Developing Best Practices for Linear Mixed Modelling in Landscape Genetics Through Landscape-directed Dispersal Simulations
Project Manager:Sara Oyler-McCance
Mixed models that account for the error structure of pairwise datasets are being utilized to compare models relating genetic differentiation with pairwise measures of landscape resistance. A model selection framework based on information criteria metrics or explained variance may help disentangle the ecological and landscape factors influencing spatial genetic structure, yet there are currently no tests of the error rates of this approach, or a consensus for the best protocols for minimizing them. The goal of this project is to develop and test a landscape-directed dispersal model to simulate a series of replicates that emulate independent empirical datasets of two species with vastly different life history and habitat use characteristics (Greater Sage-grouse and eastern fox snakes). This study develops best practices for using linear mixed models to identify the features underlying patterns of dispersal across a variety of landscapes. This research is in collaboration with University of Waterloo and is supported by Wyoming Game and Fish Department and BLM.