Statistical analyses
Generalized Linear Models were chosen assuming binomial family and logit
link function to investigate how the probability of nesting (1, presence
of nest vs 0, absence of nest) is influenced by water depth,
vegetation height, distance to the water edge, grazing intensity, and
their pair-wise interactions. Tests for potential non-linear
relationships with each of the predictor variables by introducing their
quadratic terms in the models and comparing their Akaike’s Information
Criterion (AICc corrected for small sample sizes; Burnham & Anderson,
2002) were carried out. Test for multi-collinearity (using the Variance
Inflation Factor, VIF) and over dispersion in all models, as well as
log-transformation of all predictor variables, were carried out to
normalize their residuals.
The importance of different predictor variables for nest-site selection
was investigated using multi-model inference methods (Burnham &
Anderson, 2002). First, the set of best candidate models were computed
(i.e. those with
ΔAICc<2) using an automatic model selection algorithm, and
then derived the importance of each predictor variable by summing the
AIC weights of the models where this variable is included (Burnham &
Anderson, 2002). The average predictions were computed over the set of
candidate models to analyse the relationship of nesting probability with
each of the most important predictor variables. All of the above
analyses were conducted in R v3.5.3 (Barton, 2019; R Core Team, 2019).
The density of breeding pairs was computed as several breeding pairs per
unit area (km2) of marshes where Google Earth Pro
“Add Polygon” tool was used to approximate the marshes area.