Predator-mediated local convergence fosters global microbial community divergence

Domain: biology
Published: 2026-03-18
Editorial outcome: minor_revision

Abstract
Understanding how microbial communities assemble is central to predicting ecosystem function. Although predators strongly influence bacterial communities through predation, the role of microbial predators in modulating global microbial divergence and convergence patterns remains largely neglected. Here, we integrated global-scale amplicon sequencing data, controlled field experiments, and reconstructions of natural and synthetic communities to examine predator-mediated community assembly mechanisms. We show that bacterivorous protists exert dual, scale-dependent effects on microbial communities: promoting local convergence by suppressing dominant bacterial taxa, while generating global divergence through species-specific predation effects. We find that predator identity and prey susceptibility jointly determine convergence outcomes. Communities dominated by predator-resistant taxa exhibit reduced convergence under predation pressure, revealing a predictable trait-based filtering mechanism. This work establishes bacterivorous protists as key, context-dependent agents of biogeography and suggests new opportunities for microbiome engineering, where targeted use of protists may steer microbial communities toward functional configurations that enhance soil health and ecosystem resilience.

Introduction
Understanding how microbial communities assemble is a central question in ecology, with broad implications for ecosystem management and microbiome engineering1. The interplay between biotic and abiotic variation at both local and global scales influences microbial divergence (increased dissimilarity among communities) and convergence (increased similarity among communities) patterns2–4, making it challenging to predict the trajectories of microbial communities. Recent studies revealed that abiotic factors such as pH, moisture, temperature, salinity, nutrient content, and nutrient complexity drive microbial divergence patterns5–8. While abiotic factors are important, myriad interactions among species drive microbial community composition against a background of environmental variability2,9,10. However, the role of biotic factors, particularly prey-predator interactions, in modulating microbial divergence and convergence patterns remains largely neglected.

Predatory protists (hereafter, predators) represent the primary and dominant consumers of bacteria and are one of the most crucial factors controlling bacterial community assembly through selective predation9,11. Predators target bacterial taxa based on bacterial traits or dominance, while several bacterial taxa can take advantage of predation through accelerated nutrient turnover9,12. Different predators drive bacterial communities in distinct directions through species-specific effects13–15. Nevertheless, the consequences of predation for microbial community assembly have been largely studied in isolated and in vitro systems11,12, and their influence on broader patterns such as microbial community divergence and convergence remains poorly understood.

Predators can either promote divergence or convergence in ecological communities depending on how they interact with prey species, the spatial and temporal context, and the nature of selective pressures they impose. Two contrasting hypotheses have been proposed to explain predators’ ecological impact (Fig. 1a). The divergence hypothesis posits that predators diverge communities by reducing the abundance and richness of the same bacterial taxa across ecosystems through selective-feeding16,17. In contrast, the convergence hypothesis suggests that predators converge communities by preventing dominance by a few species and allowing coexistence of a broader range of species via density-dependence feeding18–20. These hypotheses provide a framework for testing in both natural and experimental systems. Yet, despite their theoretical appeal, empirical evidence to establish which of these hypotheses explains how predators control community convergence and divergence across multiple scales and contexts is scarce.

Here, we investigate the role of predators in mediating both divergence and convergence of soil bacterial communities. First, we perform global-scale analyses of amplicon sequencing data across six continents and conduct controlled field trials on a local island where community divergence was environmentally manipulated to reveal how predator activity mediates bacterial assembly across environmental contexts. We then experimentally validate these relationships through microcosm studies with natural communities with and without defined predator introductions, followed by synthetic community (SynCom) reconstructions with selected bacterial taxa. Together, our integrative approach provides multi-scale insights for the context-dependent role of predators based on predator identity and prey susceptibility in mediating bacterial community divergence and convergence.

Results
We integrated and re-analyzed existing data from two studies, matching protist21 and bacterial6 sequenced data in 138 locations across six continents (Fig. 1b), to understand how predators affect community divergence and convergence. The soils included in this study represent a wide range of vegetation types (shrublands, grassland, and forest), edaphic characteristics such as pH, water and nutrient contents, and bioclimatic regions (arid, temperate, tropical, and continental). Predators were extracted from a publicly available amplicon sequence variant (ASV) table, which was generated by amplifying the V4-V5 region of the 18S rRNA gene using the 616*F/1132R primer set21 (“Methods,” Supplementary Fig. 1a, b). Both bacterial and predator communities and divergences showed similar distribution patterns across ecosystems and continents (Supplementary Fig. 1c–h). As predictors of predators, we used alpha (Shannon index, Evenness, and Richness) and beta (community dissimilarity) diversities. The relative abundance (RA) of predators used in all steps of our study represented their abundance distribution among all protists, which was comparable with the previous study21. Since predatory effects are species-specific9,13, RA of ubiquitous (commonly found ASVs presented in more than 60% of the samples) and endemic (ASVs presented in less than 20% of the samples) predators were also included. Abundance and divergence of predators were affected by environmental factors, especially pH, aridity, temperature, and soil carbon21, where their effect on endemic and ubiquitous predators followed opposite trends (Supplementary Fig. 2). Although we found a positive linear correlation between predator RA and bacterial divergence (Supplementary Fig. 3a), RA of each one of the top five predator ASVs that were commonly observed in most of the samples showed negative patterns (Supplementary Fig. 3b–f), indicating the possibility of their effect on community convergence. The alpha diversity indexes of predators had no effect (Supplementary Fig. 3g–i). Random forest model that identifies the key predictors of bacterial community divergence showed that RA of both ubiquitous and endemic predators and the divergence of predators were among the top factors along with pH, aridity, and temperature (Fig. 1c), showing global importance of predators for bacterial community assembly. While predator divergence and RA of endemic predators showed a positive linear relationship with bacterial divergence, RA of ubiquitous predators followed an opposite trend (Fig. 1d–f). These results may suggest a dual role of predators: while the global divergence of bacterial communities may arise from differences in the composition of predator communities across ecosystems through likely their species-specific effects, ubiquitous predators may mediate bacterial community convergence by exerting consistent effects across the globe17,19,20.

Although correlation-based, the initial results support the convergence hypothesis—that is, convergence may occur when predators suppress dominant taxa—rather than the divergence hypothesis. Therefore, we further examined the relationship between the richness of dominant bacterial taxa and the RA of ubiquitous predators. As ubiquitous predators are likely to target ubiquitous (widespread) bacterial taxa, we also assessed the correlation between them. We focused on richness over RA, as richness more directly captures the divergence and convergence patterns in bacterial communities7. We found that ubiquitous predators were positively associated with the richness of the dominant bacterial taxa (Fig. 1g, h), suggesting that predation may alleviate dominance by a few taxa and potentially promote dominance by a broad range of species.

Taken together, we do not interpret predator-mediated convergence as a universal effect. Rather, predators are likely to affect microbial communities in a species-specific manner, which may explain how convergence and divergence can coexist (Fig. 1e, f). This mechanism aligns with the Janzen–Connell hypothesis22, which posits that natural enemies reduce the dominance of abundant species, thereby promoting diversity through negative density dependence. In microbial communities, such top-down control by predators may function as a stabilizing mechanism of coexistence23, where predation suppresses competitive dominants and allows subordinate taxa to persist. This dynamic is conceptually analogous to clonal interference in microbial evolution24, in which multiple high-fitness genotypes compete for dominance, preventing any single lineage from sweeping to fixation. Similarly, predator-mediated suppression of dominant bacterial taxa may allow multiple strong competitors to coexist, thereby increasing community richness among dominant taxa and compositional convergence.

To gain deeper insight into predator-mediated convergence in bacterial communities suggested by the global observations, we conducted a local field trial under controlled ecological conditions on Ishigaki, a small tropical island (222 km2) in Japan (Fig. 2a). The relatively low initial divergence of microbial communities in this setting allow the effects of similar predators on community convergence to be more clearly observed. We artificially introduced community divergence in three fields with eight different fertilizer treatments and a pH adjustment treatment by CaCO3 addition (Supplementary Tables 1 and 2), which are known to strongly drive bacterial diversification by increasing the number of available substrates (Fig. 1c)4,6,7,25. All treatments received chemical fertilizer, while organic amendments such as filter cake, cattle manure, bagasse, and wood biochar were applied at different rates (5 or 10 tons C ha−1) to manipulate soil resource availability (Supplementary Tables 1 and 2). Soil samples were taken from each field at 0–10 cm depth, and each treatment was represented with four replicates. We hypothesized that in fields with relatively less divergent and highly abundant predators, ubiquitous predators would counteract the pH- and nutrient-driven divergence by converging bacterial communities, while endemic predators would not have a divergence effect at the local scale. Therefore, we analyzed microbial community divergence within each field.

Aligned with our expectations, bacterial divergence was generally increased with fertilizer addition (Supplementary Fig. 4a), and we observed differences in abundance and diversity of predators among treatments and fields (Supplementary Fig. 4b, c). Protist data were obtained by amplifying the V9 region of the 18S rRNA gene using the 1389F/1510R primer set26. Nonmetric multidimensional scaling (NMDS) analyses showed that predator community composition was relatively more similar to each other than those of bacteria among fields (Supplementary Fig 4d, e). RA of ubiquitous predators and divergence of predators were oppositely affected by environmental factors, especially pH, C, N, and C/N ratio, while endemic predators were not affected (Supplementary Fig. 5). To identify the key predictors of bacterial community divergence, we performed random forest analysis, which showed that RA of ubiquitous predators was among the top factors, along with C and pH (Fig. 2b). The divergence of predators was also detected as an important factor predicting bacterial community divergence (Fig. 2b). While the correlation between bacterial and predator divergences and RA of ubiquitous predators showed an opposite trend, RA of endemic predators showed no significant correlation (Fig. 2c–e), as expected. Consistent with the convergence hypothesis, we found that ubiquitous predators tend to enhance the richness of both ubiquitous and dominant bacterial taxa (Fig. 2f, g).

Taken together, these correlation-based results make the convergence hypothesis more plausible, suggesting that under local conditions where predator taxa are similar and abundant, they may contribute to bacterial community convergence or reduce nutrient- and pH-driven divergence, although nutrient and pH effects on predators cannot be excluded.

Since both global and local studies consistently showed that predators are likely to promote bacterial convergence primarily by enhancing the richness of dominant and ubiquitous bacteria, we used structural equation modeling (SEM) to mechanistically evaluate these effects. By integrating the global and local datasets, we constructed a simple model with a reduced number of predictors based on our random forest results (Figs. 1c and 2b). SEM analysis demonstrated that bacterial community convergence was largely governed by the dominant bacterial richness that was mediated by the RA of ubiquitous predators and the community similarity of predators (Fig. 3a). Although these results provided that prey-predator interaction potentially led to the convergence hypothesis, they did not offer definitive causal evidence due to the background of environmental variability. Indeed, the effects of environmental factors such as pH and nutrient levels on predator diversity and abundances (Supplementary Figs. 2 and 5) cannot be excluded in both global and local field studies. To establish causality and isolate predator effects from environmental drivers, we subsequently conducted a complementary in vitro microcosm experiment under defined conditions with and without predator introductions to test our hypothesis (Fig. 3b). The introduced predators, Acanthamoeba castellanii (Ac), a soil glissomonad (Heteromita globosa [Hg], informal designation pending formal taxonomic description), and Vermamoeba vermiformis (Vv), are widely distributed soil protists commonly found across diverse terrestrial habitats, and thus considered ubiquitous organisms. We obtained indigenous predator-free bacterial communities from five distinct soil types: forest (Com1), grassland (Com2), paddy field (Com3), soybean field (Com4), and sugarcane field (Com5). The experimental design consisted of the four protist treatments (Control with no protist addition, Ac, Hg, and Vv) for each of the five bacterial communities, making a total of 20 microcosms with six replicates each. The microorganisms were incubated in calcined clay, an inert soil substitute27,28, under defined nutrient conditions for 5 weeks. This design enabled us to directly assess the exclusive role of individual predator species in driving bacterial convergence, independent of environmental heterogeneity21. We descriptively sampled three microcosms on the 3rd and 5th weeks.

Bacterial communities consisted of a total of 2121 ASVs with an average richness of 88.9 ± 25.1, indicating successful colonization of the native communities. Results after 3 and 5 weeks showed highly similar communities in each treatment. This might be due to most communities stabilizing after 60 generations, reaching stable population equilibria4,7. The evenness of communities increased in the presence of predators (Fig. 3c), and predator treatments, especially with Ac and Vv, showed more uniform distribution of bacterial taxa at the genus level (Fig. 3c).

We found that the five communities were grouped closer to each other in predator treatments, while the control group with no predator addition showed clear separation from each other (Fig. 3d). This pattern was more strongly observed in NMDS analysis based on predator-enriched and predator-depleted ASVs, while predator-neutral ASVs grouped based on source communities (Fig. 3e–g). Consistent with NMDS visualization, the divergence of bacterial communities was higher in the control group than that of predator treatments, including predator-enriched and predator-depleted ASVs, while no difference in bacterial community divergence was observed for predator-neutral ASVs (Supplementary Fig. 6a–d). These results evidently validate the predator-mediated convergence: predator-affected ASVs caused the similarities of bacterial communities.

Although NMDS revealed broadly similar distributions among the three predator treatments, Mantel tests indicated that each predator generated distinct bacterial communities (P < 0.01). Indeed, NMDS analyses of each community (Supplementary Fig. 7) showed that Hg, a phylogenetically distinct predator from the supergroup Rhizaria, drove bacterial communities in a different compositional direction compared to the amoeba treatments from the supergroup Amoebozoa (Ac and Vv). This may support that the local convergence effect of predators can ultimately lead to global divergence, consistent with our expectations and previous findings12,13. Similar with field observations, predators increased the richness of dominant bacterial taxa (Fig. 3h), while they did not necessarily increase the richness of rare bacterial taxa (Fig. 3i). To double-check the effect of predators on dominant taxa, we further showed that the RA of dominant ASVs in the control group decreased in the predator treatments (Fig. 3j), while RA of rare bacterial ASVs in the control group increased in the presence of predators, leading to an increase in abundant bacterial richness attributed to predators (Fig. 3k). Taken together, in line with global and local field observations, we showed evidence for the convergence hypothesis: predators converge bacterial communities through consistently reducing dominant bacterial taxa and increase their richness.

Revisiting the divergence hypothesis (Fig. 1a), we found over 99% of the ASVs that were affected by predators were unique to each community (Supplementary Fig. 8), which was consistent with previous findings13. This suggests that selective predation is not uniformly directed at the same bacterial taxa. Instead, predation effects appear to be highly context-dependent, shaped by initial community composition, which disables taxonomic-based predictions13,29. Although our data do not support widespread targeting of the same bacterial taxa across ecosystems, we do not reject the possibility that certain protists may feed on similar prey under specific conditions19. Despite this nuance, the convergence hypothesis provides a more compelling explanation for the observed community divergence and convergence patterns in large-scale experiments across varied environmental conditions.

Several bacterial taxa have evolved to successfully escape from predation30, which may hinder the predator-mediated convergence. Therefore, we hypothesized that communities dominated by predator-resistant bacteria should have less convergence under predation. We constructed seven SynComs, consisting of six bacterial species (three predator-resistant and three predator-vulnerable species based on the results of the microcosm experiment). The first SynCom received equal abundances of all six bacterial species (1.8 × 106 cells g calcined clay−1), while the other six each had one dominant species (1.0 × 107 cells g calcined clay−1) and five rare ones (1.0 × 105 cells g calcined clay−1). All SynComs had the same total amount of bacteria (1.1 × 107 cells g calcined clay−1) (Fig. 4a). The experimental design consisted of the four protist treatments (Ctrl, Ac, Hg, and Vv) for each of the seven synthetic bacterial communities, making a total of 28 microcosms with 3 replicates each. The microorganisms were incubated in calcined clay, an inert soil substitute27,28, under defined nutrient conditions. We descriptively sampled the microcosms in the 3rd week. While communities without predators showed distinct patterns in each SynCom group, predator-resistant bacterial taxa dominated in the presence of predators (Fig. 4a). NMDS grouped predator treatments together, while the control groups showed higher dissimilarities (Fig. 4b). Indeed, community divergence in the control group was higher than that of predators (Fig. 4c). Crucially, the community divergence was lower when initially dominated by predator-resistant bacteria (Fig. 4d), supporting our hypothesis. We note that some predator-resistant bacteria in our study are capable of sporulation, which may have contributed to their persistence under predation, potentially modulating the degree of convergence observed. Our findings suggest that the extent of predator-mediated convergence is shaped by the traits of dominant bacterial taxa and that resistance to predation plays a key role in determining the trajectory of microbial community assembly under predator pressure.

Context-dependent predator-mediated convergence is mediated by both predator identity and prey characteristics, suggesting an evolutionary layer to microbial community responses. The effects of predators on bacterial diversity are crucial for functional redundancy and ecosystem resilience9,11. Although our results indicated that predator-mediated convergence occurs mainly due to their predation on dominant taxa, the role of resource redistribution or increased nutrient turnover following predation likely contributes to the convergence. Our previous study showed that while predator-preferred bacterial taxa overlap to some extent among distinct predator species, predators assert a stronger species-specific effect on predator-enhanced bacterial taxa13. This suggests that convergence also arises indirectly through trophic cascades or facilitative interactions, wherein predator activity releases nutrients or alters the competitive environment in ways that favor opportunistic bacterial taxa12. These context-dependent interactions could result in selective growth of functionally similar bacteria across communities, further reinforcing convergence at the compositional or functional level. For instance, increased predation has been shown to drive functional convergence among bacterial taxa exhibiting traits such as antibiotic resistance31–33, exopolysaccharide production9,34, and pathogen suppressiveness35. Here, we predicted taxonomic convergence, and future work incorporating functional metagenomics or isotope tracing may help disentangle these intertwined effects and clarify the extent to which predators drive functional convergence.

Despite the strength of our results, the interplay between abiotic and biotic drivers also remains complex. The predator diversity and abundance are controlled by climatic variables (aridity and temperature), and to some extent, by soil characteristics (pH and total C), making the generalizability of predatory effects across ecosystems remain uncertain, with likely varying magnitudes across ecosystem types21. Moreover, extreme abiotic divergence, where environmental filtering outweighs biotic interactions, may diminish the influence of predators. For instance, as most predators are aquatic organisms restricted to the water-filled pores in the soil ecosystem36, their effects on bacterial assembly are likely to be reduced under dry conditions21, which was evidenced by their low RAs in dry ecosystems (Supplementary Fig. 1a). In addition, environmental factors, especially nutrients, shape the predation patterns on bacteria13,29,37. As our in vitro experiments were conducted under defined nutrient conditions, the degree of predator-mediated convergence and the outcome of bacterial assembly may vary under different circumstances13. Despite this nuance, our model (Fig. 5a), which quantifies the net convergence effect of predatory protists, shows a highly predictive linear relationship with bacterial convergence across our global and local datasets (R² = 0.90), effectively capturing the dual role of predators. Notably, this relationship persists despite extreme abiotic gradients in climate, nutrient availability, and pH6,21. The robustness of this correlation underscores the significant role of predators in driving bacterial community assembly, likely operating in synergy with abiotic environmental filters at multiple scales.

Microbial predators have long been recognized as key biotic agents in food webs, yet their role in shaping microbial community assembly across scales has remained largely overlooked. Here, we integrate global-scale amplicon sequencing data, field experiments, controlled microcosms, and synthetic communities to show that predatory protists simultaneously mediate local convergence and global divergence of bacterial communities. This dual effect arises from a consistent mechanism: predators suppress dominant bacterial taxa, promoting compositional similarity in local settings, while species-specific predation effects lead to divergent outcomes across ecosystems, which were affected by bacterial traits (Fig. 5b). A potential limitation of our study is that the 18S rRNA gene, particularly V9 region used in local field study, may not allow reliable discrimination at the species level. Consequently, each ASV may represent a group of related protist species that could differ in ecology, prey preferences, or functional traits. While this limits the resolution of our predator–prey assignments, our multi-scale experimental approach, including microcosms and synthetic communities, helps mitigate this limitation by directly testing predatory effects.

Our findings reconcile previously conflicting models of microbial community assembly16–20 and provide empirical support for a refined convergence hypothesis: predators act as context-dependent biotic filters that mediate microbial diversity through trait-based interactions. This scale-dependent framework highlights predation as a fundamental mediator of microbial biogeography and diversity patterns. The predictable nature of predator-mediated convergence under defined conditions opens new avenues for microbiome engineering, where targeted use of predator species may help steer microbial communities to enhance functions such as nutrient cycling, disease suppression, and carbon storage31,38,39.

Methods
We integrated and re-analyzed existing data from two studies that analyzed global bacterial6 and protist21 sequences. We first matched soils samples from protist and bacterial data by using exact longitude and latitude information, allowing us to match 138 samples across six continents (Supplementary Data). The bacterial amplicon sequences were downloaded6 and re-analyzed using QIIME240. DADA241 in QIIME2 was used for error correction, removal of forward and reverse primers, quality filtering, doubleton removal, and chimera removal of the amplicon sequences, corresponding to a quality score > 30. We denoised the paired-end sequences into ASVs. QIIME2’s q2-feature-classifier plugin was used for taxonomy assignment against the latest SILVA reference database42. We obtained 27,148 ASVs, which is comparable with the original study6. For protists, we downloaded the available ASV table with taxonomic assignment21. Then, the functional group of bacterivorous protists (predators) was manually assigned based on their taxonomic annotation, following previously established classifications as previously shown21,43,44. The RA of predators used in all steps of our study represented their abundance distribution among all protists, which was comparable with the previous study21. Environmental variables were obtained from the bacterial metadata available online6. The remaining bioinformatics and all statistical analyses were conducted in R version 4.5.0 (2025.4.11; https://www.r-project.org/). To visualize the geographic distribution of global sampling sites, we generated a world map using the ggplot2 package. A base map was created using the map_data function. Sampling locations were overlaid based on their corresponding longitude and latitude coordinates from the metadata.

Community divergence was calculated as previously shown7. Briefly, beta diversities of bacteria and predators were calculated using Bray–Curtis dissimilarity matrices and quantified with the betadisper function from the Phyloseq and Vegan packages in R. We then calculated mean divergence by averaging Bray–Curtis distances between samples. Bray–Curtis dissimilarity is a widely used ecological metric that captures compositional differences based on RA. While alternative approaches such as Aitchison distance, Jaccard index, or UniFrac are also commonly used, Bray–Curtis was selected for its ecological interpretability in community ecology. Specifically, Bray–Curtis emphasizes differences in abundant taxa—which are typically the most ecologically influential in shaping ecosystem function—while downweighting rare taxa that may introduce noise in divergence estimates. This focus aligns with studying prey-predator interactions. In contrast, Aitchison distance is designed for compositional data and compares taxa based on log-ratios of their RAs. While this method is appropriate for analyzing proportional relationships, it may overemphasize low-abundance taxa due to the nature of the log-ratio transformation, especially when rare taxa exhibit high variability, as in our global dataset. Thus, Bray–Curtis provides a robust and ecologically meaningful measure of community structure change in response to predation pressure.

Alpha diversity was computed in QIIME2 using diversity metrics of the richness (observed ASVs), evenness and Shannon index. To visualize beta diversity of bacterial and protist communities, we performed NMDS based on Bray–Curtis dissimilarity using RA data. We employed a random forest permutation using the rfPermute package in R with 1000 trees and 1000 permutations to identify environmental and biotic predictors of bacterial community divergence or environmental predictors of protist divergence and RA. Importance scores and associated permutation-based P values were extracted and exported for interpretation. To visualize the linear correlation relationships, we generated a scatter plot using ggplot2, displaying a linear regression fit with 95% confidence interval between factors. Pearson’s correlation coefficient (P value) and the coefficient of determination (R²) were annotated on the plot. To identify ubiquitous ASVs, we calculated their prevalence across all samples. ASVs present in more than 60% of samples were classified as ubiquitous, while ASVs present in fewer than 20% of samples were classified as endemic, representing habitat-specific or spatially restricted taxa. Since only two bacterial ASVs were present in more than 60% of samples, we lowered the ubiquity threshold to 40% for bacteria. ASVs with a mean RA > 0.1% across all samples were classified as dominant bacterial taxa, while those with a mean RA < 0.01% were considered rare.

We conducted a local field trial in sugarcane fields on a small tropical island, Ishigaki, Japan, due to its relatively stable agricultural management, distinct climatic conditions, and ecological isolation. These features minimized confounding effects from long-range dispersal and heterogeneous land use, allowing us to more clearly assess predator-mediated microbial community dynamics under partly defined environmental conditions. Our experiment site consisted of three fields and nine treatments in each field (Supplementary Tables 1 and 2, and Supplementary Data). All treatments received chemical fertilizer addition. While Treatments 1, 7–9 received conventional chemical fertilizers (Supplementary Table 1), and the other treatments (T2–6) received reduced (70%) chemical fertilizers. Type of fertilizers included filter cake (a nutrient-rich organic byproduct of the sugarcane industry, specifically from the clarification and filtration of sugarcane juice during sugar processing), cattle manure, bagasse (a fibrous residue left after sugarcane stalks are crushed to extract their juice during sugar production), and wood biochar (Supplementary Tables 1 and 2). Filter cake, cattle manure, bagasse, and biochar were applied at a rate of 10 tons of carbon per hectare (T4–7 and 9). To create a soil carbon gradient, filter cake and cattle manure were also applied at 5 tons of carbon per hectare (T2–3). For pH adjustment, CaCO3 was applied (10.7 Mg/ha). Fertilizer types and amounts were selected based on their potential to influence bacterial diversification by increasing soil carbon, pH, and nutrient complexity (Supplementary Tables 1 and 2). Biochar was purchased from Shirotori Mokuzaikakou Cooperative Society (Gifu, Japan). The raw material consisted of 60% softwood (Cryptomeria japonica and hinoki Chamaecyparis obtusa) and 40% hardwood (Quercus spp. and Zelkova serrata). The pyrolysis was conducted at 600°C, indicating that the material was fully carbonized. After the amendment materials were applied to the soil surface, they were incorporated into the top 0–20 cm of soil using a rotary tiller. After that, chemical fertilizer was applied.

Soil samples were taken from each field at 0–10 cm depth in March 2023. Each treatment had two plots, and we sampled soils from five locations in each plot and then homogenized them after sieving through a 2 mm mesh. Microbial communities within each plot were represented with two replicates, making four replicates per treatment (Supplementary Data). Soil pH (H2O, KCl) and electric conductivity (EC) were measured using a soil to solution ratio of 1:5 after shaking for 2 h with a pH electrode (LAQUA F-72, Horiba, Ltd., Kyoto, Japan) and with a conductivity meter (ES-51, Horiba, Ltd., Kyoto, Japan), respectively. Total carbon and total nitrogen contents were determined using the dry combustion method with an elemental analyzer (Sumigraph NC-220F; Sumika Chemical Analysis Service, Osaka, Japan). Exchangeable bases (Ca2+, Mg2+, K+, Na+), exchangeable Al3+, and cation exchangeable capacity (CEC) were measured with inductively coupled plasma atomic emission spectroscopy with a spectrometer (ICPE-9000, Shimadzu Corporation, Kyoto, Japan). Base saturation was calculated by dividing exchangeable cations (Ca2+ + Mg2+ + K+ + Na+) by CEC. Available phosphorus was determined using the Bray-I method with a spectrophotometer (UV-1800, Shimadzu Corporation). Soils in three sugarcane fields were Argic Red-Yellow soils according to e-SoilMap II (https://soil-inventory.rad.naro.go.jp/eSoilMap.html), which generally correspond to Acrisols (IUSS Working Group WRB 2022).

DNA was extracted from the soil (0.5 g) using ISOIL for Bead Beating (Nippon Gene, Tokyo, Japan) according to the manufacturer’s instructions and then eluted in TE buffer (50 μL). The V4 region of the 16S rRNA gene was amplified from the extracted DNA using the universal bacterial primers (515F and 806R)45, and the V9 region of the 18S rRNA gene was amplified from the extracted DNA using the universal protist primers (1389F-1510R)26. Both primers tailed with Illumina barcoded adapters (San Diego, CA, USA). Negative control was used in all analyses from the DNA extraction through PCR to make sure contamination did not occur. Illumina MiSeq sequencing and primary analyses of raw FASTQ data were performed as described previously13,43. Bioinformatic analyses for bacteria and protists were conducted as described above. For protists, QIIME2’s q2-feature-classifier plugin was used for taxonomy assignment against the latest of the Protist Ribosomal Reference (PR2) database46. To obtain exclusive protist data, non-protist sequences (Fungi, Metazoa, unidentified Opisthokonta, Streptophyta, Rhodophyta, and unclassified eukaryotes) were removed from all samples using Qiime2 (taxa filter-table/seq). Then, predators were extracted as described above. The rest of the bioinformatics and statistics were conducted in the same way as described above, with a modification. Due to our aim, we calculated the community divergence within each field.

To evaluate the effects of protist traits on microbial community convergence, we employed piecewise SEM using the piecewiseSEM package (v2.1.2) in R (v4.3.2). This approach allows for the estimation of complex causal relationships by combining multiple linear models into a single path framework, accommodating both direct and mediated effects between variables. The SEM included three component models that were selected based on important predictors of the random forest model in both global and local analysis. Predator divergence, the RA of ubiquitous and endemic predators were modeled as predictors of dominant and ubiquitous bacterial richness. Finally, both bacterial richness metrics were included as predictors of the ultimate outcome, community convergence. Model evaluation was based on individual model fit and the overall Fisher’s C statistic for directed separation. Standardized path coefficients were extracted and interpreted to assess the strength and direction of hypothesized causal relationships. This framework allowed us to test whether protist-mediated effects on microbial community convergence are channeled through shifts in bacterial community traits, especially those affecting dominant and ubiquitous taxa.

To obtain diverse bacterial communities, we collected five soil samples from diverse fields (Supplementary Data). Forest soil (Com1) was obtained from a natural forest at Nishikan ward, Niigata prefecture (N37.785488, E138.846804); a soil from grassland (Com 2) was obtained from near shoreline at Nishikan ward, Niigata prefecture (N37.787003, E138.817610); a paddy field soil (Com3) and a soybean field (Com5) was sampled from Shindori Station in the Field Center for Sustainable Agriculture and Forestry, Niigata University, Niigata (N37.85.69, E138.96.22); a sugarcane field (Com4) was sampled from Sugarcane fields in Ishigaki as described above. Each soil collected from a 0–20 cm depth at five locations per field, sieved to 2 mm, and homogenized.

The protist-free indigenous bacterial community was obtained with a filtration method (1.2 µm pore size mixed cellulose ester membrane filters [Advantec, Tokyo, Japan]) from the collected soils as described previously13. The 50 μL of protist-free bacterial inoculum was cultured in 100 μL of the amoeba saline solution47 in 96-well microtiter plates for 3 weeks at 20°C. The absence of protists was confirmed weekly with an inverted microscope at ×100, ×200, and ×400 magnifications (Nikon Eclipse TE2000-S, Tokyo, Japan). The preparation and growth of axenic cultures of predatory protists, Acanthamoeba castellanii (Ac; Amoebozoa, Tubulinea; ~20–30 µm), Vermamoeba vermiformis (Vv; Amoebozoa, Discosea; 15–35 µm), and a soil glissomonad, Heteromita globosa LAP3-201748 (Hg; Rhizaria, Cercozoa; ~10 µm) was described elsewhere13. The NCBI GenBank accession number of H. globosa LAP3-2017 is LC76448248. Both global and local field trials demonstrated that taxonomically distinct predators show distinct impacts on bacterial communities. Therefore, the three protist species were selected based on their distinct phylogenetic backgrounds, ecological relevance as common bacterivorous predators, and ability of axenic culture.

We used calcined clay as an inert soil substitute, allowing us to control nutrient levels27,28. The calcined clay was prepared as described elsewhere13. The sterile plastic tubes (volume: 100 mL) were filled with calcined clay (27 g DW, 40 mL) and a defined nutrient medium (nutrient broth [18 g L−1], Eiken Chemical Co. Ltd., Tokyo, Japan) that supports the growth of a wider range of bacterial taxa and consists of peptone, beef extract, and sodium chloride. All of the microcosms were inoculated with the protist-free bacterial media (~107 cells per g dry calcined clay). About 500 cells g−1 soil of each axenic protist species were added into the microcosms, while the control treatment received the same amount of sterile water. We had four predator treatments (Ctrl, Control with no protist addition; Ac, Acanthamoeba castellanii; Vv, Vermamoeba vermiformis; Hg, a soil glissomonad (Heteromita globosa, informal designation pending formal taxonomic description)) for each community, making a total of 20 treatments. Each treatment was prepared with 6 replications. Microcosms were incubated at 25°C in the dark. An equal volume of nutrient medium was added every 3 days to compensate for water loss due to evaporation and uptake. Our previous experiments showed that the effect of protists on bacteria at the community level can be clearly observed in 3 weeks13,14. Therefore, we conducted sampling on the 3rd and 5th weeks. Three replicative microcosms in each treatment were destructively sampled at the 3rd and 5th weeks.

The calcined clay was mixed thoroughly, and 1 g of calcined clay was transferred to sterile tube. The 1000× dilution of the sample was inspected with an inverted microscope at ×200 and ×400 magnifications (Nikon Eclipse TE2000-S, Tokyo, Japan) to check cross-contamination and successful colonization of the protists. The afterward molecular analysis (DNA extraction, PCR, Illumina Miseq Sequencing), and the bioinformatics analyses were the same as previously described13. Here, we analyzed protist-enriched and protist-depleted bacterial taxa, using the DESeq function in the DESeq2 package in the R program, which models raw counts using a negative binomial GLM, taking into account the dispersion for each ASV49,50. Although differential abundance analysis tools have limitations, DESeq2 is one of the recommended tools for microbial datasets51. Using this model, we compared each protist treatment with the control treatment with RAs. DESeq analyses were performed separately for each community and each protist treatment. Since the microcosm experiment yielded less ASV counts compared to field studies, ASVs with a mean RA > 1% across all samples were classified as dominant bacterial taxa, while those with a mean RA < 0.1% were considered rare. To identify dominant and rare bacterial ASVs of the control group, we first isolated treatments without protist addition and normalized the ASV counts to RAs. ASVs with a mean RA > 1% across control samples were classified as dominant in control, while those with a mean RA < 0.1% were considered rare in control.

While many convergence/divergence studies compare initial and final communities to assess shifts over time4,7, our microcosm approach diverges from this by directly contrasting predator-present and predator-absent communities. This design, although lacking a defined initial baseline, enables a focused examination of predator-induced community convergence under controlled conditions as we isolate the effect of predation itself—a critical, yet often under-explored, biotic driver of microbial assembly. Moreover, in natural settings, initial community states are rarely known, and microbial communities are shaped by ongoing biotic interactions6,21,25. Our predator-absent controls serve as realistic ecological baselines, allowing us to infer the relative influence of predation without relying on potentially artificial starting conditions18,19.

To understand how predator-resistant and predator-vulnerable bacterial taxa respond to predator-mediated convergence, we selected three bacterial ASVs that were significantly depleted and three bacterial ASVs that were significantly enriched in response to protist presence in the microcosm experiment. Then, we obtained bacterial species from the Japan Microbe Collection (JCM, https://jcm.brc.riken.jp/en) that showed 100% match with our raw sequences. Our selection criteria were based on several factors. Bacterial taxa were included only if they met all of the following criteria: ubiquitous (presence in >60% of samples), either enriched or depleted by at least two out of three predators in at least three communities. The selected protist-depleted bacterial taxa were Telluria terrae (Tt, formerly known as Masilla terrae, ASV: 45af176ccb220ac7f05edaa76f14fb1; JCM No: 31606), Knoellia sinensis (Ks, ASV: a7da93a9744a7863292bcf97d47b6a10; JCM No: 11536), and Luteimonas galliterrae (Lg, ASV: 33a198a979ec6f8252b36eb5ae643d5e; JCM No: 34401), while protist-enriched bacterial taxa were Priestia megaterium (Pm, formerly known as Bacillus megaterium, ASV: 83e948f389f9d97b0c975afc880989e8; JCM No: 2506), Achromobacter denitrificans (Ad, ASV: ecc423b8fefd03a92aa1a97fc74a654c; JCM No: 9657), and Pseudomonas kilonensis (Pk, ASV: 0ddcd311e02f742e2e0e61ce02cf9c29; JCM No: 11939). Each bacterial species was grown according to the instructions of JCM. Among those bacteria, Pk, Pm, and Ad are known as competitive and fast-growing species, making them less vulnerable to predation, while Ks, Lg, and Tt are known as slow-growing and less competitive species. In addition, both Pk and Pm are known to produce secondary metabolites that may increase their survival from predation chances52,53, while Ad is known for resistance to antimicrobial compounds54 that are likely to be produced by Pk or Pm52,53. Prior to the SynCom experiment, we conducted pilot co-culture assays between each protist and bacterial isolate to confirm their resistance or vulnerability to predation, and these results were used to confirm predator-resistant and predator-vulnerable species.

The first SynCom received equal abundances of all six bacterial species (1.8 × 106 cells g calcined clay−1), while the other six each had one dominant species (1.0 × 107 cells g calcined clay−1) and five rare ones (1.0 × 105 cells g calcined clay−1). All SynComs had the same total amount of bacteria (1.1 × 107 cells g calcined clay−1). The SynComs were incubated for 3 weeks under the same experimental conditions as described above, with one modification. Here, we used one gram of calcined clay per SynCom in 2 mL tubes. Sampling and DNA extraction were also the same as described above. To quantify the gene abundances of each bacterial species, we used a quantitative real-time PCR (qPCR), for which we have designed species-specific primers (Supplementary Table 3).

Each bacterial species in each treatment was separately amplified, making a total of 504 samples. The qPCR analysis was conducted as previously described13. Briefly, one µL of the standardized DNA extracts (5 ng µL−1) was used in the qPCR analysis. The qPCR reaction (25 μL) contained 10 pmol of each primer and 5 μL of SsoFastTM EvaGreen supermix (Bio-Rad, Hercules, CA, USA) and ran in a CFX96TM Real-Time System (Bio-Rad). The qPCR program started with an initial denaturation step of 180 s at 95°C, followed by 40 cycles of denaturation (45 s, 95°C) and primer annealing (60 s, 57°C to 60°C depending on the primer set) with a final step of primer extension of 30 s at 72°C. The quality and size of the generated amplicons were checked by gel electrophoresis and melting curve analysis. We conducted these analyses using six 96-well plates, each of which contained only one primer set for only one bacterial species, and dilutions of the original of each bacterial media for calculating copy numbers. Copy numbers for each gene were calculated using a regression equation for each assay relating the cycle threshold (Ct) values to the known number of copies in the standards of each bacterial taxon. The remaining bioinformatics and statistical analyses were conducted as described above with one modification. In NMDS analysis, a log transform was applied to the ASV table consisting of 16S rRNA gene copy numbers to handle heteroscedasticity in the data55 using the log function in R, then a Bray–Curtis distance matrix was prepared. Then, the community divergence was calculated from the Bray–Curtis distance matrix using log-transformed ASV table.

To create a model that predicts predator-mediated divergence and convergence, we formulated the divergence and convergence effects of predators. As high community divergence of predators and high abundance of endemic predators correlate with bacterial community divergence, we multiply the predator divergence and RA of endemic predators for divergence impact (see formula below). We divided the RA of ubiquitous predators into predator divergence for convergence impact, as high abundance of ubiquitous predators and low community divergence of predators correlates with bacterial community convergence. Then we substate predator divergence effect from the predator convergence effect. Positive values imply that predators are more likely to be responsible for bacterial community convergence, while negative values imply the opposite.

Predators divergence impact (PDI) = RAE × DP

Predators convergence impact (PCI) = RAU / DP

Net convergence effect (NCE) = PCI - PDI

Where, RAE, relative abundance of endemic predators; RAU, relative abundance of ubiquitous predators.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Asiloglu et al. investigates the effects of bacterivorous protists (predators) on bacterial community\nassembly. Through large-scale correlation analyses, microcosm experiments, and synthetic community experiments, the\nauthors aim to demonstrate that predator identity and prey susceptibility jointly determine convergence outcomes. While the\ntopic is interesting and relevant to microbial ecology, the manuscript suffers from several critical weaknesses that undermine\nthe validity of its conclusions.\nOverall, this study attempts to infer causal relationships between predator activity and bacterial community assembly from\nglobal meta-analyses based primarily on correlation data. However, although predators can indeed regulate bacterial\ncommunities, they are also influenced by bacterial composition. Thus, describing predators as drivers of bacterial assembly\nappears overstated; terms such as predator-mediated assembly or predator–bacteria interactions would be more\nappropriate.\nThe authors use the relative abundances of ubiquitous and endemic predators to represent “species-specific” predation, but\nthe rationale for this assumption requires detailed justification. Predator abundance and distribution are shaped by both\nenvironmental factors and stochastic processes, and there is currently no direct evidence that endemic predators are truly\nspecies-specific. Moreover, the authors do not specify which bacterial taxa correspond to these so-called species-specific\npredators.\nUsing a random forest model, the authors report that the abundance of ubiquitous predators, along with pH, aridity, and\ntemperature, predicts bacterial community divergence. However, the presented results (Extended Data Fig. 2) clearly show\nstrong correlations among these environmental variables and predator abundance. Although the authors conducted\ncontrolled experiments showing that predator addition alters community richness and dominant taxa, they did not indicate\nwhether the three introduced predators belong to the ubiquitous or endemic groups, preventing meaningful linkage with the\nearlier findings. Furthermore, the study lacks post-experimental analyses to confirm whether the introduced predators\nsuccessfully colonized.\nRegarding manuscript organization, the Results section is difficult to follow. The research design is not clearly described,\nparticularly in the experimental validation part. Readers must frequently refer to the Methods section to understand the\nanalytical logic, which hinders readability. The authors should explicitly describe the analytical approach and experimental\ndesign within each subsection of the Results.\nSpecific comments\nLine 81: The authors state that environmental factors have opposite effects on endemic and ubiquitous predators. This is\nexpected, as by definition the abundances of these two groups are negatively correlated.\nLine 84: The term “impact” is inappropriate here, as the analysis only demonstrates correlations, not causality.\nLine 89: Correlation alone does not demonstrate that predators influence bacterial community assembly.\nLine 93: The correlation between predator abundance and community divergence does not imply that predators drive\nconvergence by suppressing dominant taxa.\nLines 97–99: Although divergence is sensitive to richness, both richness and taxonomic unevenness contribute to\ndivergence. The authors should explain how the results in Fig. 1g–h support the inference that “predation disrupts\ndominance by a few taxa.” Moreover, the correlation analysis cannot demonstrate that predation enhances bacterial\nrichness.\nLines 132–135: The authors introduced community divergence through fertilizer and CaCO₃ (pH adjustment) treatments and\nobserved similar correlation patterns. They infer that predators tend to converge bacterial communities or at least reduce\nnutrient- and pH-driven divergence, while endemic predators have no divergence impact due to their low abundance.\nHowever, these experiments cannot establish causality—nutrients and pH may also regulate predator distributions indirectly\nthrough their effects on bacterial communities.\nLine 151: “a total of 2,121 ASVs and an average richness of 88.9 ± 25.1” does not necessarily indicate that all communities\nare “diverse” or “rich.” The authors should define the thresholds for these terms.\nLine 169: Although differences exist between the Hg, Ac, and Av groups, it is not reliable to conclude that taxonomy-based,\nspecies-specific effects lead to global divergence based solely on these data.\nLine 173: The results in Fig. 3j and Fig. 3h are redundant; as the number of dominant species increases, the relative\nabundance of each naturally decreases.\nLine 189: In the synthetic community experiments, the authors selected predator-resistant and predator-vulnerable bacterial\nspecies based on the microcosm results. However, co-culture experiments should be included to verify whether these\nstrains are indeed resistant or vulnerable to the tested predators.", "reviewer_2": "This is an interesting article aiming to characterise the effects of protist predators on bacterial communities. This is an under\nresearched topic and so any new information here is welcome. Though this MS is somewhat focussed on soil communities,\npredators are highly likely to have important impacts on microbiomes across habitats, and so publication in a broad reach\njournal such as Ncomms is appropriate - ie the approaches to evaluate predation impacts and the novel findings are likely to\nbe more broadly of interest.\nThe main strength of the manuscript is that it uses observational omic data from distributed and local soils to ascertain\nassociative effects supporting conclusions; but then goes further to experimentally test mechanisms in manipulated\ncommunities. Convincing ecologically grounded arguments are used to explain patterns, which as stated may be broadly\napplicable. The finding that predators can influence microbiome composition is not in itself novel, though more evidence\nhere is welcomed. Where this manuscript goes further is to suggest more widespread differences in predator impact, and a\nmechanism explaining context specificity - namely that communities dominated by predator resitant taxa are less likely to be\nimpacted by predation pressure.\nMy main concern is regarding the early correlational approaches, though this is mitigated by the later experimental work.\nFirstly, based on an unpublished observation of my own - it is apparent that any molecular community metric derived from an\nenvironmental DNA sample will be as good as or better predictor of variance in another molecular metric, compared with co\nlocated environmental data. This could be using fungal, plant or protist amplicon data to predict bacterial amplicons. For\nsoils the main environmental predictor is pH; which affects diversity, dominance, composition and variance. The invoking of\nspecific types of interaction between bacteria and protist here is something of a stretch, as the communities could just be co-\nvarying with environment. The authors themselves note in the MS that protists are also structured strongly by soil properties,\nso I wonder whether the language should be more cautious in the early part of the results.\nFor example: Lines 140-143 relating to SEM.\nBacterial community change relates to bacterial community dominance: This is known and most likely due to pH effects -\ncomposition and diversity metrics tend to relate.\nThen, bacterial dominance in the SEM is driven by relative abundances of ubiquitous predators, and community composition\nof predators. The authors state this provides \" strong support for the convergence hypothesis \" but what if these community\nattributes just co vary, eg acidity selects for dominance of both predators and prey? The authors do state that this is not\ncausal, but I think \"strong support\" is misleading and it would be useful to change to \"some\" and provide some more text\nspecifically indicating other potential reasons for the observations. Indeed I feel this would only strengthen the case for the\nexperimental work.\nIt is also possible that a \"null\" hypothesis could be trialled adding more support for the earlier results - ie picking lineages of\n18S amplicons not known to predate bacteria and ascertaining their contributions to bacterial change - \"divergence\" or\n\"convergence\". I suspect strong relationships either way can be found for many non-predatory taxa, or do protists uniquely\nexplain more variance in bacterial communities?\nThe next experimental results are I think fantastic - figures 3e-3g are marvellous. Really clear demonstration of both the\nimpact of protists on community development as well as signifying differing effects of predators on specific taxa, and how this\ntranslates to community level change. Similary the syncomm experiment goes further to support the manuscripts overall\nclaims.\nWhilst the manuscript is generally well written and presented some careful checking of English may be required at proofing.", "reviewer_3": "This excellent manuscript deals with the effect of predator protists on bacterial communities diversity. They test two\nscenarios, if predation tends to reduce or increase their diversity. In that purpose, they first use global meta-analyses,\nlocal field experimentation, microcosms and in vitro experiments with known strains. Results show that predators tend to reduce\ndiversity locally by preying on the most common species, but promote diversity at large scale, due to local specialization and\ntrait-based interactions. These results show that bacterial communities (and, therefore, functions) can be steered in a\npredictable way through the use of a certain protist species. This finding opens the way for soil microbiome engineering,\nwhich would then have many practical applications like disease suppression or soil fertility increase. For these reasons, I\nrecommend the publication of this article.\nThe article is well written and easy to follow. I still have a few comments though\nLine 74: Here I would specify the marker gene (probably 18S v9)\nLine 77: The gene coding for the 18S rRNA does not allow separating between species, and especially the v9 region. Of\ncourse, one has to take the available data but one has to bear in mind that each 18S ASV represents rather a group of\nrelated species that can potentially have the same ecology, food regime, etc… or not! This is one of the potential limitations\nof the study, which can be discussed.\nLikewise, \"endemic\" is generally used for organisms that are restricted to a certain geographical area. The word\n\"ecosystem\" does not apply to, for instance, temperate grasslands; these would be \"ecosystem types\" or biomes.\nLine 78: Because the Methods section is at the end of the manuscript, it is difficult to know how the RA have been\ncalculated. I would advise to drop one line about it, or simply to refer directly to the Materials section.\nLine 116: here one could add \"by increasing the number of available substrates\"\nLine 117: Are the ubiquitous protist ASVs the same ones as in the former experiment, or have they been reclassified?\nLine 129: between (instead of “of”)\nLine 169: I am not convinced that taxonomy has a lot to do here. Acanthamoeba (Discosea) is very distantly related to\nVermamoeba (Tubulinea). I would suggest that both taxa feed on bacteria using their pseudopodia, while \"Heteromita\" uses\nits flagella, and therefore captures bacteria that are characterized by other traits. The difference would be more functional\nthan taxonomic.\nFig 5: “Vulnarable”; what is the difference between a and b?\nLine 474: UniFrac distances include a phylogenetic component, and given that the manuscript is oriented towards\ncalculating dissimilarities, these distances would be interesting to apply as well. Maybe the results do not differ substantially,\nbut I think that giving it a preliminary try (and maybe more if it’s worth doing it) would be a good idea. UniFrac does also have\na quantitative component.\nLines 541-543: It would be useful to know, at least roughly, which taxa are retained as predator and non-predator protists for\nthe calculation of the RA. Or is it the ratio of heterotrophic protists versus all eukaryotes (including fungi animals and plants)?\nHow did the authors determine that their ASVs correspond to organisms that prey on protists; did they rely on a functional\ndatabase?\nLine 571: This taxon name “Heteromita” used to include an immense diversity of taxa (basically almost all Glissomonadida)\nhas been invalidated. I guess that reference sequences for these three cultures have been published elsewhere, but I would\nprovide the at least their GenBank accession codes, and name that species according to its closest relative. Otherwise, this\nwould prevent reproducibility of the experiments.\nLine 618: Knoellia sinensis (with “s”); by the way, the fast grower Pt is capable of forming spores; how much would that\ninfluence the experience?"}, "author_response": "Response to the Reviewer Comments\nReviewer #1 (Remarks to the Author):\nThe manuscript by Asiloglu et al. investigates the effects of bacterivorous protists (predators) on bacterial community\nassembly. Through large-scale correlation analyses, microcosm experiments, and synthetic community experiments,\nthe authors aim to demonstrate that predator identity and prey susceptibility jointly determine convergence outcomes.\nWhile the topic is interesting and relevant to microbial ecology, the manuscript suffers from several critical\nweaknesses that undermine the validity of its conclusions.\nAnswer: Thank you for taking the time to evaluate our manuscript. We appreciate your constructive criticism. We\nrevised the manuscript according to your and other reviewers’ comments, as shown below (Blue text represents our\nresponses, and the line numbers of the final manuscript file is provided to show the changes). Briefly, we replaced\noverstated causal language on correlation-based results (e.g., “predator-driven”) with more appropriate terms such as\n“predator-mediated”, clarified the rationale and classification of predator types, and incorporated additional\nexplanations of experimental design directly within the Results section. We also added methodological details,\naddressed limitations of sequence resolution, and refined the English throughout the text to enhance readability and\ntransparency. In addition, we adhered to the journal’s formatting requirements. Briefly, the abstract was shortened to\n150 words, subheadings were organized, and references were adjusted.\nOverall, this study attempts to infer causal relationships between predator activity and bacterial community assembly\nfrom global meta-analyses based primarily on correlation data. However, although predators can indeed regulate\nbacterial communities, they are also influenced by bacterial composition. Thus, describing predators as drivers of\nbacterial assembly appears overstated; terms such as predator-mediated assembly or predator–bacteria interactions\nwould be more appropriate.\nAnswer: Thank you for the comment. We have reviewed and revised any overstated descriptions of predator effects.\nThe term “predator-driven” has been replaced with “predator-mediated” throughout the manuscript, including in the\ntitle. We have removed causative language where results are correlational. All revisions have been highlighted in blue\nin the revised version of the manuscript for clarity.\nThe authors use the relative abundances of ubiquitous and endemic predators to represent “species-specific” predation,\nbut the rationale for this assumption requires detailed justification. Predator abundance and distribution are shaped by\nboth environmental factors and stochastic processes, and there is currently no direct evidence that endemic predators\nare truly species-specific. Moreover, the authors do not specify which bacterial taxa correspond to these so-called\nspecies-specific predators. Using a random forest model, the authors report that the abundance of ubiquitous predators,\nalong with pH, aridity, and temperature, predicts bacterial community divergence. However, the presented results\n(Supplementary Fig. 2) clearly show strong correlations among these environmental variables and predator abundance.\nAnswer: Thank you for the comment. We agree that environmental factors may similarly influence both bacterial and\nprotist communities. We used correlation-based results to disentangle the relationship between predator abundance\nand bacterial community divergence, which was later confirmed in microcosm studies. We had a discussion on this\nmatter in the submitted initially manuscript under the subsection “Net convergence effect of predators”. However, to\nenhance clarity, the following discussion was added to the main text.\nL153-157: “Indeed, the effects of environmental factors such as pH and nutrient levels on predator diversity and\nabundances (Supplementary Figs 2 and 5) cannot be excluded in both global and local field studies. To establish\ncausality and isolate predator effects from environmental drivers, we subsequently conducted a complementary in\nvitro microcosm experiment under defined conditions with and without predator introductions.”\nAlthough the authors conducted controlled experiments showing that predator addition alters community richness and\ndominant taxa, they did not indicate whether the three introduced predators belong to the ubiquitous or endemic\ngroups, preventing meaningful linkage with the earlier findings.\nAnswer: Thank you for this helpful comment. The partial amplification of the 18S rRNA gene in our study does not\nallow precise taxonomic-level identification of environmental protists at the strain level. However, the introduced\npredators — Acanthamoeba castellanii, Vermamoeba vermiformis, and Heteromita globosa — are well-documented\ncommon soil protists reported from a wide range of terrestrial ecosystems. Therefore, we consider them to represent\nubiquitous predator taxa rather than endemic ones.\nThe following sentence was added to the main text for clarification:\nL157-159: “The introduced predators, Acanthamoeba castellanii (Ac), Heteromita globosa (Hg), and Vermamoeba\nvermiformis (Vv), are widely distributed soil protists commonly found across diverse terrestrial habitats, and thus\nconsidered as ubiquitous organisms.”\nFurthermore, the study lacks post-experimental analyses to confirm whether the introduced predators successfully\ncolonized.\nAnswer: Although we do not have exact numbers of protists at the end of the experiment, we did check the successful\ncolonization of the introduced protists. The methodology was added to the manuscript.\nL408-410: “1 g of calcined clay was transferred to a sterile tube. The 1000 × dilution of the sample were inspected\nwith an inverted microscope at ×200 and ×400 magnifications (Nikon Eclipse TE2000-S, Tokyo, Japan) to check the\nsuccessful colonisation of the protists.”\nRegarding manuscript organization, the Results section is difficult to follow. The research design is not clearly\ndescribed, particularly in the experimental validation part. Readers must frequently refer to the Methods section to\nunderstand the analytical logic, which hinders readability. The authors should explicitly describe the analytical\napproach and experimental design within each subsection of the Results.\nAnswer: Thank you for the critical comment. To enhance the readability, we added explanations to each subsection\nof the Results.\nL72-74: “The soils included in this study represent a wide range of vegetation types (shrublands, grassland, and forest),\nedaphic characteristics such as pH, water and nutrient contents, and bioclimatic regions (arid, temperate, tropical, and\ncontinental).”\nL121-124: “All treatments received chemical fertiliser, while organic amendments such as filter cake, cattle manure,\nbagasse, and wood biochar were applied at different rates (5 or 10 tons C ha⁻¹) to manipulate soil resource availability\n(Supplementary Tables 1–2). Soil samples were taken from each field at 0–10 cm depth and each treatment was\nrepresented with four replicates.”\nL157-166: “The introduced predators, Acanthamoeba castellanii (Ac), Heteromita globosa (Hg), and Vermamoeba\nvermiformis (Vv), are widely distributed soil protists commonly found across diverse terrestrial habitats, and thus\nconsidered as ubiquitous organisms. We obtained indigenous predator-free bacterial communities from five distinct\nsoil types: forest (Com1), grassland (Com2), paddy field (Com3), soybean field (Com4), and sugarcane field (Com5).\nThe experimental design consisted of the four protist treatments (Control with no protist addition, Ac, Hg, and Vv)\nfor each of the five bacterial communities, making a total of 20 microcosms with six replicates each. The\nmicroorganisms were incubated in calcined clay, an inert soil substitute 30,31, under defined nutrient conditions for 5\nweeks. This design enabled us to directly assess the exclusive role of individual predator species in driving bacterial\nconvergence, independent of environmental heterogeneity21. We descriptively sampled three microcosms on the 3rd\nand 5th weeks.”\nL205-211: The first SynCom received equal abundances of all six bacterial species (1.8×106 cells g calcined clay-1),\nwhile the other six each had one dominant species (1.0×107 cells g calcined clay-1) and five rare ones (1.0×105 cells g\ncalcined clay-1). All SynComs had the same total amount of bacteria (1.1×107 cells g calcined clay-1) (Fig. 4a). The\nexperimental design consisted of the four protist treatments (Ctrl, Ac, Hg, and Vv) for each of the seven synthetic\nbacterial communities, making a total of 28 microcosms with three replicates each. The microorganisms were\nincubated in calcined clay, an inert soil substitute 30,31, under defined nutrient conditions. We descriptively sampled\nthe microcosms in the 3rd week.\nSpecific comments\nLine 81: The authors state that environmental factors have opposite effects on endemic and ubiquitous predators. This\nis expected, as by definition the abundances of these two groups are negatively correlated.\nAnswer: Thank you for the comment. We agree that, by definition, the abundances of ubiquitous and endemic groups\nare expected to be negatively correlated. However, in our study, we defined ubiquitous ASVs as those detected in\nmore than 60% of samples and endemic ASVs as those detected in fewer than 20% of samples (as described in Lines\n81-82 for clarity). Therefore, these two groups do not necessarily show opposing patterns, since approximately 20%\nof ASVs fall between these thresholds and are not included in either category. Consequently, the relative abundances\nof ubiquitous and endemic predators are not expected to sum to 100%. Indeed, in the second experiment (the local\nfield trial), these groups did not exhibit opposing responses to environmental factors (Supplementary Fig. S5),\nsupporting this interpretation.\nLine 84: The term “impact” is inappropriate here, as the analysis only demonstrates correlations, not causality.\nAnswer: Thank you. We have softened our interpretation as follows.\nL86-87: “indicating the possibility of their effect on community convergence”\nLine 89: Correlation alone does not demonstrate that predators influence bacterial community assembly.\nAnswer: Thank you for the comment. We agree that correlation alone does not demonstrate causality. In our study,\nthis correlation-based result is intended to show an association rather than a direct causal relationship. To address this\npoint, we have revised the sentence. The following modification clarifies that our interpretation is correlative and not\na demonstration of direct causation.\nL92-95: “These results may suggest a dual role of predators: while the global divergence of bacterial communities\nmay arise from differences in the composition of predator communities across ecosystems through likely their species-\nspecific effects, ubiquitous predators may mediate bacterial community converge by exerting consistent effects across\nthe globe.”\nLine 93: The correlation between predator abundance and community divergence does not imply that predators drive\nconvergence by suppressing dominant taxa.\nAnswer: Thank you for the comment. We agree that the correlation between predator abundance and community\ndivergence does not, in itself, demonstrate that predators drive convergence by suppressing dominant taxa. However,\nthe observed correlation supports “the convergence hypothesis (Fig. 1a)”, which proposes that convergence may occur\ndue to predators suppressing dominant taxa. Therefore, we further examined the relationship between the richness of\ndominant bacterial taxa and the relative abundance of ubiquitous predators to explore this potential mechanism. At\nthis stage of the manuscript, we do not interpret the correlation as direct evidence of causality but rather as a basis for\nhypothesis-driven exploration. We revised the sentence for clarification.\nL96-98: “Although correlation-based, the initial results support the convergence hypothesis—that is, convergence\nmay occur when predators suppress dominant taxa—rather than the divergence hypothesis. Therefore, we further\nexamined the relationship between the richness of dominant bacterial taxa and the relative abundance of ubiquitous\npredators.”\nLines 97–99: Although divergence is sensitive to richness, both richness and taxonomic unevenness contribute to\ndivergence. The authors should explain how the results in Fig. 1g–h support the inference that “predation disrupts\ndominance by a few taxa.” Moreover, the correlation analysis cannot demonstrate that predation enhances bacterial\nrichness.\nAnswer: Thank you for the comment. We agree that both richness and taxonomic unevenness contribute to community\ndivergence and that correlation alone cannot demonstrate causality. In Fig. 1g–h, we observed that the relative\nabundance of ubiquitous predators was positively correlated with the richness of dominant bacterial taxa. Here,\n“dominant” taxa refer to bacterial ASVs with relative abundances exceeding 1%. This pattern suggests that in\ncommunities with higher predator abundance, more dominant bacterial taxa coexist, implying that predation may\nalleviate competitive exclusion among a few highly abundant species. In other words, predation likely reduces the\ndisproportionate dominance of a small subset of bacteria, allowing more taxa to persist above the dominance threshold.\nThis mechanism aligns with the inference that “predation disrupts dominance by a few taxa.” However, we\nacknowledge that our statement that “predation disrupts dominance by a few taxa” was based on the observed positive\nrelationship and this relationship does not establish a direct causal link. To clarify this interpretation, we have revised\nthe relevant sentence in the main text as follows:\nL101-103: “We found that ubiquitous predators were positively associated with the richness of the dominant bacterial\ntaxa (Fig. 1g–h), suggesting that predation may alleviate dominance by a few taxa and potentially promote dominance\nby broader range of species.”\nLines 132–135: The authors introduced community divergence through fertiliser and CaCO₃ (pH adjustment)\ntreatments and observed similar correlation patterns. They infer that predators tend to converge bacterial communities\nor at least reduce nutrient- and pH-driven divergence, while endemic predators have no divergence impact due to their\nlow abundance. However, these experiments cannot establish causality—nutrients and pH may also regulate predator\ndistributions indirectly through their effects on bacterial communities.\nAnswer: Thank you for the constructive comment. We agree that our correlation-based results cannot establish\ncausality and that nutrient and pH treatments may also influence predator distributions. Nevertheless, the consistent\ncorrelation patterns observed across different environmental manipulations make the convergence hypothesis (Fig.\n1a) more plausible as an explanation for our results. To clarify this point, we have revised the sentence to emphasize\nthat our findings are consistent with, but do not prove, this hypothesis.\nL141-144: “Taken together, these correlation-based results make the convergence hypothesis more plausible,\nsuggesting that under local conditions where predator taxa are similar and abundant, they may contribute to bacterial\ncommunity convergence or reduce nutrient- and pH-driven divergence, although nutrient and pH effects on predators\ncannot be excluded.”\nWe have also added text in the following section acknowledging the potential effects of environmental factors and\nexplaining that causality was further tested through a controlled microcosm experiment.\nL151-157: “Although these results provided that prey-predator interaction potentially leading to the convergence\nhypothesis, they did not offer definitive causal evidence due to the background of environmental variability. Indeed,\nthe effects of environmental factors, such as pH and nutrient levels, on predator diversity and abundance\n(Supplementary Figs 2 and 5) cannot be excluded in both global and local field studies. To establish causality and\nisolate predatory effects from environmental drivers, we subsequently conducted a complementary in vitro microcosm\nexperiment under defined conditions with and without predator introductions.”\nLine 151: “a total of 2,121 ASVs and an average richness of 88.9 ± 25.1” does not necessarily indicate that all\ncommunities are “diverse” or “rich.” The authors should define the thresholds for these terms.\nAnswer: We have revised the sentence as follows.\nL167-168: “Bacterial communities consisted of a total of 2121 ASVs with an average richness of 88.9±25.1, indicating\nsuccessful colonisation of the native communities.”\nLine 169: Although differences exist between the Hg, Ac, and Av groups, it is not reliable to conclude that taxonomy-\nbased, species-specific effects lead to global divergence based solely on these data.\nAnswer: Thank you. Indeed, traits such as the size and feeding type of the introduced predators differ. We agree with\nyou. We deleted the speculative section, and the sentence has been edited as follows.\nL184-185: “This may support that local convergence effect of predators can ultimately lead to global divergence,\nconsistent with our expectations and previous findings”\nLine 173: The results in Fig. 3j and Fig. 3h are redundant; as the number of dominant species increases, the relative\nabundance of each naturally decreases.\nAnswer: Thank you for the comment. We believe there may be a slight misunderstanding regarding Figures 3h and\n3j. Although both panels present data related to dominant ASVs, they address different aspects of the results. Figure\n3h shows the overall composition and relative abundance of dominant ASVs across all treatments, whereas Figure 3j\nillustrates explicitly the relative abundance changes of the dominant ASVs identified in the Control treatment. This\npanel is important for demonstrating that the relative abundance of these dominant Control ASVs decreases under\nprotist treatments, highlighting the suppressive effects of predators on initially dominant taxa.\nWe would also like to note that Reviewer 2 found these figures (Figs. 3e–3g) to be particularly clear and effective in\nillustrating the impact of protists on community development and taxa-specific effects. Considering both perspectives,\nwe believe Figures 3h and 3j together provide a comprehensive understanding of how protists shape community\nstructure, and therefore, with all due respect, we prefer to retain the current presentation.\nLine 189: In the synthetic community experiments, the authors selected predator-resistant and predator-vulnerable\nbacterial species based on the microcosm results. However, co-culture experiments should be included to verify\nwhether these strains are indeed resistant or vulnerable to the tested predators.\nAnswer: Thank you for this insightful comment. Actually, before conducting the synthetic community (SynCom)\nexperiments, we performed preliminary co-culture (pairwise interaction) experiments between each protist species\nand individual bacterial isolates. These pilot tests were used to assess predation effects and thereby classify the\nbacterial strains as predator-resistant or predator-vulnerable. The SynCom experiment was subsequently designed\nafter these verified interactions. To clarify this procedure, we have added the following sentence to the Methods\nsection:\nL449-451: “Prior to the SynCom experiment, we conducted pilot co-culture assays between each protist and bacterial\nisolate to confirm their resistance or vulnerability to predation, and these results were used to confirm predator-\nresistant and predator-vulnerable species.”\nReviewer #2 (Remarks to the Author):\nThis is an interesting article aiming to characterise the effects of protist predators on bacterial communities. This is an\nunder researched topic and so any new information here is welcome. Though this MS is somewhat focussed on soil\ncommunities, predators are highly likely to have important impacts on microbiomes across habitats, and so publication\nin a broad reach journal such as Ncomms is appropriate - ie the approaches to evaluate predation impacts and the novel\nfindings are likely to be more broadly of interest.\nThe main strength of the manuscript is that it uses observational omic data from distributed and local soils to ascertain\nassociative effects supporting conclusions; but then goes further to experimentally test mechanisms in manipulated\ncommunities. Convincing ecologically grounded arguments are used to explain patterns, which as stated may be\nbroadly applicable. The finding that predators can influence microbiome composition is not in itself novel, though\nmore evidence here is welcomed. Where this manuscript goes further is to suggest more widespread differences in\npredator impact, and a mechanism explaining context specificity - namely that communities dominated by predator\nresitant taxa are less likely to be impacted by predation pressure.\nAnswer: Thank you for taking your time to evaluate our manuscript. We appreciate your constructive criticism. We\nrevised the manuscript according to your and other reviewers’ comments, as shown below (Blue text represents our\nresponses, and the line numbers of the final manuscript file is provided to show the changes). Briefly, we replaced\noverstated causal language on correlation-based results (e.g., “predator-driven”) with more appropriate terms such as\n“predator-mediated,” clarified the rationale and classification of predator types, and incorporated additional\nexplanations of experimental design directly within the Results section. We also added methodological details,\naddressed limitations of sequence resolution, and refined the English throughout the text to enhance readability and\ntransparency. In addition, we adhered the manuscript to the journal’s formatting requirements. Briefly, the abstract\nwas shortened to 150 words, subheadings were organized, and references were adjusted.\nMy main concern is regarding the early correlational approaches, though this is mitigated by the later experimental\nwork. Firstly, based on an unpublished observation of my own - it is apparent that any molecular community metric\nderived from an environmental DNA sample will be as good as or better predictor of variance in another molecular\nmetric, compared with co located environmental data. This could be using fungal, plant or protist amplicon data to\npredict bacterial amplicons. For soils the main environmental predictor is pH; which affects diversity, dominance,\ncomposition and variance. The invoking of specific types of interaction between bacteria and protist here is something\nof a stretch, as the communities could just be co-varying with environment. The authors themselves note in the MS\nthat protists are also structured strongly by soil properties, so I wonder whether the language should be more cautious\nin the early part of the results. For example: Lines 140-143 relating to SEM. Bacterial community change relates to\nbacterial community dominance: This is known and most likely due to pH effects - composition and diversity metrics\ntend to relate. Then, bacterial dominance in the SEM is driven by relative abundances of ubiquitous predators, and\ncommunity composition of predators. The authors state this provides \" strong support for the convergence hypothesis\n\" but what if these community attributes just co vary, eg acidity selects for dominance of both predators and prey? The\nauthors do state that this is not causal, but I think \"strong support\" is misleading and it would be useful to change to\n\"some\" and provide some more text specifically indicating other potential reasons for the observations. Indeed I feel\nthis would only strengthen the case for the experimental work.\nAnswer: Thank you for the insightful comment. We agree with you on that protist and bacterial data deriving from\nthe same DNA may overestimate the correlation-based results. We obtained similar comments from other reviewers\nas well. We have reviewed and revised any overstated descriptions of predator effects. All revisions have been\nhighlighted in blue in the revised version of the manuscript for clarity. Briefly, the term “predator-driven” has been\nreplaced with “predator-mediated” throughout the manuscript, including in the title. We agree that correlation does\nnot prove causality; therefore, we have removed causative language when results are correlational throughout the\nmanuscript. Discussion was added regarding the potential effects of environmental factors on predators.\nL141-144: “Taken together, these correlation-based results make the convergence hypothesis more plausible,\nsuggesting that under local conditions where predator taxa are similar and abundant, they may contribute to bacterial\ncommunity convergence or reduce nutrient- and pH-driven divergence, although nutrient and pH effects on predators\ncannot be excluded.”\nWe have also added text in the following section acknowledging the potential effects of environmental factors and\nexplaining that causality was further tested through a controlled microcosm experiment.\nL151-157: “Although these results provided that prey-predator interaction potentially leading to the convergence\nhypothesis, they did not offer definitive causal evidence due to the background of environmental variability. Indeed,\nthe effects of environmental factors, such as pH and nutrient levels, on predator diversity and abundance\n(Supplementary Figs 2 and 5) cannot be excluded in both global and local field studies. To establish causality and\nisolate predatory effects from environmental drivers, we subsequently conducted a complementary in vitro microcosm\nexperiment under defined conditions with and without predator introductions.”\nIt is also possible that a \"null\" hypothesis could be trialled adding more support for the earlier results - ie picking\nlineages of 18S amplicons not known to predate bacteria and ascertaining their contributions to bacterial change -\n\"divergence\" or \"convergence\". I suspect strong relationships either way can be found for many non-predatory taxa,\nor do protists uniquely explain more variance in bacterial communities?\nAnswer: Thank you for this insightful suggestion. We agree that including a “null” comparison using non-predatory\nprotist lineages would provide an important test to evaluate whether the observed relationships are unique to predatory\ntaxa. However, in our current dataset, the relative abundances of predatory taxa were used as explanatory variables in\nStudies 1 and 2. Because non-predatory taxa can only be represented as the complementary fraction (i.e., 1 – predatory\ntaxa), testing their relationships with bacterial divergence or convergence would produce statistically redundant results.\nTherefore, this approach (although we fully agree) cannot effectively distinguish the specific effects of predatory\nversus non-predatory protists with the available data in this manuscript.\nNonetheless, our subsequent controlled experiments (Studies 3 and 4) were designed to address causality and isolate\npredator-specific effects under defined conditions. These experiments demonstrated consistent and mechanistically\ninterpretable patterns that strongly support the conclusions derived from the correlative analyses.\nWe agree that future studies incorporating community-wide functional assignments, such as non-predatory mixotrophs\nor saprotrophs as controls, will be valuable for testing whether predatory protists uniquely explain bacterial community\nvariance. We plan to incorporate this comparative framework in our future work.\nThe next experimental results are I think fantastic - figures 3e-3g are marvellous. Really clear demonstration of both\nthe impact of protists on community development as well as signifying differing effects of predators on specific taxa,\nand how this translates to community level change. Similary the syncomm experiment goes further to support the\nmanuscripts overall claims. Whilst the manuscript is generally well written and presented some careful checking of\nEnglish may be required at proofing.\nAnswer: Thank you very much for the encouraging comments and positive evaluation of our results. The manuscript\nhas been carefully rechecked, and the English language has been further refined throughout.\nReviewer #3 (Remarks to the Author):\nThis excellent manuscript deals with the effect of predator protists on bacterial communities diversity. They test two\nscenarios, if predation tends to reduce or increase their diversity. In that purpose, they first use global meta-analyses,\nlocal field experimentation, microcosms and in vitro experiments with known strains. Results show that predators tend\nto reduce diversity locally by preying on the most common species, but promote diversity at large scale, due to local\nspecialization and trait-based interactions. These results show that bacterial communities (and, therefore, functions)\ncan be steered in a predictable way through the use of a certain protist species. This finding opens the way for soil\nmicrobiome engineering, which would then have many practical applications like disease suppression or soil fertility\nincrease. For these reasons, I recommend the publication of this article. The article is well written and easy to follow.\nI still have a few comments though.\nAnswer: Thank you very much for the encouraging comments and positive evaluation of our results. We appreciate\nyour constructive criticism. We revised the manuscript according to your and other reviewers’ comments, as shown\nbelow (Blue text represents our responses, and the line numbers of the final manuscript file is provided to show the\nchanges). Briefly, we replaced overstated causal language on correlation-based results (e.g., “predator-driven”) with\nmore appropriate terms such as “predator-mediated,” clarified the rationale and classification of predator types, and\nincorporated additional explanations of experimental design directly within the Results section. We also added\nmethodological details, addressed limitations of sequence resolution, and refined the English throughout the text to\nenhance readability and transparency. In addition, we adhered the manuscript to journal’s formatting requirements.\nBriefly, the abstract was shortened to 150 words, subheadings were organized, and references were adjusted.\nLine 74: Here I would specify the marker gene (probably 18S v9)\nAnswer: Thank you. Information of the marker genes and primer sets is added for both global and local studies as\nfollows.\nL74-75: “Predators were extracted from publicly available amplicon sequence variant (ASV) table, which was\ngenerated by amplifying the V4-V5 region of the 18S rRNA gene using the 616*F/1132R primer set (methods,\nSupplementary Fig. 1a-b).”\nL130-131: “Protist data were obtained by amplifying the V9 region of the 18S rRNA gene using the 1389F/1510R\nprimer set.”\nLine 77: The gene coding for the 18S rRNA does not allow separating between species, and especially the v9 region.\nOf course, one has to take the available data but one has to bear in mind that each 18S ASV represents rather a group\nof related species that can potentially have the same ecology, food regime, etc… or not! This is one of the potential\nlimitations of the study, which can be discussed.\nAnswer: Thank you for the comment. The V9 region was only used in the local island study. We added the following\nsection to the conclusion to address the potential limitation.\nL259-263: “A potential limitation of our study is that the 18S rRNA gene, particularly V9 region used in local field\nstudy, may not allow reliable discrimination at the species level. Consequently, each ASV may represent a group of\nrelated protist species that could differ in ecology, prey preferences, or functional traits. While this limits the resolution\nof our predator–prey assignments, our multi-scale experimental approach, including microcosms and synthetic\ncommunities, helps mitigate this limitation by directly testing predatory effects.”\nLikewise, \"endemic\" is generally used for organisms that are restricted to a certain geographical area. The word\n\"ecosystem\" does not apply to, for instance, temperate grasslands; these would be \"ecosystem types\" or biomes.\nAnswer: We have redefined the usage of “endemic” in this manuscript.\nL81-82: “endemic (ASVs presented in less than 20% of the samples)”.\nLine 78: Because the Methods section is at the end of the manuscript, it is difficult to know how the RA have been\ncalculated. I would advise to drop one line about it, or simply to refer directly to the Materials section.\nAnswer: Thank you. We added an explanation on calculation of predator RA.\nL283-284: “The relative abundance (RA) of predators used in all steps of our study represented their abundance\ndistribution among all protists, which was comparable with the previous study.”\nLine 116: here one could add \"by increasing the number of available substrates\"\nAnswer: Done (L120-121).\nLine 117: Are the ubiquitous protist ASVs the same ones as in the former experiment, or have they been reclassified?\nAnswer: In each study, we reclassified the ubiquitous protists, which represent ASVs found in more than 60% of the\nsamples.\nLine 129: between (instead of “of”)\nAnswer: Done.\nLine 169: I am not convinced that taxonomy has a lot to do here. Acanthamoeba (Discosea) is very distantly related\nto Vermamoeba (Tubulinea). I would suggest that both taxa feed on bacteria using their pseudopodia, while\n\"Heteromita\" uses its flagella, and therefore captures bacteria that are characterized by other traits. The difference\nwould be more functional than taxonomic.\nAnswer: Thank you. Indeed, traits such as the size and feeding type of the introduced predators differ. We agree with\nyou. We deleted the speculative section, and the sentence has been edited as follows.\nL184-185: “This may support that local convergence effect of predators can ultimately lead to global divergence,\nconsistent with our expectations and previous findings.”\nFig 5: “Vulnarable”; what is the difference between a and b?\nAnswer: We appreciate the comment and would like to clarify the figure.\n- If the question refers to the two “vulnerable” bacteria (dark and light red colors): these colors indicate\ndifferent bacterial species that are vulnerable to predation. Different species may respond differently to the\nsame predator, which is why they are represented separately.\n- If the question refers to panels a and b (community A and B), both panels represent protist communities with\nslightly different compositions; however, the dominant predator is the same in both cases.\nLine 474: UniFrac distances include a phylogenetic component, and given that the manuscript is oriented towards\ncalculating dissimilarities, these distances would be interesting to apply as well. Maybe the results do not differ\nsubstantially, but I think that giving it a preliminary try (and maybe more if it’s worth doing it) would be a good idea.\nUniFrac does also have a quantitative component.\nAnswer: Thank you for this insightful suggestion. We calculated weighted UniFrac distances as recommended and\nobtained results that were highly consistent with those based on Bray–Curtis dissimilarity. Although minor differences\nwere observed, they did not affect the overall conclusions of the study.\nHowever, as protist feeding preferences are not strictly linked to the taxonomic identity of bacteria — and can vary\neven among closely related bacterial species — we believe that a taxonomy-independent metric such as Bray–Curtis\ndissimilarity is more appropriate for capturing predator-driven community changes. Therefore, we would like to\npresent the Bray–Curtis results in the main text.\nLines 541-543: It would be useful to know, at least roughly, which taxa are retained as predator and non-predator\nprotists for the calculation of the RA. Or is it the ratio of heterotrophic protists versus all eukaryotes (including fungi\nanimals and plants)? How did the authors determine that their ASVs correspond to organisms that prey on protists;\ndid they rely on a functional database?\nAnswer: Thank you for this valuable comment. We manually assigned ASVs to predatory protists based on their\ntaxonomic classification at the genus or family level. This approach requires specific expertise in protist taxonomy\nand functional ecology, which our group has established through multiple previous studies using the same method\n(Asiloglu et al., 2021: doi: https://doi.org/10.1016/j.soilbio.2021.108397; Bodur et al., 2025, doi:\nhttps://doi.org/10.1016/j.scitotenv.2025.179606). The original article (Oliverio et al., 2020) from which we obtained\nthe global protist community data also applied this taxon-based functional classification, and our results were\ncomparable with theirs (as mentioned in Line 80).\nTo clarify this in the manuscript, we have revised the Methods section as follows:\nL281-283: “Then functional group of bacterivorous protists (predators) was manually assigned based on their\ntaxonomic annotation following previously established classifications as previously shown21,44,45.”\nLine 571: This taxon name “Heteromita” used to include an immense diversity of taxa (basically almost all\nGlissomonadida) has been invalidated. I guess that reference sequences for these three cultures have been published\nelsewhere, but I would provide the at least their GenBank accession codes, and name that species according to its\nclosest relative. Otherwise, this would prevent reproducibility of the experiments.\nAnswer: Thank you for the comment. Heteromita globosa LAP3-2017 was isolated by us from a paddy field, and the\nrelated information, including GenBank accession codes, was previously published (*) as you mentioned. For clarity,\nwe added the information on the GenBank accession code to the methods section.\nL391-392: “The NCBI genbank accession number of H. globosa LAP3-2017 is LC764482.”\n(*): Fujino, M., Suzuki, K., Harada, N., & Asiloglu, R. (2023). Protists modulate active bacterial community\ncomposition in paddy field soils. Biology and Fertility of Soils, 59(7), 709-721.doi: https://doi.org/10.1007/s00374-\n023-01733-5\nLine 618: Knoellia sinensis (with “s”); by the way, the fast grower Pt is capable of forming spores; how much would\nthat influence the experience?\nAnswer: Thank you. The spelling of Knoellia sinensis has been corrected. Sporulation could reduce bacterial\nsusceptibility to predation and contribute to its high abundance in predator treatments. Nonetheless, vulnerable\nbacterial species consistently decreased, indicating that active predator–prey interactions occurred. Therefore, while\nsporulation may have partially protected spore-forming bacteria, the overall patterns of predator effects on bacterial\ncommunity composition remain robust. The following sentence was added to the main text.\nL216-218: “We note that some predator-resistant bacteria in our study are capable of sporulation, which may have\ncontributed to their persistence under predation, potentially modulating the degree of convergence observed.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors' careful revisions and detailed responses to my earlier comments. The manuscript has been\nsubstantially improved, and the major concerns regarding the interpretation of predator-bacteria interactions raised during\nmy initial review have been satisfactorily addressed.\nI recommend acceptance after the authors address the following minor formatting issue.\nIn Fig. 3C, the font used for the x-axis label and the genus names appears non-standard, and some letters overlap when\nviewed on my system. Please adjust the font style to ensure these labels render properly, and italicize the genus names.\nAfter this minor formatting adjustment, the manuscript does not need to be returned to me for further review.", "reviewer_2": "I am happy with the authors ammendments, have no further comments and recommend the article suitable for publication.", "reviewer_3": "I have read carefully all the answers provided, and I think that most comments have been reasonably addressed. There is\nonly one (small) point on which I disagree, is to keep the name \"Heteromita\". This species name has been invalidated; I\nwould instead name it \"soil glissomonad\", pending a formal description of the group.\nBesides this very small detail, I think that the manuscript is ready for publication."}, "author_response": "RESPONSE TO THE REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nI appreciate the authors' careful revisions and detailed responses to my earlier comments. The manuscript has been\nsubstantially improved, and the major concerns regarding the interpretation of predator-bacteria interactions raised\nduring my initial review have been satisfactorily addressed.\nI recommend acceptance after the authors address the following minor formatting issue.\nIn Fig. 3C, the font used for the x-axis label and the genus names appears non-standard, and some letters overlap\nwhen viewed on my system. Please adjust the font style to ensure these labels render properly, and italicize the\ngenus names. After this minor formatting adjustment, the manuscript does not need to be returned to me for further\nreview.\nAnswer: Thank you very much for taking your time to review our manuscript. We appreciate your efforts and\nvaluable comments. We have adjusted the kerning and italicised the genus names.\nReviewer #2 (Remarks to the Author):\nI am happy with the authors ammendments, have no further comments and recommend the article suitable for\npublication.\nAnswer: Thank you very much for taking your time to review our manuscript. We appreciate your efforts and\nvaluable comments.\nReviewer #3 (Remarks to the Author):\nI have read carefully all the answers provided, and I think that most comments have been reasonably addressed.\nThere is only one (small) point on which I disagree, is to keep the name \"Heteromita\". This species name has been\ninvalidated; I would instead name it \"soil glissomonad\", pending a formal description of the group.\nBesides this very small detail, I think that the manuscript is ready for publication.\nAnswer: Thank you very much for taking your time to review our manuscript. We appreciate your efforts and\nvaluable comments.\nFollowing your suggestion, we have revised the terminology in the main text as follows.\nL158: a soil glissomonad (Heteromita globosa [Hg], informal designation pending formal taxonomic description)\nL390: a soil glissomonad\nL403: Hg, a soil glissomonad (Heteromita globosa, informal designation pending formal taxonomic description)"}]

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