University of Cincinnati

Date: 12/6/2010

I, Heather Farrington , hereby submit this original work as part of the requirements for the degree of Doctor of Philosophy in Biological Sciences.

It is entitled: Historical Specimens Reveal a Century of Genetic Change in Darwin's Finches

Student's name: Heather Farrington

This work and its defense approved by:

Committee chair: Kenneth Petren, PhD

Committee member: Lisle Gibbs, PhD

Committee member: Theresa Culley, PhD

Committee member: Ronald Debry, PhD

Committee member: Stephen Matter, PhD

1393

Last Printed:2/24/2011 Document Of Defense Form

Historical Specimens Reveal a Century of Genetic Change in Darwin’s Finches

By Heather Farrington B.S., University of Mount Union, 1999

A Doctoral Dissertation Submitted to the Faculty of the University of Cincinnati Department of Biological Sciences Advisor: Dr. Kenneth Petren

As Partial Fulfillment of the Requirements For the Degree of Doctor of Philosophy in Biology

February 2011

ABSTRACT

Understanding how populations change through time is critical in the generation of effective conservation biology practices. However, data from multiple time points, especially those that span a significant number of years/generations are rare. In this series of studies, I used a model system, Darwin’s finches of the Galápagos Islands, to investigate questions related to both long term and short term evolution in this system. These species are thought to be in a state of decline due to recent human-induced habitat disturbances, and increasing local extinction events. Darwin’s finches are also well-represented in museum collections. Therefore, this is an ideal system in which to study both change over time and extinction events in fragmented populations. I first used a traditional phylogenetics approach using multiple nuclear introns to examine the overall evolutionary history of this species radiation. These data generated a novel evolutionary tree topology, leading to the reevaluation of the earliest species divergences in this system. I then investigated short term evolutionary trends in warbler finch populations by utilizing genetic information (in the form of microsatellite markers) generated from museum specimens collected approximately 100 years ago. There was no genetic evidence of archipelago-wide population declines in the warbler finches. Both increases and decreases in genetic diversity were noted for individual populations. Decreases in genetic diversity were attributed to fluctuations in population size due to El Niño cycles, while increases were due to migration from other populations. On the island of Española, a suspected case of “genetic rescue”, when a population with low genetic diversity is infused with genetic variation through migration, was revealed. This suggests that migration may play an important role in maintenance of genetic diversity within island populations. Lastly, I again used museum specimens to compare extinct and extant populations to determine if there was any genetic ii

indication of population declines prior to extinction events. In this rare test of the predictive ability of standing genetic variation to predict extinction risk, genetic diversity was generally equal to or greater than diversity in populations that have persisted through time. This result emphasizes the caution in using genetic data alone to monitor populations and evaluate extinction risk. All three studies yielded unexpected results, particularly those that utilized museum specimens from natural history collections. The overall conclusion of this dissertation highlights the importance of understanding population interactions in fragmented landscapes, which has major conservation implications for population persistence and viability.

iii

TABLE OF CONTENTS

Abstract…………………………………………………………………………………….….…ii

Table of Contents……………………………………………………………………………….. v

List of Tables and Figures…………………………………………………………….…….…...vi

Chapter 1: General Introduction………………………………………………………………...1

Chapter 2: Multi-locus Phylogeny of Darwin’s Finches………………………………………...9

Chapter 3: A Century of Genetic Change and Metapopulation Dynamics in the Galápagos Warbler Finches (Certhidea)……………………………………………………………………40

Chapter 4: Extinction Dynamics in Populations of Darwin’s Finches…………………………86

Chapter 5: General Conclusions………………………………………………...……………..130

LIST OF TABLES AND FIGURES

CHAPTER 2:

Table 1: Species and populations included in phylogenetic analysis

Figure 1: Map of Galápagos

Figure 2: Current phylogenies of Darwin’s finches

Figure 3: Maximum likelihood tree for multi-locus intron data set

Figure 4: Maximum likelihood tree for multi-locus intron data set including the Cocos Island finch

Figure 5: Parsimony tree based on control region data

Table S1: Nuclear intron locus information

CHAPTER 3:

Table 1: Certhidea populations used for cross-temporal analysis

Figure 1: Galápagos map indicating islands sampled

Figure 2: PCA plots of historic and modern populations

Figure 3: Summary genetic data; allelic richness, observed and expected heterozygosity

Figure 4: Average inferred population sizes ( = 4Neμ) and immigration rates

Figure 5: Historic vs. modern migration rates

Figure 6: Percent change over time (allelic richness, migration and population size)

Table S1: Museum specimen information

Table S2: MSVAR and Bottleneck results

Table S3: Table of summary genetic data

CHAPTER 4:

Table 1: Darwin’s finch population sampling information

Table 2: Summary genetic data

Figure 1: Galápagos map indicating probable extinction events

Figure 2: PCA plots of historic and modern populations

Table S1: Museum specimen information

vii

CHAPTER 1: GENERAL INTRODUCTION

Understanding the evolutionary dynamics of populations through time is essential for the effective management of biodiversity during this era of increasing human impact in the global ecosystem. Phylogenetic methods have traditionally been used to investigate the evolutionary history of populations and species. These methods typically address long-term evolutionary history and attempt to answer questions related to the timing and sequence of species and population divergences (Avise 2000). This is where my graduate study began, using novel genetic loci to reconstruct the adaptive radiation of Darwin’s finches. Previous studies using various genetic markers have been only marginally successful in resolving the evolutionary history of this group (Petren et al. 1999; Sato et al. 1999, Petren et al. 2005). Although the phylogenetic study was a valuable learning experience, my interests soon shifted to the shorter term evolutionary dynamics of populations and their implications for conservation.

Human disturbance is causing population decline and fragmentation of taxa around the world, which has stimulated a need for better understanding of how populations respond to these environmental perturbations. As population sizes decrease and/or fragmentation increases, genetic drift and inbreeding lead to reduced genetic diversity in a population (Wright 1969), limiting the evolutionary potential of a population to adapt to new environmental conditions under natural selection (Fisher 1930). Limited evolutionary potential increases the risk of extinction when environmental conditions change (Gilpin and Soulé 1986). In addition to changes occurring within a single population, as habitat fragmentation becomes more prevalent across landscapes, the evolutionary interactions among populations must also be considered

(Wright 1940; Andrewartha and Birch 1954; Levins 1970). Metapopulation studies have demonstrated the importance of gene flow to maintain genetic diversity within individual

populations (Hanski and Gaggiotti 2004), and a better understanding of metapopulation dynamics is critical to conservation management efforts in fragmented populations.

The majority of our knowledge relating to genetic changes in populations over time is due to experimental and comparative studies that lack a strong temporal component.

Experimental studies have been useful in demonstrating inbreeding depression (Willis 1993), genetic drift (Dobzhansky and Pavlovsky 1957), evolution under selection (Mather 1943), and the link between heterozygosity and fitness (Charlesworth and Charlesworth 1999). However, how well these studies translate to wild populations is unclear (Sæther and Engen 2004).

Comparative and meta-analysis approaches have been useful in demonstrating reduced genetic diversity in small vs. large populations (Leimu et al. 2006) and endangered vs. non-threatened populations or species (Spielman et al. 2004). Theoretical studies often attempt to extrapolate population changes into the past or future based on current genetic data (Beaumont 1999; Wilson et al. 2003; Kuhner 2006). However, these methods are laden with assumptions related to mutation rates and patterns, population demography and reproductive rates, and mating systems

(Excoffier and Heckel 2006), and typically do not account for demographic and environmental stochasticity.

The best way to examine genetic evolutionary processes in wild populations is to obtain actual genetic data from the past to track changes through time. However, data from multiple time points, especially those that span a significant number of years/generations are rare. If this data were obtained, it would give us unique insight into the evolutionary dynamics of wild populations and the opportunity to assess how natural populations conform to our understanding of evolutionary processes. Researchers equipped with this knowledge can develop better

conservation tools, and evaluate existing ones, for identifying and managing threatened populations.

Natural history collections are warehouses for past biodiversity. In addition to the morphological and distributional data they contain, these collections are also a rich potential source of past genetic data. The use of DNA from museum specimens has become increasingly common since genetic material was first isolated from muscle tissue of an extinct quagga in 1984

(Higuchi et al. 1984). The advent of PCR has also contributed to the increased use of “ancient”

DNA as it allows the amplification of DNA fragments from very low initial copy numbers

(Chelomina 2006). Ancient DNA is often used in phylogenetics to represent species or populations that are now extinct, or taxa that are difficult to sample in the field (Pääbo et al.

2004). More recently, ancient DNA has become increasingly important for cross-temporal comparisons, assessing genetic changes in populations over time (loss of diversity, heterozygosity, etc.) (Wandeler et al. 2007). These cross-temporal comparisons are particularly valuable for examination of threatened or endangered species, where changes in the genetic structure of declining populations are of particular concern (Nielsen, et al. 1997; Bouzat et al.

1998).

Although the DNA of museum specimens holds valuable information, there are two primary challenges to working with ancient DNA. First, DNA degrades over time in dead tissues, causing fragmentation of DNA molecules and oxidative damage to DNA strands

(Lindahl 1993), leading to both low quantity and quality of DNA template. Deamination of cytosine to uracil is the primary cause of miscoding errors in ancient DNA sequences (Binladen et al. 2006). Therefore, sequence data are often inconsistent among PCR replicates and can result in false genotypes (Wandeler et al. 2003). Mitochondrial DNA (mtDNA) has been

preferentially used from museum specimens due to its presence at much higher initial cellular concentration than nuclear DNA, increasing the probability of recovering intact and relatively undamaged DNA segments (Willerslev and Cooper 2005). However, cloning and extensive replication are still required to obtain reliable sequence data. Second, contamination from modern DNA sources is a problem, as modern, undamaged DNA will often be preferentially amplified in a PCR reaction (Willerslev and Cooper 2005).

The use of microsatellites eliminates the problems associated with direct sequencing of ancient DNA. Microsatellite data uses only fragment length differences, caused by changes in numbers of nucleotide repeats, to measure variation, which eliminates the need for excessive sequencing and/or cloning to determine a consensus sequence. However, allelic dropout is a common problem in microsatellite data obtained from museum specimens. Allelic dropout is the more efficient amplification of one of the two microsatellite alleles in a heterozygote, leading to misidentification of an individual as a homozygote (Sefc et al. 2003). Allelic dropout typically leads to the underestimation of heterozygosity in populations and/or individuals. Therefore, additional precautions must be taken to ensure quality of ancient DNA data such as replicate analyses and strict lab protocols to limit contamination from modern specimens.

The model system used for this thesis research was Darwin’s finches of the Galápagos

Islands. This system has many characteristics that make it ideal for evolutionary study. First, as a textbook example of adaptive radiation, the origination of multiple species from one ancestral stock due to ecological niche differentiation, the finches have a complex and interesting evolutionary history, which I have investigated in chapter one of this thesis. Second, most

Galápagos finch species exist as metapopulations, geographically isolated island populations linked together by low levels of migration. There are more than 20 islands of various sizes in

this system, inhabited by unique combinations of the 14 recognized endemic finch species, allowing for comparisons among multiple populations within a species, as well as comparisons between species. Furthermore, there is a wide range of human disturbance on islands of the archipelago, from relatively pristine islands to those with extensive agricultural zones and settlements. Third, whereas other island avifauna have been decimated by human activities (e.g.

Hawaii), the Galápagos avifauna is largely intact, with no known loss of species, only local extinctions of individual island populations. Lastly, there are large numbers of museum specimens available for this group, representing both extinct and extant island populations, which were collected on a series of expeditions to the Galápagos primarily between 1895 and

1910. I utilized these museum collections in chapters two and three of this thesis research to study extinction dynamics of individual populations, changes in individual populations of various sizes through time, and changes in metapopulation dynamics and gene flow through time.

The objectives of this thesis were to:

1) Reconstruct the evolutionary history of the Darwin’s finch radiation using a multi-

locus phylogeny based on nuclear introns. This phylogeny was then compared to

those previously proposed using other genetic markers.

2) Examine cross-temporal genetic diversity and migration patterns in warbler finch

populations by comparing genetic data from museum specimens to modern

populations. I hypothesized that Darwin’s finches are in a state of decline due to

recent increases in human disturbance in the archipelago. Therefore, I predicted that

genetic variation, in the form of allelic richness and heterozygosity, and migration

rates should be lower in modern populations when compared to historic levels.

3) Determine whether historic populations that are now extinct had lower genetic

diversity prior to extinction when compared to extant counterparts from the same

historic time point. I hypothesized that extinct populations were in a state of decline

at the time of sample collection. Therefore, I predicted these populations would

have reduced genetic variation prior to extinction.

REFERENCES

Andrewartha, H.G., Birch, L.C. 1954. The distribution and abundance of animals. University of Chicago Press.

Avise, J.C. 2000. Phylogeography: the history and formation of species. Harvard University Press, Cambridge, Massachusetts.

Beaumont, M.A. 1999. Detecting population expansion and decline using microsatellites. Genetics 153:2013-2029.

Binladen, J., Wiuf, C., Gilbert, M.T.P., Bunce, M., Barnett, R., Larson, G., Greenwood, A.D., Haile, J., Ho, S.Y.W., Hansen, A.J., Willerslev, E. 2006. Assessing the fidelity of ancient DNA sequences amplified from nuclear genes. Genetics 172:733-741.

Bouzat, J.L., Lewin, H.A., Paige, K.N. 1998. The ghost of genetic diversity past: historical DNA analysis of the greater prairie chicken. American Naturalist 152:1-6.

Charlesworth, B., Charlesworth, D. 1999. The genetic basis of inbreeding depression. Genetical Research 74(3):329-340.

Chelomina, G.N. 2006. Ancient DNA. Russian Journal of Genetics 42(3):219-233.

Dobzhansky, T., Pavlovsky, O. 1957. An experimental study of interaction between genetic drift and natural selection. Evolution 11:311-319.

Excoffier, L., Heckel, G. 2006. Computer programs for population genetics data analysis: a survival guide. Nature Reviews Genetics 7:745-758.

Fisher, R.A. 1930. The genetical theory of natural selection. Oxford University Press.

Gilpin, M.E., Soulé, M.E. 1986. Minimal viable populations: processes of species extinction. In: Conservation Biology: the Science of Scarcity and Diversity. Soulé, M.E. (ed). Sinauer, Sunderland, MA.

Hanski, I., Gaggiotti, O.E. 2004. Ecology, genetics, and the evolution of metapopulations. Elsevier Academic Press, San Diego, California.

Higuchi, R., Bowman, B., Freiberger, M., Ryder, O.A., Wilson, A.C. 1984. DNA sequences from the quagga, an extinct member of the horse family. Nature 312:282-284.

Kuhner, M.K. 2006. LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22(6):768-770.

Leimu, R., Mutikainen, P., Koricheva, J., Fischer, M. 2006. How general are positive relationships between plant population size, fitness and genetic variation? Journal of Ecology 94:942-952.

Levins, R. 1970. Extinction. Lecture Notes in Mathematics. 2:75-107.

Lindahl, T. 1993. Instability and decay of the primary structure of DNA. Nature 362:709-715.

Mather, K. 1943. Polygenic inheritance and natural selection. Biological Reviews 18:32-64.

Nielsen, E.E., Hansen, M.M., Loeschcke, V. 1997. Analysis of microsatellite DNA from old scale samples of Atlantic salmon Salmo salar: a comparison of genetic composition over 60 years. Molecular Ecology 6:487-492.

Pääbo, S., Poinar, H., Serre, D., Jaenicke-Després, V., Hebler, J., Rohland, N., Kuch, M., Krause, J., Vigilant, L., Hofreiter, M. 2004. Genetic analysis from ancient DNA. Annual Review of Genetics 38:645-679.

Petren, K., Grant, B.R., Grant, P.R. 1999. A phylogeny of Darwin’s finches based on microsatellite DNA length variation. Proceedings of the Royal Society of London Series B Biological Sciences. 266(1417):321-329.

Petren, K., Grant, B.R., Grant, P.R., Keller F. 2005. Comparative landscape genetics and the adaptive radiation of Darwin’s finches: the role of peripheral isolation. Molecular Ecology. 14:2943-2957.

Sato, A., O’hUigin, C., Figueroa, F., Grant, P.R., Grant, B.R., Tichy, H., Klein, J. 1999. Phylogeny of Darwin’s finches as revealed by mtDNA sequences. Proceedings of the National Academy of Sciences of the United States of America. 96(9):5101-5106.

Sæther, B., Engen, S. 2004. Stocastic population theory faces reality in the laboratory. Trends in Ecology and Evolution 19(7):351-353.

Sefc, K.M., Payne, R.B., Sorenson, M.D. 2003. Microsatellite amplification from museum feather samples: effects of fragment size and template concentration on genotyping errors. Auk 120(4):982-989.

Spielman, D., Brook, B.W., Frankham, R. 2004. Most species are not driven to extinction before genetic factors impact them. Proceedings of the National Academy of Science 101(2):15261-15264.

Wandeler, P., Hoeck, P.E.A., Keller, L.F. 2007. Back to the future: museum specimens in population genetics. Trends in Ecology and Evolution 22(12):634-642.

Wandeler, P., Smith, S., Morin, P.A., Pettifor, R.A., Funk, S.M. 2003. Patterns of nuclear degeneration over time – a case study in historic teeth samples. Molecular Ecology 12:1087-1093.

Willerslev, E., Cooper, A. 2005. Ancient DNA. Proceedings of the Royal Society B 272:3-16.

Willis, J.H. 1993. Partial self-fertilization and inbreeding depression in two populations of Mimulus gattatus. Heredity 71:145-154.

Wilson, I.J., Weale, M.E., Balding, D.J. 2003. Inferences from DNA data: population histories, evolutionary processes and forensic match probabilities. Journal of the Royal Statistical Society 166:155-188.

Wright, S. 1940. Breeding structure of populations in relation to speciation. American Naturalist 74:232-248.

Wright, S. 1969. Evolution and the genetics of populations. University of Chicago Press, Chicago.

Multi-Locus Phylogeny of Darwin’s Finches

Heather L. Farrington1*, and

Kenneth Petren1

1 Department of Biological Sciences; University of Cincinnati; Cincinnati OH 45221-0006

* Correspondence: [email protected]; +001 513-556-9719

______

Abstract

Adaptive radiations are ideal systems in which to study the processes of speciation and evolution. However, phylogenies are often difficult to resolve for recent and rapid speciation events. Here we used a multi-locus nuclear intron approach to generate a phylogeny of Darwin’s finches and compared this phylogeny to those generated by mtDNA and microsatellite data. Our data set consisted of introns from 12 genes for a total of approximately 7100 base pairs of sequence data. Resolution of relationships among several species was not possible in previous phylogenies due to the recent nature of the divergence. The most substantial discrepancy between methods was the arrangement of the Certhidea species (warbler finches) at the base of the radiation. Previous phylogenies suggested that the two Certhidea species (C. olivacea and

C. fusca) were paraphyletic; however, the multi-locus approach showed that these species are monophyletic sister taxa. This topology has important implications for the Darwin’s finch radiation including the likely phenotype of the common ancestor and the congruence between morphology and genetic divergence. This study demonstrates the need to evaluate multiple sources of data when constructing species phylogenies to avoid being misled by single gene histories.

Introduction

Adaptive radiations are unique evolutionary events that can help us understand the external ecological conditions and the internal genetic factors required to stimulate the generation of new species. Investigations into several well-known radiation events, including the

African cichlids (Barlow 2000; Seehausen 2006), Hawaiian honeycreepers (Tarr and Fleischer

1995) and the Galápagos finches (Grant 1999), have given rise to a wealth of knowledge about the events and conditions that stimulate the process of speciation and foster the formation of new phenotypes (Schluter 2000). However, despite extensive study of these systems, the evolutionary histories of adaptive radiations are often unclear. Phylogenetic reconstruction of these groups using modern genetic techniques has proven especially challenging for recent radiations

(Seehausen 2006). In many radiations, species are formed very rapidly, allowing little time for the accumulation of mutations between divergence events and for coalescence to occur between lineages (Maddison 1997), leading to poorly resolved phylogenies. Hybridization between lineages at various times during the radiation further confuses genetic signal, obscuring the evolutionary history of species (Shaw 2002; Day et al. 2007; Grant et al. 2005). Due to these characteristics of rapid speciation events, phylogenetic histories of adaptive radiations are often poorly resolved at many levels due to conflicting data among loci, limited genetic variation in recently radiated groups, or low resolution at an individual locus (Clabaut et al. 2005; Shaw

2002). Therefore, multiple loci must be used to increase the probability of recovering the true species tree, and reduce the amount of noise in the phylogenetic signal due to conflicting data among loci (Rokas 2003; Pamilo and Nei 1988; Hudson and Coyne 2002).

Because of the rapid and repeated speciation events that are characteristic of adaptive radiations, individual loci develop unique gene histories during the radiation based on their

mutation rates, inheritance patterns and linkage with other loci (Moore 1995; Hare 2001). These individual locus histories often do not reflect the true sequence of speciation events (i.e. gene trees do not equal species trees) (Nichols 2001; Hudson and Coyne 2002). Tree topologies can differ dramatically depending on the loci used (i.e., mtDNA, nuclear DNA, microsatellites,

AFLPs) for reconstruction of the phylogeny. Each of these methods carries with it its own advantages and disadvantages, including their utility at different evolutionary timescales (as a function of mutation rates), saturation rates, variation among closely related taxa, ease of data collection and availability of methods for analysis. These varying characteristics often lead to conflicting results among markers in phylogenetic reconstructions (Avise 2000). Therefore, multi-locus approaches have become increasingly common to estimate species history based on information from several different loci. Nuclear introns are well-suited for this type of analysis due to their abundance in the genome, selective neutrality, ease of amplification, and independent segregation of different genes (Prychitko and Moore 1997; Johnson and Clayton

2000; Allen and Omland 2003).

In this study we examine the utility of a multi-locus nuclear approach in reconstruction of the Darwin’s finch phylogeny, and compare the resulting trees to those previously developed using mtDNA and microsatellite methods. Current phylogenies suggest that Darwin’s finches often defy commonly-accepted evolutionary patterns, particularly the congruence of morphological and genetic divergence (Freeland and Boag 1999; Petren et al. 1999; Sato et al.

1999, Petren et al. 2005). Within Darwin’s finches, the most genetically distinct species, the warbler finches (Certhidea olivacea and Certhidea fusca), are the most morphologically similar.

Furthermore, there is often greater genetic divergence between isolated island populations within a single species, most notably among the sharp-beaked ground finch populations (Geospiza

difficilis), than between recognized species (Petren et al. 1999; Sato et al. 1999, Petren et al.

2005). Our goal in this study was to use multi-locus information to test these seemingly contradictory patterns, and specific hypotheses of monophyly and paraphyly among the warbler finches and the sharp-beaked ground finch populations, which most obviously represent these patterns. We also examined the placement of the Cocos Island finch (Pinaroloxias inornata) within the Darwin’s finch radiation. To conclude, we discuss the relative merits of different classes of molecular markers in phylogenetic reconstruction, and the potential utility of multi- locus approaches for revealing the history of rapidly speciating groups.

Resolving a phylogenetic tree for the Darwin’s finch radiation will allow us to reconstruct the ecological and genetic conditions that have stimulated speciation in this group of organisms. By deciphering the evolutionary history of this group, we can further investigate and better understand the role of such forces as genetic variation, ecology, behavior, stochastic events, biogeography, natural selection, genetic drift, population isolation, migration and hybridization, on the process of species divergence and maintenance of species boundaries

(Grant 2007). These forces are just as strong today as when the colonizing ancestors of Darwin’s finches first arrived on the Galápagos Islands.

Methods

Samples and Molecular Methods

Intron variation was analyzed in 17 individuals representing 10 species of Darwin’s finches, with an emphasis on the two Certhidea species and various island populations of

G. difficilis. These populations were chosen because they represent all major evolutionary trajectories in the Darwin’s finch radiation as revealed by molecular markers previously used in phylogenetic reconstructions of this group. Microsatellite data previously collected from over

1000 Darwin’s finch specimens, assures that our limited taxon sampling for this study does represent all the major evolutionary lineages of this radiation. Therefore, the emphasis for this study was generation of greater quantities of genetic data per individual, rather than increased taxon sampling. One closely related mainland species, Tiaris bicolor, was included as the outgroup (Table 1; Fig. 1). Whole blood samples were collected during several field expeditions and DNA was extracted using previously published methods (Petren et al. 1999).

An extensive literature search was conducted to identify potential nuclear intron primers for use in this study. Each potential locus was evaluated for PCR and sequencing quality and sequence variability for the most distantly related finch groups. Detailed information for each locus evaluated, including primer sequences, PCR conditions and literature citations, can be found in Supplementary Table 1. A total of 24 introns were screened for phylogenetic utility.

PCR products were directly sequenced using ABI 3730 DNA Analyzers with PCR primers or appropriate nested sequencing primers, as necessary (Appendix A). Loci that could not be amplified, sequenced or had no variability among the initial individuals screened were discarded

(Appendix A). Sequences were aligned using CodonCode Aligner software v.1.5.2.(CodonCode

Corporation, Dedham, MA), with additional adjustments made by eye. Variable sites suspected to be heterozygous were coded as ambiguous characters to reduce bias. The final data set included 14 introns from 12 unique genes, for a total of approximately 7100 base pairs. The genes utilized on this study were adenylate kinase (AK), lamin (Lam), glyceraldehydes-3- phosphate dehydrogenase (Gapd), -enolase (Enol), myoglobin (Myo), -fibrinogen (FIB), aldolase (ALD), ornithine decarboxylase (OD), tropomyosin (TROP), Lactate dehydrogenase

(LDH), laminin receptor precursor (LRP), and phosphoenolpyruvate carboxykinase (PEPCK).

Combined Analyses

Analyses were initially run excluding the Cocos Island finch to evaluate hypotheses within the Galápagos taxa. Hypotheses regarding the Cocos Island finch were evaluated separately due to conflicting phylogenetic signals for this species in previous studies and a previously hypothesized hybrid origin of this non-Galápagos species (Seehausen 2004). Aligned sequences were analyzed by parsimony and maximum likelihood methods as a concatenated data set. Several studies (Rokas 2003; deQueiroz 1993; deQueiroz et al. 1995; Gadagkar et al. 2005) have demonstrated that concatenation is appropriate for multi-locus data sets with low variation for several reasons. First, information to resolve certain relationships may only be present in some of the data sets, and would be lost with a consensus approach. Second, when there is conflict among characters, combining data sets may increase signal to noise ratio. Third, combined data sets may contain groups not significantly supported by a single locus. Portions of introns that were unobtainable due to PCR or sequencing difficulties were coded as missing data

(<4% of the total data set). Parsimony branch support was assessed using 10000 bootstrap replicates. The data set was analyzed first ignoring indel events and again including indels as new character states to assess the impact of these events on tree topologies and branch support.

Maximum likelihood analyses were conducted using both PAUP (Swofford 2002) and MrBayes

(Huelsenbeck and Ronquist 2001). ModelTest (Posada and Crandall 1998) was used to establish the most suitable model of evolution. Bayesian analyses were run for 2000000 generations, sampling every 1000 generations. Burn-in time was short, as likelihood values stabilized after only 10000 generations. The remaining 1990 sampled trees were used to estimate branch support. Likelihood analyses were also conducted by partitioning the data set by locus.

Partitioned analysis was run for 20000000 generations, sampling every 1000 generations. Due to

limited variation among loci, the evolutionary model generated for the concatenated data set was also used for all partitions, but allowing rates to vary. A Bayesian Estimation of Species Trees

(BEST) was also performed within the MrBayes program (Liu and Pearl 2007; Edwards et al.

2007).

Alternative tree topologies were compared using the Approximately Unbiased (AU) and

Kishino-Hasegawa (K-H) tests (Shimodaira 2002) in the software program COSEL (Shimodaira and Hasegawa 2001) with a priori hypotheses based on morphology and previously published tree topologies. Constraints of monophyly and paraphyly were applied to both the Certhidea species and the G. difficilis populations in separate analyses.

Individual Locus Analyses

Each intron locus was evaluated individually by parsimony methods to determine what tree topologies were supported by each locus (i.e., if the combined tree topology was supported by the majority of the individual loci). Additionally, 16 individual loci from a previous microsatellite study (Petren et al. 1999) were also evaluated to determine support for various tree topologies using other nuclear markers.

Mitochondrial Control Region

In order to examine congruence of phylogenetic signal between mitochondrial loci, previously unpublished control region sequences were collected for 29 Darwin’s finch and several Tiaris obscura (outgroup) samples. Sequences were aligned by eye. The total length of the aligned sequences was 1079 base pairs, with 113 parsimony informative sites. Parsimony analysis was conducted on these data and compared to the previously published cytochrome b phylogeny (Petren et al. 2005).

Results

Combined Analyses

The concatenated intron data set contained a total of 7118 base pairs. There were a total of 59 parsimony informative sites, with informative variation ranging from 1 to 14 bases for individual loci. There were also two phylogenetically informative indel events in the data set.

DNA sequences for each individual and locus were posted in GenBank (accession numbers will be added upon publication). Of the 252 intron sequences used in this study, only eight could not be obtained due to PCR or sequencing difficulties. Interestingly, two intron markers that have been used frequently in avian phylogenetic studies, -fibrinogen intron 7 (FIB7) and chromo- helicase binding protein (CHD) were of limited use in Darwin’s finches. FIB7 was very difficult to sequence due to a poly-A region in the intron, while the sex-linked CHD locus yielded no informative sequence variation. Analyses with the BEST program yielded unresolved phylogenies for the concatenated intron data set. This was not surprising as the utility of coalescent methods has been questioned in recent adaptive radiations due to extremely short internode times between speciation events (Degnan and Rosenburg 2009).

Paraphyly of Warbler Finches

Contrary to the previous phylogenies, both parsimony and maximum likelihood methods supported a single basal split between the warbler finches (Certhidea) and the remainder of the finches in the radiation using the combined data set. Within the monophyletic Certhidea clade, maximum likelihood methods divided populations according to previously recognized monophyletic species (C. olivacea and C. fusca). This topology, containing the two Certhidea species as a monophyletic group, was more strongly supported by both parsimony bootstrap and

Bayes values (Fig. 3) than the paraphyletic topology generated by mtDNA and microsatellite

methods. However, AU and K-H tests comparing these two paraphyly topologies with the multi- locus data set were not statistically significant (AU p=0.14, K-H p=0.17).

Tree topologies and support values were not substantially altered when a Bayes partitioned analysis was performed, with the exception of a reduced support value for the

Certhidea olivacea clade (98 to 83 percent support). Inclusion of informative gaps in the parsimony analysis substantially increased bootstrap support for the tree finch clade (Cactospiza pallida and Camarhynchus parvulus). Resolution between species within the monophyletic

Cethidea clade was reduced (not shown) when gaps were considered as informative.

Paraphyly of Sharp-beaked Ground FinchPopulations

In likelihood analyses, the G. difficilis populations were paraphyletic (Fig. 3). The populations from Pinta and Fernandina islands always grouped together; however, the placement of this group relative to the tree finches and other ground finches varied based on the method of analysis used. Maximum likelihood placed these populations later in the radiation, while

Bayesian methods placed these populations basal to the tree and ground finch clade as in the mtDNA and microsatellite trees. The other G. difficilis populations (Darwin and Genovesa

Islands) group together by maximum likelihood, but not Bayesian methods. Parsimony bootstrap support for the Darwin and Genovesa Island clade was less than 50%. Differences in tree topologies using a G. difficilis monophyly constraint were not significant with the AU and K-H tests (AU p=0.20, K-H p=0.23). Relationships among the tree and ground finches were largely unresolved.

Placement of Cocos Finch

The combined 15-intron data set generally agreed with the microsatellite tree on the placement of the Cocos finch, Pinaroloxias inornata (Fig. 4). This species diverged after the

Certhidea species and was basal to the ground and tree finch clades. This topology was well supported with Bayesian and maximum likelihood, but weakly supported by parsimony bootstraps. Inclusion of the Cocos finch in the data set generally reduced parsimony and

Bayesian support for the monophyly of Certhidea, although the relationship still held in all analyses.

Individual Locus analyses

Due to the limited variation in the data set, strict consensus parsimony trees using individual loci were generally unresolved, or provided support for only pairs or small subsets of taxa. Only a single locus (TROP) clearly supported monophyly of Certhidea. Relationships among the G. difficilis populations were also poorly resolved by individual loci. When examining microsatellite data from a previous study, only 4 of 16 sites supported monophyly of

Certhidea. Interestingly, two of these sites did not place Certhidea at the base of the radiation, but rather as a monophyletic clade late in the radiation.

Mitochondrial Control Region

The control region tree supports paraphyly of the warbler finches, similar to previous studies. However, the Certhidea species are reversed in the control region topology, with

Certhidea fusca at the basal position in the radiation.

Discussion

Phylogenetic Relationships and Implications for Darwin’s Finches

The most substantial difference between the multi-locus phylogeny generated in this study and the previously published phylogenies (Petren et al. 1999; Burns et al. 2002) is the relationship between the Certhidea species. The mtDNA and microsatellite phylogenies indicate that Certhidea olivacea, inhabiting the inner islands of the archipelago, and Certhidea fusca,

inhabiting the outer islands, form a paraphyletic relationship at the base of the Darwin’s finch radiation (Sato et al. 1999; Petren et al. 1999; Fig. 2). Among trees with this paraphyletic relationship, the control region locus is unique because it places C. fusca in the basal position of the radiation (Fig. 5). The control region topology seems more ecologically probable because C. fusca inhabits the oldest (eastern) and lowest islands of the archipelago (Tonnis et al. 2005;

Chrisite et al. 1992). It is possible that C. fusca populations moved westward and adapted to the highland environments of the younger, central islands, ultimately giving rise to the C. olivacea clade. This pattern, movement and radiation from older to younger islands, has been observed in several Hawaiian species that have undergone extensive radiation (Shaw 1996; Bonacum et al.

2005). A previous study has also shown that C. fusca are distributed among islands due to habitat preference for low elevation islands, explaining their additional presence on low, western, isolated islands (Tonnis et al. 2005). The greater diversity of habitat on larger, high elevation islands may have been the stimulus for the radiation of the remaining finch species. This scenario also explains why the finch radiation started off very slowly, then very rapidly increased in species number, contrary to most radiations where species diversification is initially very rapid, then declines as available niches are filled (Grant and Grant 2008; Seehausen 2006).

Although the Certhidea paraphyly can be explained ecologically, this evolutionary relationship has three main criticisms. First, the warbler finches are among the most morphologically similar of Darwin’s finches, suggesting that these species converged on, or retained, a similar morphology despite being the oldest genetically distinct lineages (Petren et al.

1999). These species are so similar, in fact, that they were recognized as a single species until genetic analysis revealed their distant relationship (Petren et al. 1999). It is typically seen that species diverge very rapidly after colonizing a new habitat due to intraspecific competition and

abundant alternate niche availability (Schluter 2000). However, why these two species converged on or maintained the same phenotypes in a novel environment is still unanswered.

Second, these phylogenies suggest the common ancestor of the Darwin’s finch radiation had a warbler-like morphology since it gave rise to two independent lineages of warbler finches very early in the radiation. However, recent studies have shown the Tiaris family from mainland

Central and South America to be the closest living relatives of Darwin’s finches (Sato et al.

2001; Burns et al. 2002). The members of the Tiaris family have a morphology more like that of

Darwin’s ground finches (Geospiza) than the warbler finches. Third, due to relatively weak parsimony and Bayes support, it is possible that this paraphyly is the result of incomplete lineage sorting due to rapid and recent species radiation (Avise et al. 1990; Parker and Kornfield 1997).

The monophyletic topology generated with the multi-locus approach is a more parsimonious explanation for the morphological similarity between the Certhidea species, and the radiation from a ground finch-like ancestral state. Although parsimony bootstrap support was similar to that of the mtDNA and microsatellite trees, Bayes support for the multi-locus tree was substantially higher than for trees generated using other methods. In addition to higher likelihood support, recent studies have shown that nuclear genes are often more useful than mtDNA and microsatellites in resolving deeper nodes in a species history (Shaw 2002). Because nuclear DNA has a coalescence time roughly four times that of mtDNA, it has a higher utility for more ancient divergences (Avise 2000). In addition, a recent study in East African cichlids

(Clabaut et al. 2005) demonstrated that nuclear loci may be less variable due to lower rates of mutation, but give more reliable phylogenetic signal than mtDNA due to a decrease in homoplasy.

The paraphyletic relationship among the various populations of Geospiza difficilis was supported by the multi-locus phylogeny, indicating greater genetic divergence between some island populations of G. difficilis than between some of the recognized Darwin’s finch species.

Although the paraphyly within the G. difficilis species is consistent between trees generated by the various methods, the the topologies differ depending on the method used. The variation in population groupings within the tree topology may be the result of local extinction events followed by recolonization from other islands, a common phenomenon in a fragmented metapopulation (Hanski and Gaggiotti 2004). Genetic distances between and within morphologically distinct species groups, especially those of the ground and tree finches, have significant overlap, causing morphological species to be non-monophyletic (Sato et al. 1999).

As with other rapid radiations, the lack of a fully resolved phylogeny is most likely due to incomplete lineage sorting and/or hybridization (Funk and Omland 2003). Incomplete coalescence is likely to have an impact on the Darwin’s finch phylogeny due to the recent and rapid speciation events among the tree and ground finches. Fragmented populations such as these result in inflated coalescent times and effective population sizes due to differential drift among populations (Whitlock and Barton 1997). With limited migration, an allele that becomes fixed in one population, is very slow to be acquired by and move through the other populations.

The first major divergence event, that between the warbler finches and the rest of the species in the radiation, occurred less than one million years ago (Freeland and Boag 1999), allowing little time for even quickly evolving mtDNA to coalesce by species. Additionally, there is evidence for hybridization among Darwin’s finches (Grant 1993). Although not a frequent occurrence, there is evidence that hybridization has happened throughout the evolutionary history of

Darwin’s finches and continues today (Grant et al. 2005). Even at low levels, production of

hybrids, especially those that are viable and produce their own offspring, can rapidly deteriorate the phylogenetic signal of a locus.

The multi-locus approach is consistent with the other phylogenetic methods in that it places the Cocos Island finch, Pinaroloxias inornata, within the Darwin’s finch radiation, although the placement of this species varies by marker. The mtDNA tree places the Cocos finch within a monophyletic clade with the Camarhyncus and Cactospiza tree finches. The multi-locus approach utilized in this study places the divergence of the Cocos finch right after that of the Certhidea species, very early in the radiation and prior to the diversification of the tree and ground finches. The early divergence of the Cocos finch agrees with the microsatellite tree topology. This topology suggests that the Cocos finch is genetically less closely related to the tree finches, which is consistent with their warbler-like morphology (small body size and long, slender beak). Because of the three warbler-like finch species at the base of the radiation, the suggested morphology of the common ancestor is also warbler-like.

Phylogenetic methods

The ultimate goal of phylogenetic studies is to determine the genetic, geographic and ecological factors that facilitate speciation and the processes by which an ancestral species gives rise to new species. As researchers begin their evolutionary studies of a group of organisms, they are faced with many options for phylogenetic reconstruction. Here we have demonstrated that the type of marker used in phylogenetic reconstruction can yield very different evolutionary topologies within closely related groups of organisms, substantially altering our understanding and interpretation of the evolutionary processes occurring during formation of a particular species group. The characteristics of, and potential information offered by, the various markers

available for phylogenetic reconstruction should be carefully considered when choosing a molecular marker for analysis.

Mitochondrial DNA has by far been the most commonly used marker in phylogenetic studies due to its rapid mutation rate and small effective population size (Avise 2000). Rapid mutation rates increase the probability of mutations occurring at internodes of the phylogenetic tree, and in turn, increase resolution among taxa (Moore 1995). The small effective population size of mtDNA due to maternal inheritance allows new haplotypes to spread rapidly through a population (Avise 2000). This effect is compounded in small populations (Nordborg 2000).

However, the maternal inheritance of mtDNA as a single linkage unit is not typical of the rest of the genome. Furthermore, the use of this marker essentially yields only the female evolutionary history, which is problematic in systems with strong sex-biased migration and/or hybrid success

(Canestrelli et al. 2007; Funk and Omland 2003).

The mtDNA region used for analysis may also influence the resulting phylogeny. The control region tree generated for Darwin’s finches generally agreed with that of the cytB data.

However, the Certhidea species at the base of the radiation were reversed, with C. fusca as the basal lineage. A study in new world jays (Bonaccorso and Peterson 2007) also revealed conflict among sites of the mitochondria. These conflicts are not due to independent histories since mtDNA is inherited as a single circular chromosome, so rate heterogeneity among loci and/or species lineages is the most likely cause for these differences in phylogenetic signal (Wendel and

Doyle 1998). The paraphyletic relationship noted in Darwin’s finches when using mtDNA is not unique. Funk and Omland (2003) found that 23% of animal species phylogenies produced using mtDNA were not monophyletic, citing introgression during evolutionary history and incomplete lineage sorting as the primary causes.

Nuclear microsatellite data, like mtDNA, are also useful for fine-scale phylogenetic resolution, and eliminate the problem of a potentially misleading single locus by examining numerous nuclear loci simultaneously. However, these loci are recorded as binary data and must be reduced to distances for tree construction. Furthermore, the high mutation rates and assumed size constraints of microsatellites often lead to increased incidences of homoplasy (Takezaki and

Nei 1996; Pollock et al. 1998). Therefore, phylogenetic trees can differ substantially depending on the mutation models and assumptions used for evolutionary inference (step-wise vs. infinite allele models) (Ellegren 2000; Oliveira et al. 2006).

The multi-locus intron method described in this paper is useful because it examines multiple loci simultaneously and yields a consensus estimate of the species phylogeny from independent loci. Introns are not prone to homoplasy like microsatellites, however, mutation rates for nuclear sequence data are much slower than for mtDNA, giving less resolution at later stages of the radiation. Nuclear sequence data is better suited for resolution of the deeper nodes of a phylogenetic tree, as demonstrated here with the new topology among the Certhidea species revealed by the multi-locus approach.

As demonstrated here in this comparative phylogenetic study, the use of different genetic markers can yield very different evolutionary histories for a given group of closely related taxa.

When constructing phylogenetic trees, the markers used should be carefully selected and, where possible, multiple markers should be used to generate an overall species phylogeny. Edwards et al. (2005) determined that nuclear and mitochondrial DNA can be complimentary, with nuclear genes resolving the deeper nodes of the tree and mtDNA allowing finer resolution at the tips.

This is the general pattern seen in the literature. A brief literature review of recent avian studies attempting to use nuclear and mtDNA data to reconstruct phylogenies found general congruence

between nDNA and mtDNA among numerous taxanomic groups including starlings and mockingbirds (Lovette and Rubenstein 2007), orioles (Allen and Omland 2003), pigeons and doves (Johnson and Clayton 2000), and woodpeckers (Weibel and Moore 2002). However, by congruence, we mean that major clades were supported by both markers, but resolution within clades was limited in nDNA and resolved to some extent by mtDNA. In other words, congruence is not the similarity between fully resolved trees, but the absence of significant conflict between nodes resolved with both markers. The studies listed above, and others (Fjeldså et al. 2005; Olsson et al. 2005; Barker 2004), showed that combining their nuclear and mitochondrial loci into a single tree yielded well-supported phylogenies for interpretation of their respective evolutionary hypotheses. However, there are also cases in the literature where nuclear and mitochondrial DNA from multiple loci cannot be reconciled, and conflict within and between loci lower statistical support for clades throughout the tree (McCracken and Sorenson

2005). Multi-locus approaches offer the advantage of pooling data from numerous sources to increase species phylogenetic signal and decrease the probability of being mislead by a single gene tree that may not reflect the true species phylogeny (de Queiroz 1993; Gadagkar et al.

2005).

Despite the advantages of a multi-locus approach, there are some situations in which resolution among taxa may be very difficult. Due to the very nature of adaptive radiations (the extremely rapid divergence of a group of species), the use of multiple markers may only be partially successful in resolving species relationships. This is due primarily to the absence of fixed genetic changes arising within taxa between divergence events, leading to incomplete lineage sorting of ancestral polymorphisms. In these cases, the use of non-genetic data, such as morphology and behavior, may be useful in determining relationships within clades.

References

Allen, E.S. and Omland, K.E. 2003. Novel intron phylogeny supports plumage convergence in orioles (Icterus). Auk. 120(4):961-969.

Avise, J.C. 2000. Phylogeography: the history and formation of species. Harvard University Press, Cambridge, Massachusetts.

Avise, J.C., Ankney, C.D., Nelson, W.S. 1990. Mitochondrial gene trees and the evolutionary relationships of mallard and black ducks. Evolution. 44(4):1109-1119.

Barker, F.K. 2004. Monophyly and relationships of wrens (Aves: Troglodytidae): a congruence analysis of heterogeneous mitochondrial and nuclear DNA sequence data. Molecular Phylogenetics and Evolution. 31:486-504.

Barlow, G.W. 2000. The cichlid fishes: nature’s grand experiment in evolution. Perseus Pub., Cambridge, Massachusetts.

Bonaccorso, E., Peterson, A.T. 2007. A multilocus phylogeny of New World jay genera. Molecular Phylogenetics and Evolution. 42:467-476.

Bonacum, J., O’Grady, P.M., Kambysellis, M., DeSalle, R. 2005. Phylogeny and age of diversification of the planitibia species group of the Hawaiian Drosophila. Molecular Phylogenetics and Evolution. 37:73-82.

Burns, K.J., Hackett, S.J., Klein, N.K. 2002. Phylogenetic relationships and morphological diversity in Darwin’s finches and their relatives. Evolution. 56(6):1240-1252.

Cansetrelli, D., Verardi, A., Nascetti, G. 2007. Genetic differentiation and history of populations of the Italian treefrog Hyla intermedia : lack of concordance between mitochondrial and nuclear markers. Genetica. 130:241-255.

Christie, D.M., Duncan, R.A., McBirney, A.R., Richards, M.A., White, W.M., Harpp, K.S., Fox, C.G. 1992. Drowned islands downstream from the Galápagos hotspot imply extended speciation times. Nature. 355:246-248.

Clabaut, C., Salzburger, W., Axel, M. 2005. Comparative phylogenetic analyses of the adaptive radiation of Lake Tanganyika cichlid fish: nuclear sequences are less homoplasious but also less informative than mitochondrial DNA. Journal of Molecular Evolution. 61(5):666-681.

Degnan, J. H., and N. A. Rosenberg. 2009. Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends in Ecology & Evolution 24:332-340.

Day, J.J., Santini, S., Garcia-Moreno, J. 2007. Phylogenetic relationships of the Lake Tanganyika cichlid tribe Lamprologini: the story from mitochondrial DNA. Molecular Phylogenetics and Evolution. 45:629-642.

de Queiroz, A. 1993. For consensus (sometimes). Systematic Biology. 42(3):368-372. de Queiroz, A., Donoghue, M.J., Kim, J. 1995. Separate versus combined analysis of phylogenetic evidence. Annual Review of Ecology and Systematics. 26:657-681.

Edwards, S.V., Jennings, W.B., Shedlock, A.M. 2005. Phylogenetics of modern birds in the era of genomics. Proceedings of the Royal Society of London Series B Biological Sciences. 272:979-992.

Edwards, S.V., Liu, L., Pearl, D.K. 2007. High-resolution species trees without concatenation. Proceedings of the National Academy of Science. 104(14):5936-5941.

Ellegren, H. 2000. Microsatellite mutations in the germ line: implications for evolutionary inference. Trends in Genetics. 16(12):551-558.

Fjeldså, J., Irestedt, M., Ericson, P.G.P. 2005. Molecular data reveal some major adaptational shifts in the early evolution of the most diverse avian family, the Furnariidae. Journal of Ornithology. 146:1-13.

Freeland, J.R. and Boag, P.T. 1999. Phylogenetics of Darwin’s finches: paraphyly in the tree finches and two divergent lineages in the warbler finch. Auk. 116(3):577-588.

Funk, D.J. and Omland, K.E. 2003. Species-level paraphyly and polyphyly: frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annual Review of Ecology Evolution and Systematics. 34:397-423.

Gadagkar, S.R., Rosenberg, M.S., Kumar, S. 2005. Inferring species phylogenies from multiple genes: concatenated sequence tree versus consensus gene tree. Journal of Experimental Zoology (Molecular and Developmental Evolution). 304B:64-74.

Grant, P. R. 1993. Hybridization of Darwin finches on Isla Daphne Major, Galápagos. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences 340:127-139.

Grant, P.R. 1999. Ecology and evolution of Darwin’s finches, second printing. Princeton University Press, Princeton, New Jersey.

Grant, P.R., Grant, B.R. 2008. How and why species multiply: the radiation of Darwin’s finches. Princeton University Press, Princeton, New Jersey.

Grant, P.R., Grant, B.R., Petren, K. 2005. Hybridization in the recent past. American Naturalist. 166(1):56-67

Hanski, I and Gaggiotti, O.E. 2004. Ecology, genetics and evolution of metapopulations. Elsevier Academic Press, Burlington, Massachusetts.

Hare, M.P. 2001. Prospects for nuclear gene phylogeography. TRENDS in Ecology and Evolution. 16(12):700-706.

Hudson, R.R., Coyne, J.A. 2002. Mathematical consequences of the genealogical species concept. Evolution. 56(8):1557-1565.

Huelsenbeck, J.P. and Ronquist, F. 2001. MRBAYES: Bayesian inference of phylogeny. Bioinformatics. 17:754-755.

Johnson, K.P. and Clayton, D.H. 2000. Nuclear and mitochondrial genes contain similar phylogenetic signal for pigeons and doves (aves: columbiformes). Molecular Phylogenetics and Evolution. 14(1):141-151.

Liu, L., Pearl, D.K. 2007. Species trees from gene trees: reconstructing Bayesian posterior distributions of a species phylogeny using estimated gene tree distributions. Systematic Biology. 56(3):504-514.

Lovette, I.J., Rubenstein, D.R. 2007. A comprehensive molecular phylogeny of the starlings (Aves: Sturnidae) and mockingbirds (Aves: Mimidae): congruent mtDNA and nuclear trees for a cosmopolitan avian radiation. Molecular Phylogenetics and Evolution. 44:1031-1056.

Maddison, W.P. 1997. Gene trees in species trees. Systematic Biology. 46(3):523-536.

McCracken, K.G., Sorenson, M.D. 2005. Is homoplasy or lineage sorting the source of incongruent mtDNA and nuclear gene trees in the stiff-tailed ducks (Nomonyx-Oxyura)? Systematic Biology. 54(1):35-55.

Moore, W.S. 1995. Inferring phylogenies from mtDNA variation: mitochondrial-gene trees versus nuclear-gene trees. Evolution. 49(4):718-726.

Nichols, R. 2001. Gene trees and species trees are not the same. Trends in Ecology and Evolution. 16(7):358-364.

Nordborg, M. Coalescent theory. 2001. In D. J. Balding, M. J. Bishop, and C. Cannings, editors, Handbook of Statistical Genetics, pages 179–212. John Wiley & Sons, Inc., Chichester, U.K.

Oliveira, E.J., Pádua, J.G., Zucchi, M.I., Vencovsky, R., Vieira, M.L.C. 2006. Origin, evolution and genome distribution of microsatellites. Genetics and Molecular Biology. 29(2):294-307.

Olsson, U., Alström, P., Ericson, P.G.P., Sundberg, P. 2005. Non-monoplyletic taxa and cryptic species – evidence from a molecular phylogeny of leaf-warblers (Phylloscopus, Aves). Molecular Phylogenetics and Evolution. 36:261-276.

Pamilo, P., Nei, M. 1988. Relationships between gene trees and species trees. Molecular Biology and Evolution. 5(5):568-583.

Parker, A., Kornfield, I. 1997. Evolution of the mitochondrial DNA control region in the mbuna (Cichlidae) species flock of Lake Malawi, East Africa. Journal of Molecular Evolution. 45:70- 83.

Pollock, D.D., Bergman, A., Feldman, M.W., Goldstein, D.B. 1998. Microsatellite behavior with range constraints: parameter estimation and improved distances for use in phylogenetic reconstruction. Theoretical Population Biology. 53:256-271.

Posada, D. and Crandall, K.A. 1998. Modeltest: testing the model of DNA substitution. Bioinformatics. 14(9):817-818.

Prychitko, T.M. and Moore, W.S. 1997. The utility of DNA sequences of an intron from the beta-fibrinogen gene in phylogenetic analysis of woodpeckers (aves: picidae). Molecular Phylogenetics and Evolution. 8(2):193-204.

Rokas, A., Williams, B.L., King, N., Carroll, S.B. 2003. Genome scale approaches to resolving incongruence in molecular phylogenies. Nature. 425:798-804.

Sato, A., Tichy, H., O’hUigin, C., Grant, P.R., Grant, B.R., Klein, J. 2001. On the origin of Darwin’s finches. Molecular Biology and Evolution. 18(3):299-311.

Schluter, D. 2000. The ecology of adaptive radiation. Oxford University Press, Oxford.

Seehausen, O. 2004. Hybridization and adaptive radiation. TRENDS in Ecology and Evolution. 19(4):198-207.

Seehausen, O. 2006. African cichlid fish: a model system for adaptive radiation research. Proceedings of the Royal Society of London Series B Biological Sciences. 273(1597):1987- 1998.

Shaw, K.L. 2002. Conflict between nuclear and mitochondrial DNA phylogenies of a recent species radiation: what mtDNA reveals and conceals about modes of speciation in Hawaiian crickets. Proceedings of the National Academy of Sciences USA. 99(25):16122-16127.

Shaw, K.L. 1996. Sequential radiations and patterns of speciation in the Hawaiian cricket genus Laupala inferred from DNA sequences. Evolution. 50(1):237-255.

Shimodaira, H. 2002. An approximately unbiased test of phylogenetic tree selection. Systematic Biology. 51(3):492-508.

Shimodaira, H. and Hasegawa, M. 2001. CONSEL: for assessing the confidence of phylogenetic tree selection. Bioinformatics. 17(12):1246-1247.

Swofford, D.L. 2002. PAUP*: phylogenetic analysis using parsimony (*and other methods). Version 4. Sinauer Associates, Sutherland, MA.

Takezaki, N. and Nei, M. 1996. Genetic distances and reconstruction of phylogenetic trees from microsatellite DNA. Genetics. 144:389-399.

Tarr, C.L., Fleischer, R.C. 1995. Evolutionary relationships of the hawaiian honeycreepers (Aves, Drepanidinae). In: Hawaiian biogeography: evolution on a hot spot archipelago. Eds. Wagner, W.L. and Funk, V.A. Smithsonian Institution Press, Washington D.C.

Tonnis, B. Grant, P.R., Grant, B.R., Petren, K. 2005. Habitat selection and ecological speciation in Galápagos warbler finches (Certhidea olivacea and Certhidea fusca). Proceedings of the Royal Society of London Series B Biological Sciences. 272:819-826.

Weibel, A.C., Moore, W.S. 2002. A test of a mitochondrial gene-based phylogeny of woodpeckers (genus Picoides) using an independent nuclear gene, -fibrinogen intron 7. Molecular Phylogenetics and Evolution. 22(2):247-257.

Wendel, J.F., Doyle, J.J. 1998. Phylogenetic incongruence: window into genome history and molecular evolution. In: Molecular systematics of plants II DNA sequencing. Eds. Soltis, D.E., Soltis, P.S., Doyle, J.J. Kluwer Academic Publishers, Boston, Massachusetts.

Whitlock, M.C., Barton, N.H. 1997. The effective size of a subdivided population. Genetics. 146:427-441.

Tables/Figures

Table 1: Species and populations included in phylogenetic analysis.

Figure 1: Map of Galápagos. Island abbreviations are those used in subsequent figures.

Figure 2: Current phylogenies for Darwin’s finches based on microsatellite and mtDNA data. Support values represent bootstraps for microsatellite data, and parsimony bootstraps (above left), Bayes support (above right) and NJ bootstraps (below) (from Petren et al. 2005). Island abbreviations are as shown in Fig. 1

Figure 3: Maximum likelihood tree for multi-locus intron data set. Values are Bayes (top) and parsimony bootstrap (bottom) support.

Figure 4: Maximum likelihood tree for multi-locus intron data set including the Cocos Island finch. Values are Bayes (top) and parsimony bootstrap (bottom) support.

Figure 5: Maximum parsimony tree of mitochondrial control region. Values represent parsimony bootstrap support.

Table S1: Loci and other info

Table 1: Species and populations included in phylogenetic analyses

Species Abbrev. Island Populations Certhidea C. Olivecea (Col) Ra, Sc, So C. fusca (Cfu) Es, Pi, Ma Geospiza G. difficilis (Gdi) Dw, Ge, Pi, Fe G. conirostris (Gco) Ge G. magnirostris (Gma) Da G. fortis (Gfo) Fe Camarhynchus parvulus (Cpa) Fl Cactospiza pallida (Cpd) Is Platyspiza crassirostris (Pcr) Pi Pinaroloxias inornata (Pin) Cocos Is. Tiaris bicolor (Tbi) Puerto Rico

(Dw)

(Pi)

(Ma)

(Ge)

(So)

(Ra) (Sc) (Fe) (Pz)

(Sf) (Co) (Is)

(Fl) (Es)

Fig. 1

Figure 2

Certhidea fusca Espanola

100 85 Certhidea fusca Pinta 100 100 99 85 Certhidea fusca Marchena 100 100 99 91 100 Certhidea olivacea Rabida 99 91 56 98 99 Certhidea olivacea SantaCruz

56 98 Certhidea olivacea Santiago

G. difficilis Darwin

G. difficilis Genovesa

G. difficilis Pinta 100 83 94 100 G. difficilis Fernandina

100 83 90 94 Platyspiza Pinta 100

90 G. magnirostris Daphne

G. conirostris Genovesa 85

56 85 G. fortis Fernandina

56 C. pallid Isabela 95 54 C. parvulus Floreana 95 54 Tiaris bicolor

Figure 3

Certhidea fusca Espanola

100 80 Certhidea fusca Pinta 100 99 Certhidea fusca Marchena 90 59 Certhidea olivacea Rabida 100 59 98 Certhidea olivacea SantaCruz

50 Certhidea olivacea Santiago

G. difficilis Darwin 95

G. difficilis Genovesa

G. difficilis Pinta 100 85 100 G. difficilis Fernandina 95

99 Platyspiza Pinta

G. magnirostris Daphne

100 G. conirostris Genovesa 63 96 56 G. fortis Fernandina

100 C. pallid Isabela 61 99 55 C. parvulus Floreana

Pinaroloxias Cocos Tiaris bicolor

Figure 4

Bootstrap 96 CerthSago22 87 CerthSago11 Certhidea 100 CerthScz8 olivacea 98 CerthFern4 CerthIsab1 DifPinta9 100 DifSago6

52 DifDarwin1 032Diff 040Con 037Mag Geospiza (ground 63 ScanDaph47 finches) 61 052Fort 82 045Scan 98 DifGenov19 DifGenov11 049Fulig 100 92 017Cacto 018Parv 69 Camarhyncus 78 030psitt and Cactospiza 422Pinar (tree finches)

100 064Platy 026Platy

63 CerthMarch 53 CerthEspan 97 423Certh Certhidea fusca 88 015Certh

80 CerthSCris CerthPinta 065TiOb

85 066TiOb Tobscura

Figure 5

Table S1:

Annealing Name Intron # Length PCR Primers Temp Source Adenylate kinase 5 529 ATTGACGGCTACCCTCGCGAGGTG 53 Shapiro and Dumbacher, Auk 2001 CACCCGCCCGCTGGTCTCTCC Friesen et al., Molecular Ecology Lamin 3 655 CCAAGAAGCAGCTGCAGGATGAGATGC 60 1997 CTGCCGCCCGTTGTCGATCTCCACCAG Glyceraldehyde-3- phosphate Friesen et al., Molecular Ecology dehydrogenase 11 323 ACCTTTAATGCGGGTGCTGGCATTGC 60 1997 CATCAAGTCCACAACACGGTTGCTGTA Friesen et al., Molecular Ecology -Enolase 8 322 TGGACTTCAAATCCCCCGATGATCCCAGC 61 1997 CCAGGCACCCCAGTCTACCTGGTCAAA Myelin proteolipid Friesen et al., Molecular Ecology protein 4 NA TACATCTACTTTAACACCTGGACCACCTG 58 1999 GGCTCCAACCTGCTCTCCATCTGCAA Heslewood et al., Electrophoresis Myoglobin 2 590 GCCACCAAGCACAAGATCCC 56 1998 CGGAAGAGCTCCAGGGCCTT Prychitko and Moore, Molecular -fibrinogen 7 NA GGAGAAAACAGGACAATGACAATTCAC 52 Biology and Evolution 2003; TCCCCAGTAGTATCTGCCATTAGGGTT 4 576 CTGTAATATCCCGGTGGTTTCAGG 51 ATTTCAGATGTTTCACCTCCCTTTC 5 560 CGCCATACAGAGTATACTGTGACAT 54 GCCATCCTGGCGATTCTGAA Friesen et al., Molecular Ecology 1997; Slade et al., Molecular Ecology Aldolase 350 TGTGCCCAGTATAAGAAGGATGG 57 1993 CCCATCAGGGAGAATTTCAGGCTCCACAA Avian ovomucoid Armstrong et al., Auk 2001 intron G NA CAAGACATACGGCAACAARTG GGCTTAAAGTGAGAGTCCCRTT Ornithine Friesen et al., Molecular Ecology decarboxylase 6&7 680 GACTCCAAAGCAGTTTGTCGTCTCAGTGT 60 1999; Allen and Omland, Auk 2003 ATTGGTGGTGGCTTCCCTGGCTCTGAAGA

Friesen et al., Molecular Ecology Ribosomal protein 40 5 314 GGGCCTGATGTGGTGGATGCTGGC 60 1999 GAGCAGGCTGCTGCTGAGAAAGC Friesen et al., Molecular Ecology Tropomyosin 5 319 GAGTTGGATCGGGCTCAGGAGCG 46 1999 AAGCACATTGCTGAAGAGGCTGACCG Internal Transcribed Freeland and Boag, Evolution 1999 Spacer NA

Friesen et al., Molecular Ecology Lactate dehydrogenase 3 612 GGAAGACAAACTAAAAGGAGAAATGATGGA 60 1999 GAGAGTCGTCTCAACCTGCTTCAGAGGAA Laurent et al., Molecular Preproinsulin 2 NA ATGGCTCTCTGGATCCGATC Phylogenetics and Evolution 2004 GCTAGTTGCAGTAGTTCTCC Laminin receptor McCracken and Sorenson, Syst. Biol., precursor 322 GGCCTGATGTGGTGGATGCTGGC 60 2005 GCTTTCTCAGCAGCAGCCTGCTC Phosphoenolpyruvate McCracken and Sorenson, Syst. Biol., carboxykinase 3 625 TCAATACCAGATTCCCAGGCTGC 60 2005 CCATGCTGAAGGGGATGACATAC 9 655 GGAGCAGCCATGAGATCTGAAGC 60 GTGCCATGCTAAGCCAGTGGG Aggrecan NA AAACTGTGAGGAAGGCTGGA 60 Smith et al., Genetica, 2005 AGGCAGGTGATAGTTGCAGG Chromodomain- GAGAAACTGTGCAAAACAG 51 Helicase- DNA Binding Protein NA TCCAGAATATCTTCTGCTCC Birks and Edwards, Molecular rhodopsin NA Phylogenetics and Evolution, 2002

Hemoglobin - A NA GGGCACCCGTGCTGGGGGCTGCCAAC TAACGGTACTTGGCAGTMAG

A Century of Genetic Change and Metapopulation Dynamics in the

Galápagos Warbler Finches (Certhidea)

Heather L. Farrington1*, and

Kenneth Petren1

1 Department of Biological Sciences; University of Cincinnati; Cincinnati OH 45221-0006

* Correspondence: [email protected]; +001 513-556-9719

Abstract

Populations that are connected by immigrants play an important role in evolutionary and conservation biology, yet we have little direct evidence of how such metapopulations change over evolutionary time. We surveyed recent and historic (1895-1905) genetic variation in 11 populations of warbler finches at 16 microsatellite loci. Although several lines of evidence predict that Darwin’s finches may be in decline, we found that the genetic diversity of warbler finches has not generally decreased, and broad scale patterns of variation remained the same over time. Migration rates and population sizes have actually increased over time, especially in larger populations, which may be explained by the increased frequency of El Niño events. Significant losses as well as gains in genetic diversity were noted for several individual island populations.

These changes were mirrored by changes in immigration rates, but the degree of change did not correspond to population size or human habitation. These results reflect the predicted stabilizing properties of metapopulations over evolutionary time. However the dramatic and unpredictable changes observed in individual populations over this short time interval suggests that caution must be used when inferring the status and connectivity of individual population fragments based on single snapshots of genetic variation.

Keywords: Certhidea olivacea, Certhidea fusca, SSR, microsatellite, museum specimens, metapopulations

Introduction

Understanding how populations change through time is fundamental to the study of evolution and conservation biology. However, for most species, historical information is not available, and even current estimates of population size can be difficult to obtain (Mills et al.

2000). For these reasons, genetic surveys of populations are being used more frequently to monitor current population size, growth trajectory, inbreeding, connectivity, and the likelihood of future persistence or decline (Antao et al. 2011, DeBarba et al. 2010, Luikart et al. 2010,

Schwartz et al. 2007, Wang 2005, Keller & Waller 2002, Moritz 1994). Genetic monitoring typically proceeds by taking a “snap-shot” of multilocus genotypes from a single time point, and extending inferences into the past or future. Many methods have recently been developed to reconstruct population history based on current genetic data (Beaumont 1999; Wilson et al. 2003;

Kuhner 2006). However, several phenomena can increase the uncertainty of past population inferences, including environmental variation leading to changes in population size (Berthier et al. 2006), bottlenecks (England et al. 2003), inbreeding (Keller & Waller 2002), drift or selection

(Willi et al. 2007), and other stochastic effects.

Population subdivision further complicates our ability to make inferences about past population history. As habitat fragmentation becomes more common in natural systems, understanding the evolutionary dynamics of partially isolated populations has become increasingly important in conservation (Kindlmann & Burel 2008). There is also a growing interest in how metapopulation dynamics can affect long-term persistence and evolution in population networks (Hanski & Gaggiotti 2004, Young and Clarke 2000). Compared to a single

large population, fragmented populations can buffer against strongly negative ecological interactions such as disease or local catastrophies (Su et al. 2009), while periodic influx of genetic variation can help buffer against local extinction through a process termed “genetic rescue” (Brown and Kodric-Brown 1977, Whitlock et al. 2000). Metapopulations may also play an important role in adaptive evolution through the interaction of genetic drift and low levels of gene flow (Whitlock et al. 2000, Church & Taylor 2002). Add to these dynamics the possibility of extinction and re-colonization, and it is easy to see that the history of genetic change in metapopulations can be complex. Empirical studies of metapopulation dynamics have generally been limited to several decades at most. We have almost no direct information about the longer term history of metapopulation dynamics and the history of genetic change for individual population fragments (Hanski & Gaggiotti 2004).

Natural history collections offer valuable opportunities to study population dynamics through time (Wandeler et al. 2007). Direct cross-temporal genetic comparisons can reveal more complex population histories than single time point estimates (Ramakrishnan & Hadly 2009).

For example, in populations with low genetic diversity, it is often difficult to determine if recent population decline or an extended history of small population size has created the observed patterns when only a single time point is available for analysis (Matocq & Villablanca 2001).

Genetic data from historically preserved specimens can serve as a reference point for past genetic diversity (Bouzat 2001). The reconstruction of population history using historic specimens is becoming increasingly common, especially in threatened and endangered taxa, including fish

(Hansen et al. 2002), mammals (Miller & Waits 2003; Pertoldi et al. 2001) and birds (Taylor et al. 2007; Johnson & Dunn 2006). However, most studies of historic collections have been limited to one or a small number of isolated populations.

The adaptive radiation of Darwin’s finches offers an ideal natural system to directly evaluate recent population history in a metapopulation context. During 1895-1905, several natural history expeditions visited the Galápagos Islands and collected large numbers of finches, at a time when human settlers numbered in the low hundreds. These specimens can provide a historical point of reference, and allow us to measure changes in genetic diversity over time across a well-defined, fragmented landscape. A 100 year time span represents roughly 25-30 generations in Darwin’s finches (average generation time of 3-5 years; Grant 1999). Based on recent studies, substantial demographic, morphological and genetic changes can occur in

Galápagos finch populations over these time scales. Natural selection on small islands has caused significant morphological changes in heritable beak traits in just a few years time (Grant

& Grant 2006; Grant et al. 2004), while El Niño climate cycles cause substantial fluctuations in population size (Grant et al. 2000). Finch populations have also shown attributes commonly associated with metapopulations, including regular exchange of immigrants (Petren et al. 2005), several population extinctions, and one colonization event (Grant & Grant 1995; Dvorak et al.

2004; Grant et al, 2005).

The past one hundred years has been a time of tremendous ecological change in the

Galápagos Islands. El Niño fluctuations produce periods of dramatic expansion and contraction in finch population sizes (Grant et al. 2000). Impacts vary according to island characteristics, but the frequency and severity of El Niño episodes appears to be on the rise (Guilderson and Schrag

1998). Resident human populations and tourism have increased exponentially over the last century, along with the introduction of non-native species. Habitat disturbance has increased

(Watson et al. 2009), diseases such as avian pox and plasmodium parasites have been introduced

(Wikelski et al. 2004; Kleindorfer & Dudaniec 2006; Levin et al. 2009, Parker et al. 2011), and

introduced dipteran nest parasites are negatively affecting reproduction and recruitment in endemic bird communities (Dudaniec & Kleindorfer 2006; Dudaniec et al. 2007).

Other island systems demonstrate how human activities can have devastating effects on endemic avian communities. For example, the Hawaiian endemic finch radiation has lost most of its species to extinction (James & Price 2008). In contrast, the endemic finches of Galápagos have not yet experienced a known species extinction, yet there are signs of decline, and their future trajectory is uncertain. Several local extinctions have been documented in the finch radiation over the past century (Grant et al. 2005; Grant 1999), and two species, the mangrove finch and medium tree finch, are now critically endangered (IUCN Redlist 2010). The increase in environmental pressures in the Galápagos over the past century allows us a unique opportunity to directly investigate how individual island populations are responding to this changing environment, and what role metapopulation dynamics may play in this system.

We compared historic and present genetic diversity in eleven different populations of warbler finches to test the hypothesis that populations are in general decline. We predicted that populations on smaller islands would show the greatest signs of decline because these islands are more prone to extended droughts associated with El Niño/La Niña climatic cycles (Grant 1999), and genetic changes should be more easily detected in smaller populations. Alternatively, finch populations on islands with permanent human settlements may be more heavily impacted by human disturbance and introduced species, and may therefore show more pronounced signatures of decline. We also tested whether the magnitude and direction of interisland migration was consistent over time, expecting that patterns would remain constant over time. Metapopulation theory predicts that even if whole metapopulations are stable, individual subpopulations may change dramatically (Hanski & Gaggiotti 2004). We therefore tested individual populations for

change over time, expecting small populations to change most, with the goal of connecting any gains or losses in genetic diversity to changes in inferred patterns of migration over time.

Methods

Study Taxa

Warbler finches are the most widespread species of Darwin’s finch. All 14 known populations are morphologically and ecologically similar (Grant & Grant 2002), but they actually comprise two genetically distinct lineages based on mtDNA and microsatellite data

(Freeland & Boag 1999; Petren et al. 1999). Certhidea olivacea, inhabits the large, central islands, while C. fusca, occupies the smaller, peripheral islands of the archipelago (Tonnis et al.

2005). These species are strictly allopatric and represent the greatest genetic divergence found in the entire Darwin’s finch radiation (Petren et al. 2005); we therefore treated them separately in this study. Warbler finches are morphologically distinct from, and distantly related to, finch species within the radiation that are known to hybridize, thus we assume that introgression is negligible

Samples

A total of 219 modern and 192 historic tissue samples were collected for cross-temporal comparisons of 11 warbler finch populations (Table 1; Fig. 1). For modern specimens, whole blood samples were collected in the field by venipuncture and dried on EDTA-treated filter paper. These samples were collected on various field expeditions to the Galápagos Islands during the years 1988-2006 (Petren et al. 2005). Museum specimen tissue for DNA extraction was obtained from toe pad shavings (approximately 3 x 2 mm) of Darwin’s finches from the

California Academy of Science, the British Museum of Natural History and the American

Museum of Natural History. The majority of specimens (~80%) were obtained from the

California Academy of Science Galápagos expedition (1905-1906) and Rollo Beck’s collection from a previous expedition in 1899. Collection dates for all museum specimens were 1897-1907

(Supp. Table S1).

Laboratory Methods

DNA was extracted from modern blood samples using previously published methods

(Petren 1998). Museum samples were stored and processed in a room dedicated to ancient DNA work, and separated from any modern specimens to avoid contamination. All equipment and work area surfaces were UV irradiated prior to and after each use. Work areas were frequently bleached, and access to the work space was restricted. DNA was extracted from museum specimens using QBiogene GeneClean Ancient DNA kits following the manufacturer protocol.

Extracted DNA was eluted to a total volume of approximately 50 μL. Blank extractions

(prepared with no tissue) were periodically processed to check reagents for contamination.

Sixteen microsatellite loci (14 autosomal and two sex-linked) previously developed for

Darwin’s finches (Petren 1998) were used to obtain genotype information from both modern and historic specimens. Amplification success declines rapidly with fragment size in degraded genetic samples (Sefc et al. 2003), thus PCR primers were redesigned to generate shorter PCR products (Petren et al. 2010). Total DNA was subjected to PCR in multiplex reactions (four loci per reaction with differing color fluorescent dyes) to increase genotyping efficiency and to conserve extracted template DNA. Negative control PCR reactions were also run with each batch of reactions prepared. PCR amplifications were performed in a total volume of 15 μL containing 7.5 μL QIAGEN multiplex PCR master mix, 0.30 μM of primers, and 1 μL of extracted DNA under the following conditions: an initial denaturation step at 95°C for 15 min, followed by 33 cycles (40 cycles for historic samples) of 30 s at 94°C, 1 min 30 s at 52°, and 1

min 30 s at 72°, and a final extension step of 72°C for 10 min. PCR reactions for historic samples were repeated three times each to recover alleles that may have failed to amplify

(dropouts), and thereby reduce genotyping error. PCR products were analyzed by fragment analysis, with a LIZ-labelled size standard, on an Applied Biosystems 3730xl DNA Analyzer at the Cornell University Life Sciences Core Laboratories Center. Sample results were genotyped by hand with the aid of GENEMAPPER software (Applied Biosystems). Modern and historic genotypes were scored independently, and historic specimens were scored without knowledge of population origin. Singleton alleles were identified by population and re-evaluated for accuracy.

Individual museum specimens with less than 50% genotype recovery across the 16 loci were excluded from analyses.

To assess the quality and repeatability of historic genotypes, a sub-set of 10 randomly chosen individuals were subjected to six-fold replicated genotyping across all loci. Replicates were scored independently then compared to quantify frequencies of allelic dropout and spurious alleles. A principal coordinates analysis (PCA) was also conducted with both modern and historic genotypes to examine the congruence between past and present data sets.

Genetic Analysis

Genetic analyses were based on data from the 14 autosomal microsatellite loci. GenAlex

(Peakall & Smouse 2006) and FSTAT (Goudet 1995) were used to calculate basic genetic summary statistics of the populations at each time point, including allelic richness (AE), and observed (Ho) and expected (He) heterozygosities. Alleleic richness was calculated using a rarefaction method with a minimum sample size of three individuals (FSTAT; Goudet 1995).

GDA (Genetic Data Analysis, version 1.0; Lewis and Zaykin 2001) was used to perform an exact test (Guo & Thompson 1992) with sequential Bonferroni correction (Rice 1989) to determine

which loci deviated significantly from Hardy-Weinberg proportions. Mean values for observed and expected heterozygosity and allelic variation were compared using Wilcoxon paired samples tests to determine if there was a significant change in any of these parameters over time within a single population. Genetic parameters were also calculated and compared for pooled populations at both time points to detect any changes in genetic diversity across the archipelago as a whole over time. In addition, theta () values (Weir & Cockerham 1984), an analogue of FST, were calculated between time points using the GDA program, and a 95% confidence interval was used to determine if significant divergence in allele frequencies occurred in each population over time.

Theta was also calculated between each population pair within species at each time point. For clarity, Weir and Cockerham’s will be referred to as FST throughout the text. Mantel matrix correlations were conducted for pair-wise FST between time points to determine if genetic structure among populations remained stable over time, and tested for significance based on

1000 permutations (Smouse, Long & Sokal 1986). We also used a resampling approach with a minimum sample size of 10 individuals and 100 replicates to determine if changes in genetic diversity could be attributable to sampling effects in historic samples. This approach was previously used for modern samples, and indicated that variation in heterozygosity measures did not differ substantially between sample sizes of six and 16 individuals (Petren et al. 2005).

To test the primary hypotheses of population decline, genetic diversity measures were evaluated across populations in a repeated measures ANOVA framework. Changes in genetic diversity measures over time (calculated as percent change from past to present) were also analyzed for correlation with island elevation (which is closely related to island size) and log10 transformed island area to test whether smaller islands had the largest changes in diversity through time.

We used three single time point estimators of population history. MSVAR (Beaumont

1999) was used to evaluate historic population size trends. Specific conditions and details are provided in the supplemental materials (Table S2). The BOTTLENECK (Cornuet & Luikart

1996) program was used to test each population at each time point for evidence of a recent population bottleneck. A two-phase mutation model (80% step-wise) was used since this is more appropriate for microsatellite data than a strict step-wise model (Luikart & Cornuet 1998). A coalescent-based method was used to estimate population size while also providing estimates of migration rates (MIGRATE 3.0.3; Beerli & Felsenstein 1999, 2001), for both past and present time points. MIGRATE is widely used (nearly 1000 citations of the combined program references listed above) and it is relatively robust with regard to missing populations and minor violations of assumptions, including constant population size, migration and mutation rates through time (migration drift equilibrium). MIGRATE was run under the maximum likelihood framework using the Brownian motion model of microsatellite evolution, with randomly generated starting trees. Unknown alleles were excluded, and searches included 10 short chains and three long chains run with an adaptive heating scheme to increase the parameter space explored. Samples were taken every 20 steps, with a burn-in of 10000. Simulations were run to evaluate all population size and bidirectional migration parameters ( and M, respectively). The

Brownian motion model implemented in MIGRATE typically provides very similar results to the more time consuming stepwise model. Following the recommendation in the manual, ten replicate runs were performed for each data set and results were averaged and compared using a

Wilcoxon test. Analyses were also performed using the by locus results generated in MIGRATE to examine changes over time in a similar fashion to genetic diversity measures (non-parametric

Wilcoxon matched pairs test across time). Mantel tests were conducted to determine if overall

migration rates (mean number of immigrants exchanged for each pair of populations) and directionality of migration (difference between bidirectional estimates of island pairs) were significantly correlated between past and present time points.

We predicted that estimated population size ( = 4Neμ) and genetic diversity estimates for C. olivacea populations should be higher overall than those for C. fusca which inhabit smaller, peripheral islands. An ANOVA was used to test the hypothesis that population sizes were generally larger for C. olivacea than for C. fusca, with factors including species, sampling period, the interaction of time and species, and island included as a random nested factor within species. A similar ANOVA was used to test whether migration rate estimates differed between

C. olivacea and C. fusca populations.

Results

Quality Analysis of Historic Genotypes

Of the 2688 historic individual x locus autosomal genotypes possible, 84% were recovered for this study. Genotyping success varied greatly by locus (66% - 99%), with four loci falling below 80% recovery. Exclusion of these four loci increased the genotyping success rate to 90%. Based on the sub-set of 10 individuals with 6-fold replication, allelic dropout was estimated to impact approximately 26% of PCR replicates, while spurious alleles affected about

4% of PCR replicates. Using this calculated allele dropout rate, the probability of missing a second allele in all three PCR replicates for a given sample was less than 2%. Twenty-five of the

154 total autosomal locus/population combinations were out of Hardy-Weinberg equilibrium after Bonferroni correction. A single locus (Gf13), accounted for 28% of these deviations, while the remaining locus/population combinations were nearly equally distributed between historic and modern samples (45% and 55%, respectively), suggesting allelic dropout in historic

specimens was not the primary cause of deviations from equilibrium. Modern and historic population genotypes clearly clustered together according to the PCA analysis, which supports the assumption that historic specimens provide reliable genetic information about these populations (Fig 2).

Genetic change over time

FST values among islands were significantly correlated between time points (C. olivacea

P < 0.02; rm = 0.88 and C. fusca P = 0.02; rm = 0.85), indicating no substantial changes in overall population structure at the landscape level across the archipelago over the past century. Four individual populations, Fernandina, Genovesa, Isabela and Santiago had significant cross- temporal FST values. Thus, substantial changes in allele frequencies occurred between sampling time points for at least one population within both the C. olivacea and C. fusca groups.

Significant FST values across time were not always accompanied by significant changes in allelic richness and heterozygosity.

Direct cross-temporal comparisons of genetic diversity did not reveal consistent declines over time across the archipelago (F1,143 = 0.22, P = 0.64 for allele richness, AE; F1,143 = 0.02, P =

0.89 for expected heterozygosity, HE). Genetic diversity was significantly higher in C. olivacea populations from larger central islands when compared to C. fusca from smaller peripheral islands at both time points (F1,9 = 22.7, P < 0.01 for AE; F1,9 = 21.7, P < 0.01 for HE), while the interaction of time and species was not significant (F1,295 = 0.78, P = 0.38 for AE; F1,295 = 0.67,

2 P = 0.41 for HE). Changes in allele richness were not correlated with island size (r = 0.02;

P = 0.70) or elevation (r2 = 0.09; P = 0.36).

When data were pooled among populations, allele richness for the metapopulation as a whole did not change over time (P = 0.22). Declines in genetic diversity over time were not noted for islands with permanent human settlements (Santa Cruz, Isabela and San Cristobal).

Four island populations (Pinta, Pinzon, San Cristobal and Santa Fe) showed no evidence of change over time in any of the genetic diversity measures evaluated, while the remaining seven islands showed significant changes in at least one genetic measurement over time (Fig. 3;

Supp. Table S3).

Genovesa and Marchena showed significant declines (P < 0.05) in allelic richness (20% and 18%, respectively), with accompanying decreases in expected heterozygosity of >25% over the time interval examined (Fig. 3). Resampling of genotypes for Genovesa indicated a standard deviation of 0.12 (6%) for allelic richness and 3.3% for expected heterozygosity. Therefore, the results were not likely a population sampling artifact. Tests for genetic bottlenecks (Cornuet &

Luikart 1996) did not reveal any evidence of recent population decline in these populations

(Supp. Table S2). Of the 14 autosomal loci examined, five and four previously variable loci have become fixed for a single allele in the two respective modern populations, making these loci uninformative for bottleneck inferences based on this method, but likely contributing to the significant within population cross-temporal FST value noted for Genovesa (Fig. 3). Fernandina was the only population to have a significant test for a population bottleneck, which was indicated by modern samples (Supp. Table S2), but there was no evidence of a bottleneck in genetic diversity measures over time.

The Española population had statistically significant increases over time (P < 0.05) in all the genetic diversity measures evaluated (increases of 18% for AE and 35% for HE). Isabela and

Santa Cruz populations also showed significant increases in observed heterozygosity over time

(P < 0.01; Fig. 3).

Overall population size trends

Inferred population sizes (4Neμ) generated by the program MIGRATE generally increased over time across all populations (F1, 207 = 6.22; P = 0.013). Population sizes were generally larger for C. olivacea populations from larger central islands than for C. fusca populations from smaller peripheral islands (F1,9 = 12.8, P = 0.006), while the time x species interaction revealed larger C. olivacea populations increased to a greater extent over time (F1, 207

= 5.78; P = 0.017). These results are in stark contrast to results obtained from a different method

(Beaumont 1999) that does not account for migration, and which indicated dramatic declines in population size (90-100%) in all warbler finch populations based only on recently collected specimens (Supp. Table S2).

Inferred migration and population size

Overall movement of finches among islands, inferred as total number of migrants received per generation for each population, was similar between C. olivacea and C. fusca (Fig.

4; F1,9 = 0.016, P = 0.90), thus there was no clear difference between smaller and larger populations. However, the proportional genetic effect of these migrants would likely be greater in the smaller C. fusca populations. Levels of migration for both species increased significantly over time (F1,207 = 0.016, P < 0.001), but these increases were most likely driven by the large inferred increases in just a few islands. The species by time interaction for migration estimates was not significant in the model (P = 0.186). Mantel tests for similarity of migration rates for individual island pairs between historic and modern data sets were highly correlated for both C. olivacea (P = 0.01; Rm = 0.92) and C. fusca (P < 0.01; Rm = 0.72) populations. Mantel tests for

patterns of directional migration revealed significant similarity for C. fusca populations over time (P = 0.01; Rm = 0.69), but not for C. olivacea (P = 0.13; Rm = 0.48), indicating substantial changes in the directionality of gene flow over time among larger central islands. Figure 5 shows directional migration rates by receiving island, each with several source islands, for the two time points. Linear relationships are apparent, but slopes are shallower than 1:1. Islands that received fewer immigrants historically tended to receive more immigrants recently, and vice versa.

Significant changes in migration rates over time were noted for two of the five C. olivacea populations. Total immigration into the Santa Cruz population increased by 33%, from estimates of 2.7 to 3.6 immigrants per generation. Estimated immigration for Santiago was one and a half times greater for the modern data set than the historic (1.1 to 2.8 immigrants per generation; Fig. 4). Simulations with migration indicated statistically significant increases in C. olivacea population sizes (4Neμ) on two of the five islands, Santiago and Pinzon, between time points (P < 0.01). However, these changes were small in magnitude, less than 7% (Fig. 4). The

Santiago population showed the largest proportional gains in inferred population size and migration (Fig. 6). Genetic diversity on Santiago also increased, but not evenly across loci, thus the changes were not considered significant.

For the C. fusca populations, immigration rates into Genovesa were lower (by 15%) in the modern data set (3.4 to 2.9 migrants per generation), although not statistically significant (P

= 0.14), while immigration into Pinta and Española were significantly higher (40% and 132%, respectively) (Fig. 4). The changes in migration rates were statistically significant (P < 0.01) for both Genovesa and Española when migration data were analyzed by locus across time.

Significant changes in population size were noted in two of the six populations; Marchena

decreased over time, while the Española population increased. However, as with C. olivacea, these changes were relatively small in magnitude, 6-8% (Fig. 4).

Changes in directly measured cross-temporal genetic diversity were closely related to changes in inferred migration rate between time points (Fig. 6). Significant correlations were noted between percent change in inferred total migration and allele richness (P < 0.01; R2 =

0.66), as well as expected heterozygosity (P < 0.01; R2 = 0.58).

Discussion

Genotypes from museum specimens allowed us to examine genetic changes in several interconnected populations of Darwin’s finches over the last century. We rejected the hypothesis that warbler finches are in general decline as reflected by genetic diversity. Decline was not associated with direct human disturbance and habitat destruction, nor was there evidence of consistent decline in smaller populations on smaller islands that are more prone to drought.

Declines in diversity occurred in two smaller populations, yet tests for genetic bottlenecks did not reveal these changes. This highlights the utility of direct cross-temporal comparisons and shows the limitations of some single time point estimators for monitoring fragmented populations. Surprisingly, several populations showed increased diversity over time, and these populations also showed increased migration rates compared to historical levels. Although the broadest patterns of immigration were consistent over time, there were substantial changes in the directionality and rate of gene exchange among specific islands over time. Local stochastic fluctuations in the midst of global stability is a feature broadly associated with metapopulations

(Hanski & Gaggiotti 2004, Young and Clarke 2000).

Ancient DNA Genotyping

The quality assessment of historic genotypes conducted for this study is informative for future cross-temporal comparisons using natural history collections. The reliability of genotyping varied by locus, with a significant negative correlation between amplification success and fragment size (P = 0.003). Reliability also varied by specimen, which may be related to environmental and storage conditions during or shortly after study skin preparation

(i.e.- temperature and humidity). Ten individuals (~5% of total museum specimens evaluated) were excluded from the study as a result of failure to recover >50% of their genotype information.

Reducing the size of targeted loci, replicate genotyping, extensive use of controls, and an internal quality analysis were the most efficient approaches to ensure high quality genetic data from these historic specimens (Gilbert et al. 2005). With allele dropout affecting approximately

26% of PCR reactions, the probability of missing a second allele in all three replicates for a given sample was less than 2%. The majority of the additional heterozygotes recovered with

6-fold replication suggest that misidentification of heterozygotes is most likely to occur when

PCR replicates fail, rather than through allelic dropout in several successful PCR reactions. Thus the highest rate of genotyping errors are expected for genotypes based on only a single successful PCR, and this affected less than 8% of the historic genotypes used for this study. In future studies, the most cost-effective way to recover accurate genetic information may be to focus on obtaining a minimum number of successful replicates for each sample. With the large number of individuals and loci evaluated in this study, and the low probability of genotyping errors with 3-fold replication, the effect of genotyping error is small relative to the magnitude of cross-temporal changes (Fig. 3). The magnitude of cross-temporal changes greatly exceeds what could be attributable to genotyping error.

Genetic diversity over time

Declines in genetic diversity were confined to two small, peripheral islands of the archipelago and were not associated with human inhabited islands where anthropogenic disturbance is most extensive. The erosion of genetic diversity in these populations is most likely due to genetic drift caused by periods of population decline associated with natural climate cycles. Genovesa and Marchena are low elevation, peripheral islands that are highly impacted by periodic El Niño events, which bring abundant rain to otherwise arid islands, generating a boom of primary productivity that continues up the food chain. This period of abundant food resources leads to a spike in reproductive output of finch populations (Grant & Grant 1992).

However, dry conditions typically follow El Niño events, resulting in food scarcity and massive mortality (Grant et al. 2000). Therefore, these periodic “boom and bust” cycles may differentially impact a subset of smaller islands and erode genetic diversity over time without evidence of a single, recent bottleneck event (Vucetich & Waite 1999).

Migration over time

We detected widespread evidence of genetic movement among warbler finch populations. Islands received an average of 3-4 migrants per generation, which lies at the lower end of the spectrum of inferred migration rates for Darwin’s finches (Petren et al. 2005). Overall patterns of immigration were relatively constant, while directional patterns were more variable over time, as indicated by matrix correlations. Substantial changes in gene flow occurred for particular islands over time and largely corresponded to cross-temporal trends in genetic diversity (Fig. 6). Surprisingly, three large island populations had substantial changes in migration rate and/or inferred population size.

Española had significant increases in all measures of genetic diversity over time which can be attributed to migration because other causes of increased genetic diversity are less likely.

Mutation is not likely to introduce substantial new genetic variation over the 100 year time frame examined here. Hybridization is not a likely factor for the morphologically and genetically distinct warbler finches, which have a lower propensity to hybridize compared to other Darwin’s finches (Grant 1999; Petren et al. 2005). Locations where specimens were collected on each island may have differed at the two time points and contributed to observed differences, but within-island geographical population structure tends to be subtle in most cases (de León et al.

2010).

Migration rate estimates increased 132% for Española between the historic and modern data sets (Fig. 4), which complements the genetic diversity increases of 18% for AE and 55% for

HO (Figs. 3 and 6). No specific source population for migrants could be identified as there were substantial increases in inferred migration noted from all other islands (90%-273%). Española had the lowest historical genetic diversity, so the increase can be seen as a recovery from a historical population crash and return to equilibrium. An influx of immigrants is expected to have a more substantial impact on genetic diversity in a genetically depauperate island than on a more genetically diverse island. The pattern is consistent with a natural genetic rescue (Brown and Kodric-Brown 1977), but any affect of the genetic diversity increase on fitness is unknown.

Metapopulation Dynamics

The Galápagos warbler finches show many of the predicted dynamics of metapopulations over century-long time scales. Metapopulation dynamics can buffer species from extinction by recolonization of empty habitat patches, but also through the maintenance of genetic variation

(Reed 2004). Although genetic diversity may be lost in individual populations, alleles may be

retained elsewhere in the metapopulation and spread to other demes by migration. Isolation from gene flow, particularly in small populations, can lead to loss of genetic diversity and inbreeding depression (Keller 1998). Periodic migration can maintain genetic variation yet still allow for local adaptation. The gain in diversity on Española shows that genetic infusion through immigration does occur and may play a role in species persistence, evolution, or both. Low levels of gene flow into small populations can be a recipe for adaptive divergence under some conditions (Whitlock et al. 2000; Church & Taylor 2002). Across all islands, past migration rates that were either high or low tended to change more and in the opposite direction (Fig. 5). This suggests that stochastic factors may be balanced or averaged out over time, a picture that is consistent with expectations based on metapopulation dynamics.

Our results suggest that migration rates and population sizes of warbler finches have increased over time. This is consistent with the known increase in frequency and severity of El

Niño events over recent time (Guilderson and Schrag 1998). El Nino events bring much rain, an increase in reproduction and an apparent increase in finch movements between islands. The increase in population sizes may reflect greater levels of and/or more successful immigration.

However, these results should be viewed with caution for several reasons. First, very recent or future introductions of diseases or parasites that could have dramatic negative effects, and the causes of the presumed extinction of the Floreana population remain unknown. Second, increased immigration could be a result of birds moving around more due to an overall decline in habitat quality. It seems conceivable that increased immigration could thus mask overall decline, but the conditions over which this could happen are unknown.

Darwin’s finches provide three illustrative examples of species that are not part of metapopulations. The mangrove finch (Cactospiza heliobates and the medium tree finch

(Camarhynchus pauper) are currently confined to single islands (Grant 1999), and they are the only two species listed as endangered, while the Cocos finch (Pinaroloxias inornata) remains undifferentiated in form on the remote and isolated Cocos Island off the coast of Costa Rica

(Grant 1999). This may be further evidence for the importance of metapopulation dynamics in the Darwin’s finch system.

Historical Inferences

In this study, direct cross-temporal comparisons using natural history collections revealed substantial changes in genetic diversity and gene flow over time. This picture of episodic events suggests that inferences of population history based on a single point in time can vary substantially, depending on when genetic samples are collected. Single point estimators of population history are thought to reflect a composite history of populations, where fluctuating parameters in a population’s history are reduced to single values reflecting means or general trends over time. The evidence that some warbler populations changed dramatically over time suggests that inferences based on single time points be regarded with caution. In our analysis, methods that take migration into account performed better than those that did not, particularly those that attempted to reconstruct ancestral population size and bottlenecks. Migration is one cause of such errors in Darwin’s finches, and migration may yield misleading results if not accounted for or ruled out in other systems. The stochastic nature of environmental change in the Galápagos is a likely cause of the observed rapid changes over time, and the uncertainties are only reduced with historical samples, which are regrettably limited in number for most systems.

It remains to be seen whether the volatile history of populations revealed here is a common feature of other natural populations, or whether it is specific to the Galápagos finches and perhaps an underlying cause of this unusually rapid adaptive radiation.

Acknowledgements

We thank Terry Chesser and Joel Cracraft of the American Museum of Natural History, and John Dumbacher, Maureen Flannery, Douglas Long and Luis Baptista of the California

Academy of Science for access to valuable historical specimens. We thank the Galápagos

National Parks and Charles Darwin Research Station for field support. We thank K. Short, J.

Niedzwiecki, and E. Ristagno for lab and field assistance and anonymous reviewers for constructive comments. This work was partially supported by the National Science Foundation

(DEB-0317687 to K.P.), Sigma Xi, The American Ornithologists’ Union and the University of

Cincinnati University Research Council and Department of Biological Sciences.

References

Antao, T., A. Perez-Figueroa, and G. Luikart. 2011. Early detection of population declines: high

power of genetic monitoring using effective population size estimators. Evol. Appl.

4:144-154.

Beaumont, M. A. 1999. Detecting population expansion and decline using microsatellites.

Genetics 153:2013-2029.

Beerli, P., and J. Felsenstein. 1999. Maximum-likelihood estimation of migration rates and

effective population numbers in two populations using a coalescent approach. Genetics

152:763-773.

Beerli, P., and J. Felsenstein. 2001. Maximum likelihood estimation of a migration matrix and

effective population sizes in n subpopulations by using a coalescent approach.

Proceedings of the National Academy of Sciences of the United States of America

98:4563-4568.

Berthier, K., N. Charbonnel, M. Galan, Y. Chaval, and J. F. Cosson. 2006. Migration and

recovery of the genetic diversity during the increasing density phase in cyclic vole

populations. Molecular Ecology 15:2665-2676.

Bouzat, J. L. 2001. The importance of control populations for the identification and management

of genetic diversity. Genetica 110:109-115.

Brown, J. H., and A. Kodric-Brown. 1977. Turnover Rates in Insular Biogeography - Effect of

Immigration on Extinction. Ecology 58:445-449.

Church, S. A., and D. R. Taylor. 2002. The evolution of reproductive isolation in spatially

structured populations. Evolution 56:1859-1862.

Cornuet, J. M., and G. Luikart. 1996. Description and power analysis of two tests for detecting

recent population bottlenecks from allele frequency data. Genetics 144:2001-2014.

De Barba, M., L. P. Waits, E. O. Garton, P. Genovesi, E. Randi, A. Mustoni, and C. Groff. 2010.

The power of genetic monitoring for studying demography, ecology and genetics of a

reintroduced brown bear population. Molecular Ecology 19:3938-3951. de Leon, L. F., E. Bermingham, J. Podos, and A. P. Hendry. 2010. Divergence with gene flow as

facilitated by ecological differences: within-island variation in Darwin's finches.

Philosophical Transactions of the Royal Society B-Biological Sciences 365:1041-1052.

Dudaniec, R. Y., B. Fessl, and S. Kleindorfer. 2007. Interannual and interspecific variation in

intensity of the parasitic fly, Philornis downsi, in Darwin's finches. Biological

Conservation 139:325-332.

Dudaniec, R. Y., and S. Kleindorfer. 2006. Effects of the parasitic flies of the genus Philornis

(Diptera : Muscidae) on birds. Emu 106:13-20.

Dvorak, M., H. Vargas, B. Fessl, and S. Tebbich. 2004. On the verge of extinction: a survey of

the mangrove finch Cactospiza heliobates and its habitat on the Galápagos Islands. Oryx

38:171-179.

England, P. R., G. H. R. Osler, L. M. Woodworth, M. E. Montgomery, D. A. Briscoe, and R.

Frankham. 2003. Effects of intense versus diffuse population bottlenecks on

microsatellite genetic diversity and evolutionary potential. Conservation Genetics 4:595-

604.

Freeland, J. R., and P. T. Boag. 1999. The mitochondrial and nuclear genetic homogeneity of the

phenotypically diverse Darwin's ground finches. Evolution 53:1553-1563.

Gilbert, M. T. P., H. J. Bandelt, M. Hofreiter, and I. Barnes. 2005. Assessing ancient DNA

studies. Trends in Ecology & Evolution 20:541-544.

Goudet, J. 1995. FSTAT (Version 1.2): A computer program to calculate F-statistics. Journal of

Heredity 86:485-486.

Grant, B. R., and P. R. Grant. 2002. Lack of premating isolation at the base of a phylogenetic

tree. American Naturalist 160:1-19.

Grant, P. R. 1999. Ecology and Evolution of Darwin's Finches. Princeton University Press,

Princeton, New Jersey.

Grant, P. R., and B. R. Grant. 1992. Demography and the Genetically Effective Sizes of 2

Populations of Darwin Finches. Ecology 73:766-784.

Grant, P. R., and B. R. Grant. 1995. The founding of a new population of Darwin's finches (vol

49, pg 229, 1995). Evolution 49:1307-1307.

Grant, P. R., and B. R. Grant. 2006. Evolution of character displacement in Darwin's finches.

Science 313:224-226.

Grant, P. R., B. R. Grant, L. F. Keller, and K. Petren. 2000. Effects of El Nino events on

Darwin's finch productivity. Ecology 81:2442-2457.

Grant, P. R., B. R. Grant, J. A. Markert, L. F. Keller, and K. Petren. 2004. Convergent evolution

of Darwin's finches caused by introgressive hybridization and selection. Evolution

58:1588-1599.

Grant, P. R., B. R. Grant, K. Petren, and L. F. Keller. 2005. Extinction behind our backs: the

possible fate of one of the Darwin's finch species on Isla Floreana, Galápagos. Biological

Conservation 122:499-503.

Guilderson, T. P., and D. P. Schrag. 1998. Abrupt shift in subsurface temperatures in the

Tropical Pacific associated with changes in El Nino. Science 281:240-243.

Guo, S. W., and E. A. Thompson. 1992. Performing the Exact Test of Hardy-Weinberg

Proportion for Multiple Alleles. Biometrics 48:361-372.

Hansen, M. M., D. E. Ruzzante, E. E. Nielsen, D. Bekkevold, and K. L. D. Mensberg. 2002.

Long-term effective population sizes, temporal stability of genetic composition and

potential for local adaptation in anadromous brown trout (Salmo trutta) populations.

Molecular Ecology 11:2523-2535.

Hanski, I., and O. E. Gaggiotti. 2004. Ecology, Genetics, and Evolution of Metapopulations.

Elsevier Academic Press.

Hanski, I., and M. Gilpin. 1991. Metapopulation Dynamics - Brief-History and Conceptual

Domain. Biological Journal of the Linnean Society 42:3-16.

James, H. F., and J. P. Price. 2008. Integration of palaeontological, historical, and geographical

data on the extinction of koa-finches. Diversity and Distributions 14:441-451.

Johnson, J. A., and P. O. Dunn. 2006. Low genetic variation in the heath hen prior to extinction

and implications for the conservation of prairie-chicken populations. Conservation

Genetics 7:37-48.

Keller, L. F. 1998. Inbreeding and its fitness effects in an insular population of song sparrows

(Melospiza melodia). Evolution 52:240-250.

Keller, L. F., P. R. Grant, B. R. Grant, and K. Petren. 2002. Environmental conditions affect the

magnitude of inbreeding depression in survival of Darwin's finches. Evolution 56:1229-

1239.

Keller, L. F., and D. M. Waller. 2002. Inbreeding effects in wild populations. Trends in Ecology

& Evolution 17:230-241.

Kindlmann, P., and F. Burel. 2008. Connectivity measures: a review. Landscape Ecology

23:879-890.

Kleindorfer, S., and R. Y. Dudaniec. 2006. Increasing prevalence of avian poxvirus in Darwin's

finches and its effect on male pairing success. Journal of Avian Biology 37:69-76.

Kuhner, M. K. 2006. LAMARC 2.0: maximum likelihood and Bayesian estimation of population

parameters. Bioinformatics 22:768-770.

Levin, II, D. C. Outlaw, F. H. Vargas, and P. G. Parker. 2009. Plasmodium blood parasite found

in endangered Galápagos penguins (Spheniscus mendiculus). Biological Conservation

142:3191-3195.

Lewis, P. O., and D. Zaykin. 2001. Genetic Data Analysis: Computer Program for the Analysis

of Allelic Data.

Luikart, G., and J. M. Cornuet. 1998. Empirical evaluation of a test for identifying recently

bottlenecked populations from allele frequency data. Conservation Biology 12:228-237.

Luikart, G., N. Ryman, D. A. Tallmon, M. K. Schwartz, and F. W. Allendorf. 2010. Estimation

of census and effective population sizes: the increasing usefulness of DNA-based

approaches. Conservation Genetics 11:355-373.

Matocq, M. D., and F. X. Villablanca. 2001. Low genetic diversity in an endangered species:

recent or historic pattern? Biological Conservation 98:61-68.

Miller, C. R., and L. P. Waits. 2003. The history of effective population size and genetic

diversity in the Yellowstone grizzly (Ursus arctos): Implications for conservation.

Proceedings of the National Academy of Sciences of the United States of America

100:4334-4339.

Mills, L. S., J. J. Citta, K. P. Lair, M. K. Schwartz, and D. A. Tallmon. 2000. Estimating animal

abundance using noninvasive DNA sampling: Promise and pitfalls. Ecological

Applications 10:283-294.

Moritz, C. 1994. Applications of mitochondrial DNA analysis in conservation – a critical review.

Molecular Ecology 3:401-411.

Patricia G. P., E. L. Buckles, H. Farrington, K. Petren, N. K. Whiteman, R. E. Ricklefs, J. L.

Bollmer, G. Jiménez-Uzcátegui. 2011. 110 Years of Avipoxvirus on the Galápagos

Islands. PLoS ONE 6(1):e15989.

Peakall, R., and P. E. Smouse. 2006. GENALEX 6: genetic analysis in Excel. Population genetic

software for teaching and research. Molecular Ecology Notes 6:288-295.

Pertoldi, C., M. M. Hansen, V. Loeschcke, A. B. Madsen, L. Jacobsen, and H. Baagoe. 2001.

Genetic consequences of population decline in the European otter (Lutra lutra): an

assessment of microsatellite DNA variation in Danish otters from 1883 to 1993.

Proceedings of the Royal Society B-Biological Sciences 268:1775-1781.

Petren, K. 1998. Microsatellite primers from Geospiza fortis and cross-species amplification in

Darwin's finches. Molecular Ecology 7:1782-1784.

Petren, K., B. R. Grant, and P. R. Grant. 1999. A phylogeny of Darwin's finches based on

microsatellite DNA length variation. Proceedings of the Royal Society of London Series

B-Biological Sciences 266:321-329.

Petren, K., P. R. Grant, B. R. Grant, A. A. Clack, and N. V. Lescano. 2010. Multilocus

genotypes from Charles Darwin's finches: biodiversity lost since the voyage of the

Beagle. Philosophical Transactions of the Royal Society B-Biological Sciences

365:1009-1018.

Petren, K., P. R. Grant, B. R. Grant, and L. F. Keller. 2005. Comparative landscape genetics and

the adaptive radiation of Darwin's finches: the role of peripheral isolation. Molecular

Ecology 14:2943-2957.

Ramakrishnan, U., and E. A. Hadly. 2009. Using phylochronology to reveal cryptic population

histories: review and synthesis of 29 ancient DNA studies. Molecular Ecology 18:1310-

1330.

Reed, D. H. 2004. Extinction risk in fragmented habitats. Animal Conservation 7:181-191.

Rice, W. R. 1989. Analyzing Tables of Statistical Tests. Evolution 43:223-225.

Schwartz, M. K., G. Luikart, and R. S. Waples. 2007. Genetic monitoring as a promising tool for

conservation and management. Trends in Ecology & Evolution 22:25-33.

Sefc, K. M., R. B. Payne, and M. D. Sorenson. 2003. Microsatellite amplification from museum

feather samples: Effects of fragment size and template concentration on genotyping

errors. Auk 120:982-989.

Slatkin, M. 1977. Gene Flow and Genetic Drift in a Species Subject to Frequent Local

Extinctions. Theoretical Population Biology 12:253-262.

Smouse, P. E., J. C. Long, and R. R. Sokal. 1986. Multiple regression and correlation extentions

of the Mantel Test of matrix correspondance. Systematic Zoology 35:627-632.

Su, M., W. L. Li, Z. Z. Li, F. P. Zhang, and C. Hui. 2009. The effect of landscape heterogeneity

on host-parasite dynamics. Ecol. Res. 24:889-896.

Taylor, S. S., I. G. Jamieson, and G. P. Wallis. 2007. Historic and contemporary levels of genetic

variation in two New Zealand passerines with different histories of decline. Journal of

Evolutionary Biology 20:2035-2047.

Tonnis, B., P. R. Grant, B. R. Grant, and K. Petren. 2005. Habitat selection and ecological

speciation in Galápagos warbler finches (Certhidea olivacea and Certhidea fusca).

Proceedings of the Royal Society B-Biological Sciences 272:819-826.

Vucetich, J. A., and T. A. Waite. 1999. Erosion of heterozygosity in fluctuating populations.

Conservation Biology 13:860-868.

Wandeler, P., P. E. A. Hoeck, and L. F. Keller. 2007. Back to the future: museum specimens in

population genetics. Trends in Ecology & Evolution 22:634-642.

Wang, J. L. 2005. Estimation of effective population sizes from data on genetic markers.

Philosophical Transactions of the Royal Society B-Biological Sciences 360:1395-1409.

Watson, J., M. Trueman, M. Tufet, S. Henderson, and R. Atkinson. 2009. Mapping terrestrial

anthropogenic degradation on the inhabited islands of the Galápagos Archipelago. Oryx

44:79-82.

Weir, B. S., and C. C. Cockerham. 1984. Estimating F-Statistics for the Analysis of Population-

Structure. Evolution 38:1358-1370.

Whitlock, M. C., P. K. Ingvarsson, and T. Hatfield. 2000. Local drift load and the heterosis of

interconnected populations. Heredity 84:452-457.

Wikelski, M., J. Foufopoulos, H. Vargas, and H. Snell. 2004. Galápagos birds and diseases:

Invasive pathogens as threats for island species. Ecology and Society 9.

Willi, Y., J. Van Buskirk, B. Schmid, and M. Fischer. 2007. Genetic isolation of fragmented

populations is exacerbated by drift and selection. Journal of Evolutionary Biology

20:534-542.

Wilson, I. J., M. E. Weale, and D. J. Balding. 2003. Inferences from DNA data: population

histories, evolutionary processes and forensic match probabilities. Journal of the Royal

Statistical Society Series a-Statistics in Society 166:155-188.

Young, A. G., and G. M. Clarke. 2000. Genetics, Demography and Viability of Fragmented

Populations. Cambridge University Press.

Tables and Figures

Table 1: Certhidea populations used for cross-temporal analysis. Time periods are historic (H) and modern (M); Sources for historic specimens are California Academy of Science (CAS),

British Natural History Museum (BNHM), and American Museum of Natural History (AMNH); n=number of samples analyzed

Figure 1: Galápagos map indicating islands sampled. A dashed line separates inner island C. olivacea occupied islands from more peripheral C. fusca occupied islands. Island abbreviations shown are used in all figures.

Figure 2: PCA plot of historic (hollow symbols, dashed lines) and modern (solid symbols and lines) genetic data. Circles are 50% centroids. The first two axes account for 51.1% and 14.5% of the variation in the data.

Figure 3: Summary genetic data; (A) allelic richness (AE), (B) expected heterozygosities (He), and (C) observed heterozygosity (Ho) calculated as means across 14 autosomal loci. Islands are presented largest to smallest within species. C. olivacea are to the left of dashed line, C. fusca to the right. P values are indicated by * (<0.05) or ** (<0.01).

Figure 4: Average (A) inferred population sizes ( = 4Neμ) and (B) total number of immigrants per generation coming into a population from all source populations, generated from 10 replicate

MIGRATE runs. White represents historic populations and shaded modern. Bars indicate

standard deviation. C. olivacea are to the left of dashed line, C. fusca to the right. P values are indicated by * (<0.05) or ** (<0.01).

Figure 5: Past vs. present migration rates (number of migrants per generation) by island for (A)

C. olivacea and (B) C. fusca populations. Solid line indicates best fit line, dotted line indicates equality of past and present values. Points below the dotted line have higher migration in the past, above the line higher migration at present.

Figure 6: Percent change in mean values of (A) allelic richness, (B) total migration, and (C) estimated population size ( = 4Neμ) from past to present.

Table 1:

Island Time Period Source Date n Española* H CAS 1906 18 M field 1988, 1997 29 Fernandina H BNHM, AMNH, CAS 1894, 1897, 1899 13 M field 1999 19 Genovesa H BNHM, CAS 1897, 1906 25 M field 1988, 1997 23 Isabela H CAS 1906 18 M field 1999 25 Marchena H BNHM, CAS 1897, 1899, 1906 22 M field 1988 8 Pinta H CAS 1899, 1906 12 M field 1997, 2001 19 Pinzón H BNHM, CAS 1899, 1906 19 M field 2004 19 San Cristóbal H BNHM, CAS 1897, 1906 20 M field 1999 19 Santa Cruz H CAS 1906 10 M field 1988-1999 15 Santa Fe H BNHM, CAS 1897, 1899, 1906 18 M field 1998-1999, 2004 12 Santiago H CAS 1906 17 M field 1996 31

* - Samples for Española come from the main island (10) and the satellite island of Gardner (8). Excluding Gardner samples did not change the overall results for genetic diversity parameters measured.

Fig 1:

(Pi)

(Ma) (Ge)

(Sa)

(Sc) (Fe) (Pz)

(Sf) (Co) (Is)

(Es)

Fig 2:

Fig. 3:

** ** *

* *

C) ** **

** *

Fig. 4:

A) ** ** * *

** ** ** **

Fig. 5:

Fig. 6:

Table S1: Museum specimen sources, accession numbers and collection dates. * = Specimen excluded from analysis due to <50% recovery of genotype data. CAS = California Academy of

Science; ANHM = American Natural History Museum; BMNH = British Museum of Natural

History

Species Museum Museum Cat. # Date Island fusca CAS 1380 1906 Genovesa olivacea CAS 4537 1905 Santiago olivacea CAS 4538 1906 Santiago olivacea CAS 4540 1906 Santiago olivacea CAS 4543* 1906 Isabela olivacea CAS 4544 1905 Santiago olivacea CAS 4545 1906 Isabela olivacea CAS 4546 1906 Isabela olivacea CAS 4547 1906 Isabela olivacea CAS 4548 1905 Pinzón olivacea CAS 4551* 1906 Santiago olivacea CAS 4553 1906 Isabela olivacea CAS 4555 1906 Isabela olivacea CAS 4556* 1906 Santiago olivacea CAS 4557 1906 Isabela olivacea CAS 4559 1906 Santiago olivacea CAS 4560* 1906 Isabela olivacea CAS 4561 1906 Santa Cruz olivacea CAS 4562 1906 Santa Cruz olivacea CAS 4565 1906 Santiago olivacea CAS 4567 1906 Isabela olivacea CAS 4568 1906 Isabela olivacea CAS 4569* 1906 Santa Cruz olivacea CAS 4570 1906 Isabela olivacea CAS 4571 1906 Isabela olivacea CAS 4575* 1906 Santiago olivacea CAS 4578 1906 Isabela olivacea CAS 4580 1905 Santa Cruz olivacea CAS 4584 1906 Isabela olivacea CAS 4586 1905 Pinzón olivacea CAS 4587 1905 Santiago olivacea CAS 4590 1905 Pinzón olivacea CAS 4591 1905 Pinzón olivacea CAS 4592 1905 Pinzón olivacea CAS 4593 1905 Pinzón olivacea CAS 4596 1905 Santiago olivacea CAS 4600 1905 Santa Cruz olivacea CAS 4602 1906 Isabela olivacea CAS 4604 1905 Pinzón olivacea CAS 4605 1905 Pinzón

Species Museum Museum Cat. # Date Island olivacea CAS 4606 1906 Isabela olivacea CAS 4611 1906 Isabela fusca CAS 4612 1905 San Cristobal fusca CAS 4614 1905 San Cristobal fusca CAS 4617 1905 San Cristobal fusca CAS 4633 1905 San Cristobal fusca CAS 4634 1905 San Cristobal fusca CAS 4636 1905 San Cristobal fusca CAS 4637 1905 San Cristobal fusca CAS 4640 1905 San Cristobal fusca CAS 4669 1906 Pinta fusca CAS 4670 1906 Pinta fusca CAS 4671 1906 Marchena fusca CAS 4672 1906 Pinta fusca CAS 4673 1906 Marchena fusca CAS 4674 1906 Marchena fusca CAS 4678 1906 Genovesa fusca CAS 4679 1906 Genovesa fusca CAS 4680 1906 Genovesa fusca CAS 4682 1906 Genovesa fusca CAS 4683 1906 Genovesa fusca CAS 4684 1906 Genovesa fusca CAS 4686 1906 Genovesa fusca CAS 4687 1906 Genovesa fusca CAS 4689 1906 Genovesa fusca CAS 4691 1906 Genovesa fusca CAS 4694 1905 Espanola fusca CAS 4698 1905 Espanola fusca CAS 4702 1906 Espanola fusca CAS 4706 1906 Espanola fusca CAS 4710* 1906 Espanola fusca CAS 4714 1906 Espanola fusca CAS 4718 1905 Espanola fusca CAS 4721 1905 Espanola fusca CAS 4725 1906 Espanola fusca CAS 4733 1905 Espanola fusca CAS 4737 1906 Espanola fusca CAS 4741 1906 Espanola fusca CAS 4745 1906 Espanola fusca CAS 4748 1905 Santa Fe fusca CAS 4749 1906 Santa Fe fusca CAS 4750 1905 Santa Fe fusca CAS 4753* 1905 Santa Fe fusca CAS 4759 1905 Santa Fe fusca CAS 4764 1905 Santa Fe fusca CAS 4766 1906 Santa Fe fusca CAS 4767 1905 Santa Fe fusca CAS 4771 1906 Santa Fe olivacea CAS 4777 1905 Pinzón 80

Species Museum Museum Cat. # Date Island olivacea CAS 4778 1906 Santiago olivacea CAS 4779 1905 Isabela olivacea CAS 4781 1906 Santa Cruz olivacea CAS 4782 1905 Pinzón olivacea CAS 4785 1905 Pinzón olivacea CAS 4787 1906 Santa Cruz olivacea CAS 4790 1905 Santiago fusca CAS 4798 1906 Marchena fusca CAS 4799 1906 Marchena fusca CAS 4804 1906 Espanola olivacea CAS 4807 1905 Santiago olivacea CAS 4808 1905 Santiago olivacea CAS 4811 1905 Santa Cruz olivacea CAS 4812 1905 Santiago olivacea CAS 4815 1906 Santa Cruz olivacea CAS 4817 1906 Santa Cruz olivacea CAS 4820* 1906 Santa Cruz olivacea CAS 4821 1905 Pinzón olivacea CAS 4825 1905 Santa Cruz olivacea CAS 4829 1905 Santiago olivacea CAS 4830 1905 Santiago olivacea CAS 4831 1905 Pinzón olivacea CAS 4833 1905 Santiago olivacea CAS 4834 1905 Santiago olivacea CAS 4836 1906 Isabela olivacea CAS 4840 1906 Isabela fusca CAS 4844 1905 San Cristobal fusca CAS 4845 1905 San Cristobal fusca CAS 4854 1905 San Cristobal fusca CAS 4855 1905 San Cristobal fusca CAS 4881 1906 Pinta fusca CAS 4882 1906 Pinta fusca CAS 4883 1906 Pinta fusca CAS 4884 1906 Pinta fusca CAS 4885 1906 Marchena fusca CAS 4886 1906 Marchena fusca CAS 4887 1906 Marchena fusca CAS 4889 1906 Marchena fusca CAS 4890 1906 Marchena fusca CAS 4891* 1906 Marchena fusca CAS 4892 1906 Marchena fusca CAS 4893 1906 Pinta fusca CAS 4894 1906 Marchena fusca CAS 4895 1906 Pinta fusca CAS 4896 1906 Pinta fusca CAS 4898 1906 Marchena fusca CAS 4899 1906 Marchena fusca CAS 4900 1906 Marchena fusca CAS 4907 1906 Genovesa 81

Species Museum Museum Cat. # Date Island fusca CAS 4908 1906 Genovesa fusca CAS 4914 1906 Espanola fusca CAS 4919 1905 Espanola fusca CAS 4925 1905 Espanola fusca CAS 4933 1905 Espanola fusca CAS 4940 1906 Espanola fusca CAS 4963 1906 Santa Fe fusca CAS 4964 1906 Santa Fe fusca CAS 4966 1906 Santa Fe fusca CAS 81104 1899 Genovesa fusca CAS 81105 1899 Genovesa fusca CAS 81106 1899 Genovesa fusca CAS 81107 1899 Genovesa fusca CAS 81108 1899 Genovesa fusca CAS 81113 1899 Marchena fusca CAS 81114 1899 Marchena fusca CAS 81115 1899 Marchena fusca CAS 81116 1899 Pinta fusca CAS 81117 1899 Marchena fusca CAS 81118 1899 Pinta olivacea CAS 81141 1899 Fernandina olivacea CAS 81142 1899 Fernandina olivacea CAS 81143 1899 Fernandina fusca CAS 81155 1899 Santa Fe fusca CAS 81156 1899 Santa Fe olivacea AMNH 522519 1894 Fernandina olivacea AMNH 522520 1894 Fernandina olivacea AMNH 522521 1894 Fernandina olivacea AMNH 522522 1894 Fernandina olivacea AMNH 522523 1894 Fernandina olivacea AMNH 522524 1894 Fernandina olivacea AMNH 522525 1894 Fernandina fusca BNHM 1885.4.1.191 1897 Marchena fusca BNHM 1899.9.1.105 1897 San Cristobal fusca BNHM 1899.9.1.106 1897 San Cristobal fusca BNHM 1899.9.1.107 1897 San Cristobal fusca BNHM 1899.9.1.108 1897 San Cristobal fusca BNHM 1899.9.1.109 1897 San Cristobal fusca BNHM 1899.9.1.110 1897 San Cristobal fusca BNHM 1899.9.1.111 1897 San Cristobal fusca BNHM 1899.9.1.112 1897 San Cristobal fusca BNHM 1899.9.1.114 1897 Genovesa fusca BNHM 1899.9.1.115 1897 Genovesa fusca BNHM 1899.9.1.116 1897 Genovesa fusca BNHM 1899.9.1.117 1897 Genovesa fusca BNHM 1899.9.1.118 1897 Genovesa fusca BNHM 1899.9.1.119 1897 Genovesa fusca BNHM 1899.9.1.120 1897 Genovesa fusca BNHM 1899.9.1.128 1897 Marchena 82

Species Museum Museum Cat. # Date Island fusca BNHM 1899.9.1.129 1897 Marchena fusca BNHM 1899.9.1.138 1897 Santa Fe fusca BNHM 1899.9.1.140 1897 Santa Fe fusca BNHM 1899.9.1.141 1897 Santa Fe fusca BNHM 1899.9.1.142 1897 Santa Fe fusca BNHM 1899.9.1.143 1897 Santa Fe olivacea BNHM 1899.9.1.144 1899 Pinzón olivacea BNHM 1899.9.1.88 1897 Fernandina olivacea BNHM 1899.9.1.89 1897 Fernandina olivacea BNHM 1899.9.1.90 1897 Fernandina olivacea BNHM 1899.9.1.91 1899 Pinzón olivacea BNHM 1899.9.1.92 1899 Pinzón olivacea BNHM 1899.9.1.93 1899 Pinzón olivacea BNHM 1899.9.1.94 1899 Pinzón olivacea BNHM 1899.9.1.95 1899 Pinzón

Table S2: MSVAR Summary Data and BOTTLENECK results

MSVAR Summary Data; current and ancestral estimated population sizes and percent decline

% ISLAND Current N Ancestral N DECLINE Scruz 5923 2288534399.97% Sant 2922 860369099.97% Genov 0 8621834100.00% Espanola 35 7021060100.00% Pinzon 4032 2723913099.99% Marchena 0 9130271100.00% SanFe 3 17884472100.00% SanCristo 175 8042603100.00% Pinta 2 16383225100.00% Fern 1595 1892171499.99% Isab 5513 1619611999.97%

MSVAR simulations were run for 10000 interations with a generation time of 5 years and a mutation rate of 10-4

BOTLLENECK data (Wilcoxon test for heterzygote excess p values)

Island Historic Modern Española 0.966 0.812 Fernandina 0.548 0.032* Genovesa 0.999 0.455 Isabela 0.793 0.948 Marchena 0.932 0.723 Pinta 0.999 0.903 Pinzón 0.768 0.971 San Cristóbal 0.866 0.729 Santa Cruz 0.837 0.984 Santa Fe 0.773 0.773 Santiago 0.852 0.749

Table S3: Genetic data summary table

P A AE HHO E (FST) H M H M H M H M H M Española 79 93 2.4 3.4 1.7 2.0 0.22 0.34 0.26 0.35 0.075 0.002** 0.010** 0.001** ns Fernandina 100 86 5.3 5.6 3.2 3.1 0.52 0.53 0.60 0.57 0.041 ns ns ns * Genovesa 100 64 4.3 2.3 2.0 1.6 0.32 0.20 0.34 0.25 0.052 0.03* 0.042* ns * Isabela 93 100 5.6 7.6 3.1 3.2 0.43 0.58 0.58 0.61 0.030 ns 0.003** ns * Marchena 100 71 3.6 2.2 2.2 1.8 0.36 0.24 0.42 0.28 0.056 0.002** ns 0.004** ns Pinta 93 100 3.5 3.5 2.1 2.2 0.36 0.42 0.38 0.40 0.018 ns ns ns ns Pinzón 100 100 5.7 5.6 3.0 2.8 0.45 0.49 0.60 0.57 0.010 ns ns ns ns San Cristóbal 100 100 5.6 5.9 2.8 2.9 0.42 0.53 0.53 0.56 0.000 ns ns ns ns Santa Cruz 100 93 4.3 7.7 3.1 3.4 0.37 0.64 0.56 0.62 0.044 ns 0.010** ns ns Santa Fe 93 93 3.9 3.5 2.4 2.3 0.42 0.42 0.46 0.42 0.050 ns ns ns ns Santiago 100 100 4.4 7.6 2.6 3.2 0.45 0.62 0.53 0.64 0.073 ns ns ns *

P = % polymorphic loci; A = Average alleles per locus; AE = Allelic richness; HO = Observed heterozygosity; HE = Expected heterozygosity; = Weir & Cockerham theta calculated between time points within a single population; P values are indicated by * (<0.05) or ** (<0.01)

Extinction Dynamics in Populations of Darwin’s Finches

Heather L. Farrington1*, and

Kenneth Petren1

1 Department of Biological Sciences; University of Cincinnati; Cincinnati OH 45221-0006

* Correspondence: [email protected]; +001 513-556-9719

Abstract

There is a great deal of debate over the role of genetics in the process of extinction.

Regardless of the direct impact of genetic traits, substantial genetic changes such as reduced allelic diversity and heterozygosity should occur as populations decline prior to extinction.

Therefore, many studies routinely assess population size and viability based on diversity at neutral genetic markers. To test the effectiveness of predicting population extinction based on genetic variation, we used museum skins of Darwin’s finches collected ~100 years ago to compare populations that are currently extinct to those that have persisted since the time of sample collection. Microsatellite genotypes from 16 loci were used to compare genetic diversity parameters between extinct and extant pairs of similar populations within species. We found only one instance where genetic diversity was reduced in the now extinct population when compared to an extant counterpart. In most cases, genetic diversity was greater in extinct populations when compared to extant. This study demonstrates the difficulty of evaluating population status and extinction risk based on genetic data alone, especially when environmental stochasticity is high and populations are fragmented and exchange low levels of immigrants.

Migration into declining populations may function to mask the genetic erosion expected as populations decline to extinction.

Introduction

There is a long-standing debate over the relative importance of genetic factors in estimating population extinction risk (Soulé and Mills 1992). Lande (1988) supported the notion that stochastic demographic changes such as bottlenecks, population densities, sex ratios and age structures, are of more immediate concern in conservation management, but that genetic factors could play a decisive role in determining the long-term persistence and adaptability of a population (Lande and Shannon 1996). Although extinction is, by definition, a demographic process, with the end result being a population size of zero, there is a growing body of evidence supporting the role of genetics in population extinctions (Frankham 2005). Reductions in population size increase the probability of inbreeding, which in turn increase losses of rare alleles due to drift, decrease heterozygosity, and increase the likelihood of expression of deleterious recessive mutations in homozygotes (Keller and Waller 2002). Decreases in heterozygosity have been linked to reduced population fitness, which reduces reproductive output and/or juvenile and adult survival (Keller and Waller 2002; Britten 1996; David 1998;

Hansson and Westerberg 2002), further lowering population numbers, and increasing the genetic impacts on the population. Once a population enters this cycle, called the “extinction vortex”

(Gilpin and Soulé 1986), it is unlikely to recover without influx of new alleles by migration from other populations (Keller and Waller 2002), natural mutations to create new alleles, or careful management to reduce inbreeding and increase individual reproduction and survival (Gautschi et al. 2003).

Regardless of the direct contribution that genetics plays in extinction, recent studies have shown that genetic changes occur very rapidly in declining populations, and that populations are seldom driven to extinction before substantial genetic changes can be detected (Spielman et al.

2004). Therefore, the use of genetic data in population viability estimates is rapidly increasing, with levels of current genetic diversity playing a growing role in population assessment (Brook et al. 2002; Frankham 2005; O’Grady et al. 2006). However, it is uncertain how reliable the use of genetic data is in the prediction of extinction risk.

When evaluating genetic diversity in threatened or endangered species, populations are often compared to non-threatened sister taxa as a reference point for potential genetic diversity

(Spielman et al. 2004). Furthermore, the ultimate fate of the population of interest is currently unknown because it is not possible to verify that extinction will or will not occur within a specified timeframe. There have been several examples of species persisting over extended time periods despite low genetic diversity (i.e. Johnson et al. 2009; Ardern and Lambert 1997); therefore, reduced genetic diversity is not a guarantee for imminent extinction. Additionally, human intervention on behalf of endangered and threatened species may artificially prolong the persistence of a population and alter natural patterns of extinction.

As habitat fragmentation becomes more prevalent across landscapes, the evolutionary interactions among populations must also be considered when evaluating population sustainability (Wright 1940; Andrewartha and Birch 1954; Levins 1970). Metapopulation studies have demonstrated the importance of gene flow to maintain genetic diversity within individual populations, and the complexity of extinction and recolonization dynamics in these systems (Hanski and Gaggiotti 2004). A better understanding of metapopulations is critical to conservation management efforts in fragmented populations for the development of more effective species monitoring practices.

Over the past century, resident human populations and tourism in the Galápagos Islands have increased exponentially, along with the introduction of non-native species. In addition to

the increases in habitat disturbance and alteration (Watson et al. 2009), diseases such as avian pox and plasmodium parasites have been introduced (Wikelski et al. 2004; Kleindorfer &

Dudaniec 2006; Levin et al. 2009), and an introduced nest parasite, Philornis downsi, are negatively impacting the endemic bird communities (Dudaniec & Kleindorfer 2006; Dudaniec et al. 2007). Based on research of extinction patterns on other remote islands, there is a strong expectation that Galápagos bird communities should be in decline (Blackburn et al. 2004;

Steadman 2006; Pratt et al. 2009). Although there have been no known bird species extinctions in the Galápagos, current population level extinction rates are approximately 100 times greater than during the era prior to human colonization (Steadman 1986, 2006; Steadman et al. 1991).

Extinction events are notoriously difficult to verify without extensive targeted sampling effort; however, at least fourteen island finch populations are suspected to be effectively extinct based on recent field observations (Grant 1999; Grant et al. 2005). These extinction events have affected all of the major groups of Darwin’s finches, the ground, tree and warbler finches, and have occurred on islands that vary in size and degree of human disturbance (Fig. 1; Table 1).

Therefore, this system provides a unique opportunity to examine recent local extinction events in a fragmented landscape.

The species in the Darwin’s finch radiation exist as a series of metapopulations, a group of geographically isolated populations connected by low levels of gene flow (Hanski & Gilpin

1991). With the exception of the Cocos finch and two endangered populations in the Galápagos

(Camarhyncus heliobates and Camarhyncus pauper), each finch species is found on multiple islands within the archipelago. Local extinction and recolonization events are a natural part of metapopulation dynamics. However, when population extinction rates begin to exceed recolonization rates, the entire metapopulation is in jeopardy (Hanski and Gaggiotti 2004).

The objective of this study was to examine the genetic characteristics of island populations of Darwin’s finches that have gone extinct within the past 100 years using museum specimens. The goal was to determine whether genetic characteristics were affected by population declines prior to extinction, and if genetic profiles could have been used to predict these extinction events. We tested the hypothesis that populations would show significantly reduced genetic variation (in terms of heterozygosity and allelic diversity) prior to extinction when compared to populations of the same species on ecologically similar islands that have persisted over time. This hypothesis is based on the assumption that decreasing population size over time will lead to inbreeding and loss of genetic variation in populations prior to extinction.

Methods

Samples and Laboratory Methods

The goal of this study was to compare genetic profiles of museum specimens collected approximately 100 years ago from presently extinct populations, and compare those to specimens collected at the same time period from populations that are still extant today. Islands chosen for comparison to extinct populations were influenced by the availability of museum specimens; however, an effort was made to compare extinct and extant populations from ecologically similar islands (based on elevation, area, etc.). A total of 215 historic tissue samples representing both extinct and extant populations were collected (Table 1; Fig. 1). Museum specimen tissue for DNA extraction was obtained from toe pad shavings (approximately

3 x 2 mm) of Darwin’s finches from the California Academy of Science, the British Museum of

Natural History and the American Museum of Natural History. The majority of specimens

(84%) were obtained from the California Academy of Science Galápagos expedition

(1905-1906) and Rollo Beck’s collections from previous expeditions in the late 1890’s. We also

included six genotypes from samples collected by Charles Darwin during his 1835 visit to the

Galápagos during the Beagle voyage (Petren et al 2010).

Museum samples were stored and processed in a room dedicated to ancient DNA work, and separated from any modern specimens to avoid contamination. All equipment and work area surfaces were UV irradiated prior to and after each use. Work areas were frequently bleached, and access to the work space was restricted. DNA was extracted from museum specimens using

QBiogene GeneClean Ancient DNA kits following the manufacturer protocol. Extracted DNA was eluted to a total volume of approximately 50 μL. Blank extractions (prepared with no tissue) were periodically processed to check reagents for contamination.

Sixteen microsatellite loci (14 autosomal and two sex-linked) previously developed for

Darwin’s finches (Petren 1998) were used to obtain genotype information from historic specimens. As amplification success declines rapidly with fragment size in degraded genetic samples (Sefc et al. 2003), the original primers were modified to generate shorter PCR products

(Petren et al. 2010). Total DNA was subjected to PCR in MultiPlex reactions (four loci per reaction with differing color fluorescent dyes) to increase genotyping efficiency and to conserve extracted template DNA. Negative control PCR reactions were also run with each batch of reactions prepared. PCR amplifications were performed in a total volume of 15 μL containing

QIAGEN multiplex PCR master mix, 0.30 μM of primers, and 1 μL of extracted DNA under the following conditions: an initial denaturation step at 95°C for 15 min, followed by 40 cycles of

30 s at 94°C, 1 min 30 s at 52°, and 1 min 30 s at 72°, and a final extension step of 72°C for

10 min. PCR reactions for historic samples were run in triplicate to reduce allelic dropout rate and genotyping error. Previous studies have shown that increasing the number of independent

PCR replicates (up to six) does not significantly increase the quality of the data set for samples

collected on the Beck and 1906 expeditions (Farrington and Petren in prep). PCR products were analyzed by fragment analysis, with a LIZ-labelled size standard, on an Applied Biosystems

3730xl DNA Analyzer at the Cornell University Life Sciences Core Laboratories Center.

Sample results were genotyped by hand with the aid of GENEMAPPER software (Applied

Biosystems). Genotype traces were evaluated without knowledge of population origin to limit scoring bias. Individual museum specimens with less than 50% genotype recovery across the

16 loci were excluded from analyses.

Genetic Analysis

Genetic analyses were based on data from the 14 autosomal microsatellite loci. GenAlex

(Peakall and Smouse 2006) and FSTAT (Goudet 1995) were used to calculate basic genetic summary statistics for each population, including allelic richness (AE), and observed (Ho) and expected (He) heterozygosities. Alleleic richness was calculated using a rarefaction method with a minimum sample size of two individuals (FSTAT; Goudet 1995). Genetic analyses were also conducted for modern populations using existing microsatellite data sets to obtain average, minimum and maximum values for these diversity measures in current populations. GENETIC

DATA ANALYSIS (GDA, version 1.0; Lewis and Zaykin 2001) was used to perform an exact test (Guo and Thompson 1992) with sequential Bonferroni correction (Rice 1989) to determine which loci deviated significantly from Hardy-Weinberg proportions. Mean values for observed and expected heterozygosity and allelic variation were compared between extinct and extant population pairs using Wilcoxon paired samples tests.

Principal coordinates analyses (PCA) were also conducted for each species with both modern and historic genotypes to examine the distinctiveness of extinct populations compared to current populations.

The BOTTLENECK (Cornuet and Luikart 1996) program was used to test each historic population for evidence of a recent population bottleneck. A two-phase mutation model (with

80% step-wise) was used since this is more appropriate for microsatellite data than a strict step- wise model (Luikart and Cornuet 1996).

Results

Of the 3010 historic individual x locus autosomal genotypes possible, 82% were recovered for this study. Genotyping success varied greatly by locus (66% - 97%), with six loci falling below 80% recovery. Exclusion of these six loci increased the genotyping success rate to

89%. Previous studies have estimated allelic dropout to be 20-26% for Darwin’s finch specimens from the California Academy of Science collection (Farrington and Petren in prep;

Petren et al. in prep). Twenty of the 224 total autosomal locus/population combinations were out of Hardy-Weinberg equilibrium after Bonferroni correction. A single locus (Gf8), accounted for

25% of these deviations, while there was no apparent pattern of deviation among the remaining loci and populations.

For clarity in the results and discussion, historic populations (those with data generated from museum specimens) will be referred to as “extinct” for populations no longer present on a given island, or “reference” for populations that are currently extant and chosen for comparisons at historic time points. Each extinct population was compared to a reference population of the same species from an ecologically similar island. Data generated from recently collected blood samples in the field (within the past 10-15 years) will be referred to as “modern” populations.

Genetic diversity measures were calculated for modern populations to determine current levels of genetic diversity for various island populations of each species.

We compared allelic richness and observed and expected heterozygosity based on historic samples from extinct and reference populations within each species to determine if populations had reduced genetic diversity prior to extinction. Only one population comparison,

Platyspiza crassirostris, had significantly lower allelic richness (P = 0.01) and expected heterozygosity (P = 0.04) values for the extinct population when compared to its reference population. Although the difference was not significant for observed heterozygosity, this value for the extinct population was lower than in any modern population of this species (Table 2).

All of the remaining significant tests indicated that population genetic diversity measures in extinct populations were higher than those for reference populations. For the G.magnirostris comparison between the extinct San Cristobal and reference Pinta populations, all three genetic diversity measures were significantly higher in the extinct population (P < 0.05), with values for allelic richness and expected heterozygosity well above the range for modern populations. The extinct San Cristobal population tested positive for a population bottleneck (P = 0.01), despite its high genetic diversity, suggesting a very recent bottleneck event. The other G.magnirostris comparison, between the extinct Darwin and reference Genovesa populations, revealed no significant differences. However, nearly all the historic genetic diversity values for Darwin and

Genovesa G. magnirostris were below levels of current populations, as might be expected from very small, peripheral islands with low population numbers (Table 2). The historic reference

Genovesa population also tested positive for a population bottleneck (P = 0.02).

All remaining comparisons of genetic diversity measures between historic extinct and reference populations had at least one value that was significantly higher in the extinct population. Most notably, the extinct Camarhynchus psittacula population from Floreana had higher genetic diversity across all three measures (P < 0.05) when compared to its historic

reference population, as well as higher values compared to modern populations (Table 2). The extinct G. difficilis population from Santa Cruz also had higher allelic richness (P = 0.01) and expected heterozygosity (P < 0.01) when compared to its historic reference population and modern population values. The significant difference in observed heterozygosity (P < 0.01) noted for the historic extinct and reference populations of G. fortis was most likely due to the unusually low value in the reference population used for comparison. The remaining historic population comparisons, C. fusca and G. scandens, had significantly higher values for allelic richness and observed heterozygosity, respectively, for extinct populations when compared to corresponding reference populations (Table 2).

We conducted PCA analyses to, first, evaluate the genetic distinctiveness of extinct populations and, second, to verify that reference populations did not change substantially over the past 100 years. PCA plots included both historic extinct and reference populations, as well as all available genetic data from modern populations of each species. PCA analyses suggested that extinct Certhidea fusca and Geospiza difficilis populations were genetically distinct from modern populations (Figure 2). Some divergence was also noted in the extinct Platyspiza crassirostris population. Historic G.magnirostris populations from Darwin and Wolf were somewhat distinct from other populations of this species. This is not surprising due to the isolation of these islands from the rest of the archipelago.

Discussion

The general pattern revealed from these data is contrary to our predictions. In most cases, genetic diversity measures were greater, often significantly so, in populations that are now extinct when compared to their extant counterparts a century ago. Only one of eight population comparisons in Darwin’s finches, that of Platyspiza crassirostris, showed the expected pattern of

lower genetic diversity in a population prior to extinction when compared to a reference population from the same point in history. Due to the current rates of avian population extinctions in the Galápagos, combined with increasing habitat disturbance and invasive species, it seems apparent that Darwin’s finch populations are in a state of decline. However, most samples collected 100 years ago gave no indication that populations were headed toward extinction; therefore, historic genetic information did not accurately reflect extinction risk in

Darwin’s finch populations over the past century. This result has significant implications for conservation since genetic diversity measures are commonly used for population monitoring.

To further investigate our unexpected results, we must first examine the overall quality of the data set. When amplifying DNA from historic specimens, there is a much higher risk of genotyping error than with modern specimens collected from fresh tissues, due to degradation of genetic material over time (Axelsson et al. 2008). Previous studies have quantified allelic dropout, the misidentification of a heterozygote as a homozygote, to occur in 20-26% of PCR reactions for museum specimens collected in the early 1900s (Petren et al. 2010; Farrington and

Petren ms). Triplicate replication of each genotype, as performed for this data set, greatly reduces the likelihood of missing a second allele in a heterozygote. Furthermore, the samples used for comparison in this study are all historic specimens, therefore, the probability of genotyping errors should be relatively uniform across loci and individuals, introducing no obvious bias in data quality across populations. Sample sizes used in these analyses were respectable and similar between extinct and reference populations in most cases, eliminating sampling effort as a bias for genetic diversity estimates. Also, the large number of loci examined increases the quality of the genetic data. Based on the size of the data set and modified laboratory procedures used specifically for historic specimens, we are confident that our results

are not due to a low quality data set. We conclude there is little chance that bias introduced by genotyping of museum specimens could produce a pattern of higher genetic diversity in extinct populations.

Another important consideration is whether 100 years is a reasonable timeframe in which to utilize genetic data for prediction of extinction risk. A century represents roughly 25-30 generations in Darwin’s finches (average generation time of 3-5 years; Grant 1999). Although we do not know the exact timing of these extinction events, there is anecdotal evidence that several of the populations investigated here were extinct as early as the 1960s (Grant et al. 2005), potentially as few as 15 generations after the California Academy of Science (CAS) expedition in 1905. One hundred year time horizons are also commonly used in population viability analyses (IUCN; Mace and Lande 1991; Fagan et al. 2001; O’Grady et al. 2008; D’Elia and

McCarthy 2008). Based on the temporal proximity between sample collection and population extinction, we would expect to see some evidence of population decline in the form of reduced genetic variation in these populations.

The number of specimens collected on the CAS expedition can be used as a general, yet imperfect, estimate of relative abundance. Specimens from now extinct populations made up a smaller proportion of total number of specimens collected on a given island when compared to the reference populations in all comparisons conducted (Table 1). This result must be regarded with caution because many factors may account for sampling differences among species, but the pattern supports our initial expectations and appears to conflict with the patterns of past genetic diversity.

Before investigating possible scenarios which may explain our results, some background on the Darwin’s finch radiation is needed. An important characteristic of Darwin’s finches is

that most species exist as a series of fragmented populations on different islands, and these island populations often show locally adapted traits. Body size, beak size and beak shape are heritable traits (Grant 1999; Keller et al. 2001), and closely associated with foraging ecology and selection pressures (Grant 1999). Each island in the archipelago is unique in its combination of available resources, competing species, and environmental conditions, generating intense local selection pressures which lead to morphological divergence among island populations (Grant 1999). For example, Charles Darwin collected G. magnirostris specimens from the now extinct populations on Floreana and San Cristobal that had beak dimensions far larger than any other finch populations in the radiation (Grant 1999; Petren et al. 2010). Patterns of morphological divergence among populations are reflected in patterns of genetic divergence at presumably neutral loci (Petren et al. 2010).

Despite the patterns of morphological variation among island populations of several species, a growing body of evidence indicates that populations are connected by regular immigrant exchange and gene flow (Petren et al. 2005). Gene flow from various source populations can bring together unique combinations of genes that may serve as a catalyst for adaptive divergence under some conditions (Whitlock et al. 2000; Church & Taylor 2002).

Migrants can contribute genetic variation to a population, limiting the effects of inbreeding depression (Bouzat et al. 2009), and maintain genetic diversity, and therefore adaptive potential, in changing environments (Frankham 2003). Therefore, strong local selection maintains differences among populations despite gene flow between islands that would be expected to homogenize populations (Petren et al. 2005). The combined forces of local adaptation and metapopulation dynamics have led to unique genotypes and phenotypes in particular island

populations, and this novel genetic diversity is lost when a local extinction event occurs (Petren et al. 2010).

With population extinctions impacting several different Darwin’s finch species and occurring on a variety of islands, it is likely that an array of factors is responsible, including habitat disturbance, introduced species and disease, and stochastic environmental events. These factors, in combination with the metapopulation dynamics occurring in this system, may make genetic monitoring for population declines in these species difficult.

Rapid environmental change

An important assumption of viability analyses and efforts to predict persistence or extinction of a population is that environmental conditions are constant (Chisholm and Wintle

2007). However, in the Galápagos, the environment has changed dramatically over the past 100 years and is likely a major cause of the inability of genotypic information to predict extinction.

Human habitat alteration is likely to play a large role in population extinctions. Tourism in the

Galápagos has increased approximately 9% per year over the past 25 years, while the resident population more than doubled between 1990 and 2006. These increases have been accompanied by growing numbers of introduced species (Charles Darwin Foundation 2007). The island of

Floreana, which has the longest history of human habitation and disturbance, has had an unusually high occurrence of population extinctions over the past century (Grant et al. 2005).

However, the islands of Santa Cruz and San Cristobal currently have the largest human settlements.

Although increased human disturbance is certainly cause for concern at present, many of these extinction events are thought to have occurred prior to the recent exponential increases in

resident populations and tourism. In addition, many extinction events have occurred on uninhabited islands with little human disturbance (Grant 1999; Table 1).

Introduced diseases are also an imminent threat that could quickly drive a population to extinction, particularly when populations are naive to these threats (Atkinson and Samuel 2010;

LaPointe et al. 2009). Several extinction evens in Hawaiian forest birds have been attributed to introduced diseases such as avian pox and malaria (Smith et al 2006; van Riper and Scott 2001).

Avian malaria and other diseases have already been detected in endemic birds of the Galápagos

(Thiel et al. 2005), and movement of birds among islands can disperse pathogens between populations.

A recent study has proposed that avipox viruses were introduced to the Galápagos during the nineteenth century, and pox-like lesions were noted in the CAS expedition logs and on collected specimens (Parker et al. in review). Therefore, if populations declined after disease introduction, we should detect evidence of population bottlenecks or ongoing population declines from historic samples, but we did not.

Stochastic environmental change

Stochastic events can greatly affect both the short and long term persistence of a population (Melbourne and Hastings 2008), but are often not taken into account when evaluating population viability (Sæther and Engen 2004). In a relatively long-lived species with overlapping generations, there is a lag time between demographic and genetic changes in a population, especially if older, more genetically diverse individuals are still contributing to the gene pool. The length of this lag time is influenced by the rate of population decline and the lifespan of individuals (Gaggioti and Vetter 1999). Stochastic processes operating on very short time scales, may bring about rapid extinction with little or no genetic change in the population.

100

Extinction events in the finches could be related to El Niño/La Niña cycles that cause large fluctuations in population sizes from year to year (Grant 1999). El Niño years bring abundant rains to otherwise arid islands, stimulating high reproductive rates in the finches.

However, when dry La Niña conditions return, populations crash and breeding is often greatly reduced, or even eliminated, for several seasons (Grant et al. 2000). These fluctuations may affect demographic structure and sex ratios within populations, altering population growth rates, and therefore recovery, after population crashes. Recent studies have shown that El Niño/La

Niña events have been increasing in severity over time (Guilderson and Schrag 1998), and will become an increasing threat to these island populations.

A single El Niño/La Niña cycle could decimate a population; however, it is more likely that populations would be impacted by several of these cycles before extinction. Periodic “boom and bust” cycles erode genetic diversity over time (Vucetich & Waite 1999). Therefore, if the extinct populations examined in this study were subjected to frequent changes in population size, genetic evidence of decline should have been noted. In addition, large fluctuations in population size are assumed to be more pronounced on smaller peripheral islands of the archipelago, but extinction events are not biased toward these types of islands.

Metapopulation structure

Environmental factors are likely contributing to declines in Darwin’s finch populations; however, the question remains why historic populations showed little evidence of reduced genetic variation, and showed inflated levels of genetic diversity in many cases, prior to extinction. The answer might be found in the connectivity between populations in the archipelago.

101

In a metapopulation framework, migration into a small or declining population may help to maintain high levels of genetic variation in the population (Smith et al. 2006). This “genetic rescue” effect is enhanced the smaller and more inbred the resident population becomes due to heterosis of outbred individuals (Ingvarsson and Whitlock 2000). Evidence of genetic rescue has recently been found through cross-temporal genetic comparisons of historic and modern populations of warbler finches (Farrington and Petren in prep). Therefore, demographically the population may be declining, but the genetic signature may not reflect that decline.

This potential genetic “masking” of population decline is supported by the lack of reduced genetic diversity prior to extinction, and relative abundances of extinct populations at the time of the CAS expedition. Based on the history of natural and human-induced pressures imposed on these populations, we suspect that many of these populations were already in a state of decline, and should, therefore, have reduced genetic diversity. In addition, these extinct populations appeared to be smaller than their reference population counterparts, but their genetic diversity was much higher than expected based on relative abundance.

Recolonization

As local extinctions are a part of metapopulation dynamics, so too are recolonizations of unoccupied habitat patches. However, only a single colonization event has been documented in the Galápagos in recent history (Grant and Grant 1995). This leads us to ask why islands are not being recolonized after extinction events.

Previous studies have suggested that Darwin’s finches are capable of colonizing even the most remote islands in the archipelago, and that migration rates should be high enough to promote re-establishment of populations (Petren et al. 2005). The arrival and establishment of a

102

new population of G. magnirostris on the island of Daphne demonstrates that new habitats can be successfully colonized (Grant and Grant 1995).

Without any obvious barriers to recolonization of open habitats, why are extinct populations not being re-established? First, the quality of the habitat may have been reduced, particularly on human inhabited islands, rendering open patches unsuitable. Second, productivity in existing finch populations may be reduced overall due to habitat deterioration or disease, leading to a decline in total potential migrants in the metapopulation. Genetic methods for estimation of migration rates may not reflect a very recent reduction in overall migration rates (Bossart and Prowell 1998). Allee effects may also play a role in the successful colonization of an empty habitat patch. Immigrants to an island may not breed, and therefore initiate population growth, or stay in a habitat patch, if potential mates are difficult to find (Veit and Lewis 1996). Therefore, existing populations may be more likely to attract migrants than unoccupied habitats. Site fidelity may also limit recolonization of open habitat patches

(Matthiopoulos et al. 2005), and some finch species have shown preferential immigration to habitats similar to those in which they were raised (Tonnis et al. 2005).

Conclusion

Neutral genetic variation is increasingly being used to aid in population monitoring for threatened and endangered species. These markers are particularly useful for hard to census animals, and can be a helpful tool in management of captive breeding populations. As populations become increasingly fragmented over the landscape, it is critical to understand the within and between patch dynamics of a species system for effective conservation management.

With population extinctions impacting several different Darwin’s finch species and occurring on a variety of islands, it is likely that an array of factors is responsible, including habitat

103

disturbance, introduced species and disease, and stochastic environmental events. Infrequent recolonization after extinction suggests that habitat quality has deteriorated and/or complex recolonization dynamics may be at work, such that empty habitat patches are not perceived as suitable. Our results suggest that genetic monitoring of fragmented populations may be complicated further by migration. It appears that genetic monitoring would be most effective in situations involving steady population declines, stable environmental conditions, and no migration. However, these conditions are rarely met in natural systems. Further study is needed to understand the extinction dynamics within natural metapopulation systems, and develop more effective methods of monitoring these populations for evidence of decline.

Acknowledgements

We thank Terry Chesser and Joel Cracraft of the American Museum of Natural History, and John Dumbacher, Maureen Flannery, Douglas Long and Luis Baptista of the California

Academy of Science for access to valuable historical specimens. We thank the Galápagos

National Parks and Charles Darwin Research Station for field support. We thank K. Short, J.

Niedzwiecki, and E. Ristagno for lab and field assistance and anonymous reviewers for constructive comments. This work was partially supported by the National Science Foundation

(DEB-0317687 to K.P.), Sigma Xi, The American Ornithologists’ Union and the University of

Cincinnati University Research Council.

References

Andrewartha, H.G., Birch, L.C. 1954. The distribution and abundance of animals. University

of Chicago Press.

104

Ardern, S. L., and D. M. Lambert. 1997. Is the black robin in genetic peril? Molecular Ecology

6:21-28.

Atkinson, C. T., and M. D. Samuel. 2010. Avian malaria Plasmodium relictum in native

Hawaiian forest birds: epizootiology and demographic impacts on 'apapane Himatione

sanguinea. Journal of Avian Biology 41:357-366.

Axelsson, E., E. Willerslev, M. T. P. Gilbert, and R. Nielsen. 2008. The effect of ancient DNA

damage on inferences of demographic histories. Molecular Biology and Evolution

25:2181-2187.

Blackburn, T. M., P. Cassey, R. P. Duncan, K. L. Evans, and K. J. Gaston. 2004. Avian

extinction and mammalian introductions on oceanic islands. Science 305:1955-1958.

Bossart, J. L., and D. P. Prowell. 1998. Genetic estimates of population structure and gene flow:

limitations, lessons and new directions. Trends in Ecology & Evolution 13:202-206.

Bouzat, J. L., J. A. Johnson, J. E. Toepfer, S. A. Simpson, T. L. Esker, and R. L. Westemeier.

2009. Beyond the beneficial effects of translocations as an effective tool for the genetic

restoration of isolated populations. Conservation Genetics 10:191-201.

Britten, H. B. 1996. Meta-analyses of the association between multilocus heterozygosity and

fitness. Evolution 50:2158-2164.

Brook, B. W., D. W. Tonkyn, J. J. Q'Grady, and R. Frankham. 2002. Contribution of inbreeding

to extinction risk in threatened species. Conservation Ecology 6.

Charles Darwin Foundation, Parque Nacional Galápagos & Instituto Nacional de Galápagos.

2007. Galápagos Report 2006-2007. Charles Darwin Foundation, Puerto Ayora,

Galápagos, Ecuador.

105

Chisholm, R. A., and B. A. Wintle. 2007. Incorporating landscape stochasticity into population

viability analysis. Ecological Applications 17:317-322.

Church, S. A., and D. R. Taylor. 2002. The evolution of reproductive isolation in spatially

structured populations. Evolution 56:1859-1862.

Cornuet, J. M., and G. Luikart. 1996. Description and power analysis of two tests for detecting

recent population bottlenecks from allele frequency data. Genetics 144:2001-2014.

David, P. 1998. Heterozygosity-fitness correlations: new perspectives on old problems. Heredity

80:531-537.

D'Elia, J., and S. McCarthy. 2010. Time Horizons and Extinction Risk in Endangered Species

Categorization Systems. Bioscience 60:751-758.

Dudaniec, R. Y., B. Fessl, and S. Kleindorfer. 2007. Interannual and interspecific variation in

intensity of the parasitic fly, Philornis downsi, in Darwin's finches. Biological

Conservation 139:325-332.

Dudaniec, R. Y., and S. Kleindorfer. 2006. Effects of the parasitic flies of the genus Philornis

(Diptera : Muscidae) on birds. Emu 106:13-20.

Fagan, W. F., E. Meir, J. Prendergast, A. Folarin, and P. Karieva. 2001. Characterizing

population vulnerability for 758 species. Ecology Letters 4:132-138.

Farrington, H., K. Petren. In prep. A century of genetic change and metapopulation dynamics in

the Galápagos warbler finches (Certhidea)

Frankham, R. 2003. Genetics and conservation biology. Comptes Rendus Biologies 326:S22-

S29.

Frankham, R. 2005. Genetics and extinction. Biological Conservation 126:131-140.

106

Gaggiotti O.E., R.D. Vetter. 1999. Effect of life history strategy, environmental variability, and

overexploitation on the genetic diversity of pelagic fish populations. Canadian Journal of

Fisheries and Aquatic Sciences 56, 8:1376-1388.

Gautschi, B., J. P. Muller, B. Schmid, and J. A. Shykoff. 2003. Effective number of breeders and

maintenance of genetic diversity in the captive bearded vulture population. Heredity

91:9-16.

Gilpin, M. E., and M. E. Soule. 1986. Minimum viable populations: processes of species

extinction. Soule, M. E. (Ed.). Conservation Biology: The Science of Scarcity and

Diversity. Xiii+584p. Sinauer Associates, Inc.: Sunderland, Mass., USA. Illus. Paper.

Maps:19-34.

Goudet, J. 1995. FSTAT (Version 1.2): A computer program to calculate F-statistics. Journal of

Heredity 86:485-486.

Grant, P. R. 1999. Ecology and Evolution of Darwin's Finches. Princeton University Press,

Princeton, New Jersey.

Grant, P. R., and B. R. Grant. 1995. The founding of a new population of Darwin's finches (vol

49, pg 229, 1995). Evolution 49:1307-1307.

Grant, P. R., B. R. Grant, L. F. Keller, and K. Petren. 2000. Effects of El Niño events on

Darwin's finch productivity. Ecology 81:2442-2457.

Grant, P. R., B. R. Grant, K. Petren, and L. F. Keller. 2005. Extinction behind our backs: the

possible fate of one of the Darwin's finch species on Isla Floreana, Galápagos. Biological

Conservation 122:499-503.

Guilderson, T. P., and D. P. Schrag. 1998. Abrupt shift in subsurface temperatures in the

Tropical Pacific associated with changes in El Nino. Science 281:240-243.

107

Guo, S. W., and E. A. Thompson. 1992. Performing the Exact Test of Hardy-Weinberg

Proportion for Multiple Alleles. Biometrics 48:361-372.

Hanski, I., and O. E. Gaggiotti. 2004. Ecology, Genetics, and Evolution of Metapopulations.

Elsevier Academic Press.

Hanski, I., and M. Gilpin. 1991. Metapopulation Dynamics - Brief-History and Conceptual

Domain. Biological Journal of the Linnean Society 42:3-16.

Hansson, B., and L. Westerberg. 2002. On the correlation between heterozygosity and fitness in

natural populations. Molecular Ecology 11:2467-2474.

Ingvarsson, P. K., and M. C. Whitlock. 2000. Heterosis increases the effective migration rate.

Proceedings of the Royal Society of London Series B-Biological Sciences 267:1321-

1326.

Johnson, J. A., R. E. Tingay, M. Culver, F. Hailer, M. L. Clarke, and D. P. Mindell. 2009. Long-

term survival despite low genetic diversity in the critically endangered Madagascar fish-

eagle. Molecular Ecology 18:54-63.

Keller, L. F., P. R. Grant, B. R. Grant, and K. Petren. 2001. Heritability of morphological traits

in Darwin's Finches: misidentified paternity and maternal effects. Heredity 87:325-336.

Keller, L. F., and D. M. Waller. 2002. Inbreeding effects in wild populations. Trends in Ecology

& Evolution 17:230-241.

Kleindorfer, S., and R. Y. Dudaniec. 2006. Increasing prevalence of avian poxvirus in Darwin's

finches and its effect on male pairing success. Journal of Avian Biology 37:69-76.

Lande, R. 1988. Genetics and demography in biological conservation. Science 241:1455-1460.

Lande, R., and S. Shannon. 1996. The role of genetic variation in adaptation and population

persistence in a changing environment. Evolution 50:434-437.

108

LaPointe, D. A., E. K. Hotmeister, C. T. Atkinson, R. E. Porter, and R. J. Dusek. 2009.

Experimental infection of Hawai'i 'Amakihi (Hemignathus virens) with West Nile virus

and competence of a co-occurring vector, Culex quinquefasciatus: potential impacts on

endemic Hawaiian avifauna. Journal of Wildlife Diseases 45:257-271.

Levin, II, D. C. Outlaw, F. H. Vargas, and P. G. Parker. 2009. Plasmodium blood parasite found

in endangered Galápagos penguins (Spheniscus mendiculus). Biological Conservation

142:3191-3195.

Levins, R. 1970. Extinction. Lecture Notes in Mathematics. 2:75-107.

Lewis, P. O., and D. Zaykin. 2001. Genetic Data Analysis: Computer Program for the Analysis

of Allelic Data.

Luikart, G., and J. M. Cornuet. 1998. Empirical evaluation of a test for identifying recently

bottlenecked populations from allele frequency data. Conservation Biology 12:228-237.

Mace, G. M., and R. Lande. 1995. Assessing extinction threats: Toward a reevaluation of IUCN

threatened species categories. Readings from Conservation Biology: Genes, populations,

and species:10-19.

Matthiopoulos, J., J. Harwood, and L. Thomas. 2005. Metapopulation consequences of site

fidelity for colonially breeding mammals and birds. Journal of Animal Ecology 74:716-

727.

Melbourne, B. A., and A. Hastings. 2008. Extinction risk depends strongly on factors

contributing to stochasticity. Nature 454:100-103.

Melbourne, B. A., and A. Hastings. 2008. Extinction risk depends strongly on factors

contributing to stochasticity. Nature 454:100-103.

109

O'Grady, J. J., B. W. Brook, D. H. Reed, J. D. Ballou, D. W. Tonkyn, and R. Frankham. 2006.

Realistic levels of inbreeding depression strongly affect extinction risk in wild

populations. Biological Conservation 133:42-51.

O'Grady, J. J., D. H. Reed, B. W. Brook, and R. Frankham. 2008. Extinction risk scales better to

generations than to years. Animal Conservation 11:442-451.

Patricia G. P., E. L. Buckles, H. Farrington, K. Petren, N. K. Whiteman, R. E. Ricklefs, J. L.

Bollmer, G. Jiménez-Uzcátegui. In review. 110 Years of Avipoxvirus on the Galápagos

Islands

Peakall, R., and P. E. Smouse. 2006. GENALEX 6: genetic analysis in Excel. Population genetic

software for teaching and research. Molecular Ecology Notes 6:288-295.

Petren, K. 1998. Microsatellite primers from Geospiza fortis and cross-species amplification in

Darwin's finches. Molecular Ecology 7:1782-1784.

Petren, K., H. Farrington, B. Fessl, H. Vargas, and A. A. Clack. in prep. A genetic signature of

impending extinction in Darwin's rarest finch.

Petren, K., H. Farrington, B. Fessl, H. Vargas, and A. A. Clack. in prep. A genetic signature of

impending extinction in Darwin's rarest finch.

Petren, K., P. R. Grant, B. R. Grant, A. A. Clack, and N. V. Lescano. 2010. Multilocus

genotypes from Charles Darwin's finches: biodiversity lost since the voyage of the

Beagle. Philosophical Transactions of the Royal Society B-Biological Sciences

365:1009-1018.

Petren, K., P. R. Grant, B. R. Grant, and L. F. Keller. 2005. Comparative landscape genetics and

the adaptive radiation of Darwin's finches: the role of peripheral isolation. Molecular

Ecology 14:2943-2957.

110

Pratt, T. K., C. T. Atkinson, P. C. Banko, J. D. Jacobi, and B. L. Woodworth. 2009. Conservation

biology of Hawaiian forest birds: implications for island avifauna. Yale University Press.

Rice, W. R. 1989. Analyzing Tables of Statistical Tests. Evolution 43:223-225.

Saether, B. E., and S. Engen. 2004. Stochastic population theory faces reality in the laboratory.

Trends in Ecology & Evolution 19:351-353.

Sefc, K. M., R. B. Payne, and M. D. Sorenson. 2003. Microsatellite amplification from museum

feather samples: Effects of fragment size and template concentration on genotyping

errors. Auk 120:982-989.

Smith, J. N. M., L. F. Keller, A. B. Marr, P. Arcese. 2006. Conservation and biology of small

populations: the song sparrow of Mandarte Island. Oxford University Press.

Smith, K. F., D. F. Sax, and K. D. Lafferty. 2006. Evidence for the role of infectious disease in

species extinction and endangerment. Conservation Biology 20:1349-1357.

Soulé, M.E., Mills, L.S. 1992. Conservation genetics and conservation biology: a troubled

marriage. In: Species and Ecosystem Conservation. Sandlund, O.T., Hindar, K., Brown,

A.H.D. (eds.). Scandinavian University Press, Oslo.

Spielman, D., B. W. Brook, and R. Frankham. 2004. Most species are not driven to extinction

before genetic factors impact them. Proceedings of the National Academy of Sciences of

the United States of America 101:15261-15264.

Steadman, D. W. 1986. Holocene vertebrate fossils from Isla Floreana Galápagos Ecuador.

Smithsonian Contributions to Zoology:I-IV, 1-104.

Steadman, D. W. 2006. Extinction & Biogeography of Tropical Pacific Birds. Extinction &

Biogeography of Tropical Pacific Birds.

111

Steadman, D. W., T. W. Stafford, D. J. Donahue, and A. J. T. Jull. 1991. Chronology of holocene

vertebrate extinction in the Galápagos Islands Quaternary Research 36:126-133.

Thiel, T., N. K. Whiteman, A. Tirape, M. I. Baquero, V. Cedeno, T. Walsh, G. J. Uzcategui, and

P. G. Parker. 2005. Characterization of canarypox-like viruses infecting endemic birds in

the Galápagos Islands. Journal of Wildlife Diseases 41:342-353.

Tonnis, B., P. R. Grant, B. R. Grant, and K. Petren. 2005. Habitat selection and ecological

speciation in Galápagos warbler finches (Certhidea olivacea and Certhidea fusca).

Proceedings of the Royal Society B-Biological Sciences 272:819-826. van Riper, C., III, and J. M. Scott. 2001. Limiting factors affecting Hawaiian native birds.

Studies in Avian Biology:221-233.

Veit, R. R., and M. A. Lewis. 1996. Dispersal, population growth, and the Allee effect:

Dynamics of the house finch invasion of eastern North America. Am. Nat. 148:255-274.

Vucetich, J. A., and T. A. Waite. 1999. Erosion of heterozygosity in fluctuating populations.

Conservation Biology 13:860-868.

Watson, J., M. Trueman, M. Tufet, S. Henderson, and R. Atkinson. 2009. Mapping terrestrial

anthropogenic degradation on the inhabited islands of the Galápagos Archipelago. Oryx

44:79-82.

Whitlock, M. C., P. K. Ingvarsson, and T. Hatfield. 2000. Local drift load and the heterosis of

interconnected populations. Heredity 84:452-457.

Wikelski, M., J. Foufopoulos, H. Vargas, and H. Snell. 2004. Galápagos birds and diseases:

Invasive pathogens as threats for island species. Ecology and Society 9.

Wright, S. 1940. Breeding structure of populations in relation to speciation. American

Naturalist 74:232-248.

112

Tables and Figures

Table 1: Darwin’s finch populations used for analysis.

Table 2: Summary genetic data; allelic richness (AE), observed heterozygosity (Ho), and expected heterozygosities (He) calculated as means across 14 autosomal loci. Grand means and ranges for modern populations are presented for comparison. P values are indicated by * (<0.05) or ** (<0.01).

Figure 1: Galápagos map indicating probable extinction events of island populations.

Figure 2 a-g: PCA plots of extinct and extant population pairs plotted with modern populations of the same species. X = Extinct population; R = Reference population; MOD = modern samples from reference population.

113

Island Island CAS Expedition Info # Area Elevation Human Species Number % CAS 1 2 Island Status Island Samples (km ) (m) Settlement per Island Collected Collected2 Certhidea (warbler Ext Floreana 16 171 550 Yes 9 53 4.7 finch) Ref Pinta 12 60 635 No 9 10 6.8 Geospiza difficilis Ext Santa 24 904 870 Yes 10 33 9.1 (sharp-beaked Cruz ground finch) Ref Santiago 14 572 920 Intermittent 10 46 20.7 G. fortis (medium Ext Espanola 15 58 220 No 4 15 4.5 ground finch) Ref Floreana 8 171 550 Yes 9 361 32.6 Camarhynchus Ext Floreana 13 171 550 Yes 9 9 0.8 psittacula* (large Ref Marchena 14 130 340 No 7 25 30.5 tree finch) G. scandens (cactus Ext Pinzon 10 18 440 No 7 13 8.6 finch) Ref Santa Fe 13 24 255 No 6 47 35.6 G. magnirostris Ext San 6 552 730 Yes 7 0 0** (large ground finch) Cristobal Ref Pinta 15 60 635 No 8 36 24.7 Platyspiza Ext Floreana 14 171 550 Yes 9 48 4.3 crassirostris* Ref San 15 552 730 Yes 7 27 5.7 (vegetarian finch) Cristobal G. magnirostris* Ext Darwin 11 2 168 No 4 3 12 (large ground finch) Ref Genovesa 15 17 65 No 4 10 20 1 Ext = Extinct; Ref = Reference (currently extant) populations 2 Using CAS 1905-1906 expedition data only; proportion of the total finches collected for a specified island identified as the species of interest * Likely extinct based on recent field observations ** Collected by Charles Darwin in 1835; population extinct at the time of the CAS expedition 114

Table 2:

Ae Ho He Species C. Psittacula Extinct Floreana 2.28 0.5 0.52 Extant Marchena 1.83 0.23 0.37 p value 0.013* 0.002** 0.013*

Modern Pops Mean 2.0 0.45 0.4 Range 1.7-2.2 0.36-0.49 0.27-0.47 N 5 5 5

Species Certhidea Extinct Floreana 2.23 0.48 0.52 Extant Pinta 1.84 0.36 0.38 p value 0.03* 0.199 0.060

Modern Pops Mean 2.1 0.46 0.48 Range 1.5-2.6 0.2-0.64 0.25-0.64 N 11 11 11

Species G. fortis Extinct Espanola 2.82 0.64 0.72 Extant Floreana 2.76 0.39 0.66 p value 0.76 0.002** 0.101

Modern Pops Mean 2.8 0.69 0.71 Range 2.6-3.0 0.60-0.76 0.67-0.77 N 11 11 11

Species G. scandens Extinct Pinzon 2.73 0.45 0.65 Extant SantaFe 2.4 0.3 0.58 p value 0.07 0.009** 0.426

Modern Pops Mean 2.7 0.66 0.64 Range 2.3-3.0 0.57-0.72 0.48-0.77 N 8 8 8

Species G. difficilis Extinct SantaCruz 2.67 0.55 0.67 Extant Santiago 2.28 0.4 0.53 p value 0.011* 0.091 0.003**

Modern Pops Mean 2.1 0.49 0.48 Range 1.8-2.4 0.36-0.62 0.36-0.6 N 6 6 6

115

Ae Ho He Species Platyspiza Extinct Floreana 1.97 0.35 0.43 Extant SanCristobal 2.31 0.38 0.53 p value 0.013* 0.685 0.040*

Modern Pops Mean 2.1 0.47 0.44 Range 1.8-2.5 0.37-0.54 0.35-0.59 N 6 6 6

Species G. magnirostris Extinct SanCristo 3.16 0.59 0.76 Extant Pinta 2.47 0.4 0.6 p value 0.005** 0.020* 0.009**

Species G. magnirostris Extinct Darwin 2.23 0.34 0.51 Extant Genovesa 2.4 0.46 0.58 p value 0.23 0.132 0.326

Modern Pops Mean 2.7 0.64 0.64 Range 2.4-2.9 0.57-0.71 0.60-0.68 N 10 10 10

116

Fig. 1:

117

Fig. 2 a) Difficilis

118

b) Certhidea

119

c) Fortis

120

d) Platyspiza

121

e) Psittacula

122

f) Scandens

123

g) Magnirostris

124

Table S1: Museum specimen sources, accession numbers and collection dates. * = Specimen excluded from analysis due to <50% recovery of genotype data. CAS = California Academy of

Science; ANHM = American Natural History Museum; BMNH = British Museum of Natural

History

Species Museum Mus. # Date Island Camarhynchus psittacula CAS 1464 1906 Floreana Camarhynchus psittacula CAS 86752 1898 Floreana Camarhynchus psittacula CAS 8395 1906 Floreana Camarhynchus psittacula CAS 8398 1906 Floreana Camarhynchus psittacula CAS 8390 1906 Floreana Camarhynchus psittacula CAS 8396 1906 Floreana Camarhynchus psittacula CAS 8391 1906 Floreana Camarhynchus psittacula CAS 8397 1906 Floreana Camarhynchus psittacula BNHM 1899.9.1.438 1897 Floreana Camarhynchus psittacula AMNH 518008 1897 Floreana Camarhynchus psittacula AMNH 518010 1897 Floreana Camarhynchus psittacula AMNH 518017 1897 Floreana Camarhynchus psittacula AMNH 518009 1897 Floreana Camarhynchus psittacula CAS 8393* 1906 Floreana Camarhynchus psittacula CAS 8394* 1906 Floreana Camarhynchus psittacula CAS 8308 1906 Marchena Camarhynchus psittacula CAS 8312 1906 Marchena Camarhynchus psittacula CAS 8313 1906 Marchena Camarhynchus psittacula CAS 8314 1906 Marchena Camarhynchus psittacula CAS 8315 1906 Marchena Camarhynchus psittacula CAS 8317 1906 Marchena Camarhynchus psittacula CAS 8320 1906 Marchena Camarhynchus psittacula CAS 8326 1906 Marchena Camarhynchus psittacula CAS 8280 1906 Marchena Camarhynchus psittacula CAS 8284 1906 Marchena Camarhynchus psittacula CAS 8301 1906 Marchena Camarhynchus psittacula BNHM 1899.9.1.451 1897 Marchena Camarhynchus psittacula BNHM 1899.9.1.450 1897 Marchena Camarhynchus psittacula BNHM 1885.12.14.548 1868 Marchena Certhidea olivacea CAS 4883 1906 Pinta Certhidea olivacea CAS 4884 1906 Pinta Certhidea olivacea CAS 6765 1906 Floreana Certhidea olivacea CAS 4654 1906 Floreana Certhidea olivacea CAS 4656 1906 Floreana Certhidea olivacea CAS 4860 1906 Floreana Certhidea olivacea CAS 4865 1906 Floreana Certhidea olivacea CAS 4868 1906 Floreana Certhidea olivacea CAS 4874 1906 Floreana Certhidea olivacea CAS 4876 1906 Floreana Certhidea olivacea CAS 4642 1906 Floreana

125

Species Museum Mus. # Date Island Certhidea olivacea CAS 4795 1906 Floreana Certhidea olivacea CAS 4864 1906 Floreana Certhidea olivacea CAS 4666 1898 Floreana Certhidea olivacea CAS 4647 1898 Floreana Certhidea olivacea CAS 4658 1898 Floreana Certhidea olivacea CAS 4649 1898 Floreana Certhidea olivacea CAS 4660 1898 Floreana Certhidea olivacea CAS 81116 1899 Pinta Certhidea olivacea CAS 81118 1899 Pinta Certhidea olivacea CAS 4893 1906 Pinta Certhidea olivacea CAS 4672 1906 Pinta Certhidea olivacea CAS 4881 1906 Pinta Certhidea olivacea CAS 4895 1906 Pinta Certhidea olivacea CAS 4896 1906 Pinta Certhidea olivacea CAS 4669 1906 Pinta Certhidea olivacea CAS 4670 1906 Pinta Certhidea olivacea CAS 4882 1906 Pinta Geospiza difficilis CAS 6879 1906 Santa Cruz Geospiza difficilis CAS 6880 1906 Santa Cruz Geospiza difficilis CAS 6884 1906 Santa Cruz Geospiza difficilis CAS 6885 1906 Santa Cruz Geospiza difficilis CAS 6922 1906 Santa Cruz Geospiza difficilis CAS 6924 1906 Santa Cruz Geospiza difficilis CAS 6872 1906 Santa Cruz Geospiza difficilis CAS 6881 1906 Santa Cruz Geospiza difficilis CAS 6874 1906 Santa Cruz Geospiza difficilis CAS 6875 1906 Santa Cruz Geospiza difficilis CAS 6917 1906 Santa Cruz Geospiza difficilis CAS 6918 1906 Santa Cruz Geospiza difficilis CAS 6928 1906 Santa Cruz Geospiza difficilis CAS 6868 1906 Santa Cruz Geospiza difficilis CAS 6869 1906 Santa Cruz Geospiza difficilis CAS 6887 1906 Santa Cruz Geospiza difficilis CAS 6883 1906 Santa Cruz Geospiza difficilis CAS 6925 1906 Santa Cruz Geospiza difficilis CAS 6919 1906 Santa Cruz Geospiza difficilis CAS 6881 1906 Santa Cruz Geospiza difficilis CAS 6892 1905 Santiago Geospiza difficilis CAS 6894 1905 Santiago Geospiza difficilis CAS 6901 1905 Santiago Geospiza difficilis CAS 6908 1905 Santiago Geospiza difficilis CAS 6914 1905 Santiago Geospiza difficilis CAS 6934 1905 Santiago Geospiza difficilis CAS 6937 1905 Santiago Geospiza difficilis CAS 6939 1905 Santiago Geospiza difficilis CAS 6942 1905 Santiago Geospiza difficilis CAS 6895 1905 Santiago Geospiza difficilis CAS 6911 1905 Santiago Geospiza difficilis CAS 6891 1905 Santiago 126

Species Museum Mus. # Date Island Geospiza difficilis CAS 6940 1906 Santiago Geospiza difficilis CAS 6938 1906 Santiago Geospiza fortis CAS 5536 1906 Espanola Geospiza fortis CAS 6761 1906 Espanola Geospiza fortis CAS 5545 1906 Espanola Geospiza fortis CAS 6152 1906 Espanola Geospiza fortis CAS 6157 1906 Espanola Geospiza fortis CAS 6179 1906 Espanola Geospiza fortis CAS 5538 1906 Espanola Geospiza fortis CAS 6162 1906 Espanola Geospiza fortis CAS 5503 1905 Espanola Geospiza fortis CAS 6194 1905 Espanola Geospiza fortis CAS 6285 1905 Espanola Geospiza fortis CAS 6123 1906 Espanola Geospiza fortis CAS 6760 1906 Espanola Geospiza fortis CAS 5556 1905 Espanola Geospiza fortis CAS 5095 1905 Espanola Geospiza fortis CAS 6051 1905 Floreana Geospiza fortis CAS 6319 1905 Floreana Geospiza fortis CAS 6344 1905 Floreana Geospiza fortis CAS 6345 1905 Floreana Geospiza fortis CAS 6347 1905 Floreana Geospiza fortis CAS 5269 1905 Floreana Geospiza fortis CAS 5197 1905 Floreana Geospiza fortis CAS 5244 1905 Floreana Geospiza fortis CAS 6342* 1905 Floreana Geospiza fortis CAS 6346* 1905 Floreana Geospiza fortis CAS 5239* 1905 Floreana Geospiza fortis CAS 5228* 1905 Floreana Geospiza magnirostris AMNH 516977 ~1900 Darwin Geospiza magnirostris AMNH 516986 ~1900 Darwin Geospiza magnirostris AMNH 516989 ~1900 Darwin Geospiza magnirostris AMNH 516976 ~1900 Darwin Geospiza magnirostris AMNH 516978 ~1900 Darwin Geospiza magnirostris AMNH 516982 ~1900 Darwin Geospiza magnirostris AMNH 516985 ~1900 Darwin Geospiza magnirostris AMNH 516981 ~1900 Darwin Geospiza magnirostris AMNH 516979 ~1900 Darwin Geospiza magnirostris AMNH 516988 ~1900 Darwin Geospiza magnirostris AMNH 516984 ~1900 Darwin Geospiza magnirostris AMNH 517047* ~1900 Darwin Geospiza magnirostris CAS 5972 1906 Genovesa Geospiza magnirostris CAS 80471 1899 Genovesa Geospiza magnirostris CAS 80472 1899 Genovesa Geospiza magnirostris CAS 80473 1899 Genovesa Geospiza magnirostris CAS 80477 1899 Genovesa Geospiza magnirostris CAS 80478 1899 Genovesa Geospiza magnirostris CAS 5101 1906 Genovesa Geospiza magnirostris CAS 5119 1906 Genovesa 127

Species Museum Mus. # Date Island Geospiza magnirostris CAS 5312 1906 Genovesa Geospiza magnirostris CAS 5958 1906 Genovesa Geospiza magnirostris CAS 5960 1906 Genovesa Geospiza magnirostris CAS 5963 1906 Genovesa Geospiza magnirostris CAS 5979 1906 Genovesa Geospiza magnirostris CAS 5126 1906 Genovesa Geospiza magnirostris CAS 80479 1899 Genovesa Geospiza magnirostris CAS 5123 1906 Pinta Geospiza magnirostris CAS 5127 1906 Pinta Geospiza magnirostris CAS 5131 1906 Pinta Geospiza magnirostris CAS 5135 1906 Pinta Geospiza magnirostris CAS 5139 1906 Pinta Geospiza magnirostris CAS 5145 1906 Pinta Geospiza magnirostris CAS 5961 1906 Pinta Geospiza magnirostris CAS 5977 1906 Pinta Geospiza magnirostris CAS 5987 1906 Pinta Geospiza magnirostris CAS 5990 1906 Pinta Geospiza magnirostris CAS 5993 1906 Pinta Geospiza magnirostris CAS 5970 1906 Pinta Geospiza magnirostris CAS 5975 1906 Pinta Geospiza magnirostris CAS 5981 1906 Pinta Geospiza magnirostris CAS 5971 1906 Pinta Geospiza magnirostris BNHM 1855.12.19.83 1835 San Cristobal Geospiza magnirostris BNHM 1837.2.21.396 1835 San Cristobal Geospiza magnirostris BNHM 1855.12.19.104 1835 San Cristobal Geospiza magnirostris BNHM 1855.12.19.81 1835 San Cristobal Geospiza magnirostris BNHM 1855.12.19.80 1835 San Cristobal Geospiza magnirostris BNHM 1855.12.19.113 1835 San Cristobal Geospiza scandens CAS 7204 1906 Pinzon Geospiza scandens CAS 7381 1906 Pinzon Geospiza scandens CAS 7383 1906 Pinzon Geospiza scandens CAS 7386 1905 Pinzon Geospiza scandens CAS 7384 1905 Pinzon Geospiza scandens CAS 7385 1905 Pinzon Geospiza scandens CAS 7201 1905 Pinzon Geospiza scandens CAS 7382 1905 Pinzon Geospiza scandens CAS 7202 1906 Pinzon Geospiza scandens CAS 7203 1906 Pinzon Geospiza scandens CAS 7200* 1905 Pinzon Geospiza scandens CAS 7380* 1905 Pinzon Geospiza scandens CAS 7387* 1905 Pinzon Geospiza scandens CAS 7114 1905 Santa Fe Geospiza scandens CAS 7125 1905 Santa Fe Geospiza scandens CAS 7128 1905 Santa Fe Geospiza scandens CAS 7131 1905 Santa Fe Geospiza scandens CAS 7250 1905 Santa Fe Geospiza scandens CAS 7330 1905 Santa Fe Geospiza scandens CAS 7340 1905 Santa Fe Geospiza scandens CAS 7143 1905 Santa Fe 128

Species Museum Mus. # Date Island Geospiza scandens CAS 7331 1905 Santa Fe Geospiza scandens CAS 7123 1905 Santa Fe Geospiza scandens CAS 7133 1905 Santa Fe Geospiza scandens CAS 7332 1905 Santa Fe Geospiza scandens CAS 7341 1905 Santa Fe Platyspiza crassirostris CAS 86727 1961 Floreana Platyspiza crassirostris CAS 86728 1961 Floreana Platyspiza crassirostris CAS 86729 1961 Floreana Platyspiza crassirostris CAS 8401 1906 Floreana Platyspiza crassirostris CAS 8585 1906 Floreana Platyspiza crassirostris CAS 8586 1906 Floreana Platyspiza crassirostris CAS 8474 1906 Floreana Platyspiza crassirostris CAS 8490 1906 Floreana Platyspiza crassirostris CAS 8489 1906 Floreana Platyspiza crassirostris CAS 8559 1906 Floreana Platyspiza crassirostris CAS 8406 1906 Floreana Platyspiza crassirostris CAS 8612 1906 Floreana Platyspiza crassirostris CAS 8455 1906 Floreana Platyspiza crassirostris CAS 8479 1905 Floreana Platyspiza crassirostris CAS 8603* 1906 Floreana Platyspiza crassirostris BNHM 1899.9.1.395 1897 San Cristobal Platyspiza crassirostris BNHM 1899.9.1.399 1897 San Cristobal Platyspiza crassirostris BNHM 1899.9.1.396 1897 San Cristobal Platyspiza crassirostris BNHM 1899.9.1.398 1897 San Cristobal Platyspiza crassirostris BNHM 1899.9.1.397 1897 San Cristobal Platyspiza crassirostris BNHM 1899.9.1.400 1897 San Cristobal Platyspiza crassirostris CAS 8482 1906 San Cristobal Platyspiza crassirostris CAS 8521 1906 San Cristobal Platyspiza crassirostris CAS 8509 1906 San Cristobal Platyspiza crassirostris CAS 8511 1906 San Cristobal Platyspiza crassirostris CAS 8465 1905 San Cristobal Platyspiza crassirostris CAS 8528 1906 San Cristobal Platyspiza crassirostris CAS 8501 1906 San Cristobal Platyspiza crassirostris CAS 8456 1906 San Cristobal Platyspiza crassirostris CAS 8436 1906 San Cristobal

129

CHAPTER 5: GENERAL CONCLUSION

This set of studies has addressed several important questions regarding the evolutionary history and population genetics of Darwin’s finch populations. All three studies produced unexpected results that emphasize the importance of continued study of this system and remind us that natural processes may not always conform to our expectations. The primary findings of this thesis research are summarized below.

The finch phylogeny generated in chapter two was the first use of a multi-locus nuclear intron data set for tree generation in this group. The data produced a novel evolutionary tree, with a basal topology different from all previously published trees, leading to the reconsideration of evolutionary events at the earliest stages of this adaptive radiation.

The “ancient” DNA data sets used in chapters three and four are among the largest used in the literature to date, with a combined total of nearly 400 historic specimens used. Historic

Darwin’s finch specimens revealed a great deal about how these populations are changing over time. There was no genetic evidence of archipelago-wide population declines in the warbler finches, as was expected due to increased disturbance of Galápagos habitat in recent years. Both increases and decreases in genetic diversity were noted for individual populations. Decreases in genetic diversity were attributed to fluctuations in population size due to El Niño cycles, while increases were due to migration from other populations. On one island, a suspected case of

“genetic rescue” was revealed. These results emphasize the importance of metapopulation dynamics in this system for population persistence.

Perhaps the most surprising result of this thesis was the lack of reduced genetic diversity prior to population extinction, as revealed with historic specimens. As populations decline to extinction, inbreeding and genetic drift lead to reduced genetic diversity in a population.

130

However, genetic diversity prior to extinction was generally equal to or greater than diversity in populations that have persisted through time. Although it is difficult to explain these results, it is possible that metapopulation dynamics have played a role in maintaining genetic diversity in declining populations. This study also highlights the difficulties with using genetic data alone to evaluate extinction risk, particularly when there is gene flow among populations.

These results clearly show that the Darwin’s finch radiation has a great deal more to teach us about evolutionary processes, particularly in metapopulations. These studies showcase how natural history collections can help us better understand evolutionary processes and make more informed conservation decisions for species existing in fragmented landscapes.

131