Determining marine bioregions: A comparison of quantitative approaches. Issue 10 (9th August 2020)
- Record Type:
- Journal Article
- Title:
- Determining marine bioregions: A comparison of quantitative approaches. Issue 10 (9th August 2020)
- Main Title:
- Determining marine bioregions: A comparison of quantitative approaches
- Authors:
- Hill, Nicole
Woolley, Skipton N. C.
Foster, Scott
Dunstan, Piers K.
McKinlay, John
Ovaskainen, Otso
Johnson, Craig - Editors:
- Pearse, Will
- Abstract:
- 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 ofAbstract: 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. … (more)
- Is Part Of:
- Methods in ecology and evolution. Volume 11:Issue 10(2020)
- Journal:
- Methods in ecology and evolution
- Issue:
- Volume 11:Issue 10(2020)
- Issue Display:
- Volume 11, Issue 10 (2020)
- Year:
- 2020
- Volume:
- 11
- Issue:
- 10
- Issue Sort Value:
- 2020-0011-0010-0000
- Page Start:
- 1258
- Page End:
- 1272
- Publication Date:
- 2020-08-09
- Subjects:
- biogeography -- bioregionalization -- community ecology -- ecological statistics -- ecoregionalization
Ecology -- Periodicals
Evolution -- Periodicals
577 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)2041-210X ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1111/2041-210X.13447 ↗
- Languages:
- English
- ISSNs:
- 2041-210X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 14407.xml