Semiparametric Regression in Capture-Recapture Modeling

O Gimenez (1) , C Crainiceanu (2) , C Barbraud (3) , S Jenouvrier (3) , B J T Morgan

1 CEFE - Centre d’Ecologie Fonctionnelle et Evolutive
2 JHU - Johns Hopkins University
3 CEBC - Centre d'Études Biologiques de Chizé

Capture-recapture models were developed to estimate survival using data arising from marking and monitoring wild animals over time. Variation in survival may be explained by incorporating relevant covariates. We propose nonparametric and semiparametric regression methods for estimating survival in capture-recapture models. A fully Bayesian approach using Markov chain Monte Carlo simulations was employed to estimate the model parameters. The work is illustrated by a study of Snow petrels, in which survival probabilities are expressed as nonlinear functions of a climate covariate, using data from a 40-year study on marked individuals, nesting at Petrels Island, Terre Adélie.

Mots clés: environmental covariates; penalized splines; Auxiliary variables; Bayesian inference; Demographic rates; Environmental covariates; Penalized splines; WinBUGS; auxiliary variables; demographic rates

Domaines: Mathématiques [math]; Sciences de l'environnement

O Gimenez, C Crainiceanu, C Barbraud, S Jenouvrier, B J T Morgan. Semiparametric Regression in Capture-Recapture Modeling. Biometrics, 2006, 62 (3), pp.691 - 698. ⟨10.1111/j.1541-0420.2005.00514.x⟩. ⟨hal-03498951⟩