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Greater sage-grouse population structure (1: finest-scaled, tier one) in the western United States

Dates

Publication Date
Start Date
1950
End Date
2019

Citation

O’Donnell, M.S., Edmunds, D.R., Aldridge, C.L., Heinrichs, J.A., Monroe, A.P., Coates, P.S., Prochazka, B.G., Hanser, S.E., and Wiechman, L.A., 2022, Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States): U.S. Geological Survey data release, https://doi.org/10.5066/P991D45Q.

Summary

This data, grsg_lcp_ThiessenPoly_mst1, is one of five hierarchical delineations of greater sage-grouse population structure. The data represent Thiessen polygons of graph constructs (least-cost path minimum spanning tree [LCP-MST]) that defined our population structure of sage-grouse breeding sites in the western United States. This data was developed by applying dispersal and genetic rules to decompose the fully connected population structure (graph) into the product presented here. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. [...]

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Shapefile: grsg_lcp_ThiessenPoly_mst1.zip
grsg_lcp_ThiessenPoly_mst1.CPG 5 Bytes
grsg_lcp_ThiessenPoly_mst1.dbf 30.91 KB
grsg_lcp_ThiessenPoly_mst1.prj 413 Bytes
grsg_lcp_ThiessenPoly_mst1.sbn 3.61 KB
grsg_lcp_ThiessenPoly_mst1.sbx 516 Bytes
grsg_lcp_ThiessenPoly_mst1.shp 2.88 MB
grsg_lcp_ThiessenPoly_mst1.shx 2.71 KB

Purpose

The hierarchical graphs (subpopulations) denote the degree to which wildlife dispersal affected population structure and will support analyses of population trends across different temporal and spatial scales of population organizational units to inform adaptive management. We also provide data describing the relative importance of local populations and where to potentially avoid landscape disturbances that may negatively affect population connectivity using centrality measures supported by graph theory. The results of applying graph analytics (centrality indices) to our population structure are intended to help managers make decisions on where habitat restoration and protections against habitat loss can improve and/or sustain population connectivity.

Additional Information

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