Multiple drivers jointly shape the diversity, composition, and assembly processes of soil protist communities across temperate grasslands

Protists, as prevalent members of soil microbial communities, play crucial roles in ecosystem functioning. A comprehensive understanding of their spatial distribution patterns and assembly processes is essential for the accurate prediction of their responses to environmental disturbances. We conducted a large-scale field study across 1,700 km of temperate grasslands in the eastern Eurasian steppe. Our results revealed that the α diversity of soil protists increased linearly along the latitudinal gradient from south to north, influenced primarily by a decrease in mean annual temperature and an increase in soil total organic carbon and total phosphorus. Conversely, community dissimilarity (i.e., β diversity) decreased from south to north, being predominantly driven by mean annual temperature and soil pH. Despite having a mean annual temperature below zero, the meadow steppe is the grassland type that is likely to provide optimal conditions for protists. In addition, our study indicates that soil protist communities in temperate grasslands were strongly driven by stochastic processes such as dispersal limitation and drift. Mean annual temperature and soil pH regulated the balance between drift and environmental filtering, with the relative importance of determinism increasing with rising temperature and soil pH. Overall, our results reveal that changes in the diversity, composition, and community assembly processes of soil protists in temperate grasslands were determined by multiple factors, with climatic factors contributing the most. These findings deepen our understanding of biogeographical patterns and the underlying mechanisms of soil protists in temperate grassland ecosystems, thereby providing scientific evidence that may aid in predicting the dynamics of soil protist communities in the context of climate change. Multiple drivers jointly shape the diversity, composition, and assembly processes of soil protist communities across temperate grasslands

Protists, as prevalent members of soil microbial communities, play crucial roles in ecosystem functioning. A comprehensive understanding of their spatial distribution patterns and assembly processes is essential for the accurate prediction of their responses to environmental disturbances. We conducted a large-scale field study across 1,700 km of temperate grasslands in the eastern Eurasian steppe. Our results revealed that the α diversity of soil protists increased linearly along the latitudinal gradient from south to north, influenced primarily by a decrease in mean annual temperature and an increase in soil total organic carbon and total phosphorus. Conversely, community dissimilarity (i.e., β diversity) decreased from south to north, being predominantly driven by mean annual temperature and soil pH. Despite having a mean annual temperature below zero, the meadow steppe is the grassland type that is likely to provide optimal conditions for protists. In addition, our study indicates that soil protist communities in temperate grasslands were strongly driven by stochastic processes such as dispersal limitation and drift. Mean annual temperature and soil pH regulated the balance between drift and environmental filtering, with the relative importance of determinism increasing with rising temperature and soil pH. Overall, our results reveal that changes in the diversity, composition, and community assembly processes of soil protists in temperate grasslands were determined by multiple factors, with climatic factors contributing the most. These findings deepen our understanding of biogeographical patterns and the underlying mechanisms of soil protists in temperate grassland ecosystems, thereby providing scientific evidence that may aid in predicting the dynamics of soil protist communities in the context of climate change.

Biogeography; soil macroecology; soil protist; latitudinal gradient; community assembly processes; temperate grassland 1. Introduction Protists are unicellular or colonial microeukaryotes with diverse morphologies and lifestyles. They play multifaceted roles in nutrient and energy cycling, decomposition, and microbial population control (Geisen et al. 2018). While belowground biodiversity has received significant attention in recent years, research on soil communities has predominantly focused on nematodes, bacteria, and fungi, leaving protists relatively understudied (Geisen et al. 2017, Guerra et al. 2020). However, significant advancements have recently been made in revealing the taxonomic and functional diversity of soil protists (Adl et al. 2019), as well as in understanding the environmental preferences of protist taxa (Ekelund and Rønn 1994, Oliverio et al. 2020). The diversity, composition, and assembly of protist communities have been shown to vary significantly across terrestrial habitats (Oliverio et al. 2020, Wu et al. 2022, Dong et al. 2024), with a range of complex, interacting factors, such as elevation, climate, soil properties, vegetation type, microbial communities, and anthropogenic activities, being identified (Geisen et al. 2018, Nguyen et al. 2021, Verdon et al. 2025). Nevertheless, changes in protist communities in temperate grasslands are not well understood, particularly with regard to the relative contributions of major factors driving diversity and composition across large geographic and climate gradients. This knowledge gap in soil protist biogeography and macroecology limits the predictive power of soil biodiversity and related ecosystem functioning in the context of climate change. The biogeographical patterns of soil protist communities and the underlying processes have recently been studied (Bates et al. 2013, Oliverio et al. 2020, Wu et al. 2022). In most natural ecosystems, protist α diversity is primarily influenced by the water-energy balance, resulting in a hump-shaped latitudinal pattern with a peak in the temperate zone (Bates et al. 2013, Fernández et al. 2016, Aslani et al. 2022). Protist diversity has also been shown to follow a distance-decay relationship (Aslani et al. 2022, Kang et al. 2022, Wu et al. 2022). However, the investigation of the large-scale distribution of soil protists may be complicated by ecosystem types. In forests, the latitudinal diversity of protists aligns with both the temperature hypothesis (i.e., α diversity increases with mean annual temperature) and the metabolic niche theory (i.e., β diversity decreases as temperature rises, Wu et al. 2022). In contrast, protist diversity in deserts is reduced by increasing aridity along longitudes (Dong et al. 2024). To date, large-scale studies of grassland protists have mainly focused on alpine regions (Kang et al. 2022) or anthropogenic factors (Chen et al. 2022b, Luo et al. 2023, Zhao et al. 2023). In order to assess the distribution patterns of soil protists, evidence from grassland ecosystems spanning large latitudinal and longitudinal ranges in temperate regions is required. Multiple environmental factors have been shown to jointly drive variations in protist communities (Geisen et al. 2018, Oliverio et al. 2020). Soil water availability is critical for the survival of soil protists (Ekelund and Rønn 1994, Geisen et al. 2014), which depends on climatic factors such as temperature and precipitation (Hu et al. 2022). Consequently, precipitation and aridity significantly impact protist diversity and community composition on a large spatial scale (Oliverio et al. 2020, Chen et al. 2022a). Soil pH also exerts a strong effect on protist community dynamics due to the distinct environmental preferences of different taxa (Shen et al. 2014, Dupont et al. 2016, Aslani et al. 2022). Additionally, land-use practices such as fertilization, mowing, and grazing can influence soil properties, microbial communities, and vegetation characteristics, leading to indirect cascading effects on protist communities (Lentendu et al. 2014, Fiore-Donno et al. 2020, Hu et al. 2022, Zhao et al. 2023). Although deterministic processes imposed by environmental gradients can strongly influence the dynamics of soil protist communities, stochastic processes such as immigration, emigration, birth, and death are also involved in community assembly (Zhou and Ning 2017, Aslani et al. 2022). The relative contribution of deterministic and stochastic processes to microbial community assembly varies considerably between bacteria, fungi, and protists (Aslani et al. 2022, Kang et al. 2022, Dong et al. 2024). While the assembly of soil bacterial communities is driven by soil pH and mean annual temperature (Tripathi et al. 2018, Chen et al. 2023), aridity has been identified as the primary factor in the assembly of protist communities (Chen et al. 2022a). Harsh environmental conditions (e.g., high temperatures) can eliminate non-adapted taxa from a community, thereby increasing the relative importance of determinism (Guo et al. 2018, Luan et al. 2020). To understand the source of variations in protist communities in natural temperate grasslands, the phylogenetic bin-based null model (Ning et al. 2020) can be used to quantify community assembly processes and test which climatic or soil factors modulate these processes. To explore biogeographic patterns and infer the assembly processes of soil protists, we conducted a large-scale, comprehensive sampling campaign in natural grasslands along a 1,700 km transect in the eastern part of the Eurasian steppe. Herein, we investigated how protist α and β diversity, community structure, and assembly processes vary across temperate grassland soils. Our objectives were to (1) determine the latitudinal distribution patterns of soil protist communities in the natural temperate grasslands in the region of eastern Eurasia; (2) identify the main factors influencing soil protist community as well as their relative contributions; and (3) reveal the underlying processes governing protist community assembly and the changes in their relative contribution along environmental gradients. 2. Materials and methods 2.1. Study area and climate factors The study was conducted in the eastern part of the Eurasian steppe belt (40°01’N–50°10’N, 106°45’E–125°15’E, 155–1,535 m above sea level) spanning a distance of 1,750 km from August to September in 2021. Seventeen sites in fenced grasslands in northern China (Heilongjiang Province and the Inner Mongolia Autonomous Region; Figure 1A; Table S1) were selected. This area experiences temperate monsoon and continental climates (Li et al. 2021a). According to the WorldClim database (v. 2.1), mean annual temperature (MAT) increases from –2.16 to +8.20°C from the northeast to the southwest, while mean annual precipitation (MAP) decreases from 493 to 144 mm (Fick and Hijmans 2017). The aridity index (AI, the ratio of MAP to potential evapotranspiration) was obtained from the Global AI and Potential Evapotranspiration Database (v. 3) and ranges from 0.08 (arid) to 0.52 (dry subhumid) (United Nations Environment Programme 1992, Zomer et al. 2022). The study area covered four major grassland types formed by a variety of environmental conditions: temperate meadow (TM), meadow steppe (MS), typical or dry steppe (TS), and desert steppe (DS) (Wang et al. 2014, Li et al. 2020, Li et al. 2021a, Table S1). G rasslands are dominated by the Stipa genus, including S. baicalensis, S. grandis, and S. klemenzii (Bai et al. 2012). 2.2. Field sampling and soil factor measurements Five 1 m × 1 m plots were selected on a flat, open area at each site (Figure S1). At each plot, a composite soil sample and the above-ground vegetation were collected. Each soil sample was divided into two subsamples: ~300 g for soil analysis, which was kept on ice; and ~10 g for protist community analyses, which was frozen and stored in liquid nitrogen. The soils were analyzed for pH, electrical conductivity (EC), soil water content (SWC), total carbon (TC), total organic carbon (TOC), total nitrogen (TN), the TC to TN ratio (C/N), total phosphorus (TP), available phosphorus (AP), nitrate nitrogen (\(\text{NO}_{3}^{-}\)-N), and ammonium nitrogen (\(\text{NH}_{4}^{+}\)-N). SWC was determined gravimetrically. Soil pH and EC were analyzed using a PHS-3C pH meter and a DDS-307a conductivity meter (Leici, Shanghai, China) at a soil:water ratio of 1:5. Soil TC and soil TN were measured using a Vario MAX Cube analyzer (Elementar, Germany). Soil TP was determined following wet digestion using a catalyst (K 2 SO 4 :CuSO 4 :Se, 100:10:1) and concentrated H 2 SO 4 in a SmartChem600 (KPM Analytics, USA). Soil AP was measured using the Olsen method (Olsen and Sommers 1982). Soil TOC was determined using hydrochloric acid with a Vario TOC analyzer (Elementar, Germany).\(\text{NO}_{3}^{-}\)-N and \(\text{NH}_{4}^{+}\)-N were extracted from soils with KCl and measured by an Alliance Futura (KPM Analytics, USA). Plant samples were sterilized in an oven at 105°C for 30 min, then dried at 65°C for 48 h until a constant mass was achieved. The dried samples were ground using a ball mill and sieved (0.15 mm sieve) to measure the plant TC, TN, and TP contents (Cao et al. 2020). Plant TC and TN were determined using a Vario MAX cube (Elementar, Germany). Plant TP analysis was based on the molybdenum blue method. 2.3. DNA extraction, PCR assays, and processing of sequencing data To characterize protist communities, the V4 region of the 18S rRNA gene was broadly targeted using the primer set FW-TAReuk454FWD1 (CCAGCASCYGCGGTAATTCC) and TAReukREV3 (ACTTTCGTTCTTGATYRA; Stoeck et al. 2010). DNA extraction, PCR reactions, and paired-end sequencing (Illumina MiSeq sequencer) were performed at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). Raw sequences were processed using the Quantitative Insights into Microbial Ecology 2 standard procedure. The nt_v20210917 was used to blast amplicon sequence variants (ASVs) against the NCBI classification database as of December 2022. Any ASV represented by fewer than 10 reads across the dataset was excluded. Non-protist ASVs, which were assigned to Metazoa, multicellular fungi, and multicellular Viridiplantae, were excluded prior to further analysis (Adl et al. 2019, Oliverio et al. 2020). 2.4. Statistical analyses Data analysis was performed using the R programming language (R Development Core Team, v. 4.2.1) and the following packages: vegan v. 2.6-4 (Oksanen et al. 2018), picante v. 1.8.2 (Kembel et al. 2010), ggcor v. 0.9.8.1 (Huang et al. 2020), betapart v. 1.6 (Baselga et al. 2018), geosphere v. 1.5-18 (Hijmans 2022), rfPermute v. 2.2 (Archer 2021), A3 v. 1.0.0 (Fortmann-Roe 2015) , ggplot2 v. 3.4.1 (Wickham 2016), and ggtern v. 3.5.0 (Hamilton and Ferry 2013). The map of the study sites was created using QGIS 3.28.0. All environmental factors were standardized and centered. Pearson’s correlations between latitude, longitude, altitude, and environmental factors measured at 17 sites were calculated as an average of five replicates using the quickcor function in the ggcor package (Figure S2). To reduce the dimensionality of the environmental factors, a principal components analysis (PCA) was conducted on the following variables: climate (MAT, MAP, and AI); soil (pH, EC, SWC, TC, TOC, TN, C/N, TP, AP,\(\text{NO}_{3}^{-}\)-N, and \(\text{NH}_{4}^{+}\)-N); and plant (above-ground biomass (AGB), TN, TC, C/N, and TP; Figure S3). The environmental factors that contributed most to the first four axes (explaining 80% of the total variance) were used in the subsequent analysis (Table S2). For each study site, α diversity indices (observed richness (Sobs), the Shannon index, and Faith’s phylogenetic diversity index) were calculated, as well as β diversity represented by the Bray-Curtis distances between plots, which was calculated using the avgdist function in the vegan package . The Sørensen dissimilarity index was further partitioned into nestedness (species loss) and turnover (species replacement) for the presence/absence ASV data (package betapart ; Menegotto et al. 2019). Differences between sites and grassland types were analyzed using a one-way ANOVA with Duncan’s test at P < 0.05 in SPSS 22.0 (IBM SPSS Inc., Chicago, IL, USA; Table S3). The relationships between environmental factors and protist community variables were determined using two-tailed Pearson’s correlation tests ( quickcor function in the ggcor package) and ordinary least squares models ( lm function). To cluster protist communities, Principal Coordinate Analysis (PCoA) was performed based on Bray-Curtis distances using the cmdscale function in vegan . The dissimilarity between grassland communities was tested using Permutational Multivariate ANOVA (PERMANOVA, adonis2 function in vegan ; method = ”bray”). Distance-decay patterns were explored across all 85 communities and within each grassland type. Distance-based redundancy analysis (dbRDA, dbrda function in the vegan package) was applied on the square root of Bray-Curtis distances to investigate the impact of environmental variables on β diversity. The models were selected using ordistep ( P < 0.05, the vegan package ) and tested for significance using anova, as well as for co-correlation according to the variance inflation factor (VIF < 10 and AI, Fiore-Donno et al. 2020). Variation partitioning analysis (VPA; varpart function in vegan ) was used to assess the contribution of climatic factors, soil properties, and plant characteristics. Factors selected using dbRDA and tested using the anova.cca function in vegan . Multiple Regression on distance Matrices (MRM; distance based; ecodist package; Lichstein 2007) and random forest (RF) models ( rfPermute function in rfPermute and a3 function in A3 ) were employed to identify the primary environmental factors driving protist communities. Geographic coordinates were used to determine the geographic distances between sites (Haversine distances, distm function in the geosphere package ). Environmental distances between sites were calculated based on the Euclidean dissimilarity of environmental factors. The relative influence of deterministic and stochastic processes on community assembly was assessed using the phylogenetic bin-based null model ( iCAMP, Ning et al. 2020) in the iCAMP package (v. 1.5.12). To compare the contributions of each ecological process, the iCAMP results were summarized for each grassland type and site, with the significance calculated based on 1,000 bootstraps. 3. Results 3.1. Soil protist community composition A total of 2,168,214 protist sequences were obtained from 85 soil samples and clustered into 6,967 ASVs. Twenty-one phyla were detected, including the heterotrophic Cercozoa (30%) and Ciliophora (17%), the parasitic Apicomplexa (13%), the phototrophic Chlorophyta (13%), and the heterotrophic Evosea (7.7%; Figure S4A). Of the 458 genera, the most abundant were Xiphocephalus (4.3%) and Cryptosporidium (2.5%) from Apicomplexa and Filamoeba (2.4%) from Evosea. Significant variations in soil protist composition were observed between grassland types at the phylum level, with the highest relative abundance of Ciliophora found in the DS and the highest abundance of Apicomplexa found in the TS ( P < 0.05; Figure S4C; Table S3). A significant negative correlation was observed between soil pH and the relative abundance of Cercozoa ( P < 0.05; Figures S5G and S6). Furthermore, SWC and soil TOC were negatively correlated with the relative abundance of Ciliophora ( P < 0.05; Figures S5H, I, and S7A) but positively correlated with Evosea. The latter was also positively correlated with MAP and AI ( P < 0.05; Figure S5). 3.2. Protist diversity The richness, Shannon index, and phylogenetic diversity of soil protist communities varied significantly between the 17 sites and grassland types (Table S3). The highest richness and phylogenetic diversity were observed in the protist communities of sites DR, ZY, and BKT, and the lowest α diversity was in CG (Figure S8). Protist α diversity increased along the latitude gradient from south to north ( P < 0.001), with the highest α diversity found in the MS (Figures 2 and S8F). MAT was the strongest predictor of variation in richness and phylogenetic diversity (Figure 2E, F). MAT decreased protist richness, Shannon’s index, and phylogenetic diversity (Figures S6 and S9). Conversely, soil TOC and soil TP exhibited positive correlations with richness and phylogenetic diversity, while AI increased phylogenetic diversity (Figures S6 and S9). 3.3. Protist diversity Protist communities displayed significant variation between sites and grassland types (Figures 3B and S8D; Table S3). Community dissimilarity decreased from south to north, with the least heterogeneous communities occurring in the MS (Figure 3B). Soil protist communities differed significantly between grassland types (PERMANOVA; Table S4). The grassland type MS was the only one where protist communities clustered together, whereas communities in other grasslands were comparatively scattered (R 2 = 0.115, P < 0.001; Figure S10A). Soil protist communities in 85 soil samples and within the MS exhibited significant distance-decay patterns ( P < 0.01; Figures 1H and S11A). Variations in soil protist communities were significantly related to MAT, AI, and MAP, as well as other factors ( P < 0.001; Figure 3C; Table S5). Out of these factors, climate explained 34.7% of the variation and had a major role in structuring soil protist communities, while 56.4% of the variation remained unexplained (Figure S10B). MAP had the strongest influence on the total protist community (Figure S12A), with additional joint effects from MAT and pH ( R 2 = 0.278, P < 0.001), and further from plant TN ( R 2 = 0.328, P < 0.001; Table S6). Community dissimilarity increased significantly with increasing MAT and soil pH (Figures S6 and S11B, C). Additionally, MAT was the best predictor of β diversity ( P < 0.05; Figure 3D). The Sørensen dissimilarity index averaged 0.831, with the turnover component contributing 0.782 and the nestedness component contributing 0.048 (Figure S13A). Sørensen dissimilarity and the turnover component increased with geographic distance, MAP, pH, and EC (Figures S13B, C, and S14). In addition, our study found that protist α and β diversity were negatively correlated (Figure S11D-F). 3.4 Community assembly processes and their driving factors Stochastic processes (i.e., dispersal limitation (34.2%), homogenizing dispersal (0.51%), and drift (45.7%)) were dominant in soil protist communities (Figure S15A). Additionally, homogeneous selection (17.1%) substantially contributed to structuring soil protist communities, whereas heterogeneous selection (2.6%) did not. Deterministic processes were significantly more important in the DS than in the MS (Figure 4A). Dispersal limitation was more important in the TM than in the DS (Figure 4D), whereas drift had the least impact in the TM (Figure 4F). Cercozoa and Ciliophora communities were less deterministically driven (12.78% and 17.54%, respectively) than Apicomplexa (30.55%, P < 0.05; Figure S15). Soil TOC and TN significantly decreased the relative influence of determinism, while MAT and soil pH significantly increased it (Figures 5 and S6). MAT and soil pH increased the relative importance of homogeneous selection, while soil pH decreased the relative importance of drift (Figures S6 and S16C-E). Conversely, soil nutrient content (TOC and TN) decreased the relative importance of homogeneous selection (Figures S6 and S16F, G). Soil TOC (28.0%) and MAT (20.0%) were the strongest predictors of determinism ( P < 0.05), while MAP was the best predictor of the relative contribution of drift (20.8%, P < 0.05; Figures 5I and S7B). 4 Discussion 4.1 Soil protist community composition and diversity Although protists are common members of the soil microbiome, most research has focused on other components (Geisen et al. 2017, Geisen et al. 2018, Guerra et al. 2020). Protist α diversity and community composition are often assessed together to reveal the taxonomic or functional groups that contribute most to responses to environmental conditions and ecosystem functions. Phagotrophic Cercozoa and heterotrophic Ciliophora predominated in soil protist communities in grasslands (Figure S4A, Chen et al. 2022b, Zhao et al. 2023), as well as in other habitats (Xiong et al. 2022, Oliverio et al. 2020, Verdon et al. 2025), due to their ability to adapt to cold and dry conditions (Pawlowski et al. 2012, Oliverio et al. 2020). The correlations found between the relative abundance of soil protists and environmental factors in our study further proved that environmental conditions significantly affect protist phyla (Shen et al. 2014, Wu et al. 2022). Ciliophora were particularly abundant in sites with low soil water and nutrient content, specifically in desert steppes (Figures S4C and S5H, I). This suggests that Ciliophora may have evolved to withstand natural stress, as has also been observed in grasslands on the Tibetan Plateau and in deserts in northwestern China (Zhao et al. 2023, Dong et al. 2024). In our study, high soil pH (6–10 pH) reduced the relative abundance of Cercozoa (Figure S5G), and a similar effect was observed in soils with low pH (pH 4–6 (Shen et al. 2014), pH 4–7 (Wu et al. 2022)). This implies that the majority of Cercozoa prefer soils with a neutral or low pH (Bates et al. 2013, Oliverio et al. 2020). These results enhance our understanding of the distribution patterns of dominant protist phyla in temperate grassland soils under varying environmental conditions. In our study, soil protist α diversity (richness, Shannon’s index, and phylogenetic diversity) increased from south to north along the latitude, which was strongly driven by regional changes in MAT and peaked in the meadow steppe (Figure 2). However, this pattern did not align with global protist biodiversity (Bates et al. 2013, Aslani et al. 2022). Certain environmental conditions related to water and/or energy likely promote the growth and metabolic rates of protists, thereby increasing niche sizes and influencing α diversity (Okie et al. 2015, Gray et al. 2016, Geisen et al. 2018). Soil TOC and TP content were positively correlated with the α diversity of soil protists in the studied grasslands (Figure S9, Zhao et al. 2023), which is consistent with the more individuals hypothesis (Storch et al. 2018). Conversely, a lack of resources (e.g., water and nutrients) in sites with higher MAT is likely to limit protist functions directly and indirectly (e.g., by affecting bacterial communities), subsequently reducing α diversity (Figures S2, S6, and S9, Lentendu et al. 2014, Nguyen et al. 2021, Geisen et al. 2018). Increasing aridity likely suppressed protist growth and reduced phylogenetic diversity (Figure S9L), in line with patterns observed in temperate deserts (Dong et al. 2024). The reason for this is that both ecosystems are drylands (AI < 0.65), where soil water availability is often a primary limiting factor to biota and driver for ecosystem functioning (Li et al. 2021b, Hu et al. 2022). Conversely, in forests where soil moisture and aridity are not limiting factors, α diversity is usually positively affected by MAT and soil pH (Wu et al. 2022). Overall, our results highlight the importance of regional (climate) and local (soil) factors in determining protist α diversity, providing new evidence of favorable environmental conditions for protists in temperate grasslands. 4.2 diversity of protist communities β diversity provides a quantitative measure of variation in community composition across habitats. Similar to other large-scale studies (Bates et al. 2013, Aslani et al. 2022), the soil protist communities in our study region exhibited a significant distance-decay pattern, as well as within the grassland type meadow steppe (Figures 1H and S11A). However, the distance-decay relationship was not evident in desert steppes, possibly due to higher habitat homogeneity and/or dispersal potential (Zinger et al. 2014). Furthermore, the turnover component, which dominated the community dissimilarity of soil protists (Figure S13A), was also observed in other small-bodied terrestrial organisms at a large spatial scale spanning several ecosystems (Soininen et al. 2018). Dispersal limitation, geographic distance, and the heterogeneity of certain environmental variables (e.g., MAP, pH, and EC in our study; Figure S14C) are likely to explain the strong turnover of protist communities (Menegotto et al. 2019, Zhang et al. 2020, Su et al. 2024). Our study indicates that MAT, MAP, and AI were the strongest factors in predicting protist β diversity in temperate grasslands (Figures 3C, D, and S12A), aligning with previous findings (Oliverio et al. 2020, Kang et al. 2022, Wu et al. 2022). In addition to MAP, soil pH was found to increase community dissimilarity linearly (Figure S11C). Soil pH directly alters the activity, diversity, and composition of protists and indirectly affects bacterial, fungal, and other above- and below-ground communities that interact with soil protists (Dupont et al. 2016, Geisen et al. 2018). However, our results are contrary to those in forest ecosystems that showed a linear decrease in community dissimilarity with increasing MAT above zero (Wu et al. 2022). In forests, higher MAT increased the growth rates of soil protists and broadened ecological niches, in accordance with the metabolic-niche theory (Okie et al. 2015, Gray et al. 2016). In our study, although MAT and available energy decreased from south to north, MAP, SWC, and, consequently, water availability increased (Figure 1 and S2). Therefore, water deficit in the southern part of our research area possibly acted as a limiting factor, providing an unstable and heterogeneous environment with narrower ecological niches for protists (Okie et al. 2015, Fernández et al. 2016). Our study also indicates that soil protist communities in temperate grasslands were distinctly separated into meadow steppe, temperate meadow, typical (dry) steppe, and desert steppe, each grassland type forming specific environmental conditions (Figure 3C) (Wang et al. 2014, Li et al. 2020). Protist communities in meadow steppes were the most homogeneous, driven by the combined effects of lower MAT, higher MAP, and neutral soil pH compared to the other grassland types (Figures 3B and S11). These factors likely filtered out taxa unable to withstand MAT below zero. In contrast, the heterogeneity of soil protist communities in temperate meadows could be attributed to the high pH (Figures 3B and S11C), which likely facilitated taxonomic and functional divergence (Muscarella and Uriarte 2016, Denelle et al. 2019). Surprisingly, we identified a significant negative relationship between α diversity and β diversity (Figure S11D-F), which represented a relatively rare pattern in soil microbial research. This pattern is likely to emerge when specific favorable environmental conditions broaden realized niches, thereby increasing the number of taxa and their occurrence frequencies in different sites (Okie et al. 2015, Feng et al. 2020, Liu et al. 2022). For example, in the McMurdo Dry Valleys in Antarctica, the α diversity of bacteria decreased with elevation due to a decline in air temperatures, whereas bacterial β diversity increased. This suggests that variations in the microbial community can be predicted by metabolic niche theory, as warmer conditions may increase the metabolic rates and niche width of microbes (Okie et al. 2015). In Neotropical rainforests, high α diversity and low β diversity were observed in the heterotrophic protists Cercozoa and Ciliophora. These patterns are likely determined by local-scale selection-based processes and regional-scale dispersal limitation (Lentendu et al. 2018). The results of the present study, therefore, imply that a contrasting relationship between protist α and β diversity may occur in temperate grasslands due to specific environmental conditions and strong dispersal limitation. 4.3 Assembly processes of protist communities Overall, stochastic processes predominated the soil protist communities of the dominant phyla. In our temperate grasslands (40–50°N), stochasticity accounted for 80.4% of community assembly, likely because protist cyst formation buffers them against environmental selection (Ekelund and Rønn 1994, Nemergut et al. 2013, Geisen et al. 2018). Within habitat types, meadow steppe communities exhibited even higher stochasticity (84.9 ± 1.24%) than desert steppe (79.2 ± 2.14%; Figure 4A), reflecting stronger deterministic filtering in harsher, nutrient-poor desert conditions (Guo et al. 2018, Luan et al. 2020, He et al. 2024, Chen et al. 2023). By contrast, the more productive meadow steppe with greater soil nutrient content could support wider niches and higher biodiversity, thereby amplifying the contribution of random processes (Chase 2010, Li et al. 2020). Dispersal limitation (34.2%) and ecological drift (45.7%) were the dominant stochastic processes across our large study region (Figure S15). Although protists disperse passively via water, wind, or animals, geographic barriers, such as the Greater Khingan Range, and their relatively larger cell size, compared to bacteria, impede long-distance immigration (Stegen et al. 2013, Zhou and Ning 2017, Nemergut et al. 2013). Moreover, limited dispersal could intensify drift, since small protist populations are highly susceptible to random reproductive and mortality events, especially following disturbances (Dini-Andreote et al. 2015, Kang et al. 2022). Despite the strong influence of stochastic processes, homogeneous selection still contributed significantly (17.1%) to community assembly, underscoring the major impact of specific environmental factors (Figure S15A). This process may vary, depending on habitat type and organism. For example, protist communities in croplands and forests were governed by homogeneous selection, rather than other processes (Chen et al. 2022b, Wu et al. 2022), and bacterial communities often become deterministic in low-diversity or post-disturbance contexts (Xun et al. 2019, Jiao et al. 2020, He et al. 2024). Our study found that the contribution of homogeneous selection increased from Ciliophora (15.5%) to Chlorophyta (21.4%) to Apicomplexa (26.1%; Figure S15), indicating that environmental filtering exerts a stronger influence on less abundant taxa. This finding highlights that rare groups, with narrower niche breadths, are more vulnerable to selective pressures and may even be driven to local extinction by severe disturbances (Jiao and Lu 2019). Along the environmental gradients, increases in soil total organic carbon and total nitrogen reduced the relative importance of homogeneous selection, likely because higher nutrient availability expands niche space and biodiversity, thereby diluting selective pressures and favoring stochasticity (Zhang et al. 2016, He et al. 2021, Luan et al. 2020). Conversely, rising MAT and soil pH intensified homogeneous selection and decreased statistical drift (Figures 5 and S16), as extreme temperatures and pH narrow niche breadths and reinforce environmental filtering (Tripathi et al. 2018, He et al. 2021, Guo et al. 2018). In arid grasslands, elevated MAT exacerbates desiccation stress, further amplifying deterministic effects on protist community structure (Ekelund and Rønn 1994, Huang et al. 2017, Dong et al. 2024). Overall, this study revealed the significant impacts of soil pH and MAT on the community assembly of soil protists in temperate grasslands. 5. Conclusions In summary, our study shows that variations in the diversity, composition, and community assembly processes of soil protists in temperate grasslands of the eastern Eurasian steppe are influenced by mean annual temperature and soil properties, such as pH and organic carbon content. Protist α diversity in temperate grasslands increased linearly from south to north, which is contrary to other continental-scale studies, thus providing an alternative scenario for the biogeography of soil protists. Additionally, our study emphasizes contrasting patterns between α and β diversity, suggesting the influence of environmental filtering and strong dispersal limitation. Mean annual temperature and soil pH may regulate the balance between drift and homogeneous selection, underlying the patterns of soil protist communities in temperate grasslands. As the study region has experienced significant temperature increases and expanded dryland areas over the past four decades (Huang et al. 2017), climate warming and drought are likely to intensify environmental filtering processes of microbial communities, resulting in changes in ecosystem functions (Guo et al. 2018, Chen et al. 2023). Our study provides a scientific basis for protecting temperate grassland ecosystems and implies that soil protist communities can serve as bioindicators for assessing the impact of climate change on ecosystem stability and functionality. Declaration of competing interest The authors declare that they have no competing interests.

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Authors Metrics & Citations Metrics Article Usage 215views 151downloads Citations Download citation Anastasiia Zhukova, Tianyu Zhang, Jiazheng Xv, et al. Multiple drivers jointly shape the diversity, composition, and assembly processes of soil protist communities across temperate grasslands. Authorea. 06 August 2025. DOI: https://doi.org/10.22541/au.175447830.08005635/v1 DOI: https://doi.org/10.22541/au.175447830.08005635/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.