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

Autor: O'Donnell MS; U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA., Edmunds DR; U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA., Aldridge CL; U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA., Heinrichs JA; Natural Resource Ecology Laboratory, U.S. Geological Survey, Fort Collins Science Center Colorado State University Fort Collins Colorado USA., Monroe AP; U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA., Coates PS; U.S. Geological Survey Western Ecological Research Center Dixon California USA., Prochazka BG; U.S. Geological Survey Western Ecological Research Center Dixon California USA., Hanser SE; U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado USA., Wiechman LA; U.S. Geological Survey Ecosystems Mission Area Fort Collins Colorado USA.
Jazyk: angličtina
Zdroj: Ecology and evolution [Ecol Evol] 2022 Nov 30; Vol. 12 (12), pp. e9565. Date of Electronic Publication: 2022 Nov 30 (Print Publication: 2022).
DOI: 10.1002/ece3.9565
Abstrakt: 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 ( Centrocercus urophasianus ), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.
Competing Interests: Authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or otherwise that might be perceived as influencing objectivity of this research.
(© 2022 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)
Databáze: MEDLINE
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