Human land use and sex reversal
We quantified land use in a 500-m wide belt zone around each pond using geoinformatics software as described in detail in (Bókony et al., 2018). We divided each belt zone into the following 8 land-use categories: natural vegetation (e.g. woodlands, non-agricultural meadows), arable fields, pastures, residential areas, public built areas (e.g. commercial and industrial areas), roads with vehicular traffic, railroads, and water; and we calculated the proportion of area falling into each of these categories (Table S1). Because railroads and water were present only around 2 and 3 ponds, respectively, and covered very small areas (Table S1), we omitted these from further analyses. We used two alternative approaches to quantify the intensity of anthropogenic land use for each capture site. First, we summed the proportions of arable land, pastures, residential and public built-up areas, and roads for each pond; we will refer to this variable as ”total anthropogenic land cover”. In the second approach, we performed a principal components analysis (PCA) using the 6 landscape variables, which yielded two axes with >1 eigenvalue, explaining 82.1% of variation in total. Urban landscape areas loaded positively on the first axis whereas agricultural landscape areas loaded positively on the second axis (Table S4, Figure S4). We will refer to the habitat scores along these two axes as ”urban PC scores” and ”agricultural PC scores”. We analysed the relationship between these habitat variables and XX/male ratio of the adult frogs in generalized linear models with binomial error distribution, using the brglm function in R package brglm(Kosmidis, 2019). This analysis weights each site by sample size (the number of phenotypic males in our case) and appropriately handles separation (i.e. in our dataset, there were no sex-reversed adults at certain sites) by the maximum penalized likelihood method. One model contained ”total anthropogenic land cover” as the only predictor, whereas the other model contained the two PCA score variables simultaneously.