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A comparison of popular approaches to optimize landscape resistance surfaces
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A comparison of popular approaches to optimize landscape resistance surfaces
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Journal Article
A comparison of popular approaches to optimize landscape resistance surfaces
2019
Overview
ContextLandscape resistance surfaces are often used to address questions related to movement, dispersal, or population connectivity. However, modeling landscape resistance is complicated by the selection of the most appropriate analytical approach and the assignment of resistance values to landscape features.ObjectivesWe compare three common approaches used in landscape genetics to assign resistance values to landscape features and assess the ability of each approach to correctly identify the data generating resistance surfaces from competing resistance surfaces, as well as the accuracy of each method in recreating the true resistance surface.MethodsUsing simulated genetic data and landscape resistance surfaces, three optimization approaches were evaluated: constrained optimization using reciprocal causal modeling (RCM-CO), constrained optimization using linear mixed effects (MLPE-CO) models, and true optimization using ResistanceGA, which combines MLPE models with a genetic algorithm.ResultsAll methods had low type I error (20% or less) when the simulated surface was continuous, but only MLPE-CO and ResistanceGA had low type I error (10% or less) when the simulated surface was categorical. Error was substantially lower with ResistanceGA than MLPE-CO or RCM-CO for multivariate surfaces. Correlation between true and optimized resistance surfaces was generally high with MLPE-CO and ResistanceGA, but low with RCM-CO.ConclusionsMLPE-based approaches (ResistanceGA and MLPE-CO) were superior to RCM-CO, highlighting their value for landscape genetic analyses. The overall performance, objectivity, and accessibility of ResistanceGA underscore its value as a tool for inferring resistance values from genetic data to better understand how landscapes affect dispersal, movement and population connectivity.
Publisher
Springer Nature B.V
Subject
