Data collection
Our empirical approach to study the relationship between elevation and
plant pathogens might be sensitive to the relatively small number of
replicates across a single environmental gradient in a single year, as
trophic interactions can vary across space and time, and are often
context dependent (Roslin et al., 2017; Liu, Chen, et al., 2020).
Therefore, to test the generality of our main results from the field
survey, we conducted a systematic
literature search in ISI Web of Science and China National
Knowledge Infrastructure (www.cnki.net). We searched for research on
foliar fungal pathogen [(fungal OTU* OR fungi OTU* OR fung*
operational taxonomic unit OR fung* abundance OR fung* richness) AND
(elevation* OR altitud*) AND (folia* OR leaf OR leaves)], foliar
fungal diseases
[(plant
disease* OR pathogen* OR infect* OR epidemic*) AND (inciden* OR
prevalen* OR load* OR severity OR occur* OR abundance) AND (elevation*
OR altitud*) AND (folia* OR leaf OR leaves)] and soil plant pathogens
[(fungal OTU* OR fungi OTU* OR
fung* operational taxonomic unit OR fung* abundance OR fung* richness)
AND (elevation* OR altitud*) AND (soil OR belowground OR
underground)]. We finally identified 41 papers (providing a total of
62 effect sizes) that met our criteria (Fig. S1.1; Table S1.1):
(i ) focused on the relationship between elevation and foliar
fungal diseases and foliar/soil plant pathogens in nonagricultural
ecosystems; and (ii ) reported sample sizes greater than three.
Detailed process for literature screening and basic information of 41
papers were provided in Fig. S1.4 and Table S1.1.
We collected the OTU table for studies on foliar and soil plant
pathogens, identified the putative plant pathogens according to the
aforementioned methods, and calculated ffpOTUs (i.e. foliar
fungal pathogen OTU richness), sfpOTUs and sfpRA for each
study. All plant pathogenic genera identified in the meta-analysis are
listed in the Table S2.1. We extracted the sample sizes and Pearson’s
correlation coefficients (r ) from the main text, tables, figures
(using WebPlotDigitizer v. 4.4; Rohatgi, 2020), or raw data. We also
recorded background information on location and the elevation range of
sampling (as highest sampling
elevation minus lowest sampling elevation) from original papers, then we
extracted the mean annual temperature and mean annual precipitation of
the lowest elevation location for each study based on the WorldClim
database (Fick and Hijmans, 2017).