Abstract
Landscape genetic studies typically focus on the evolutionary processes that give rise to spatial patterns that are quantified at a single point in time. Although landscape change is widely recognized as a strong driver of microevolutionary processes, few landscape genetic studies have directly evaluated the change in spatial genetic structure (SGS) over time with concurrent changes in landscape pattern. We introduce a novel approach to analyze landscape genetic data through time. We demonstrate this approach using genotyped samples (n = 569) from a large black bear (Ursus americanus) population in Michigan (USA) that were harvested during 3 years (2002, 2006, and 2010). We identified areas that were consistently occupied over this 9‐year period and quantified temporal variation in SGS. Then, we evaluated alternative hypotheses about effects of changes in landscape features (e.g., deforestation or crop conversion) on fine‐scale SGS among years using spatial autoregressive modeling and model selection. Relative measures of landscape change such as magnitude of landscape change (i.e., number of patches changing from suitable to unsuitable states or vice versa), and during later periods, measures of fragmentation (i.e., patch aggregation and cohesion) were associated with change in SGS. Our results stress the importance of conducting time series studies for the conservation and management of wildlife inhabiting rapidly changing landscapes.
Highlights
Understanding how landscape alteration will influence species distributions and connectivity is the cornerstone to development of successful conservation, restoration, and management strategies (Bolliger, Le Lay, & Holderegger, 2010; Fahrig, 1997)
Using a times series approach in a landscape genetic framework, we (i) develop a set of landscape resistance models that incorporate a suite of resistance surfaces representing alternative hypotheses concerning the associations between interindividual genetic distance and landscape resistance distance; (ii) identify and compare the best performing landscape resistance models among years; (iii) evaluate whether local spatial genetic structure (SGS) patterns changed over time; and (iv) quantify landscape change and use spatial autoregressive model selection to test for associations between changes in SGS and landscape change
Our study illustrates the advantages of joint use of time series genetic and landscape data when using established
Summary
Understanding how landscape alteration will influence species distributions and connectivity is the cornerstone to development of successful conservation, restoration, and management strategies (Bolliger, Le Lay, & Holderegger, 2010; Fahrig, 1997). Assessments of changes in genetic diversity provide a means to evaluate trends in connectivity, to infer demographic histories of populations, and to gauge loss of genetic diversity (Schwartz et al, 2007) Implementing such studies is challenging because sampling the same population(s) at regular intervals is difficult despite the potential value for conservation and management. Such studies are limited in part by the rarity of multiple samples of the same population from different time points, but are further impeded by the necessity of obtaining complementary time series landscape data Despite these challenges, time series landscape genetic studies are valuable as natural ecosystems are spatially heterogeneous and landscape composition/configurations can change over time, sometimes drastically so, due to natural and anthropogenic factors (Bolliger et al, 2010; Spear et al, 2010). Using a times series approach in a landscape genetic framework, we (i) develop a set of landscape resistance models that incorporate a suite of resistance surfaces representing alternative hypotheses concerning the associations between interindividual genetic distance and landscape resistance distance; (ii) identify and compare the best performing landscape resistance models among years; (iii) evaluate whether local SGS patterns changed over time; and (iv) quantify landscape change and use spatial autoregressive model selection to test for associations between changes in SGS and landscape change
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