Data analysis
All statistical analysis were performed in R v3.5.2 (R Core Team, 2018) using packages car (Fox & Weisberg, 2011), iNEXT (Chao et al., 2014), MASS (Venables & Ripley, 2002) and mvabund (Wang, Naumann, Eddelbuettel, & Warton, 2018). A significance level of α = 0.05 was considered. To test for sexual size dimorphism, we compared all the adult bird’s measurements (wing, 3rd primary, tail, tarsus, weight, bill length, depth and width) using a MANOVA and subsequent univariate tests. Dietary analysis and comparisons were all done at 3 taxonomic levels: highest prey resolution (all prey items to the most resolved possible taxonomic levels, which varied across taxonomic groups), family and order. To compare the average number of prey taxa detected per dropping of males and females, we used a GLM with a Poisson error distribution. The overall richness of prey ingested by both sexes was estimated using Hill numbers with the double of the reference sample size to avoid extrapolation bias (Chao et al., 2014). We compared the estimated richness considering sample coverage and not sample size (Chao & Jost, 2012). Instead of comparing the 95% confidence interval, a very conservative approach, we considered that differences were significant if the 84% confidence interval (a proxy for α = 0.05) of both estimates did not overlap (MacGregor-Fors & Payton, 2013). Finally, we also compared the diet composition between sexes using Generalized Linear Models for Multivariate Abundance Data with a binomial distribution (manyglm and anova.manyglmfunctions). We did not include in diet analysis possible confounding variables as sampling day or sample collection localization, because they do not differ between sexes (sampling day (1stApril = day 1), GLM with negative binomial distribution: LR Chisq = 1.066, df = 1, p = 0.302; latitude, GLM with Poisson distribution: LR Chisq = 2.149, df = 1, p = 0.143; longitude, GLM with negative binomial distribution: LR Chisq = 2.056, df = 1, p = 0.152).