Monte Carlo estimation of stage structured development from cohort data - Jonas Knape; Perry de Valpine (Ecology, 2016)
Cohort data are frequently collected to study stage‐structured development and mortalities of many organisms, particularly arthropods. Such data can provide information on mean stage durations, among‐individual variation in stage durations, and on mortality rates. Current statistical methods for cohort data lack flexibility in the specification of stage duration distributions and mortality rates. In this paper, we present a new method for fitting models of stage‐duration distributions and mortality to cohort data. The method is based on a Monte Carlo within MCMC algorithm and provides Bayesian estimates of parameters of stage‐structured cohort models. The algorithm is computationally demanding but allows for flexible specifications of stage‐duration distributions and mortality rates. We illustrate the algorithm with an application to data from a previously published experiment on the development of brine shrimp from Mono Lake, California, through nine successive stages. In the experiment, three different food supply and temperature combination treatments were studied. We compare the mean duration of the stages among the treatments while simultaneously estimating mortality rates and among‐individual variance of stage durations. The method promises to enable more detailed studies of development of both natural and experimental cohorts. An R package implementing the method and which allows flexible specification of stage duration distributions is provided.