Data from: 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
(MMPP) (Freed & Shepp, 1982) allows, in a similar way to a
multistate model, to model opportunistic data using several states. To
this end, several multistate models were rewritten as MMPP models,
showing, the potential for this approach to address the ecological
questions as multistate models, but using an extended data framework. In
particular, it is a useful framework for dealing with data that has
unordered levels of uncertainty. 4.The methods were illustrated using
simulations and analysis of data on the Alpine ibex (Capraibex).