Statistical Analysis
All analyses were conducted in R Language and Environment for
Statistical Version 4.0 (R Core Team 2020) and α was set to p=0.05. All
reported values are means ± standard error unless otherwise stated. Our
dependent variables of interest were: prevalence (defined as proportion
of jars with at least one infected animal in observed by the end of the
experiment), and the average time to visible infection for individuals
in each jar (calculated by the number of new infections on each
observation day). For D. magna development rates, the time to
emergence of hatchlings in days was calculated for ephippia-only
treatments, and the average time to maturity (develop first egg clutch)
for all treatments.
The data for the live D. magna treatment and the data from the
ephippia treatment were analysed separately. For the ephippia treatment,
average time to hatch, time to maturation, and time to infection (all in
days) were compared among ephippia treatments using linear regressions
with temperature as the independent variable. The effect of temperature
on prevalence was analysed using a generalized linear model with a
binomial distribution (package MASS ). For the live D.
magna treatments, the dependent variables time to maturation, time to
infection and prevalence at the end of the experiment were compared
among host resistotype and temperature treatments. Here, we used
generalized linear mixed effects models (package lme4 ) with
different error distributions depending on the nature of the data
(binomial for prevalence, normal for all others) using temperature and
host resistotype and their interactions as fixed variables, with clone
nested within resistotype as a random effect
(DV~Temp*Resistotype+(1|Clone:Resistotype)).
When interactions were not significant, they were removed from the model
and only main effects were assessed.