Statistical analysis
We used the metafor package (v. 3.0.2; Viechtbauer, 2010) to calculate the mean effect size of elevation on ffpOTUs , foliar fungal diseases, sfpOTUs, and sfpRA , with ‘study’ nested in ‘paper’ as random effects (Nakagawa et al., 2017). The effect size (Z ) was considered to be significant when the 95% confidence interval of the mean did not include zero (Lajeunesse, 2013). We tested the overall effect of elevation on ffpOTUs , foliar fungal diseases, sfpOTUs , and sfpRA , and respective effect in forest and grassland ecosystem for foliar fungal disease (due to insufficient study in grassland ecosystems for other response variables). We then introduced mean annual temperature, mean annual precipitation, latitude and elevation to test the context dependence of effect size (Z ). The amount of heterogeneity explained by each variable was estimated by the Q m statistic and its corresponding P value (Viechtbauer, 2010). For assessing the potential publication bias, we conducted Kendall’s rank test for funnel plot asymmetry (Borenstein et al., 2009), and also ran a meta-regression between effect size (Z ) and studies’ publication years/journal impact factors. All statistical analyses were conducted using R v. 4.1.1 (R Development Core Team, 2021).
Results
Field survey along an elevational gradient