Cargando…

Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation

The increased pressure on the marine ecosystems highlights the need for policies and integrated approaches for sustainable management of coastal areas. Spatial planning based on geographic information of human activities, ecological structures and functions, and their associated goods and services i...

Descripción completa

Detalles Bibliográficos
Autores principales: Bergström, Per, Lindegarth, Susanne, Lindegarth, Mats
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717332/
https://www.ncbi.nlm.nih.gov/pubmed/26811765
http://dx.doi.org/10.1002/ece3.1823
_version_ 1782410634649927680
author Bergström, Per
Lindegarth, Susanne
Lindegarth, Mats
author_facet Bergström, Per
Lindegarth, Susanne
Lindegarth, Mats
author_sort Bergström, Per
collection PubMed
description The increased pressure on the marine ecosystems highlights the need for policies and integrated approaches for sustainable management of coastal areas. Spatial planning based on geographic information of human activities, ecological structures and functions, and their associated goods and services is a fundamental component in this context. Here, we evaluate the potential of predictive modeling to provide spatial data on one ecosystem function, mussel growth for use in such processes. We developed a methodology based on statistical modeling, spatial prediction, and mapping for the relative growth of the blue mussel, Mytilus edulis. We evaluated the performance of different modeling techniques and classification schemes using empirical measurements of growth from 144 sampling sites and data on biological, chemical, and physical predictors. Following comparisons of the different techniques and schemes, we developed random forest models to predict growth along the Swedish west coast. Implemented into GIS the best model produced in this study predicts that low, intermediate, and high growth rates can be expected in 53%, 32%, and 15% of modeled area, respectively. The results of this study also suggest that the nature and quality of predictor data hold the key to improving the predictive power of models. On a more general note, this study exemplifies a feasible approach based on measuring, modeling, and mapping for obtaining scientifically based spatial information on ecosystem functions and services affected by a complex set of factors. Such information is fundamental for maritime spatial planning and ecosystem‐based management and its importance is likely to increase in the future. Because of its close link to nutrient assimilation and production yield, site‐specific information of soft tissue growth such as the map of predicted growth rate developed in this study can be used as a tool for optimizing actions aimed at mitigating eutrophication and aquaculture operations and in maritime spatial planning processes of coastal areas.
format Online
Article
Text
id pubmed-4717332
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-47173322016-01-25 Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation Bergström, Per Lindegarth, Susanne Lindegarth, Mats Ecol Evol Original Research The increased pressure on the marine ecosystems highlights the need for policies and integrated approaches for sustainable management of coastal areas. Spatial planning based on geographic information of human activities, ecological structures and functions, and their associated goods and services is a fundamental component in this context. Here, we evaluate the potential of predictive modeling to provide spatial data on one ecosystem function, mussel growth for use in such processes. We developed a methodology based on statistical modeling, spatial prediction, and mapping for the relative growth of the blue mussel, Mytilus edulis. We evaluated the performance of different modeling techniques and classification schemes using empirical measurements of growth from 144 sampling sites and data on biological, chemical, and physical predictors. Following comparisons of the different techniques and schemes, we developed random forest models to predict growth along the Swedish west coast. Implemented into GIS the best model produced in this study predicts that low, intermediate, and high growth rates can be expected in 53%, 32%, and 15% of modeled area, respectively. The results of this study also suggest that the nature and quality of predictor data hold the key to improving the predictive power of models. On a more general note, this study exemplifies a feasible approach based on measuring, modeling, and mapping for obtaining scientifically based spatial information on ecosystem functions and services affected by a complex set of factors. Such information is fundamental for maritime spatial planning and ecosystem‐based management and its importance is likely to increase in the future. Because of its close link to nutrient assimilation and production yield, site‐specific information of soft tissue growth such as the map of predicted growth rate developed in this study can be used as a tool for optimizing actions aimed at mitigating eutrophication and aquaculture operations and in maritime spatial planning processes of coastal areas. John Wiley and Sons Inc. 2015-12-02 /pmc/articles/PMC4717332/ /pubmed/26811765 http://dx.doi.org/10.1002/ece3.1823 Text en © 2015 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Research
Bergström, Per
Lindegarth, Susanne
Lindegarth, Mats
Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title_full Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title_fullStr Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title_full_unstemmed Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title_short Modeling and predicting the growth of the mussel, Mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
title_sort modeling and predicting the growth of the mussel, mytilus edulis: implications for planning of aquaculture and eutrophication mitigation
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717332/
https://www.ncbi.nlm.nih.gov/pubmed/26811765
http://dx.doi.org/10.1002/ece3.1823
work_keys_str_mv AT bergstromper modelingandpredictingthegrowthofthemusselmytilusedulisimplicationsforplanningofaquacultureandeutrophicationmitigation
AT lindegarthsusanne modelingandpredictingthegrowthofthemusselmytilusedulisimplicationsforplanningofaquacultureandeutrophicationmitigation
AT lindegarthmats modelingandpredictingthegrowthofthemusselmytilusedulisimplicationsforplanningofaquacultureandeutrophicationmitigation