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\title{Watershed unit is conductive to uncover microbial biogeography}



\author[1]{Xiao-Yan Yang}%
\author[1]{wei Deng}%
\author[1]{Fa Zhang}%
\author[1]{Shuoran Liu}%
\author[1]{Davide Fornacca}%
\author[1]{Na Li}%
\author[1]{Yanpeng Li}%
\author[1]{Xi-Jie Fan}%
\author[1]{xin Zhang}%
\author[1]{Li-Lei Liu}%
\author[1]{Ya-Jiao Zhu}%
\author[1]{Kun Tan}%
\author[1]{Yin Gao}%
\author[1]{Wen Xiao}%
\author[2]{Qinghua Cai}%
\affil[1]{Dali University}%
\affil[2]{Institute of Hydrobiology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\selectlanguage{english}
\begin{abstract}
Biogeography is flawed by a poor understanding of microbial
distribution, due to the lack of systematical research framework,
especially appropriate study units. We studied the biogeographic
patterns of Nematode-Trapping Fungi by collecting and analyzing 2,250
specimens from 228 sites in Yunnan Province, China. We found typical
watershed patterns both at the level of species and gene of
Nematode-Trapping Fungi. The results showed that microbial biogeography
could be better understood by 1) using watersheds as research units, 2)
removing the coverup of widespread species, and 3) applying good
sampling efforts and strategies. We suggest that watersheds could help
unify the understanding of biogeographic patterns of animal, plant, and
microbe, and may also help account for the historical and contemporary
factors driving species distribution.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Biogeographic patterns and the underlying mechanisms are essential in
biodiversity research. The understanding of biogeographic patterns is
crucial for predicting how global change affects biodiversity, designing
effective conservation strategies, and achieving sustainable development
(Gaston 2000). Studies on biogeographic patterns can be challenging due
to the interactions among multiple biotic and abiotic factors, and to
complex interrelationships among animals, plants, and microorganisms
(Dolan 2006; Delavaux \emph{et al.,} 2019). Previous research focusing
solely on macrobiota biodiversity may therefore be inadequate to clarify
the full view of biogeography (Mascarenhas \emph{et al.} , 2020).
Despite microbes being the most important biotic driver of global
material and energy flows, biogeographic research on microbiota has long
lagged behind that of macrobiota, and there are debates on whether
heterogeneous biogeographic patterns exists in microbes (Martiny
\emph{et al.} , 2006; Meyer \emph{et al.,}2018). The followings may be
the main obstacles of microbial biogeography.

The primary obstacle for the lagged research on microbial biogeography
is evaluating microbial diversity accurately. Microbial communities
contain extremely high microbial biodiversities and nonculturable taxa.
So, the traditional pure culture methods cannot deal with the obstacle.
Recent advances in metagenomics have finally made fast evaluations of
microbial diversity possible (Hanson \emph{et al.} 2012), while concerns
about the new methodologies remain. First, metagenomic approaches use
operational taxonomic units (OTU) clustered based on arbitrary identity
thresholds (usually 97\%), which reduces the resolution of the taxonomic
data set and generates confusing patterns (Storch \emph{et al.} 2008).
Second, metagenomic approaches are not concern on the strain level and
unable to match OTUs with actual taxa or identify dormant microorganisms
(Dolan 2006; Land \emph{et al.,}2015; Locey \emph{et al.,} 2020). Third,
metagenomic approaches controversially neglect the rare taxa which are
often more important than common taxa in analyses of microbial diversity
(Gobet \emph{et al.,} 2012). Although current reaserch have begun to use
amplicon sequence variants (ASV) with higher resolution to replace OTU,
ASV is still an operational taxonomic unit in essence, which has not
been completely solved those problems.

At last, the core obstable is the choice of research units. Many studies
about microbial biodiversity lack systematic sampling designs, clear
research boundaries, and appropriate research scales (Ladau \&
Eloe-Fadrosh 2019). The worse is that biogeographic patterns are
particularly sensitive to above factors (Bay \emph{et al.,} 2020). Those
caused confused results, and the confusions make it impossible for
researchers to distinguish variation in microbial biodiversity caused by
contemporary or historical factors (Castle \emph{et al.,} 2019).

We propose three solutions to overcoming above obstacles and clarify the
confusions. First, microbial biogeographic studies should depend on
natural units with clear boundaries. Current studies on microbial
biodiversity use either an arbitrary grid as unit of analysis or
nothing, which may result in mixing up the effects of various factors.
Natural units with clear boundaries should also be the fundamental need
for the study about biodiversity, because coupling animal, plant and
microbial biodiversity distributions is inevitable for future
biogeographic research (Peters \emph{et al.,} 2016). While biogeographic
units for animals and plants have long been defined (e.g., ecoregions,
biota, biogeographic provinces) (Hausdorf 2002; Smith \emph{et
al.,}2018), they do not match well with each other; great differences
were among different regions and taxa (Smith \emph{et al.,} 2020). We
propose that natural watersheds are ideal units for biodiversity
research, because they are relatively independent or ``close'' units of
material and energy flow, and their boundaries can be natural barriers
of transmission (Timur 2013). In addition, the contemporary and
historical effects driving species distribution should change gradiently
in different scales of watersheds, and watershed units could help us
distinguish contemporary and historical effects.

Second, a framework that considers the importance of rare species and
removing the coverup of widespread species should be established.
Because rare species comprise a large part of the microbial community
and contribute greatly to the microbial geographical patterns (Lynch \&
Neufeld 2015), their narrow distribution cannot be ignored. However,
most of the existing research focuses on common species which widely
distributed. The widespread species with large biomass are likely to
masked the heterogeneous biogeographic patterns of microbial community.
This may be due in part to the overconfidence in technology and resource
limitations, so inadequate sampling efforts are engaged in current
metagenomic approaches. Limitations on the resolution of metagenomic
approaches often results in the removal of rare taxa data during data
processing (Bay \emph{et al.,} 2020). While the pure culture methods may
be more effective at detecting rare species, its high workload makes it
difficult to achieve comprehensive monitoring for huge microbial
communities. To better account for the issue, we suggest 1) selecting a
cultivatable and relatively small microbial taxon as the research
object, 2) sampling adequately with stratified sampling strategy, 3)
removing the coverup of widespread species at species level, and 4)
analysing the widespread species biogeographic patterns at genetic
level. These strategies will avoid the common problems plaguing the
existing research system and should therefore set the standard for
comprehensively understanding the biogeographic patterns of
microorganisms.

Third, we suggest that studies about microbial biodiversity should be
conducted in highly heterogenous environments. Microorganism dispersal
is largely driven by both abiotic and biotic factors, and microbes are
readily able to adapt and survive in a variety of environments. Indeed,
studies conducted in homogeneous environments and within small spatial
scales often reveal that either microorganisms are randomly distributed,
or the weakness of environmental effects and dispersal limitations fail
to indicate any non-random patterns in these region (Meyer \emph{et
al.,} 2018; Liu \emph{et al.,} 2020). We believe it is easier to observe
the spatial distribution pattern of microorganisms on large scales with
high environmental heterogeneity.

Consequently, we conducted this research in Yunnan Province, China,
which holds six major international rivers belonging to the Pacific and
Indian Ocean systems. The watersheds were largely shaped by the
collision of the Indian Ocean and Eurasian plates, with the formation of
the Qinghai-Tibet Plateau. The spatial heterogeneity in Yunnan is very
high, given that mountainous areas occupy 88.6\% of the province.. Using
the stratified sampling method, we collected 2,250 specimens from 228
sites in Yunnan Province (Figure 1, 2). We combined pure culture and
molecular methods to investigate how common and rare Nematode-Trapping
Fungi (NTF, a cultivable microbial group consisting of 99 known species
) taxa are spatially distributed across Yunnan. By doing so, we aim to
verify our three proposed recommendations regarding 1) using watersheds
as the research units, 2) removing the coverup of widespread species,
and 3) stratified sampling from heterogenous environments, to overcome
the challenges of microbial biodiversity research and to test our
hypothesis that the microbial biogeographic patterns could be found in
the perspective of watersheds.

\textbf{1 Materials and Methods}

\textbf{1.1 Study area}

Yunnan province is located between 21\selectlanguage{ngerman}° 8' 32''\textasciitilde{}29° 15'
8'' N and 97° 31' 39''\textasciitilde{}106° 11' 47'' E, with a total
area of 394,100 km\textsuperscript{2} and spans an altitude of 76-6740
m. This area is characterized by sharp altitudinal gradient and the six
large rivers -- Mekong (\begin{CJK}{UTF8}{gbsn}澜沧江, \end{CJK}\selectlanguage{english}Lancang Jiang), Irrawaddy (\begin{CJK}{UTF8}{gbsn}独龙江,
\end{CJK}\selectlanguage{english}Dulong Jiang), Salween (\begin{CJK}{UTF8}{gbsn}怒江, \end{CJK}\selectlanguage{english}Nu Jiang), Red River (\begin{CJK}{UTF8}{gbsn}红河, \end{CJK}\selectlanguage{english}Hong He),
Pearl River (\begin{CJK}{UTF8}{gbsn}珠江, \end{CJK}\selectlanguage{english}Zhu Jiang) and Yangtze River (\begin{CJK}{UTF8}{gbsn}金沙江, \end{CJK}\selectlanguage{english}Jinsha Jiang)
cutting across the province. This complex topography helped shape the
region's relatively closed ecological zones, where biodiversity is
extremely high.

\textbf{1.2 Research objects}

Nematode-Trapping Fungi (NTF) consist of eukaryotic microorganisms with
terrestrial and aquatic biotypes belonging to the Orbiliaceae family.
NTF are predatory microbes and, as their name suggests, prey on
nematodes and other small organisms by producing trapping structures
with their vegetative hypha. Globally distributed across various
habitats and extreme environments, 101 NTF species have so far been
reported, including those in the \emph{Arthrobotrys}
,\emph{Drechslerella} and \emph{Dactylella} genera. All NTF species can
be pure cultured and their morphological characteristics are observable
under a low-magnification microscope. That NTF can be studied
qualitatively and semi-quantitatively make them an ideal taxa for
studying patterns in the spatial distribution of microbial diversity.

\textbf{1.3 Stratified sampling}

We systematically established 282 stratified sampling sites acording to
Yunnan's hydrographic network. We were unable to sample from sites
located in inaccessible terrain, including deep valleys and high
mountains. Sites with strong human disturbance was also avoided. At last
228 randomly distributed sites (Ripley's K, \emph{p} = 0.2435) were
remained(Figure 2). To ensure the phenological consistency and clear
aquatic-terrestrial boundary of the sites, sampling was conducted from
the southernmost to the northernmost and between February 27th, 2014 and
June 8th, 2014. At each site, 5 sampling points were set paralleled with
the river both in the aquatic and terrestrial habitats with a distance
of 10 meters (Figure 1b). Totally 2,250 sampling points were set (Figure
1a), 30 sampling points were lost due to no soil there. Using the
five-point sampling method in 1m\selectlanguage{ngerman}×1m squares (Figure 1b), samples
consisted of \textasciitilde{}50 g of mixed soil (or sediment) and were
collected at a depth of 0\textasciitilde{}10 cm below the surface at
each sampling point. Totally we collected 2,250 specimens from 228
sites. Samples were then stored in a disposable self-sealing bag at room
temperature. We recorded habitat information, including location,
longitude, latitude, and altitude of each sampling site. Samples were
sent to the laboratory within 1 week of collection, immediately followed
by NTF identification processing.

\textbf{1.4 NTF classification and identification}

Isolation and purification: Using the five-point spreading method,
samples were plated on petri-dishes with CMA medium with
\textasciitilde{}5,000 individuals of \emph{Panagrellusredivivus} to
induce NTF germination. We plated three replicates per sample for a
total of 6,750 petri-dishes plated. The petri-dishes were incubated at
room temperature (25-28\selectlanguage{english}). We examined the petri-dishes once after 3
weeks and again after 4-5 weeks using a stereomicroscope to isolate all
the species. During each examination, we used single spore isolation to
collect and purify any germinated NTF on CMA medium. A sterile toothpick
was used during collection. Pure cultures were preserved until we were
able to isolate and purify cultures from all specimens.

Morphological identification: All preserved pure cultures were revived
in CMA medium and placed on a temporary slide using the inserting and
sticking method. Morphological characteristics of NTF, including
conidia, conidiophores and chlamydospore, were then photographed using a
microscope camera (Olympus BX51, Japan). For each NTF strain, the type
of trapping device was observed and confirmed using the observation
chamber and nematode induction method. Strains were identified according
to Li \emph{et al.,} 2014.

Molecular identification: Each NTF strain was inoculated on PDA medium
to gather the mycelium. We then extracted DNA from the mycelium using a
CTAB protocol. The fragments of ITS (Internal Transcribed Spacer), TUB
(\selectlanguage{greek}β-\selectlanguage{english}tubulin gene), TEF (Translation Elongation Factor 1-alpha) and RPB2
(RNA polymerase II) were amplified by PCR and sequenced by BioSune
Biotechnology Co., Ltd. (Shanghai, China). The homologous ITS and TUB
sequences were aligned using the BLAST toolkit from NCBI (National
Center for Biotechnology Information Search database).

We were able to identify and classify specific NTF species using
morphological and molecular identifications, referring to the taxonomic
system used by Li \emph{et al.,} 2014.

\textbf{1.5 Data analysis}

1.5.1 Analysis of NTF species composition and distribution

The occurrence frequency (OF) for each species and sampling site was
calculated respectively. OF (\%) was calculated as: (number of specimens
with NTF occurrence / total number of specimens) \selectlanguage{ngerman}× 100\%. Species with
OF \textgreater{} 5\% were defined as widespread species, while species
with OF \textless{} 1\% were defined as rare species.

Using the latitude and longitude of each site and NTF OFs, we then
plotted distribution maps based on genus, species, and rare species with
the \emph{raster} , \emph{sf} and \emph{ggplot2} packages in R version
4.02.

The mean variance ratio was used for evaluating NTF species
distributions. A mean variance ratio of 1 indicated a random
distribution (conforming to the Poisson's distribution); a mean variance
ratio \textgreater{}1 indicated a clustered distribution; a mean
variance ratio \textless{}1, indicated an even distribution. The
significance of these distributions was verified using a T-test.

We divided sampling areas into 9 grids using QGIS version 3.10. We
selected the six grids with the largest sampling areas (grids A, B, C,
D, E and F) for analysis (Figure S1 a). The watershed boundaries of
Yunnan Province were extracted from a derived digital elevation model
(CGIAR-CSI SRTM v4.1) at 90-meters resolution using GIS geoprocessing
algorithms (Figure S1 b). We constructed upset plots using grid and
watershed units with the \emph{UpSetR} package in R version 4.02.

1.5.2 Genetic divergence and bigeographical distribution analysis of

\emph{Arthrobotrys oligospora}

The DNA sequencing of ITS, TUB, TEF and RPB2 fragments was conducted on
the strains of \emph{Arthrobotrys oligospora} , which were found at
every sampling site. The four sequences from each strain were summarized
in a txt file and converted into a fasta file. MAFFT version 7 was used
to generate the multi-sequence matrix (homologous sequences searching),
and Bioedit was used to manually improve the accuracy of the alignment.
We used jmodeltest software to select the optimal calculating
alternative model for the piecing sequence. The phylogenetic tree was
constructed following the Maximum Likelihood method (ML) in the way of
partition calculation using IQ-Tree version 1.6.5 software. We used
FigTree version 1.3.1, Microsoft Word (Microsoft office 2007) and
Photoshop (Adobe Photoshop CS5 V12.0) to read the phylogenetic tree.

1.5.3 Biogeographical distribution of NTF in Yunnan

A first prediction was performed using the different watershed as unique
predictors of the phylogenetic tree clades distribution
of\emph{Arthrobotrys oligospora} . We assigned clades to watersheds
using the majority rule and assessed classification accuracy using error
matrix metrics.

From the original dataset, we generated 45 different splits between
train and test samples with train ratios comprising between 30\% and
70\% (step=5). Every ratio step included five different mixes of
train/test samples. Voronoi (Thiessen) polygons were computed around
each training set and assigned the clade corresponding to their
originating point. The polygons were used to predict the test dataset
clades.

1.5.4 Multivariate machine learning model

We compiled a dataset of 97 variables divided based on bioclimatic (24),
topographic (6), vegetation (12), and soil properties (55) using QGIS
(Table S1). In addition to the watershed, 19 predictors were selected
among an initial pool of 97 variables. Table S2 shows the selection
results after multinomial logistic regression and collinearity
screening. In these cases, we always retained the values for the first
layer.

To select the most suitable predictor variables to include in our
machine learning model, we hierarchically screened each variable in our
dataset. We iteratively preformed multinomial logistic regressions to
assess the effects of each predictor variable on clade. We assessed the
relationships among predictor variables using correlation matrices
(Pearson's and Kendall's), and assessed multicollinearity using the
Variance Inflation Factor.

The selected predictor variables and watershed predictors constituted
the final dataset to investigate patterns in clade distribution using an
Extra-Trees classifier (Extremely Randomized Trees). We aimed to improve
the prediction ability from the previous analysis (watershed subdivision
as a unique predictor), and therefore used the same accuracy metrics as
in that analysis. Hyperparameters were optimized with Grid Search and
Cross Validation techniques, though we also considered bootstrapping and
accuracy estimations on the out-of-bag samples. Relative feature
importance was calculated for the best model.

\textbf{Results}

\textbf{The species composition and distribution of NTF in Yunnan
Province}

We identified 44 NTF species from 2,250 samples. These species belonged
to three genera: \emph{Arthrobotrys} (29 species), \emph{Dactylella} (9
species), and \emph{Drechslerella} (6 species) (Table S3). The rank
order of occurrence frequency (OF) of NTF species corresponded to a
logarithmic distribution model (\emph{R\textsuperscript{2}} = 0.940)
(Figure 3). Four species were widely distributed (mean OF = 5.22\%), and
contributed to 9.09\% of the total species richness and 62.32\% of the
total OF. Conversely, 31 species were classified as rare (mean OF =
0.18\%), and contributed to 70.45\% of the total species richness and
7.52\% of the total OF.

A mean variance ratio of 1.07 (\emph{t} = 0.7029\selectlanguage{english},\emph{p}\textgreater{}
0.05) indicated that NTF species were randomly distributed (Figure 4).

\textbf{NTF spatial distribution patterns}

At the genus level, \emph{Arthrobotrys} was regionally distributed and
found in 98.68\% of the sampling sites, while \emph{Dactylella}
and\emph{Drechslerella} were local distributed and found in 7.02\% and
7.89\% of the sampling sites respectively (Figure 5).

Among the 44 species detected in this study, the 4 widespread species
were found in 83.30\%, 56.58\%, 47.37\% and 41.23\% of the sampling
sites. The 31 rare species found in less than 10\% of the sampling sites
(range: 0.41-9.21\%) (Figure 6).

When the all species were superposed in the map, NTF was found to be
randomly distributed across the entire study area (mean variance ratio =
1.014, t = 0.153, p = 0.878). When the widespread species were removed,
the cumulative distribution of rare and non-widely distributed species
OFs was non-random and clustered (rare species: means variance ratio =
1.284, t = 3.022, p = 0.003; non-widely distributed species: means
variance ratio = 1.326, t = 3.471, p \textless{} 0.001). The widespread
species masked the non-random patterns.

\textbf{NTF distribution: watersheds \emph{vs} grids}

Of the 19 rare species with non-single site distributions within grids
or watersheds, 63.16\% were distributed in the same or adjacent grid,
and 84.21\% were distributed in the same or adjacent watershed. With the
exception of \emph{Drechslerella dactyloides} , 8 of the 9 rare species
that only appeared in 2 or 3 sites were distributed in the same or
adjacent watershed (Figure 7).

At genetic level, the phylogenetic tree revealed that the widespread
species \emph{A. oligospora} was divided into 5 large clades. When grid
was used as the unit of analysis, no clear distribution pattern of these
clades was detected. When watershed was used as the unit of analysis,
the distribution of these clades was consistent with the natural
watershed divisions in Yunnan Province, except for Irrawaddy River,
which has no corresponding clade. The strains within the other clades
were distributed within their corresponding watersheds. The
64.70\%\selectlanguage{english},60.71\%\selectlanguage{english},80.00\%\selectlanguage{english},63.89\% and 61.11\% of the strains from clade
1 to clade 5 were correspondingly distributed in the Yangtze River, Red
River, Pearl River, Mekong, and Salween-Irrawaddy watersheds
respectively (Figure 8).

Based on our phylogenetic tree, the spatial distribution \emph{A.
oligospora} was machine learned using the randomly generated Voronoi
polygons and the watersheds. We found that the 45 maps generated using
polygons all had lower accuracy than the maps watersheds generated
(Table S4). On average, the accuracy of the polygons was low (mean:
36\%, median: 38\%), with all but one prediction falling below 50\%. By
assigning the clades according to the watershedes, 68.8\% of clades were
classified correctly (Figures S2, panel a). Watershed explained nearly
70 \% of\emph{A. oligospora} distribution in Yunnan. None of the
additional climate, topographic, soil and vegetation variables
significantly improved the model (Figure S3\selectlanguage{english},Table S5).

We were better able to capture the distribution pattern of NTF when
using watershed as the unit of analysis compared with grid (Figure 9).
Only 17 intersections of were found when we constructed upset plots
using watershed units. We found 19 species distributed in only one
watershed unit, and that 76.2\% of all species were distributed in the
adjacent watersheds. There was a large gap between the dataset (variance
was 7.89, and mean value was 2.53). Despite finding 22 combinations when
constructing the integrative diagrams by grid, only 11 species were
distributed in only one grid and 55.0\% of all species were distributed
in adjacent grids. There was a small fluctuation between the dataset
(variance was 5.04, and mean value was 1.77). Ultimately, using
watershed units led to a higher observation of endemic species, more
species distributed within adjacent or similar units, and a more varied
species composition, compared with using grid units.

\textbf{Discussion}

\textbf{Widespread species masked thebiogeographic patterns of microbes}

Nearly half of the reported NTF species (N = 44) in 3 genera were found
during this study. At the species level, the distribution pattern was
random and had no heterogeneous pattern. The number of common species
only accounted for less than 5\% of the total species but contributed to
70\% of the OF. When excluding the 4 widespread species, the pattern
tended to be non-random with a clear heterogenous pattern. If the
distribution of the species with high OF were gradually superimposed,
the patterns of NTF tended to be random. The results indicated that the
extreme high OF and wide distribution of the common species mask
microbial heterogeneous biogeographic patterns.

The 31 rare species were restricted to 0.44\% \textasciitilde{} 9.21\%
of the sampling sites. The genera \emph{Dactylella}
and\emph{Drechslerella} were restricted in 7.02\% and 7.89\% of the
sampling sites respectively. Because the limited distribution and the
existence of endemic species were representative of the biogeographic
pattern in itself\textsuperscript{5}, we concluded that NTF has
heterogeneous biogeographic patterns..Although the rare species were low
in abundance, they played a key role in understanding microbial
biogeographic patterns. A low sampling effort may have limited any
observations of these species. In this study, rare species (OF
\textless{} 1\%) accounted for 70.45\% of the 2,250 specimens collected
in Yunnan Province. Had the sampling effort been reduced by 10\%, we
estimated that 29 of all species (93.54\% of rare species) would have
been more difficult to observe. Studies that used metagenomics to
investigate microbial diversity patterns reported a sampling intensity
interval between 3\textasciitilde{}300 samples, with an average of 40
samples collected (summarised according to articles published in The
ISME Journal and Microbiome over the past five years). Sampling efforts
were subsequently reduced to same time and resources when using
metagenomic methods (Karimi \emph{et al} ., 2018). With low sampling
efforts, many rare species would be ignored.

\textbf{Watersheds are crucial for reveal microbial distribution
patterns}

Our results showed that the heterogeneous distribution of microbial
diversity could be better explained by using watersheds as the units
compared with grids. More endemic species were observed using watershed
units (19 species by using watersheds vs 11 species by using grids).
Additionally, 84.21\% rare species were clustered in the same or
adjacent watersheds, while only 63.16\% were clustered in the same or
adjacent grids.

At the genetic level, the 5 clades of \emph{A. oligospora} were
consistent with the natural watershed divisions and these divisions were
further supported by machine learning results. The
64.70\%\selectlanguage{english},60.71\%\selectlanguage{english},80.00\%\selectlanguage{english},63.89\% and 61.11\% of the strains from clade
1 to clade 5 were correspondingly distributed in the Yangtze River, Red
River, Pearl River, Mekong, and Salween-Irrawaddy watersheds
respectively. These findings suggest that watersheds are the most
crucial factor in explaining the observed spatial distribution pattern
of \emph{A. oligospora} , more so than other environmental factors.
Historical events leading to the formation of Yunnan's six large
watersheds, including the uplift of Qinghai-Tibet Plateau and the
spatial compression of the Hengduan Mountains, therefore likely shaped
the spatial distribution of NTF genetic diversity. Because environments
in mountainous region were geographically isolated by mountains, rivers,
and other natural barriers, many new species evolved here (Ding \emph{et
al} ., 2020). As such, patterns of microbial diversity are easier to
observe in this region given its clear watershed boundaries, complex
terrains, high environmental heterogeneity, and abundant biodiversity.

Watersheds with clear boundaries and ecogeographical significance make
them natural division units for biogeographic research. The dividing
ridges between adjacent watersheds are critical dispersal barriers, the
relatively isolated materials and energy flow were shaped. Previous
studies have considered watersheds as ecosystem boundaries, and
discussed the possibility of setting watersheds as the appropriate units
in landscape ecology (Berkes \emph{et al.,} 1998). For example, Paula et
al. (2018) found that watersheds were better units when predicting
diversity based on environmental heterogeneity. The hierarchical
structure of watersheds also makes them appropriate for understanding
how historical and contemporary factors contribute to diversity
patterns. Moving from large scales (secondary or tertiary watersheds) to
smaller scales (sub-watersheds), the influence of ridge barriers
hindering distribution gradually decrease while environmental influences
increase. The effects of historical events and contemporary
environmental background are therefore perfectly coupled by watershed
units. As such, using watersheds research units should help solve most
of the problems in studying the spatial distribution of microbial
diversity.

Meanwhile, from the perspective of watershed, the possibility of
integrating plants, animals and microorganisms biogeographic research
could be provided. As with microorganisms, biogeographic research on
plants and animals is also plagued by confusing or deficient research
units, methods, and analyses. For instance, the biogeographic provinces
between the plants and animals were not coincident. Watershed units can
provide an alternative that is applicable to multiple biological groups,
allowing for research that consider the interactive effects of plants,
animals, and microorganisms in shaping biogeographic patterns. For the
core issues of biogeography, including the historical and environmental
influences on the spatial distribution, the scale effects on the
biological distribution patterns could be also solved. Finally, we
believe that watersheds could support explanations on the origin of
biodiversity, which is a fundamental scientific problem.

\textbf{Conclusion}

We did find definited biogeographic pattern of NTF in Yunnan.
Heterogeneous pattern of NTF can not be found, if widespread species
were included, or rare species were removed. Watershed unit, not grid,
revealed clear distribution pattern of NTF at species and genetic level.
Suggestions on future microbial biogeography studies include: employing
watershed as basic unit; removing the impact of widespread species,
valuing the contribution of rare species; employing stratified sampling
strategy and good sampling effort; attaching importance to pure culture
research and working on small taxa.

\textbf{Acknowledgements:} This work was funded by the National Natural
Science Foundation of China (U1602262, 31360013, 31760126), supported by
the Second Tibetan Plateau Scientific Expedition and Research Program
(STEP), Grant No. SQ2019QZKK2002. We thank Chen Jin-Hui (\begin{CJK}{UTF8}{gbsn}陈锦辉\end{CJK}\selectlanguage{english}) from
Kunming Institute of Zoology, Chinese Academy of Sciences for sample
collection. Thanks to Jacob B Kraus, Ren Guo-Peng (\begin{CJK}{UTF8}{gbsn}任国鹏\end{CJK}\selectlanguage{english}), She Rong
(\begin{CJK}{UTF8}{gbsn}佘容\end{CJK}\selectlanguage{english}), Zhang Cai-Cai (\begin{CJK}{UTF8}{gbsn}张彩彩\end{CJK}\selectlanguage{english}) and Zhu Xiao-Ming (\begin{CJK}{UTF8}{gbsn}朱晓明\end{CJK}\selectlanguage{english}) from Institute
of Eastern-Himalaya Bodiversity research, Dali University for
discussions. Thanks to the followings for their contributions in
laboratory research, they are Fang Yi-Hao (\begin{CJK}{UTF8}{gbsn}房以好\end{CJK}\selectlanguage{english}), Zhou Han-Yu
(\begin{CJK}{UTF8}{gbsn}周汉宇\end{CJK}\selectlanguage{english}), Wang Jia-Liang (\begin{CJK}{UTF8}{gbsn}王家亮\end{CJK}\selectlanguage{english}), Wang Chuan-Qi (\begin{CJK}{UTF8}{gbsn}王传琪\end{CJK}\selectlanguage{english}), Zhou Xin-Juan
(\begin{CJK}{UTF8}{gbsn}周新娟\end{CJK}\selectlanguage{english}), Wang Hai-Qing (\begin{CJK}{UTF8}{gbsn}王海清\end{CJK}\selectlanguage{english}), Ma Rui (\begin{CJK}{UTF8}{gbsn}马瑞\end{CJK}\selectlanguage{english}), Li Fei-Teng (\begin{CJK}{UTF8}{gbsn}李飞腾\end{CJK}\selectlanguage{english}),
Li Jing-Chao (\begin{CJK}{UTF8}{gbsn}李静超\end{CJK}\selectlanguage{english}), Jiang Lin (\begin{CJK}{UTF8}{gbsn}姜林\end{CJK}\selectlanguage{english}), Gu Rong (\begin{CJK}{UTF8}{gbsn}顾荣\end{CJK}\selectlanguage{english}), Wen Hui (\begin{CJK}{UTF8}{gbsn}温慧\end{CJK}\selectlanguage{english}),
Ni Jun (\begin{CJK}{UTF8}{gbsn}倪俊\end{CJK}\selectlanguage{english}), Liao Yong-Hong (\begin{CJK}{UTF8}{gbsn}廖永红\end{CJK}\selectlanguage{english}), Liu Sheng-Bin (\begin{CJK}{UTF8}{gbsn}刘胜斌\end{CJK}\selectlanguage{english}), Duan
Ji-Xia (\begin{CJK}{UTF8}{gbsn}段继霞\end{CJK}\selectlanguage{english}), Liu Na (\begin{CJK}{UTF8}{gbsn}刘娜\end{CJK}\selectlanguage{english}), Sun Hui-Fang (\begin{CJK}{UTF8}{gbsn}孙慧芳\end{CJK}\selectlanguage{english}), Fan Xian-Qin
(\begin{CJK}{UTF8}{gbsn}范先芹\end{CJK}\selectlanguage{english}), Chen Xiao-Xi (\begin{CJK}{UTF8}{gbsn}陈晓溪\end{CJK}\selectlanguage{english}), Wu Tao (\begin{CJK}{UTF8}{gbsn}吴涛\end{CJK}\selectlanguage{english}), Mao Xu-Dan (\begin{CJK}{UTF8}{gbsn}毛旭丹\end{CJK}\selectlanguage{english}),
Wang Ting-Ping (\begin{CJK}{UTF8}{gbsn}王廷平\end{CJK}\selectlanguage{english}), Lin Yan-Yun (\begin{CJK}{UTF8}{gbsn}林燕云\end{CJK}\selectlanguage{english}), Huang Yuan-Tong (\begin{CJK}{UTF8}{gbsn}黄圆通\end{CJK}\selectlanguage{english}),
Xie Rong (\begin{CJK}{UTF8}{gbsn}谢蓉\end{CJK}\selectlanguage{english}), Wang Yan-Fang (\begin{CJK}{UTF8}{gbsn}王彦芳\end{CJK}\selectlanguage{english}), Xia Feng-Yu (\begin{CJK}{UTF8}{gbsn}夏丰雨\end{CJK}\selectlanguage{english}), Guo
Hao-Miao (\begin{CJK}{UTF8}{gbsn}郭浩妙\end{CJK}\selectlanguage{english}), Zhang Ying (\begin{CJK}{UTF8}{gbsn}张莹\end{CJK}\selectlanguage{english}), Li Yi-Lun (\begin{CJK}{UTF8}{gbsn}李宜伦\end{CJK}\selectlanguage{english}), Yuan Jia-Xing
(\begin{CJK}{UTF8}{gbsn}袁佳兴\end{CJK}\selectlanguage{english}), Wang Shi-Xiong (\begin{CJK}{UTF8}{gbsn}王世雄\end{CJK}\selectlanguage{english}), Zhou Fang-Yuan (\begin{CJK}{UTF8}{gbsn}周方圆\end{CJK}\selectlanguage{english}), Xiong
Meng-Jia (\begin{CJK}{UTF8}{gbsn}熊梦佳\end{CJK}\selectlanguage{english}), Liu Tao (\begin{CJK}{UTF8}{gbsn}刘韬\end{CJK}\selectlanguage{english}), Chen Zhi-Hong (\begin{CJK}{UTF8}{gbsn}陈志鸿\end{CJK}\selectlanguage{english}), Lu Ya-Xian
(\begin{CJK}{UTF8}{gbsn}陆亚娴\end{CJK}\selectlanguage{english}), Gao Yu-Ting (\begin{CJK}{UTF8}{gbsn}高玉婷\end{CJK}\selectlanguage{english}), Li Zheng-Qiang (\begin{CJK}{UTF8}{gbsn}李政强\end{CJK}\selectlanguage{english}), Feng Xing-Xing
(\begin{CJK}{UTF8}{gbsn}冯鑫鑫\end{CJK}\selectlanguage{english}), Chen Yu-Xiu (\begin{CJK}{UTF8}{gbsn}陈玉秀\end{CJK}\selectlanguage{english}), Zhang Xiao-Yu (\begin{CJK}{UTF8}{gbsn}张晓禹\end{CJK}\selectlanguage{english}), Huang Zhi-Hao
(\begin{CJK}{UTF8}{gbsn}黄志豪\end{CJK}\selectlanguage{english}), Fan Ji-Zhuang (\begin{CJK}{UTF8}{gbsn}范集壮\end{CJK}\selectlanguage{english}), Huang Fei-Hong (\begin{CJK}{UTF8}{gbsn}黄飞鸿\end{CJK}\selectlanguage{english}), Ni Shui-Qing
(\begin{CJK}{UTF8}{gbsn}倪水清\end{CJK}\selectlanguage{english}), Zhu Zi-Zhong (\begin{CJK}{UTF8}{gbsn}朱自忠\end{CJK}\selectlanguage{english}), Liao Guang-Hui (\begin{CJK}{UTF8}{gbsn}廖光辉\end{CJK}\selectlanguage{english}), Wang Zhu-Hua
(\begin{CJK}{UTF8}{gbsn}王柱华\end{CJK}\selectlanguage{english}), Feng Bing (\begin{CJK}{UTF8}{gbsn}冯冰\end{CJK}\selectlanguage{english}), Xu Xiao-Yu (\begin{CJK}{UTF8}{gbsn}徐潇雨\end{CJK}\selectlanguage{english}), Yan Li-Hong (\begin{CJK}{UTF8}{gbsn}严丽红\end{CJK}\selectlanguage{english}),
Yue Xiao-Lan (\begin{CJK}{UTF8}{gbsn}岳晓兰\end{CJK}\selectlanguage{english}), Teng Man (\begin{CJK}{UTF8}{gbsn}藤曼\end{CJK}\selectlanguage{english}), Li Shuang-Mei (\begin{CJK}{UTF8}{gbsn}李双美\end{CJK}\selectlanguage{english}), Zhao
Jia-Jing (\begin{CJK}{UTF8}{gbsn}赵家静\end{CJK}\selectlanguage{english}), Zhang Da-Bo (\begin{CJK}{UTF8}{gbsn}张大波\end{CJK}\selectlanguage{english}), Li Zheng-Xiong (\begin{CJK}{UTF8}{gbsn}李正雄\end{CJK}\selectlanguage{english}), Zhang
Xun-Gang (\begin{CJK}{UTF8}{gbsn}张勋刚\end{CJK}\selectlanguage{english}), Zhang Zan (\begin{CJK}{UTF8}{gbsn}张赞\end{CJK}\selectlanguage{english}), Huo Bang-Guo (\begin{CJK}{UTF8}{gbsn}伙帮国\end{CJK}\selectlanguage{english}), Yao Lu-Jun
(\begin{CJK}{UTF8}{gbsn}姚路军\end{CJK}\selectlanguage{english}), An Pan (\begin{CJK}{UTF8}{gbsn}安攀\end{CJK}\selectlanguage{english}), Yang Zheng-Chao (\begin{CJK}{UTF8}{gbsn}杨正超\end{CJK}\selectlanguage{english}), Yang Yao-Quan
(\begin{CJK}{UTF8}{gbsn}杨耀权\end{CJK}\selectlanguage{english}), Zhou Fa-Ping (\begin{CJK}{UTF8}{gbsn}周发平\end{CJK}\selectlanguage{english}), Niu Kai-Yang (\begin{CJK}{UTF8}{gbsn}牛开阳\end{CJK}\selectlanguage{english}), Xiong
He-Ju(\begin{CJK}{UTF8}{gbsn}熊和菊\end{CJK}\selectlanguage{english}), Lin Hui-Biao (\begin{CJK}{UTF8}{gbsn}林慧彪\end{CJK}\selectlanguage{english}), Chen Xiao-Jun (\begin{CJK}{UTF8}{gbsn}陈晓俊\end{CJK}\selectlanguage{english}), Zhou
Jian-Fen (\begin{CJK}{UTF8}{gbsn}周建芬\end{CJK}\selectlanguage{english}), Wang Ya-Juan (\begin{CJK}{UTF8}{gbsn}王娅娟\end{CJK}\selectlanguage{english}), Qi Yu-Kun (\begin{CJK}{UTF8}{gbsn}齐宇坤\end{CJK}\selectlanguage{english}), Huang
Shuai-Yi (\begin{CJK}{UTF8}{gbsn}黄帅义\end{CJK}\selectlanguage{english}), and Wang Ai-Di (\begin{CJK}{UTF8}{gbsn}王爱迪\end{CJK}\selectlanguage{english}).

\textbf{Author Contributions:}Y.X.Y, X.W and C.Q.H conceived the study;
Y.X.Y, X.W, D.W, Z.F, L.S.R and D.F undertook comparative and analytical
work and wrote the paper; L.N, T.K and G.Y performed data analyses;
Y.X.Y, X.W, D.W, Z.F, L.S.R, L.Y.P, F.X.J, Z.X, L.L.L, Z.Y.J coordinated
the laboratory research work; Y.X.Y, X.W, D.W, Z.F, L.S.R, D.F, C.Q.H
edited the manuscript.

\textbf{Data accessibility statement:} The data supporting the results
will be archived in Dryad, and the data DOI will be included at the end
of the article.

\textbf{Competing interests:} The authors declare no competing
interests.

\textbf{References}

Bay, S. K., McGeoch, M. A., Gillor, O., Wieler, N., Palmer, D. J.,
Baker, D. J. et al. (2020). Soil Bacterial Communities Exhibit Strong
Biogeographic Patterns at Fine Taxonomic Resolution. \emph{mSystems} ,
5, e00540-20.

Berkes, F., Kislalioglu, M., Folke, C., Gadgil, M. (1998). Exploring the
Basic Ecological Unit: Ecosystem-like Concepts in Traditional
Societies.\emph{Ecosystems} , 1, 409-415.

Castle, S. C., Samac, D. A., Sadowsky, M. J., Rosen, C. J., Gutknecht,
J., \& Kinkel, L. L. (2019). Impacts of Sampling Design on Estimates of
Microbial Community Diversity and Composition in Agricultural
Soils.\emph{Microb Ecol} , 78, 753--763.

Delavaux, C. S., Weigelt, P., Dawson, W., Duchicela, J., Essl, F., van
Kleunen, M. et al. (2019). Mycorrhizal fungi influence global plant
biogeography. \emph{Nat Ecol Evol} , 3, 424--429.

Ding, W. N., Ree, R. H., Spicer, R. A., \& Xing, Y. W. (2020). Ancient
orogenic and monsoon-driven assembly of the world's richest temperate
alpine flora. \emph{Science} , 369, 578--581.

Dolan, J. R. (2006). Microbial Biogeography? \emph{J Biogeogr} , 33,
199-200.

Garc\selectlanguage{ngerman}ía-Llamas, P., Calvo, L., Cruz, M. D., Suárez-Seoane, S. (2018).
Landscape heterogeneity as a surrogate of biodiversity in mountain
systems: What is the most appropriate spatial analytical
unit?\emph{Ecological indicators} , 85, 285-294.

Gaston, K. J. (2000). Global patterns in biodiversity. \emph{Nature} ,
405, 220-7.

Gobet, A., Böer, S. I., Huse, S. M., van Beusekom, J. E., Quince, C.,
Sogin, M. L. et al. (2012). Diversity and dynamics of rare and of
resident bacterial populations in coastal sands. \emph{ISME J} , 6,
542--553.

Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C., \& Martiny, J. B.
(2012). Beyond biogeographic patterns: processes shaping the microbial
landscape. \emph{Nat Rev Microbiol} , 10, 497--506.

Hausdorf, B. (2002). Units in Biogeography. \emph{Syst Biol} , 4,
648-652.

Karimi, B., Terrat, S., Dequiedt, S., Saby, N., Horrigue, W., Lelièvre,
M. et al. (2018). Biogeography of soil bacteria and archaea across
France. \emph{Sci Ad} , 4, eaat1808.

Ladau, J., \& Eloe-Fadrosh, E. A. (2019). Spatial, Temporal, and
Phylogenetic Scales of Microbial Ecology. \emph{Trends Microbiol} , 27,
662--669.

Land, M., Hauser, L., Jun, S. R., Nookaew, I., Leuze, M. R., Ahn, T. H.
et al. (2015). Insights from 20 years of bacterial genome
sequencing.\emph{Funct Integr Genomics} , 15, 141--161.

Li, J., Hyde, K. D., Zhang, K. Q. (2014). \emph{Nematode-Trapping Fungi}
. Springer, Netherlands, pp. (13-40).

Liu, S, Wang, H, Tian, P, Yao, X, Sun, H, Wang, Q, Delgado-Baquerizo, M.
(2020). Decoupled diversity patterns in bacteria and fungi across
continental forest ecosystems. \emph{Soil Biol Biochem} , 144, 107763.

Locey, K. J., Muscarella, M. E., Larsen, M. L., Bray, S. R., Jones, S.
E., \& Lennon, J. T. (2020). Dormancy dampens the microbial
distance-decay relationship. \emph{Philos Trans R Soc Lond B Biol Sci} ,
375, 20190243.

Martiny, J. B., Bohannan, B. J., Brown, J. H., Colwell, R. K., Fuhrman,
J. A., Green, J. L. et al. (2006). Microbial biogeography: putting
microorganisms on the map. \emph{Nat Rev Microbiol} , 4, 102--112.

Mascarenhas, R., Ruziska, F. M., Moreira, E. F., Campos, A. B., Loiola,
M., Reis, K. et al. (2020). Integrating Computational Methods to
Investigate the Macroecology of Microbiomes. \emph{Front Genet} , 10,
1344.

Meyer, K. M., Memiaghe, H., Korte, L., Kenfack, D., Alonso, A., \&
Bohannan, B. (2018). Why do microbes exhibit weak biogeographic
patterns?. \emph{ISME J} , 12, 1404--1413.

Peters, M. K., Hemp, A., Appelhans, T., Behler, C., Classen, A., Detsch,
F. et al. (2016). Predictors of elevational biodiversity gradients
change from single taxa to the multi-taxa community level. \emph{Nat
Commun} , 7, 13736.

Smith, J. R., Hendershot, J. N., Nova, N., Daily, G. C. (2020). The
biogeography of ecoregions: Descriptive power across regions and
taxa.\emph{J Biogeogr} , 47, 1413-1426.

Smith, JR, Letten, AD, Ke, PJ, Anderson, CB, Hendershot, JN, Dhami, MK.
et al. A global test of ecoregions. \emph{Nat Ecol Evol} , 2018
Dec;2(12):1889-1896.

Storch, D. \& Izling, A. L. (2008). The Concept of Taxon Invariance in
Ecology: Do Diversity Patterns Vary with Changes in Taxonomic
Resolution? \emph{Folia Geobot} , 43, 329-344.

Timur, O. (2013). \emph{Advances in Landscape Architecture.} IntechOpen,
London, pp. (176-206).

Ynch, M. D., \& Neufeld, J. D. (2015). Ecology and exploration of the
rare biosphere. \emph{Nat Rev Microbiol} , 13, 217--229.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

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

\end{center}
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Figure
2 Geographic environments of the sampling sites.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image14/image14}

\end{center}
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Figure
3 The species composition and occurrence frequency ordering of the
Nematode-Trapping Fungi.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image15/image15}

\end{center}
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Figure
4 Biogeographic patterns of species number of Nematode-Trapping Fungi.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image16/image16}

\end{center}
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Figure 5 Biogeographic patterns of each genus of Nematode-Trapping
Fungi.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image19/image19}

\end{center}
\end{figure}

Figure
6 Biogeographic patterns of each species of Nematode-Trapping Fungi.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image20/image20}

\end{center}
\end{figure}

Figure 7 Biogeographic patterns of rare species in the pespective of a)
grids and b) watersheds.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image22/image22}

\end{center}
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Figure 8 Biogeographic patterns of widespread species \emph{A.
oligospora} based on a) genetic clades respectively in the pespective of
b) watersheds and c) grids\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image23/image23}

\end{center}
\end{figure}

Figure 9 The upset plot of species in the perspective of a) grids and b)
watershed units. The distribution pattern was uniform when using grids
(c) and was heterogeneous when using watershed (d).

\selectlanguage{english}
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