Inferring landscape effects on gene flow: a new model selection framework

Abstract

Populations in fragmented landscapes experience reduced gene flow, lose genetic diversity over time and ultimately face greater extinction risk. Improving connectivity in fragmented landscapes is now a major focus of conservation biology. Designing effective wildlife corridors for this purpose, however, requires an accurate understanding of how landscapes shape gene flow. The preponderance of landscape resistance models generated to date, however, is subjectively parameterized based on expert opinion or proxy measures of gene flow. While the relatively few studies that use genetic data are more rigorous, frameworks they employ frequently yield models only weakly related to the observed patterns of genetic isolation. Here, we describe a new framework that uses expert opinion as a starting point. By systematically varying each model parameter, we sought to either validate the assumptions of expert opinion, or identify a peak of support for a new model more highly related to genetic isolation. This approach also accounts for interactions between variables, allows for nonlinear responses and excludes variables that reduce model performance. We demonstrate its utility on a population of mountain goats inhabiting a fragmented landscape in the Cascade Range, Washington.

Estimating effective landscape distances and movement corridors: comparison of habitat and genetic data

“…As shown in this study, connectivity models and the corridors suggested by them depend strongly on the analytical methods used for creating and utilizing underlying resistance surfaces. Several published studies have suggested ways for finding optimal resistance values (e.g., Kuroe et al 2011, Shirk et al 2010, Graves et al 2014, and these studies have certainly improved resistancebased connectivity modeling. However, the effects of different conceptual approaches underlying resistance models (habitat suitability vs. resistance to inter-population movement) or the analytical choices made during their creation and analysis (additive vs. multiplicative models, least-cost vs. circuit-theory) appear to be at least as important as the numerical optimization of resistance values.…”

“…Specifically, we used genetic samples of brown bears genotyped at 17 polymorphic microsatellite loci to quantify genetic structure and measured the genetic distance among samples as the proportion of shared alleles (Bowcock et al 1994). The relationship between the genetic structure observed within the bear population and likely drivers of landscape resistance was systematically evaluated through v www.esajournals.org reciprocal causal modeling (Cushman et al 2006(Cushman et al , 2013b and the multi-model optimization approach developed by Shirk et al (2010). The resulting resistance models included variables of landscape composition (percentage of landscape cover by mixed forest and agricultural lands), landscape configuration (cohesion of mixed forest and shrubland) and canopy cover.…”

“…Resistance values have usually been determined by assigning resistance scored on an arbitrary scale (Beier et al 2008, Pereira et al 2011) on the basis of expert opinion itself or in combination with literature review (Larkin et al 2004, Kautz et al 2006. However, such approaches have frequently been shown to perform poorly (Beier et al 2008, Shirk et al 2010. To address these limitations, some authors have suggested that habitat suitability models predicting species occurrence on the basis of empirical data may provide a better estimation of resistance (Ferreras 2001, Chetkiewicz et al 2006, O'Brien et al 2006, Beier et al 2008).…”

Can Orchards Help Connect Mediterranean Ecosystems? Animal Movement Data Alter Conservation Priorities

“…This high resistance value is used because, given roads are narrow (only 2-3 pixels wide in most cases), animals will encounter roads only for a short distance. Moreover, empirical studies (e.g., Shirk et al 2010 for mountain goats) have found similarly high relative resistances for roads, and although responses to roads will be species-specific, the logic of using values which are orders of magnitude higher than other resistance values is consistent. Road resistance values of similar magnitude were used for mammalian connectivity modeling by the Washington Habitat Connectivity Working Group (WHCWG 2010).…”

Modelling Marten (Martes americana) Movement Costs in a Boreal Forest: Effects of Grain Size and Thematic Resolution

“…To circumvent this problem, methods are needed to provide truly empirical movement resistance estimates. Recent studies have attempted to estimate resistance by comparing model fit to real data [17][18][19]. Recently, Driezen et al [20] presented a method of validating the best least-cost path tested model with real dispersal paths, and reiterated the necessity to work with habitat specialist species to estimate cost values throughout the landscape.…”