a; Mikkelsen & Irwin, 2021; McCallum et al., 2023; Nikelski et al., 2023) to determine if there was a relationship between recombination and differentiation across a wide sampling for birds. In avian genomes, average rates of recombination are generally higher in smaller chromosomes (Groenen et al., 2009; Backström et al., 2010; Kawakami et al., 2014; Peñalba et al., 2020). Given this pattern between recombination and chromosome size, and lower recombination regions association with elevated differentiation (chapter 4), I predicted that across birds, larger chromosomes may have higher levels of differentiation. Alternatively, smaller chromosomes with higher recombination may have higher levels of differentiation due to the benefits of linking together selected loci mentioned above and being more gene-dense (Smith et al., 2000; Federico et al., 2005; Waters et al., 2021). Extending from concepts in chapter 4, I used this data to determine if there were consistent relationships between FST and chromosome length and if the strength of this association was predicted by the age of the species pair. Younger species genomic landscapes may be more impacted by recent linked selection in low recombination areas, whereas older species may have accumulated more differentiation across the genome, particularly in gene-dense micro-chromosomes. I predicted that FST would be higher in larger chromosomes with lower rates of recombination, creating a positive association between FST and chromosome size. I also predicted that if age of divergence influenced patterns, it would weaken the association in older pairs because differentiation has accumulated to the point where genome-wide, all chromosomes are equally differentiated. 13 The questions asked in this dissertation surround important themes in evolution that range from applied to broad questions. Delineating the number of species is generally important for understanding global biodiversity, and describing patterns of differentiation and introgression puts into perspective the role historic gene flow plays in current geographic variation. More broadly, I investigated questions about how varying recombination (both in inversions and recombination landscapes) contributes to genomic variation. Understanding how variable recombination is associated with observed patterns of differentiation advances our understand on how recombination and selection interact to influence genomic patterns. Through each chapter, important insights emerge about how ancient lineages persist in the genomes of extant populations and how regions of low recombination disproportionately associate with elevated differentiation. 14 Chapter 2: Genomic divergence and introgression between cryptic species of a widespread North American songbird 2.1 Introduction Understanding how many species exist is an important aspect of biology (May, 1988; Mora et al., 2011). Phenotypic differences are often used to describe species, but this can be challenging when individuals have similar physical appearances. Distinct populations that are not easily distinguishable based on morphology are otherwise known as cryptic species (Bickford et al., 2007). Across various clades of organisms, there are examples of cryptic speciation in fish (Hudson et al., 2017), primates (Poelstra et al., 2021), birds (Pulido-Santacruz et al., 2018), and plants (Spriggs et al., 2019). Cryptic speciation poses a challenge to studies of biodiversity because if prevalent, it would mean species counts are underestimated. To help navigate this problem, genomics has a strong role to play. Using genomic data can help to identify differentiated populations that may have been difficult to distinguish otherwise (Mikhailov et al., 2022) and to infer species status among populations (Dufresnes et al., 2018). While delineating cryptic populations is important, understanding the processes underlying genomic differentiation between populations can provide important insight into speciation. There is growing appreciation in the role hybridization and introgression can play in the speciation process (Taylor & Larson, 2019). This reticulated evolution can result in organisms that are a mosaic ancestry of other lineages (Yang et al., 2021; Fu et al., 2022), some of which may no longer exist (Nilsson et al., 2018; Rocha et al., 2022). While these processes may complicate the delineation of cryptic species, describing newly discovered evolutionary 15 lineages provides opportunities to investigate the role reticulated evolution had in their formation. In North America, the white-breasted nuthatch (Sitta carolinensis) is a widespread and well-known songbird, and a compelling system to investigate cryptic speciation. There are several described subspecies of white-breasted nuthatch (Phillips, 1986; Grubb Jr. & Pravosudov, 2020). These descriptions are based on plumage, vocalizations, bill dimensions, and other morphological characteristics (Phillips, 1986; Grubb Jr. & Pravosudov, 2020). However, variations in these traits can be subtle, leading to some debate regarding subspecies and their ranges (Grinnell, 1926; Grinnell & Miller, 1944; Aldrich, 1944; Hawbecker, 1948; D. S. Wood, 1992). Differences in vocalizations between populations are, in some cases, more apparent than differences in morphological characteristics (Pandolfino & Pieplow, 2015). Earlier genetic work revealed four well-diverged populations of white-breasted nuthatches across North America, with researchers proposing they are multiple species based on mtDNA and several nuclear genetic markers (Spellman & Klicka, 2007; Walstrom et al., 2012). However, white-breasted nuthatches are currently treated as a single species based on a decision from the American Ornithological Society in 2013, citing the need for additional study (AOS, 2013). In the present study, we used genomic sequencing of white-breasted nuthatches from a wide geographic range of sampling to address two key questions. First, we ask whether there are multiple species of white-breasted nuthatch. By using loci across the genome and sampling across the range, we used overall levels of differentiation and frequency of hybrid individuals in areas of contact as proxies for reproductive isolation between putative species. The potential for hybridization may be limited by whether and to what degree ranges overlap, a factor that we 16 consider when inferring conclusions from the study. Genome-wide levels of differentiation can also help provide clues on the degree of isolation between populations (Malone & Fontenot, 2008; Sánchez-Guillén et al., 2014). Second, we ask whether introgression between distinct populations has had a strong role in shaping patterns of genomic differentiation. While introgression is usually viewed as hindering differentiation and speciation, introgression can also spread adaptively advantageous variants (e.g. Pardo-Diaz et al. 2012; Oziolor et al. 2019; Todesco et al. 2020; Stolle et al. 2022) that can enhance the potential for subsequent local adaptation and speciation (Delmore et al., 2015; Irwin et al., 2016). To provide a strong genomic foundation for our study, we present a newly constructed chromosome-level reference genome for white-breasted nuthatch. This allowed us to map our sequencing reads to a reference genome of our organism as opposed to a more distantly related species. In the Sitta genus there are a few genome assemblies available with the Eurasian nuthatch (Sitta europaea; Feng et al., 2020); BioProject PRJNA545868) being one, but that assembly is not at chromosome scale. We sampled white-breasted nuthatches from across North America using both field and museum sources, greatly expanding sample sizes and geographic coverage from previous, white-breasted nuthatch genetic studies. We then sequenced these samples using genotyping-by-sequencing (GBS; Elshire et al., 2011; Alcaide et al., 2014) and mapped the sequences to our reference genome. To test the amount of asymmetrical introgression between populations we utilized ABBA-BABA tests (Green et al., 2010; Durand et al., 2011). The null model used in these tests is based on population relationships being fully explained by a bifurcating phylogeny. If genomic data deviate significantly from expectations given such a phylogeny, the conclusion is that some 17 other factor—e.g., introgression and/or selection—is needed to explain the data. In each test we determine, given a pair of populations, if there is an excess sharing of derived alleles from a third population when compared to an outgroup. We extended this by also examining the fd statistic (Martin et al., 2015), which is a modified ABBA-BABA test, in sliding windows across the genome to see if certain genomic regions contained excess allele sharing. Entering this study, we had several predictions. We predicted that our genomic sequencing would recover the four previously described populations (Spellman & Klicka, 2007; Walstrom et al., 2012). If reproductive isolation was strong between populations, we anticipated finding few to no hybrids between populations. An alternative possibility is that reproductive isolation could be weak, such that we would find extensive hybrid zones in areas of contact between populations. In our tests of introgression, assuming populations had diverged in allopatry, we anticipated finding weak signals of asymmetrical introgression. However, if introgression had occurred, it likely did so in the recent past, assuming these are young species when barriers to gene flow would be most porous. 2.2 Methods 2.2.1 Genome Assembly We sent a single blood sample of a male white-breasted nuthatch collected near Hillsboro, Oregon to Dovetail Genomics (Scotts Valley, California, USA) to sequence and assemble a reference genome. First, DNA was sequenced using PacBio Sequel II (one SMRT cell), and then reads were assembled using wtdbg2 (Ruan & Li, 2020). Purge_dups (Guan et al., 2020) was then used on the assembly and also filtered for any contamination. Then Chicago proximity ligation 18 and HiRise (Putnam et al., 2016) were used to order and orientate scaffolds, including breaking apart incorrectly joined scaffolds. Finally, Hi-C (Lieberman-Aiden et al., 2009) data was used to further connect scaffolds using HiRise. BUSCO (v 4.0.5) was then used to assess the completeness of the assembly (Simão et al., 2015). Using the final assembly, we compared the synteny of the white-breasted nuthatch genome to the zebra finch (Taeniopygia guttata; Bioproject PRJNA558048; Wada et al. 2006; Warren et al. 2010; Korlach et al. 2017; Formenti et al. 2021; Rhie et al. 2021) and brown creeper (Certhia americana; Bioproject PRJNA604561; Manthey et al. 2021) genomes using D-GENIES (Cabanettes & Klopp, 2018). While there is a Sitta genome assembly available (Sitta europaea; Feng et al., 2020); BioProject PRJNA545868) it is highly fragmented, so it is not ideal to compare alignments with our genome. 2.2.2 Field Methods We used 6-meter and 12-meter mist nets to capture birds in the field with audio lures. The audio lure played a white-breasted nuthatch vocalization, eliciting a territorial response from a nearby bird. Depending on locations, we used playbacks sourced from either California, Colorado, or West Virginia (Macaulay Library at the Cornell Lab of Ornithology: ML56836, ML120272, ML105294, ML77256), with our intent being to use the playback that elicited the strongest response in the local population. We sampled 61 birds in the spring of 2019 and 2 birds in the spring of 2020. Our 2020 field season was abruptly canceled due to the start of the COVID-19 pandemic. Once each bird was captured, we affixed a USGS numbered aluminum leg band and collected a small (i.e., 5 to 40 µl) blood sample from the brachial vein, and stored the sample in Queen’s lysis buffer (Seutin et al., 1991). Each bird was released at its capture site. 19 2.2.3 Lab Methods In addition to the blood samples collected from the field, we obtained 337 tissue samples from museum collections. Sample locations are shown in Figure 2.2. Our sampling included locations that we hypothesized were contact zones between populations based on Walstrom et al. (2012), who refer to the four populations as East; Pacific; Rockies North/Eastern Sierra Nevada; and Rockies South/Great Basin/Mexico. In our analyses, we refer to these populations as East, Pacific, Rocky Mountain North (RN), and Rocky Mountain South (RS), respectively. Our samples included three red-breasted nuthatches (Sitta canadensis). DNA from tissue and blood samples was extracted using a phenol-chloroform isolation method. DNA concentrations were standardized to ~20 ng/µl for further analysis. DNA quality was visually assessed by running a sample in agarose gel using electrophoresis, and all 399 were deemed of sufficient quality for further sequencing library preparation. Museum samples exhibited more degradation than field samples but still had high molecular weight DNA. Four Genotyping-by-Sequencing (GBS; Elshire et al. 2011) libraries were prepared using the protocol described in Alcaide et al. 2014 and Toews et al. 2016 with minor modifications described in Geraldes et al. 2019. Broadly, our library approach used the PstI enzyme to digest DNA, which was then ligated to barcodes used for demultiplexing and common adapters. These were then amplified using PCR, pooled, and sent for sequencing. Each library contained a DNA negative control (no DNA sample). In our fourth library we included 12 technical replicates (i.e., the same individuals sequenced twice in two different libraries) from our previous libraries. Each library was sequenced on an Illumina NovaSeq 6000 SP flow cell with 150 bp paired-end reads at The Centre for Applied Genomics (TCAG) in Toronto, Canada. In a 5th GBS library, we sequenced 35 samples along with samples for a different sequencing project not associated with 20 this study. Each of our four GBS libraries produced an average of approximately 531 million paired reads (with a range from 512 to 540 million), totaling 2.1 billion sequencing reads. 2.2.4 Data filtering Our data processing pipeline is derived and modified from the pipeline used by Irwin et al. (2016). Sequenced reads were demultiplexed using process_radtags from Stacks 2.60 (Rochette et al., 2019). Inspection of demultiplexed reads using FastQC (Andrews, 2010) showed some samples contained “read through”, or when insert fragments are too short, and the sequencing read includes the adapter. Using Trimmomatic 0.39 (Bolger et al., 2014) we made custom adapter files for trimming that contained a portion of the common adapter with the barcode for the forward read and a portion of the common adapter for the reverse read. These sequences were used in Trimmomatic for “palindrome mode” on each sample to remove any adapters and barcodes. If read-through was detected, we allowed Trimmomatic to discard the reverse read since it contained identical information. We also included a sequence of G’s for single-end trimming to remove poly-G tails known to affect NovaSeq sequencing (De-Kayne et al., 2021). We used the following settings for trimming: ILLUMINACLIP:adapter.fa:2:5:5:2. These settings are low but were used to ensure all adapters and barcodes were removed. We also trimmed reads in Trimmomatic for a minimum Phred score of 33 and according to the following settings: TRAILING:3 SLIDINGWINDOW: 4:10 MINLEN:30. Reads were then aligned to the white-breasted nuthatch assembly described above using BWA mem version 0.7.17 (Li & Durbin, 2009). Picard 2.26.3 (https://broadinstitute.github.io/picard/) was then used to add read group headers and convert .sam to .bam files. Following this Samtools 1.12 (Li et al., 2009) was used to merge single-end (reads with no mate) and paired-end reads and index. Single Nucleotide 21 Polymorphisms (SNPs) were then called using GATK v3.8 (McKenna et al., 2010) HaplotypeCaller, and GenotypeGVCF to create a single VCF. In our initial VCF file, 5,347,033 SNPs were called. We then filtered and processed the VCF files using custom Perl scripts that removed loci with observed heterozygosity greater than or equal to 0.6 and a mapping quality (MQ) score less than 20 (Owens et al., 2016). Eleven samples were removed because they had over 58% missing loci from the total SNPs called (due to low read depth, presumably because of low DNA concentration or quality). We then used VCFtools (Danecek et al., 2011) to filter out SNPs that were not biallelic or had a Genotype Quality score below 10 (genotypes with low certainty), a minor allele count less than 2, or contained more than 10% missing data. Kinship between individuals was assessed using SNPrelate IBDKing (Zheng et al., 2012). We removed individuals from pairs or sets with kinship coefficients greater than 0.02 until there were no familial relationships. In total, 24 individuals were removed who had familial relationships. After the filtering of sites and individuals, we retained 370,410 SNPs and 360 individuals. The median average depth across individuals in the set of filtered SNPs was 33.7. The minimum and maximum average depth per individual were 5.4 and 54.9, respectively. The 2.5% and 97.5% quantiles of mean read depth across individuals were 18.7 and 43.8. Two hybrid individuals between the Rocky Mountain and Eastern populations and two intermediate samples between the Rocky Mountain populations (identified in our PCA) were excluded from all analyses except PCA and Admixture (Alexander et al., 2009). In analyses that excluded hybrid and intermediate individuals, there were 365,667 SNPs and 356 individuals. In our tests of introgression using Dsuite (Malinsky et al., 2021), we filtered SNPs using the same filtering as listed above but included red-breasted nuthatch samples, resulting in 635,938 SNPs. In some analyses (see phylogeny and genotype by individual section), we subsampled 22 individuals “arbitrarily.” By this, we mean we selected samples within a population or PCA cluster indiscriminately but picked samples from different parts of a population range and different GBS libraries. We also calculated within-population nucleotide diversity (pW; Nei & Li, 1979) in sliding windows using R scripts from Irwin et al. (2018). For each population (PCA cluster of white-breasted nuthatch), we used 23 individuals and included invariant sites by using the “allSites” option in the GATK GenotypeGVCF command. This data was filtered using VCFtools, removing sites with more than two alleles, depth less than 10, genotype quality less than 10, more than 40% missing genotypes, and indels. Sliding windows of 5000 sequenced bp were used and were non-overlapping. 2.2.5 Analysis of population structure To assess the genetic structure among individuals based on genome-wide SNPs, we conducted a Principal Component Analysis (PCA) with the filtering listed above using a custom script (Irwin et al., 2018) in the R language (R Core Team, 2021). In this PCA, missing data were imputed with PCAMethods using ‘svdImpute’ (Stacklies et al., 2007). Using this dataset including hybrids and intermediates we also performed an analysis of population structure using Admixture (Alexander et al., 2009). In our Admixture analysis, the lowest CV error was for the number of populations (K) of 4, as expected based on our PCA. Admixture figures for K of 3, 4, and 5 were produced using pophelper in R (Francis, 2017). We then used VCFtools (Danecek et al., 2011) to calculate the weighted average genome-wide FST (a relative measure of genetic differentiation; Weir & Cockerham, 1984) while excluding hybrids and intermediate samples between each population. To support the status of the F1 hybrids between Rocky Mountain and 23 Eastern populations found in our PCA we used genotype by individual plots (described below) and a single representative scaffold to show that almost all chosen SNPs were heterozygous for hybrids across the scaffold. For this plot, we used a subset of RS and Eastern individuals, and we selected only high FST SNPs (>0.9). 2.2.6 Phylogeny estimation To estimate the best phylogeny to represent the history of population splitting among white-breasted nuthatch populations, we arbitrarily selected 10 individuals from each population and 3 red-breasted nuthatches as outgroups to construct a phylogeny using BEAST v2.6.3 (Bouckaert et al., 2014) and SNAPP (Bryant et al., 2012). We filtered SNPs to allow for no missing data, no indels, only biallelic SNPs, genotyping quality greater than 10, and a minor allele count of 1. SNPs for these samples were filtered from our initial VCF file (post MQ and heterozygosity filtering). To reduce linkage disequilibrium between SNPs we thinned SNPs using VCFtools to ensure no SNP was within 5kb of any other SNP. SNPs from the Z chromosome were also excluded from this analysis. We then used the snapp_prep.rb script (Stange et al., 2018) to generate the input file for our analysis. This script sets the model parameters of the analysis to those described by Stange et al. (2018). We constrained the “crown” divergence using a normal distribution with a mean of 17.5 mya and a standard deviation of 1.25 mya. The estimated time to the most recent common ancestor between white-breasted nuthatches and red-breasted nuthatches is ~17.5 mya based on Pasquet et al. (2014) where they used two mitochondrial markers and one nuclear marker (time approximated from figure 2 of Pasquet et al., 2014). The standard deviation used resulted in a similar 95% HPD interval (~15 – 20 mya) of the white-breasted and red-breasted nuthatch to that 24 of Pasquet et al. (2014), approximately 16-20 mya. Using the snapp_prep.rb we randomly selected 3,000 SNPs and ran the analysis for 5,000,000 MCMC iterations. The analysis was stopped at 2,579,700 iterations because the allotted computational time requested for the analysis expired. Inspection of the snapp.log file using Tracer v1.7.1 (Rambaut et al., 2018) showed each model parameter had an effective sample size (ESS) > 700. We then used TreeAnnotator v2.6.2 (Drummond & Rambaut, 2007) to generate a maximum clade credibility tree with mean node heights and 10% burn-in. We then used FigTree v1.4.4 (https://github.com/rambaut/figtree) to produce the phylogeny generated by TreeAnnotator. 2.2.7 Inference of introgression Given our estimated phylogeny produced as described above (see “results”), we then tested whether the population genomic variation is consistent with that phylogeny or whether additional gene flow between some pairs of populations needs to be invoked. For this purpose, we conducted ABBA-BABA tests (Green et al., 2010; Durand et al., 2011). The purpose of this test is to determine if there is excess derived allele (“B”) sharing based on an outgroup defining the ancestral allele (“A”) (Green et al., 2010; Durand et al., 2011). The “ABBA” or “BABA” pattern are orientations of derived and ancestral alleles in a four-group phylogeny that can indicate introgression between groups. Generally, examples of the test define the groups as “P1”, “P2”, “P3”, and “P4” in this phylogeny orientation: ((P1,P2), P3),P4); “P1” and “P2” are sister taxa, “P3” is a more distantly related group, and “P4” is the outgroup (Green et al., 2010; Durand et al., 2011). Based on this, there are four ways in which we can test for introgression between groups of nuthatches using the red-breasted nuthatch as the “P4” outgroup. For tests one and two, we placed RS and RN as “P1” and “P2”, and Eastern or Pacific as “P3”, testing for excess allele sharing with a Rocky Mountain population and the Pacific or Eastern population. In tests 3 25 and 4 we then used RS or RN as “P1”, Pacific as “P2”, and Eastern as “P3” to test for allele sharing between either the Rockies or Pacific with the Eastern population. These tests were conducted using Dsuite (Malinsky et al., 2021). To extend these tests we examined how excess allele sharing varied across the genome. Using Dsuite, we conducted ABBA-BABA tests in sliding windows across chromosomes (100 SNP window and 100 SNP step) and chose to use the fd statistic, which may have some benefits above the traditional D statistic (Martin et al., 2015). In particular, we were interested in whether regions of high differentiation between RN and RS (the two most closely related groups based on the phylogeny and overall FST) might have a history of introgression from the Pacific population. Sliding windows of fd were plotted with FST between RS and RN using code derived from Roelfs (2019). From these sliding windows testing for introgression, we chose 4 small representative regions of the genome to investigate further. Using custom R code (Irwin et al., 2016, 2018), we compared genotypes of 10 arbitrarily selected samples from each of the populations. We then subsetted SNPs from these regions to examine population structure across all samples using the PCA methods described above. We then repeated this PCA step with only RS individuals to see if geography explained the occurrence of RN/RS genotypes in these regions for the RS population. Using SNPs from these regions, we constructed phylogenies using the same approach as described above for the whole-genome phylogeny except we reduced the MCMC iterations to 3,000,000 and SNPs were not thinned for distance. Considering SNPs in these regions are not fixed between RN and RS, we used sample positions on the SNP subset PCAs to arbitrarily select RN and RS individuals who grouped with the predominately RN or RS cluster for our phylogenetic analyses. In these analyses, 5 individuals from each population were chosen along 26 with the three red-breasted nuthatches as the outgroup. ESS for model parameters of each phylogeny were > 690. 2.3 Results The total length of our new white-breasted nuthatch genome assembly is ~1.032 Gb, comprising 360 scaffolds with an N50 of 57.57 Mb. The largest scaffold in the assembly is 154 Mb and is homologous to chromosome 2 of the Zebra finch genome. The BUSCO analysis shows 98.43% complete BUSCOs, indicating a highly complete genome. There is a high degree of synteny between the white-breasted nuthatch genome and other bird genomes, and our largest scaffolds (Scaffold 1 -39) represent complete or nearly complete chromosomes (appendix. A.1). There are also some notable differences. Several smaller scaffolds in the white-breasted nuthatch map to larger chromosomes in the zebra finch and brown creeper. These may either represent fission/fusion events or unassembled scaffolds. For this reason, we did not adopt chromosome names for scaffolds despite most scaffolds representing full chromosomes. Instead, scaffolds in our reference genome are numbered using arbitrary integers starting with 1 for the first scaffold in the assembly, 2 for the second, and so forth. We note that Scaffold 3 in our assembly aligns to the avian Z chromosome in both the zebra finch and brown creeper. Principal components analysis (PCA) reveals four well-separated discrete clusters (fig. 2.1a), which we refer to as Rocky Mountain South (RS; N = 150), Rocky Mountain North (RN; N = 138), Eastern (N = 23), and Pacific (N = 45). Sample locations are shown in figure 2.2b. Estimates of weighted genome-wide FST between populations (i.e., PCA clusters) range from 0.13 to 0.61 (table 2.1). 27 Figure 2.1. a) PCA of 370,410 genome-wide SNPs from 360 white-breasted nuthatch samples across North America. PC axes 1 and 2 explain 14.7% and 12.6% of the variation, respectively. b) Geographic locations of samples. c) Admixture analysis of 370,410 SNPs and 360 white-breasted nuthatch individuals with K=4. Populations are color-coded based on population. RN and RS have the largest amount of mixed ancestry, with RN having the highest frequency of individuals with portions of Pacific ancestry. RS/RN intermediate (Int.) samples and Rocky Mountain/Eastern hybrids show mixed ancestry between their respective populations. D) Phylogeny of white-breasted nuthatch populations based on 3,000 randomly selected autosomal SNPs. Node dates are in millions of years and each node had a posterior probability of 1. Node bars reflect the 95% HPD interval. wbnu_plate1to5_360_NoHighReKin_variants_wholegenome_MQ20_het06_Miss09_NoOut_biallelic_GQ10_noIndel_mac2_plink.4.LatSort.QK=40.000.250.500.751.00EasternHybrid_RN_EHybrid_RS_EPacific RNRN_RS_CrossRS| ||| | || |as.character(metdat$location)East Pacific RNA) B)C) D)RSAncestry fractionRS/RN Int.Eas /Rocky HybridPC 1 (14.7%)PC 2 (12.6%)-50 0 50 100 150-250-200-150-100-500PC 1 (14.7%)PC 2 (12.6%)EasternHybrid_RN_EHybrid_RS_EPacificRNRN_RS_IntRSPopulationEasternHybrid_RN_EHybrid_RS_EPacificRNRN_RS_CrossRS2.0EastRNRed_breastedRSPacific0.15417.4491.6050.648 28 Table 2.1. Genome-wide weighted average FST between populations. Our analysis using Admixture confirms findings from the PCA. For K = 4, we see discrete groupings of Pacific and Eastern populations. RS and RN appear as a gradient of mixed ancestry (fig. 2.1c). In our admixture plot, samples are organized by latitude within groups, and we can see shared ancestry of RN and RS appears at least partially related to latitude. RN appears to have some individuals with Pacific ancestry. We also show Admixture results for K=3 and K=5 (appendix. A.2). K = 3 groups Rocky Mountain populations, and K=5 reveals additional RS structure with a cluster of individuals from Mexico. K=4 was most supported by cross-validation (CV error = 0.14037), whereas K=3 and K=5 had CV errors of 0.15277 and 0.14074 respectively (appendix A.3). The phylogeny constructed between populations leads to a broad range of divergence dates between these populations (fig. 2.1d). The oldest split date occurs between the East and the other populations at 1.6 mya, the Pacific and the Rockies then diverged at 0.65 mya, and the most recent split occurs between Rocky Mountain populations at 0.15 mya. Our PCA revealed evidence for two F1 hybrids between Eastern and Rocky Mountain individuals. In the Admixture analysis, each F1 appears to have half Eastern ancestry as Pacific RN RS EasternPacificRN 0.30RS 0.36 0.13Eastern 0.61 0.46 0.431 29 indicated by their intermediate position in the PCA and by long stretches of heterozygous genotypes (appendix. A.4) across Scaffold 1. Both F1 individuals were found in areas where the Rocky Mountain and Eastern populations are in closest proximity. Despite the presence of these F1 hybrids, there is no evidence of F2s or other recent multigenerational hybrids or backcrosses between the Eastern and Rocky Mountain populations, which are widely separated clusters on the PCA plot (fig. 2.1a). Between the Rocky Mountain populations, we see a moderate degree of admixed ancestry. However, we identified two that may represent either more recent admixture or the geographic area where populations transition from more RS ancestry to more RN ancestry. These individuals are found in Western Nevada and cluster more closely with RN along PC 1 (fig. 2.1a) and RS along PC 3 (appendix. A.5). Our Admixture analysis revealed that these individuals have equal amounts of RN and RS ancestry with a small portion of Pacific ancestry. To test for asymmetrical introgression between populations, we performed ABBA-BABA tests with the red-breasted nuthatch as an outgroup (fig. 2.2). In each test, we found a significantly positive D-statistic. The standard interpretation of this result is that it would indicate greater introgression between P3 (Pacific/Eastern) and P2 (Pacific/RN) than between P3 and P1 (RN/RS). This rejects the null model of no introgression or introgression that occurred equally between P3 and P2 and between P3 and P1. In other words, this interpretation of these tests suggests excess derived allele sharing between RN, Pacific, and Eastern populations. 30 Figure 2.2. Four ABBA-BABA tests using the Red-breasted Nuthatch as an outgroup. Each test shows a significant positive D statistic (unadjusted p-value). A positive D statistic indicates excess ABBA allele patterns. Focusing on the patterns of allele sharing between RS, RN, and the Pacific our sliding window analysis found specific areas of the genome that showed a high signal of introgression (fig. 2.3). When these windowed fd (ABBA-BABA test variant; Martin et al., 2015) values are compared to FST between RN and RS, we see again a large portion of overlap between high fd and elevated FST. For the remaining portions of the genome where fd is closer to zero, introgression has either not occurred or is in equal amounts between RS, RN, and the Pacific. RS RN Pacific Red-breasted RS RN Eastern Red-breastedRN Pacific Eastern Red-breasted RS Pacific Eastern Red-breastedD = 0.098p-value = 7.2 × 10-7D = 0.24p-value = 4.9 × 10-11D = 0.34p-value = 4.3 × 10-19D = 0.18p-value = 5.7 × 10-10A) B)C) D)A B AB A B ABA B AB A B AB 31 Figure 2.3. A) FST between RN and RS is shown for SNPs across the genome. Scaffolds 14, 15, 38, and 39 are not included as they did not contain enough SNPs for Dsuite windows. SNPs with NA or negative values for FST are omitted along with SNPs from scaffolds less than 1 Mb leaving 210,858 SNPs. Dotted vertical lines indicated scaffold boundaries and plotted in scaffold order (i.e., scaffold 1, 2, 3 etc.). The green line represents fd in 100 SNP sliding windows. In panels B-E, we show a few scaffolds in detail as examples. In these examples, we highlight in red the region used to construct phylogenies. 32 To further investigate these areas, we constructed phylogenies from four regions on different scaffolds that had high FST and elevated fd, indicating introgression between Pacific and RN. Based on our ABBA-BABA tests and assuming the positive D statistic is the result of introgression between RN (“P2) and Pacific (“P3”) we would expect the Pacific and RN to be closely related sister taxa followed by the Eastern population with divergence dates occurring more recently in time than the genome-wide estimates. Phylogenies constructed from these regions do not match these expectations (fig. 2.4). In each phylogeny, we see node dates between RN, Pacific, and Eastern similar to that of the genome-wide dates and instead see RS is older than all other nuthatch populations. This means that the ABBA patterns found in our tests are the result of excess ancestral alleles in RS, rather than shared derived alleles between RN and the Pacific. 33 Figure 2.4. Phylogenies of the four selected regions identified from FST and fd scans. In each phylogeny, RS is older than all other white-breasted nuthatch populations. A) Scaffold 1 52 – 58 Mb region (1253 SNPs). B) Scaffold 2 54-59 region (615 SNPs). C) Scaffold 29 12 – 14.1 Mb (1232 SNPs). D) Scaffold 34 2-4 Mb region (644 SNPs). Node indicates the age in millions of years and node bars indicate the 95% HPD of the node height. Across all phylogenies posterior probabilities for all nodes > 0.97. Examining these regions using genotype-by-individual plots and PCA’s (fig. 2.5 and appendix. A.6), we see that RS has highly divergent continuous haplotype blocks in these regions. These areas are not fixed, so some individuals in RN and RS contain the alternate population haplotype or are heterozygous for the region. This is made clearer by the PCAs of SNPs in these regions. Each PCA reflects a similar pattern where PC2 separates RN, Eastern, 2.0RSPacificRNEastRed_breasted2.210.641.7917.382.0PacificRNRSRed_breastedEast17.421.511.980.452.0Red_breastedRSRNEastPacific17.421.750.691.342.0PacificRNRSRed_breastedEast0.693.517.441.61A) B)C) D) 34 and Pacific and PC 1 separates RS from all other populations. Between RN and RS there is a low frequency of samples that group in the alternate populations cluster, and we see some samples cluster between the predominately RN and RS groups, which are heterozygotes for these differentiated regions. Figure 2.5. Genotype by individuals plot (A and C) of genomic regions selected for further analysis with corresponding PCA’s (B and D). Each row is an individual and each column is a SNP. Individuals are color-coded on the sides to denote populations based on genome-wide SNPs. Genotype colors are determined by the reference allele. Dark purple is homozygous reference, light purple is homozygous alternate, and light/dark purple is heterozygous. Only SNPs with FST > 0.25 between RN and RS are shown in the genotype by individual plots to reduce complexity. PCA of scaffold 1 region (B) is of 741 SNPs and the PCA of the scaffold 2 region (D) is of 530 SNPs. 35 We then repeated this PCA step using only RS individuals to determine if geography explained sample distributions within clusters (appendix. A.7). The high-frequency RS cluster contained individuals across the range of RS. The middle PCA cluster and low-frequency cluster contained no samples from Mexico, which is the most southern portion of the RS range. Finally, we examined nucleotide diversity for each population of white-breasted nuthatch to infer some of the evolutionary history of RS (appendix. A.8). One possibility is that RS as a population is much older and has recently homogenized with RN. If this is the case, we might expect nucleotide diversity in the Rocky Mountains to be much higher than other populations. We find that Rocky Mountain nucleotide diversity is comparable with Eastern nucleotide diversity, but that Pacific diversity is much lower. 2.4 Discussion These results show that white-breasted nuthatches, currently treated as a single species, in fact consist of four distinct clusters that show much genetic differentiation. Comparisons among these groups show a variety of levels of reproductive isolation, ranging from moderate FST (0.13) and moderate admixture (RN vs. RS) to high FST (0.43 to 0.46) and only a few F1 hybrids (Eastern vs. the Rocky Mountain populations). With high amounts of differentiation between populations and clear differences in song (Pandolfino & Pieplow, 2015) we suspect that both pre and postzygotic isolation is playing a role in white-breasted nuthatches. Thus white-breasted nuthatches provide an excellent system for the study of the “speciation continuum” (Drès & Mallet, 2002; Stankowski & Ravinet, 2021), the concept of different pairs of populations being at different stages of the speciation process—from undifferentiated groups within populations freely interbreeding to highly differentiated populations displaying complete reproductive 36 isolation. The comparison of RN and RS apparently represents an early stage of speciation, with some areas of the genome differentiated but some gene flow still occurring. Pacific is further along in the continuum of differentiation from the Rocky Mountain populations. Finally, the Eastern population compared with the Rocky Mountain populations represents a near-final stage of speciation, with high levels of genome-wide differentiation and reproductive isolation indicated by no evidence for F2’s or backcrosses despite the presence of two F1 hybrids. The presence of these F1 hybrids between the Eastern and Rocky Mountain populations shows that premating isolation is not complete, yet the distant separation of genomic clusters and the lack of advanced hybrids between these populations indicates that hybrids may have low fitness. If this is the case, postzygotic isolation is apparently strong. Production of low-fitness hybrids can suppress the reproductive output of a population, leading to decline and inability for two species to coexist in the same region (Mikkelsen & Irwin, 2021; Irwin & Schluter, 2022). Such dynamics produced by low hybrid fitness could also limit range overlap between the other forms of white-breasted nuthatch, if hybrids have low fitness. Limited hybrid frequency may also be caused by geographic separation of population ranges limiting opportunities for mating. Further fine-scale sampling is needed to fully understand the degree (or lack thereof) of overlap between populations. Our sampling and results revealed that the different genetic groups come into close proximity, and the sampling of hybrid individuals shows that at least the parents of these offspring overlapped geographically. In California, Pandolfino et al. (2017) found areas where individuals singing Rocky Mountain and Pacific song types co-occurred, suggesting that their ranges do overlap. We also suspect Rocky Mountain populations come into closer contact in Northwest Nevada but we lack sampling from this region. Given genome-wide levels of differentiation we speculate that the Rocky Mountain, 37 Pacific, and Eastern populations have accumulated some genetic incompatibilities and that geographic barriers are insufficient to explain the genomic patterns. Our tests of introgression indicate that the genetic variation is not fully explained by a bifurcating phylogeny, such that introgression has occurred between divergent populations. One concern raised regarding ABBA-BABA tests is that introgression from ghost lineages, populations that are currently obscure or extinct, can cause the test to imply different routes of introgression than those that actually occurred (Hibbins & Hahn, 2022; Tricou et al., 2022). If ghost lineage introgression occurred in white-breasted nuthatches, it would likely occur with RS as RS is typically used as P1 in our ABBA-BABA tests. In this scenario, we would see an excess of ABBA patterns (or a positive D statistic) which are commonly interpreted as introgression between P2 and P3. But in the case of ghost introgression, ABBA patterns are generated by introgression of ancestral alleles in P1. Further scrutiny of these genomic regions does not support the typical interpretation of our tests for introgression. If introgression truly occurred between these populations, we would expect dates of divergence to occur more recently in time than genome-wide estimates. Instead, the major difference we find is that RS is much older in these genomic regions. These patterns with our ABBA-BABA tests and phylogenies match the expectations of what one would expect if a ghost lineage introgressed into RS (Hibbins & Hahn, 2022; Tricou et al., 2022). Our Admixture analysis did show that some RN individuals had some Pacific ancestry. So, it may be that introgression among the other three groups is occurring; however, given that ghost lineage introgression can also bias Admixture analyses (Lawson et al., 2018) we are cautious about interpreting this further. Interestingly, in our ABBA-BABA test where RS was not used (fig. 2.2c) we did observe a significant positive D statistic. We believe that this is driven in part by ancestral RS haplotypes 38 occurring in RN. For example, we highlighted four regions from our Dsuite analysis using genotype-by-individual plots (fig. 2.5a-d; appendix A.6 a-d). These regions show that there are alternate haplotypes between RS and the other nuthatch groups. These haplotypes are not completely fixed between populations and occur in high frequency in RS or RN and then at a much lower frequency in the other population. This interpretation is also supported by our tests because the overall D statistic is much lower when RN is used (fig. 2.2c) than when RS is tested with Pacific and the Eastern population (fig. 2.2d). Some of this signal may be caused by genuine introgression between Pacific and the Eastern population. If this is the case, introgression likely occurred in the more distant past as these populations are currently well separated geographically. RS containing excess ancestral alleles in these areas could have occurred through different mechanisms. The first is that RS is in fact a much older population and recently came into contact with RN and their admixture homogenized much of the genome erasing the genome-wide evidence for the older divergence. However, as we noted in our results section, we would anticipate RS and RN to have much higher nucleotide diversity but diversity within these populations is comparable to the Eastern population. So, it is unlikely that this scenario explains our observed patterns. Another possibility is that selection on standing variation may have been responsible for generating ABBA-BABA patterns in alleles. Bias from selection has been noted by others (Durand et al., 2011) and is possible in principle. For selection to fully explain patterns of derived and ancestral alleles in white-breasted nuthatches this would require parallel selection in RN, Pacific, and Eastern populations. While we cannot fully exclude this possibility, we believe introgression from extinct/unsampled population into RS is a more parsimonious explanation. 39 These patterns of differentiation and introgression between RN and RS complicate the interpretation of whether these two lineages represent separate species. Genomic admixture varying partially by latitude implies a modest level of reproductive isolation, and further genomic sampling in Western Nevada may reveal more hybridization between these populations. However, large regions of the genome show high differentiation between RN and RS. Some of these appear to have been due to introgression from an unsampled population into RS, and these are now playing a role in the differentiation between RS and RN. There are several possibilities regarding how these regions may drive or inhibit speciation between RS and RN. The first is that these introgressed areas may be globally adaptive creating the appearance of differential selection (Booker et al., 2021) for white-breasted nuthatches and have yet to sweep through RN. If so, once introgressed these peaks of differentiation between RN and RS will collapse and the genomes will homogenize. The other possibility is that these regions are locally adaptive (or experiencing background selection [Charlesworth et al., (1993)] ) for RS only. This could promote differentiation between Rocky Mountain populations and increase reproductive isolation between them. These two outcomes point to two different scenarios; this is either very early in the speciation process or mid-sweep of haplotypes experiencing linked selection. With our current data, it is unclear which is occurring. Long-term monitoring of populations and these regions of differentiation could represent a unique opportunity to track these regions to determine if they continue to sweep through RS and into RN. Despite the unclear species status of RN and RS, this pair in some ways is the most fascinating for future research. These large tracts of introgression promoting differentiation between RN and RS in otherwise homogenized genomes are of great interest in terms of how 40 these haplotypes may relate to white-breasted nuthatch biology. In principle, we could determine the genes in these regions and explore their function; however, because these are large regions, we would likely have thousands of genes to explore, and we know little about how they would relate to the ecology of the birds. Detailed ecological comparisons and life history studies of white-breasted nuthatch populations are needed before we can revisit these genomic regions of interest. Our study focused on genomic data. Given these results, it would be interesting to revisit an analysis of plumage and morphological variation. Earlier analyses of plumage and morphological variation in white-breasted nuthatches provide conflicting evidence. Grinnell (1926), Grinnell and Miller (1944), Aldrich (1944), and Hawbecker (1948) described geographic variation in plumage with much discussion on ranges of western populations. Wood (1992) examined differences between described Eastern subspecies and suggested they should be considered a single subspecies. Aldrich (1944) provided a range map based on phenotype, but it differs markedly from ranges based on our genomic data. Our RS range is from Southwest Nevada to the Front Range of Colorado and from Northern Colorado to Mexico. RS largely overlaps with nelsoni by Aldrich (1944), but nelsoni extends into Wyoming and Central Montana. On the southern part of the range nelsoni stops in Northern Mexico, and the remaining Mexico populations are listed as four different subspecies. Our RN range is from British Columbia to Western Nevada and from Central Washington to Eastern Wyoming. The core of this range overlaps with Aldrich’s (1944) range of tenuissima but Aldrich (1944) included samples from Southern California in tenuissima. Aldrich (1944) described two eastern subspecies but in the genomic data the eastern population appears as one tightly grouped population from Eastern Colorado to the East coast. Unlike the other populations, the range of 41 the Pacific population is similar between Aldrich’s (1944) aculeata subspecies and our own analysis, except for Southern California. These comparisons of morphological differences contrast with variation in vocalizations that much more closely correspond with genomic population structuring (Pandolfino & Pieplow, 2015). In an analysis of vocalizations of white-breasted nuthatches across the US and Canada, Pandolfino & Pieplow (2015) determined there were three song groups: Eastern, Pacific, and Rocky Mountain (“interior west” in their study). The Eastern and Pacific populations had some similarities in their vocalization, but the Pacific population also had a “tooey” song which did not occur in other populations. Rocky Mountain populations also had a “quank” (“rapid” and “disyllabic”) call only found in those populations. Pandolfino & Pieplow (2015) could not find a vocalization that separated any Rocky Mountain groups but noted that Rocky Mountain populations differed in mean pitch for the “disyllabic quank”. Considering our genomic results, revisiting the question of morphological variation in white-breasted nuthatch would be of interest. Large collections of specimens exist in multiple museums, and current technology may be more suited to quantify variation (reviewed by Mason and Bowie 2020) that was difficult to study previously (plumage patterns, paleness of the back, etc.). Structuring a morphological analysis based on the genomic delineations might reveal some diagnostic morphological features that have otherwise been missed. The construction of a chromosome-level reference genome for the white-breasted nuthatch is a major contribution of this study. To our knowledge, the only available assembly for the Sitta genus is a genome of a Eurasian nuthatch (Sitta europaea; Feng et al., 2020); BioProject PRJNA545868). When we aligned the nuthatch genome to reference genomes for the zebra finch (Taeniopygia guttata; Bioproject PRJNA558048; Wada et al. 2006; Warren et al. 2010; Korlach 42 et al. 2017; Rhie et al. 2021; Formenti et al. 2021) and brown creeper (Certhia americana; Bioproject PRJNA604561; Manthey et al. 2021), we found that overall there was a high degree of synteny but that there were several cases of scaffolds mapping to the same chromosomes (i.e., two scaffolds mapping to portions of a single chromosome). This can be explained by fission or fusion events during the evolutionary time separating these species or by incomplete assemblies based on available data. Some karyotypic studies that include Sitta suggest that there may be more rearrangements in this genus of Passerine than expected (Shields, 1970; Nanda et al., 2010). In the future, karyotypes for each putative species and fluorescent in situ hybridization (FISH) probes would help resolve if there are large rearrangements in the genome. The white-breasted nuthatch system contains an unusual combination of elements that make it of interest to the field of evolution. The first is that is comprised of 4 differentiated clades that likely represent at least 3 species that, based on morphology, are presently treated as one species. The other aspect is that introgression appears to have played an important role in shaping differentiation between the youngest populations; RN and RS. Surprisingly, this introgression appears to be sourced from an older lineage of white-breasted nuthatches. Other studies have highlighted the evidence of possible introgression from extinct species (e.g., Szöllősi et al. 2013; Zhang et al. 2019; Grant and Grant 2021). White-breasted nuthatches seem to represent such an example and make us consider how much genomic variation in avian systems may be accounted for by introgression from now-extinct or unsampled populations and species. 43 Chapter 3: Polymorphic haplotype blocks present in a passerine 3.1 Introduction Genomic sequencing has provided remarkable insight into evolutionary processes (e.g. Hohenlohe et al., 2010; Brawand et al., 2014; Evans et al., 2014; Riesch et al., 2017; Liu et al., 2018; Morales et al., 2018; Edelman et al., 2019; Todesco et al., 2020; Hager et al., 2022). When examining patterns of differentiation, attention is often focused on genome-wide signals to understand population structure. However, on a more local genomic scale, of specific regions or chromosomes, patterns of population structure can vary widely (e.g. Li & Ralph, 2019). This variation leads to a fundamental yet elusive question: why is genomic differentiation often so heterogeneous? Various factors can influence differentiation. This includes recombination, which varies across genomes and can modify differentiation landscapes (Kong et al., 2002; Backström et al., 2010; Samuk et al., 2017; Booker et al., 2020; Turbek et al., 2021). Introgression from other species (e.g. Oziolor et al., 2019) and the lower effective population size of sex chromosomes can influence local genomic variation (e.g. Mank et al., 2010). Structural elements of chromosomes like centromeres and inversions are other important factors affecting population structure (e.g. Ellegren et al., 2012; Funk et al., 2021) as they also reduce recombination (Sturtevant, 1921; Beadle, 1932; Mather, 1936; Sturtevant & Mather, 1938; Brazier & Glémin, 2022; Rönspies et al., 2022). Inversions are structural variants that represent special cases of low-recombination regions (Sturtevant, 1921). An inversion is a sequence of DNA flipped in orientation compared to the original DNA sequence (Sturtevant, 1921). Recombination in individuals heterozygous for 44 an inversion is suppressed within the region (Sturtevant & Beadle, 1936; Sturtevant & Mather, 1938; Rönspies et al., 2022). This is partly because if crossing-over occurs between the inversion and colinear region, it interferes with the proper segregation of chromatids resulting in non-viable gametes (Sturtevant & Beadle, 1936). Recombination is also suppressed because inversion heterozygotes disrupt the cross-over process during meiosis resulting in changes in cross-over frequency across the genome (Schultz & Redfield, 1951; Lucchesi & Suzuki, 1968; Joyce & McKim, 2010; Crown et al., 2018). Various models have been proposed regarding how inversions, via the suppression of recombination, could promote local adaptation and create barriers to gene flow resulting in speciation (Noor et al., 2001; Rieseberg, 2001; Navarro & Barton, 2003; Kirkpatrick & Barton, 2006; Faria & Navarro, 2010; Huang & Rieseberg, 2020). Several empirical examples have highlighted how large inversions are related to different phenotypes involved in local adaptation (Tuttle et al., 2016; Jay et al., 2018; Todesco et al., 2020; Funk et al., 2021; Sanchez-Donoso et al., 2022). In avian taxa specifically, it was initially proposed that inversions may be relatively uncommon (Ellegren, 2010), but Hooper & Price (2017) have shown they can occur more frequently. Using short-read data to find inversions can be challenging. Signals of local genomic structure such as centromeres, inversions, or regions of low recombination may be obscured in population structure analyses by genome-wide signals. Genome scans using metrics like FST, a relative measure of genetic differentiation (Wright, 1951; Weir & Cockerham, 1984), are generally pairwise measurements and may not reveal such patterns if an inversion or haplotype is polymorphic within a species. However, genetically divergent inversions can produce characteristic patterns in principal component analyses (PCA) of variation in specific genomic regions, differing substantially from the genome-wide pattern ( e.g. Berg et al., 2016; Knief et 45 al., 2016; Huang et al., 2020): when individuals group into three distinct clusters, these can represent a middle cluster that is heterozygous for the inversion, with clusters on either side representing the two homozygotes for alternative inversion types (Lundberg et al., 2017). While not strictly necessary to explain such a pattern, genomic inversions can produce these cluster types. Broadly, these genomic features provide important context to speciation and could improve our understanding of variation beyond that of nucleotide differences. In the present study, we sought to detect the occurrence of inversion-like patterns using genomic sequencing data from white-breasted nuthatches (Sitta carolinensis). In a previous assessment of the population structure of white-breasted nuthatches (See Chapter 2), we identified four well-differentiated clades (Eastern, Rocky Mountain North [RN], Rocky Mountain South [RS], and Pacific) that are likely at least three different species, in agreement with previous genetic studies (Spellman & Klicka, 2007; Walstrom et al., 2012). Using the sequences from that study, here we employed two complementary approaches to identify possible inversions. First, we used a local-PCA approach (Li & Ralph, 2019) using SNP (single nucleotide polymorphism) windows within each population of white-breasted nuthatches (one analysis per population) to identify genomic regions that show unusual patterns of relationships among individuals (in comparison with most genomic regions)—we refer to these regions as outliers. This methodology conducts PCAs in genomic windows and then identifies PCA pattern outliers by assessing differences using multidimensional scaling (Li & Ralph, 2019). In our secondary approach, we conducted PCA’s across all samples from all populations using SNPs on individual scaffolds/chromosomes. After we identified suspected inversions, we revisualized our data using patterns of linkage disequilibrium (LD) and genotypes of individuals to further investigate if these areas are likely inversions. In some of the regions identified, this three-cluster 46 PCA pattern occurs across multiple cryptic species, suggesting the sharing of polymorphisms despite the otherwise high genomic differentiation between the species. We explored these areas in further detail to determine whether introgression or shared ancestry explains the shared polymorphisms. Introgression has been an important factor in explaining genomic variation in white-breasted nuthatches (Chapter 2) where one population, Rocky Mountain South, appears to have signals of introgression from a ghost lineage/unsampled population (Chapter 2). In figure 3.1, we illustrate two possible scenarios that could result in the sharing of these polymorphisms between species. To differentiate these possibilities, we would rely on phylogeny topology. In a scenario where the origin of the polymorphisms is in the common ancestral population we would expect two clades based on the inversion haplotypes. Within each we would then expect to see branching patterns of populations similar to that of the genome-wide divergence patterns between populations (fig. 3.1a). In a scenario of recent origin of the polymorphism followed by introgression, we would expect samples of one haplotype to be phylogenetically nested within those of the alternate haplotype (fig. 3.1b). 47 Figure 3.1. Diagram of species tree and haplotype trees. Below each tree is the resulting phylogeny of haplotypes across populations. A) A tree example for standing variation explaining the presence of haplotypes across populations. B) A scenario of introgression explaining the presence of haplotypes in both populations. 3.2 Methods 3.2.1 Data processing A total of 399 genetic samples from white-breasted nuthatches across North America were collected from wild-caught individuals and museum specimens as described in Chapter 2. DNA was extracted from samples and used for genotyping-by-sequencing (GBS; Elshire et al., 2011; Alcaide et al., 2014). GBS libraries were prepared with the PstI enzyme following the protocol described by Alcaide et al. (2014). GBS reads were aligned using bwa mem 0.7.17 (Li & Durbin, 2009) to a white-breasted nuthatch genome produced from our previous genomic Ancestral Recent IntrogressionPac hap 1 Pac hap 1RN hap 1RN hap 1RS hap 1Pac hap 2RS hap 2RN hap 2Pac hap 2RN hap 2RS hap 2RS hap 1A) B) 48 study (Chapter 2), and SNPs were called using GATK 3.8 (McKenna et al., 2010) and filtered for quality using VCFtools 0.1.14 (Danecek et al., 2011). Some individuals were excluded from the analysis due to missing data or close familial relationships with other individuals in the dataset. Further details regarding the bioinformatic pipeline are found in Chapter 2. We excluded hybrid or intermediate individuals between species, as identified in Chapter 2, from our analysis of individual chromosomes. This exclusion was done to maximize the ability of the PCAs to identify unusual population structure patterns rather than show covariance from other factors like having hybrid individuals. Using the genome alignment from Chapter 2 to the zebra finch (Taeniopygia guttata BioProject PRJNA5580480; Wada et al., 2006; Warren et al., 2010; Korlach et al., 2017; Formenti et al., 2021; Rhie et al., 2021), we determined which scaffolds are homologous to zebra finch chromosomes 3.2.2 Bioinformatic analyses To identify possible inversions we employed two complementary PCA-based approaches. For our first analysis, we subsetted samples based on populations identified in Chapter 2: Eastern (N=23), Pacific (N=45), Rocky Mountain South (RS) (N=150), and Rocky Mountain North (RN) (N=138). Retaining samples from only one population at a time, we filtered our initial dataset of SNPs (after we filtered SNPs based on mapping quality and heterozygosity) using the filtering parameters applied to the dataset of all individuals. SNPs passing filtration appeared at least twice in the population, occurred in at least 90% of individuals, had a genotype quality greater than 10, were biallelic, and were not indels. This filtering was repeated for each individual population and retained 111,190 SNPs in the Eastern population, 50,869 SNPs in the Pacific, 156,202 SNPs in RN, and 207,424 SNPs in RS. For each population-specific dataset we 49 converted the files to bcf files and indexed them using bcftools (Li, 2011; Danecek et al., 2021) and used them as input for lostruct (Li & Ralph, 2019) to conduct local PCA’s in 50 SNP windows. Scaffolds 14, 15, 38, and 39 were excluded as they did not have enough SNPs to form a 50 SNP window in each population. These PCA windows were then used as input for a dissimilarity matrix which was then visualized with multidimensional scaling (MDS) (Li & Ralph, 2019). We then plotted MDS values across the genome based on modified code from Roelfs (2019). Windows that deviated the furthest along multi-dimensional scaling axes indicated possible genomic regions where population structure differed from the rest of the genome-wide pattern (Li & Ralph, 2019). These identified areas were then be used for further investigation using PCA for a three-cluster-like pattern. Following lostruct, we then conducted principal component analyses (PCA) of individual scaffolds, which largely correspond to chromosomes, using a set of 365,667 filtered SNPs used in in Chapter 2. In this analysis step we examined scaffolds with PCA if they were greater than 1 Megabase (Mb). SNP counts per scaffold/chromosome ranged from 34,372 down to 30 (appendix B.1). PCAs were done using custom R scripts that imputed missing data (Stacklies et al., 2007; Irwin et al., 2016, 2018; R Core Team, 2021). These PCAs of individual scaffolds largely showed similar patterns, with Rocky Mountain populations close together in PC space and other populations more differentiated; however, several scaffolds showed unusual structures indicative of an inversion and not similar to the genome-wide pattern (see Results). We explored these scaffolds further to investigate putative inversions. We were primarily interested in three-cluster patterns within populations. We also noted that Scaffolds 29, 33, 34, 38 and 39 showed a similar three-group structure between population. Scaffolds 29, 33, and 34 were identified in in Chapter 2 as regions with a high signal of introgression indicated by sliding windows of ABBA- 50 BABA tests (Green et al., 2010; Durand et al., 2011). Specifically in the cases of 29 and 34 these regions are likely the result of introgression from a ghost lineage/unsampled population that has introgressed into RS (Chapter 2) and for this reason these regions were excluded in our analyses. For scaffolds 38 and 39, because of the small number of SNPs found on these scaffolds (30 and 134 SNPs), we chose to exclude them from further analyses. For each scaffold with a three-group structure, samples located in the middle were labeled as “Hets” for heterozygotes, and the other groups were arbitrarily grouped as “Hap1” or “Hap2” for either haplotype 1 or 2. We revisualized the data in the PCA using patterns of LD, measured as r2 (i.e., 0 between SNPs means they are unlinked while 1 would mean they are perfectly linked) between SNPs from Plink 1.9 (Purcell et al., 2007), and plots of genotypes per individual produced by custom R scripts (Irwin et al., 2016, 2018). For LD plots, we examined putative haplotype clusters if they contained more than 10 individuals. Some clusters contained a small number of individuals (for example hap 2 of scaffold 22) and thus SNPs generated considerable noise in LD plots. Finally, in LD plots only, we removed SNPs with minor allele counts less than 5 to reduce noise from rare variants that may have high LD due to chance. 3.2.3 Comparisons of Phylogeny topology We examined whether haplotypes moved across population boundaries through gene flow or are maintained ancestral polymorphisms by comparing phylogenies from genome-wide SNPs to phylogenies from loci within polymorphic regions identified by our PCA methods. We used the same methodology utilizing BEAST 2 (Bouckaert et al., 2014) and SNAPP (Bryant et al., 2012), to construct phylogenies as done in Chapter 2. This included constraining the root node with a normal distribution that has a mean of 17.5 Mya and a standard deviation of 1.25 based on an 51 estimated date from Pasquet et al. (2014), as well as using snapp_prep.rb (Matschiner, 2022; https://github.com/mmatschiner/snapp_prep) to generate input files, Tracer v1.7.1 (Rambaut et al., 2018) to determine effective sample sizes in each run (> or equal to 419), TreeAnnotator v2.6.2 (Drummond & Rambaut, 2007) to build a maximum clade credibility tree using mean node heights and a 10% burn-in, and FigTree v1.4.4 (https://github.com/rambaut/figtree ) to plot the phylogeny. For each run, we used 3,000,000 MCMC iterations with a 10% burn-in. SNPs from the following areas were used to build phylogenies for scaffold 2, 11, 20, and 22, respectively: 39 Mb – 126 Mb, 3 Mb – 4 Mb and 5.9 Mb – 6.3 Mb, 0 Mb – 11.5 Mb, and 7.5 Mb – 20.4 Mb. The red-breasted nuthatch (Sitta canadensis) was used as the outgroup. As done in Chapter 2, loci used in phylogeny construction had to be present in all individuals, biallelic, not an insertion or deletion, not located on the Z chromosome, and with a 10 or greater genotype quality. SNPs used for genome-wide phylogenies were thinned to be at least 100kb apart. For BEAST analyses of inversion regions, we excluded “Hets” individuals as their genotypes would bias divergence estimates between polymorphisms. In each analysis, we randomly selected 1500 SNPs as input except for the scaffold 11 haplotype phylogeny. In this scaffold, because the polymorphism is smaller and in two distinct areas of the scaffold, we retained a smaller number of loci, 514 SNPs, as input for the phylogeny. Sample sizes of groups used in phylogenies varied and are listed in the figure captions (see “results”). 3.3 Results Within individual populations of white-breasted nuthatch, we utilized lostruct (Li & Ralph, 2019) as a first step to identify areas of interest where genomic regions deviate along MDS axes for investigating possible inversions (fig. 3.22). In the Eastern population, PCA windows in 52 scaffold 2 noticeably deviated along MDS axis 1. In the Eastern, RS, and RN populations, windows in scaffold 22 all deviated across MDS axis 2. In the RN population, there is some deviation along MDS axis 1 in scaffold 3. Figure 3.2. MDS values for PCA windows across the genome for each population of white-breasted nuthatches. Each dot represents a 50 SNP PCA. To the right of each plot, we show an approximate range of the population across North America based on sampling from Chapter 2. Colored bars denote scaffolds where deviation in MDS scores was found. Orange is scaffold 2, green scaffold 3, and pink scaffold 22. -0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09MDS1-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09-1*MDS2-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09MDS1-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09MDS2-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09-1*MDS1-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09-1*MDS2-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09-1*MDS1-0.50.00.50.0e+00 2.5e+08 5.0e+08 7.5e+08 1.0e+09Genome Position-1*MDS2EasternRNRSPacific 53 Following our local PCA analysis, we examined population structure across all populations on a per scaffold basis. Most scaffolds exhibited structure similar to that of the genome-wide pattern shown in Chapter 2 (appendix B.2-B.5): Three genomically distinct clusters corresponding to Pacific, Eastern, and Rocky Mountain (RS/RN) populations. The Rocky Mountain populations have the lowest genome-wide differentiation (Chapter 2) so on some scaffolds, these populations do not form distinct clusters. We also investigated the population-specific regions indicated in lostruct (Eastern scaffold 2 and RN scaffold 3) using PCA to see if these genomic windows deviated because they contain three-cluster patterning. In this approach, we identified Scaffolds 11, 20, and 22 as having a three-cluster pattern of variation that can be indicative of inversions (fig. 3.3). For the scaffold 22 PCA (fig. 3.3d) we show PC 2 and 3 as PC 1 and 2 do not show the three-cluster pattern as effectively (appendix B.6). 54 Figure 3.3. PCA’s of individual scaffolds that show 3-cluster patterns consistent with two major haplotype groups. A) Scaffold 2 using only Eastern individuals (23) and 10,400 SNPs. B) Scaffold 11 PCA with 356 white-breasted nuthatch samples and 3099 SNPs. C) Scaffold 20 PCA with 356 white-breasted nuthatch samples and 9370 SNPs. PC axes 1 and 2 are shown in the supplementary materials. D) Scaffold 22 PCA using 9336 SNPs. PC 2 and 3 are shown as the best separate the three putative inversion clusters, PC 1 and 2 are shown in the supplementary materials. Colored stars highlight putative inversion type; black is homozygote hap 1, grey is het, and gold is hap 2. For the scaffold 2 Eastern specific region, we did confirm that three-cluster pattern is shown among Eastern individuals in this genomic region (fig. 3.3a). In scaffold 20, the three-cluster PCA pattern only occurred within the Eastern population (appendix B.7a). When we -30 -20 -10 0 10 20-30-20-10010PCA of chrScaffold_22: all SNPsPC2PC3EasternRSRNPacific-20 -10 0 10-20-15-10-505PCA of chrScaffold_11: all SNPsPC1PC2EasternRSRNPacific-20 0 20 40 60-40-30-20-1001020PCA of chrScaffold_2: all SNPsPC1PC2Eastern0 10 20 30 40-20-15-10-5051015PCA of chrScaffold_20: all SNPsPC1PC2EasternRSRNPacificPC 1PC 2PC 1PC 2PC 1PC 5PC 2PC 3A) B)C) D)Scaffold 2 Scaffold 11Scaffold 20 Scaffold 22 55 inspected SNP loadings of Scaffold 20 we identified 2 SNPs with high loading (appendix B.7b). When we removed these SNPs and repeated the PCA, the three-cluster pattern in the Eastern population was no longer apparent and population clustering corresponded to the genome-wide pattern (appendix B.7c). Further inspection of PC axes did reveal this structure in the Eastern population (fig. 3.3c). It is unclear why these two high-loading SNPs caused this Eastern population structure to be shown in PC 1. Inspection of these SNPs showed they were missing across all but one individual in the Eastern population. In other words, these high-loading SNPs seemed to cause real variation to appear in an initial PC axis but should be excluded and the pattern is then recovered by inspecting PC 5. When we inspect RN individuals only in a PCA of scaffold 3 SNPs (appendix B.8) we do not see a three-cluster pattern. High-scoring MDS windows from this scaffold are likely driven by admixture with RS because in a PCA of all individuals in scaffold 3 (appendix B.2) RN individuals appear as a gradient along PC1 with RS. After identifying possible inversion regions, we examined SNP loadings across each scaffold (fig. 3.4). If PCA axes reflected variation driven by an inversion, we expected clusters of SNPs with high PC loadings. In each region, we observed this expected pattern. Across the scaffolds, concentrations of highly loaded SNPs identify the genomic regions largely responsible for the patterns observed in PCAs. 56 Figure 3.4. SNP loadings of PC axes corresponding to PCAs in figure 3. SNP loadings are plotted along scaffolds and shown in absolute values (SNP loadings can be negative and positive, but the absolute value in both cases indicates the strength of their contribution to the PC axis). Purple lines highlight areas with elevated SNP loadings. Scaffold 22 was the only region to be identified by both the local PCA and scaffold PCA approaches, highlighting the usefulness of these complementary approaches. Lostruct identified the Eastern specific region on scaffold 2, whereas the PCA by scaffold approach found regions not indicated by deviation along MDS axes in lostruct. For the areas identified with both approaches: scaffold 2, 11, 20, and 22 these correspond to zebra finch chromosomes 2, 27, 11, and 10 inferred from genome alignments performed in Chapter 2. A) B)0.0e+00 5.0e+06 1.0e+07 1.5e+07 2.0e+070.000.020.040.060.08Scaffold 22 PositionPC 3 SNP loadings0.0e+00 5.0e+06 1.0e+07 1.5e+07 2.0e+070.000.050.100.150.20Scaffold 20 PositionPC 5 SNP loadings0.0e+00 5.0e+07 1.0e+08 1.5e+080.000.010.020.030.04Scaffold 2 PositionPC 1 SNP loadings0e+00 1e+06 2e+06 3e+06 4e+06 5e+06 6e+060.000.020.040.060.08Scaffold 11 PositionPC 1 SNP loadingsC) D) 57 To infer recombination patterns between polymorphisms in this region, we revisualized our data using LD between SNPs for all individuals and then within individuals of a single haplotype (fig. 3.5). Generally, LD blocks appear in these scaffolds when examined across all individuals. Within individual haplotypes, LD blocks are no longer observed. In scaffold 11 and 20 (fig. 3.5a and c), there is a gap of low LD between areas of high LD. It is not immediately clear what the reason is for this gap; in the case of scaffold 11 it may be that an assembly error is responsible. For scaffold 20, the signal of LD is weak in the region identified through SNP loadings, but this area seems to have higher LD among SNPs compared to those from the rest of scaffold 20. It may be that this region is a recent polymorphism and has yet to accumulate significant LD among SNPs. In LD plots sometimes only one haplotype is shown as the alternate haplotype contained too few individuals (< 10), creating excess noise. 58 Figure 3.5. LD (R2) between SNPs of Scaffolds 2, 11, and 20. Left most plots represent LD of SNPs with only individuals of “Hap1” of that scaffold, the middle plots show LD of SNPs for all individuals, and the right plots show LD of only “Hap2” individuals. SNP counts represent all the SNPs used by plink to calculate R2, but only R2 between SNPs greater than 0.7 is shown to reduce plot complexity. A) Eastern specific scaffold 2 LD of 3836 SNPs for 19 individuals of Hap1 and LD of 5209 SNPs for all 23 Eastern individuals. B) LD patterns for the scaffold 11 haplotype found in the Pacific and Rocky Mountain populations. 708 SNPs from 89 Rocky Mountain individuals of Hap1, LD of 1342 SNPs across all Rocky Mountain individuals, and LD of 611 SNPs from 64 Hap2 Rocky Mountain individuals. C) LD of 973 SNPs for 14 Hap1 Eastern individuals for scaffold 20 and LD of 1409 SNPs across all 23 Eastern individuals. Scaffold 22 has a small region of elevated LD maintained within just one haplotype near the end of the scaffold (fig. 3.6b). We explored this region of scaffold 22 further using a PCA of SNPs at this region (fig. 3.6c), which revealed a complex pattern of structuring between 59 populations. In this PCA, we again see the three population structures among Pacific and Rocky Mountain populations, but now the Eastern population also shows this pattern. Interestingly, we see intermediates between Eastern and Rocky Mountain clusters. The position of these intermediates on the PCA suggests they are heterozygous for Rocky Mountain and Eastern-specific haplotypes at the end of scaffold 22. From this data, we can infer that there is a polymorphism occurring in this region of scaffold 22 for both the Eastern population and the Rocky Mountain population. We also observe persistent LD within a single haplotype group of the Rockies because some individuals are heterozygous for the Eastern version at the end of the scaffold. 60 Figure 3.6. A) Linkage disequilibrium of 4268 SNPs across all individuals in the Rocky Mountain populations for scaffold 22. B) linkage disequilibrium of 3724 SNPs across only hap 1 individuals in the Rocky Mountain populations for scaffold 22. SNP counts represent all the SNPs used by plink to calculate R2 but only R2 between SNPs greater than 0.7 is shown to reduce plot complexity. C) PCA of 984 SNPs from the 18.5-20 Mb end of scaffold 22. We then examined the genotypes of individuals across each scaffold (fig. 3.7). Visualizing genotypes for these areas of the scaffolds helps determine whether individuals who are in the middle of three clusters in a PCA do in fact have long stretches of heterozygous SNPs and thus consistent with an inversion hypothesis. We see that this is largely true for scaffold 2, 11, and scaffold 22 (fig. 3.7 a, b, and d). For scaffold 20, this pattern is much more subtle, in that PC 1PC 2SNP A LocationSNP B LocationA) B)-20 -15 -10 -5 0 5 10-10-50510PCA of chrScaffold_22: all SNPsPC1PC2EasternRSRNPacific0.0 × 1005.0 × 1061.0 × 1071.5 × 1072.0 × 1070.0 × 100 5.0 × 106 1.0 × 107 1.5 × 107 2.0 × 107BP_ABP_B0.0 × 1005.0 × 1061.0 × 1071.5 × 1072.0 × 1070.0 × 100 5.0 × 106 1.0 × 107 1.5 × 107 2.0 × 107BP_ABP_BSNP A LocationSNP B LocationC) 61 heterozygous individuals (rows with predominately purple) do not have continuous heterozygous genotypes. In scaffold 22 (fig. 3.7d) we also see heterozygous RN individuals with mostly purple genotypes contain stretches of red homozygous reference genotypes. These genotype plots also provide insight into the cross-population patterns observed in our PCAs for scaffold 11 and 22. In the case of scaffold 11, each population of white-breasted nuthatches appears to contain both haplotypes because similar genotypes occur in the same genomic regions across populations. In scaffold 22, the distribution of haplotypes across the scaffold in the Rocky Mountain populations is not the same as those in the Pacific or the Eastern. In the Rocky Mountains, haplotypes cover over half of the scaffold. In the Pacific and East, haplotypes are found in smaller fragmented regions, mostly towards the end of the scaffold. 62 Figure 3.7. Genotype by individual plots of the four regions identified by lostruct and PCA. In each panel we show genotypes (columns) plotted across individuals (rows). Colors next to rows denote the population of the individuals and correspond to population colors used in previous figures. The number of individuals shown are reduced, and SNPs were filtered based on FST > 0.5 between haplotypes also to reduce complexity. In regions where the putative polymorphism appeared across multiple populations, FST was calculated between haplotypes in the Rocky Mountain populations. In the case of scaffold 2, Haplotype 2 only contains 1 individual so FST based SNP filtering is done between Hap1 and Hets. Blue and red represent homozygous SNPs and purple indicates heterozygous SNPs. The reference genome used in our study is a Pacific individual so in plots containing all Eastern individuals (A and C) colors of homozygous SNPs are determined by frequency within the hap groups. In panels with individuals from all populations (B and D) homozygous SNP color is determined by the reference allele. A) genotypes of 23 Eastern individuals for scaffold 2. B) Genotypes across 139 white-breasted nuthatches for scaffold 11. C) Genotypes of 23 Eastern individuals for scaffold 20. D) Scaffold 22 genotypes across 89 white-breasted nuthatches. 63 We have established that white-breasted nuthatches harbor polymorphic chromosome regions where recombination is suppressed between types. Some of these haplotype polymorphisms appear across multiple white-breasted nuthatch species. For each polymorphism, we compared an estimated phylogeny based on SNPs from outside this region with one based on SNPs from within the region (figs. 3.8-3.9). For both haplotype phylogenies in scaffolds 11 and 22, we see that the first split occurs between haplotypes and that the following branching events occur between populations within haplotypes. The oldest split occurs between the Eastern and Pacific/Rocky Mountain populations followed by the Pacific and the Rockies. The final and most recent split occurs between Rocky Mountain populations. This phylogeny topology is consistent with the whole-genome phylogeny observed in Chapter 2 as well as those in figs. 3.8a and 3.9a. These results from phylogenies support our model of haplotypes that arose in the common ancestor (fig. 3.1a). We also constructed phylogenies for the Eastern-specific haplotypes of scaffold 2 and 20 (appendix B.9-B.10). These haplotypes form monophyletic groups and diverged recently relative to the split between the Eastern population and other white-breasted nuthatches. The scaffold 20 Eastern specific haplotypes are the youngest in our comparisons which may partially explain why patterns of linkage disequilibrium are subtle in this region. 64 Figure 3.8. A) Phylogeny of 1500 SNPs randomly selected across the genome. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), Eastern (2), Pacific (4), RN (4), and RS (4). B) Phylogeny of 514 SNPs selected from within the scaffold 11 haplotype. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), East hap 1 (2), Pacific hap 1 (2), Pacific hap 2 (2), RN hap 1 2.0RS_hap1Pac_hap1RN_hap1Red_breastedEast_hap1Pac_hap2RS_hap2RN_hap20.060.450.182.190.3217.40.942.0PacificRNRed_breastedRSEastern17.41 0.220.611.43A)B) 65 (2), RN hap 2 (2), RS hap 1 (2), and RS hap 2 (2). The same samples are used in both A and B. Bars on nodes represent the 95% HPD. 66 Figure 3.9. A) Phylogeny of 1500 SNPs randomly selected from across the genome. Sample sizes are listed in parentheses next to populations: Red-breasted Nuthatch (3), Pacific (3), RS (6), RN (4), East (6)). B) 2.0Red_breastedEast_hap_1East_hap_2RS_hap_2RS_hap_1Pac_hap_1RN_hap_1RN_hap_20.681.360.370.1217.431.070.252.0EasternPacificRNRed_breastedRS0.110.781.5617.39A)B) 67 Phylogeny using 1500 SNPs randomly selected from within the scaffold 22 haplotype. Sample sizes are listed in parentheses next to populations: Euro (1), Red-breasted Nuthatch (3), Pacific hap 1 (3), RS hap 1 (3), RS hap 2 (3), RN hap 1 (3), RN hap 2 (1), East hap 1 (3), and East hap 2 (3). The same samples are used in both A and B. Bars on nodes represent the 95% HPD. 3.4 Discussion Inversions can cause suppressed recombination over large chromosomal regions, such that selection can drive adaptive divergence between inversion haplotypes (Tuttle et al., 2016; Jay et al., 2018; Huang et al., 2020; Sanchez-Donoso et al., 2022). Recent advances in long-read sequencing have opened opportunities to document the occurrence of inversions, showing they can be prolific within a single species (Harringmeyer & Hoekstra, 2022). In a time when there were few chromosome-level genomes to compare, it was inferred that inversions may be less common in birds than in other groups (Backström et al., 2008; Ellegren, 2010). In contrast to these early inferences, Hooper and Price (2017) have shown that inversions can be common in birds. Unambiguously identifying inversions requires the use of long read data; however, applying PCA methods to short read data has shown promise in identifying regions that may be inversions (Lundberg et al., 2017; Li & Ralph, 2019; Huang et al., 2020). To further our understanding of inversions in birds, we examined SNP data in white-breasted nuthatches (Chapter 2) to identify possible inversions using complementary PCA approaches. We have uncovered several haplotype polymorphisms with reduced recombination using two PCA-based approaches. This pattern consists of three clearly separated groups within a PCA that correspond to homozygotes and heterozygotes. In some cases, these regions are large. The region of reduced recombination found on scaffold 2 is ~87 Mb and the region scaffold 22 is ~12 68 Mb. What is unusual in the white-breasted nuthatch system is that in some cases, polymorphic haplotype structure appears shared across different putative species. Beyond these regions that we identified based on our PCA methods, the majority of the white-breasted nuthatch genome shows a clear pattern of differentiated and geographically restricted populations that may best be described as distinct species, which is a reflection of the whole-genome pattern we observed in Chapter 2. Patterns uncovered using SNP loadings and LD were largely consistent with inversion patterns because with inversions, recombination is suppressed in heterozygotes (Sturtevant & Beadle, 1936; Sturtevant & Mather, 1938; Rönspies et al., 2022) but can occur freely in homozygotes for an inversion type. This expected pattern of increased LD when examining all individuals was subtle in the Eastern specific region on scaffold 20, suggesting this region is not an inversion or perhaps a recently formed inversion. Based on our data, it is difficult to distinguish. Scaffold 11 showed an unusual pattern in SNP loadings and LD with two areas of low SNP loadings and low LD separated by almost 3 Mb. We suggest that the separation of these regions is due to an assembly error because these two areas have a clear signal of LD. In other words, these areas do not appear to recombine, and there is an association of genotypes between the two regions. It is not obvious what biological scenario, rather than a technical artifact, could explain the gap between these regions. Scaffold 22 had particularly complex patterns of LD, such that even within individuals of a single haplotype (hap 1), elevated LD persisted at the end of the chromosome. It was clear from this region that there is an Eastern-specific haplotype for which some RS/RN individuals 69 were heterozygous. This would explain why LD is elevated at the end of the scaffold, even within one haplotype of the Rocky Mountain population. Examining genotypes for individuals in the genomic regions detected helped confirm the patterns observed in the PCAs that genotype clusters (stretches of homozygosity/heterozygosity) occur in these areas for different PCA groups. Scaffolds 2 and 11 (assuming the pattern in 11 truly is an assembly error) seem to have the strongest support for an inversion. In these scaffolds, the regions identified through SNP loadings seem to be lacking recombination. Scaffold 20, as predicted through LD and SNP loading, has a subtle pattern of genotype clustering in these regions. Scaffold 22 genotypes show three distinct types in the Rocky Mountains and heterozygous individuals largely have heterozygous stretches of SNPs. However, for some of these heterozygous individuals in RN, we observed stretches of homozygous SNPs in an otherwise heterozygous individual. There are several possibilities that might explain this observation, the first is that a single cross-over occurred in the RN population. This would mean that at least the entire area identified is not an inversion, as crossing over should be suppressed in a true inversion. Another scenario is a double crossover may have occurred that overlapped with the boundary of the polymorphism. Genotypes by individual plots in scaffold 22 also provide insight into the apparent sharing of these haplotypes across populations based on PCA approaches. If the same polymorphism were responsible for the patterns we observed in our PCAs, then we would expect the same genotypes to occur in the same areas of the scaffold for each of the populations of white-breasted nuthatches. In scaffold 22, genotype clusters were smaller and fragmented in the Pacific and Eastern populations, demonstrating that the mirrored clustering we observed among samples in PCAs of scaffold 22 is driven by independent polymorphisms between the Rockies 70 and Pacific/Eastern populations. This pattern could be the result of recurrent selective sweeps or background selection (Charlesworth et al., 1993) located in this genomic region (Delmore et al., 2015; Nam et al., 2015; Irwin et al., 2016; Munch et al., 2016). In our final analysis we used phylogenies to examine if the cross-population occurrence of polymorphism patterns was the consequence of introgression or standing ancestral variation. We determined this by comparing phylogeny topologies based on genome-wide SNPs to those based on SNPs from the polymorphism regions. These analyses supported a hypothesis of standing ancestral variation. It appears that polymorphisms in both scaffolds 11 and 22 are old and were circulating in populations prior to many of the putative species divergence dates. As we documented in our previous analysis of this white-breasted nuthatch genomic data, we found that introgression has occurred into RS through an unsampled or extinct lineage of nuthatch (Chapter 2). This may be relevant in our present study for scaffold 22 as this large polymorphism is old, only occurs in the Rockies, and might be a genomic relic from this unsampled lineage. Given our current data, it is difficult to discern its origin. For the majority of this study, we focused on identifying putative inversions, but without long-read data we recognize that we cannot definitively confirm these regions as inversions and should therefore consider alternative explanations. One is that centromeres may be responsible, as these regions have low recombination rates and have been implicated in areas of divergence (Beadle, 1932; Mather, 1936; Ellegren et al., 2012; Vijay et al., 2017; Weissensteiner et al., 2017). However, centromeres usually have generally low recombination (Beadle, 1932; Mather, 1936; Mahtani & Willard, 1998; Choo, 1998), so we should have seen persistent LD within both haplotypes if they were centromeres. It would also not explain why there are two distinct haplotypes within populations. 71 Another possibility is that these are selected haplotypes maintained by balancing selection (if some regions are truly inversions, balancing selection may maintain them as well). Balancing selection can be an important mechanism for maintaining diversity, and recent work has shown it has been involved in maintaining venom diversity in rattlesnakes (Schield et al., 2022). In a system of balancing selection, we would expect to observe two differentiated haplotypes where realized recombination is suppressed relative to outside the region (Charlesworth, 2006) assuming recombinants have lower fitness. However, unlike an inversion where cross-over events often result in non-viable gametes (Sturtevant & Beadle, 1936), balancing selection of linked haplotypes could be more tolerant of some recombination (Ward et al., 2002; Carbone et al., 2007). In the case of the large Rocky Mountain haplotype on scaffold 22, genotype patterns showed recombination may have occurred in this region, casting some doubt on an inversion hypothesis. But if it is unlikely to be an inversion, balancing selection may maintain these haploblocks. While it may be unclear what exact mechanism might influence these polymorphic haplotypes, finding them is interesting through the lens of evolution and speciation. It has been argued that ancestral polymorphisms can be sorted unequally and appear as areas of divergent selection between species (Guerrero & Hahn, 2017). This perspective is particularly relevant when considering the broader implications of these haplotypes. Over the course of differentiation, different haplotypes could have been lost in different populations of white-breasted nuthatches. This loss of haplotypes would result in areas of differentiation between putative species because each species is fixed for alternate haplotypes. One could reasonably conclude that these areas were the result of divergent selection when in fact this genomic area of high differentiation was due to the loss of one ancestral haplotype. With ever-increasing amounts 72 of genomic data in birds, it would be interesting to see how prevalent polymorphic haplotypes are in avian populations, whether derived from inversions or not. Describing the frequency of these haplotypes would provide an empirical perspective on the rarity of haplotype polymorphisms and how frequently we might expect these polymorphisms to be unequally sorted over speciation. 73 Chapter 4: Correlated landscapes of recombination and differentiation in an avian speciation continuum 4.1 Introduction A long-standing area of interest is how recombination influences evolution and speciation (Crow & Kimura, 1965; Hill & Robertson, 1966; Eshel & Feldman, 1970; Butlin, 2005). Within-chromosome recombination occurs when homologous chromosomes cross over during meiosis, resulting in the exchange of genetic material between those chromosomes (Creighton & McClintock, 1931). Crossing over during meiosis appears to be an obligate process (Darlington & Dark, 1932; Callan & Montalenti, 1947; Owen, 1949), as the lack of it is associated with genetic disorders (Hassold & Hunt, 2001). While recombination has an important function for properly segregating chromosomes, it also has implications for evolution. Broad-scale patterns of recombination across the genome can be influenced by genome organization. Many avian genomes are comprised of a few large chromosomes and many smaller chromosomes, often referred to as macro- and micro-chromosomes (Newcomer, 1957; Takagi & Makino, 1966; Ellegren, 2010). Obligate crossing over in meiosis occurs at least once per chromosome arm or in some organisms once per chromosome (Darlington & Dark, 1932; Pardo-Manuel de Villena & Sapienza, 2001; Borodin et al., 2008; Roesti et al., 2013), resulting in per base pair recombination rates being higher in micro-chromosomes than in macro-chromosomes (Kaback et al., 1992; Kaback, 1996; Backström et al., 2010; Kawakami et al., 2014; Hagen et al., 2020). 74 Recombination can vary on both local and broad scales. On the local scale of a few kilobases, there can be areas of highly elevated recombination known as “hotspots” (Lichten & Goldman, 1995). In some organisms (e.g. mammals), the gene PRDM9 is associated with hotspots where its protein product modifies methylation patterns and binds to specific DNA regions promoting crossing-over (see Paigen & Petkov, 2018 for review), but this gene is not found in avian genomes (Oliver et al., 2009; Baudat et al., 2010; Myers et al., 2010; Parvanov et al., 2010; Singhal et al., 2015). Recombination hotspots appear highly conserved within birds (Singhal et al., 2015). Broad-scale recombination on the megabase scale (which would include recombination hotspots and non-hotspots) also appears to have some degree of conservation, but recombination on this broad scale may change more over evolutionary time than hotspots (Backström et al., 2010; Singhal et al., 2015; Kawakami et al., 2017; Turbek et al., 2021). Broad-scale patterns can be shaped by proximity to telomeres/centromeres (Brazier & Glémin, 2022) and levels of methylation (Marand et al., 2017). It has been suggested that the relationship of broad-scale recombination rates between species depends on the conservation of karyotype structure (Backström et al., 2010; Auton et al., 2012; Singhal et al., 2015). Genomes often show highly heterogenous recombination landscapes, and areas of high or low recombination might influence differentiation patterns but in distinct ways. Empirically, regions of low recombination have sometimes been associated with areas of increased differentiation (e.g. Keinan & Reich, 2010; Geraldes et al., 2011; Nachman & Payseur, 2012; Burri et al., 2015; Samuk et al., 2017; Manthey et al., 2021). This can be a result of linked selection or reduced gene flow in low recombination areas (Nachman & Payseur, 2012). Under a model with selection, regions of low recombination will experience more genetic hitch-hiking (Maynard Smith & Haigh, 1974; Slatkin & Wiehe, 1998). 75 While the above considerations lead to the prediction that regions of reduced recombination are likely to have higher differentiation than high recombination regions, the opposite prediction might also be made in some situations. If recombination rate is sufficiently high, beneficial mutations at different genes in different but homologous chromosomes can recombine onto the same linked version of that chromosome, facilitating selection (Fisher, 1930; Muller, 1932; Crow & Kimura, 1965; Hill & Robertson, 1966; Felsenstein, 1974; Roze & Barton, 2006). Higher recombination regions can also harbor a larger fraction of introgressed loci from other species because recombination can more efficiently separate neutral or beneficial variants from deleterious ones in the background of the recipient species (Janoušek et al., 2015; Juric et al., 2016; Schumer et al., 2018; Martin et al., 2019). If genomic regions of higher recombination are more efficient at bringing loci into positive epistasis or harbor a larger percentage of loci introgressed from a third population, they may appear more differentiated in comparisons of two populations. White-breasted nuthatches (Sitta carolinensis) are a powerful system to investigate questions regarding recombination and differentiation because they consist of three putative species representing the range of the speciation continuum (Spellman & Klicka, 2007; Walstrom et al., 2012; Chapter 2). Thus, we can make increasingly distant comparisons of recombination and differentiation within the same related group of species. These comparisons allow us to delineate when shifts in recombination have taken place during speciation and determine the strength of associations between recombination and differentiation across varying evolutionary distances. In a previous analysis, we used GBS (genotyping-by-sequencing; Elshire et al., 2011) to assess genome-wide patterns of differentiation and introgression between putative species (Chapter 2). Here we use whole-genome sequencing from a subset of samples from each putative 76 species to estimate rates of recombination. We then compare these rates of recombination to estimates of differentiation produced from the larger GBS dataset. By using FST from the GBS dataset our estimates of recombination and FST are semi-independent in that they are not derived from identical sequencing input. In our study, we ask two key questions: 1) How similar are broad-scale patterns of recombination between putative species of white-breasted nuthatch? 2) What is the relationship between recombination rates and differentiation across the genome? For question one, we expect that recombination landscapes will be most similar in closely related species and that this similarity will decrease with more distantly related species. Because white-breasted nuthatches are comprised of four populations with increasing levels of differentiation and divergence (Chapter 2) we can compare these populations recombination landscapes to determine if they match this expectation. For our second question, we hypothesized that differentiation among populations will largely be concentrated in areas of low recombination. This would occur because these areas are disproportionally affected by linked selection fixing large regions of the genome (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993). An alternative hypothesis is that areas of high recombination may have elevated differentiation. This could occur because, in regions where recombination occurs more frequently, selected loci may be brought into linkage more quickly and spread to fixation (Fisher, 1930; Muller, 1932; Hill & Robertson, 1966; Felsenstein, 1974). Until recently, the generation and comparison of recombination landscapes has been a difficult undertaking. Cryptic speciation in white-breasted nuthatches provides an excellent opportunity to observe the degree to which recombination landscapes change between recently diverged but clearly differentiated species. This system also allows us to explore the association 77 between recombination and differentiation. A finding of a strong association between recombination and differentiation would provide clues as to what areas of the genome may be important to the speciation process. 4.2 Methods For the whole genome sequencing (WGS), we sequenced 16 samples as preliminary data; 4 from each genomic population of white-breasted nuthatch using Nextera DNA Flex kits on one lane of Illumina HiSeq4000 resulting in 422,671,247 paired-reads. An additional 40 samples, 10 from each genomic population of white-breasted nuthatch, were sequenced using Illumina DNA Prep (formerly Nextera DNA Flex) on one lane of the NovaSeq S4 flowcell resulting in 2,919,372,139 paired-reads at the Centre for Applied Genomics in Toronto, Canada. The target insert size was 350 bp for both data sets, and the read length was 150 bp. Samples were chosen arbitrarily but selected in a way that they reflected the geographic range of the four white-breasted nuthatch genomic groups. The additional 40 WGS samples were sequenced previously using a reduced-representation GBS approach in our previous study (Chapter 2), and entire genomes were sequenced in the present study for the first time. WGS reads were trimmed and adapters were removed with Trimmomatic. We used TRAILING:3 SLIDINGWINDOW:4:10 MINLEN:30 to trim reads based on quality and ILLUMINACLIP:$EBROOTTRIMMOMATIC/adapters/NexteraPE-PE.fa:2:30:10 to remove adapters that may occur on shorter reads. We mapped trimmed reads to our white-breasted nuthatch reference genome (Chapter 2) using BWA mem 0.7.17 (Li & Durbin, 2009), marked duplicates using Picard 2.26.3 (https://broadinstitute.github.io/picard/), and then used GATK 3.8 (McKenna et al., 2010) to identify variant sites. Initial filtering of all called variants removed loci 78 with mapping quality (MQ) less than 20 and heterozygosity greater than 0.6 using scripts from Owens et al. (2016). Following this we used VCFtools (Danecek et al., 2011) to eliminate individuals and variants with too many missing genotypes. Eight individuals were removed for missing genotypes at more than 10% of total SNPs. Variants were removed if they were insertion-deletions, sites with more than 2 alleles, genotyping quality less than 10, minor allele count less than 2, and sites with any amount of missing genotypes across all individuals which resulted in 1,343,993 SNPs being retained across the full population dataset. Using this SNP dataset we confirmed expected population assignment of individuals, based on geographic location of sample or previous population assignment using GBS data (Chapter 2), using principal component analysis (PCA) scripts developed by Irwin et al. (2016) and Irwin et al. (2018). As input for recombination maps, we filtered SNPs on a per-population basis (i.e., filtered SNPs from the post-MQ and heterozygosity filtered data using the same vcftools parameters described above but with only Eastern, RN, RS, or Pacific individuals). In this step, we allowed up to 10% missing genotypes per SNP and after filtering this left 3,954,110 SNPs in the Eastern population, 2,000,835 in the Pacific population, 3,645,276 SNPs in Rocky Mountain North (RN), and 4,824,767 SNPs in Rocky Mountain South (RS). Mean depth of individuals at these filtered loci ranged from 4.5 – 18.9, with a median mean depth of individuals of 14.6. To estimate recombination rates in putative species of white-breasted nuthatch, we used ReLERNN (Adrion et al., 2020; https://github.com/kr-colab/ReLERNN), which is a machine learning-based approach to estimating recombination. Using variant information and coalescence simulations from msprime (Kelleher et al., 2016; Baumdicker et al., 2022), ReLERNN predicts per base pair recombination rates in genomic windows using provided SNP data and a trained 79 recurrent neural network. Simulation steps take various population genetic parameters such as generation time and mutation rate. For each population, we ran ReLERNN twice to ensure that runs produced similar results (see R2 in “results”). We randomly selected one of these runs for further analyses and excluded the other. For ReLERNN runs, we used a “upperRhoThetaRatio” of 30 and an assumed generation time of 2.36. Generation time (in years) was calculated as (1/mortality)+age of first reproduction (IUCN Standards and Petitions Committee, 2022). For average mortality (1-survival rate), we used a survival rate of 0.265 per year. This value comes from survival estimates of white-breasted nuthatches in Ohio and Wisconsin (Doherty Jr. & Grubb Jr., 2002; Cava et al., 2014). Cava et al. (2014) survival is reported as “apparent annual survival” for males and female, the mean survival from these two values is 0.265. Doherty Jr. & Grubb Jr. (2002) survival is “apparent survival” with and without supplemented food, the average of these values is 0.265. Other studies (Karr et al., 1990; DeSante & Kaschube, 2006) have reported higher survival rates, but because these survival estimates are highly similar and from independent observations, we choose these estimates. For the age of first reproduction, we used one year (Grubb Jr. & Pravosudov, 2020). The generation time used in our study closely corresponds to the generation length reported by Bird et al. (2020) for white-breasted nuthatches of 2.2556. We did not provide a demographic history for ReLERNN during the simulation step and instead used the default approach where ReLERNN assumes a constant population size derived from the provided variant information and a provided mutation rate (Adrion et al., 2020). Adrion et al. (2020) state that ReLERNN is robust to some misspecification in parameters like mutation and effective population size. Generally, if these parameters are misspecified in our analyses, this is not a concern for our results, as any misspecification would impact genome-wide estimates for 80 the entire population equally. Our questions do not involve comparisons of average genome-wide recombination rates between populations but rather comparisons of the shape of the genomic landscape of recombination (i.e., are areas of high recombination in one population also high in another population?). The rest of the run parameters we used matched the recommended default settings. We also performed runs of autosomes and the Z chromosome (Scaffold 3) separately using the same parameter settings. For the Z chromosome, we anticipated recombination rates to be higher in the pseudoautosomal region (PAR). To identify the likely location of the PAR we used sequenced alignment and read depth. We aligned the white-breasted nuthatch (Chapter 2) to the hooded crow genome (Corvus cornix cornix; BioProject: PRJNA192205; Genome assembly: ASM73873v4; Poelstra et al., 2014; Vijay et al., 2016; Weissensteiner et al., 2017) because the PAR location has recently been identified in the hooded crow (Catalán et al., 2021). To align sequences we used D-Genies (Cabanettes & Klopp, 2018). We confirmed that the area of increased estimated recombination on the Z of the white-breasted nuthatch overlaps with the identified PAR in the hooded crow (Catalán et al., 2021). The PAR of the hooded crow is also orthologous to the PAR in the collared flycatcher (Smeds et al., 2014; Catalán et al., 2021). Following this, we used read depth to identify the location of the PAR. First, we identified the sex of individuals by examining the mean read depth ratio between Z reads and an arbitrary autosome (scaffold 7). For calculating mean read depth, we only included sites with a max mean depth of 15 to minimize biases of very high-depth sites. For Z SNPs, we estimated mean depth per individual using sites located between 20 Mb – 60 Mb to avoid bias from the PAR that we suspected occurred at or near the beginning of the chromosome. After identifying 81 the sex of individuals, we examined the read depth ratio between males and females across sites with no missing data and a max mean read depth of 15 on the Z. After estimating recombination within individual populations, we tested the associations of recombination rate variation between populations using 1 Mb windows and linear regressions (R2). Regions at the ends of scaffolds that were smaller than 1 Mb or were not present in all populations were excluded from further analysis. This left 999 windows to analyze across the genome. Measurements of genomic differentiation (FST) were generated using VCFtools from previously reported GBS (genotyping-by-sequencing) data of white-breasted nuthatches (Chapter 2). The library preparation followed the protocol described by Alcaide et al., (2014). The bioinformatics pipeline used for processing the GBS data followed a similar process in that reads were mapped using BWA mem and SNP calling was done using GATK 3.8. SNPs were then filtered using the scripts/steps described above. More information can be found in Chapter 2. FST is based on SNPs post-filtering. We chose to use FST measured from our GBS data because it included more samples. Recombination rates and FST were compared in 1 Mb windows using Spearman’s rank correlation (Spearman, 1904). We randomly chose one population’s recombination landscape to plot against FST in the main text and provided the plot for the other population’s recombination landscape in the supplement. One complication surrounding our ability to accurately estimate recombination rates is that they are estimates based on population genomic data, which can be impacted by other factors like selection (Adrion et al. 2020), effective population size (Ne; Slatkin, 1994), and recent gene flow (Samuk & Noor, 2022). Without pedigree information or gametic sequencing 82 (Dréau et al., 2019), our estimates of recombination will likely have some biases due to these other factors. To address this, we examined the relationship of pairwise FST to recombination landscapes of both populations in the FST comparison and a third “unrelated population” (i.e., FST of RS/RN plotted against Eastern recombination rates). Depending on the FST comparison we chose to use either the Eastern, RS, or Pacific recombination landscape. The Eastern population was used because its divergence date appears to be the oldest of all white-breasted nuthatches (Chapter 2) and would have had the most time to recover from biases due to gene flow (Samuk & Noor, 2022). RS and Pacific were used to address any potential confounding effects that may occur by having a population in the FST comparison also used as the recombination landscape (i.e., selection in one population creating the appearance of a low recombination region in an area of high FST). Our reasoning for this approach is that if we see consistent relationships between FST and recombination despite the population’s recombination landscape used this suggests that there is likely a real relationship between the two not solely driven by another confounding variable (i.e., higher FST in truly lower areas of recombination). Stated differently, if a genomic region is indicated as having low recombination in multiple populations, this suggests this region is more likely to be an area of lower recombination and not the result of bias from some other factor like selection. In each comparison of FST and recombination, we fit a cubic spline to the full data along with subsets according to chromosome/scaffold size: macro, intermediate, and micro. These were defined as > 40 Mb, 40-20 Mb, and < 20 Mb (Axelsson et al., 2005). Cubic splines of partitioned data allow us to determine if patterns of FST and recombination are independent of chromosome size type. All analyses were conducted in R (R Core Team, 2021). 83 4.3 Results Genomic variation according to our whole genome sequencing clearly separated our samples into the four clusters (appendix C.1) that had been previously observed with more limited sequencing (Spellman & Klicka, 2007; Walstrom et al., 2012; Chapter 2). White-breasted nuthatches are made up of four highly differentiated populations and based on our GBS analyses (Chapter 2), RS and RN are the most closely related, followed by the Pacific, then the Eastern population. As an initial assessment of recombination rate outputs from ReLERNN, we examined recombination in relation to the size of scaffolds (fig. 4.1), which largely correspond to whole chromosomes (Chapter 2). We observe across all populations that with increasingly smaller chromosomes, median recombination rates increase. This corresponds to other recombination patterns seen in birds (Stapley et al., 2008; Backström et al., 2010; Kawakami et al., 2014; Hagen et al., 2020). Across the genome, recombination rates were highly variable (appendix C.2-5); for larger scaffolds, recombination appeared low in the center and higher at the ends. In scaffold 3 (Z chromosome), we see recombination is much higher at the start of the scaffold (appendix C.6). Through our approach of sequence alignment to the hooded crow genome and measuring read depth between male and female nuthatches, we identified the putative PAR region of the white-breasted nuthatch genome located approximately 2,533,000 bp – 3,150,000 bp. This higher rate of recombination is due, at least in part, to the PAR (appendix C.7). 84 Figure 4.1. Median recombination rate (cross over/base pair) for each scaffold plotted against the length of the scaffold. Scaffolds largely correspond to whole chromosomes, so as expected, the per-base recombination rate increases in smaller chromosomes. In our next step we examined the relationship of recombination landscapes among different populations of white-breasted nuthatch. We observed significant positive correlations (fig. 4.2) in all comparisons, with all p-values < 2 ´ 10-16. Coefficients of determination (R2) values ranged from 0.61 to 0.84, depending on the comparison (table 4.1). R2 was highest when comparing RS and RN, which have the lowest genome-wide weighted FST (Chapter 2). Replicate runs were usually most correlated (except for RN, where R2 was slightly lower than with RS/RN comparison), confirming that repeated runs of the same population data using this machine 0.0e+00 5.0e+07 1.0e+08 1.5e+082.0e-086.0e-081.0e-071.4e-07Chr bpEast Median co/bp0.0e+00 5.0e+07 1.0e+08 1.5e+082.0e-086.0e-081.0e-071.4e-07Chr bpPacific Median co/bp0.0e+00 5.0e+07 1.0e+08 1.5e+082e-086e-081e-07Chr bpRN Median co/bp0.0e+00 5.0e+07 1.0e+08 1.5e+082.0e-086.0e-081.0e-07Chr bpRS Median co/bp 85 learning approach produce very similar recombination landscapes. This lower relationship between replicate runs in RN is in part due to variation between runs on the Z chromosome (scaffold 3), excluding that chromosome R2 is 0.862. A part of these associations of recombination landscapes between populations is driven by the relationship between recombination rate and chromosome size. Given the overall similarity between recombination landscapes from different populations, it is likely that within chromosome recombination rate variation is similar among populations as well. Table 4.1. Strengths of association (measured via R2 values) of recombination rate in 1 Mb windowed averages across the genome. R2 values with the same two populations (e.g., Pacific vs Pacific) are between replicate runs of ReLERNN for the same population data whereas all others are comparisons are between population recombination landscapes retained for further analysis. Pacific RN RS EasternPacific 0.9135RN 0.6316 0.8177RS 0.6347 0.8448 0.8884Eastern 0.6092 0.6908 0.7193 0.88571 86 Figure 4.2. 1 Mb windowed averages of recombination rates (cross-over per bp per generation) are plotted between populations. A linear regression is fitted to the comparison (red). Columns indicate which population is on the x-axis and rows indicate which population is on the y-axis. Figure was made using code derived from Chang (2018). Our second test involved comparing the relationship between FST and recombination rates. To calculate FST we used 1 Mb windows. In each window, median SNP counts within 1 Mb windows ranged from 128 to 240. The distribution of SNP counts per FST window are shown in appendix C.8. To examine genome-wide variation in FST and recombination, we plotted FST across the genome with corresponding population recombination rates below (fig. 4.3). Qualitatively, regions of low recombination tended to correspond to areas of elevated FST. 0.0e+00 5.0e-08 1.0e-07 1.5e-070.0e+001.0e-07one_mb_recom_combined$recomRate_East0.0e+001.0e-070.0e+001.0e-070.0e+00 5.0e-08 1.0e-07 1.5e-070.0e+001.0e-07one_mb_recom_combined$recomRate_Pac0.0e+00 5.0e-08 1.0e-07 1.5e-07one_mb_recom_combined$recomRate_RS0.0e+00 5.0e-08 1.0e-07 1.5e-07 0.0e+00 5.0e-08 1.0e-07 1.5e-070.0e+001.0e-07one_mb_recom_combined$recomRate_RNEASTPa ificRSRNco/bp 1 Mb avg.co/bp 1 Mb avg. 87 Figure 4.3. For 999 1Mb genomic widows, we show FST between putative species pairs across the genome from largest to smallest scaffolds. Continuous genome position is for plotting purposes only. Beneath each FST comparison, we plot the average recombination rate (cross over/base pair) for that window. Line colors correspond to population recombination rates; RS (teal), RN (dark blue), Pacific (dark green), Eastern (light blue). This plot was generated from code adapted from Roelfs (2019). In our direct assessment of this relationship, we compared both populations' recombination rates to FST in 1 Mb windows (fig. 4.4, and appendix C.9). In all tests, we found significantly negative values of Spearman’s rank correlation (table 4.2; p-value < 2.2 ´ 10-16). Across comparisons, regions of the highest FST were found in areas of low recombination (fig. 4.4). We also compared pairwise FST to recombination rates in a population not included in the FST calculation. In these comparisons with another population's recombination landscape, we found the relationship between low recombination and high FST was preserved (fig. 4.5). Across all associations tested, we found negative correlation values that varied between -0.29 and -0.85 0e+00 4e+08 8e+080.00.40.8FST RS/RN0e+00 4e+08 8e+080.00.40.8FST RN/Pac0e+00 4e+08 8e+080.00.40.8FST RS/Pac0e+00 4e+08 8e+080.0e+001.5e-07co/bp0e+00 4e+08 8e+080.0e+001.5e-07co/bp0e+00 4e+08 8e+080.0e+001.5e-07co/bp0e+00 4e+08 8e+080.00.40.8FST RS/East0e+00 4e+08 8e+080.00.40.8FST RN/East0e+00 4e+08 8e+080.00.40.8FST East/Pac0e+00 4e+08 8e+080.0e+001.5e-07Genome Positionco/bp0e+00 4e+08 8e+080.0e+001.5e-07Genome Positionco/bp0e+00 4e+08 8e+080.0e+001.5e-07Genome Positionco/bp 88 (table 4.2). This relationship was maintained even when genome-wide FST was high (comparisons with the eastern population). Across all chromosome/scaffold categories, we see this relationship of high FST regions in areas of low recombination, showing the genome-wide pattern is not driven by a certain chromosome type (see cubic spline colors in fig 4.4. and 4.5). Table 4.2. Strength of association between FST landscapes and recombination landscapes. In each row, windowed FST between a pair of populations is tested against a population recombination landscape using Spearman rank correlation (Cor column). Each “Cor” column refers to the Spearman rank correlation between FST of the population pair and the preceding “Recom. Group”. For example, the top row of the last “Cor” column is the Spearman rank correlation value between windowed FST of RS/RN and the Eastern population's recombination landscape. FST pair Recom. Group 1 Cor Recom. Group 2 Cor Recom. Group 3 CorRS/RN RS -0.76 RN -0.86 East -0.67RS/Pac RS -0.58 Pac -0.54 East -0.51RN/Pac RN -0.56 Pac -0.45 East -0.39RS/East RS -0.33 East -0.41 Pac -0.29RN/East East -0.57 RN -0.60 Pac -0.47Pac/East East -0.60 Pac -0.55 RS -0.531 89 Figure 4.4. Pairwise windowed FST between populations is plotted against the windowed recombination rate in one of the FST populations. The other population’s recombination rate is shown in appendix C.9. Orange is a cubic spline fit to all windows. Green, purple, and yellow are cubit splines fit to windows from chromosomes > 40 Mb, 40-20 Mb, and < 20 Mb, respectively. Each dot represents a 1 Mb window. 0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RSFST RS RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RSFST RS Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RNFST RN Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RSFST RS East0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST East RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST Pac East 90 Figure 4.5. Pairwise weighted average FST in 1 Mb windows plotted against the recombination rate of a population not occurring in the FST comparison. For further information regarding dots and cubic splines see figure 4.4. 4.4 Discussion Crossing-over of homologous chromosomes during meiosis results in within-chromosome recombination, a fundamental process in gamete formation. The frequency at which recombination occurs shapes the inheritance of loci, patterns of differentiation, and may influence the speciation process (Haldane et al., 1915; Haldane, 1919; Butlin, 2005; Noor & Bennett, 2009; Keinan & Reich, 2010; Nachman & Payseur, 2012; Samuk et al., 2017). Here we examined how recombination rates vary among differentiated groups of white-breasted nuthatch and how differentiation between populations is associated with recombination rates. Generally, patterns of recombination in white-breasted nuthatches are similar to other birds (Stapley et al., 2008; Groenen et al., 2009; Kawakami et al., 2014; Hagen et al., 2020) in that average 0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST RS RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST RS Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST RN Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate PacFST RS East0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate PacFST East RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RSFST Pac East 91 chromosome-wide recombination rate decreases as chromosome size increases, and in some larger chromosomes, recombination rates appear to be higher at the ends. In our study, we sought to answer two key questions: 1) How similar are within-population recombination landscapes among putative species of white-breasted nuthatch, and 2) What is the relationship between genomic differentiation and recombination? We hypothesized that broad-scale recombination would be conserved based on previous studies of recombination hotspots (Singhal et al., 2015). We also hypothesized that because regions of low recombination have more potential to be influenced by linked selection (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993; review by Nachman, 2002), areas of high differentiation would tend to be in low recombination areas. Others who have examined broad-scale recombination between populations in birds have found that recombination landscapes are conserved between closely related species or populations (Singhal et al., 2015; Kawakami et al., 2017; Turbek et al., 2021). In the case of the Collared and Pied Flycatcher (Ficedula albicollis and Ficedula hypoleuca), there was an appreciable decline in the association of recombination landscapes between Collared and Pied Flycatchers (Kawakami et al., 2017). The power of the white-breasted nuthatch system is that we can compare recombination landscapes between increasingly more differentiated populations. Across all populations, broad-scale patterns of recombination have a high degree of conservation. Unsurprisingly, the populations with the lowest levels of differentiation, RS and RN (Chapter 2), have the most associated recombination landscapes. Similarity in recombination landscapes declines when compared to the Pacific or Eastern population. It has been suggested that broad-scale changes in recombination can be partially attributed to karyotypic changes (Dumas & Britton-Davidian, 2002; Auton et al., 2012; Singhal 92 et al., 2015). The reason for this is that fission or fusion of chromosomes would either increase or reduce recombination between loci. It is unknown if putative, white-breasted nuthatch species harbor such interchromosomal differences. However, we know there may be some inversions within chromosomes based on Chapter 3. Constructing representative karyotypes for each of the four populations would be an interesting line of investigation in this regard. Generally, these comparisons of recombination landscapes provides useful insight into how much change in recombination should be expected across the speciation continuum. Our data support the hypothesis that regions of elevated differentiation are associated with areas of low recombination. It has been noted that when genetic drift has a large influence (e.g. in small populations) and selection is weak, chance effects can result in FST outliers tending to occur in regions of low recombination; however, in this scenario the average FST does not vary with recombination rate (Booker et al., 2020). Linked selection (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993) is likely responsible for the patterns in FST and recombination that we have observed. It has been argued that background selection can elevate FST, especially within regions of low recombination (Charlesworth et al., 1993, 1997; Burri, 2017). Recent work has shown that when genome-wide average FST is low, FST is robust to the effects of background selection (Matthey‐Doret & Whitlock, 2019). Since white-breasted nuthatches span a range of genome-wide average differentiation (Chapter 2) it is likely some combination of background and positive linked selection are driving our observed patterns. In the Z chromosome (scaffold 3), recombination is noticeably higher at the beginning of the scaffold, which is likely to be the pseudoautosomal region (PAR). We used sequence alignment and read-depth comparisons between males and females to confirm that the PAR is likely located in this region and at least partially responsible for elevated recombination rates in 93 this region. The estimated length of the white-breasted nuthatch PAR is 617 kb which is similar to the hooded crow (Catalán et al., 2021), collared flycatcher (Smeds et al., 2014), and zebra finch (Singhal et al., 2015), but PAR length is variable across birds (Zhou et al., 2014). In many cases the PAR includes the end of the chromosome (Smeds et al., 2014; Zhou et al., 2014; Catalán et al., 2021), but in the white-breasted nuthatch we found ~ 2.5 Mb of sequence that occurred before the apparent PAR. In principle, the PAR could have expanded through translocation events (Otto et al., 2011; Bergero et al., 2013). However, if this had occurred, we would expect both males and females to contain this newly translocated sequence and thus have a 1:1 ratio in read depth, which is not what we observed. We suspect this region may be an assembly error, because the PAR should not be surrounded by non-PAR sequence as crossing over would transfer this to the W chromosome. Therefore, we believe this assembly sequence preceding the putative PAR region should be located somewhere else on the Z chromosome. It is important to note that indirect methods of mapping recombination, as we have used here, come with crucial caveats. Linkage disequilibrium is not generated simply by variation in recombination but is also influenced by demographic effects (Slatkin, 1994; Rogers, 2014), selection (Maynard Smith & Haigh, 1974), and gene flow (Samuk & Noor, 2022). ReLERNN simulates a constant population size and is argued to be reliable even when demographic histories are modeled inaccurately (Adrion et al., 2020). To address both selection and gene flow, we compared FST to several recombination landscapes. In a region where a selective sweep has occurred, the rate of crossing-over may be the same, but linkage disequilibrium of sites will have increased due to reductions in variation from selection. By comparing an FST landscape to several population recombination maps, we at least partially control for within-population selective sweeps. Gene-flow can also significantly bias recombination estimates (Samuk & Noor, 2022). 94 In white-breasted nuthatches there are clear signals of introgression (Chapter 2), which is a concern when estimating recombination. Again, by comparing an FST landscape to multiple recombination landscapes this helps address this issue. We find that there is a significant negative relationship between FST and recombination rates, supporting our overall conclusions that high FST areas are associated with regions of low recombination. Our results show recombination rates in white-breasted nuthatches are characterized by increasing recombination rates in smaller chromosomes and elevated recombination near the ends of some chromosomes. We show that between populations, the recombination landscape is conserved at some level but can change in short evolutionary periods of time. Our results support regions of elevated differentiation being generally found in regions of low recombination. We extend this observation to show that this relationship between recombination and differentiation is maintained despite increasingly differentiated comparisons. We find this conclusion to be particularly important when considering heterogeneous differentiation across the genome: even as genome-wide differentiation increases, the underlying recombination landscape is still strongly associated with the distribution of differentiation. This points towards regions of low recombination playing an important role in shaping landscapes of differentiation and may be important for maintaining species boundaries. If areas of low recombination are more influenced by linked selection and resistant to gene flow (Nachman & Payseur, 2012), they can build up incompatible loci (Dobzhansky, 1936; Muller, 1942). These loci may then act as barriers to gene-flow (Dobzhansky, 1936; Muller, 1942). 95 Chapter 5: Relationships between genomic differentiation, chromosome size, and divergence date in birds 5.1 Introduction Microchromosomes are interesting genomic elements found in some clades of vertebrates and are characterized as being extremely small relative to other chromosomes (Waters et al., 2021). Microchromosomes are frequently found in avian taxa (e.g., Hillier et al., 2004; Warren et al., 2010; Ellegren et al., 2012; Peñalba et al., 2020; Zhu et al., 2021) and possess unique characteristics. The first is that microchromosomes are packed together in the nucleus of cells and have frequent interactions with other microchromosomes (Habermann et al., 2001; Perry et al., 2021; Waters et al., 2021). Another interesting aspect of microchromosomes is that they often have higher per base-pair rates of recombination (Groenen et al., 2009; Backström et al., 2010; Kawakami et al., 2014; Hagen et al., 2020; Peñalba et al., 2020; Manthey et al., 2021). This is primarily due to obligate cross-over events (Jones, 1984) at least once per chromosome (Roesti et al., 2013) or chromosome arm (Pardo-Manuel de Villena & Sapienza, 2001). Variation in recombination rates is an important factor in shaping overall genome-wide patterns of differentiation (FST; Wright, 1951). In regions with lower frequencies of recombination, elevated differentiation is often observed between species. This pattern has been observed in birds (Turbek et al., 2021), humans (Keinan & Reich, 2010), sunflowers (Renaut et al., 2013), and sticklebacks (Samuk et al., 2017). Differing mechanisms have been suggested to explain these observations. In scenarios of only genetic drift, loci within low recombination regions can have increased variation in FST, and this greater variation in outcomes might create 96 the appearance of outlier loci (Booker et al., 2020). Linked selection (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993; Kim & Stephan, 2000), which can include both positive selection or background selection, has been invoked to explain elevated areas of differentiation: sites under selection can cause reduced variation in the surrounding area of low recombination (reviewed by Cutter & Payseur, 2013; Burri, 2017). When considering differentiation and chromosome size in light of how it influences recombination, one expectation would be to assume that there is a positive association between differentiation and chromosome length. This expectation is derived from the assumption that linked selection would have a large impact in areas of low recombination (as explained above), and if low areas of recombination occur more frequently in large chromosomes, we should expect larger chromosomes to have higher levels of differentiation. An alternative expectation is that differentiation may be higher in smaller chromosomes. More recombination events will have more opportunities to bring together useful combinations of alleles by reducing Hill-Robertson interference (i.e., beneficial alleles competing for fixation on different chromosomes; Hill & Robertson, [1966]). If this outweighs the effects of linked selection in low-recombining regions of large chromosomes, negative associations between differentiation and chromosome size may be observed. An additional consideration with avian genomes is that smaller chromosomes tend to be more gene-dense (Smith et al., 2000; Federico et al., 2005; Waters et al., 2021). Higher rates of recombination, combined with higher gene density, may allow smaller chromosomes to differentiate more quickly because with more genes in a given area, we might expect microchromosomes to be disproportionally subject to selection. Some evidence for this may be seen in some systems where gene density is associated with repeated differentiation (Delmore et al., 2018). 97 The interaction between differentiation, recombination, and chromosome size is potentially influenced by the divergence time between a given pair of populations. Because relative differentiation (FST) is expressed as a ratio between zero and one, in the extreme case, populations may differentiate so much that there are largely fixed differences genome-wide. In such a case, there would be no apparent relationship between differentiation and chromosome size. This would not because a relationship does not exist, but only because divergence age has obscured the pattern. Another possible scenario is this relationship declines because between a given pair, gene flow ceases and large lower-recombining chromosomes no longer play a major role in maintaining species boundaries. This allows differentiation to accumulate faster on smaller higher recombining chromosomes resulting in a negative relationship between FST and chromosome size. Considering how chromosome size shapes rates of recombination and how recombination may be associated with patterns of differentiation across genomes, we sought to determine if there are predictable relationships between differentiation and chromosome size in avian genomes. To answer this question, we developed two alternative predictions: 1) All else being equal, macrochromosomes, due to their lower rates of recombination, will have higher average measurements of FST than smaller chromosomes. This would result in a positive relationship between chromosome size and FST. 2) Alternatively, microchromosomes may have similar or higher levels of FST relative to macrochromosomes through the combined interactions of elevated recombination and gene density. This would result in an equivalent mean level of differentiation across chromosomes or a negative relationship between chromosome size and FST. To test our hypotheses, we aggregated genomic data from 32 species comparisons of birds (see table 1 for 98 citations) and examined the relationships between chromosome size and FST. Most of these comparisons are from passerine species; however, three are hummingbird species pairs. Our predictions are not mutually exclusive and it is possible that genomes of different species pairs could be more influenced by different underlying evolutionary processes. As we previously described, the age of the species pairs may influence our inferences between chromosome size and differentiation. Hence, we also examined how the direction and strength of association between FST and chromosome size varied with the age of species divergence. We suspect that younger species pairs that have not accumulated much genome-wide divergence may show clearer relationships between FST and chromosome size, whereas in older species, genome-wide differentiation may be so high that any relationship between differentiation and chromosome size has become obscured over evolutionary time. Combining data from a variety of population genomic studies, we gain an opportunity to try to unravel how genome organization itself may relate to the patterns of differentiation we observe between species. If certain chromosomes are repeatedly implicated in the differentiation process, these areas may play vital roles in establishing reproductive isolation across taxa. Improving our understanding of where hotspots of differentiation are likely to occur in the genome opens up the possibility to predict genome evolution and to identify speciation-promoting regions, especially in incipient species. 5.2 Methods To examine the relationship between differentiation and chromosome size across passerine birds, we gathered genomic datasets from 32 species comparisons (table 5.1). These were based on already processed sequencing reads that had been mapped to reference genomes, which was 99 followed by genotyping of single nucleotide polymorphisms and calculation of estimates of FST in small genomic regions (i.e., “windows”). The methodologies (reference genome used, SNP calling algorithm, and FST window size) varied across datasets and are summarized in table 5.1, along with the core citation for which each dataset was generated. In table 5.1, “window size” that says “sequenced bp” indicates that a study used an approach that determined the window size of FST based on a number of sequenced sites rather than physical distance measured in base pairs across the genome. This is done using custom R scripts ( Irwin et al., 2016, 2018) and the “allSite” command in GATK’s genotypeGVCF (McKenna et al., 2010). For each study, FST calculation varied. In studies that used the previously mentioned custom R scripts, FST was calculated according to Weir & Cockerham (1984). This FST calculation was also used in the hummingbird comparisons (Henderson & Brelsford, 2020). Manthey et al. (2021) used the method of Reich et al. (2009) to calculate FST, and studies that used ANGSD to call SNPs also produced FST using ANGSD (Korneliussen et al., 2014). In each case, weighted estimators of FST were used (i.e., sums of numerators divided by sums of denominators) in genomic windows. For each chromosome, we took the mean FST across windows per chromosome and examined the relationship between mean chromosome FST and log-transformed chromosome size using correlation tests (i.e., cor.test() function in R [R Core Team, 2021]). Table 5.1. Summary of species comparisons used in study. When a single species is listed in “species names” column this indicates the comparison is between subspecies or putative species. An integer next to a single species name means multiple comparisons with different subspecies/putative species. S. townsendi / S. virens, Geothlypis tolmiei / G. philadelphia, and V. cyanoptera / V. chrysoptera original data citations are listed but FST per chromosome is derived from Irwin et al. (2018) and Wood et al. (2023) respectively. Sitta carolinensis comparisons 1-6 are with the following populations described in Chapter 2: 1) Rocky Mountain North and 100 South; 2) Rocky South and Pacific; 3) Rocky North and Pacific; 4) Rocky South and East; 5) Rocky North and East; 6) East and Pacific. S. coronata is a comparison between S. coronata coronata and S. coronata auduboni. Z. leucophrys is a comparison between Z. leucophrys gambelii and Z. leucophrys pugetensis. Phylloscopus trochiloides comparisons 1-3 are as follows: 1) Phylloscopus trochiloides trochiloides and Phylloscopus trochiloides plumbeitarsus; 2) Phylloscopus trochiloides trochiloides and Phylloscopus trochiloides viridanus; 3) Phylloscopus trochiloides plumbeitarsus and Phylloscopus trochiloides viridanus. Certhia americana is a comparison between Northern and Southern lineages. “Seq.” column is the sequencing strategy used, whether genotyping-by-sequencing (GBS; Elshire et al., 2011) or whole genome sequencing (WGS). In the “window size” column, “sequenced bp” indicates the FST window was determined by the number of sequenced sites. Because the Z chromosome is a sex chromosome that is hemizygous in female birds and tends to have higher differentiation than the autosomes (reviewed by Irwin, 2018), we excluded it from our analyses. In the case of the Certhia americana comparison (Manthey et al., 2021), hummingbird species pairs (Henderson & Brelsford, 2020), and the Troglodytes pacificus / T. Species comparison window size reference genome SNP calling Seq. citation(s)Sitta carolinensis1 5k sequenced bp S. carolinensis GATK GBS Chapter 2S. carolinensis2 5k sequenced bp S. carolinensis GATK GBS Chapter 2S. carolinensis3 5k sequenced bp S. carolinensis GATK GBS Chapter 2S. carolinensis4 5k sequenced bp S. carolinensis GATK GBS Chapter 2S. carolinensis5 5k sequenced bp S. carolinensis GATK GBS Chapter 2S. carolinensis6 5k sequenced bp S. carolinensis GATK GBS Chapter 2Setophaga pharetra/S. pityophila 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. coronata 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. castanea/S. striata 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. nigrescens/S. graciae 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. pensylvanica/S. petechia 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. occidentalis/S. townsendi 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. citrina/S. ruticilla 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. americana/S. pitiayumi 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. palmarum/S. coronata 10 Kb S. coronata ANGSD WGS Baiz et al. 2021S. virens/S. chrysoparia 10 Kb S. coronata ANGSD WGS Baiz et al. 2021Vermivora bachmanii/V. chrysoptera 10 Kb S. coronata ANGSD WGS Wood et al. 2023V. bachmanii/V. cyanoptera 10 Kb S. coronata ANGSD WGS Wood et al. 2023V. cyanoptera/V. chrysoptera 10 Kb S. coronata ANGSD WGS Toews et al. 2016S. townsendi/ S. virens 10k sequenced bp Taeniopygia guttata GATK GBS Kenyon et al. 2017Geothlypis tolmiei/ G. philadelphia 10k sequenced bp T. guttata GATK GBS Porter 2015Zonotrichia atricapilla/Z. leucophrys 10k sequenced bp T. guttata GATK GBS McCallum et al. In prepZ. leucophrys 10k sequenced bp T. guttata GATK GBS McCallum et al. In prepPhylloscopus trochiloides1 10k sequenced bp T. guttata GATK GBS Alcaide et al. 2014P. trochiloides2 10k sequenced bp T. guttata GATK GBS Alcaide et al. 2014P. trochiloides3 10k sequenced bp T. guttata GATK GBS Alcaide et al. 2014Emberiza citrinella/E. leucocephalos 10k sequenced bp T. guttata GATK GBS Nikelski et al. 2022Certhia americana 100 Kb C. americana GATK WGS Manthey et al. 2021Calypte anna/C. costae 100 Kb C. anna Samtools WGS Henderson and Brelsford 2020Archilochus colubris/A. alexandri 100 Kb C. anna Samtools WGS Henderson and Brelsford 2020Selasphorus sasin/S. rufus 100 Kb C. anna Samtools WGS Henderson and Brelsford 2020Troglodytes pacificus/T. hiemalis 10k sequenced bp Ficedula albicollis GATK GBS Mikkelsen and Irwin 20212 101 hiemalis comparison (Mikkelsen & Irwin, 2021), those authors (J. Manthey, pers. comm.; A. Brelsford, pers. comm.; E. Mikkelsen, pers. comm.) provided us with the calculated estimates of FST per chromosome so we were able to exclude the Z and recalculate the correlation coefficient and p-value for the relationship between chromosome size and FST. Some of the species comparisons used an older version of the zebra finch reference genome for sequence alignment and SNP calling (Alcaide et al., 2014; Irwin et al., 2018; Nikelski et al., 2023), which included chromosome 1B. In the case of the flycatcher genome assembly, this 1B segment was found to be connected to the much larger chromosome 1 (Kawakami et al., 2014). So, this genomic region is excluded in some comparisons where 1B is listed as a separate chromosome but not in other comparisons where the 1B region is connected to the larger chromosome. Given how small this genomic region is (~1.08 Mb in the prior Zebra Finch assembly: Bioproject PRJNA17289) its inclusion in comparisons where it is not listed as a separate chromosome is likely to have little impact on the overall estimate of FST for that chromosome. In the case of the Sitta carolinensis estimates, the reference genome used was near chromosome-scale (Chapter 2). Most scaffolds in this genome correspond to whole chromosomes, but some appear to be fragments (Chapter 2). Scaffolds > 1 Mb were included for the S. carolinensis comparisons because, currently, it is not clear whether these regions are real chromosomal fission events or unassembled chromosome regions. We also examined whether the strength of the relationship (correlation) between chromosome size and FST was related to the date of divergence between the species pairs. We relied on TimeTree.org (Kumar et al., 2022) to find species divergence dates. Divergence dates are either based on a single estimate from a study or a median date based on several studies. The majority of these dates are based on mtDNA loci or mtDNA loci with a few nuclear loci, 102 whereas the minority are based on genome-wide SNPs. In some cases, we found divergence dates by searching the literature and incorporated dates from additional studies when not included in TimeTree. In the case of the hummingbird divergence dates, the dates reported by McGuire et al., (2014) and Lessard et al., (2016) were averaged with the dates reported by Abrahamczyk & Renner, (2015). We also took an average for the divergence date between Phylloscopus plumbeitarsus and Phylloscopus trochiloides using dates estimated from Päckert et al. (2012) and Alström et al. (2018). For Setophaga coronata coronata and Setophaga coronata auduboni, it seems that significant mitochondrial introgression has occurred (Mila et al., 2007; Brelsford & Irwin, 2009; Toews et al., 2014). Thus, using a divergence time between Setophaga coronata coronata and Setophaga nigrifrons may be more appropriate as Setophaga nigrifrons mtDNA haplotype occurs in the southern range of Setophaga coronata auduboni (Toews et al., 2014). Average mtDNA sequence divergence between Setophaga coronata coronata and Setophaga nigrifrons is 4.1% (Toews et al., 2014) and assuming ~2.1% divergence per million years (Weir & Schluter, 2008) this gives a divergence date of 1.95 mya. However, we assume the true date of divergence between Setophaga coronata auduboni and Setophaga coronata coronata likely occurred between 16,000 years ago (the approximate date reported by Mila et al. 2007) and 1.95 mya. So for the purposes of our analysis, we averaged the two for a date of 0.983 mya as a very rough approximation. A summary of divergence information can be found in table 5.2. Because some populations/species appear in multiple comparisons, there is potentially a problem of non-independence in a statistical test that includes all species pairs. To control for this, we also examined the association between divergence time and the correlation coefficient of FST and chromosome size using reduced portions of our data set. We arbitrarily retained only one 103 species pair from those species that appeared more than once in our dataset (12 comparisons excluded) and repeated our analyses. All analyses were conducted using R (R Core Team, 2021). We also noted in our regression analyses that chromosomes could substantially deviate from the regression line. Pearson’s correlation is sensitive to outliers. To ensure our conclusions were robust to outlier chromosomes we repeated our analyses excluding chromosomes < 2Mb, as these smaller chromosomes often deviated the most, and repeated our analyses using Spearman’s rank correlation which is non-parametric and is insensitive to outliers (de Winter et al., 2016). 5.3 Results Across the 32 species comparisons, relationships between chromosome size and FST were highly variable (fig. 5.1; not including those where the relationship between chromosome size and FST were shown in the original publication). Pearson’s correlation tests showed that seventeen had associations significantly different from zero (with p-values less than 0.05; table 5.2). Of these significant values, two had negative relationships, Setophaga pharetra / S. pityophila and Troglodytes pacificus / T. hiemalis. 104 Figure 5.1. Mean FST per log chromosome size across 27 species comparisons of birds. Certhia americana, Troglodytes sp., and hummingbird comparisons are not shown as original the manuscripts displayed their relationships. trochiloides_trochiloides2 trochiloides_trochiloides3 virens_chyrsopariapharetra_pityophila tolmiei_philadelphia townsendi_virens trochiloides_trochiloides1nigrescens_graciae occidentalis_townsendi palmarum_coronata pensylvanica_petechiacitrinella_leucocephalos coronata_coronata cyanoptera__chrysoptera leucophrys_leucophryscarolinensis_carolinensis5 carolinensis_carolinensis6 castanea_striata citrina_ruticillacarolinensis_carolinensis1 carolinensis_carolinensis2 carolinensis_carolinensis3 carolinensis_carolinensis4americana_pitiayumi atricapilla_leucophrys bachmanii_chrysoptera bachmanii_cyanoptera14 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 1914 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 1914 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 1914 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 19 15 16 17 18 1914 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 1914 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 1914 15 16 17 18 19 15 16 17 18 19 14 15 16 17 18 19 14 15 16 17 18 190.250.300.350.400.450.20.30.40.50.300.330.360.390.100.150.200.220.240.260.280.150.200.250.300.350.250.300.350.400.450.200.250.300.350.400.150.200.250.0200.0220.0240.0260.350.400.450.500.000.050.100.150.200.150.160.170.180.100.150.200.250.300.20.30.40.50.30.40.50.60.100.150.200.070.080.090.100.150.200.250.250.300.350.400.380.390.000.050.100.150.200.20.30.40.50.0100.0150.0200.0250.0300.210.230.250.270.560.580.600.620.300.350.40Log(Chromosome Size)F ST 105 Table 5.2. Summary of Pearson correlation coefficients and p-values from linear regressions of FST and chromosome length. Divergence dates are included with the date source. All divergence dates are medians provided by TimeTree.org except for Sitta carolinensis, Setophaga coronata, Certhia americana, Phylloscopus trochiloides, Troglodytes species, Zonotrichia species, Vermivora species, and hummingbird species. Source refers to the study that reported the date of divergence. “mya” refers to the divergence date of the pair in millions of years. Given that relationships were inconsistent across comparisons, we sought to determine if age of divergence between populations might influence the relationship between chromosome size and differentiation. In fact, we observed a significant negative relationship (Pearson correlation -0.50, p-value 0.0033; Spearman rank correlation -0.41, p-value = 0.019; adjusted R2 = 0.23) between age of divergence and correlation coefficient (r) between FST and chromosome size (fig. 5.2). When we retained only one pair from the species that occurred multiple times (12 Comparison cor. p-value mya sourceSitta carolinensis1 0.43 0.0064 0.154 Chapter 2S. carolinensis2 0.42 0.0083 0.648 Chapter 2S. carolinensis3 0.32 0.052 0.648 Chapter 2S. carolinensis4 0.3 0.07 1.605 Chapter 2S. carolinensis5 0.43 0.0075 1.605 Chapter 2S. carolinensis6 0.47 0.0028 1.605 Chapter 2S. pharetra/S. pityophila -0.51 0.0037 4.16 Barker et al. 2015S. coronata -0.28 0.14 1.033 Mila et al. 2007; Toews et al. 2014S. castanea/S. striata -0.15 0.43 2.89 Barker et al. 2015S. nigrescens/S. graciae 0.47 0.0092 0.397 Barker et al. 2015S. pensylvanica/S. petechia 0.029 0.88 1.98 Barker et al. 2015S. occidentalis/S. townsendi 0.15 0.42 0.318 Barker et al. 2015; Weir et al. 2004S. citrina/S. ruticilla -0.063 0.74 3.87 Barker et al. 2015S. americana/S. pitiayumi -0.11 0.55 0.393 Barker et al. 2015S. palmarum/S. coronata 0.094 0.62 2.81 Barker et al. 2015S. virens/S. chrysoparia 0.57 0.0011 0.225 Barker et al. 2015V. bachmanii/V. chrysoptera 0.62 0.00023 2.82 Wood et al. 2023V. bachmanii/V. cyanoptera 0.62 0.00028 2.82 Wood et al. 2023V. cyanoptera/V. chrysoptera 0.7 0.000019 1.11 Wood et al. 2023S. townsendi/ S. virens 0.071 0.72 0.801 Barker et al. 2015; Weir et al. 2004G. tolmiei/ G. philadelphia 0.49 0.0066 1.05 Barker et al. 2015; Weir et al. 2004Z. atricapilla/Z. leucophrys 0.89 1.1x1010 0.75 McCallum et al. In prepZ. leucophrys 0.72 0.000012 0.12 McCallum et al. In prepP. trochiloides1 -0.34 0.067 1.4 Pa¨ckert et al. 2012; Alstro¨m et al. 2018P. trochiloides2 -0.13 0.5 2.3 Alstro¨m et al. 2018P. trochiloides3 0.21 0.28 2.3 Alstro¨m et al. 2018E. citrinella/E. leucocephalos 0.74 0.0000037 0.201 Irwin et al. 2009; Barker et al. 2015; Hooper et al. 2017C. americana 0.9 7x1010 1.06 Manthey et al. 2021C. anna/C. costae 0.56 0.0011 2.075 McGuire et al. 2014; Abrahamczyk and Renner 2015; Lessard et al. 2016A. colubris/A. alexandri 0.068 0.72 1.255 McGuire et al. 2014; Abrahamczyk and Renner 2015; Lessard et al. 2016S. sasin/S. rufus 0.29 0.109 1.035 McGuire et al. 2014; Abrahamczyk and Renner 2015; Lessard et al. 2016T. pacificus/T. hiemalis -0.47 0.0099 4.3 Toews and Irwin 20082 106 comparisons excluded), this significant negative relationship was maintained with both Pearson’s correlation (cor. = -0.59 with a p-value = 0.006) and Spearman rank correlation (rho = -0.53 with a p-value of 0.017) (appendix D.1). Figure 5.2. Pearson correlation coefficient (r) between FST and log chromosome length plotted against divergence date of the species in millions of years. A linear regression is fitted through the points with a 95% confidence band in grey. Species comparisons are colored by genus, and non-passerine birds are shown with a diamond symbol. In figure 5.2, we also showed points as colors corresponding to their respective genus. For the overall negative trend between FST /chromosome length and divergence, one concern was that it may be driven by phylogenetic patterns among sampled species. From this data we see no -0.50.00.50 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia 107 evidence that genus differences are playing a role in the overall patterns between FST /chromosome length and divergence age. In fact, within Setophaga, there is a significantly negative trend with the correlation of FST and chromosome size and divergence time (Pearson’s correlation = -0.62, p-value = 0.043). We repeated our analyses using Spearman’s rank correlation to test the relationship (appendix D.2) of divergence date and Pearson’s correlation coefficients between FST and chromosome size excluding chromosomes 2 Mb or smaller (appendix D.3-4). FST values in these smaller chromosomes often widely deviated from linear regression lines. Repeating our analysis using Spearman’s rank correlation provides an additional estimate of relationships that are more robust than Pearson’s to outlier values (de Winter et al., 2016). In these additional tests excluding smaller chromosomes and using Spearman rank correlation as input on the full data set, we found significant negative associations between the relationship of FST and chromosome size and divergence date (p-values < 0.05). For specific correlation values and p-values see appendix D.5. In our reduced data set where all comparisons with repeated populations are removed (12 pairs excluded) we found that using Spearman values (appendix D.6) and Pearson with < 2 Mb scaffolds/chromosomes removed (appendix D.7) the association with divergence time was still significant (p-value < 0.05; appendix D.8). 5.4 Discussion Genome differentiation is not a homogeneous process (Rieseberg & Burke, 2001; Wu, 2001), and physical characteristics of genomes may allow us to predict where differentiation may accumulate first. Using genomic sampling across species of birds, we asked if chromosome size, with its effects on recombination, may predict differentiation across the genome. We 108 hypothesized that lower recombination rates on larger chromosomes would promote higher differentiation resulting in a positive association between chromosome size and FST. An alternative that we proposed was that smaller chromosomes with higher recombination rates might be more efficient at bringing together beneficial mutations (Hill & Robertson, 1966) and more frequently subject to selection due to higher gene density (Smith et al., 2000; Federico et al., 2005; Waters et al., 2021). This would tend to produce a negative relationship between chromosome size and FST. In the 32 comparisons we examined, relationships between FST and chromosome size were highly variable. Seventeen of these comparisons showed significant relationships (p-value < 0.05) with 2 having significant negative relationships and 15 significant positive relationships. For all comparisons (including significant and non-significant trends) 8 had negative trends, and 24 had positive ones. These results suggest that our two predictions were not mutually exclusive, in that some comparisons may be shaped by linked selection driving higher differentiation in large chromosomes whereas in other systems differentiation in gene-dense microchromosomes may be balancing out or outweighing these effects. One variable that may influence the patterns we observe is the age of divergence. We would expect more elevated and homogeneous differentiation between older species than younger ones, which might obscure any relationship with chromosome size. We did observe a significant negative association between correlation estimates and age of divergence. Importantly, this relationship does not appear to be due to differentiation reaching its max estimate of one in older species, but instead accumulating equivalent or greater levels of differentiation in smaller chromosomes. This finding shows that the age of taxon pairs is an important factor impacting the slope of the relationship between FST and chromosome size. In a 109 way, this supports both of our predictions when taking into account the age of the species pair. It may be that in younger species pairs in which hybridization and gene flow is still substantial, larger chromosomes with longer regions of reduced recombination differentiate faster than smaller chromosomes. Then as divergence time increases and the influence of gene flow is reduced, differentiation accumulates faster on smaller chromosomes because there are more potential targets for selection. Depending on the evolutionary history of populations, the timing of hybridization and introgression, and the selective benefit of introgressing alleles, introgressed loci can be found in higher density in regions of high recombination or less commonly in areas of low recombination (Schumer et al., 2018; Leitwein et al., 2019; Aguillon et al., 2022). If hybridization and introgression are predominately influencing high recombination areas in young species then this is generally consistent with our results here, where younger taxa tend to exhibit a positive correlation between chromosome length and differentiation (fig. 5.2), although there are exceptions. While divergence date may impact associations of FST and chromosome length, weak relationships in some young species comparisons may simply be because meaningful variation in differentiation across chromosomes has yet to accumulate (e.g., if drift was the predominate process of differentiation between taxa). In this study, we were limited in some aspects. The first is sample size; 32 comparisons, despite representing many years of fieldwork and sequencing, is a modest sample size for this analysis. With ever-increasing genomic data sets being produced in birds (e.g., Bravo et al., 2021), including average differentiation per chromosome are worthwhile supplemental analyses, especially those that use chromosome-level assemblies for species of interest. Having these supplemental analyses available, future researchers can significantly increase sampling sizes to further expand on the role chromosome size has in the process of differentiation. We would be 110 eager to see if our conclusions are replicated with increased sampling, including a wider array of bird clades and also incorporating non-avian taxa that have microchromosomes (Ohno et al., 1969; Morescalchi et al., 1979; Menezes et al., 2019; Schield et al., 2019). The second aspect is that the genomic datasets in this analysis varied widely in their methodology from sequencing approach to SNP calling pipeline, to the choice of the reference genome. It is unclear how these differences may influence our observations, but ideally, each dataset would result from the same sequencing approach and the same SNP calling pipeline with the closest reference genome possible. More distant reference genomes may influence our estimates of differentiation because a greater percentage of reads are lost during mapping; however, this would likely not influence variation in differentiation among chromosomes within a given comparison. In the case of S. carolinensis, certain scaffolds may represent real chromosomal fission events or are possibly assembly errors. If they are assembly errors and some scaffolds are fragments from large chromosomes (i.e., a small scaffold is treated as a micro-chromosome when it should in fact be linked to a larger scaffold), they may have higher FST than expected based on their size and erroneously flatten associations between FST and chromosome length. It is also important to note that our study contains a certain amount of pseudo-replication. This occurs because some of the populations occur in multiple comparisons (e.g., the Rocky South population of S. carolinensis compared multiple times to other S. carolinensis populations). We wanted to ensure this pseudo-replication did not bias our conclusions. After removing all observations where a population occurs more than once or all but one of these comparisons, we find this significant negative relationship between the correlation of FST and chromosome size and divergence date still occurs. 111 A similar question regarding divergence and chromosome size has been investigated in mammals using sequence divergence. Tigano et al. (2022) observed in some mammal clades that sequence divergence is negatively correlated with chromosome size. Our study differs from theirs in that we examined patterns among birds, which is a benefit because most avian genomes contain many small microchromosomes (Ellegren, 2010; Waters et al., 2021), providing greater insight into how the length of chromosomes influences differentiation. We also chose to examine FST rather than sequence divergence. Sequence divergence and FST are different measurements of differentiation (see Cruickshank & Hahn, 2014), the aforementioned being analogous to DXY (Nei, 1987) or pB (Charlesworth, 1998). Thus, these two metrics are susceptible to different evolutionary forces, specifically FST to local adaptation (Cruickshank & Hahn, 2014). Sequence divergence is less sensitive to local adaptation (Cruickshank & Hahn, 2014), so it may not reveal to the full extent the interactions of selection and chromosome length. To our knowledge, the interaction of FST and chromosome length has not been directly studied in a diverse sampling of birds. In this initial assessment of genomic data from birds, we show that variation in chromosome size may give rise to predictable patterns of genome-wide differentiation between species in that if the age of the pair is known, one may be able to reasonably predict where areas of high differentiation will be found. We uncovered a positive association that is more apparent in younger species and becomes strongly negative with increasing age of the taxon pair. In this aspect, this study represents an important first step in disentangling how macrochromosomes and microchromosomes contribute to the evolutionary trajectories of avian genomes. 112 Chapter 6: Conclusion 6.1 Summary of key results Genomic data from white-breasted nuthatches reveal four clearly differentiated groups that are likely three distinct species. These groups broadly correspond to different geographic regions of North America: the Pacific coast, the Northern area of the Interior West or “Rocky Mountain North” in our studies (Washington, Oregon, Idaho, Montana, Wyoming, and extending into Canada), the Southern area of the interior west or “Rocky Mountain South” in our studies (Colorado, Utah, Nevada, New Mexico, Arizona, and into Mexico), and the Eastern U.S. (Eastern Colorado to the Carolinas). The US contains all four populations, two populations are present in Canada (RN and East), and Mexico has only RS. These populations span a gradient of differentiation from somewhat differentiated (RN and RS) to highly differentiated (Eastern and Pacific). Introgression has clearly been involved in at least the divergence of the Rocky Mountain populations as areas for elevated introgression overlap extensively with areas of differentiation between RN and RS. Surprisingly, this introgression appears to be the result of a ghost or unsampled lineage of white-breasted nuthatch that has been preserved within the genome of RS. Across populations of white-breasted nuthatches, there are several areas of the genome that may be inversions. Some of these are restricted to single populations, whereas others are shared across multiple populations. Polymorphisms that are shared across populations appear to be old and are likely the result of standing ancestral variation. While inversions cannot be concluded definitively with the short read data that was used, the Eastern-specific scaffold 2 regions and the shared scaffold 11 may be the most likely candidates for inversions given that no 113 recombinant haplotypes were observed. The Eastern-specific scaffold 20 region had only subtle patterns of linkage disequilibrium, and the shared scaffold 22 region had heterozygous individuals that appeared to be recombinant, making these areas less likely to be the result of inversions. However, if these genomic patterns are not due to inversions, it is unclear what else, besides selection, could generate linkage disequilibrium and produce within a population two distinct haplotypes. I speculate that balancing selection may be involved in these areas. In white-breasted nuthatches, rates of recombination across the genome are generally well correlated between populations. There is an apparent phylogenetic signal where the strongest association is between the least differentiated populations and this association declines slightly with increasingly differentiated groups. Recombination landscapes are also strongly associated with FST between populations in those genomic regions. Since FST is a pairwise measurement involving two populations we compared the between-population FST landscape to the recombination landscapes of both populations. In all comparisons FST was significantly negatively associated with recombination. My final data chapter examined how FST varied with chromosome length in a broad sampling of avian species pairs. The relationship between FST and chromosome length was highly variable among pairs, but this association was negatively associated with divergence time. This result extends the inference around recombination and differentiation from my previous chapter to show that in younger species, large chromosomes (with lower recombination) are more likely to have higher levels of differentiation. As species pairs diverge further over time, differentiation accumulates on smaller chromosomes and eventually exceeds that of larger chromosomes, creating a negative relationship. This may be driven by two factors occurring independently or in unison. The first is the increased efficiency of selection in regions with more 114 recombination by reducing Hill-Robertson interference (Hill & Robertson, 1966) and the second is that more selection may occur in smaller chromosomes as they typically contain more genes in birds (Smith et al., 2000; Federico et al., 2005; Waters et al., 2021). 6.2 Unanswered questions While my dissertation answered many questions surrounding the evolutionary history of white-breasted nuthatches and added to the understanding of recombination’s interaction with differentiation, there are several fruitful areas of future research to be examined that were either not possible to answer with the data in my research or beyond the scope of my questions. Regarding white-breasted nuthatches, ecological studies and gene-focused genetic studies could be done to understand better the areas of genomic differentiation observed between populations, especially the regions of introgression between Rocky Mountain South and North. Initially, I proposed examining the relationship between beak morphology and tree bark structure between the putative species and whether genomic regions were associated with these traits. The COVID pandemic that occurred during my doctoral work made these plans unfeasible, but future research could examine these patterns and may reveal a possible ecological explanation for the divergence of white-breasted nuthatches. It would also be interesting to 1) provide a high-quality annotation for the white-breasted nuthatch genome produced from my work and 2) assemble chromosome-level reference genomes for the other white-breasted nuthatch species. With a proper annotation, one could see if genes within areas of differentiation are overrepresented for a specific function (although this would be best if done in conjunction with ecological studies). As well, if there was a representative genome for each group, one could align these and see if there are structural 115 rearrangements between populations. This would be interesting because it may confirm the polymorphic haploblocks identified in chapter 3 as inversions. It would also be interesting to expand the scope of chapter 5. A much larger project could be done by incorporating genomic data from more species of birds, and to compare the relationships of FST and chromosome size between birds with many micro-chromosomes and those with few micro-chromosomes (de Oliveira et al., 2005; Nanda et al., 2006; Mead et al., 2021). This project could extend beyond birds by investigating FST and chromosome size in other species with micro-chromosomes like insects (Menezes et al., 2019) and fish (Ohno et al., 1969). If the patterns I found in chapter 5 are supported in a larger study with more bird species and other organisms with micro-chromosomes, it would provide strong support that areas of low recombination are the areas that we most expect to have elevated differentiation early in speciation. 6.3 Broad implications The genomic data in my dissertation greatly extended the work of Spellman & Klicka (2007) and Walstrom et al. (2012) who initially demonstrated four clades of nuthatches distributed across North America. My work has shown these populations are remarkably different within their genomes and likely represent three distinct species, with the species status between Rocky North and Rocky South less clear. It is likely that white-breasted nuthatches will need a taxonomic revision describing them as a Pacific nuthatch, a Western mountain nuthatch (western used to reflect the range of the “Rocky mountain” populations that extend from British Columbia to Mexico), and an Eastern nuthatch. Prior to the use of genetic data, different populations of white-breasted nuthatches were not considered to be distinct species (Grinnell, 1926; Aldrich, 1944; 116 Grinnell & Miller, 1944; Hawbecker, 1948; Wood, 1992; Spellman & Klicka, 2007; Walstrom et al., 2012). The research presented in my dissertation contributes to our understanding of speciation in several ways. The first is how it has highlighted a dramatic case of cryptic species. Species are usually defined based on their morphological differences compared to other species. These are differences that we, as humans, can perceive, and we might be missing some important distinctions. For example, birds can see in ultraviolet light, meaning they can detect color variation that goes unnoticed by human observers and can be important differences between species (Bleiweiss, 2004). The second is the role of introgression in influencing patterns of differentiation and introgression from ghost lineages. Many peaks in sliding windows testing for introgression overlapped with areas of elevated differentiation in the RS and RN populations of white-breasted nuthatches. This showed rather that introgression can be a primary source of genomic variation that differentiates populations. ABBA-BABA tests supported more ABBA than BABA patterns, a result which is often interpreted as introgression of derived lineages among the second and third lineages used within the test (Green et al., 2010; Durand et al., 2011). In white-breasted nuthatches, we inferred this pattern was instead caused by introgressed ancestral alleles. This exact introgression scenario has been highlighted by Tricou et al. (2022) and complicates the interpretation of introgression tests. Broadly, this demonstrates how differences accumulated from unsampled or extinct lineages can contribute to currently observed population patterns. The results in my third chapter showed that polymorphic chromosome regions, which in some cases are likely inversions, can be common within populations. This is inferred because I used PCA approaches based on short read data to identify these regions. Long read data is more 117 effective at locating inversions (Harringmeyer & Hoekstra, 2022) so it is likely that the regions I identified are an under representation of the total number of inversions present. In each case where these regions were found across various populations of white-breasted nuthatches, they appeared to be old. These findings illustrate that while large-scale genome structure seems conserved within some clades of birds (Ellegren, 2010), within chromosome structure might be more dynamic. In the fourth and fifth chapters of my dissertation I examined the relationship between genomic differentiation and areas of low recombination. While this association has been noted by others (Geraldes et al., 2011; Nachman & Payseur, 2012; Burri et al., 2015; Turbek et al., 2021), my work adds to this by demonstrating their relationship in white-breasted nuthatches and also across a sampling of bird species. Linked selection by either selective sweeps or background selection (Maynard Smith & Haigh, 1974; Charlesworth et al., 1993) in areas of low recombination may be where the “magic” happens in speciation, where incompatibilities build up that maintain species boundaries. 118 References Abrahamczyk, S., & Renner, S. S. (2015). 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K=5 reveals additional structure in RS by grouping samples from Mexico. wbnu_plate1to5_360_NoHighReKin_variants_wholegenome_MQ20_het06_Miss09_NoOut_biallelic_GQ10_noIndel_mac2_plink_LatSort.5.QK=50.000.250.500.751.00EasternHybrid_RN_EHybrid_RS_EPacific RNRN_RS_CrossRS MX| ||| | || | |as.character(metdat2$location)wbnu_plate1to5_360_NoHighReKin_variants_wholegenome_MQ20_het06_Miss09_NoOut_biallelic_GQ10_noIndel_mac2_plink_LatSort.3.QK=30.000.250.500.751.00EasternHybrid_RN_EHybrid_RS_EPacific RNRN_RS_CrossRS| ||| | || |as.character(metdat$location)Ancestry FractionAncestry FractionRSRNPacificEastRockies/East Hybrids RS/RN int.RNPacificEastRockies/East Hybrids RS/RN int.RS MXA)B) 171 A.3 Cross-validation error (CV) across varying K in Admixture analysis. K=4 has lowest CV and is most supported. K CV error1 0.203232 0.172833 0.152774 0.140375 0.140746 0.141537 0.142238 0.142349 0.1471310 0.149071 172 A.4 Genotypes for 10 Eastern, 10 RS, and 2 putative F1 hybrids are shown. Individuals are color-coded according to population. Light purple and dark purple represent homozygote SNPs, the color is determined by the frequency of the SNP in the population. Half-light purple and half dark purple are heterozygous SNPs. Only SNPs with FST of >0.9 are shown. 173 A.5 PCA of white-breasted nuthatch samples using PC 2 and 3. For formatting and analysis details, see figure 2.1 in the main text. PC 2 and 3 explain 12.6% and 7% of the variation in the data, respectively. -250 -200 -150 -100 -50 0-50050PC 2 (12.6%)PC 3 (7%)EasternHybrid_RN_EHybrid_RS_EPacificRNRN_RS_IntRS 174 A.6 This figure corresponds with figure 2.5; see figure for formatting details. Genotypes across scaffolds 29 and 34 for a subset of individuals in each population are shown (A and C) next to corresponding PCA’s of SNPs from that region for all individuals (B and D). Genotypes with FST < or equal to 0.25 between RN and RS are excluded in the genotype by individual plots The scaffold 29 PCA is of 1,111 SNPs (B) and the scaffold 34 PCA is of 635 SNPs (D). 175 A.7 PCAs using only RS individuals for the regions of interest focused on in figure 2.5 and appendix A.6. A) PCA of scaffold 1 SNPs from 52 Mb – 58 Mb region (398 SNPs). B) PCA of scaffold 2 SNPs from 54 Mb – 59 Mb region (248 SNPs). C) PCA of scaffold 29 SNPs from 12 Mb – 14.1 Mb region (661 SNPs). D) PCA of scaffold 34 SNPs from 2 Mb – 4 Mb region (407 SNPs). Similar to figure 2.5 and appendix A.6, PCAs contain 3 clusters, a predominately RS cluster, a lower frequency cluster that is the RN haplotype, and a middle cluster of heterozygotes. In the case of the scaffold 34 region there is only a RS haplotype and heterozygotes. 0 5 10 15 20-10-505PCA of chrScaffold_1: all SNPsPC1PC2AZNMNVMXUTCOWY-15 -10 -5 0-505PCA of chrScaffold_2: all SNPsPC1PC2AZNMNVMXUTCOWY-5 0 5 10 15-505PCA of chrScaffold_29: all SNPsPC1PC2AZNMNVMXUTCOWY0 5 10 15-6-4-20246PCA of chrScaffold_34: all SNPsPC1PC2AZNMNVMXUTCOWYPC 1PC 2PC 1PC 1 PC 1PC 2PC 2PC 2A) B)C) D) 176 A.8 Log transformed nucleotide diversity from sliding windows across the genome for each population of white-breasted nuthatches. Each dot is a 5,000 sequenced bp window and is based on sequencing from 23 individuals. Eastern RS RN Pacific-10-8-6-4Populationlog(nuc. diversity) 177 Appendix B Supplementary information for Chapter 3 B.1 Post-filtering SNP counts for scaffolds 1 Mb or greater. Sca↵old SNPs1 259842 343723 122424 159695 157616 150427 143768 123449 1390610 1362911 309912 606113 366014 8915 4716 118817 293018 1228419 939220 937221 1225122 933623 1284324 1222125 658026 743027 1107328 1145829 970830 590331 789432 1010433 760234 451435 596536 589737 165238 3039 1341 178 B.2 PCA’s of SNPs from Scaffolds 1-9. -20 0 20 40-50-40-30-20-10010PCA of chrScaffold_5: all SNPsPC1PC2-30 -20 -10 0 10 20 30 40-40-30-20-100PCA of chrScaffold_8: all SNPsPC1PC2-20 0 20 40-50-40-30-20-100PCA of chrScaffold_4: all SNPsPC1PC2-10 0 10 20 30 40-30-20-10010PCA of chrScaffold_6: all SNPsPC1PC2-40 -30 -20 -10 0 10 20 30-40-30-20-10010PCA of chrScaffold_7: all SNPsPC1PC2-60 -40 -20 0 20 40-60-40-200PCA of chrScaffold_1: all SNPsPC1PC2-40 -20 0 20 40 60-60-40-200PCA of chrScaffold_2: all SNPsPC1PC2-30 -20 -10 0 10 20 30-30-20-10010PCA of chrScaffold_3: all SNPsPC1PC2-20 0 20 40-50-40-30-20-100PCA of chrScaffold_9: all SNPsPC1PC2 179 B.3 PCA’s of SNPs from scaffolds 10-19. -20 -10 0 10 20 30 40-40-30-20-100PCA of chrScaffold_10: all SNPsPC1PC2-30 -20 -10 0 10 20 30-40-30-20-100PCA of chrScaffold_12: all SNPsPC1PC2-10 0 10 20 30-15-10-5051015PCA of chrScaffold_13: all SNPsPC1PC2-4 -2 0 2-2-10123PCA of chrScaffold_14: all SNPsPC1PC2-2 -1 0 1 2 3 4-10123PCA of chrScaffold_15: all SNPsPC1PC2-5 0 5 10-10-505PCA of chrScaffold_16: all SNPsPC1PC2-20 -15 -10 -5 0 5-15-10-505PCA of chrScaffold_17: all SNPsPC1PC2-20 -10 0 10 20 30 40 50-30-20-1001020PCA of chrScaffold_18: all SNPsPC1PC20 10 20 30 40-50510152025PCA of chrScaffold_19: all SNPsPC1PC2 180 B.4 PCA’s of SNPs scaffolds 21-30. -20 -10 0 10 20 30 40-40-30-20-100PCA of chrScaffold_21: all SNPsPC1PC20 10 20 30 40-100102030PCA of chrScaffold_23: all SNPsPC1PC20 10 20 30 40-30-20-100PCA of chrScaffold_24: all SNPsPC1PC20 10 20 30-20-15-10-505PCA of chrScaffold_25: all SNPsPC1PC2-20 -10 0 10 20 30-30-20-10010PCA of chrScaffold_26: all SNPsPC1PC2-10 0 10 20 30 40-30-20-10010PCA of chrScaffold_27: all SNPsPC1PC20 10 20 30 40-100102030PCA of chrScaffold_28: all SNPsPC1PC2-30 -20 -10 0 10 20 30 40-40-30-20-10010PCA of chrScaffold_29: all SNPsPC1PC2-30 -20 -10 0 10-20-15-10-505PCA of chrScaffold_30: all SNPsPC1PC2 181 B.5 PCA’s of SNPs from scaffolds 31-39 0 10 20 30 40-25-20-15-10-505PCA of chrScaffold_31: all SNPsPC1PC2-30 -20 -10 0 10-20-10010PCA of chrScaffold_32: all SNPsPC1PC2-20 -10 0 10 20 30-30-20-10010PCA of chrScaffold_33: all SNPsPC1PC2-20 -10 0 10 20-25-20-15-10-505PCA of chrScaffold_34: all SNPsPC1PC2-5 0 5 10 15 20 25 30-505101520PCA of chrScaffold_35: all SNPsPC1PC2-5 0 5 10 15 20 25 30-20-15-10-505PCA of chrScaffold_36: all SNPsPC1PC2-5 0 5 10 15-15-10-50PCA of chrScaffold_37: all SNPsPC1PC2-1 0 1 2 3-1.0-0.50.00.51.01.52.0PCA of chrScaffold_38: all SNPsPC1PC2-6 -4 -2 0 2-2-10123PCA of chrScaffold_39: all SNPsPC1PC2 182 B.6 PCA of Scaffold 22 SNPs across all white-breasted nuthatch samples after filtering. This PCA corresponds to figure 3.3d PCA but shows PC axes 1 and 2. -10 0 10 20 30 40-20-10010PCA of chrScaffold_22: all SNPsPC1PC2EasternRSRNPacific 183 B.7 A) PCA of 9732 SNPs from Scaffold 20 across 356 white-breasted nuthatch samples. B) SNP loadings across the scaffolds, two high loading SNPs are marked in red. C) PCA excluding those two high loading SNPs seen in panel B. -50 0 50-80-60-40-2002040PCA of chrScaffold_20: all SNPsPC1PC2EasternRSRNPacific0 10 20 30 40-25-20-15-10-505PCA of chrScaffold_20: all SNPsPC1PC2EasternRSRNPacificPC 1PC 2PC 1PC 2A)C)B)0.0e+00 5.0e+06 1.0e+07 1.5e+07 2.0e+070.00.20.40.60.8Scaffold 20 PositionPC 1 SNP loadings 184 B.8 PCA of RN individuals (138 samples) using 4861 SNPs from scaffold 3. -10 0 10 20 30 40-20-1001020PCA of chrScaffold_3: all SNPsPC1PC2RN 185 B.9 Genome-wide SNP phylogeny (A) compared to SNPS from the region of high LD in scaffold 2 (B) each phylogeny uses 1500 randomly selected SNPs. Sample sizes are as follows: 3 Red-breasted Nuthatches, 2 Pacific, 2 RN, 2 RS, and 2 Eastern. In the region specific phylogeny (B) East hap 1 and East hap 2 contain only 1 individual each. 2.0RSEast_hap1East_hap2RNPacificRed_breasted0.251.80.4617.450.742.0Red_breastedRNEasternRSPacific0.181.517.440.62A)B) 186 B.10 Phylogenies of genome-wide SNPs (A) and Scaffold 20 region-specific SNPs from the area of elevated LD (B). Phylogeny details are the same as those in appendix B.9 except sample sizes of populations are larger. Non-eastern populations contain 3 individuals, and the Eastern population has 8, 4 of each haplotype. 2.0RNRSPacificEasternRed_breasted0.150.6817.391.612.0PacificEast_hap_2RSEast_hap_1RNRed_breasted0.060.381.490.5517.38A)B) 187 Appendix C Supplementary information for Chapter 4 C.1 PCA of 1,343,993 genome-wide SNPs with no missing data across 48 white-breasted nuthatches. PC 1 explains 30.2% of the variation in the data and PC 2 explains 18.5%. -200 0 200 400 600-300-200-1000100200300PCA of whole_genome: all SNPsPC1PC2EasternRSRNPacific 188 C.2 Recombination rate across scaffolds for RS (Rocky Mountain South). Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffold_14 Scaffold_38 Scaffold_39 Scaffold_15Scaffold_12 Scaffold_36 Scaffold_11 Scaffold_13 Scaffold_17 Scaffold_37 Scaffold_16Scaffold_29 Scaffold_30 Scaffold_31 Scaffold_32 Scaffold_33 Scaffold_34 Scaffold_35Scaffold_22 Scaffold_23 Scaffold_24 Scaffold_25 Scaffold_26 Scaffold_27 Scaffold_28Scaffold_8 Scaffold_9 Scaffold_10 Scaffold_18 Scaffold_19 Scaffold_20 Scaffold_21Scaffold_2 Scaffold_1 Scaffold_3 Scaffold_4 Scaffold_7 Scaffold_5 Scaffold_69.0e+051.2e+061.5e+061.8e+061.50e+061.55e+064.0e+058.0e+051.2e+068.0e+051.0e+061.2e+060e+002e+064e+066e+060e+002e+064e+066e+060e+002e+064e+066e+062e+064e+066e+061e+062e+063e+064e+061e+062e+063e+061e+062e+063e+060e+005e+061e+070e+005e+061e+070.00e+002.50e+065.00e+067.50e+061.00e+071.25e+070e+003e+066e+069e+060e+003e+066e+069e+060e+002e+064e+066e+068e+060e+002e+064e+066e+060.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070e+001e+072e+073e+074e+070e+001e+072e+073e+070e+001e+072e+073e+070e+001e+072e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+071.0e+081.5e+080.0e+003.0e+076.0e+079.0e+071.2e+080e+002e+074e+076e+078e+070e+002e+074e+076e+070e+002e+074e+076e+070e+002e+074e+076e+070e+001e+072e+073e+074e+070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-07Positionco/bp 189 C.3 Recombination rate across scaffolds for RN (Rocky Mountain North). Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffold 38 is dropped because it only had enough SNPs to form one window. Scaffold_14 Scaffold_39 Scaffold_15Scaffold_12 Scaffold_36 Scaffold_11 Scaffold_13 Scaffold_17 Scaffold_37 Scaffold_16Scaffold_29 Scaffold_30 Scaffold_31 Scaffold_32 Scaffold_33 Scaffold_34 Scaffold_35Scaffold_22 Scaffold_23 Scaffold_24 Scaffold_25 Scaffold_26 Scaffold_27 Scaffold_28Scaffold_8 Scaffold_9 Scaffold_10 Scaffold_18 Scaffold_19 Scaffold_20 Scaffold_21Scaffold_2 Scaffold_1 Scaffold_3 Scaffold_4 Scaffold_7 Scaffold_5 Scaffold_61.8e+061.9e+062.0e+065.00e+057.50e+051.00e+061.25e+061.50e+061.10e+061.15e+061.20e+061.25e+060e+002e+064e+066e+062e+064e+066e+060e+002e+064e+066e+062e+064e+066e+061e+062e+063e+064e+061e+062e+063e+061e+062e+063e+060e+005e+061e+070e+005e+061e+070.00e+002.50e+065.00e+067.50e+061.00e+071.25e+070e+003e+066e+069e+060e+003e+066e+069e+060e+002e+064e+066e+068e+060e+002e+064e+066e+060.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070e+001e+072e+073e+074e+070e+001e+072e+073e+070e+001e+072e+073e+070e+001e+072e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+071.0e+081.5e+080.0e+003.0e+076.0e+079.0e+071.2e+080e+002e+074e+076e+078e+070e+002e+074e+076e+070e+002e+074e+076e+070e+002e+074e+076e+070e+001e+072e+073e+074e+070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-07Positionco/bp 190 C.4 Recombination rate across scaffolds for the Pacific population. Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffolds 14, 15, and 38 are dropped because they only had enough SNPs to form one window. Scaffold_11 Scaffold_13 Scaffold_17 Scaffold_37 Scaffold_16 Scaffold_39Scaffold_32 Scaffold_33 Scaffold_34 Scaffold_35 Scaffold_12 Scaffold_36Scaffold_26 Scaffold_27 Scaffold_28 Scaffold_29 Scaffold_30 Scaffold_31Scaffold_20 Scaffold_21 Scaffold_22 Scaffold_23 Scaffold_24 Scaffold_25Scaffold_6 Scaffold_8 Scaffold_9 Scaffold_10 Scaffold_18 Scaffold_19Scaffold_2 Scaffold_1 Scaffold_3 Scaffold_4 Scaffold_7 Scaffold_50e+002e+064e+066e+062e+064e+066e+061e+062e+063e+064e+061e+062e+063e+061.5e+062.0e+062.5e+063.0e+068.0e+051.0e+061.2e+061.4e+063e+066e+069e+060e+003e+066e+069e+062e+064e+066e+068e+062e+064e+066e+062e+064e+066e+062e+064e+066e+060.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070e+005e+061e+070e+005e+061e+070.00e+002.50e+065.00e+067.50e+061.00e+071.25e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070e+001e+072e+073e+074e+070e+001e+072e+073e+074e+070e+001e+072e+073e+070e+001e+072e+073e+070e+001e+072e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+071.0e+081.5e+080.0e+003.0e+076.0e+079.0e+071.2e+080e+002e+074e+076e+078e+070e+002e+074e+076e+070e+002e+074e+076e+070e+002e+074e+076e+070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-07Positionco/bp 191 C.5 Recombination rate across scaffolds for the Eastern white-breasted nuthatch population. Scaffolds are arranged from largest to smallest. The red line indicates the estimated rate, and the grey lines represent the 95% confidence interval. Scaffold_14 Scaffold_38 Scaffold_39 Scaffold_15Scaffold_12 Scaffold_36 Scaffold_11 Scaffold_13 Scaffold_17 Scaffold_37 Scaffold_16Scaffold_29 Scaffold_30 Scaffold_31 Scaffold_32 Scaffold_33 Scaffold_34 Scaffold_35Scaffold_22 Scaffold_23 Scaffold_24 Scaffold_25 Scaffold_26 Scaffold_27 Scaffold_28Scaffold_8 Scaffold_9 Scaffold_10 Scaffold_18 Scaffold_19 Scaffold_20 Scaffold_21Scaffold_2 Scaffold_1 Scaffold_3 Scaffold_4 Scaffold_7 Scaffold_5 Scaffold_67.50e+051.00e+061.25e+061.50e+061.75e+062.00e+061.35e+061.40e+061.45e+061.50e+061.55e+061.60e+068.0e+051.0e+061.2e+061.4e+066.0e+058.0e+051.0e+061.2e+060e+002e+064e+066e+060e+002e+064e+066e+060e+002e+064e+066e+062e+064e+066e+061e+062e+063e+064e+061e+062e+063e+061e+062e+063e+060e+005e+061e+070e+005e+061e+070.0e+002.5e+065.0e+067.5e+061.0e+070e+003e+066e+069e+060e+003e+066e+069e+060e+002e+064e+066e+068e+060e+002e+064e+066e+060.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070.0e+005.0e+061.0e+071.5e+070e+001e+072e+073e+074e+070e+001e+072e+073e+070e+001e+072e+073e+070e+001e+072e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+061.0e+071.5e+072.0e+070.0e+005.0e+071.0e+081.5e+080.0e+003.0e+076.0e+079.0e+071.2e+080e+002e+074e+076e+078e+070e+002e+074e+076e+070e+002e+074e+076e+070e+002e+074e+076e+070e+001e+072e+073e+074e+070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-070.0e+005.0e-081.0e-071.5e-07Positionco/bp 192 C.6 Recombination rates on Scaffold 3 (Z chromosome) for each population (or putative species) of white-breasted nuthatch. The green dot highlights the region suspected to be the PAR and corresponds to the region identified in appendix C.7. 0e+00 2e+07 4e+07 6e+070.0e+005.0e-081.0e-071.5e-07PositionEast co/bp0e+00 2e+07 4e+07 6e+070.0e+005.0e-081.0e-071.5e-07PositionRN co/bp0e+00 2e+07 4e+07 6e+070.0e+005.0e-081.0e-071.5e-07PositionRS co/bp0e+00 2e+07 4e+07 6e+070.0e+005.0e-081.0e-071.5e-07PositionPac co/bp 193 C.7 A) Genome alignment between the white-breasted nuthatch and hooded crow genomes. The section of the hooded crow genome that corresponds to the pseudoautosomal region (PAR) and aligns to the white-breasted nuthatch is shown in the yellow-box. B) Read depth ratio between the mean depth of male vs female white-breasted nuthatches for 10,714 SNPs across the Z chromosome (scaffold 3). The red line represents non-overlapping sliding window of median depth using a 100 SNP window. C) This panel corresponds with panel B but only shows 0 – 5 Mb position SNPs. The green line highlights the region (2,533,000 bp – 3,150,000 bp) of the suspected PAR. 0e+00 2e+07 4e+07 6e+070.51.01.52.02.53.0position along Z (scaffold 3)M/F read depth0e+00 1e+06 2e+06 3e+06 4e+06 5e+061.01.52.02.53.0position along Z (scaffold 3)M/F read depthA BC 194 C.8 Distribution of SNP counts for 1 Mb windows of FST between populations. For each comparison, the median SNP count is listed. SNPs per 1 Mb Window of FST RS/RNSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070SNPs per 1 Mb Window of FST RS/PacSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070SNPs per 1 Mb Window of FST RN/PacSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070SNPs per 1 Mb Window of FST RS/EastSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070SNPs per 1 Mb Window of FST RN/EastSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070SNPs per 1 Mb Window of FST Pac/EastSNPs per 1 Mb windowFrequency0 200 400 600 800 1000 1200010203040506070Median = 205 Median = 196 Median = 164Median = 240 Median = 213 Median = 128 195 C.9 Recombination rate in 1 Mb windows is compared to the weighted average FST between populations. This figure corresponds to main figure 4. In this figure, we show FST compared to the recombination landscape of the second population in the FST pair which was not used in figure 4. See figure 4.4 for information on dots and spline colors. 0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RNFST RS RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate PacFST RS Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate PacFST RN Pac0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate EastFST RS East0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate RNFST East RN0.0e+00 5.0e-08 1.0e-07 1.5e-070.00.20.40.60.81.0Recombination Rate PacFST Pac East 196 Appendix D Supplementary information for Chapter 5 D.1 Pearson correlation values between FST and chromosome size compared to divergence times in a dataset where duplicate species/populations have been removed. -0.50.00.50 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia 197 D.2 Spearman rank correlation values for the relationship of FST and log chromosome length plotted against divergence date for the species pair using comparisons for all species pairs. -0.50.00.51.00 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia 198 D.3 Relationships between mean FST and log chromosome size for 27 avian species pairs only including scaffolds/chromosomes > 2 Mb. This corresponds to figure 1 in the main text, please see this figure for formatting details. trochiloides_trochiloides2 trochiloides_trochiloides3 virens_chyrsopariapharetra_pityophila tolmiei_philadelphia townsendi_virens trochiloides_trochiloides1nigrescens_graciae occidentalis_townsendi palmarum_coronata pensylvanica_petechiacitrinella_leucocephalos coronata_coronata cyanoptera__chrysoptera leucophrys_leucophryscarolinensis_carolinensis5 carolinensis_carolinensis6 castanea_striata citrina_ruticillacarolinensis_carolinensis1 carolinensis_carolinensis2 carolinensis_carolinensis3 carolinensis_carolinensis4americana_pitiayumi atricapilla_leucophrys bachmanii_chrysoptera bachmanii_cyanoptera15 16 17 18 19 15 16 17 18 19 16 17 18 1916 17 18 19 15 16 17 18 19 15 16 17 18 19 15 16 17 18 1916 17 18 19 16 17 18 19 16 17 18 19 16 17 18 1915 16 17 18 19 16 17 18 19 16 17 18 19 15 16 17 18 1915 16 17 18 19 15 16 17 18 19 16 17 18 19 16 17 18 1915 16 17 18 19 15 16 17 18 19 15 16 17 18 19 15 16 17 18 1916 17 18 19 15 16 17 18 19 16 17 18 19 16 17 18 190.300.350.400.450.300.350.400.450.500.300.320.340.360.380.100.150.200.240.250.260.270.280.290.150.200.250.300.350.300.350.400.450.250.300.350.400.180.200.220.240.0220.0240.0260.4000.4250.4500.4750.100.150.200.150.160.170.180.100.150.200.250.300.30.40.50.450.500.550.600.0750.1000.1250.1500.06750.07000.07250.07500.07750.150.200.250.250.300.350.400.380.390.000.050.100.150.200.350.400.450.500.0150.0200.0250.0300.210.230.250.270.560.570.580.590.300.350.40Log(Chromosome Size)F ST 199 D.4 Pearson correlation values (excluding chromosomes/scaffolds that are 2 Mb or smaller) for FST and log chromosome size plotted against the divergence date of the pair. -0.50.00.50 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia 200 D.5 Correlation and p- values between the association of FST /chromosome length and divergence date using the full data set. Row names indicate the input values (i.e., correlation coefficient for the relationship of FST and chromosome length) used to test the association with divergence date. This includes Pearson correlation coefficients with 2 Mb chromosomes/scaffolds removed, Spearman rank correlation, and Spearman rank correlation with 2Mb chromosomes/scaffolds removed. Column names indicate the test used to examine the relationship between FST /chromosomes length and divergence date. Pearson Pearson_pvalue Spearman Spearman_pvalue Pearson_2mb_remove -0.41 0.02 -0.37 0.035 Spearman -0.48 0.0053 -0.43 0.014 201 D.6 Spearman rank correlation values between FST and chromosome size compared to divergence times in a dataset where duplicate species/populations have been removed. -0.50.00.51.00 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia 202 D.7 Pearson correlation values between FST and chromosome size with scaffolds/chromosomes < 2Mb excluded plotted against divergence age using the reduced portion of our dataset where species that appear more than once in comparisons are removed. D.8 Correlation and p-values of the association between FST/chromosome length and divergence date retaining only 1 pair for populations that occur multiple times. For information regarding row and column names see appendix D.5. Pearson Pearson_pvalue Spearman Spearman_pvalue Pearson_2mb_remove -0.52 0.018 -0.50 0.027 Spearman -0.55 0.012 -0.55 0.014 -0.50.00.50 1 2 3 4divergence date (mya)correlation between FST and chromosome sizegenusArchilochusCalypteCerthiaEmberizaGeothlypisPhylloscopusSelasphorusSetophagaSittaTroglodytesVermivoraZonotrichia
Thesis/Dissertation
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Zoology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Irwin, Darren E
Introgression, recombination, and genomic differentiation in a cryptic species complex of North American passerines
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