2.4 STATISTICAL ANALYSIS
The results of PCR screening were used to classify each specimen as
positive or negative to at least one infection and calculate the
parasite richness in each specimen (i.e. , the number of different
parasite targets detected in each sample). Parasite richness and
presence/absence of infection were included as dependent variables in
different generalized linear mixed models to estimate their relationship
with the investigated covariates. The responses of the two species were
tested separately and covariates were chosen following our ecological
expectation. Specifically, in all the models we included the same
covariates namely the percentage of green habitat and its fragmentation
(ENN) as well as the abundance and diversity of floral resources.
Moreover, each model encompassed the distance from hives and the number
of hives in the surroundings. The amount of impervious cover, initially
included in the models with the other covariates as a descriptor of
urbanization, was excluded by the models because of the high
collinearity with the percentage of green habitat evaluated through
variance inflation factor (VIF) (see also the correlation plot in
Supporting information, Figure S2). Presence/absence of infections were
used as dependent variables in a Generalized Linear Mixed Model (GLMM)
(Magnusson et al., 2017) with binomial distribution (accounting for
binary presence/absence data) to evaluate changes in the probability of
being infected in response to the mentioned independent variables.
Changes in parasite species richness per sample in response to the
considered covariates have been evaluated through GLMM with Poisson
distribution (accounting to count data), in both the models sampling
sites were included as a random effect. Final models were obtained
through a backward stepwise model selection approach based on AIC (Zuur
et al., 2009). Data analysis was performed using R (version 3.6.1; R
CoreTeam 2019).