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Applications of hidden hybrid Markov/semi‐Markov models: from stopover duration to breeding success dynamics
Author(s) -
Choquet Rémi,
Béchet Arnaud,
Guédon Yann
Publication year - 2014
Publication title -
ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.17
H-Index - 63
ISSN - 2045-7758
DOI - 10.1002/ece3.962
Subject(s) - hidden markov model , computer science , markov process , mark and recapture , toolbox , markov model , markov chain , duration (music) , imperfect , state (computer science) , machine learning , artificial intelligence , mathematics , algorithm , statistics , population , art , linguistics , philosophy , demography , literature , sociology , programming language
Usually in capture–recapture, a model parameter is time or time since first capture dependent. However, the case where the probability of staying in one state depends on the time spent in that particular state is not rare. Hidden Markov models are not appropriate to manage these situations. A more convenient approach would be to consider models that incorporate semi‐Markovian states which explicitly define the waiting time distribution and have been used in previous biologic studies as a convenient framework for modeling the time spent in a given physiological state. Here, we propose hidden Markovian models that combine several nonhomogeneous Markovian states with one semi‐Markovian state and which (i) are well adapted to imperfect and variable detection and (ii) allow us to consider time, time since first capture, and time spent in one state effects. Implementation details depending on the number of semi‐Markovian states are discussed. From a user's perspective, the present approach enhances the toolbox for analyzing capture–recapture data. We then show the potential of this framework by means of two ecological examples: (i) stopover duration and (ii) breeding success dynamics.

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