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Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems

1. Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with bio...

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Autores principales: Roland Pitcher, C., Lawton, Peter, Ellis, Nick, Smith, Stephen J., Incze, Lewis S., Wei, Chih‐Lin, Greenlaw, Michelle E., Wolff, Nicholas H., Sameoto, Jessica A., Snelgrove, Paul V. R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Blackwell Publishing Ltd 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412211/
https://www.ncbi.nlm.nih.gov/pubmed/22879681
http://dx.doi.org/10.1111/j.1365-2664.2012.02148.x
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author Roland Pitcher, C.
Lawton, Peter
Ellis, Nick
Smith, Stephen J.
Incze, Lewis S.
Wei, Chih‐Lin
Greenlaw, Michelle E.
Wolff, Nicholas H.
Sameoto, Jessica A.
Snelgrove, Paul V. R.
author_facet Roland Pitcher, C.
Lawton, Peter
Ellis, Nick
Smith, Stephen J.
Incze, Lewis S.
Wei, Chih‐Lin
Greenlaw, Michelle E.
Wolff, Nicholas H.
Sameoto, Jessica A.
Snelgrove, Paul V. R.
author_sort Roland Pitcher, C.
collection PubMed
description 1. Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with biological patterns. Instead, biodiversity may respond nonlinearly and to interactions between environmental variables. 2. To investigate the role of the environment in driving patterns of biodiversity composition in large marine regions, we collated multiple biological survey and environmental data sets from tropical NE Australia, the deep Gulf of Mexico and the temperate Gulf of Maine. We then quantified the shape and magnitude of multispecies responses along >30 environmental gradients and the extent to which these variables predicted regional distributions. To do this, we applied a new statistical approach, Gradient Forest, an extension of Random Forest, capable of modelling nonlinear and threshold responses. 3. The regional‐scale environmental variables predicted an average of 13–35% (up to 50–85% for individual species) of the variation in species abundance distributions. Important predictors differed among regions and biota and included depth, salinity, temperature, sediment composition and current stress. The shapes of responses along gradients also differed and were nonlinear, often with thresholds indicative of step changes in composition. These differing regional responses were partly due to differing environmental indicators of bioregional boundaries and, given the results to date, may indicate limited scope for extrapolating bio‐physical relationships beyond the region of source data sets. 4.  Synthesis and applications. Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms.
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spelling pubmed-34122112012-08-07 Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems Roland Pitcher, C. Lawton, Peter Ellis, Nick Smith, Stephen J. Incze, Lewis S. Wei, Chih‐Lin Greenlaw, Michelle E. Wolff, Nicholas H. Sameoto, Jessica A. Snelgrove, Paul V. R. J Appl Ecol Environmental Drivers 1. Environmental variables are often used as indirect surrogates for mapping biodiversity because species survey data are scant at regional scales, especially in the marine realm. However, environmental variables are measured on arbitrary scales unlikely to have simple, direct relationships with biological patterns. Instead, biodiversity may respond nonlinearly and to interactions between environmental variables. 2. To investigate the role of the environment in driving patterns of biodiversity composition in large marine regions, we collated multiple biological survey and environmental data sets from tropical NE Australia, the deep Gulf of Mexico and the temperate Gulf of Maine. We then quantified the shape and magnitude of multispecies responses along >30 environmental gradients and the extent to which these variables predicted regional distributions. To do this, we applied a new statistical approach, Gradient Forest, an extension of Random Forest, capable of modelling nonlinear and threshold responses. 3. The regional‐scale environmental variables predicted an average of 13–35% (up to 50–85% for individual species) of the variation in species abundance distributions. Important predictors differed among regions and biota and included depth, salinity, temperature, sediment composition and current stress. The shapes of responses along gradients also differed and were nonlinear, often with thresholds indicative of step changes in composition. These differing regional responses were partly due to differing environmental indicators of bioregional boundaries and, given the results to date, may indicate limited scope for extrapolating bio‐physical relationships beyond the region of source data sets. 4.  Synthesis and applications. Gradient Forest offers a new capability for exploring relationships between biodiversity and environmental gradients, generating new information on multispecies responses at a detail not available previously. Importantly, given the scarcity of data, Gradient Forest enables the combined use of information from disparate data sets. The gradient response curves provide biologically informed transformations of environmental layers to predict and map expected patterns of biodiversity composition that represent sampled composition better than uninformed variables. The approach can be applied to support marine spatial planning and management and has similar applicability in terrestrial realms. Blackwell Publishing Ltd 2012-05-31 2012-06 /pmc/articles/PMC3412211/ /pubmed/22879681 http://dx.doi.org/10.1111/j.1365-2664.2012.02148.x Text en © 2012 The Authors. Journal of Applied Ecology © 2012 British Ecological Society Open access.
spellingShingle Environmental Drivers
Roland Pitcher, C.
Lawton, Peter
Ellis, Nick
Smith, Stephen J.
Incze, Lewis S.
Wei, Chih‐Lin
Greenlaw, Michelle E.
Wolff, Nicholas H.
Sameoto, Jessica A.
Snelgrove, Paul V. R.
Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title_full Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title_fullStr Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title_full_unstemmed Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title_short Exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
title_sort exploring the role of environmental variables in shaping patterns of seabed biodiversity composition in regional‐scale ecosystems
topic Environmental Drivers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3412211/
https://www.ncbi.nlm.nih.gov/pubmed/22879681
http://dx.doi.org/10.1111/j.1365-2664.2012.02148.x
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