Seasonal survival and reversible state effects in a long-distance migratory shorebird.

Events during one stage of the annual cycle can reversibly affect an individual's condition and performance not only within that stage, but also in subsequent stages (i.e. reversible state effects). Despite strong conceptual links, however, few studies have been able to empirically link individual-level reversible state effects with larger-scale demographic processes. We studied both survival and potential reversible state effects in a long-distance migratory shorebird, the Hudsonian Godwit Limosa haemastica. Specifically, we estimated period-specific survival probabilities across the annual cycle and examined the extent to which an individual's body condition, foraging success and habitat quality during the nonbreeding season affected its subsequent survival and reproductive performance. Godwit survival rates were high throughout the annual cycle, but lowest during the breeding season, only slightly higher during southbound migration and highest during the stationary nonbreeding season. Our results indicate that overwintering godwits foraging in high-quality habitats had comparably better nutritional status and pre-migratory body condition, which in turn improved their return rates and the likelihood that their nests and chicks survived during the subsequent breeding season. Reversible state effects thus appeared to link events between nonbreeding and breeding seasons via an individual's condition, in turn affecting their survival and subsequent reproductive performance. Our study thus provides one of the few empirical demonstrations of theoretical predictions that reversible state effects have the potential to influence population dynamics.

Population size and stopover duration estimation using mark-resight data and Bayesian analysis of a superpopulation model.

We present a novel formulation of a mark-recapture-resight model that allows estimation of population size, stopover duration, and arrival and departure schedules at migration areas. Estimation is based on encounter histories of uniquely marked individuals and relative counts of marked and unmarked animals. We use a Bayesian analysis of a state-space formulation of the Jolly-Seber mark-recapture model, integrated with a binomial model for counts of unmarked animals, to derive estimates of population size and arrival and departure probabilities. We also provide a novel estimator for stopover duration that is derived from the latent state variable representing the interim between arrival and departure in the state-space model. We conduct a simulation study of field sampling protocols to understand the impact of superpopulation size, proportion marked, and number of animals sampled on bias and precision of estimates. Simulation results indicate that relative bias of estimates of the proportion of the population with marks was low for all sampling scenarios and never exceeded 2%. Our approach does not require enumeration of all unmarked animals detected or direct knowledge of the number of marked animals in the population at the time of the study. This provides flexibility and potential application in a variety of sampling situations (e.g., migratory birds, breeding seabirds, sea turtles, fish, pinnipeds, etc.). Application of the methods is demonstrated with data from a study of migratory sandpipers.