Dominance and natural suppression of bacterial plant pathogens across global soils

Domain: chemistry
Published: 2026-03-06
Editorial outcome: minor_revision

Abstract
Soils are the primary environmental reservoir of plant pathogens impacting food production and
ecosystem productivity worldwide. Yet, some soils can also suppress pathogens through
environmental and microbial regulation. Here we integrate 1,602 soil metagenomes from 59
countries with a greenhouse experiment to identify 32 dominant pathogens, including Ralstonia
solanacearum, Clavibacter michiganensis, and Streptomyces europaeiscabiei. Pathogen hotspots
occur primarily in warm ecosystems and agricultural soils, whereas higher soil microbial diversity,
increased soil organic carbon and colder climatic conditions are associated with lower pathogen
prevalence. Non-pathogenic Streptomyces spp., arbuscular mycorrhizal fungi, and biosynthetic
gene clusters encoding terpenes and polyketides are associated with reduced pathogen prevalence.
Predictive modelling suggests that several dominant bacterial pathogens are likely to increase in
prevalence under future climate scenarios, particularly in tropical and subtropical regions. By
identifying global drivers of dominant pathogens and their suppression, this study provides a
foundation for improved surveillance and management of plant disease risks under climate change.

Introduction
Plant diseases caused by bacterial pathogens substantially reduce global primary productivity and
threaten food security1-3. In agricultural systems, they directly undermine crop yields, while in
natural ecosystems their increased prevalence can lower primary productivity, diminishing
biodiversity and ecosystem carbon storage4. Although soils are the largest reservoir of plant
pathogens, they also possess an inherent capacity to suppress pathogen abundance and disease
outbreaks5. Understanding how bacterial pathogens are distributed across soils, and how they
interact with co-existing microbial communities, is therefore essential for identifying the
environmental and biological conditions that promote or limit their prevalence.

Despite evidence that soils play a central role in regulating the survival, proliferation, and
suppression of plant pathogens, major knowledge gaps persist regarding the ecology of soil-borne
bacterial plant pathogens. Three factors largely explain this deficiency. First, most previous
research has focused on fungal and oomycetes pathogens6-8, while bacterial plant pathogens have
received comparatively less attention, in part because conventional markers such as 16S rRNA
genes have limited power to distinguish pathogenic from non-pathogenic species. Second, we
currently lack a comprehensive, standardized assessment of the global biogeography and
environmental controls of bacterial pathogens in soils. Unlike fungal and oomycete taxa, whose
global distributions are known to be strongly shaped by temperature and precipitation6, comparable
insights for bacterial plant pathogens remain unresolved owing to limited taxonomic resolution
and the absence of global-scale metagenomic datasets. Finally, although some soils naturally
suppress pathogen abundance and infection, the specific biological and environmental
determinants of this suppression remain poorly defined across global ecosystems. In particular, the
roles of microbial diversity, functional traits, and competing microbial taxa in constraining
bacterial pathogens in soils have not been quantified at a global scale. Together, these gaps
severely limit our ability to anticipate pathogen emergence, identify soils with inherent suppressive
properties, and develop targeted strategies to monitor and mitigate plant disease risks under rapid
environmental change. Given that plant diseases already impose multibillion-dollar annual losses9-
11 and threaten global food security2,12-15, resolving these fundamental unknowns is urgently
needed to inform future surveillance, management, and policy interventions.

To address existing knowledge gaps, we combined data from public databases (1,402 samples)
with a standardized global field survey (200 samples)16, together comprising 1,602 soil
metagenomes from 59 countries (Supplementary Figure 1 and Supplementary Data 1), to: (1)
determine the global distribution of dominant soil-borne bacterial plant pathogens; (2) identify the
environmental conditions, soil microbial diversity, and microbial and metabolic traits associated
with pathogen-suppressive soils; and (3) predict the current and future global distributions of these
dominant bacterial plant pathogens. To achieve this, we integrated metagenomic and pathogen
genomic analyses with amplicon sequencing and qPCR data from a global field survey, together
with machine-learning and predictive modelling approaches. To further validate the microbial
diversity–suppression relationship identified in the global dataset, we conducted a controlled
greenhouse experiment to directly test the effects of soil biodiversity on pathogen suppression.
Emerging global evidence suggests that higher microbiome diversity can constrain human and
animal pathogens (identified based on 16s rRNA genes) in the plant rhizosphere17. Thus, we
predict that soil biodiversity similarly functions as a key biotic barrier that suppresses soil-borne
bacterial plant pathogens by outcompeting pathogens in the soil. We also expect that warmer and
drier climates, and lower soil organic carbon (OC) would be associated with a higher prevalence
of soil-borne bacterial plant pathogens6,7,18. Our overarching aim was to identify globally dominant
soil-borne bacterial plant pathogens, characterize their distribution and ecological drivers, and
determine the environmental and biological factors that promote natural pathogen suppression.

Results
Dominant bacterial plant pathogens across global soils
First, we constructed a comprehensive bacterial plant pathogen database comprising 310 pathogen
genomes and identified their abundance across global soils using a global soil metagenome dataset.
107 distinct taxa were identified as potential plant pathogens, among which 32 species emerged as
dominant pathogens based on their frequency (detected in 80% of samples) and relative abundance
(top 30%, with average relative abundance ≥ 0.1%) (Fig. 1). These dominant pathogens included
several species from the genera Streptomyces, Rhodococcus, Agrobacterium, Clavibacter,
Ralstonia, Burkholderia, Paracidovorax, Pseudomonas, and Xanthomonas, all known to cause
diseases in a broad range of plants across diverse ecosystems. For example, Ralstonia
solanacearum, the causative agent of bacterial wilt, ranks as the second most destructive bacterial
pathogen globally due to its extensive strain diversity, broad host range, and deleterious impact on
plant productivity19,20. Notably, we identified four Streptomyces species (i.e., S. europaeiscabiei,
S. acidiscabies, S. turgidiscabies, S. scabiei), ranked highly among the dominant pathogens (Fig.
1a and c). This lineage is prominently linked to potato common scab disease21. The adaptability of
these Streptomyces spp. to various environments, particularly their ability to persist as spores in
soil for extended periods and their substantial dispersal abilities, likely contributes to their
dominance21,22.

Comparative genomic analyses further revealed that dominant pathogen species were enriched in
genes associated with signalling and cellular processes, particularly those involved in sugar
transport systems, energy metabolism and carbohydrate metabolism, relative to non-dominant
species (Supplementary Figure 2; Supplementary Table 1). These genes are integral to cellular
communication and function, regulate cell growth and interactions, all of which are critical to
ecological adaptability23,24. Moreover, these genomic signatures support that the pathogenicity
involves signalling mechanisms and ability to penetrate cell wall and membranes25 and underscore
the key genomic adjustments that underpin their prevalence in soil environments.

We next investigated the correlation between the prevalence of key pathogens identified in our
database and independent global observation resources. Our correlation analysis revealed a
positive association between the relative abundance of R. solanacearum (rho = 0.44, P = 0.02, 28
countries) in our dataset and independent observation resource of this pathogen26 (Fig. 2b).
Similarly, the relative abundance of Clavibacter michiganensis (rho = 0.42, P = 0.0047), Robbsia
andropogonis (rho = 0.61, P = 0.0058), and Xanthomonas translucens (rho = 0.53, P = 0.0066)
were positively correlated with their recorded observation in various countries by the EPPO Global
Database (https://gd.eppo.int)27 (Fig. 2b). These country-level correlations demonstrate that the
 that the
relative abundance patterns inferred from our metagenomic database are consistent with
independent global surveillance records, supporting the reliability of our pathogen prevalence
estimates. If these findings are further confirmed at local and regional scales by real farm data, our
framework could support the development of more effective disease surveillance systems,
enabling timely and targeted management and prevention strategies. Taken together, our work
provides insights on the globally dominant bacterial plant pathogens and advances our
understanding of their distribution.

Hotspots of soil bacterial plant pathogens
Next, we sought to understand the conditions that promote the global distribution of bacterial plant
pathogens. Their relative abundance showed distinct patterns across terrestrial soil biomes, with
significantly higher relative abundance observed in farmland compared to other land-use types
(Fig. 2a). This suggests that soils influenced by farm management practices (e.g., monoculture and
use of agrochemicals) may support higher pathogen abundance than natural soils28. In particularly,
pathogens from the genus Pseudomonas, including P. corrugata, P. fuscovaginae, P. marginalis,
P. mediterranea, and P. palleroniana, as well as Pectobacterium carotovorum, were notably more
abundant in farmlands (over 50%, Fig. 1b). These findings highlight the potential role of
agricultural management in shaping the prevalence and distribution of bacterial pathogens.

Random forest analyses identified mean annual temperature (MAT) as a significant factor
affecting the relative abundance of most dominant pathogens (29 out of 32) (Fig. 1c). The influence
of MAT varied among pathogens but generally showed a positive effect on their total abundance
(Fig. 1c, Fig. 2c), suggesting that rising temperatures may exacerbate prevalence of majority of
pathogens6,29,30. This increased prevalence is likely promoted by temperature-induced shortened
incubation periods of pathogens and extended growing seasons of hosts3. The influence of mean
annual precipitation (MAP) varied across pathogen taxa. For example, Streptomyces spp. and
Clavibacter michiganensis showed negative correlations with MAP (Fig. 1c and Supplementary
Figure 3), whereas Rhizobium rhizogenes, Burkholderia spp., Ralstonia solanacearum species
complex, and Xanthomonas spp. exhibited positive correlations with MAP (Fig. 1c and
Supplementary Figure 3). These patterns suggest niche partitioning among pathogens based on
water availability and specific ecological preferences31-36. Taking together, we showed that warmer
ecosystems and farmlands support the largest soil bacterial plant pathogen prevalence worldwide.

The ecology of soil pathogen suppression
We evaluated which set of soil properties and environmental conditions associated with the lowest
prevalence of pathogens, reflecting natural soil pathogen suppression. Correlation analyses
showed that cold ecosystems, as well as soils with higher organic carbon (OC) content, finer soil
textures, and greater microbial diversity, consistently exhibited lower relative abundances of
pathogens (Fig. 1c). To further elucidate the ecological pathways driving these patterns, we
conducted structural equation modelling (SEM) using available metadata. SEM analyses indicated
that MAP exerts direct positive effects on vegetation (measured by NDVI), which in turn enhances
microbial diversity and, ultimately, lower pathogen relative abundance (Fig. 2c). Consistent results
from both correlation and SEM analyses showed that regions characterized by higher microbial
diversity, colder climates, finer soil textures, and elevated soil OC content tend to exhibit lower
pathogen prevalence at the global scale (Fig. 1 and 2). These findings align with research
conducted at plot and farm scales37-41. For example, increasing soil OC, such as through organic
amendments, has been reported to reduce soil-borne diseases by stimulating microbial activity42.
However, the relationship between soil OC and pathogen abundance is complex and varies among
different pathogens and regions. Some studies have reported that increased OC may also lead to
an increase the prevalence of certain fungal pathogens43. This divergence is likely linked to nutrient
preference for soil borne bacterial and fungal/ oomycetes pathogens44, reflecting the distinct
environmental preferences of various pathogens. Overall, our study shows that microbial diversity
and specific soil properties are associated with lower abundances of bacterial plant pathogens
across terrestrial ecosystems.

To further support the observed relationship between soil microbial diversity and pathogen
suppression, we quantified microbial diversity using both shotgun metagenomic and amplicon
sequencing data and conducted a greenhouse experiment in which soil microbial diversity was
experimentally manipulated. We first assessed the microbial taxa diversity (based on the Kraken
2 database)45 and functional diversity (based on KEGG database)46 from 1,602 metagenomic data
and explored their association with the relative abundance of bacterial plant pathogens (Fig. 1 and
Fig. 3a). For the 200 globally standardized field samples, we quantified bacterial abundance using
16S qPCR and characterized bacterial and fungal community diversity through amplicon
sequencing, further exploring their relationships with pathogens. Microbial taxonomic diversity
estimated by Kraken2 and KEGG functional diversity both exhibited modest yet statistically
significant negative correlations with the total relative abundance of dominant bacterial plant
pathogens (Kraken2: rho = −0.051, P = 0.039; KEGG: rho = −0.13, P = 2.44 × 10⁻⁷; n = 1,602;
Fig. 3a). Analyses of bacterial and fungal diversity indices from amplicon sequencing data also
showed a significant negative correlation with total relative abundance of bacterial plant pathogens
(bacteria: rho = -0.20, P = 0.006; fungi: rho = -0.18, P = 0.013; n = 200) (Fig. 3b). To
experimentally validate these patterns, we manipulated soil microbial diversity using a dilution-
to-extinction approach47, incubated the inoculated soils for six months, and subsequently
introduced pathogens for short-term monitoring. For this validation experiment, we selected two
model and dominant pathogens, which have differential mode of infection and environmental
preferences, S. scabiei (a necrotrophic pathogen favouring drier soils)21 and R. solanacearum (a
vascular wilt pathogen thriving in moist environments)20, allowing us to assess whether the
observed diversity–pathogen relationship is consistent across two contrasting and ecologically
distinct model pathogens during early invasion stages. Pathogen abundance was quantified by
qPCR and decreased with increasing soil microbial diversity (Fig. 3c), providing experimental
support for a negative association between microbial diversity and early pathogen invasion
dynamics. Although similar diversity–pathogen patterns have been reported in plot- and pot-scale
studies for other pathogens41,47, future studies incorporating a broader range of pathogen taxa will
be required to assess the generality of this relationship. Additionally, we observed a significantly
negative correlation between soil total bacterial abundance (measured by 16S qPCR) and the
relative abundance of bacterial plant pathogens (rho = -0.30, P = 2.529e-05) in our standardised
global survey study (Fig. 3d), consistently supporting the notion that increased microbial diversity
and bacterial abundance correlates with reduced pathogen load and disease incidence48. Soils with
high microbial diversity and biomass are likely to facilitate complex interactions among coexisting
microbes, encompassing competition, predation, symbiosis, and parasitism48-50, at the expense of
pathogen dominance. Collectively, our results provide global-scale evidence, supported by a
greenhouse validation experiment, that higher soil microbial diversity and bacterial abundance are
associated with reduced abundance of bacterial plant pathogens. These insights reinforce the
importance of agricultural and land-management practices that enhance soil microbial diversity
and biomass to maintain ecosystem services including the defence against plant pathogens51-53.

Microbial taxa and potential metabolic traits linked to soil pathogen suppression
Given the importance of soil microbial diversity in shaping patterns of pathogen prevalence, we
further examined specific microbial taxa within soil microbiomes that are associated with lower
pathogen abundance. Correlation analyses involving 9,619 bacterial taxa identified using the
Kraken 2 database revealed 526 taxa that were negatively correlated with the total relative
abundance of dominant pathogens (using rho < -0.2 and p < 0.0001 as thresholds). Taxa belonging
to the phyla Actinomycetota and Bacillota were predominant among these negatively correlated
groups, accounting for 34.6% and 16.5% of all negatively correlated taxa, respectively. In
particular, the relative abundance of non-pathogenic Streptomyces spp. was negatively associated
with pathogen abundance (rho = -0.08, p = 0.00195) (Fig. 3e), consistent with their known capacity
to produce a wide array of antibiotics and secondary metabolites that inhibit plant pathogens54.
Beyond bacterial communities, we also identified several fungal functional groups that were
negatively associated with pathogen prevalence, including Arbuscular Mycorrhizal fungi (rho = -
0.17, p = 0.017) and lichenized fungi (rho = -0.17, p = 0.017) (Fig. 3f). These fungal groups were
also linked to significant increases in soil bacterial and fungal diversity and abundance
(Supplementary Figure 4), suggesting their potential contribution to pathogen-suppressive soil
contexts across terrestrial ecosystems. Overall, these findings highlight the multifaceted microbial
features associated with lower pathogen abundance in soils across broad ecological contexts,
involving both bacterial antagonists and beneficial fungal symbionts.

To further support our findings, we examined the potential microbial metabolites traits associated
with pathogen-suppressive soils. Metagenomic data were annotated using the antiSMASH
database55 to assess correlations between biosynthetic gene clusters (BGCs) encoding microbial
metabolites and pathogen abundance. We found that gene clusters encoding terpene (rho = -0.11,
p = 6.063e-06), and type III polyketide synthases (T3PKS; rho = -0.10, p = 2.494e-05) showed the
strongest negative associations with pathogen abundance (Fig3. g). Terpenes, the largest class of
natural products56, are produced by diverse bacteria and fungi, particularly members of the
Streptomycetaceae, and are well documented for their antimicrobial activity57, including effects
on cellular respiration58 and membrane integrity57. Polyketides, another diverse class of natural
products with antimicrobial properties, similarly showed significantly negative associations with
pathogen abundance in our dataset (p < 0.05) and have been widely reported to contribute to
disease suppression by microbes such as Bacillus spp.59, Paenibacillus spp.60, Trichoderma spp.61,
and Flavobacterium spp.5. In addition to terpenes and T3PKS, gene clusters encoding indole,
ranthipeptides, and NRPS-like compounds also exhibited negative correlations with the relative
abundances of specific dominant pathogens (Fig. 3h). Notably, these negative associations
between biosynthetic gene clusters and pathogen abundance were consistent across both total
dominant pathogens and individual dominant pathogen taxa (Fig. 3h). Together, these findings
provide insights into specific microbial taxa and biosynthetic gene clusters associated with the
secondary metabolite biosynthetic capacity of pathogen-suppressive soils at a global scale,
highlighting their potential as candidate microbial features or putative indicators of reduced
pathogen prevalence.

Expansion risk of bacterial pathogens under climate change
Having identified current pathogen hotspots and the ecological drivers associated with soil
pathogen suppression, we next projected the distributions of dominant bacterial plant pathogens
under global change scenarios. By integrating key environmental predictors, we generated
predictive maps of both current and future distributions across global soils, explaining 64–77% of
the spatial variation in pathogen prevalence (Supplementary Table 2). Our analysis indicated a
notable prevalence of pathogens in areas with high temperatures, particularly in northern regions
of South America, North Africa, and South Asia (Fig. 4a). Future climate projections, especially
under global warming scenarios, suggest not only increased relative abundance in these areas but
also the emergence of new hotspots in northern Asia (Fig. 4b). This projected expansion mirrors
the predicted trends for fungal plant pathogens, which are similarly expected to intensify under
global change scenarios6. Streptomyces europaeiscabiei, a species predominant in arid regions, is
currently distributed across all continents, with higher relative abundance in Africa, Australia,
southwestern region of North America, and the southwestern part of Asia (Fig. 4c). Predictive
models forecast a future expansion of S. europaeiscabiei into northern Asia (Fig. 4d), suggesting
both range shifts and increases in dominance. Similarly, Ralstonia solanacearum, which thrives in
humid conditions, currently dominates equatorial regions including South Asia, northern South
America, northern Australia, eastern North America, and Central Africa (Fig. 4e). Future
projections indicate an increased dominance of this pathogen near equatorial zones (Fig. 4f).
Importantly, and despite employing independent analytical approaches, the distribution patterns of
R. solanacearum from our models closely matched records from the EPPO Global Database
(https://gd.eppo.int/)27, supporting the robustness of our predictive framework. Overall, our
predictive maps indicate a further increase in dominant bacterial plant pathogens across much of
the globe under anticipated future climatic conditions (Fig. 4 and Supplementary Figure 5).
Together, these results provide insights into the current and future global distributions of bacterial
plant pathogens and establish a basis for future empirical validation at regional and local scales,
as well as for the development of monitoring and disease risk assessment frameworks under global
change.

Overall, our findings have important implications for future research and for the monitoring and
management of plant disease risks. Future work should assess how the global drivers of pathogen
prevalence and suppression identified here interact with local factors, such as land-use practices,
crop management (e.g., resistant varieties, crop rotation), and regional socio-economic
constraints—to shape disease outcomes. While this study focuses on pathogen dominance and
suppression, predicting pathogen virulence and disease severity associated with dominance
represents an important next research direction. Establishing locally relevant thresholds for
microbial diversity, the abundance of key microbial taxa such as non-pathogenic Streptomyces
spp., or the presence of specific biosynthetic gene clusters could also enable the development of
practical indicators of pathogen-suppressive soils. Such indicators would provide a valuable basis
for improved surveillance systems and for designing targeted interventions, including microbial
inoculants or metabolite-based approaches, to mitigate disease risks in both agricultural and natural
ecosystems.

We present the comprehensive catalogue of globally dominant soil-borne bacterial plant pathogens
and provide insights into the environmental and biological factors associated with pathogen natural
suppression. Our results show that high soil microbial diversity, increased soil organic carbon, and
colder climates consistently coincide with lower pathogen prevalence worldwide. We also identify
specific microbial groups, including non-pathogenic Streptomyces spp., arbuscular mycorrhizal
fungi, and lichenized fungi, and biosynthetic gene clusters encoding terpenes and polyketides as
potential indicators of pathogen-suppressive soils. Our analyses indicate that several dominant
bacterial pathogens are likely to expand or increase in prevalence under future climate scenarios,
highlighting regions where disease risks may intensify. These findings advance our understanding
of the ecological processes shaping pathogen distributions and provide a foundation for developing
improved surveillance tools, management practices, and future research aimed at mitigating the
impacts of soil-borne bacterial diseases on global food production and ecosystem functioning.

Methods
Global soil metagenomics (shotgun sequencing based) database
We developed global soil metagenomics database by combining metagenomes obtained from
public databases (including 1,402 metagenomes) and a standardized global soil sampling
(including 200 metagenomes). The public databases including the published global standardized
soil metagenome dataset, and soil metagenomes from global regions available from the NCBI SRA
database62,63. For the standardized global soil samples, topsoil (~0–10 cm) samples were collected
from a global soil survey conducted between 2016 and 2019, employing standardized field
protocols for all sampling procedures16. Shotgun metagenomic sequencing was conducted at the
Next Generation Sequencing (NGS) Facility (Western Sydney University, Australia) using the
Illumina NovaSeq platform to generate approximately 10 Gb of 150 bp paired-end high-quality
reads per sample. In total, the combined database comprises soil metagenomes derived from 317
independent NCBI BioProjects, spanning data release years from 2011 to 2024 (Supplementary
Data 1).

Our final global dataset comprises 1,602 soil metagenomes collected from 23 terrestrial biomes
spanning 59 countries across all continents (Supplementary Figure 1). This dataset covers a broad
range of climate (arid, temperate, tropical, cold, and polar), and major vegetation types, including
grasslands, shrublands, forests, wetlands, and farmlands. We included soils from both agricultural
and natural ecosystems because natural soils provide an essential ecological baseline, can act as
reservoirs of pathogen inoculum, and offer an optimal context for examining natural pathogen
suppression, an ecological process that is often obscured by intensive management in agricultural
systems. Sampling locations were selected to capture a robust representation of Earth's
geographical, climatic and edaphic diversity, and all sites corresponded to natural or agricultural
soils rather than controlled experimental samples. For example, the latitude of the sampling
locations ranges from –77.6 to 80.7; the longitude ranges from –159.6 to 172.2, and plant cover
ranges between 0.1% and 100%. The mean annual temperature (MAT) varies between –24.8 and
27.8 ℃; the mean annual precipitation (MAP) ranges from 6 to 5895 mm, and the soil pH ranges
from 3.7 to 9.11. This broad coverage improves the representativeness of the dataset, enabling a
more comprehensive understanding of how environmental factors shape the global distribution of
soil plant bacterial pathogens.

Metagenomic analysis and potential bacterial plant pathogens detection
The global soil metagenomics database (1,602 samples) was used for the detection of potential
bacterial plant pathogens. Metagenomic tools like Kraken 245,64, MetaPhlAn265, and MG-RAST66,
enable high-resolution identification of microbial taxonomic information. However, these tools
are generally not tailored for the specific detection of bacterial plant pathogens. To enhance the
accuracy in identifying bacterial plant pathogens in soil samples, we developed a specialized, high-
quality database focused on bacterial plant pathogens (BPPDB). Guided by previous literature,
this database comprises 113 recognized bacterial plant pathogen species represented by 310
complete genomes obtained from the National Center for Biotechnology Information (NCBI). By
capturing the majority of globally important bacterial plant pathogens, those responsible for a
substantial proportion of plant disease worldwide, BPPDB provides a biologically meaningful and
robust framework for large-scale pathogen identification, while maintaining an appropriate
balance between taxonomic coverage and genomic quality. The nucleotide sequences of these
pathogen genomes were then translated into protein sequences, followed by the construction of a
protein database using the DIAMOND67.

For the analysis of 1,602 shotgun metagenomic datasets, we first quality-filtered the raw sequences
using fastp (v0.20.1)68. The obtained high-quality reads were then assembled using Megahit
(v1.2.9)69, retaining contigs ≥1000 bp in length. We next used Prokka (v1.14.5)70 for gene
prediction to identify the open reading frames (ORFs, i.e., protein-coding genes). These ORFs
were then clustered at a 95% similarity threshold using CD-HIT (v4.8.1)71, to generate a non-
redundant gene catalogue. The read counts and Reads Per Million (RPM) values for each predicted
non-redundant gene were quantified using Salmon (v1.6.0)72. Subsequently, for the taxonomic
annotation of bacterial plant pathogens, we conducted an alignment of the translated protein
sequences from our soil metagenomic datasets against our customized bacterial plant pathogens
database using DIAMOND. This alignment was conducted under specific parameters: a maximum
e-value threshold of 1 × 10−10, a minimum percent identity of over 80%, and baseline alignment
length of at least 100 amino acids. The relative abundances of these bacterial phylotypes were
expressed as RPM across all samples, allowing direct comparison of relative abundance among
samples with differing sequencing depths and read yields73. In total, we identified 107 bacterial
pathogen species, of which 32 were classified as globally dominant based on their high frequency
of occurrence (present in at least 80% of samples) and high relative abundance (top 30%, with a
mean relative abundance ≥ 0.1%) across global soils (Fig. 1).

Cross-validation of pathogen detection
Comparison with Kraken 2 databases. We used a combination of Kraken 2 (v2.1.1)45 and Bracken
(v2.6.2)74 tools based on the standard Kraken database (as of March 2023) for the taxonomic
annotation at the reads level. We discerned 98 bacterial phylotypes overlapped with our custom
plant pathogenic bacterial database, from a total of 10,014 bacterial phylotypes detected. The
identified table was rarefied to 100,000 reads per sample for alpha-diversity estimation and
normalized using the cumulative sum scaling (CSS) method for beta-diversity analyses. Our results
indicated a significant and positive correlation between the alpha and beta diversity of bacterial
plant pathogens identified from our custom database workflow and those identified from the
Kraken 2 database (alpha diversity: r = 0.25, P < 2.2 × 10 −16; Beta diversity: r = 0.39, P < 2.2 ×
10 −16) (Supplementary Figure 6a & b). Furthermore, the relative abundances of most pathogens
identified from our database showed significantly positive correlation with those identified by
Kraken 2 database (Selected 15 pathogens, Pearson r 0.25–0.67, P < 2.2e-16; Supplementary
Figure 7).

Comparison with the PHI-base. We mapped aligned translated protein sequences from soil
metagenomic datasets to the validated virulence genes curated in the Pathogen-Host Interactions
database (PHI-base)75 utilizing the DIAMOND67 tool (e-value threshold of 1 × 10−10, percent
identity > 80%). This method effectively identified a significant count of 2250 functional genes
associated with bacterial pathogenicity and virulence. There are 23 annotated bacterial plant
pathogen species overlapped between the PHI-base and our custom database. Among them, the
relative abundance of most overlapping species showed a significant positive correlation between
two methods. In the analysis of individual species correlations, the top 12 most positively
correlated species exhibited Pearson r values ranging from 0.14 to 0.80, with P-values ranging
from < 6.591e-09 to < 2.2e-16 (Supplementary Figure 8).

Comparison with coverM quantification. We further performed an independent validation using
an alternative read-alignment–based method, CoverM76, to assess the robustness of our pathogen
abundance quantification. Estimates derived from Salmon and CoverM were highly consistent
(Pearson r = 0.95, P < 2.2e-16 for total pathogen abundance), with many individual pathogens
showing correlations exceeding 0.90 (P < 2.2e-16, Supplementary Figure 9).

Comparison with expanded pathogen database. To address the potential overestimation arising
from closely related non-pathogenic taxa, we expanded the bacterial plant pathogen database
(BPPDB) to include 20 genomes from closely related non-pathogenic species. Pathogen
abundances estimated derived from the expanded database were highly concordant with those
obtained using the pathogen-only database (Supplementary Figure 10, Pearson r > 0.98, P < 2.2e-
16).

Comparison with quantitative PCR of specific dominant pathogens. From the standardised
dataset, we retrieved DNA and performed pathogen-targeted qPCR assays to further validate the
abundances of bacterial plant pathogens identified using our custom database. Specifically, the
absolute abundance of Ralstonia solanacearum, the dominant plant pathogen identified in our data,
was quantified via real-time qPCR employing the primers FliCF
(GAACGCCAACGGTGCGAACT) and FliCR (GGCRGGCCTTCAGGGAGGTC)77. For these
assays, we randomly selected 70 samples spanning a range of biomes. The qPCR reactions were
assembled in a total volume of 10 μl, comprising 1 μ l of DNA template, 5 μl of 2×PowerUp SYBR
Green Master Mix (Thermo Fisher Scientific), 3.2 μl of PCR-grade water, and 0.4 μl of both the
forward and reverse primers (10 μmol μl−1 each). The assays were carried out on a LightCycler
480 (Roche) utilizing a 3-step melt cure program on a LightCycler 480 (Roche), under the
following parameters: an initial denaturation at 95°C for 5 minutes, succeeded by 40 cycles of
denaturation at 95°C for 30 s, annealing at 60°C for 45 s, and elongation at 72°C for 60 s.
Following qPCR, amplification specificity was assessed through melting curve analysis, with
samples showing specific amplification retained for further analysis. The R. solanacearum isolate,
purchased from the Department of Primary Industries (DPI, NSW), provided the genomic DNA
for establishing standards in the qPCR assays. Standard curves were constructed using a tenfold
serial dilution. The comparison between the abundance of R.solanacearum from our custom
database and the absolute abundance from qPCR revealed a significant positive correlation (rho =
0.28, P = 0.018; Supplementary Figure 6c), demonstrating a strong concordance in the detection
and quantification of this pathogen.

Taxonomic microbial diversity and qPCR-based abundance
Soil microbial diversity was assessed using both metagenomic and amplicon sequencing data, with
amplicon sequencing conducted only for the 200 standardized global soil samples. For the 1,602
metagenomic samples, taxonomic profiles were generated using the Kraken 2 database. The
standardized dataset comprising the 200 global soil samples included both shotgun amplicon
sequencing data generated on the Illumina MiSeq platform, consisting of 16S rRNA gene and ITS
amplicon datasets (n = 200 each, 400 datasets in total). Bacterial and fungal communities were
characterized by amplifying the 16S rRNA and 18S rRNA genes using the 341F/805R and
Euk1391f/EukBr primer sets, respectively78,79. Samples were sequenced on the Illumina MiSeq
platform (2 × 300 PE) at the Next Generation Sequencing (NGS) Facility (Western Sydney
University, Australia) using a paired-end sequencing protocol. The microbial diversity of these
200 amplicon sequencing data was characterized using QIIME (v1.9.1)80, USEARCH (v10.0)81,
and UNOISE382. Amplicon Sequencing Variants (ASVs) were identified at the 100% identity level.
Representative sequences of bacterial ASVs were annotated against the SILVA database83, and
fungal ASVs were classified using both SILVA and the Protist Ribosomal Reference (PR2)
databases84. ASVs that could not be confidently assigned to bacterial or fungal lineages were
excluded from downstream analyses. The remaining reads were subsequently clustered into 37,653
bacterial and 6,083 fungal ASVs. For alpha-diversity estimation, the bacterial and fungal ASV
tables were rarefied to 10,000 sequences per sample (resulting in the removal of one sample) and
4,000 sequences per sample (removing 25 samples), respectively, as supported by the rarefaction
curves shown in Supplementary Figure 12. Despite the inherent limitations of short-read 18S data,
the fungal dataset provides reliable comparative diversity patterns suitable for large-scale cross-
biome analyses85. Microbial alpha-diversity was calculated in QIIME.

We also carried out 16S qPCR experiment for the 200 standardised global dataset to measure the
bacterial abundance in soil, using the primer set Eub338/Eub51886,87. The qPCR reactions were
assembled in a total volume of 10 μl, comprising 1 μl of DNA template, 5 μl of 2×PowerUp SYBR
Green Master Mix (Thermo Fisher Scientific), 3.2 μl of PCR-grade water, and 0.4 μl of both the
forward and reverse primers (10 μmol μl−1 each). The assays were carried out on a LightCycler
480 (Roche) utilizing a 3-step melt cure program, using a 3-step melt cure program on a
LightCycler 480 (Roche), under the following parameters: an initial denaturation at 95°C for 5
minutes, succeeded by 45 cycles of denaturation at 95°C for 30 s, annealing at 57°C for 30 s, and
elongation at 72°C for 30 s. Standards were generated by cloning target sequences from pure
cultures into pUC57-Amp vector and weIre then transformed into chemically competent
Escherichia coli cells. Standard curves were generated using a tenfold serial dilution of plasmid
DNA.

Greenhouse experimental validation of soil microbial diversity-pathogen abundance
relationships
To validate the observed correlation between soil microbial diversity and pathogen abundance, a
controlled greenhouse experiment was conducted. Soil samples were collected from the
Hawkesbury Forest Experiment (HFE) site at the Hawkesbury Institute for the Environment (HIE),
Western Sydney University in December 2023. The soils were homogenized and sieved (2 mm
mesh) prior to experimental setup. A portion of the soil was temporarily stored at room temperature
(~25 ̊ C) and maintained at a 70% water-holding capacity moisture level for soil inoculation
preparation. The other portion of the soil was sterilized by > 50 kGy γ radiation with a 60Co source.
Soil microcosms were established using dilution-to-extinction approach47. An initial soil inoculum
(100-fold diluted inoculum) was prepared by mixing 20g unsterilized soil with 100 mL sterilized
demineralized water using a blender for 2 min. Four microbial diversity gradients including
sterilized soils, 10-6 diluted soils (by adding 150 mL106-fold diluted inoculums to 1.5 kg soil), 10-
3 diluted soils (by adding 150 mL 103-fold diluted inoculum to 1.5 kg soil), and natural soil. The
inoculated soils were incubated at room temperature for six months to facilitate microbial
community assembly and stabilization.

The representative pathogen strains S. scabiei and R. solanacearum were obtained from the New
South Wales Department of Primary Industries (DPI, Australia), originally isolated from diseased
potato and tomato plants, respectively. Bacterial cultures were grown in nutrient broth medium
under constant agitation (180 rpm) for 72 hr at 28°C. Cell densities were normalized to OD600 =
1.0 using sterilized water. Soil microcosms with defined microbial diversity gradients were
established in 3-L polyethylene pots containing 1.5 kg of sterilized soil. Following a 7-day pre-
incubation period with daily watering to 60% water-holding capacity, each microcosm was
inoculated with 15 mL bacterial suspension through soil drenching. Each treatment was replicated
five times. The applied inoculum corresponded to an estimated initial concentration of ~10⁶ CFU
per gram of soil, which lies within the commonly used range for soil-borne pathogen introduction
assays in controlled experiments88. All experimental units were maintained in controlled
greenhouse conditions (without host plants) with standardized irrigation protocols. Soil samples
were collected at 3 days, 14 days, and 21 days post-inoculation and immediately store at -20°C for
subsequent molecular analysis. At each time point, around 5 g of soil was collected from the top
0–5 cm layer using sterile corers, pooled from three subsites within the same pot to reduce spatial
heterogeneity. Soil was also sampled prior to pathogen inoculation to assess background pathogen
levels.

Total DNA was extracted from collected soil samples using the Powersoil DNA Isolation Kit (Mo
Bio Laboratories, Carlsbad, CA, USA), following the manufacturer’s instructions. The quality of
DNA was assessed using a NanoDrop 2000 spectrophotometer (Thermo Scientific, USA). The
absolute abundances of the inoculated pathogens were quantified by quantitative PCR (qPCR).
The absolute abundance of S. Scabiei was quantified using primers Stx1a
(GTGGACCGTGGAGCATCT) and Stx1b ( CAGTTCGGCGTAACTCAGC)89. For R.
solanacearum, primers FliCF (GAANCGCCAACGGTGCGAACT) and FliCR
(GGCGGCCTTCAGGGAGGTC)77 were used. Each qPCR reaction was performed in a total
volume of 10 μl, comprising 1 μl of DNA template, 5 μl of 2×PowerUp SYBR Green Master Mix
(Thermo Fisher Scientific), 3.2 μl of PCR-grade water, and 0.4 μl of both the forward and reverse
primers (10 μmol μl−1 each). The assays were carried out on a LightCycler 480 (Roche) utilizing
a 3-step melt cure program on a LightCycler 480 (Roche). For S. scabiei, the thermal profile
consisted of an initial denaturation at 95 °C for 5 min, followed by 40 cycles of denaturation at
95 °C for 15 s, annealing at 62 °C for 30 s, and extension at 72 °C for 45 s. For R. solanacearum, the
profile included an initial denaturation at 95 °C for 5 min, followed by 40 cycles of denaturation
at 95 °C for 30 s, annealing at 60 °C for 45 s, and extension at 72 °C for 60 s. Amplification
specificity was verified by melting curve analysis, and only samples showing specific
amplification were retained for further analysis.

Profiling functional genes of metagenomic data and pathogen genomes
For the metagenomic data, the functional genes of the ORFs were annotated using the eggNOG-
mapper v1.0.3, based on the eggNOG database (version 5.0)90. The annotation results were then
reorganized into Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology (KO)46 profiles.
Comparative genomic analysis was conducted to explore the genomic drivers determining the
distribution of pathogens. Initially, we categorized our pathogens into dominant and non-dominant
groups and compared the functional gene differences between these two groups. Differential
abundance analysis of functional genes between dominant and non-dominant pathogens was
performed using the generalized linear model (GLM) approach of EdgeR in the ‘edgeR’ R
package91, applying a trimmed mean of M-values (TMM) normalization method and a threshold
of significance set at P < 0.001. To identify microbial metabolic traits contributing to pathogen
suppression, we further annotated bacterial biosynthetic gene clusters (BGCs) in our metagenomic
dataset and examined their correlations with pathogen abundance. BGCs were predicted using
antiSMASH (v7.1)55 database on contigs larger than 3 kb. For precise product identification, the
predicted BGCs were aligned to those in the MIBiG92 repository using BLASTP.

Correlation between pathogen abundance and their known global distribution
We downloaded reported global distribution data of some bacterial plant pathogens from previous
studies and public databases to assess potential correlation between the distribution of bacterial
plant pathogens and their global distribution. We downloaded the data of the distribution of several
bacterial plant pathogens from the European and Mediterranean Plant Protection Organization
(EPPO) Global Database (https://gd.eppo.int/)27. This data was then correlated with our data to
further elucidate the links between the abundance of these pathogens and their known global
distribution. We also extracted the global distribution data of R. solanacearum species complex
from a previous study26, and performed a correlation analysis with our dataset.

Environmental factors and soil properties measure
For both standardized global soil sampling and from public databases ((1,602 samples in total)),
climatic information, including mean annual temperature (MAT), mean annual precipitation
(MAP), diurnal temperature range (MDR), and seasonality of temperature (TSEA) and
precipitation (PSEA), was extracted from the WorldClim 2.0 database93 (1 km2 resolution). Aridity
index data were acquired from the Global Aridity Index and Potential Evapotranspiration Climate
Database (version 3)94. Assessment of plant cover (the fraction of ground covered by green
vegetation) were obtained from Copernicus Global Land Service95. The Normalized Difference
Vegetation Index (NDVI) were derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+)
imagery (with a pixel resolution of 30 m x 30 m), employing methods previously described96. In
the case of the global standardized dataset, soil properties were determined using standardized
protocols. Soil properties, such as pH, texture (quantified by clay percentage), salinity, total
organic nitrogen, and OC content were analysed using methodologies detailed in previous
studies96-98.

Evaluating the ecological drivers of bacterial plant pathogens
Machine learning random forest analyses were then performed using the ‘randomForest’99 and
‘rfPermute’ package100 in R to explore the ecological factors (such as MAT, MAP, NDVI, soil
properties, and microbial diversity) that drive the relative abundance of bacterial plant pathogens.
Each RF model was trained with 9,999 trees (ntree = 9999), using the default number of variables
randomly sampled at each split. A fixed random seed was used to ensure reproducibility. Variable
importance was quantified based on the Mean Decrease in Accuracy (%IncMSE) metric obtained
via the importance() function. The top-ranked predictors were exported for reproducibility.
Statistical significance of predictor importance was further assessed using rfPermute, which
applies permutation-based testing to derive p-values for each predictor’s contribution. All analyses
were repeated across 32 pathogen variables using a standardized workflow to ensure consistency
of variable importance ranking and model performance. Spearman correlation analyses were
further conducted to evaluate the correlations between ecological factors and the relative
abundance of all plant pathogens and the dominant plant pathogens using the R package ‘psych’101.
All statistical analyses were conducted and visualized in R (http://www.r-project.org).

Structural Equation Modeling (SEM)102 was further utilized to identify the direct and indirect
impacts of climatic factors, vegetation, soil properties, and microbial diversity on the relative
abundance of dominant bacterial plant pathogens. The SEM constructs were developed using IBM
SPSS Amos 21 software (Amos Development Corporation), following the methodology
established in previous studies6,52. Model estimation was performed using maximum likelihood.
Considering the non-normal distribution of some introduced variables, bootstrap tests were applied
to evaluate the probability of path coefficients differing from zero. Model fit was evaluated through
a chi-square test, the root mean squared error of approximation (RMSEA), and the Bollen–Stine
bootstrap test. Our models showed a robust fit, as indicated by the following metrics: chi-square/df
= 0.892, P = 0.345; RMSEA = 0.00, P = 0.821; and bootstrap P = 0.383.

Global mapping of bacterial plant pathogens
We next utilized ordinary least squares models to predict the current and future global distribution
of bacterial plant pathogens. Predictors for our global maps were selected through machine
learning using random forest regression, including climatic factors (MAP, MAT, MDR, and
PSEA), elevation, slope, vegetation (NDVI), and soil properties (organic carbon, texture, and pH).
Multicollinearity was assessed and used to filter predictors. To assess the accuracy of the models,
we quantified the discrepancy between the parameter space of the predictors and the original
dataset103. Areas in the projection distant from the sample points were pinpointed using a masking
criterion based on a P-value less than 0.01. We used the Mahalanobis distance to every point in
the multidimensional space, which spans across ten dimensions as determined by the exogenous
variables. This distance was measured in relation to both the center of the known distribution and
the convex hull encompassing all data points used in the model. Environmental outliers were
defined as locations whose Mahalanobis distance exceeded the 0.95 quantile of the χ² distribution
(df = 10), indicating that their environmental profiles did not fall within the multivariate envelope
of the sampled observations104.

To project the distribution of bacterial plant pathogens globally in future climates (2050), we reran
our original model, using climatic and land use available data sets following previous studies6,105-
108. The land-use projections were provided by the Land-use Harmonized v2.0 project
(http://luh.umd.edu/)109 whereas climatic variables were obtained from MIROC6 global climate
model (CMIP6 Landscape 2019)110. Key constant variables in these projections were elevation,
slope, and soil properties. We used a mix of historical and future datasets from ISMIP2a under
Representative Concentration Pathways (RCP) 2.6, RCP6.0, and RCP8.5 for the year 2050,
integrating these with Shared Socioeconomic Pathways (SSP)105,111.

Map images in Figure 2c, 4a–f and Supplementary Figure 1, 5a–m were created in R using
ggplot2112.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents a global atlas of soil-borne pathogens using 1,602 soil metagenomes from over 50 countries. They used amplicon sequencing, qPCR, greenhouse validation experiments, and performed predictive modeling for climate change scenarios. This is a timely study that provides interesting insights. For example, how pathogen suppression may be negatively linked to mycorrhizal fungi, non-pathogenic Streptomyces, organic carbon, and some of the biosynthetic gene clusters. I also appreciate their attempts to predict things under climate change. However, there are several methodological, and conceptual issues that must be addressed before the manuscript can be considered further.\nIntroduction\nLn 93-94: But why? What are the current challenges that prevent such studies?\nLn 96-98: that’s not completely right. Check out this study (doi:10.1016/j.oneear.2024.06.005) that found that high microbial diversity prevents pathogen prevalence worldwide.\nLn 115-117: I believe the authors can be more specific and directional in their hypothesis. It’s a bit vague at the moment. A lot of factors will jointly either promote or suppress sounds too ambiguous.\nMethods\nDatabase assembly: this section is confusing as the authors discuss amplicon data and metagenome data simultaneously. I suggest separating the two types of data. However, I do have some other questions. Did you only select field samples or you also selected greenhouse samples? I ask because while both would show a location on global map, they are completely different. Plant pathogen studies are often conducted in the greenhouse. Some of the land use sites also do not make sense. For example, wetland, forest, and bare land—what’s the rationale when you are interested in plant pathogens? Secondly, did you select studies that used same/ similar sequencing platforms and depths? Microbiome meta-analyses are always tricky because primer sets are different, instruments are different, terrible reverse reads, meta-data are not available most of the times, etc. How did you ensure/overcome this? Ln 755 says: “We used shotgun metagenomic data for the detection of potential bacterial plant pathogens”- which samples are these? Standardized or global? Lastly, the custom dataset is a strength, but with 137 species and 310 genomes, it probably doesn’t represent the global diversity of plant pathogens. The authors used Salmon for quantification. While it’s a widely used tool, there are inherent biases with regards to its correction assumptions and how it handles similar isoforms. The protein-level homology cut-off used in this study can overestimate the detection of pathogens by including non-pathogenic close relatives. This also becomes a problem since the correlations for the top 12 most positively correlated species were as low as 0.14.\nFungal data: The Euk primers are good for a lot of eukaryotic organisms, so it seems like one stone for many birds. However, for fungi, it provides very poor resolution and this becomes an even bigger problem since the typical amplicon size derived from these primers is really short (~180 bp). Furthermore, the threshold for rarefying fungal data is also too low. I feel the fungal dataset is suboptimal. These caveats should be mentioned explicitly and rarefaction plots should be provided for both bacteria and fungi.\nGreenhouse study: I appreciate that the authors attempted to validate their diversity-pathogen relationship, but there are important issues. First of all, it is not clear if this greenhouse study was conducted in the presence of a host plant or not. If not, why do it in the greenhouse? Secondly, the authors used OD600 and inoculated a 15 mL suspension. OD600 is a crude measure and can be affected by dead cells and different sizes, especially in this case. Cell count using Flow Cytometry would have been better. More importantly, 15 mL reads arbitrary- why 15 and not 10 or 20? Was it adequate for a 3 L pot? Did you just add it on top of the pots or a few mm into the soil? When you collected samples, did you collect from the topsoil, especially since the pot size was 3 L. Also, how much soil sample was collected at each time point? Important details are missing. Lastly, the validation only involved two pathogens- Streptomyces scabiei and Ralstonia solanacearum, so the findings must be interpreted with caution.\nStatistics: Provide important details on RF, SEM and network analysis? P values, correlation thresholds, etc.? Ln 968: how did you identify outliers and how many were they?\nResults and Discussion\nDominant pathogen: I find their threshold for defining a dominant pathogen too stringent. Prevalence of 100% of samples and relative abundance in the top 30% can bias the dataset towards only ubiquity and ignore locally devastating pathogens. Basically, relative abundance and presence (ubiquity) do not necessarily correspond to virulence potential.\nThe relationship between microbial diversity and pathogen is not really strong. Sometimes it was even less than 0.01. I wouldn’t call it ‘robust’. Exercise caution while interpreting.\nWhile climatic factors are certainly important for pathogen prevalence, as shown in this study, management and agronomic factors are also critical. For example, the application of fungicides or pesticides, adoption of resistant varieties and crop rotation, etc. can determine pathogen outbreaks and prevalence at a local scale, potentially impacting global scale projections done in this study. I would be careful in interpretations and conclusions and not attribute everything to climate change.\nFigures 1 and 3 are just too dense. Too much to process in my opinion.", "reviewer_2": "This manuscript entitled “Dominance and natural suppression of bacterial plant pathogens across global soils” by Gao et al., identified 32 dominant bacterial plant pathogens, their genomic traits, and evidence of natural soil suppression, by combining a global survey and public databases, including 1,602 soil metagenomes and an independent greenhouse experiment. The authors found that pathogen suppression was strongest in global cold-climate hot-spots, driven by high soil organic carbon, microbial diversity, and abundance; non-pathogenic Streptomyces, mycorrhizal fungi, lichenized fungi, and terpene/polyketide gene clusters all negatively correlated with pathogen prevalence; and that future climates are projected to increase many pathogens, particularly in tropical Africa, North America, and Eurasia. This study establishes a foundational atlas of soil-borne plant pathogens, advancing ecological knowledge and guiding future surveillance and management to protect food security and sustainability. Overall, the research topic is very interesting and important, but I have several critical comments.\nMajor comments:\n1. The dilution-to-extinction approach presents a potential confounding factor, as it might inadvertently introduce bacterial pathogens. The manuscript should address the extent of this potential influence. Furthermore, the experimental design requires clarification regarding the controls used—for instance, whether a set of dilutions was maintained without pathogen inoculation to account for background effects. Additionally, it is crucial to justify whether the chosen sampling period (3 to 21 days post-inoculation) was sufficient for the microbial community to stabilize.\n2. The authors found that bacterial and fungal diversity were very important for pathogen suppression, but the description of bacterial and fungal sequencing protocols lacks sufficient detail. I think the authors should be more detailed in bacterial and fungal sequence. After resampling, each fungal sample had 2,000 sequences. Does this sequence depth adequately capture the diversity of the entire fungal community? I recommend including a rarefaction curve to address this concern. Additionally, the authors should expand their Structural Equation Modeling (SEM) to include microbial metabolic traits. The current model, which focuses on diversity, is incomplete given the documented crucial role of metabolic traits in pathogen suppression.\n3. The Methods section lacks sufficient detail. Although Random Forest models and were employed, their implementation is not described. The manuscript must clarify the cross-validation procedure, the specific parameters used, and the performance metrics (e.g., whether the F1-score was applied). I suggest consulting the documentation for relevant packages (e.g., rfPermute or A3) to ensure these crucial details are explicitly reported.\n4. The manuscript would benefit from stylistic refinement to enhance its overall clarity and fluency. Numerous sentences are structurally unwieldy and hinder readability. For instance, the sentence in Lines 324-327 is characteristic of this issue, as it relies heavily on repetitive verbs such as “provide” and “offer”. A more concise and dynamic revision would significantly improve the impact. Additionally, I think grammar across the main text should be carefully re-checked by authors, since I found many grammar mistakes.\nSome other minor comments:\n1. Lines 52-56: This sentence is somewhat unwieldy; simplifying it would greatly improve clarity and flow.\n2. Lines 69-72: This sentence is vague. Please clarify.\n3. Line 89: Replace “e.g.” with “e.g.,”.\n4. Line 108: Is “soil microbial biodiversity”?\n5. Lines 132-133: Throughout the manuscript, italics should be used for all formally recognized taxonomic genera and species, such as Streptomyces, Rhodococcus, and Agrobacterium.\n6. Line 195: What dose “the system-level” mean?\n7. Line 230: Please clarify the source of the Streptomyces Scabiei and Ralstonia Solanacearum strains. Were they isolated from soil?\n8. Line 226-232: The dilution-to-extinction approach could also introduce bacterial pathogens, and if so, to what extent?\nFurthermore, what is the design of the control for this experiment (e.g., without inoculating the pathogen at each dilution gradient)?\n9. Why is the natural soil treatment for Ralstonia solanacearum missing from Figure 3? I think the authors should add it.\n10. Why only bacterial abundance? I suggest the authors should also investigate the relationship between fungal abundance and bacterial plant pathogens.\n11. Line 235 and 240: If fungal abundance was not assessed, I suggest replacing “abundances” and with “bacterial abundances”.\n12. Line 251-252: What dose “total abundance” mean? Is relative abundance of non-pathogenic Streptomyces spp.? Please clarify.\n13. Line 252: I suggest replacing “Streptomyces spp.” with “Streptomyces spp.”.\n14. Lines 282-283: Change “the role of microbial diversity, community and biosynthesis gene clusters” to “the role of microbial diversity and biosynthesis gene clusters”.\n15. Line 361: Replace “e.g.” with “e.g.,”.\n16. Lines 324-327: The language lacks lexical variety, with excessive use of “provide”, “offer” and global, resulting in clunky and disjointed prose.\n17. Line 325: Change “and provide” to “, offering”.\n18. Line 839: For fungal communities, with 2,000 sequences per sample, were the rarefaction curves saturated? I suggest that the corresponding figure be supplemented in the appendix to confirm sequencing depth adequacy.\n19. Line 862: Is “100 dilution” the correct way to refer to this? Please clarify.\n20. Lines 872-876: Was the sampling period (3 to 21 days post-inoculation) sufficient for microbial diversity and biomass to stabilize following inoculation？\n21. Line 906: The quotation marks around 'edgeR' should be in English – style.\n22. Line 939: The same comment applies as mentioned above.\n23. Line 89, 233 and 1061: Throughout the manuscript, is 16s qPCR or 16S qPCR? I suggest replacing “16s qPCR” with “16S qPCR”.\n24. The Reference formatting style and In-text citation style should be followed according to Guide to Authors."}, "author_response": "We sincerely reviewers for very positive and constructive comments. We have now addressed\nall issues raised by reviewers and as a result we believe that the quality of manuscript has\nimproved significantly. Please see below for point to point responses.\nPlease note that changes made are highlighted in red fonts.\nReviewer 1 comments\nThis study presents a global atlas of soil-borne pathogens using 1,602 soil metagenomes from\nover 50 countries. They used amplicon sequencing, qPCR, greenhouse validation\nexperiments, and performed predictive modeling for climate change scenarios. This is a\ntimely study that provides interesting insights. For example, how pathogen suppression may\nbe negatively linked to mycorrhizal fungi, non-pathogenic Streptomyces, organic carbon, and\nsome of the biosynthetic gene clusters. I also appreciate their attempts to predict things under\nclimate change. However, there are several methodological, and conceptual issues that must\nbe addressed before the manuscript can be considered further.\nAnswer: We sincerely thank the reviewer for the positive and constructive evaluation of our\nwork. We appreciate the recognition of the study’s novelty, global scope, and its contributions\nto understanding pathogen suppression and climate change impacts. Following the reviewer’s\nconstructive feedback, we have carefully revised the manuscript to address all methodological\nand conceptual issues raised.\nIntroduction\nLn 93-94: But why? What are the current challenges that prevent such studies?\nAnswer: We thank the reviewer for this valuable comment. We have almost re-written Abstract\nand Introduction sections to address all issues raised by current challenges and flow of the\nsections. To address this issue, we revised the sentence to clarify the main reasons why such\nglobal assessments have been limited. The revised text now reads in lines 119-122 “Unlike\nfungal and oomycete taxa—whose global distributions are known to be strongly shaped by\ntemperature and precipitation6—comparable insights for bacterial plant pathogens remain\nunresolved owing to limited taxonomic resolution and the absence of global-scale\nmetagenomic datasets.’\nLn 96-98: that’s not completely right. Check out this study (doi:10.1016/j.oneear.2024.06.005)\nthat found that high microbial diversity prevents pathogen prevalence worldwide.\nAnswer: We thank the reviewer for pointing out this important reference, which deal with\nhuman and animal pathogens. We now cited (doi:10.1016/j.oneear.2024.06.005) and have\nrevised the text to be more precise. The revised sentence (lines 145–148) reads as ‘Emerging\nglobal evidence suggests that higher microbiome diversity can constrain human and animal\npathogens (identified based on 16s rRNA genes) in the plant rhizosphere17. Thus, we predict\nthat soil biodiversity similarly functions as a key biotic barrier that suppresses soil-borne\nbacterial plant pathogens by outcompeting pathogens in the soil.’.\nLn 115-117: I believe the authors can be more specific and directional in their hypothesis. It’s\na bit vague at the moment. A lot of factors will jointly either promote or suppress sounds too\nambiguous.\nAnswer: We thank the reviewer for this valuable suggestion. We have revised the hypothesis\nto be more specific and directional. In the revised version, we now explicitly describe the\nexpected relationships between key environmental and microbial factors and pathogen\nprevalence. The revised text (lines 148–153) reads as ‘We also expect that warmer and drier\nclimates, and lower soil organic carbon would be associated with a higher prevalence of soil-\nborne bacterial plant pathogens6,7,18. Our overarching aim was to identify globally dominant\nsoil-borne bacterial plant pathogens, characterize their distribution and ecological drivers, and\ndetermine the environmental and biological factors that promote natural pathogen\nsuppression.’.\nMethods\nDatabase assembly: this section is confusing as the authors discuss amplicon data and\nmetagenome data simultaneously. I suggest separating the two types of data.\nAnswer1: We agree that the original structure may have caused confusion. In the revised\nmanuscript, we have now separated the description of metagenomic datasets and amplicon-\nbased datasets into two independent subsections to improve clarity. Now the methods part\n‘Global soil metagenomics (shotgun sequencing based) database’ only have the description of\nthe database of Metagenome (line 772). The method of amplicon data is separated with section\ntitle ‘Taxonomic microbial diversity and qPCR-based abundance’ (line 892).\nHowever, I do have some other questions. Did you only select field samples or you also selected\ngreenhouse samples? I ask because while both would show a location on global map, they are\ncompletely different. Plant pathogen studies are often conducted in the greenhouse.\nAnswer: In this study, we restricted our analyses to field-collected samples and excluded\ngreenhouse or pot-based experiments to identify globally dominant species, their drivers, and\ndistribution. This approach ensures that all samples reflect pathogen distribution in natural or\nagricultural ecosystems rather than in controlled environments. We have included a sentence\nto clarify this in lines 789-792 ‘Sampling locations were selected to capture a robust\nrepresentation of Earth's geographical, climatic and edaphic diversity, and all sites\ncorresponded to natural or agricultural soils rather than controlled experimental samples.’\nSome of the land use sites also do not make sense. For example, wetland, forest, and bare\nland—what’s the rationale when you are interested in plant pathogens?\nAnswer: Thank you for raising this point. We examine the distribution of plant pathogens\nacross diverse terrestrial ecosystems, rather than restricting the scope to agricultural soils, was\nmotivated by several key scientific considerations. First, soil-borne plant pathogens are not\nexclusive to croplands. Many pathogens threaten forests, grasslands, and other natural areas1\nthat provide vital ecosystem services, such as timber production and livestock grazing.\nLimiting the analysis to farmland would therefore overlook a significant part of their global\necology and impact. Second, natural ecosystems can act as reservoirs for plant pathogens,\nwhich may persist there as latent or opportunistic taxa2. These reservoirs can serve as sources\nof inoculum that are subsequently introduced into agricultural systems3 via hydrological\npathways, aeolian dispersal, wildlife movement, or human-mediated transport. Third, natural\nsoils provide a critical ecological baseline and are the ideal system in which to study the innate\ncapacity of soil to suppress pathogens4—a phenomenon often obscured by intensive\nmanagement in agricultural settings. Notably, our comparison across land-use types revealed\nthat farmland harbours higher pathogen abundance than natural ecosystems, underscoring the\nvalue of a globally representative sampling framework for characterizing the distribution of\nplant pathogens. To clarify this rationale, we have added a sentence in lines 785–789 read as\n‘We included soils from both agricultural and natural ecosystems because natural soils provide\nan essential ecological baseline, can act as reservoirs of pathogen inoculum, and offer an\noptimal context for examining natural pathogen suppression—an ecological process that is\noften obscured by intensive management in agricultural systems.’\nSecondly, did you select studies that used same/ similar sequencing platforms and depths?\nMicrobiome meta-analyses are always tricky because primer sets are different, instruments are\ndifferent, terrible reverse reads, meta-data are not available most of the times, etc. How did you\nensure/overcome this? Ln 755 says: “We used shotgun metagenomic data for the detection of\npotential bacterial plant pathogens”- which samples are these? Standardized or global?\nAnswer: We agree with the reviewer’s comments regarding technical heterogeneity in\nmetagenome meta-analyses. This is exact reason, we exclusively used metagenomic (shotgun)\nsequencing data for pathogen identification (removing any bias linked to primers, PCR,\nplatforms etc) 5,6. For further standardisation and to minimize potential biases associated with\nsequencing depth, all shotgun metagenomic datasets were normalized using Reads Per Million\n(RPM) prior to pathogen abundance estimation. RPM scales read counts to a per-million-reads\nbasis, allowing relative abundance to be compared across samples with different sequencing\ndepths and read yields. This approach effectively reduces artefacts caused by variations in\nsequencing platforms, or low-quality reverse reads6. We have clarified this in the lines 827-\n830 ‘The relative abundances of these bacterial phylotypes were expressed as RPM across all\nsamples, allowing direct comparison of relative abundance among samples with differing\nsequencing depths and read yields79.’.\nWe used shotgun metagenomic dataset of 1,602 samples included all metagenomes both from\npublic downloaded and standardised datasets for pathogen detection. We have clarified this in\nthe lines 800-801 ‘The global soil metagenomics database (1,602 samples) was used for the\ndetection of potential bacterial plant pathogens.’.\nComment: Lastly, the custom dataset is a strength, but with 137 species and 310 genomes, it\nprobably doesn’t represent the global diversity of plant pathogens.\nAnswer: We agree with the reviewer that our current custom pathogen genome database may\nnot yet capture the full global diversity of all known or emerging bacterial plant pathogens.\nThis limitation largely reflects the present status of publicly available genomic resources: many\nplant pathogens exhibit substantial intraspecific genomic variation (including subspecies,\npathovars, and race-level differentiation)7,8, and rapid emergence species still lack complete or\nhigh-quality genome assemblies9,10. These factors inevitably constrain the taxonomic and\ngenomic completeness of any curated pathogen database at this stage. Nevertheless, our custom\ndatabase includes the majority of taxonomically representative genomes of major bacterial\nplant pathogens, covering 32 dominant species that collectively account for a substantial\nportion of the global plant disease burden11,12. We therefore believe that the database used\nprovides a biologically meaningful and robust foundation for large-scale pathogen detection,\nwhile maintaining a balance between database coverage and genomic quality. We have\nexpanded the methods section to include this justification in lines 803-812 reads as ‘To enhance\nthe accuracy in identifying bacterial plant pathogens in soil samples, we developed a\nspecialized, high-quality database focused on bacterial plant pathogens (BPPDB). Guided by\nprevious literature, this database comprises 113 recognized bacterial plant pathogen species\nrepresented by 310 complete genomes obtained from the National Center for Biotechnology\nInformation (NCBI). By capturing the majority of globally important bacterial plant\npathogens—those responsible for a substantial proportion of plant disease worldwide—\nBPPDB provides a biologically meaningful and robust framework for large-scale pathogen\nidentification, while maintaining an appropriate balance between taxonomic coverage and\ngenomic quality.’.\nComment: The authors used Salmon for quantification. While it’s a widely used tool, there are\ninherent biases with regards to its correction assumptions and how it handles similar isoforms.\nThe protein-level homology cut-off used in this study can overestimate the detection of\npathogens by including non-pathogenic close relatives. This also becomes a problem since the\ncorrelations for the top 12 most positively correlated species were as low as 0.14.\nAnswer: We thank the reviewer for this comment. As the reviewer noted, Salmon is widely\nused for metagenomic quantification, and in our workflow, it was applied at the nucleotide\nlevel. Therefore, potential biases associated with isoform-level corrections in transcriptomic\napplications are not relevant in this study. To address reviewer’s this comment, we further\nassess the robustness of our pathogen abundance estimates, we performed an independent\nvalidation using an alternative read-alignment–based method, CoverM13. Pathogen abundances\nquantified by Salmon and CoverM were highly consistent (Pearson r = 0.95, P < 2.2 × 10⁻¹⁶\nfor total pathogen abundance), and many individual pathogens exhibited correlations greater\nthan 0.90 (see figure below). This strong agreement indicates that quantification is not sensitive\nto the choice of method.\nProtein-level similarity could detect conserved sequences shared with non-pathogenic close\nrelatives. However, several aspects of our workflow greatly reduce this risk. First, we used a\ncurated pathogen-only genome database (BPPDB) containing genomes exclusively from\nbacterial plant pathogens, which restricts homology searches to bona fide pathogen genomes\nrather than across broad taxonomic groups. Second, strict amino acid identity and alignment\ncoverage thresholds were applied in DIAMOND14, minimising matches arising from generic\nhousekeeping proteins. Finally, to address the concern regarding possible overestimation due\nto close non-pathogenic relatives, we expanded our reference database by including 20\ngenomes from close related non-pathogenic species. Re-analysis demonstrated that pathogen\nabundances obtained from the expanded database showed very high correlation with those\nderived from the pathogen-only database (Pearson r > 0.98, P < 2.2e-16; see figure below).\nThis confirms that our approach does not misclassify close relatives. In addition, cross-\nvalidation with Kraken2-based taxonomic classification also showed strong agreement across\nmethods, further supporting the robustness of our pathogen detection and quantification\npipeline. We have included these cross-validations in the Methods (lines 859–869), which now\nread ‘Comparison with coverM quantification. We further performed an independent\nvalidation using an alternative read-alignment–based method, CoverM76, to assess the\nrobustness of our pathogen abundance quantification. Estimates derived from Salmon and\nCoverM were highly consistent (Pearson r = 0.95, P < 2.2e-16 for total pathogen abundance),\nwith many individual pathogens showing correlations exceeding 0.90 (P < 2.2e-16, Extended\nData Fig. S9). Comparison with expanded pathogen database. To address the potential\noverestimation arising from closely related non-pathogenic taxa, we expanded the bacterial\nplant pathogen database (BPPDB) to include 20 genomes from closely related non-pathogenic\nspecies. Pathogen abundances estimates derived from the expanded database were highly\nconcordant with those obtained using the pathogen-only database (Extended Data Fig. S10,\nPearson r > 0.98, P < 2.2e-16).’\nWe appreciate the reviewer’s concern regarding the relatively low correlation coefficient (r =\n0.14) observed for Erwinia amylovora when comparing our custom pathogen database with\nthe validated virulence genes curated in the PHI-base. We would like to clarify that this value\nrepresents the lowest correlation among the top 12 positively correlated species. In fact, most\nof the top 12 species exhibited much stronger correlations, including Xanthomonas campestris\n(r = 0.80), Burkholderia glumae (r = 0.76), and Pseudomonas syringae (r = 0.69), all of which\ndemonstrate high consistency across databases. The lower correlation for E. amylovora, though\nstatistically significant, is not unexpected. PHI-base is a manually curated repository of\nexperimentally validated pathogen–host interaction and virulence genes15, rather than a\nquantitative database of species-level pathogen abundance. Consequently, the abundance of\nvirulence genes recorded in PHI-base may not scale proportionally with metagenomic pathogen\nabundance, particularly for species with variable accessory genomes or strain-level\nheterogeneity. Furthermore, when comparing pathogen abundance profiles derived from our\ncustom database with those obtained using the Kraken2 database, the majority of species\nexhibited strong positive correlations, supporting the robustness of our detection framework\n(Extended Data Fig. S8). Therefore, we believe that the low correlation for a single species to\nPHI-base does not undermine the overall reliability of our pathogen detection framework,\nwhich is consistently supported by multiple independent annotation strategies.\nResponse Fig. 1 Correlation of pathogen relative abundances quantified using Salmon\nversus CoverM. Relative abundances of bacterial plant pathogens quantified using Salmon\nshowed strong positive correlations with those obtained using CoverM. Several representative\npathogens are displayed to illustrate the consistency between the two quantification methods.\nShown were log-transformed values. P values for linear regressions were calculated by using\npearson’s correlation. The grey area represents the 95% confidence interval for the fit\n(Extended Data Fig. S9).\nResponse Fig. 2 Correlation of pathogen relative abundances estimated using the\npathogen-only versus expanded databases. Relative abundances of bacterial plant pathogens\nestimated using pathogen-only database showed strong positive correlations with those\nestimated using expanded database (contaminated with the genomes of non-pathogenic\nspecies). Shown were log-transformed values. P values for linear regressions were calculated\nby using pearson’s correlation. The grey area represents the 95% confidence interval for the fit\n(Extended Data Fig. S10).\nComment: Fungal data: The Euk primers are good for a lot of eukaryotic organisms, so it seems\nlike one stone for many birds. However, for fungi, it provides very poor resolution and this\nbecomes an even bigger problem since the typical amplicon size derived from these primers is\nreally short (~180 bp). Furthermore, the threshold for rarefying fungal data is also too low. I\nfeel the fungal dataset is suboptimal. These caveats should be mentioned explicitly, and\nrarefaction plots should be provided for both bacteria and fungi.\nAnswer: We thank the reviewer for these insightful comments. We acknowledge that universal\neukaryotic primers targeting the 18S rRNA region have lower taxonomic resolution for fungi\ncompared with ITS markers. However, 18S-based amplicons remain widely used in large-scale\nfungal ecology and biogeography studies16, particularly when the goal is to quantify broad-\nscale diversity patterns rather than species-level taxonomy. Importantly, our dataset yielded\nmore than 6,000 high-quality fungal ASVs across global biomes, a richness level comparable\nto (or exceeding) those reported in ITS amplicon surveys17-19. This demonstrates that, despite\nthe inherent limitations, the 18S dataset in this study provides robust and representative fungal\ndiversity patterns suitable for the large-scale comparative analyses.\nTo assess the adequacy of sequencing depth, we have now included rarefaction curves for both\nbacterial and fungal datasets (Extended Data Fig. S10 and see figure below). For fungal data,\nwe additionally increased the rarefaction depth from 2,000 to 4,000 reads per sample and found\nthat diversity estimates (e.g., Shannon index) were highly correlated (r = 0.99) between the two\ndepths (see figures below), indicating that our results are robust to sequencing depth. We\ntherefore believe that, despite the inherent limitations of short-read 18S data, the fungal dataset\nprovides reliable comparative diversity patterns suitable for large-scale cross-biome analyses.\nWe have added a clarifying sentence in lines 910–912 reads as ‘Despite the inherent limitations\nof short-read 18S data, the fungal dataset provides reliable comparative diversity patterns\nsuitable for large-scale cross-biome analyses85.’.\nResponse Fig. 3 a, Rarefaction curves of bacterial and fungal communities. b, Relationship\nbetween Shannon diversity indices calculated at 4,000 and 2,000 rarefied reads. P values were\nobtained using Spearman’s correlation. The grey shading denotes the 95% confidence interval\nof the fitted regression (Extended Data Fig. S12).\nComment: Greenhouse study: I appreciate that the authors attempted to validate their diversity-\npathogen relationship, but there are important issues. First of all, it is not clear if this\ngreenhouse study was conducted in the presence of a host plant or not. If not, why do it in the\ngreenhouse? Secondly, the authors used OD600 and inoculated a 15 mL suspension. OD600 is\na crude measure and can be affected by dead cells and different sizes, especially in this case.\nCell count using Flow Cytometry would have been better. More importantly, 15 mL reads\narbitrary- why 15 and not 10 or 20? Was it adequate for a 3 L pot? Did you just add it on top\nof the pots or a few mm into the soil? When you collected samples, did you collect from the\ntopsoil, especially since the pot size was 3 L. Also, how much soil sample was collected at each\ntime point? Important details are missing. Lastly, the validation only involved two pathogens-\nStreptomyces scabiei and Ralstonia solanacearum, so the findings must be interpreted with\ncaution.\nAnswer: We thank the reviewer for the constructive comments.\nThe experiment was performed without host plants, because the aim of this validation was to\nprovide a controlled microcosm system to test whether the negative relationship between soil\nmicrobial diversity and pathogen abundance observed in the global dataset is also detectable\nunder standardized experimental condition, independent of plant-mediated immunity or\nrhizosphere effects. We have now revised the text to make this clear in lines 954-956 ‘All\nexperimental units were maintained in controlled greenhouse conditions (without host plants)\nwith standardized irrigation protocols.’.\nWe agree that OD600 is a relatively coarse estimate of cell density. However, it is routinely\nused in soil inoculation experiments and provides sufficient reproducibility when the same\ngrowth phase and optical calibration are used20-22. To ensure the accuracy and consistency of\nour inoculum, we standardized the bacterial suspensions to a target OD600 and then estimated\nthe corresponding CFU/mL values. For the inoculation volume (15 mL), actually we added 1.5\nkg soil to a 3 L pot, so the 15 mL suspension is for 1.5 kg soil. This dosage estimated around\n1×10⁶ CFU per gram of soil, which falls within the well-established range used in soil pathogen\nintroduction assays23. We have clarified this in lines 952-954 ‘The applied inoculum\ncorresponded to an estimated initial concentration of ~10⁶ CFU per gram of soil, which lies\nwithin the commonly used range for soil-borne pathogen introduction assays in controlled\nexperiments88.’.\nAt each time point, around 5 g of soil was collected from the top 0–5 cm layer using sterile\ncorers, pooled from three subsites within the same pot to reduce spatial heterogeneity. These\ndetails have been added to the revised text in lines 957-959 ‘At each time point, around 5 g of\nsoil was collected from the top 0–5 cm layer using sterile corers, pooled from three subsites\nwithin the same pot to reduce spatial heterogeneity.’.\nFor the validation work, we chose Ralstonia solanacearum and Streptomyces scabiei because\nthey are among the most dominant pathogens, differ strongly in phylogeny (Gram-negative vs.\nGram-positive) and Mean Annual Precipitation (MAP) preference (MAP- negative vs. MAP-\npositive), allowing us to test whether the diversity–suppression pattern generalizes across\ncontrasting pathogen types irrespective of microbial traits and environmental preference. We\nnow clearly describe the choice of these organisms in lines 272-280 ‘For this validation\nexperiment, we selected two model and dominant pathogens, which have differential mode of\ninfection and environmental preferences—S. scabiei (a necrotrophic pathogen favouring drier\nsoils)21 and R. solanacearum (a vascular wilt pathogen thriving in moist environments)20—\nallowing us to test whether the diversity–suppression pattern generalizes across contrasting\npathogen types irrespective of microbial traits and environmental preference. Although our\nfindings are supported by plot/ pot scale studies for other pathogens41,47, future studies should\nincorporate a broader spectrum of pathogens to generalize the diversity-pathogen inhibition\nrelationship observed in this study.’.\nComment: Statistics: Provide important details on RF, SEM and network analysis? P values,\ncorrelation thresholds, etc.? Ln 968: how did you identify outliers and how many were they?\nAnswer: We appreciate the reviewer’s comments. We have now added a more detailed\ndescription of the statistical procedures used for the random forest (RF) and structural equation\nmodelling (SEM) analyses. These details have been incorporated into the revised manuscript\nin lines 1025-1036 ‘Machine learning random forest analyses were then performed using the\n‘randomForest’99 and ‘rfPermute’ package100 in R to explore the ecological factors (such as\nMAT, MAP, NDVI, soil properties, and microbial diversity) that drive the relative abundance\nof bacterial plant pathogens. Each RF model was trained with 9,999 trees (ntree = 9999), using\nthe default number of variables randomly sampled at each split. A fixed random seed was used\nto ensure reproducibility. Variable importance was quantified based on the Mean Decrease in\nAccuracy (%IncMSE) metric obtained via the importance() function. The top-ranked predictors\nwere exported for reproducibility. Statistical significance of predictor importance was further\nassessed using rfPermute, which applies permutation-based testing to derive p-values for each\npredictor’s contribution. All analyses were repeated across 32 pathogen variables using a\nstandardized workflow to ensure consistency of variable importance ranking and model\nperformance.’, and in lines 1043-1047 ‘The SEM constructs were developed using IBM SPSS\nAmos 21 software (Amos Development Corporation), following the methodology established\nin previous studies6,52. Model estimation was performed using maximum likelihood.\nConsidering the non-normal distribution of some introduced variables, bootstrap tests were\napplied to evaluate the probability of path coefficients differing from zero.’.\nPlease note that the term ‘outliers’ in the context of our paper refers to environmental\nextrapolation points rather than data errors. We have clarified this definition in the revised text,\nlines 1065-1068 ‘Environmental outliers were defined as locations whose Mahalanobis\ndistance exceeded the 0.95 quantile of the χ² distribution (df = 10), indicating that their\nenvironmental profiles did not fall within the multivariate envelope of the sampled\nobservations104.’.\nResults and Discussion\nComment: Dominant pathogen: I find their threshold for defining a dominant pathogen too\nstringent. Prevalence of 100% of samples and relative abundance in the top 30% can bias the\ndataset towards only ubiquity and ignore locally devastating pathogens. Basically, relative\nabundance and presence (ubiquity) do not necessarily correspond to virulence potential.\nAnswer: We thank the reviewer for this comment. We agree that a 100% prevalence threshold\nwould be overly stringent and could bias the analysis toward ubiquitous taxa. We clarify that\ndominant pathogens were defined using a prevalence threshold of > 80%, and the 100%\nprevalence in the manuscript was a textual error that has now been corrected. The 80%\nthreshold was chosen to identify globally dominant pathogens, and lowering this threshold\nyielded consistent results (Supplementary Dataset). We have correlated sentence in lines 830-\n833 read as ‘In total, we identified 107 bacterial pathogen species, of which 32 were classified\nas globally dominant based on their high frequency of occurrence (present in at least 80% of\nsamples) and high relative abundance (top 30%, with a mean relative abundance ≥ 0.1%)\nacross global soils (Fig. 1).’.\nWe agree that global prevalence does not equate to virulence, and that locally devastating\npathogens may be missed by a frequency-based definition. Indeed, we did explore the\ncorrelation between pathogens abundance and validated virulence genes curated in the\nPathogen–Host Interaction database (PHI-base), and a lot of pathogen species demonstrate\npositive correlation (Extended Data Fig. S8). However, the main objective of this study was to\ncharacterize globally dominant pathogens, their drivers and distributions and abundance\npatterns; detailed assessments of virulence traits and epidemic severity are beyond the scope\nof our manuscript but represent an important direction for future research. We have included a\nclarification in line 371-373 ‘While this study focuses on pathogen dominance and suppression,\npredicting pathogen virulence and disease severity associated with dominance represents an\nimportant next research direction.’\nComment: The relationship between microbial diversity and pathogen is not really strong.\nSometimes it was even less than 0.01. I wouldn’t call it ‘robust’. Exercise caution while\ninterpreting.\nAnswer: We thank the reviewer for this comment. We agree that the correlation between\nmicrobial diversity (based on Kraken2 metagenomic estimates) and pathogen abundance is not\nnumerically strong (r = –0.051, P = 0.039; n = 1,602), although statistically significant. Effect\nsizes in the range of r = 0.05~0.20 have also been reported in other global soil microbiome\nstudies24,25, and are considered ecologically meaningful when the direction of association is\nconsistent across spatial scales. Moreover, pathogen abundance is shaped by a suite of\ninteracting abiotic and biotic factors, and shotgun metagenomic meta-analyses inherently carry\nsubstantial biological and technical noise, which typically reduces effect sizes. Importantly,\nwhen microbial diversity was derived from our standardized amplicon dataset, the negative\nassociation strengthened (r = –0.22, P = 0.003). This pattern was further supported by our\nindependent greenhouse experiment. Collectively, these results indicate a modest but\nstatistically supported negative relationship between microbial diversity and pathogen\nabundance. We have included a clarification in lines 260-266 ‘Microbial taxonomic diversity\nestimated by Kraken2 and KEGG functional diversity both exhibited modest yet statistically\nsignificant negative correlations with the total relative abundance of dominant bacterial plant\npathogens (Kraken2: rho = −0.051, P = 0.039; KEGG: rho = −0.13, P = 2.44 × 10⁻⁷; n = 1,602;\nFig. 3a). Analyses of bacterial and fungal diversity indices from amplicon sequencing data\nshowed a strong and significant negative correlation with total relative abundance of bacterial\nplant pathogens (bacteria: rho = -0.20, P = 0.006; fungi: rho = -0.18, P = 0.013; n = 200) (Fig.\n3b).’, and in lines 270-272 ‘Our data demonstrate a clear link between higher microbial\ndiversity and the early suppression of pathogen invasion, with more diverse soils reducing the\nabsolute abundances of both pathogens as quantified by qPCR (Fig. 3c).’.\nComment: While climatic factors are certainly important for pathogen prevalence, as shown in\nthis study, management and agronomic factors are also critical. For example, the application of\nfungicides or pesticides, adoption of resistant varieties and crop rotation, etc. can determine\npathogen outbreaks and prevalence at a local scale, potentially impacting global scale\nprojections done in this study. I would be careful in interpretations and conclusions and not\nattribute everything to climate change.\nAnswer: We thank the reviewer for this comment. We fully agree that agronomic practices—\nsuch as pesticide application, crop rotation, and the use of resistant cultivars—can influence\npathogen dynamics at local or field scales. We also believe it is of value to further explore these\ndrivers of pathogen distributions and suppression are affected by regional and local conditions\nincluding management practices (e.g., use of resistant varieties, antibiotics, and crop-rotations).\nThere, we have clarified this in the manuscript, lines 368-371 ‘Future work should assess how\nthe global drivers of pathogen prevalence and suppression identified here interact with local\nfactors—such as land-use practices, crop management (e.g., resistant varieties, crop rotation),\nand regional socio-economic constraints—to shape disease outcomes.’\nComment: Figures 1 and 3 are just too dense. Too much to process in my opinion.\nAnswer: Thank you for this comment. We appreciate the concern regarding the amount of\ninformation presented in Figures 1 and 3. These figures summarise key components of\ndominant and natural suppression of bacterial plant pathogens and therefore necessarily\nintegrate several result layers within a single overview. To improve clarity while retaining these\nessential elements, we have refined the figure legends and added clearer guidance in the main\ntext to help readers navigate the figure panels. We hope this improves readability while\npreserving the comprehensive structure that is important for interpreting the global patterns\npresented.\nReviewer #2 (Remarks to the Author):\nThis manuscript entitled “Dominance and natural suppression of bacterial plant pathogens\nacross global soils” by Gao et al., identified 32 dominant bacterial plant pathogens, their\ngenomic traits, and evidence of natural soil suppression, by combining a global survey and\npublic databases, including 1,602 soil metagenomes and an independent greenhouse\nexperiment. The authors found that pathogen suppression was strongest in global cold-climate\nhot-spots, driven by high soil organic carbon, microbial diversity, and abundance; non-\npathogenic Streptomyces, mycorrhizal fungi, lichenized fungi, and terpene/polyketide gene\nclusters all negatively correlated with pathogen prevalence; and that future climates are\nprojected to increase many pathogens, particularly in tropical Africa, North America, and\nEurasia. This study establishes a foundational atlas of soil-borne plant pathogens, advancing\necological knowledge and guiding future surveillance and management to protect food security\nand sustainability. Overall, the research topic is very interesting and important, but I have\nseveral critical comments.\nAnswer: We sincerely thank the reviewer for the positive and encouraging assessment of our\nwork. We appreciate the recognition of our study’s novelty, global scope, and contribution to\nunderstanding the ecological drivers of soil-borne bacterial plant pathogens and natural soil\nsuppression. We have carefully addressed all of the reviewer’s comments by adding new\nanalyses, clarifying methodological details, and improving the overall clarity and precision of\nthe manuscript. All revisions are detailed in the point-by-point responses below.\nMajor comments:\n1: The dilution-to-extinction approach presents a potential confounding factor, as it might\ninadvertently introduce bacterial pathogens. The manuscript should address the extent of this\npotential influence. Furthermore, the experimental design requires clarification regarding the\ncontrols used—for instance, whether a set of dilutions was maintained without pathogen\ninoculation to account for background effects. Additionally, it is crucial to justify whether the\nchosen sampling period (3 to 21 days post-inoculation) was sufficient for the microbial\ncommunity to stabilize.\nAnswer: We thank the reviewer for this important comment. We acknowledge that soil dilution\nmay theoretically introduce low-abundance background pathogens. To minimize this\npossibility, we: (i) collected the source soil from an untreated natural site with no known history\nof plant disease; (ii) prepared all dilution treatments from the same initial soil, ensuring that\nany background pathogen load was consistent across treatments; and (iii) included a pathogen-\nfree control (dilution-only, no inoculation) and measure the pathogen background levels across\ntreatments. We have now included a new Extended figure (Extended Data Fig. S11) to show\nthe qPCR quantification of S. scabiei and R. solanacearum in the dilution controls prior to\npathogen inoculation, confirming negligible background levels across treatments (the\nabundance of background pathogens in the soil was less than 0.01% of the abundance of the\ninoculated pathogens, figure shows below). These results indicate that background pathogen\nlevels are minimal and would not bias the experimental outcomes or confound the\ninterpretation of the inoculated pathogen dynamics.\nWe appreciate the reviewer’s comment regarding the 21-day duration of our greenhouse\nexperiment. Prior to pathogen inoculation, soils were incubated for six months to allow\nmicrobial communities to establish and stabilize, and for microbial biomass to recover\nfollowing inoculation at different levels of microbial diversity. Following pathogen inoculation,\nthe short-term monitoring period was intentionally chosen to capture the initial establishment\nof the pathogens and the early suppressive responses of the soil microbiome. While longer-\nterm monitoring would indeed be valuable to assess the temporal stability of this effect and\nrepresents an important direction for future research, the current timeframe was appropriate to\ntest for the presence of a diversity-mediated suppression effects. To clarify the scope of our\nstudy, we have revised the manuscript in lines 941-942 ‘The inoculated soils were incubated\nat room temperature for six months to facilitate microbial community assembly and\nstabilization.’, and in lines 266-269 ‘To experimentally validate these patterns, we manipulated\nsoil microbial diversity using a dilution-to-extinction approach50, incubated the inoculated soils\nfor six months, and subsequently introduced pathogens for short-term monitoring.’. We also\nadded further clarifications in lines 270-272 ‘Our data demonstrate a clear link between higher\nmicrobial diversity and the early suppression of pathogen invasion, with more diverse soils\nreducing the absolute abundances of both pathogens as quantified by qPCR (Fig. 3c).’.\nResponse Fig. 4 Background levels of target pathogens in the greenhouse experiment.\nqPCR-based quantification of background levels of S. scabiei and R. solanacearum in soils\nwith different microbial diversity prior to pathogen inoculation. The average level of the\ninoculated pathogen—calculated across all treatments and time points—shows that, by\ncomparison, the background pathogen level is minimal and would not influence the results\n(Extended Data Fig. S11).\n2: The authors found that bacterial and fungal diversity were very important for pathogen\nsuppression, but the description of bacterial and fungal sequencing protocols lacks sufficient\ndetail. I think the authors should be more detailed in bacterial and fungal sequence. After\nresampling, each fungal sample had 2,000 sequences. Does this sequence depth adequately\ncapture the diversity of the entire fungal community? I recommend including a rarefaction\ncurve to address this concern. Additionally, the authors should expand their Structural Equation\nModeling (SEM) to include microbial metabolic traits. The current model, which focuses on\ndiversity, is incomplete given the documented crucial role of metabolic traits in pathogen\nsuppression.\nAnswer: We appreciate the reviewer’s constructive suggestions. We have now included the\ndetails of the amplicon data in lines 895-901 ‘The standardised databased of total of 200 global\nsoil samples also had information available for amplicon sequencing (Miseq Illimuna) on\nbacterial 16S rRNA and fungal 18S rRNA genes were amplified using the 341F/805R and\nEuk1391f/EukBr primer sets, respectively78,79. Samples were sequenced on the Illumina MiSeq\nplatform (2 × 300 PE) at the Next Generation Sequencing (NGS) Facility (Western Sydney\nUniversity, Australia) using a paired-end sequencing protocol. The microbial diversity of these\n200 amplicon sequencing data was characterized using QIIME (v1.9.1)80, USEARCH (v10.0)81,\nand UNOISE382.’, and lines 905-912 ‘The remaining reads were subsequently clustered into\n37,653 bacterial and 6,083 fungal ASVs. For alpha-diversity estimation, the bacterial and\nfungal ASV tables were rarefied to 10,000 sequences per sample (resulting in the removal of\none sample) and 4,000 sequences per sample (removing 25 samples), respectively, as supported\nby the rarefaction curves shown in Extended Data Fig. S12. Despite the inherent limitations of\nshort-read 18S data, the fungal dataset provides reliable comparative diversity patterns suitable\nfor large-scale cross-biome analyses85.’.\nWe have now included rarefaction curves for both bacterial and fungal datasets (Extended Data\nFig. S10) to evaluate sequencing depth adequacy. The fungal dataset was further rarefied to\n4,000 reads per sample, and the results were compared with those obtained at 2,000 reads.\nDiversity estimates were highly consistent across depths (Shannon index correlation = 0.99, P\n< 0.001) (see Response Fig. 5), indicating that sequencing depth did not bias our results.\nBased on these results, we adopted the 4,000-read rarefaction depth for all subsequent analyses to\nbalance sequencing coverage and sample retention.\nWe thank the reviewer for the suggestion to include microbial metabolic traits in the Structural\nEquation Model (SEM), and we agree that metabolic traits play an important ecological role.\nIn response, we tested the inclusion of microbial metabolic diversity (derived from the diversity\nof biosynthetic gene clusters identified by antiSMASH) in the SEM. However, this variable\ndid not show a significant effect on pathogen suppression, suggesting that overall metabolic\ndiversity is not a relevant driver (see Response Fig. 5). Instead, specific secondary metabolites\nabundance, rather than total metabolic diversity, appear to mediate suppression (as\ndemonstrated in Fig. 3). Therefore, we kept the initial SEM focused on microbial diversity,\nwhich best explains the observed patterns.\nResponse Fig. 5 a, Rarefaction curves of bacterial and fungal communities. b, Relationship\nbetween Shannon diversity indices calculated at 4,000 and 2,000 rarefied reads. P values were\nobtained using Spearman’s correlation. The grey shading denotes the 95% confidence interval\nof the fitted regression. c, Structural equation model (SEM) incorporating soil microbial\nmetabolic trait diversity, illustrating the direct and indirect effects of environmental variables\non pathogen abundance. d, Total standardized effects from the SEM (sum of direct and indirect\neffects) on the relative abundance of plant pathogens. RMSEA, root mean square error of\napproximation; df, degrees of freedom.\n3: The Methods section lacks sufficient detail. Although Random Forest models and were\nemployed, their implementation is not described. The manuscript must clarify the cross-\nvalidation procedure, the specific parameters used, and the performance metrics (e.g., whether\nthe F1-score was applied). I suggest consulting the documentation for relevant packages (e.g.,\nrfPermute or A3) to ensure these crucial details are explicitly reported.\nAnswer: We thank the reviewer for this comment. Detailed information on the Random Forest\n(RF) implementation has now been added to the revised Methods section in lines 1025-1036\n‘Machine learning random forest analyses were then performed using the ‘randomForest’99 and\n‘rfPermute’ package100 in R to explore the ecological factors (such as MAT, MAP, NDVI, soil\nproperties, and microbial diversity) that drive the relative abundance of bacterial plant\npathogens. Each RF model was trained with 9,999 trees (ntree = 9999), using the default\nnumber of variables randomly sampled at each split. A fixed random seed was used to ensure\nreproducibility. Variable importance was quantified based on the Mean Decrease in\nAccuracy (%IncMSE) metric obtained via the importance() function. The top-ranked predictors\nwere exported for reproducibility. Statistical significance of predictor importance was further\nassessed using rfPermute, which applies permutation-based testing to derive p-values for each\npredictor’s contribution. All analyses were repeated across 32 pathogen variables using a\nstandardized workflow to ensure consistency of variable importance ranking and model\nperformance.’.\n4: The manuscript would benefit from stylistic refinement to enhance its overall clarity and\nfluency. Numerous sentences are structurally unwieldy and hinder readability. For instance, the\nsentence in Lines 324-327 is characteristic of this issue, as it relies heavily on repetitive verbs\nsuch as “provide” and “offer”. A more concise and dynamic revision would significantly\nimprove the impact. Additionally, I think grammar across the main text should be carefully re-\nchecked by authors, since I found many grammar mistakes.\nAnswer: We appreciate the reviewer’s suggestion regarding language clarity and fluency. The\nmanuscript has been carefully revised throughout to improve sentence readability and stylistic\nconsistency. Abstract and Introduction sections have almost re-written. Specifically, we have\nrephrased the sentence highlighted by the reviewer in lines 384-386 “Here we present the first\ncomprehensive catalogue of globally dominant soil-borne bacterial plant pathogens and\nprovide new insights into the environmental and biological factors associated with pathogen\nnatural suppression.” In addition, the entire manuscript has been thoroughly proofread to\ncorrect grammatical and stylistic issues, ensuring clarity and consistency across sections.\nSome other minor comments:\n1. Lines 52-56: This sentence is somewhat unwieldy; simplifying it would greatly improve\nclarity and flow.\nAnswer: Thanks, this is now revised (lines 59-61).\n2. Lines 69-72: This sentence is vague. Please clarify.\nAnswer: Thanks, this is now revised (lines 74-76).\n3. Line 89: Replace “e.g.” with “e.g.,”.\nAnswer: Thanks, this is now revised (lines 205 and 370).\n4. Line 108: Is “soil microbial biodiversity”?\nAnswer: Thanks, this is now revised (line 138).\n5. Lines 132-133: Throughout the manuscript, italics should be used for all formally recognized\ntaxonomic genera and species, such as Streptomyces, Rhodococcus, and Agrobacterium.\nAnswer: Thanks, this is now revised throughout the manuscript.\n6. Line 195: What dose “the system-level” mean?\nAnswer: Thanks, this is now revised (lines 234-235).\n7. Line 230: Please clarify the source of the Streptomyces Scabiei and Ralstonia Solanacearum\nstrains. Were they isolated from soil?\nAnswer: Thanks, we have added this detail in lines 944-946 ‘The representative pathogen\nstrains S. scabiei and R. solanacearum were obtained from the New South Wales Department\nof Primary Industries (DPI, Australia), originally isolated from diseased potato and tomato\nplants, respectively.’.\n8. Line 226-232: The dilution-to-extinction approach could also introduce bacterial pathogens,\nand if so, to what extent?\nFurthermore, what is the design of the control for this experiment (e.g., without inoculating the pathogen at each dilution gradient)?\nAnswer: Thanks. We have now included an Extended figure (Extended Data Fig. S11) to show\nthe qPCR quantification of the background of S. scabiei and R. solanacearum in the dilution\ncontrols prior to pathogen inoculation. These results confirm negligible background levels\nacross treatments.\n9. Why is the natural soil treatment for Ralstonia solanacearum missing from Figure 3? I think\nthe authors should add it.\nAnswer: We appreciate the reviewer’s suggestion. The result for Ralstonia solanacearum in\nthe natural soil treatment has now been included (Fig 3c).\n10. Why only bacterial abundance? I suggest the authors should also investigate the relationship\nbetween fungal abundance and bacterial plant pathogens.\nAnswer: We appreciate the reviewer’s insightful suggestion. In the present study, only\nbacterial abundance was quantified because the global soil DNA extracts were primarily used\nfor shotgun metagenomic sequencing, 16S/18S amplicon sequencing, and 16S rRNA–based\nqPCR, and amplicon sequence (16S and 18S) and 16S rRNA-based qPCR, resulting in\ninsufficient residual DNA for reliable fungal quantification. We fully agree that exploring the\nrelationship between fungal abundance and bacterial pathogens would provide valuable\ncomplementary insights, and we plan to address this aspect in future work using newly\ndeveloped datasets.\n11. Line 235 and 240: If fungal abundance was not assessed, I suggest replacing “abundances”\nand with “bacterial abundances”.\nAnswer: Thanks, this is now revised (lines 258, 284, and 289).\n12. Line 251-252: What dose “total abundance” mean? Is relative abundance of non-pathogenic\nStreptomyces spp.? Please clarify.\nAnswer: Thanks, we now revised as relative abundance of non-pathogenic Streptomyces spp.in\nline 303.\n13. Line 252: I suggest replacing “Streptomyces spp.” with “Streptomyces spp.”.\nAnswer: Thanks, this is now revised (line 303).\n14. Lines 282-283: Change “the role of microbial diversity, community and biosynthesis gene\nclusters” to “the role of microbial diversity and biosynthesis gene clusters”.\nAnswer: Thanks, this is now revised (lines 332-334).\n15. Line 361: Replace “e.g.” with “e.g.,”.\nAnswer: Thanks, this is now revised (lines 205 and 370).\n16. Lines 324-327: The language lacks lexical variety, with excessive use of “provide”, “offer”\nand global, resulting in clunky and disjointed prose.\nAnswer: Thanks, we now revised this sentence in lines 384-386 reads as ‘Here we present the\nfirst comprehensive catalogue of globally dominant soil-borne bacterial plant pathogens and\nprovide new insights into the environmental and biological factors associated with pathogen\nnatural suppression.’.\n17. Line 839: For fungal communities, with 2,000 sequences per sample, were the rarefaction\ncurves saturated? I suggest that the corresponding figure be supplemented in the appendix to\nconfirm sequencing depth adequacy.\nAnswer: Thanks, we have now included rarefaction curves for both bacterial and fungal\ndatasets (Extended Data Fig. S12) to evaluate sequencing depth adequacy.\n18. Line 862: Is “100 dilution” the correct way to refer to this? Please clarify.\nAnswer: Thanks, we now revised as ‘100-fold diluted inoculum’ in line 937.\n19. Lines 872-876: Was the sampling period (3 to 21 days post-inoculation) sufficient for\nmicrobial diversity and biomass to stabilize following inoculation？\nAnswer: We thank the reviewer for this comment. This concern has been addressed in our\nresponse to Comment 1 above, where we explain that after diversity inoculation, soils were\nincubated for six months to stabilise community and recovery of biomass. Once that was\nachieved, pathogens were inoculated and incubated for 21-day experimental period to capture\nthe early establishment and suppression phase of the microbial community Please see the\nclarification in lines 270-272, 266-269, and 941-942.\n20. Line 906: The quotation marks around 'edgeR' should be in English – style.\nAnswer: Thanks, this is now revised (line 990).\n21. Line 89, 233 and 1061: Throughout the manuscript, is 16s qPCR or 16S qPCR? I suggest\nreplacing “16s qPCR” with “16S qPCR”.\nAnswer: Thanks, this is now revised (lines 258, 282, and 914).\n23. The Reference formatting style and In-text citation style should be followed according to\nGuide to Authors.\nAnswer: Thanks, we have now checked all the references and make sure they are formatted\naccording to the journal´s guidelines."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have made a reasonable effort to address my previous comments, and the manuscript has improved since the initial submission. However, a few issues still remain that should be addressed before publication. These are mainly related to interpretations rather than data quality or analytical approaches.\nIn several places, statistically significant but relatively small effect sizes are described using language that implies strong or ‘robust’ relationships. Given the scale of the analysis and the inherent noise in global metagenomic datasets, I think the authors should describe these associations as consistent or supported rather than strong. They can tone down the claims and conclusions, particularly in the abstract, results and conclusions.\nSecondly, the manuscript sometimes moves too quickly from correlations to implied mechanisms. For example, while the greenhouse experiment provides decent support for the diversity-pathogen relationship, its scope is limited to two pathogens (which they mentioned) and early invasion dynamics, and more importantly, in the absence of host plants. The experiment should therefore be discussed as a validation of patterns rather than as evidence of general or long-term suppression mechanisms.\nLastly, several microbial taxa and biosynthetic gene clusters are described as indicators of pathogen-suppressive soils. This kind of pitching overstates their current practical or diagnostic value, as the authors did not evaluate any thresholds or predictive performances. Referring to these features as candidate or putative indicators would be better.", "reviewer_2": "Excellent work on the revisions to this manuscript. I do, however, have a few remaining comments. One point that requires particular attention is the need for a clearer description of the methodology concerning meta-data.\nSpecific comments:\n1. Lines 134-135: Public databases (1,402 samples): How many articles are included, and what is the publication date range (e.g., 2015–2025)? Are all the samples soil samples? Please also include an Excel table listing the basic information of all the articles.\n2. Line 176: Change “exhibit” to “exhibited”.\n3. Line 239-244: First occurrence of an abbreviation: write out the full term, e.g., organic carbon (OC).\n4. Line 257: Are the “200 globally standardized field soil samples” part of the meta-analysis, or were they subjected to amplicon sequencing by the authors? Please clarify.\n5. Lines 895-898: The current amplicon dataset is already quite extensive, with 200 global soil samples subjected to amplicon sequencing targeting bacterial 16S rRNA and fungal 18S rRNA genes. However, does this truly represent global diversity? The authors should enrich the global amplicon dataset in their manuscript.\n6. How many amplicon sequencing datasets were generated from the 200 global soil samples? Please clarify.\n7. Lines 1082-1083: Please upload 1,402 samples of shotgun metagenomics from public databases, as “PRJNA1162941” only includes 200 global shotgun metagenomics samples. No data was found when accessing this link: “https://dataview.ncbi.nlm.nih.gov/object/PRJNA1012541?reviewer=6lp8udtdrbrk2kgs6u8v0f”.\n8. In Figure 4, the axis labels should read “Longitude” and “Latitude”.\n9. According to academic publishing standards, should ODI links be provided for all cited references?\n10. Line 762: The font size of the short title in the Legend is inconsistent with that in the preceding section. Please verify this."}, "author_response": "Response to the reviewers\nPlease note that the line number included below correspond to track_changed version of the\nmanuscript.\nReviewer #1 (Remarks to the Author):\nThe authors have made a reasonable effort to address my previous comments, and the\nmanuscript has improved since the initial submission. However, a few issues still remain that\nshould be addressed before publication. These are mainly related to interpretations rather than\ndata quality or analytical approaches.\nAnswer: We thank the reviewer for their careful re-evaluation of the manuscript and for the\npositive assessment that the manuscript has improved since the initial submission. We\nappreciate the reviewer’s continued constructive feedback and have addressed the remaining\nissues related to interpretation as detailed below.\nIn several places, statistically significant but relatively small effect sizes are described using\nlanguage that implies strong or ‘robust’ relationships. Given the scale of the analysis and the\ninherent noise in global metagenomic datasets, I think the authors should describe these\nassociations as consistent or supported rather than strong. They can tone down the claims and\nconclusions, particularly in the abstract, results and conclusions.\nAnswer: We thank the reviewer for this helpful suggestion. In accordance with the reviewer’s\ncomment and, given the global scale of the analysis and the inherent variability of metagenomic\ndatasets, the strength of the associations should be described more cautiously. We have revised\nthe language throughout the manuscript to better reflect the magnitude of the observed effects.\nSpecifically, we have tone down the claims with using more conservative wording (e.g.,\n“consistent”, “supported”, or “associated”) in the Abstract, Results, and Conclusions (lines 59,\n257, and 294 et al).\nSecondly, the manuscript sometimes moves too quickly from correlations to implied mechanisms.\nFor example, while the greenhouse experiment provides decent support for the diversity-pathogen\nrelationship, its scope is limited to two pathogens (which they mentioned) and early invasion\ndynamics, and more importantly, in the absence of host plants. The experiment should therefore\nbe discussed as a validation of patterns rather than as evidence of general or long-term\nsuppression mechanisms.\nAnswer: We have revised the text to more clearly frame the greenhouse dilution-to-extinction\nexperiment as a validation of the observed diversity–pathogen relationship, rather than as\nevidence for general or long-term suppression mechanisms. Accordingly, we have softened\nmechanistic language throughout this section and clarified that the experiment provides\nexperimental support for the consistency of the diversity–suppression relationship under\ncontrolled conditions, rather than demonstrating universal or long-term pathogen suppression\nmechanisms (lines 237–248).\nLastly, several microbial taxa and biosynthetic gene clusters are described as indicators of\npathogen-suppressive soils. This kind of pitching overstates their current practical or diagnostic\nvalue, as the authors did not evaluate any thresholds or predictive performances. Referring to\nthese features as candidate or putative indicators would be better.\nAnswer: We thank the reviewer for this helpful suggestion. We have revised the text throughout\nthis section to consistently refer to these taxa and biosynthetic gene clusters as candidate or\nputative indicators of pathogen-suppressive soils, rather than definitive indicators (lines 299-\n304).\nReviewer #2 (Remarks to the Author):\nExcellent work on the revisions to this manuscript. I do, however, have a few remaining\ncomments. One point that requires particular attention is the need for a clearer description of\nthe methodology concerning meta-data.\nAnswer: We thank the reviewer for the positive assessment of the revisions. We have revised\nthe Methods section to explicitly clarify the sources, curation, and use of meta-data in the\nanalyses (lines 724-726, lines 839-846).\nSpecific comments:\n1. Lines 134-135: Public databases (1,402 samples): How many articles are included, and what\nis the publication date range (e.g., 2015–2025)? Are all the samples soil samples? Please also\ninclude an Excel table listing the basic information of all the articles.\nAnswer: We thank the reviewer for this helpful suggestion and for seeking further clarification\nregarding the publicly sourced datasets. The 1,402 publicly available samples were compiled\nfrom independent NCBI BioProjects, rather than from a fixed number of peer-reviewed articles.\nWe note that many BioProjects do not have an associated publication at the time of data release,\nor may be linked to multiple publications, making a one-to-one correspondence between\nsamples and articles infeasible.\nWe have clarified this point in the manuscript and now report the number of contributing\nBioProjects and their data release years, and confirm that all publicly sourced samples included\nin this study are soil samples. The revised text now reads: “In total, the combined database\ncomprises soil metagenomes derived from 317 independent NCBI BioProjects, spanning data\nrelease years from 2011 to 2024 (Supplementary Dataset).” (lines 724-726). In addition, we\nhave provided an Excel table listing key metadata for all public datasets, including sample\nidentifiers, BioProject accession numbers, release years, and relevant environmental\ndescriptors, to enhance transparency and reproducibility.\n2. Line 176: Change “exhibit” to “exhibited”.\nAnswer: Thanks. We now revised.\n3. Line 239-244: First occurrence of an abbreviation: write out the full term, e.g., organic\ncarbon (OC).\nAnswer: Thanks. We now revised (line 116).\n4. Line 257: Are the “200 globally standardized field soil samples” part of the meta-analysis,\nor were they subjected to amplicon sequencing by the authors? Please clarify.\nAnswer: We thank the reviewer for raising this point. The 200 globally standardized field soil\nsamples were independently collected and subjected to amplicon sequencing and shotgun\nmetagenomic sequencing by the authors. We have clarified this distinction in the manuscript to\navoid ambiguity (lines 839-840).\n5. Lines 895-898: The current amplicon dataset is already quite extensive, with 200 global soil\nsamples subjected to amplicon sequencing targeting bacterial 16S rRNA and fungal 18S rRNA\ngenes. However, does this truly represent global diversity? The authors should enrich the global\namplicon dataset in their manuscript.\nAnswer: We thank the reviewer for this point. We agree that, despite its relatively large size,\nthe amplicon dataset comprising 200 globally distributed soil samples does not fully represent\nglobal microbial diversity. The primary purpose of this dataset was not to exhaustively capture\nglobal diversity, but to provide an independent, standardized field-based dataset spanning\nmultiple regions, used to validate the diversity–pathogen relationships identified in the global\nmeta-genomes under consistent sampling and sequencing conditions.\n6. How many amplicon sequencing datasets were generated from the 200 global soil samples?\nPlease clarify.\nAnswer: We thank the reviewer for this point. All 200 samples, we have now clarified this in\nline 842-846.\n7. Lines 1082-1083: Please upload 1,402 samples of shotgun metagenomics from public\ndatabases, as “PRJNA1162941” only includes 200 global shotgun metagenomics samples.\nNo data was found when accessing this link:\n“https://dataview.ncbi.nlm.nih.gov/object/PRJNA1012541?reviewer=6lp8udtdrbrk2kgs6u8v0\nf&#x201D;.\nAnswer: We thank the reviewer for raising this point and apologize for any confusion caused\nby the data availability description. The BioProject PRJNA1162941/ PRJNA1249113/\nPRJNA1249114 contains only including the 200 globally standardized soil samples subjected\nto shotgun metagenomic sequencing and 16S/ITS amplicon sequencing. The 1,402 shotgun\nmetagenomic samples from public databases were not generated by this study and therefore\nwere not re-uploaded to a new BioProject. Instead, these publicly available datasets remain\narchived under their original SRA accessions and BioProject identifiers. We have provided a\ncomprehensive Supplementary Dataset listing all public shotgun metagenomic samples used\nin the meta-analysis, including their SRA run accessions and corresponding BioProject IDs.\n8. In Figure 4, the axis labels should read “Longitude” and “Latitude”.\nAnswer: We thank the reviewer for pointing this out. The axis labels in Figure 4 have been\ncorrected to “Longitude” and “Latitude”.\n9. According to academic publishing standards, should ODI links be provided for all cited\nreferences?\nAnswer: Thanks. We have checked the references to ensure consistency with the journal’s\nformatting guidelines.\n10. Line 762: The font size of the short title in the Legend is inconsistent with that in the\npreceding section. Please verify this.\nAnswer: Thanks, we now revised."}]

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