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The role of insularity in promoting intraspecific differentiation in Song Sparrows Wilson, Amy 2008

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THE ROLE OF INSULARITY IN PROMOTING INTRASPECIFIC DIFFERENTIATION IN SONG SPARROWS  by  AMY WILSON  B.Sc., University of Calgary, 2000 M.Sc., University of British Columbia, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in THE FACULTY OF GRADUATE STUDIES  (Forestry) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2008  © Amy Wilson, 2008  Abstract  Islands are valuable research systems for evolution and conservation, but most work has focused on oceanic islands. Far less study has occurred on near-shore islands where interisland and island-mainland dispersal is an important microevolutionary process. Further studies in near-shore systems would aid the expansion of island evolutionary theory and conservation initiatives. In this thesis, I studied populations of Song Sparrows (Melospiza melodia) on near-shore islands along the Pacific coast of North America to examine the causes and consequences of dispersal for microevolutionary and ecological processes. Within an island metapopulation, where inter-island distances ranged from 200m to 2km, male and female immigration rates were influenced by adult density and sex ratio respectively, suggesting that intrasexual territoriality influences immigration. Islands differed in immigration levels, with low immigration and high resident recruitment on more isolated islands. I next examine genetic structuring at a larger spatial scale (0-300km). I found that the scale of genetic structuring within continuously distributed populations was less than 10km, suggesting that Song Sparrows are a sedentary passerine. Regional comparisons revealed that holding geographic distance constant, larger genetic distances occur in areas located at subspecific boundaries or across water barriers. The apparent reduction in dispersal to islands had broad-scale consequences. Across Pacific Coast islands, island populations consistently had lower genetic variation than mainland populations. Small and remote island populations tended to have the lowest genetic variation. From an in situ conservation stance, populations on large, remote islands could be important contributors to intraspecific genetic diversity because of high genetic differentiation. Finally, I link genetic structuring with contemporary dispersal and show that migration rates among the Channel Islands are low, suggesting that these islands are demographically independent. The absence of shared mtDNA haplotypes between extant and extinct populations suggests that inter-island migration was historically low, potentially explaining why the two extirpated islands have not been recolonized. Collectively, my thesis results increase our understanding of the mechanisms of divergence on insular populations by examining factors affecting dispersal, the spatial scale of divergence and estimating the consequences of reduced gene flow on islands for broadscale patterns of genetic variation, microevolution and demographic stability.  ii  Table of contents  Abstract .......................................................................................................................................... ii Table of contents .......................................................................................................................... iii List of tables................................................................................................................................... v List of figures ................................................................................................................................ vi Acknowledgements .................................................................................................................... viii Dedication ..................................................................................................................................... ix Statement of co-authorship .......................................................................................................... x Chapter 1 − Thesis introduction .................................................................................................. 1 Introduction ...............................................................................................................................................1 Thesis outline and chapter descriptions ....................................................................................................5 Study species..............................................................................................................................................7 Study areas ................................................................................................................................................7 References................................................................................................................................................10  Chapter 2 −Influential factors for natal dispersal in an avian island metapopulation. ....... 14 Introduction .............................................................................................................................................14 Methods ...................................................................................................................................................15 Data analyses ..........................................................................................................................................16 Results .....................................................................................................................................................18 Discussion................................................................................................................................................19 References................................................................................................................................................29  Chapter 3 − Micro-spatial genetic structure in Song Sparrows: A comparison among subspecies. .................................................................................................................................... 32 Introduction .............................................................................................................................................32 Methods ...................................................................................................................................................35 Data analysis ...........................................................................................................................................36 Results .....................................................................................................................................................40 Discussion................................................................................................................................................41 References................................................................................................................................................53  iii  Chapter 4 − The contribution of island populations to in situ genetic conservation. ........... 61 Chapter 5 − Inter-island genetic divergence of an avian endemic on the Channel Islands. ......................................................................................................................................... 62 Introduction .............................................................................................................................................62 Methods ...................................................................................................................................................64 Data analysis ...........................................................................................................................................66 Results .....................................................................................................................................................69 Discussion................................................................................................................................................71 References................................................................................................................................................81  Chapter 6 – Conclusions............................................................................................................. 87 Thesis conclusions and implications .......................................................................................................87 Future directions .....................................................................................................................................90 References................................................................................................................................................96  Appendices ................................................................................................................................... 99 Appendix 1. Derivations for partitioning the components of genetic variation. .....................................99 Appendix 2. Parsimony mtDNA haplotype network for Channel Island Song Sparrows ......................100 Appendix 3. Permits...............................................................................................................................101  iv  List of tables  Table 2.1. Island characteristics and recruitment histories of monitored island populations during 1998 to 2004. Recruitment of residents and immigrants was calculated as the mean number of recruits per year. ................................................................................... 23 Table 2.2. Models of factors affecting: a) female immigration rate, b) male immigration rate and c) individual dispersal from natal island. The top three models are shown along with the intercept-only model for both female and male immigration candidate model sets. ΔAICc is the change in AICc between that model and the best fitting model, wi is model weight (a measure of the relative likelihood of each model) and K is the number of model parameters. ............................................................................................................ 24 Table 2.3. Models of annual survival probabilities (φ) for adult Song Sparrows in the Southern Gulf Islands, British Columbia, 1998–2004. In each model island is a categorical variable and the intercept and recapture rate are also included as parameters. Subscripts denote models including the parameters of breeding island (ISL), gender (SEX), and residency status (DISP). ............................................................................... 25 Table 2.4. Best model estimates of annual survival (φ), and 95% CI for adult Song Sparrows across 1998-2004. ............................................................................................................ 26 Table 3.1. Summary of published microsatellite studies for passerine species where spatial genetic structure was examined as a minor or major component of the study. The average distance interval (Avg. dist) and distance ranges (Dist. range) were calculated based on published coordinates or obtained from maps. The reported outcome of the tests for IBD is provided in the fourth column as: IBD pattern not present (N), IBD pattern present (Y) and not reported (NR). .................................................................................. 47 Table 5.1 Estimates of contemporary (mC) migration rates between Song Sparrow populations within the Channel Islands as estimated from BayesAss (Wilson & Rannala 2003). The mode migration rate is provided along with the 95% credible interval. The uninformative credible interval was: (0, 0.11). ............................................................... 76 Table 5.2. Estimates of pairwise divergence between Song Sparrow populations on Santa Cruz (SCRU), Santa Rosa (SROS), San Miguel (SMIG), Santa Barbara (SBI) and San Clemente (SCLE) islands. Genetic distances were calculated based on 512 bp of the mitochondrial control region and were corrected for within-population divergence. ..... 77  v  List of figures  Figure 2.1. Study area and continuously monitored islands (black) in the Haro Strait, BC, Canada. Inset map shows general area of study site........................................................ 27 Figure 2.2. Influence of adult sex ratio (a), and density (b), in the recipient population on sex-specific immigration rates. Female immigration is indicated as solid circles and male immigration is indicated as open triangles. ..................................................................... 28 Figure 3.1. The predicted genetic structure patterns as a function of relative influences of drift and gene flow between sampled populations. Studies that predominantly sample population pairs which have very high gene flow would be expected to have corrected pairwise genetic distances centering on zero, with no geographic relationship (A). Studies that include population pairs representing a wider range of gene flow/drift levels would be expected to show the increasing genetic distance and variance associated with an ibd pattern (B). Studies that compare populations which are isolated would be expected to show high pairwise genetic distances, along with high variance with no geographic relationship due to predominant drift forces (C) (Modified from Hutchison & Templeton 1999).............................................................................................................. 48 Figure 3.2. a) Map of sampling sites of Song Sparrow populations: 1) Georgia Basin, 2) San Francisco Bay and 3) Salton Sea. Inset B depicts sampling along the BC Mainland and Vancouver Island: 1) Campbell River, 2) Powell River, 3) Sechelt, 4) Qualicum, 5) Duncan, 6) Sooke and 7) Delta. Inset c) depicts the sampling sites within the Southern Gulf Islands: 1, 2) Shell Islands, 3-5) Dock Islands, 6) Reay, 7) Mandarte, 8) Halibut and 9) Sidney. ......................................................................................................................... 49 Figure 3.3. Correlogram of the correlation coefficient (r) between genetic and geographic distance at four distance classes. Correlation coefficients were calculated at the individual level for Song Sparrow populations within a) San Francisco Bay and b) Georgia Basin and c) the Salton Sea. The permuted 95% confidence interval (dashed lines) around the null of r = 0, and the bootstrapped 95% confidence intervals around the correlation for each distance class are also shown. Please note the y-axis scaling difference for plot c. ........................................................................................................ 50 Figure 3.4. Correlogram of the correlation coefficient (r) between genetic and geographic distance at four distance classes. Correlation coefficients were calculated at the individual level for Song Sparrow populations in the Southern Gulf Islands. The permuted 95% confidence interval (dashed lines) around the null of r = 0, and the bootstrapped 95% confidence intervals around the correlation for each distance class are also shown. ...................................................................................................................... 51  vi  Figure 3.5. Relationship between pair-wise genetic distance (G’ST) and log geographic distance (km) for San Francisco Bay (grey circles) and the BC mainland and Vancouver Island populations (white circles). ................................................................................... 52 Figure 5.1. Map of the Channel Islands showing the geographic distribution of unique mitochondrial DNA haplotypes. Each pie chart indicates the haplotype frequency for samples collected from that population. Song Sparrows have extant populations on San Miguel, Santa Rosa and Santa Cruz islands and extirpated populations on Santa Barbara and San Clemente islands. ............................................................................................... 78 Figure 5.2 Results of clustering analysis in structure (Pritchard et al. 2000) The blue and red areas represent tightly-spaced columns each of which represents the admixture coefficient for a single individual bird. Birds are grouped by sampling location within Santa Cruz, Santa Rosa or San Miguel islands. The height of each column indicates the proportion of ancestry for each individual that is attributed to the two genetic clusters . 79 Figure 5.3 Inter-island and mainland genetic differentiation patterns as a function of geographical distance in the four Channel Island taxa for which genetic data is available. The taxa are the Channel Island fox (Wayne 1991), San Clemente Loggerhead Shrike (Eggert et al, 2004), Island Scrub Jay (Delaney & Wayne 2005), and Channel Island Song Sparrow (present study). Units of genetic differentiation for the three avian species are FST obtained from microsatellites, while allozyme data for the Channel Island fox are Nei’s genetic distances. ................................................................................................... 80 Figure 6.1. The extent of genetic differentiation (G’ST) of Song Sparrow populations on the Channel Islands, Vancouver Island, Haida Gwaii and the Aleutian Islands from the corresponding mainland populations as a function of geographic distance. ................... 94 Figure 6.2. The retention of allelic richness for island populations in reference to closest mainland populations as a function of distance (km) from the island to the mainland. Populations included are: Vancouver Island, Channel Islands (Santa Cruz, Santa Rosa and San Miguel), Haida Gwaii and Alaskan Islands (Kodiak, Adak and Attu). The sizes of the circles are proportional to log island size (km2). ................................................... 95  vii  Acknowledgements  It takes a flock of people to fledge a PhD student and I would like to take this opportunity to thank them. First of all, I thank my supervisor Peter Arcese for being supportive and generous with his time and for believing that someone can work on plants then birds. I also thank my supervisory committee members Rick Taylor, Darren Irwin and Sally Aitken who provided considerable guidance and thoughtful feedback. The data collection for chapter 2 has been the result of many people, most recently, were Scott Wilson, Danielle Dagenais, Andrew Johnston, Amy Marr, Katie O’Connor and Simone Runyan. Scott Wilson and Christy Begus aided me for the field collection for chapters 3 and 4. This work was made possible by grants provided by NSERC, W. and H. Hesse, NSF, UBC Graduate Fellowship, UBC Dept Forestry, California Genetic Conservation Program, American Ornithologists Union and Friends of Ecological Reserves. Site access was provided by the Tsawout and Tseycum Bands, A. and H. Brumbaum, T. and M. Boyle, Parks Canada, Environment Canada and the National Park Service. During my time as a student, I have received valuable guidance from Bart van der Kamp, Jamie Smith, Jerram and Ester Brown and the botany faculty at the University of Calgary. I am indebted to Carol Ritland, Allyson Miscampbell and Hesther Yueh, who not only provided molecular expertise, but were solid friends and prevented me from turning into a muggle. I also thank Cindy Prescott for her help through a major difficulty concerning lost paperwork. My graduate experience was enriched by friendship both inside and outside of UBC, most notably from the Calgary ex-pats, Andrea Griffiths and Michelle Buresi. Very special thanks go to Tony, Mavi, Lenny and Deuce Wilson for helping me keep things in perspective and for grudgingly providing hugs. I owe my parents Sandy and Keith everything, as they have always been supportive from the very, very beginning and worked very hard so that I could go along this path. Their patience and unwavering belief in me has been instrumental for enabling me to come this far and to keep going. My greatest thanks and love go to my husband Scott. I could never come up with words meaningful enough to thank Scott, and since I keep ringing up a debt I won’t try here, but there is nothing that I have done or will do in my career or otherwise, that does not have some element of his guidance embedded in it. Since words do not suffice, I will try to show my appreciation by doing something that benefits the wildlife that has brought us both so much joy.  viii  Dedication  To my Mum and Dad, Sandy and Keith.  ix  Statement of co-authorship  Chapter 2 was co-authored by my advisor Peter Arcese. Chapter 4 was co-authored by Peter Arcese, Lukas Keller, Yvonne Chan, Michael Patten, Christin Pruett, and Kevin Winker. The latter four authors contributed raw genetic data which had been published elsewhere. Lukas Keller generated the genotypic data for a subset of the Southern Gulf Islands during his doctoral research under the supervision of Peter Arcese.  All co-authors provided valuable input and comments on manuscripts, however, I designed and conducted the research, conducted all analyses and wrote all manuscripts.  x  Chapter 1 − Thesis introduction  Introduction Dispersal is a fundamental parameter for both ecological and evolutionary processes. At the ecological level, dispersal among populations can increase the persistence of both individual populations and metapopulations by augmenting population size and reducing the influence of random fluctuations in reproductive rates and survivorship, collectively termed demographic stochasticity (Hanski 1999, Clobert et al. 2001). The risk of extirpation due to demographic stochasticity increases as populations decrease in size, such that in the absence of immigration, very small populations may undergo frequent extirpations. For example, among Fritillary butterfly (Melitaea cinxia) populations, the persistence of very small populations was dependent on immigrants from the larger, more stable populations (Hanski 1999). Given the substantial contribution of dispersal to population viability, it is important to understand what factors influence dispersal rates. Dispersal, however, is a complex, multi-staged process, and is influenced by a range of evolutionary, ecological and life history factors, operating over different time and spatial scales. Examples of evolutionary influences on dispersal rates are inbreeding avoidance (Pusey 1987) and spatial and temporal environmental variability. The influence of ecological drivers of dispersal may be more temporally and spatially variable. In the case of density-dependent dispersal, the population structure of the recipient or source populations can influence immigration and emigration patterns (Sæther et al. 1999). Stable metapopulation dynamics result when emigration is positively density-dependent (higher from high density populations) and immigration is negatively density-dependent (higher into lower density populations). Emigration tends to be positively density dependent within avian populations (Matthysen 2005); however, less is known regarding the factors influencing immigration rates because of the logistical difficulties in tracking individuals as they disperse to distant areas. The strength of density-dependence and other influences will be strongly modified by the extent of population isolation and the permeability of the intervening matrix between populations (Clobert et al. 2001, Bowler & Benton 2005). Although the dispersal of individuals can have evolutionary implications by increasing population persistence, it is the dispersal of genes, termed as gene flow that has greater 1  evolutionary consequences. Dispersal is an obvious prerequisite for gene flow, so at the landscape level, factors that influence dispersal will also influence gene flow rates, but dispersal and gene flow rates may not be equivalent. Discrepancies between gene flow and dispersal rates occur when immigrants have reduced reproductive success or if selection against hybrids occurs (dispersal > gene flow, Bensch et al. 1998, Alexandrino et al. 2005), or if immigrants have a selective advantage, leading to their genes spreading rapidly (dispersal < gene flow, Ingvarsson & Whitlock 2000). Within population genetic and evolutionary models, gene flow is a central parameter, which reintroduces lost alleles or homogenizes allelic frequencies. By homogenizing allelic frequencies, gene flow can be antagonistic to adaptive divergence by impeding genetic changes in response to localized selection pressures. Stated formally, the ‘divergence with gene flow’ model suggests that adaptive divergence can only occur if the influence of selection is high relative to the level of gene flow (Endler 1977, Rice & Hostert 1993). Evidence for the divergence with gene flow model comes from negative correlations between phenotypic divergence and inferred gene flow levels. However, a negative correlation is also consistent with phenotypic divergence impeding gene flow (Garant et al. 2007, Rasanen & Hendry 2008). Measuring patterns of dispersal and gene flow Evaluating the influential factors and consequences of dispersal and gene flow requires that these parameters can be accurately measured. Dispersal patterns are typically measured using mark-recapture methods which, although providing a definitive account of dispersal events, do not reliably capture long distance events and are labour intensive (Koenig et al. 1996). Dispersal events can also be measured using molecular mark-recapture techniques, where repeated samplings of particular genotypes are synonymous with ‘recaptures’. More commonly, provided sufficient numbers of molecular markers are available, contemporary dispersal rates are estimated by identifying first-generation immigrants using population genetic assignment techniques (Goudet et al. 2001). Longer-term estimates of dispersal and gene flow can be inferred from the extent of genetic differentiation. Estimates of dispersal based on genetic differentiation assume that while dispersal rates will be lower than gene flow, the relationship will be consistent. Estimating gene flow rates from genetic differentiation can also be problematic, as many models rely on unrealistic assumptions (Whitlock & McCauley 1999) or have limited reliability for high gene 2  flow species (Waples 1998). Analytical methods are continually improving, with Bayesian approaches offering some of the most promising techniques (Shoemaker et al. 1999, Beaumont & Rannala 2004, Excoffier & Heckel 2006). Despite these advances, it remains difficult to estimate migration in cases of low genetic differentiation, and migration estimates are rarely placed in the context of contemporary demography (Waples & Gaggiotti 2006). Islands as ecological models for dispersal and gene flow Novel ecological pressures, increased isolation, and well-defined borders make island populations ideal for studying the dynamics of dispersal and gene flow processes operating over both ecological and evolutionary timescales (Grant 1998, Emerson 2002). However, much of the theory of the genetics of insular avian populations comes from studies of oceanic islands. Oceanic islands are ideal models for allopatric divergence, whereas studies of continental islands are more suitable for examining parapatric or ‘divergence with gene flow’ evolutionary models. Continental islands are closer to the mainland (Williamson 1981), such that high rates of postcolonization immigration may impede evolutionary changes on continental islands (Endler 1977). The close proximity of continental islands also results in these islands having a larger subset of the mainland fauna, which may reduce the extent to which the selective environment differs from the mainland, as compared to oceanic islands. Genetic changes on islands The potential for evolutionary distinctiveness is higher for island populations because for many species, open water barriers reduce gene flow rates. Reduced levels of gene flow into island populations should result in lower genetic diversity and higher genetic divergence between island populations as compared to mainland populations. Meta-analyses have confirmed that island populations typically have reduced genetic diversity as compared to similarly isolated mainland populations (Frankham 1997). The genetic divergence of island populations depends on the extent to which water is a barrier for a species. Genetic distance will more rapidly increase as the permeability of the intervening matrix decreases. Reduced gene flow into island populations is the most obvious factor influencing island genetic structure, however there are other influential factors such as founder effects during colonization and the effective population size (Ne) of the population.  3  Founder effects Colonization is a sampling process, such that the amount of genetic diversity captured by the founding population will have a positive but saturating relationship with the size of that founding population, Therefore, if the numbers of founders are small, allelic richness will be reduced within the island populations, with disproportionate losses of rare alleles and more significant changes in frequency occurring for loci consisting of many alleles (Luikart et al. 1998, Keller et al. 2001). Actual estimates of founding flock sizes have ranged from 30 for the Galápagos finches (Vincek et al. 1997) to 200 for the Silvereyes of Heron Island (Clegg et al. 2002). Within archipelagos founder effects are compounded across sequential, interisland colonizations. Sequential colonizations lead to a repeated sub-sampling of genetic diversity, potentially resulting in considerable losses in allelic richness, particularly for island populations that are at the end of the colonization sequence (Clegg et al. 2002, Pruett & Winker 2005). Subsequent genetic changes within island populations will be determined by post-colonization levels of genetic drift and gene flow. Population Ne and time duration of isolation The influence of post-colonization genetic drift and gene flow are determined by the effective population size, Ne. The genetic divergence of a population is approximated by the equation: FST=1/ (1+4Nem), where Ne is the effective population size and m is the migration rate into the population (Wright 1965). From this simplified equation, it is clear that, all else equal, genetic divergence will be lower if either Ne or m are high. As the effective population size decreases, drift becomes increasingly strong, and the presence of particular alleles will depend on their population-level frequencies. The duration of isolation (T) also interacts with the effective population size to influence genetic differentiation, which can be seen in the equation: FST = 1- e -T2Ne (Wright 1951). It is clear from this equation that a large population would require longer time periods to reach the same numerical value of FST than a small population. Similarly, holding the effective population size constant, older island populations should be more genetically diverged. An important consideration is, however, that populations that undergo periodic extinction-recolonization events will lose all previously accumulated genetic divergence, regardless whether this divergence is adaptive or non-adaptive. 4  Thesis outline and chapter descriptions Models within conservation and evolutionary biology rely heavily on estimates of dispersal and gene flow therefore, I will be focusing on the dynamics of these two parameters at a range of spatial scales. Chapters 2 and 3 are mechanistic and deal with the factors that influence the extent and scale of dispersal and gene flow. Chapters 4 and 5 are pattern-based, and examine the scales of isolation under which island populations demonstrate biologically important divergence. These four research chapters are summarized below. Contemporary patterns of dispersal are difficult to observe, therefore, the determinants of dispersal are often unknown. In chapter 2, I use a long-term mark-recapture study within the Southern Gulf Islands in British Columbia to demonstrate that dispersal patterns are influenced by local population structure, in such a way that is consistent with intra-sexual competition for breeding resources. The most supported predictors of female and male immigration were sex ratio and adult density respectively. I also reported lower apparent survival for females, suggesting that vacancy rate rather than innate dispersal differences may account for the femalebias among immigrants. A second important result were the strong differences in dispersal patterns across islands. Within our study area, small islands that were closely spaced had the majority of breeding vacancies filled by immigrants. Conversely, on Mandarte Island which was the largest, most isolated study island, the primary means of recruitment were birds born on the island. These recruitment asymmetries have implications ranging from predictions of inbreeding risk to broad-scale population models that assume spatial uniformity in recruitment. The main conclusions of this demographic study are applicable to the fine-scale, proximate determinants of dispersal. However, the limits of dispersal or the influence of geographical and environmental factors on dispersal could not be adequately addressed with mark-recapture data alone. Long-term patterns of dispersal can be inferred from patterns in population genetic structure. In chapter 3, I adopted a molecular approach to examine the spatial scale over which genetic structuring occurs, which provides a valid estimate of the spatial scale over which migration is a strong determinant of population structure. Genetic samples were collected from 200 individuals in 15 populations across the BC Georgia Basin. Through collaboration with Y. Chan and M. Patten, I was able to expand the genetic data set to include San Francisco Bay and the Salton Sea. This collaborative data set enabled the examination of the extent of geographical 5  variation in genetic structuring and the contribution of localized factors to this variation. I found that the spatial scale of genetic structuring was less than 10 km. Geographic distance was not strongly correlated with genetic structuring, whereas salinity and subspecific membership contributed to increased genetic distances among individuals. I also analyzed individuals from the Southern Gulf Islands, where I demonstrated that across the same geographical scales, individuals on island populations are more genetically related than individuals within mainland populations, confirming that even the small (1- 5 km) water barriers separating the Southern Gulf Islands can be a dispersal impediment for Song Sparrows. Chapters 4 and 5 focus on the broad-scale consequences of water barriers on patterns of genetic diversity and differentiation. In chapter 4, I examine the distribution of intraspecific diversity across all major archipelagoes along the Pacific Coast. Based on this large data set, I was able to confirm the general expectation that allelic diversity is reduced on islands, and that this decline is most significant on remote or very small islands. Gene diversity (analogous to heterozygosity), only declined on very remote or relatively small islands. However, tiny islands (< 4 ha) that have high immigration and frequent population turnovers did not have reduced gene diversity. Lastly, I addressed the extent to which islands contribute to in situ conservation in cases where these islands already held conservation priority based on recognized endemism or other non-genetic criteria. I found that islands could contribute positively to in situ conservation of genetic diversity if differentiation is also considered. In chapter 5, I examine the micro-evolutionary relationships within the Channel Islands, which although close to the mainland, are geologically old with high numbers of endemic subspecies. Despite some ecological study, there have been limited genetic analyses of avian taxa within these islands, and no studies of single avian taxon across multiple islands. There is significant taxonomic variation in endemism across the Channel Islands, and the first question becomes, to what extent does isolation and species-level vagility account for these differences? The Channel Island Song Sparrow is a good study species for this question because as an endemic subspecies, it represents moderate morphological divergence and populations are present on several of the Channel Islands. I found relatively strong inter-island differentiation and sufficiently low migration rates to suggest that contemporary populations are demographically independent. Inter-island distances had a major influence on genetic affinities 6  among the islands, whereas ecological similarity as measured by shared avian and plant species did not. In comparison with the limited genetic data from more morphologically divergent Channel Island taxa, Song Sparrow divergence patterns were lower, which is consistent with the hypothesis that dispersal rates are inversely correlated with subspecific divergence. Study species Distribution and subspecific divergence The Song Sparrow (Melospiza melodia), is a passerine that has a transcontinental distribution across North America. Within the eastern part of their range, Song Sparrows are migratory, while Pacific coast forms are year-round residents, although some altitudinal migration is expected (Arcese et al. 2002). One remarkable aspect of Song Sparrows is their substantial intraspecific divergence. There are over 25 diagnosable Song Sparrow subspecies (Arcese et al. 2002), whereas the average for North American passerines species is 3.3 subspecies (Klicka & Zink 1999). The majority of these subspecies occur along coastal areas, particularly in California and on offshore islands of western North America (Zink & Dittmann 1993). Phenotypic variation among among subspecies is extensive, with substantial clines in body mass and plumage. For example, body mass ranges from 20g in southern California to 40g in the Aleutian Islands (Aldrich 1984). Although Song Sparrow subspecies are morphologically diagnosable, in two wideranging surveys of mitochondrial divergence (Zink & Dittman 1993, Fry & Zink 1998), haplotypes were not specific to subspecies groups and were not structured geographically. For example, haplotypes from Mexico were found in Alaskan populations. The fact that there were no subspecies-specific haplotypes suggested to the authors that Song Sparrows had recently diverged (Zink & Dittman 1993, Fry & Zink 1998). The alternative hypothesis to a young and rapid divergence is that incomplete lineage sorting is preventing the relationships among lineages from to reflecting actual organismal relationships. The apparent lack of concordance between morphology and genetics makes Song Sparrows a promising model for increasing our understanding of both the mechanisms of divergence and the interpretation of genetic patterns. Study areas This thesis is based on published and unpublished genetic data collected across coastal North America. The comparative analyses within Chapters 3 and 4 incorporate previously 7  published data from Song Sparrow populations in San Francisco Bay (Chan & Arcese 2002, 2003), Salton Sea (Patten et al. 2004) and mainland and island populations in Alaska and northern BC (Pruett & Winker 2005). I collected new genetic samples across the coastal BC mainland, along the eastern coast of Vancouver Island and in the Southern Gulf Islands, which I collectively refer to as the Georgia Basin. Existing but unpublished genotypes were available for the Southern Gulf Islands and Triangle Island from L. Keller. In addition, I collected new genetic data for all extant Song Sparrow populations on the Californian Channel Islands. These new or unpublished genetic data were incorporated in Chapters 3 and 4, while Chapter 5 only includes the Channel Island data. Details on the study areas where new data was collected is provided below. Vancouver Island and BC Coast A single subspecies, M. m. morphna occurs continuously across the southern BC coast. Based on Breeding Bird Survey data (Sauer et al. 2006), Song Sparrow population densities within this region are highest in the lower mainland, and decline northward along coastal areas as forested habitat increases. The Georgia Basin has been unglaciated for approximately 13 KYA, although a glacial refugium on the western side of Vancouver Island has been proposed (Pielou 1991). Vancouver Island is connected to the coastal mainland by a stepping-stone route provided by the Broughton archipelago and Southern Gulf Islands. Channel Islands The Channel Islands archipelago consists of eight islands, situated 20 to almost 120 km offshore from Los Angeles (Figure 5.1). There is a high level of endemism on these near-shore islands, which varies among taxa. Examples range from taxa that are undifferentiated from the mainland form, to those that are endemic to particular islands or to the entire Channel Island group. This variability among species, offers substantial potential insight into the intrinsic and extrinsic drivers of subspecific differentiation. Major geological events occurred approximately 10 to 12 KYA, which is when the Northern Channel Islands were formed from the submergence of the super-island Santarosae (Johnson 1978, Bloom 1983, Schoenherr et al. 2003). The history of human influence has also had profound effects on the Channel Island flora and fauna, beginning with the appearance of the Chumash First Nations as long as 13 KYA (Schoenherr et al. 2003). Human influence accelerated considerably in the 1800s, when 8  Chumash settlements were replaced with intensive agricultural operations. During the past century, sheep and cattle farming occurred extensively across the archipelago, leading to the complete denuding of San Miguel, Santa Barbara and San Nicolas islands (Schoenherr et al. 2003). This intensive disturbance led to multiple extinctions and extirpations of endemic flora and fauna. Two of these extirpations involved Song Sparrows, which were last seen on Santa Barbara and San Clemente Islands in 1967 and 1968 respectively (Collins 2008). Traditionally, the Song Sparrow populations of the Channel Islands have been delineated into three subspecies: M .m clementae on Santa Rosa and San Clemente islands, M .m. micronyx on San Miguel Island and M. m. graminea on Santa Barbara Island. However, it was recently proposed that only a single subspecies, M.m.graminea, a subspecies of special concern (Collins 2008), is diagnosable across the islands (Patten 2001).  9  References 1. Aldrich JW (1984) Ecogeographical variation in size and proportions of Song Sparrows (Melospiza melodia). Ornithological Monographs 35. 2. Alexandrino J, Baird SJE, Lawson L, Macey JR, Moritz C, Wake DB (2005) Strong selection against hybrids at a hybrid zone in the Ensatina ring species complex and its evolutionary implications. Evolution 59, 1334-1347. 3. 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Chan Y, Arcese P (2002) Subspecific differentiation and conservation of Song Sparrows (Melospiza melodia) in the San Francisco Bay region inferred by microsatellite loci analysis. Auk 119, 641-657. 9. Chan Y, Arcese P (2003) Morphological and microsatellite differentiation in Melospiza melodia (Aves) at a microgeographic scale. J. Evol Biol 16, 939-947. 10. Clegg SM, Degnan SM, Kikkawa J, Moritz C, Estoup A, Owens IPF (2002) Genetic consequences of sequential founder events by an island-colonizing bird. Proc Natl Acad Sci USA 99: 8127-8132. 11. Clobert J, Danchin E, Dhondt AA, Nichols JD (2001) Dispersal. Oxford University Press, New York.  10  12. Collins PW (2008) Channel Island Song Sparrow (Melospiza melodia graminea). In: Studies in Western Birds no. 1 (eds. Shuford WD & Gardali T), pp. 425-431, Western Field Ornithologists, Camarillo, California, and California Department of Fish and Game, Sacramento. 13. Emerson BC (2002) Evolution on oceanic islands: molecular phylogenetic approaches to understanding pattern and process. Mol Ecol 11, 951-966. 14. Endler JA (1977) Geographic Variation, Speciation, and Clines. Monogr. Popul. Biol. 10, Princeton Univ. Press, Princeton, NJ 15. Excoffier L, Heckel G (2006) Computer programs for population genetics data analysis: a survival guide. Nat Rev Genet 7, 745-758. 16. Frankham R (1997) Do island populations have lower genetic variation than mainland populations? Heredity 78, 311 327 17. Fry AJ, Zink RM (1998) Geographic analysis of nucleotide diversity and Song Sparrow (Aves: Emberizidae) population history. Mol Ecol 7, 1303-1313. 18. Garant D, Forde SE, Hendry AP (2007) The multifarious effects of dispersal and gene flow. Funct Ecol 21, 434-443. 19. Goudet, J. 2001. Fstat, a program to estimate and test gene diversities and fixation indices. Version 2.9.3. http://www.unil.ch/izea/softwares/fstat.html 20. Grant, P.R. (ed.) (1998) Evolution on Islands. Oxford University Press, New York. 21. Hanski I (1999) Metapopulation ecology. - Oxford University Press. Oxford 22. Ingvarsson, P. & Whitlock, M.C. (2000). Heterosis increases the effective migration rate. Proc. R Soc. Lond. B 267, 1321–1326. 23. Johnson DL (1978) The origin of island mammoths and the Quaternary land bridge history of the Northern Channel Islands, California. Quat Res 10, 204-225. 24. Keller LF, Jeffery KJ, Arcese P, Beaumont MA, Hochachka WM, Smith JNM, Bruford MW (2001) Immigration and the ephemerality of a natural population bottleneck: evidence from molecular markers. Proc Roy Soc B 268, 1387-1394 25. Klicka J, Zink RM (1999) Pleistocene effects on North American songbird evolution. Proc Roy Soc B 266:695-700.  11  26. Koenig WD, Van Vuren D, Hooge PN (1996) Detectability, philopatry, and the distribution of dispersal distances in vertebrates. Trends Ecol Evol 11, 514-517. 27. Luikart G, Sherwin WB, Steele BM, Allendorf FW (1998). Usefulness of molecular markers for detecting population bottlenecks via monitoring genetic change. Mol. Ecol 7, 963-974. 28. Matthysen E (2005) Density-dependent dispersal in birds and mammals. Ecography 28, 403416. 29. Patten MA (2001) The roles of habitat and signalling in speciation: evidence from a contact zone of two Song Sparrow (Melospiza melodia) subspecies. Ph.D Dissertation, University of California Riverside. 30. Patten MA, Rotenberry JT, Zuk M (2004) Habitat selection, acoustic adaptation, and the evolution of reproductive isolation. Evolution 58, 2144-2155. 31. Pielou EC (1991) After the Ice Age: the Return of Life to Glaciated North America. University of Chicago Press, Chicago. 32. Pruett CL, Winker K (2005) Northwestern Song Sparrow populations show genetic effects of sequential colonization. Mol Ecol 14, 1421–1434 33. Pusey AE (1987) Sex-biased dispersal and inbreeding avoidance in birds and mammals. Trends Ecol Evol 2, 295–299. 34. Räsänen K, Hendry AP (2008) Disentangling interactions between adaptive divergence and gene flow when ecology drives diversification. Ecol Lett 11, 624-636. 35. Rice WR, Hostert EE (1993) Laboratory experiments on speciation - What have we learned in 40 years. Evolution 47, 1637-1653. 36. Sæther BE, Engen S & Lande R (1999) Finite metapopulation models with densitydependent migration and stochastic local dynamics. Proc Roy Soc B 266, 113-118. 37. Sauer JR, Hines JE, Fallon J (2006) The North American Breeding Bird Survey, Results and Analysis 1966 - 2006. Version 6.2.2006. USGS Patuxent Wildlife Research Center, Laurel, MD http://www.mbr-pwrc.usgs.gov/bbs/ [Accessed 2 Jan 2008] 38. Schoenherr AA, Feldmeth CR, Emerson MJ (2003). Natural history of the islands of California. California natural history guides, 61. Berkeley: University of California Press. 39. Shoemaker JS, Painter IS, Weir BS (1999) Bayesian statistics in genetics - a guide for the uninitiated. Trends in Genetics 15, 354-358. 12  40. Vincek V, O’Huigin C, Satta Y et al. (1997) How large was the founding population of Darwin’s finches? Proc Roy Soc B 264, 111–118. 41. Waples RS (1998) Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J Heredity 89, 438-450. 42. Waples RS, Gaggiotti O (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol 15, 1419-1439. 43. Whitlock MC, Mccauley DE (1999) Indirect measures of gene flow and migration: FST≠1/(4Nm+1). Heredity 82, 117–125. 44. Williamson M (1981) Island populations. Oxford University Press. 45. Wright S (1951) The genetical structure of populations. Ann Eugen 13, 323-354. 46. Wright S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395-420. 47. Zink RM, Dittmann DL (1993) Gene flow, refugia, and evolution of geographic-variation in the Song Sparrow (Melospiza melodia). Evolution 47, 717-729.  13  Chapter 2 −Influential factors for natal dispersal in an avian island metapopulation1.  Introduction Dispersal patterns influence genetic and demographic processes that affect the ecology, evolution and conservation of species, and can be divided into three functionally distinct stages: emigration, migration and immigration (Ims and Yoccoz 1997). Behavior in each stage can be influenced by a range of factors related to the intrinsic state of individuals including age and sex, and extrinsic factors such as the demographic structure of recipient and donor populations, and patch size and isolation (reviewed in Clobert et al. 2001, Bowler and Benton 2005). In this study, we focused on the combined influence of population demography and island characteristics on sex-specific dispersal patterns among small island populations. Theory suggests that as populations decline in size, random variation in demographic rates such as survivorship and fecundity increase the chance that one or both sexes are extirpated (Lande et al. 2003). Thus, understanding how sex-biased dispersal patterns arise has become important to predicting landscape scale patterns of population persistence (Dale 2001). Several studies have shown that the immigration rates of particular sexes were more strongly influenced by the density of same-sex individuals of root voles Microtus oeconomus (Andreassen and Ims 2001) and by the availability of mates in superb fairy-wrens Malurus cyaneus (Pruett-Jones and Lewis 1990) and black-throated blue warblers Dendroica caerulescens (Marra and Holmes 1997). The idea that intra-sexual competition for mates and territories influences dispersal also underpins long-standing hypotheses for the evolution of sex-biased dispersal (Murray 1967, Greenwood 1980, Waser 1985). For example, intra-sexual competition in Song Sparrows (Melospiza melodia) affects male fitness by limiting access to territories and mates, and affects female fitness by limiting access to male parental care (Arcese 1989a, b, Smith et al. 2006). Thus, we might expect immigration in Song Sparrow populations to be related to sex-specific 1  This chapter has been published. Wilson AG, Arcese P (2008) Influential factors for natal dispersal in an avian island metapopulation. Journal of Avian Biology 36, 341-347. [doi: 10.1111/j.2008.0908-8857.04239.x] 14  variation in survival, to be highest in the sex in short supply, and to decline as same-sex density increases. Testing these predictions requires a system wherein immigrants and residents are reliably identified, populations can be studied over multiple years to obtain robust survival estimates (Sandercock 2006), and variation in population structure is sufficient to influence immigration measurably. However, such studies are logistically difficult and, as a consequence, these predictions are rarely tested in natural songbird populations. We tested these predictions by recording detailed data on demography and dispersal patterns in nine island populations of Song Sparrows wherein all residents were individuallymarked and all immigration events were monitored for seven consecutive years. We show here that: i) recruitment patterns varied among islands and ii) male and female immigration responded differently to population structure, but in a manner consistent with the predictions above. Methods Our data were collected during the breeding season from March to August in 1998 to 2004 on Mandarte Island and eight other small islands (Table 2.1) in the Haro Strait, c. 20 km northeast of Victoria, B.C., Canada (48°N, 123°W, Fig. 2.1). By April 1998, all established adults on these islands had been marked uniquely with three colored plastic bands and one numbered aluminum band. With few exceptions, all nine islands were monitored every 2-7 d during the breeding season, such that all territorial birds were identified, all nests were located (mainly during incubation), and all birds hatched on the island were banded as nestlings. In four of 800 breeding attempts (0.005%), we banded young as dependent fledglings (< 24 days of age). In the unlikely event that successful nesting attempts were missed, if these fledgling birds recruited on their natal island, they would erroneously be categorized as immigrants. Although it is possible that we missed some nesting attempts, and thus failed to band a small number of locally hatched young, the small size of our study islands, their simple vegetation structure, and the consistent application of monitoring methods based on many years of experience with the species, suggests to us that few if any successful nests escaped detection. We were therefore confident in defining as ‘immigrants’ birds that we did not record has having hatched on the island where they eventually bred, and as ‘residents’ birds that bred in their natal population. Natal dispersal in Song Sparrows occurs in the hatching year (Arcese 1989c); thus we refer to 15  non-territorial juveniles as ‘juvenile dispersers,’ most of which were not re-sighted the following spring. Wilson and Arcese (2006) and Smith et al. (2006) provide more detailed descriptions of methods. Data analyses Factors influencing immigration rates We used generalized linear models to test for effects of population structure on the annual number of immigrant and resident recruits for both sexes (Poisson errors, log-link function) by examining models with sex ratio and the density of adults, females and males (standardized by island-specific maximum value) as predictors. Island was included as a blocking variable in each case. These counts of recruitment only included immigrants and resident recruits in their first breeding year. Population parameters were calculated based on the pre-dispersal conditions, so only included individuals present in the current breeding year that were not first-year birds. We calculated relative density as the current number of territorial adults divided by the maximal number of territorial adults known to be present on that particular island from 1998 to 2004. Since our study islands differ in total area and the amount and quality of suitable breeding habitat for Song Sparrows, we felt that relative density was a better index of the potential number of vacancies than an area-based measure. Sex ratio was calculated as the proportion of adult males relative to all territorial adults (Wilson and Hardy 2002). To evaluate the effects of these three variables (sex ratio, island and relative density) on immigration in each sex, we used Akaike's Information Criterion (AIC; Akaike 1973). Because we consider only three predictor variables in our analyses, and each represented a reasonable a priori hypothesis, we considered all combinations of variables in eight candidate models which included an ‘intercept-only’ model. Island size and isolation effects were tested in univariate correlations of the annual number of immigrants versus island size (ha) and isolation (an index created by summing the land area in a 1.5 km buffer around the focal island; Table 2.1). Island size and isolation were calculated in ArcGIS 9.0 (ESRI, Redlands, California) based on spatial data described in detail by Jewell et al. (2007). Because estimates of island size derived from remote sensed images vary slightly on tidal cycle and source, estimates used here may differ slightly from those reported elsewhere. Our choice of buffer width was based on cumulative experience suggesting that  16  dispersal by juvenile Song Sparrows is rare to populations more than 1.5km from the next nearest island. We also examined factors affecting dispersal at the individual-level using observations of 53 individuals from 49 different broods that were hatched and banded on monitored islands, and that later bred on their natal island or another monitored island. Using logistic models, we examined the influence of two predictor variables: adult relative density (defined above) and the island on which they bred (categorical coding) on individual dispersal outcomes: immigrant (1) or resident (0). We used AIC model selection to evaluate among three candidate models based on all combinations of these two variables. Survival analyses We estimated annual apparent survival (φ) and recapture probabilities (p) using the Cormack-Jolly-Seber recapture model as implemented in the program MARK (White and Burnham 1999). The extent to which apparent survival underestimates actual survival is determined by the rate of permanent emigration by adults (Turchin 1998, Sandercock 2006). Permanent emigration is very rare in this system (Smith et al. 2006), therefore our estimates of apparent survival should be very close to actual adult survival rates occurring in our study system. For the purpose of this study we assumed that re-sightings were equivalent to recaptures. Since these islands are very small and easily searched, and because Song Sparrows reside on or near their territories year-round, we restricted recapture probability (p) to be constant across islands and years. We tested for variation in adult survival in relation to island, dispersal status and gender, but lacked sufficient data to test for island by year interactions. Birds that recruited to island populations before our current study were not included in models incorporating dispersal status as this was unknown for these individuals. These birds were included, however, to estimate sexspecific apparent survival. Overall, the effects of breeding island, gender and residency status (immigrant or resident) were examined using seven candidate models, based on all variable combinations. Model selection Model selection for all analyses was conducted using Akaike's information criterion (AIC; Akaike 1973) for small samples (AICc). Lower AICc values indicate a more parsimonious 17  model and we considered models with ΔAICc < 2 to be well supported by the data (Burnham and Anderson 2002). The Akaike weights (wi) were also used as a measure of support for different models in the candidate set, with a higher weighting indicating higher support (Burnham and Anderson 2002). To examine the influence of different predictor variables for each analysis, we also examined the combined weights of all models in the set that contained the respective variables. Since all models were considered, variables were represented an equal number of times, as required for variable weight calculations (Burnham and Anderson 2002). All analyses were conducted using the STATISTICA 7 package (StatSoft 2006). Results Juvenile dispersal patterns We observed 60 cases where a marked juvenile was re-sighted away from its natal island, with six of these cases leading to recruitment within the study area. Several marked juveniles were re-sighted on three or more islands. Island-hopping also occurred in birds that returned to breed on their natal island. Juvenile dispersers were identified off their natal island as early as 38 days of age, or about 2 weeks after becoming independent from parental care. These 60 dispersing juveniles included 9 sets of siblings (19 birds) that were re-sighted together on a nonnatal island, perhaps because similarly-aged fledglings often flock together (unpubl. data). Factors influencing immigration rates We identified 230 breeding recruits in 7 yrs, including 80 immigrants and 150 residents. Males constituted 58.67 % of the resident recruits and 41.25% of the immigrant recruits, suggesting a substantial female-bias in dispersal overall (χ2= 6.34, df = 1, P = 0.01). Female immigration models with the most support were those specifying sex ratio and relative adult density, followed by the global model (Table 2.2a). The combined weight of models containing density was 0.53 compared to 0.97 for sex ratio, which we interpret as strong evidence that sex ratio primarily influenced female immigration. Immigration by females increased as sex ratio became biased in favor of males (β = 2.75, SE = 1.05, Fig 2.2a), whereas density had no clear effect (β = -0.80, SE = 1.00). The male immigration model specifying density had the most support (Table 2.2b), but the other candidate models were also supported. Yet, the combined weight for models including density was 0.79 versus 0.40 and 0.28 for island and sex ratio, respectively. We interpret these results as indicating that density was the most influential 18  variable affecting male immigration, which declined as density increased (β = -1.67 SE = 0.96, Fig 2.2b). Sex ratio had no strong influence on male immigration (β = -0.81 SE = 1.03). When testing if conditions in the natal population influenced emigration, we found that the best-supported model included gender, with females being slightly more likely to disperse and recruit within our study area compared to males (β = 0.46 SE = 0.29, Table 2.2c). Adult population density and sex ratio were less, but similarly influential (Table 2.2c). After splitting subjects by gender, we found very weak support for an effect of sex ratio on female emigration based on cumulative weights across models (sex ratio, 0.52; density 0.38), but equivocal results for males. Islands also differed consistently in recruitment pattern, mainly because Mandarte Island had consistently low immigration and high resident recruitment rates. Immigrants comprised about 4% of recruits on Mandarte Island, compared to 50 to 96% elsewhere (Table 2.1). Four islands (Rubly, Little Shell, Dock 3 and Imrie Islands) experienced complete turnovers of the female population, wherein all females present in a prior year were replaced by immigrants. Univariate analyses of the effect of island characteristics on immigration suggested a weak positive influence of island area (rS=0.25, p<0.05), and a weak negative influence of isolation (rS=-0.29, p<0.05) on immigration. Survival analyses For all survival analyses including or excluding birds with unknown residency, the most supported model included island and sex (φ ISL,SEX; Table 2.3). We then used our top model (φ ISL,SEX),  including all birds, to calculate sex- and island-specific survival (Table 2.4), and found  that females (0.517 ± 0.03 SE) experienced lower apparent survival than males (0.647 ± 0.024 SE). Discussion Factors affecting immigration and emigration Immigration and emigration are often assumed to balance, but this assumption is known often to be false in nature (Clobert et al. 2001). An important result in our study is that islands differed strongly in the contributions of immigrants versus residents to local recruitment, and that immigration and emigration rates differed partly as a consequence of population structure, island area and isolation. Resident recruitment into populations on the small islands we studied was 19  very low (Table 2.1), with most vacancies being taken up by immigrants, except on Mandarte Island. A pattern of high emigration from smaller islands has also been reported for common terns Sterna hirundo (Dittmann et al. 2005), and lesser kestrels Falco naumanni (Serrano et al. 2005). Although it is possible that juvenile survival declined with island area, this seems unlikely given that adult survival on all islands was similar to that observed on Mandarte Island, where recruitment by resident juveniles is often high. Positive effects of island area on recruitment might also be mediated via density, given that we observed a negative correlation between density and island area (rS = -0.62, P < 0.05), and because emigration is often linked to density in bird populations (Matthysen 2005). Although population density was not the only supported predictor of emigration in our individual-level analyses (Table 2.2c), long-term monitoring on Mandarte Island shows that recruitment by residents declined as adult density increased (Arcese et al. 1992, Wilson and Arcese 2003). For instance, after a severe winter in 1989 reduced the adult population to six birds, 82% of the fledged young recruited as first year adults in 1990, compared to the long-term average of 24%. Further evidence that density affected immigration was provided by our observation that annual immigration rates varied inversely with population density on nine focal islands over seven years. It is also possible that isolation and density might interact to influence dispersal if, for example, birds balance the likelihood of obtaining a breeding territory on their natal island against the risks of dispersing to a new and unfamiliar population. Unlike the other islands we studied, Mandarte Island often has a surplus of non-territorial ‘floaters’ during the breeding period and independent young are seen in undefended areas late in the season. Mandarte Island is also among the most isolated islands we studied (Table 2.1). These factors might combine to cause a higher fraction of juveniles on Mandarte Island to remain as residents than on less isolated islands which have little undefended habitat. Factors affecting sex-specific immigration and emigration rates Dispersal in most species of birds is female-biased, both in terms of distance travelled and the proportion of the sex dispersing (Greenwood 1980, Clarke et al. 1997). Sex-specific differences in avian dispersal have been linked previously to ectoparasite levels (Heeb et al. 1999), patch characteristics (Dittman et al. 2005), population density (Delestrade et al. 1996) and the territorial and mating system (Greenwood 1980). Our results indicate that sex-specific 20  responses to population structure also contribute to annual variation in immigration rate by males and females. We found that immigration rates of males declined as population density increased, and that immigration rates of females increased as sex ratio became biased towards an excess of adult males. Studies of the marsh tit Parus palustris (Nilsson 1989), great tit Parus major (Delestrade et al. 1996), and red squirrel Sciurus vulgaris L. (Wauters et al. 2004) also suggest that population density was more influential of dispersal in the more philopatric and territorial sex. Male Song Sparrows are also strongly territorial (Arcese 1989a) and were less likely to disperse among island populations than females in this study, consistent with the hypothesis that investment in territory defense affects dispersal bias in birds (Greenwood 1980). On Mandarte Island, the fraction of adult males without territories or mates rose with male density, and these floaters and unmated males experienced lower lifetime reproductive success as a consequence (Smith and Arcese 1989, Smith et al. 2006). In contrast, female Song Sparrows rarely remain unmated in our study area once general breeding has commenced (Arcese 1989b). However, competition does become evident among female Song Sparrows as the ratio of territorial males to females declines, the rate of polygyny increases, and access to male parental care limits female reproductive rate (Arcese 1989b). Thus, our observation that female immigration was more prevalent into populations when males outnumbered females is consistent with the hypothesis that territorial behavior in females arises as a consequence of competition for male parental care (Arcese 1989b) and influences patterns of dispersal (Arcese 1989c). Greenwood (1980) also noted that emigration by female pied flycatchers Ficedula hypoleuca was higher in polygynous than monogamous populations. Sex-specific variation in adult survival should also affect the probability of gaining a breeding territory close to the natal area (Murray 1967, Waser 1985). We found that adult female Song Sparrows suffered higher annual mortality than males (Table 2.4), as previously found for Song Sparrows on Mandarte Island (Smith et al. 2006) and many other species of birds (Breitwisch 1989, Payevsky et al. 1997, Liker and Szekely 2005). Although some reported differences in apparent survival rate may arise as a consequence of breeding site fidelity (Sandercock and Gratto-Trevor 1997), adult female Song Sparrows rarely disperse more than a territory width between years (Smith et al. 2006, unpubl. data). Thus, we suggest that in our 21  study, higher average mortality in females than males facilitated female immigration but impeded male immigration. Dispersal and metapopulation dynamics We found that immigrants often constituted a substantial fraction of local population size, and in six cases immigration by females prevented the extirpation of that sex from the island. Breeding vacancies and extirpations should be more common in females as their survival declines relative to males, but our observation that a relative scarcity of females enhanced female immigration should also act to stabilize population size. Density-dependence in male immigration, which we also observed, should also tend to stabilize populations. We expected this ‘rescue effect’ of immigration to be more pronounced on smaller islands due to a higher risk of extinction, and on less isolated islands due to a higher frequency of immigration (Hanski 1999, Brown and Kodric-Brown 1977). Our results support these ideas because immigrants contributed less to population size as islands became more isolated. Imrie, the most isolated island studied (Table 2.1) had no female immigrants during three years despite only males being present. Extirpation was averted after a female immigrant re-established the breeding population. Mandarte Island, which is similarly isolated, has experienced very low immigration rates, and rebounded slowly from severe population declines, mainly via recruitment by residents (Arcese et al. 1992, Smith et al. 2006). Overall, our results suggest that immigration varied with local demography in a sex-specific way, stabilized population numbers and reduced extinction rates in the smallest populations.  22  Table 2.1.  Island characteristics and recruitment histories of monitored island populations  during 1998 to 2004. Recruitment of residents and immigrants was calculated as the mean number of recruits per year.  Island Dock 1 Dock 2 Dock 3 Reay Little Shell Ker Imrie Rubly Mandarte  Avg. Pop. Size  Land area  Size  Recruitment of  Recruitment of  (± SD)  within 1.5  (ha)  residents (± SD)  immigrants (± SD)  7.43 (± 3.74) 7.14 (± 2.73) 2.71 (± 1.60) 6.14 (± 2.12) 6.50 (± 2.07) 11.86 (± 3.93) 4.75 (± 1.50) 8.17 (± 1.33) 60.00 (± 15.03)  17.84 km radius 12.21 (ha) 34.63 43.64 63.12 54.41 0.84 83.35 2.14  0.51 0.42 0.23 0.53 0.64 3.88 1.17 2.86 8.06  0.14 (± 0.38) 0.57 (± 0.79) 0.14 (± 0.38) 0.57 (± 0.79) 0.17 (± 0.41) 1.00 (± 1.41) 0.75 (± 0.96) 0.17 (± 0.41) 18.29 (± 7.65)  1.14 (± 1.21) 0.71 (± 0.75) 0.57 (± 0.79) 0.71 (± 0.76) 1.00 (± 0.63) 2.86 (± 2.48) 1.00 (± 0.82) 3.83 (± 2.56) 0.71 (± 1.11)  23  Table 2.2. Models of factors affecting: a) female immigration rate, b) male immigration rate and c) individual dispersal from natal island. The top three models are shown along with the intercept-only model for both female and male immigration candidate model sets. ΔAICc is the change in AICc between that model and the best fitting model, wi is model weight (a measure of the relative likelihood of each model) and K is the number of model parameters.  Model a) Female immigration rate Sex ratio + island Sex ratio + density + island Sex ratio + density Intercept only b) Male immigration rate Density Density + island Sex ratio + density Intercept only c) Individual dispersal Sex Sex ratio Density  AICc  ∆ AICc  wi  K  125.90 127.55 131.65 153.18  0.00 1.65 5.75 27.35  0.64 0.28 0.04 0.00  3 4 3 1  109.46 111.11 111.59 118.00  0.00 1.65 2.13 8.55  0.40 0.18 0.14 0.00  2 3 3 1  75.15 77.03 77.08  0.00 1.87 1.92  0.36 0.14 0.14  2 3 3  24  Table 2.3. Models of annual survival probabilities (φ) for adult Song Sparrows in the Southern Gulf Islands, British Columbia, 1998–2004. In each model island is a categorical variable and the intercept and recapture rate are also included as parameters. Subscripts denote models including the parameters of breeding island (ISL), gender (SEX), and residency status (DISP).  Model φ ISL,SEX φ ISL,SEX,DISP φ ISL φ ISL,DISP φ SEX  AICc 1378.98 1381.55 1381.74 1383.47 1388.92  ∆AICc 0.00 2.57 2.75 4.48 9.94  wi 0.61 0.17 0.15 0.06 0.00  K 12 14 11 12 4  25  Table 2.4. Best model estimates of annual survival (φ), and 95% CI for adult Song Sparrows across 1998-2004. Parameter φ Sex-specific survival Male 0.65 Female 0.52 Island-specific survival  95 % CI  Dock 1 Dock 2 Dock 3 Imrie Ker Little Shell Mandarte Reay  0.64 0.57 0.47 0.84 0.50 0.74 0.62 0.62  (0.50-0.75) (0.42-0.71) (0.26-0.70) (0.52-0.96) (0.39-0.61) (0.57-0.86) (0.57-0.67) (0.46-0.58)  Rubly  0.28  (0.16-0.44)  Recapture Rate  0.99  (0.98-1.0)  (0.60-0.69) (0.46-0.58)  26  Figure 2.1. Study area and continuously monitored islands (black) in the Haro Strait, BC, Canada. Inset map shows general area of study site.  27  Figure 2.2. Influence of adult sex ratio (a), and density (b), in the recipient population on sexspecific immigration rates. Female immigration is indicated as solid circles and male immigration is indicated as open triangles.  28  References 1. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In: Petrov, B. N. and Csaki, F. (eds.) Second Int. Symp. Inform. theor. Budapest, Academiai Kiado, pp. 267–281. 2. Andreassen, H. P. and Ims, R. A. 2001. Dispersal in patchy vole populations: role of patch configuration, density dependence, and demography. - Ecology 82: 2911–2926. 3. Arcese, P. 1989 a. Territory acquisition and loss in the Song Sparrow. - Anim. Behav. 37: 4555. 4. Arcese, P. 1989 b. Intrasexual competition and the mating system of primarily monogamous birds: the case of the Song Sparrow. - Anim. Behav. 38: 96-111. 5. Arcese, P. 1989 c. Intrasexual competition, mating system and natal dispersal in Song Sparrows. - Anim. Behav. 38: 958-979. 6. Arcese, P., Smith, J. N. M., Hochachka, W., Rodgers, C. and Ludwig, D. 1992. Stability, regulation and the determination of abundance in an insular Song Sparrow population. Ecology 73: 805-822. 7. Bowler, D. E. and Benton, T. G. 2005. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. - Biol. Rev. 80: 205-225. 8. Breitwisch, R. 1989. Mortality patterns, sex ratios, and parental investment in monogamous birds. – Curr. Ornit. 6:1–50. 9. Brown, J. H. and Kodric-Brown, A. 1977. Turnover rates in insular biogeography: effect of immigration on extinction. - Ecology 58: 445–449. 10. Burnham, K. P. and Anderson, D. R. 2002. Model selection and inference: a practical information-theoretic approach. - Springer, New York. 11. Clarke, A. L., Sæther, B. E. and Roskaft, E. 1997. Sex biases in avian dispersal: a reappraisal. - Oikos 79: 429-438. 12. Clobert, J., Danchin, E., Dhondt, A. A. and Nichols, J. D. 2001. Dispersal. - Oxford University Press, New York: 13. Dale, S. 2001. Female-biased dispersal, low female recruitment, unpaired males, and the extinction of small and isolated bird populations. - Oikos 92: 344–356.  29  14. Delestrade, A., McCleery, R. H. and Perrins, C. M. 1996. Natal dispersal in a heterogeneous environment: the case of the great tit in Wytham. - Acta Oecologia 17: 519-529. 15. Dittmann, T., Zinsmeister, D. and Becker, P. H. 2005. Dispersal decisions: common terns, Sterna hirundo, choose between colonies during prospecting. - Anim. Behav. 70: 13-20. 16. Greenwood, P.J. 1980. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28: 1140–1162. 17. Hanski, I. 1999. Metapopulation ecology. - Oxford University Press. Oxford. 18. Heeb, P., Werner, I., Mateman, A. C., Kolliker, M., Brinkhof, M. W. G., Lessells, C. M. and Richner, H. 1999. Ectoparasite infestation and sex-biased local recruitment of hosts. - Nature 400: 63-65. 19. Ims, R. A. and Yoccoz, N. G. 1997. Studying transfer in metapopulations: emigration, migration and immigration. - In: Hanski, I. and Gilpin, M. (eds) Metapopulation biology: ecology, genetics and evolution. Academic Press, London, pp. 247-265. 20. Jewell, K. J., Arcese, P. and Gergel, S. A. 2007. Robust predictions of species distribution: spatial habitat models for a brood parasite. - Biol. Cons. 140: 259-272. 21. Lande, R., Engen, S. and Sæther, B. E. 2003. Stochastic population dynamics in ecology and conservation. - Oxford University Press, Oxford. 22. Liker, A. and Szekely, T. 2005. Mortality costs of sexual selection and parental care in natural populations of birds. - Evolution 59: 890-897. 23. Marra, P. P. and Holmes, R. T. 1997. Avian removal experiments: do they test for habitat saturation or female availability? - Ecology 78: 947-952. 24. Matthysen, E. 2005. Density-dependent dispersal in birds and mammals. - Ecography 28: 403-416. 25. Murray, B. G. 1967. 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Program MARK: survival estimation from populations of marked animals. - Bird Study 46: 120–139. 39. Wilson, K. and Hardy, I. C. W. 2002. Statistical analysis of sex ratios: an introduction. - In: Hardy, I. C. W. (ed.). Sex ratios: concepts and research methods. Cambridge University Press, Cambridge, pp. 366–382. 40. Wilson, S. and Arcese, P. 2006. Nest depredation, brood parasitism, and reproductive variation in island populations of Song Sparrows (Melospiza melodia). – Auk 123: 784-794. 41. Wilson, S. and Arcese, P. 2003. El Niño drives timing of breeding but not population growth in the Song Sparrow (Melospiza melodia). – Proc. Natl. Acad. Sci. 100:11139-11142.  31  Chapter 3 − Micro-spatial genetic structure in Song Sparrows: A comparison among subspecies2.  Introduction To some extent, restrictions on dispersal will exist for all natural populations, leading to the non-random spatial distribution of individuals with respect to their genetic similarity, referred to as spatial genetic structure (Wright 1943). In the absence of selection, more related individuals will occur in closer proximity, leading to positive genetic structure. The spatial extent over which positive genetic structure is detectable has been referred to in the literature as the ‘genetic patch width’ (Epperson 2003) and the ‘patch size’ (Sokal 1979, Sokal & Wartenberg 1983, Smouse & Peakall 1999), the latter of these terms is adopted in this paper. According to theory, the geographical scale over which dispersal becomes restricted should vary among species as a consequence of differences in vagility (Bohonak 2002). Differences in genetic structuring between populations of the same species will arise because of the influence of localized landscape features (e.g. physical or ecological barriers) on dispersal patterns. The relative influence of distance and landscape features on gene flow can be determined by examining the correlations between genetic distance and landscape or population features. Testing for the presence of an ‘isolation by distance’ (IBD) pattern can be particularly informative for determining which factors are potentially influencing dispersal. An IBD pattern describes a situation where geographically close populations are more genetically similar to each other than to more distantly spaced populations (Wright 1943). Therefore, testing for IBD is equivalent to asking whether the genetic distance between populations is associated with the geographic distance between them. If gene flow declines as the distances between populations increase, this will result in a positive correlation between genetic and geographic distance, and an IBD  pattern. The magnitude and variance of pairwise genetic distances (e.g. FST) as a function of  geographic distance will depend on the relative influence of gene flow versus drift (Figure 3.1).  2  A version of this chapter will be submitted for publication. Wilson AG, Arcese P, Chan Y, Patten M,. Micro-spatial genetic structure in Song Sparrows: A comparison among subspecies. 32  In addition to restricting gene flow, selection may further influence genetic structure if neutral loci are linked with selected loci (Epperson 1990). The absence of IBD patterns among populations does not mean that geographic distance does not influence gene flow. The correct interpretation of an absence of an IBD pattern is that dispersal dynamics are more complicated than a simple distance-dependent relationship, and are also influenced by other ecological or physical barriers. For several species, genetic structure has been determined to be more closely correlated to ecological gradients (Cooper 2000, Ruegg et al. 2006), dispersal barriers (Lee et al. 2001, Funk et al. 2005, Crispo et al. 2006, Pérez-Espona et al. 2008) and reproductive barriers (Irwin et al. 2005), than to measures of straight-line geographic distances between populations. In some cases, geographic distance became influential once the confounding influence of migration barriers was accounted for (Keyghobadi et al. 1999). Alternatively, the absence of genetic structuring may be an artefact of sampling scale (Smouse & Peakall 1999), low statistical power (Waples 1998), or a major departure from migration-drift equilibrium (Hutchison & Templeton 1999), rather than a true absence of a distance effect. An IBD pattern would not be expected between populations that are in close proximity and experience high gene flow rates, or between populations that have been too recently isolated for the accumulation of genetic differences (Fig. 3.1 A, Slatkin 1993, Hutchinson & Templeton 1999, Crispo & Hendry 2005). Populations that are completely isolated will also lack an IBD relationship, because there is an absence of migration regardless of distance (Figure 3.1 C). In these cases, the genetic distances are determined by drift, leading to high variances in population pairwise genetic distances, such that population size should be influential in determining the genetic similarity between populations than geographic proximity (Hutchinson & Templeton 1999). The situations of high gene flow or high drift can be distinguished by comparing both the genetic distances and the variances in genetic distance (Hutchinson & Templeton 1999 Fig 3.1 A & Fig 3.1 C). The inferences that can be made from the spatial genetic structure have significant relevance for conservation applications and evolutionary models. Knowing the scale at which gene flow predominates indicates an approximate scale of demographic independence (Scribner et al. 2005). Spatial genetic structure also indicates the spatial scale over which populations 33  diverge genetically due to dispersal limitations, which is crucial information for ‘divergence with gene flow’ evolutionary models (Endler 1973, Rice & Hostert 1993, Orr & Smith 1998). Although testing for IBD patterns is common practice, few studies include enough populations over appropriate spatial scales to examine the extent or causes of spatial variation in genetic structure. This is particularly true in passerines, where the average sampling interval in genetic studies is c. 640 km (Table 3.1), which may be due to trends of low genetic divergence within avian populations relative to other taxa (Crochet 2000). Therefore, we have limited data regarding the fine-scale genetic structuring within avian populations (but see Friesen et al. 1996, Woxvold et al. 2006) and as a consequence, a limited understanding of the factors that influence genetic structuring. Song Sparrows (Melospiza melodia) are a particularly suitable model species for questions related to the scale of spatial genetic structure for several reasons. First, there are 25 Song Sparrow subspecies unevenly distributed across North America (Patten 2001, Arcese et al. 2002), making it possible to investigate the mechanisms leading to variation in genetic structure, and ultimately, subspecific divergence. Secondly, the low molecular divergence reported in some studies (Chan & Arcese 2003) is at odds with the field data of high philopatry of Song Sparrows (Nice 1937, Johnson 1956, Halliburton & Mewaldt 1976), and is non-concordant with patterns of subspecific membership (Barrowclough 1983, Zink & Dittman 1993, Fry & Zink 1998, but see Pruett et al. 2008a). The interpretation of these patterns is hindered by the absence of information on the spatial scale at which Song Sparrows would diverge due to inherent dispersal limitations, and to what extent genetic structure differs across regions. Lastly, it is difficult to interpret the significance of the magnitude of genetic distance between any two subspecies if the range of distances occurring among populations within a subspecies is unknown. Therefore, these cases of non-concordance represent an opportunity to establish biologically reasonable interpretations of genetic patterns. In this study, we compare the scale, strength and influences of genetic structure across three regions differing in subspecies distribution. We focused on Song Sparrows in three regions: southern British Columbia (BC), San Francisco Bay, and the Salton Sea (Figure 3.2). In southern BC, a single subspecies (M. m. morphna) is continuously distributed across coastal BC, Vancouver Island and the Southern Gulf Islands. There are five subspecies occurring in the San 34  Francisco Bay region (M. m. samuelis, M. m. maxillaris and M. m. pusillula, M. m. gouldii and M. m. heermanni, Chan & Arcese 2002) and two subspecies occur in close proximity in the Salton Sea area (M. m. heermanni and M. m. fallax, Patten et al. 2004). Subspecific designation in the San Francisco Bay and Salton Sea is based on morphological traits. We first test the prediction that given the sedentary nature of the M. melodia subspecies we studied, positive genetic structuring would be detectable at the population level. In each of these regions, we estimate the patch size and the magnitude of the autocorrelation coefficients. We next test the hypothesis that geographic differences in spatial genetic structure are due to alternative factors that influence local levels of gene flow. To address this hypothesis within these three regions, we evaluated the relative contributions of geographic distance, and locally variable covariates such as population size, water barriers, sub-specific membership and salinity on genetic structuring. These covariates were chosen based on their hypothesized effects on genetic divergence through an influence on drift or gene flow as a result of dispersal limitations, mating preferences or in the case of salinity, demonstrated physiological differences (Basham & Mewaldt 1987). We show that positive genetic structure can be detected at a fine scale within an avian population, even in cases where spatial structure was not present among populations. We also demonstrate that geographical variation in genetic structuring exists, and discuss the contribution of locally variable factors to this variation. Methods Field protocol Data were available from previously published work for San Francisco Bay (214 individuals across nine sites and five subspecies, Chan & Arcese 2002, 2003, Figure 3.2a), and the Salton Sea (58 individuals across two subspecies, Patten et al. 2004, Figure 3.2a). In 2005, 150 genetic samples across eight sites were collected across coastal BC and Vancouver Island, in a pairwise manner across the Georgia Strait (Fig 3.2b). Unpublished genetic data were available for the Southern Gulf Islands (211 individuals across 10 islands, Fig. 3.2c). During the breeding season, adult birds were captured in mist nets using playbacks of male song. All birds were banded with a numbered metal band, Universal Transverse Mercator (UTM) coordinates were recorded and blood samples were taken. We took blood samples by first cleaning the area with sterile isopropanol swabs and then puncturing the brachial vein using a sterile 30 gauge needle. 35  The 20-50μl blood sample was collected in plain glass capillary tubes and immediately placed in 1ml of Queen’s Lysis buffer (Seutin et al. 1991). Bleeding was stopped with gentle pressure and puncture sites were cleaned with a sterile swab before birds were released to their territories. DNA extraction and microsatellite amplification DNA was extracted using the GenElute™ Blood Genomic DNA Miniprep Kit (SigmaAldrich Canada Ltd.) according to the manufacturer’s instructions. Individuals were genotyped at eight microsatellite markers: Mme1, Mme 2, Mme3, Mme7, Mme8, Mme12, (Jeffery et al. 2001), Escu1 (Hanotte et al. 1994) and GF5 (Petren 1998). Mme3 and Mme7 are z-linked loci, for which we scored the second allele in all females as absent. The polymerase chain reactions (PCR) were carried out in 15μl volumes containing approximately 100ng genomic DNA, 10mM Tris-HCl (pH 8.3), 50mM KCl, 1.5-2mM MgCl2, 0.2mM dNTPs (Invitrogen), 0.16ug/μl bovine serum albumin (BSA), 0.1% Triton X-100 (Sigma-Aldrich Canada Ltd.), 1pmol of each primer, 0.3 pmol of M13 Infrared Label (LiCor) and 0.5U of Taq polymerase (Roche). PCR reactions were carried out in a MJ Research PTC-100 thermal cycler (MJ Research, Inc.), using the following profile: 94°C for 2 min, followed by 35 cycles of 60s at 94 C, 60s at the locus-specific annealing temperature (details in Chan & Arcese 2002) and 60s at 72°C, followed by a final extension of 10 min at 72°C. PCR products were fractionated on 7% polyacrylamide gels using a LI-COR 4200 DNA analyzer. In all gel runs, alleles were calibrated against a size standard (50– 350 bp, LI-COR), and a species-specific allele ladder. Gel results were visualized using Base ImagIR (LI-COR, Lincoln, NB, USA) and loci were scored manually using RFLP scan (Scanalytics, CSP Inc., Fairfax, VA, USA). Data analysis Migration–drift equilibrium In cases of low genetic differentiation, it is very difficult to distinguish whether the cause is very recent separation of large populations (non-equilibrium drift model) or ongoing gene flow (equilibrium model). Populations may lack an IBD signal because they have failed to reach equilibrium (Slatkin 1993). We used the program 2MOD v0.2 (Beaumont 2001) to evaluate whether the population structure within these populations had attained an equilibrium between gene flow and drift or if a non-equilibrium drift model was more likely (Ciofi et al. 1999; Beaumont 2001). The 2MOD program uses Markov chain Monte Carlo simulations to calculate 36  the likelihood of the observed data under the alternatives of an equilibrium or non-equilibrium model. The posterior probability of a particular model is calculated based on the number of times a model had the highest likelihood. In the equilibrium model, population structure is determined by a balance between migration and drift. Conversely, the drift model represents situations where populations are isolated and subsequent patterns of divergence in allele frequencies are determined only by drift. The contribution of mutation is assumed to be negligible in both the equilibrium and drift models (Ciofi et al. 1999). We ran the model three times for 300 000 iterations with a 30 000 iteration burn-in. Individual spatial autocorrelation Spatial autocorrelation coefficients can be used in population genetic studies to provide a measure of the genetic similarity of individuals as a function of the geographic distance between them. We analyzed the scale of spatial genetic structure using multivariate spatial autocorrelation analysis (Smouse & Peakall 1999) as implemented in GENALEX version 6.01 (Peakall & Smouse 2006). This method produces an autocorrelation coefficient (r(h)) among individuals for multiple distance class intervals (h), and is calculated as: In this equation, xij(h) and xii(h) are off-diagonal and diagonal elements respectively of h distance class matrices (X(h) matrices) and cij and cii are off-diagonal and diagonal elements respectively, of the genetic covariance matrix (C matrix). Inter-individual spatial relationships are classified into h distance class intervals, each having a separate X(h) matrix. For example, if the spatial separation of individuals i and j occurs in the distance class interval h = 3, then xij(3) = 1, whereas this element in the other X matrices will be zero (i.e. xij(1), xij(2), xij(4)… xij(h) = 0). The diagonal element  is the number of individuals occurring within a particular distance class h  for the ith individual. Within the genetic covariance matrix, cii is equivalent to the distance of the ith individual from the sample genetic mean, whereas the cij elements are equivalent to the tendency of individuals to covary in the same direction from the sample genetic mean. Therefore, a positive autocorrelation coefficient r(h) indicates that individuals occurring at a particular distance class h have smaller genetic distances between each other than they do to the sample genetic mean.  37  If gene flow is limited by distance at the spatial scale of the study, a positive correlation (r(h)) is expected at the shorter distance classes. However, this correlation will become zero at distance classes where drift predominates over gene flow, removing the correlation between genetic and geographic distance. The distance class at which a correlation coefficient intercepts the axis of r = 0, is taken as an estimate of the patch size (Sokal 1979, Peakall et al. 2003). Beyond the y-intercept, subsequent oscillations of the plot are due to the fact that the covariance matrix averages zero, so a positive correlation in one distance class leads to negative correlations in other distance classes. However, this compensation is not necessarily of the same magnitude, because it is the covariance matrix, not the autocorrelation coefficients that average to zero (Peakall pers. comm., Smouse & Peakall 1999). Spatial autocorrelation was calculated for each region separately (San Francisco Bay, Georgia Basin and the Salton Sea), providing three estimates of spatial autocorrelation over the same distance classes. We divided the sampling range (25 km) into four distance classes: 0.5, 2, 10 and 25 km. Each distance class includes all pairwise spatial separation values greater than the preceding class value and smaller than the focal distance class value (e.g. Distance class 2 includes all values ≤ 2 and > 0.5 km). For the Southern Gulf Islands we used a distance class set of 0.5, 2, 5 and 10 km. Different distance class choices were evaluated, but did not alter the resulting interpretation. Statistical significance for the multivariate spatial autocorrelation is based on 95% confidence intervals generated by bootstrapping around the estimated r for each distance class and a 95% null interval is generated around the value of r = 0. A statistically significant autocorrelation is one whose 95% confidence interval, do not overlap with the 95% confidence intervals of the null. For all analyses, we used 1000 permutations and bootstrap iterations. Isolation by distance We calculated genetic differentiation using a standardized G’ST, which was particularly valuable for this study as our microsatellite loci are highly variable. This standardization is necessary because GST is a measure of how variation is apportioned among populations, but the identity of the alleles is not accounted for. Therefore, with highly variable markers, even in cases of maximal differentiation, the maximal value of GST will be less than 1 (Hedrick 1999, 2005). We calculated a standardized G’ST by using the program RECODEPC v. 0.1 (Meirmans 2006). RECODEPC is a utility that recodes the data so as to maximize between-population variation, such 38  that the differentiation estimator calculated from this recoded data represents GSTMAX (Hedrick 2005). The standardized G’ST is then calculated by dividing the FST-value based on the original data by the FST -value based on the recoded data. Pairwise FST values (Weir & Cockerham 1984) and significance were calculated based on 1000 permutations using FSTAT v 2.9.3.2 (Goudet 2001). We used the standardized G’ST measures within partial Mantel tests to evaluate the influence of several environmental and biological boundaries on the genetic relationships between populations. We tested for an IBD relationship between inter-populational genetic distance (G’ST) and the straight-line geographic distance, with the Mantel test using the program IBDWS v. 3.15 (Jensen et al. 2005). Within the San Francisco Bay populations, we controlled for salinity and subspecific membership. In this study, salinity refers to the salinity of the sea water, measured in practical salinity units (psu) at sampling sites, for which we used the distance values extracted by Chan & Arcese (2003). For San Francisco Bay where multiple subspecies co-occur, comparisons between different subspecies were coded as ‘1’ and comparisons within the same subspecies were coded as ‘0’. The Salton Sea sample was not included in the IBD analysis because only two populations were sampled. Within the Georgia Basin, we controlled for the water barrier imposed by the Georgia Strait by coding populations on the same side of the Strait as ‘0’ and populations which are separated by the Strait with a ‘1’. Within the Southern Gulf Islands, we controlled for island size and island population size, which were coded as continuous variables. Island sizes were calculated using ArcGIS (ArcGIS 9.2, ESRI, Redlands, CA), while island population sizes were known from ongoing demographic monitoring (Wilson & Arcese 2008). Statistical significance was calculated based on 20,000 permutations. We also tested for phylogeographic structure in allelic identity within subspecific groups, by examining if RST differs from FST using the program SPAGeDi v 1.2 (Hardy & Vekemans 2002). RST will differ from FST in cases where the mutation rate has greater influence on genetic differentiation than migration (Hardy et al. 2003), leading to spatial clustering of closely related alleles. Statistical significance was determined based on 10,000 permutations and was corrected for multiple comparisons using the false discovery rate (Benjamini & Hochberg 1995).  39  Results Migration–drift equilibrium Within M. m. morpha populations, the likelihood was highest for the migration-drift equilibrium model for maintaining the pattern of population structure (p = 0.85). Among subspecific groups in San Francisco Bay, the migration-drift equilibrium model also had the highest probability (p = 0.99). The highest support for the equilibrium models suggests that the genetic structure in all populations is more likely due to the balance between on-going gene flow and genetic drift, rather than recent separation and drift alone. Individual spatial autocorrelation The patch size is estimated to be the smallest distance class at which the correlation coefficient is not significantly different from zero. Within both San Francisco Bay and the Georgia Basin, genetic and geographic distances were correlated at distance classes less than 10 km. In San Francisco Bay, the correlation at the shortest distance class (≤ 0.5 km) was considerably higher (r = 0.039, 95% CI: 0.045, 0.033, Fig 3.3 a) than the correlation at the same distance class within the Georgia Basin populations (r = 0.020, 95% CI: 0.026, 0.015, Fig 3.3 b). Within the Salton Sea, the spatial correlation at the 0.5km distance class was relatively high (r = 0.067, 95% CI: 0.173,-0.015, Fig 3.4 c), but due to limited sample size, the 95% confidence interval of the estimate included zero, precluding a robust estimate of patch size in the region. In the Georgia Basin and San Francisco Bay regions, however, the autocorrelation coefficient was non-significant at a distance class of ≤ 10 km, providing an estimate of a patch size that falls within a range of 2-10 km for both regions. The intense microspatial sampling within the San Francisco Bay region enabled a 5km distance class to be tested, which was not significant (r = 0.007, 95% CI: 0.015,-0.002), refining the genetic patch estimate to 2-5 km for the San Francisco Bay area. Within the Southern Gulf Islands, the correlation at the first distance class of ≤ 0.5 km was high (r = 0.077, 95% CI: 0.082-0.072, Fig 3.4), but was not statistically significant at distance classes beyond 2 km. Isolation by distance For the San Francisco Bay populations, based on the Mantel test, we did find a modest correlation between the straight-line geographic distance and genetic distance (G’ST) between populations (r =0.028, p = 0.09). This correlation increased positively and became statistically 40  significant after statistically controlling for salinity (r = 0.37, p = 0.04). Similarly, there was a strong correlation between genetic distance and salinity after controlling for geographic distance (r = 0.30, p = 0.05). When subspecific membership was controlled for, the strength of the IBD relationship was reduced and became statistically insignificant (r = 0.21, p = 0.18, Figure 3.5), suggesting the contribution of subspecies to genetic structuring. When M. m. pusillula was excluded from analyses, there was no significant correlation between genetic distance and geographic distance after controlling for salinity (r = 0.30, p = 0.18). Similarly when M. m. pusillula was excluded from analyses, the previously strong correlation between genetic distance and salinity after controlling for geographic distance also became non-significant (r = -0.23, p = 0.81). For the BC populations, the Mantel test revealed no correlation between the straight-line geographic and genetic distance (G’ST) between populations (r = -0.036, p = 0.56), even after accounting for populations separated by the Strait of Georgia (r = -0.011, p = 0.54). In contrast, we found strong IBD in the Southern Gulf Islands, even after correcting for variation in island size (r = 0.62, p = 0.0003) and population size (r = 0.62, p = 0.0003). In the analysis of phylogeographic structuring, multi-locus estimates of RST were not significantly larger than randomly generated values, but locus-specific estimates were significant for seven pairwise comparisons following correction for the false discovery rate. These differences occurred primarily between M. m. gouldii and the other San Francisco Bay subspecies (M. m. samuelis, M. m. maxillaris, and M. m. pusillula) at Mme2, and between M .m. gouldii and M. m. pusillula at the two sex-linked loci (Mme3 and Mme7). Other cases of significant structuring occurred between the Salton Sea populations of M. m. heermanni and two San Francisco Bay subspecies: M. m. samuelis, and M. m. maxillaris, at one sex-linked locus (Mme7). Discussion Using multivariate spatial autocorrelation methods, we were able to determine that the patch size in continuously distributed Song Sparrow populations is less than 10 km. The patch size was comparable between BC and San Francisco Bay populations although, the latter had higher structuring at similar distances. However, for the Georgia Basin, few individuals were sampled in the 5 km distance class leading to wide confidence intervals, such that the patch size 41  estimate is in the 2-10 km range. Further refinement of this estimate for the Georgia Basin would require additional sampling within the 5 km distance. Multivariate spatial autocorrelation is infrequently used within vertebrate genetic surveys (but see Peakall et al. 2003), so there are few studies available to compare the patch size found in this study. When this method was applied within populations of white-breasted thrashers (Ramphocinclus brachyurus, Temple et al. 2006) and superb fairy wrens (Malurus cyaneus, Double et al. 2005), these studies were able to detect positive genetic structure at scales of less than 200 m. Factors influencing fine-scale patterns of genetic structure Sampling scale influences the detection of IBD, but environmental and biological factors also contribute to the scale and magnitude of genetic structuring. Although the scale was comparable across Song Sparrow populations, the magnitude of genetic structuring differed among populations. The stronger structuring found in the Californian populations could be due to differences in population age, population densities, or levels of gene flow among subspecies. Differences in population age Spatial genetic structure is predicted to increase with population age, as older populations would have sufficient time to attain equilibrium (Epperson 2003, 2005). In accordance with this prediction, some studies have also found decreased IBD correlations in more recently colonized or northern latitudes areas (Castric & Bernatchez 2003). At the regional level, the BC Georgia Basin would have been colonized since the last glacial maximum, which is estimated at 14 KYA (Mandryk et al. 2001; Clague & James 2002). The southern California region was not glaciated in the Quarternary (Pielou 1991) so the maximal age of populations in this region would considerably pre-date populations in the BC region. The Californian populations sampled in this study, however, would have more recent local colonization dates. The contemporary marshes within the San Francisco Bay area have an estimated age ranging from 2 to 6 KYA (Atwater et al. 1979). Even more recent are the populations around the Salton Sea, which would have only become substantial within the past century with the creation of this inland sea from the diversion of the Colorado River into the Salton Sink (Patten et al. 2003). At the short geographic distances, and small Ne considered in this study, however, spatial genetic structure can develop quickly (Slatkin 1993, Epperson 2005). It follows that the equilibrium model was supported in all of the Song Sparrow populations, such that differences in equilibrium as a function of population age 42  are less likely. In a recent meta-analysis, time since colonization was not strongly predictive of the presence of IBD patterns (Crispo & Hendry 2005), with factors such as population density, topography and dispersal rates having a stronger potential influence. Differences in population densities Population size influences genetic structure, because small populations approach equilibrium faster and are thus more likely to demonstrate IBD patterns or other spatial correlations over shorter time scales than larger populations, which approach equilibrium at a slower rate (Slatkin 1993). Increasing population density or non-uniform densities (i.e. clustering) is also expected to increase the magnitude of genetic structuring (Wright 1943, Epperson 2005). Based on breeding bird survey data (Sauer et al. 2006), Song Sparrow densities were higher in the San Francisco Bay than in BC region, but overall presence was comparable. In the Salton Sea area, however, Song Sparrows were infrequently encountered and densities were considerably lower. The aridity within the Salton Sea area and reduced suitable habitat likely leads to smaller, more isolated populations. Differences in gene flow Higher correlation coefficients at short distance intervals are indicative that dispersal may be restricted (Rousset 1997). This effect can be seen in comparisons among populations that are expected to differ in vagility. For example, in two species groups of the house wren (Troglodytes [a.] aedon, T. [a.] musculus), the strong IBD patterns found in sedentary populations were absent in the less differentiated migratory populations (Arguedas & Parker 2000). Similarly, populations of Song Sparrows that were migratory, or were not restricted to a one-dimensional distribution by landscape barriers, had low genetic differentiation and low IBD patterns (Pruett et al. 2008b). It is likely that the greater genetic distance among individuals within the San Francisco Bay and Salton Sea as compared to the BC populations are related to lower levels of gene flow between subspecies. Although these populations are of similar age and seem to have attained a migration-drift equilibrium, this equilibrium occurs at a much lower migration level between the subspecies than within the single subspecies of M. m. morphna. Low rates of gene flow are also suggested by the significant phylogeographic structuring at some loci for some of the San Francisco Bay subspecies, particularly M. m. gouldii. The lower gene flow between subspecies 43  within San Francisco Bay and the Salton Sea as compared to the inter-populational divergence within a single subspecies in the Georgian Basin confirms i) a degree of reproductive isolation among subspecies occurring in the San Francisco Bay and Salton Sea, and ii) suggests that sudden structuring over short spatial scales may indicate the presence of unrecognized cryptic groups. The higher structuring within the Californian subspecies is therefore likely due to sampling in a subspecific contact zone, as has been found in other hybrid zones (Lugon-Moulin et al. 1999). It is also possible, at lower latitudes, gene flow among populations even within a subspecies is more restricted (Martin & MacKay 2004). Examining the effect of latitude would require increased sampling across a wider geographic range within one of these Californian subspecies, to determine if the spatial genetic correlations within a single Californian subspecies would be similar to the spatial genetic correlations within the BC subspecies M. m. morphna. Factors influencing patterns of isolation by distance By testing for IBD, one is evaluating whether geographic distance is strongly correlated with genetic distance, which if true, suggests that at the scale of the study i) populations are at a migration-drift equilibrium, or ii) geographic distance is closely associated with migration rates (Slatkin 1993, Hutchison & Templeton 1999). If the chosen measure of geographic distance is not reflective of actual dispersal routes (i.e. does not account for barriers), or if other variables are more influential in determining gene flow, then the IBD relationship would be weakened or absent. In other populations where simple correlations between genetic and geographic distance were absent, genetic distance was more closely related to differences in maximum altitude (yellowhammer, Emberiza citrinella, Lee et al. 2001), rainfall (southern brown bandicoot, Isoodon obesulus, Cooper 2000) or other ecological indices (Swainson's thrush, Catharus ustulatus, Ruegg et al. 2006). Within the San Francisco Bay area, the candidate environmental variable was salinity, which ranges widely over short distances (BDAT 2007, Chan & Arcese 2003). Our analysis reconfirms previous work suggesting salinity is a contributor to spatial genetic structure, due to the apparent isolation of the salt-tolerant M. m. pusillula (Basham & Mewaldt 1987, Chan & Arcese 2003) from the other San Francisco Bay subspecies.  44  Within BC populations of M. m. morphna, we evaluated the influence of water barriers on genetic structure because many of the sampled individuals occurred on small islets or on Vancouver Island. The sampling design within BC was designed to evaluate the contribution of small-scale water versus land barriers on IBD patterns. Even small water barriers can impede recolonization on islands (Paine 1985) and across rivers (Smith et al. 2005). The increased genetic structure among insular individuals in M. m. morphna suggests that even small water barriers can have detectable effects on microspatial genetic structure. It is possible that within the islands, social barriers to dispersal also lead to higher structuring, because the population saturation on small islands inhibits immigration (Wilson & Arcese 2008). Yet in our study, and other avian studies (e.g. Geospiza sp., Petren et al. 2005), the IBD pattern was unaffected by island size or population size, providing support for distance-limited gene flow as an explanation for the increased genetic divergence of island populations. Conclusions Estimating the extent and spatial scale of genetic structure in Song Sparrows has facilitated the interpretation of outstanding issues in this model system related to outbreeding depression and the potential for genetic divergence on near-shore islands. In M. m. morphna, Marr et al. (2002) reported weak evidence of outbreeding depression within an island population for the F2 generation of immigrants, consistent with the idea that immigrants had originated from genetically divergent source populations. Mark-recapture data indicates that immigrants in this island metapopulation likely originate locally (Wilson unpubl. data.). In contrast, our current results suggest that although genetic structuring is higher among the Southern Gulf Island populations as compared to the mainland populations, genetic structuring within M. m. morphna is low compared to other Song Sparrow subspecies. This suggests that the evidence of outbreeding depression reported by Marr et al. (2002) is probably unrelated to simple landscape measures of neutral genetic divergence, but may warrant further study of the ecological or social determinants of immigrant success or localized genetic factors (Templeton 1986, Knowlton & Jackson 1993). Within Song Sparrow populations or species with similar vagility and population structure, we suggest that populations that are greater than 10 km apart from each other may begin to diverge genetically. The similar scale over which these subspecies are structured 45  suggests that the patch size is closely related to specific dispersal tendency, but that the magnitude of genetic divergence is influenced by more localized factors.  46  Table 3.1. Summary of published microsatellite studies for passerine species where spatial genetic structure was examined as a minor or major component of the study. The average distance interval (Avg. dist) and distance ranges (Dist. range) were calculated based on published coordinates or obtained from maps. The reported outcome of the tests for  IBD  is provided in the  fourth column as: IBD pattern not present (N), IBD pattern present (Y) and not reported (NR).  Passerine species  Avg. dist  Dist. range  (km)  (km)  IBD  Reference  Sagebrush Brewer’s sparrow 172 4-453 NR Croteau et al. (2007) House wren 209 11-441 Y§ Arguedas & Parker (2000) Blue manakin 218 67-414 Y Francisco (2000) et al. (2007) Yellowhammer 246 75-496 N Lee et al. (2001) Laysan finch 377 68-610 NR Tarr et al. (1998) Reed bunting 386 45-898 Y Grapputo et al. (1998) South Island robin 528 27-1026 Y Boessenkool et al. (2007) Swainson's thrush 606 83-877 Y Ruegg et al. (2006 ) Chestnut-backed chickadee 632 100-1586 N Burg et al. (2006) Black-throated blue warbler 677 163-1249 NR Davis et al. (2006) Satin bowerbird 681 87-1638 Y Nicholls et al. (2006) Florida grasshopper sparrow 805 278-1509 NR Bulgin et al. (2003) Yellow warbler 810 111-2227 Y Gibbs et al. (2000) Little greenbul 894 25-2595 Y Smith et al. (2005) Cerulean warbler 1043 240-1770 N Veit et al. (2005) Wilson's warbler 1888 389-4100 N Clegg et al. (2003) §Mixed: absent in migratory and present in sedentary populations.  47  Figures  Figure 3.1. The predicted genetic structure patterns as a function of relative influences of drift and gene flow between sampled populations. Studies that predominantly sample population pairs which have very high gene flow would be expected to have corrected pairwise genetic distances centering on zero, with no geographic relationship (A). Studies that include population pairs representing a wider range of gene flow/drift levels would be expected to show the increasing genetic distance and variance associated with an  IBD  pattern (B). Studies that compare  populations which are isolated would be expected to show high pairwise genetic distances, along with high variance with no geographic relationship due to predominant drift forces (C) (Modified from Hutchison & Templeton 1999).  48  Figure 3.2. a) Map of sampling sites of Song Sparrow populations: 1) Georgia Basin, 2) San Francisco Bay and 3) Salton Sea. Inset B depicts sampling along the BC Mainland and Vancouver Island: 1) Campbell River, 2) Powell River, 3) Sechelt, 4) Qualicum, 5) Duncan, 6) Sooke and 7) Delta. Inset c) depicts the sampling sites within the Southern Gulf Islands: 1, 2) Shell Islands, 3-5) Dock Islands, 6) Reay, 7) Mandarte, 8) Halibut and 9) Sidney.  49  Figure 3.3. Correlogram of the correlation coefficient (r) between genetic and geographic distance at four distance classes. Correlation coefficients were calculated at the individual level for Song Sparrow populations within a) San Francisco Bay and b) Georgia Basin and c) the Salton Sea. The permuted 95% confidence interval (dashed lines) around the null of r = 0, and the bootstrapped 95% confidence intervals around the correlation for each distance class are also shown. Please note the y-axis scaling difference for plot c.  50  Figure 3.4. Correlogram of the correlation coefficient (r) between genetic and geographic distance at four distance classes. Correlation coefficients were calculated at the individual level for Song Sparrow populations in the Southern Gulf Islands. 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J., Hoffman, J. I. & Amos, W. 2006. Dispersal, philopatry and intergroup relatedness: fine-scale genetic structure in the white-breasted thrasher, Ramphocinclus brachyurus. Mol. Ecol. 15: 3449-3458. 84. Templeton, A.R. 1986. Coadaptation and outbreeding depression. In: Conservation biology: the science of scarcity and diversity (M. E. Soulé, ed), pp 105–116. Sinauer Associates, Sunderland, MA. 85. Veit, M. L., Robertson, R. J., Hamel, P. B. & Friesen, V. L. 2005. Population genetic structure and dispersal across a fragmented landscape in cerulean warblers (Dendroica cerulea). Conserv. Genet. 6: 159-174. 86. Waples, R. S. 1998. Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Heredity 89: 438-450. 87. Weir, B. S. & Cockerham, C. C. 1984. Estimating F-statistics for the analysis of populationstructure. Evolution 38: 1358-1370. 88. Wilson, A.G., & Arcese, P. 2008. Influential factors for natal dispersal in an avian island metapopulation. J. Avian Biol. 36: 341-347 89. Woxvold, I. A., Adcock, G. J. & Mulder, R. A. 2006. Fine-scale genetic structure and dispersal in cooperatively breeding apostlebirds. Mol. Ecol. 15: 3139-3146. 90. Wright, S., 1943. Isolation by distance. Genetics 28: 114– 138 91. Zink, R. M. & Dittmann, D. L. 1993. Gene flow, refugia, and evolution of geographicvariation in the Song Sparrow (Melospiza melodia). Evolution 47: 717-729.  60  Chapter 4 − The contribution of island populations to in situ genetic conservation.  This chapter has been removed from the electronic thesis due to copyright restrictions.  The citation for this chapter is: Wilson AG, Arcese P, Keller L, Pruett C, Winker K, Patten M, Chan Y (2008). The contribution of island populations to in situ genetic conservation. Conservation Genetics in press [doi: 10.1007/s10592-008-9612-3].  Summary Genetic variation is often lower within island populations, however, islands may also harbor divergent genetic variation. The likelihood that insular populations are genetically diverse or divergent should be influenced by island size and isolation. We tested this assumption by comparing patterns of genetic variation across all major island song sparrow (Melospiza melodia) populations along the Pacific North American coast. Allelic richness was moderately lowered even on islands that are close to large, potential source populations. The most significant differences in allelic richness occurred on very small or highly remote islands. Gene diversity was significantly lower only on remote or very small islands. We found that island populations contribute to regional genetic variation through both the amount of genetic variation and the uniqueness of that variation. The partitioning of this contribution was associated with the size and isolation of the island populations.  61  Chapter 5 − Inter-island genetic divergence of an avian endemic on the Channel Islands3.  Introduction Isolation, novel ecological pressures, and well-defined borders make island populations ideal systems for studying micro-evolutionary processes (Grant 1998, Emerson 2002). Island and mainland populations frequently diverge in life history, morphology and ecology (Grant 1998), and these changes can occur over relatively small geographic distances (Mayr 1942, Power 1979). Microevolutionary processes on islands are most often studied on remote islands, where allopatric divergence is the primary model. Fewer microevolutionary studies have been carried out on near-shore island systems, where a divergence with gene flow model (Endler 1973, Orr & Smith 1998) is more appropriate. Unlike remote islands, where even the most vagile of species are isolated, near-shore islands provide variation in migration rates such that the divergence with gene flow model predictions can be assessed. Within a near-shore island system, the divergence with gene flow model would predict that island populations with high gene flow would have reduced evolutionary divergence from each other. A follow-up prediction would be that interspecific variation in dispersal ability across any ecological or topographical barriers could result in interspecific variation in subspecific differentiation. The California Channel Islands are a near-shore archipelago (Figure 5.1) with a biogeographic and geological history that is well suited to evaluate hypotheses of microevolutionary divergence. The Channel Island archipelago is composed of eight islands that are clustered into a northern and southern group. The northern Channel Islands form a chain which lying from east to west include Anacapa, Santa Cruz, Santa Rosa and San Miguel islands. The southern Channel Islands are more dispersed and are composed of Santa Catalina, Santa Barbara, San Clemente and San Nicolas islands. Counting across flora and fauna, the eight Channel Islands support almost 140 endemic species and subspecies (Johnson 1972, Raven & Axelrod 1978, Junak et al. 1995, Schoenherr et al. 2003). These levels of endemism are remarkable, given the close proximity (20 to 120 km) of the Channel Islands to the mainland. 3  A version of this chapter will be submitted for publication. Wilson AG and Arcese P. Interisland genetic divergence of an avian endemic on the Channel Islands. 62  There is also considerable taxonomic variability in patterns of subspecific differentiation across the Channel Islands, with the occurrence of archipelago endemics, island endemics or species completely undifferentiated from continental populations (Johnson 1972). The geological history of these islands is well characterized, providing time estimates for key events such as the formation of the super-island Santarosae, and its subsequent separation into the four northern Channel Islands, 10 to 12 KYA (Bloom 1983, Porcasi et al. 1999). Based on bathymetry, Santa Cruz Island was the first island to separate from Santarosae Island, followed by the separation of Santa Rosa and San Miguel islands a few hundred years later (Porcasi et al. 1999). This geological timeline and inter-specific variation in subspeciation enables us to examine the influence of vagility and historical occupancy on patterns of endemicity within Channel Island avifauna. Long-standing hypotheses about the colonization and differentiation of the Channel Island fauna have been based on patterns of morphological divergence and distribution (Johnson 1972, Power 1979). These models predicted that endemics should have low dispersal tendencies and long population histories on the islands. This prediction was upheld in the Island Scrub Jay (Aphelocoma insularis), which occurs only on Santa Cruz Island and based on molecular data, diverged from mainland populations 150 KYA (Delaney & Wayne 2005). A more moderate divergence pattern is present in the Channel Island Song Sparrows (Melospiza melodia), which have traditionally been recognized as three subspecies across four islands: M .m clementae on Santa Rosa and San Clemente, M .m. micronyx on San Miguel Island and M. m. graminea on Santa Barbara Island. A more recent evaluation suggested, however, that the island populations should be recognized as a single subspecies, M. m. graminea (Patten 2001). Song Sparrows were extirpated from Santa Barbara Island and San Clemente in 1967 and 1968, due to extensive habitat loss (Schoenherr et al. 2003, Collins 2008). Conversely, Song Sparrows are now present within disturbed areas on Santa Cruz Island, despite being historically very rare (Miller 1956, Sheldon 1990), suggesting that their presence is due to the expansion of a small population or increased migration from nearby Santa Rosa or San Miguel islands. We first evaluate the hypothesis that subspecific differentiation in the Channel Islands is influenced by the migration rate of the species. This hypothesis gains support if genetic differentiation among Channel Island Song Sparrow populations is i) higher when compared to other non-insular Song Sparrow populations and ii) lower than the more morphologically 63  divergent taxa present in the Channel Islands. We next evaluate the hypothesis that distance has been a significant determinant of inter-island dispersal rates. The alternative is that the Channel Islands are a saturated system, such that ecology rather than distance is limiting Song Sparrow distribution. Based on fossil evidence, Song Sparrows have been present on San Miguel Island for at least 39 KY, so would have been present during the Santarosae period. Depending on the degree to which water barriers impede gene flow in Song Sparrows, the submergence of Santarosae could have led to complete isolation between populations on San Miguel, Santa Rosa and Santa Cruz islands, and thus strong genetic differences between these island populations should be present. Similarly, the large water barriers separating the northern and southern Channel Islands should also have restricted inter-island gene flow. The water-barrier hypothesis would be supported if i) levels of gene flow among island populations are low and ii) there is a positive relationship between inter-island genetic and geographic distances. We then discuss the implication of these migration patterns for the conservation management of current Channel Island Song Sparrow populations. Methods Field protocol Contemporary genetic samples were collected from free-living individuals on San Miguel (N=20, 2006, 2007), Santa Rosa (N=27, 2006) and Santa Cruz islands (N=21, 2006). Adult birds were captured in mist nets using playbacks of male song. Each captured bird was banded with a uniquely numbered USFW aluminum band and a blood sample was taken from the brachial vein. Blood collection procedures involved wiping the elbow area with a sterile isopropanol swab, then puncturing the brachial vein using a sterile 30-gauge needle. A 20-50 μl blood sample was then collected in a plain glass capillary tube and placed in 1 mL of Queen’s Lysis buffer (Seutin et al. 1991). The 30-gauge needle was ideal for blood collection, because a sufficient sample was collected with minimal injury to the bird. Blood flow at the puncture site was stopped with slight pressure, cleaned again with sterile swabs and birds were released back into their territories. DNA samples for the extirpated populations of Santa Barbara (N=5) and San Clemente (N=6) islands were obtained from museum specimens archived at the Museum of Vertebrate Zoology, UC Berkeley and San Diego Natural History Museum. 64  DNA extraction and microsatellite genotyping of contemporary samples DNA was extracted from the blood samples using the GenElute™ Blood Genomic DNA Miniprep Kit (Sigma-Aldrich Canada Ltd.) according to manufacturer’s instructions. We followed manufacturing instructions, along with the modifications suggested by Bush (2007). Individuals from contemporary populations were genotyped at nine microsatellite markers: Mme1, Mme 2, Mme3, Mme7, Mme8, Mme12, Escu1, GF5 and PSAP335 (details provided in Chan & Arcese 2002). Mme3 and Mme7 are z-linked loci, which we dealt with by scoring the second allele in all females as absent. Microsatellites PCR reactions were performed in a 15 μl volumes containing approximately 100 ng genomic DNA, 10 mM Tris-HCl (pH 8.3), 50 mM KCl, 1.5-2 mM MgCl2, 0.2 mM dNTPs (Invitrogen), 0.16 μg/μl bovine serum albumin (BSA), 0.1% Triton X-100 (Sigma-Aldrich Canada Ltd.), 1pmol of each primer, 0.3 pmol of M13 Infrared Label (LiCor) and 0.5U of Taq polymerase (Roche). PCR reactions were carried out using standard cycling conditions with locus specific annealing temperatures as detailed in Chan & Arcese (2002). Microsatellite PCR products were fractionated on 7% polyacrylamide gels using a LI-COR 4200 DNA analyzer. Allele sizes were calibrated against both a commercial size standard (50–350 bp, LI-COR), and a species-specific allele ladder. Gel results were visualized using Base ImagIR (LI-COR, Lincoln, NB, USA), and loci were scored manually using RFLP scan (Scanalytics, CSP Inc., Fairfax, VA, USA). Sequencing reactions We generated mtDNA sequences for extirpated populations using museum toe pads and from blood samples of contemporary populations. We extracted DNA from the museum toe pads in a dedicated laboratory using QIAamp DNA micro kit (Qiagen). In order to deal with the potentially degraded DNA from museum specimens, we designed primers to amplify small (< 300bp) segments of DNA using the program Primer3 v 0.4.0 (Rozen & Skaletsky 2000). We based primer design on the M.melodia control region sequences provided in Fry and Zink (1998; GenBank Accession: AF053828–AF053882). We amplified the D-loop region of the control region using three sets of primers: SOSP -1, SOSP -2 and SOSP-3 which amplified 310, 310 and 192 bp respectively. Primer sequences were SOSP-1 F [5'-GCTCTTTTGCGCTATTGGTT-3'], SOSP-1R [5'-GAATGGGGTCAAAGTGCATC-3'], SOSP-2F [5'-TGTAATGGTTGCCGGACATA-3'], SOSP-2R [5'TTGATGACGAATGGTTTGGA-3], SOSP-3F  [5'-TCCAAACCATTCGTCATCAA-3'] and SOSP-3R [5’65  CGTGGGGGTGTGGTTAGTAG-3'].  Template amplifications were performed in 30 µl volumes  containing 50 ng of DNA, 10 mM Tris-HCl (pH 8.3), 50mM KCl, 2.5mM MgCl2, 0.2 mM dNTPs (Invitrogen), 2.4 µg/μl BSA, 0.1% Triton X-100 (Sigma-Aldrich Canada Ltd.), 10 pmol of each primer, and 1.0 U of Taq polymerase (Strata-gene). The cycling profile was 95 ° for 1 min, 35 cycles of denaturation at 95 °C for 30 sec, annealing at 56 °C for 30 sec, extension at 72 °C for 45 sec, followed by a final extension for 5 min at 72°C. Sequencing reactions were performed commercially at Macrogen-Korea using Big Dye chemistry and an ABI3730 XL automatic DNA sequencer. Data analysis Genetic differentiation and genetic structure To test for departures from Hardy–Weinberg (HWE) and linkage equilibrium, we used GENEPOP v 3.4 (Raymond & Rousset 1995). The HWE test was based on 400 batches and 3000 iterations, while the linkage equilibrium test was based on 800 batches and 10,000 iterations. We calculated a standardized genetic differentiation measure (G’ST) due to the highly polymorphic microsatellite markers used in this study (Hedrick 1999, 2005). We calculated the uncorrected GST using FSTAT v 2.9.3.2 (Goudet 2001). We then recoded our data set into maximally differentiated populations with the program RECODEPC v. 0.1 (Meirmans 2006), which was then analyzed using FSTAT to calculate G’ST MAX. To obtain G’ST, we divided the uncorrected GST estimate by the maximal divergence possible G’ST MAX. Significance was based on 1000 permutations. We also tested for phylogeographic structure among the extant island populations. Under a step-wise mutation model, if drift is the primary cause of differentiation the RST and FST estimators will be equivalent. Alternatively, if mutation is contributing to between-population differentiation, then alleles will not be randomly distributed with respect to size and RST will exceed FST. The relationship between these two estimators forms the basis of the allele-size randomization test implemented in the program SPAGeDi v1.2 (Hardy & Vekemans 2002). All statistical tests were corrected for multiple comparisons using the false discovery rate (Benjamini & Hochberg 1995).  66  Contemporary rates of immigration We estimated the rates of immigration among the extant island populations using BAYESASS v 2.3 (Wilson & Rannala 2003) and GENECLASS2 (Piry et al. 2004). BAYESASS estimates asymmetric migration rates (m), population allelic frequencies (P), the individual migrant ancestry and the population-level inbreeding coefficient (F). We ran ten replicates of Markov chain Monte Carlo (MCMC) chains that were 3×107 iterations in length, with a burn-in period of 2×106 iterations. Estimates were collected every 2000 iterations for the estimation of the posterior distributions of each statistic. In order to satisfy the recommended Markov chain acceptance ratio range of 40-60% (Wilson & Rannala 2003), we used delta values of 0.15, 0.15 and 0.20 for the migration rates (m), population allele frequencies (p) and inbreeding coefficient (F) respectively. The widths of the credible intervals from our data were narrower than the uninformative interval, suggesting that our data can be informative for inferring the migration rate. Uninformative data would lead to the posterior overlapping the prior. If the inference model assumptions are not met, or if divergence is too low, BAYESASS estimates may not converge under typical run length, making it necessary to do convergence tests (Faubet et al. 2007). Interrun variability, however, was low across ten replicate runs, and parameter estimation was very similar over runs differing in initial seed values and run length, so Bayesian deviance tests as suggested by Faubet et al. (2007) were unnecessary. As a complement to the BAYESASS analysis, we also calculated population assignment probabilities using GeneClass2 (Piry et al. 2004). The assignment probabilities were calculated based on the Bayesian criterion (Rannala & Mountain 1997), and the Monte-Carlo re-sampling algorithm (Paetkau et al. 2004). As a third corroboration of the BAYESASS and Geneclass2 analysis, we also used the Bayesian clustering program STRUCTURE v2.2.3 (Pritchard et al. 2000) to estimate the number of genetically distinct clusters (K) among the three extant islands. We evaluated models with correlated allele frequencies and considered models with admixture and no-admixture. For all models, we ran ten replicate runs of 106 iterations with a burn-in of 105 iterations, across each value of K from 1 to 4. We compared models varying in K by calculating posterior probabilities as suggested by Pritchard et al. (2000). Given that widespread habitat loss across San Miguel and Santa Rosa islands likely had a strong impact on Song Sparrow population sizes, we also examined if bottlenecks were 67  discernable using the program BOTTLENECK 1.2.02 (Cornuet & Luikart 1996; Piry et al. 1999). The rationale underlying this analysis is that rare alleles will be disproportionately lost in bottlenecked populations, leading to excess heterozygosity (Cornuet & Luikart 1996). We ran the analysis under all three mutation models, infinite alleles model (IAM), the stepwise-mutation model (SMM), and the two-phase model (TPM) at 70% SMM and 30% IAM (Di Rienzo et al. 1994). Although, the loci used in this study are a mix of di-repeats or imperfect di-repeats, which may be better described by the IAM model (Di Rienzo et al. 1994, Estoup et al. 1995). Given that our sample sizes were < 30, significance testing was based on 1000 iterations and the Wilcoxon signed rank test based on recommendations by Luikart et al. (1998). Finally, we also examined whether any signatures of population expansion were present, using the k and g tests (Reich et al. 1999) as implemented in the program KGTESTS (Bilgin 2007). Expanding populations should show reduced variance in allele length within a locus (k-test), and similar allele-length variation among loci (g-test) (Reich et al. 1999). Sequence data analysis We analyzed 30 sequences from extant populations on Santa Rosa, Santa Cruz and San Miguel islands and 11 sequences from the extirpated populations of Santa Barbara and San Clemente islands. Sequences were aligned using the ClustalW module provided in MEGA4 (Tamura et al. 2007) and collapsed into haplotypes using the program DnaSP (Rozen et al. 2003). We used the Bayesian Information Criterion (BIC) in Modeltest 3.8 (Posada 2006) to determine the optimal substitution model. The model with the lowest BIC was a general time reversible model with invariable substitution rates among sites (GTR+I). We used DAMBE (Xia & Xie 2001) to calculate the genetic distances among haplotypes based on the GTR model. Using these genetic distances, we calculated the average pairwise distance between the island populations, with a correction for the average within-group distance for each population. We used these distances to evaluate the contribution of geographic distance to the divergence among islands using the partial Mantel test as implemented in the program IBDWS v. 3.15 (Jensen et al. 2005). As a covariate to geographic distance, we calculated biogeographical similarity using the program EstimateS v7.5 (Colwell 2005), which we are using as a proxy for ecological similarity between islands. Biogeographical similarity between the islands was calculated based on shared 68  taxa (Chao et al. 2005), compiled from the presence and absence data for all native flora and fauna (Johnson 1972, Junak et al. 1995, Schoenherr et al. 2003). Results Genetic differentiation and genetic structure All island populations were in HWE and linkage equilibrium. Our results suggest that extant populations of Channel Island Song Sparrows exhibit considerable genetic divergence using both standardized (G’ST) and uncorrected measures of genetic differentiation (GST). Santa Rosa and Santa Cruz island populations were moderately diverged from each other, despite being geographically close (G’ST=0.13, GST = 0.03). Santa Rosa and San Miguel island populations were considerably more divergent (G’ST=0.33, GST = 0.12), with the highest divergence occurring between San Miguel and Santa Cruz islands (G’ST=0.38, GST = 0.14). A statistically significant signal of phylogeographic structuring was present at a single locus (PSAP) for one pairwise comparison between San Miguel and Santa Rosa islands (p = 0.01). For all other loci and pairwise comparisons, there was no significant phylogeographic structuring. Contemporary rates of immigration Results from the BAYESASS analyses suggested that immigration rates into the San Miguel Island population were low, with immigration rates estimate ranging from mC = 0.011 from Santa Rosa Island and mC = 0.008 from Santa Cruz Island (Table 5.1). Santa Rosa Island also had low immigration rates from both San Miguel (mC = 0.006) and Santa Cruz islands (mC = 0.006). Conversely, Santa Cruz Island appears to receive considerable immigration from Santa Rosa Island (mC = 0.3). Higher migration rates between Santa Rosa and Santa Cruz islands were also suggested by the GeneClass2 population assignment test. All sampled birds had the highest probability of originating from the island of capture. In the analysis for detection of firstgeneration migrants, one individual from Santa Cruz Island was assigned to Santa Rosa Island (log(Lhome /Lmax= 2.51, p = 0.001), and another bird sampled from Santa Rosa Island was assigned to Santa Cruz Island (-log(Lhome /Lmax = 2.88, p= 0.002). Results for our STRUCTURE analyses supported the migration patterns suggested from BAYESASS, as the model with the highest posterior probability distinguished two genetic clusters (K = 2; p = 1.0). The populations on 69  Santa Cruz and Santa Rosa islands each exhibited similar membership in the same cluster, whereas 17 individuals from San Miguel had complete membership in a second cluster (Figure 5.2). The results of bottleneck test depended strongly on the underlying mutation model. The one-tailed probability of heterozygosity excess under the IAM model was significant following false discovery corrections within San Miguel (p= 0.013) and Santa Rosa Island (p= 0.013), but was not significant for Santa Cruz Island (p= 0.037). following correction for the false discovery rate (27 comparisons, pcrit= 0.018). Under the TMP model, statistical significance was not below the critical value (p= 0.018) for Santa Rosa (p= 0.019), Santa Cruz (p = 0.024) and San Miguel Islands (p= 0.098). Under the SMM model, none of the islands had heterozygote excesses (San Miguel p= 0.23, Santa Rosa p= 0.29 and Santa Cruz p = 0.28). Our loci are a mix of di-repeats or imperfect di-repeats, which may be better described by the IAM model (Di Rienzo et al. 1994, Estoup et al. 1995), therefore I am interpreting these results to be support the occurrence of past bottlenecks. For the bottleneck test, the one-tailed probability of heterozygote excess under the IAM model was significant within San Miguel (p = 0.009) and Santa Rosa (p = 0.006) islands, but was not significant for Santa Cruz Island (p = 0.037), following correction for the false discovery rate (27 comparisons, pcrit= 0.018). Therefore, we are interpreting these results as a signal for the occurrence of past bottlenecks in San Miguel and Santa Rosa islands. The results from the k and g-tests were non-significant for all populations, but these tests have low statistical power for nine loci (Reich et al. 1999). For the intralocus k-test, two, four and six loci had expansion signals on Santa Cruz, Santa Rosa and San Miguel respectively, however, this is not statistically significant based on k-test criteria. For the g-test, the critical value of g = 0.18 (Reich et al. 1999) was exceeded in Santa Cruz (g = 2.28), Santa Rosa (g = 1.85) and San Miguel islands (g = 0.80). Sequence data analysis Within the 41 samples across the five islands of the northern and southern Channel Islands, we found 10 haplotypes, all of which were unique from other published sequences for Song Sparrow populations (Fry & Zink 1998). There were no haplotypes shared between the northern and southern Channel Islands. Within the populations of Santa Rosa, Santa Cruz and 70  San Miguel islands, six haplotypes were recovered, only one of which was present on all three islands (Haplotype 3, Figure 5.1). Within the populations on Santa Barbara and San Clemente islands, four haplotypes were recovered, one of which (Haplotype 9) was present on both islands. The parsimony haplotype network is provided in appendix 5.1. Based on the pairwise population genetic distances (Table 5.2), the islands followed an isolation by distance pattern, with log geographic distance being predictive of genetic distance (r = 0.74, p = 0.05). There was a nonsignificant, but negative, relationship between genetic distance and biogeographical distance (r = -0.38, p = 0.15). The partial correlation between genetic and geographic distance decreased slightly when corrected for the contribution of biogeographical distance (r = 0.69, p = 0.04) to genetic distances between populations. Discussion Patterns of subspecific divergence within Channel Island taxa have been attributed to both colonization history and dispersal tendency (Johnson 1972), with the prediction that taxa that show high inter-island divergence or high divergence from the mainland are likely to have had longer population histories on the islands and low dispersal tendencies. In comparison with these other taxa, Song Sparrows had higher inter-island divergence than the endemic subspecies, San Clemente Loggerhead Shrikes (Lanius ludovicicanus mearnsi, Eggert et al. 2004, Figure 5.3). In contrast, the inter-island genetic divergence between populations of the Channel Island Song Sparrow were lower than the genetic divergence between the endemic species of Island Scrub Jay and the mainland species of Scrub Jay (Figure 5.3). With respect to population history, based on sequence divergence from the mainland congener, the Island Scrub Jay has been isolated on the Channel Islands for an estimated 150 KY (Delaney & Wayne 2005). Estimating the divergence time of the Channel Island Song Sparrow based on haplotype sequence divergence was not done due to inadequate mainland sampling. However, fossil evidence places a minimal colonization date of 39 KYA for Song Sparrows on San Miguel Island, so it is possible that Song Sparrows colonized the northern Channel Islands after the Island Scrub Jay, have had intermittent extinctions, or perhaps colonized at a similar time but did not differentiate to the same degree due to larger effective population sizes. Historic Song Sparrow extinctionrecolonization cycles seem possible, given the extirpations of Song Sparrows that have occurred on Santa Barbara and San Clemente islands (Collins 2008). Furthermore, species turnovers 71  (replacement of species with a different species) have been documented in other Channel Island avifauna (Jones & Diamond 1976), but to a lesser extent among the endemic taxa (Lynch & Johnson 1974). Therefore, the role of vagility and/or population history on subspecific divergence seems to be supported by the tendency of taxa which show strong intraspecific divergence, such as the Island Scrub Jay and Channel Island Fox, to also show significant genetic divergence (Figure 5.3). This hypothesis, however, requires that additional taxa be surveyed with modern molecular methods, particularly for those species which show little to no inter-island divergence, such as the Orange-crowned Warbler (Vermivora celata sordida). At a regional level, divergence patterns of Song Sparrow populations suggest that genetic differentiation is associated with subspecific differentiation (Pruett et al. 2008). Indeed, many Song Sparrow populations resident on large islands have been recognized as endemic subspecies based on external morphology, and the most genetically divergent populations also tend to occur on islands (Pruett et al. 2008). The inter-island genetic differentiation and subspecific differentiation among the Channel Island populations were comparable to the Aleutian Islands, which are separated by a 1600 km matrix of ocean barriers and island stepping-stones (Pruett & Winker 2005). The similarity in morphological and genetic divergence between Song Sparrow populations on the Channel and Aleutian Islands over very different geographical and temporal scales is likely because populations on the Channel Islands are both smaller and older than the Aleutian Islands. The isolation by distance pattern among the Channel Islands suggests that distance is likely an important factor underlying inter-island divergence patterns. It has already been shown that Song Sparrow populations can diverge over relatively short geographic scales, with increased genetic structure occurring across water barriers (Chapter 3). However, island configurations and sizes have been quite variable in the history of the Channel Islands (Porcasi et al. 1999, Kinlan et al. 2005). During the mid-Pleistocene (700 - 128 KYA), the only islands which were not submerged were Santa Catalina Island and high elevation portions of Santa Cruz and Santa Rosa islands (Johnson 1978, 1983). All Channel Island taxa predating the midPleistocene must have originated on Santa Catalina, Santa Cruz or Santa Rosa islands (Miller 1985), and dispersed later. Therefore, although water barriers are likely an important structuring force within the Channel Islands, the role of island age and size may also be influential. 72  Examples of other potential factors influencing dispersal among the Channel Islands are paleoclimate (Johnson 1972), and interspecific competition (Miller 1951). The presence of shared endemics between San Clemente and Santa Rosa has been attributed to similar environmental conditions during the Xerothermic period (Johnson 1972). Although the affinity between San Clemente and Santa Rosa islands may be present for some avian taxa (Johnson 1972), it was not present in the genetic relationships among Song Sparrow populations on these two islands (Table 5.2). Biogeographical distance among the islands was not significantly correlated with Song Sparrow genetic differentiation, but our analysis was only a superficial treatment, and the role of ecology for intraspecific divergence of Channel Island taxa remains an interesting avenue of research. According to fossil evidence, Song Sparrows have been present on the Channel Islands for at least 39 KY (Gunthrie 1992). Therefore, during the time of Santarosae, it is likely a single Song Sparrow population was distributed across this superisland, which subsequently became subdivided into three populations about 10 to 12 KYA (Porcasi et al.1999). Based on contemporary estimates of migration rates (Table 5.1) and patterns of shared haplotypes across islands (Figure 5.1), it seems likely that low levels of migration occurred among the northern Channel Islands after the submergence of Santarosae. The absence of shared haplotypes between the northern and southern Channel Islands suggest that more limited gene flow occurred between these two groups. When compared to the available sequences from mainland Song Sparrow populations (Fry & Zink 1998), the haplotypes recovered on the Channel Islands were unique and monophyletic. However, inferences regarding the genealogical relationships of the Channel Island haplotypes would be strengthened considerably with additional sampling on the mainland. Divergence between the northern and southern Channel Islands has also been reported in an endemic moth (Argyrotaenia isolatissima, Rubinoff & Powell 2004) and in deer mice (Peromyscus maniculatus, Ashley & Wills 1987). Genetic estimates of immigration can have substantial relevance to conservation biology by estimating the importance of immigration rates to the persistence of particular populations (Hastings 1993, Waples & Gaggiotti 2006, Wood & Gross 2008). Our estimates of contemporary gene flow suggest that inter-island migration occurs at too low of a rate for immigration to contribute to demographic stability. An exception to this conclusion, however, is the high levels 73  of immigration estimated between Santa Rosa and Santa Cruz Island, which may reflect the recent colonization of Santa Cruz Island by Santa Rosa colonists in the 19th century, rather than high contemporary rates of migration. Historical accounts suggest that Song Sparrows were very rare or absent on Santa Cruz Island prior to the 1950s (Sheldon 1990, Miller 1956), perhaps as a consequence of competitive exclusion by the resident Rufous-crowned Sparrow (Aimophila ruficeps) on Santa Cruz Island (Miller 1956). The contemporary Song Sparrow distribution within Santa Cruz Island is concentrated within the central valley and appears to be closely associated with fennel (Foeniculum vulgare; pers. obs.) which has recently spread over an estimated 6.4% of the coastal sage and grassland habitats of this island (Beatty & Licari 1992). Therefore, Song Sparrow establishment from Santa Rosa Island to Santa Cruz Island may have been facilitated by the introduction and spread of fennel after the 1850s (Greene 1886). Similarly, population increases in the Spotted Skunk (Spilogale gracilis amphiala) on Santa Cruz Island were hypothesized to have been a response to habitat change leading to competitive release from the declining Channel Island Fox (Jones et al. 2008). Sequential colonization events or recurrent gene flow should be reflected in the patterns of diversity (Wayne et al. 1991), where sources have higher allelic richness than the recipient populations (Hewitt 1996). Allelic richness declines across the Channel Islands in a manner consistent with a stepping stone migration, with similar allelic richness (AR) patterns within Santa Cruz Island (AR = 6.54 ± 0.59) and Santa Rosa Island (AR = 6.06 ± 0.98), but with a substantial drop in allelic richness on San Miguel Island (AR = 4.79 ± 0.93) (Chapter 4). Based on the low estimated migration rates among some of the islands (Table 5.1, Figure 5.2), this loss of allelic diversity is consistent with a pattern of drift-mediated losses accruing over long periods of isolation. Overall, patterns of migration, genetic structuring (Figures 5.2), and historical records suggest that immigrants from Santa Rosa Island could have augmented low numbers of Song Sparrows resident on Santa Cruz Island. Although there was no genetic signal of an expansion for the Santa Cruz Island population, we cannot exclude the possibility that a small resident population expanded in response to habitat changes within the central valley. Habitat loss has been considerable across the Channel Islands, particularly on San Miguel Island which was essentially denuded by cattle grazing in the early 1900s (Schoenherr et al. 2003). Assuming the IAM mutation model, our data suggest that historic bottlenecks did occur 74  on Santa Rosa and San Miguel islands. Based on the probable range of Ne for these islands (Ne: < 50, Wilson unpubl. data) and the expected duration of bottleneck signals (Luikart et al. 1998), the presence of a bottleneck signal on Santa Rosa and San Miguel islands would correspond most closely to recent events such as the period of intense grazing on these islands. The absence of a statistically significant bottleneck signal within the Santa Cruz Island population is consistent with the historical records of low Song Sparrow densities on this island (Sheldon 1990, Miller 1956), such that any declines in such a low population would be unlikely to be detected as a bottleneck signal (Luikart et al. 1998). The rates of migration between extant populations suggest that natural levels of immigration would be insufficient for the natural recolonization of Santa Barbara or San Clemente islands by colonists from either other Channel Islands or the mainland. The shortest open-ocean distances from the mainland to Santa Barbara or San Clemente islands are 64 and 95 km respectively. This open-ocean distance is likely a substantial barrier for resident Song Sparrow populations, as evidenced by the low migration rates among the northern Channel Islands, the spatial genetic structure (Chapter 3), and dispersal ecology (Chapter 2) of other Song Sparrow subspecies. Low migration rates may also explain the absence of any observations of breeding Song Sparrows on Santa Barbara Island over the last 40 years. It is also possible, however, that the absence of suitable habitat has impeded Song Sparrows from reestablishing a breeding population on Santa Barbara Island. The Channel Island Song Sparrow is a threatened endemic subspecies (Collins 2008), such that estimating the extent of demographic independence among populations is crucial for informed management. Although the Song Sparrows are a common species overall, Channel Island Song Sparrows offer an interesting model for studying extinction dynamics in near-shore islands. Local extirpations of Song Sparrows, perhaps as a consequence of habitat loss, account for two of the four avian extirpations observed in this archipelago to date. Because of the sedentary nature of non-migratory Song Sparrows, dispersal patterns inferred here provide a baseline estimate of the degree to which immigration might be expected to contribute to population stability for Channel Island fauna. Overall, our results suggest that natural population augmentation and recolonization is unlikely for Channel Island Song Sparrows, therefore, maintaining existing populations should receive high conservation priority. 75  Tables Table 5.1 Estimates of contemporary (mC) migration rates between Song Sparrow populations within the Channel Islands as estimated from BAYESASS (Wilson & Rannala 2003). The mode migration rate is provided along with the 95% credible interval. The uninformative credible interval was: (0, 0.11). Recipient population  SMIG  SROS  SCRU  Donor population  mC (95% CI)  SMIG  0.982 (0.93-1.00)  SROS  0.011 (0.00-0.049)  SCRU  0.008 (0.00-0.034)  SMIG  0.006 (0.00-0.028)  SROS  0.987 (0.96-1.00)  SCRU  0.007 (0.00-0.031)  SMIG  0.014 (0.00-0.048)  SROS  0.301 (0.25-0.33)  SCRU  0.685 (0.67-0.73)  76  Table 5.2. Estimates of pairwise divergence between Song Sparrow populations on Santa Cruz (SCRU), Santa Rosa (SROS), San Miguel (SMIG), Santa Barbara (SBI) and San Clemente (SCLE) islands. Genetic distances were calculated based on 512 bp of the mitochondrial control region and were corrected for within-population divergence.  SMIG  SROS  SCRU  SBI  SMIG  0  SROS  0.000  0  SCRU  0.002  0.001  0  SBI  0.006  0.006  0.005  0  SCLE  0.004  0.004  0.004  0.002  SCLE  0  77  Figures Figure 5.1.  Map of the Channel Islands showing the geographic distribution of unique  mitochondrial DNA haplotypes. Each pie chart indicates the haplotype frequency for samples collected from that population. Song Sparrows have extant populations on San Miguel, Santa Rosa and Santa Cruz islands and extirpated populations on Santa Barbara and San Clemente islands.  78  Figure 5.2 Results of clustering analysis in  STRUCTURE  (Pritchard et al. 2000) The blue and red  areas represent tightly-spaced columns each of which represents the admixture coefficient for a single individual bird. Birds are grouped by sampling location within Santa Cruz, Santa Rosa or San Miguel islands. The height of each column indicates the proportion of ancestry for each individual that is attributed to the two genetic clusters  79  Figure 5.3  Inter-island and mainland genetic differentiation patterns as a function of  geographical distance in the four Channel Island taxa for which genetic data is available. 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Wood CC, Gross MR (2008) Elemental conservation units: Communicating extinction risk without dictating targets for protection. Conservation Biology 22, 36-47. 73. Xia X, Xie Z (2001) DAMBE: Software package for data analysis in molecular biology and evolution. J Heredity 92, 371-373. 74. Zink RM, Dittmann DL (1993) Gene flow, refugia, and evolution of geographic-variation in the Song Sparrow (Melospiza melodia). Evolution 47, 717-729.  86  Chapter 6 – Conclusions  Thesis conclusions and implications Understanding how the ecology of dispersal, sampling scale, population history and dispersal barriers affect the spatial genetic structure of species is essential to integrating genetic data into conservation plans because these factors help to estimate the scale of population connectivity and the potential for evolutionarily divergent populations to exist. Island populations have long served as models for the study of dispersal and evolutionary divergence because islands provide well-defined boundaries between populations, and because island populations often display morphological and genetic divergence from their mainland counterparts. In this thesis, I compared insular and mainland populations of Song Sparrows across the Pacific Coast of North America to examine some of the patterns and processes underlying geographic variation in genetic structure. The first consistent point to emerge is that considerable geographic variation in genetic structure and dispersal patterns can occur among different Song Sparrow populations. Furthermore, this variation can occur over short geographical scales. Results in chapter 2 show that near-shore islands in the Georgia Basin separated by less than 7 km can differ by over 90% in average immigration rate. During 1998-2004, only 4% of all firstyear birds recruiting to Mandarte Island were immigrants, the remaining recruits being born on Mandarte Island in the previous year. Low gene diversity on Mandarte Island, as compared to much smaller islands with higher migration rates (Chap. 4) suggests that heterogeneity in immigration also influenced patterns of genetic diversity. Mandarte Island lies between 2 and 7 km away from other islands, suggesting that relatively small water barriers can restrict Song Sparrow dispersal and gene flow. The issue of fine-scale dispersal patterns is expanded upon in Chapter 3, where I showed that genetic structuring at < 2 km is higher among individuals on islands than among mainland individuals (Chap. 3, Fig 3.4). This result provides further evidence for both the existence of fine-scale genetic differences among resident Song Sparrows populations in western North America and for the restrictive influence of water barriers on gene flow. 87  Fine-scale genetic structuring was also detected across mainland sites, with the approximate patch size being less than 10 km in continuously distributed Song Sparrow populations (Chap. 3, Fig 3.3). Among the three regions, the patch size was similar, but regions differed in the strength of structuring over similar spatial scales, due to the influence of localized factors. In Chapter 4, I showed that patterns of genetic diversity within island populations can also be similar, despite very different spatial scales of isolation or population sizes. The demonstration of geographic variation in genetic structuring implies that different factors are operating within different populations. I examined factors such as island size, geographic distance, intervening matrix and biological barriers. Island size had clear effects on genetic structuring, which is evident by the similar levels of divergence and reduced genetic variation seen in populations on both the Channel Islands (small, close to shore) and the Aleutian Islands (larger, very remote; Chap 4, 5). I interpreted these differences as being due to drift, driven by a small effective population size (Ne) on the Channel Islands and strong isolation in the Aleutian Islands. However, based on fossil records and Pleistocene glaciations patterns, Song Sparrow populations on the Channel Islands could be 28 KY older than populations on the Aleutian Islands (Guthrie 1992, Stilwell and Kaufman 1996). Spatial genetic structuring is also influenced by the interaction between the intervening matrix, geographic distance and barriers. Although many studies report the presence or absence of genetic structure or isolation by distance (IBD) patterning, often too few populations are sampled to make strong inferences regarding the influence of environmental variables or geographic distance. Testing for IBD is an explicit test of the hypothesis that measures of geographical distance (or factors highly correlated with distance) are the primary determinants of genetic structure at the scale of the study. Therefore, the absence of IBD suggests that alternate hypotheses involving other ecological or historical factors should be evaluated. The results in Chapter 3 demonstrate the value of multivariate spatial autocorrelation analyses for detecting fine-scale patterns of genetic structure not detected using typical Mantel tests. By sampling over similar scales, I was also able to conclude that distance over water acts as a stronger dispersal barrier than distance over land. A second conclusion was that the genetic distances between closely occurring populations of different subspecies are much higher than the genetic distances between distant populations of a single subspecies. These conclusions are 88  based on the higher spatial correlations over shorter geographic space and higher genetic divergence among subspecies over small spatial scales (Chap 3, Figs. 3.4, 3.5). A practical message from my results is that studies that are focused on inferring contemporary patterns of dispersal from genetic structure with passerine populations should consider sampling from populations that are separated by distances less than 10 km, because sampling intervals may otherwise exceed the underlying genetic patch (Smouse & Peakall 1999). Implications for the conservation of populations on near-shore islands The islands along the Pacific Coast of North America have rarely been the subject of genetic studies, such that we understand very little about patterns of genetic variation or divergence among them (Cook & MacDonald 2001). The few published studies showed that Pacific Coast island populations had diverged genetically from mainland populations (Bidlack & Cook 2001, Small et al. 2003, Burg et al. 2005, Pruett & Winker 2005). By comparing across island populations of a widely distributed passerine, from Alaska to California, I was able to demonstrate that island populations can be contributors to intraspecific genetic diversity. Despite the fact that allelic richness was lower on most islands than on mainland populations, total intraspecific diversity would be lower if particular islands were not present (Chap 4, Fig 4.2, 4.3). The islands contributing the most to intraspecific diversity were Vancouver Island due to high diversity, and the Channel Islands due to high differentiation. From the perspective of in situ genetic conservation, I showed that increasingly remote islands had a high differentiation contribution, even if low diversity reduced the overall contribution. Therefore, the generalization of island populations having low levels of genetic diversity (Frankham 1997) holds for Song Sparrows. I have, however, refined this generalization by showing that island size may be a stronger predictor of diversity and differentiation than island isolation (Figures 6.1 & 6.2). Island isolation can also be expected to influence the contribution of immigration to population viability. I showed in the Southern Gulf Islands that immigration can prevent extirpation, but that for more isolated islands the potential for ‘demographic rescue’ via immigration was much lower. Patterns of genetic divergence and recolonization within the Channel Islands are consistent with these fine-scale ecological patterns. The strong genetic divergence of San Miguel Island suggests that inter-island immigration is not a substantial contributor to population viability. This also implies that if the San Miguel Island Song Sparrow 89  population were extirpated, the chances of natural recolonization over ecological timescales would be low. The fact that the Song Sparrow populations extirpated from San Clemente and Santa Barbara islands have not been recolonized also supports this conclusion. Implications for microevolution on near-shore islands So what do these patterns indicate about the processes that have lead to divergence in Song Sparrows, or in the way that genetic differences should be interpreted? Before my study, spatial genetic data had only been collected from Song Sparrow populations showing high morphological variation (Chan & Arcese 2002, 2003, Patten et al. 2004, Pruett & Winker 2005). Yet, a comprehensive examination of intraspecific divergence requires a clear determination of the range of genetic differences that occur among populations within a subspecies, due to differences in population size or isolation by distance. Comparisons between subspecies are more complicated as they include the effects of selection, partial reproductive isolation and different histories. I confirmed that genetic differences between populations of M. m. morphna separated by over 100 km were smaller than genetic differences occurring over only 20 km between populations of subspecies in San Francisco Bay (Chap 3. Fig 3.5). Given that Vancouver Island has avian endemics (Campbell et al. 1990), there was the potential for cryptic divergence between Song Sparrow populations on Vancouver Island and the southern coast of BC. The microsatellite data, however, do not support the existence of any divergent groups along the eastern coast of Vancouver Island. Islands with endemic Song Sparrow subspecies (Aleutian and Channel islands) showed greater island to mainland genetic differences than populations on islands without endemic Song Sparrow subspecies (Vancouver Island, Figure 6.1). Any hypotheses regarding whether Song Sparrows diverged more rapidly on islands requires that the phylogeography of Song Sparrows can be resolved and this is made difficult by incomplete lineage sorting of mtDNA sequences (Fry & Zink 1998, Maddison & Knowles 2006). Future directions The conclusions within this thesis are relevant to two broad categories, the conservation of genetic diversity and the use of molecular markers to infer dispersal. Areas within these objectives in particular need of further study are i) clarifying the association between nonadaptive and adaptive genetic diversity and ii) assessing the reliability of inferences that can be made in systems with low genetic differentiation. 90  Adaptive versus neutral genetic variation An ongoing debate that is pertinent to my thesis is the extent to which patterns of diversity at neutral loci reflect the diversity at adaptive loci. Some meta-analyses have found positive correlations between neutral diversity and quantitative variation (Merilä & Crnokrak 2001), while others have found no association (Reed & Frankham 2001). If there is no reliable correlation between quantitative variation and neutral molecular markers, studies focusing on adaptive genetic variation may need to measure quantitative variation directly (Reed & Frankham 2001). For many vertebrates, it is not yet possible to survey allele frequencies at candidate quantitative trait loci across natural populations, and therefore, phenotypic variation is often taken as a proxy for adaptive variation. Yet, using phenotypic variation as a proxy for quantitative variation can be problematic, due to phenotypic plasticity, errors in estimating additive genetic variation, environmental effects and errors in trait measurement. The correlation between molecular diversity and adaptive diversity may be more reliable if molecular makers are either closely linked with candidate genes, or are accurate estimates of genome-wide diversity (Bonin et al. 2007). Single nucleotide polymorphisms (SNPs) are promising markers because they measure diversity across a greater proportion of the genome, and therefore may provide better estimates of genome-wide diversity (Morin et al. 2004). If adaptive and neutral variation could be concurrently screened using molecular markers, it would be possible to examine how the balance between selection and drift varies among traits and in response to variable population sizes, population history and dispersal barriers. Within the Song Sparrow system, the hypotheses regarding whether phenotypic variation is due to adaptive versus non-adaptive factors (Aldrich 1984, Chan & Arcese 2002) can really only be tested if adaptive variation can be directly measured. Estimating dispersal among populations Estimating migration rates based on indices of genetic differentiation can be very difficult, as many methods rely on unrealistic population models (Whitlock & McCauley 1999). New analytical models can now accommodate increasingly complex population models, and are capable of estimating multiple parameters concurrently, allowing typical assumptions to be relaxed (Beaumont & Rannala 2004, Excoffier & Heckel 2006). However, the issue of estimating migration rates among populations that are not genetically differentiated is 91  problematic (Waples 1998), even when using these novel analytical techniques. Based on simulated data, reliable parameter estimates could not be recovered using several popular programs under conditions of low genetic divergence (MIGRATE: Abdo et al. 2004, BAYESASS: Faubet et al. 2007). The inability to estimate migration under conditions of low genetic divergence is an important issue because it prevents the detection of potentially important barriers to dispersal. Based on the dispersal ecology of Song Sparrows in the Southern Gulf Islands and the spatial genetic structure provided in Chapter 3, it would be expected that the 32 km wide Georgia Strait should constitute a significant barrier. Yet, the genetic divergence among populations was too low for current analytical methods to estimate migration parameters. For contemporary conservation purposes, Waples & Gaggiotti (2006) suggest that estimates of migration rates should be evaluated against the theoretical value of migration that leads to populations being demographically independent. For many species, however, the populations that are demographically interdependent will likely be genetically similar, so current analytical methods based on typical numbers of loci and sampled individuals, will have difficulty in defining the extent of connectivity relevant to demographic independence. A possible approach for estimating migration parameters in complex or difficult situations would be to use population simulations. Programs are available which are able to simulate a range of demographic scenarios ranging from simple (EASYPOP, Balloux 2001) to increasingly complex models (SIMCOAL2, Laval & Excoffier 2004). Parameters derived from the simulations are subsequently compared to empirical data to determine which simulated scenario is most compatible with the data, and whether or not additional loci or sampled individuals could provide additional power (Waples 1998). Simulations have been recently used to infer ancient demography in mammals (Hadley et al. 2004), and as a complement to genetic and radiotracking data to infer the influence of roads on bobcat and coyote dispersal (Riley et al. 2006). Similar analyses examining the influence of variation in population size, secondary contact and postglacial colonization scenarios could be very helpful in further revealing factors that have shaped the evolutionary history of Song Sparrows.  92  Thesis summary I have shown that at a microspatial scale, population structure and island size and isolation can lead to considerable spatial and temporal differences in dispersal. I have also shown that the scale of genetic structure driven by contemporary dispersal operates at a much smaller scale than previously expected. Factors such as island size, isolation and subspecific identity had predictable effects on dispersal patterns and genetic structure among populations. However, the applicability of these conclusions to conservation prioritization or management could be strengthened considerably i) if future studies included surveys of adaptive genetic variation, and ii) if genetically-based migration estimates were accurate and precise enough to determine if populations are demographically independent, which is currently difficult in populations with low genetic differentiation.  93  Figures Figure 6.1. The extent of genetic differentiation (G’ST) of Song Sparrow populations on the Channel Islands, Vancouver Island, Haida Gwaii and the Aleutian Islands from the corresponding mainland populations as a function of geographic distance.  94  Figure 6.2. The retention of allelic richness for island populations in reference to closest mainland populations as a function of distance (km) from the island to the mainland. Populations included are: Vancouver Island, Channel Islands (Santa Cruz, Santa Rosa and San Miguel), Haida Gwaii and Alaskan Islands (Kodiak, Adak and Attu). The sizes of the circles are proportional to log island size (km2).  95  References 1. Abdo Z, Crandall KA, Joyce P (2004) Evaluating the performance of likelihood methods for detecting population structure and migration. Mol Ecol 13, 837–851. 2. Aldrich JW (1984) Ecogeographical variation in size and proportions of Song Sparrows (Melospiza melodia). Ornithological Monographs 35 3. Balloux F (2001) EASYPOP (version 1.7): A computer program for population genetics simulations. J Hered 92, 301-302. 4. Beaumont MA, Rannala B (2004) The Bayesian revolution in genetics. Nat Rev Genet 5, 251-261. 5. Bidlack A, Cook JA (2001) Reduced genetic variation in insular northern flying squirrels (Glaucomys sabrinus) along the North Pacific Coast. Anim Cons 4, 283-290. 6. Bonin A, Nicole F, Pompanon F, Miaud C, Taberlet P (2007) Population adaptive index: A new method to help measure intraspecific genetic diversity and prioritize populations for conservation. Cons Biol 21, 697–708. 7. Burg TM, Gaston AJ, Winker K, Friesen VL (2005) Rapid divergence and postglacial colonization in western North American Steller's jays (Cyanocitta stelleri) Mol Ecol 14, 3745–3755. 8. Campbell RW, Dawe NK, McTaggart-Cowan I, Cooper JM, Kaiser GW, McNall MCE. (1990) The Birds of British Columbia. Volume II: Diurnal birds of prey through woodpeckers. Royal British Columbia Museum, Environment Canada and the Canadian Wildlife Service. Victoria, BC. 9. Chan Y, Arcese P (2002) Subspecific differentiation and conservation of Song Sparrows (Melospiza melodia) in the San Francisco Bay region inferred by microsatellite loci analysis. Auk 119, 641-657. 10. Chan, Y. & Arcese, P. 2003. Morphological and microsatellite differentiation in Melospiza melodia (Aves) at a microgeographic scale. J Evol Biol 16, 939-947. 11. Cook JA, MacDonald SO (2001) Should endemism be a focus of conservation efforts along the North Pacific Coast of North America? Biol Conserv 97, 207-213. 12. Excoffier L, Heckel G (2006) Computer programs for population genetics data analysis: a survival guide. Nature Reviews Genetics 7, 745-758. 96  13. Faubet P, Waples RS, Gaggiotti OE (2007) Evaluating the performance of a multi-locus Bayesian method for the estimation of migration rates. Mol Ecol 16, 1149-1166. 14. Frankham R (1997) Do island populations have lower genetic variation than mainland populations? Heredity 78, 311 327. 15. Fry AJ, Zink RM (1998) Geographic analysis of nucleotide diversity and Song Sparrow (Aves: Emberizidae) population history. Mol Ecol 7, 1303-1313. 16. Guthrie DA (1992) A late Pleistocene avifauna from San Miguel Island, California. Papers on avian paleontology Sci.Ser. 36 (ed. Campbell E), pp 319-327. Natural History Museum. Los Angeles, CA. 17. Hadly EA, Ramakrishnan U, Chan YL, van Tuinen M, O'Keefe K, Spaeth PA, Conroy CJ (2004) Genetic response to climatic change: Insights from ancient DNA and phylochronology. PLOS Biology 2: 1600-1609. 18. Hastings A (1993) Complex interactions between dispersal and dynamics: lessons from coupled logistic equations. Ecology 74, 1362-1372. 19. Laval G, Excoffier L (2004) SIMCOAL 2.0: a program to simulate genomic diversity over large recombining regions in a subdivided population with a complex history. Bioinformatics 20, 2485-2487. 20. Maddison WP, Knowles LL (2006) Inferring phylogeny despite incomplete lineage sorting. Systematic Biology 55, 21-30. 21. Merilä J, Crnokrak P (2001) Comparison of marker gene and quantitative genetic differentiation among populations. J Evol Biol. 14, 892-903. 22. Morin PA, Luikart G, Wayne RK (2004) SNPs in ecology, evolution and conservation. Trends Ecol Evol 19, 208-216. 23. Patten MA, Rotenberry JT, Zuk M (2004) Habitat selection, acoustic adaptation, and the evolution of reproductive isolation. Evolution 58, 2144-2155. 24. Pruett CL, Winker K (2005) Northwestern Song Sparrow populations show genetic effects of sequential colonization. Mol Ecol 14, 1421–1434. 25. Reed DH, Frankham R (2001) How closely correlated are molecular and quantitative measures of genetic variance? A meta-analysis. Evolution 55, 1095–1103.  97  26. Riley SPD, Pollinger JP, Sauvajot RM, York EC, Bromley C, Fuller TK, Wayne RK (2006) A southern California freeway is a physical and social barrier to gene flow in carnivores. Mol Ecol 15, 1733–1741. 27. Small MP, Stone KD, Cook JA (2003) American marten (Martes americana) in the Pacific Northwest: population differentiation across a landscape fragmented in time and space. Mol Ecol 12, 89-103. 28. Smouse PE, Peakall R (1999) Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82: 561-573. 29. Stilwell KB, Kaufman DS (1996) Late Wisconsin glacial history of the northern Alaska Peninsula, southwestern Alaska, USA. Arctic Alpine Res 28: 475 487. 30. Waples RS (1998) Separating the wheat from the chaff: Patterns of genetic differentiation in high gene flow species. J. Heredity 89, 438-450. 31. Waples RS, Gaggiotti O (2006) What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol Ecol 15, 1419-1439. 32. Whitlock MC, Mccauley DE (1999) Indirect measures of gene flow and migration: FST≠1/ (4Nm+1). Heredity 82, 117–125.  98  Appendices  Appendix 1. Derivations for partitioning the components of genetic variation. The estimators of absolute and relative differentiation are defined as DST=hT-hS and GST = DST/hT, respectively. Where hT is the total gene diversity and hS is the average diversity. These values can be calculated to include a focal population k or without the population (hT/k, hS/k). We use these estimators along with the diversities calculated for the particular population, denoted as: hTk, hSk to estimate the total contribution of population k (CT(k)) to the total diversity of the sample. By arranging and equating DST and GST we obtain the formula: hT - hT/k = (hS - hS/k) + (DST - DST/k), which provides the framework for CTk to be partitioned into a diversity CSk and differentiation CDk component (Petit et al. 1998). The derivation of the actual contribution of population k, CTk and CSk involves a substitution and reduction, while CDk is obtained from the difference between CTk and CSk leading to the formulas: CTk  2 hT hT / k n hT  C Sk  1 hk hs / k n 1 hT  C Dk  CTk  C Sk  Formulae for allelic richness calculated using similar equations with the substitution of gene diversity with allelic richness rarefaction (r) estimate based on a g sample size. The total r T(g) is the allelic richness of the whole population, while the r S(g) is the locus-specific average. The formulas for the total contribution of population k to allelic richness (CTRk(g)) and the components of diversity (CSRk(g)) and differentiation (CDRk(g)) are provided below: CTRk ( g )  rT ( g )  rT / k ( g )  rT ( g ) 1  C SRk ( g )  1 rk ( g ) rs / k ( g ) C DRk ( g ) n 1 rT ( g )  CTRk ( g )  C SRk ( g )  99  Appendix 2. Parsimony mtDNA haplotype network for Channel Island Song Sparrows Parsimony haplotype network estimated using the computer program TCS v1.21 (Clement et al. 2000), based upon 512 bp of the mitochondrial control region. Ovals and boxes, (the latter of which has the highest root probability) are proportional in size to the number of individuals sampled possessing that haplotype. Haplotype designations and sampling locations are provided in the caption for figure 5.1. Each branch segment connecting haplotypes represent single nucleotide changes and small, unfilled circles between segments represent unsampled, intermediate haplotypes. Haplotype one is unconnected because it could not be joined under the 95% parsimony criterion  100  Appendix 3. Permits Animal Care  101  Environment Canada Permit  102  US Federal Bird Banding Permit-Master  US Federal Bird Banding Permit-Subpermit  103  California Department of Fish and Game  104  National Park Service- Channel Islands  105  106  University of California Animal Care  107  

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