The Efficient Semiparametric Regression Modeling of Capture-Recapture Data: Assessing the Impact of Climate on Survival of Two Antarctic Seabird Species

Abstract

A nonparametric approach has recently been proposed for estimating survival in capture–recapture models, which uses penalized splines to achieve flexibility in exploring the relationships with environmental covariates. However, this method is highly time-consuming because it is implemented through a fully Bayesian approach using Markov chain Monte Carlo simulations. To cope with this issue, we developed a two-step approach in which the existing method is used in conjunction with a multivariate normal approximation to the capture–recapture data likelihood. The ability of our approach to capture various nonlinearities in demographic parameters was validated by carrying out a simulation study. Two examples dealing with Snow petrel and Emperor penguin capture–recapture data sets were also considered to illustrate our procedure, including the relationship between survival rate, population size and climatic covariates. Keywords Auxiliary variables · Bayesian inference · Bivariate smoothing · Computational efficiency · Demographic rates · Environmental covariates · Interactions · Multivariate normal approximation · Penalized-splines · WinBUGS