Genetic and ecological evidence of long‐term translocation success of the federally endangered Stephens' kangaroo rat

Abstract

Abstract The efficacy of translocation as a method for conserving species in peril has not been fully evaluated. Post‐release monitoring rarely involves long‐term assessments and initial success metrics may not translate to population viability. In particular, genetic factors may play a critical role in fitness following release as founder effect, genetic drift, inbreeding depression and outbreeding depression can influence extinction risk. As part of a multi‐year recovery effort, we translocated populations of the endangered Stephens' kangaroo rat (Dipodomys stephensi; SKR), in order to restore population connectivity. Here we evaluate the success of the translocation efforts through long‐term population estimation and genetic analyses. We conducted post‐release assessments for 9 years, and used Bayesian clustering analysis and principal components analysis (PCA) of 24 microsatellite markers developed for this species to assess genetic admixture between source and translocated populations. Demographic results indicate long‐term translocation success, as evidenced by founder survival and establishment, and population growth and regulation. Genetic results show admixture at the release site between genetically discrete source populations, provide definitive evidence that translocated individuals, not immigrants, produced offspring at the release site, that these offspring successfully reproduced and that even with moderate to great genetic differentiation of source populations, outbreeding depression was not detected. Despite the historical failure of heteromyid translocation efforts, our long‐term study illustrates how the development of species‐specific best practices led to an effective strategy for SKR recovery. More broadly, our results suggest efforts that use a systematic hypothesis‐testing framework alongside long‐term monitoring offer great promise in improving translocation outcomes.

Keywords

• Bayesian clustering analysis
• long‐term success
• parentage analysis
• PCA clustering analysis
• population estimation
• recovery