Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method
We present a hierarchical Bayesian modelling (HBM) framework for estimating riverine fish population size from successive removal data via electrofishing. It is applied to the estimation of the population of Atlantic salmon (Salmo salar) juveniles in the Oir River (France). The data set consists of...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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2008
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crcansciencepubl:10.1139/f07-153 2024-06-23T07:51:23+00:00 Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method Rivot, Etienne Prévost, Etienne Cuzol, Anne Baglinière, Jean-Luc Parent, Eric 2008 http://dx.doi.org/10.1139/f07-153 http://www.nrcresearchpress.com/doi/pdf/10.1139/f07-153 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Fisheries and Aquatic Sciences volume 65, issue 1, page 117-133 ISSN 0706-652X 1205-7533 journal-article 2008 crcansciencepubl https://doi.org/10.1139/f07-153 2024-06-13T04:10:51Z We present a hierarchical Bayesian modelling (HBM) framework for estimating riverine fish population size from successive removal data via electrofishing. It is applied to the estimation of the population of Atlantic salmon (Salmo salar) juveniles in the Oir River (France). The data set consists of 10 sampling sites sampled by one or two removals over a period of 20 years (1986–2005). We develop and contrast four models to assess the effect of temporal variations and habitat type on the density of fish and the probability of capture. The Bayes factor and the deviance information criterion are used to compare these models. The most credible and parsimonious model is the one that accounts for the effects of the years and the habitat type on the density of fish. It is used to extrapolate the population size in the entire river reach. This paper illustrates that HBM successfully accommodates large but sparse data sets containing poorly informative data for some units. Its conditional structure enables it to borrow strength from data-rich to data-poor units, thus improving the estimations. Predictions of the population size of the entire river reach can be derived, while accounting for all sources of uncertainty. Article in Journal/Newspaper Atlantic salmon Salmo salar Canadian Science Publishing Canadian Journal of Fisheries and Aquatic Sciences 65 1 117 133 |
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Open Polar |
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Canadian Science Publishing |
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crcansciencepubl |
language |
English |
description |
We present a hierarchical Bayesian modelling (HBM) framework for estimating riverine fish population size from successive removal data via electrofishing. It is applied to the estimation of the population of Atlantic salmon (Salmo salar) juveniles in the Oir River (France). The data set consists of 10 sampling sites sampled by one or two removals over a period of 20 years (1986–2005). We develop and contrast four models to assess the effect of temporal variations and habitat type on the density of fish and the probability of capture. The Bayes factor and the deviance information criterion are used to compare these models. The most credible and parsimonious model is the one that accounts for the effects of the years and the habitat type on the density of fish. It is used to extrapolate the population size in the entire river reach. This paper illustrates that HBM successfully accommodates large but sparse data sets containing poorly informative data for some units. Its conditional structure enables it to borrow strength from data-rich to data-poor units, thus improving the estimations. Predictions of the population size of the entire river reach can be derived, while accounting for all sources of uncertainty. |
format |
Article in Journal/Newspaper |
author |
Rivot, Etienne Prévost, Etienne Cuzol, Anne Baglinière, Jean-Luc Parent, Eric |
spellingShingle |
Rivot, Etienne Prévost, Etienne Cuzol, Anne Baglinière, Jean-Luc Parent, Eric Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
author_facet |
Rivot, Etienne Prévost, Etienne Cuzol, Anne Baglinière, Jean-Luc Parent, Eric |
author_sort |
Rivot, Etienne |
title |
Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
title_short |
Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
title_full |
Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
title_fullStr |
Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
title_full_unstemmed |
Hierarchical Bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
title_sort |
hierarchical bayesian modelling with habitat and time covariates for estimating riverine fish population size by successive removal method |
publisher |
Canadian Science Publishing |
publishDate |
2008 |
url |
http://dx.doi.org/10.1139/f07-153 http://www.nrcresearchpress.com/doi/pdf/10.1139/f07-153 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
Canadian Journal of Fisheries and Aquatic Sciences volume 65, issue 1, page 117-133 ISSN 0706-652X 1205-7533 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/f07-153 |
container_title |
Canadian Journal of Fisheries and Aquatic Sciences |
container_volume |
65 |
container_issue |
1 |
container_start_page |
117 |
op_container_end_page |
133 |
_version_ |
1802642477920813056 |