Presenter: Todd Cross, University of Montana and USFS National Genomics Lab for Wildlife & Fish Conservation
We genotyped 1499 greater sage- grouse from 297 leks across Montana, North Dakota and South Dakota using a 15-locus microsatellite panel, then examined spatial autocorrelation, spatial principal components analysis, and hierarchical Bayesian clustering to identify population structure. Our results show that at distances of up to ~240 km individuals exhibit greater genetic similarity than expected by chance, suggesting that the cumulative effect of short-range dispersal translates to long-range connectivity. We also found two levels of hierarchical genetic subpopulation structure. These subpopulations occupy significantly different elevations and are surrounded by divergent vegetative communities with different dominant subspecies of sagebrush, each with distinct terpene defense.
We propose five management groups reflective of genetic structure. These genetic groups are largely coincident with existing priority areas for conservation. On average, 85.8% of individuals within each conservation priority area assign to a distinct subpopulation. Our results largely support existing management decisions regarding subpopulation boundaries.
About the presenter: Todd Cross is a Ph. D. candidate at the University of Montana stationed at the Forest Service’s National Genomics Laboratory for Wildlife & Fish Conservation, where he is involved in a large-scale greater sage-grouse conservation genetics project which aims to understand population structure and to identify landscape and environmental features critical to maintaining genetic connectivity for this species of great conservation concern. His efforts are in close collaboration with state and federal agencies and non-governmental organizations involved with sage grouse management across the eleven western states within the species’ range. Together, they are tackling an unprecedentedly large study which will provide a comprehensive view of genetic population structure, patterns of diversity, and qualitative connectivity data.
