Modeling animals' behavioral response by Markov chain models for capture-recapture experiments.
A bivariate Markov chain approach that includes both enduring (long-term) and ephemeral (short-term) behavioral effects in models for capture-recapture experiments is proposed. The capture history of each animal is modeled as a Markov chain with a bivariate state space with states determined by the capture status (capture/noncapture) and marking status (marked/unmarked). In this framework, a conditional-likelihood method is used to estimate the population size and the transition probabilities. The classical behavioral model that assumes only an enduring behavioral effect is included as a special case of the bivariate Markovian model. Another special case that assumes only an ephemeral behavioral effect reduces to a univariate Markov chain based on capture/noncapture status. The model with the ephemeral behavioral effect is extended to incorporate time effects; in this model, in contrast to extensions of the classical behavioral model, all parameters are identifiable. A data set is analyzed to illustrate the use of the Markovian models in interpreting animals' behavioral response. Simulation results are reported to examine the performance of the estimators.
Yang
Hsin-Chou
HC
Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan. hsinchou@ibms.sinica.edu.tw
Chao
Anne
A
D000818:Animals; D003411:Arvicolinae; D001522:Behavior, Animal; D003198:Computer Simulation; D008390:Markov Chains; D015233:Models, Statistical; D011156:Population Density