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Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved manageme...

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Autores principales: Yates, Katherine L., Mellin, Camille, Caley, M. Julian, Radford, Ben T., Meeuwig, Jessica J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4917103/
https://www.ncbi.nlm.nih.gov/pubmed/27333202
http://dx.doi.org/10.1371/journal.pone.0155634
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author Yates, Katherine L.
Mellin, Camille
Caley, M. Julian
Radford, Ben T.
Meeuwig, Jessica J.
author_facet Yates, Katherine L.
Mellin, Camille
Caley, M. Julian
Radford, Ben T.
Meeuwig, Jessica J.
author_sort Yates, Katherine L.
collection PubMed
description Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability.
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spelling pubmed-49171032016-07-08 Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes Yates, Katherine L. Mellin, Camille Caley, M. Julian Radford, Ben T. Meeuwig, Jessica J. PLoS One Research Article Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modelling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modelling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability. Public Library of Science 2016-06-22 /pmc/articles/PMC4917103/ /pubmed/27333202 http://dx.doi.org/10.1371/journal.pone.0155634 Text en © 2016 Yates 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 (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yates, Katherine L.
Mellin, Camille
Caley, M. Julian
Radford, Ben T.
Meeuwig, Jessica J.
Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title_full Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title_fullStr Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title_full_unstemmed Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title_short Models of Marine Fish Biodiversity: Assessing Predictors from Three Habitat Classification Schemes
title_sort models of marine fish biodiversity: assessing predictors from three habitat classification schemes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4917103/
https://www.ncbi.nlm.nih.gov/pubmed/27333202
http://dx.doi.org/10.1371/journal.pone.0155634
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