title: sGD: software for estimating spatially explicit indices of genetic diversity.
abstract: Anthropogenic landscape changes have greatly reduced the population size, range and migration rates of many terrestrial species. The small local effective population size of remnant populations favours loss of genetic diversity leading to reduced fitness and adaptive potential, and thus ultimately greater extinction risk. Accurately quantifying genetic diversity is therefore crucial to assessing the viability of small populations. Diversity indices are typically calculated from the multilocus genotypes of all individuals sampled within discretely defined habitat patches or larger regional extents. Importantly, discrete population approaches do not capture the clinal nature of populations genetically isolated by distance or landscape resistance. Here, we introduce spatial Genetic Diversity (sGD), a new spatially explicit tool to estimate genetic diversity based on grouping individuals into potentially overlapping genetic neighbourhoods that match the population structure, whether discrete or clinal. We compared the estimates and patterns of genetic diversity using patch or regional sampling and sGD on both simulated and empirical populations. When the population did not meet the assumptions of an island model, we found that patch and regional sampling generally overestimated local heterozygosity, inbreeding and allelic diversity. Moreover, sGD revealed fine-scale spatial heterogeneity in genetic diversity that was not evident with patch or regional sampling. These advantages should provide a more robust means to evaluate the potential for genetic factors to influence the viability of clinal populations and guide appropriate conservation plans.
authors.affiliation: Climate Impacts Group, Joint Institute for the Study of the Atmosphere (JISAO), University of Washington, Box 355672, Seattle, Washington 98195-5762, USA. ashirk@u.washington.edu
authors.forename: A J
authors.initials: AJ
authors.lastname: Shirk
authors.forename: S A
authors.initials: SA
authors.lastname: Cushman
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003247:Conservation of Natural Resources / Q000379:methods; D003710:Demography; D014644:Genetic Variation; D005828:Genetics, Population; D012418:Ruminants / Q000235:genetics*; D012984:Software; D014861:Washington