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Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments

Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantify...

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Autores principales: Dunstan, Piers K., Althaus, Franziska, Williams, Alan, Bax, Nicholas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3350518/
https://www.ncbi.nlm.nih.gov/pubmed/22606271
http://dx.doi.org/10.1371/journal.pone.0036558
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author Dunstan, Piers K.
Althaus, Franziska
Williams, Alan
Bax, Nicholas J.
author_facet Dunstan, Piers K.
Althaus, Franziska
Williams, Alan
Bax, Nicholas J.
author_sort Dunstan, Piers K.
collection PubMed
description Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantifying biodiversity to describe patterns of biodiversity in an area that includes two marine reserves in deep water off southern Australia. We used biological data collected during a recent survey, combined with extensive physical data to model, predict and map three different attributes of biodiversity: distributions of common species, beta diversity and rank abundance distributions (RAD). The distribution of each of eight common species was unique, although all the species respond to a depth-correlated physical gradient. Changes in composition (beta diversity) were large, even between sites with very similar environmental conditions. Composition at any one site was highly uncertain, and the suite of species changed dramatically both across and down slope. In contrast, the distributions of the RAD components of biodiversity (community abundance, richness, and evenness) were relatively smooth across the study area, suggesting that assemblage structure (i.e. the distribution of abundances of species) is limited, irrespective of species composition. Seamounts had similar biodiversity based on metrics of species presence, beta diversity, total abundance, richness and evenness to the adjacent continental slope in the same depth ranges. These analyses suggest that conservation objectives need to clearly identify which aspects of biodiversity are valued, and employ an appropriate suite of methods to address these aspects, to ensure that conservation goals are met.
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spelling pubmed-33505182012-05-17 Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments Dunstan, Piers K. Althaus, Franziska Williams, Alan Bax, Nicholas J. PLoS One Research Article Understanding patterns of biodiversity in deep sea systems is increasingly important because human activities are extending further into these areas. However, obtaining data is difficult, limiting the ability of science to inform management decisions. We have used three different methods of quantifying biodiversity to describe patterns of biodiversity in an area that includes two marine reserves in deep water off southern Australia. We used biological data collected during a recent survey, combined with extensive physical data to model, predict and map three different attributes of biodiversity: distributions of common species, beta diversity and rank abundance distributions (RAD). The distribution of each of eight common species was unique, although all the species respond to a depth-correlated physical gradient. Changes in composition (beta diversity) were large, even between sites with very similar environmental conditions. Composition at any one site was highly uncertain, and the suite of species changed dramatically both across and down slope. In contrast, the distributions of the RAD components of biodiversity (community abundance, richness, and evenness) were relatively smooth across the study area, suggesting that assemblage structure (i.e. the distribution of abundances of species) is limited, irrespective of species composition. Seamounts had similar biodiversity based on metrics of species presence, beta diversity, total abundance, richness and evenness to the adjacent continental slope in the same depth ranges. These analyses suggest that conservation objectives need to clearly identify which aspects of biodiversity are valued, and employ an appropriate suite of methods to address these aspects, to ensure that conservation goals are met. Public Library of Science 2012-05-11 /pmc/articles/PMC3350518/ /pubmed/22606271 http://dx.doi.org/10.1371/journal.pone.0036558 Text en Dunstan et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Dunstan, Piers K.
Althaus, Franziska
Williams, Alan
Bax, Nicholas J.
Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title_full Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title_fullStr Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title_full_unstemmed Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title_short Characterising and Predicting Benthic Biodiversity for Conservation Planning in Deepwater Environments
title_sort characterising and predicting benthic biodiversity for conservation planning in deepwater environments
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3350518/
https://www.ncbi.nlm.nih.gov/pubmed/22606271
http://dx.doi.org/10.1371/journal.pone.0036558
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