ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms

Abstract

O_LIUnderstanding how landscape features affect functional connectivity among populations is a cornerstone of landscape genetic analyses. However, parameterization of resistance surfaces that best describe connectivity is largely a subjective process that explores a limited parameter space.\nC_LIO_LIResistanceGA is a new R package that utilizes a genetic algorithm to optimize resistance surfaces based on pairwise genetic distances and either CIRCUITSCAPE resistance distances or cost distances calculated along least cost paths. Functions in this package allow for the optimization of both categorical and continuous resistance surfaces, as well as the simultaneous optimization of multiple resistance surfaces.\nC_LIO_LIThere is considerable controversy concerning the use of Mantel tests to accurately relate pairwise genetic distances with resistance distances. Optimization in uses linear mixed effects models with the maximum likelihood population effects parameterization to determine AICc, which is the fitness function for the genetic algorithm.\nC_LIO_LIResistanceGA fills a void in the landscape genetic toolbox, allowing for unbiased optimization of resistance surfaces and for the simultaneous optimization of multiple resistance surfaces to create a novel composite resistance surface.\nC_LI