Multievent: An Extension of Multistate Capture-Recapture Models to Uncertain States

International audience Capture-recapture models were originally developed to account for encounter probabilities that are less than 1 in free-ranging animal populations. Nowadays, these models can deal with the movement of animals between different locations and are also used to study transitions be...

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Bibliographic Details
Published in:Biometrics
Main Author: Pradel, Roger
Other Authors: Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD France-Sud )
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2005
Subjects:
Memory model
Arnason-Schwarz model
Breeding propensity
Canada goose
Hidden Markov model
Jolly- Move model
[SDE]Environmental Sciences
Canada
Canada Goose
Online Access:https://hal.archives-ouvertes.fr/hal-02126357
https://doi.org/10.1111/j.1541-0420.2005.00318.x
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Summary:International audience Capture-recapture models were originally developed to account for encounter probabilities that are less than 1 in free-ranging animal populations. Nowadays, these models can deal with the movement of animals between different locations and are also used to study transitions between different states. However, their use to estimate transitions between states does not account for uncertainty in state assignment. I present the extension of multievent models, which does incorporate this uncertainty. Multievent models belong to the family of hidden Markov models. I also show in this article that the memory model, in which the next state or location is influenced by the previous state occupied, can be fully treated within the framework of multievent models.

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