Data collection and transformations
We obtained primary literature data directly from the 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 sought to
obtain the 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 the median instead of the 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; Wanet al. 2014). The 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 their variances
(Rosenberg et al. 2013). Also, in cases where at least one of the
categories had no observations (e.g., no survival in the polluted
treatment), we applied Yate’s continuity correction to avoid dividing by
zero (Yates 1934). Log odds ratios were then converted to d , and
the variances of log odds ratios were converted to variances ofd , assuming that a continuous logistic distribution underlies
each discrete trait (Hasselblad & Hedges 1995). Finally, we estimated
Hedge’s g and its variance by applying the sample size correctionJ 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, the stressor effects and sampling errors are not independent, as
they were estimated against the same 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) and included
these variance-covariance matrices in our statistical analyses (see
below). In a few experiments (n = 8), large covariances between effects
and small sample sizes resulted in variance-covariance matrices with
negative eigenvalues, which were therefore not positive definite. We
dealt with this issue by adjusting these 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 the results with the estimated covariances and show
the results under the RVE in the Supporting Material.