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

Abstract

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 leastcost 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

A genetic warning system for a hierarchically structured wildlife monitoring framework

“…Implementation of this approach requires range-wide spatial population clusters and range-wide genetic data. Significant work went into developing the population clusters used here (O'Donnell et al, 2022a). However, the development of hierarchical population clusters could be based on any two regional and local biologically based spatial divisions for any given species distribution.…”

“…We used the greater sage-grouse hierarchical population framework (O'Donnell et al, 2022a) based on population structure (O'Donnell et al, 2022b) and a clustering algorithm (Coates et al, 2021;O'Donnell et al, 2019). The population structure was characterized by graph theory (mathematics of pairwise relationships), describing the connectivity based on multiple biological parameters, including a resistance surface describing functional habitat characteristics, a range of dispersal distances, an estimate of gene flow distance, effects of landscape features on movement, and connectivity based on least-cost paths.…”

“…Here, we developed the described statistical model as part of a genetic warning system (GWS) for greater sage-grouse (Centrocercus urophasianus) based on two measures of genetic diversity (A R and H E ) for previously developed hierarchically nested population clusters (Coates et al, 2021;O'Donnell et al, 2022a). Within this warning system, we defined and then applied thresholds of genetic diversity to identify local areas with low genetic diversity, and thus elevated conservation concern.…”

Defining fine‐scaled population structure among continuously distributed populations

“…Software ( lcp_centrality ) supporting this project (O'Donnell et al, 2022b; https://doi.org/10.5066/P9QQ39WG) and population connectivity data derived for sage‐grouse (O'Donnell et al, 2022a; https://doi.org/10.5066/P991D45Q) are freely available to the public. The greater sage‐grouse lek data (source) have limited availability owing to unique restrictions held by each state (data are managed by 11 western states and are not public due to the sensitivity of the species).…”