Ethics Statement

Permits to collect and import S. o. citrinellus fecal samples included Costa Rican Ministry of Energy and the Environment permit ACOPAC-INVN-14-08 and Centers for Disease Control and Prevention permit 2008-08-151. Also, an IACUC animal care protocol was approved by Columbia University for this research (AC-AAAA5583).

Sampling and DNA Extraction

Fecal samples were collected from S. o. citrinellus in the Central Pacific region of Costa Rica from September 2008 – April 2009 (Figure 1). S. o. citrinellus is not continuously distributed between sampling locations as they are restricted to larger secondary forest fragments [33], [40]; two groups (PD and O) were sampled in oil palm plantations but adjacent to a forest fragment or riparian forest. From 304 fecal samples, we verified genotypes for 233 S. o. citrinellus individuals, comprised of 10 to 20 adult individuals from each of 14 groups. The average size of sampled groups was 39 individuals (range 18–67). Whenever possible (N = 13 groups), more than 10 individuals per group were sampled to increase the precision of genetic analyses in detecting dispersal and migration [41]. Samples were stored in 8 ml plastic tubes with RNAlater buffer (Ambion) at −4°C in the field and −20°C in the laboratory. Eleven additional DNA samples from individuals of the southern subspecies S. o. oerstedii, used as an outgroup, were contributed by G. Gutierrez (University of Costa Rica) for a total of 244 samples.

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Figure 1. Sampled S. o. citrinellus groups in the Central Pacific region of Costa Rica.

Sampled S. o. citrinellus groups in the Central Pacific region of Costa Rica, showing the limit of 500 m asl to their distribution and different classes of matrix habitat as defined by a manual land cover classification.

https://doi.org/10.1371/journal.pone.0043027.g001

We extracted DNA using QIAamp DNA Stool Minikits (Qiagen) with small modifications to the “Isolation of DNA from Stool for Human DNA Analysis” protocol (see [42], [43]). We used real-time quantitative PCR after extraction to quantify the amount of nuclear DNA in each sample with iQ SYBR Green Supermix (Bio-Rad) and universal primate primers amplifying approximately 200 bp of nuclear DNA [44]. We included samples with greater than 0.5 ng/µl DNA concentrations (averaged over two replicate runs) in our genotyping analyses.

Microsatellite Genotyping

We PCR amplified 17 autosomal microsatellite markers on multiplex panels of three or four markers (CJ7 [45]; D17s804, D3s1210, D3s1229, D3s1776, D4s111, D5s111, D8s165, D8s260 [46]; Leon 15, Leon 21 [47]; LL118, LL157, LL311 [48]; Locus5 [49]; SB38 [50]; Table S1) using Multiplex PCR Kits (Qiagen; for reaction concentrations and conditions see [43]). PCR products were electrophoresed on an ABI 3730 DNA Analysis System with GENESCAN 500 ROX size standard, and genotypes were called using GeneMapper software (ABI). We confirmed heterozygous genotypes by scoring alleles at least four times and homozygous genotypes at least seven times since allelic dropout is often a problem when amplifying microsatellite markers from fecal samples [51], [52], [53]. We used the program MICROCHECKER [54] to test for null alleles, and removed one of the 17 microsatellite markers because it was found to possibly contain null alleles (D13s160). We tested for linkage disequilibrium and deviations from Hardy-Weinberg equilibrium (HWE) across markers in ARLEQUIN v 3.1 [55] and found no evidence for linkage disequilibrium. We did find violations of HWE when analyzing samples at the species and population levels, consistent with a Wahlund effect [56], [57]. At the group level, three groups had one marker that was significantly out of HWE (D3s1766 at Gamalotillo, Leon21 at Chirraca, and Leon15 at MANP), likely due to the presence of related individuals in the sample [58], [59], [60].

Analysis of Population Genetic Structure

We ran our multilocus genotypes in STRUCTURE v 2.2 [61] and BAPS v 2 [62] to infer the number of genetic clusters in our dataset. We ran 10 independent iterations of K = 1−16 in STRUCTURE for 2,000,000 Markov Chain Monte Carlo (MCMC) generations with a 200,000 burn-in period, assuming correlated allele frequencies and admixture. We inferred K using ln P(X | K) and the ΔK method [63], where optimum K has the highest ΔK value, or rate of change in the log probability of the data between successive K-values. We ran BAPS under the individual clustering module and the default settings (stochastic optimization) also for 10 separate iterations for a maximum K of 1–21. Both programs were run without spatial information.

We also examined genetic structure in S. o. citrinellus microsatellite data with pairwise tests for differentiation among groups and populations using F-statistics [64] calculated with Weir and Cockerham’s [65] estimators in FSTAT v 2.9 [66], for 10,000 randomizations not assuming HWE. Bonferroni corrections were used throughout when conducting multiple comparisons.

We detected first generation migrants and admixed individuals using STRUCTURE and GENECLASS v 2.0 [67], [68]. Migrants were defined as individuals that were assigned to one population but geographically sampled from another population. Admixed individuals were defined as those individuals that could not be confidently assigned to either population following the ranking and plotting approach of Beaumont et al. [69]. To detect first generation migrants in STRUCTURE, we ran the program using the cluster memberships inferred as described above using the ΔK method as prior population information. We conducted several runs using a range of values for MIGRPRIOR (0.001−0.1) following Pritchard et al. [70]. Because choice of MIGRPRIOR did not significantly affect program outputs, we present results from MIGRPRIOR  =  0.09, the average migration rate between populations of S. o. citrinellus found using the software BAYESASS [71], following Liu et al. [16]. Burn-in and run length were the same as earlier runs of STRUCTURE without prior population information. We also performed an exclusion test and used the ‘Detect first generation migrants’ option in GENECLASS [68], [72], using both L_h and L_h/L_max, which represent, respectively, the most appropriate statistic when all potential source populations have not been sampled and when they have [72]. The probability of individual genotypes coming from each population was calculated by comparing individual genotypes to 10,000 simulated individuals per population [72].

We tested for genetic signatures of a recent population bottleneck using BOTTLENECK [73]. We tested our data under the Infinite Alleles Model (IAM), the Stepwise Mutation Model (SSM), and the Two Phase Model (TPM) with 10,000 replications using a sign test. The sign test compares observed and expected heterozygosity excess. If excess is higher than expected (based on equilibrium) for a large majority of markers in a population, the population may have recently experienced a genetic bottleneck [73], [74].

Land Cover Classification

Using 32 geo-referenced aerial photographs (taken with a DCS camera, each 10×10.5 km), a 90×7 km multispectral MASTER line image taken in the year 2005 obtained from the Centro Nacional de Alta Technología (CENAT) in Costa Rica, and a forest cover dataset generated by EOSL, CCT and FONAFIFO [75] with Landsat 7 TM satellite imagery from the year 2000, we delineated five habitat classes (forest, cattle pasture, rivers, oil palm plantations, and residential areas; Figure 1) manually in ArcGIS v 9.3 (ESRI) at a 20×20 m resolution for the 1800 km² study area. We defined residential areas as clusters of human residences less than 100 m apart and consisting of a total area greater than 3 hectares. Our classification also included an “other non-forest” category, which included shrimp farms, rice plantations, abandoned lots, and residences that were not concentrated enough to fit our definition of a residential area. Ninety-eight percent of 131 ground reference points were accurately reflected in the classification.

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Figure 2. Distribution of three genetic clusters estimated in STRUCTURE (A) and BAPS (B).

Distribution of three genetic clusters estimated in STRUCTURE (A) and BAPS (B). Vertical lines are broken into colored segments showing the proportion of each individual assigned to each K (within S. o. citrinellus: western cluster – black, eastern cluster 1– light grey, eastern cluster 2– grey; S. o. oerstedii – white). Sample locations are listed at the bottom of the figure, and are arrayed in west to east order (left to right).

https://doi.org/10.1371/journal.pone.0043027.g002

Landscape Genetic Analyses

We estimated pairwise genetic relationships between individuals using Rousset’s â [76] and Moran’s I [77], [78] to measure both distance (â) and similarity (I) [30]. We measured two types of geographic distances: Euclidean distances, straight-line distances on a map, and least-cost geographic distances, where the costs of dispersing across different habitat classes were incorporated into the measure of geographic distance. We calculated Euclidean geographic distances in ArcMap v 9.3 (ESRI) using the sampled coordinates and we calculated least-cost geographic distances for the five habitat classes identified above (forests, oil palm plantations, cattle pastures, residential areas, and rivers) using the COSTDISTANCE function in ArcGIS v 9.3 (ESRI). We varied the cost of one class while keeping all others at an equal, low cost (1) and then repeated this process for each habitat class. We assessed least-cost distances for a range of 6 arbitrary cost values (10, 50, 100, 1000, 5000, 10000) to account for sensitivity [79], [80].

We performed Mantel tests of matrix correspondence [81] between genetic distances and geographic distances in ZT [82]. Also, because least-cost distances and Euclidean distances are not independent, we performed partial Mantel tests [83] in ZT to test the strength of relationships between genetic and least-cost distance matrices while controlling for the effect of Euclidean distance [84], [85]. Partial correlations show high power and accuracy in their ability to infer the effect of landscapes on dispersal when there is a strong contrast between the permeability of different landscape elements [86]. Significance was assessed with 10,000 permutations.

Recent studies have suggested that the spatial scale of landscape genetic analyses can have important effects on results, especially inferences about which landscape features affect gene flow [87], [88], [89]. To test the stability of our inferences across spatial scales, we repeated the above analyses including only pairs of samples within genetic clusters of S. o. citrinellus as inferred by analyses of population genetic structure, excluding between population pairs.

We used the results of our least-cost distance analyses to generate a resistance surface characterizing the cumulative effects of landscape heterogeneity on gene flow in S. o. citrinellus, implemented in the software CIRCUITSCAPE 3.5 [90]. Instead of calculating a single least-cost path, CIRCUITSCAPE incorporates aspects of electronic circuit theory (i.e. electronic resistance) with a random walk approach to visualize resistance patterns across the landscape [91], [92], [93]. We tested for relationships between pairwise resistance distances (generated using the “pairwise” mode in CIRCUITSCAPE) and genetic distances using simple and partial Mantel tests as described above. Also, we produced cumulative current flow maps in the “all to one” mode, with focal points as sampled groups and an 8-neighbor connection scheme, and the source current for each group scaled to group size. The habitat grid encompassed a 5–25 km buffer around peripheral focal points, with forests, rivers, residential areas and cattle pastures at very low costs (<10) and oil palm plantations at a moderate cost of 20, following recommendations from the CIRCUITSCAPE manual (resistance values above 20 are considered moderate, while values above 200 are considered high). We ran the program under several other parameterizations with similar results to those presented.

Analysis of Population Genetic Structure

Bayesian clustering analysis revealed at least two genetically distinct populations within S. o. citrinellus in the Central Pacific of Costa Rica. The most likely number of clusters across the whole sample, which included outgroup S. o. oerstedii individuals, was four in both STRUCTURE and BAPS (Figure 2,S1), although K = 2 or 3 were only slightly less likely (Figure S1). For K = 4, one cluster represents samples from the subspecies S. o. oerstedii while the other three clusters are within S. o. citrinellus. The first cluster within S. o. citrinellus includes almost all individuals from western groups. The other two clusters do not seem to be geographically separated and include mostly members from eastern groups (Figure 2). The two eastern clusters likely represent ancestral polymorphism that has not been sorted out in this population. We defined a western population and an eastern population of S. o. citrinellus based on the strong geographic clustering inferred using STRUCTURE and BAPS. We also found significant population differentiation using F-statistics between the defined eastern and western populations of S. o. citrinellus (F_ST = 0.0903, P = 0.05). Microsatellite allelic diversity in populations of S. o. citrinellus ranged from 3 to 15 alleles (mean = 8.1) in the western population and from 5 to 27 (mean = 10.6) in the eastern population. Pairwise F_ST values among groups of S. o. citrinellus ranged from 0.016–0.19, with a mean of 0.103 (Table 1). Pairwise F_ST values among groups from the same population (mean = 0.06, range 0.016–0.11) were less than pairwise F_ST values among groups from different populations (mean = 0.14, range 0.070–0.19, Table 1).

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Table 1. Pairwise F_ST values among groups of S. o. citrinellus.

https://doi.org/10.1371/journal.pone.0043027.t001

Analyses using STRUCTURE and GENECLASS estimated 7 likely migrants and 10 potentially admixed individuals between the western and eastern populations (Table 2). STRUCTURE estimated 1 potential migrant (individual G1, P = 0.019), while GENECLASS estimated the same individual as a migrant in addition to 6 others (P<0.01) using both likelihood methods (L_h and L_h/L_max). These 7 migrants were assigned to a cluster where they were not geographically sampled in GENECLASS and also had lower probabilities of belonging to their geographic origin cluster compared to other individuals in STRUCTURE (Table 2). Ten to 40 km separated sampled sites and inferred populations of origin.

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Table 2. Results of migrant detection analyses.

https://doi.org/10.1371/journal.pone.0043027.t002

In STRUCTURE, we found breaks in mean Q-values at Q = 0.2 and 0.8 and therefore defined individuals with mean Q-values from 0.2 to 0.8 as potentially admixed [22], [23], [70], [83], [89]. We found 20 individuals with mean Q values between 0.2 and 0.8 (Figure S2), and 10 were also assigned in GENECLASS to >1 cluster with a high probability (>0.2) of assignment to a cluster other than the origin (Table 2). In one case (individual K1, from Chirraca), an individual had low probability of being in either cluster as estimated by GENECLASS, but STRUCTURE identified it as a potential migrant (P = 0.046). We interpreted this individual as potentially admixed, or a migrant from an unsampled ‘ghost’ population.

We found no evidence of a bottleneck from the microsatellite data. None of the sign or Wilcoxon tests across any models suggested heterozygosity excess. Similarly, all mode-shift tests showed normal L-shaped distributions.

Landscape Genetic Analyses

Genetic distances were correlated with Euclidean geographic distances both within populations (eastern population, â: r = 0.14, P = 0.0021; I: r = −0.10, P<0.0001; western population, â: r = 0.17, P<0.0001; I: r = −0.32, P<0.0001) and when the entire sample was considered (â: r = 0.21, P<0.0001; I: r = −0.18, P<0.0001). Across the entire sample, oil palm plantations at a cost of 10 were the only habitat class for which Mantel’s r-values between least-cost and genetic distance matrices were consistently larger than Mantel’s r-values between genetic and Euclidean distance matrices; these results held for both Moran’s I and Rousset’s â and in both simple and partial Mantel tests (Figure 3, Table S2). However, when only within population pairs were considered, Mantel’s r-values between least-cost and genetic distance matrices for oil palm plantations did not differ greatly from Mantel’s r-values between genetic and Euclidean distance matrices (Figure 4, Table S3,S4). For both the eastern and western within population pairs, there were large, significant partial Mantel’s r-values for Rousset’s â when costs of 5,000 and 10,000 were given to oil palm plantations, but they were not greater than the Mantel’s r-value between genetic and Euclidean distance matrices, and Moran’s I did not show the same pattern (Figure 4, Table S3,S4).

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Figure 3. Results of Mantel tests of least-cost distances against genetic relationships, including all pairs of individuals.

Results of Mantel tests of least-cost distances against genetic relationships, including all pairs of individuals. Negative Mantel’s r-values are given (left) for Morans’ I for easier comparison with trends in Rousset’s â (right). Filled symbols (diamonds for simple Mantel tests and circles for partial Mantel tests) represent statistically significant Mantel’s r-values in the expected direction (positive for â and negative for I). Dotted lines represent the Mantel’s r-value for Euclidean distance against genetic distance.

https://doi.org/10.1371/journal.pone.0043027.g003

Least-cost distances for forests showed the expected relationship for non-barrier habitat classes when all sample pairs were considered, with absolute Mantel’s r-values almost consistently decreasing with increasing cost in both simple and partial Mantel tests (Figure 3, Table S2). This pattern was less clear when only within population pairs were considered (Figure 4, Table S3,S4).

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Figure 4. Results of Mantel tests of least-cost distances against genetic relationships, including only pairs within populations.

Results of Mantel tests of least-cost distances against genetic relationships, including only pairs within the eastern population (black) or western population (grey). Least-cost distances are for oil palm plantations, cattle pastures, forests, rivers, and residential areas (top to bottom). Negative Mantel’s r-values are given (left) for Morans’ I for easier comparison with trends in Rousset’s â (right). Filled symbols represent statistically significant Mantel’s r-values in the expected direction (positive for â and negative for I).

https://doi.org/10.1371/journal.pone.0043027.g004

Pairwise resistance distances calculated in CIRCUITSCAPE showed strong relationships with genetic distance when all samples were considered (simple Mantel, â: r = 0.29, P<0.0001; I: r = −0.25, P<0.0001; partial Mantel controlling for Euclidean distance, â: r = 0.24, P<0.0001; I: r = −0.21, P<0.0001). Relationships were not as strong when only within population pairs were considered (eastern population, simple Mantel â: r = 0.17, P = 0.001; I: r = −0.11, P<0.0001; partial Mantel â: r = 0.09, P>0.05; I: r = −0.04, P = 0.042; western population, simple Mantel â: r = 0.14, P = 0.02; I: r = −0.23, P<0.0001; partial Mantel â: r = 0.07, P>0.05; I: r = −0.08, P>0.05). When controlling for the effect of one another, resistance distances and least-cost distances for oil palm plantations at a cost of 10 both showed significant relationships with genetic distances (Table 3). However, least-cost distances showed stronger relationships with genetic distances when controlling for resistance distances than vice versa, at least when all pairs or only western pairs were considered (Table 3).

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Table 3. Results of partial Mantel tests between genetic distances (Moran’s I and Rousset’s â), resistance distances (generated in CIRCUITSCAPE), and the best least-cost distances (oil palm plantations at a cost of 10, “Palm10”).

https://doi.org/10.1371/journal.pone.0043027.t003

The resistance surface output from CIRCUITSCAPE showed that even with a moderate cost, oil palm plantations cause an extensive area of low current flow in the middle of the landscape due to the cumulative costs of traversing through this expansive matrix habitat type (Figure 5). The resistance surface also shows that current flow is stronger among sites in the eastern population than in the western population (Figure 5), which is consistent with the consistently larger Mantel’s r-values between genetic and geographic distance matrices in the western population (Figure 4).

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Figure 5. Cumulative resistance surface created in CIRCUITSCAPE.

Cumulative resistance surface created in CIRCUITSCAPE. Forests, rivers, residential areas and cattle pastures were given very low resistance values (<10) and oil palm plantations were given a moderate resistance of 20.

https://doi.org/10.1371/journal.pone.0043027.g005

Population Genetic Structure

Clustering analysis and F-statistics revealed that S. o. citrinellus are structured into two genetically distinct populations, an eastern and a western population. These populations have also been supported by AMOVAs as well as mtDNA haplogroups [42], [43]. Genetic differentiation between populations was significant but weak, which is consistent with recent separation or a high level of gene flow between them. There was a high level of agreement between clustering algorithms in terms of assignment of individuals to the two subpopulations, including the inference of recent migrants, despite the different algorithms that each program uses to describe population membership (GENECLASS calculates the probability that an individual belongs to a population, whereas STRUCTURE calculates the proportion of an individual’s genome that is characteristic of a population; Table 2). These results suggest the two populations are connected by gene flow despite their genetic distinctiveness. Importantly, individuals inferred to be migrants or of mixed or ambiguous ancestry were scattered throughout the sampled area, indicating weak differentiation across the landscape (Figure 1, Table 2).

We note that our sample contains more individuals from the eastern population, which contains more contiguous forest patches, and fewer individuals from the western population, where forest patches are less contiguous and more isolated. STRUCTURE and BAPS have been shown to produce spurious results if samples are obtained unevenly from a continuous population [94]. Also, previous publications based on simulation results have suggested that clustering analyses will group all individuals from the largest, most continuous region together, while grouping all other individuals in a second, less well resolved cluster [95]. However, this was not the case in our results; the western population, which was less continuously sampled due to the natural distribution of S. o. citrinellus in the area, showed a more resolved cluster than the eastern population, which showed a less well resolved cluster with evidence of ancestral polymorphism.

Landscape Genetic Analysis

In addition to significant differentiation among populations, we also found significant genetic differentiation among several groups of S. o. citrinellus (but not all) using pairwise F_ST comparisons. These groups are in fragments of secondary forest separated by varying types of unsuitable habitats such as cattle pasture, African oil palm plantations, and rice plantations. While F-statistics effectively measure spatial variance in gene frequencies, we used an individual-based landscape genetic approach to instead measure aspects of spatial patterns of gene frequencies while taking into account landscape heterogeneity to better understand the forces behind the patterns of population genetic structure shown here.

Our results suggest that landscape heterogeneity affects genetic relationships in S. o. citrinellus and that different matrix habitat classes have different effects on dispersal in the studied landscape. Least-cost distances for oil palm plantations at a cost of 10 had stronger relationships with genetic distances than Euclidean distances in both simple and, more importantly, partial Mantel tests, suggesting that these least-cost distances contributed different information from Euclidean distances and that oil palm plantations represent moderate barriers to gene flow. When we restricted the analyses to within population pairs, none of the least-cost distances had stronger effects than Eucidean distances, suggesting that the effect of oil palm plantations on gene flow is largely scale-dependent and manifests during longer dispersal events between populations.

Similarly, the resistance surface showed how oil palm plantations, even when given only moderate resistance values, impede current flow because they dominate the landscape and the cost of crossing them accumulates over large distances. The strongest current flow in the resistance surface was in the eastern population near Manuel Antonio National Park (MANP), where there is not only the highest density of natural forest and monkey groups in the region, but also a break in the oil palm plantations due to complex topography. We found weaker isolation by distance in the eastern population as compared to the western population, also likely due to this break in the oil palm plantations.

By contrast, we found that cattle pastures, rivers, and residential areas do not differ greatly from Euclidean distance in their effects on genetic distance. Cattle pastures, rivers, and residential areas in this region are often surrounded by live fences of fruiting trees, which might explain why they did not show strong negative effects on S. o. citrinellus gene flow in this landscape. Alternatively, the landscape composition and configuration of these habitat types is quite different from that of oil palm plantations, which occur as large, contiguous expanses. Cattle pastures, rivers, and residential areas, by contrast, are smaller and more isolated from one another. In this particular landscape, these features did not influence gene flow, but if we tested multiple landscapes with a range of variability in the composition and configuration of landscape features, we might find different results [80], [86], [88].

We can lend further support to this idea by examining differences in landscape configuration between the eastern and western populations in our dataset. For example, in the western population, increasing cost for residential areas did not change Mantel’s r-values between least-cost and genetic distances, but in the eastern population, residential areas showed a non-barrier pattern, with increasing costs resulting in decreasing Mantel’s r-values. It is likely that residential areas are non-barriers in both populations, but this analysis was not sensitive enough to pick up the signal in the western population, where residential areas are smaller in number and more spread out in comparison to the eastern population.

A disadvantage to the Mantel test framework is that it is difficult to choose among closely related models or models with only slightly different Mantel’s r-values [96]. Here, pairwise resistance distances created in CIRCUITSCAPE produced similar Mantel’s r-values to least-cost distances for oil palm plantations at a cost of 10. Ours is one of a small number of empirical studies that has used CIRCUITSCAPE to characterize landscape connectivity in human-dominated environments, and the other studies have found that resistance distances outperform least-cost distances in how well they characterize gene flow [92], [97]. Here, partial Mantel tests controlling for the effect of least-cost distances on resistance and vice versa suggested that oil palm plantations at a cost of 10 had the stronger relationship with genetic distance when all pairs were considered and when only western pairs were considered (Table 3). However, the Mantel’s r-values were within a reasonable margin of error of one another. Both distances assigned moderate costs to oil palm plantations and had stronger relationships with genetic distances than Euclidean distance matrices when between population pairs were included in the analysis, showing a consistent trend.

It is possible that the temporal scale of our landscape data has influenced our results [98], [99]. Long, overlapping generations affect the power of landscape genetic approaches to detect the effects of current or even historical landscape patterns on genetic structure, and this issue is particularly important for long-lived primate taxa. A recent simulation study showed that it would take 1–15 generations to detect barriers to gene flow using Mantel’s r [99]. Saimiri have an average generation time of 3–6 years [100], meaning that at least 12 and up to 25 generations have likely transpired since the major transformation of the Central Pacific landscape in the early 1900s (although at that time the plantations were of banana, and later replaced with African oil palm, they were of the same configuration and area) [38]. Thus, we should have been able to detect barriers to gene flow caused by landscape features in this study. However, genetic distance measures such as Rousset’s â are based on F_ST and as such may reflect processes that are more likely to be apparent in historical landscape data [4]. Although we have shown some effect of landscape heterogeneity on gene flow using current landscape data, historical data from the early 1900s may show a stronger relationship. Unfortunately, such data not readily available.

Although we tested for a population contraction and found no evidence for a bottleneck, sequencing additional loci, in particular nuclear introns and coding mtDNA loci, would allow for a more robust analysis of alternative hypotheses through Approximate Bayesian Computation (ABC) [101]. ABC would allow us to test various alternative scenarios of population expansion and/or contraction and also to test for a temporal correlation between increasing genetic structure and the expansion of banana and oil palm plantations in the Central Pacific of Costa Rica.

Another issue that is common in many landscape genetic studies is that the different focal matrix habitats used in our analysis are likely of different ages. Rivers are older than the other four habitat classes we considered, and some residential areas are likely younger than the cattle pastures and oil palm plantations, most of which were established in the early 1900s. Future analyses might address this issue by including different sets of models and molecular markers that pinpoint different temporal scales in order to distinguish between the effects of historical and recent landscape changes on population genetic structure [102].

Implications for Conservation Management

This study exemplifies how important it is to conduct landscape genetic analyses in species that show evidence of population genetic structure in heterogeneous landscapes. Furthermore, we highlight the importance of quantifying the relative effects of different matrix habitat classes in landscape genetic analyses [4], [18], [103], instead of assuming that all non-suitable habitats have a uniform effect on dispersal and gene flow. Because we distinguished among matrix habitat classes, we have a finer understanding of what does and does not constitute a barrier to S. o. citrinellus gene flow in the Central Pacific Costa Rican landscape. We are also able to make more detailed recommendations to conservation managers regarding the types of matrix habitat that S. o. citrinellus may or may not use to disperse among patches of forest in the Central Pacific. In a concurrent study, we used resistance surfaces to test different biological corridor configurations for their potential ability to augment gene flow through oil palm plantations [42]. Another strategy to augment gene flow through oil palm plantations might be to plant understory vegetation, which has been shown to increase bird richness in oil palm plantations in eastern Guatemala [104].

However, we must recognize that our results are specific to the Central Pacific landscape, and conservation managers should be careful not to apply our results in other landscapes or for other populations of S. oerstedii. For example, in a landscape where cattle pastures dominate instead of oil palm plantations, a separate landscape genetic study would be necessary to measure the relative effects of each matrix habitat to determine whether it might be more important for conservation managers to augment gene flow through cattle pastures rather than oil palm plantations.

When attempting to translate the results of any landscape genetic analysis to patterns of functional connectivity, we must also acknowledge that measures of genetic distance do not equate to animal movement patterns. Simulations that model the sums of individual behavioral decisions are likely necessary to best inform conservation management of taxa in heterogeneous landscapes [105], [106], [107], [108]. A possible next step would be to incorporate a recently published new form of population viability analysis that uses individual-based models (simulations) that incorporate behavioral decisions alongside models of landscape change over time [109].