Experiment One
We used the lme4 () package to examine whether pesticide type,
class, or chemical identity explained the greatest amount of variation
in snail biomass. To do so, we separately predicted infected and
uninfected B. glabrata and B. truncatus biomass using a
categorical random term for pesticide, nested within class, nested
within type to account for the nested structure of our experimental
design (1 | type / class / chemical). These models did not
consider the control tanks because they were not hierarchically nested.
To determine whether our nested experimental treatments had significant
effects on snails, we then performed a likelihood ratio test to compare
a full nested model for each snail species to a null (intercept only)
linear model that did not include the random effect. If our nested model
was significant, we then used the experimental level that explained the
most variation in snails as a fixed categorical predictor of either
infected or uninfected B. glabrata or uninfected B.
truncatus total snail biomass using separate regressions with a
Gaussian error distribution in the glmmTMB package (Brooks et
al., 2017) in R v4.0 (RCoreTeam, 2013). We did not analyze infectedB. truncatus biomass due to very low egg viability, and, thus,
infection rates for this species (Table S3). For each response, we also
fit models including a random term for spatial block and/or a term for
zero-inflation. We considered models having water and solvent tanks
treated separately or combined as one control group, resulting in 9
possible models for each species. We ranked models using AICc values,
and assessed significance of predictors for each response only for the
model with the lowest AICc value (Tables S4-S6). We plotted the results
of our regression models using the visreg package (Rosseel,
2012).
A combined factor and path analysis was used for the first experiment to
explore potential causal pathways of agrochemicals on snail egg
production and biomass using the lavaan package (Rosseel, 2012)
in R statistical software (RCoreTeam, 2013). Because our sample size of
75 tanks restricted the number of causal pathways we could infer, we
first constructed a latent variable for phytoplankton chlorophyll a
(F0) in all weeks, periphyton chlorophyll a
(F0) in all weeks, snail egg counts in all weeks, andH. verticillata abundance in all weeks (1-12). For each latent
variable, we used modification indices to add any missing covariances
until each model fit the observed data (CFI > 0.95).
The scores for each latent variable model were then extracted using the
predict function in lavaan and used for construction of final structural
equation models to separately predict Bi. glabrata and Bu.
truncatus biomass (Tables S7-S8). We examined pathways that were
previously known for agrochemicals i.e. top down-effects of insecticides
on snail predators and bottom-up effects of fertilizer or herbicide on
periphyton (Halstead et al., 2018). We also examined d-separate tests
and included any potential missing pathways until our model had a good
fit to observed data