Defining biologically relevant and hierarchically nested population units to inform wildlife management.

Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial "K"luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (