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 the non-independence of effects and sampling
errors measured in the same experiment. All models also included
observation-level random intercepts, so residual variation within
studies could be estimated. Full and reduced models (including the
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 the 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
the effects of moderators in the final models. Meta-analysis results
were plotted using the R package orchaRd (Nakagawa et al.2021). We tested the significance of statistical contrasts between
fitness and infectivity response variables in Q2 using Wald-type
chi-square tests, computed with the function ‘anova’.