Gene Flow in Complex Landscapes: Testing Multiple Hypotheses with Causal Modeling

Abstract

Predicting population-level effects of landscape change depends on identifying factors that influence population connectivity in complex landscapes. However, most putative movement corridors and barriers have not been based on empirical data. In this study, we identify factors that influence connectivity by comparing patterns of genetic similarity among 146 black bears (Ursus americanus), sampled across a 3,000-km(2) study area in northern Idaho, with 110 landscape-resistance hypotheses. Genetic similarities were based on the pairwise percentage dissimilarity among all individuals based on nine microsatellite loci (average expected heterozygosity=0.79). Landscape-resistance hypotheses describe a range of potential relationships between movement cost and land cover, slope, elevation, roads, Euclidean distance, and a putative movement barrier. These hypotheses were divided into seven organizational models in which the influences of barriers, distance, and landscape features were statistically separated using partial Mantel tests. Only one of the competing organizational models was fully supported: patterns of genetic structure are primarily related to landscape gradients of land cover and elevation. The alternative landscape models, isolation by barriers and isolation by distance, are not supported. In this black bear population, gene flow is facilitated by contiguous forest cover at middle elevations.

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

“…Although landscape genetics has certainly proven to be a robust technique for species with extensive field data (for example, Cushman et al, 2006;Shafer et al, 2012), application to poorly understood species can be difficult. Ideally, investigators should develop specific hypotheses about how landscape heterogeneity impacts gene flow in a focal species within the study area and parameterize resistance models accordingly (for example, Balkenhol et al, 2009;Anderson et al, 2010).…”

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

“…However, our results suggest roads do not unduly influence functional connectivity. Indeed, while previous landscape genetic studies have found roads as a limiting factor to gene flow in black bears, the relative influence of roads varies among populations (Cushman et al., 2006; Short Bull et al., 2011), and in some cases, roads may serve to facilitate rather than impede bear movement (Balkenhol & Waits, 2009). …”

“…The relative support for each model was based on the difference in the partial Mantel correlation after partialling out the resistance distance of the other model. The model with significant and positive relative support values for all comparisons was considered the best candidate model (Cushman et al., 2006, 2013). …”

“…These “snapshots” of data only measure populations in their current state (Anderson et al., 2010; Martensen, Saura, & Fortin, 2017; Schwartz, Luikart, & Waples, 2007). Therefore, inferences can be problematic when studying long‐lived and iteroparous species such as black bears ( Ursus americanus ) that are sensitive to landscape features (i.e., land cover) that are expected to undergo future modification (Cushman, McKelvey, Hayden, & Schwartz, 2006; Cushman, Wasserman, Landguth, & Shirk, 2013). Thus, incorporating a time series approach becomes necessary to make inferences about how a species responds to landscape or environmental change (Sun & Hedgecock, 2017).…”

Detecting black bear source–sink dynamics using individual-based genetic graphs

“…However, for species that are continuously rather than patchily distributed, use of genetic data to estimate gene flow has been hampered by an inability to delineate populations. Use of individualbased measures of pairwise genetic relatedness allows for investigations of fine-scale genetic structure and dispersal patterns of continuously distributed species without a priori delineation of populations [17,18]. Pairwise relatedness represents the degree of shared ancestry, or the probability that two alleles at a locus, one taken at random from each of two individuals, are identical by descent [19].…”