A general modeling framework for open wildlife populations based on the Polya tree prior.
Bayesian nonparametrics
Polya tree
capture-recapture
count data
ring recovery
statistical ecology
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
09 2023
09 2023
Historique:
received:
14
05
2021
accepted:
22
07
2022
medline:
13
9
2023
pubmed:
7
9
2022
entrez:
6
9
2022
Statut:
ppublish
Résumé
Wildlife monitoring for open populations can be performed using a number of different survey methods. Each survey method gives rise to a type of data and, in the last five decades, a large number of associated statistical models have been developed for analyzing these data. Although these models have been parameterized and fitted using different approaches, they have all been designed to either model the pattern with which individuals enter and/or exit the population, or to estimate the population size by accounting for the corresponding observation process, or both. However, existing approaches rely on a predefined model structure and complexity, either by assuming that parameters linked to the entry and exit pattern (EEP) are specific to sampling occasions, or by employing parametric curves to describe the EEP. Instead, we propose a novel Bayesian nonparametric framework for modeling EEPs based on the Polya tree (PT) prior for densities. Our Bayesian nonparametric approach avoids overfitting when inferring EEPs, while simultaneously allowing more flexibility than is possible using parametric curves. Finally, we introduce the replicate PT prior for defining classes of models for these data allowing us to impose constraints on the EEPs, when required. We demonstrate our new approach using capture-recapture, count, and ring-recovery data for two different case studies.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2171-2183Informations de copyright
© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.
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