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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...
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This data, grsg_lcp_ThiessenPoly_mst3, 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...
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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...
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This data, grsg_lcp_ThiessenPoly_mst4, 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...
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The betweenness (bc; grsg_lcp_betweenness_centrality) defines the importance of a node in a graph based on how many times it occurs in the shortest path between all pairs of nodes. In other words, a node is important if it is included in many shortest paths between other nodes because it serves as a bridge between different parts of the graph. The data were defined from least-cost paths (LCPs) constructed into minimum spanning trees (MSTs). The bc identified major corridors spanning the sage-grouse range where nodes had a larger number of connections with other nodes, reflecting regions where leks potentially play larger roles of sage-grouse continuity based on graph theory analytics. We identified a threshold of...
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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...
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This data, grsg_lcp_ThiessenPoly_mst5, 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...
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This data, grsg_lcp_ThiessenPoly_mst2, 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...
    map background search result map search result map Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States) Greater sage-grouse betweenness centrality of fully connected population structure in the western United States Greater sage-grouse closeness centrality of fully connected population structure in the western United States Greater sage-grouse population structure (1: finest-scaled, tier one) in the western United States Greater sage-grouse population structure (2: fine-scaled, tier two) in the western United States Greater sage-grouse population structure (3: moderate-scaled, tier three) in the western United States Greater sage-grouse population structure (4: coarsest-scaled, tier four) in the western United States Greater sage-grouse population structure (5: fully connected, tier five) in the western United States Greater sage-grouse closeness centrality of fully connected population structure in the western United States Greater sage-grouse betweenness centrality of fully connected population structure in the western United States Greater sage-grouse population structure (5: fully connected, tier five) in the western United States Greater sage-grouse population structure (1: finest-scaled, tier one) in the western United States Greater sage-grouse population structure (2: fine-scaled, tier two) in the western United States Greater sage-grouse population structure (3: moderate-scaled, tier three) in the western United States Greater sage-grouse population structure (4: coarsest-scaled, tier four) in the western United States Greater sage-grouse population structure and connectivity data to inform the development of hierarchical population units (western United States)