DOI: 10.5061/dryad.gqnk98spf
title: Data from: Contrasting effects of elevation on above and belowground plant pathogens

description: Plant fungal diseases have a great influence on both photosynthesis and
 ecosystem function. However, how the elevation gradient, which is
 one of the most biogeographic factors, affects diseases is scarce. Here,
 we combined a field survey and a meta-analysis to test how elevation
 affect foliar fungal diseases and soil fungal pathogens through different
 paths. We arranged 30 plots along 3200 m ~ 4000 m in a Qinghai-Tibetan
 alpine meadow and collected the data of foliar fungal diseases, plant
 composition and soil properties to study how environment-mediated (through
 changes in temperature and humidity), plant community-mediated (through
 changes in plant biomass, richness, evenness, phylogenetic structure and
 community composition) and soil mediated (through changes in soil
 properties) effects of elevation on foliar fungal diseases and soil fungal
 pathogens. Based on linear models, we found that elevation decreased soil
 fungal pathogen richness rather than community pathogen load of foliar
 diseases. Specifically, a combination of community proneness and Pielou’s
 evenness index was the best model in predicting pathogen load. The
 structural equation model further confirmed that although elevation
 significantly changed both the plant community indices and soil
 properties, elevation mainly drove pathogen load via plant
 community-mediated effects, but decreased soil fungal pathogen richness
 through temperature. A systematic meta-analysis composed of 48 studies
 from 31 literatures confirmed our main conclusions that elevation did not
 significantly foliar fungal diseases, but decreased soil fungal pathogen
 richness significantly, indicating contrast effects of elevation in
 driving above- and belowground plant pathogens. Hence, we distinguished
 the different mechanisms for different parts of the plant pathogens in one
 system, and our study will improve the predictability of plant diseases,
 especially under the background of global climate change.
description: <b>Field survey along
 e</b><b>levation</b> <b><i>Study
 site</i></b><i>
 </i><b><i>and</i></b><i>
 </i><b><i>plots
 establishment</i></b> Our survey
 was conducted along an elevation gradient ranging from 3200 m to 4000 m on
 the south slope of Qilian Mountains in Menyuan County, located in the
 northeastern Qinghai-Tibetan Plateau. The climate is continental monsoon
 climate, with a 6 months growing season from mid-April to mid-October (Ma
 et al., 2017). The vegetation is alpine meadow, generally dominated by
 some genera of perennial herbaceous (e.g. <i>Gentiana</i>,
 <i>Kobresia</i>, <i>Poa</i> and
 <i>Saussurea</i>), with the species composition shift along
 elevation. We established thirty 0.5 × 0.5 m plots (6 replications for
 each of five elevations) along the elevation gradient: 3200 m
 (37°36'39'' N, 101°18'16'' E), 3400 m
 (37°39'58'' N, 101°20'20'' E), 3600 m
 (37°41'47'' N, 101°21'34'' E), 3800 m
 (37°42'13'' N, 101°22'14'' E) and 4000 m
 (37°42'29'' N, 101°22'27'' E). The plots
 were randomly selected with at least 10 m buffer zone between two adjacent
 plots at each elevation. <b><i>Plant and soil
 sampling and climate monitoring</i></b> In early August 2020, we harvested
 all the plant aboveground parts at ground level and sorted them into plant
 species at plot-level. Then we dried them at 65℃ for 48 hours to constant
 mass and weighed to 0.01 g, and sumed the dry biomass from a plot as the
 aboveground biomass (hereafter as ‘<i>AB</i>’, see Table S1
 for abbreviations). We also collected four soil cores (5 cm in diameter
 and 10 cm in depth) from each plot and pooled them as one sample. All fine
 roots were collected from each sample and were flushed by water, then we
 dried them for plant belowground biomass (<i>BB</i>). Soil
 moisture content (W; %) was measured gravimetrically after 5 h of
 desiccation at 120℃. A pH analyzer and a conductivity analyzer were used
 to measure the soil pH (pH) and soil conductivity (C; ms/s), respectively.
 5 grams of fresh soil were extracted with 50 ml 0.2 M KCl for 1 h at 60
 rev s<sup>-1</sup> using a shaker, then nitrate-nitrogen
 (NO<sub>3</sub><sup>-</sup>;<i>
 </i>mg/kg) and ammonium-nitrogen
 (NH<sub>4</sub><sup>+</sup>;<i>
 </i>mg/kg) were measured using an auto-analyzer (AA3, Bran-Luebbe,
 Germany). We recorded foliar fungal disease
 severity following the methods provided in Liu<i> </i>et al.
 (2017). In brief, we recorded disease severity (i.e<i>.</i> %
 leaf area coverd by fungal lesion;
 <i>V<sub>i</sub></i>) from five leaves randomlty
 selected from five individuals for each plant species in each plot. We
 recorded all available leaves for species with less than 25 leaves.
 Pathogen identification mainly followed identification manuals including
 Fungal Identification Manual (Wei, 1979), and also previous studies in
 this area (Zhang, 2009; Liu et al., 2019; Liu et al., 2020b).
 We placed a Temperature-Humidity
 Recorder Cos-03-0 (Renke Control Technology Co., Ltd., Jinan, Shandong,
 China) at each elevation, which continuously monitored the ambient
 temperature and humidity every 60 seconds for nonstop 72 hours in early
 August. Then we calculated the mean daily temperature
 (<i>MDT</i>) and mean daily humidity (<i>MDH</i>)
 for each elevation. <b><i>DNA extraction and
 PCR amplification </i></b> Soil total DNA was extracted by
 using a TIANGEN Magnetic Soil And Stool DNA Kit (TIANGEN Biotech Co.,
 Ltd., Beijing, China) following the manufacturer’s protocol. The
 concentration and purity of the DNA were accessd by agarose gel
 electrophoresis. The primers internal transcribed spacer 1 (ITS1) region
 was amplified with the forward primer ITS1F (5’-GGAAGTAAAAGTCGTAACAAGG-3’)
 and the reverse primer ITS1R (5’-GCTGCGTTCTTCATCGATGC-3’) using polymerase
 chain reactions (PCRs) (Gardes & Bruns, 1993). PCRs were performed
 in a 30 μL mixture composed of 15 µL of Phusion Master Mix (2 ×), 10 µL of
 DNA template, 3 µL of primer, and 2 µL of dd H<sub>2</sub>O.
 The PCR reactions were conducted as follows: a denaturation at 98 ℃ for 1
 min, followed by 30 cycles of 30 s at 98 ℃, 30 s at 55 ℃ for annealing and
 30 s at 72 ℃ for elongation, finally 5 mins at 72 ℃ for extention and
 reaction termination. <b><i>High-throughput
 sequencing and processing of the sequencing
 data</i></b> The library
 was constructed by a TruSeq DNA PCR-Free Library Preparation Kit
 (Illumina, San Diego, CA), then we used the Qubit@ 2.0 Fluorometer (Thermo
 Fisher Scientific, Waltham, MA) and Agilent 2100 Bioanalyzer system
 (Agilent Technologies, Santa Clara, CA) to assesse the quality of the
 library. The Illumina NovaSeq 6000 platform (Illumina, San Diego, CA) was
 operated to quantified and sequenced the constructed library (Novogene
 Co., Ltd., Beijing, China). Then non-clustering direct denoising to
 generate OTU based on unoise3 (UNBIAS) (https://www.drive5.com/usearch/manual/unoise_algo.html), and rare sequences (reads &lt; 8) were removed to avoid the possible spurious reads generated by sequencing errors. UNITE 2020 (http://unite.zbi.ee; Kõljalg et al., 2005) was used to cluster the sequences into operational taxonomic units (OTUs). A fungal species can be defined as a plant fungal pathogen when it belonged to a genus with the majority of their species can induce plant disease symptoms (Liang et al., 2016), referenced to the published data (Tedersoo et al., 2014) and FUNGuide database (https:// github.com/UMNFuN/FUNGuild; Nguyen et al., 2015). Then we calculated the accumulated OTU number of soil fungal pathogens (<i>sfpOTUs</i>), and also their relative abundance (<i>sfpRA</i>; sequences of soil fungal pathogens divided by the total number of sequences) for each sample respectively. Soil fungal pathogens relative abundance (<i>sfpRA</i>) was log-transformed to achieve the normality of residuals in the following analysis. <b><i>Community pathogen load </i></b> Community pathogen load (<i>PL</i>) was defined as (Mitchell et al., 2002): where <i>S</i> is the total number of plant species in a certain plot, <i>b<sub>i</sub> </i>is the aboveground biomass of plant species <i>i</i>. We defined a ‘disease proneness index’ (hereinafter ‘<i>P<sub>i</sub></i>’) for each species as the average severity index (<i>V<sub>i</sub></i>) across 30 plots of plant species <i>i</i>. We then calculated a ‘community proneness index’ (hereinafter ‘<i>p</i>’) for each plot by calculating a plant aboveground biomass-weighted average of the <i>P<sub>i</sub></i> for each plot (Liu et al., 2017): where <i>p</i> is the expected community pathogen load base on constituent host plants (Liu et al., 2017). Community pathogen load (<i>PL</i>) and disease proneness index (<i>p</i>) were log-transformed to achieve the normality of residuals in the following analysis. <b><i>Analysis</i></b> <b>(1) Soil properties</b> We used rda function in vegan package (Oksanen et al., 2020) to conduct principal component analysis (PCA) to summarize the soil properties (W, pH, C, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>), with the first principal component (<i>Soil PCA1</i>) explained 41.63% of the total variance (Table S2). Then we calculated the Spearman rank-order correlation between <em>Soil PCA1</em> and each of soil properties (W, pH, C, NO<sub>3</sub><sup>-</sup>, NH<sub>4</sub><sup>+</sup>) by using cor.test function. Specifically, the <i>Soil PCA1</i> is negatively related to C, pH and NH<sub>4</sub><sup>+</sup>, while positively related to W and NO<sub>3</sub><sup>-</sup>. <b>(2) Plant community-level indices </b>For each plot, we calculated the plant species richness (<i>SR</i>) and Pielou's evenness index (<i>Ev</i>) using diversity function in vegan package. Then we built a plant phylogenetic tree based on <i>rbcL</i> and <i>matK</i> genes, following the methods provided in Liu et al. (2015). In brief, MEGA X (Kumar et al., 2018) was used to conduct multiple sequence alignment by employing ClustalW method (Larkin et al., 2007). Then we concatenated the aligned two genes sequences into a matrix. For plant species that missing <i>rbcL</i> or <i>matK</i> sequences, we used their congeneric representatives in NCBI (National Center for Biotechnology Information). We then selected the top-ranked maximum-likelihood model of nucleotide substitution using Akaike’s information criterion (AIC) by jModelTest v2.1.7 (Darriba et al., 2012), and built maximum-likelihood phylogenies using PhyML v3.1<b> </b>(Guindon et al., 2010) with the aligned and concatenated sequences and also the optimal nucleotide substitution model (GTR + G). A 500 bootstrap replicates were run to evaluate the nodal support on maximum-likelihood phylogenies. We calculated several phylogenetic indices by picante package (Kembel et al., 2010), including standardized effect size of Faith's phylogenetic diversity, mean pairwise distance (weighted by aboveground biomass), mean nearest taxon distance (weighted by aboveground biomass; <i>MNTD</i>). Among these indices, <i>MNTD</i> was the best predictor for community pathogen load (<i>PL</i>), hence, we only used<i> MNTD</i> in the following analysis, given the high collinearity among these phylogenetic indices. <strong>(3) Factors affecting foliar fungal diseases and soil pathogens </strong>At plant population-level, we evaluated how elevation (hereafter<em> E</em><i>le</i>) affects <i>V<sub>i</sub></i> of each plant species based on a series of linear models, and calculated the corresponding <i>F</i>- and <i>P</i>-values. We set the <i>Ele</i> as independent variable, <i>PL </i>and soil pathogen indices (<i>sfpOTUs, sfpRA</i>) as response variables in a series of linear models to test the direct effect of <i>Ele</i>. Due to the collinearity among various community-level indices (<i>SR</i>, <i>Ev</i>,<i> p</i>,<i> MNTD</i>,<i> AB, BB</i>), environmental factors (<i>MDT</i>,<i> MDH</i>) and soil properties (<i>Soil PCA1</i>) (Fig. S1), we set them as independent variables in a series of linear models to test their effects on <i>PL </i>and various soil pathogen indices. Then we compared these models with the null model (i.e. the intercept-only model) based on Akaike's information criterion corrected for small sample sizes (AIC<sub>c</sub>). We also calculated the log-likelihood (LL) and AIC<sub>c</sub> based parameters: change in AIC<i><sub>c</sub></i> relative to the top-ranked model (DAIC<sub>c</sub>), AIC<sub>c</sub> weight<i> </i>(<i>w</i>AIC<sub>c</sub>) and the percent deviance explained (<i>De</i>) (Burnham et al., 2011), to estimate their possibilities to be the best predictor in predicting<i> PL </i>and various soil pathogen indices. We plotted the correlation matrix for all biotic or abiotic variables (<i>Ele</i>, <i>SR</i>, <i>Ev</i>,<i> p</i>,<i> MNTD</i>,<i> AB, BB, MDT, MDH, Soil PCA1</i>), then we calculated the Pearson’s correlation between these variables and <i>PL</i>/various soil pathogen indices (Fig. S1a). We also conducted Mantel test based “bray-curtis” distance between <i>sfpOTUs</i> and biotic or abiotic variables using the ggcor package (Huang et al., 2020) (Fig. S1b). The Permutational multivariate analysis of variance (PERMANOVA) was conducted to test the compositional difference of soil fungal pathogens along the elevation gradient. We then established a piecewise structural equation model (piecewise SEM; Lefcheck, 2016) to test how elevation affects <i>PL</i> and soil pathogen indices through community-mediated, environment-mediated and soil-mediated effects (Fig. S7). Our piecewise SEM comprised a series of linear models. To simplify the paths, we only retained <i>Ev</i><i> </i>to character plant diversity, because <i>Ev </i>was the best predictor for <i>PL</i>. And we retained <i>sfpOTUs </i>for soil pathogens, given it was sensitive to environmental factors. We calculated the standardized path coefficients (scaled by their mean and standard deviation) and corresponding significance (<i>P</i> values) for each path of the final models. The Fisher’s C test and AIC were used to test the goodness-of-fit of piecewise SEM by using piecewiseSEM package (Lefcheck, 2016). We further identified the effects of each index in piecewise SEM (<i>Ev</i>, <i>p</i>, <i>Soil PCA1</i>, <i>MDT</i>) on <i>PL</i> and <i>sfpOTUs</i> after accounting for the potential impact from others by residual plots. <b>Meta-analysis</b> <b><i>Data collection</i></b> To test the universality of our main results of the field survey, we conducted a systematic literature search in <i>ISI</i> <i>Web of Science</i> and China National Knowledge Infrastructure (www.cnki.net). We did the search for 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 (tree* OR forest* OR grass* OR shrub*)] and soil plant pathogens [(fungal OTU* OR fungi OTU* OR fung* operational taxonomic unit OR fung* abundance) AND (elevation* OR altitud*) AND (soil OR belowground OR underground)] respectively. We initially obtained a set of 2680 publications, with 2168 for foliar fungal diseases and 512 for soil plant pathogens. We included only those studies that: (<i>i</i>) focusing on the relationship between elevation and foliar fungal diseases/soil plant pathogens in nonagricultural ecosystems; (<i>ii</i>) reporting more than three sample sizes. Finally, we indentified 48 studies from 31 literatures met our criteria (Table S8). We collected the OTU table for studies focued on soil plant pathogens, identified the plant pathogens according to abovementioned methods, and calculated <i>sfpOTUs </i>for each of study. We extracted the sample sizes and Pearson’s correlation coefficients (<i>r</i>) from the main taxt, tables, figures (by WebPlotDigitizer Version 4.4; Rohatgi, 2020) or the raw data. We also recorded the background information of lacation, mean annual temperature, mean annual precipitation and the elevation span of sampling. <b><i>Effect sizes</i></b> We calculated the Fisher’s <i>z</i>-transformation of Pearson’s correlation coefficients (<i>r</i>) as effect size (Rosenberg et al., 2013): the corresponding variance for each <i>Z</i> was estimated as: where <i>n </i>is the sample size. Positive values of <i>Z</i> indicate elevation increases plant diseases/pathogens, while negative for decrease. <b><i>Analysis</i></b> We used the rma.mv function in the metafor package (Viechtbauer, 2010) to calculated the mean effect size of elevation on foliar fungal diseases/<i>sfpOTUs</i>, with ‘study’ nested in ‘paper’ as random structure (Nakagawa et al., 2017). The effect size (<i>Z</i>) 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 foliar fungal diseases and <i>sfpOTUs</i> respectively. Then introduced mean annual temperature, mean annual precipitation, latitude and elevation span of sampling to test the context dependence of effect size (<i>Z</i>). The amount of heterogeneity explained by each variable was estimated by <i>Q</i><sub>m</sub> statistic and its corresponding <i>P</i> 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 did the meta-regression between effect size (<i>Z</i>) and literatures’ published year/journal impact factor. All statistical analyses were conducted using R v4.1.1 (R Development Core Team, 2021).
description: (1) HaibeiData.xlsx This .xlsx file contains
 the raw data analyzed in the "<b>Field survey"
 </b>(which includes the data of plant community, <em>Soil
 PCA1</em>, environmental foctors and disease indices for each
 plot). (2)MetaData.xlsx This
 .xlsx file contains the raw data analyzed in the
 "<b>Meta-analysis"</b>(which includes the citation
 information, geographic information (location), research object
 information and statistical indices for each study).