  We discuss the semiparametric modeling of mark-recapture-recovery data where
the temporal and/or individual variation of model parameters is explained via
covariates. Typically, in such analyses a fixed (or mixed) effects parametric
model is specified for the relationship between the model parameters and the
covariates of interest. In this paper, we discuss the modeling of the
relationship via the use of penalized splines, to allow for considerably more
flexible functional forms. Corresponding models can be fitted via numerical
maximum penalized likelihood estimation, employing cross-validation to choose
the smoothing parameters in a data-driven way. Our contribution builds on and
extends the existing literature, providing a unified inferential framework for
semiparametric mark-recapture-recovery models for open populations, where the
interest typically lies in the estimation of survival probabilities. The
approach is applied to two real datasets, corresponding to grey herons (Ardea
Cinerea), where we model the survival probability as a function of
environmental condition (a time-varying global covariate), and Soay sheep (Ovis
Aries), where we model the survival probability as a function of individual
weight (a time-varying individual-specific covariate). The proposed
semiparametric approach is compared to a standard parametric (logistic)
regression and new interesting underlying dynamics are observed in both cases.
