Integrated population modeling identifies low duckling survival as a key driver of decline in a European population of the Mallard

Yannick Wiegers, Eelke Jongejans* (Corresponding author), Chris A.M. Van Turnhout, Loes van den Bremer, H.P. van der Jeugd, Erik Kleyheeg

*Corresponding author for this work

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Abstract

Europe’s highest densities of breeding Mallards (Anas platyrhynchos) are found in the Netherlands, but the breeding population there has declined by ~30% since the 1990s. The exact cause of this decline has remained unclear. Here, we used an integrated population model to jointly analyze Mallard population survey, nest survey, duckling survival, and band-recovery data. We used this approach to holistically estimate all relevant vital rates, including duckling survival rates for years for which no explicit data were available. Mean vital rate estimates were high for nest success (0.38 ± 0.01) and egg hatch rate (0.96 ± 0.001), but relatively low for clutch size (8.2 ± 0.05) compared to populations in other regions. Estimates for duckling survival rate for the three years for which explicit data were available were low (0.16–0.27) compared to historical observations, but were comparable to rates reported for other regions with declining populations. Finally, the mean survival rate was low for ducklings (0.18 ± 0.02), but high and stable for adults (0.71 ± 0.03). Population growth rate was only affected by variation in duckling survival, but since this is a predominantly latent state variable, this result should be interpreted with caution. However, it does strongly indicate that none of the other vital rates, all of which were supported by data, was able to sufficiently explain the population decline. Together with a comparison with historic vital rates, these findings point to a reduced duckling survival rate as the likely cause of the decline. Candidate drivers of reduced duckling survival are increased predation pressure and reduced food availability, but this requires future study. Integrated population modeling can provide valuable insights into population dynamics even when empirical data for a key parameter are partly missing.
Original languageEnglish
Pages (from-to)1-12
JournalOrnithological Applications
Volume124
DOIs
Publication statusPublished - 2022

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