Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus
Growth modelling is essential to inform fisheries management but is often hampered by sampling biases and imperfect data. Additional methods such as interpolating data through back-calculation may be used to account for sampling bias but are often complex and time-consuming. Here, we present an appr...
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Online Access: | http://hdl.handle.net/10451/55425 https://doi.org/10.3390/fishes7010052 |
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ftunivlisboa:oai:repositorio.ul.pt:10451/55425 2023-05-15T17:41:27+02:00 Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus Neves, Ana Vieira, Ana Rita Sequeira, Vera Silva, Elisabete Silva, Frederica Duarte, Ana Marta Mendes, Susana Ganhão, Rui Assis, Carlos Sampaio e rebelo, Rui Magalhães, Maria Filomena Gil, Maria Manuel Gordo, Leonel Serrano 2022-12-16T07:46:03Z http://hdl.handle.net/10451/55425 https://doi.org/10.3390/fishes7010052 eng eng MDPI European Maritime and Fisheries Fund MAR2020 project “VALOREJET: Valorização de espécies rejeitadas e de baixo valor comercial MAR-01.03.01-FEAMP-0003 FCT CEECIND/02705/2017 FCT CEECIND/01528/2017 FCT UIBD/04292/2020 http://hdl.handle.net/10451/55425 doi:10.3390/fishes7010052 openAccess http://creativecommons.org/licenses/by/4.0/ CC-BY article 2022 ftunivlisboa https://doi.org/10.3390/fishes7010052 2022-12-21T01:05:04Z Growth modelling is essential to inform fisheries management but is often hampered by sampling biases and imperfect data. Additional methods such as interpolating data through back-calculation may be used to account for sampling bias but are often complex and time-consuming. Here, we present an approach to improve plausibility in growth estimates when small individuals are under-sampled, based on Bayesian fitting growth models using Markov Chain Monte Carlo (MCMC) with informative priors on growth parameters. Focusing on the blue jack mackerel, Trachurus picturatus, which is an important commercial fish in the southern northeast Atlantic, this Bayesian approach was evaluated in relation to standard growth model fitting methods, using both direct readings and back-calculation data. Matched growth parameter estimates were obtained with the von Bertalanffy growth function applied to back-calculated length at age and the Bayesian fitting, using MCMC to direct age readings, with both outperforming all other methods assessed. These results indicate that Bayesian inference may be a powerful addition in growth modelling using imperfect data and should be considered further in age and growth studies, provided relevant biological information can be gathered and included in the analyses. info:eu-repo/semantics/publishedVersion Article in Journal/Newspaper Northeast Atlantic Universidade de Lisboa: repositório.UL Fishes 7 1 52 |
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Open Polar |
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Universidade de Lisboa: repositório.UL |
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ftunivlisboa |
language |
English |
description |
Growth modelling is essential to inform fisheries management but is often hampered by sampling biases and imperfect data. Additional methods such as interpolating data through back-calculation may be used to account for sampling bias but are often complex and time-consuming. Here, we present an approach to improve plausibility in growth estimates when small individuals are under-sampled, based on Bayesian fitting growth models using Markov Chain Monte Carlo (MCMC) with informative priors on growth parameters. Focusing on the blue jack mackerel, Trachurus picturatus, which is an important commercial fish in the southern northeast Atlantic, this Bayesian approach was evaluated in relation to standard growth model fitting methods, using both direct readings and back-calculation data. Matched growth parameter estimates were obtained with the von Bertalanffy growth function applied to back-calculated length at age and the Bayesian fitting, using MCMC to direct age readings, with both outperforming all other methods assessed. These results indicate that Bayesian inference may be a powerful addition in growth modelling using imperfect data and should be considered further in age and growth studies, provided relevant biological information can be gathered and included in the analyses. info:eu-repo/semantics/publishedVersion |
format |
Article in Journal/Newspaper |
author |
Neves, Ana Vieira, Ana Rita Sequeira, Vera Silva, Elisabete Silva, Frederica Duarte, Ana Marta Mendes, Susana Ganhão, Rui Assis, Carlos Sampaio e rebelo, Rui Magalhães, Maria Filomena Gil, Maria Manuel Gordo, Leonel Serrano |
spellingShingle |
Neves, Ana Vieira, Ana Rita Sequeira, Vera Silva, Elisabete Silva, Frederica Duarte, Ana Marta Mendes, Susana Ganhão, Rui Assis, Carlos Sampaio e rebelo, Rui Magalhães, Maria Filomena Gil, Maria Manuel Gordo, Leonel Serrano Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
author_facet |
Neves, Ana Vieira, Ana Rita Sequeira, Vera Silva, Elisabete Silva, Frederica Duarte, Ana Marta Mendes, Susana Ganhão, Rui Assis, Carlos Sampaio e rebelo, Rui Magalhães, Maria Filomena Gil, Maria Manuel Gordo, Leonel Serrano |
author_sort |
Neves, Ana |
title |
Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
title_short |
Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
title_full |
Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
title_fullStr |
Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
title_full_unstemmed |
Modelling Fish Growth with Imperfect Data: The Case of Trachurus picturatus |
title_sort |
modelling fish growth with imperfect data: the case of trachurus picturatus |
publisher |
MDPI |
publishDate |
2022 |
url |
http://hdl.handle.net/10451/55425 https://doi.org/10.3390/fishes7010052 |
genre |
Northeast Atlantic |
genre_facet |
Northeast Atlantic |
op_relation |
European Maritime and Fisheries Fund MAR2020 project “VALOREJET: Valorização de espécies rejeitadas e de baixo valor comercial MAR-01.03.01-FEAMP-0003 FCT CEECIND/02705/2017 FCT CEECIND/01528/2017 FCT UIBD/04292/2020 http://hdl.handle.net/10451/55425 doi:10.3390/fishes7010052 |
op_rights |
openAccess http://creativecommons.org/licenses/by/4.0/ |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3390/fishes7010052 |
container_title |
Fishes |
container_volume |
7 |
container_issue |
1 |
container_start_page |
52 |
_version_ |
1766143027579977728 |