Hierarchical Bayesian analysis of capturemarkrecapture data
We present a hierarchical Bayesian model (HBM) for capturemarkrecapture (CMR) data analysis. It aims at estimating the probability of capture (θ i ) and the total population size (N i ) in a series of I years i = 1,...,I. The HBM assumes that the θ i s and N i s are sampled from a common probabili...
Published in: | Canadian Journal of Fisheries and Aquatic Sciences |
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Online Access: | http://dx.doi.org/10.1139/f02-145 http://www.nrcresearchpress.com/doi/pdf/10.1139/f02-145 |
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crcansciencepubl:10.1139/f02-145 2023-12-17T10:27:26+01:00 Hierarchical Bayesian analysis of capturemarkrecapture data Rivot, E Prévost, E 2002 http://dx.doi.org/10.1139/f02-145 http://www.nrcresearchpress.com/doi/pdf/10.1139/f02-145 en eng Canadian Science Publishing http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining Canadian Journal of Fisheries and Aquatic Sciences volume 59, issue 11, page 1768-1784 ISSN 0706-652X 1205-7533 Aquatic Science Ecology, Evolution, Behavior and Systematics journal-article 2002 crcansciencepubl https://doi.org/10.1139/f02-145 2023-11-19T13:38:39Z We present a hierarchical Bayesian model (HBM) for capturemarkrecapture (CMR) data analysis. It aims at estimating the probability of capture (θ i ) and the total population size (N i ) in a series of I years i = 1,...,I. The HBM assumes that the θ i s and N i s are sampled from a common probability distribution with unknown parameters. It is compared with the model assuming independence between years in the θ i s and N i s (ABM). We show how a transfer of information between years is organized by the HBM. We compare the merits of HBM vs. ABM to estimate the spawning run and smolt run of an Atlantic salmon (Salmo salar) population of the River Oir (France) over a period of 17 years. In the spawners case, yearly data are poorly informative. Consequently, the HBM greatly improves posterior inferences compared with the ABM in terms of dispersion and robustness to the choice of prior. In the smolts case, the HBM does not significantly improve inferences compared with the ABM because data are more informative. We discuss why hierarchical modeling should be recommended in any ecological study where the data are collected on several sampling units that share some common features. Article in Journal/Newspaper Atlantic salmon Salmo salar Canadian Science Publishing (via Crossref) Canadian Journal of Fisheries and Aquatic Sciences 59 11 1768 1784 |
institution |
Open Polar |
collection |
Canadian Science Publishing (via Crossref) |
op_collection_id |
crcansciencepubl |
language |
English |
topic |
Aquatic Science Ecology, Evolution, Behavior and Systematics |
spellingShingle |
Aquatic Science Ecology, Evolution, Behavior and Systematics Rivot, E Prévost, E Hierarchical Bayesian analysis of capturemarkrecapture data |
topic_facet |
Aquatic Science Ecology, Evolution, Behavior and Systematics |
description |
We present a hierarchical Bayesian model (HBM) for capturemarkrecapture (CMR) data analysis. It aims at estimating the probability of capture (θ i ) and the total population size (N i ) in a series of I years i = 1,...,I. The HBM assumes that the θ i s and N i s are sampled from a common probability distribution with unknown parameters. It is compared with the model assuming independence between years in the θ i s and N i s (ABM). We show how a transfer of information between years is organized by the HBM. We compare the merits of HBM vs. ABM to estimate the spawning run and smolt run of an Atlantic salmon (Salmo salar) population of the River Oir (France) over a period of 17 years. In the spawners case, yearly data are poorly informative. Consequently, the HBM greatly improves posterior inferences compared with the ABM in terms of dispersion and robustness to the choice of prior. In the smolts case, the HBM does not significantly improve inferences compared with the ABM because data are more informative. We discuss why hierarchical modeling should be recommended in any ecological study where the data are collected on several sampling units that share some common features. |
format |
Article in Journal/Newspaper |
author |
Rivot, E Prévost, E |
author_facet |
Rivot, E Prévost, E |
author_sort |
Rivot, E |
title |
Hierarchical Bayesian analysis of capturemarkrecapture data |
title_short |
Hierarchical Bayesian analysis of capturemarkrecapture data |
title_full |
Hierarchical Bayesian analysis of capturemarkrecapture data |
title_fullStr |
Hierarchical Bayesian analysis of capturemarkrecapture data |
title_full_unstemmed |
Hierarchical Bayesian analysis of capturemarkrecapture data |
title_sort |
hierarchical bayesian analysis of capturemarkrecapture data |
publisher |
Canadian Science Publishing |
publishDate |
2002 |
url |
http://dx.doi.org/10.1139/f02-145 http://www.nrcresearchpress.com/doi/pdf/10.1139/f02-145 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
Canadian Journal of Fisheries and Aquatic Sciences volume 59, issue 11, page 1768-1784 ISSN 0706-652X 1205-7533 |
op_rights |
http://www.nrcresearchpress.com/page/about/CorporateTextAndDataMining |
op_doi |
https://doi.org/10.1139/f02-145 |
container_title |
Canadian Journal of Fisheries and Aquatic Sciences |
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59 |
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11 |
container_start_page |
1768 |
op_container_end_page |
1784 |
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1785579296228638720 |