Meta-analyses
We analyzed effect sizes (Hedge’s g ) for both Q1 and Q2 with
multi-level meta-analytic (MLMA) models, fitted in R v 4.1.2 (R Core
Team 2021) and using the package metafor version 3.0-2
(Viechtbauer 2010). We employed a model selection approach based on the
Akaike Information Criterion (AIC) to identify the most important
moderators explaining heterogeneity in effect sizes and the most
parsimonious model (Arnold 2010). This required first fitting the full
model and all reduced models via maximum likelihood (ML) estimation. For
Q1, the full model included the moderator variables infection status,
fitness trait, stressor type, and all their interactions. The full model
for Q2 included response trait, stressor type, and their interaction.
All models accounted for non-independence of effects and sampling errors
measured in the experiment. Models also included observation-level
random intercepts, so residual variation within studies could be
estimated. Full and reduced models (including intercept-only model) were
compared using the ‘dredge’ function of the R package MuMIn v
1.43.17 (Bartón 2020). The highest-ranking model based on small sample
size corrected AIC (AICc) was then refitted via restricted
maximum-likelihood (REML) estimation to interpret moderators and
evaluate publication bias and heterogeneity.
We report meta-analytic mean estimates and 95% confidence intervals for
effects of moderators in final models. Meta-analysis results were
plotted using the R package orchaRd (Nakagawa et al.2021). We tested significance of statistical contrasts between fitness
and infectivity response variables in Q2 using Wald-type chi-square
tests, computed with the function ‘anova’.