Pair and brood numbers
Mann-Whitney U -test for independent samples was used to compare the overall pair and brood densities of all the studied duck species between Maaninka and Evo. Furthermore, as mallard and teal are generalist species and common at both study areas, hence providing sufficient data, their pair and brood densities and brood production between the two areas were compared separately. G -test for goodness-of-fit was used to compare species-specific proportions of brooded and non-brooded pairs in 2017 and 2018 at Evo with those at Maaninka.
Pair and brood data were zero-inflated, and when exploring the non-zero part, there was still overdispersion. Thus zero-inflated negative binomial models were used to explain variation in the number of pairs and broods using glmmTMB (Brooks et al., 2017). All the analyses were done in R 3.4.0 (R Core Team, 2017) and the data exploration was done by following the protocol by Zuur et al. (2010). Water body size was controlled for by including shoreline length (“SHORE”) as an explanatory variable in all the models. Field percent (“FIELD”) was used to indicate the type of the surrounding landscape around each wetland (1 km buffer around the wetland) in every model. The amount of food (“FOOD”, continuous) and wetland type (“TYPE”, two levels: lake or pond) were used as wetland-level explanatory variables. In addition, pair number (“PAIRS”) was included in the models explaining brood numbers. However, as data exploration revealed that pair number and shoreline length were strongly correlated (Pearson correlation r > 0.6), shoreline length was discarded and the pair number was kept, because the latter should more directly determine possible brood production. Due to the nested structure of the data, wetland ID was entered as random factor. Year effect was excluded because it failed to improve model fit. All possible model combinations were fitted. We chose between the best fitting models by using model specific AIC-values and weights (ΔAIC<2, where Δ = AICi – AICmin) (Burnham & Anderson, 2002). Due to model selection uncertainty, we calculated the model-averaged slopes (β-values) of the variables weighted by the Akaike weights and their unconditional standard errors and 95% unconditional confidence intervals; all models in the candidate set were used (see Burnham & Anderson, 2002).