Movement and dispersal are essential to the long term persistence and viability of populations, but habitat loss and fragmentation threaten these processes and present challenges to the management of species on the landscape. Further, movement is often difficult or impossible to directly observe, necessitating indirect measures, such as genetics, to infer successful movement of individuals across the landscape. Although measures of gene flow can provide information about the connectivity or isolation of populations, understanding how habitat and landscape features affect movement is essential for managing spatial population dynamics. Landscape genetics has emerged as a field especially suited to questions related to spatial population genetic processes. From its inception, an allure of landscape genetics was the potential to use spatial genetic data to determine how landscape features affect gene flow. However, it has remained exceedingly challenging for researchers to objectively determine how landscape features affect movement and gene flow across landscapes. To address this issue, the R package called ResistanceGA (Resistance optimization using Genetic Algorithms) has been developed, which provides a framework for conducting unbiased analyses of landscape surfaces to determine their effect on gene flow.
Results/Conclusions
Through the use of genetic algorithms, ResistanceGA is able to optimize the resistance values of categorical (e.g., land cover, roads) and continuous (e.g., temperature, slope) resistance surfaces, as well as multiple surfaces simultaneously. Simulations revealed that accuracy increases with sample size, and accuracy decreases as variance in pairwise genetic data increases. In general, ResistanceGA is capable of correctly identifying the data generating resistance surface, even when highly correlated surfaces are present. Optimized landscape resistance surfaces can provide novel insights into how landscapes impede, inhibit, or promote movement. Such information is invaluable to the spatial management of populations, including preservation of core habitat, connectivity corridors, or reduction of highly resistant or impermeable habitat. Identifying how and which landscape features affect gene flow has been a formidable challenge facing researchers. ResistanceGA provides a framework for optimizing resistance surfaces, and is a viable solution to overcoming previously encountered challenges.