Abundance models improve spatial and temporal prioritization of conservation resources. Issue 7 (1st October 2015)

• Record Type: Journal Article
• Authors: Johnston, Alison; Fink, Daniel; Reynolds, Mark D.; Hochachka, Wesley M.; Sullivan, Brian L.; Bruns, Nicholas E.; Hallstein, Eric; Merrifield, Matt S.; Matsumoto, Sandi; Kelling, Steve
• Is Part Of: Ecological applications. Volume 25:Issue 7(2015)
• Page Start: 1749
• Page End: 1756
• Publication Date: 2015-10-01
• Subjects: abundance -- agriculture -- citizen science -- conservation prioritization -- dynamic conservation -- eBird -- precision conservation -- spatiotemporal models -- species distribution models -- waterbirds; Ecology -- Periodicals; Environmental protection -- Periodicals; Biology, Economic -- Periodicals; 577.05
• DOI: 10.1890/14-1826.1
• Languages: English
• ISSNs: 1051-0761

Abstract

Abstract : Conservation prioritization requires knowledge about organism distribution and density. This information is often inferred from models that estimate the probability of species occurrence rather than from models that estimate species abundance, because abundance data are harder to obtain and model. However, occurrence and abundance may not display similar patterns and therefore development of robust, scalable, abundance models is critical to ensuring that scarce conservation resources are applied where they can have the greatest benefits. Motivated by a dynamic land conservation program, we develop and assess a general method for modeling relative abundance using citizen science monitoring data. Weekly estimates of relative abundance and occurrence were compared for prioritizing times and locations of conservation actions for migratory waterbird species in California, USA. We found that abundance estimates consistently provided better rankings of observed counts than occurrence estimates. Additionally, the relationship between abundance and occurrence was nonlinear and varied by species and season. Across species, locations prioritized by occurrence models had only 10–58% overlap with locations prioritized by abundance models, highlighting that occurrence models will not typically identify the locations of highest abundance that are vital for conservation of populations.