Supplementary material for "Dynamics of a goshawk population across half a century is driven by the variation of first-year survival"

Abstract

Population dynamics are driven by stochastic and density-dependent processes acting on demographic rates. Individuals differ demographically, and to capture these differences, models of population dynamics are usually structured by age and stage, rarely by sex. An effect of sex on population dynamics is expected if the dynamics of males and females differ, requiring an unequal sex ratio at birth and/or sex-specific survival probabilities. Goshawks (Accipiter gentilis) show large sexual size dimorphism and differential survival, but it is unknown whether males and females contribute differently to population dynamics. We studied a goshawk population in northern Germany over 47 years using brood monitoring data, collected feathers and nestling ringing data. We jointly analyzed the data using a two-sex integrated population model and performed retrospective and prospective population analyses to understand whether the demographic drivers of population change differ between the sexes. The population showed large fluctuations, during which the number of breeding pairs doubled, but the long-term trend of the population was slightly negative. Female survival exceeded male survival during the first year of life. Females started to reproduce at a younger age than males, productivity increased with female age, the sex ratio of nestlings was male biased and there was moderate male immigration. Despite these differences, temporal variation in sex ratio did not contribute to population dynamics and the contribution of temporal variation in survival was similar for both sexes. Variation in first-year survival was the strongest driver in this population, regulated by a weak density-dependent feedback acting through female first-year survival. Overall, the contributions of the two sexes to population dynamics were similar in this monogamous species with strong sexual size dimorphism.

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Data files and code for carrying out all analyses and generating all figures presented in the paper. The six data files are provided in csv format. There are five code files written for R, but some of the main analyses require the NIMBLE software. The main code (IPM_Code.txt) contains a description of the data, code for loading and managing the data, and for fitting the integrated population models. The other files contain custom written functions (Functions.txt), code for performing posterior predictive tests (PPT_Code.txt), code for reporting results and performing various other analyses (Output_analyses_Code.txt), and code for generating figures (Figures_Code.txt).