2022 Early Hearing Detection & Intervention Virtual Conference

March 13 - 15, 2022

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9/26/2018 | 2:10 PM - 2:25 PM | Multi-Scaled Clustering Framework Using Graph Theory to Inform Long-Term Monitoring and Analyses of Greater Sage-Grouse Populations | Eccles Conference Center Auditorium

Multi-Scaled Clustering Framework Using Graph Theory to Inform Long-Term Monitoring and Analyses of Greater Sage-Grouse Populations

Population monitoring of Greater sage-grouse is important for managing range-wide declines, which may be indicative of the declining overall health of the sagebrush biome. To facilitate a systematic, statistically repeatable, and hierarchical approach for long-term monitoring of sage-grouse, we developed nested, regionalized polygons of similar habitat conditions to help inform management questions across multiple spatial scales. Our study areas included the states of Nevada and Wyoming (United States) because of their disparate habitat conditions. We clustered sage-grouse leks and partitioned the landscape using multiple spatial scales of biologically relevant landscape characteristics, habitat connectivity, inter-lek movement distances (isolation-by-distance), and barriers restricting sage-grouse movement. We first developed a least-cost minimum spanning tree developed from lek locations (vertices) and terrain-based least-cost paths (edges). Second, we decomposed the connected graph into subgraphs based on barriers to sage-grouse movements, and third, we further decomposed the subgraphs by including a maximum edge length (15 km) calculated from inter-lek movement distances. We used these disconnected graphs and multi-scaled covariates to inform the Spatial “K”luster Analysis by Tree Edge Removal (SKATER) clustering algorithm in an agglomerative approach. In Nevada, the finest-scaled clusters captured 90% of sage-grouse movements when we linked a single “home” cluster polygon to individual sage-grouse home ranges, while mid-level scales captured 97% - 99% of movements within home clusters. Hierarchical clusters will support a variety of management and research needs relying on scale-dependent questions, including population monitoring and habitat modeling.

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Presenters/Authors

Julie Heinrichs (), Colorado State University, Julie.Heinrichs@colostate.edu;