Latent Spatial Models and Sampling Design for Landscape Genetics
The goal of this study is to develop a spatially-explicit approach for modeling genetic variation across space and to illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We are using a multinomial data model for categorical microsatellite allele data and introduced a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for Greater Sage-grouse. This research is in collaboration with Pennsylvania State University, USGS, Colorado State University, USFS, and the University of Montana.