Hierarchical population structure in greater sage-grouse provides insight into management boundary delineationAuthor(s): Todd B. Cross; David E. Naugle; John C. Carlson; Michael K. Schwartz
Source: Conservation Genetics. 17: 1417-1433.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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Understanding population structure is important for guiding ongoing conservation and restoration efforts. The greater sage-grouse (Centrocercus urophasianus) is a species of concern distributed across 1.2 million km2 of western North America. We genotyped 1499 greater sagegrouse 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 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 its own chemical defense against herbivory. We propose five management groups reflective of genetic subpopulation structure. These genetic groups are largely synonymous 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.
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CitationCross, Todd B.; Naugle, David E.; Carlson, John C.; Schwartz, Michael K. 2016. Hierarchical population structure in greater sage-grouse provides insight into management boundary delineation. Conservation Genetics. 17: 1417-1433.
Keywordscentrocercus urophasianus, microsatellites, non-invasive sampling, genetic diversity, spatial principal components analysis
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