Bioregions in marine environments: Combining Biological and Environmental Data for Management and Scientific Understanding

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Woolley , S , Bax , N , Currie , J , Dunn , D , Hansen , C , Hill , N , O'Hara , T , Ovaskainen , O , Sayre , R , Vanhatalo , J & Dunstan , P 2020 , ' Bioregions in marine environments: Combining Biological and Environmental Data for Management and Scientific Understanding ' , BioScience , vol. 70 , no. 1 , pp. 48-59 . https://doi.org/10.1093/biosci/biz133

Title: Bioregions in marine environments: Combining Biological and Environmental Data for Management and Scientific Understanding
Author: Woolley, Skipton; Bax, Nicolas; Currie, Jock; Dunn, Daniel; Hansen, Cecilie; Hill, Nicole; O'Hara, Timothy; Ovaskainen, Otso; Sayre, Roger; Vanhatalo, Jarno; Dunstan, Piers
Contributor organization: Organismal and Evolutionary Biology Research Programme
Research Centre for Ecological Change
Otso Ovaskainen / Principal Investigator
Department of Mathematics and Statistics
Environmental and Ecological Statistics Group
Biostatistics Helsinki
Date: 2020-01
Language: eng
Number of pages: 12
Belongs to series: BioScience
ISSN: 0006-3568
DOI: https://doi.org/10.1093/biosci/biz133
URI: http://hdl.handle.net/10138/311327
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.
Subject: 1172 Environmental sciences
112 Statistics and probability
biogeography
community ecology
statistics
marine biology
GENERALIZED LINEAR-MODELS
POINT PROCESS MODELS
SPECIES DISTRIBUTION
BIAS CORRECTION
BIODIVERSITY
IMPLEMENTATION
PREDICTIONS
ECOREGIONS
FRAMEWORK
REGIONS
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion
Funder: Suomen Akatemia Projektilaskutus
SUOMEN AKATEMIA
Grant number:


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