Multivariate-State Hidden Markov Models for Mark-Recapture Data

• Activity Number: 675
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Devin Johnson* and Jeff Laake and Rod Towell
• Companies: NOAA National Marine Mammal Lab and NOAA National Marine Mammal Lab and NOAA National Marine Mammal Lab
• Keywords: capture-recapture ; Hidden Markov Model ; multistrata ; multivariate ; state uncertainty

Abstract

Multistrata capture-recapture 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 and Bayesian inference. The proposed HMM models are demonstrated with two data sets from sea lion vital rate studies. The first analysis examines dependency in loss of flipper tags, the second, examines reproductive state in different locations.