Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 Mar 31;73(3):e70072. doi: 10.1111/jeu.70072

Protistan Predators Outshine Fungi in Forest Soil Activity

Longfei Kang 1,2, Kenneth Dumack 1,3,✉
PMCID: PMC13037694  PMID: 41915129

ABSTRACT

Despite extensive research on fungal communities in forest soils, our understanding of the whole eukaryotic diversity and distribution remains limited. Moreover, traditional amplicon sequencing methods often introduce severe PCR and primer biases, further hindering accurate assessment of the microbial community composition in forest soils. To address these challenges, this study used a public metatranscriptomic data set to analyze 51 forest soil samples comprising four countries (Canada, France, Spain, and Sweden). Our results reveal that Arcellinida, a eukaryotic order of shell‐bearing amoebae, represent the most abundant eukaryotic taxon in forest soils, with an average relative abundance of 12.6%. This finding challenges the conventional view that fungi dominate eukaryotic diversity in these ecosystems. Furthermore, our study demonstrates that Arcellinida (R 2 = 0.066, p = 0.006) and soil pH (R 2 = 0.126, p < 0.001) are key biological and environmental drivers, respectively, shaping the composition of eukaryotic communities in forest soils, suggesting distinct impact on the microbial community through predation. These findings offer novel insights into the ecological significance of microbial eukaryotes in forest ecosystems and provide a new framework for investigating the predatory dynamics centered on Arcellinida in forest soil microbial networks.

Keywords: Arcellinida, forest soil ecosystems, metatranscriptomics, microbial communities, microbial ecology

1. Introduction

Forests play a crucial role in terrestrial ecosystems, contributing significantly to global carbon sequestration, biodiversity conservation, and climate regulation (Khadakkar 2022; Leal Filho et al. 2023; Liu et al. 2025). They are often referred to as the lungs of the Earth, providing essential ecosystem services such as water purification, soil stabilization, and habitat provision for a vast array of species. Forest soils, as a complex and dynamic environment that supports a diverse array of life forms, are considered biodiversity hotspots, hosting a wide range of plant, animal, and microbial species (Meena et al. 2023). Microorganisms (e.g., bacteria, fungi, protists and archaea) play essential roles in nutrient cycling, organic matter decomposition, and soil structure formation, thereby influencing forest soil multifunctionality and productivity (Bastida et al. 2021; Fierer and Jackson 2006). In this century, fungi have become one of the most comprehensively and thoroughly investigated eukaryotic communities in forest ecosystems due to their important ecological functions and roles, including decomposing organic matter, forming mutualistic relationships with terrestrial plants, and protecting their host plants against pathogens (Auer et al. 2024; Bahram et al. 2016; Mundra et al. 2021; Wu et al. 2019). However, large‐scale investigations into the diversity and ecological functions of other eukaryotic groups remain limited, and their ecological roles are still poorly understood to date.

Protists are polyphyletic single‐celled eukaryotes and key components of soil food webs, acting as predators of bacteria and fungi (Bonkowski 2004; Clarholm 1985; Estermann et al. 2023; Geisen et al. 2015). Obviously, they play important roles in controlling microbial community structure and function, which in turn affects soil fertility and ecosystem stability in forest soils (Geisen et al. 2018; Oliverio et al. 2020). Arcellinida, commonly known as testate amoebae, are a group of protists belonging to the phylum Amoebozoa. They are widely distributed in terrestrial ecosystems and characterized by their unique, often rigid shells that provide both protection and a means for identification (Dumack et al. 2024). This distinctive feature sets them apart from other protists and makes them valuable subjects for ecological and evolutionary studies. Moreover, Arcellinida act as top predators in microbial food webs, regulating bacterial and fungal populations (García‐Bodelón et al. 2025), and it is hypothesized that their shell functions as a microbial weapon enabling them to rupture large prey like fungal hyphae and nematodes (Dumack et al. 2024; Estermann et al. 2023). Numerous studies have shown that these organisms are primarily identified and enumerated morphologically and are routinely used to provide insights into both the stability and the general ecological health of specialized environments (e.g., soils, sediments, bogs and lakes) (García‐Bodelón et al. 2025; González‐Miguéns et al. 2024; Patterson and Kumar 2002; Useros et al. 2024). They are routinely used for specific applications such as monitoring perturbations in peatland restoration management (Carballeira and Pontevedra‐Pombal 2021; Marcisz et al. 2016) and to evaluate the impact of anthropogenic disturbance on soil properties (Heger et al. 2013; Wanner and Dunger 2001). There is thus an extensive scientific literature on the sensitivity of these organisms to different environmental perturbations (Freitas et al. 2022). Arcellinida also serve as sensitive bioindicators of environmental changes, reflecting disturbances caused by pollution, climate variations, and land management practices (Su et al. 2024). Additionally, these organisms contribute to organic matter decomposition and participate in the carbon and nitrogen cycles (Dufour et al. 2024). Their feeding activities break down complex organic compounds, releasing nutrients back into the soil, thus promoting nutrient availability for plants and other organisms (Heger et al. 2012). Other literature emphasized that Arcellinida also can function as a model group for studying microbial biogeography (Singer et al. 2019; Smith and Wilkinson 2007). The ecological roles of Arcellinida highlight their importance in maintaining the stability and function of terrestrial ecosystems. Therefore, understanding the distribution, community dynamics, and ecological interactions of Arcellinida is essential for developing effective conservation and management strategies.

The traditional classification of Arcellinida relies heavily on morphological characteristics, particularly the size, shape, and composition of their shells. These shells can be composed of self‐secreted elements (e.g., calcareous, siliceous, or chitinoid) or recycled organic and mineral particles (Dufour et al. 2024; Gomaa et al. 2017; González‐Miguéns et al. 2022). Recently, molecular studies have significantly influenced the classification of Arcellinida. Molecular approaches, such as SSU rRNA gene sequencing, have revealed much greater biodiversity than previously estimated through morphological methods (Mauricio‐Sánchez et al. 2025). For example, studies on genera like Nebela, Difflugia, and Hyalosphenia have identified cryptic lineages that are genetically distinct but morphologically indistinguishable (Dufour et al. 2024). These findings challenge the traditional reliance on shells as a primary taxonomic character and highlight the importance of molecular tools in resolving the phylogenetic relationships within Arcellinida (Gomaa et al. 2017; Kosakyan et al. 2016). While morphological characteristics remain important for the initial identification of Arcellinida, molecular biology has become essential for resolving their phylogenetic relationships and uncovering hidden diversity. Future taxonomic studies will likely continue to integrate morphological and molecular approaches to better understand the complexity of Arcellinida.

In recent years, metabarcoding has significantly expanded our understanding of microbial eukaryotic diversity (Cristescu 2014; Useros et al. 2024), such as the transformative impact of metabarcoding approaches targeting the small subunit ribosomal RNA (18S in eukaryotes) on understanding eukaryotic diversity (Santoferrara et al. 2020). However, these methods exhibit significant biases and limitations when applied to Arcellinida (Berney et al. 2004; Lara et al. 2022). Specifically, the 18S rRNA sequences of Arcellinida exhibit high evolutionary heterogeneity, often containing lineage‐specific structural elements and introns, which can extend the amplicon length beyond the capacity of conventional sequencing platforms (Gomaa et al. 2017; Lara et al. 2008). Additionally, sequencing biases may arise from primer mismatches with specific Arcellinida sequences and the amplicon length exceeding the preferred range of sequencing platforms (Choi and Park 2020; Xie et al. 2011). This technical limitation directly results in a significant underrepresentation of certain subgroups (e.g., Glutinoconcha) in the EukBank database, while the suborder Phryganellina, which contains fewer described species, seems disproportionately represented (González‐Miguéns et al. 2022). Therefore, although metabarcoding remains an effective tool for assessing microbial eukaryotic diversity, fully revealing the true environmental diversity of taxa with complex rDNA structures, such as Arcellinida, requires the integration of alternative sequencing technologies (Berney et al. 2015; Geisen et al. 2015; Lara et al. 2022).

In this study, we used a metatranscriptomic approach to reveal the community composition and diversity of prokaryotic and eukaryotic communities in forest ecosystems on a wide scale (Canada, France, Spain and Sweden). Moreover, the driving mechanisms for diversity of prokaryotic and eukaryotic communities were identified. Our results revealed high relative abundance and diversity of Arcellinida, in which communities differed strongly between sites. This study highlighted the importance of Arcellinida in forest ecosystems for the first time.

2. Materials and Methods

2.1. Data Access and Zone Description

In this study, we used a publicly available dataset provided by Auer et al. (2024). Briefly, four coniferous forest zones were selected, and 51 soil samples were collected from Montmorency, Canada (Canada_Mrcy, n = 9), Champenoux, France (France_Chpx, n = 18), Aspurz, Spain (Spain_Aspz, n = 12), and Lamborn, Sweden (Sweden_Lbrn, n = 12). The forest types in Canada_Mrcy, France_Chpx, Spain_Aspz, and Sweden_Lbrn are boreal, temperate, sub‐Mediterranean, and boreal, respectively. We used these metatranscriptomic data (RNA) to evaluate the prokaryotic and eukaryotic communities in forest ecosystems. A detailed geographical and climatic description of these four coniferous forest zones is provided in Table S1.

In the Canadian Montmorency zone, the forest is dominated by balsam fir ( Abies balsamea ), and the altitude ranges between 600 and 1000 m. The soil is classified as Humo‐Ferric Podzols or Orthic (Auer et al. 2024). In the French Champenoux zone, the altitude is 254 m, and the dominant tree species is silver fir ( Abies alba ), with ground vegetation consisting of Fraxinus excelsior seedlings. The Spanish Aspurz zone is a mixed forest with a Mediterranean climate. The forest is dominated by Scots pine ( Pinus sylvestris ), and the altitude is 615 m. In the Swedish Lamborn zone, the predominant tree species is Scots pine ( Pinus sylvestris ), and the altitude is approximately 273 m. The mean stem density is 750 ha−1, and the soil is characterized as sandy moraine/podzol (Lindahl et al. 2007). For each soil sample, a detailed description of artificial disturbances, management practices, and nutrient status is provided in Table S2.

2.2. Bioinformatics Processing

RNA extraction and quality filtering: (i) RNA was extracted as part of a sequential co‐isolation procedure following sample collection. Detailed protocols for RNA extraction and cDNA library preparation are provided in Auer et al. (2024); (ii) The raw metatranscriptomic data quality was evaluated using FastQC v.0.11.9 (Andrews 2010); (iii) TrimGalore v.0.6.7 (Krueger et al. 2021) was employed to identify and remove adapters, trim low‐quality bases (quality score < 30), and discard the last 10 bases at the 3′ end; (iv) Paired‐end reads were assembled into contigs using Mothur v.1.45.3 (Schloss et al. 2009); (v) Contigs were subsequently screened using parameters optimized for the dataset, including a minimum length of 90 bp and a maximum of 2 ambiguous bases (Heck et al. 2023).

After raw data quality control (RDQC), sequences were screened using the BLASTN algorithm (Camacho et al. 2009) against specific databases. Eukaryotic and prokaryotic taxa were identified using the PR2 v5.0.0 database (Guillou et al. 2013) and the SILVA 138 SSU Ref NR99 database (Pruesse et al. 2007), respectively. The best hit for each sequence was retained based on an e‐value threshold of 1e−35 for PR2 and 1e−30 for SILVA, along with a bit‐score threshold of 140 and an identity threshold above 92% (Heck et al. 2023). Sequences from Archaeplastida (plants) and their chloroplasts were excluded to focus on the forest soil microbial community. Taxon counts were aggregated at the genus level to define OTUs for downstream analyses. Data filtering and analysis were performed in R v.4.0.6 (R Core Team 2021) using the packages tidyr v.1.1.3 (Wickham and Girlich 2022), dplyr v.1.0.7 (Wickham et al. 2022), ape v.5.6‐2 (Paradis et al. 2004), lattice v.0.20‐41 (Sarkar 2008), and vegan v.2.5‐7 (Dixon 2003). Visualization was performed using ggplot2 v.3.3.4 (Wickham 2016).

2.3. Visualization and Statistical Analyses

To visualize the variations in prokaryotic and eukaryotic community composition among different forest zones, stacked bar plots of the top 20 most abundant communities (at the order and genus levels) were created using the vegan v.2.5‐7 (Dixon 2003), car v.3.1‐2 (Fox and Weisberg 2018), ggplot2 v.3.3.4 (Wickham 2016), and reshape2 v.1.4.4 (Wickham 2020) packages in R software v.3.8.5. The relative read numbers of forest soil microbial kingdoms were analyzed using Microsoft Office Excel (v.2010) and the Statistical Package for the Social Sciences (SPSS, v.22.0) (Table 1 and Tables S1–S3). To further elucidate the influences of environmental factors and microbial taxa (top 10 most abundant communities at the order level) on the community structure of prokaryotes and eukaryotes, non‐metric multidimensional scaling (NMDS) plots were constructed using the envfit, vegdist, and metaMDS functions from the vegan package (Table S3). These plots were generated based on the Bray‐Curtis distance method (Moebius‐Clune et al. 2013).

TABLE 1.

Relative read numbers of microbial kingdoms in a forest ecosystem.

Domain Groups Canada_Mrcy Sweden_Lbrn France_Chpx Spain_Aspz Average
(Percentage, %)
Prokaryotes Bacterial 70.5 ± 0.47 a 70.4 ± 0.20 a 70.4 ± 0.36 a 70.4 ± 0.28 a 70.4
Archaea 0.17 ± 0.02 d 0.29 ± 0.01 d 0.26 ± 0.05 d 0.27 ± 0.10 d 0.25
Eukaryotes Protists 18.9 ± 1.35 b 22.3 ± 2.14 b 19.6 ± 1.29 b 19.4 ± 1.06 b 20.1
Fungi 10.4 ± 2.09 c 6.99 ± 1.37 c 9.53 ± 2.16 c 9.61 ± 1.90 c 9.13
Animal (Metazoa) 0.036 ± 0.024 e 0.036 ± 0.021 e 0.210 ± 0.114 d 0.284 ± 0.087 d 0.14
Prokaryotes/Eukaryotes 70.7/29.3 70.7/29.3 70.7/29.3 70.7/29.3 /
Open in a new tab

Note: Distributions (mean ± SE) of bacteria, archaea, protists, fungi and metazoa at the Canada_Mrcy (n = 9), Sweden_Lbrn (n = 12), France_Chpx (n = 16) and Spain_Aspz (n = 12) sites.

2.4. Language Editing

The authors used the generative AI tool ChatGPT (OpenAI) solely for superficial language editing (grammar and clarity) in parts of the manuscript. It did not contribute to the generation of scientific content, data interpretation, or conclusions, nor to figure preparation or image manipulation. The authors retain full responsibility for all aspects of the work.

3. Results

3.1. Sequence Reads Distribution

The taxonomic richness of microbial organisms associated with forest soil was found to be high. Specifically, a total of 11,026,251 SSU rRNA contiguous sequence reads were identified. These sequence reads were assigned to 1527 distinct orders (Table S4) and 4083 distinct species. A consistent distribution pattern of sequence reads was observed across different sampling sites. Despite the differences between various sampling sites, eukaryotes (29.3% of all reads and 31.8% of order richness) were predominantly represented by protists, which accounted for 89.9% of eukaryotic orders and contributed 64.4% of the eukaryotic sequence reads (Table 1 and Table S4). Fungi made up 8.8% of eukaryotic orders and were responsible for 35.5% of the sequence reads, while microscopic Metazoa comprised 1.2% of eukaryotic order richness and contributed 0.13% of the sequence reads. Prokaryotes represented the majority of sequence reads and order richness, with approximately 70.7% and 68.2%, respectively. Among prokaryotes, bacteria accounted for 98.3% of all prokaryotic orders, representing approximately 99.7% of all prokaryotic sequence reads. Archaea represented approximately 1.7% of prokaryotic order richness and contributed approximately 0.36% of all prokaryotic sequence reads.

3.2. Microbial Community Composition

Moreover, at the order level, the composition of eukaryotic community was dominated by Arcellinida (Protists_Amoebozoa), making up 25.4% of all eukaryotic rRNA reads, followed by Placidida (10.4%, Protists_TSAR), Aphelidiales (8.6%, Fungi_Obazoa), Agaricomycotina (7.62%, Fungi_Obazoa), Saccharomycotina (6.2%, Fungi_Obazoa), Hymenostomatia (4.7%, Protists_TSAR), Pezizomycotina (3.6%, Fungi_Obazoa) and Cryomonadida (3.1%, Protists_TSAR). The prokaryotic community was dominated by Frankiales (8.9%, Bacteria_Actinobacteriota), followed by Enterobacterales (8.3%, Bacteria_Proteobacteria), Burkholderiales (5.2%, Bacteria_Proteobacteria), Acidobacteriales (5.1%, Bacteria_ Acidobacteriota), Pedosphaerales (4.2%, Bacteria_Verrucomicrobiota), Subgroup_2 (3.8%, Bacteria_Acidobacteriota), Polyangiales (3.1%, Bacteria_Myxococcota), and Gemmatales (2.5%, Bacteria_Planctomycetota) (Figure 1). At the genus level, Hyalosphenia (19.1%, Protists_ Amoebozoa) predominated in all eukaryotic rRNA reads, followed by the second most abundant genus Wobblia (8.8%, Protists_TSAR). Other highly abundant groups included Aphelidium (5.9%, Fungi_Obazoa), Tetrahymena (4.7%, Protists_TSAR), Candida (4.5%, Fungi_Obazoa), and Bullinularia (2.8%, Protists_Amoebozoa), while the prokaryotic community was dominated by Acidothermus (8.0%, Bacteria_Acidobacteriota), followed by Klebsiella (4.2%, Bacteria_ Proteobacteria), Shigella (3.26%, Bacteria_Proteobacteria), Uncultured bacterium NA1 (2.9%, Bacteria_Acidobacteriota), Streptococcus (2.4%, Bacteria_Firmicutes), and Uncultured_1 (2.2%, Bacteria_Planctomycetota) (Figure 2).

FIGURE 1.

FIGURE 1

Open in a new tab

Microbial community composition in different forest soils at the order level. Stacked bar plots display the relative abundance of the 20 most abundant eukaryotic (a) and prokaryotic (b) taxa.

FIGURE 2.

FIGURE 2

Open in a new tab

Microbial community composition in different forest soils at the genus level. Stacked bar plots display the relative abundance of the 20 most abundant eukaryotic (a) and prokaryotic (b) taxa. Shigella represents Escherichia–Shigella. Uncultured bacterium NA1 and NA2 remain unidentified at the genus level but have been classified at higher taxonomic levels as Bacteria_Acidobacteriota_Acidobacteriae_Subgroup_2_uncultured‐bacterium and Bacteria_Proteobacteria_Gammaproteobacteria_WD260_uncultured‐bacterium, respectively. Uncultured 1 and 2 represent Bacteria_Planctomycetota_Planctomycetes_Gemmatales_Gemmataceae_uncultured and Bacteria_Proteobacteria_Alphaproteobacteria_Acetobacterales_Acetobacteraceae_uncultured, respectively. Uncultured bacterium 1, 2, 3, and 4 represent Bacteria_Planctomycetota_Phycisphaerae_Tepidisphaerales_WD2101_soil_group_uncultured‐bacterium, Bacteria_Proteobacteria_Alphaproteobacteria_Elsterales_uncultured_uncultured_bacterium, Bacteria_Acidobacteriota_Acidobacteriae_Acidobacteriales_uncultured_uncultured_bacterium, and Bacteria_Acidobacteriota_Acidobacteriae_Acidobacteriales_uncultured_uncultured_bacterium, respectively.

3.3. Environmental and Biological Factors Shape Microbial Communities

To further assess the potential impact of soil environmental factors (altitude, pH, SOC, STN, AMP, and AMT) and microbial groups (the top 10 most abundant microbial orders) on microbial community composition in forest soil ecosystems, we performed non‐metric multidimensional scaling (NMDS) analyses for eukaryotic (Figure 3a) and prokaryotic (Figure 3b) rRNA sequence datasets based on Bray‐Curtis distances. For eukaryotic taxa, the coordinate points of microbial communities in different forest soils were significantly separated, with only minor overlap in sampling plots between France_Chpx and Spain_Aspz (Figure 3a). Moreover, soil pH (R 2 = 0.126, p < 0.001), AMT (R 2 = 0.116, p < 0.001), and SOC (R 2 = 0.041, p = 0.010) had significant effects on eukaryotic communities, explaining 12.6%, 11.6%, and 4.1% of the variation, respectively (Table S3). Arcellinida (R 2 = 0.066, p = 0.006) and Placidida (R 2 = 0.064, p = 0.009) accounted for 6.6% and 6.4% of the eukaryotic community composition, respectively. Hymenostomatia (R 2 = 0.057, p = 0.016) and Agaricomycotina (R 2 = 0.036, p = 0.048) had weaker structuring effects on the eukaryotic community composition, explaining 5.7% and 3.6% of the variation, respectively.

FIGURE 3.

FIGURE 3

Open in a new tab

Non‐metric multidimensional scaling (NMDS) on the Bray‐Curtis distance between each sample. Dimensionality reduction of the Bray‐Curtis distance shows the eukaryotic (a) and prokaryotic (b) communities in forest soils. Dots in different colors (red, green, blue and yellow) represent sampling sites, while dots with varying transparency represent different treatments. Arrows show envfit results. Red and blue arrows represent the explanatory power of environmental factors and microbial groups (order level) on community composition, respectively.

For prokaryotic taxa, the coordinate points representing microbial communities in various forest soils also showed significant separation, with minor overlap in sampling plots between the Canada_Mrcy and Sweden_Lbrn zones (Figure 3b). The prokaryotic diversity metric was highly correlated with AMT (R 2 = 0.092, p < 0.001), pH (R 2 = 0.077, p < 0.001), and altitude (R 2 = 0.042, p < 0.001), explaining 9.2%, 7.7%, and 4.2% of the variation, respectively (Table S3). Polyangiales (R 2 = 0.079, p = 0.001), Burkholderiales (R 2 = 0.062, p = 0.008), Frankiales (R 2 = 0.053, p = 0.011) and Acidobacteriales (R 2 = 0.030, p = 0.048) explained 7.9%, 6.2%, 5.3% and 4.8% of the prokaryotic community composition, respectively. All these taxa (eukaryotic and prokaryotic groups) were highly influenced by soil pH, explaining the variation in the communities of eukaryotes (12.6%) and prokaryotes (7.7%). In contrast, SOC, STN and AMP contributed to a lower extent, explaining marginal percentages (< 4.1%) of the variation in the three ordinations.

4. Discussion

Our findings showed that Arcellinida had the highest relative abundance (with an average of 12.6%) among eukaryotes at the order level (Figure 1). This result further confirms that Arcellinida, rather than individual fungal taxa, is the dominant eukaryotic order in forest ecosystems. Specifically, the relative abundance of Arcellinida was significantly higher at the Canadian site compared to other sites. We infer that this pattern is primarily driven by the local climate, characterized by a low mean annual temperature and high annual precipitation (Table S1), which promotes the accumulation of organic matter in forest soils. The influence of these climatic factors, particularly the mean annual temperature (R 2 = 0.116, p = 0.001), on the community diversity of eukaryotic taxa has also been demonstrated in our study (Figure 3a). Additionally, Arcellinida exhibited a preference for specific microhabitats within forest ecosystems, showing significantly higher relative abundance in the top soil layers compared to the lower layers. This suggests that Arcellinida spp. tend to aggregate in relatively stable environments, where the rich organic matter in the upper soil layers provides ample nutritional resources. Furthermore, this distribution pattern aligns with that of bacteria and fungi, reinforcing the idea that the distribution and abundance of predators are, to some extent, influenced by the availability of their prey. The findings from Berlinches de Gea et al. (2024) and Xiong et al. (2018) also confirm this pattern. Other studies have also highlighted that environmental and nutrient limitations not only restrict the distribution and growth rate of Arcellinida but also drive them to develop new survival strategies (Porfirio‐Sousa et al. 2024; Useros et al. 2023).

Moreover, prokaryotes (bacteria and archaea), which account for more than 70% of reads in our metatranscriptomic result (Table 1), are fundamental drivers of forest soil functioning. Major bacterial groups such as Proteobacteria (Enterobacterales, Rhizobiales, Haliangiales, etc.), Acidobacteria (Acidobacteriales, Solibacterales, etc.) Actinobacteria (Frankiales, Solirubrobacterales, etc.) and Bacteroidota (Chitinophagales, Sphingobacteriales, etc.) (Figure 1) play key roles in organic matter decomposition and the cycling of carbon, nitrogen and phosphorus, thereby regulating nutrient availability (Meng et al. 2024; Revillini et al. 2025) and energy flow (Fierer 2017; Goberna and Verdú 2018) in soil ecosystems.

In addition to their metabolic functions, prokaryotes structure microbial communities through both bottom‐up resource provision and top‐down regulation via predation by microbial eukaryotes (Groß et al. 2023). Such predation‐driven interactions can alter bacterial community composition and turnover, contributing to ecosystem stability and the maintenance of ecosystem functioning through functional redundancy and resilience (Tian et al. 2023). These processes provide an ecological context for our observation of abundant Arcellinida and highlight the importance of trophic interactions linking prokaryotic and eukaryotic communities.

In forest ecosystem systems, Arcellinida play important ecological and biogeochemical roles through complex interactions with bacteria and fungi, including symbiosis and diverse antagonisms, such as predation or competition (de Berlinches Gea et al. 2025). They regulate microbial community structure and influence nutrient cycling and ecosystem functioning (García‐Bodelón et al. 2025). Accordingly, their community composition and diversity show pronounced variation across microhabitats, soil types, and climatic gradients (Racoma et al. 2025).

In conclusion, we conclude that PCR‐ and primer‐based metabarcoding underestimates the abundance of a historically, morphologically well‐studied group. With metatranscriptomics this methodological hurdle does no longer exist and we encourage forest ecologists to use this method to achieve a more comprehensive overview of the microbial community in forest soils. In this study, we decided for a broad, higher‐taxonomy perspective, but in future species‐level analyses can be conducted.

Author Contributions

Longfei Kang: writing – original draft, investigation, visualization, software, methodology, formal analysis, validation, funding acquisition. Kenneth Dumack: conceptualization, visualization, methodology, data curation, supervision, resources, project administration, funding acquisition, writing – review and editing.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Location and climatic description of the four coniferous forest sites.

Table S2: Soil types and bioSample description of the four coniferous forest sites.

Table S3: PERMANOVA results for taxonomic groups (eukaryotic and prokaryotic communities) across different environmental and biological factors.

JEU-73-e70072-s002.docx (44.5KB, docx)

Table S4: Order‐level taxonomic assignment of metatranscriptomic rRNA reads for prokaryotes and eukaryotes.

JEU-73-e70072-s001.xlsx (339.8KB, xlsx)

Acknowledgments

This study was funded by the Sino‐German (CSC‐DAAD) Postdoc Scholarship Program (2023‐57678375). The authors also thank the Project of Shaanxi Provincial Department of Education (24JK0749), Research Program of Higher‐level Talents from Yulin University (2023GK84, 2024XZGY05 and 20250710) for providing the support. We sincerely appreciate the time and effort of the reviewers in providing valuable feedback to improve this manuscript. Open Access funding enabled and organized by Projekt DEAL.

Data Availability Statement

The data that support the findings of this study are available in JGI Gold at https://gold.jgi.doe.gov/project?id=Gp0225829, reference number SAMN08776709–SAMN06265162. These data were derived from the following resources available in the public domain: Auer et al. (2024), https://doi.org/10.1111/nph.19471.

References

  1. Andrews, S. 2010. “Fastqc‐A Quality Control Tool for High Throughput Sequence Data.” https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
  2. Auer, L. , Buée M., Fauchery L., et al. 2024. “Metatranscriptomics Sheds Light on the Links Between the Functional Traits of Fungal Guilds and Ecological Processes in Forest Soil Ecosystems.” New Phytologist 242, no. 4: 1676–1690. 10.1111/nph.19471. [DOI] [PubMed] [Google Scholar]
  3. Bahram, M. , Kohout P., Anslan S., Harend H., Abarenkov K., and Tedersoo L.. 2016. “Stochastic Distribution of Small Soil Eukaryotes Resulting From High Dispersal and Drift in a Local Environment.” ISME Journal 10, no. 4: 885–896. 10.1038/ismej.2015.164. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bastida, F. , Eldridge D. J., García C., Png G. K., Bardgett R. D., and Delgado‐Baquerizo M.. 2021. “Soil Microbial Diversity–Biomass Relationships Are Driven by Soil Carbon Content Across Global Biomes.” ISME Journal 15, no. 7: 2081–2091. 10.1038/s41396-021-00906-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Berlinches de Gea, A. , Geisen S., Grootjans F., Wilschut R. A., and Schwelm A.. 2024. “Species‐Specific Predation Determines the Feeding Impacts of Six Soil Protist Species on Bacterial and Eukaryotic Prey.” European Journal of Protistology 94: 126090. 10.1016/j.ejop.2024.126090. [DOI] [PubMed] [Google Scholar]
  6. Berney, C. , Fahrni J., and Pawlowski J.. 2004. “How Many Novel Eukaryotic ‘Kingdoms’? Pitfalls and Limitations of Environmental DNA Surveys.” BMC Biology 2, no. 1: 13. 10.1186/1741-7007-2-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Berney, C. , Geisen S., Van Wichelen J., et al. 2015. “Expansion of the ‘Reticulosphere’: Diversity of Novel Branching and Network‐Forming Amoebae Helps to Define Variosea (Amoebozoa).” Protist 166, no. 2: 271–295. 10.1016/j.protis.2015.04.001. [DOI] [PubMed] [Google Scholar]
  8. Bonkowski, M. 2004. “Protozoa and Plant Growth: The Microbial Loop in Soil Revisited.” New Phytologist 162, no. 3: 617–631. 10.1111/j.1469-8137.2004.01066.x. [DOI] [PubMed] [Google Scholar]
  9. Camacho, C. , Coulouris G., Avagyan V., et al. 2009. “BLAST+: Architecture and Applications.” BMC Bioinformatics 10, no. 1: 421. 10.1186/1471-2105-10-421. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Carballeira, R. , and Pontevedra‐Pombal X.. 2021. “Diversity of Testate Amoebae as an Indicator of the Conservation Status of Peatlands in Southwest Europe.” Diversity 13, no. 6: 269. 10.3390/d13060269. [DOI] [Google Scholar]
  11. Choi, J. , and Park J. S.. 2020. “Comparative Analyses of the V4 and V9 Regions of 18S rDNA for the Extant Eukaryotic Community Using the Illumina Platform.” Scientific Reports 10, no. 1: 6519. 10.1038/s41598-020-63561-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Clarholm, M. 1985. “Interactions of Bacteria, Protozoa and Plants Leading to Mineralization of Soil Nitrogen.” Soil Biology and Biochemistry 17, no. 2: 181–187. 10.1016/0038-0717(85)90113-0. [DOI] [Google Scholar]
  13. Cristescu, M. E. 2014. “From Barcoding Single Individuals to Metabarcoding Biological Communities: Towards an Integrative Approach to the Study of Global Biodiversity.” Trends in Ecology & Evolution 29, no. 10: 566–571. 10.1016/j.tree.2014.08.001. [DOI] [PubMed] [Google Scholar]
  14. de Berlinches Gea, A. , Walochnik J., Boenigk J., et al. 2025. “Protists as Determinants of the One Health Framework.” ISME Journal 19, no. 1: wraf179. 10.1093/ismejo/wraf179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dixon, P. 2003. “VEGAN, a Package of R Functions for Community Ecology.” Journal of Vegetation Science 14, no. 6: 927–930. 10.1111/j.1654-1103.2003.tb02228.x. [DOI] [Google Scholar]
  16. Dufour, O. , Hoffman E., Sleith R., Katz L. A., Weiner A., and Sehein T.. 2024. “Testate Amoebae (Arcellinida, Amoebozoa) Community Diversity in New England Bogs and Fens Assessed Through Lineage‐Specific Amplicon Sequencing.” European Journal of Protistology 92: 126049. 10.1016/j.ejop.2023.126049. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Dumack, K. , Lara E., Duckert C., et al. 2024. “It's Time to Consider the Arcellinida Shell as a Weapon.” European Journal of Protistology 92: 126051. 10.1016/j.ejop.2024.126051. [DOI] [PubMed] [Google Scholar]
  18. Estermann, A. H. , Teixeira Pereira Bassiaridis J., Loos A., et al. 2023. “Fungivorous Protists in the Rhizosphere of Arabidopsis thaliana—Diversity, Functions, and Publicly Available Cultures for Experimental Exploration.” Soil Biology and Biochemistry 187: 109206. 10.1016/j.soilbio.2023.109206. [DOI] [Google Scholar]
  19. Fierer, N. 2017. “Embracing the Unknown: Disentangling the Complexities of the Soil Microbiome.” Nature Reviews Microbiology 15: 579–590. 10.1038/nrmicro.2017.87. [DOI] [PubMed] [Google Scholar]
  20. Fierer, N. , and Jackson R. B.. 2006. “The Diversity and Biogeography of Soil Bacterial Communities.” Proceedings of the National Academy of Sciences 103, no. 3: 626–631. 10.1073/pnas.0507535103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Fox, J. , and Weisberg S.. 2018. An R Companion to Applied Regression. SAGE Publications. [Google Scholar]
  22. Freitas, Y. d. G. C. , Ramos B. R. D., da Silva Y. G., et al. 2022. “Testate Amoebae: A Review on Their Multiple Uses as Bioindicators.” Acta Protozoologica 61: 1–21. 10.4467/16890027AP.22.001.15671. [DOI] [Google Scholar]
  23. García‐Bodelón, Á. , Baković N., Cano E., Useros F., Lara E., and González‐Miguéns R.. 2025. “Predators in the Dark: Metabarcoding Reveals Arcellinida Communities Associated With Bat Guano, Endemic to Dinaric Karst in Croatia.” Microbial Ecology 87, no. 1: 166. 10.1007/s00248-024-02483-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Geisen, S. , Koller R., Hünninghaus M., Dumack K., Urich T., and Bonkowski M.. 2015. “The Soil Food Web Revisited: Diverse and Widespread Mycophagous Soil Protists.” Soil Biology and Biochemistry 94: 10–18. 10.1016/j.soilbio.2015.11.010. [DOI] [Google Scholar]
  25. Geisen, S. , Mitchell E. A. D., Adl S., et al. 2018. “Soil Protists: A Fertile Frontier in Soil Biology Research.” FEMS Microbiology Reviews 42, no. 3: 293–323. 10.1093/femsre/fuy006. [DOI] [PubMed] [Google Scholar]
  26. Goberna, M. , and Verdú M.. 2018. “Phylogenetic‐Scale Disparities in the Soil Microbial Diversity–Ecosystem Functioning Relationship.” ISME Journal 12, no. 9: 2152–2162. 10.1038/s41396-018-0162-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gomaa, F. , Lahr D. J. G., Todorov M., Li J., and Lara E.. 2017. “A Contribution to the Phylogeny of Agglutinating Arcellinida (Amoebozoa) Based on SSU rRNA Gene Sequences.” European Journal of Protistology 59: 99–107. 10.1016/j.ejop.2017.03.005. [DOI] [PubMed] [Google Scholar]
  28. González‐Miguéns, R. , Cano E., García‐Gallo Pinto M., et al. 2024. “The Voice of the Little Giants: Arcellinida Testate Amoebae in Environmental DNA‐Based Bioindication, From Taxonomy Free to Haplotypic Level.” Molecular Ecology Resources 24, no. 7: e13999. 10.1111/1755-0998.13999. [DOI] [PubMed] [Google Scholar]
  29. González‐Miguéns, R. , Todorov M., Blandenier Q., et al. 2022. “Deconstructing Difflugia: The Tangled Evolution of Lobose Testate Amoebae Shells (Amoebozoa: Arcellinida) Illustrates the Importance of Convergent Evolution in Protist Phylogeny.” Molecular Phylogenetics and Evolution 175: 107557. 10.1016/j.ympev.2022.107557. [DOI] [PubMed] [Google Scholar]
  30. Groß, V. , Reinhard A., Petters S., Pichler M., and Urich T.. 2023. “Adding Complexity to Soil Food Webs: Myxobacteria Have Broad Predation Spectra With Bacteria, Yeasts and Filamentous Fungi In Vitro.” European Journal of Soil Biology 117: 103508. 10.1016/j.ejsobi.2023.103508. [DOI] [Google Scholar]
  31. Guillou, L. , Bachar D., Audic S., et al. 2013. “The Protist Ribosomal Reference Database (PR2): A Catalog of Unicellular Eukaryote Small Sub‐Unit rRNA Sequences With Curated Taxonomy.” Nucleic Acids Research 41, no. D1: D597–D604. 10.1093/nar/gks1160. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Heck, N. , Freudenthal J., and Dumack K.. 2023. “Microeukaryotic Predators Shape the Wastewater Microbiome.” Water Research 242: 120293. 10.1016/j.watres.2023.120293. [DOI] [PubMed] [Google Scholar]
  33. Heger, T. J. , Mitchell E. A. D., and Leander B. S.. 2013. “Holarctic Phylogeography of the Testate Amoeba Yalosphenia Papilio (Amoebozoa: Arcellinida) Reveals Extensive Genetic Diversity Explained More by Environment Than Dispersal Limitation.” Molecular Ecology 22, no. 20: 5172–5184. 10.1111/mec.12449. [DOI] [PubMed] [Google Scholar]
  34. Heger, T. J. , Straub F., and Mitchell E. A. D.. 2012. “Impact of Farming Practices on Soil Diatoms and Testate Amoebae: A Pilot Study in the DOK‐Trial at Therwil, Switzerland.” European Journal of Soil Biology 49: 31–36. 10.1016/j.ejsobi.2011.08.007. [DOI] [Google Scholar]
  35. Khadakkar, S. 2022. “Primary Forest Loss in Biodiverse Indian States.” Science 377, no. 6606: 586–587. 10.1126/science.add8815. [DOI] [PubMed] [Google Scholar]
  36. Kosakyan, A. , Gomaa F., Lara E., and Lahr D. J. G.. 2016. “Current and Future Perspectives on the Systematics, Taxonomy and Nomenclature of Testate Amoebae.” European Journal of Protistology 55: 105–117. 10.1016/j.ejop.2016.02.001. [DOI] [PubMed] [Google Scholar]
  37. Krueger, F. , James F., Ewels P., Afyounian E., and Schuster‐Boeckler B.. 2021. “FelixKrueger/TrimGalore: V0.6.7—DOI via Zenodo.” Zenodo. 10.5281/zenodo.5127899.
  38. Lara, E. , Heger T. J., Ekelund F., Lamentowicz M., and Mitchell E. A. D.. 2008. “Ribosomal RNA Genes Challenge the Monophyly of the Hyalospheniidae (Amoebozoa: Arcellinida).” Protist 159, no. 2: 165–176. 10.1016/j.protis.2007.09.003. [DOI] [PubMed] [Google Scholar]
  39. Lara, E. , Singer D., and Geisen S.. 2022. “Discrepancies Between Prokaryotes and Eukaryotes Need to be Considered in Soil DNA‐Based Studies.” Environmental Microbiology 24, no. 9: 3829–3839. 10.1111/1462-2920.16019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Leal Filho, W. , Nagy G. J., Setti A. F. F., et al. 2023. “Handling the Impacts of Climate Change on Soil Biodiversity.” Science of the Total Environment 869: 161671. 10.1016/j.scitotenv.2023.161671. [DOI] [PubMed] [Google Scholar]
  41. Lindahl, B. D. , Ihrmark K., Boberg J., et al. 2007. “Spatial Separation of Litter Decomposition and Mycorrhizal Nitrogen Uptake in a Boreal Forest.” New Phytologist 173, no. 3: 611–620. 10.1111/j.1469-8137.2006.01936.x. [DOI] [PubMed] [Google Scholar]
  42. Liu, Y. , Wang H., Schindlbacher A., et al. 2025. “Soil Respiration Related to the Molecular Composition of Soil Organic Matter in Subtropical and Temperate Forests Under Soil Warming.” Soil Biology and Biochemistry 201: 109661. 10.1016/j.soilbio.2024.109661. [DOI] [Google Scholar]
  43. Marcisz, K. , Colombaroli D., Jassey V. E. J., et al. 2016. “A Novel Testate Amoebae Trait‐Based Approach to Infer Environmental Disturbance in Sphagnum Peatlands.” Scientific Reports 6, no. 1: 33907. 10.1038/srep33907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Mauricio‐Sánchez, E. , Soler‐Zamora C., and Lara E.. 2025. “The Devil Is in the Details: Cylindrifflugia luciferina Spec. Nov. Reveals the Phylogenetic Backbone of the Infraorder Cylindrothecina (Amoebozoa, Arcellinida).” European Journal of Protistology 97: 126131. 10.1016/j.ejop.2024.126131. [DOI] [Google Scholar]
  45. Meena, M. , Yadav G., Sonigra P., et al. 2023. “Multifarious Responses of Forest Soil Microbial Community Toward Climate Change.” Microbial Ecology 86, no. 1: 49–74. 10.1007/s00248-022-02051-3. [DOI] [PubMed] [Google Scholar]
  46. Meng, Y. , Chen H., Wang B., et al. 2024. “Soil Biota Associated With Soil N Cycling Under Multiple Anthropogenic Stressors in Grasslands.” Applied Soil Ecology 193: 105134. 10.1016/j.apsoil.2023.105134. [DOI] [Google Scholar]
  47. Moebius‐Clune, D. J. , Moebius‐Clune B. N., van Es H. M., and Pawlowska T. E.. 2013. “Arbuscular Mycorrhizal Fungi Associated With a Single Agronomic Plant Host Across the Landscape: Community Differentiation Along a Soil Textural Gradient.” Soil Biology and Biochemistry 64: 191–199. 10.1016/j.soilbio.2012.12.014. [DOI] [Google Scholar]
  48. Mundra, S. , Kjønaas O. J., Morgado L. N., Krabberød A. K., Ransedokken Y., and Kauserud H.. 2021. “Soil Depth Matters: Shift in Composition and Inter‐Kingdom Co‐Occurrence Patterns of Microorganisms in Forest Soils.” FEMS Microbiology Ecology 97, no. 3: fiab022. 10.1093/femsec/fiab022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Oliverio, A. M. , Geisen S., Delgado‐Baquerizo M., Maestre F. T., Turner B. L., and Fierer N.. 2020. “The Global‐Scale Distributions of Soil Protists and Their Contributions to Belowground Systems.” Science Advances 6, no. 4: eaax8787. 10.1126/sciadv.aax8787. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Paradis, E. , Claude J., and Strimmer K.. 2004. “APE: Analyses of Phylogenetics and Evolution in R Language.” Bioinformatics 20, no. 2: 289–290. 10.1093/bioinformatics/btg412. [DOI] [PubMed] [Google Scholar]
  51. Patterson, R. T. , and Kumar A.. 2002. “A Review of Current Testate Rhizopod (Thecamoebian) Research in Canada.” Palaeogeography, Palaeoclimatology, Palaeoecology 180, no. 1: 225–251. 10.1016/S0031-0182(01)00430-8. [DOI] [Google Scholar]
  52. Porfirio‐Sousa, A. L. , Tice A. K., Morais L., et al. 2024. “Amoebozoan Testate Amoebae Illuminate the Diversity of Heterotrophs and the Complexity of Ecosystems Throughout Geological Time.” Proceedings of the National Academy of Sciences 121, no. 30: e2319628121. 10.1073/pnas.2319628121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pruesse, E. , Quast C., Knittel K., et al. 2007. “SILVA: A Comprehensive Online Resource for Quality Checked and Aligned Ribosomal RNA Sequence Data Compatible With ARB.” Nucleic Acids Research 35, no. 21: 7188–7196. 10.1093/nar/gkm864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. R Core Team . 2021. “A Language and Environment for Statistical Computing.” https://cir.nii.ac.jp/crid/1370298757441457025.
  55. Racoma, C. J. M. , Tomacas L. L., Dagamac N. H. A., and Tekle Y. I.. 2025. “Underexplored Microbiota of Testate Amoeba in Southeast Asian Ecosystems: A Systematic Review and Meta‐Analysis.” Journal of Eukaryotic Microbiology 72, no. 5: e70047. 10.1111/jeu.70047. [DOI] [PubMed] [Google Scholar]
  56. Revillini, D. , Searcy C. A., and Afkhami M. E.. 2025. “50‐Year Fire Legacy Regulates Soil Microbial Carbon and Nutrient Cycling Responses to New Fire.” Soil Biology and Biochemistry 208: 109868. 10.1016/j.soilbio.2025.109868. [DOI] [Google Scholar]
  57. Santoferrara, L. , Burki F., Filker S., Logares R., Dunthorn M., and McManus G. B.. 2020. “Perspectives From Ten Years of Protist Studies by High‐Throughput Metabarcoding.” Journal of Eukaryotic Microbiology 67, no. 5: 612–622. 10.1111/jeu.12813. [DOI] [PubMed] [Google Scholar]
  58. Sarkar, D. 2008. “A Technical Overview of Lattice.” In Lattice: Multivariate Data Visualization With R, 13–33. Springer. 10.1007/978-0-387-75969-2_2. [DOI] [Google Scholar]
  59. Schloss, P. D. , Westcott S. L., Ryabin T., et al. 2009. “Introducing Mothur: Open‐Source, Platform‐Independent, Community‐Supported Software for Describing and Comparing Microbial Communities.” Applied and Environmental Microbiology 75, no. 23: 7537–7541. 10.1128/AEM.01541-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Singer, D. , Mitchell E. A. D., Payne R. J., et al. 2019. “Dispersal Limitations and Historical Factors Determine the Biogeography of Specialized Terrestrial Protists.” Molecular Ecology 28, no. 12: 3089–3100. 10.1111/mec.15117. [DOI] [PubMed] [Google Scholar]
  61. Smith, H. G. , and Wilkinson D. M.. 2007. “Not All Free‐Living Microorganisms Have Cosmopolitan Distributions—The Case of Nebela (Apodera) Vas Certes (Protozoa: Amoebozoa: Arcellinida).” Journal of Biogeography 34, no. 10: 1822–1831. 10.1111/j.1365-2699.2007.01733.x. [DOI] [Google Scholar]
  62. Su, J. , Mazei Y. A., Tsyganov A. N., et al. 2024. “Functional Traits Data for Testate Amoebae of Northern Holarctic Realm.” Scientific Data 11, no. 1: 1028. 10.1038/s41597-024-03874-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Tian, W. , Hawlena D., Pagès J. F., Zhong Z., and Wang D.. 2023. “Fear of Predators Alters Herbivore Regulation of Soil Microbial Community Function.” Ecology and Evolution 13, no. 7: e10207. 10.1002/ece3.10207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Useros, F. , García‐Cunchillos I., Henry N., Berney C., and Lara E.. 2024. “How Good Are Global DNA‐Based Environmental Surveys for Detecting All Protist Diversity? Arcellinida as an Example of Biased Representation.” Environmental Microbiology 26, no. 3: e16606. 10.1111/1462-2920.16606. [DOI] [PubMed] [Google Scholar]
  65. Useros, F. , González‐Miguéns R., Soler‐Zamora C., and Lara E.. 2023. “When Ecological Transitions Are Not So Infrequent: Independent Colonizations of Athalassohaline Water Bodies by Arcellidae (Arcellinida; Amoebozoa), With Descriptions of Four New Species.” FEMS Microbiology Ecology 99, no. 8: fiad076. 10.1093/femsec/fiad076. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wanner, M. , and Dunger W.. 2001. “Biological Activity of Soils From Reclaimed Open‐Cast Coal Mining Areas in Upper Lusatia Using Testate Amoebae (Protists) as Indicators.” Ecological Engineering 17, no. 2: 323–330. 10.1016/S0925-8574(00)00148-8. [DOI] [Google Scholar]
  67. Wickham, H. 2016. ggplot2: Elegant Graphics for Data Analysis. Springer. [Google Scholar]
  68. Wickham, H. 2020. “reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package.” R Package Version 1.4.4.
  69. Wickham, H. , François R., Henry L., and Müller K.. 2022. “dplyr: A Grammar of Data Manipulation.” R Package Version 1.0.7.
  70. Wickham, H. , and Girlich M.. 2022. “Tidyr: Tidy Messy Data.” R Package Version 1.2.0.
  71. Wu, D. , Zhang M., Peng M., Sui X., Li W., and Sun G.. 2019. “Variations in Soil Functional Fungal Community Structure Associated With Pure and Mixed Plantations in Typical Temperate Forests of China.” Frontiers in Microbiology 10: 1636. 10.3389/fmicb.2019.01636. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Xie, Q. , Lin J., Qin Y., Zhou J., and Bu W.. 2011. “Structural Diversity of Eukaryotic 18S rRNA and Its Impact on Alignment and Phylogenetic Reconstruction.” Protein & Cell 2, no. 2: 161–170. 10.1007/s13238-011-1017-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Xiong, W. , Jousset A., Guo S., et al. 2018. “Soil Protist Communities Form a Dynamic Hub in the Soil Microbiome.” ISME Journal 12, no. 2: 634–638. 10.1038/ismej.2017.171. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1: Location and climatic description of the four coniferous forest sites.

Table S2: Soil types and bioSample description of the four coniferous forest sites.

Table S3: PERMANOVA results for taxonomic groups (eukaryotic and prokaryotic communities) across different environmental and biological factors.

JEU-73-e70072-s002.docx (44.5KB, docx)

Table S4: Order‐level taxonomic assignment of metatranscriptomic rRNA reads for prokaryotes and eukaryotes.

JEU-73-e70072-s001.xlsx (339.8KB, xlsx)

Data Availability Statement

The data that support the findings of this study are available in JGI Gold at https://gold.jgi.doe.gov/project?id=Gp0225829, reference number SAMN08776709–SAMN06265162. These data were derived from the following resources available in the public domain: Auer et al. (2024), https://doi.org/10.1111/nph.19471.

Articles from The Journal of Eukaryotic Microbiology are provided here courtesy of Wiley

RESOURCES