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...
Autores principales: | , , |
---|---|
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 |