Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany
Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection proce...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Language: | English |
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2022
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Online Access: | http://dx.doi.org/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/full-xml/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2020-0368 |
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crcansciencepubl:10.1139/cjfas-2020-0368 2024-09-15T17:56:07+00:00 Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany Lebot, Clément Arago, Marie-Andrée Beaulaton, Laurent Germis, Gaëlle Nevoux, Marie Rivot, Etienne Prévost, Etienne 2022 http://dx.doi.org/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/full-xml/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2020-0368 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Fisheries and Aquatic Sciences volume 79, issue 4, page 533-547 ISSN 0706-652X 1205-7533 journal-article 2022 crcansciencepubl https://doi.org/10.1139/cjfas-2020-0368 2024-08-01T04:10:02Z Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a hierarchical Bayesian modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon (Salmo salar) populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates, and fishing effort. Additional data of total or partial abundance collected in four closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied. Article in Journal/Newspaper Atlantic salmon Salmo salar Canadian Science Publishing Canadian Journal of Fisheries and Aquatic Sciences |
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Open Polar |
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Canadian Science Publishing |
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crcansciencepubl |
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English |
description |
Estimation of abundance with wide spatiotemporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a hierarchical Bayesian modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon (Salmo salar) populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates, and fishing effort. Additional data of total or partial abundance collected in four closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied. |
format |
Article in Journal/Newspaper |
author |
Lebot, Clément Arago, Marie-Andrée Beaulaton, Laurent Germis, Gaëlle Nevoux, Marie Rivot, Etienne Prévost, Etienne |
spellingShingle |
Lebot, Clément Arago, Marie-Andrée Beaulaton, Laurent Germis, Gaëlle Nevoux, Marie Rivot, Etienne Prévost, Etienne Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
author_facet |
Lebot, Clément Arago, Marie-Andrée Beaulaton, Laurent Germis, Gaëlle Nevoux, Marie Rivot, Etienne Prévost, Etienne |
author_sort |
Lebot, Clément |
title |
Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
title_short |
Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
title_full |
Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
title_fullStr |
Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
title_full_unstemmed |
Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on Atlantic salmon populations of Brittany |
title_sort |
taking full advantage of the diverse assemblage of data at hand to produce time series of abundance: a case study on atlantic salmon populations of brittany |
publisher |
Canadian Science Publishing |
publishDate |
2022 |
url |
http://dx.doi.org/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/full-xml/10.1139/cjfas-2020-0368 https://cdnsciencepub.com/doi/pdf/10.1139/cjfas-2020-0368 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
Canadian Journal of Fisheries and Aquatic Sciences volume 79, issue 4, page 533-547 ISSN 0706-652X 1205-7533 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/cjfas-2020-0368 |
container_title |
Canadian Journal of Fisheries and Aquatic Sciences |
_version_ |
1810432331217895424 |