Genetic Diversification in the East Himalayas As Revealed by Comparative Phylogeography of the Black-Throated Bushtit and Elliot’S Laughing Thrush

QUAN Q et al.: Genetic diversification in the East Himalayas

ACCEPTED ARTICLE

Genetic diversification in the East Himalayas as revealed by comparative phylogeography of the black-throated bushtit and Elliot’s laughing thrush

 Qing QUAN, Yanhua QU, Fumin LEI

Key Laboratory of Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China

Abstract The Southwest Mountainous region of the eastern Himalayas is a hotspot with extraordinarily high biodiversity and endemism, but the processes that have driven this unique diversity are largely unknown. We evaluated processes that have contributed to the current observed high genetic diversity in this region by integrating comparative phylogeography with ecological niche modeling in a study of two representative birds of the Southwest Mountains: the black-throated bushtit (Aegithalos concinnus) and the Elliot’s laughing thrush (Garrulax elliotii). Mitochondrial DNA analyses revealed multiple divergent genetic lineages, which are roughly congruent with the north, south and east eco-subregion division of the Southwest Mountains. This strong geographical structure in these two species suggests that lineage diversification has proceeded in situ between the eco-subregions of the Southwest Mountains. During Pleistocene glaciations, the two species responded differently to climatic fluctuations. A. concinnus maintained rather stable habitats, mostly evergreen forests, during glacial cycles and thus kept a stable population size and further accumulated genetic diversity. In contrast, G. elliotii, which is mostly active in shrublands, has shifted its suitable habitats with glacial cycles. This species dispersed to low elevation areas during glacial periods, which provided multiple opportunities for gene admixture. The admixture causes the mixing of previously isolated genetic lineages and thus obscures the pattern of genetic variation [Current Zoology 61 () : – , 2015 ].

Keywords East Himalayas, Southwest Mountains of China, Genetic diversity, Comparative phylogeography, Ecological niche modeling

The eastern Himalayan region is a global biodiversity hotspot and holds great importance for biodiversity conservation (Myers et al., 2000). The Southwest Mountains of China, which are located at the eastern end of the Himalayan range and at the southeastern corner of the Tibetan plateau, possess extremely rich fauna in terms of species diversity and endemism (Zhang et al., 2009; Tang, 1996; Lei et al., 2003a; 2003b). Caused by the tectonic uplift of the

Received Dec. 19, 2014; accepted Feb. 9, 2015.

 Corresponding author. E-mail: [email protected]

QUAN Q et al.: Genetic diversification in the East Himalayas

Himalayas in the Pliocene, the Southwest Mountains are characterized by a series of parallel alpine ridges reaching altitudes over 5,000 m above sea level (m.a.s.l.) with altitudinal differences often exceeding 2,000 m between valleys and mountain tops (Zhang et al., 2009). Mountainous areas often play an important role in avian speciation and diversification because their topographic complexity can lead to ecological stratification and environmental heterogeneity (Fjeldså et al., 2012). In fact, the Southwest Mountains have been considered an archipelago of evolutionary islands where populations, subspecies and species have become isolated and evolved independently (Qu et al., 2014; 2015).

The glaciations that took place during the Pleistocene have also most likely contributed to genetic diversification in the Southwest Mountains. During Pleistocene glaciations, only high elevations of this region were glaciated (Zhou et al., 2006), with the lower limit of glaciation estimated at above 2,000 m.a.s.l. (Li et al., 1991). Accordingly, areas of low elevation may have provided mild glacial refugia, and species may have stayed in the multiple, separated micro-refugia because of the topographical complexity (Qu et al., 2011). If so, genetic diversification of montane species in the Southwest Mountains could be maintained and increased further. The black-throated bushtit Aegithalos concinnus and the Elliot’s laughing thrush Garrulax elliotii are commonly found in southern China, mainly distributed in the Southwest Mountains. A. concinnus inhabits broadleaf evergreen forests from 1,400 to 3,200 m.a.s.l, and G. elliotii is commonly found in shrublands between 2,000 and 4,500 m.a.s.l (Cheng et al., 1987). Previous mitochondrial DNA analyses identified multiple genetic lineages in these two species (Qu et al., 2011; Dai et al., 2012), yet how the topographical complexity and distinct history of the Pleistocene glaciations in the Southwest Mountains contributed to the genetic diversity in this region is not well understood. In this study, we investigate the relative importance of topographical complexity and Pleistocene glaciations on the genetic diversification in A. concinnus and G.elliotii by integrating comparative phylogeography with ecological niche modeling. We aim to 1) determine whether there are multiple genetic lineages in the Southwest Mountains; 2) if so, determine whether these lineages are congruent with eco-subregion divisions or different mountain systems; and 3) assess the divergence time between these lineages and how changes in habitats during the Pleistocene glaciations have contributed to intraspecific divergence.

1 Materials and Methods 1.1 Study area and sampling sites

The Southwest Mountains can generally be divided into several eco-subregions (Zhang et al., 1997; Yu et al., 2007; Zhang et al., 2009) (also see Fig. 1). The south eco-subregion is characterized by tropical or subtropical broadleaved forests with a mixture of evergreen and deciduous species, and the north eco-subregion is typified by alpine meadows and shrublands. The eastern subregion is dominated by the temperate evergreen coniferous and broadleaved mixed forests. The sampling of the two species is shown in Figure 1 and Table S1. These samples were collected at sites across the Southwest Mountains with an elevational distribution between 1,500 and 4,000 m.a.s.l.

1.2 Genetic datasets

We used a mitochondrial DNA locus that combined three genes original published in Qu et al. (2011) and Dai et al. (2012): an 880 bp fragment of cytochrome b, a 707 bp fragment of COI gene and a 1,003 bp fragment of DN2 gene for G. elliotii; and a 886 bp fragment of cytochrome b, a 1,236 bp fragment of COI gene and a 958 bp fragment of DN2 gene for A. concinnus. Only subsections of samples from the Southwest Mountains were used in this study.

QUAN Q et al.: Genetic diversification in the East Himalayas

1.3 Genetic diversity

We calculated the number of segregating sites and nucleotide diversity for each species using DnaSP 5.1 (Rozas et al., 2009). Two additional neutrality tests, Tajima’s D and Fu’s FS (Fu, 1997) were used to detect departures from the mutation-drift equilibrium that would be indicative of changes in historical demography and natural selection. We also calculated the uncorrected P distance within and between lineages revealed from phylogenetic trees (see Results). All tests were implemented in DnaSP. 1.4 Phylogenetic reconstruction

Maximum likelihood (ML) and Bayesian inference (BI) were employed to infer the phylogenetic relationship of the two species. The maximum likelihood trees were reconstructed in the program PHYML 3.0 (Guindon and Gascuel, 2003). Based on the Akaike Information Criterion (AIC, Akaike, 1973), the model HKY, invariable sites and gamma distribution were used as suggested by Modeltest 3.7 (Posada and Crandall, 1998). The base frequency and the ratio of transition/transversion were optimized using the maximum likelihood criterion in PHYML. Outgroups were the black- faced laughing thrush G. affinis, exquisite laughing thrush G. formosus and streaked laughing thrush G. lineatus for G. elliotii, and the silver-throated bushtit A. caudatus for A. concinnus. Bayesian analyses were performed with MrBayes 3.1 (Ronquist and Huelsenbeck, 2003) with default parameters using the selected models generated by MrModeltest 2.2 (Nylander, 2002). Two independent runs of Monte Carlo Markov Chains (MCMCs) were carried out with trees sampled every 100 generations for 2–5×106 generations until the average standard deviation of split frequency was below 0.01.

A total of 25% of the generations were discarded as burn-in and posterior probabilities were estimated for the remaining saved generations. 1.5 Divergence time estimation

Molecular dating analyses were carried out for combined data using the substitution rates recently calibrated for mitochondrial genes in Hawaiian Honeycreepers, based on calibrations using the ages of the Hawaiian Islands: a mutation rate of (1.4 ± 0.2) × 10-8 for cyt-b, (1.6 ± 0.2) × 10-8 for COI and (2.5 ± 0.03) × 10-8 for ND2, (Lernet et al,. 2011). We used the relaxed uncorrelated lognormal model (Drummond and Rambaut, 2007) implemented in BEAST 1.7.1 (Drummond and Rambaut, 2007) to consider the uncertainty of the mutation rate. Mutation rates were set to be uniform prior with an initial value of 0.014, 0.016 and 0.025 for cyt-b, COI and ND2, respectively. Tree topologies and divergence times were estimated simultaneously in BEAST, thus incorporating topological uncertainty into the divergence time estimate. Each gene was allowed its own independent evolutionary model and parameter. The models for nucleotide substitutions used for genes were selected by applying the Akaike information criterion in MODELTEST 3.7. A constant coalescent model was used in all BEAST runs. For each species, two independent analyses were run for 200 million generations, sampling every 1000. Convergence to the posterior distributions of divergence times and parameter estimates was examined in TRACER 1.5 (Rambaut and Drummond, 2007), and the burn-in was set at 25%. The maximum-credibility trees, representing the maximum posterior topology, were calculated after removing burn-ins in TREEANNOTATOR. 1.6 Ecological niche modeling

Ecological niche models were constructed for the two species to explore the degree to which its distribution has changed since the Pleistocene glaciations. We collected occurrence records from National Zoological Museum, Institute of Zoology, Chinese Academy of Sciences and China Bird Report (http://birdtalker.net/report/index.asp). We 3

QUAN Q et al.: Genetic diversification in the East Himalayas georeferenced the records in Google Maps. We only used field records of populations in the Southwest Mountains and total 161 and 67 records were used for G. elliotii and A. concinnus, respectively. The records of each species were randomly separated into two subsets, one for model training and the other for model evaluation. Current distribution ranges of these species in BirdLife International (2015) were used to outline the accessible area in ArcMap and to extract current climatic layers as the background for model training. The results of model training were projected to current, last interglacial glaciation (LIG) and last glacial maximum (LGM) layers. All the climatic layers were from Worldclim at 10 minute resolution (Hijmans et al., 2005) except that LIG layers were resampled at 30 second. We applied two Global Climate Models to estimate the suitable habitats during LGM, the Community Climate System Model (CCSM) and the Model for Interdisciplinary Research on Climate (MIROC) (Otto-Bliesner et al., 2006; Braconnot et al., 2007). To minimize redundancy in autocorrelation between climatic variables, we kept only those variables closely related to temperature and precipitation (Water-Energy theory, Hijmans et al., 2005; Tingley et al., 2009). The final models included seven variables (annual mean temperature, mean temperature diurnal range, max temperature of the warmest month, min temperature of the coldest month, annual precipitation, precipitation of the wettest month and precipitation of the driest month) because these variables did not include spatial artifacts. Ecological niche models were constructed using the maximum entropy machine learning algorithm in MAXENT (Phillips et al., 2006) with 100 replicates. The other settings were set as default. We performed partial ROC analysis to evaluate the prediction results using the Tool for Partial-ROC Ver 1.0 (Barve 2008) following Peterson (2008). We used default settings except that the 1- omission value was set to 0.9. We applied Z-test to test if the AUC ratio from the partial ROC approach is significantly larger than 1. Z value larger than 2.58 indicates the predictions are much better than the random distributions. We turned the median grids of MAXENT’s outputs into binary plots by setting the threshold at a 10 percentile training presence. Cells with values above the threshold represent the areas suitable for the species.

2 Results 2.1 Genetic diversity For A. concinnus, the combined length of the mitochondrial locus (3,080 bp) contained 607 polymorphic sites. These polymorphic sites defined 57 unique haplotypes. The nucleotide diversity is 0.0273. The tests of population expansion, indicated by Tajima’s D and Fu’s FS, did not deviate significantly from neutrality (Table 1). For G. elliotii, a 2463 bp mitochondrial DNA fragment included 93 polymorphic sites and 52 haplotypes. The nucleotide diversity is

0.003 and the results of Tajima’s D and Fu’s FS tests indicated an over-abundance of singleton mutations and rare alleles (Table 1). 2.2 Phylogenetic reconstruction and divergence time estimation In G. elliotii, both the ML and BI trees based on combined data (cyt-b+COI+ND2, Fig. 2) or each gene alone (Fig. S1) are composed of two major lineages . The geographic distributions of the two lineages appeared uneven (Fig. S1), with the majority of the first lineage found mainly in populations in the north and east subregions (north-east lineage); the second lineage is most common in the south subregion (south lineage). The two lineages are not strongly supported by the posterior probability (PP < 95%) and bootstrap value (ML < 70) (Fig. 2). The uncorrected P distance is 0.003 between the two lineages (Table 2). Divergence time between the two lineages fell within the late Pleistocene glacial period (MIS6): 0.25 (95% HPD, 0.16–0.37 mya). The coalescent time for each lineage is 0.16 (95% HPD, 0.1–0.24) for the south lineage and 0.13 (95% HPD, 0.08–0.2) mya for the north-east lineage (Fig. 2). In A. concinnus, the ML and BI trees showed similar topology, dividing individuals into two major lineages. The

QUAN Q et al.: Genetic diversification in the East Himalayas first lineage comprised sampling sites from the south eco-subregion of the Southwest Mountains (south lineage), and the second was restricted to sampling sites from the north and east eco-subregions (north-east lineage). The north-east lineage was further divided into two clades, which are roughly congruent with north and east eco-subregions, respectively in combined data and each gene (Fig. 2 and Fig. S1). All three lineages are strongly supported by the posterior probability (PP > 95%) and bootstrap value (ML > 70). The uncorrected P distance is 0.045 between the two lineages (Table 2). The estimated divergence time between the south and north-east lineages was 1.53 (95% HPD: 1.1– 2.02) mya, and the coalescent time was 0.18 (0.11–0.25) and 0.22 (0.14–0.31) mya for the south and north-east lineages, respectively (Fig. 2). 2.3 Ecological niche modeling

The ecological niche models developed under the current climate condition accurately predicted the present distribution of each species. AUC ratio from partial RPC are 1.309 ± 0.063 (Z = 155.10 > 2.58) and 1.695±0.061 (Z = 360.29 > 2.58) for G. elliotii and A. concinnus. The area under the ROC curve (AUC) was close to one (0.943 for A. concinnus and 0.893 for G. elliotii), indicating better than random predictions (0.5 = random, 1 = maximum). The climatic suitability for the two species predicted by MAXENT during the LGM and LIG somehow changed dramatically between the species, indicating that the two species have responded differently to the LGM and LIG climatic fluctuations. In A. concinnus, these suitable habitats were more or less similar to the habitats predicted as suitable during the LGM and LIG, suggesting a long-term persistence of the stable habitat. By contrast, in G. elliotii, rather small areas of habitats were predicted as suitable during the LGM but much larger areas in the LIG compared to current habitats, indicating that suitable habitats of this species had shifted with climatic changes (Fig. 3). 3 Discussion 3.1 Lineage diversification (in situ diversification) within the Southwest Mountains Topographical complexity and ecological heterogeneity are likely to have contributed to the genetic diversification of the two species in the Southwest Mountains of China. In agreement with this, the analyses of mitochondrial DNA revealed multiple genetic lineages in A. concinnus and G. elliotii. In A. concinnus, three lineages were observed, a south lineage (including individuals from localities in the south eco-subregion of the Southwest Mountains), a north lineage (including individuals from localities in the north eco-subregion), and an east lineage (including individuals from localities in the east eco-subregion). In G. elliotii, the genetic divergence is less distinct, but two lineages could still be detected, which were generally congruent with a division between the south and north-east eco-subregions (south lineage included individuals from localities in the south eco-subregion, and the north-east lineage contained individuals from localities in the north and the east eco-subregions). This strong geographical structure in these two species suggests that lineage diversification has proceeded in situ between the eco-subregions of the Southwest Mountains. In their cases, the steep mountains and deep valleys of the Kangding-Muli-Baoxin Divide, recognized as a geographic barrier by Zhang et al. (2009) and Li (1989), appear to have effectively prevented gene flow among subregional populations. Across the entire study area, the correlation between the phylogeographic pattern and subregion division is strong, which may explain from population genetic point why the Southwest Mountains have higher avian endemism due to ‘‘ecological island’’ effect (the subregion’s isolation as being ecological isolation (Lei et al., 2015). The coalescence times of these genetic lineages date back between 0.11 and 0.31 (G. elliotii) and 1.1 and 2.02 (A. concinnus) mya. The in situ diversification within the Southwest Mountains thus mostly developed in the late Pleistocene. Similar Pleistocene allopatric divergences were observed for a few bird species inhabiting the eastern and

QUAN Q et al.: Genetic diversification in the East Himalayas western Himalayas (Päckert et al., 2012), and south and north Southwest Mountains (Qu et al., 2014). These results suggest that different eco-subregions and mountain systems in the Southwest Mountains have served as physical barriers since the Pleistocene. These barriers have effectively prevented gene flow between populations, subspecies and species in different eco-subregions. The diversification in situ is thus the dominant model of diversification in the species occurring whinin the Southwest Mountains of China. The in situ diversification in the Southwest Mountains mimics that of archipelago islands, which are separated by areas of unsuitable habitats that act as barriers to gene flow (Qu et al., 2011, 2014, 2015). Isolated ecosystems such as mountaintops and mountain systems have been shown to exhibit island-like properties (Masta, 2000; Smith and Farrell, 2005; Shepard and Burbrink, 2008; 2009). In the Southwest Mountains, species were often restricted to specific eco- subregions and mountain systems, where organisms have been isolated since the Pleistocene. This may explain the extraordinary high number of endemic genetic lineage, subspecies, species and genera in this region (Lei et al., 2003a; 2003b). 3.2 Different response to the Pleistocene glaciations in the two species The Pleistocene glacial cycles have induced environmental changes in East Asia, which caused shifts in the geographic ranges of many species (Li et al., 2009; Liu et al., 2012; Wang et al. 2013; Lei et al., 2014). Nevertheless, two species in the Southwest Mountains of China responded differently to Pleistocene glacial cycles, as evidenced from their different stable habitat distribution and patterns of genetic variation. The ecological niche models predict that the suitable habitats of A. concinnus stayed stable during the LGM, LIG and present time, whereas those of G. elliotii dramatically changed with climatic fluctuations, with the smallest habitats in the LGM and the largest ones in the LIG. Different patterns of stable habitat for the two species is reasonable because the pollen-based paleovegetation data indicate that cool-temperate vegetation, such as shrublands, which are the suitable habitat for G. elliotii, expanded to lower elevations during glacial periods and contracted to high elevations during warmer and wetter interglacial periods (Kou et al., 2006; Neigel and Avise, 1986; Wang et al., 2001; Yu et al., 2007). However, the broad-leaved and seasonal forests, which are the suitable habitats for A. concinnus, stayed stable in low elevations of the Southwest Mountains during glacial periods. A different stable habitat pattern led to a different demographic history. A stable population size and long-term persistence of population structure are observed for A. concinnus, whereas signal of population expansion and genetic admixture are detected for G. elliotii. Different stable habitat patterns and demographic history have further contributed to the various genetic diversification patterns of the two species. For G. elliotii, glacial-induced expansion may bring separated populations into contact, thus increasing opportunities for gene flow and obscuring previous genetic divergence. Indeed, we found a shallow phylogeographic division in this species and highly shared ancestry between genetic lineages. It is probable that isolated genetic lineages periodically expanded to low elevations where they admixed, and established a contact zone along their boundaries. The frequent occurrence of the admixture has dramatically reduced the genetic diversity in G. elliotii. By contrast, a stable habitat and demographic history through the Pleistocene allowed the genetically distinct lineages of A. concinnus to be maintained, thus further increasing the genetic diversity of this species. It is likely that the relatively mild climate in the low elevation areas in the Southwest Mountains makes it possible to maintain stable populations during colder periods, causing less drastic demographic fluctuations, such as the bottlenecks and expansions experienced by the other high elevation species, G. elliotii. However, considering that the populations of G. elliotii from eco-subregions frequently made contact during glacial expansions, we may expect that different populations merged into the same gene pool. However, a rather large degree of genetic divergence between lineages indicates that they have been separated from each other for a relatively long 6

QUAN Q et al.: Genetic diversification in the East Himalayas period (two lineages diverged by 0.24 mya). Although the south and north-east lineages are intermixed, no haplotypes are shared between the lineages. This lack of sharing suggests that the two lineages are at an intermediate divergence stage from paraphyly to reciprocal monophyly. Thus, for the species in the Southwest Mountains, we conclude that the high diversity in this region is the result of a long-term in situ diversification.

Acknowledgements We sincerely thank Chuanying Dai for providing mtDNA sequences of the Aegithalos concinnus. We also thanks Town Peterson for providing the analysis protocol for the ecological niche model, and Robert G. Moyle for the protocol of genetic analyses. This research was supported by grants from the National Science Foundation of China (Nos 31471990, 31172064 to Y.Q. and 31330073, 30925008 to F.L.) and the Ministry of Science and Technology of the People’s Republic of China (MOST Grant No. 2011FY120200-3).

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Table 1 Mitochondrial DNA estimated population genetic summaries for the two species

Species N bp S π Tajima’s D Fu’s FS Garrulax elliotii 80 2463 93 0.00328 -1.945* -37.21***

Aegithalos concinnus 72 3080 607 0.0273 -1.3247 -2.009

* P < 0.005; *** P < 0.0001.

Table 2 Divergence (uncorrect P distance) between the major clades of two species

Species Da (South Clade) Da (North Clade) Da (South versus North) Garrulax elliotii 0.002 0.002 0.003

Aegithalos concinnus 0.003 0.003 0.045

QUAN Q et al.: Genetic diversification in the East Himalayas

Fig. 1 Study area and topography of the Southwest Mountains Sampling sites for the two species; open circles represent G. elliotii and filled circles represent A. concinnus. Sampling sites are indicated by each population’s name (Table S1). The bold dashed line represents the boundary between recognized ecological subregions in the eastern Himalayan mountain range (Zhang et al., 2009).

QUAN Q et al.: Genetic diversification in the East Himalayas

Fig. 2 The mitochondrial DNA phylogenetic trees of Garrulax elliotii and Aegithalos concinnus The mean coalescent time and its 95% highest posterior density (HPD) intervals are shown above the nodes, and the branch supports for main nodes under the nodes by three approaches (BEAST/BI/ML). The stars in the trees show individuals clustered within one lineage, although they geographically belong to other lineage’s subregions.

Fig. 3 The suitable habitats predicted by the ecological niche modeling for current, last glacial Maximum (LGM) and last interglacial (LIG) distributions for Garrulax elliotii and Aegithalos concinnus, respectively

QUAN Q et al.: Genetic diversification in the East Himalayas

Supplementary Materials

Table S1 Genbank accession numbers of all sequences used in this study

Table S1.1 Aegithalos concinnus

Collection n Vouche Localities Coordinate Cyt-b COI ND2 umber number

GS1 1152 Wenxian N32.70, E105.21 HQ605327 HQ605124 HQ605530

GS2 1154 Wenxian N32.70, E105.21 HQ605328 HQ605125 HQ605531

GS3 1182 Wenxian N32.70, E105.21 HQ605329 HQ605126 HQ605532

GS4 1212 Wenxian N32.70, E105.21 HQ605330 HQ605127 HQ605533

SC1 3661 Panzhihua N26.45, E101.6 HQ605393 HQ605190 HQ605596

SC2 3818 Yanbian N27.10, E101.53 HQ605404 HQ605201 HQ605610

SC3 3819 Yanbian N27.10, E101.53 HQ605407 HQ605204 HQ605610

SC4 3844 Yanbian N27.10, E101.53 HQ605416 HQ605213 HQ605619

SC5 3845 Yanbian N27.10, E101.53 HQ605417 HQ605214 HQ605620

SC6 3898 Yanbian N27.10, E101.53 HQ605418 HQ605215 HQ605621

SC7 3986 Pangzhihua N26.45, E101.6 HQ605419 HQ605216 HQ605622

SC8 3994 Pangzhihua N26.45, E101.6 HQ605420 HQ605217 HQ605623

SC9 4097 Miyi N27.12, E101.89 HQ605421 HQ605218 HQ605624

SC10 4180 Miyi N27.12, E101.89 HQ905191 HQ906394 HQ905597

SC11 8788 Luding N29.64, E102.12 HQ605395 HQ605192 HQ605598

SC12 8789 Luding N29.64, E102.12 HQ605396 HQ605193 HQ605599

SC13 10139 Yaan N30.07, E102.99 HQ605397 HQ605194 HQ605560

SC14 8902 Guangyuan N32.42, E105.84 HQ605398 HQ605195 HQ605601

SC15 8903 Guangyuan N32.42, E105.84 HQ605399 HQ605196 HQ605602

SC16 8904 Guangyuan N32.42, E105.84 HQ605400 HQ605197 HQ605603

SC17 8951 Guangyuan N32.42, E105.84 HQ605401 HQ605198 HQ605604

QUAN Q et al.: Genetic diversification in the East Himalayas

SC18 8952 Guangyuan N32.42, E105.84 HQ605402 HQ605199 HQ605605

SC19 8953 Guangyuan N32.42, E105.84 HQ605403 HQ605200 HQ605606

SC20 8954 Guangyuan N32.42, E105.84 HQ605405 HQ605202 HQ605608

SC21 8955 Guangyuan N32.42, E105.84 HQ605406 HQ605203 HQ605609

SC31 13217 Miyi N27.12, E101.89 HQ605408 HQ605205 HQ605611

SC32 13218 Miyi N27.12, E101.89 HQ605409 HQ605206 HQ605612

SC33 13219 Miyi N27.12, E101.89 HQ605410 HQ605107 HQ605613

SC34 13220 Miyi N27.12, E101.89 HQ605411 HQ605208 HQ605614

SC35 13221 Miyi N27.12, E101.89 HQ605412 HQ605209 HQ605615

SC36 13222 Miyi N27.12, E101.89 HQ905413 HQ605210 HQ605616

SC37 13223 Miyi N27.12, E101.89 HQ605414 HQ605211 HQ605617

SC38 13224 Miyi N27.12, E101.89 HQ605415 HQ605212 HQ605618

SX1 4610 Taibaishan N34.00, E107.78

SX2 4657 Taibaishan N34.00, E107.78 HQ605241 HQ605444 HQ605647

SX3 4658 Taibaishan N34.00, E107.78 HQ605247 HQ605450 HQ605653

SX4 4661 Taibaishan N34.00, E107.78 HQ605248 HQ605451 HQ605654

SX13 8723 Taibaishan N34.00, E107.78 HQ605234 HQ605437 HQ605640

SX14 8724 Taibaishan N34.00, E107.78 HQ605235 HQ605438 HQ605641

SX15 8725 Taibaishan N34.00, E107.78 HQ605236 HQ605439 HQ605642

SX16 8726 Taibaishan N34.00, E107.78 HQ605237 HQ605440 HQ605643

SX17 8727 Taibaishan N34.00, E107.78 HQ605238 HQ605441 HQ605644872

SX18 8728 Taibaishan N34.00, E107.78 HQ605239 HQ605442 HQ605645

SX19 8729 Taibaishan N34.00, E107.78 HQ605240 HQ605443 HQ605646

SX20 8730 Taibaishan N34.00, E107.78 HQ605242 HQ605445 HQ605648

SX21 8731 Taibaishan N34.00, E107.78 HQ605243 HQ605446 HQ605649

SX22 8732 Taibaishan N34.00, E107.78 HQ605244 HQ605447 HQ605650

SX23 8733 Taibaishan N34.00, E107.78 HQ605246 HQ605448 HQ605651

SX24 8734 Taibaishan N34.00, E107.78 HQ605246 HQ605449 HQ605652

YN1 5207 Kunming N25.23, E102.59 HQ605249 HQ605452 HQ605655

QUAN Q et al.: Genetic diversification in the East Himalayas

YN2 5208 Kunming N25.23, E102.59 HQ605253 HQ605456 HQ605659

YN3 5209 Kunming N25.23, E102.59 HQ605263 HQ605466 HQ605669

YN4 5344 Gaoligong N25.59, E98.73 HQ605267 HQ605470 HQ605673

YN5 5356 Gaoligong N25.59, E98.73 HQ605268 HQ605471 HQ605674

YN6 5357 Gaoligong N25.59, E98.73 HQ605269 HQ605472 HQ605675

YN7 5358 Gaoligong N25.59, E98.73 HQ605270 HQ605473 HQ605676

YN8 5359 Gaoligong N25.59, E98.73 HQ605271 HQ605474 HQ605677

YN9 5360 Gaoligong N25.59, E98.73 HQ605272 HQ605475 HQ605678

YN10 5361 Gaoligong N25.59, E98.73 HQ605250 HQ605453 HQ605656

YN12 5363 Gaoligong N25.59, E98.73 HQ605252 HQ605455 HQ605658

YN21 12087 Lincang N23.99, E100.27 HQ605254 HQ605457 HQ605660

YN22 12088 Lincang N23.99, E100.27 HQ605255 HQ605458 HQ605661

YN23 12089 Lincang N23.99, E100.27 HQ605256 HQ605459 HQ605662

YN24 12090 Baoshan N24.82, E98.77 HQ605257 HQ605460 HQ605663

YN25 12091 Baoshan N24.82, E98.77 HQ605258 HQ605461 HQ605664

YN27 12093 Lincang N23.99, E100.27 HQ605260 HQ605463 HQ605666

YN28 12095 Lijiang N26.92, 100.24 HQ605261 HQ605464 HQ605667

YN29 12096 Lijiang N26.92, 100.24 HQ605262 HQ605465 HQ605668

YN30 12097 Lijiang N26.92, 100.24 HQ605264 HQ605467 HQ605670

YN31 12098 Lijiang N26.92, 100.24 HQ605266 HQ605468 HQ605671

Table S1.2 Garrulax elliotii

Field number Vouche Localities Coordinate Cyt-b COI ND2

number

1_CD 4957 Changdu N31.11, E97.10 HM601543 HM601623 HM601703

2_CD 4958 Changdu N31.11, E97.10 HM601544 HM601624 HM601704

3_CD 4959 Changdu N31.11, E97.10 HM601545 HM601625 HM601705

4_CD 4960 Changdu N31.11, E97.10 HM601546 HM601626 HM601706

5_CD 5131 Changdu N31.11, E97.10 HM601547 HM601627 HM601707

QUAN Q et al.: Genetic diversification in the East Himalayas

6_MK 5089 Mangkang N29.40, E98.32 HM601548 HM601628 HM601708

7_MK 5096 Mangkang N29.40, E98.32 HM601549 HM601629 HM601709

8_MK 5099 Mangkang N29.40, E98.32 HM601550 HM601630 HM601710

9_MK 5101 Mangkang N29.40, E98.32 HM601551 HM601631 HM601711

10_MK 5111 Mangkang N29.40, E98.32 HM601552 HM601632 HM601712

11_MK 5113 Mangkang N29.40, E98.32 HM601553 HM601633 HM601713

12_MK 5115 Mangkang N29.40, E98.32 HM601554 HM601634 HM601714

13_MK 5117 Mangkang N29.40, E98.32 HM601555 HM601635 HM601715

14_MK 5119 Mangkang N29.40, E98.32 HM601556 HM601636 HM601716

15_MK 5121 Mangkang N29.40, E98.32 HM601557 HM601637 HM601717

16_MK 5123 Mangkang N29.40, E98.32 HM601558 HM601638 HM601718

17_MK 5129 Mangkang N29.40, E98.32 HM601559 HM601639 HM601719

18_MK 5132 Mangkang N29.40, E98.32 HM601560 HM601640 HM601720

19_MK 5134 Mangkang N29.40, E98.32 HM601561 HM601641 HM601721

20_YB 4885 Yanbian N27.10, E101.55 HM601562 HM601642 HM601722

21_RT 4887 Rangtang N32.19, E100.59 HM601563 HM601643 HM601723

22_RT 4888 Rangtang N32.19, E100.59 HM601564 HM601644 HM601724

23_YA 4988 Yaan N30.07, E102.99 HM601565 HM601645 HM601725

24_YA 5044 Yaan N30.07, E102.99 HM601566 HM601646 HM601726

25_KD 201 Kangding N30.04, E101.59 HM601567 HM601647 HM601727

26_KD 208 Kangding N30.04, E101.59 HM601568 HM601648 HM601728

27_LWQ 246 Leiwuqi N31.12, E96.36 HM601569 HM601649 HM601729

28_WX 1240 Wenxian N32.72, E105.11 HM601570 HM601650 HM601730

29_WX 1228 Wenxian N32.72, E105.11 HM601571 HM601651 HM601731

30_LHS 6450 Lianhuashan N34.70, E103.40 HM601572 HM601652 HM601732

31_ZN 7785 Zhuoni N34.58, E103.49 HM601573 HM601653 HM601733

32_ZN 7786 Zhuoni N34.58, E103.49 HM601574 HM601654 HM601734

33_BC 7775 Beichuan N31.50, E104.27 HM601575 HM601655 HM601735

34_BC 7776 Beichuan N31.50, E104.27 HM601576 HM601656 HM601736

35_BC 7777 Beichuan N31.50, E104.27 HM601577 HM601657 HM601737

36_BC 7778 Beichuan N31.50, E104.27 HM601578 HM601658 HM601738

37_BC 7779 Beichuan N31.50, E104.27 HM601579 HM601659 HM601739

38_BC 7780 Beichuan N31.50, E104.27 HM601580 HM601660 HM601740

39_BC 7781 Beichuan N31.50, E104.27 HM601581 HM601661 HM601741

40_SLJ 8056 Shennongjia N31.46, E110.28 HM601582 HM601662 HM601742

41_SLJ 8089 Shennongjia N31.46, E110.28 HM601583 HM601663 HM601743

QUAN Q et al.: Genetic diversification in the East Himalayas

42_SLJ 8126 Shennongjia N31.46, E110.28 HM601584 HM601664 HM601744

43_SLJ 8398 Shennongjia N31.46, E110.28 HM601585 HM601665 HM601745

44_SLJ 8414 Shennongjia N31.46, E110.28 HM601586 HM601666 HM601746

45_FP 7962 Foping N32.81, E106.25 HM601587 HM601667 HM601747

46_FP 7950 Foping N32.81, E106.25 HM601588 HM601668 HM601748

47_BLJ 139 Wenxian N32.96, E104.68 HM601589 HM601669 HM601749

48_BLJ 151 Wenxian N32.96, E104.68 HM601590 HM601670 HM601750

49_YA 4989 Yaan N29.98, E102.98 HM601591 HM601671 HM601751

50_YA 5045 Yaan N29.98, E102.98 HM601592 HM601672 HM601752

51_YA 5046 Yaan N29.98, E102.98 HM601593 HM601673 HM601753

54_DGP 6136 Taibaishan N34, E107.78 HM601594 HM601674 HM601754

55_TBS 8750 Taibaishan N34, E107.78 HM601595 HM601675 HM601755

56_TBS 8751 Taibaishan N34, E107.78 HM601596 HM601676 HM601756

57_YA 10127 Yaan N29.98, E102.98 HM601597 HM601677 HM601757

DL1 5063 Dali N25.34, E100.13 HM601598 HM601678 HM601758

DL2 5064 Dali N25.34, E100.13 HM601599 HM601679 HM601759

DL3 5067 Dali N25.34, E100.13 HM601600 HM601680 HM601760

DL4 5068 Dali N25.34, E100.13 HM601601 HM601681 HM601761

DL5 5069 Dali N25.34, E100.13 HM601602 HM601682 HM601762

DL6 5071 Dali N25.34, E100.13 HM601603 HM601683 HM601763

DL7 5073 Dali N25.34, E100.13 HM601604 HM601684 HM601764

DL8 5077 Dali N25.34, E100.13 HM601605 HM601685 HM601765

DL9 5078 Dali N25.34, E100.13 HM601606 HM601686 HM601766

DL10 5080 Dali N25.34, E100.13 HM601607 HM601687 HM601767

DL11 5084 Zhongdian N27.48, E99.42 HM601608 HM601688 HM601768

ZD1 4862 Zhongdian N27.48, E99.42 HM601609 HM601689 HM601769

ZD2 4863 Zhongdian N27.48, E99.42 HM601610 HM601690 HM601770

ZD3 4864 Zhongdian N27.48, E99.42 HM601611 HM601691 HM601771

ZD4 4865 Zhongdian N27.48, E99.42 HM601612 HM601692 HM601772

ZD5 4866 Zhongdian N27.48, E99.42 HM601613 HM601693 HM601773

ZD6 4867 Zhongdian N27.48, E99.42 HM601614 HM601694 HM601774

ZD7 4868 Zhongdian N27.48, E99.42 HM601615 HM601695 HM601775

ZD8 4869 Zhongdian N27.48, E99.42 HM601616 HM601696 HM601776

ZD9 4875 Zhongdian N27.48, E99.42 HM601617 HM601697 HM601777

ZD10 4876 Zhongdian N27.48, E99.42 HM601618 HM601698 HM601778

ZD11 4877 Zhongdian N27.48, E99.42 HM601619 HM601699 HM601779

QUAN Q et al.: Genetic diversification in the East Himalayas

ZD12 4878 Zhongdian N27.48, E99.42 HM601620 HM601700 HM601780

ZD13 4879 Zhongdian N27.48, E99.42 HM601621 HM601701 HM601781

ZD14 4880 Zhongdian N27.48, E99.42 HM601622 HM601702 HM601782

QUAN Q et al.: Genetic diversification in the East Himalayas

Fig. S1 Cytochrome b, CoI and ND2 gene trees of Garrulax elliotii and Aegithalos concinnus