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