Hierarchical Bayesian analysis of capture–mark–recapture data

We present a hierarchical Bayesian model (HBM) for capture–mark–recapture (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...

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Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Authors: Rivot, E, Prévost, E
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2002
Subjects:
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Atlantic salmon
Salmo salar
Online Access:http://dx.doi.org/10.1139/f02-145
http://www.nrcresearchpress.com/doi/pdf/10.1139/f02-145
Description
Summary:We present a hierarchical Bayesian model (HBM) for capture–mark–recapture (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.

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