One metric or many? Refining the analytical framework of landscape resistance estimation in individual-based...

One of the allures of landscape genetics is the ability to leverage pairwise genetic distance metrics to infer how landscape features promote or constrain gene flow (i.e. landscape resistance surfaces). Critically, properly parameterized landscape resistance surfaces are foundational to applied conservation and management decisions. As such, there has been considerable effort expended assessing methods and metrics to estimate landscape resistance from genetic data (Balkenhol et al., Ecography, 32, 2009, 818; Peterman et al., Landsc. Ecol., 34, 2019, 2197; Shirk et al., Mol. Ecol. Resour., 17, 2017, 1308; Shirk et al., Mol. Ecol. Resour., 18, 2018, 55). Nonetheless, a primary challenge to assessing the effects of landscapes on gene flow is in the estimation of landscape resistance values, and this problem becomes increasingly challenging as more landscape features or land cover classes are considered. It quickly becomes infeasible to adequately assess the potential parameter space through manual or systematic assignment of resistance values. The development of ResistanceGA (Peterman, Methods Ecol. Evol., 9, 2018, 1638) provided a framework for using genetic algorithms to optimize landscape resistance values and identify the best statistical relationship between pairwise effective distances and genetic distances. ResistanceGA has seen extensive use in both population- and individual-based landscape genetic analyses. However, there has been relatively limited assessment of ResistanceGA's ability to identify the landscape features affecting gene flow (but see Peterman et al., Landsc. Ecol., 34, 2019, 2197; Winiarski et al., Mol. Ecol. Resour., 20, 2020, 1583) or the sensitivity of ResistanceGA results to the choice of genetic distance metric used. In the current issue of Molecular Ecology Resources, Beninde et al. (2023) aim to address these knowledge gaps by examining the impact of individual-based genetic distance measures on landscape genetic inference.