Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal

Abstract

Landscape resistance reflects how difficult it is for genes to move across an area with particular attributes (e.g. land cover, slope). An increasingly popular approach to estimate resistance uses Mantel and partial Mantel tests or causal modelling to relate observed genetic distances to effective distances under alternative sets of resistance parameters. Relatively few alternative sets of resistance parameters are tested, leading to relatively poor coverage of the parameter space. Although this approach does not explicitly model key stochastic processes of gene flow, including mating, dispersal, drift and inheritance, bias and precision of the resulting resistance parameters have not been assessed. We formally describe the most commonly used model as a set of equations and provide a formal approach for estimating resistance parameters. Our optimization finds the maximum Mantel r when an optimum exists and identifies the same resistance values as current approaches when the alternatives evaluated are near the optimum. Unfortunately, even where an optimum existed, estimates from the most commonly used model were imprecise and were typically much smaller than the simulated true resistance to dispersal. Causal modelling using Mantel significance tests also typically failed to support the true resistance to dispersal values. For a large range of scenarios, current approaches using a simple correlational model between genetic and effective distances do not yield accurate estimates of resistance to dispersal. We suggest that analysts consider the processes important to gene flow for their study species, model those processes explicitly and evaluate the quality of estimates resulting from their model.

Identification of landscape features influencing gene flow: How useful are habitat selection models?

“…; Legendre and Fortin ), specifically the reliance on potentially biased significance tests (Graves et al. ), partial Mantel tests are an appropriate tool for characterizing the data distribution and shape of the landscape genetics relationships (Legendre and Fortin ) and still offer valuable initial tests especially when complemented with other analyses (Cushman et al. ).…”

Optimizing the trade‐off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual‐based sampling scheme

“…; Graves et al . ), Mantel tests are suitable for IBD detection in the case of genetic mutation–migration–drift equilibrium (Guillot & Rousset ) and for IBB detection in the case of a total barrier to gene flow (Jaquiéry et al . ; Graves et al .…”

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).…”