Experiment Two
We performed the same regressions as for experiment one to predict
infected, uninfected, and total snail biomass in experiment two using a
categorical random term for pesticide, nested within class, and compared
it to a null (intercept) model using likelihood ratio tests. To identify
which herbicides have strong effects in the absence of predation, we
used the fixed categorical predictor for pesticide identity to
separately predict natural-log transformed infected, uninfected, and
total B. glabrata snail biomass, in tanks without crayfish
predators, using a Gaussian error distribution in the glmmTMBpackage (Brooks et al., 2017) in R v4.0 (RCoreTeam, 2013). Total biomass
included living and dead snails to examine whether herbicides caused
increases in snails early in the experiment despite those snails
perishing before the end of the experiment.
To test for an interaction between crayfish predators and herbicides in
experiment two, we separately predicted log-transformed uninfected,
infected, and total Bi. glabrata snail biomass, using Gaussian
error distributions that considered all possible interactions between a
fixed categorical term for crayfish presence and fixed categorical terms
for pesticide, class, or type. We also considered a random term for
block, a term for zero-inflation, and whether water and solvent tanks
should be treated separately or as a single control group, resulting in
20 possible crayfish interaction models. We compared all interaction
models to one another, a model using only crayfish presence and a null
(intercept only) model using AICc. We also used a Pearsons’ correlation
coefficient to assess the relationship between infected and total snail
biomass, and performed a negative binomial regression to predict total
cercariae counts in tanks using shedding snail biomass.