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.