Data collection and transformations
We obtained primary literature data directly from main text, tables, supporting material, or raw data files whenever available. Otherwise, we digitized data from figures using PlotDigitizer (https://plotdigitizer.com). Stressor effects were standardized to unbiased mean differences (Hedge’s g ) from both continuous and discrete variables (Hedges 1981). For continuous variables, we obtained mean and standard deviation (SD) of fitness traits and infectivity metrics in environments with different exposure to stressors. If SD was not reported, an error estimate (standard error (SE), 95% confidence interval (CI) or Wald’s CI) was converted to SD, assuming normality. If a study reported median instead of mean (n = 13 effects in four studies), we estimated the mean following Hozo et al. (2005). If dispersion was only reported as data range or interquartile range (n = 8 effects in one study and n = 5 effects in three studies, respectively), we approximated SD (Lajeunesse 2013; Wan et al. 2014). Mean and SD of response variables were then used to calculate standardized mean differences (d) and their variances.
Many studies (n = 67) used discrete variables to quantify infection prevalence and/or survivorship. In these cases, we calculated odds ratios between environmental treatments and estimated variances (Rosenberg et al. 2013). In cases where at least one category had no observations (e.g., no survival in polluted treatment), we applied Yate’s continuity correction to avoid dividing by zero (Yates 1934). Log odds ratios were then converted to d , and variances of log odds ratios were converted to variances of d , assuming a continuous logistic distribution underlies each discrete trait (Hasselblad & Hedges 1995). Finally, we estimated Hedge’s g and its variance by applying sample size correction J to all values of d and their variances (Hedges 1981).
Most experiments (n = 108) contrasted host fitness traits and infectivity across three or more environmental treatments or in more than one-time interval. For example, a control group could be compared to two levels of chemical pollution or at both 24 and 48 hpi. In these cases, stressor effects and sampling errors were not independent, as they shared control group or time baseline. To account for correlated sampling errors between these effects, we computed covariances in sampling errors between effects in multiple-comparison designs following Viechtbauer (2010). We included these variance-covariance matrices in our statistical analyses (see below). For a few experiments (n = 8) where large covariances between effects and small sample sizes resulted in variance-covariance matrices with negative eigenvalues (i.e., not positive definite), we adjusted covariance estimates to produce the nearest positive definite matrix using the R package Matrix(Douglas & Maechler 2021). As an alternative approach to estimating sampling error covariances, we adjusted fixed effect coefficients using the robust variance estimator (RVE) (Hedges et al. 2010), as implemented in the R package clubSandwich (Pustejovsky 2020). Here, we focus on results with estimated covariances and show results under the RVE in Supporting Material.