Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.

Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM) approach is used to describe the abundance of 40 species groups (i.e. functional groups) acro...

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Autores principales:	Michael Drexler, Cameron H Ainsworth
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c27a2fd16da0425287d5aefb5a3d9131
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spelling	oai:doaj.org-article:c27a2fd16da0425287d5aefb5a3d91312021-11-18T07:45:45ZGeneralized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.1932-620310.1371/journal.pone.0064458https://doaj.org/article/c27a2fd16da0425287d5aefb5a3d91312013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23691223/?tool=EBIhttps://doaj.org/toc/1932-6203Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM) approach is used to describe the abundance of 40 species groups (i.e. functional groups) across the Gulf of Mexico (GoM) using a large fisheries independent data set (SEAMAP) and climate scale oceanographic conditions. Predictor variables included in the model are chlorophyll a, sediment type, dissolved oxygen, temperature, and depth. Despite the presence of a large number of zeros in the data, a single GAM using a negative binomial distribution was suitable to make predictions of abundance for multiple functional groups. We present an example case study using pink shrimp (Farfantepenaeus duroarum) and compare the results to known distributions. The model successfully predicts the known areas of high abundance in the GoM, including those areas where no data was inputted into the model fitting. Overall, the model reliably captures areas of high and low abundance for the large majority of functional groups observed in SEAMAP. The result of this method allows for the objective setting of spatial distributions for numerous functional groups across a modeling domain, even where abundance data may not exist.Michael DrexlerCameron H AinsworthPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 5, p e64458 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael Drexler Cameron H Ainsworth Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
description	Spatially explicit ecosystem models of all types require an initial allocation of biomass, often in areas where fisheries independent abundance estimates do not exist. A generalized additive modelling (GAM) approach is used to describe the abundance of 40 species groups (i.e. functional groups) across the Gulf of Mexico (GoM) using a large fisheries independent data set (SEAMAP) and climate scale oceanographic conditions. Predictor variables included in the model are chlorophyll a, sediment type, dissolved oxygen, temperature, and depth. Despite the presence of a large number of zeros in the data, a single GAM using a negative binomial distribution was suitable to make predictions of abundance for multiple functional groups. We present an example case study using pink shrimp (Farfantepenaeus duroarum) and compare the results to known distributions. The model successfully predicts the known areas of high abundance in the GoM, including those areas where no data was inputted into the model fitting. Overall, the model reliably captures areas of high and low abundance for the large majority of functional groups observed in SEAMAP. The result of this method allows for the objective setting of spatial distributions for numerous functional groups across a modeling domain, even where abundance data may not exist.
format	article
author	Michael Drexler Cameron H Ainsworth
author_facet	Michael Drexler Cameron H Ainsworth
author_sort	Michael Drexler
title	Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
title_short	Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
title_full	Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
title_fullStr	Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
title_full_unstemmed	Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.
title_sort	generalized additive models used to predict species abundance in the gulf of mexico: an ecosystem modeling tool.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/c27a2fd16da0425287d5aefb5a3d9131
work_keys_str_mv	AT michaeldrexler generalizedadditivemodelsusedtopredictspeciesabundanceinthegulfofmexicoanecosystemmodelingtool AT cameronhainsworth generalizedadditivemodelsusedtopredictspeciesabundanceinthegulfofmexicoanecosystemmodelingtool
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Generalized additive models used to predict species abundance in the Gulf of Mexico: an ecosystem modeling tool.

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