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;Vi ) 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 (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 S was the total number of plant species in a certain plot,
and bi was the aboveground biomass of plant
species i . We then defined a ‘disease proneness index’ (hereafter
‘Pi ’) for each species as the average severity
index (Vi )
across 30 plots of plant species i . We then calculated a
‘community proneness index’
(hereafter ‘Proneness ’) for each plot by calculating a plant
aboveground biomass-weighted average of the Pifor 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 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 (Vi ) in this plot
and weighted by its aboveground biomass. Despite a similar mathematical
formula for PL andProneness , 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). PL and 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 (PL ) and disease
proneness index (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 inISI 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. Alternaria ,Epicoccum , Fusicladium ), Leotiomycetes (e.g. Coma ,Erysiphe , Scytalidium ), Sordariomycetes (e.g.Fusarium , Neonectria , 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
(sfpOTUs ; OTU richness of soil fungal pathogens), and also the
relative abundance of soil fungal pathogens (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 (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 (sfpRA ), and the richness of soil
pathogens (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.