Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Sep 1.
Published in final edited form as: Am J Phys Anthropol. 2020 Jul 8;173(1):21–33. doi: 10.1002/ajpa.24099

Population genetics of wild Macaca fascicularis with low-coverage shotgun sequencing of museum specimens

Lu Yao 1, Kelsey Witt 2,3, Hongjie Li 4, Jonathan Rice 3, Nelson R Salinas 1,*, Robert D Martin 5,6, Emilia Huerta-Sánchez 2, Ripan S Malhi 4
PMCID: PMC8329942  NIHMSID: NIHMS1723213  PMID: 32643146

Abstract

Objectives:

Long-tailed macaques (Macaca fascicularis) are widely distributed throughout the mainland and islands of Southeast Asia, making them a useful model for understanding the complex biogeographical history resulting from drastic changes in sea levels throughout the Pleistocene. Past studies based on mitochondrial genomes (mitogenomes) of long-tailed macaque museum specimens have traced their colonization patterns throughout the archipelago, but mitogenomes trace only the maternal history. Here, our objectives were to trace phylogeographic patterns of long-tailed macaques using low-coverage nuclear DNA (nDNA) data from museum specimens.

Methods:

We performed population genetic analyses and phylogenetic reconstruction on nuclear single nucleotide polymorphisms (SNPs) from shotgun sequencing of 75 long-tailed macaque museum specimens from localities throughout Southeast Asia.

Results:

We show that shotgun sequencing of museum specimens yields sufficient genome coverage (average ~1.7%) for reconstructing population relationships using SNP data. Contrary to expectations of divergent results between nuclear and mitochondrial genomes for a female philopatric species, phylogeographical patterns based on nuclear SNPs proved to be closely similar to those found using mitogenomes. In particular, population genetic analysis and phylogenetic reconstruction from the nDNA identify two major clades within M. fascicularis: Clade A includes all individuals from the mainland along with individuals from northern Sumatra, while Clade B consists of the remaining island-living individuals, including those from southern Sumatra.

Conclusions:

Overall, we demonstrate that low-coverage sequencing of nDNA from museum specimens provides enough data for examining broad phylogeographic patterns, although greater genome coverage and sequencing depth would be needed to distinguish between very closely related populations, such as those throughout the Philippines.

Keywords: Long-tailed macaque, Macaca, Southeast Asia, phylogeography, nuclear DNA, next-generation sequencing

Introduction

Southeast Asia is a geographically complex region including an extensive mainland and thousands of islands ranging from less than 10 km2 to 743,330 km2 (Figure 1). Throughout the Pleistocene glacial periods, the Sunda continental shelf (Sundaland) was above sea level, serving as a land bridge connecting the continental islands to the mainland, thus significantly influencing the colonization patterns of organisms in the region (Delson, 1980; Fooden, 2006; Heaney, 1986; Jansa, Barker, & Heaney, 2006; Outlaw & Voelker, 2008; Steppan, Zawadzki, & Heaney, 2003). To the east of the shelf is the Huxley-Wallace line, a deep sea barrier that separates the Sunda shelf from the oceanic islands, which have never been connected to the mainland. Wallace (1863) drew the southern end of the line to separate Bali and Lombok islands at the Strait of Lombok. This barrier extends northward between Borneo and Sulawesi and then eastward between Mindanao and Sanghir Islands. Huxley (1868) subsequently revised the upper end of this line to pass directly northward, such that most islands of the Philippines lie to the east of the barrier (Figure 1; Huxley-Wallace line).

Figure 1.

Figure 1.

Open in a new tab

Map of Southeast Asia indicating major colonization patterns based on nuclear SNP analyses. The light blue arrow indicates the major colonization pattern of specimens that cluster within Clade A. The pink arrows indicate colonization patterns of specimens that cluster within Clade B. The ends of the arrows do not indicate exact localities. The blue star indicates the site of the Toba eruption.

On either side of the Huxley-Wallace line, faunal composition is largely distinct as most non-volant organisms had to swim, raft, or be conveyed by humans to the oceanic islands (Heaney, Balete, & Rickart, 2016; Heaney, 1985). A combination of glacial periods and human-mediated colonization has thus generated complex phylogeographic patterns of organisms in this region. However, long-tailed macaques (Macaca fascicularis) are an evident exception, occurring widely on both sides of the Huxley-Wallace line. This primate species is therefore an excellent study subject for understanding the phylogeography of terrestrial organisms in Southeast Asia. The 22 recognized Macaca species are currently split into seven species groups, based on a combination of geographical, behavioral, morphological and genetic evidence (Fooden, 1976; Groves, 2001; Li et al., 2009; Tosi, Morales, & Melnick, 2003; Zinner et al., 2013). In the most recent revision of species group classifications (Zinner et al., 2013), M. fascicularis was allocated to its own monotypic species group containing 10 recognized subspecies (Fooden, 1995; see Fig. 1).

Earlier studies of M. fascicularis indicated that this species initially colonized Sundaland in the Pliocene (~5.3-2.6 Ma) when the climate was relatively cold and became isolated when sea levels rose (Delson, 1980). Fossil evidence on Java indicates that M. fascicularis was able to significantly expand its range throughout the early Holocene and middle Pleistocene (Aimi & Aziz, 1985; Delson, 1980; Fooden, 2006). More recent time-calibrated molecular phylogenies concord in estimating the most recent arrival of M. fascicularis on the larger Sunda shelf islands to approximately 1.7-2 Ma (Liedigk et al., 2015; Tosi & Coke, 2007; Yao, Li, Martin, Moreau, & Malhi, 2017).

Using a dataset composed of both mitochondrial DNA (mtDNA) and Y-chromosomal loci, Tosi & Coke (2007) demonstrated that long-tailed macaques fall into two major clusters, one containing mainland Indochinese specimens (Clade A) and another that is composed of insular specimens (Clade B). One thing of note is that the mtDNA showed that southern Sumatran individuals cluster with conspecifics from the islands while the mainland clade consisted exclusively of mainland individuals. However, analysis of two Y-chromosomal loci revealed a different pattern, with Sumatran lineages stemming from the mainland and others stemming from the Sunda shelf islands (Tosi & Coke, 2007). Other studies corroborated these two highly structured clades using mtDNA fragments (D. Smith, John, & George, 2007) and mitogenomes (Liedigk et al. 2015). Yao et al. (2017) later tripled the overall sample size by adding newly shotgun sequenced museum specimens from Sumatra and eastern oceanic islands to the data from Liedigk et al. (2015). The resulting mitogenome phylogeny again indicated the existence of two major clades: Clade A including lineages from both the mainland and northern Sumatra and an insular Clade B including lineages from southern Sumatra and all other islands. This split within the island of Sumatra, dated to ~1.88 Ma, is attributable to the aftermath of the super-eruption of a volcano on Sumatra approximately 73 kya that wiped out organisms inhabiting the region (Ambrose, 2003; Chesner, Rose, Deino, Drake, & Westgate, 1991; Williams et al., 2009). Replacement populations then recolonized Sumatra from the nearby mainland to the north and from Borneo to the east, thus giving rise to the observed divide on Sumatra. A further finding is that, all oceanic Philippine specimens appear to stem from Borneo (Liedigk et al., 2015; Yao et al., 2017) reflecting two waves of colonization into the Philippines, one moving directly north through Palawan island and another moving directly east through the Sulu archipelago (Smith et al., 2014; Yao et al., 2017).

Most of these phylogeographic studies were based on mtDNA, which evolves relatively rapidly in comparison to nuclear DNA (nDNA), making it an excellent tool to study evolution on fast time scales, such as evolution of organisms that became isolated on islands (Evans et al., 2012). However, mtDNA is exclusively maternally inherited (Brown, George, & Wilson, 1979) so data for other genomic regions, including the autosomes and sex chromosomes, would be necessary to trace the patrilineal phylogeography. In M. fascicularis, females are philopatric (Pusey & C., 1987; Ruiter & Geffen, 1998), so nDNA would be necessary to explore effects of the dispersal of male macaques. Furthermore, mtDNA is often considered to be a single marker, which can lead to a disparity between mtDNA and species phylogenies (Rogers & Gibbs, 2014). Finally, uniparental markers such as mtDNA do not allow for assessment of admixture within individuals, which requires autosomal DNA.

Numerous studies have compared results of analyses of mtDNA and nDNA from various organisms, including guenons (Guschanski et al., 2013), Iberian lizards (Godinho, Crespo, & Ferrand, 2008), bats (Riesle-Sbarbaro et al., 2018; Dool et al., 2016), fishes (Egger, Koblmüller, Sturmbauer, & Sefc, 2007; Wallis et al., 2017), birds (Hung, Drovetski, & Zink, 2016), dogs (Leathlobhair et al., 2018), and anurans (Barrow, Lemmon, & Lemmon, 2018). Some of these studies indicate different phylogeographic patterns derived from nuclear or mitochondrial evidence (Riesle-Sbarbaro et al., 2018; Egger et al., 2007; Guschanski et al., 2013; Wallis et al., 2017) whereas others yielded complementary results but with nDNA revealing more than mtDNA (Dool et al., 2016; Godinho et al., 2008; Leathlobhair et al., 2018). Very few studies have assessed the concordance between mtDNA- and nDNA-based phylogenies for primates (Guschanski et al., 2013; Tosi & Coke, 2007), but a comparison of both is needed to examine sex-biased dispersal. Long-tailed macaques have social organizations where females are philopatric (Pusey & Packer, 1987; Ruiter & Geffen, 1998), so nDNA results are expected to diverge from mtDNA results in that a clear pattern of colonization would not be expected with nDNA due to the dispersing nature of male long-tailed macaques.

When examining phylogeography, demographic changes in populations over time, or assessing population structure prior to human intervention, ancient DNA analysis of museum specimens can be a powerful tool. First, current regulations often restrict tissue collection and importation from live primates, so the large sample sizes make museum specimens a valuable resource. Additionally, museum specimens were collected before significant levels of human impact affected their distributions, allowing scientists to fully understand the natural phylogeography of a species. However, sequencing high-depth (number of times each base pair is sequenced) and high-coverage genomes (the proportion of the genome sequenced) from ancient remains can be challenging, as the DNA that remains in the organism is often degraded and fragmented. Exposure to sunlight, heat, and moisture all negatively impact DNA preservation (Hofreiter, Serre, Poinar, Kuch, & Pääbo, 2001; Pääbo et al., 2004), and the preparation and storage method of specimens may also increase the rate of DNA degradation (Gansauge & Meyer, 2014).

However, Guschanski et al. (2013) found that museum specimen antiquity, weight, and specimen part from which the sample was collected have no effect on the percentage of endogenous DNA (DNA originating from the organism) sequenced. Contamination of samples with modern DNA is also a common problem in ancient DNA studies (Malmström, Storå, Dalén, Holmlund, & Götherström, 2005; Sampietro et al., 2006). Due to these limitations, the majority of ancient DNA studies focus on mtDNA, which is present in a much higher copy number than nuclear genomes and is therefore more likely to be preserved.

Studies that compare ancient mtDNA and nDNA sequences often use nDNA from modern individuals from a closely-related population (S. K. Brown, Darwent, & Sacks, 2013) or focus on a small number of nuclear genes (Cairns & Wilton, 2016; Fabre et al., 2014; Woods, Turvey, Brace, MacPhee, & Barnes, 2018) or regions (Rohland et al., 2010). However, entire nuclear genomes have been successfully sequenced from ancient specimens to clarify the history of mammoths (Palkopoulou et al., 2015) as well as domestic horses (Librado et al., 2015), goats (Daly et al., 2018), and dogs (Frantz et al., 2016; Leathlobhair et al., 2018). Some of these studies focus on generating high-depth genomes of a small number of individuals (Frantz et al., 2016; Librado et al., 2015; Palkopoulou et al., 2015), while others sequence low-depth genomes of larger numbers of individuals, with depth ranging from less than 0.01x to 14.9x (Daly et al., 2018; Leathlobhair et al., 2018). This latter approach lends itself well to museum specimens, where the rate of genomic DNA recovery may be low. It is possible that even genomes with lower breadth of coverage (less than 5% of the genome) and low-depth genomes (less than 1x) can generate useful demographic information.

In this study, we analyze nDNA sequences from M. fascicularis museum specimens obtained by shotgun sequencing in order to examine phylogeographic and population-level signatures using autosomal evidence, compare these patterns to those using mitogenomes, and test whether low-coverage nuclear genome data could yield sufficient data for population genetic analyses. Because of the nature of inheritance for nDNA and mtDNA, given the female philopatry of long-tailed macaques, we hypothesized that the results would demonstrate a discordance in phylogeographic patterns using the two types of genomic data.

Materials and Methods

All molecular lab work was conducted at the Malhi Ancient DNA Lab and the Malhi Molecular Anthropology Lab at the University of Illinois at Urbana-Champaign (UIUC) and the Pritzker Lab for Molecular Systematics and Evolution at the Field Museum of Natural History (FMNH). In order to minimize contamination, a clean-room facility (the Malhi ancient DNA Lab), contamination prevention protocols, and negative controls were used at each extraction and amplification step (see Lindo et al., 2016). No animals were sacrificed for this study.

Sample Collection

All samples used in this study were obtained from wild M. fascicularis specimens collected 50 to 150 years ago and housed in collections at The Field Museum of Natural History (FMNH) in Chicago, American Museum of Natural History (AMNH) in New York, Smithsonian Institution National Museum of Natural History (NMNH) in Washington, and the Naturalis Biodiversity Center (RMNH) in Leiden, Netherlands (Supplemental Data 1, 2). We utilized dried tissues from the surface of the skull or within the braincase of museum specimens instead of often-used skins and toe pads as some museums forbid any sampling from those parts. Because skins and toe pads are often used for non-genetic studies, it is important to preserve them.

DNA Extraction, Library Preparation, and Sequencing

The digestion, extraction, library preparation, and sequencing of all specimens used in this study are detailed in Yao et al. (2017). In brief, the dried tissue samples were initially digested overnight in a proteinase K solution at 37°C. We then used a Qiagen extraction kit to extract the DNA, which was confirmed via amplification and sequencing of a 200 bp section of M. fascicularis cytochrome b. Genomic libraries with Illumina platform-specific oligonucleotide adapters unique to each library were prepared for samples from which we successfully extracted DNA. This was done using the NEBNext Ultra DNA Library Prep Kit following the TruSeq DNA Sample Preparation V2 protocol by Illumina and using unique NEBNext Multiplex Dual Index primers in the library amplification step. The libraries were assessed for fragment size and quantification using an Agilent 2100 Bioanalyzer or an AATI Fragment Analyzer. For sequencing, batches of 25-30 samples were pooled together (3 μl of each sample at 10 nM in the pool) and shotgun sequenced on six lanes of the ILLUMINA HiSeq2500 at the Roy J. Carver Biotechnology Center at UIUC, which generated results with 100-bp single-end reads.

DNA Alignment and Assessment

To trim DNA sequences and remove adaptors, the Trimmomatic program was used. This also reduces false variant discovery, as reads below standard quality resulting from DNA damage are ignored (Kircher, Sawyer, & Meyer, 2012). Each sample was mapped against the M. fascicularis reference genome (GenBank Assembly Accession: GCA_000364345.1; Build 5.0) using BOWTIE 2 (Langmead & Salzberg, 2013). SAMtools (Li et al., 2009) was used to sort, index, and quantify contamination by examining informative sites and removing duplicate reads that may result from PCR amplification.

Once the sequences were assembled for all 149 individuals, sequencing depth (average number of unique reads that cover each nucleotide in the genome) and genome coverage (percentage of genome sequenced that is covered by at least one read) were calculated. Sequencing depth was calculated by dividing the total nucleotides sequenced from all reads that have been mapped to the reference genome by the total number of nucleotides in the reference genome (2,770,485,009 bp).

Population Genomic Analyses

After mapping the reads to the reference genome, we used ANGSD version 0.920 to filter the data and identify single-nucleotide polymorphisms (SNPs) (Korneliussen, Albrechtsen, & Nielsen, 2014). We used genotype likelihoods rather than SNP calls (Li, 2011), which can be more accurate for low-coverage data, and only retained SNPs that were present in at least half of the individuals analyzed and had a p-value of 0.01 or less, indicating the statistical likelihood of the position being a variable site. Of the 149 sequenced specimens, we selected individuals for population genomic analyses and phylogenetic reconstruction that had at least one million reads mapping to the reference genome (samples that had ~0.4-15.6% genome coverage were included) and had sequence data for at least 1% of the SNPs found in at least five individuals. This resulted in a final dataset of 75 individuals and over 21,500 genome-wide SNPs, each of which was present in at least half of the individuals (deposited in the NCBI Sequence Read Archive). These SNPs were not evenly distributed across the genome (Supplemental Data 3), so we created data subsets consisting of one randomly-selected SNP per 50-kilobase window to control for linkage. These linkage-controlled datasets only contained 179 SNPs, so for many of these analyses we used only the larger dataset or compared the dataset to multiple linkage-controlled subsets. We will refer to the full dataset with 21,500 SNPs as the 21K SNP dataset, and the data subsets controlling for linkage as the unlinked SNP datasets.

We used PCAngsd to measure admixture for 2-5 clusters for these individuals and perform principal components analyses (PCAs) for examining the genetic distances amongst individuals and populations (Skotte, Korneliussen, & Albrechtsen, 2013; Meisner & Albrechtsen, 2018). The admixture calculations and PC As were calculated both using the 21K SNP dataset and five replicates of the unlinked SNP datasets. In order to analyze population differentiation, we used ANGSD to calculate pairwise FST values between all populations with at least n=5 using realSFS (Nielsen, Korneliussen, Albrechtsen, Li, & Wang, 2012), calculated using a folded site frequency spectrum (SFS) of the 21K SNP dataset. The folded SFS for each of these populations was also used to calculate Tajima’s D, nucleotide diversity, and Watterson’s theta for each chromosome of each individual. ANGSD was also used to estimate each individual’s heterozygosity for comparison of genetic differentiation amongst individuals in our study by extracting the heterozygosity results from the folded SFS. A Kruskal-Wallis test was used to determine if the heterozygosity measurements were significantly different between populations, and a Dunn’s test was used to identify the populations that were statistically distinct, using a Bonferroni correction. The populations were also categorized by the type of island they derived from (Mainland, Continental, and Oceanic), and these categories were tested for statistically significant differences using the same methods.

Phylogenetic reconstruction

To assess the phylogenetic relationship between individuals, we used a pseudoautosomal sampling method to randomly select one read at each position in the 21K SNP dataset for each of the 75 individuals (e.g. Sikora et al., 2014; Allentoft et al., 2015; Loosdrecht et al., 2018). Low-coverage ancient DNA data can make it difficult to call genotypes. By sampling a single read at each position, thus generating a haploid “genome” for each individual, the ambiguity in the data can be decreased (Orlando, Gilbert, & Willerslev, 2015). These pseudoautosomal haplotypes were selected using a python script that selected a single read that mapped to a SNP from the ANGSD SNP calls (generated using the options –doMajorMinor 4 –doMaf 2) for each individual. We generated 100 of these pseudoautosomal datasets and inferred a maximum likelihood tree for each in RAxML version 8.2.12 (Stamatakis, 2014) using a rapid Bootstrap analysis with 1000 bootstraps, a GTRCAT substitution model, and a Lewis ascertainment bias correction for the absence of invariable sites. We chose the GTRCAT model as it has been demonstrated to show similar performance to the GTR-Gamma model, but with shorter runtime (Izquierdo-Carrasco, Smith, & Stamatakis, 2011). The rhesus macaque reference genome was used as an outgroup; we used MUMmer version 3.94 to align the two macaque genomes, the delta-filter −1 command to filter the alignment and the show-snps command to identify variant sites (Kurtz et al., 2004). The outgroup was then added to the maximum likelihood trees using an evolutionary placement algorithm (Berger, Krompass, & Stamatakis, 2011), after which we reconstructed an extended majority rule consensus tree of 100 trees (Aberer, Pattengale, & Stamatakis, 2010). We also generated maximum likelihood trees using one hundred replicates of the unlinked SNP dataset, and created a consensus tree of those replicates using the methods above. All phylogenetic trees were visualized using the R packages dendextend1.9.0 (Galili, 2015) and circlize 0.4.5 (Gu, Gu, Eils, Schlesner, & Brors, 2014).

Results

Genome Coverage and Sequencing Depth

A total of 149 M. fascicularis museum specimens were shotgun sequenced at the Roy J. Carver Biotechnology Center at UIUC. For all samples, the average sequencing depth was ~0.019x, the genome coverage was ~1.71%, and the average endogenous content was 37.72%. Of these, 75 individuals were included in our study (Supplemental Data 1 and 2). Average sequencing depth and genome coverage for this subset of samples were ~0.036x and ~3.31%, respectively. Only three of the 75 individuals (specimen numbers NMNH 477845, NMNH 83272, and FMNH 105689) had greater than 10% genome coverage, while the genome coverage of all other individuals included in the analyses ranged from <0.01-9.5%.

Phylogeny

A phylogeny of the 75 individuals was reconstructed based on maximum-likelihood analyses using the 21K SNP dataset (Figure 2, Supplemental Data 4). The single Nias island specimen (NMNH 121873) was found to be sister to all other long-tailed macaques sequenced for this study, which is an unlikely and probably incorrect placement within the phylogeny because of a combination of low genome coverage and low sequencing depth. There is also the possibility that this specimen was mislabeled, but mitochondrial genome analyses previously showed this specimen clustered properly within the phylogeny (Yao et al., 2017) Notably, two well-supported clades were defined, with Clade A including individuals from the mainland and northern Sumatra along with two individuals from Koh Kut and Mindanao islands, of which the Mindanao individual is an outlier considered to be a recent migrant. These clades were also supported in 100% of the trees constructed using the unlinked SNP datasets, although no other clades had strong support using the smaller SNP sets (Supplemental Data 5). The northern Sumatran specimens were nested within a mainland subgroup. Clade B is an insular clade composed of individuals from all other islands. Within Clade B, specimens from the continental shelf islands (Java, Bali, Penida, Bangka, southern Sumatra) diverged first. The southern Sumatran specimens were clustered in a subgroup together with specimens from Bangka and Borneo islands. All other Borneo specimens were clustered with oceanic island specimens. Most specimens from Mindanao formed a subclade with most of the specimens from Borneo. All other oceanic island specimens formed a sister group to the Mindanao/Borneo lineage, but the nodes are not strongly significant (bootstrap values <95%).

Figure 2.

Figure 2.

Open in a new tab

Maximum likelihood phylogeny of M. fascicularis

Reconstructed phylogeny of 75 M. fascicularis museum specimens based on SNP data. Green=mainland, orange=Sundaland, blue=oceanic islands. All significant nodes (>=95%) are marked with a red point.

Principal Component Analysis

The PCA of the 21K SNP dataset neatly illustrated the biogeographical pattern of long-tailed macaques in Southeast Asia by clustering the specimens into three main geographic clades/subclades which roughly matched clades from the phylogenetic analysis. However, each principal component had a low contribution to the total variation (Figure 3). PC1 explains 8.4% of the genomic variance and separated specimens in Clade A, the mainland clade, from specimens in Clade B, the insular clade. Clade A was composed of mainland individuals and northern Sumatran specimens along with two specimens from Mindanao and Koh Kut islands. Clade B consisted of all other insular specimens, including all specimens from southern Sumatra. PC2 explained 2.3% of the genomic variance and divided the specimens in Clade B into two separate subclades. One subclade contained specimens from the southern islands, including Java, Bali, Penida, and Bangka islands. This southern subclade also included specimens from southern Sumatra and Nias Island, which is an oceanic island. The other subclade included specimens from the northeastern islands, including Borneo and all the Philippine islands.

Figure 3.

Figure 3.

Open in a new tab

Principal Component Analysis (PC1 vs. PC2)

The colors represent population types. Mainland specimens are colored in green, continental shelf island specimens are colored in orange, and oceanic island specimens are colored in blue. The shapes are labelled based on island according to the legend.

We also examined other principal components, which explained even less of the genetic variance. For example, PC3 explained only 1.8% of the variance and did not confidently distinguish major clades and subclades within Southeast Asia (Supplemental Data 6). We also compared the results of the 21K dataset to the unlinked datasets. The principal component analyses of the unlinked datasets similarly show separation between clades A and B along PC1 and separation of continental and oceanic island populations along PC2 (Supplemental Data 7). However, the clear separation between northern and southern subclades within the insular Clade B is not present in PC2 for the unlinked datasets.

Fixation index

The Fst analysis compared the six geographic “populations” in our dataset with sample sizes of at least five individuals: Bali, Borneo, Java, Mindanao, Negros, and the mainland (Figure 4). As other analyses, including the PCA and the phylogeny, demonstrated that one individual sampled from Mindanao was a recent migrant from the mainland, it was removed from the Mindanao population prior to making the Fst calculations. The Fst values ranged from 0.023 (between Mindanao and Borneo) to 0.073 (between the mainland and Negros). The mainland had relatively higher genetic differentiation from all other islands, while other populations with more pairwise genetic similarity include Borneo with Java and the Philippine islands (Mindanao and Negros) with each other and with Borneo.

Figure 4.

Figure 4.

Open in a new tab

Fixation index (Fst) plot

Heterozygosity and Tajima’s D

Heterozygosity was calculated for each individual (Figure 5), although the values are low, reflecting the low sequencing depth. However, the heterozygosity values were not biased by the amount or depth of data as there was no association between heterozygosity and sequencing depth or genome coverage (Supplemental Data 8). These heterozygosity values can therefore be compared between individuals in our study.

Figure 5.

Figure 5.

Open in a new tab

Heterozygosity and Tajima’s D by island populations

All results are colored by population type. A) The distribution of heterozygosity values for each population. Points indicate outliers. A single horizontal line indicates the heterozygosity value for a single individual from an island (or locality). B) Tajima’s D calculations for every population with n of at least 5. The values calculated for each chromosome are all plotted together. C) A comparison of Watterson’s theta (tW) and π (tP) for each chromosome and population. The black line indicates a 1:1 ratio.

Individual heterozygosity values were separated for different island populations in order to examine patterns of heterozygosity throughout Southeast Asia (Figure 5A). Bangka, Koh Kut, Nias, Busuanga, Maripipi, and Palawan islands each only had a sample size of one, but they were nevertheless plotted in order to indicate where a representative of each of these islands lies within the heterozygosity distribution. When comparing the different population types, the heterozygosity values were shown to be significantly different (p=0.040), indicating that the Mainland population heterozygosity values were significantly higher than heterozygosity values for Continental (p=0.044) and Oceanic (p=0.016) island populations. This suggests that the macaque populations likely underwent a bottleneck when they migrated from the mainland to the outlying islands. However, no statistically significant differences in heterozygosity were identified between specific populations (p=0.271).

Tajima’s D was also calculated for the populations with a sample size of at least 5 (Figure 5B). Oceanic island populations show a weakly positive Tajima’s D value, indicating a lack of rare alleles compared to continental island and mainland populations. This again may indicate an ongoing bottleneck for oceanic island populations (Tajima, 1989), which is consistent with the recent split time of continental and oceanic island populations around 60,000 years before present (Yao et al., 2017). A comparison of π and Watterson’s theta, the two estimates compared using Tajima’s D, for each population (Figure 5C) demonstrates that π is higher than Watterson’s theta for oceanic island populations, while the opposite is true for both continental island and mainland populations. This also suggests that oceanic island populations recently experienced a founder effect, while the other populations likely show a population expansion (Tajima, 1989). However, these results must be interpreted with caution, as a number of factors can impact Tajima’s D, such as selection and rapid population expansion or contraction (Stajich & Hahn, 2005).

Admixture

PCAngsd calculated the most statistically significant number of clusters to be two using the 21K SNP dataset. When the five unlinked datasets were analyzed, the most significant number of clusters varied from 3 to 5, depending on which subset was selected. We therefore examined 2-5 clusters using the 21K SNP dataset to compare genetic diversity (Figure 6). Based on the admixture plots, northern Sumatran specimens had genetic ancestries most similar to selected individuals from the mainland, specifically from the southern end of Thailand (FMNH 105689, NMNH 251661, NMNH 83272). The one specimen from Koh Kut (NMNH 254741) and two from Mindanao island (FMNH 56493, FMNH 61026) had similar genetic ancestries to the remainder of the mainland specimens.

Figure 6.

Figure 6.

Open in a new tab

Admixture plot using 21K SNP Dataset

Admixture plots showing results for 2-5 clusters. Green text indicates the mainland while orange text indicates a continental shelf island and blue text indicates an oceanic island.

Southern Sumatran specimens were genetically similar to individuals from Bali, Penida, Java, Bangka, and Borneo islands. The specimen from Nias, which is an oceanic island to the west of Sumatra, was genetically similar to this group of continental shelf islands as well. However, when five clusters are analyzed (K=5), Bornean specimens had genetic ancestries that were more similar to specimens from the oceanic Philippine islands. While the unlinked datasets had more variable admixture results, all of them showed separation between individuals from the mainland relative to all island populations at K=2, and the majority showed affinity between the Philippine islands (e.g., Negros, Mindanao, Bohol, and Luzon) at K=2 (Supplemental Data 9).

Discussion

Next generation sequencing has been a major development for the field of ancient DNA, but most studies focus on capturing genomic regions of interest to obtain greater sequencing depth (Cairns & Wilton, 2016; Lindo et al., 2016; Rohland et al., 2010; Woods et al., 2018). Yet shotgun sequencing without targeting specific genomic regions could be beneficial in that the mitogenome, a common target for ancient DNA studies, can potentially be sequenced at high coverage (Yao et al., 2017) because of its high copy number, allowing investigators to skip a time-consuming and costly targeted capture step. We show in this study that shotgun sequencing museum specimens also has a further benefit of yielding nDNA sequences at low coverage that allow for population genetic analyses, although these analyses are necessarily more limited than analyses using targeted techniques. For example, although we were able to identify major phylogeographic patterns with high confidence (bootstrap values >95%), relationships between closely related individuals and populations were not statistically significant (bootstrap values <95%), suggesting that there was not enough genomic coverage to fully distinguish closely related individuals. Additionally, while we were able to use a dataset of over 21,500 SNPs for our analyses, these SNPs were clustered within fewer than 200 50 kb windows, further limiting our ability to examine genome-wide trends. Measures of genetic diversity that require high confidence in individual genotypes, such as heterozygosity, can be compared between studies, but only if sufficient quantities of high-coverage SNPs can be recovered. Other genomic analysis methods that require high-coverage genomes, such as the Pairwise Sequentially Markovian Coalescent (PSMC, Li & Durbin, 2011) or selection scans that require the analysis of contiguous haplotypes (e.g. Sabeti et al., 2007), cannot be used at all. Nonetheless, we were able to successfully recover SNPs throughout the nuclear genome and use them to better understand phylogeographic patterns amongst insular and mainland populations of M. fascicularis in Southeast Asia.

While we were able to use a dataset of over 21,500 SNPs for our analyses, these SNPs were clustered within fewer than 200 50 kb windows, further limiting our ability to examine genome-wide trends. It is unclear why the data are clustered in the genome this way, but we are nevertheless confident in our findings. First, the relationships between populations are consistent with what was previously known about the demographic history of M. fascicularis in Southeast Asia. Second, comparisons of the 21K SNP dataset with the smaller, unlinked SNP datasets show that resolution is lost when using the smaller dataset but the separation of clades A and B is maintained. Finally, previous work has demonstrated that fewer than 1000 SNPs can be sufficient to reconstruct the demography of closely-related populations (Kodaman et al., 2013; Schablitsky et al., 2019; Wright et al., 2019).

One unanticipated finding was that all population genetic analyses and phylogenetic reconstruction of our M. fascicularis specimens generally align with previous phylogeographic analyses. Because long-tailed macaque females are philopatric (Pusey & Packer, 1987; Ruiter & Geffen, 1998), the nDNA results were expected to diverge significantly from those based on mtDNA as a result of male dispersal. However, using low coverage nuclear genomic data, we demonstrate that these data still yield two major clades – A and B – within M. fascicularis as shown in previous studies (Tosi & Coke, 2007; Yao et al., 2017).

It is unexpected that mtDNA and nDNA analyses yield matching phylogenies despite the potential effects of female philopatry versus male migration, but we do have a potential explanation. The expectation that mtDNA and nDNA will yield different trees could apply only within land masses and not between them. In a contiguous area, females being philopatric and males migrating could result in different phyleographic patterns based on mtDNA and Y-chromosomal DNA/nDNA. However, our study compared populations separated on islands. Therefore, both males and females likely became isolated on islands, and the isolation of islands limited male dispersal between islands. As migration of both males and females is needed for any founder population, this may partially or completely eliminate any effect of female philopatry versus male dispersal at the outset.

In this nDNA phylogeny, we observe the presence of members of both clades on the island of Sumatra, with northern Sumatran specimens falling into Clade A along with mainland populations and almost all other insular specimens, including southern Sumatran specimens, clustering within Clade B. When comparing pairwise FST values for these populations, the Mainland individuals, representing Clade A, similarly show higher genetic differentiation from all other populations, which are all part of Clade B. This marked division within long-tailed macaques on Sumatra has been suggested to be a result of the super-eruption at the site of Lake Toba (Yao et al., 2017) that occurred approximately 73kya (Chesner et al., 1991). The super-eruption led to deforestation as a result of prolonged cooling, thus wiping out populations of plants and animals in the region (Ambrose, 2003; Williams et al., 2009). Organisms had to recolonize the island afterwards, but the site of the Toba eruption on Sumatra also formed a persisting geographic barrier that separated the north from the south (Ambrose, 2003). Accordingly, organisms from the mainland colonized northern Sumatra, and organisms from Borneo and Bangka islands colonized southern Sumatra. Males who were part of a female’s family group likely migrated to Sumatra with the females. This geographic barrier also affected other primates and resulted in similar phylogeographic patterns for orangutans (Nater et al., 2011, 2015, 2017) and gibbons (Thinh et al., 2010; Whittaker, Morales, & Melnick, 2007) in both sexes. Although this split is evidenced by mitogenomes (Yao et al., 2017) and two Y-chromosomal loci (Tosi & Coke, 2007), we did not expect to see the pattern using nDNA because of male dispersal. Once the forest grew back around the site of the Toba eruption, males would have the opportunity to cross the former barrier, but our population genetic and phylogenetic results indicate that admixture likely did not occur between northern and southern Sumatran specimens.

The higher heterozygosity in mainland specimens suggest that long-tailed macaques originated from the mainland and spread throughout the Southeast Asian islands. Using mtDNA fragments, Smith et al. (2007) showed that specimens from the oceanic Philippines and Mauritius islands had reduced genetic heterogeneity in comparison to mainland and continental shelf insular individuals because of the founder effect, in which the population of origin is defined by a high level of genetic variation that then decreases with distance from the origin. Our results confirm the presence of a founder effect. Mainland specimens have high genetic diversity compared to continental and oceanic islands. Our results suggest that M. fascicularis began colonizing the Southeast Asian islands via the Greater Sunda Islands (Sumatra, Borneo, and Java), which would constitute the most accessible route into the oceanic islands. Southern Sumatran specimens have lower levels of heterozygosity than northern Sumatran ones and are closer to the heterozygosity levels in other insular specimens than to the substantial variation levels in northern Sumatran or mainland specimens. This aligns with the possibility that M. fascicularis were wiped out on Sumatra due to the Toba eruption and recolonized southern Sumatra from Borneo. Similarly, the high heterozygosity levels in northern Sumatra could reflect a scenario in which macaques from the mainland recolonized the northern end of Sumatra after the Toba eruption.

The admixture analyses not only distinguish Clades A and B, but also further distinguish individuals within Clade A. It is clear that the northern Sumatran macaques are closely related only to certain macaques from the mainland, specifically in southern Thailand, which is a part of the Malay Peninsula. This indicates that macaques began colonizing the peninsula before crossing over to Sumatra, as was previously suggested (Liedigk et al., 2015; Tosi et al., 2003; Yao et al., 2017). Although no specimens from Malaysia were included in the current analysis, previous studies using mitogenomes indicate that northern Sumatran specimens stem directly from Malaysia (Liedigk et al., 2015; Yao et al., 2017).

In addition to the major division between two clades, our population genetic analyses clearly reveal another pattern within Clade B, which had been previously suggested by the mitogenome analysis presented by Yao et al. (2017). Specifically, there are two major subclades within Clade B. One includes specimens from Southeast Asian islands in the south (southern Sumatra, Java, Bali, Penida, Bangka, and southwest Borneo). The other subclade within Clade B includes specimens that reside on the more northern Southeast Asian islands (all Philippine islands and Borneo). These two subclades are also represented by the FST results, where individuals from Borneo are more closely related to individuals from the Philippine islands (Mindanao and Negros) than to other populations. This division within Clade B is particularly evident in the principal component analysis, suggesting that macaques colonized Java and the Lesser Sunda Islands to the east via southern Sumatra, whereas there was a separate colonization in the north from Borneo into the Philippine islands. Admixture results also illustrate these two subclades within Clade B. Specimens belonging to the southern subclade are similar with five distinct genetic signatures while specimens belonging to the northern subclade have different population structure with fewer than five genetic signatures.

Despite some of the shortcomings with using low-coverage nuclear genomic data, we demonstrate that these types of data can be beneficial for performing basic population genetic analyses for examining broad phylogeographic patterns across a large sample size and can reveal patterns that may not be clearly represented using mitogenomes. In particular, not only do we confirm the deep division into a mainland Clade A and an insular Clade B, but we also identify a clear distinction between two subclades within the insular Clade B using population genomic analyses. This study underlines the need for more thorough analysis of specimen conditions on sequencing coverage and depth, presents a thorough examination of phylogeographic patterns of long-tailed macaques, and provides a framework for future genomic studies of museum specimens.

Supplementary Material

Supplemental Data 1. Table of samples

Supplemental Data 1. Table of samples

All samples sequenced for this study. Samples are listed by specimen ID number from their respective museums. The 75 samples analyzed in this study are marked with an “x” in the “Included” column. “Total_Nt_across_reads” refers to the sum of all nucleotides sequenced across all reads. “Total_Mapped_Nucleotides” refers to the number of nucleotides that mapped to the reference genome. “SNP_Coverage” refers to the proportion of the over 21,500 SNPs called in the 75 individuals included in this study.

Supplemental Data 3. SNP positions across chromosomes.

SNP positions across chromosomes. Large red dots indicate chromosome length, and blue dots indicate SNP locations on that chromosome.

Supplemental Data 4. Maximum likelihood consensus tree (Newick format)

Maximum likelihood consensus tree (Newick format)

Supplemental Data 2A. Map of Southeast Asia with museum specimen ID numbers corresponding to those in Supplementary Data 1.

Supplemental Data 2. Map of Southeast Asia with museum specimen ID numbers corresponding to those in Supplementary Data 1.

A. A map of Southeast Asia derived from Liedigk et al. (2015) marked with specimens included in the analyses of this study. The regions shaded in black, dark grey, and light grey regions indicate the regions of M. f aureus, M. f. fascicularis, and M. f. philippinensis, respectively. The region with dark grey lines indicated an area of likely intergradation of M. fascicularis and M. f. philippinensis (Fooden, 1995, 2006). Black circles represent exact localities whereas white circles represent approximate localities. The current site of Lake Toba is marked with a blue star.

Supplemental Data 2B A map of the Philippines (magnified from the boxed area in Figure 1A)

B. A map of the Philippines (magnified from the boxed area in Figure 1 A) derived from Heaney (1986), marked with specimens analyzed in this study.

Supplemental Data 5. Maximum likelihood phylogeny of M. fascicularis using an unlinked dataset

Supplemental Data 5. Maximum likelihood phylogeny of M. fascicularis using an unlinked dataset Green=mainland, orange=Sundaland, blue=oceanic islands. The significant node (>=95%) is marked with a red point.

Supplemental Data 6. Principal component analysis (PC3 vs. PC1 and PC2)

Supplemental Data 6. Principal component analysis (PC3 vs. PC1 and PC2) A plot of PC1 vs. PC3 (A) and PC2 vs. PC3 (B). The colors represent population types. Mainland specimens are colored in green, continental shelf island specimens are colored in orange, and oceanic island specimens are colored in blue. The shapes are labelled based on island according to the legend.

Supplemental Data 7. Principal component analysis (PC1 and PC2)

Supplemental Data 7. Principal component analysis (PC1 and PC2) A plot of PC1 vs. PC2 using five different subsets of SNPS (one SNP randomly selected per 50 kb window). The colors represent population types. Mainland specimens are colored in green, continental shelf island specimens are colored in orange, and oceanic island specimens are colored in blue. The shapes are labelled based on island according to the legend.

Supplemental Data 8. Heterozygosity plotted against sequencing depth and position

Supplemental Data 8. Heterozygosity plotted against sequencing depth and position

Supplemental Data 9. Admixture plots using five unlinked SNP datasets

Supplemental Data 9. Admixture plots using five unlinked SNP datasets Admixture plots showing results for 2-5 clusters for each dataset. Green text indicates the mainland while orange text indicates a continental shelf island and blue text indicates an oceanic island.

Acknowledgements

We are thankful for the kind cooperation from the following individuals for providing access to their respective museum collections for dried tissues: the late William Stanley and Dr. Lawrence Heaney at The Field Museum of Natural History (FMNH), Eileen Westwig and Neil Duncan at the American Museum of Natural History (AMNH), Darrin P. Lunde at the Smithsonian Institution National Museum of Natural History (NMNH) and Pepijn Kamminga and Steven van der Mije at the Naturalis Biodiversity Center (Leiden, Netherlands). We would also like to acknowledge the reviewers for their efforts in revising this manuscript. Data collection, sample preparation, and sequencing were previously supported by the NSF DDIG DEB-1501733 to Robert D. Martin and Lu Yao, grants to Lu Yao (NSF GRFP 100152161, The Field Museum Women-in-Science Graduate Fellowship, Arts and Science Fund from the Arts, Science and Culture Initiative at University of Chicago, and Hinds Fund Award, Field Museum Pritzker Lab Award, and Travel Award from the Committee on Evolutionary Biology at University of Chicago), research funding from The Field Museum to Robert D. Martin and research funds from the University of Illinois Urbana-Champaign to Ripan S. Malhi. The remainder of this research was supported by the Gerstner Postdoctoral Fellowship at the American Museum of Natural History to Lu Yao. Kelsey E. Witt and Emilia Huerta-Sanchez were funded by NSF award #1557151 and NIH award #1R35GM128946-01.

Footnotes

Publisher's Disclaimer: This is the author manuscript accepted for publication and has undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version Record. Please cite this article as doi: 10.1002/ajpa.24099

Data Availability Statement:

The data that support the finds of this study are openly available in Genbank SRA at http://www.ncbi.nlm.nih.gov/bioproject/634778 (BioProject ID PRJNA634778) and Github at https://github.com/kelsey-witt/macaque-lowcvg-pipeline (scripts/pipeline).

References

  1. Aberer AJ, Pattengale ND, & Stamatakis A (2010). Parallel computation of phylogenetic consensus trees. Procedia Computer Science, 1(1), 1065–1073. 10.1016/j.procs.2010.04.118 [DOI] [Google Scholar]
  2. Aimi M, & Aziz F (1985). Vertebrate fossils from the Sangiran dome, Mojokerto, Trinil, and Sambungmacan areas. Quaternary Geology of the Hominid Fossil Bearing Formations in Java. [Google Scholar]
  3. Allentoft ME, Sikora M, Sjögren K-G, Rasmussen S, Rasmussen M, Stenderup J, … Willerslev E (2015). Population genomics of Bronze Age Eurasia. Nature, 522(7555), 167–172. 10.1038/nature14507 [DOI] [PubMed] [Google Scholar]
  4. Ambrose SH (2003). Did the super-eruption of Toba cause a human population bottleneck? Reply to Gathome-Hardy and Harcourt-Smith. Journal of Human Evolution, 45(3), 231–237. [DOI] [PubMed] [Google Scholar]
  5. Barrow LN, Lemmon AR, & Lemmon EM (2018). Targeted Sampling and Target Capture: Assessing Phylogeographic Concordance with Genome-wide Data. Systematic Biology, 67(6), 979–996. 10.1093/sysbio/syy021 [DOI] [PubMed] [Google Scholar]
  6. Berger SA, Krompass D, & Stamatakis A (2011). Performance , Accuracy , and Web Server for Evolutionary Placement of Short Sequence Reads under Maximum Likelihood. Systematic Biology, 60(3), 291–302. 10.1093/sysbio/syr010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brown SK, Darwent CM, & Sacks BN (2013). Ancient DNA evidence for genetic continuity in arctic dogs. Journal of Archaeological Science, 40(2), 1279–1288. 10.1016/j.jas.2012.09.010 [DOI] [Google Scholar]
  8. Brown WM, George M, & Wilson AC (1979). Rapid evolution of animal mitochondrial DNA. Proceedings of the National Academy of Sciences, 76(4), 1967 LP–1971. https://doi.org/10.1073/pnas.76.4.1967 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cairns KM, & Wilton AN (2016). New insights on the history of canids in Oceania based on mitochondrial and nuclear data. Genetica, 144(5), 553–565. 10.1007/s10709-016-9924-z [DOI] [PubMed] [Google Scholar]
  10. Chesner CA, Rose WI, Deino A, Drake R, & Westgate JA (1991). Eruptive history of Earth’s largest Quaternary caldera (Toba, Indonesia) clarified. Geology, 19(3), 200–203. 10.1130/0091-7613(1991)019&lt;0200:EHOESL&gt;2.3.CO;2 [DOI] [Google Scholar]
  11. Cunningham AA, Wood JLN, Nicolas V, Sargan DR, Suu-Ire R, de Vries S, … Amponsah-Mensah K (2018). The Gambian epauletted fruit bat shows increased genetic divergence in the Ethiopian highlands and in an area of rapid urbanization. Ecology and Evolution, (October), 1–18. 10.1002/ece3.4709 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Daly KG, Delser PM, Mullin VE, Scheu A, Mattiangeli V, Teasdale MD, … Erek CM (2018). Ancient goat genomes reveal mosaic domestication in the Fertile Crescent. Science, 361, 85–88. [DOI] [PubMed] [Google Scholar]
  13. Delson E (1980). Fossil macaques, phyletic relationships and a scenario of deployment (pp. 10–30). Van Nostrand Reinhold. [Google Scholar]
  14. Dool SE, Puechmaille SJ, Foley NM, Allegrini B, Bastian A, Mutumi GL, … Jacobs DS (2016). Nuclear introns outperform mitochondrial DNA in inter-specific phylogenetic reconstruction: Lessons from horseshoe bats (Rhinolophidae: Chiroptera). Molecular Phylogenetics and Evolution, 97, 196–212. 10.1016/j.ympev.2016.01.003 [DOI] [PubMed] [Google Scholar]
  15. Egger B, Koblmüller S, Sturmbauer C, & Sefc KM (2007). Nuclear and mitochondrial data reveal different evolutionary processes in the Lake Tanganyika cichlid genus Tropheus. BMC Evolutionary Biology, 7, 1–14. 10.1186/1471-2148-7-137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Evans A, Jones D, Boyer A, Brown J, Costa D, Ernest S, … Uhen M (2012). The maximum rate of mammal evolution. Proc National Acad Sci, 109(11), 4187–4190. 10.1073/pnas.1120774109 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fabre P-H, Vilstrup JT, Raghavan M, Der Sarkissian C, Willerslev E, Douzery EJP, & Orlando L (2014). Rodents of the Caribbean: origin and diversification of hutias unravelled by next-generation museomics. Biology Letters, 10(7), 20140266–20140266. 10.1098/rsbl.2014.0266 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fooden J (1976). Provisional Classification and key to living species of macaques {(Primates:} Macaca). Folia Primatol, 25, 225–236. [DOI] [PubMed] [Google Scholar]
  19. Fooden J (1995). Systematic Review of Southeast Asian Longtail Macaques, Macaca fascicularis {(Raffles,} [1821]). Fieldiana Zoology, 81. [Google Scholar]
  20. Fooden J (2006). Comparative Review of {Fascicularis-Group} Species of Macaques {(Primates:} Macaca). Fieldiana, 107. [Google Scholar]
  21. Frantz LAF, Mullin VE, Pionnier-Capitan M, Lebrasseur O, Ollivier M, Perri A, … Larson G (2016). Genomic and archaeological evidence suggests a dual origin of domestic dogs. Science, 352(6290), 1228–1231. [DOI] [PubMed] [Google Scholar]
  22. Galili T (2015). Data and text mining dendextend : an R package for visualizing , adjusting and comparing trees of hierarchical clustering, 31(July), 3718–3720. 10.1093/bioinformatics/btv428 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gansauge M, & Meyer M (2014). Selective enrichment of damaged DNA molecules for ancient genome sequencing, 1–8. 10.1101/gr.174201.114.24 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Godinho R, Crespo EG, & Ferrand N (2008). The limits of mtDNA phylogeography: Complex patterns of population history in a highly structured Iberian lizard are only revealed by the use of nuclear markers. Molecular Ecology, 17(21), 4670–4683. 10.1111/j.1365-294X.2008.03929.x [DOI] [PubMed] [Google Scholar]
  25. Groves CP (2001). Primate taxonomy.
  26. Gu Z, Gu L, Eils R, Schlesner M, & Brors B (2014). circlize implements and enhances circular visualization in R, 30(19), 2811–2812. 10.1093/bioinformatics/btu393 [DOI] [PubMed] [Google Scholar]
  27. Guschanski K, Krause J, Sawyer S, Valente LM, Bailey S, Finstermeier K, … Savolainen V (2013). Next-generation museomics disentangles one of the largest primate radiations. Systematic Biology, 62(4), 539–554. 10.1093/sysbio/syt018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Heaney L (1986). Biogeography of mammals in {SE} Asia: estimates of rates of colonization, extinction and speciation. Biol J Linn Soc, 25(1-2), 127–165. 10.1111/j.1095-8312.1986.tb01752.x [DOI] [Google Scholar]
  29. Heaney LR (1985). Zoogeographic evidence for Middle and Late Pleistocene land bridges to the Philippine Islands, 127–143. [Google Scholar]
  30. Heaney LR, Balete DS, & Rickart EA (2016). The Mammals of Luzon Island: Biogeography and Natural History of a Philippine Fauna. Johns Hopkins University Press. [Google Scholar]
  31. Hofreiter M, Serre D, Poinar HN, Kuch M, & Pääbo S (2001). Ancient DNA. Nature Reviews. Genetics, 2(5), 353–9. 10.1038/35072071 [DOI] [PubMed] [Google Scholar]
  32. Hung CM, Drovetski SV, & Zink RM (2016). Matching loci surveyed to questions asked in phylogeography. Proceedings of the Royal Society B: Biological Sciences, 283(1826), 12–14. 10.1098/rspb.2015.2340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Huxley TH (1868). On the Classification and Distribution of the Alectoromorphœ and Heteromorphœ. Zoological Society. [Google Scholar]
  34. Izquierdo-Carrasco F, Smith SA, & Stamatakis A (2011). Algorithms, data structures, and numerics for likelihood-based phylogenetic inference of huge trees. BMC Bioinformatics, 12. 10.1186/1471-2105-12-470 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Jansa S, Barker F, & Heaney L (2006). The pattern and timing of diversification of Philippine endemic rodents: Evidence from mitochondrial and nuclear gene sequences. Systematic Biology, 55(1), 73–88. 10.1080/10635150500431254 [DOI] [PubMed] [Google Scholar]
  36. Kircher M, Sawyer S, & Meyer M (2012). Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Research, 40(1), 1–8. 10.1093/nar/gkr771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kodaman N, Aldrich MC, Smith JR, Signorello LB, Bradley K, Breyer J, Cohen SS, Long J, Cai Q, Giles J, Bush WS, Blot WJ, Matthews CE, & Williams SM (2013). A Small Number of Candidate Gene SNPs Reveal Continental Ancestry in African Americans. Annals of Human Genetics, 77(1), 56–66. 10.1111/j.1469-1809.2012.00738.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Korneliussen TS, Albrechtsen A, & Nielsen R (2014). Open Access ANGSD : Analysis of Next Generation Sequencing Data, 1–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, & Salzberg SL (2004). Versatile and open software for comparing large genomes. Genome Biology, 5(2), 12. Retrieved from http://www.tigr.org/software/mummer. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Langmead B, & Salzberg SL (2013). Fast gapped-read alignmnet with Bowtie 2. Nature Methods, 9(4), 357–359. 10.1038/nmeth.1923.Fast [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Leathlobhair MN, Perri AR, Irving-Pease EK, Witt KE, Linderholm A, Haile J, … Frantz LAF (2018). The Evolutionary History of Dogs in the Americas. Science, 85(July), 81–85. 10.1126/SCIENCE.AAO4776 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Li H (2011). A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics, 27(21), 2987–2993. 10.1093/bioinformatics/btr509 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Li H, & Durbin R (2011). Inference of human population history from individual whole-genome sequences. Nature, 475(7351), 493–496. 10.1038/nature10231 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, … Durbin R (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078–2079. 10.1093/bioinformatics/btp352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Li J, Han K, Xing J, Kim H-S, Rogers J, Ryder O, … Batzer M (2009). Phylogeny of the macaques {(Cercopithecidae:} Macaca) based on Alu elements. Gene, 448(2), 242–249. 10.1016/j.gene.2009.05.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Librado P, Der Sarkissian C, Ermini L, Schubert M, Jónsson H, Albrechtsen A, … Orlando L (2015). Tracking the origins of Yakutian horses and the genetic basis for their fast adaptation to subarctic environments. Proceedings of the National Academy of Sciences, 112(50), 201513696. 10.1073/pnas.1513696112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Liedigk R, Kolleck J, Böker K, Meijaard E, Badrul M-Z, Muhammad A-L, … Roos C (2015). Mitogenomic phylogeny of the common long-tailed macaque {(Macaca) fascicularis fascicularis). {BMC} Genomics, 16(1), 222. 10.1186/s12864-015-1437-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Lindo J, Huerta-Sánchez E, Nakagome S, Rasmussen M, Petzelt B, Mitchell J, … Malhi RS (2016). A time transect of exomes from a Native American population before and after European contact. Nature Communications, 7, 13175. 10.1038/ncomms13175 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Loosdrecht M Van De, Bouzouggar A, Humphrey L, Posth C, Barton N, Aximu-petri A, … Krause J (2018). Pleistocene North African genomes. Science, 552, 548–552. [DOI] [PubMed] [Google Scholar]
  50. Malmström H, Storå J, Dalén L, Holmlund G, & Götherström A (2005). Extensive human DNA contamination in extracts from ancient dog bones and teeth. Molecidar Biology and Evolution, 22(10), 2040–7. 10.1093/molbev/msi195 [DOI] [PubMed] [Google Scholar]
  51. Meisner J, & Albrechtsen A (2018). Inferring population structure and admixture proportions in low-depth NGS data. Genetics, 210(2), 719–731. 10.1534/genetics.118.301336 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nater A, Greminger MP, Arora N, Schaik CP, Goossens B, Singleton T, … Krützen M (2015). Reconstructing the demographic history of orangutans using Approximate Bayesian Computation. Mol Ecol, 24(2), 310–327. 10.1111/mec.13027 [DOI] [PubMed] [Google Scholar]
  53. Nater A, Mattle-Greminger MP, Nurcahyo A, Nowak MG, de Manuel M, Desai T, … Krützen M (2017). Morphometric, Behavioral, and Genomic Evidence for a New Orangutan Species. Current Biology, 27, 1–12. 10.1016/j.cub.2017.09.047 [DOI] [PubMed] [Google Scholar]
  54. Nater A, Nietlisbach P, Arora N, Schaik C, Noordwijk M, Willems E, … Krützen M (2011). {Sex-Biased} Dispersal and Volcanic Activities Shaped Phylogeographic Patterns of Extant Orangutans (genus: Pongo). Mol Biol Evol, 28(8), 2275–2288. 10.1093/molbev/msr042 [DOI] [PubMed] [Google Scholar]
  55. Nielsen R, Korneliussen T, Albrechtsen A, Li Y, & Wang J (2012). SNP calling, genotype calling, and sample allele frequency estimation from new-generation sequencing data. PLoS ONE, 7(7). 10.1371/journal.pone.0037558 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Orlando L, Gilbert MTP, & Willerslev E (2015). Reconstructing ancient genomes and epigenomes. Nature Reviews. Genetics, 16(7), 395–408. 10.1038/nrg3935 [DOI] [PubMed] [Google Scholar]
  57. Outlaw DC, & Voelker G (2008). Pliocene climatic change in insular Southeast Asia as an engine of diversification in Ficedula flycatchers. Journal of Biogeography, 35(4), 739–752. 10.1111/j.1365-2699.2007.01821.x [DOI] [Google Scholar]
  58. Pääbo S, Poinar H, Serre D, Jaenicke-Despres V, Hebler J, Rohland N, … Hofreiter M (2004). Genetic analyses from ancient DNA. Annual Review of Genetics, 38, 645–79. 10.1146/annurev.genet.37.110801.143214 [DOI] [PubMed] [Google Scholar]
  59. Palkopoulou E, Mallick S, Skoglund P, Enk J, Rohland N, Li H, … Dalén L (2015). Complete genomes reveal signatures of demographic and genetic declines in the woolly mammoth. Current Biology, 25(10), 1395–1400. 10.1016/j.cub.2015.04.007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Pusey AE, & C. P (1987). Dispersal and philopatry. In Smuts BB, Cheney DL, Seyfarth RM, Wrangham RW, & Struhsaker TT (Eds.), Primate Societies (pp. 250–266). Chicago: University of Chicago Press. [Google Scholar]
  61. Rogers J, & Gibbs RA (2014). Comparative primate genomics: Emerging patterns of genome content and dynamics. Nature Reviews Genetics, 15(5), 347–359. 10.1038/nrg3707 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Rohland N, Reich D, Mallick S, Meyer M, Green RE, Georgiadis NJ, … Hofreiter M (2010). Genomic DNA sequences from mastodon and woolly mammoth reveal deep speciation of forest and savanna elephants. PLoS Biology, 8(12), 16–19. 10.1371/journal.pbio.1000564 [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Ruiter JR, & Geffen E (1998). Relatedness of matrilines, dispersing males and social groups in long–tailed macaques (Macaca fascicularis). Proceedings of the Royal Society of London. Series B: Biological Sciences, 265(October 1997), 79–87. 10.1098/rspb.1998.0267 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, … Stewart J (2007). Genome-wide detection and characterization of positive selection in human populations. Nature, 449(7164), 913–918. 10.1038/nature06250 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sampietro ML, Gilbert MTP, Lao O, Caramelli D, Lari M, Bertranpetit J, & Lalueza-Fox C (2006). Tracking down human contamination in ancient human teeth. Molecidar Biology and Evolution, 23(9), 1801–7. 10.1093/molbev/ms1047 [DOI] [PubMed] [Google Scholar]
  66. Schablitsky JM, Witt KE, Madrigal JR, Ellegaard MR, Malhi RS, & Schroeder H (2019). Ancient DNA analysis of a nineteenth century tobacco pipe from a Maryland slave quarter. Journal of Archaeological Science, 105(February), 11–18. 10.1016/j.jas.2019.02.006 [DOI] [Google Scholar]
  67. Sikora M, Carpenter ML, Moreno-Estrada A, Henn BM, Underhill P. a, Sánchez-Quinto F, … Bustamante CD (2014). Population genomic analysis of ancient and modern genomes yields new insights into the genetic ancestry of the Tyrolean Iceman and the genetic structure of Europe. PLoS Genetics, 10(5), e1004353. 10.1371/journal.pgen.1004353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Skotte L, Korneliussen TS, & Albrechtsen A (2013). Estimating individual admixture proportions from next generation sequencing data. Genetics, 195(3), 693–702. 10.1534/genetics.113.154138 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Smith DG, Ng J, George D, Trask JS, Houghton P, Singh B, … Kanthaswamy S (2014). A genetic comparison of two alleged subspecies of Philippine cynomolgus macaques. American Journal of Physical Anthropology, 155(1), 136–148. 10.1002/ajpa.22564 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Smith D, John M, & George D (2007). Mitochondrial {DNA} variation within and among regional populations of longtail macaques {(Macaca} fascicularis) in relation to other species of the fascicularis group of macaques. Am J Primatol, 69(2), 182–198. 10.1002/ajp.20337 [DOI] [PubMed] [Google Scholar]
  71. Stajich JE, & Hahn MW (2005). Disentangling the effects of demography and selection in human history. Molecular Biology and Evolution, 22(1), 63–73. 10.1093/molbev/msh252 [DOI] [PubMed] [Google Scholar]
  72. Stamatakis A (2014). RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30, 1312–1313. 10.1093/bioinformatics/btu033 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Steppan Scott J, Zawadzki Christopher, & Heaney Lawrence R. (2003). Molecular phylogeny of the endemic Philippine rodent Apomys (Muridae) and the dynamics of diversification in an oceanic archipelago. Biological Journal of the Linnean Society, 80(4), 699–715. [Google Scholar]
  74. Tajima F (1989). Statistical Method for Testing the Neutral Mutation Hypothesis by DNA Polymorphism. Genetics, 123, 585–595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Thinh V, Mootnick A, Geissmann T, Li M, Ziegler T, Agil M, … Roos C (2010). Mitochondrial evidence for multiple radiations in the evolutionary history of small apes. {BMC} Evol. Biol, 10, 74. 10.1186/1471-2148-10-74 [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Tosi AJ, & Coke CS (2007). Comparative phylogenetics offer new insights into the biogeographic history of Macaca fascicularis and the origin of the Mauritian macaques. Mol. Phylogenet. Evol, 42(2), 498–504. 10.1016/j.ympev.2006.08.002 [DOI] [PubMed] [Google Scholar]
  77. Tosi AJ, Morales JC, & Melnick DJ (2003). Paternal, Maternal, and Biparental Molecular Markers Provide Unique Windows onto the Evolutionary History of Macaque Monkeys Published by : Society for the Study of Evolution Stable URL : http://www.jstor.org/stable/3448864 PATERNAL , MATERNAL , AND BIP. Society, 57(6), 1419–1435. [DOI] [PubMed] [Google Scholar]
  78. Wallace AR (1863). On the physical geography of the Malay Archipelago. The Journal of the Royal Geographical Society of London, 33, 217–234. [Google Scholar]
  79. Wallis GP, Cameron-Christie SR, Kennedy HL, Palmer G, Sanders TR, & Winter DJ (2017). Interspecific hybridization causes long-term phylogenetic discordance between nuclear and mitochondrial genomes in freshwater fishes. Molecular Ecology, 26(12), 3116–3127. 10.1111/mec.14096 [DOI] [PubMed] [Google Scholar]
  80. Whittaker DJ, Morales JC, & Melnick DJ (2007). Resolution of the Hylobates phylogeny: Congruence of mitochondrial D-loop sequences with molecular, behavioral, and morphological data sets. Molecular Phylogenetics and Evolution, 45(2), 620–628. [DOI] [PubMed] [Google Scholar]
  81. Williams MAJ, Ambrose SH, van der Kaars S, Ruehlemann C, Chattopadhyaya U, Pal J, & Chauhan PR (2009). Environmental impact of the 73ka Toba super-eruption in South Asia. Palaeogeography, Palaeoclimatology, Palaeoecology, 284(3), 295–314. [Google Scholar]
  82. Woods R, Turvey ST, Brace S, MacPhee RDE, & Barnes I (2018). Ancient DNA of the extinct Jamaican monkey Xenothrix reveals extreme insular change within a morphologically conservative radiation. Proceedings of the National Academy of Sciences of the United States of America, 115(50), 12769–12774. 10.1073/pnas.1808603115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Wright B, Farquharson KA, McLennan EA, Belov K, Hogg CJ, & Grueber CE (2019). From reference genomes to population genomics: Comparing three reference-aligned reduced-representation sequencing pipelines in two wildlife species. BMC Genomics, 20(1), 1–10. 10.1186/s12864-019-5806-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Yao L, Li H, Martin RD, Moreau CS, & Malhi RS (2017). Tracing the phylogeographic history of Southeast Asian long-tailed macaques through mitogenomes of museum specimens. Molecular Phylogenetics and Evolution, 116(November 2016), 227–238. 10.1016/j.ympev.2017.08.006 [DOI] [PubMed] [Google Scholar]
  85. Yao L, Li H, Martin RD, Moreau CS, & Malhi RS (2017). Tracing the phylogeographic history of Southeast Asian long-tailed macaques through mitogenomes of museum specimens. Molecular Phylogenetics and Evolution, 116. 10.1016/j.ympev.2017.08.006 [DOI] [PubMed] [Google Scholar]
  86. Zinner D, Fickenscher GH, Roos C, Anandam MV, Bennett EL, Davenport TRB, … Eudey AA (2013). Family Cercopithecidae (old world monkeys). In Handbook of the Mammals of the World-Primates. Lynx. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Data 1. Table of samples

Supplemental Data 1. Table of samples

All samples sequenced for this study. Samples are listed by specimen ID number from their respective museums. The 75 samples analyzed in this study are marked with an “x” in the “Included” column. “Total_Nt_across_reads” refers to the sum of all nucleotides sequenced across all reads. “Total_Mapped_Nucleotides” refers to the number of nucleotides that mapped to the reference genome. “SNP_Coverage” refers to the proportion of the over 21,500 SNPs called in the 75 individuals included in this study.

NIHMS1723213-supplement-Supplemental_Data_1__Table_of_samples.pdf (124.9KB, pdf)
Supplemental Data 3. SNP positions across chromosomes.

SNP positions across chromosomes. Large red dots indicate chromosome length, and blue dots indicate SNP locations on that chromosome.

NIHMS1723213-supplement-Supplemental_Data_3__SNP_positions_across_chromosomes_.pdf (10.2KB, pdf)
Supplemental Data 4. Maximum likelihood consensus tree (Newick format)

Maximum likelihood consensus tree (Newick format)

NIHMS1723213-supplement-Supplemental_Data_4__Maximum_likelihood_consensus_tree__Newick_format_.pdf (18.9KB, pdf)
Supplemental Data 2A. Map of Southeast Asia with museum specimen ID numbers corresponding to those in Supplementary Data 1.

Supplemental Data 2. Map of Southeast Asia with museum specimen ID numbers corresponding to those in Supplementary Data 1.

A. A map of Southeast Asia derived from Liedigk et al. (2015) marked with specimens included in the analyses of this study. The regions shaded in black, dark grey, and light grey regions indicate the regions of M. f aureus, M. f. fascicularis, and M. f. philippinensis, respectively. The region with dark grey lines indicated an area of likely intergradation of M. fascicularis and M. f. philippinensis (Fooden, 1995, 2006). Black circles represent exact localities whereas white circles represent approximate localities. The current site of Lake Toba is marked with a blue star.

NIHMS1723213-supplement-Supplemental_Data_2A__Map_of_Southeast_Asia_with_museum_specimen_ID_numbers_corresponding_to_those_in_Supplementary_Data_1_.pdf (1MB, pdf)
Supplemental Data 2B A map of the Philippines (magnified from the boxed area in Figure 1A)

B. A map of the Philippines (magnified from the boxed area in Figure 1 A) derived from Heaney (1986), marked with specimens analyzed in this study.

NIHMS1723213-supplement-Supplemental_Data_2B_A_map_of_the_Philippines__magnified_from_the_boxed_area_in_Figure_1A_.pdf (960.9KB, pdf)
Supplemental Data 5. Maximum likelihood phylogeny of M. fascicularis using an unlinked dataset

Supplemental Data 5. Maximum likelihood phylogeny of M. fascicularis using an unlinked dataset Green=mainland, orange=Sundaland, blue=oceanic islands. The significant node (>=95%) is marked with a red point.

NIHMS1723213-supplement-Supplemental_Data_5__Maximum_likelihood_phylogeny_of_M__fascicularis_using_an_unlinked_dataset.pdf (90.6KB, pdf)
Supplemental Data 6. Principal component analysis (PC3 vs. PC1 and PC2)

Supplemental Data 6. Principal component analysis (PC3 vs. PC1 and PC2) A plot of PC1 vs. PC3 (A) and PC2 vs. PC3 (B). The colors represent population types. Mainland specimens are colored in green, continental shelf island specimens are colored in orange, and oceanic island specimens are colored in blue. The shapes are labelled based on island according to the legend.

NIHMS1723213-supplement-Supplemental_Data_6__Principal_component_analysis__PC3_vs__PC1_and_PC2_.pdf (26.1KB, pdf)
Supplemental Data 7. Principal component analysis (PC1 and PC2)

Supplemental Data 7. Principal component analysis (PC1 and PC2) A plot of PC1 vs. PC2 using five different subsets of SNPS (one SNP randomly selected per 50 kb window). The colors represent population types. Mainland specimens are colored in green, continental shelf island specimens are colored in orange, and oceanic island specimens are colored in blue. The shapes are labelled based on island according to the legend.

NIHMS1723213-supplement-Supplemental_Data_7__Principal_component_analysis__PC1_and_PC2_.pdf (45.1KB, pdf)
Supplemental Data 8. Heterozygosity plotted against sequencing depth and position

Supplemental Data 8. Heterozygosity plotted against sequencing depth and position

NIHMS1723213-supplement-Supplemental_Data_8__Heterozygosity_plotted_against_sequencing_depth_and_position.pdf (15.1KB, pdf)
Supplemental Data 9. Admixture plots using five unlinked SNP datasets

Supplemental Data 9. Admixture plots using five unlinked SNP datasets Admixture plots showing results for 2-5 clusters for each dataset. Green text indicates the mainland while orange text indicates a continental shelf island and blue text indicates an oceanic island.

NIHMS1723213-supplement-Supplemental_Data_9__Admixture_plots_using_five_unlinked_SNP_datasets.pdf (39.1KB, pdf)

Data Availability Statement

The data that support the finds of this study are openly available in Genbank SRA at http://www.ncbi.nlm.nih.gov/bioproject/634778 (BioProject ID PRJNA634778) and Github at https://github.com/kelsey-witt/macaque-lowcvg-pipeline (scripts/pipeline).

RESOURCES