Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States)
Dates
Publication Date
2022-07-25
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
We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity [...]
Summary
We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. 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. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.
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.
