RT Journal Article SR Electronic T1 Multivariate State Hidden Markov Models for Mark-Recapture Data JF bioRxiv FD Cold Spring Harbor Laboratory SP 025569 DO 10.1101/025569 A1 Devin S. Johnson A1 Jeff L. Laake A1 Sharon R. Melin A1 Robert L. DeLong YR 2015 UL http://biorxiv.org/content/early/2015/11/19/025569.1.abstract AB 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, the model 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 (HMM) 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.