Bioregions in Marine Environments: Combining Biological and Environmental Data for Management and Scientific Understanding

Author:

Woolley Skipton N C1ORCID,Foster Scott D2,Bax Nicholas J13,Currie Jock C4,Dunn Daniel C5,Hansen Cecilie6,Hill Nicole13,O’Hara Timothy D7ORCID,Ovaskainen Otso89,Sayre Roger10,Vanhatalo Jarno P811ORCID,Dunstan Piers K1

Affiliation:

1. Oceans and Atmospheres, Commonwealth Scientific and Industrial Research Organization (CSIRO), Hobart, Australia

2. Data61, CSIRO, also in Hobart

3. Institute for Marine and Antarctic Studies, University of Tasmania, Hobart

4. Nelson Mandela University and the South African National Biodiversity Institute, Cape Town, South Africa

5. Marine Geospatial Ecology Lab, Duke University, Durham, North Carolina

6. Institute of Marine Research, Bergen, Norway

7. Museums Victoria, Melbourne, Victoria, Australia

8. Organismal and Evolutionary Biology Research Progamme, University of Helsinki, Finland

9. Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway

10. Land Change Science Program, US Geological Survey, Reston, Virginia

11. Department of Mathematics and Statistics, University of Helsinki, Finland

Abstract

AbstractBioregions 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.

Funder

Academy of Finland

Research Council of Norway

Jane and Aatos Erkko Foundation

Publisher

Oxford University Press (OUP)

Subject

General Agricultural and Biological Sciences

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