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Greater sage-grouse closeness centrality of fully connected population structure 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

Closeness centrality (cc; grsg_lcp_closeness_centrality) measures the average length of the shortest path between the node and all other nodes in the graph. The more central a node, the closer it is to all other nodes and the more likely information/movements can flow to other nodes. Closeness is computed as one divided by the average path lengths from a node to its neighbors, which assumes that important nodes are close to other nodes. The data were defined from least-cost paths (LCPs) constructed into minimum spanning trees (MSTs). We identified a threshold of the cc normalized value (>0.047) where patterns of network connectivity occurred in our graph. The cc identified leks with the greatest number of shortest paths between neighboring [...]

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Shapefile: grsg_lcp_closeness_centrality.zip
grsg_lcp_closeness_centrality.CPG 5 Bytes
grsg_lcp_closeness_centrality.dbf 219 Bytes
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grsg_lcp_closeness_centrality.sbx 116 Bytes
grsg_lcp_closeness_centrality.shp 1.41 MB
grsg_lcp_closeness_centrality.shx 108 Bytes

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.

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