Landscape heterogeneity predicts gene flow in a widespread polymorphic bumble bee, Bombus bifarius (Hymenoptera: Apidae)
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- Lozier, J.D., Strange, J.P. & Koch, J.B. Conserv Genet (2013) 14: 1099. doi:10.1007/s10592-013-0498-3
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Bombus bifarius is a widespread bumble bee that occurs in montane regions of western North America. This species has several major color pattern polymorphisms and shows evidence of genetic structuring among regional populations, and the taxonomic status of regional populations has repeatedly been debated. We test whether observed structure is evidence for discrete gene flow barriers that might indicate isolation or instead reflects clinal variation associated with spatially limited dispersal in a complex landscape. We first consider color pattern variation and identify geographical patterns of B. bifarius color variation using cluster analysis. We then use climate data and a comprehensive set of B. bifarius natural history records with an existing genetic data set to model the distribution of environmentally suitable habitat in western North America and predict pathways of potential gene flow using circuit theory. Resistance distances among populations that incorporate environmental suitability information predict patterns of genetic structure much better than geographic distance or Bayesian clustering alone. Results suggest that there may not be barriers to gene flow warranting further taxonomic considerations, but rather that the arrangement of suitable habitat at broad scales limits dispersal sufficiently to explain observed levels of population differentiation in B. bifarius.
KeywordsLandscape geneticsIsolation by distanceIsolation by resistanceEnvironmental niche modelMicrosatellitesCircuit theoryColor polymorphism
Understanding the processes governing genetic diversity and differentiation of populations is a central focus of population genetics. Gene flow is the major force that maintains connectivity among populations, and many studies seek to identify relationships between space and genetic variation with the aim of identifying barriers to gene flow (Manel et al. 2003; Storfer et al. 2007; Guillot et al. 2009). Classic approaches involve testing for correlations between pairwise genetic (e.g. FST or other measures) and geographic distance separating individuals or populations assuming models of isolation by distance (IBD) (Slatkin 1993; Rousset 1997). A significant IBD signature, for example, suggests that a species is at genetic equilibrium with spatially restricted dispersal, while lack thereof can suggest that long-distance dispersal is sufficiently common to overcome IBD signatures or that populations are not at equilibrium (Slatkin 1993; Hutchison and Templeton 1999; Guillot et al. 2009). Model-based approaches such as Structure or Geneland (e.g., Falush et al. 2003; Guillot 2005) can also be used to assign genotypes to potentially unknown source populations, and may reveal more discrete patterns of population structure that indicate barriers to gene flow, as might be found for recently isolated species.
For organisms that occur in regions with substantial environmental heterogeneity, populations will not be continuously distributed throughout the landscape, but will occur primarily in areas with suitable ecological conditions. Likewise, dispersal will not be random with respect to two-dimensional space, but will occur with higher probability along paths of high habitat suitability. Identifying how such heterogeneity affects inferences of gene flow from genetic data is one of the primary goals of landscape genetics (Manel et al. 2003; Storfer et al. 2007; Storfer et al. 2010). Isolation by resistance (IBR) modeling incorporates spatial data to create a “resistance” surface representing the probability with which a species can disperse across a landscape. IBR models often outperform simple IBD analyses because they can better predict actual pathways of dispersal among populations (McRae 2006; McRae and Beier 2007) and identify environmental factors shaping these paths (Cushman et al. 2006). Accurate inference of connectivity has clear implications for conservation, providing a foundation for predicting future gene flow as the spatial arrangement of suitable habitat is altered by climate and land use changes (Cushman et al. 2006; Wilson et al. 2005; McRae and Beier 2007; Storfer et al. 2010). Similarly, because IBR can be used to distinguish the signatures of apparent isolation from one of equilibrium gene flow, such methods can improve detection of more discrete population structure that might represent subspecies or other evolutionarily significant units, versus patterns that can largely be attributed to range dimensions (McRae and Beier 2007).
Montane species such as B. bifarius are likely to be particularly affected by range dimensions (Cushman et al. 2006; Schwartz et al. 2009; Wasserman et al. 2010). Because the habitat mosaic associated with elevation changes may limit dispersal pathways, discrepancies between distance and resistance may be especially strong, altering our perceptions of population connectivity. Environmental niche models (ENMs) offer a promising tool to quantify these complex species distributions (Elith and Leathwick 2009). By modeling the relationship between species observations and environmental characteristics associated with those localities, ENMs generate a spatial prediction of environmental suitability that can be applied to estimate gene flow routes or barriers (Rissler and Apodaca 2007; McRae et al. 2008; Abellán et al. 2011; Chan et al. 2011). ENM predictions can also identify the environmental variables contributing most to a species’ distribution (Elith et al. 2010; Warren 2012) and, when integrated with population genetic data, might have the greatest impacts on gene flow.
A comprehensive understanding of B. bifarius population structure serves several purposes. First, this species is a potential candidate for development as a commercial pollinator in western NAm (Strange 2010), and the nature of genetic structure in B. bifarius, including the need for delimitation of species or other evolutionarily significant units, will provide data for assessing risks associated with domestic transport for pollination services (Goulson 2010; Duennes et al. 2012; Williams et al. 2012b). Second, other montane western NAm bumble bees have suffered serious declines in recent decades (Cameron et al. 2011). Earlier analyses for B. bifarius show that populations at higher elevations tend to show greater genetic isolation (Lozier et al. 2011), and although this species is currently common, dispersal could be challenged if pathways of suitable habitat are altered in the future (Wilson et al. 2005). By understanding how environmental variables, including climate, shapes dispersal routes in a species that remains abundant, we may gain insights into factors that could contribute to such declines in other species.
To more explicitly model the forces shaping gene flow in B. bifarius, we apply novel landscape genetic approaches to previously generated microsatellite data that focused on describing general population genetic patterns for B. bifarius and other NAm Bombus (Lozier et al. 2011). First we utilize natural history collection specimens to test whether previously suggested subspecific B. bifarius groups can be recovered with a quantitative analysis of color pattern variation. We then apply an existing genetic data set toward testing the hypothesis that environmental heterogeneity, rather than pure IBD or discrete barriers to gene flow, shapes patterns of genetic differentiation among populations of this polymorphic species. Our analysis provides the first attempt to link ENMs with population genetics in bumble bees, and offers a straightforward approach for studying gene flow and testing species-level taxonomic hypotheses in other montane Bombus.
Analysis of color pattern
To quantify pile color, we examined setae for 10 individuals from a set of western NAm populations representing regions included in genetic analyses (Fig. 1) as well as from a number of geographically intermediate populations from which we did not have sufficient genetic data (Supplementary Table 1). Pinned specimens reflecting the geographic range of our genetic analysis were selected from a recent study (Cameron et al. 2011). We scored ten body regions associated with setae color variability in bumble bees (Supplementary Fig. 1; from Williams 2007) and are similar to those historically used to qualitatively diagnose B. bifarius subspecies (Stephen 1957; Thorp et al. 1983).
Setae color values were captured with a digital photograph of the dorsal region of each specimen using a Keyence VHx-500f camera-mounted dissecting scope. Magnification was kept constant and photographs of each specimen were taken within 2 h of each other in the late evening to reduce variability from ambient sunlight. Setae colors were sampled from a single pixel in each body region using the RGB color model (red, green, blue), which were later transformed to a HSL color model (hue, saturation, lightness), which has proven valuable to studies of insect color evolution and mating systems (Davis et al. 2007). We excluded a small number of poor-quality specimens with matted setae that produced extreme outlier color values.
To determine our modeling approach, we visualized the distribution of the HSL color model data for each body region with histograms and box plots with R 2.15.1 (R Core Team 2012). Therefore, each body region was associated with three measurements of color properties for 30 data points per specimen. H was normally distributed, but S and L were positively skewed. Non-normality of the data was confirmed using a Generalized Shapiro–Wilk test (MVW = 0.9652, P = 0.0353) and we used a non-parametric K-means cluster analysis to identify distinct color phenotypes in two-dimensional space. To determine the optimal K for our analysis we calculated the within group sum of squares for all values between one and the maximum number of geographic regions (eight or eleven). We also conducted a Monte Carlo Cluster Analysis to determine the optimal K for the final K-means cluster analysis. HSL values were transformed from RGB values using the color library in Python 2.7.3 (http://www.python.org). All color pattern statistical analyses were executed in R with the car, psych, cluster, mclust, and adegenet libraries.
We take our molecular data from an earlier study of population structure in NAm Bombus (Lozier et al. 2011). Briefly, these data consist of coarsely sampled populations from throughout the B. bifarius range in the U.S.A., including sites in Alaska, California, Colorado, Idaho, Montana, Nevada, Oregon, Utah, Washington, and Wyoming. Specimens were genotyped for nine microsatellite markers (doi:10.5061/dryad.d403s) that showed no deviations from Hardy–Weinberg equilibrium, and full-sibs were excluded. Here we exclude sites with fewer than 10 sampled individuals to minimize noise from sampling error. We also excluded a single Alaska site because of the large distance from all other samples. The final data set consisted of 26 populations and 447 individuals (Fig. 1).
Environmental niche model (ENM)
Analysis of environmental variable importance for the Maxent environmental niche model used to estimate heterogeneity of habitat suitability for B. bifarius
Training gain with onlya
Training gain withoutb
Test gain with onlyc
Test gain withoutd
Max temperature of warmest month
Mean temperature of wettest quarter
Precipitation of driest month
Annual mean temperature
Precipitation of wettest month
Mean temperature of driest quarter
Min temperature of coldest month
Estimating resistance and distance among populations
We developed estimates of population connectivity by combining Maxent models of habitat suitability with the circuit theory approach implemented in Circuitscape V3.5 (Shah and McRae 2008). Circuitscape uses electrical circuit theory to generate IBR models of gene flow. The resistance distance between two points reflects the likelihood of potential gene flow, integrating over all possible alternative pathways of dispersal. When spatial heterogeneity in habitat quality is an important predictor of species dispersal, this approach should improve the relationship with genetic distance over IBD analyses (McRae and Beier 2007). We used the logistic output raster from Maxent as a conductance (the inverse of resistance) layer for Circuitscape, where areas of high predicted suitability values specify greater conductance, and thus the potential for greater gene flow; water was specified as “no data.” We ran Circuitscape in pairwise mode with the four-neighbor cell connection scheme calculated using average resistances. Because of the resolution of environmental data used for distribution models, the two coastal islands (San Juan Island and Orcas Island, WA) were connected to the mainland by 1–2 cell-wide corridors, but otherwise surrounded by completely unsuitable habitat that excluded gene flow. Given our interest in improving inferences about coarse regional-scale population structure, we considered such narrow corridors to be a sufficient representation of restricted over-water dispersal, rather than attempting to assign specific resistance values to water. As seen in results (below) this seems adequate at this regional scale.
To compare our IBR model to more standard IBD approaches, we estimated resistance distances using a flat two-dimensional landscape with the same dimensions as the Maxent logistic output raster but where all cells were given conductance values of 1.0. Results using this flat landscape scenario closely mirrored patterns from analysis of ln-transformed great circle distances (not shown), and because it shares spatial properties with the raster-based resistance estimates, we focus our results on the former for more straightforward comparison of IBD and IBR.
To estimate potential locations of discrete population structure in the data we applied the Bayesian clustering algorithm employed in Geneland 4.0.0 (Guillot 2005), implemented in R, which explicitly takes into account the spatial location of sampling sites and estimates the optimal number of population clusters. Site coordinates were provided for each individual, with a random adjustment of [−0.001 − 0.001] decimal degrees. We ran five replicates for two million iterations (thinning = 200) with the number of possible clusters K ranging from 2 to 5 and checked for consistency. We processed a final run on a landscape of 100 × 100 cells and with a burn-in of 2,000 iterations, fixing the maximum K to four, the value with the highest posterior density from all preliminary runs (e.g., Leaché 2011). Given the geographic patterns evident in both color pattern and Geneland clusters (see below), we treat these clusters as a proxy to test the alternate hypothesis of barriers to gene flow between B. bifarius subspecies, without proposing an explicit mechanism for these barriers.
Pairwise FST was estimated using Genepop 4.1.2 (Rousset 2008) and transformed to FST/(1-FST). Note that FST is highly correlated with other measures of genetic distance/differentiation (e.g. Jost 2008) in B. bifarius. We used the R package ecodist (Goslee and Urban 2007) to perform Mantel tests and partial Mantel tests comparing the relationship between each of the landscape distances (geographic = IBD and resistance = IBR) and the FST/(1-FST) matrix. To examine the potential effects of discrete population structure, we also performed Mantel tests coding population pairs as derived from the same (0) or different (1) genetic clusters as identified with Geneland and applying partial Mantel tests to partial out effects of IBD or IBR. Although Mantel tests have been criticized (Raufaste and Rousset 2001), several studies have demonstrated the continued utility of this approach for distinguishing IBD and IBR phenomena (Cushman and Landguth 2010; Legendre and Fortin 2010; Landguth et al. 2011). Models were compared by visual examination of distance scatter plots, Mantel r-values, and Bonferroni adjusted P-values for tests of positive correlation between matrices, estimated with 10,000 permutations of the response matrix in ecodist (Goslee and Urban 2007; Legendre and Fortin 2010).
The optimal cluster size for the second analysis including populations from geographically intermediate regions was estimated at K = 6 (Fig. 3b, Supplementary Table 1). Similar to the first analysis (Fig. 3a), three clusters representing black-banded B. bifarius nearcticus individuals overlap substantially. Adjacent to the black-banded clusters, three more clusters overlapped, representing red-banded B. bifariusbifarius, and also included the San Juan Islands population (‘vancouverensis’ variety), both of which have red abdominal setae. Even so, pigmentation of the San Juan Island population remains somewhat distinct (Fig. 3b). Overall, there is a greater degree of intermediate color variation than observed in Fig. 3a when intermediate populations were included, although the three typical forms are still the most extreme phenotypes, and the clouds representing red ‘bifarius’ and black ‘nearcticus’ forms remain largely distinguishable.
Ecological niche model
Average AUC for the cross validated B. bifarius distribution model was 0.820 (s.d. = 0.002) for training and 0.810 (s.d. = 0.017) for test points, and the overall pattern of high-suitability regions reflects previous coarse-grained distribution maps for this species (e.g. Thorp et al. 1983). The Maxent analysis revealed annual precipitation to be a strong limiting factor for B. bifarius, with this variable having the strongest influence on the final model as indicated by percent contribution (52.1 %), highest gain when used in isolation, and greatest decrease in gain when excluded (Table 1). Annual precipitation also overwhelmingly provided the greatest contribution to preliminary models run using all 19 BIOCLIM variables. Overall, the model predicts suitable habitat to be strongly associated with forested mountain ranges in the western US, with drier intervening basins and deserts constituting relatively low suitability habitat through which dispersal might be limited.
The clustering approach implemented in Geneland consistently detected four genetic clusters (Supplementary Fig. 2), versus the three most obvious clusters that were observed in earlier Structure analyses (Lozier et al. 2011). Results suggest an optimal partitioning of populations in southeastern, northern, southwestern, and northwestern populations (Fig. 1). If clustering is restricted to K = 3 in Geneland, however, results are comparable to Structure (Lozier et al. 2011) and the geographic distribution of three major color forms, with southeastern and northwestern sites separated from more central sites (diamond + triangle populations; Fig. 1). The new Geneland cluster results from the split of populations from California and Oregon, similar to the subclusters observed within the ‘nearcticus’ form with the color data. However, FST indicates that this cluster is very weakly differentiated from other central populations (Fig. 1), and we evaluate both the K = 3 and K = 4 models.
Results from Mantel and partial Mantel tests of effects of distance, resistance, or discrete population structure on genetic differentiation [FST/(1-FST)] among 26 B. bifarius populations
FST/(1–FST) ~ resistance
FST/(1–FST) ~ distance
FST/(1–FST) ~ resistance + distance
FST/(1–FST) ~ clusterK4
FST/(1–FST) ~ clusterK4 + distance
FST/(1–FST) ~ clusterK4 + resistance
FST/(1–FST) ~ clusterK3
FST/(1–FST) ~ clusterK3 + distance
FST/(1–FST) ~ clusterK3 + resistance
GESTE F-model analysis of resistance vs. distance for mainland populations, demonstrating the impact of landscape resistance on genetic structure over distance alone
Constant + distance-based connectivity
Constant + resistance-based connectivity
Constant + distance-based + resistance-based connectivity
Parameter (factor) of model 3
Posterior mode [95 % HPDI]
−4.26 [−4.70; −3.89]
α2 (resistance-based connectivity)
−0.642 [−1.08; −0.253]
0.384 [0.150; 0.962]
We also tested whether regional genetic clustering may reflect isolation of populations not explained by spatial distance or landscape resistance alone. We used partial Mantel tests to examine the relationship between genetic distance and Geneland cluster assignment for each population pair (same cluster = 0 vs. different cluster = 1) and partialling out resistance or spatial distances. We predict that if discrete gene flow barriers exist among clusters, then partial Mantel test r-values should remain significantly positive, whereas if distance/resistance accounts for most of the genetic structure, there will be no significant correlation. A simple Mantel test showed a highly significant effect of cluster membership alone for both K = 3 and 4 (r = 0.527 and 0.681, respectively). When IBD was taken into account, a significant relationship between genetic distance and clustering remained, albeit with reduced r-values (r = 0.385 and 0.581; P < 0.005). However, partialling out resistance distances substantially reduced the clustering effect (r = 0.156 and 0.225; P > 0.05 after Bonferroni correction). Together, these results suggest that limited, but not complete, isolation of populations associated with habitat heterogeneity offers a reasonable explanation for the B. bifarius data.
The use of range dimensions and spatial data on environmental heterogeneity to estimate gene flow corridors among populations can often improve inferences about population genetic processes (Cushman et al. 2006; McRae and Beier 2007; Goulson et al. 2010; Apodaca et al. 2012; Devitt et al. 2013). We show that ENMs and isolation by resistance analysis can explain much of the genetic differentiation among B. bifarius populations, providing additional insights over the use of geographic distance and clustering methods alone (Lozier et al. 2011).
In our experience, the majority of B. bifarius specimens from the field or natural history collections can be readily assigned subjectively to one of three morphological groups based on their geographic provenance (Fig. 1), including the two widely accepted subspecies B. bifarius bifarius (red-banded) and B. bifarius nearcticus (black-banded)(Thorp et al. 1983, Koch et al. 2012), and a third phenotype that likely reflects the vancouverensis form described by Cresson (1878) and Viereck et al. (1904) but later grouped with B. bifarius bifarius (Stephen 1957). Given these color phenotypes, and comparable patterns of regional genetic clustering (Fig. 1, 3 Supplementary Fig. 2; Lozier et al. 2011), we were thus interested in testing whether reproductive isolation could be responsible for structuring diversity in B. bifarius, in contrast to a null model of migration-drift equilibrium (McRae and Beier 2007; Frantz et al. 2009).
Color pattern morphometrics for populations used in genetic analyses initially revealed five distinct phenotypic clusters in B.bifarius, which were subsequently collapsed to three groups in two-dimensional space as three of the initial five were superimposed. These final three groups corresponded directly to the above morphological groups, with the nearcticus black-banded form comprising the three overlapping clusters, suggesting greater variation within this form. As suggested by this sub-clustering, the subsequent inclusion of geographically intermediate populations indicates greater complexity, with a number of intermediate pigment clusters obscuring the much simpler three color-group scenario. Such patterns support the correlation of genetic distance with resistance that suggests that gene flow in B. bifarius is largely shaped by range-wide variation in environmental suitability, with populations connected by narrow bands of suitable habitat showing larger effective separation than those in more homogenous regions (Fig. 4). Our results do not necessarily indicate that there have never been important periods of historical isolation that have shaped variation in B. bifarius, but the linear IBR relationship and diversity of intermediate coloration suggests that extant populations are governed to a large extent by equilibrium processes.
Equilibrium does not imply an absence of ecologically important limits to dispersal across the B. bifarius range, however. Incorporating the ENM-based B. bifarius distribution into genetic analyses demonstrates that both overly dry habitats and bodies of water are important for restricting gene flow. Linear features and climatic gradients often constitute dispersal barriers for terrestrial organisms (Storfer et al. 2010), but it does not appear that these factors completely impede gene flow, evidenced by the relatively low r-values for partial Mantel tests including both cluster membership and resistance. We suspect the clustering observed in Geneland and Structure can be largely attributed to the “clines versus cluster” dilemma (Frantz et al. 2009; Guillot et al. 2009), where populations along a spatial gradient can be assigned to unique genetic clusters in the absence of true barriers. The fairly weak clustering observed from structure (Lozier et al. 2011) suggested the potential for ongoing gene flow among geographic regions, but insufficient genetic data or incomplete lineage sorting might conceivably affect such analyses in large recently isolated populations (Latch et al. 2006; Orozco-terWengel et al. 2011). The present results provide further evidence that ongoing gene flow is an important part of the genetic structure of this species, and that species-level taxonomic revisions are likely unwarranted without additional evidence. Such ongoing gene flow may also explain the intermediate phenotypes falling between color pattern cluster ellipses (Fig. 3). In contrast, B. ephippiatus, another new world species that exhibits substantial color polymorphism, comprises a set of distinct lineages associated with both geographic isolation and color pattern (Duennes et al. 2012). This study adds to the mounting evidence for a complex relationship between population structure and color pattern variation, and suggests that insights into phylogeography and species delimitation of Bombus with respect to color pattern variability will benefit from in depth analyses on a case-by-case basis.
The lack of evidence for strongly isolated populations certainly raises questions regarding the maintenance of color polymorphisms in B. bifarius. The convergence of multiple co-distributed species on similar color patterns in western NAm (Stephen 1957; Thorp et al. 1983) strongly suggests an adaptive function, such as Müllerian mimicry or thermoregulation (Williams 2007). At this point it is difficult to address the specific adaptive mechanisms driving the spatial distribution of these polymorphisms in B. bifarius. Additional research should focus on the extent to which the distribution of color patterns reflects local adaptation versus primarily neutral processes. The study of the San Juan Island vancouverensis phenotype could be particularly interesting, as despite proximity to the black-banded nearcticus color form, these B. bifarius more closely resemble the eastern red-banded bifarius phenotype (Fig. 1, 3). Genome-wide analyses may enable detection of genetic variants associated more closely with color than with space, which might represent candidate genome regions underlying adaptive genetic structure.
The relatively homogenous ranges of many bumble bees may explain why, in contrast to B. bifarius, little genetic structure has been identified in several other NAm Bombus species (Lozier et al. 2011). For example, ENMs for three eastern species (B. impatiens, B. bimaculatus, and B. pensylvanicus) predict large continuous distributions with few environmental barriers to dispersal (Cameron et al. 2011). Isolation on islands appears important in eastern species, but there was no evidence for a relationship between geographic and genetic distances such as that observed for B. bifarius (Lozier et al. 2011). It may thus be the case that substantial habitat heterogeneity is required to produce detectable genetic structure in bumble bees collected at broad geographic scales (Wasserman et al. 2010). Similar patterns are apparent in Europe, where it is often necessary to examine populations isolated by islands or mountain ranges to identify strong intraspecific differentiation (Estoup et al. 1996; Widmer et al. 1998; Widmer and Schmid-Hempel 1999; Darvill et al. 2010; Goulson et al. 2011).
This is not to say that sampling at the appropriate scale and incorporating relevant environmental data will not prove useful for studying patterns of dispersal at finer scales in other bumble bees. Our study focused on broad scale effects of climate on gene flow in B. bifarius, but lacks the necessary resolution to identify how local landscape features may impact dispersal. More intensive local studies with appropriately scaled landscape data would be worthwhile (e.g. Murphy et al. 2010; Wasserman et al. 2010). For example, Goulson et al. (2011) applied Circuitscape models incorporating bathymetric data to infer patterns of isolation and drift associated with sea level changes in B. hortorum populations. Fine-scale analyses incorporating other land use variables in a causal-modeling framework (e.g. Cushman et al. 2006; Wasserman et al. 2010) may provide valuable data on how dispersal in bumble bees is affected by particular components of urban or agricultural landscape (Jha and Kremen 2013). Efforts with a focus on spatial relationships among individuals, rather than populations, may be particularly valuable (Knight et al. 2009; Goulson et al. 2010).
From a conservation perspective, our results also suggest that research into the effects of increased habitat isolation associated with climate change is warranted. Although B. bifarius is not currently in obvious decline (Cameron et al. 2011), montane species are predicted to become increasingly isolated with climate change as populations track suitable conditions upslope (Hill et al. 2010; McCain and Colwell 2011). Given that gene flow in B. bifarius can at least in part be predicted by the distribution of climatically suitable habitat, environmental change may result in increased genetic isolation (Wilson et al. 2005). In terms of environmental drivers of gene flow, it is interesting that precipitation consistently contributed most to the B. bifarius ENM as opposed to temperature, which was our a priori expectation for this primarily montane species. It may be that overly wet conditions limit opportunities for foraging (Williams 1986), or contribute to flooding of suitable nest sites for principally underground nesting species like B. bifarius (Sakagami 1976; Harder 1986; Williams and Osborne 2009). At the opposite extreme, limitations from overly dry conditions may reflect biotic interactions, as sufficient yearly precipitation, including snow, is likely to be crucial for providing floral resources required throughout the colony lifespan (e.g., Aldridge et al. 2011). Further research into the effects of variable precipitation on bee distributions is warranted, particularly in light of potential climatic changes that could increasingly disrupt the synchrony of plant-pollinator interactions.
Taking range dimensions and variation in environmental suitability into account is likely to improve inferences about population structure at the range-wide scale in bumble bees that occur in montane environments. B. bifarius exhibits striking color pattern differentiation and regional genetic clustering that has led to hypotheses about sub-specific or specific taxonomic status of populations. However, accounting for environmental suitability across the landscape suggests that ongoing gene flow along restricted spatial dimensions can explain observed genetic patterns. The ENM approach used here requires extensive spatially referenced observation data for the taxa of interest, but the increasing availability of digitized natural history collections should alleviate this limitation for many Bombus species (Koch and Strange 2009; Cameron et al. 2011; Bartomeus et al. 2011). Likewise, the increasing availability of high-resolution land use and environmental data will likely make studies of dispersal or gene flow possible at finer scales. Understanding how gene flow is limited by physical and environmental factors at both broad and fine spatial scales will be important for predicting how these important pollinators may respond to changing climate and land use practices in the coming century.
We thank the many curators and institutions listed in Cameron et al. (2011) for access to B. bifarius specimens used for our ENM. We thank J. Knoblett for assistance in generating molecular data, and S. Jha and reviewers for comments on an earlier version of this manuscript. The data for this research was supported in part by a grant from the United States Department of Agriculture (CSREES-NRI 2007-02274)