Greater sage-grouse genetic data and R code for evaluating conservation translocations in the northwestern United States, 1992–2021

Conservation translocations are a common wildlife management tool that can be difficult to implement and evaluate for effectiveness. Genetic information can provide unique insight regarding local impact of translocations (e.g., presence and retention of introduced genetic variation) and identifying suitable source and recipient populations (e.g., adaptive similarity). We developed two genetic data sets and wrote statistical code to evaluate conservation translocation effectiveness into the isolated northwestern region of the greater sage-grouse (Centrocercus urophasianus) distribution and to retrospectively evaluate adaptive divergence among source and recipient populations. Our first data set was microsatellite-based and derived from biological samples (feathers, tissue, and blood) collected from the translocation source populations and the northwestern recipient populations (in Washington state) before and after translocation. These data were used to evaluate neutral change in genetic variation resulting from translocation efforts. We wrote code for statistical analyses to evaluate two things in our microsatellite-based data. First, we developed a simulation model to predict the genetic effect of conservation translocations and compare the predictions to what was observed. Second, we developed a statistical model to estimate the probability that individuals sampled post-translocation are the offspring of two individuals from the same population or from individuals from two distinct populations. Our second data set was whole-genome sequencing data (derived from tissue and blood samples) for the source and Washington populations prior to translocation efforts. These data were used to characterize genome-wide adaptive divergence patterns that may influence translocation outcomes.