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Determining marine bioregions: A comparison of quantitative approaches.
- Source :
- Methods in Ecology & Evolution; Oct2020, Vol. 11 Issue 10, p1258-1272, 15p
- Publication Year :
- 2020
-
Abstract
- Areas that contain ecologically distinct biological content, called bioregions, are a central component to spatial and ecosystem‐based management. We review and describe a variety of commonly used and newly developed statistical approaches for quantitatively determining bioregions.Statistical approaches to bioregionalization can broadly be classified as two‐stage approaches that either 'Group First, then Predict' or 'Predict First, then Group', or a newer class of one‐stage approaches that simultaneously analyse biological data with reference to environmental data to generate bioregions. We demonstrate these approaches using a selection of methods applied to simulated data and real data on demersal fish. The methods are assessed against their ability to answer several common scientific or management questions.The true number of simulated bioregions was only identified by both of the one‐stage methods and one two‐stage method. When the number of bioregions was known, many of the methods, but not all, could adequately infer the species, environmental and spatial characteristics of bioregions. One‐stage approaches, however, do so directly via a single model without the need for separate post‐hoc analyses and additionally provide an appropriate characterization of uncertainty.One‐stage approaches provide a comprehensive and consistent method for objectively identifying and characterizing bioregions using both biological and environmental data. Potential avenues of future development in one‐stage methods include incorporating presence‐only and multiple data types as well as considering functional aspects of bioregions. [ABSTRACT FROM AUTHOR]
- Subjects :
- BIOTIC communities
Subjects
Details
- Language :
- English
- ISSN :
- 2041210X
- Volume :
- 11
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Methods in Ecology & Evolution
- Publication Type :
- Academic Journal
- Accession number :
- 146199583
- Full Text :
- https://doi.org/10.1111/2041-210X.13447