We develop alternative strategies for building and fitting parametric capture-recapture models for closed populations which can be used to address a better understanding of behavioral patterns. In the perspective of transition models, we first rely on a conditional probability parameterization. A large subset of standard capture-recapture models can be regarded as a suitable partitioning in equivalence classes of the full set of conditional probability parameters. We exploit a regression approach combined with the use of new suitable summaries of the conditioning binary partial capture histories as a device for enlarging the scope of behavioral models and also exploring the range of all possible partitions. We show how one can easily find unconditional MLE of such models within a generalized linear model framework. We illustrate the potential of our approach with the analysis of some known datasets and a simulation study.
Keywords: Behavioral response; Mark-recapture; Markov models; Memory effect; Memory-related summary statistics; Population size.
© 2015, The International Biometric Society.