Multistate capture–recapture models for irregularly sampled data

Abstract

Multistate capture-recapture data comprise individual-specific sighting histories, together with information on individuals’ states related, for example, to breeding status, infection level, or geographical location. Such data are often analysed using the Arnason–Schwarz model, where transitions between states are modelled using a discrete-time Markov chain, making the model most easily applicable to regular time series. When time intervals between capture occasions are not of equal length, more complex time-dependent constructions may be required, increasing the number of parameters to estimate, decreasing interpretability, and potentially leading to reduced precision. Here we develop a multi-state model based on a state process operating in continuous time, which can be regarded as an analogue of the discrete-time Arnason–Schwarz model for irregularly sampled data. Statistical inference is carried out by regarding the capture-recapture data as realisations from a continuous-time hidden Markov model, which allows the associated efficient algorithms to be used for maximum likelihood estimation and state decoding. To illustrate the feasibility of the modelling framework, we use a long-term survey of bottlenose dolphins where capture occasions are not regularly spaced through time. Here, we are particularly interested in seasonal effects on the movement rates of the dolphins along the Scottish east coast. The results reveal seasonal movement patterns between two core areas of their range, providing information that will inform conservation management.