Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights

Abstract Ecologists and fisheries managers are interested in monitoring economically important marine fish species and using this data to inform management strategies. Determining environmental factors that best predict changes in these populations, particularly under rapid climate change, are a pri...

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Autor principal:	Hannah E. Correia
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d0622fa9f09d40f3b0789ad7346476b3
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spelling	oai:doaj.org-article:d0622fa9f09d40f3b0789ad7346476b32021-12-02T16:57:37ZSemiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights10.1038/s41598-021-89398-82045-2322https://doaj.org/article/d0622fa9f09d40f3b0789ad7346476b32021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89398-8https://doaj.org/toc/2045-2322Abstract Ecologists and fisheries managers are interested in monitoring economically important marine fish species and using this data to inform management strategies. Determining environmental factors that best predict changes in these populations, particularly under rapid climate change, are a priority. I illustrate the application of the least squares-based spline estimation and group LASSO (LSSGLASSO) procedure for selection of coefficient functions in single index varying coefficient models (SIVCMs) on an ecological data set that includes spatiotemporal environmental covariates suspected to play a role in the catches and weights of six groundfish species. Temporal trends in variable selection were apparent, though the selection of variables was largely unrelated to common North Pacific climate indices. These results indicate that the strength of an environmental variable’s effect on a groundfish population may change over time, and not necessarily in-step with known low-frequency patterns of ocean-climate variability commonly attributable to large-scale regime shifts in the North Pacific. My application of the LSSGLASSO procedure for SIVCMs to deep water species using environmental data from various sources illustrates how variable selection with a flexible model structure can produce informative inference for remote and hard-to-reach animal populations.Hannah E. CorreiaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hannah E. Correia Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
description	Abstract Ecologists and fisheries managers are interested in monitoring economically important marine fish species and using this data to inform management strategies. Determining environmental factors that best predict changes in these populations, particularly under rapid climate change, are a priority. I illustrate the application of the least squares-based spline estimation and group LASSO (LSSGLASSO) procedure for selection of coefficient functions in single index varying coefficient models (SIVCMs) on an ecological data set that includes spatiotemporal environmental covariates suspected to play a role in the catches and weights of six groundfish species. Temporal trends in variable selection were apparent, though the selection of variables was largely unrelated to common North Pacific climate indices. These results indicate that the strength of an environmental variable’s effect on a groundfish population may change over time, and not necessarily in-step with known low-frequency patterns of ocean-climate variability commonly attributable to large-scale regime shifts in the North Pacific. My application of the LSSGLASSO procedure for SIVCMs to deep water species using environmental data from various sources illustrates how variable selection with a flexible model structure can produce informative inference for remote and hard-to-reach animal populations.
format	article
author	Hannah E. Correia
author_facet	Hannah E. Correia
author_sort	Hannah E. Correia
title	Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
title_short	Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
title_full	Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
title_fullStr	Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
title_full_unstemmed	Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
title_sort	semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d0622fa9f09d40f3b0789ad7346476b3
work_keys_str_mv	AT hannahecorreia semiparametricmodelselectionforidentificationofenvironmentalcovariatesrelatedtoadultgroundfishcatchesandweights
_version_	1718382530905767936

Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights

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