Conserving migratory populations requires knowing the location of migratory corridors. GPS collars have greatly advanced knowledge of ungulate migration corridors by allowing direct observation of migratory movements. Due to logistics and limited funding, many populations cannot be collared, making it difficult to identify their migration corridors. We developed a novel approach to predicting corridors that uses maximum likelihood to fit cost distance movement models to GPS data. Fitted models can be used to predict the locations of corridors used by populations with limited or no collar data. We demonstrated that maximum likelihood estimation can recover resistance parameters used to simulate movements. Next, we used data from multiple mule deer migrations in Idaho and Wyoming to fit cost distance models with the covariates including date of peak green-up, vegetation, human footprint, topography, and others.
Results/Conclusions
We found that environmental variables influence the movements of migrating mule deer in ways not captures by standard habitat modeling approaches. Our fitted cost distance models predicted paths that more closely matched empirical tracks than paths generated from the traditional approach of parameterizing cost distance models from step selection or resource selection functions. In addition to the practical benefit of mapping corridors, this approach can address conceptual ideas in migration ecology that center around navigation, seasonal range decisions, fidelity, and movement constraints. Models of predictive corridors can also be used to assess the potential for climate or land use change to influence future movements of migrating animals.