Evolutionary Dynamics of Bird Populations in Southeast Asia Haw Chuan Lim Louisiana State University and Agricultural and Mechanical College, Haw Chuan [email protected]

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2010 Evolutionary dynamics of bird populations in Southeast Asia Haw Chuan Lim Louisiana State University and Agricultural and Mechanical College, [email protected]

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EVOLUTIONARY DYNAMICS OF BIRD POPULATIONS IN SOUTHEAST ASIA

A Dissertation

Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Philosophy

The Department of Biological Sciences

Haw Chuan Lim B. Env.Sc., University of Wollongong, Australia, 1996 M.S., National University of Singapore, Singapore, 2002 August 2010 Acknowledgments

I am very thankful to Dr. Frederick Sheldon, my advisor, for agreeing to take me as a graduate student. Up to that point, I only had experience as an avian ecologist. The members of my graduate committee, Drs. Robb Brumfield, Bryan Carstens, Phil Stouffer, and Fred, are prominent experts in their fields, and they have been very generous in the giving their time and providing advice. I also benefited greatly from interactions with other staff and faculty members of the Museum of Natural Science and the Department of Biological Sciences. Some of these people are: Steve Cardiff, Prosanta Chakrabarty, Nanette Crochet, John McCormack, Matthew

Davis, Donna Dittmann, Elizabeth Derryberry, David Foltz, Mark Hafner, Michael Hellberg,

Dan Lane, Susan Murray, Van Remsen and Andrew Whitehead. Office administrator extraordinaire Ms. Tammie Jackson was always very helpful and cheerful. I owe much to fellow graduate students for their friendships and lending of support. Special thanks are due to Matthew

Carling, Zachary Cheviron, Ron Eytan and James Maley for their liberal sharing of know-how, and stimulating discussions. I also thank David Anderson, Phred Benham, Gustavo Bravo, Curt

Burney, Santiago Claramunt, Jesus Fernandez, Dency Gawin, Richard Gibbons, Sarah Hird,

Heather and Nathan Jackson, Eric Johnson, Ben Marks, Verity Mathis, John McVay, Jonathan

Myers, Luciano Naka, Brian O‟Shea, Noah Reid, Eric Rittmeyer, Cesar Sanchez, Thomas

Valqui, Danielle Ward, and many others; they greatly enriched my time here at LSU. My fieldwork would not have been possible without permission from the Malaysian Wildlife

Department and the Sarawak Forestry Department. I also thank the following museums and institutions for generous tissue loans: LSU Museum of Natural Science, Academy of Natural

Sciences of Philadelphia, National Museum of Natural History, National University of

Singapore, Field Museum of Natural History, American Museum of Natural History and Burke

Museum. Funding for my project came from: the National Science Foundation (DEB-0228688 to my advisor), the American Ornithologists‟ Union, American Museum of Natural History, LSU

University Museum of Natural Science Birdathon and Prepathon Funds, LSU Department of

Biological Sciences and LSU Biograd Research Fund. Most of all, I thank my wife, Ching Chi, and my daughter, Megan, for their support. Without Ching Chi‟s sacrifices, I would not have been able to begin to pursue a doctorate education here at LSU.

iii

Table of Contents

Acknowledgments...... ii

Abstract ...... v

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

Chapter 2: The Cause of Extensive Color Polymorphism in the Oriental Dwarf Kingfisher Ceyx erithaca ...... 3 Introduction ...... 3 Methods ...... 7 Results ...... 15 Discussion ...... 26

Chapter 3: Coalescent Simulation and Comparative Niche Modeling Reveal Historical Mechanisms That Promoted Population Divergence ...... 32 Introduction ...... 32 Methods ...... 34 Results ...... 42 Discussion ...... 47

Chapter 4: Demographic and Evolutionary Responses to Climatic and Geographical Fluctuations: A Multilocus and Multi-Species Perspective ...... 54 Introduction ...... 54 Methods ...... 60 Results ...... 67 Discussion ...... 79

Chapter 5: Overall Conclusions ...... 88

References ...... 90

Appendix 1: Supplementary Data ...... 105

Appendix 2: Permission from Journal of Avian Biology ...... 130

Vita ...... 135

Abstract

A confluence of factors determines the biological diversity we observe today. Here, I conducted three investigations of the historic, geographic and ecological factors that shaped the morphological and genetic diversity of rainforest birds in Southeast Asia.

(1) In the Oriental Dwarf Kingfisher (Ceyx erithaca) complex, the birds are highly polymorphic

in plumage. Quantitative analysis of this variation indicated that a large proportion of birds in

Sumatra, the Malay Peninsula and Borneo have plumages intermediate between the northerly

black form from mainland Asia and the southerly rufous form from Java. Phylogenetic

analyses indicated that birds from continental Southeast Asia (north of the Malay Peninsula)

were well differentiated from those from insular Southeast Asia. This genetic distinction

correlates well with a fixed plumage difference (mantle coloration). Coalescent analyses

showed that the plumage polymorphism was caused by past genetic introgression between the

two parental forms.

(2) I sampled 16 lowland rainforest bird species primarily from the Malay Peninsula and Borneo

to test the long-standing hypothesis that animals on different Sundaic landmasses intermixed

extensively when low sea-levels during the Last Glacial Maximum (LGM) exposed land-

bridges. This hypothesis was rejected in all but five species through coalescent simulations.

Environmental niche modeling showed that the presence of unsuitable habitats between

western and eastern Sundaland during the LGM coincided with deeper inter-population

genetic divergences. The distinctiveness of the northeastern Borneo populations of some

species may be underlain by a combination of factors that included riverine barriers, LGM

expansion of montane forests and regional physiography.

(3) I further investigated the population divergence and demographic histories of three bird

species that possessed disparate ecological characteristics. Multilocus analyses revealed

changes in effective population sizes that were driven by long-term changes in the

environment, instead of high-frequency glacial cycles. Populations from Borneo exhibited

stronger demographic growth than those from mainland Southeast Asia, suggesting regional

differences in environmental changes or directional colonization. The species with the widest

habitat breadth also showed the greatest amount of inter-landmass gene flow. This adds to the

growing body of empirical work indicating an association between a species‟ ecological

characteristics and its population connectivity over evolutionary time-scales.

Chapter 1: General Introduction

Deciphering mechanisms that underlie the generation, maintenance and distribution of biological diversity is a central challenge in evolutionary biology and ecology. I posit that these mechanisms can be grouped into the following three main areas: (1) the ecology of the organisms under investigation; (2) the spatial or geographical setting of the diversification; and

(3) historical contingencies, such as the geological history of a region or the phylogenetic history of a taxon. In different systems, the relative importance of different mechanisms, and the way they interact with each other, vary (de Aguiar et al. 2009). The refugial hypothesis of tropical diversification provides a simplistic example of how all three groups of factors could potentially work together to shape biological diversity of a biogeographic region (Haffer 1997). Under this hypothesis, historical climatic fluctuations that were a result of orbital forcing, variation in solar output, and other changes external or internal to the climate system, caused widespread vegetation changes in a region. The precise placement of different vegetation types depended on the geography of the region (e.g., presence of mountain chains and large bodies of water). When the magnitude of shift in the vegetational communities was severe enough (e.g., from closed forests to open savannahs), species that originally inhabited the region could no longer do so because niche conservatism (tendency of a species to retain ancestral characteristics) or other ecological constraints prevented them from using the new habitats (Wiens and Graham 2005;

Losos 2008). In response to the formation of new habitat barriers, population-level evolutionary or ecological processes, such as genetic drift and natural selection, worked to create and distribute biological diversity along different axes (e.g., morphological, genetic, and community structuring).

Given their vast pools of biological diversity, the tropics, ironically, are also one of the world‟s most poorly studied terrestrial biomes with regards to the origins of biological diversity.

This is particularly true of Southeast Asia. In this dissertation, I explored how the dynamic nature of Southeast Asia shaped the demographic history of its birds, as well as the history of population divergence and the distribution of genetic and morphological diversity. Given the historical focus of my investigation, the approaches I took emphasized estimating genealogical relationships, reconstruction of paleohabitats, and testing of population divergence hypotheses.

Many of the processes I studied occurred at the level of populations. As such, my analytical approaches took into account uncertainties associated with the coalescent process – the random merging of genetic lineages as time goes backwards (Kingman 1982). In chapter two, I carried out phylogenetics analyses, multivariate analyses of morphological data, and coalescence-based analyses to decipher the factors that underpin extensive plumage color polymorphism in the

Oriental Dwarf Kingfisher Ceyx erithaca, a species that occurs in southern Asia. The species‟ plumage polymorphism has long intrigued ornithologists and has been attributed to natural selection, contemporary hybridization, or past introgression of genes (Voous 1951; Ripley and

Beehler 1987). In the third chapter, I took a comparative approach and investigated the pattern of population divergence in 16 species of birds across Sundaland (insular Southeast Asia). Using ecological niche modeling and reconstructed paleohabitats, I tested the relative importance of habitat and non-habitat (riverine) barriers in determining population structure. In chapter four, I focused on three bird species that are differentiated in terms of habitat breadth. Using multiple genetic loci (mitochondrial and nuclear), I determined how evolutionary demographic history varied from species to species, and in different parts of Sundaland.

Chapter 2: The Cause of Extensive Color Polymorphism in the Oriental Dwarf Kingfisher Ceyx erithaca

Introduction Color polymorphism is the expression of genetically determined color variation in conspecifics of the same age and sex classes that cannot be explained by recurrent mutations alone (Buckley

1987). A recent review has shown that color variation occurs in approximately 3.5% of the world‟s bird species (Galeotti et al. 2003), and most of this variation is probably highly heritable and has simple Mendelian underpinnings (Roulin 2004). Research on color polymorphism has been focused mainly on its evolutionary origins (Roulin 2004), how it is maintained by selection

(natural or sexual) in the face of gene flow (Lank 2002), and its molecular basis (Mundy 2005).

Numerous forms of selection have been found or posited to be important in maintaining color polymorphism. For example, divergent selection has been shown to play an important role in the song sparrow (Melospiza melodia). Birds living in more humid environments tend to have darker feathers because they are more resistant to bacterial-induced degradation (Burtt and Ichida

2004). Frequency-dependent selection is invoked to explain the high degree of polymorphism in birds-of-prey, as variation makes it more difficult for prey to form “avoidance images” (Rohwer and Paulson 1987, but see Krüger and Lindstrom 2001). On the other hand, polymorphism can also persist through non-adaptive processes. In the classic case of the lesser snow goose (Chen caerulescens caerulescens), different morphs (light and dark) that evolved in allopatry came into secondary contact because of habitat changes in their wintering grounds (Cooke et al. 1988).

Although positive assortative mating helps prevent a complete merging of morphs, no fitness differences have been associated with coloration (Cooke et al. 1985). The same has also been suggested for fulmars (Fulmarus glacialis glacialis) in the North Atlantic. Continuous variation in plumage melanism in this subspecies has been suggested to be the result of an influx of Reprinted by permission of Journal of Avian Biology 3 melanistic characteristics from the dark Pacific fulmar (F. g. rodgersii) during warm interglacial periods (Van Franeker and Wattel 1982).

In the Indo-Malayan region, the prevalence of intermediate plumage phases of the forest- dwelling Oriental Dwarf Kingfisher (Ceyx erithaca) has long intrigued ornithologists (Ripley

1942; Voous 1951; Sims 1959; Ripley and Beehler 1987). This “species” is comprised of two principal color forms: the black form, which is sometimes referred to as the black-backed kingfisher, and the rufous form, which is sometimes designated as a separate species, rufous- backed kingfisher, C. rufidorsa (Inskipp et al. 1996). The black form has a lilac-brown crown, mainly orange-rufous underpart, blackish-blue back, ultramarine neck patches, black forehead and black wings. The rufous form resembles the black form, except that black or blue coloration is replaced by lilac-rufous in the neck, wing coverts, forehead and scapulars, and by rusty brown

(with some lilac) in the mantle. One notable exception to this pattern is the Borneo subspecies,

C. e. motleyi, which has a rusty brown rather than black mantle. Ceyx erithaca has a more northerly geographic distribution of the two forms, occupying western South Asia, parts of

Indochina, the Malay Peninsula, Sumatra and Borneo (Fry 1992) (Fig. 2.1). The distribution of

C. rufidorsa is more insular and southerly. It occupies the Malay Peninsula, Sumatra, Borneo and

Java, as well as parts of the Philippines and other small islands (Ripley and Beehler 1987; Fry

1992). In allopatry, the two forms appear to be distinct species. However, where they overlap, plumages that form a continuum between the two “parental” types are common (Ripley and

Beehler 1987). Intermediate individuals can resemble pure erithaca but without black or blue in one or more of the aforementioned plumage regions. Plumage regions in individuals can also exhibit intermediate characteristics, with dark coloration incompletely expressed.

Figure 2.1. Distribution of different color forms of Ceyx erithaca. Map is modified from (Ripley and Beehler 1987), and Woodall (2001).

In this study, I attempted to decipher the evolutionary underpinnings of the pervasive color polymorphism in Oriental Dwarf Kingfishers and the specific status of its various forms using molecular genetic techniques. Several mechanisms could have created the geographically structured color polymorphism observed in these birds. First, selective forces may favor the dark morph to the north and rufous morph in the south (primary intergradation). Intermediate individuals could also be the result of secondary contact of substantially diverged parental forms.

In this case, hybridization occurs in the zone of overlap producing intermediate phenotypes

(contemporary hybridization). Lastly, the polymorphism could be the result of introgression of genes while the lineages were in the early phases of divergence (past introgression). In this

5 model, contemporary hybridization is curtailed because of the build–up of reproductive isolating mechanisms or because the two lineages have limited contact with each other (e.g., due to a range shift). Despite this effective separation, introgressed color alleles continue to persist alongside native ones, resulting in plumage polymorphism.

To distinguish among these possibilities, I employed multilocus phylogenetic and coalescence-based analyses. If the cause of polymorphism is primary intergradation, I do not expect concordant patterns of variation between neutral molecular loci and plumage characters that are presumably under selection (Barton and Hewitt 1985). Under this model, plumage differences would have arisen in situ out of an essentially continuous population. Therefore, the black and rufous forms should not possess distinct genotypes in neutral loci that are not linked to those under selection. If the variation results from secondary contact and subsequent hybridization, I expect genetic markers to be substantially diverged. Additionally, depending on the symmetry of gene flow, some mismatches between plumage and morph-typical mtDNA haplotypes are expected. Because genetic mixing is contemporaneous in this case, I also expect phenotypically intermediate individuals to exhibit substantial admixture of nuclear markers. If introgressive hybridization (i.e., hybridization that leads to movement of genes beyond the hybrid zone) occurred in the more distant past, the two forms should not share complete haplotypes at loci that have high mutation rates and small effective population sizes (e.g., mitochondrial loci). I should also be able to reject an isolation-only model of divergence based on expectations derived from the neutral coalescent process (Kingman 1982). Recent analytical advances, such as the approach developed by Nielsen and Wakeley (2001), now allow researchers to distinguish between the retention of ancestral polymorphism and gene flow

6 subsequent to divergence as the cause of allele sharing in recently diverged populations or closely related species.

I also inspected museum specimens for size and plumage characteristics. Size characters were measured and used in multivariate analyses to cluster birds into groups. In comparing plumages, I considered two issues overlooked in some earlier studies (Voous 1951; Sims 1959;

Ripley and Beehler 1987). Previous studies failed to take into account that Ceyx erithaca (except the Borneo subspecies) is migratory (Fry 1992; Wells 1999). This failure caused an overestimation of the number of “pure” resident C. erithaca in areas visited by wintering C. erithaca, leading to an incorrect picture of the distribution of color morphs and the possible causes of polymorphism. Also, previous investigations did not report mantle coloration found in different populations. As it turns out, mantle color is an important indicator of phylogenetic affinity.

Methods Sampling for Genetic Analyses

I obtained individuals representing populations from the following kinds of localities: where the two color forms overlap (Sumatra, Malay Peninsula and Borneo), and where only one form is found (rufous form only – Java; black form only – continental Southeast Asia) (Fig. 2.1, Table

2.1). The four specimens representing the black form were collected in Singapore. I am confident that they were migrants from northern continental areas because Singapore does not have a resident population of Ceyx (Wang and Hails 2007), and the individuals were window-killed in urban localities during the migration season. Because of uncertainty over the taxonomic status of these individuals, I refer to and group specimens based on their geographic origins. The single sample from Java was designated as Javan. No voucher specimen is associated with this sample,

7 but the bird was confirmed in the field to have no dark coloration (N. Sodhi, pers. comm.).

Further, only the rufous form is known to occur in Java. Birds collected in Singapore were designated as continental. The remaining samples (n = 17) were collected in the zone of overlap and have been designated as mixed. These specimens have a wide distribution of plumage scores

(Table 2.1), and were all collected outside of the migratory season (see Morphological data).

Laboratory Methods and Genetic Markers

Genomic DNA was extracted from muscle or blood using DNeasy tissue kit following manufacturer‟s protocol (Qiagen Inc.). DNA of only one individual (Javan sample, S07-1) was obtained from blood. For each individual, I carried out standard PCR to amplify DNA at one mitochondrial locus (NADH dehydrogenase-2 [ND2], 806 bp) and five nuclear loci. The nuclear loci, each containing a majority of intron sites, were from the following genes: glyceraldehyde-3- phosphate dehydrogenase (GAPDH, 342 bp), ornithine decarboxylase (ODC, 599 bp), α- tropomyosin (TROP, 424 bp), myoglobin (MYO, 643 bp) and β-actin (AlatBact, 421 bp).

Sources of primer sequences for the loci were: Hackett (1996) and Shapiro et al. (2004) for ND2;

Primmer et al. (2002) for GAPDH, ODC and TROP; Heslewood et al. (1998) and Slade et al.

(1993) for MYO, and Marks (2008) for AlatBact. For thermocyling, the locus specific annealing temperatures (X oC) were 55oC for ND2 and AlatBact, 58 oC for MYO, 60oC for ODC and

TROP, and 64oC for GADPH. The thermocycler profile for each reaction was: an initial denaturation step at 95°C for 2 minutes, followed by 35 cycles of the following steps: 95°C for

30 seconds, annealing at X°C for 30s, and extension at 72°C for 45 seconds. The final step of the reaction was a 5 minutes extension step at 72°C. PCR products were cleaned with 20% polyethylene glycol and cycle-sequenced with BigDye Terminator version 3.0 cycle sequencing kit (Applied Biosystems Inc.) in both directions. Sequence analysis was carried out in an ABI

Table 2.1. Sample designation, source, plumage score and collection locality of samples sequenced for this study Label Source1 Collection Plumage Mantle Locality Coordinates Number2 score3 color (Lat., Long.) continental AMNH DOT9643 8 black SINGAPORE 1o22‟N 103o48‟E continental AMNH DOT9655 8 black SINGAPORE 1o22‟N 103o48‟E continental Burke 67542 8 black SINGAPORE 1o22‟N 103o48‟E continental Burke 73854 8 black SINGAPORE 1o22‟N 103o48‟E Javan NUS S07-1 04 rufous4 Linggo Forest, Java, 6o58‟S INDONESIA 109o30‟E mixed LSUMNS B36384 NA rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B36401 8 rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B38550 8 rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B38570 7 rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B38571 8 rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B38586 2 rufous Tawau Hills, Sabah, 4o22‟N MALAYSIA 117o47‟E mixed LSUMNS B38611 NA rufous Imbak Valley, Sabah, 5o06‟N MALAYSIA 117o01‟E mixed LSUMNS B38619 7 rufous Kinabalu Park, Sabah, 6o05‟N MALAYSIA 116o33‟E mixed LSUMNS B47086 6 rufous Kinabalu Park, Sabah, 6o05‟N MALAYSIA 116o33‟E mixed LSUMNS B47126 NA rufous Kinabalu Park, Sabah, 6o05‟N MALAYSIA 116o33‟E mixed LSUMNS B47135 8 rufous Kinabalu Park, Sabah, 6o05‟N MALAYSIA 116o33‟E mixed LSUMNS B47161 0 rufous Klias Forest Reserve, 5o23‟N Sabah, MALAYSIA 115o45‟E mixed LSUMNS B47231 6 rufous Klias Forest Reserve, 5o23‟N Sabah, MALAYSIA 115o45‟E mixed LSUMNS B52068 5 rufous Sedenak Forest Reserve, 1o42‟N Johor, MALAYSIA 103o29‟E mixed LSUMNS B52103 2 rufous Sedenak Forest Reserve, 1o42‟N Johor, MALAYSIA 103o29‟E mixed Burke 67478 0 rufous Luang, Sumatra, 2o16‟N INDONESIA 101o02‟E mixed Burke 67495 1 rufous Lingkung, Sumatra, 0o33‟N INDONESIA 100o21‟E Outgroup Alcedo crista LSUMNS B39522 Gonja Triangle, GHANA 8o47‟N 1o25‟E A. leucogastra LSUMNS B39420 Assim Foso, GHANA 5o20‟N 1o14‟E 1 AMNH = American Museum of Natural History; Burke = University of Washington Burke Museum of Natural History and Culture; NUS = National University of Singapore; LSUMNS = Louisiana State University Museum of Natural Science. 2 In bold = ingroup samples with intron haplotype data. 3 Methods of scoring can be found under Morphological data; NA = voucher specimen not available. 4 Score given based on field identification.

Prism 3100 automated DNA sequencer (Applied Biosystems Inc.). Generated sequences were assembled and aligned by eye using Sequencher version 4.1 (Gene Codes Corp.). To discern nuclear allelic phases when an individual had more than one heterozygous nucleotide position within a nuclear locus, PCR products were TA cloned (Stratagene) and sequenced in both directions. Sequences of the cloned products were then compared against those derived from direct sequencing of PCR products. Only a subset of samples from the zone of overlap were selected to have their nuclear alleles cloned and identified (Table 2.1). All generated DNA sequences were deposited in GenBank (accession numbers: GQ861094 – GQ861237).

Polymorphism and Selective Neutrality

I calculated nucleotide diversity (π), Tajima‟s D (Tajima 1989), and Fu and Li‟s D* (Fu and Li

1993) using DNAsp version 4.5 (Rozas et al. 2003). The latter two statistics were used to test if loci evolved according to the Wright-Fisher model, and significance testing was done via 1,000 coalescent simulations. In addition, I performed the McDonald-Kreitman (MDK) test using ND2 sequences (McDonald and Kreitman 1991). This test examines whether the ratio of synonymous to nonsynonymous fixed differences between species is the same as the ratio of synonymous to nonsynonymous polymorphism within species. Different types of polymorphic sites (fixed differences, exclusive polymorphisms and shared polymorphisms) were calculated with the program SITES (http://lifesci.rutgers.edu/~heylab/heylabsoftware.htm). For statistical analysis, I used the following groupings: continental, mixed, and mixed + Javan. All statistics were calculated based on completely known haplotypes.

Phylogenetic Analyses

To establish the phylogenetic context and the relative amount of intragroup divergence for C. erithaca and C. rufidorsa, I compared their ND2 and MYO sequences to an extensive set of

10 outgroup taxa drawn from GenBank. I used these two genes because their sequences were widely available. The number of outgroup sequences for ND2 and MYO were 36 and 25, respectively

(Appendix 1 Table 1). ND2 and MYO sequences of two additional C. erithaca individuals (both derived from Laos; Appendix 1 Table 1) were downloaded and included in the analyses.

Although C. erithaca and C. rufidorsa have been compared in a subfamily-wide phylogenetic study (Moyle et al. 2007), only one representative of each taxon was included, and C. rufidorsa was represented by an individual from Borneo in the zone of overlap. For the MYO data, I employed diplotypic sequence data (heterozygous sites in sequence chromatograms were coded according to IUPAC degeneracy codes) to match the data found in GenBank. For each dataset, I first ran MODELTEST version 3.7 and selected the optimal evolutionary model based on

Akaike‟s information criterion. These parameters were then transferred into PAUP* version

4.0b10 or MrBayes version 3.1.2 to carry out maximum likelihood (ML) and Bayesian phylogenetic analyses. To assess node confidence in ML reconstructions, I ran 100 bootstrap replicates in PAUP* (Felsenstein 1985). Each replicate used a neighbor joining tree as the starting tree, and TBR branch swapping was limited to 10,000 swaps per replicate. The analysis in MrBayes was carried out with the following settings: two simultaneous runs and four incrementally heated Metropolis-coupled Markov chains (MCMC) of 5 million generations each.

Chains were sampled every 100 generations, and the first 25% of the samples was discarded as burn-in. Indels in MYO were ignored during tree building, but subjected to simple coding

(Simmons and Ochoterena 2000), and subsequently mapped onto the tree. To assess phylogenetic concordance between ND2 and MYO data, I used a Shimodaira-Hasegawa (SH) test (Shimodaira and Hasegawa 1999) and two C. lepidus as outgroups. The best ML tree for each dataset was found in PAUP* using heuristic searches and the random-taxon-addition option

(Nrep = 10). If more than one best ML tree was found, I used the 50% majority-rule consensus tree as the most likely topology.

For intron sequences with known allelic phases (Table 2.1), I employed the four-gamete test implemented in SITES to examine intralocus recombination (Hudson and Kaplan 1985). For each locus, I then used non-recombining DNA blocks that contained the most parsimony- informative sites for further phylogenetic analyses. Using these trimmed intron datasets, I first constructed a statistical parsimony network for each locus using TCS version 1.21 (Clement et al. 2000), with the connection limit set at 95%. This was followed by Bayesian phylogenetic analyses in MrBayes using Alcedo cristata and A. leucogaster as outgroups and the trimmed loci as data partitions. I used a mixed model approach; each data partition was initiated with a locus- specific evolutionary model (e.g., for TROP, the model was nst=6, rates=equal) determined with

MODELTEST. In the mixed model analysis, all parameters, with the exception of topology, were unlinked between partitions. I carried out two simultaneous Bayesian runs using the same settings as described above.

Test for Isolation with Gene Flow

Because alleles can be shared between groups as a result of retained ancestral polymorphism or gene flow after population splitting, I used two coalescent approaches to differentiate the relative roles of these forces in shaping genetic patterns in the kingfishers. I tested for gene flow during divergence between the continental and mixed populations. Gene flow was possible because during periods of low sea-level (i.e., during glacial periods), when the shallow Sunda shelf was exposed, mainland and island populations could come into extensive contact (Voris 2000). The first approach was to test the fit of my nuclear haplotype data to an isolation-only model of population divergence implemented in the Wakeley-Hey program (Wakeley and Hey 1997). This

12 approach uses the proportions of different kinds of polymorphisms between the two taxa (fixed differences, shared polymorphisms and exclusive polymorphisms), and determines if they are consistent with null (isolation-only) data simulated with estimated locus-specific population recombination rates, 4Ner (Hudson 1987). This model also assumes that all variation is neutral and effective population sizes (of the ancestral population and the two descendent ones) remained constant. I carried out 10,000 coalescent simulations, and tested for significance using

2 the WH (Wang et al. 1997) and the χ (Kliman et al. 2000) statistics. The WH statistic measures the maximum difference in fixed differences among loci plus the maximum difference in shared polymorphism among loci. If two populations experienced some gene flow after divergence, and loci exhibit differential introgression, the WH statistic will have a value higher than that expected under an isolation-only model (i.e., I expect loci to exhibit a large variance in levels of net divergence). The χ2 statistic, on the other hand, evaluates the discrepancy between observed and expected values for each polymorphism type at each locus.

To estimate divergence time and migration rates simultaneously between the two populations, I used a Markov chain Monte Carlo method implemented in the program IM (Hey and Nielsen 2004). For this, I used non-recombining DNA blocks of five loci: ND2 (all 806 bp),

AlatBact, TROP, GADPH and MYO. ODC, which did not vary between the two groups, was excluded because IM requires a locus to be polymorphic in at least one of the populations (Won and Hey 2005). In addition to estimating effective population sizes, population splitting time and directional migration rates, I also estimated the number and timing of migration events for each locus (using option –p3 in IM). After preliminary runs to establish appropriate prior distributions and to verify convergence of parameter estimates, I carried out a final run of 10 million steps with a burn-in period of 1 million steps. In this process, I used a total of six chains and a

13 geometric heating scheme (g1 = 0.95, g2 = 0.8). Prior distributions of the final run were as follow: t = 15, qerithaca = qmixed = 5, qancestral = 15, merithaca = mmixed = 10, and I assumed each locus to mutate according to the Hasegawa-Kishino-Yano model (Hasegawa et al. 1985). To translate mutation-rate-scaled parameter estimates into number of years, I assumed the following: intronic substitution rate = 1.8 x 10-9 substitutions/site/year (Axelsson et al. 2004), ND2 substitution rate

= 1.0 x 10-8 substitutions/site/year (Weir and Schluter 2008), and a generation time of two years

(Woodall 2001).

Morphological Data

I inspected a total of 150 voucher specimens of C. erithaca, C. rufidorsa or intermediate individuals obtained from the American Museum of Natural History (AMNH), University of

Washington Burke Museum of Natural History and Culture (Burke), Louisiana State University

Museum of Natural Science (LSUMNS) and National University of Singapore Raffles Museum of Biodiversity Research (RMBR). Plumage coloration of each specimen was scored according to Ripley and Beehler (1987), with modifications as follow. In addition to scoring the extent of dark coloration in four previously studied plumage regions (forehead, side of neck, scapulars and wing coverts), I also noted mantle coloration to be primarily black or rusty brown. For each plumage region, with the exception of mantle, I assigned one of three scores (0, 1 or 2), with 2 representing the darkest coloration and 0 the absence of dark coloration. After discarding potential migrants, I partitioned the plumage data (n = 108) by geographic region and examined the frequency distribution of plumage scores. A bird was classified as a potential migrant or wintering bird if it was a C. erithaca and was collected between August and March (Fry 1992).

For each bird, I also measured three morphometric characters: bill depth (at the anterior end of the nares), wing chord length and tarsus length (from the notch at the intertarsal joint to the bend

14 of the foot). Dimensions were measured with digital calipers to two decimal places for bill depth and tarsus length, and to one decimal place for wing chord length. Each character was measured three times and the average recorded. Sex, when noted on the specimen tag, was recorded.

Individuals were also identified as subadults according to the criteria of Robson (2005).

For the subset of specimens with known sex (potential migrants included, n = 132), I tested for the effects of sex and age on the three morphometric characters using general linear modeling. Because age was found to have a significant effect on bill depth, I removed subadults from all subsequent multivariate analyses. Using this reduced dataset, I carried out explorative

Principal Component Analysis (PCA) to reveal data structure. I also conducted Discriminant

Function Analyses (DFA) with the sexes separate (and without subadults), because sex was found to have an effect on wing chord length. I used DFA to determine if group membership could be predicted by the morphometric characters.

Results Nucleotide Variation and Neutrality Statistics

Phased nuclear intron haplotype data were collected from all continental and Javan individuals, and eight mixed individuals. ND2 data were collected from all ingroup individuals (n = 22)

(Table 2.1). Continental birds exhibited higher levels of nucleotide diversity for three (ND2,

GAPDH and MYO) out of the four variable loci than all other birds combined, despite the former‟s small sample size (Table 2.2). AlatBact and ODC showed little or no within-group nucleotide variation. Among the loci, GADPH showed the most within-group variation. Negative

Tajima‟s D or Fu and Li‟s D* suggested the presence of positive selection at some loci or that the populations experienced expansion. However, most values became statistically insignificant after multiple-test corrections. MDK tests revealed no departure from the neutral hypothesis at

ND2, regardless of whether I tested continental against mixed (Fisher‟s exact test, P = 0.165), or continental against mixed + Javan (Fisher‟s exact test, P = 0.240). ND2 had many more between-group fixed differences than the nuclear loci; among the nuclear loci, only AlatBact showed fixed differences. AlatBact was also the only locus with no within group polymorphism.

ODC had only one polymorphic site.

Phylogenetic Analyses

In the ND2 Bayesian consensus tree, mixed and Javan individuals formed a monophyletic group, and continental individuals formed a separate clade (Fig. 2.2A). These two clades are sisters, and their closest relative is a group of kingfishers from Southeast Asia and Australasia. In contrast, continental, mixed and Javan individuals formed an almost complete polytomy in the MYO tree

(Fig. 2.2B). Nonetheless, the data captured their collective sister relationship to the same group of Southeast Asian and Australasian kingfisher species. Using SH test (full optimization, 1,000 bootstrap replicates), I found that the MYO ML consensus topology (not shown, based on 35 equally probable ML trees) provided a significantly worse fit to the ND2 data when compared to the best ND2 ML topology (P = 0.000). The reciprocal test (best ND2 ML topology compared to

MYO consensus topology under MYO data), however, was not statistically significant (P =

0.142), suggesting that the polytomy observed in the MYO phylogenetic tree was a result of insufficient phylogenetic signal rather than true lack of concordance with the ND2 data. The four gamete test indicated no sign of intralocus recombination in the entire length of the AlatBact or ODC loci. For the remaining nuclear loci, I carried out further analyses using the non- recombining blocks of DNA. The respective sizes of these blocks were: TROP = 311 bp,

GAPDH = 120 bp, and MYO = 55 bp. As shown by the haplotype networks, haplotype sharing was common (Fig. 2.3A). In fact, in every locus except for AlatBact, the most common

Table 2.2. Length, nucleotide diversity (π) and neutrality statistics of loci used in this study. Numbers in bold represent statistically significant values. NA = statistic could not be calculated. continental mixed mixed + Javan π x 10-3 Tajima‟s Fu and Li‟s π x 10-3 Tajima‟s Fu and Li‟s π x 10-3 Tajima‟s Fu and Li‟s Locus bp (variance) D D* (variance) D D* (variance) D D* n = 4 n = 17 n = 18 ND2 806 3.10 -0.797 -0.798 1.00 -1.83 -2.09 1.50 -2.10 -2.72 (0.0007) (P = 0.172) (P = 0.167) (0.0001) (P = 0.080) (P = 0.028) (0.0003) (P = 0.002) (P = 0.005)

n = 8 n = 16 n = 18 TROP 424 1.77 -1.45 -1.57 2.08 -0.836 -0.634 1.88 -0.942 -0.701 (0.0008) (P = 0.042) (P = 0.036) (0.0005) (P = 0.240) (P = 0.248) (0.0005) (P = 0.210) (P = 0.210) AlatBact 421 0 NA NA 0 NA NA 0 NA NA ODC 599 0 NA NA 0 NA NA 0.18 -1.16 -1.50 (0.0000) (P = 0.080) (P = 0.086) GAPDH 342 10.7 -0.275 -0.202 7.82 -0.429 0.095 7.36 -0.492 0.036 (0.0052) (P = 0.340) (P = 0.356) (0.0016) (P = 0.270) (P = 0.508) (0.0015) (P = 0.279) (P = 0.483) MYO 643 6.83 -0.627 -0.610 4.12 -1.25 -1.35 4.33 -1.19 -0.935 (0.0017) (P = 0.219) (P = 0.207) (0.0006) (P = 0.035) (P = 0.057) (0.0005) (P = 0.058) (P = 0.112)

A. ND2 B. MYO

Figure 2.2. Phylogenetic relationships (50% majority-rule Bayesian consensus tree) of C. erithaca, with an extensive set of outgroup taxa, based on ND2 (A) and MYO (B). Both trees were rooted with two Halcyon malimbica sequences. Thickened branches represent nodes with posterior probability of > 0.90 or bootstrap support of > 75%. Only branches directly connected to the focal taxa have their nodal support shown numerically. Integers above branches in (B) represent indels, which are reported according to the following format: position in contig/size of indel with respect to the root. Indel 1: 31/-2; indel 2: 44/-5; indel 3: 224/-14; indel 4: 345/+1; indel 5: 346/-6; indel 6: 433/-2; indel 7: 523/+13 or + 14.

18 haplotype was shared by members of all three groups. In ODC, every individual had the same haplotype, except for one variant in the Javan bird. Two fixed differences in AlatBact completely differentiated mixed and Javan individuals from continental ones. Combining all the intron loci in a mixed-model Bayesian analysis, I found that continental individuals were derived and genealogically nested within the mixed + Javan clade (Bayesian posterior probability =

0.94), which otherwise showed little internal structure (Fig. 2.3B).

Divergence with Gene Flow Analyses

The number of fixed differences and different kinds of polymorphism in nuclear loci between continental and mixed or between continental and mixed + Javan are shown in Table 2.3. The estimated per generation population-recombination rates averaged across all loci were: continental = 31.59 and mixed = 25.32. Based on WH analysis, the fit of these nuclear data to an isolation-only model between continental and mixed was equivocal. I detected a significant departure from the model using the χ2 statistic (P = 0.012), suggesting substantial gene flow, but not with the WH statistic (P = 0.440). P-values here represent the proportion of times that simulated values exceeded or were equal to the observed value. These tests were one-tailed, the alternative hypothesis being that gene flow occurred during divergence. Although the data did not fit the isolation-only model well, I used them to estimate population sizes and divergence times for comparison with IM results (Table 2.4).

IM analysis indicated that migration rates with the highest posterior probability in either direction (continental  mixed) were nonzero, thus providing additional support against the isolation-only model (Table 2.4, see also Appendix 1 Fig. 1A and 1B,). The magnitude of gene flow in either direction appeared broadly similar, with mixed individuals going into continental populations slightly more often (Table 2.4). The posterior probability of divergence time peaked

A. B.

Figure 2.3. (A) Statistical parsimony haplotype networks based on non-recombining blocks of nuclear loci. Each line represents one mutational step. Size of each circle is proportional to the number of sequences it represents. (B) Bayesian consensus tree based on a mixed-model analysis of the combined nuclear intron dataset (fully phased non-recombining blocks only). The tree was rooted with Alcedo cristata and A. leucogaster sequences. Thickened branches represent nodes that have posterior probabilities > 0.90 (also shown numerically).

20 around 2.0-3.0, corresponding to 1.9-2.9 million years, and remained flat after that (Appendix 1

Fig. 1C), possibly an indication of insufficient data to reject much older dates.

The discrepancy between divergence time estimates obtained from WH and IM was large, but not surprising. Given that WH assumes an isolation-only model, the presence of post- divergence gene flow could make its estimate of splitting time shorter than it actually is. To take a closer look at gene-flow timing, I followed Won and Hey (2005) and recorded the number and mean times of migration events over the course of the IM simulation. To produce an overall picture of mean migration time, I averaged across loci probability distributions of the timing of migration events, after weighting each locus-specific distribution by the number of migration events in the locus (Appendix 1 Figs. 2A and 2B). The most likely estimates of mean migration times from continental into mixed populations, and from mixed into continental, are 1.67 x 105

(95% C.I. = 1.23 x 105 – 2.96 x 106) years ago, and 1.96 x 105 (95% C.I. = 1.45 x 105 – 2.93 x

106) years ago, respectively.

Morphological Data

Resident birds in Borneo exhibit a continuous distribution of plumage scores, contrasting with those from Java, Palawan, and continental Asia (other than the Malay Peninsula), which tended to possess scores that clustered tightly at one or the other end of the spectrum (Fig. 2.4).

Qualitatively, color distributions of Peninsular Malaysia and Sumatra birds were similar; birds from both regions showed broad plumage score distributions, but were never very dark in coloration. Inspection of mantle color in my genetic samples produced an interesting result. All birds in the mixed + Javan clade (Figs. 2.2A and 2.3B) had rusty brown mantles, regardless of color pattern in the rest of the plumage. On the other hand, all birds in the continental clade had a bluish-black mantle and plumage scores of eight. This led me to hypothesize that mantle

Table 2.3. Frequency of different categories of polymorphic sites between continental and mixed, and between continental and mixed + Javan (in parentheses) Locus FD1 ExP12 ExP23 SP4 ND2 33(32) 4 5(9) 1(1) TROP 0(0) 2 3(3) 1(1) ABACT 2(2) 0 0(0) 0(0) ODC 0(0) 0 0(1) 0(0) GAPDH 0(0) 5 5(5) 5(5) MYO 0(0) 9 9(10) 4(4) 1 Fixed differences 2 Exclusive polymorphism in continental 3 Exclusive polymorphism in mixed or mixed + Javan 4 Shared polymorphism

Table 2.4. Parameters and their confidence intervals estimated with WH and IM. Ne = effective population size. C.I. = confidence intervals. * Estimates are based on visual inspection of posterior probability plot (Appendix 1 Fig. 1C) because IM did not produce one sharp peak in the posterior distribution. 5 5 Ne (x 10 ) Ne (x 10 ) Divergence Migration rate Migration rate continental mixed time continental  mixed  (x 106 yr) mixed continental (x 10-7 per yr) (x 10-7 per yr) WH Estimate 2.35 1.87 0.034 NA NA 95% C.I. 0.000524 – 20.9 0.000428 – 11.7 0 – 0.222 IM Estimate 1.27 1.62 1.9 – 2.9* 3.60 13.9 95% C.I. 0.643 – 4.98 0.759 – 4.06 0.671 – 66.2 0.980 – 68.9

22 coloration is a fixed difference between the two clades. After eliminating potential migrants from the voucher specimens, I found that most birds could be partitioned into the two groups based on mantle coloration and geographic origin. The group possessing the dark mantle occurred only in the north (India, Myanmar, and Hainan Island, China). The southernmost black-backed non- migrant specimen in my sample was from latitude 16°48' N in Myanmar, although presumed migrants were found as far south as Singapore. The northernmost specimen from the Malay

Peninsula that possessed a rusty brown back was collected at latitude 4°23'N (Kuala Tahan). An individual with possibly intermediate mantle color (blackish-brown) was collected at 5°20'N,

101°22'E, a location that is about 50 km south of the Thai-Malaysian border. However, this result was only tentative given that no quantitative measurements were taken (e.g., using a spectrophotometer).

Principal component analysis of morphometric data indicate that individuals tend to cluster based on mantle coloration, lending support to the idea that the two back-color morphs form natural groupings (Fig. 2.5). The first two principal components account for 82.9% of the total variation; rufous-backed birds have deeper bills, and longer tarsi and wing chords. For a more quantitative look at the morphometric data, I partitioned the dataset without subadults into male- (n = 56) and female-only (n = 50) datasets, and conducted linear discrimination function analyses separately. Classification success for both the male-only and female-only analyses was high. In the male-only analysis, the proportion of individuals correctly assigned to its true group was 0.857; the same for female-only analysis was 0.900. In both analyses, the probability of correctly identifying a black-backed bird (male-only analysis: 0.917, female-only analysis:

0.905) was slightly higher than the probability of correctly identifying a rufous-backed bird, which are 0.841 and 0.897, respectively.

India, Burma and Java (n = 11) Borneo (n = 35) Hainan Is., China (n = 9)

14 14 14

12 12 12

10 10 10

8 8 8

6 6 6 Frequency 4 4 4

2 2 2

0 0 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8

Plumage score Peninsular Malaysia (n = 23) Sumatra and adjacent islands (n = 24) Palawan, the Philippines (n = 6)

14 14 14

12 12 12

10 10 10

8 8 8

6 6 6 Frequency 4 4 4

2 2 2

0 0 0 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8

Plumage score Figure 2.4. Frequency distributions of plumage color scores in five geographic regions. A score of 8 indicates pure black form, and a score of 0 indicates pure rufous form.

Table 2.5. Plot of principal components 1 (x-axis) and 2 (y-axis). The PCA is based on a morphometric dataset with subadults removed. Birds are labeled according to their mantle colors. PC1 = 0.600*Bill depth + 0.564*Wing chord length + 0.568*Tarsus length; PC2 = -0.029*Bill depth + 0.724*Wing chord length - 0.689*Tarsus length.

Discussion Cause of Plumage Polymorphism

The genetic data reveal that there are only two distinct lineages in the Ceyx erithaca/rufidorsa complex: the rufous-backed clade comprising individuals in the zone of overlap (Sumatra,

Peninsular Malaysia and Borneo) and Java, and the black-backed clade consisting of continental birds north of these regions. ND2 data indicate that the two groups are reciprocally monophyletic, whereas combined nuclear intron data indicate that the black-backed clade is genealogically nested within the rufous-backed clade. All birds from the zone of overlap, regardless of plumage score, are genetically more closely related to the Javan bird than to the continental population. Moreover, this dichotomous genetic distinction correlates well with a fixed plumage color difference (i.e., mantle color), despite the prevalence of color intermediacy in other parts of the plumage. I note, however, that an increase in the size of the genetic data would make confidence in its correlation with plumage characteristics stronger. Inspection of museum specimens indicates that all individuals collected south of the Thai-Malaysian border

(winter migrants excepted) are rufous-backed birds. They possess a range of plumages, with the dispersion of scores particularly wide in Sumatra, Peninsular Malaysia and Borneo. The darkest rufous-backed birds (plumage score = 7-8) are found in Borneo, and they have been classified as distinct subspecies, C. e. motleyi. All black-backed birds were collected north of Peninsular

Malaysia, and their plumage scores did not fall below seven. A third line of evidence suggesting that there are indeed two distinct groups of birds is provided by multivariate analysis of morphometric data; rufous-backed birds are generally larger, with deeper bills, and longer wings and tarsi than black-backed birds.

Although the distribution of plumages superficially resembles a cline from darker birds in the north to redder birds south, it is unlikely that this “clinal” pattern has arisen in situ due to

26 selection (primary intergradation) because of the deep genetic divergence between morphotypes; such a divergence is not expected when a population is more or less continuous. Irwin (2002), using coalescence simulations, showed that spurious phylogeographic breaks can arise even when there is no barrier to gene flow if a non-recombining genetic marker is used (e.g., an mtDNA marker). There is little risk that this is happening in the kingfishers because the mtDNA phylogeographic structure is corroborated by both the nuclear dataset and a fixed difference in mantle color. My data also do not support the hypothesis of contemporary hybridization. If such hybridization is occurring, I would expect ND2 haplotypes belonging to the two lineages to co- occur in Sumatra, Peninsular Malaysia and Borneo, where individuals with intermediate plumages are common. Not once, in the zone of overlap, have I found an ND2 haplotype similar to those obtained from birds derived from north of Peninsular Malaysia; all phenotypically intermediate individuals possessed a rufous-backed ND2 haplotype. Although haplotype sharing occurs for the nuclear intron markers, the combined dataset produced a tree that clearly differentiated rufous-backed birds of all sorts of coloration from the black-backed ones. Thus, the lack of genetically admixed individuals also argues against contemporary hybridization.

My results support the third causal hypothesis, which is that the plumage polymorphism in the Ceyx erithaca/rufidorsa group is the result of introgressive hybridization that occurred when the two forms were diverging from one another. Using the Wakeley-Hey program, I rejected an isolation-only model of divergence with the χ2 statistic, but not with the WH statistic.

I do not consider the results to be contradictory because the WH statistic is probably less powerful. By examining only two properties (among-locus variation in fixed differences and shared polymorphism), it considers a small portion of the potentially useful information in the data. IM produced the same conclusion as the χ2 analysis while exploiting different genetic

27 information (Wakeley 1996). IM also suggests that this gene flow, on average, occurred more recently than the estimated time of initial population divergence, thus supporting the scenario of post-divergence gene flow.

So, what was the cause of extensive gene exchanges after initial population splitting?

Most likely, periodic gene flow occurred because of the dynamic geography of the Indo-Malayan

Archipelago during the last few million years. The currently exposed landmasses of the Indo-

Malayan region, including the Malay Peninsula, Borneo, Sumatra, and Java, sit atop a shallow continental shelf. During glacial periods, low eustatic sea-level exposed much of the shelf, which then connected islands and landmasses to each other (Voris 2000). If populations in the north and south became isolated and diverged from one another due to the narrowness of land connection when sea-level was high, the effect of falling sea-level was to create an extensive contact zone between these two (continental and insular) forms. To determine if the two forms still have the ability to interbreed, genetic and ecological comparisons need to be made in the small area of peninsular Thailand (8 o50‟N - 10o40‟N) where resident populations of both black-backed and rufous-backed forms can be found (Robson 2002). If hybrids occur there, then genetic introgression may be on-going but strongly curtailed by the narrow contact zone in the confined

Isthmus of Kra.

A Common Pattern?

To my knowledge, few avian species or species-complexes exhibit a spatially expansive, graduated (or continuous) plumage polymorphism that has been attributed to introgression of genes between divergent forms. The kingfishers‟ unusual case appears to derive from its unique geographic circumstances. Several requirements have converged to cause such a wide and ancient polymorphic zone. First, the extreme forms have been isolated long enough (perhaps

28 accompanied by small effective population sizes) for fixed plumage differences to emerge, but not long enough for the evolution of substantial reproductive isolation. Moreover, periods of contact must have been transitory, long enough to create plumage polymorphism but not long enough for the populations to merge entirely. A similar phenomenon may be at work in the variable wheatear (Oenanthe picata) of Central Asia. Panov (1992) hypothesized that the three plumage forms (picata, capistrata and opistholeuca) first diverged in allopatry and subsequently came into secondary contact. Because of extensive intermixing, intermediate plumages are as common as 70% locally, but are generally found in 5-30% of the individuals in each sampled population. Because mates are selected apparently without discrimination based on plumage, populations appear to be merging. What is unique in the Oriental Dwarf Kingfishers, according to my interpretation, is that although they have experienced secondary contacts during periods of low sea-level, this contact is severed currently. Black-backed plumage alleles that had introgressed are lingering in the southern populations, creating extensive plumage polymorphism. I am not certain why black-backed birds do not exhibit plumage polymorphism

(since the magnitude of gene flow in either direction was the same), but it is possible that black alleles are dominant to rufous ones. The genetic basis of structural coloration, as in the kingfishers (Prum 2006), requires further research. Another interesting observation is that although past genetic introgression likely happened, I did not find a black-backed mtDNA haplotype in the south or a rufous-backed haplotype in the north. It is possible that such introgressed mtDNA haplotypes are rare, and locating them will require much better sampling, particularly in the putative zone of contact in the Isthmus of Kra. An alternative explanation is that Haldane‟s rule was at work (i.e., the heterogametic sex is less fit during hybridization).

The contact dynamics between black-backed and rufous-backed kingfishers could be the tip of an iceberg – a small indication of a possibly common phenomenon in Southeast Asia in which sister taxa (or populations) from the continent and islands have interacted periodically in step with glacioeustatic fluctuations (Heaney 1986; Inger and Voris 2001). Such interaction is possible in any geographic region where land configuration has changed dramatically with rising or falling sea-level. For example, in eastern Australia, the Australian magpie Gymnorhina tibicen is composed of two principal plumage types: the black-backed form (consisting of four subspecies) and the white-backed form (consisting of three subspecies) (Schodde and Mason

1999). The latter is found in southeastern Australia and the island of Tasmania, the former in the rest of eastern Australia, and the two hybridize in a zone that spans at least 32oS-37 oS. Given the shallow genetic divergence between the white-backed and black-backed forms (Hughes et al.

2001), it is possible that the white-backed birds arose during recent interglacial isolation on

Tasmania, and subsequently invaded mainland Australia. In Southeast Asia, genetic evidence for or against intermittent contact is scant, given how poorly this region has been studied. Work on the giant freshwater prawn (Macrobrachium rosenbergii) by de Bryun et al. (2005) found no general introgression of mtDNA between continental and insular forms. Only at one site, located just south of the Isthmus of Kra, (9 o58‟N, 98o37‟E) did different haplotypes come in contact. In the limited biogeographic literature comparing continental and insular Southeast Asia, the main focus has been on the distinctiveness of the regions‟ respective biota, as shaped by climatological, ecological, or paleogeographical factors (Hughes et al. 2003; Woodruff 2003).

In summary, I found two distinct lineages within the Oriental Dwarf Kingfisher. The genetically delineated groups correspond to a fixed difference in mantle coloration and differences in size. The data support the hypothesis that extensive plumage polymorphism in the

30 rufous-backed clade of Malaya, Sumatra, and Borneo is caused by past introgression of genes, possibly during periods of low eustatic sea-level. Given that climate change-driven range shifts were common during earth‟s history, it likely that many other taxa have also undergone episodic isolation and contact. Examination of multiple genetic loci will be instrumental in detecting such events when genetic introgression does not manifest itself as phenotypic polymorphism.

Chapter 3: Coalescent Simulation and Comparative Niche Modeling Reveal Historical Mechanisms That Promoted Population Divergence

Introduction Around the time Alfred Russel Wallace wrote his now famous paper on Amazonian biogeography (Wallace 1852), George Windsor Earl published a pamphlet detailing the shallowness of the sea that connects landmasses of the Indo-Malayan subregion (now termed

Sundaland) of the Malay Archipelago (Earl 1853). Earl‟s observation helped Wallace explain why faunal communities on various landmasses in the subregion are similar, but are collectively distinct from those of the Austro-Malayan subregion across Wallace‟s line. Since then, many biogeographers have come to the conclusion that exposure of the shallow Sunda shelf when sea- levels fell during glacial periods led to extensive region-wide faunal exchanges (Banks 1949;

Darlington 1957; Medway 1972; Heaney 1986). However, recent genetic studies began to paint a different picture of population connectivity by detecting deep genetic divergences among populations that occupy different Sundaic landmasses (Gorog et al. 2004; Buckley-Beason et al.

2006; Steiper 2006). For rainforest adapted species, there were two likely classes of barriers. The first potential barriers were rivers, which bathymetric analysis indicates traversed the now- submerged Sunda shelf (Voris 2000). The riverine barrier hypothesis of population divergence arguably has its origin in Wallace‟s Amazonian work (Wallace 1876), but has received mixed support there since then (Capparella 1991; Ayers and Clutton-Brock 1992; Gascon et al. 2000). It is also possible that populations of Sundaic forest species were isolated in refuges by habitat barriers, as increased aridity and a reduction of maritime influence over Sundaland during glacial periods likely led to an expansion of savanna or seasonal vegetation (Cannon et al. 2009). The refuge hypothesis, though contested (Colinvaux et al. 2000), is one of the main hypotheses

32 proposed to explain diversity in tropical South America and Africa (Haffer 1997; Anthony et al.

2007; Marks 2010). Here, I combined genetic data and environmental niche modeling (ENM) to resolve a two tiered question: was there extensive population intermixing when Sundaic landmasses were connected during the Last Glacial Maximum (LGM), and – if not – was prolonged isolation caused by habitat or riverine barriers?

To investigate the impact of late Pleistocene climatic fluctuation on inter-population genetic differentiation, I performed statistical phylogeographic analyses on 16 passerine species that inhabit the understory of lowland rainforest. I focused on samples from sites within

Sundaland, which allowed me to test hypotheses regarding genetic breaks across the Sunda shelf, and within Borneo. I used coalescence simulations to differentiate the following alternative hypotheses of species response to land-bridge formation: (1) the existence of land connection led to extensive inter- and intra-landmass genetic exchange (Dispersal Hypothesis); and (2) focal populations remained isolated despite physical connectivity (Isolation Hypothesis). The

Dispersal Hypothesis predicts that, for each species, there was one panmictic population across

Sundaland during the LGM, when all the landmasses were connected. In contrast, the Isolation

Hypothesis posits that divergences among populations predate at least the LGM. Because of large confidence intervals, I did not attempt to estimate specific population splitting times with my single-locus mitochondrial dataset. Instead, I proposed and tested population divergence models that are topologically consistent with the observed phylogeographic trees. Next, I carried out ENM to determine the relative importance of habitat and riverine barriers as isolating mechanisms. Species-specific niche models were generated with current distribution records and climatic data, and then projected onto a LGM climate model for Sundaland. My hypotheses led to two different predictions: (1) if habitat barriers were important, paleodistributions of species

33 that experienced isolation would be more fragmented (i.e., projections would indicate a higher proportion of unsuitable habitat) than those species that did not; and (2) if habitat barriers were not important in maintaining population isolation, species with or without deep genetic breaks should have qualitatively similar paleodistributions (Wiens and Graham 2005).

Methods Data Collection

I studied forest or forest-edge passerine species that have parts of or their entire geographic ranges in Sundaland. These species are: Hypothymis azurea and Terpsiphone paradisi

(Monarchidae); Copsychus malabaricus (Muscicapidae); Alophoixus phaeocephalus, Pycnonotus plumosus, and Tricholestes criniger (Pycnonotidae); Orthotomus sericeus (Cisticolidae);

Macronous gularis, Malacocincla malaccensis, Pellorneum capistratum, Stachyris erythroptera, and Stachyris poliocephala (Timaliidae); Arachnothera longirostra and Hypogramma hypogrammicum (Nectariniidae); Prionochilus maculatus (Dicaeidae); and Philentoma pyrrhoptera (Incertae sedis). I obtained samples mainly from the following Malaysian regions:

Peninsular Malaysia (PEN), western Borneo (WB) and northeastern Borneo (NEB), and supplemented them with those from continental Asia, Sumatra, and the Philippines (Fig. 3.1).

Samples were collected either by me, loaned by natural history museums or, for a small number, were downloaded DNA sequences from GenBank (average n = 25.0 individuals/species).

For phylogeographic tree estimation, I added two closely-related outgroup taxa per study species. The majority (> 85%) of the samples were derived from vouchered specimens deposited in museums (Appendix 1 Table 2). DNA was obtained from each sample with (if pectoral muscle) DNeasy Tissue Kit (Qiagen Inc.) or (if whole blood) proteinase K digestion followed by standard phenol-chloroform extraction. For each species, I sequenced 726-1096 bp (average:

Figure 3.1. Map of Southeast Asia showing sampling localities (triangles = focal populations; squares = all other populations). Transects for which paleohabitat suitability scores were calculated are indicated by rectangles lying between PEN and WB (transect A), and between WB and NEB (transect B). Area of exposed continental shelf when sea-level was 120 m below current level is shown in a darker shade of gray (note: for clarity, not all exposed shelves outside of Sundaland are shown), and only the three largest paleo-rivers are shown. BOR = Borneo, Nth. SEA = northern Southeast Asia, PHI = the Philippines, and SUM = Sumatra. The site of origin of one sample (T. paradisi; Primorskiy Kray, Russia; UWBM 71896) is not shown.

850.3 bp) fragments of the NADH2 gene using previously published PCR primers and protocols

(Slikas et al. 2000; Zou et al. 2007), and BigDye Terminator Cycle Sequencing Kits (Applied

Biosystems Inc.). All DNA sequences were aligned and edited in Sequencher version 4.7 (Gene

Codes Corp.) and subsequently deposited in GenBank. Only samples from the focal regions

(PEN, NEB and WB) were used in parametric bootstrap tests of divergence hypotheses.

Phylogenetic Analysis and Parametric Bootstrapping

I used Bayesian and maximum likelihood (ML) methods to infer phylogeographic relationships.

Bayesian analyses were carried out in MrBayes version 3.12 (Huelsenbeck and Ronquist 2001).

Each analysis consisted of two independent runs of four heated Markov chains; each chain was 5 x 106 generations in length (10% burn-in) and was sampled every 100 generations. I performed

ML analyses in PAUP* version 4.0 (Swofford 2002) using heuristic searches and random stepwise sequence addition (nrep = 10). Nodal support for clades was assessed with Bayesian posterior probabilities (BPP), and by ML nonparametric bootstrapping (number of pseudoreplicates = 100). In each type of analysis, an appropriate nucleotide substitution model was selected based on the Akaike Information Criterion calculated in MODELTEST version 3.7

(Posada and Crandall 1998). I grouped the phylogeographic patterns into five topological classes, as shown in Figs. 3.2A-E.

Using the observed phylogeographic patterns as guides, I generated population divergence hypotheses depicted in Figs. 3.2A-D. In these models, the hypothesized timing of population splits matches times of significant paleoclimatic change. Depending on the gene tree topology, PEN and Borneo populations were hypothesized to have diverged after the LGM (21 thousand years ago [kya]) or after the Penultimate Glacial Maximum (PGM, 140 kya), as a result of rising sea-level. If within-Borneo phylogeographic structure was detected, I posited the timing of this population split to correspond to the onset of the Last Glacial Period (LGP, 110 kya) or the onset of the Penultimate Glacial Period (PGP, 200 kya), i.e., when climatic cooling probably had induced vegetational changes that impeded faunal movement (Allen et al. 1999; González et al. 2008). Data from the one species with polytomous phylogeographic relationships among focal populations (P. capistratum) were tested against divergence hypothesis of the fourth phylogeographic pattern (Fig. 3.2D). Given an observed gene tree topology, many temporal variants (e.g., trees with increasingly older nodes) are likely compatible with the data. Instead of an exhaustive investigation, I elected to test plausible models that focused on the salient

Figure 3.2. Five general phylogeographic patterns shown by the 16 study species (A-E; see text for descriptions), and the corresponding population divergence hypotheses. Each tree is a Bayesian majority-rule consensus tree, and each branch tip represents one individual. Geographical codes follow those of Fig. 3.1. Thickened branches represent Bayesian posterior or ML bootstrap nodal support of > 0.90 or > 75%, respectively. Scale bar units = substitutions/site.

37 biogeographic question, which is whether focal populations were connected during the most recent episodes of land connection.

Following this, I carried out coalescent simulations to generate data (DNA sequences) that resembled the observed data in all important aspects (e.g., population mutation rate [θ], sample size, and Ti/Tv ratio), except for the parameter I was testing – population divergence time. To carry out the simulations, I first estimated θ for each population with the program IM, which analyzes the dynamics of two recently diverged populations (Hey and Nielsen 2004). For species exhibiting phylogeographic patterns in Figs. 3.2A and 3.2B, individuals from the two

Borneo populations were combined and estimates of θ’s were obtained from one pairwise IM

PEN-(WB+NEB) analysis. For the remaining phylogeographic patterns (Figs. 3.2C-E), I carried out three pairwise IM analyses. For a pair of sister populations, estimates of their θ’s were based on the IM analysis between them. For the third population, its θ estimate was the average of two pairwise analyses between this third population and each of the two sisters. In the case where the three clades formed a polytomy (Fig. 3.2E), each estimate of θ was the average of the two relevant pairwise analyses. For each species, I first conducted multiple short IM runs to obtain the appropriate prior values. Final θ’s were peak values derived from IM analyses with the following settings: HKY mutation model, length of 6 million generations (10% burn-in), and six heated MCMC chains. For one species (H. azurea), θ was estimated in ARLEQUIN version 3.0 as nucleotide diversity because the IM analyses failed to converge (Excoffier et al. 2005). When simulating DNA sequences, I assumed ancestral θ’s to be the average of those of the two daughter lineages because ancestral θ’s were generally poorly estimated. Data from population divergence trees with reciprocally monophyletic clades often do not provide sufficient information to estimate ancestral population sizes (Edwards and Beerli 2000). Estimated θ’s

38 were translated into effective population sizes using two DNA substitution rates: 1 x 10-8 substitutions/site/year (Weir and Schluter 2008) and a faster variant of 4 x 10-8 substitutions/site/year (Ho et al. 2005), and a generation time of one year. Using the same substitution rates, I simulated DNA sequences with SIMCOAL version 2.1.2, which incorporates both coalescent and nucleotide substitution stochasticities (Laval and Excoffier 2004). For each species and mutation rate, coalescent simulations were carried out 500 times; ARLEQUIN was then used to calculate Nei and Li‟s net nucleotide divergence, πnet, using the Tamura and Nei model of nucleotide substitution (Nei and Li 1979; Takahata and Nei 1985). I rejected a hypothesized time of divergence if the observed πnet fell above 95% of the simulated values. In

th none of my tests was the observed πnet smaller than the simulated 5 percentile value.

Environmental Niche Modeling

Niche models of each species were constructed by analyzing present-day distribution data and climatic variables in the program MAXENT version 3.2 (Phillips et al. 2006). Distribution data comprised specimen-based locality information downloaded from the online database ORNIS and my own data. I consulted online and regional gazetteers to convert textual descriptions of locations into geographic coordinates (accuracy = 0.01o for both latitude and longitude).

Duplicate locations were removed. In one species, T. paradisi, I excluded northern migratory subspecies (e.g., incei) from niche modeling because they are likely highly divergent from non- migratory populations. Also, because T. paradisi shows little phylogeographic structure within

Sundaland, a more restrictive geographic sample (probably leading to a reduced sampling of environmental space) is more conservative given the comparative approach adopted here. The final distribution dataset consisted of an average of 121.1 unique locations per species (range:

45-420) (Appendix 1 Fig. 3). To avoid over-fitting the models and to improve model

39 transferability across time (Peterson et al. 2007), I selected a subset of relatively independent

“bioclim” variables from the WorldClim dataset by conducting correlation analyses followed by hierarchical clustering (Appendix 1 Fig. 4) (Hijmans et al. 2005). These climatic layers were cropped so that they encompassed all distribution records used in this study (approximate extent of layers: 23o39‟N-11o58‟S, 90o33‟E-127 o57‟E). The climatic variables eventually selected represent the following dimensions of modern Southeast Asian climate: temperature (BIO5 and

BIO6), precipitation (BIO13 and BIO14), seasonality (BIO4 and BIO15), and isothermality

(BIO3). I ran MAXENT using default settings, while randomly selecting 25% of the samples as test samples. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) statistic and binomial tests of whether test samples were predicted better than by chance. Finally, niche models were projected onto a climate model of the LGM generated under the Community Climate System Model (CCSM) version 3 to estimate each species‟ paleodistribution (Kiehl and Gent 2004). CCSM was chosen because a global fully- coupled model is better at simulating the tropical El Niño-Southern Oscillation that dominates the climate patterns of my study region (Bush 2007). A climate model of the LGM is appropriate for investigating the impact of species paleodistribution on genetic structure because the climatic regime of the LGM is likely representative of those of earlier glacial maxima (Bintanja et al.

2005). Even if populations diverged during earlier maxima, habitat barriers during the LGM, if they existed, could still maintain separation among populations.

I took two approaches to assess the level of LGM habitat connectivity among the focal populations. In the first, I delineated two transects that straddle the geographic space between

PEN and WB (Fig. 3.1, transect A), and between WB and NEB (Fig. 3.1, transect B). In each transect, I calculated the proportion of space (i.e., number of pixels) that each species was

40 predicted to be present during the LGM (hereafter called the transect suitability score). To convert MAXENT‟s default logistic output with continuously distributed probability of presence values (0-1) into paleodistributions that contained only presence/absence data, I used an objective criterion – equality of test sensitivity and specificity – to define thresholds of occurrence (Cantor et al. 1999). By using the same criterion across species, I removed arbitrarily selected thresholds as a confounding factor in defining what was suitable. I then performed

Spearman‟s rank correlations to quantify the relationship between the transect suitability scores of each species and the corresponding Tamura-Nei model corrected πnet (PEN-WB or WB-NEB) values. In the second approach, in order to locate areas with large differences in habitat suitability among species possessing different phylogeographic patterns, I first statistically normalized (mean = 0, standard deviation [S.D.] = 1) each species‟ logistic paleodistribution output (cropped to the appropriate extents, depending on the contrast). I then contrasted normalized paleodistributions of species with PEN-Borneo divergence time < 21 kya against those with PEN-Borneo divergence time > 21 kya. A second contrast was performed between species with WB-NEB divergence time < 110 kya and those with WB-NEB divergence time >

110 kya. For paleodistributions to be contrasted statistically, the within-group mean and S.D. of probability of presence value for every pixel location on the map were calculated using mathematical functions in ArcMap version 9.2 (ESRI Inc.). To reduce the number of between- group tests, I overlaid a grid with 0.5o x 0.5o cells and averaged the mean or S.D. values of all pixels that belong to the same cell. In each pairwise comparison between phylogeographic groups, Student‟s t-tests were performed between spatially corresponding cells. To identify climatic conditions underlying the formation of putative habitat barriers, I extracted climate data from regions where predicted LGM suitability differed the most between phylogeographic

41 groups (i.e., areas with statistically significant difference). Between two groups of species, I considered the mean value from each 0.5o x 0.5o grid cell as one data point and used simple whisker-and-box plots to visualize differences in each climatic variable.

Results Five phylogeographic patterns were recovered by Bayesian and ML phylogenetic analyses. The observed patterns were: little or no phylogeographic structure among the focal populations (Fig.

3.2A); a genetic break between PEN and Borneo population in which either PEN or Borneo populations form a well-supported clade (Fig. 3.2B); the three focal populations reciprocally monophyletic, and the two Borneo populations as sisters (Fig. 3.2C); the three focal populations reciprocally monophyletic, and the PEN and WB populations as sisters (Fig. 3.2D); and the three populations in distinct clades, but inter-population relationships not ascertained with confidence

(Fig. 3.2E). In S. poliocephala, the Borneo populations formed two weakly supported clades

(BPP ≤ 0.89, bootstrap support ≤ 72%). Because of this within-Borneo phylogeographic structure, I included this species under the third phylogeographic pattern and tested its data against the corresponding divergence model (Fig. 3.2C). Except in H. hypogrammicum, Sumatra samples, if available, always clustered with those from PEN. On the other hand, despite being from the same landmass, samples from PEN and other parts of mainland Asia formed separate, strongly supported clades in some species (e.g., H. azurea). Only in one case (in A. phaeocephalus) was sharing of haplotypes between the two Borneo populations detected when there was within-Borneo phylogeographic structure. The introgressed haplotype was left out of the IM analyses as well as the subsequent parametric bootstrap analyses to test the initial time of population divergence. Based on coalescent simulations, the divergences between PEN and

Borneo populations occurred before 21 kya in all but five species (Table 3.1; at P = 0.05).

Among the seven species in which within-Borneo phylogeographic structure was detected in the gene trees (Figs. 3.2C-E), the divergence time between WB and NEB populations predates 110 kya (i.e., the onset of the LGP) in all species other than S. poliocephala (Table 3.1). In four species, I failed to reject the initially-proposed population divergence hypotheses. However, less stringent hypotheses with more recent divergence events and the same topologies as the original hypotheses were rejected (Table 3.1).

Niche models produced by MAXENT predicted species‟ distributions significantly better than expected at random. Across all species, AUC values were high (> 0.89) compared to the value expected under random prediction (0.5). Binomial tests of model performance also indicated that the models performed significantly better than random predictions (Appendix 1

Fig. 3). For several species, the western coast of India (Western Ghats) and the Philippines were predicted to be suitable climatically, under current conditions, even though many of the study species are absent from these areas. This suggests that other biogeographic barriers (e.g., deep straits between most of the Philippines and Sundaland) have been important in excluding some of the species from these areas. Paleodistributions of several species indicate that central

Sundaland was relatively unsuitable climatically during the LGM (Appendix 1 Fig. 3).

Species that have PEN and Borneo populations that diverged prior to 21 kya tend to have lower transect suitability scores within transect A (51.2% ± 14.0; mean ± S.E.) than species whose populations diverged more recently than 21 kya (77.5% ± 19.5) (Table 3.2). The

Spearman‟s rank correlation between PEN-WB inter-population πnet, corrected for sequence length, and transect suitable scores was negative (ρ = -0.210) but insignificant (P = 0.436). On the other hand, species exhibiting strong within-Borneo population structure (WB-NEB divergence time > 110 kya) have similar transect suitability scores (55.3% ± 16.8) in transect B

Table 3.1. Study species categorized by phylogeographic pattern and population divergence hypothesis tested (A-D). Θ = population mutation rate. Test significance is equivalent to the proportion of simulated πnet values exceeding the observed values (** < 0.01, * < 0.05, NS > 0.05). Coalescent simulations were carried with two mutation rates (low µ = 1 x 10-9 substitutions[sub]/site/Ma; high µ = 4 x 10-9 sub/site/Ma) except when I failed to reject the divergence hypothesis using the lower µ. Hypoth. Ingroup Observed per bp π (x10-2) Taxa bp Model2 Estimated θ (x 10-3) net ; Tested1 N Significance of divergence tests: low µ/high µ PEN BOR PEN-BOR H. azurea A 25 976 TrN + I 6.72 8.58 0.00; NS T. paradisi A 17 860 HKY + I 1.31 3.10 0.15; NS O. sericeus A 17 1096 TrN + I 0.015 3.21 0.05; NS P. pyrrhoptera A 18 846 TrN + Γ 1.18 8.68 0.13; NS H. hypogrammicum A 33 881 K81uf + Γ 2.48 4.88 0.24; NS P. plumosus3 B 26 851 HKY + I 6.56 5.38 1.72; **/NS (**/**) S. erythroptera B 29 762 TIM + I 3.19 10.6 10.6; **/** A. longirostra3 B 33 781 K81uf + Γ 3.72 1.38 0.67; NS (**/*) P. maculatus B 23 874 TIM + I 2.88 6.10 3.57; **/**

Hypoth. Ingroup Observed per bp π (x10-2) Taxa bp Model1 Estimated θ (x 10-3) net ; Tested1 N Significance of divergence tests: low µ/high µ PEN WB NEB PEN-WB WB-NEB PEN-NEB C 18.3 4.89 20.7 A. phaeocephalus 25 849 GTR + Γ 1.84 6.32 4.16 **/** **/* **/** C 12.1 3.67 9.15 M. gularis 25 797 TrN + Γ 1.40 1.33 3.89 **/** **/** **/** C 3.42 2.00 3.10 S. poliocephala 18 726 TIM + I 0.62 1.71 7.47 **/* **/NS **/** D 2.09 3.18 2.37 C. malabaricus4 37 841 GTR + I 2.05 0.96 0.26 **/NS (**/**) **/* (**/**) **/NS (**/*) D 1.43 7.15 9.26 T. criniger4 28 814 TrN + I 6.38 4.23 2.81 */NS (**/**) **/** (**/**) **/** (**/**) D 18.9 24.7 20.9 P. capistratum 18 823 TVM + I 3.57 2.97 3.30 **/** **/** **/** D 4.05 13.3 14.1 M. malaccensis 27 797 TrN + Γ 2.58 3.65 3.17 **/* **/** **/** 1 The population divergence hypotheses tested correspond to those found in Fig. 3.2. 2 Outgroup taxa included in model selection process. 3 For these species, I also tested the hypothesis in which PEN-BOR divergence time is 21 kya (results in parentheses). 4 For these species, I also tested the hypothesis in which PEN-WB divergence is 21 kya, and the divergence time between their ancestral population and NEB is 110 kya (results in parentheses).

44 compared to species whose populations are more recently diverged (< 110 kya) (55.0% ± 12.5)

(Table 3.2). The Spearman‟s rank correlation between WB-NEB inter-population πnet distances and transect suitability scores was not significant (ρ = 0.132, P = 0.625).

Table 3.2. Transect suitability score for species grouped according to results of parametric bootstrap tests. Group 1 = PEN-BOR divergence time < 21 kya, no structure within BOR; group 2 = PEN-BOR divergence time > 21 kya, WB- NEB divergence time < 110 kya; group 3 = PEN-BOR divergence time > 21 kya, WB-NEB divergence time > 110 kya. Transect Phylogeographic Species Suitability group Score (%) A B 1 H. azurea 97.2 44.1 1 H. hypogrammicum 100.0 96.8 1 O. sericeus 0.0 0.0 1 P. pyrrhoptera 100.0 93.8 1 T. paradisi 90.4 41.5 2 A. longirostra 14.8 77.8 2 P. maculatus 46.7 16.3 2 P. plumosus 0.0 1.2 2 S. erythroptera 91.2 78.6 2 S. poliocephala 100.0 100.0 3 A. phaeocephalus 3.7 22.5 3 C. malabaricus 99.8 96.2 3 M. gularis 0.3 0.1 3 M. malaccensis 8.7 38.0 3 P. capistratum 97.6 74.7 3 T. criniger 100.0 100.0

Average paleodistributions for species showing either recent (< 21 kya) or older (> 21 kya) PEN-Borneo divergence dates are consistent in indicating that northwestern Sundaland, western Java and NEB contained climatically suitable habitats during the LGM (Appendix 1 Fig.

5A and 5B). However, paleohabitat suitability differed for these two groups of species in other areas. Habitat suitability for species with older (> 21 kya) PEN-Borneo divergence time was higher in central Sumatra and eastern Borneo, and less so in north-central and southern

Sundaland (Fig. 3.3A). Although only 6.5% of 1232 grid cells showed a significant difference in

45 probability of presence between the two species groups (two-tailed t-test, α = 0.10), the spatially clustered nature of such cells suggests that the pattern is likely driven by real differences in paleodistributions. When species were grouped according to their WB-NEB divergence times (< or > 110 kya), species with older within-Borneo divergence dates found south-central Borneo more suitable and central Borneo, which is occupied by highlands, less so (Fig. 3.3B, Appendix

1 Figs. 5C and 5D). However, as in the previous contrast, the number of cells with significantly different probability of presence is small, but they are spatially clustered. In 4.1% of the cells, species with older within-Borneo divergence have significantly higher probability of presence values; this group of species has significantly lower probability of presence in 3.4% of the cells.

When contrasting species that have recent (< 21 kya) or older (> 21 kya) PEN-Borneo divergences, the regions in which each group has significantly higher LGM habitat suitability were well differentiated climatically (Fig. 3.4A). Specifically, the regions predicted to be less suitable to the species with deeper divergences had greater seasonality in temperature (BIO4) and precipitation (BIO15), and were warmer and wetter during the warmest and wettest month

(BIO5 and BIO13), respectively. Areas more suitable to this group of species were marginally warmer and wetter during the coldest and driest month (BIO6 and BIO14), respectively.

Between species that have shallow (< 110 kya) or deeper (> 110 kya) WB-NEB divergences, habitats that were predicted to be less suitable for the latter group were much wetter during the driest month (BIO14, Fig. 3.4B), whereas habitats that were highly suitable were generally warmer (BIO5 and BIO6). Differences in seasonality in temperate and precipitation appear to be less important when compared to the previous contrast. Not surprising for this tropical system, isothermality (single-day temperature fluctuation relative to whole-year temperature fluctuation,

BIO3) was not an important differentiating factor in either of the contrasts.

A. B.

Figure 3.3. Maps of (A) Sundaland and (B) Borneo showing t-statistic values in 0.5o x 0.5o grid cells. (A) The t-statistic value was calculated based on the following contrast: PEN-Borneo divg < 21 kya - PEN-Borneo divg > 21 kya. (B) The t-statistic value was calculated based on the following contrast: WB-NEB divg < 110 kya - WB-NEB divg > 110 kya. is the per-cell probability of presence (PP) value averaged over all species within each phylogeographic group. Values greater than 1.76 and less than -1.76 are statistically significant (Student‟s t-test, two-tailed, α = 0.10).

Discussion Phylogeographic Patterns and Taxonomic Congruence

With the realization that the Sunda Islands were periodically connected to the mainland in the past (Molengraaff 1921; Van Bemmelen 1949), biogeographers reasonably expected close genetic relationships among populations of species on all islands (Inger and Chin 1962; Heaney

1986). However, by rejecting the Dispersal Hypothesis in 11 of the 16 study species, I demonstrate a clear need to revise the view that glacial-period land-bridges fomented extensive exchanges among rainforest faunas. Of these 11 species, five have at least one large (> 10%) inter-population πnet value, further suggesting ancient (possibly pre-Pleistocene) divergence and long-term persistence. These findings add to the small but growing body of literature suggesting that substantial complexity underlies population genetic structure in this geographic region

Figure 3.4. Standard whisker-and-box plots of climatic variables from the (A) PEN-Borneo contrast and the (B) WB-NEB contrasts. “S” represents climatic values from the regions where species with shallower divergences have high predicted probability of presence (open blue squares in Fig. 3.3), whereas “d” represents climatic values from the regions where species with deeper divergences have high predicted probability of presences (open red squares in Fig. 3.3). Outliers (those with values >1.5 * the interquantile range from the edge of a box) are shown as asterisks. BIO3 = isothermality (mean diurnal range/temperature annual range) (* 100); BIO4 = temperature seasonality (S.D. *100); BIO5 = maximum temperature of warmest month; BIO6 = minimum temperature of coldest month; BIO13 = precipitation of wettest month; BIO14 = precipitation of driest month; BIO15 = precipitation seasonality (Coefficient of Variation). Units of temperature and precipitation are in (°C * 10), and mm, respectively.

(Brandon-Jones 1998; Gathorne-Hardy et al. 2002; Campbell et al. 2004; Moyle et al. 2005;

Sheldon et al. 2009b). The distinctive difference in some species between populations in NEB and WB is particularly remarkable because these are currently connected by appropriate lowland habitats. In six species, separation between these populations occurred before 110 kya. The only case of genetic introgression between the two populations (in A. phaeocephalus, involving only one individual) that was detected might have been anticipated based on non-genetic evidence. In addition to the two subspecies in WB (diardi) and NEB (connectens), a third subspecies

(sulphuratus) is found in Borneo. This third subspecies occupies central Borneo and is morphologically intermediate between the other two (Fishpool and Tobias 2005), suggesting secondary contact followed by gene flow.

Current taxonomy corroborates the genetic data remarkably well (Smythies 1999;

Dickinson 2003; Wells 2007). Among the species that have a Borneo and a PEN clade (including

H. hypogrammicum), taxonomy correctly identifies these two as separate subspecies 60% of the time. In two species (P. plumosus and S. erythroptera), taxonomy separates Borneo individuals into two subspecies that are purported to occupy northeastern and western Borneo. For the species in which Sundaic populations form one clade (excluding H. hypogrammicum), taxonomy matches genetic data 50% of the time; in the species where there are discordance (T. paradisi and O. sericeus), Borneo and PEN populations are identified as separate subspecies. For the remaining species, taxonomy corroborates genetic information (i.e., PEN, WB and NEB are recognized as supporting three different subspecies), with the exceptions of S. poliocephala and

T. criniger. Borneo individuals of T. criniger are considered as belonging to one subspecies, when there are clearly two Borneo clades. S. poliocephala is considered monotypic whereas in the gene tree, populations from PEN, WB and NEB form three weakly to moderately supported

49 clades. Overall, there is a tendency to over-split taxonomically when populations are perceived to be separated by barriers.

Additionally, I uncovered interesting but preliminary phylogeographic patterns for individuals occurring north of Sundaland on mainland Asia. In some species (H. azurea, A. longirostra and M. gularis), the northern individuals form one or more clades that are distinct from the Sundaic ones. In others (T. paradisi and C. malabaricus), the northern individuals cannot be differentiated from (at least) Peninsular Malaysia individuals. The zoogeographic transition along the Thai-Malay Peninsula, and the propensity of some Sundaic genetic lineages to extend beyond the Isthmus of Kra (the traditional northern limit of Sundaland), which is associated with a vegetation transition and Neogene marine transgressions (Woodruff 2003), needs to be further investigated.

Habitat Barriers as A Cause of Lineage Isolation

The confluence of advances in climate modeling, niche modeling and genetic analysis allows forces that shaped the large-scale distribution of genetic diversity to be investigated with unprecedented rigor (e.g., Graham et al. 2006; Carstens and Richards 2007; McCormack et al.

2008; Carnaval et al. 2009). Although they have relatively coarse resolution, the widely used continental- or global-scale climate layers have proven to be useful because an organism‟s tolerance to various climatic conditions, in the broadest sense, defines its fundamental niche

(Kearney and Porter 2009). For example, many of the study species that have low inter- population divergences, presumably because they were better able to cross habitat barriers, also have wide latitudinal ranges – an indication that they occupy broad environmental niches.

Through the use of reconstructed paleodistributions, I demonstrate that species exhibiting significant differentiation between their PEN and Borneo populations had a lower probability of

50 occurrence in the large swath of emergent land that ran approximately north-south in central

Sundaland in the LGM. This area coincided with the location of a LGM savanna or seasonal vegetation corridor, hypothesized based on geomorphological, palynological, non-avian biogeographical, and other evidence (Heaney 1991; Bird et al. 2005). Characterization of the climate of the region unfavorable to the deeply divergent species supports the notion that the area was more seasonal. Riverine barriers were unlikely to have produced the pattern of LGM habitat discontinuity; moreover, central Sundaland was dissected by the upper reaches of paleo-rivers, which probably have reduced effectiveness in limiting dispersal (Colwell 2000). However, I cannot completely rule out the scenario that rivers acted in concert with habitat changes to impede population exchanges of some species across the Sunda shelf. According to my modeling, the corridor of unsuitable habitat between PEN and Borneo appears to be partitioned into northern and southern parts, although other paleo-evidence is ambiguous with respect the corridor‟s continuity (Cannon et al. 2009). The area of potential corridor closure coincides with the Karimata Strait (Fig. 3.1), in which lie several islands that contain forest-dependent mammal communities – an indication that these islands were covered by forests before being isolated by a rising sea (Meijaard and van der Zon 2003). This area also corresponds to the location of the shallowest sea between Borneo and Sumatra, and could be an important faunal conduit during glacial periods.

In Borneo, the dominant LGM habitat barrier appears to be upland in nature since species that are more deeply structured found the elevated central portion of Borneo less suitable. This is also supported by my climate analysis, which shows that habitats less suitable for this group of species were generally wetter and cooler. It is also possible that rivers have played a role in isolating populations here. As a result of climate cooling, upper montane forests descended to as

51 low as 1,300 m a.s.l. during the LGM (Morley 2000), which then pushed lowland forests into relatively narrow coastal strips (Cannon et al. 2009). River stretches, which have a stronger barrier effect closer their mouths, could then counteract the connecting effects of these corridors of lowland forest. In addition, I postulate that the physiography of NEB (i.e., being almost a peninsula) likely contributed to increased isolation of its populations (Sheldon et al. 2009a). I note that in both PEN-Borneo and within-Borneo analyses, the transect based approach is less effective in detecting habitat barriers, probably as a result of range shifts that moved areas containing major differences in suitability away from the pre-defined transects.

Because of model and sampling inadequacies, studies that rely on distribution reconstruction have their limitations (Peterson and Nyari 2007). These limitations may include an inability to project niche models across time because of a lack of analogous climatic conditions, and the failure of a niche model to take into account all important aspects of a species‟ niche (e.g., choice of microhabitat). One notable example of the second issue from my study is the tailorbird O. sericeus. It has a PEN-Borneo divergence time of < 21 kya, but was predicted to have experienced a massive range contraction in Sundaland in the LGM (Table 3.2 and Appendix 1 Fig. 3D). This apparent contradiction may be explained by the fact that it inhabits scrub and forest edge (Sheldon et al. 2001), a habitat generalism that was not effectively accounted for by my analytical approach. Nevertheless, the conclusions from my study should remain robust to technical limitations common to studies that use paleodistributions. This is because by averaging the paleodistributions of species that exhibit the same phylogeographic pattern, and then comparing these aggregated paleodistributions in a statistical framework, I minimize overreliance on the reconstructed distribution of any particular species. Finally, for some species, it is possible that the issue of a late Pleistocene barrier is irrelevant. Some lineages,

52 particularly those in northeastern Borneo, are so old that they may be reproductively isolated from the other populations. To verify this, multiple characters of individuals found across potential contact zones need to be studied.

Chapter 4: Demographic and Evolutionary Responses to Climatic and Geographical Fluctuations: A Multilocus and Multi-Species Perspective

Introduction Pleistocene climatic fluctuations strongly influenced the demographic and population divergence histories of plants and animals, primarily by causing distributional changes (Allen et al. 1999;

Hewitt 2000). In Europe and North America, expansion of ice sheets generally caused organisms to retreat into southern refugia (Hewitt 2004; Weir and Schluter 2004; Lao et al. 2008), although some species continued to persist in enclaves of suitable habitat in the north (Hickerson and

Cunningham 2005; Stewart 2008; Marko et al. 2010). Population genetic studies often reveal that the previously glaciated regions harbor less genetic diversity (e.g., Michaux et al. 2003; Boulet and Gibbs 2006; Lemmon et al. 2009), probably as a result of rapid recolonization and growth followed by the exclusion of other genotypes by the original colonizers (Ibrahim et al. 1996).

This interpretation is supported by another commonly observed pattern, which is that populations occupying deglaciated regions exhibit signatures of recent demographic expansion (Lessa et al.

2003). Together, these forms of evidence engender the „Expansion-Contraction‟ paradigm of northern temperate Pleistocene biogeography, which is generally applicable at least to terrestrial ecosystems (Provan and Bennett 2008).

In contrast, the impact of Pleistocene climatic fluctuations on the distribution of genetic diversity of organisms at lower latitudes or the tropics is less well understood. In part, our ignorance stems from a relative paucity of studies of Pleistocene effects in the tropics, but it also results from the lack of one dominant proximate driver (i.e., glaciation) behind distributional shifts and population isolation. So far, a range of climate-shift related mechanisms have been proposed as having a major impact on population structuring and divergence. These include:

54 formation of forest refugia during cooler, more arid periods (Haffer 1997); presence of environmentally stable areas in tropical mountains that allowed lineages to persist and subsequently radiate (Fjeldsa and Bowie 2008); and climatic shifts creating greater vegetation heterogeneity or disturbances that facilitated selection-driven divergence (Vanzolini and

Williams 1981; Colinvaux 1993). Because climatic changes were less dramatic at lower latitudes than in the far north, lineages at lower latitudes were likely to persist for longer periods of time

(Weir and Schluter 2007). This presents additional challenges for scientists because of the need to disentangle temporally distinct causal factors that produced superficially similar spatial patterns (Soltis et al. 2006).

For a general picture of the important evolutionary processes that underlie tropical genetic diversity to emerge, it is critical to develop an understanding of the mechanisms that were at work in different places (Moritz et al. 2000). Among the most poorly studied major tropical regions is Southeast Asia, which harbors a disproportionately large amount of biological diversity given its size (Myers et al. 2000). My study focuses on parts of insular Southeast Asia

(corresponding to the biogeographic region of Sundaland), which is highly distinctive in having the currently emergent landmasses (Borneo, Java, Sumatra and the Malay Peninsula) resting atop a shallow continental shelf, the Sunda shelf. Because of its shallow nature, the shelf was periodically exposed by glacio-eustatic reductions in sea-level, resulting in land bridges connecting the Sunda islands to one another and the mainland (Voris 2000). The existence of periodic land connections has led some biogeographers to postulate that the region‟s fauna experienced extensive population mixing from time to time (Medway 1972; Heaney 1986).

However, glacial-period Sundaland was not a monolithic block of tropical lowland rainforest, the dominant vegetation type of today‟s Sundaland. Bathymetric studies have shown that the shelf

55 was dissected by paleo-rivers, some of which were large rivers that carried the combined flow of several modern-day rivers (Gibbons and Clunie 1986; Voris 2000). In recent years, it has also become increasingly clear that the climatic regime in glacial periods was much different from today. Not only was it colder and drier, but maritime influence over Sundaland decreased as the land area increased, and these climatic changes probably resulted in an expansion of seasonal forest, savanna woodland and upland forest at the expense of lowland rainforest (Morley 2000;

Gathorne-Hardy et al. 2002; Bird et al. 2005; Cannon et al. 2009). The few genetic studies conducted in this region indicate that populations of rainforest species on different landmasses are highly differentiated, such that their initial divergences likely predated recent glacial cycles, if not the entire Pleistocene (Gorog et al. 2004; Steiper 2006). Others show that habitat generalists or open-habitat animals have diverged more recently, suggesting gene flow during

Pleistocene land connections (Fernando et al. 2003; Campbell et al. 2004; Sheldon et al. 2009b).

This suggests that it is important to take into account the life history traits and ecological characteristics of a species when investigating its evolutionary responses to climatic fluctuations

(Bohonak 1999).

In this study, I focus on the following passerine species that are codistributed in

Sundaland: Arachnothera longirostra (Little Spiderhunter; Nectariniidae), Malacocincla malaccensis (Short-tailed Babbler; Timaliidae) and Orthotomus sericeus (Rufous-tailed

Tailorbird; Cisticolidae). All three species are found in Borneo, the Malay Peninsula, Sumatra and adjacent islands (Fig. 4.1), although A. longirostra is found as far as southern China and parts of India (Grimmett et al. 1998; MacKinnon and Phillipps 2000). Data from multiple genetic loci were combined to study the impact of past environmental fluctuation on their demographic and population divergence histories, and the association between these histories and the species‟

Figure 4.1. Sampling localities (triangles) of all samples from which ND2 or nuclear sequences were derived (except for those in Myanmar and Vietnam). Sarawak and Sabah are Malaysian states on the island of Borneo. For clarity, sampling points in the northern Malay Peninsula and extreme southern Malay Peninsula (island of Singapore) were collapsed into single symbols. Regions with elevations ≥ 1,000 m are shown in gray.

57 general ecological characteristics. All three species are birds of the understory, but differ in respect to habitat breadth. O. sericeus, a shrub-level gleaning insectivore, inhabits the widest variety of habitat types. It occupies lowland rainforest edges, secondary forests, scrub and riparian vegetation, back mangroves, and heath forests (Sheldon et al. 2001; Wells 2007). It also occurs in rainforest interior when its congeneric competitors are absent (Wells 1978). On the other hand, M. malaccensis, is an insectivore that occupies mainly lowland primary or mature secondary lowland rainforests, foraging only up to 1 m above the forest floor (Teesdale 1972;

Wells 2007). It is sensitive to habitat degradation, and avoids cultivated rural landscapes (Peh et al. 2005). The habitat breadth of A. longirostra, an insectivore/nectarivore, is intermediate between those of the previous two species. Although it occupies lowland rainforests (primary and secondary), it can also be found in forest edges, closed-canopy scrubs, and is adaptable to moderate levels of forest conversion (e.g., it is found in structurally complex exotic tree plantations) (Mann 2008; Sheldon et al. 2009c). Because of their disparate ecological characteristics, these three species are expected to have had different evolutionary responses to environmental fluctuations in Sundaland. I predict that the species with the widest habitat breadth (O. sericeus) to be least affected by the habitat and riverine barriers that probably arose when the Sunda shelf was exposed. As such, it should exhibit the highest level of across-shelf, inter-population gene flow, followed by A. longirostra and M. malaccensis. Moreover, regional differences can also drive differences in demographic history of populations. Based on palynological data and vegetation modeling, it has been shown that lower montane vegetation or hill forests in Sundaland might have descended to 200 m above sea-level during glacial maxima

(Flenley 1998; van der Kaars et al. 2001; Cannon et al. 2009). If this was the case, lowland rainforests in parts of Borneo were periodically reduced to relatively narrow coastal strips. This

58 is because central Borneo is dominated by highlands, and the continental shelves (which were exposed during glacial maxima) in northern and eastern Borneo during LGM did not extend much further than current coastlines. As a consequence, species dependent on lowland rainforests might have experienced demographic fluctuations. In western Sundaland, a north- south savanna corridor is likely to have occurred, but its dimensions are debated. (Bush and

Flenley 2007). At its purported maximal extent, this corridor might have displaced most of the lowland rainforest on the Malay Peninsula, although refugial areas were thought to be available on nearby Sumatra and in the emergent shelf (now Melaka Straits) between them (Morley 2000).

By taking a comparative phylogeographic approach, I hope to shed light on the ecological underpinnings of differential responses to the region‟s past environmental changes (Avise 1998).

However, because of the region‟s highly dynamic history (e.g., landmasses were separated and reconnected multiple times), it is also necessary to employ a data-rich approach that allows fairly complex demographic histories to be deciphered. For species that have been influenced by recent isolation events, uncertainties around estimates of inter-population genetic divergence are dominated by coalescent stochasticity, that is, the random sorting of ancestral lineages in descendent populations (Hudson and Turelli 2003). To manage this source of uncertainty, I sampled multiple unlinked loci; the variance in coalescence time among loci provides an indication of the ancestral population size, which directly affects coalescent stochasticity

(Edwards and Beerli 2000). To investigate the process of inter-population migration after initial divergence, I applied the coalescence-based „Isolation-with-Migration” (IM) analytical approach

(Hey and Nielsen 2004), which has been used successfully to tease apart ongoing gene flow and divergence time in a variety of within-species and between-species studies (e.g., Won and Hey

2005; Kondo et al. 2008; Hurt et al. 2009). To examine population size changes through time, I

59 use a combination of summary statistics, mismatch distributions, an IM model that incorporates size changes (Hey 2005), and the Bayesian skyline approach (Drummond et al. 2005). Although the data requirement limited my ability to examine a larger number of species, and thus the ability to derive a quantitative association between ecology and the degree of genetic differentiation, this study, as one of the first multilocus-based study in Sundaland, still reveals interesting demographic and evolutionary patterns, and provides a framework for future work.

Methods Sampling and Laboratory Methods

I sequenced nuclear intronic loci and combined them with sequences of the mitochondrial

NADH dehydrogenase 2 (ND2; 781 – 1096 bp) gene collected in chapter three, although not all individuals were sequenced for both the ND2 gene and the nuclear loci (Appendix 1 Table 3).

For the new (i.e., nuclear) dataset, individuals of each species were collected from the Malay

Peninsula (PEN), and the states of Sarawak and Sabah in Malaysian Borneo (hereafter called the focal regions). For O. sericeus, I supplemented the nuclear sequences with those from a Sumatra individual, because of the small sample size from the Malay Peninsula (Table 4.1). The Malay

Peninsula and Sumatra are separated by a narrow and shallow strait (approximate depth = 30 m), thus enabling them to be connected physically ~50% of the time during the last 250 thousand years (kyr) as seas depth fluctuated between the current level and 120 m below that (Voris 2000).

Further, comparative phylogeographic analyses of mitochondrial DNA (mtDNA) from multiple species of birds have shown that individuals from Sumatra and the Malay Peninsula often are often very closely related genetically (chapter three). In each species, the average number of individuals for which mtDNA and nuclear data are available is 25.7 (range: 17-33) and 18.7

(range: 15-25), respectively (Table 4.1), with the majority (71.4%) of the samples derived from individuals that are museum vouchered (Appendix 1 Table 3).

Table 4.1. The number of individuals sampled sorted by species, geographical locality and the type of genetic locus. ND2 data was collected in chapter three. Used in Northern multilocus Malay Peninsula Sarawak Sabah Sumatra Southeast Asia analyses1 A. longirostra ND2 10 8 11 1 3 29 nuclear 6 6 4 0 0 16 M. malaccensis ND2 8 9 8 2 0 25 nuclear 9 8 8 0 0 25 O. sericeus ND2 4 6 6 1 0 17 nuclear 4 6 4 1 0 15 1 For O. sericeus, I supplemented Malay Peninsula individuals with one Sumatra individuals. Otherwise, individuals from outside of the Malay Peninsula, Sarawak and Sabah were not included in multilocus analyses (i.e., Isolation-with-migration, Bayesian skyline and summary statistics calculations).

DNA was extracted from ethanol-preserved pectoral muscle using DNeasy Tissue Kits

(QIAGEN Inc.) or from blood preserved in Queen‟s Lysis Buffer (Seutin et al. 1991) using a standard phenol-chloroform protocol. I amplified 9 nuclear autosomal and 1 Z-linked loci using published PCR primers and thermal cycling conditions. The autosomal loci are: 16214, 20771,

27189 (Backstrom et al. 2008), ARNTL, GARS, PER2, VIM (Kimball et al. 2009), GADPH and

TGFβ2 (Primmer et al. 2002). The Z-linked locus was BRM15 (Borge et al. 2005). PCR products were purified with 20% polyethylene glycol and cycle-sequenced in both directions using the PCR primers and the BigDye Terminator Cycle-Sequencing Kit version. 3.1 (Applied

Biosystems Inc.). I cleaned the cycle-sequencing products with Sephadex G-50 fine (GE

Healthcare) before analyzing them on an ABI Prism 3100 Genetic Analyzer (Applied

Biosystems). I assembled, called „double peaks‟, and edited sequences in the program

Sequencher version 4.7 (GeneCodes Corp.).

I first resolved the gametic phase of alleles containing more than one heterozygous nucleotide position computationally with the program PHASE version 2.1 (Stephens and

Donnelly 2003), or Champuru version 1.0 (Flot 2007) if the alleles from an individual were heterozygous with respect to base insertion or deletion. Haplotypes fully resolved by Champura were specified as known (by using the –k option) during PHASE analyses. If phase probabilities of nucleotide sites (other than those containing unique polymorphisms) were below 0.85 after these computational steps, I resolved the gametic phases of selected individuals using molecular cloning. I cloned PCR products using the Stratagene PCR cloning Kit (Agilent Technologies) and then picked four clones (with the appropriate insert) from each individual randomly. Picked cloned were PCR-amplified using the M13 primers and then sequenced. While this level of replication in sequencing was usually enough to resolve sequence incongruence that arose out of polymerase error (by nucleotide misincorportation) or in vitro PCR recombination (Bradley and

Hillis 1997), I increased the number of clones sequenced when necessary. I then re-ran PHASE analyses with these fully resolved haplotypes specified as „known‟. I continued this iterative process of PHASE analysis and molecular cloning until all ambiguous sites (except for sites with unique polymorphism) had phase probabilities of ≥ 0.85. Harringan et al. (2008) showed that, for their samples (n = 32), which were composed of relatively complex heterozygous sequences, haplotype inference was correct whenever phase probabilities exceeded 0.60.

Data Analyses

ND2 sequences were used to construct parsimony haplotype networks with TCS version 1.21

(Clement et al. 2000). I designated populations based on information obtained from the ND2 gene (haplotype network and AMOVA), since, compared to nuclear loci, mtDNA loci are leading indicators of population structure (Zink and Barrowclough 2008). For A. longirostra and

O. sericeus, I combined individuals from Sabah and Sarawak (into a single population) because conspecfics from these regions either share haplotypes or form a clade in the ND2 gene tree. By contrast, M. malaccensis individuals from the three focal regions form reciprocally monophyletic clades (chapter three), and were thus designated as three populations. Other than haplotype network construction, I carried out all data analyses using only individuals from the focal regions, except for the one O. sericeus individual from Sumatra. I calculated the number of segregating sites (S), nucleotide diversity (π, Nei 1987), Tajima‟s D (Tajima 1989), and Ramos-

Onsins and Rozas‟ R2 (Ramos-Onsins and Rozas 2002) for each population of each species. The significance of the latter two statistics was tested by comparing observed values against those generated from 1,000 coalescent simulations. Both Tajima‟s D and R2 use information in the nucleotide sequences (number of segregating sites, pairwise differences, and/or number of singleton mutations) to detect a departure from the neutral model of evolution. When intralocus recombination was detected, I used only the largest non-recombining DNA block to calculate and test the significance of these two statistics because recombination affects the power of these tests (Wall 1999). I also calculated shared polymorphisms and fixed differences between pairs of populations. All of the above calculations were performed in DNAsp version 5.1 (Rozas et al.

2003). For the ND2 data alone, I also calculated AMOVA, population-specific mismatch distributions, and statistics that test for sudden population expansion (sum of squared deviation

[SSD] from a distribution expected under sudden expansion and Harpending‟s Raggedness index) in Arlequin version 3.11 (Harpending 1994; Schneider and Excoffier 1999; Excoffier et al. 2005). Intralocus recombination of nuclear loci was detected with a phylogenetic sliding- window method – the pruned probabilistic divergence measure method (Husmeier et al. 2005).

This method counteracts a diffusion in the divergence signal as the number of taxa increases by

63 reducing the tree space (i.e., by pruning) through topology-based clustering of phylogenetic trees

(Stockham et al. 2002). I executed this analysis in the program Topali version 2.5 with step size and window size set at 10 bp and 160 bp, respectively (Milne et al. 2004). For each species-locus combination, I selected the 15 most divergent sequences for recombination analysis because this is the maximum number allowed when analyses are carried out in the online server.

To obtain estimates of divergence time (t), effective population size (Ne) and the level of gene flow (m), mitochondrial and nuclear data were combined and analyzed in the coalescence- based programs IM or IMa version 2.0 (Hey and Nielsen 2007). If intralocus recombination was detected in a nuclear locus, I selected the largest non-recombining block of DNA for analysis.

For M. malaccensis, I first analyzed the data using IMa version 2.0 because it can model the divergence history of more than two populations. Subsequent to this, I analyzed sequences of M. malaccensis individuals derived from the Malay Peninsula and Sarawak in IM because these populations are sister to each other. For each species, I conducted IM analyses using the basic six-parameter model, as well as the seven-parameter size-change model (Hey 2005). In addition to the above-mentioned parameters, the size-change model provides information on the proportion (s or 1-s) of the ancestral population that founded each daughter population, and the

(exponential) population size changes that follow. The rate of population expansion (i.e., the exponential growth constant) was calculated using point estimates of parameters and the equation r = Ln(N1/s*NA)/t, where N1 is the final effective population size of population 1 and s*NA is the proportion of the ancestral population that founded population 1 at the time of splitting (t). Initial IM or IMa runs were conducted with broad priors in order to identify appropriate upper limits that would encompass all or most of the entire posterior distributions.

Final runs have 10 million generations (10% burn-in, sampling every 100th generations) with 1

64 cold chain and 5 heated chains (geometric heating). I ensured parameter convergence and proper chain mixing by checking trend lines, effective sample sizes and results from independent runs.

Here, for each species, I present results from two final runs, one conducted under the (six- parameter) constant population model, and one conducted under the (seven-parameter) size- change model. All migration rates shown pertain to gene flow rates as time moves forward.

To convert estimated parameters scaled to the neutral mutation rate (µ) into parameters with absolute units (e.g., years, number of individuals), I estimated the mutation rates of eight loci, namely: ARNTL, GAPDH, GARS, PER2, TGFβ2, VIM, BRM and ND2. For the first six loci, I took sequences from one parrot (Micropsitta finchii, Psittacula alexandri or Amazona amazonica), one suboscine (Tyrannus tyrannus or Mionectes rufiventris) and one oscine

(Bombycilla garrulous or Regulus calendula). These sequences were either downloaded from

GenBank (Appendix 1 Table 4) or were unpublished (R. Kimball, per comm.). I then estimated the best sequence substitution model for each locus using MODELTEST version 3.7 (Posada and

Crandall 1998). Following this, I calculated the mutation rate of each locus under two estimates of divergence times between oscines and suboscines (62.5 million years [Myr] and 78.7 Myr;

Ericson et al. 2002), and the locus-specific model of sequence evolution. The mutation rates applied in IM or IMa version 2 were the averages of rates based on the two divergence times

(average rate for autosomal loci = 1.67 x 10-9 substitutions/site/year[sub/s/yr]). The rates I obtained are similar to the average rate for autosomal loci obtained from a chicken-turkey comparison, 1.35 x 10-9 sub/s/yr (Ellegren 2007). For BRM, I applied the rate (1.80 x 10-9 sub/s/yr) calculated based on Axelsson et al.‟s Chicken-Turkey data (2004). By comparing ND2- and cytochrome b (CytB)-based intraspecific genetic distances of the study species, I approximated the mutation rate of ND2 to be about 1.3 times that of CytB. Using this conversion

65 ratio, I calculated ND2 mutation rate for my species based on Weir and Schluter‟s (2008) study in which the average passerine CytB mutation rate was 1.035 x 10-8 sub/s/yr. To calculate the generation time (G) of each species, I used the equation G = α + (sv/1 – sv), where α is age at first breeding (which I assumed to be one year for all species) and sv is the estimated adult survival rate (Saether et al. 2005). Because I lacked estimates of sv for the study species, I used mass-corrected survival rates of ecologically equivalent species from the Neotropics as proxies

(Brawn et al. 1995).

For each population of each species, I also conducted an extended Bayesian skyline

(EBS) analysis (Heled and Drummond 2008), using the program Beast version 1.5 (Drummond and Rambaut 2007). This analytical approach uses coalescence times among alleles from a population to estimate population size through time without relying on parametric models of population size changes or a pre-specified number of size-change events (or size steps).

However, although it is more flexible than IM in terms of estimating of population size changes, it does not take into account inter-population gene flow. As suggested by one of Beast‟s author

(A. Drummond), I did not attempt to differentiate the likelihood of different demographic models

(e.g., extended skyline vs. constant population size) given my data using Bayes factors because the method for calculating them in Tracer version 1.5 may produce unreliable results. Instead, I used the exclusion of zero from the 95% confidence interval around an estimation of the number of size-change steps as an indication of population size-change through time. As in the IM analyses, individuals of A. longirostra from Sarawak and Sabah were combined, as were those of

O. sericeus. In the EBS analyses, I used a strict molecular clock model because I failed to reject clock-like evolution in all populations using the fastest evolving locus, ND2. I selected the GTR

+ Γ + I and HKY + Γ + I substitution models for the ND2 and nuclear loci, respectively.

Analyses were run for 60-80 million generations, with the MCMC chain sampled every 1000 generations. I discarded the initial 10% of the samples as burn-in. To take advantage of Beast‟s ability to incorporate probabilistic calibration priors (i.e., priors with pre-defined distributions), for each nuclear locus, I specified a mutation rate prior that was normally distributed. Each normal distribution was specified such that it was centered on the mean mutation rate for that locus, and its 2.5th and 97.5th percentile coincided with the rates calculated based on the younger and older oscine-suboscine divergence times, respectively. The use of normal distributions here was appropriate because I imported mutation rate estimations that were based on one secondary calibration point (i.e., oscine-suboscine split) (Ho 2007). For the ND2 data, I used the lognormal distribution as the prior distribution because this distribution most closely resembles the spread of rates calculated from multiple avian taxon-pairs (Weir and Schluter 2008). The real-space mean of the lognormal distribution corresponds to the mean passerine mutation rate, after the

ND2-CytB correction; the 95% distribution of the lognormal distribution encompassed the fastest and slowest passerine rates reported by Weir and Schluter (2008).

Results Population Structure, Summary Statistics and Mismatch Distributions

The average number of alleles sequenced per gene (mtDNA and nuclear) for A. longirostra, M. malaccensis and O. sericeus was 30.3, 43.8 and 28.0, respectively (Table 4.1). Because of occasional PCR or sequencing failures, not all samples yielded DNA sequences for all genes.

ND2 haplotype network of A. longirostra showed that one haplotype predominated in Borneo

(Fig. 4.2A). In contrast, individuals from the Malay Peninsula possessed a number of haplotypes separated from each other by one mutational step. Haplotypes from northern Southeast Asia

(Myanmar and Vietnam) were separated from Sundaic haplotypes by multiple mutational steps.

AMOVA showed that, within Sundaland, most of the genetic variation was explained by differences between PEN and Borneo (85.3%). For M. malaccensis, TCS returned three separate networks when the connection limit was set at 95% (networks of populations also failed to connect to each other when the limit was 90%; Fig. 4.2B), as a result of strong between- population differentiation (uncorrected inter-population P-distances: 3.4-10.3%). Individual haplotypes were segregated according to the population of origin, with Sumatra samples nesting with the ones from PEN; a large majority (93.11%) of the genetic variation was explained by differences among the three populations. The O. sericeus network indicated haplotype sharing between PEN and Sumatra, and little overall population structuring with 72.65% of genetic variation explained by within population differences (Fig. 4.2C).

Tajima‟s D and R2 largely corroborate each other in terms of identifying departures from a neutral model of evolution (i.e., a significant result in one statistic was often matched by the same in the other; Table 4.2). In all species, the Borneo population or populations have proportionately more strongly-negative Tajima‟s D values (P < 0.10; 18.2-45.5%) and smaller R2 values (P < 0.10; 27.3-45.5%) than populations in the Malay Peninsula (P < 0.10; Tajima‟s D: 0-

11.1%; R2: 0-27.3%). As expected, nuclear loci contained more inter-population shared polymorphisms than fixed differences; only in M. malaccensis were inter-population nucleotide fixed differences evident in the nuclear loci (Table 4.2).

Three populations (both populations of A. longirostra, and the Sarawak population of M. malaccensis) showed distinctly unimodal mismatch distributions indicative of sudden population expansion (Fig. 4.2). One population (Sabah population of M. malaccensis) showed a bimodal distribution and the remaining populations have multimodal mismatch distributions (Fig. 4.2).

Although the SSD and the Raggedness Index tests failed to reject sudden population expansion

Figure 4.2. Haplotype networks and mismatch distributions of ND2 sequences from each species, sorted by population. Each line (separated by circles or squares) on the haplotype networks represents one inferred nuclear substitutional change. Each circle represents one haplotype, the size of which is proportional to the frequency of the haplotype (smallest circle = 1 individual). In the mismatch distributions, both observed and simulated data (under a model of sudden population expansion) are shown.

Table 4.2. Genetic summary statistics for each genetic locus.

Popula- P- P- 1 2 3 4 5 6 7 Locus Length (bp) tion n S π (%) Tajma's D value R2 value Pair SP FD A. longirostra 16214 349 B 20 24 1.45 -1.10 * 0.094 * 19 0

PEN 12 20 2.11 0.20 0.170

20771 465 B 18 4 0.18 -0.84 0.105 ** 4 0

PEN 12 8 0.32 -1.78 ** 0.109 **

27189 497 B 20 7 0.48 0.64 0.168 6 0

PEN 12 13 0.70 -1.10 0.171

ARNTL 506 B 20 6 0.14 -1.89 ** 0.091 *** 5 0

PEN 12 7 0.47 0.08 0.147

GAPDH 314 B 20 7 0.46 -0.90 0.124 2 0

PEN 10 8 0.72 -0.89 0.114 **

GARS 419 B 20 6 0.55 1.13 0.186 4 0

PEN 12 8 0.58 -0.34 0.128

PER2 643 B 18 5 0.24 0.16 0.145 3 0

PEN 12 6 0.33 0.26 0.161

TGFB 539 B 20 15 0.93 0.69 0.165 12 0

PEN 12 17 1.21 0.69 0.172

VIM 499 B 20 20 1.10 -0.12 0.124 11 0

PEN 10 14 1.15 0.73 0.183

BRM 400 B 15 7 0.35 -1.59 * 0.085 *** 2 0

PEN 9 6 0.49 -0.52 0.190

ND2 781 B 19 4 0.05 -1.86 ** 0.130 1 5

PEN 10 6 0.26 -0.81 0.129 **

B Proportion of P-value < 0.1 36.4% 36.4%

PEN Proportion of P-value < 0.1 9.1% 27.3%

M. malaccensis 16214 398 SB 16 10 0.63 -0.65 0.104 * SB-SR 3 0

SR 16 18 1.01 -1.05 0.083 ** SR-PEN 4 0

PEN 16 6 0.45 -0.08 0.147 PEN-SB 3 0

20771 432 SB 16 16 0.84 -1.34 0.097 * SB-SR 0 0

SR 16 3 0.28 0.92 0.195 SR-PEN 2 0

PEN 18 2 0.20 1.13 0.212 PEN-SB 0 0

27189 497/390 SB 14 17 0.98 0.36 0.137 SB-SR 1 1

SR 16 12 0.42 -1.62 * 0.099 ** SR-PEN 0 0

PEN 12 1 0.03 -1.14 0.276 PEN-SB 0 1

ARNTL 629 SB 16 9 0.37 -0.54 0.114 SB-SR 2 0

SR 14 9 0.22 -1.93 ** 0.138 SR-PEN 1 0

PEN 16 6 0.24 -0.52 0.134 PEN-SB 0 0

GAPDH 326 SB 16 13 1.06 -0.44 0.124 SB-SR 1 0

(table continued)

Popula- P- P- 1 2 3 4 5 6 7 Locus Length (bp) tion n S π (%) Tajma's D value R2 value Pair SP FD

SR 16 4 0.35 -0.32 0.134 SR-PEN 0 0

PEN 16 0 0.00 PEN-SB 0 2

GARS 363 SB 16 6 0.48 -0.13 0.141 SB-SR 1 0

SR 16 3 0.18 -0.81 0.129 SR-PEN 1 0

PEN 14 2 0.18 0.04 0.179 PEN-SB 1 0

PER2 638/183 SB 14 32 1.59 0.02 0.122 SB-SR 15 0

SR 16 34 1.13 -1.23 0.085 *** SR-PEN 12 0

PEN 16 18 0.89 0.18 0.136 PEN-SB 6 0

TGFB 521/388 SB 16 19 0.76 -1.27 0.100 ** SB-SR 1 0

SR 16 18 0.87 -0.65 0.148 SR-PEN 3 0

PEN 16 3 0.24 1.14 0.210 PEN-SB 1 2

VIM 512 SB 16 11 0.38 -1.79 * 0.077 *** SB-SR 1 0 SR 16 8 0.28 -1.45 0.116 * SR-PEN 0 0

PEN 14 2 0.08 -0.96 0.145 PEN-SB 0 0

BRM 404 SB 12 13 0.91 -0.63 0.113 * SB-SR 1 0

SR 11 2 0.13 -0.78 0.165 SR-PEN 0 1

PEN 14 1 0.11 0.84 0.220 PEN-SB 0 1

ND2 797 SB 8 10 0.49 0.05 0.153 SB-SR 0 77

SR 9 13 0.45 -1.22 0.113 ** SR-PEN 1 23

PEN 8 14 0.72 0.33 0.167 PEN-SB 1 78

SB Proportion of P-value < 0.1 18.2% 45.5%

SR Proportion of P-value < 0.1 36.3% 45.5%

PEN Proportion of P-value < 0.1 0% 0%

O. sericeus 16214 403 B 18 2 0.08 -1.10 0.126 0 0

PEN 10 0 0.00

20771 465 B 20 4 0.13 -1.41 0.089 *** 0 0

PEN 10 0 0.00

27189 483 B 20 8 0.22 -1.82 * 0.101 * 2 0

PEN 10 3 0.16 -1.03 0.209

ARNTL 637 B 20 5 0.22 0.01 0.136 0 0

PEN 10 1 0.03 -1.11 0.300

GAPDH 335 B 20 8 0.70 0.14 0.143 4 0

PEN 10 5 0.50 -0.23 0.141 *

GARS 363 B 20 4 0.30 -0.18 0.131 2 0

PEN 10 2 0.11 -1.40 0.200

PER2 630 B 20 2 0.12 0.70 0.183 1 0

PEN 10 1 0.07 -0.82 0.233

TGFB 563 B 20 9 0.46 0.06 0.136 5 0

PEN 10 5 0.32 0.12 0.186

(table continued)

Popula- P- P- 1 2 3 4 5 6 7 Locus Length (bp) tion n S π (%) Tajma's D value R2 value Pair SP FD VIM 531 B 20 1 0.04 -0.59 0.095 0 0

PEN 10 1 0.07 0.01 0.178

BRM 367 B 15 7 0.65 0.41 0.154 4 0

PEN 8 4 0.58 1.70 0.268

ND2 1096 B 12 19 0.39 -0.63 0.109 * 0 0

PEN 5 0 0.00 0.126

B Proportion of P-value < 0.1 18.2% 27.3%

PEN Proportion of P-value < 0.1 11.1% 11.1%

1 When intra-locus recombination was detected, the size of the largest non-recombining DNA block is given after “/”. All genetic diversity parameters were calculated using the full sequences. 2 Population designations according to ND2 gene tree. B = Borneo; SB = Sabah; and SR = Sarawak. 3 Number of chromosome sampled; not all individuals were successfully sequenced for every locus. 4 Number of segregating sites. 5 The population pair compared when calculating shared polymorphism or fixed differences. This is only relevant for M. malaccensis which has three populations. SB = Sabah; SR = Sarawak; PEN = Malay Peninsula. 6 Number of shared polymorphism. 7 Number of fixed differences.

(at P = 0.05) in all populations, populations that did not posses unimodal mismatch distributions generally have low P-values, suggesting poor match between the model and the empirical data

(Table 4.3). Time of expansion for populations with unimodal distributions ranged from 20 kyr to 172 kyr (Table 4.3). The two peaks in the mismatch distribution of the Sabah population of M. malaccensis correspond approximately to 140 kyr and 240 kyr.

Isolation-With-Migration Analyses

Overall, intralocus recombination was not detected except in three loci in M. malaccensis (Table

4.2). In A. longirostra, estimates for Ne – PEN and Ne – Borneo under the size-change model have wider confidence intervals compared to the constant population model. Among the three Ne estimates, that of PEN was the highest (0.68-1.27 x 106 individuals) under both models, with the

Table 4.3. Results of tests of observed mismatch distributions against a model of sudden demographic expansion. (Note: estimates of time of expansion may not be reliable if observed data do not fit the expansion model well, see text for more explanations). Raggedness Time of 95% confidence SSD1 P-value P-value Population index expansion interval (kyr) (kyr) lower upper A. longirostra PEN 0.073 0.073 0.238 0.074 110.3 17.3 195.1 Borneo 0.002 0.600 0.280 0.560 20.0 0.0 78.2 M. malaccensis

PEN 0.078 0.110 0.214 0.152 414.7 20.0 689.0 Sarawak 0.007 0.780 0.044 0.764 172.1 20.7 308.9 Sabah 0.022 0.572 0.057 0.762 232.4 76.6 401.3 O. sericeus

Borneo 0.037 0.114 0.115 0.082 333.7 91.9 563.9 1 Sum of squared deviation

PEN and Borneo populations diverging around 0.63-0.69 Myr ago (Fig. 4.3A). The posterior distribution of mPEN -> Borneo peaked at zero under the constant size model (but note a smaller secondary peak), unlike the posterior distribution produced by the size-change model (peak estimate: 1.13 x 10-6 /individual/yr). Because only a small proportion (0.05%) of the ancestral population founded the Borneo population, growth experienced by the latter population was high

(r = 12.94) compared to the population in PEN (r = 1.73, Table 4.4).

6 In M. malaccensis, IMa version 2 estimated Ne – Sabah to be large (1.33 x 10 individuals:

95% highest posterior density [HPD] = 1.00-1.80 x 106 individuals). This population diverged from PEN and Sarawak populations around 3.84 Myr ago (95% HPD = 2.68-5.96 Myr), and had effectively zero gene flow with the populations in PEN and Sarawak since then (i.e., posterior distributions of migration rate peaked at zero, curves not shown). Under both IM models, Ne –

Sarawak was higher than Ne – PEN and Ne – Ancestral (Fig. 4.3B), but was similar to Ne – Sabah estimated under IMa version 2. The rate of gene flow was slightly higher in the direction of Sarawak into

PEN than in the opposite direction (Fig. 4.3B). The estimate of divergence time showed a

Figure 4.3A. Probability densities of each parameter calculated under the constant population (orange) and size-change (blue) isolation-with-migration models for A. longirostra. The peak value of each parameter and its 95% highest posterior density (in parenthesis) are also shown. Within each species, the scale of the x-axes of equivalent parameters are kept the same to aid visualization of relative proportions.

Figure 4.3B. Probability densities of each parameter calculated under the constant population (orange) and size-change (blue) isolation-with-migration models for M. malaccensis. The peak value of each parameter and its 95% highest posterior density (in parenthesis) are also shown. Within each species, the scale of the x-axes of equivalent parameters are kept the same to aid visualization of relative proportions.

Figure 4.3C. Probability densities of each parameter calculated under the constant population (orange) and size-change (blue) isolation-with-migration models for O. sericeus. The peak value of each parameter and its 95% highest posterior density (in parenthesis) are also shown. Within each species, the scale of the x-axes of equivalent parameters are kept the same to aid visualization of relative proportions.

76 bimodal distribution under the size-change model, with the two peak estimates being approximately 0.49 Myr and 1.32 Myr (Table 4.4). This could represent two alternative demographic histories that were similarly probable. I carried out separate analyses with upper t prior limits that excluded the older divergence time. This resulted in a higher estimate of Ne –

5 5 Ancestral (2.51 x 10 individuals; 95% HPD = 1.19-4.24 x 10 ; curve not shown), which corresponds to the secondary peak slightly evident in the original posterior distribution (Fig.

4.3B). Based on the combinations of longer divergence time-smaller Ne – Ancestral and shorter divergence time-larger Ne – Ancestral, I obtained constant growth rates that ranged between 2.38 and

4.44, with rates in Sarawak higher than in PEN (Table 4.4).

For O. sericeus, t was estimated to be between 0.77 and 0.92 Myr ago. However, the posterior distributions have long flat tails that did not fall to zero (Fig. 4.3C). The size of the

Borneo population was the largest, followed by the PEN population and the ancestral population.

Gene flow was asymmetrical, with a high level going from PEN into Borneo, and effectively no gene flow going in the opposite direction. The size-change model of IM did not converge upon one estimate for the splitting parameter (s) – the posterior distribution showed two peaks, one at zero and the other one at one (curve not shown). Therefore, I assumed both daughter populations were founded by 100% of the ancestral population (which was small) before calculating the constant growth rates of each population. As in A. longirostra, growth rate was higher in Borneo

(4.11) than in PEN (1.25, Table 4.4).

Bayesian Skyline Analyses

Reconstruction of population size through time using the EBS approach indicated that three populations had experienced significant size changes (Table 4.4, Fig. 4.4); the 95% HPD of the number of size-change steps for these populations did not include zero. These populations were:

Table 4.4. Estimates of changes in Ne and other method-specific parameters from IM (size-change model) and Extended Bayesian skyline analyses. Population size estimates have been rounded to three significant figures.

Results from IM analyses Results from Extended Bayesian Skyline analyses Proportional No. of size- contribution change Initial by ancestral Initial Final Exponential steps population Final Divergence population population population growth (mean; 95% size population time (Myr) Population (%) size (peak) size (peak) constant (r) Population HPD) (median) size (median) A. longirostra 0.633 PEN 99.95 423000 1270000 1.73 PEN 0.64; 0-2 642000 800000 Borneo 0.05 211 765000 12.94 Borneo 0.63; 0-2 520000 628000 M. malaccensis 0.4931 PEN 10.85 27200 151000 3.48 PEN 0.54; 0-2 200000 215000 Sarawak 89.15 224000 1990000 4.44 Sarawak 1.37; 1-3 190000 2560000 1.340 PEN 8.95 6010 146000 2.38 Sabah 3.32; 3-5 221000 11400000 Sarawak 91.05 61100 1810000 2.53 O. sericeus 0.770 PEN 1002 25300 66000 1.25 PEN 0.74; 0-3 86000 93000 Borneo 1002 25300 602000 4.11 Borneo 1.55; 1-3 51200 513000 1 Analysis carried out with a lower t upper prior that excluded the older divergence time. 2 Each daughter population was assumed to be founded by 100% of the ancestral population.

78 the two Borneo populations of M. malaccensis, and the Borneo population of O. sericeus. The

Sarawak population of M. malaccensis underwent a sharp increase around 500-600 kyr ago and grew about five-fold to its current size. The Sabah population of M. malaccensis, on the other hand, first experienced a steady increase in size to a maximum of about 15 million individuals followed by a decline (about 630 kyr ago), and then underwent another increase (about 270 kyr ago). The final size of this population, as indicated by the EBS analysis, was around 11 million individuals. The Borneo population of O. sericeus grew ten-fold from an initial size of around

50,000 individuals over a span of several hundred thousand years (Table 4.4, Fig. 4.4). In all instances except for the Sabah population of M. malaccensis, estimates of final population sizes based on the EBS analyses were consistent with those produced by IM or IMa.

Discussion Population Structure and Changes in Population Size

The three study species showed distinct differences in population divergence and demographic histories. The species with the strongest mtDNA structure was M. malaccensis. Its three focal populations were reciprocally monophyletic, and their haplotype networks failed to connect because of large among-population genetic distances. A. longirostra exhibited intermediate differentiation among its populations. Sabah and Sarawak individuals shared haplotypes, but at least five mutational steps separated the PEN and Borneo populations. Across Sundaland, populations of O. sericeus were even less structured; because of small among-individual differences, individuals were intermixed on phylogenetic trees (chapter three). Within Borneo, however, there was a lack of haplotype sharing between the O. sericeus populations in Sabah and

Sarawak (unlike A. longirostra), and this is suggestive of intermediate differentiation. In the terminology of Omland et al. (2006), this can be described as „allotypy‟, whereby the ancestral

Figure 4.4. Extended Bayesian skyline plots of populations of the three study species. Blue = median value; green and red lines = 95% confidence interval.

80 haplotype (likely resembling the Malay Peninsula haplotype because of the latter‟s central location in the network) has been lost but an individual may still have as its closest genetic relative a non geographic-neighbor. In general, evidence from different types of analyses (IM, mismatch distribution, summary statistics and EBS) indicated that the Borneo population(s) of all three species underwent larger population size changes than their peninsular counterparts.

These evidence, with respect to the Borneo population(s), include: larger exponential growth constants; unimodal or bimodal mismatch distributions; confidence limits of number of size change steps in EBS analyses that did not include zero; and more loci showing significantly negative Tajima‟s D or smaller R2. It can be argued that selective sweeps can also account for negative Tajima‟s D or smaller R2. However, selective sweeps are essentially locus-specific events, and the prevalence of statistically significant values made demographic expansion the more parsimonious explanation (Jensen et al. 2005).

The lack of complete concordance among results derived from different analytical methods also highlighted differences in their underlying assumptions. In the Borneo population of A. longirostra, the extensive sharing of haplotypes among individuals, and the unimodal mismatch distribution indicated very recent (~ 20 kyr ago) population expansion. This conclusion was not supported by the EBS plot, which showed a constant population size through time (except, perhaps, for a slight upward trend close to the present). This disagreement may be attributed to the fact that EBS analyses infer size changes by detecting changes in coalescence times among alleles across different time periods (Pybus et al. 2000). If a population was founded by a small number of genetically diverse individuals (for example, a random subset of a much larger population), coalescence times among alleles would still be long, resulting in an estimate of effective population size that was much larger than the actual numerical size. In

81 general, an EBS analysis did not detect a significant change in population size if the final size was not at least an order of magnitude larger than the pre-splitting ancestral population size, as estimated by IM. For example, the starting sizes of the PEN and Sarawak populations of M. malaccensis, as estimated by the EBS analyses, were similar to the size of their ancestral population estimated by IM. Only in the Sarawak population was population growth registered in the EBS plot because its final population size (2.6 x 106) was much larger than the ancestral population size (0.0067 x 106 – 0.25 x 106). In contrast to the EBS result, the PEN population, as modeled by IM, also experienced growth because it was founded by only a small fraction of the ancestral population. This is not saying that an EBS analysis provides an erroneous picture of population size changes, since it calculates effective population size, which is an idealized concept that describes the amount of genetic drift or inbreeding experienced by the actual population (Wright 1931).

A lack of congruence in the timing of population expansion, as estimated by mismatch distributions and the EBS analyses, was also evident. In all cases where population increases were detected by EBS plots (in the two Borneo populations of M. malaccensis and the Borneo population of O. sericeus), the timings of initial expansion were older than those (presumably equitable) events detected by mismatch distributions by a factor of 2-3. For example, the mismatch method estimated population expansion in the Sarawak population of M. malaccensis began about 170 kyr ago, whereas in the EBS plot the increase began close to 500 kyr ago. These discrepancies could be caused by a combination of factors that include: low precision in the analyses, errors in the DNA substitutional rate employed (too slow in the nuclear loci, which dominated the EBS analyses, or too fast in ND2), and the lack of DNA substitution-model correction in the mismatch analyses causing systematic underestimation of genetic distances

(hence expansion times). In any case, both sets of results indicated that population expansion, if it occurred, predated the LGM (except for the Borneo population of A. longirostra) – a period during which rainforest dependent species in Sundaland were said to be confined to a few refugia

(Brandon-Jones 1998; Gathorne-Hardy et al. 2002). Instead of bearing the signatures of severe population reductions during the LGM, the results here indicated that the dynamics of most populations were driven by longer term changes in the environment. One such example was the gradual amelioration of climate after the Mid-Pleistocene transition (1250 – 700 kyr ago); this period of climate amelioration was typified by increased sea temperatures, and reduced aridity in

Africa and Asia (Clark et al. 2006). In support of the notion that conditions improved for forest- dependent species after the Mid-Pleistocene transition, a deep sea pollen record that spanned approximately 1 Myr showed that elements of lowland rainforest (e.g., Dipterocarpacea and

Celastraceae) appeared to have expanded around 600 kyr ago (Sun et al. 2003). Population reductions during periods corresponding to glacial maxima might have little impact on long-term population sizes of tropical species because such bottleneck events were generally short in duration, and were likely to be less severe than those in the temperate zone (Jouzel et al. 1993;

Colinvaux et al. 2000; Lambeck et al. 2002).

The greater rate of expansion experienced by populations in Borneo compared to those in the Malay Peninsula can be explained by at least two non-mutually exclusive phenomena. One is that Borneo has experienced greater vegetational changes that favored forest-dependent species.

This might be the case for the Sarawak population of M. malaccensis, whose ancestral population was likely to be situated in Borneo since the Sabah population of M. malaccensis is sister to both the PEN and Sarawak population. Unfortunately, the current state of knowledge on variation in vegetation turnover within Sundaland is not sufficient enough to provide a

83 conclusive support for this hypothesis (Bush and Flenley 2007). Pollen records, which are vital to our understanding of temporal vegetational changes, often contain components that have been transported long distances by water or wind. This reduces our ability to infer past vegetation formations with great spatial precision. Another possible explanation for greater population expansion in Borneo is that its population expanded after being colonized by a small number of individuals derived from the mainland. Evidence suggests that this explanation is applicable to the Borneo population of A. longirostra; it was founded by a small fraction (0.05%) of the ancestral population, and population expansion occurred subsequent to population divergence. A. longirostra could be absent from Borneo at the time of population splitting, or the existing population was simply replaced when the colonizers expanded. For O. sericeus, IM failed to indicate what fraction of the ancestral population founded the Borneo population, most likely as a result of the former‟s small size. However, the asymmetrical gene flow going from PEN into

Borneo suggests that the Borneo population received migrants from PEN while undergoing expansion. Interestingly, both the mismatch distribution and EBS plot indicated two episodes of population expansions in the Sabah population of M. malaccensis (subspecies sordida).

Although IMa did not detect such a fluctuation because the size-change model was not implemented, the relatively small long-term effective population size (compared to the final size estimated by the EBS approach) was indicative of the population having experienced bottleneck event(s). This dramatic size fluctuation could be related to the population being situated at a corner of Sundaland (surrounded by seas even during glacial maxima). Without the ability to shift distribution as environmental conditions fluctuated, it responded by expanding and contracting. Such a strong population fluctuation has been found in microorganisms (e.g., Chong et al. 2010) and temperate organisms (e.g., Galbreath et al. 2009), but has heretofore not been

84 reported for a tropical vertebrate species. Further work is needed to investigate if other Sabah endemics (e.g., Pitta ussheri) have similar demographic histories.

Relationship between Population Connectivity and Ecological Characteristics

In accordance to the predictions made based on ecological characteristics, inter-landmass gene flow, as estimated by IM, was highest in O. sericeus, followed by A. longirostra and M. malaccensis. In general, the difference in gene flow rate was about an order of magnitude between successive pairs of species (in at least one direction). Estimates of gene flow produced by the constant population and size-change models generally agreed with each other, increasing my confidence in systematic among-species differences. The only time the two models produced noticeably incongruent results in gene flow was in the estimation of mPEN -> Borneo in A. longirostra. This could have resulted from an assumption of the constant population model being violated since the Borneo population likely underwent a strong expansion. The number of studies on gene flow in tropical birds over evolutionary time scales in a comparative framework is limited, giving few empirical findings (primarily from the Neotropics) to compare the results against. In a study of five species of Amazonian birds (three thamnophilids, one tyrannid, and one dendrocolaptid) that occupied continuous forests and natural forest fragments (> 1000 years old), Bates (2002) found that the thamnophilids had the most genetically structured populations, with populations in fragments possessing private alleles as well as allele-frequencies that were substantially different from those in the continuous forest sites. He speculated that the differences could be due to phylogenetic-related ecological differences, with the thamnophilids being the least vagile group of the three. Echoing this finding was the study of Brawn et al.

(1996). They conducted an examination of populations of three species of tanagers (Thraupidae) in Panama and on a nearby archipelago that had been separated from the mainland about 10 kyr

85 previously. They concluded that the species most strongly differentiated between the island and mainland populations was also the least vagile (Ramphocelus dimidiatus).

In Southeast Asia, the babblers (Timaliidae), of which M. malaccensis is a typical member, are arguably the Old-World ecological equivalents of the Neotropical antbirds

(thamnophilids). Both families are insectivorous and forest-dependent, and they share morphological characteristics such as short rounded wings, strong legs and feet, and proportionately large bills. Studies conducted on ecological time scales are providing mounting evidence that such understory insectivores are highly sensitive to forest fragmentation and disperse poorly across gaps (Stouffer and Bierregaard 1995; Sekercioglu et al. 2002; Stratford and Robinson 2005; Moore et al. 2008). In contrast, the spiderhunters, because they are partially nectivorous (Cheke and Mann 2008), are likely to be more vagile because of the patchiness and temporal heterogeneity of their food resources (Wong 1986; Sodhi 2002). This relationship between dispersal propensity and population structure of a nectarivore is reflected in the low inter-population genetic divergences (≤ 2.4%) in an African sunbird species complex (Nectarina olivacea/N. obscura) over large spatial scales (9000 km) (Bowie et al. 2004). In a study that looked at 40 bird species that possessed trans-Andean distributions, Burney and Brumfield

(2009) found that canopy species had the least amount of cross-Andes genetic differentiation.

This ability to overcome the barrier effect of an uplifting Andes is probably related to the fact that the canopy species are able to utilize a wider range of environments (that differ in microclimate, light regime, vegetation structure, etc.) than understory species (Harris and Reed

2002; Walther 2003). In the same way, given the habitat generalism of O. sericeus, it seems reasonable that it possessed a greater propensity to cross the emergent Sunda shelf during glacial

86 periods, which, at a minimum, would have contained patches of non lowland-rainforest vegetation (Bird et al. 2005).

Conclusions

This study provided insights into the long-term population dynamics of three species of understory birds from a poorly studied tropical area. The multilocus analyses revealed changes in

Ne that were driven by long-term changes in the environment, instead of high-frequency glacial cycles. Regional differences in habitat changes through time or directional colonization likely underlied differences in dynamics between PEN and Borneo populations. The study also added to the growing body of empirical work that detects an association between a species‟ ecological characteristics and dispersal propensity, and the amount of inter-population gene flow experienced over evolutionary time scales (e.g., Crawford et al. 2007). Despite considerable advances in the analytical methods used to construct divergence and demographic histories, no single method was able to fully handle the complexities of empirical data (e.g., Strasburg and

Rieseberg 2010). Therefore, it is important to identify the limitations of individual methods, and apply complementary approaches.

Chapter 5: Overall Conclusions

The use of heritable markers to study organismal evolution represents a giant step in the advancement of science (Huxley 1942). The advent of molecular techniques (e.g., Mullis and

Faloona 1987), coalescent theory (Kingman 1982) and spatial-phylogenetic thinking (Avise

2009) further enhance our ability to understand the processes that are involved in the generation and distribution of biological diversity. Due to historical inertia and its poor accessibility,

Southeast Asia, a region harboring multiple biological hotspots, has been poorly studied in this respect. In my dissertation, a wide spectrum of approaches such as morphometric analysis, ecological niche modeling, coalescence-based analysis of population demography were used to investigate how the dynamic nature of Southeast Asia‟s geography and climate affected the evolution of bird populations.

In chapter two, I found that the extensive plumage color polymorphism of the Oriental

Dwarf Kingfisher is likely a result of past genetic introgression between the northern black form and the southern rufous form. In order for morphological divergence to manifest, the two forms likely experienced extended isolation from each other after initial population split. Following this, they probably came into contact when the Sunda shelf was exposed due to lowered sea levels. Coupled with a lack of complete reproductive isolation, genetic introgression occurred, resulting in many of the rufous-backed birds possessing plumage characteristics of the black form. Further investigations of hybridization or genetic introgression will require sampling at the current zone of overlap (at the Isthmus of Kra) between the black- and rufous-backed birds. In chapter three, I studied the phylogeographic pattern of 16 bird species that occupy lowland rainforest or rainforest edges. The study uncovered diverse phylogeographic patterns.

Particularly striking was the genetic distinctiveness of some populations from northeastern

Borneo. Results from ecological niche modeling and habitat “retrodiction” indicated that the distribution of habitat during the LGM was important in shaping today‟s phylogeographic patterns. Chapter four investigated the demographic history and degree of population connectivity of three focal species using multilocus data. The results showed a correlation between the habitat breadth (and perceived dispersal propensity) of each species, and the level of inter-population gene flow. Populations occupying Borneo showed signatures for stronger demographic expansion, suggesting fundamentally different long-term environmental changes in various parts of Sundaland.

This series of studies provides a useful framework from which future, more detailed studies can be launched. For example, one can proceed to characterize putative contact zones situated in the Isthmus of Kra or between northeastern and the rest of Borneo. As it becomes increasing easy to collect large amount of genetic and environmental (contemporary or historical) data, future investigators can rigorously test alternative phylogeographic and diversification hypotheses using statistical approaches. Such work will not only have conservation implications, but will also make fundamental contributions to our knowledge of how the world‟s biological diversity was assembled.

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Appendix 1: Supplementary Data

Appendix 1 Table 1. Accession numbers of sequences used in phylogenetic analyses that were downloaded from GenBank. Species Accession number for ND2 Accession number for MYO Locality Ceyx fallax DQ640784 Sulawesi Ceyx erithaca DQ640792 DQ640821 Laos Ceyx erithaca EF585385 EF585367 Laos Ceyx melanurus DQ640785 Philippines Ceyx lepidus DQ640788 DQ640817 New Guinea Ceyx lepidus DQ469008 DQ640816 Solomon Islands Ceyx lepidus DQ469002 Solomon Islands Ceyx lepidus DQ469005 Solomon Islands Ceyx lepidus DQ469007 Solomon Islands Ceyx lepidus DQ469009 Solomon Islands Ceyx lepidus DQ469010 Solomon Islands Ceyx lepidus DQ469011 Solomon Islands Ceyx lepidus DQ640787 Solomon Islands Ceyx lepidus DQ469003 DQ640819 Philippines Ceyx lepidus DQ469004 Philippines Ceyx lepidus DQ640790 Philippines Alcedo argentata DQ640786 DQ640815 Philippines Alcedo cyanopecta DQ640789 DQ640818 Philippines Alcedo pusilla DQ640778 DQ640806 Solomon Islands Alcedo websteri DQ640779 DQ640807 Bismarck Archipelago Alcedo azurea DQ111832 DQ640805 New Guinea Alcedo atthis EF585373 EF585355 France Alcedo quadribrachys EF585382 EF585364 Cameroon Alcedo cristata EF585374 EF585356 Malawi Alcedo cristata EF585375 EF585357 Malawi Alcedo cristata EF585380 EF585362 Principe Island Alcedo cristata EF585381 EF585363 Principe Island Alcedo cristata EF585383 EF585365 Sao Tome Island Alcedo cristata EF585384 EF585366 Sao Tome Island Alcedo leucogaster EF585376 EF585358 Cameroon Alcedo leucogaster EF585377 EF585360 Cameroon Alcedo leucogaster EF585378 EF585359 Bioko Island Alcedo leucogaster EF585379 EF585361 Bioko Island Ceyx lecontei EF585386 EF585368 Cameroon Ispidina picta EF585387 EF585369 Cameroon Ispidina picta EF585388 EF585380 Gabon Halcyon malimbica EF585389 EF585371 Cameroon Halcyon malimbica DQ640771 DQ640796 Not provided

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Appendix 1 Table 2. Sample number and collection locality of study samples. Institutional abbreviations: AMNH = American Museum of Natural History; FMNH = Field Museum of Natural History; KUMNH = Kansas University Museum of Natural History; LSUMNS = Louisiana State University Museum of Natural Science; NMNH = National Museum of Natural History, UWBM = University of Washington Burke Museum. GenBank = DNA sequence of sample was downloaded from GenBank. When no institution is given, sample is an unvouchered blood sample. Species name Ingroup Country State/Province/ Institution Sample sample County number size

Hypothymis azurea 25 Vietnam Quang Nam AMNH 10806 Taiwan Taichung AMNH 5151 Outgroup taxa Taiwan Nantou AMNH 5242 Terpsiphone viridis Myanmar Pegu NMNH B00619 AF407058 Genbank Myanmar Sagaing NMNH B05696 Terpsiphone paradisi Myanmar Sagaing NMNH B06138 EF052688 Genbank Thailand Nakhon Ratchasima GenBank EF052695 Malaysia Terengganu A6 Malaysia Johor LSUMNS 52065 Malaysia Sabah LSUMNS 38545 Malaysia Sabah LSUMNS 38573 Malaysia Sabah LSUMNS 47055 Malaysia Sabah LSUMNS 47056 Malaysia Sabah LSUMNS 47144 Malaysia Sarawak LSUMNS 52195 Malaysia Sarawak LSUMNS 52199 Malaysia Sarawak LSUMNS 52204 Malaysia Sarawak LSUMNS 57007 Malaysia Sarawak LSUMNS 57038 Malaysia Sarawak KUMNH 82047 Indonesia Sumatra UWBM 67496 Philippines Luzon KUMNH 20165 Philippines Mindanao KUMNH 19054 Philippines Palawan KUMNH 12697 Philippines Palawan KUMNH 12746 Terpsiphone paradisi 17 Vietnam Quang Nam AMNH 10736 Vietnam Quang Nam AMNH 10754 Outgroup taxa Vietnam Quang Nam AMNH 10763 Hypothymis azurea Vietnam Quang Nam AMNH 12233 BH2 Russia Primorskiy Kray UWBM 71896 Myiagra alecto Thailand Nakhon Ratchasima GenBank EF052688 DQ084078 Genbank Malaysia Terengganu D36 Malaysia Terengganu E16 Malaysia Terengganu E24 Malaysia Johor PS20 Malaysia Johor PS22 Malaysia Sabah LSUMNS 47028 Malaysia Sabah LSUMNS 47112 Malaysia Sabah LSUMNS 47115 Malaysia Sabah LSUMNS 57443 Malaysia Sarawak LSUMNS 52156 Malaysia Sarawak LSUMNS 52162 Copsychus malabaricus 37 Vietnam Quang Nam AMNH 10770

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(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

captive FMNH 363785 Outgroup taxa Myanmar Sagaing NMNH B02488 Copsychus luzoniensis Myanmar Sagaing NMNH B05754 15805 KUMNH Myanmar Sagaing NMNH B05755 Myiagra alecto Myanmar Sagaing NMNH B06132 DQ084078 Genbank Myanmar Sagaing NMNH B06137 Myanmar Sagaing NMNH B06190 China Hainan LSUMNS B51225 Vietnam GenBank DQ466859 Malaysia Terengganu A12 Malaysia Terengganu D33 Malaysia Terengganu E20 Malaysia Terengganu E23 Malaysia Terengganu I2 Singapore Singapore UWBM 81954 Malaysia Johor LSUMNS 52060 Malaysia Johor LSUMNS 52067 Malaysia Johor LSUMNS 52082 Malaysia Johor BS3 Malaysia Johor BS4 Malaysia Johor LSUMNS 52094 Malaysia Johor PB32 Malaysia Johor PB33 Malaysia Johor PB7 Malaysia Sabah LSUMNS B36312 Malaysia Sabah LSUMNS B36334 Malaysia Sabah LSUMNS B38608 Malaysia Sabah LSUMNS B46976 Malaysia Sabah LSUMNS B46991 Malaysia Sabah LSUMNS B47003 Malaysia Sabah LSUMNS B47014 Malaysia Sabah LSUMNS B47016 Malaysia Sarawak LSUMNS 52152 Malaysia Sarawak LSUMNS 52196 Malaysia Sarawak LSUMNS 57052 Malaysia Sarawak UWBM 81962 Alophoixus 25 Malaysia Terengganu D28 phaeocephalus Malaysia Terengganu E12 Outgroup taxa Malaysia Terengganu E4 Alophoixus bres Malaysia Johor LSUMNS 52092 DQ402228 Genbank Malaysia Johor LSUMNS 52097 Alophoixus ochraceus Malaysia Johor PB23 DQ402229 Genbank Malaysia Johor PS8 Malaysia Johor PS9 Malaysia Sabah LSUMNS 36338 Malaysia Sabah LSUMNS 38568 Malaysia Sabah LSUMNS 51067 Malaysia Sabah LSUMNS 51120 Malaysia Sabah LSUMNS 51123 Malaysia Sabah LSUMNS 51131 Malaysia Sabah LSUMNS 51136

107

(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

Malaysia Sabah LSUMNS 52068 Malaysia Sarawak LSUMNS 52154 Malaysia Sarawak LSUMNS 52155 Malaysia Sarawak LSUMNS 57006 Malaysia Sarawak LSUMNS 57042 Malaysia Sarawak LSUMNS 57068 Malaysia Sarawak UWBM 81929 Malaysia Sarawak UWBM 81940 Malaysia Sarawak UWBM 81951 Malaysia Sarawak UWBM 82026 Pycnonotus plumosus 26 Malaysia Johor LSUMNS 52080/BS16 Singapore Singapore C0704 Outgroup taxa Singapore Singapore L0623 Pycnonotus melanicterus Singapore Singapore L0970 DQ402243 Genbank Singapore Singapore L0978 Pycnonotus brunneus Singapore Singapore L0620 DQ402233 Genbank Singapore Singapore PU10 Singapore Singapore PU9.2 Singapore Singapore TL1 Singapore Singapore TL15 Malaysia Sabah LSUMNS 46957 Malaysia Sabah LSUMNS 46965 Malaysia Sabah LSUMNS 46969 Malaysia Sabah LSUMNS 47033 Malaysia Sabah LSUMNS 47151 Malaysia Sabah LSUMNS 47186 Malaysia Sabah LSUMNS 51052 Malaysia Sabah LSUMNS 51061 Malaysia Sarawak LSUMNS 57032 Malaysia Sarawak LSUMNS 57047 Malaysia Sarawak LSUMNS 57053 Malaysia Sarawak LSUMNS 58202 Malaysia Sarawak LSUMNS 58218 Malaysia Sarawak UKNHM 81934 Malaysia Sarawak UKNHM 81936 Malaysia Sarawak UNIMAS B1395 Tricholestes criniger 28 Malaysia Terengganu D12 Malaysia Terengganu D29 Outgroup taxa Malaysia Terengganu D30 Alophoixus Malaysia Terengganu E1 phaeocephalus B52154 LSUMNS Malaysia Johor LSUMNS 52061 Alophoixus bres Malaysia Johor BH17 DQ402228 Genbank Malaysia Johor BH22 Malaysia Johor LSUMNS 52084 Malaysia Johor LSUMNS 52089 Malaysia Johor LSUMNS 52093 Malaysia Johor LSUMNS 52071 Malaysia Johor LSUMNS 52078 Malaysia Johor LSUMNS 52106 Malaysia Johor LSUMNS 52102 Malaysia Sabah LSUMNS 36385

108

(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

Malaysia Sabah LSUMNS 38560 Malaysia Sabah GenBank DQ402223 Malaysia Sabah LSU 38607 Malaysia Sabah LSU 47133 Malaysia Sabah LSU 57471 Malaysia Sabah GenBank DQ861969 Malaysia Sarawak LSUMNS 52211 Malaysia Sarawak LSUMNS 57056 Malaysia Sarawak LSUMNS 57081 Malaysia Sarawak LSUMNS 57088 Malaysia Sarawak UWBM 81905 Malaysia Sarawak UWBM 81906 Malaysia Sarawak UWBM 81973 Orthotomus sericeus 17 Singapore Singapore UWBM 81966 Singapore Singapore UWBM 81969 Outgroup taxa Singapore Singapore J1165 Orthotomus sutorius Singapore Singapore J1167 DQ871365 Genbank Malaysia Sabah LSUMNS 36342 Orthotomus atrogularis Malaysia Sabah LSUMNS 46989 DQ871379 Genbank Malaysia Sabah LSUMNS 47010 Malaysia Sabah LSUMNS 47191 Malaysia Sabah LSUMNS 51012 Malaysia Sarawak LSUMNS 52137 Malaysia Sarawak LSUMNS 52144 Malaysia Sarawak LSUMNS 52153 Malaysia Sarawak LSUMNS 52167 Malaysia Sarawak LSUMNS 58212 Malaysia Sarawak LSUMNS 58216 Malaysia Sarawak LSUMNS 58217 Indonesia Sumatra UWBM 67468 Macronous gularis 25 Vietnam Quang Nam AMNH 10805 Vietnam Quang Nam AMNH 10811 Outgroup taxa Vietnam Ha Giang AMNH 10980 Stachyris erythroptera Myanmar Sagaing NMNH B06135 FJ460771 Genbank Myanmar Sagaing NMNH B06140 Macronous ptilosus Thailand Nakhon Ratchasima GenBank DQ861941 B36391 LSUMNS Singapore Singapore J1125 Singapore Singapore J1126 Singapore Singapore J1127 Singapore Singapore J1130 Singapore Singapore PT1 Malaysia Sabah LSUMNS 46955 Malaysia Sabah LSUMNS 46956 Malaysia Sabah LSUMNS 46958 Malaysia Sabah LSUMNS 46996 Malaysia Sabah LSUMNS 47013 Malaysia Sabah LSUMNS 47187 Malaysia Sabah LSUMNS 47189 Malaysia Sabah LSUMNS 47240 Malaysia Sabah LSUMNS 51031 Malaysia Sabah LSUMNS 51033 Malaysia Sarawak LSUMNS 52121

109

(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

Malaysia Sarawak LSUMNS 52129 Malaysia Sarawak LSUMNS 52135 Malaysia Sarawak LSUMNS 57031 Malacocincla malaccensis 27 Malaysia Terengganu D18 Malaysia Terengganu D22 Outgroup taxa Malaysia Terengganu D23 Malacopteron affine Malaysia Terengganu D25 B57084 LSUMNS Singapore Singapore UWBM 81952 Malacopteron magnum Singapore Singapore UWBM 81953 B36423 LSUMNS Singapore Singapore UWBM 81955 Singapore Singapore UWBM 81957 Malaysia Sabah LSUMNS 47052 Malaysia Sabah LSUMNS 47072 Malaysia Sabah LSUMNS 47097 Malaysia Sabah LSUMNS 51049 Malaysia Sabah LSUMNS 51078 Malaysia Sabah LSUMNS 51083 Malaysia Sabah LSUMNS 51119 Malaysia Sabah LSUMNS 51504 Malaysia Sarawak LSUMNS 52119 Malaysia Sarawak LSUMNS 52150 Malaysia Sarawak LSUMNS 52157 Malaysia Sarawak LSUMNS 52175 Malaysia Sarawak LSUMNS 52197 Malaysia Sarawak LSUMNS 52209 Malaysia Sarawak LSUMNS 57011 Malaysia Sarawak LSUMNS 57020 Malaysia Sarawak LSUMNS 58215 Indonesia Sumatra UWBM 67490 Indonesia Sumatra UWBM 67491 Pellorneum capistratum 18 Malaysia Johor LSUMNS 52066 Malaysia Johor LSUMNS 52073 Outgroup taxa Malaysia Johor LSUMNS 52098 Malacopteron affine Malaysia Johor PS13 B57084 LSUMNS Malaysia Johor PS2 Illadopsis rufipennis Malaysia Johor PS23 EU686329 Genbank Malaysia Sabah LSUMNS 46960 Malaysia Sabah LSUMNS 46993 Malaysia Sabah LSUMNS 46995 Malaysia Sabah LSUMNS 47175 Malaysia Sabah LSUMNS 47201 Malaysia Sabah LSUMNS 47202 Malaysia Sabah LSUMNS 51058 Malaysia Sabah LSUMNS 51121 Malaysia Sarawak LSUMNS 52128 Malaysia Sarawak LSUMNS 52182 Malaysia Sarawak LSUMNS 52183 Malaysia Sarawak LSUMNS 58211 Stachyris erythroptera 29 Malaysia Terengganu A18 Malaysia Terengganu A19 Outgroup taxa Malaysia Terengganu E3 Stachyris poliocephala Malaysia Terengganu E5

110

(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

B52138 LSUMNS Malaysia Terengganu E8 Stachyris nigriceps Malaysia Terengganu I5 D8 Malaysia Terengganu I6 Malaysia Terengganu I7 Singapore Singapore A411 Singapore Singapore J1128 Malaysia Sabah LSUMNS 36388 Malaysia Sabah LSUMNS 36389 Malaysia Sabah LSUMNS 36417 Malaysia Sabah LSUMNS 38584 Malaysia Sabah LSUMNS 38597 Malaysia Sabah LSUMNS 38598 Malaysia Sabah LSUMNS 57414 Malaysia Sabah LSUMNS 57428 Malaysia Sarawak LSUMNS 52149 Malaysia Sarawak LSUMNS 52187 Malaysia Sarawak LSUMNS 52188 Malaysia Sarawak LSUMNS 58214 Malaysia Sarawak UWBM 81930 Malaysia Sarawak UWBM 82019 Malaysia Sarawak UWBM 82054 Malaysia Sarawak UWBM 82058 Indonesia Sumatra UWBM 67471 Indonesia Sumatra UWBM 67497 Indonesia Sumatra UWBM 67498 Stachyris poliocephala 18 Malaysia Terengganu D27 Malaysia Terengganu D26 Outgroup taxa Malaysia Terengganu D24 Pomatorhinus schisticeps Malaysia Johor LSUMNS 52107 DQ861944 Genbank Malaysia Johor LSUMNS 52096 Stachyris nigriceps Malaysia Sabah LSUMNS 57467 D8 Malaysia Sabah LSUMNS 51066 Malaysia Sabah LSUMNS 51065 Malaysia Sabah LSUMNS 47082 Malaysia Sabah LSUMNS 47008 Malaysia Sabah LSUMNS 36426 Malaysia Sabah LSUMNS 36418 Malaysia Sarawak UNIMAS SM37 Malaysia Sarawak LSUMNS 52140 Malaysia Sarawak LSUMNS 52138 Malaysia Sarawak LSUMNS 52134 Malaysia Sarawak LSUMNS 52127 Indonesia Sumatra UWBM 67500 Arachnothera longisrostra 33 Myanmar Pegu NMNH B00165 Vietnam Quang Nam AMNH 12304 Outgroup taxa Vietnam Quang Nam AMNH 10813 Arachnothera affinis Malaysia Terengganu E21 B36402 LSUMNS Malaysia Terengganu D3 Arachnothera affinis Malaysia Terengganu D2 B36309 LSUMNS Malaysia Johor LSUMNS 52095 Malaysia Johor LSUMNS 52077 Malaysia Johor BH21

111

(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

Malaysia Johor LSUMNS 52086 Malaysia Johor LSUMNS 52063 Malaysia Johor LSUMNS 52059 Malaysia Singapore UWBM 81972 Malaysia Sabah LSUMNS 51143 Malaysia Sabah LSUMNS 51059 Malaysia Sabah LSUMNS 50992 Malaysia Sabah LSUMNS 47184 Malaysia Sabah LSUMNS 47073 Malaysia Sabah LSUMNS 47064 Malaysia Sabah LSUMNS 46994 Malaysia Sabah LSUMNS 46985 Malaysia Sabah LSUMNS 46961 Malaysia Sabah LSUMNS 38610 Malaysia Sabah LSUMNS 36406 Malaysia Sarawak UWBM 82077 Malaysia Sarawak UWBM 82076 Malaysia Sarawak UWBM 81911 Malaysia Sarawak UWBM 81901 Malaysia Sarawak LSUMNS 58219 Malaysia Sarawak LSUMNS 52171 Malaysia Sarawak LSUMNS 52143 Malaysia Sarawak LSUMNS 52122 Indonesia Sumatra UWBM 67469 Hypogramma 33 Malaysia Terengganu E25 hypogrammicum Malaysia Terengganu A9 Outgroup taxa Malaysia Terengganu A7 Dicaeum trigonostigma Malaysia Terengganu A5 AY136609 Genbank Malaysia Terengganu A15 Nectarinia senegalensis Malaysia Terengganu A10 AF447293 Genbank Malaysia Johor PS14 Malaysia Johor PB29 Malaysia Johor PB28 Malaysia Johor LSUMNS 52083 Malaysia Johor LSUMNS 52085 Malaysia Sabah LSUMNS 51127 Malaysia Sabah LSUMNS 51126 Malaysia Sabah LSUMNS 51116 Malaysia Sabah LSUMNS 51102 Malaysia Sabah LSUMNS 51100 Malaysia Sabah LSUMNS 47233 Malaysia Sabah LSUMNS 47230 Malaysia Sabah LSUMNS 47081 Malaysia Sabah LSUMNS 47070 Malaysia Sabah LSUMNS 46954 Malaysia Sabah LSUMNS 36403 Malaysia Sarawak UWBM 81984 Malaysia Sarawak LSUMNS 57089 Malaysia Sarawak LSUMNS 57059 Malaysia Sarawak LSUMNS 57021 Malaysia Sarawak LSUMNS 52207

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(table continued) Species name Ingroup Country State/Province/ Institution Sample sample County number size

Malaysia Sarawak LSUMNS 52194 Malaysia Sarawak LSUMNS 52181 Malaysia Sarawak LSUMNS 52180 Malaysia Sarawak LSUMNS 52132 Malaysia Sarawak LSUMNS 52125 Indonesia Sumatra UWBM 67464 Prionochilus maculatus 23 Malaysia Terengganu E2 Malaysia Terengganu D4 Outgroup taxa Malaysia Terengganu C1 Dicaeum trigonostigma Malaysia Johor PS3 AF290101 Genbank Malaysia Johor PS26 Dicaeum aeneum Malaysia Johor PS16 DQ468972 Genbank Malaysia Johor PS12 Malaysia Johor 52072 Malaysia Sabah LSUMNS 57415 Malaysia Sabah LSUMNS 51125 Malaysia Sabah LSUMNS 51073 Malaysia Sabah LSUMNS 51055 Malaysia Sabah LSUMNS 36382 Malaysia Sabah LSUMNS 36381 Malaysia Sabah LSUMNS 36379 Malaysia Sarawak UWBM 82050 Malaysia Sarawak UWBM 81907 Malaysia Sarawak LSUMNS 52210 Malaysia Sarawak LSUMNS 52208 Malaysia Sarawak LSUMNS 52205 Malaysia Sarawak LSUMNS 52193 Malaysia Sarawak LSUMNS 52192 Malaysia Sarawak LSUMNS 52178 Philentoma pyrrhoptera 18 Malaysia Terengganu E18 Malaysia Terengganu E17 Outgroup taxa Malaysia Terengganu D5 Philentoma velatum Malaysia Johor PS19 AY816228 Genbank Malaysia Johor PB9 Vanga curvirostris Malaysia Johor PB10 AY701508 Genbank Malaysia Johor LSUMNS 52069 Malaysia Johor LSUMNS 52081 Malaysia Sabah LSUMNS 51101 Malaysia Sabah LSUMNS 38609 Malaysia Sabah LSUMNS 38572 Malaysia Sabah LSUMNS 36390 Malaysia Sabah LSUMNS 36375 Malaysia Sarawak UWBM 82029 Malaysia Sarawak UWBM 82025 Malaysia Sarawak UWBM 82011 Malaysia Sarawak LSUMNS 57096 Malaysia Sarawak LSUMNS 57063 Malaysia Sarawak LSUMNS 57013

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Appendix 1 Table 3. Sample number and collection locality of study species. Institutional abbreviations: AMNH = American Museum of Natural History; FMNH = Field Museum of Natural History; KUMNH = University of Kansas Museum of Natural History; LSUMNS = Louisiana State University Museum of Natural Science; NMNH = National Museum of Natural History, UWBM = University of Washington Burke Museum. When no institution is given, the sample is unvouchered blood. Species name Country State/ Area of Collection Instituion Sample Locus Province number sequenced A. longisrostra Indonesia Sumatra Lunang Town UWBM 67469 ND2 only A. longisrostra Malaysia Johor Panti Forest LSUMNS 52059 ND2 only A. longisrostra Malaysia Johor Bukit Hantu LSUMNS 52063 Both A. longisrostra Malaysia Johor Bukit Sedenak LSUMNS 52077 Both A. longisrostra Malaysia Johor Bukit Hantu LSUMNS 52086 ND2 only A. longisrostra Malaysia Johor Panti Forest LSUMNS 52095 Both A. longisrostra Malaysia Johor Bukit Hantu BH21 ND2 only A. longisrostra Malaysia Sabah Tawau Hills Park LSUMNS 36406 ND2 only A. longisrostra Malaysia Sabah Imbak Valley LSUMNS 38610 Both A. longisrostra Malaysia Sabah Kinabalu National Park LSUMNS 46961 Both A. longisrostra Malaysia Sabah Kinabalu National Park LSUMNS 46985 ND2 only A. longisrostra Malaysia Sabah Kinabalu National Park LSUMNS 46994 ND2 only A. longisrostra Malaysia Sabah Kinabalu National Park LSUMNS 47064 ND2 only A. longisrostra Malaysia Sabah Kinabalu National Park LSUMNS 47073 ND2 only A. longisrostra Malaysia Sabah Klias Forest Reserve LSUMNS 47184 Both A. longisrostra Malaysia Sabah Mendolong LSUMNS 50992 ND2 only A. longisrostra Malaysia Sabah Tawau Hills Park LSUMNS 51059 Both A. longisrostra Malaysia Sabah Tawau Hills Park LSUMNS 51143 ND2 only A. longisrostra Malaysia Sarawak Padawan District LSUMNS 52122 Both A. longisrostra Malaysia Sarawak Padawan District LSUMNS 52143 Both A. longisrostra Malaysia Sarawak Matang Town LSUMNS 52171 Both A. longisrostra Malaysia Sarawak Tatau Town LSUMNS 52229 Nuclear only A. longisrostra Malaysia Sarawak Batu Niah LSUMNS 52237 Nuclear only A. longisrostra Malaysia Sarawak Universiti Malaysia LSUMNS 58219 Both Sarawak A. longisrostra Malaysia Sarawak Kubah National Park UWBM 81901 ND2 only A. longisrostra Malaysia Sarawak Bako National Park UWBM 81911 ND2 only A. longisrostra Malaysia Sarawak Bako National Park UWBM 82076 ND2 only A. longisrostra Malaysia Sarawak Bako National Park UWBM 82077 ND2 only A. longisrostra Malaysia Singapore Bukit Timah UWBM 81972 Both A. longisrostra Malaysia Terengganu Kenyir Lake D2 Both A. longisrostra Malaysia Terengganu Kenyir Lake D3 Both A. longisrostra Malaysia Terengganu Kenyir Lake E21 ND2 only A. longisrostra Myanmar Pegu Yedashe NMNH B00165 ND2 only A. longisrostra Vietnam Quang Tra My District AMNH 10813 ND2 only Nam A. longisrostra Vietnam Quang Tra My District AMNH 12304 ND2 only Nam M. malaccensis Indonesia Sumatra Lingkung District UWBM 67490 ND2 only M. malaccensis Indonesia Sumatra Lingkung District UWBM 67491 ND2 only M. malaccensis Malaysia Sabah Crocker Range Nat‟l LSUMNS 36355 Nuclear only Park M. malaccensis Malaysia Sabah Crocker Range Nat‟l LSUMNS 36392 Nuclear only Park M. malaccensis Malaysia Sabah Kinabalu National Park LSUMNS 47052 Both M. malaccensis Malaysia Sabah Kinabalu National Park LSUMNS 47072 Both

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(table continued) Species name Country State/ Area of Collection Instituion Sample Locus Province number sequenced M. malaccensis Malaysia Sabah Kinabalu National Park LSUMNS 47097 ND2 only M. malaccensis Malaysia Sabah Tawai Forest Reserve LSUMNS 50341 Nuclear only M. malaccensis Malaysia Sabah Tawau Hills Park LSUMNS 51049 Both M. malaccensis Malaysia Sabah Tawau Hills Park LSUMNS 51078 ND2 only M. malaccensis Malaysia Sabah Tawau Hills Park LSUMNS 51083 ND2 only M. malaccensis Malaysia Sabah Tawau Hills Park LSUMNS 51119 Both M. malaccensis Malaysia Sabah Tawau Hills Park LSUMNS 51504 ND2 only M. malaccensis Malaysia Sabah Ulu Tungud Forest LSUMNS 57422 Nuclear only Reserve M. malaccensis Malaysia Sarawak Padawan District LSUMNS 52119 Both M. malaccensis Malaysia Sarawak Matang Town LSUMNS 52150 Both M. malaccensis Malaysia Sarawak Matang Town LSUMNS 52157 Both M. malaccensis Malaysia Sarawak Sungei Merah LSUMNS 52175 Both M. malaccensis Malaysia Sarawak Sebankoi LSUMNS 52197 ND2 only M. malaccensis Malaysia Sarawak Sebankoi LSUMNS 52209 ND2 only M. malaccensis Malaysia Sarawak Batu Niah LSUMNS 52246 Nuclear only M. malaccensis Malaysia Sarawak Samarakan LSUMNS 57011 ND2 only M. malaccensis Malaysia Sarawak Samarakan LSUMNS 57020 ND2 only M. malaccensis Malaysia Sarawak Miri-Bintulu Road LSUMNS 58161 Nuclear only M. malaccensis Malaysia Sarawak Miri-Bintulu Road LSUMNS 58168 Nuclear only M. malaccensis Malaysia Sarawak Tatau Town LSUMNS 58186 Nuclear only M. malaccensis Malaysia Sarawak Universiti Malaysia LSUMNS 58215 ND2 only Sarawak M. malaccensis Malaysia Terengganu Kenyir Lake D18 Both M. malaccensis Malaysia Terengganu Kenyir Lake D22 Both M. malaccensis Malaysia Terengganu Kenyir Lake D23 Both M. malaccensis Malaysia Terengganu Kenyir Lake D25 ND2 only M. malaccensis Singapore Singapore Central Catchment UWBM 81952 ND2 only M. malaccensis Singapore Singapore Central Catchment UWBM 81953 Both M. malaccensis Singapore Singapore Central Catchment UWBM 81955 Both M. malaccensis Singapore Singapore Central Catchment UWBM 81957 Both M. malaccensis Singapore Singapore Central Catchment K0306 Nuclear only M. malaccensis Singapore Singapore Central Catchment K0307 Nuclear only M. malaccensis Singapore Singapore Central Catchment K0412 Nuclear only O. sericeus Indonesia Sumatra Lunang Town UWBM 67468 Both O. sericeus Malaysia Sabah Crocker Range Nat‟l LSUMNS 36342 Both Park O. sericeus Malaysia Sabah Kinabalu National Park LSUMNS 46989 Both O. sericeus Malaysia Sabah Kinabalu National Park LSUMNS 47010 ND2 only O. sericeus Malaysia Sabah Klias Forest Reserve LSUMNS 47191 Both O. sericeus Malaysia Sabah Mendolong LSUMNS 51012 Both O. sericeus Malaysia Sarawak Padawan District LSUMNS 52137 Both O. sericeus Malaysia Sarawak Padawan District LSUMNS 52144 ND2 only O. sericeus Malaysia Sarawak Matang Town LSUMNS 52153 Both O. sericeus Malaysia Sarawak Matang Town LSUMNS 52167 Both O. sericeus Malaysia Sarawak Batu Niah LSUMNS 58160 Nuclear only O. sericeus Malaysia Sarawak Tatau Town LSUMNS 58190 Nuclear only O. sericeus Malaysia Sarawak Universiti Malaysia LSUMNS 58212 Both Sarawak O. sericeus Malaysia Sarawak Universiti Malaysia LSUMNS 58216 ND2 only Sarawak O. sericeus Malaysia Sarawak Universiti Malaysia LSUMNS 58217 ND2 only Sarawak O. sericeus Singapore Singapore Bukit Batok UWBM 81966 Both 115

(table continued) Species name Country State/ Area of Collection Instituion Sample Locus Province number sequenced O. sericeus Singapore Singapore Marina South UWBM 81969 Both O. sericeus Singapore Singapore Pulau Ubin J1165 Both O. sericeus Singapore Singapore Pulau Ubin J1167 Both

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Appendix 1 Table 4. Species and source of sequences used to calibrate mutation rate of six nuclear loci. Locus Parrot Source Suboscine Source Oscine Source species species species ARNTL Micropsitta Unpublished, Tyrannus Unpublished, Bombycilla Unpublished, finchii R. Kimball tyrannus R. Kimball garrulous R. Kimball GAPDH Amazona GenBank, Mionectes GenBank, Bombycilla GenBank, amazonica AY194433 rufiventris EU231662 garrulous EU272099 GARS Micropsitta Unpublished, Tyrannus Unpublished, Regulus Unpublished, finchii R. Kimball tyrannus R. Kimball calendula R. Kimball PER2 Psittacula Unpublished, Tyrannus Unpublished, Regulus Unpublished, alexandri R. Kimball tyrannus R. Kimball calendula R. Kimball TGFβ2 Micropsitta GenBank, Tyrannus GenBank, Regulus GenBank, finchii EU737405 tyrannus EU737471 calendula EU737446 VIM Psittacula Unpublished, Tyrannus Unpublished, Regulus Unpublished, alexandri R. Kimball tyrannus R. Kimball calendula R. Kimball

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A. continental into mixed B. mixed into continental

0.005 0.004

0.004 0.003

probability 0.003 0.002 0.002 0.001 0.001

0 0 Marginal posterior probability posterior Marginal Marginal posterior posterior Marginal 0 2 4 6 8 10 0 2 4 6 8 10

Migration rate Migration rate C. Divergence time 0.0015

0.0012

0.0009

0.0006

0.0003

Marginal posterior probability posterior Marginal 0 2 4 6 8 10

Time Appendix 1 Figure 1. Posterior probability plots of migration rates (A, B) and divergence times (C) between continental and mixed populations, calculated using the program IM. All parameters are scaled by neutral mutation rate (see text for explanations).

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A. continental into mixed

Marginal posterior probability posterior Marginal Time

B. mixed into continental

Marginal posterior probability posterior Marginal

Time Appendix 1 Figure 2. Posterior probability plots of weighted mean migration times averaged across all five loci (one mitochondrial and four autosomal introns), calculated using the program IM. Time is scaled by neutral mutation rate (see text for explanations).

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Hypothymis azurea, n = 420 AUC = 0.919 (Training data); 0.896 (Test data) P = 0.00226 (Fractional predicted area = 0.927) Niche model projected on current climate Niche model projected on paleoclimate

Terpsiphone paradisi, n = 92 AUC = 0.901 (Training data); 0.925 (Test data) P = 0.100 (Fractional predicted area = 0.905) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3A. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Copsychus malabaricus, n = 162 AUC = 0.943 (Training data); 0.940 (Test data) P = 0.0394 (Fractional predicted area = 0.928) Niche model projected on current climate Niche model projected on paleoclimate

Alophoixus phaeocephalus, n = 54 AUC = 0.978 (Training data); 0.975 (Test data) P = 0.0152 (Fractional predicted area = 0.725) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3B. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Pycnonotus plumosus, n = 139 AUC = 0.923 (Training data); 0.950 (Test data) P = 0.00062 (Fractional predicted area = 0.735) Niche model projected on current climate Niche model projected on paleoclimate

Tricholestes criniger, n = 60 AUC = 0.958 (Training data); 0.957 (Test data) P = 0.00812 (Fractional predicted area = 0.726) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3C. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Orthotomus sericeus, n = 134 AUC = 0.950 (Training data); 0.992 (Test data) P = 0.00076 (Fractional predicted area = 0.710) Niche model projected on current climate Niche model projected on paleoclimate

Macronous gularis, n = 172 AUC = 0.918 (Training data); 0.868 (Test data) P = 0.00604 (Fractional predicted area = 0.872) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3D. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Malacocincla malaccensis, n = 80 AUC = 0.972 (Training data); 0.960 (Test data) P = 0.00173 (Fractional predicted area = 0.728) Niche model projected on current climate Niche model projected on paleoclimate

Pellorneum capistratum, n = 63 AUC = 0.966 (Training data); 0.949 (Test data) P = 0.00695 (Fractional predicted area = 0.718) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3E. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

124

Stachyris erythroptera, n = 71 AUC = 0.949 (Training data); 0.980 (Test data) P = 0.00332 (Fractional predicted area = 0.715) Niche model projected on current climate Niche model projected on paleoclimate

Stachyris poliocephala, n = 59 AUC = 0.981 (Training data); 0.972 (Test data) P = 1.73E-14 (Fractional predicted area = 0.056) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3F. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Arachnothera longirostra, n = 233 AUC = 0.939 (Training data); 0.949 (Test data) P = 0.00049 (Fractional predicted area = 0.842) Niche model projected on current climate Niche model projected on paleoclimate

Hypogramma hypogrammicum, n = 88 AUC = 0.981 (Training data); 0.972 (Test data) P = 2.66E-13 (Fractional predicted area = 0.105) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3G. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Prionochilus maculatus, n = 66 AUC = 0.960 (Training data); 0.973 (Test data) P = 0.0297 (Fractional predicted area = 0.726) Niche model projected on current climate Niche model projected on paleoclimate

Philentoma pyrrhoptera, n = 45 AUC = 0.964 (Training data); 0.937 (Test data) P = 0.0274 (Fractional predicted area = 0.721) Niche model projected on current climate Niche model projected on paleoclimate

Appendix 1 Figure 3H. Ecological niche models of study species projected on current (WorldClim) and Last Glacial Maximum (CCSM) climatic models. White squares are geographical localities used to construct the models; purple squares are geographical localities randomly selected by MAXENT to verify the models; n = total number of geographic localities used for a particular species. AUC = area under the (receiver operating characteristics) curve; P = probability of the null hypothesis that test points are predicted no better than by a random prediction with the specified fractional predicted area.

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Appendix 1 Figure 4. Dendogram of cluster analysis on all 19 Asian BIOCLIM variables. Cluster analysis was carried out using median linkage, and correlation distances. B1 = BIO1, B2 = BIO2, and so on.

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A B

C D

Appendix 1 Figure 5. Averaged normalized paleodistributions of species showing peninsular Malaysia- Borneo divergence of (A) < 21 Ka or (B) > 21 Ka. Averaged normalized paleodistributions of species showing western Borneo-northeastern Borneo divergence of (C) < 110 Ka or (D) > 110 Ka. Red represents more suitable habitats and blue, less suitable habitats.

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Appendix 2: Permission from Journal of Avian Biology

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License Number 2458261271588 License date Jun 29, 2010

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Licensed content publication Journal of Avian Biology

Licensed content title Extensive color polymorphism in the southeast Asian oriental dwarf

kingfisher Ceyx erithaca: a result of gene flow during population divergence?

Licensed content authorLim Haw Chuan, Sheldon Frederick H., Moyle Robert G.

Licensed content date Jun 22, 2010

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Vita

Haw Chuan Lim was born in 1973 in Johor Bahru, Malaysia, to Swee Eng and Roy Weng Chin

Lim. Primary education was pleasantly blissful because the school he attended was where his mother was serving as a teacher. However, for secondary school and junior college, he had to commute across an international border to Singapore on a daily basis. His interest in the conservation of wildlife and biological diversity prompted him to pursue a degree in environmental sciences at Wollongong, New South Wales, Australia. There, he had first-hand exposure to biological research and museum work. Upon his return to Southeast Asia, after a short stint in Papua New Guinea, he took up a scientific research job at the only wetland reserve in Singapore – Sungei Buloh Wetland Reserve. Besides gaining valuable work and field experience, he also met his wife, Ching Chi, who was a volunteer there. After working on migratory shorebirds, mangroves and wetland ecosystems for a couple of years, he joined Dr.

Navjot Sodhi‟s laboratory at the National University of Singapore. Over there, he worked on government-funded projects looking at habitat selection and management of invasive and native birds, as well as the environmental impact of coastal land reclamations. He joined Dr. Frederick

Sheldon‟s laboratory in 2004 to study avian population genetics.

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