Main Effect and Publication Bias
We analyzed effect size data for each of the three categories of our data (prevalence, intensity, parasitoid) according to the following scheme. First, we fit a random effects model (REM) to estimate the overall effect of predators on parasites in prey. We report the size and direction of the overall effect as well as I2, a measure of heterogeneity that can be interpreted as the proportion of total variation that is due to between study variation (Higgins & Thompson 2002). We also used these models to diagnose publication bias in the data by visualizing the relationship between effect size and variance with a funnel plot and testing for a significant correlation between these traits using a rank-order correlation test. If significant correlation was detected, we used the trim-and-fill method (Duval & Tweedie 2000) to determine whether introduction of studies to balance the diagnosed bias would alter the main effect.