2.4 Statistical analysis
All statistical analyses were performed with R (version 3.4.1). The
different statistical models built to analyze the data are described in
the supplementary material (Table S1 ). Mortality during
development (death = 0, emerged = 1) was analyzed with Generalized
Linear Model (GLM) with binomial distribution of errors. Explanatory
variables were glyphosate concentration and glyphosate type (pure or in
formulation) for the experiment 1 and glyphosate exposition and food
treatments for the experiment 2. A GLM with normal distribution of
errors was used to test for difference in development time (calculated
as the number of days from hatching to emergence) and wing length (mm)
among the larval treatments. Explanatory variables were mosquito sex,
glyphosate concentration and glyphosate type (pure or in formulation)
for the experiment 1 and mosquito sex, glyphosate exposition and food
treatments for the experiment 2. Female mosquito-centred traits (blood
meal size, number of eggs, infection prevalence and oocyst burden),
which may depend on which bird mosquitoes fed on, were analyzed fitting
bird as a random factor into the models (to account for the spatial
pseudoreplication), using lmer or glmer (package: lme4,
Bates et al., 2015) according to whether the errors were normally (blood
meal size, number of eggs and oocyst burden) or binomially (prevalence)
distributed. Glyphosate concentration and glyphosate type (pure or in
formulation) for the experiment 1 and glyphosate exposition and food
treatments for the experiment 2 were used as fixed factors. Blood meal
size was also added as a fixed factor when it was not a response
variable.
Maximal models, including all higher-order interactions, were simplified
by sequentially eliminating non-significant interactions and terms to
establish a minimal model (Crawley, 2012). The significance of the
explanatory variables was established using either a likelihood ratio
test (which is approximately distributed as a Chi-square distribution,
Bolker, 2008) or an F test. The significant Chi-square or F values given
in the text are for the minimal model, whereas non-significant values
correspond to those obtained before the deletion of the variable from
the model. A posteriori contrasts were carried out by aggregating
factor levels together and by testing the fit of the simplified model
using LRT (Crawley, 2012).
Results