Utility of computer simulations in landscape genetics

Abstract

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space-time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio-temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial-temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus-specific spatial patterns due to micro-scale environmental selection.

Understanding and monitoring the consequences of human impacts on intraspecific variation

“…Fortunately, spatial simulation approaches in landscape genetics can help to cope with heterogeneity and incompleteness and can identify factors affecting genetic variation (Epperson et al., 2010; Landguth, Cushman, & Balkenhol, 2015). For instance, simulations have successfully identified agents that restricted gene flow in heterogeneous environments for American pikas, Ochotona princeps (Castillo, Epps, Davis, & Cushman, 2014), and American marten, Martes americana (Wasserman, Cushman, Schwartz, & Wallin, 2010).…”

Isolation-by-distance in landscapes: considerations for landscape genetics

“…The latter is regarded as the most simple landscape genetic pattern that would be obtained even if there were no landscape effects and migration was thus only constrained by distance between demes (Spear et al, 2005;Balkenhol et al, 2009;Jenkins et al, 2010). This notion may have originated from spatially explicit simulation studies of IBD patterns, in which demes or individuals are usually placed in regular lattices throughout homogeneous spaces (Guillot et al, 2009;Epperson et al, 2010). Indeed, distance-constrained migration in such models produces IBD patterns that are not influenced by any landscape elements.…”

“…Ever since Wright (1943) described isolation-by-distance (IBD), patterns of spatial genetic structure have been extensively studied in population genetic simulation models (Epperson, 2003;Epperson et al, 2010) and in natural populations (Crispo and Hendry, 2005;Jenkins et al, 2010;Storfer et al, 2010). In most of these studies, migration probability is a function of geographic straight-line distance.…”

“…In the present study, we therefore assess to what degree habitat configuration influences equilibrium and non-equilibrium IBD patterns. We abandon the regular lattice setup of demes or individuals, as commonly used in population genetic simulation studies (Doligez et al, 1998;Epperson et al, 2010), and instead use irregular habitat configurations and deme topologies, which better reflect the natural study systems typically used in landscape genetics. Hutchison and Templeton (1999) described hypothetical and empirical IBD patterns and showed that an F ST -distance relationships are not always of 'case-I' type, which is characterized by monotonically increasing pairwise F ST values with increasing interdeme distance due to an equilibrium of gene flow and random genetic drift ( Figure 1; Hutchison and Templeton, 1999).…”

Fine-scale landscape genetics of the American badger (Taxidea taxus): disentangling landscape effects and sampling artifacts in a poorly understood species

“…Multiple statistics in landscape genetics already have elevated type I error rates (that is, false significance; Balkenhol et al, 2009;Graves et al, 2013;Guillot and Rousset, 2013), and with spatially biased sampling further creating non-random genetic variation Oyler-McCance et al, 2013), authors need a method to prevent erroneous conclusions about gene flow. Gene flow simulations provide a means of replication within a single landscape and control over processes that result in observed genetic variation (for example, Epperson et al, 2010;Landguth et al, 2010) and thus can quantify how often statistics falsely identify focal landscape factors as significant (that is, type I error rates for each landscape factor). By quantifying type I errors using gene flow simulations, investigators can better understand how landscape heterogeneity impacts gene flow in poorly understood species and separate those effects from sampling artifacts.…”