Switching state-space models for modeling penguin population dynamics

Tracking individual animals through time using mark-recapture methods is the gold standard for understanding how environmental conditions influence demographic rates, but applying such tags is often infeasible due to the difficulty of catching animals or attaching marks/tags without influencing behavior or survival. Due to the logistical challenges and emerging ethical concerns with flipper banding penguins, relatively little is known about spatial variation in demographic rates, spatial variation in demographic stochasticity, or the role that stochasticity may play in penguin population dynamics. Here we describe how adaptive importance sampling can be used to fit age-structured population models to time series of point counts. While some demographic parameters are difficult to learn through point counts alone, others can be estimated, even in the face of missing data. Here we demonstrate the application of adaptive importance sampling using two case studies, one in which we permit immigration and another permitting regime switching in reproductive success. We apply these methods to extract demographic information from several time series of observed abundance in gentoo and Adelie penguins in Antarctica. Our method is broadly applicable to time series of abundance and provides a feasible means of fitting age-structured models without marking individuals.