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\begin{document}

\title{Above- and belowground plant pathogens along elevational gradients:
patterns and potential mechanisms}



\author[1]{Ziyuan  Lin}%
\author[2]{Fletcher Halliday}%
\author[1]{Peng Zhang}%
\author[1]{Xingxing Wang}%
\author[1]{Fei Chen}%
\author[1]{Anya Shi}%
\author[1]{Juanjuan Shi}%
\author[1]{Yao Xiao}%
\author[1]{Xiang Liu}%
\affil[1]{Lanzhou University}%
\affil[2]{University of Zurich}%


\vspace{-1em}



  
  \date{May 24, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Plant pathogens are important for ecosystem functioning and community
assembly and respond to a variety of biotic and abiotic factors, which
change along elevation gradients. Thus elevational gradients are a
valuable model system for exploring how plant community, soil
properties, and environmental factors influence pathogens. Yet, how
these factors influence pathogens in nature remains poorly understood.
We tested patterns and potential mechanisms of plant fungal pathogens
along elevational gradients by combining a field survey in the Tibetan
Plateau with a global meta-analysis. We found that increasing elevation
was associated with a decrease in soil fungal pathogen richness but not
foliar fungal disease symptoms. Elevation mainly related to soil fungal
pathogen richness through abiotic factors. Whereas no evidence supported
association between elevation and foliar fungal disease. The
meta-analysis suggests some generality in the results of the field
survey: elevation was associated with a decrease in soil fungal pathogen
richness, but had no consistent relationship with foliar fungal disease
or pathogens. Our study reveals distinct patterns of above- and
belowground plant pathogen along elevation gradients and provides new
insight into the potential mechanisms in shaping these patterns.%
\end{abstract}%



\sloppy


\textbf{Title:} \textbf{Above- and belowground plant pathogens along
elevational gradients: patterns and potential mechanisms}

\textbf{Abstract}

Plant pathogens are important for ecosystem functioning and community
assembly and respond to a variety of biotic and abiotic factors, which
change along elevation gradients. Thus elevational gradients are a
valuable model system for exploring how plant community, soil
properties, and environmental factors influence pathogens. Yet, how
these factors influence pathogens in nature remains poorly understood.
We tested patterns and potential mechanisms of plant fungal pathogens
along elevational gradients by combining a field survey in the Tibetan
Plateau with a global meta-analysis. We found that increasing elevation
was associated with a decrease in soil fungal pathogen richness but not
foliar fungal disease symptoms. Elevation mainly related to soil fungal
pathogen richness through abiotic factors. Whereas no evidence supported
association between elevation and foliar fungal disease. The
meta-analysis suggests some generality in the results of the field
survey: elevation was associated with a decrease in soil fungal pathogen
richness, but had no consistent relationship with foliar fungal disease
or pathogens. Our study reveals distinct patterns of above- and
belowground plant pathogen along elevation gradients and provides new
insight into the potential mechanisms in shaping these patterns.

\textbf{Key-words:} Biodiversity-disease Relationship, Community Disease
Proneness, Community Pathogen Load, Foliar Fungal Pathogen,
Meta-analysis, Soil Fungal Pathogen

\textbf{Introduction}

Pathogens that cause plant diseases can maintain plant diversity by
inducing both negative density dependence and life-history trade-offs
(Allan et al., 2010; Cappelli et al., 2020), yet the factors that drive
pathogen abundance and diversity remain poorly understood. Previous
studies in agroecosystems suggest that environmental heterogeneity
(shaped by multiple abiotic and biotic factors) can regulate plant
pathogens (Stukenbrock and McDonald, 2008). However, whether variation
in such factors drive geographic patterns of plant pathogens in natural
ecosystem remains an open question.

Abiotic factors such as temperature and precipitation can affect plant
pathogens both directly and indirectly through changes in host plant
communities and soil properties (Liu et al., 2019). High temperatures
often promote both foliar fungal diseases (e.g. Roy et al., 2004; Liu et
al., 2019) and soil pathogens (Delgado-Baquerizo et al., 2020). Warming
can benefit pathogen fitness by increasing pathogen survival, growth and
transmission (Siebold and Tiedemann, 2013), extending the favorable time
for pathogen growth (Roy et al., 2004), or narrowing the generation gap
of pathogens (Bebber, 2015). Additionally, humidity can increase plant
disease by promoting pathogens' spore germination and growth (Romero et
al., 2021).

Abiotic factors are also predicted to influence the diversity,
phylogenetic structure, and composition of host plant communities (Zhu
et al., 2020), and could thereby indirectly affect plant pathogens
(Halliday et al., 2021). In disease ecology, pathogens commonly increase
in prevalence and severity with decreasing host diversity and
corresponding changes in host density (e.g. via changes in host richness
and evenness; Keesing et al., 2010; Halliday and Rohr, 2019). This
negative biodiversity-disease relationship is potentially caused by
reduced encounter rates, susceptible host regulation, and non-random
host species loss (Keesing et al., 2006; Halliday, Rohr, et al., 2020).
Accumulated empirical evidence from grasslands and temperate and
subtropical forests supports the existence of negative
biodiversity-disease relationships in natural communities (e.g. Mitchell
et al., 2002; Rottstock et al., 2014; and Liu, Chen, et al., 2020 for a
meta-analysis; but see Halliday et al 2017, 2020). Furthermore, changes
in diversity are often accompanied by compensatory shifts in the density
of component host species in grasslands (Mitchell et al., 2002). The
density-dependent transmission of foliar fungal diseases causes
communities with higher host density to suffer more seriously from
disease at the host population level (Burdon and Chilvers, 1982), and
soil pathogens may rely on plant biomass since more biomass will provide
more nutrient substance and greater chance for pathogenicity (Liu et
al., 2021). Furthermore, disease proneness (i.e. expected community
pathogen load based on constituent host plant species) can explain why
host diversity loss is strongly associated with increased foliar fungal
disease in alpine meadows (Liu et al., 2017). A plant species with high
disease proneness (i.e. possess good growth but weak defensive
abilities) can harbor more pathogens, and thus a host community with a
higher proportion of more disease-prone species is expected to have a
higher community pathogen load (Liu et al., 2017).

In addition to plant community characteristics, soil properties can also
potentially regulate plant pathogens. Both foliar and soil pathogens are
likely to benefit from high soil nutrients in both agro- and natural
ecosystems (Huber and Watson, 1974; Liu et al., 2017). Soil nutrients
can promote pathogens by increasing tissue nitrogen concentration
(Veresoglou et al., 2013), which is one of the most important limiting
factors for many pathogens, especially for those that extract nutrients
only from living plant tissues (i.e. biotrophic pathogens; Liu, Lu, et
al., 2020).

Despite the well-characterized impacts of single abiotic or biotic
factors on plant pathogens, how these soil-, plant community-, and
environment mediated effects combine to generate patterns of above- and
belowground plant pathogens across biogeographic gradients remains
poorly understood. For instance, a previous study found that fungal
diseases on \emph{Phragmites australis} increased with latitude in North
America, indicating that geographical gradients were associated with the
distribution of pathogens (Allen et al., 2020). However, studies along
latitude are massive logistical undertakings, and their results are
easily affected by the potential confounding factors of geology and
biogeographic history (Halbritter et al., 2018). Compared to other
biogeographical gradients, elevational change generates highly variable
ecological conditions (including soil, plant community, environment) at
a relatively small spatial scale (Rowe, 2009), providing an excellent
`natural laboratory' to study how biotic and abiotic factors affect
plant pathogens (Halliday et al., 2021).

Here, following this framework, we considered soil-, environment-, and
plant community-mediated effects to explore patterns and potential
mechanisms of plant pathogens along elevation gradients. However,
quantifying plant pathogen communities is complicated by the fact that
potential plant pathogens do not always cause disease (i.e. the plant
disease triangle; Liu and He, 2019). To overcome this challenge, we
measured plant pathogen communities in two ways. We measured foliar
fungal disease as a quantitative measurement of the disease severity
(i.e. relative abundance, rather than absolute abundance) of pathogens
that are currently causing disease on plants. Leaves are readily
surveyed for disease, allowing for accurate and reliable surveys of
pathogen relative abundance in plant communities. However, the disease
observed on a leaf is only one small part of the total pathogen
potential of an ecological community. In contrast with leaves, soils can
serve as a reservoir of plant pathogens capable of causing disease both
above-and below ground, even when those pathogens are not currently
causing disease on a plant (Delgado-Baquerizo et al., 2020). Surveying
the richness and relative abundance of soil fungal pathogen communities
using sequencing-based approaches has become a powerful approach to
assess the pathogen potential of a community (van Agtmaal et al., 2017).

We combined these two complementary approaches by measuring foliar
fungal disease, soil fungal pathogens, plant community characteristics
(richness, evenness, biomass and proneness), environmental conditions,
and soil properties along an elevational gradient in an alpine meadow in
the northeastern Qinghai-Tibetan Plateau to explore patterns of above-
and belowground plant pathogens. We sought to answer the following
questions: (\emph{i} ) how do abiotic and biotic factors (soil
properties, plant community, environment conditions, above- and
belowground plant pathogens) change along elevational gradients?;
(\emph{ii} ) how do soil properties, plant community and environmental
conditions affect above- and belowground plant pathogens?; and
(\emph{iii} ) what is the relative importance of the plant community-,
environment- and soil-mediated effects for the correlation between
elevation and above- and belowground plant pathogens? We additionally
performed a systematic meta-analysis to explore the correlation between
elevation and above- and belowground plant pathogens and to assess the
generality of our main conclusions.

\textbf{Materials and methods}

\textbf{Field survey along an elevational gradient}

\emph{\textbf{Study site} \textbf{and} \textbf{plot establishment}}

We conducted our survey along an elevational gradient ranging from 3200
m to 4000 m a.s.l. on the south slope of the Qilian Mountains in Menyuan
County, located on the northeastern Qinghai-Tibetan Plateau in China
(Fig. S1.1). The region has a continental monsoon climate, with a
6-month growing season from mid-April to mid-October. The study took
place in alpine meadow habitat, dominated by some perennial herbaceous
genera (e.g. \emph{Gentiana} , \emph{Kobresia} , \emph{Poa}
and\emph{Saussurea} ), with the species composition shifting with
elevation. We established thirty 0.5 \selectlanguage{ngerman}× 0.5 m plots (6 replicates for
each of five elevations) along the elevational gradient at 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). We randomly placed the
plots with at least a 10 m buffer zone between two adjacent plots at
each elevation.

Plant and soil sampling and climate monitoring

In early August 2020, we harvested all the plant aboveground parts at
ground level and sorted them to species at the plot level. We then dried
plants at 65 \selectlanguage{english} for 48 hours to constant mass and weighed them to 0.01 g;
we then summed the dry biomass of each species from each plot to
quantify aboveground biomass (hereafter `\emph{AGB} ', see Table 1 for
abbreviations). We also collected four soil cores (5 cm in diameter and
10 cm in depth) in the four corners of each plot and pooled them to form
one sample per plot. We flushed all fine roots collected from each
sample with water and then dried them to measure plant belowground
biomass (\emph{BGB} ). We measured soil moisture content (W; \%)
gravimetrically after 5 h of desiccation at 120 \selectlanguage{english} and used a pH analyzer
and a conductivity analyzer to measure the soil pH (pH) and soil
conductivity (C; ms/s), respectively. We extracted 5 g of fresh soil
with 50 ml 0.2 M KCl for 1 h at 60 rev s\textsuperscript{-1} using a
shaker, then measured nitrate-nitrogen
(NO\textsubscript{3}\textsuperscript{-}; mg/kg) and ammonium-nitrogen
(NH\textsubscript{4}\textsuperscript{+}; mg/kg) using an auto-analyzer
(AA3, Bran-Luebbe, Germany).

We placed a Temperature-Humidity Recorder Cos-03-0 (Renke Control
Technology Co., Ltd., Jinan, Shandong, China) at each elevation, which
continuously monitored ambient temperature and humidity every 60 seconds
for 72 hours in early August 2020. We then calculated the mean daily
temperature (\emph{MDT} ) and mean daily humidity (\emph{MDH} ) for each
elevation. Given the relatively short-term temperature and humidity may
not represent the environmental conditions that were present during
pathogens development, we only test the associations between elevation
with \emph{MDT} and\emph{MDH} (thereby confirming whether the
elevational gradient can serve as a temperature and humidity gradient),
but we did not included these measurements in other analyses.

Bioinformatic analysis

We extracted DNA from the soil samples and implemented PCR
amplification. We then constructed our library and assessed its quality.
We operated the Illumina NovaSeq 6000 platform to quantify and sequence
the constructed library for soil fungi. We then used non-clustering
direct denoising to generate operational taxonomic units (OTUs) and
identified the fungus at the genus level (Detailed supplementary methods
in Section S1.1 in Supporting information).

Measurement of above- and belowground plant pathogens

For foliar fungal diseases, we recorded foliar fungal disease severity
following the methods provided in Liu et al. (2017). In brief, we
visually recorded disease severity (i.e. \% leaf area covered by fungal
lesion;\emph{V\textsubscript{i}} ) from five leaves randomly selected
from five individuals for each plant species in each plot. We recorded
all available leaves for species with less than 25 leaves. We then
calibrated our records by comparing diseased leaves to reference images
of known disease severity. We identified the foliar fungal pathogens
using an Olympus CX33 light microscope (Shinjuku, Japan) following
identification manuals, including the Fungal Identification Manual (Wei,
1979), Plant Disease Diagnosis (Lu, 1997), and also previous studies in
this area (Zhang, 2009; Liu et al., 2019; Liu, Lu, et al., 2020). We
defined community pathogen load (\emph{PL} ) following Mitchell et al.
2002 as:

\begin{equation}
PL=\frac{\sum_{i=1}^{S}{b_{i}V_{i}}}{\sum_{i=1}^{S}b_{i}}\nonumber \\
 \end{equation}

where \emph{S} was the total number of plant species in a certain plot,
and \emph{b\textsubscript{i}} was the aboveground biomass of plant
species \emph{i} . We then defined a `disease proneness index'
(hereafter `\emph{P\textsubscript{i}} ') for each species as the average
severity index (\emph{V\textsubscript{i}} ) across 30 plots of plant
species \emph{i} . We then calculated a `community proneness index'
(hereafter `\emph{Proneness} ') for each plot by calculating a plant
aboveground biomass-weighted average of the \emph{P\textsubscript{i}}for
each plot (Liu et al., 2017):

\begin{equation}
Proneness=\frac{\sum_{i=1}^{S}{b_{i}P_{i}}}{\sum_{i=1}^{S}b_{i}}\nonumber \\
 \end{equation}

where \emph{Proneness} was the expected community pathogen load based on
constituent host plant species, which was measured independent of the
actual disease in a given plot (Liu et al., 2017). Specifically, each
species in each plot was assigned a value of disease proneness based on
averaging its disease severity (\emph{V\textsubscript{i}} ) in this plot
and weighted by its aboveground biomass. Despite a similar mathematical
formula for \emph{PL} and\emph{Proneness} , these values represent two
distinct characters of plant community (the actual amount of disease in
a community and the amount that would be expected based on the identity
of species present and their relative abundances alone). \emph{PL} and
\emph{Proneness} are not always correlated with one another (e.g. Liu et
al., 2019), and are often used together in disease ecology studies (e.g.
Mitchell et al., 2002; Johnson et al., 2013; Liu et al. 2017; Liu et
al., 2019). We log-transformed community pathogen load (\emph{PL} ) and
disease proneness index (\emph{Proneness} ) to achieve normality of
residuals in the following analysis.

For soil fungal pathogens, we defined fungal taxa as putative plant
fungal pathogens when they include any pathogenic species which were
reported to induce any plant disease symptoms (e.g. canker, rot, leaf
spot, blight, rust and mildew) (Liang et al., 2016), as determined by
references to published data (Tedersoo et al., 2014), paper in\emph{ISI}
\emph{Web of Science} (if any paper reported their pathogenicity) and
the FUNGuild algorithm (Nguyen et al., 2015). Indeed, even if some
genera with mixed feeding strategies (e.g. parasitic, mutualistic and
saprophytic) were presumed as pathogens based on the above method in
this study, they still represent pathogen potential. For instance,
genera belong to Dothideomycetes (e.g. \emph{Alternaria}
,\emph{Epicoccum} , \emph{Fusicladium} ), Leotiomycetes (e.g.
\emph{Coma} ,\emph{Erysiphe} , \emph{Scytalidium} ), Sordariomycetes
(e.g.\emph{Fusarium} , \emph{Neonectria} , \emph{Valsa} ) and other
classes were presumed as plant pathogenic. All plant pathogenic genera
identified in the field study are listed in Table S2.1. We then
calculated the accumulated OTU number of soil fungal pathogens
(\emph{sfpOTUs} ; OTU richness of soil fungal pathogens), and also the
relative abundance of soil fungal pathogens (\emph{sfpRA} ; copy number
of soil fungal pathogens divided by the total number of copy number of
soil fungus) for each sample. We log-transformed soil fungal pathogen
relative abundance (\emph{sfpRA} ) to achieve normality of residuals in
the following analysis.

Limitations and caveats in methodology

This study includes three complementary measurements of plant pathogens:
the relative abundance of pathogens causing foliar disease, the relative
abundance of soil pathogens (\emph{sfpRA} ), and the richness of soil
pathogens (\emph{sfpOTUs} ). We measured damaged on the leaves and
fungal pathogen communities in the soil. Although these two approaches
do not provide identical assessments of pathogen richness and disease
outcomes in foliar and soil- compartments, we believe that this approach
is justified, as it reflects the most commonly used approach in these
two respective fields of research, and represents comprehensive
characteristics of both above- and belowground fungal plant pathogen
communities, and thereby provides a more comprehensive understanding of
how pathogen communities respond to changing environmental conditions.
Furthermore, although the measurements are not identical, they are often
positively correlated with one another, and this correlation often
transcends study systems (Rottstock et al., 2014; Liu et al., 2016;
Halliday et al., 2017; Halliday et al., 2020b). Therefore, we feel
confident that the distinct measurements in our study can provide
insight into the biogeographic pattern of pathogens across elevation
gradients.

In this study, we measured foliar fungal diseases and soil pathogens as
representatives of above- and belowground plant pathogens respectively
to bring together research from two different fields that tend to study
pathogens in different ways, which provides complementary information
using complementary measurement approaches. However, we applied these
measurements with caveats that visual assessment for foliar fungal
diseases does not include all pathogens and can result in an incomplete
assessment of pathogen diversity, while sequencing-based assessment for
soil fungal pathogens is not directly related to any particular disease
outcome. Overall, these measurements still reflect the most commonly
used approach in these two respective fields of research, and thereby
provide a more comprehensive understanding of how pathogen communities
respond to changing environmental conditions.

Root diseases resulted by root-borne pathogens can also affect host
mortality, growth and productivity, and further influence ecological
succession and biogeochemistry process, thereby regulating ecosystem
functioning (e.g. Hansen \& Goheen, 2000; Healey et al., 2016). Future
studies could combine surveys of foliar and root fungal diseases by both
visual measurements and sequencing to more comprehensively explore the
responses of above- and belowground plant pathogens to abiotic and
biotic factors and their impacts on ecosystem functioning.

The results of the field survey might be sensitive to limitations of the
empirical approach. For soil fungal sequencing, fungal ITS1 region may
suffer from certain taxonomic biases, like high proportion of mismatches
and biased amplification of certain fungal taxa (e.g. basidiomycetes;
Tedersoo \& Lindahl, 2016). However, fungal ITS1 region indeed possess
some advantages which outperformance to other regions, for example, it
is easily discriminate fungal taxa from plants and provides wider
richness and taxonomic coverage (Mbareche et al., 2020). Future studies
could overcome these challenges by incorporating multiple sequencing
regions, and using more advanced methods (e.g. exact sequence variants
(ESVs), which generate a greater resolution than OTU-based methods;
Mbareche et al., 2020). In fact, quantitative PCR is a good choice to
calculate the abundance of pathogens (Tellenbach et al., 2010), although
the small datasets prevent us from further analyses regarding the
absolute abundance of pathogens. In fact, unlike the foliar fungal
disease, the relative abundance of soil pathogen profiles cannot
indicate the absolute abundance of pathogens. Hence, we can only
conclude that elevation had no associated with the relative abundance of
soil pathogens. Our empirical results also stem from a single location
in a single year, and thus our results might be sensitive to local
environmental conditions that are characteristic of the particular year
of sampling. Although our empirical results largely agree with the
results of the meta-analysis, this does not negate the limitations of
the empirical study (i.e., relatively small sample size, single
gradient, single year). Future studies conducted over multiple
elevational gradients and multiple years remain the gold standard for
empirical field surveys along elevational gradients. Large-scale and
long-term studies of biotic and abiotic drivers of disease across
environmental contexts remain a pressing need if ecologists want
identify the underlying effects of temporal dynamics and spatial
heterogeneity in the community ecology of infectious disease.

Statistical analysis

\emph{(i) Changes of abiotic and biotic factors along elevational
gradients}

We conducted principal component analysis (PCA) to summarize soil
properties (W, pH, C, NO\textsubscript{3}\textsuperscript{-} and
NH\textsubscript{4}\textsuperscript{+}) using the ``vegan'' package (v.
2.5.7; Oksanen et al., 2020) (Fig. S1.2). We then calculated the
Spearman rank-order correlation between \emph{Soil PCA1} and each of the
soil properties (W, pH, C, NO\textsubscript{3}\textsuperscript{-} and
NH\textsubscript{4}\textsuperscript{+}) (Fig. S1.2). We also calculated
the plant species richness (\emph{SR} ) and Pielou's evenness index
(\emph{Evenness} ) using the ``vegan'' package for each plot. To test
correlations among various variables, we plotted the correlation matrix
for all biotic or abiotic variables (\emph{Elevation} , \emph{SR}
,\emph{Evenness} , \emph{Proneness} , \emph{AGB, BGB, MDT, MDH}
and\emph{Soil PCA1} ), and we then calculated the Pearson's correlation
between these variables and \emph{PL} /various soil pathogen indices
(Fig. S1.3a). We also conducted Mantel tests based on ``Bray-Curtis''
distance between \emph{sfpOTUs} and biotic or abiotic variables using
the ``ggcor'' package (v. 0.9.8.1; Huang et al., 2020) (Fig. S1.3b).

To minimize the influence of the potential spatial autocorrelation on
the results, we used the respective coordinates of each sample plot to
generate a spatial matrix. Specifically, given the sample plots were
distributed along a cambered mountain slope which approximated to the
spherical surface, we calculated the spatial matrix based on spherical
correlation structure (i.e. ``corSpher'' class) using the ``nlme''
package (v. 3.1-152; Pinheiro et al., 2021). We then introduced the
spatial matrix into a series of linear mixed-effects models with five
elevations as a random effect in following analyses, using the ``nlme''
package. We set \emph{Elevation} as independent variables in a series of
linear mixed-effects models to test its associations with various
community-level indices (\emph{SR} , \emph{Evenness} , \emph{Proneness}
,\emph{AGB} and \emph{BGB} ) and soil properties (\emph{SoilPCA1} ),
respectively. At the plant community level, we set \emph{Elevation} as
the independent variable and \emph{PL} and soil pathogen indices
(\emph{sfpOTUs} and\emph{sfpRA} ) as response variables in a series of
linear mixed-effects models to test the direct correlations between
\emph{Elevation} and above- and belowground plant pathogens.

For the soil fungal pathogen community, we conducted permutational
multivariate analysis of variance (PERMANOVA) to test the compositional
difference of soil fungal pathogens along the elevational gradient. A
significant result of PERMANOVA supports the hypothesis that pathogen
communities change along elevational gradients.

\emph{(ii) Factors affecting above- and belowground plant pathogens}

We set various community-level indices (\emph{SR} , \emph{Evenness}
,\emph{Proneness} , \emph{AGB} and \emph{BGB} ), and soil properties
(\emph{Soil PCA1} ) as independent variables in a series of linear
mixed-effects models to test their effects on \emph{PL} ,
\emph{sfpOTUs}and \emph{sfpRA} , respectively. We using ``MuMIn''
package (v. 1.47.1; Barto\selectlanguage{polish}ń, \selectlanguage{english}2022) to conducted full model selections
based on a series of linear mixed-effects models for \emph{PL} ,
\emph{sfpOTUs} and\emph{sfpRA} , respectively. We then extracted the
effect sizes with 95\% confidence intervals from weighted average
standardized coefficients from models with \selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{} 4 based on
model selections, and 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\textsubscript{c}) using the
``MuMIn'' package. We also calculated the log-likelihood (LL) and
AIC\textsubscript{c} based parameters: change in
AIC\emph{\textsubscript{c}} relative to the top-ranked model
(\selectlanguage{greek}Δ\selectlanguage{english}AIC\textsubscript{c}), AIC\textsubscript{c} weight (\emph{w}
AIC\textsubscript{c}) and the percent deviance explained (\emph{De} )
(Burnham et al., 2011), to estimate their possibilities of being the
best predictor of \emph{PL} and various soil pathogen indices. When the
ratio of\emph{w} AIC\textsubscript{c} of predictor to \emph{w}
AIC\textsubscript{c} of null model more than 1.5, it indicates that the
corresponding variable is associated with\emph{PL} , \emph{sfpOTUs} and
\emph{sfpRA} (Burnham et al., 2011).

\emph{(iii) Plant community-, environment- and soil-mediated
relationships between elevation and above- and belowground plant
pathogens}

To explore how soil properties, plant community and environment
conditions mediated the relationship between elevation and above- and
belowground plant pathogens, we used the ``piecewiseSEM'' package (v.
2.1.2; Lefcheck, 2016) to build a structural equation model (SEM) based
on a series of linear mixed- effects models to test the relationship
between elevation and \emph{PL} and \emph{sfpOTUs} including paths that
were mediated by the plant community and environment (i.e. elevation may
first influence plant community characters, soil properties and
environmental conditions, which then indirectly change plant fungal
pathogen communities) (Halliday et al., 2021; Fig. 2a). We did not
include \emph{MDT} in the SEM, given that there was a strong
collinearity between \emph{MDT} and \emph{elevation} (Pearson' \emph{r}=
0.985). We calculated the standardized path coefficients (scaled by
their mean and standard deviation) and corresponding significance
(\emph{P} values) for each path of the final models. Statistically
significant path(s) (\emph{P} \textless{} 0.1) highlight potentially
important mechanisms generating relationships between elevation and
above- and belowground plant pathogens. We used the Fisher's C test to
test the goodness-of-fit of the SEM. We then used partial residual plots
to interpret the relationships highlighted in the model (Grace, 2006).
In brief, we extracted the residuals of each variable in SEM and then
set the residuals of plant pathogen related variables (i.e. \emph{PL}and
\emph{sfpOTUs} ) as functions of residuals of explanatory variable (i.e.
\emph{Elevation} , \emph{Soil PCA1} , \emph{Evenness}
and\emph{Proneness} ) in a series of linear models (Grace, 2006).
Significant results of residual analysis correspond to significant paths
in the SEM.

\textbf{Meta-analysis}

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 \emph{ISI}
\emph{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): (\emph{i} ) focused on the relationship between elevation
and foliar fungal diseases and foliar/soil plant pathogens in
nonagricultural ecosystems; and (\emph{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 \emph{ffpOTUs} (i.e. foliar
fungal pathogen OTU richness), \emph{sfpOTUs} and \emph{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 (\emph{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).

Effect sizes

We calculated effect sizes as the Fisher's \emph{z} -transformation of
Pearson's correlation coefficients (\emph{r} ) (Rosenberg et al., 2013):

\begin{equation}
Z=\operatorname{}\left(\frac{1+r}{1-r}\right)\nonumber \\
 \end{equation}

The corresponding variance for each \emph{Z} was estimated as:

\begin{equation}
Var=\frac{1}{n-3}\nonumber \\
 \end{equation}

where \emph{n} is the sample size. Positive values of \emph{Z} indicate
that increasing elevation is associated with increases in foliar fungal
disease or foliar/soil pathogens, while negative values indicate
decreases.

Statistical analysis

We used the \emph{metafor} package (v. 3.0.2; Viechtbauer, 2010) to
calculate the mean effect size of elevation on \emph{ffpOTUs} , foliar
fungal diseases, \emph{sfpOTUs,} and \emph{sfpRA} , with `study' nested
in `paper' as random effects (Nakagawa et al., 2017). The effect size
(\emph{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 \emph{ffpOTUs} , foliar fungal
diseases, \emph{sfpOTUs} , and \emph{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 (\emph{Z} ). The amount of heterogeneity explained by each
variable was estimated by the \emph{Q} \textsubscript{m} statistic and
its corresponding \emph{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 (\emph{Z} ) and studies' publication years/journal
impact factors. All statistical analyses were conducted using R v. 4.1.1
(R Development Core Team, 2021).

\textbf{Results}

\textbf{Field survey along an elevational gradient}

\emph{(i) Changes of abiotic and biotic factors along elevational
gradients}

For soil properties, the first principal component (\emph{Soil PCA1} )
explained 41.63\% of the total variance and \emph{Soil PCA1} was
positively correlated with pH, C, and
NO\textsubscript{3}\textsuperscript{-} and negatively correlated with W
and NH\textsubscript{4}\textsuperscript{+} (Fig. S1.2).

We found 73 plant species in total along the elevational gradient in the
alpine meadow. \emph{Elevation} had a strong correlation with
\emph{MDT}rather than \emph{MDH} , showed a highly collinearity between
them (Fig. S1.3). Due to this collinearity, we feel confident
interpreting elevation as a proxy for temperature. Besides,
increasing\emph{Elevation} was associated with a significant decrease in
\emph{AGB}(\emph{P} = 0.006, Marginal\emph{R\textsuperscript{2}} =
0.733), \emph{SR} (\emph{P} = 0.028, Marginal
\emph{R\textsuperscript{2}} = 0.482) and \emph{Soil PCA1}(\emph{P} =
0.038, Marginal \emph{R\textsuperscript{2}} = 0.610) (Fig. S1.5).

At the plant population-level, fungal leaf spot was the most commonly
identified symptom, compared with others (e.g. rusts, blight, smuts and
downy mildew; Table S1.3). Multiple species belonging to several genera
(i.e. \emph{Alternaria} , \emph{Ascochyta} , \emph{Peronospora}
,\emph{Puccinia} , \emph{Trichometasphaeria} , \emph{Urosystis}
and\emph{Ustiligo} ) were identified as pathogens associated with the
observed foliar fungal diseases (Table S1.2). Host species varied
in\emph{V\textsubscript{i}} from 0 to 27.78 (\emph{Deschampsia
caespitosa} ) (Table S1.2). With respect to soil pathogens, we
identified putative plant fungal pathogens belonging to 106 genera. Soil
plant fungal pathogens occupied an average of about 7.72\% relative
abundance, compared to all soil fungi across all plots.

For foliar fungal diseases and soil pathogens,
increasing\emph{Elevation} was associated with a significant reduction
in \emph{sfpOTUs} (\emph{P} = 0.020, Marginal
\emph{R\textsuperscript{2}} = 0.433), but not \emph{PL}(\emph{P} =
0.403, Marginal \emph{R\textsuperscript{2}} = 0.034) or\emph{sfpRA}
(\emph{P} = 0.973, Marginal \emph{R\textsuperscript{2}}\textless{}
0.001) (Fig. S1.6). PERMANOVA results indicated that the composition of
soil fungal pathogens varied with elevation (\emph{F}
\textsubscript{4,25} = 2.744, \emph{P} \textless{} 0.001) (Table S1.3).

\emph{(ii) Factors affecting above- and belowground plant pathogens}

Among different plant community-level indices, environmental factors and
soil properties, linear mixed-effects models result showed that
only\emph{Proneness} (\emph{P} \textless{} 0.001,
Marginal\emph{R\textsuperscript{2}} = 0.348) had a significant positive
effect on \emph{PL} , while no variable had a significant effect
on\emph{sfpOTUs} and \emph{sfpRA} (Fig. S1.7, S1.8, S1.9). Among various
univariable and multivariable linear mixed-effects models, only
univariable of \emph{Pronenss} (\emph{P} \textless{} 0.001)
and\emph{Eveness} (\emph{P} = 0.043) had significant effects on
\emph{PL} , and the combination of these factors (\emph{Proneness} +
\emph{Eveness} ) provided the best fit (AIC\textsubscript{c} =
82.510,\emph{w} AIC\textsubscript{c} = 0.269, R\textsuperscript{2} =
0.438) compared to the null model (AIC\textsubscript{c} =
93.719,\emph{w} AIC\textsubscript{c} = 0.001), accounting for \emph{c.}
44\% of the deviance explained in \emph{PL} (Fig. 1a; Table
S1.4).\emph{sfpOTUs} were directly associated with \emph{Elevation}
(\emph{P}= 0.005), and strongly influenced by \emph{Soil PCA1} (\emph{P}
= 0.021) (Fig. 1b). The combination of \emph{Elevation} and \emph{Soil
PCA1}provided the best fit (AIC\textsubscript{c} = 76.842,\emph{w}
AIC\textsubscript{c} = 0.294, R\textsuperscript{2} = 0.534)
for\emph{sfpOTUs} among various multivariable models (Table
S1.5).\emph{sfpRA} had no statistically significant association with any
ecological variable tested, and was therefore not included in the SEM
(Table S1.6)

\emph{(iii) Plant community-, environment- and soil-mediated
relationships between elevation and above- and belowground plant
pathogens}

The final SEM (Fisher's C = 11.855, \emph{P} = 0.158, \emph{d.f.} = 8)
revealed that increasing \emph{Elevation} was associated with a
significant decrease in \emph{Soil PCA1} (standardized path coefficient
\selectlanguage{greek}β \selectlanguage{english}= -0.768, \emph{P} = 0.038) (Fig. 2b; Table S1.7).
While\emph{Elevation} showed strong negative direct correlation
with\emph{sfpOTUs} (\selectlanguage{greek}β \selectlanguage{english}= -1.109, \emph{P} = 0.015) but not \emph{PL} (\selectlanguage{greek}β \selectlanguage{english}=
-0.159, \emph{P} = 0.563). Furthermore, \emph{Evenness} (\selectlanguage{greek}β \selectlanguage{english}=
-0.296,\emph{P} = 0.082) and \emph{Proneness} (\selectlanguage{greek}β \selectlanguage{english}= 0.665, \emph{P}
\textless{} 0.001), rather than \emph{Soil PCA1} (\selectlanguage{greek}β \selectlanguage{english}= -0.180, \emph{P} =
0.482), were associated with variation in \emph{PL} . In contrast,
significant reduction in \emph{sfpOTUs} was associated with increasing
\emph{Soil PCA1} (\selectlanguage{greek}β \selectlanguage{english}= -0.540, \emph{P} = 0.026), but not \emph{Evenness}
(\selectlanguage{greek}β \selectlanguage{english}= 0.063, \emph{P} = 0.672) or \emph{Proneness} (\selectlanguage{greek}β \selectlanguage{english}= 0.097, \emph{P} =
0.529) (Fig. 2b; Table S1.7). The final SEM accounted for 45.772\% of
variation in \emph{PL} and 55.855\% of variation in \emph{sfpOTUs} . In
addition, the residual plots also showed that changes in
both\emph{Proneness} and \emph{Evenness} were associated with variation
in\emph{PL} , while increasing \emph{Soil PCA1} was associated with a
significant decrease in \emph{sfpOTUs} (Fig. S1.10). This indicates the
important role of plant community in explaining variation in \emph{PL} ,
and highlights the indirect connection between elevation and belowground
plant pathogens via abiotic factors.

\textbf{Meta-analysis}

Kendall's rank test for funnel plot asymmetry indicated that no
publication bias existed for all the tests (\emph{P} \textgreater{}
0.05) (Table S1.8; Fig. S1.11), and there was no significant correlation
between effect size (\emph{Z} ) and studies' publication years/journal
impact factors (\emph{P} \textgreater{} 0.05) (Fig. S1.12).

Overall, elevation was not associated with foliar fungal pathogen OTU
richness (\emph{Z} \selectlanguage{ngerman}± 95\% CI = -0.157 ± 0.263, \emph{P} = 0.242), foliar
fungal diseases (\emph{Z} ± 95\% CI = -0.047 ± 0.242, \emph{P} = 0.703)
or \emph{sfpRA} (\emph{Z} ± 95\% CI =-0.101 ± 0.183, \emph{P} = 0.281),
whereas increasing elevation was significantly associated with
\emph{sfpOTUs} (\emph{Z} ± 95\% CI = -0.257 ± 0.172, \emph{P} = 0.003)
(Fig. 3; Fig. S1.13; Table S1.9). Elevation was not significantly
associated with foliar fungal disease in forest (\emph{Z} ± 95\% CI =
-0.127 ± 0.440, \emph{P} = 0.571) or grassland (\emph{Z} ± 95\% CI =
0.023 ± 0.153, \emph{P} = 0.772) ecosystems (Fig. 3; Fig. S1.13; Table
S1.9). There was no significant correlation between the climatic
variables (mean annual temperature, mean annual precipitation) and the
effect size (\emph{Z} ) on \emph{ffpOTUs} , foliar fungal
diseases,\emph{sfpOTUs} or \emph{sfpRA} (Fig. S1.14; Table S1.10).
Nevertheless, increasing absolute latitude was associated with
decreasing effect size (\emph{Z} ) on \emph{ffpOTUs} (\emph{Q}
\textsubscript{m} = 4.231,\emph{P} = 0.040) but not foliar fungal
disease (\emph{Q} \textsubscript{m} = 0.364, \emph{P} = 0.547),
\emph{sfpOTUs}(\emph{Q} \textsubscript{m} = 1.264, \emph{P} = 0.261) or
\emph{sfpRA}(\emph{Q} \textsubscript{m} = 1.647, \emph{P} = 0.199) (Fig.
S1.14; Table S1.10). Furthermore, studies conducted over larger
elevational ranges tended to observe stronger negative relationships
between elevation and \emph{sfpOTUs}(\emph{Q} \textsubscript{m} = 4.572,
\emph{P} = 0.033) and \emph{sfpRA}(\emph{Q} \textsubscript{m} = 4.469,
\emph{P} = 0.033), though the elevational range of sampling did not
moderate this relationship for\emph{ffpOTUs} (\emph{Q} \textsubscript{m}
= 0.150, \emph{P} = 0.698) or foliar fungal disease (\emph{Q}
\textsubscript{m} = 0.415, \emph{P} = 0.520) (Fig. S1.14; Table S1.10).

\textbf{Discussion}

\textbf{Changes of abiotic and biotic factors along elevational
gradients}

In our field survey, abiotic and biotic factors (soil properties, plant
community characters and environment conditions) greatly varied along
elevational gradients, consistent with past studies (e.g. Lomolino,
2001), and those abiotic and biotic factors, in turn, explained
relationships between elevation and above- belowground plant pathogens.
By integrating this field survey with a systematic meta-analysis, we
showed a general, negative association between elevation and soil
pathogen richness, but did not find support for a general association
with foliar fungal pathogen richness, foliar fungal diseases or soil
pathogen relative abundance. Our meta-analysis further suggests that the
elevation range of sampling may potentially shape which relationships
are observed between elevation and soil fungal pathogens. Larger
elevation gradients mean that studies are comparing among different
habitat types with different sets of species, whereas smaller elevation
gradients are usually making comparisons across environmental conditions
within a single habitat type. These results provided important evidence
that elevational patterns of plant pathogens are consistent despite
great variation in plant and pathogen species pools.

In our field survey, we did not find any evidence supporting a direct
association between elevation and foliar fungal diseases at either the
host population or community levels. These results are inconsistent with
a previous study in the Swiss Alps along a 1101 m elevational gradient,
which found that elevation not only associated with disease directly,
but also indirectly regulated plant diseases by shifting the
relationship between host plant composition and disease (Halliday et
al., 2021). The differences between these two studies might be due to
variation in climate (higher temperature and precipitation in the Swiss
Alps), vegetation type (our site was a typical alpine meadow, while the
Swiss Alps had more montane habitat), and/or elevation range of sampling
(1101 m for Swiss Alps \emph{vs.} 800 m here). Elevation is also
associated with a series of changes in plant communities, soil
properties and other environmental factors (Lomolino, 2001). These
factors may have both positive (e.g. warming; Siebold and Tiedemann,
2013; Liu et al., 2019) and negative (e.g., dilution effect; Mitchell et
al., 2002; Rottstock et al., 2014; Halliday et al., 2020) effects on
foliar fungal diseases. These effects could also offset each other, and
environmental gradients can further modify how host community structure
affects disease (Halliday et al., 2021), resulting in imperceptible
associations between elevation with foliar fungal diseases. Hence, the
relationship between elevation and foliar fungal diseases was difficult
to explain using temperature, precipitation, latitude and elevation
range of sampling in our meta-analysis.

\textbf{Factors affecting above- and belowground plant pathogens}

Our results provide empirical support that both \emph{Evenness}
and\emph{Proneness} can shape community pathogen loads, highlighting the
importance of including host community indices into disease ecology
models (Liu et al., 2017; Halliday et al., 2019).
\emph{Proneness}increased with elevation, indicating that plant
communities at higher elevations contained greater proportions of
competent species (i.e. species with good growth but weak defensive
abilities, thus harboring more pathogens; Liu et al., 2017), though this
point was not fully supported by the study in the Swiss Alps (Halliday
et al., 2021). We attribute the variation in the plant species level
proneness index partly to the growth-defense trade-off, which suggests
that the enhancement of defense systems comes at the cost of reduced
growth, based on the assumption that plants share limited resources
(Coley et al., 1985; Cappelli et al., 2020). The community proneness
index was the best predictor of pathogen loads along the elevational
gradient in the alpine meadow (explaining \emph{\textasciitilde{}}
34.0\% of variation), suggesting that plant communities dominated by
host species with higher proneness (i.e. weak defense systems) might
experience more serious disease. Thus, our study calls for the inclusion
of host community composition into models that predict infectious
disease (Halliday, Rohr, et al., 2020).

Our study provided empirical evidence for a negative
biodiversity-disease relationship, which ecologists have long debated
(Rottstock et al., 2014; Halliday et al., 2019; Halliday and Rohr, 2019;
Liu, Chen, et al., 2020). Pielou's evenness index was an effective
predictor of pathogen load, even along such a sharp elevation gradient,
consistent with the theoretical prediction that host community evenness
should be a better predictor of disease than species richness (Chen and
Zhou, 2015). The evenness index incorporates the distribution of
relative abundances among host species, which can better describe the
mechanism of encounter reduction between host biodiversity and plant
diseases (Mitchell et al., 2002; Liu et al., 2016). Therefore, a higher
community evenness index translates into a lower probability of focal
individuals being infected through encounter reduction (Keesing et al.,
2006).

\textbf{Plant community-, environment- and soil-mediated relationships
between elevation and above- and belowground plant pathogens}

Together, our results provide broad evidence that above- and belowground
pathogens had distinct geographical vertical pattern which were shaped
by different mechanisms. For foliar fungal pathogens, we found empirical
evidence that plant community characteristics (i.e. community proneness
index and evenness), rather than soil properties, were the main drivers
of community pathogen load in our field survey. However, results did not
support any direct or indirect relationship between elevation and foliar
fungal disease.

These results highlight the importance of host identity to determine
community-level diseases. In the field survey, shifts in plant community
proneness to diseases ultimately led to difference in community pathogen
load, a finding that is consistent with other studies from both plant
diseases (Mitchell et al., 2002; Liu et al., 2017) and
also\emph{Ribeiroia ondatrae} caused amphibian diseases (Johnson et al.,
2013). However, although only a small part of the 73 plant species'
distributions overlaps with each other and generates considerable
variance in the composition of host communities for pathogens along the
elevation gradient, the degree of communities prone to disease did not
synchronously change. Therefore, plant community mediated effect was not
found in our study.

In general, our results indicated that abiotic factors mediated the
association between elevation and soil fungal pathogen richness, while
elevation showed no significant direct or indirect association with
foliar fungal diseases and soil fungal pathogen relative abundance.
Indeed, different measurements for pathogens (i.e. OTU richness versus
relative abundance) may explain the inconsistent responses of pathogens
to abiotic and biotic factors. For instance, positive plant richness-
pathogen diversity relationship and negative diversity- disease
relationship can be observed in natural plant communities, given that
increasing plant species diversity provides more diverse hosts while
simultaneously inducing dilution effects (Rottstock et al., 2014).
However, there is insufficient evidence to determine the relative
strength of which pathogen richness responds to the abiotic and biotic
environment. In addition, the different patterns of above- and
belowground plant pathogens along elevational gradients can be partly
explained by their differences in life history characteristics. Rusts
(e.g. \emph{Phragmidium} , \emph{Puccinia} and \emph{Uromyces} ) are the
dominant foliar fungal pathogens in our study site (Liu et al., 2019);
these fungi belong to a~group of obligate biotrophic pathogens that can
only extract nutrients from living plant cells (Duplessis et al., 2021)
and have relatively narrow host ranges (one or just a few
phylogenetically close plant species; Zhang, 2009). In contrast, soil
plant pathogens are commonly necrotrophic with a relatively broad host
range (Delgado-Baquerizo et al., 2020). Therefore, we expect that
negative biodiversity-disease relationships might occur more commonly
for foliar pathogens; foliar diseases are strongly dependent on their
hosts, so they can be easily captured by host composition rather than
environmental factors (e.g. temperature). For soil biota, we found
empirical evidence that increasing elevation was associated with
reductions in soil fungal pathogen richness via changes in soil
properties (\emph{Soil PCA1} ). Our meta-analysis providing further
evidence of a general negative association between soil fungal pathogen
richness and elevation among studies. However, elevation had no
significant association with soil fungal pathogen relative abundance.
Unlike the severity of foliar fungal disease or fungal pathogen relative
abundance, soil fungal pathogen richness depends on the relative rate of
fungal colonization and extinction of fungal taxa. On the one hand,
temperature may promote soil fungal pathogen richness through the
following three mechanisms: First, temperature is a key limiting factor
for plant biomass and richness (Chu et al., 2019). Broad evidence from
plants suggests that the species richness of lower trophic levels can
determine the diversity of higher trophic levels, including fungal
pathogens (Kamiya et al., 2014; Rottstock et al., 2014; Liu et al.,
2016). Moreover, increased plant biomass provides greater host
availability and more diverse habitats for pathogens. These
plant-mediated paths can shape a potential negative relationship between
elevation and soil fungal pathogen richness. Second, temperature can
promote coevolution between hosts and pathogens, given the stronger
inter-trophic interactions in warm areas (Roslin et al., 2017; Liu,
Chen, et al., 2020); this is thought to be a main factor shaping
pathogen richness. Third, temperature can determine the distribution of
soil pathogens via environment filtering (Tedersoo et al., 2014); the
fitness of pathogens is largely affected by temperature and humidity
through impacts on survival, growth, dispersal and reproduction, both at
local and global scales (Tedersoo et al., 2014; Liu et al., 2019;
Delgado-Baquerizo et al., 2020).

On the other hand, soil properties may be a regulator of plant
pathogens. Firstly, soil nutrients may benefit pathogens in both agro-
and natural ecosystems by increasing tissue nitrogen concentration
(Huber and Watson, 1974; Veresoglou et al., 2013; Liu et al., 2017).
Secondly, soil properties can alter the competition dynamics and shape
plant community composition (e.g. inducing the light asymmetry under
nitrogen addition; Xiao et al., 2021), which possibly indirectly
influence soil plant pathogens by preferring species with better growth
ability (Cappelli et al., 2020). The soil fungal pathogen relative
abundance showed no significant variation along the elevation gradient,
despite considerable changes in temperature and soil properties. We
attribute this to the small proportion of soil fungal pathogens relative
to total soil fungi (i.e. percentage of soil fungal pathogens copy
number; on average of 7.72\% in our study), which may potentially limit
the ability of soil pathogens to respond to biotic and abiotic
variables.

\textbf{Conclusion}

Our multi-method approach provides evidence that community pathogen load
is more commonly associated with host community composition (evenness
and proneness) than with linear changes along the elevational gradient.
In contrast, we did find that increasing elevation was associated with
soil fungal pathogen richness (but not relative abundance), via the
effects of temperature and soil properties. These results highlight
elevational patterns of above- and belowground plant pathogens may be
regulated by distinct mechanisms, and are consistent with a growing body
of studies of foliar diseases (Mitchell et al., 2002; Rottstock et al.,
2014; Cappelli et al., 2020; Halliday et al., 2021) and soil pathogens
(Tedersoo et al., 2014; Delgado-Baquerizo et al., 2020). More
importantly, there is increasing evidence that environmental gradients
can modify how host community structure affects disease (Halliday,
Heckman, et al., 2020; Halliday et al., 2021). These interaction effects
may help explain the variation among different studies, highlighting
that only looking at environmental factors or community structure would
miss key information relevant to diseases. Nevertheless, for the first
time, to the best of our knowledge, we distinguish the different
mechanisms driving different groups of plant pathogens in an alpine
meadow. Because of rapid changes in both plant communities and climate
change in the Anthropocene, our study implies that incorporating
different information to build models predicting above- and belowground
plant pathogens is of great importance for understanding ecosystem
health.

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\textbf{Table 1.} All abbreviations and their corresponding annotations
in the main text.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Category} & \textbf{Abbreviation} &
\textbf{Annotation}\tabularnewline
\midrule
\endhead
Soil properties & \emph{Soil PCA1} & The first principal component of
soil properties PCA\tabularnewline
Plant community characters & \emph{AGB} & Plant aboveground
biomass\tabularnewline
& \emph{BGB} & Plant belowground biomass\tabularnewline
& \emph{SR} & Plant community species richness\tabularnewline
& \emph{Evenness} & Plant community Pielou's evenness
index\tabularnewline
& \emph{Proneness} & Plant community proneness index\tabularnewline
Environmental conditions & \emph{MDT} & Mean daily
temperature\tabularnewline
& \emph{MDH} & Mean daily humidity\tabularnewline
Plant pathogen indices & \emph{PL} & Plant community pathogen
load\tabularnewline
& \emph{sfpOTUs} & Soil fungal pathogen OTU richness\tabularnewline
& \emph{sfpRA} & Soil fungal pathogen relative abundance\tabularnewline
\bottomrule
\end{longtable}



\textbf{Figure legends}

\textbf{Figure 1.} Effects of \emph{Elevation} , soil properties
(\emph{Soil PCA1} ), aboveground biomass (\emph{AGB} ), Belowground
biomass (\emph{BGB} ), Pielou's evenness index (\emph{Evenness} ),
community proneness index (\emph{Proneness} ) and species richness
(\emph{SR} ) on (a) community pathogen load (\emph{PL} ), (b) soil
fungal pathogen OTU richness (\emph{sfpOTUs} ) and (c) soil fungal
pathogen relative abundance (\emph{sfpRA} ). Effect sizes with 95\%
confidence intervals are weighted average standardized coefficients from
models with \selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{} 4 based on model selections (Table S4, S5,
S6). Blue lines indicate significant negative effects, grey lines
indicate non-significant effects, and red lines indicate significant
positive effects.

\textbf{Figure 2.} The piecewise structural equation model. (a) The
hypothesized piecewise structural equation model. Hypothesized plant
community-mediated and soil-mediated association between elevation and
community pathogen load and soil fungal pathogen OTU richness.
Specifically, paths a-c represent the association between elevation and
host plant community and soil properties, paths d, e represent the
direct association between elevation and community pathogen load and
soil fungal pathogen OTU richness, while paths f-h and paths i-k
represent the community-mediated and soil-mediated effects on community
pathogen load and soil fungal pathogen OTU richness, respectively. (b)
The final piecewise structural equation model. Numbers on arrows are
standardized path coefficients (scaled by their mean and standard
deviation), and asterisks indicate statistical significance (***\emph{P}
\textless{} 0.001; * 0.01 \textless{} \emph{P} \textless{} 0.05; + 0.05
\textless{} P \textless{} 0.1). Red arrows represent significant
positive relationships while blue arrows represent significant negative
relationships at the \emph{P} \textless{} 0.1 level; gray arrows with
dashed lines represent insufficient statistical evidence for path
coefficients (\emph{P} \textgreater{} 0.1). Arrow thickness is
proportional to the strength of the path. \emph{De}indicates the
deviance explained by predictive variables.

\textbf{Figure 3.} Overall result of the meta-analysis testing the
associations between elevation and foliar fungal pathogen OTU richness,
foliar fungal diseases, soil fungal pathogen OTU richness and soil
fungal pathogen relative abundance. Shown are the mean values of the
effect sizes (\emph{Z} ) and the corresponding 95\% confidence intervals
(95\% CI).

\textbf{Figure 1.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

