2018 journal article
Persistence-Based Area Prioritization for Conservation: Applying Occupancy and Habitat Threats and Risks Analyses
JOURNAL OF FISH AND WILDLIFE MANAGEMENT, 9(2), 543–553.
author keywords: conservation area selection; occupancy; persistence; risk assessment; threats
UN Sustainable Development Goal Categories
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: December 31, 2018
AbstractEffective habitat conservation is predicated on maintaining high levels or increasing local persistence probability of the species it purports to protect. Thus, methodological approaches that improve the inferential value of local persistence are of utmost value to guide conservation planning as they inform area selection processes. Herein we used the painted bunting Passerina ciris, a species of conservation interest in North Carolina, as an illustrative case that combined single-season, single-species occupancy analyses and a threats and risk decision support tool to rank five areas of conservation interest in terms of local persistence probability. We used survey data from two seasons (2008–2009) grouped into 21 natal dispersal sampling units and land-cover data from 12 habitat classes to establish the relationship between local occupancy probability and habitat. Occupancy increased most strongly with increasing amount of maritime forest. Projections to year 2050, relative to year 2000, indicated that a potential loss of maritime forest of 200–1,300 ha, depending on the area of interest. Projected loss was lowest at Bald Head Island–Wilmington (2%) and highest at Camp Lejune (27%). Bald Head Island–Wilmington ranked highest in projected local persistence probability (0.91; 95% confidence interval [CI] = 0.53–0.99), whereas Top Sail–Hammocks Beach Park ranked lowest (0.28; 95% CI = 0.03–0.82). Estimates of local persistence offer decision-makers another criterion to prioritize areas for conservation and help guide efforts aimed at maintaining or enhancing local persistence. These include in situ habitat management, expanding or connecting existing areas of interest. In the future, we recommend the use of multiseason occupancy models, coupled with measures of uncertainty of land-cover projections, to strengthen inferences about local persistence, particularly useful in nonstationary landscapes driven by human activities.