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Filters: Tags: Greater Sage-Grouse (X) > partyWithName: Adrian P. Monroe (X)

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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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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 betweenness 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 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