Influence of landscape heterogeneity on the functional connectivity of Allegheny woodrats (<Emphasis Type="Italic">Neotoma magister</Emphasis>) in Virginia

Allegheny woodrats (Neotoma magister) exist as groups of metapopulations due to their dependence on naturally disjunct rocky outcrops in the eastern United States. Severe demographic declines of Allegheny woodrats have occurred in many parts of the range due to a myriad of interacting processes, therefore identifying factors that help maintain the integrity of metapopulations is needed to guide conservation efforts. One factor considered critical to metapopulation persistence is maintaining functional connectivity. Therefore, our objective was to identify landscape factors that influence gene flow in Allegheny woodrats. We sampled 159 individuals from throughout the distribution in Virginia, genotyped them at 22 microsatellite loci, and modeled effects of land cover, roads, large rivers, and elevation on gene flow. The model that parameterized areas?≤?750 m in elevation as a barrier to gene flow performed best among the tested models, which indicates that functional connectivity is highest in high elevation habitats. We found little evidence for anthropogenic factors influencing gene flow, but continued study across extant metapopulations is needed in more peripheral areas to evaluate if anthropogenic barriers impact functional connectivity. Overall, our study emphasizes the importance of considering elevation for maintaining functional connectivity of Allegheny woodrats in the face of ongoing demographic declines in the eastern United States.     

Performance of partial statistics in individual‐based landscape genetics

Individual‐based landscape genetic methods have become increasingly popular for quantifying fine‐scale landscape influences on gene flow. One complication for individual‐based methods is that gene flow and landscape variables are often correlated with geography. Partial statistics, particularly Mantel tests, are often employed to control for these inherent correlations by removing the effects of geography while simultaneously correlating measures of genetic differentiation and landscape variables of interest. Concerns about the reliability of Mantel tests prompted this study, in which we use simulated landscapes to evaluate the performance of partial Mantel tests and two ordination methods, distance‐based redundancy analysis (dbRDA) and redundancy analysis (RDA), for detecting isolation by distance (IBD) and isolation by landscape resistance (IBR). Specifically, we described the effects of suitable habitat amount, fragmentation and resistance strength on metrics of accuracy (frequency of correct results, type I/II errors and strength of IBR according to underlying landscape and resistance strength) for each test using realistic individual‐based gene flow simulations. Mantel tests were very effective for detecting IBD, but exhibited higher error rates when detecting IBR. Ordination methods were overall more accurate in detecting IBR, but had high type I errors compared to partial Mantel tests. Thus, no one test outperformed another completely. A combination of statistical tests, for example partial Mantel tests to detect IBD paired with appropriate ordination techniques for IBR detection, provides the best characterization of fine‐scale landscape genetic structure. Realistic simulations of empirical data sets will further increase power to distinguish among putative mechanisms of differentiation.     

Evaluating the influence of life‐history characteristics on genetic structure: a comparison of small mammals inhabiting complex agricultural landscapes

Conversion of formerly continuous native habitats into highly fragmented landscapes can lead to numerous negative demographic and genetic impacts on native taxa that ultimately reduce population viability. In response to concerns over biodiversity loss, numerous investigators have proposed that traits such as body size and ecological specialization influence the sensitivity of species to habitat fragmentation. In this study, we examined how differences in body size and ecological specialization of two rodents (eastern chipmunk; Tamias striatus and white‐footed mouse; Peromyscus leucopus) impact their genetic connectivity within the highly fragmented landscape of the Upper Wabash River Basin (UWB), Indiana, and evaluated whether landscape configuration and complexity influenced patterns of genetic structure similarly between these two species. The more specialized chipmunk exhibited dramatically more genetic structure across the UWB than white‐footed mice, with genetic differentiation being correlated with geographic distance, configuration of intervening habitats, and complexity of forested habitats within sampling sites. In contrast, the generalist white‐footed mouse resembled a panmictic population across the UWB, and no landscape factors were found to influence gene flow. Despite the extensive previous work in abundance and occupancy within the UWB, no landscape factor that influenced occupancy or abundance was correlated with genetic differentiation in either species. The difference in predictors of occupancy, abundance, and gene flow suggests that species‐specific responses to fragmentation are scale dependent.     

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

Human-altered environments often challenge native species with a complex spatial distribution of resources. Hostile landscape features can inhibit animal movement (i.e., genetic exchange), while other landscape attributes facilitate gene flow. The genetic attributes of organisms inhabiting such complex environments can reveal the legacy of their movements through the landscape. Thus, by evaluating landscape attributes within the context of genetic connectivity of organisms within the landscape, we can elucidate how a species has coped with the enhanced complexity of human altered environments. In this research, we utilized genetic data from eastern chipmunks (Tamias striatus) in conjunction with spatially explicit habitat attribute data to evaluate the realized permeability of various landscape elements in a fragmented agricultural ecosystem. To accomplish this we 1) used logistic regression to evaluate whether land cover attributes were most often associated with the matrix between or habitat within genetically identified populations across the landscape, and 2) utilized spatially explicit habitat attribute data to predict genetically-derived Bayesian probabilities of population membership of individual chipmunks in an agricultural ecosystem. Consistency between the results of the two approaches with regard to facilitators and inhibitors of gene flow in the landscape indicate that this is a promising new way to utilize both landscape and genetic data to gain a deeper understanding of human-altered ecosystems.     

Hybrid swarm between divergent lineages of mule deer (Odocoileus hemionus)

Studies of hybrid zones have revealed an array of evolutionary outcomes, yet the underlying structure is typically characterized as one of three types: a hybrid zone, a hybrid swarm or a hybrid taxon. Our primary objective was to determine which of these three structures best characterizes a zone of hybridization between two divergent lineages of mule deer (Odocoileus hemionus), mule deer and black-tailed deer. These lineages are morphologically, ecologically and genetically distinct, yet hybridize readily along a zone of secondary contact between the east and west slopes of the Cascade Mountains (Washington and Oregon, USA). Using microsatellite and mitochondrial DNA, we found clear evidence for extensive hybridization and introgression between lineages, with varying degrees of admixture across the zone of contact. The pattern of hybridization in this region closely resembles a hybrid swarm; based on data from 10 microsatellite loci, we detected hybrids that extend well beyond the F1 generation, did not detect linkage disequilibrium at the centre of the zone and found that genotypes were associated randomly within the zone of contact. Introgression was characterized as bidirectional and symmetric, which is surprising given that the zone of contact occurs along a sharp ecotone and that lineages are characterized by large differences in body size (a key component of mating success). Regardless of the underlying mechanisms promoting hybrid swarm maintenance, it is clear that the persistence of a hybrid swarm presents unique challenges for management in this region.     

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

Landscape genetics is a powerful tool for conservation because it identifies landscape features that are important for maintaining genetic connectivity between populations within heterogeneous landscapes. However, using landscape genetics in poorly understood species presents a number of challenges, namely, limited life history information for the focal population and spatially biased sampling. Both obstacles can reduce power in statistics, particularly in individual-based studies. In this study, we genotyped 233 American badgers in Wisconsin at 12 microsatellite loci to identify alternative statistical approaches that can be applied to poorly understood species in an individual-based framework. Badgers are protected in Wisconsin owing to an overall lack in life history information, so our study utilized partial redundancy analysis (RDA) and spatially lagged regressions to quantify how three landscape factors (Wisconsin River, Ecoregions and land cover) impacted gene flow. We also performed simulations to quantify errors created by spatially biased sampling. Statistical analyses first found that geographic distance was an important influence on gene flow, mainly driven by fine-scale positive spatial autocorrelations. After controlling for geographic distance, both RDA and regressions found that Wisconsin River and Agriculture were correlated with genetic differentiation. However, only Agriculture had an acceptable type I error rate (3–5%) to be considered biologically relevant. Collectively, this study highlights the benefits of combining robust statistics and error assessment via simulations and provides a method for hypothesis testing in individual-based landscape genetics.     

High gene flow in the American badger overrides habitat preferences and limits broadscale genetic structure

Habitat associations are a function of habitat preferences and dispersal capabilities, both of which can influence how species responded to Quaternary climatic changes and contemporary habitat heterogeneity. Predicting resultant genetic structure is not always straightforward, especially in species where high dispersal potential and habitat preferences yield opposing predictions. The American badger has high dispersal capabilities that predict widespread panmixia, but avoids closed‐canopy forests and clay soils, which could restrict gene flow and create ecologically based population genetic structure. We used mitochondrial sequence and microsatellite data sets to characterize how these opposing forces contribute to genetic structure in badgers at a continent‐wide scale. Our data revealed an overall lack of ecologically based population genetic structure, suggesting that high dispersal capabilities were sufficiently realized to overcome most habitat‐based genetic structure. At a broadscale, badger gene flow is limited only by geographic distance (isolation by distance) and large water barriers (Lake Michigan and the Mississippi River). The absence of genetic structure in a species with strong avoidance of unsuitable habitats advances our understanding of when and how genetic structure emerges in widespread, highly mobile species.     

Landscape genetics of an endangered salt marsh endemic: Identifying population continuity and barriers to dispersal

Preserving the genetic diversity of endangered species is fundamental to their conservation and requires an understanding of genetic structure. In turn, identification of landscape features that impede gene flow can facilitate management to mitigate such obstacles and help with identifying isolated populations. We conducted a landscape genetic study of the endangered salt marsh harvest mouse (Reithrodontomys raviventris), a species endemic to the coastal marshes of the San Francisco Estuary of California. We collected and genotyped?>?500 samples from across the marshes of Suisun Bay which contain the largest remaining tracts of habitat for the species. Cluster analyses and a population tree identified three geographically discrete populations. Next, we conducted landscape genetic analyses at two scales (the entire study area and across the Northern Marshes) where we tested 65 univariate models of landscape features and used the best supported to test multivariable analyses. Our analysis of the entire study area indicated that open water and elevation (>?2 m) constrained gene flow. Analysis of the Northern Marshes, where low elevation marsh habitat is more continuous, indicated that geographic distance was the only significant predictor of genetic distance at this scale. The identification of a large, connected population across Northern Marshes achieves a number of recovery targets for this stronghold of the species. The identification of landscape features that act as barriers to dispersal enables the identification of isolated and vulnerable populations more broadly across the species range, thus aiding conservation prioritization.