Aug 26, 2015

Multivariate State Hidden Markov Models for Mark-Recapture Data

BioRxiv : the Preprint Server for Biology
Devin S. JohnsonRobert L. DeLong

Abstract

State-based Cormack-Jolly-Seber (CJS) models have become an often used method for assessing states or conditions of free ranging animals through time. Although originally envisioned to account for differences in survival and observation processes when animals are moving though various geographical strata, it has evolved to model vital rates in different life-history or diseased states. We further extend this useful class of models to the case of multivariate state data. Researchers can record values of several different states of interest, e.g., geographic location and reproductive state. Traditionally, these would be aggregated into one state with a single probability of state uncertainty. However, by modeling states as a multivariate vector, one can account for partial knowledge of the vector as well as dependence between the state variables in a parsimonious way. A hidden Markov model formulation allows straightforward maximum likelihood inference. The proposed HMM models are demonstrated with a case study using data from a California sea lion vital rates study.

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Aggregation
Research Personnel
Zalophus califonianus
Genetic Vectors
Case-Control Studies
Solid state

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