Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments

Although models of carrying capacity have been around for some time, their use in aquaculture management has been limited. This is partially due to the cost involved in generating and testing the models. However, the use of more generic and flexible models could facilitate the implementation of mode...

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Autores principales:	R Filgueira, J Grant, R Stuart, MS Brown
Formato:	article
Lenguaje:	EN
Publicado:	Inter-Research 2013
Materias:	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/44d1cfdb7b794d07b440e08bd809d4cb
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id	oai:doaj.org-article:44d1cfdb7b794d07b440e08bd809d4cb
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spelling	oai:doaj.org-article:44d1cfdb7b794d07b440e08bd809d4cb2021-11-17T10:04:58ZEcosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments1869-215X1869-753410.3354/aei00078https://doaj.org/article/44d1cfdb7b794d07b440e08bd809d4cb2013-07-01T00:00:00Zhttps://www.int-res.com/abstracts/aei/v4/n2/p117-133/https://doaj.org/toc/1869-215Xhttps://doaj.org/toc/1869-7534Although models of carrying capacity have been around for some time, their use in aquaculture management has been limited. This is partially due to the cost involved in generating and testing the models. However, the use of more generic and flexible models could facilitate the implementation of modelling in management. We have built a generic core for coupling biogeochemical and hydrodynamic models using Simile (www.simulistics.com), a visual simulation environment software that is well-suited to accommodate fully spatial models. Specifically, Simile integrates PEST (model-independent parameter estimation, Watermark Numerical Computing, www.pesthomepage.org), an optimization tool that uses the Gauss-Marquardt-Levenberg algorithm and can be used to estimate the value of a parameter, or set of parameters, in order to minimize the discrepancies between the model results and a dataset chosen by the user. The other critical aspect of modelling exercises is the large amount of data necessary to set up, tune and groundtruth the ecosystem model. However, ecoinformatics and improvements in remote sensing procedures have facilitated acquisition of these datasets, even in data-poor environments. In this paper we describe the required datasets and stages of model development necessary to build a biogeochemical model that can be used as a decision-making tool for bivalve aquaculture management in data-poor environments.R FilgueiraJ GrantR StuartMS BrownInter-ResearcharticleAquaculture. Fisheries. AnglingSH1-691EcologyQH540-549.5ENAquaculture Environment Interactions, Vol 4, Iss 2, Pp 117-133 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5
spellingShingle	Aquaculture. Fisheries. Angling SH1-691 Ecology QH540-549.5 R Filgueira J Grant R Stuart MS Brown Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
description	Although models of carrying capacity have been around for some time, their use in aquaculture management has been limited. This is partially due to the cost involved in generating and testing the models. However, the use of more generic and flexible models could facilitate the implementation of modelling in management. We have built a generic core for coupling biogeochemical and hydrodynamic models using Simile (www.simulistics.com), a visual simulation environment software that is well-suited to accommodate fully spatial models. Specifically, Simile integrates PEST (model-independent parameter estimation, Watermark Numerical Computing, www.pesthomepage.org), an optimization tool that uses the Gauss-Marquardt-Levenberg algorithm and can be used to estimate the value of a parameter, or set of parameters, in order to minimize the discrepancies between the model results and a dataset chosen by the user. The other critical aspect of modelling exercises is the large amount of data necessary to set up, tune and groundtruth the ecosystem model. However, ecoinformatics and improvements in remote sensing procedures have facilitated acquisition of these datasets, even in data-poor environments. In this paper we describe the required datasets and stages of model development necessary to build a biogeochemical model that can be used as a decision-making tool for bivalve aquaculture management in data-poor environments.
format	article
author	R Filgueira J Grant R Stuart MS Brown
author_facet	R Filgueira J Grant R Stuart MS Brown
author_sort	R Filgueira
title	Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
title_short	Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
title_full	Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
title_fullStr	Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
title_full_unstemmed	Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
title_sort	ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments
publisher	Inter-Research
publishDate	2013
url	https://doaj.org/article/44d1cfdb7b794d07b440e08bd809d4cb
work_keys_str_mv	AT rfilgueira ecosystemmodellingforecosystembasedmanagementofbivalveaquaculturesitesindatapoorenvironments AT jgrant ecosystemmodellingforecosystembasedmanagementofbivalveaquaculturesitesindatapoorenvironments AT rstuart ecosystemmodellingforecosystembasedmanagementofbivalveaquaculturesitesindatapoorenvironments AT msbrown ecosystemmodellingforecosystembasedmanagementofbivalveaquaculturesitesindatapoorenvironments
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Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments

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