Molecular Ecology (2010) 19, 3603–3619 doi: 10.1111/j.1365-294X.2010.04745.x
Inferring landscape effects on gene flow: a new model selection framework
A. J. SHIRK,* D. O. WALLIN,* S. A. CUSHMAN,† C. G. RICE‡ and K. I. WARHEIT‡ *Huxley College of the Environment, Western Washington University, Bellingham, WA 98225, USA, †Rocky Mountain Research Station, United States Forest Service, Missoula, MT 59808, USA, ‡Washington Department of Fish and Wildlife, Olympia, WA 98501, USA
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.
Keywords: circuit theory, gene flow, isolation by distance, landscape resistance, mountain goat Received 16 December 2009; revision received 19 May 2010; accepted 19 May 2010
explicit understanding of how landscapes influence Introduction gene flow. Habitat fragmentation can limit the spread of genetic Despite the importance of genetic connectivity to variation across a landscape (gene flow) and reduce the the viability of fragmented populations, few studies local effective population size (Keyghobadi 2007). Small employ appropriate empirical data to derive landscape populations isolated from gene flow lose genetic diver- resistance models. Beier et al. (2008) recently cited 24 sity over time and are therefore susceptible to extinction studies that produced maps of corridors, linkages or by genetic processes, including inbreeding depression cost surfaces to guide connectivity conservation deci- (Crnokrak & Roff 1999), random fixation of deleterious sions. Among these, 15 were based on expert opinion alleles (Lynch et al. 1995; Lande 1998) and loss of adap- and literature review alone. The lack of empirical data tive potential (Lande 1995; Swindell & Bouzat 2005). to inform these models makes them highly subjective, Improving connectivity in fragmented landscapes, and given that gene flow is difficult to intuit quantita- therefore the probability of population persistence, is tively, regardless of the modeller’s knowledge of the now a major conservation priority (Crooks & Sanjayan species (Wang et al. 2008). While expert knowledge is 2006). Designing effective reserve systems and wildlife important for identifying landscape variables that may corridors for this purpose, however, requires a spatially shape gene flow and formulating initial hypotheses, unvalidated models parameterized entirely by experts Correspondence: Andrew J. Shirk, Tel: 360 753 7694; have the potential to misinform connectivity assess- E-mail: [email protected] ments.