Seasonal survival and reversible state effects in a long-distance migratory shorebird
Swift, Rose et al. (2020), Seasonal survival and reversible state effects in a long-distance migratory shorebird, Dryad, Dataset, https://doi.org/10.5061/dryad.v15dv41t4
1. 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.
2. 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.
3. 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.
4. 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.
For detailed description of methods see associated manuscript.
Additionally, see README.rtf for explanations of each dataset and the variables within.