Multistate capture-recapture analysis under imperfect state observation: an application to disease models.

Published online
15 Apr 2009
Content type
Journal article
Journal title
Journal of Applied Ecology

Conn, P. B. & Cooch, E. G.
Contact email(s)

Publication language
USA & New York


Multistate capture-recapture models are frequently used to estimate the survival and state transition parameters needed to parameterize stage-structured population models, tools that are important for conservation and management. Typically, such models assume that all encountered individuals can be assigned to a particular state without error or ambiguity, a requirement which is difficult to meet in practice. Model extensions to relax this assumption would increase the richness of ecological data sets available for estimating life-history and stage-transition parameters with multistate models. One relatively common analytical approach when confronted with ambiguity in state determination is to censor all encounters where the state of an animal cannot be ascertained. Here, we present an alternative approach, which uses a hidden Markov (or multievent) modelling framework that can incorporate data from encounters of unknown state. Using simulation, we show that our approach leads to estimators of state-specific survival and transition probabilities that are more precise, and sometimes considerably so, than methods based on censoring. We demonstrate our approach using field data from a study of the dynamics of conjunctivitis in the house finch Carpodacus mexicanus Müller. A fundamental challenge in modelling disease dynamics involves the estimation of the rates of entry and exit from one or more disease states, which can be complicated when disease state is uncertain. We show that incorporating data from unknown states made substantial improvements to parameter precision. Synthesis and applications. Missing or incomplete records are an unfortunate but common feature of many ecological field studies, often diminishing the quality and quantity of data. Our approach of treating state as a hidden Markov process allows such records to be used, increasing the precision of survival and state transition parameters in multistate mark-recapture studies. Our approach is more general than other approaches in the literature, and does not require specialized sampling designs or ancillary information to inform state assignment. We suggest that ecologists consider using this modelling approach instead of censoring records whenever state information is missing.

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