Bioregions in Marine Environments: Combining Biological and Environmental Data for Management and Scientific Understanding. (18th December 2019)

• Record Type: Journal Article
• Authors: Woolley, Skipton N C; Foster, Scott D; Bax, Nicholas J; Currie, Jock C; Dunn, Daniel C; Hansen, Cecilie; Hill, Nicole; O'Hara, Timothy D; Ovaskainen, Otso; Sayre, Roger; Vanhatalo, Jarno P; Dunstan, Piers K
• Is Part Of: BioScience. Volume 70:Number 1(2020:Jan.)
• Page Start: 48
• Page End: 59
• Publication Date: 2019-12-18
• Subjects: biogeography -- community ecology -- statistics -- marine biology; Life sciences -- Periodicals; Life sciences -- Research -- Periodicals; Life sciences -- United States -- Periodicals; Life sciences -- Government policy -- United States -- Periodicals; Biology -- Periodicals; Biotechnology industries -- Periodicals; 570
• DOI: 10.1093/biosci/biz133
• Languages: English
• ISSNs: 0006-3568

Abstract

Abstract: Bioregions are important tools for understanding and managing natural resources. Bioregions should describe locations of relatively homogenous assemblages of species occur, enabling managers to better regulate activities that might affect these assemblages. Many existing bioregionalization approaches, which rely on expert-derived, Delphic comparisons or environmental surrogates, do not explicitly include observed biological data in such analyses. We highlight that, for bioregionalizations to be useful and reliable for systems scientists and managers, the bioregionalizations need to be based on biological data; to include an easily understood assessment of uncertainty, preferably in a spatial format matching the bioregions; and to be scientifically transparent and reproducible. Statistical models provide a scientifically robust, transparent, and interpretable approach for ensuring that bioregions are formed on the basis of observed biological and physical data. Using statistically derived bioregions provides a repeatable framework for the spatial representation of biodiversity at multiple spatial scales. This results in better-informed management decisions and biodiversity conservation outcomes.