Most studies reported multiple effect sizes. To account for any dependencies within studies, multi-level models were fitted using restricted maximum-likelihood estimation using the metafor package (v3.0-2) [29 (link)] in R v. 4.0.5 [30 ,31 ]. Nesting the effects reported within the study ID allowed for differentiation of the effect sizes due to sampling variation within and between studies [32 ]. Pathogen species, host species and vector species were also included as random effects, allowing multiple representations of the same species to be accounted for [33 (link)]. Taxonomic subgroup analysis was performed by calculating mean effect sizes for each pathogen genus and vector genus. The contribution of ecological and methodological predictors to the overall effect was then assessed using univariate models (electronic supplementary material, table S1). Omnibus tests were used to assess differences in mean effect size between groups, and likelihood ratio tests using maximum-likelihood estimation were used to assess the significance of each predictor.
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