Markov-modulated Poisson processes as a new framework for analyzing capture-recapture data

1.Opportunistic capture-recapture data consists of observations over non-constant time-intervals and so fails to satisfy the basic assumptions of traditional capture-recapture models. Analyzing opportunistic capture-recapture data is often done by discretizing time-intervals or summarizing data, but without taking into account the continuous-time process of the state and/or the capture.

2.To deal with non-constant time-intervals, continuous-time closed capture-recapture models have been proposed by Yip et al. (1996), Hwang & Chao (2002), Schofield et al. (2017) for estimating population size. More recently, a continuous-time Cormack-Jolly-Seber model has been proposed by Fouchet et al. (2016) to reduce bias in survival rates, and a two-state process has been proposed by Choquet et al. (2017) to estimate reproduction rates and survival rates of young within a season.

3.The aim of the current study is to demonstrate how an approach based on a Markov-modulated Poisson process (MM...