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Modeling Animals' Behavioral Response by Markov Chain Models for Capture-Recapture Experiments
Hsin-Chou Yang and Anne Chao
Vol. 61, No. 4 (Dec., 2005), pp. 1010-1017
Published by: International Biometric Society
Stable URL: http://www.jstor.org/stable/3695912
Page Count: 8
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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.
Biometrics © 2005 International Biometric Society