Abstract
Isolation by distance (IBD) is a natural pattern not readily incorporated into theoretical models nor traditional metrics for differentiating populations, although clinal genetic differentiation can be characteristic of many wildlife species. Landscape features can also drive population structure additive to baseline IBD resulting in differentiation through isolation‐by‐resistance (IBR). We assessed the population genetic structure of boreal caribou across western Canada using nonspatial (STRUCTURE) and spatial (MEMGENE) clustering methods and investigated the relative contribution of IBD and IBR on genetic variation of 1,221 boreal caribou multilocus genotypes across western Canada. We further introduced a novel approach to compare the partitioning of individuals into management units (MU) and assessed levels of genetic connectivity under different MU scenarios. STRUCTURE delineated five genetic clusters while MEMGENE identified finer‐scale differentiation across the study area. IBD was significant and did not differ for males and females both across and among detected genetic clusters. MEMGENE landscape analysis further quantified the proportion of genetic variation contributed by IBD and IBR patterns, allowing for the relative importance of spatial drivers, including roads, water bodies, and wildfires, to be assessed and incorporated into the characterization of population structure for the delineation of MUs. Local population units, as currently delineated in the boreal caribou recovery strategy, do not capture the genetic variation and connectivity of the ecotype across the study area. Here, we provide the tools to assess fine‐scale spatial patterns of genetic variation, partition drivers of genetic variation, and evaluate the best management options for maintaining genetic connectivity. Our approach is highly relevant to vagile wildlife species that are of management and conservation concern and demonstrate varying degrees of IBD and IBR with clinal spatial genetic structure that challenges the delineation of discrete population boundaries.
Highlights
Genetic approaches are increasingly being applied to delineate boundaries around demographically divergent groups of individuals, often termed populations (Waples & Gaggiotti, 2006) or management units (Palsbøll, Berube, & Allendorf, 2007; Yannic et al, 2016; Zannèse et al, 2006)
To partition drivers of spatial genetic variation, we introduce an extension of MEMGENE analysis that allows comparison between the proportion of genetic variation attributed to Euclidean distance between samples (IBD) and the proportion attributed to the landscape (IBR) (Galpern & Peres‐Neto, 2014)
We showed that Isolation by distance (IBD) played a dominant role in shaping spatial patterns of genetic variation across the landscape
Summary
Genetic approaches are increasingly being applied to delineate boundaries around demographically divergent groups of individuals, often termed populations (Waples & Gaggiotti, 2006) or management units (Palsbøll, Berube, & Allendorf, 2007; Yannic et al, 2016; Zannèse et al, 2006). Due to different levels of anthropogenic disturbance and general landscape heterogeneity across the study area, we tested landscape effects on genetic variation separately for each first‐order cluster identified by STRUCTURE and MEMGENE. Isolation by distance was significant across the full study area and all first‐ and second‐order clusters (p < 0.005; Table 3), providing additional evidence that spatial proximity among individuals explains some of the genetic variation within clusters. The MU scenarios explaining the highest proportion of spatial genetic patterns across the study area included both Scenario 1 (reflecting second‐order STRUCTURE results) and Scenario 2 (reflecting second‐ order MEMGENE results), followed closely by Scenario 4 (combining local populations and observed genetic structure) (Table 5 [a]; Figure 5), Scenario 3 (reflecting local populations) best reflected the spatial proximity of individuals only (Table 5 [b]; Figure 5). MU boundaries and spatial proximity of individuals was Scenario 1, followed by Scenario 2, Scenario 4, and lastly Scenario 3 (Table 5 [c]; Figure 5)
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