# Vegetation dominantly shapes the elevational patterns of archaeal and protistan communities and their ecological functions in the Hengduan Mountains, Tibetan Plateau

Bao-min Yao, Graeme Nicol, Yu Shi, Christina Hazard, Zhi-ming Zhang, Lin Zhang, Li-li Han, Fang He, Si-yi Liu, Guo-xin Sun, et al.

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## To cite this version:

Bao-min Yao, Graeme Nicol, Yu Shi, Christina Hazard, Zhi-ming Zhang, et al.. Vegetation dominantly shapes the elevational patterns of archaeal and protistan communities and their ecological functions in the Hengduan Mountains, Tibetan Plateau. Functional Ecology, 2025, 39 (9), pp.2327-2341. 10.1111/1365-2435.70114. hal-05409002

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Submitted on 10 Dec 2025

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# Vegetation dominantly shapes the elevational patterns of archaeal and protistan communities and their ecological functions in the Hengduan Mountains, Tibetan Plateau

Bao-Min Yao<fnref n="1" />,<fnref n="2" /> | Graeme W. Nicol³ | Yu Shi⁴ | Christina Hazard³ | Zhi-Ming Zhang⁵ |
Lin Zhang⁶ ID | Li-Li Han<fnref n="1" /> | Fang He<fnref n="1" />,⁵ | Si-Yi Liu<fnref n="1" /> | Guo-Xin Sun<fnref n="1" />,<fnref n="2" /> | Yuan Ge<fnref n="1" />,<fnref n="2" /> ID |
Bao-Dong Chen<fnref n="1" />,<fnref n="2" /> ID | Cong-Cong Shen<fnref n="1" /> | Chuan-Hong Li<fnref n="1" />,<fnref n="2" /> | Li-Mei Zhang<fnref n="1" />,<fnref n="2" /> ID

**Correspondence**

Li-Mei Zhang

Email: zhanglm@rcees.ac.cn

**Funding information**

Second Tibetan Plateau Scientific
Expedition and Research Program
(2019QZKK0306, 2019QZKK0308)

Handling Editor: Pengshuai Shao

## Abstract

1. While elevational patterns of soil bacterial and fungal communities in mountain eco-systems have been well documented, the assembly processes of soil archaeal and protistan communities, and their linkage with ecosystem multifunctionality (EMF), remain poorly understood, particularly under the combined influence of vegetational, edaphic and climatic factors in the alpine forests of the Tibetan Plateau.

2. To address this gap, an intensive quadrat survey along elevational gradients across three typical mountains in the south-eastern Tibetan Plateau was conducted.

3. Protistan and plant communities exhibited a consistent hump-shaped alpha diversity pattern along the elevation, whereas archaeal diversity followed a 'U'-shaped pattern. Though climatic factors covaried with elevation, vegetation type emerged as the dominant driver, explaining the greatest variation in archaeal (22.51%) and protistan (19.74%) community assembly, followed by elevation-dependent edaphic (16.17% and 12.63%) and climatic factors (10.28% and 11.35%). Stochastic processes dominated archaeal and protistan community assembly in mid-elevation zones, while deterministic processes accounted for a higher proportion in low- and high-elevation zones, in which harsh environmental conditions under the dry-hot valley and timberline ecotones exerted stronger environmental selection pressures. Along the elevational gradient, archaeal dominance shifted from ammonia-oxidizing *Nitrososphaerales* (>90% relative abundance) to non-ammonia-oxidizing *Gagatemarchaeaceae* (>80%), while photoautophic protists declined but parasitic protists increased, corresponding to

This is an open access article under the terms of the [Creative Commons Attribution License](https://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). *Functional Ecology* published by John Wiley & Sons Ltd on behalf of British Ecological Society.

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<footnote n="1">¹State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China;</footnote>

<footnote n="2">²University of Chinese Academy of Sciences, Beijing, China; ³Environmental Microbial Genomics Group, Laboratoire Ampère, École Centrale de Lyon, CNRS UMR 5005, Université de Lyon, Ecully, France; ⁴State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, Henan, China; ⁵School of Ecology and Environmental Sciences & Yunnan Key Laboratory for Plateau Mountain Ecology and Restoration of Degraded Environments, Yunnan University, Kunming, China and ⁶State Key Laboratory of Tibetan Plateau Earth System Science, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing, China</footnote>
vegetation succession from shrubland to broad-leaved forest and then to coniferous forest. The soil EMF index, derived from soil and plant variables, was lowest under low-elevation zones (Shrub) and peaked under sub-alpine elevation zones (Fabri forest), aligning with trends in individual function category, that is soil nutrient cycling, microbial activity and plant performance. Notably, EMF correlated positively with archaeal ($R^2=0.67$) and protistan ($R^2=0.69$) beta diversity but not alpha diversity, underscoring the importance of specific microbial taxa in driving ecosystem functions.

4. Overall, our study highlights the dominant role of vegetation in structuring archaeal and protistan communities in the alpine forest ecosystem of the south-eastern Tibetan Plateau, and provides new insights into the distinct ecophysiology and ecological functions of archaea and protists. These findings offer valuable information for predicting below-ground biodiversity responses to climate change and their feedback on ecosystem functions.

### KEYWORDS

alpine forest soil, archaea and protist, ecosystem multifunctionality, environmental heterogeneity, *Nitrososphaerota*, south-eastern Tibetan Plateau

# 1 | INTRODUCTION

Mountain ecosystems play a pivotal role in addressing global climate and environmental challenges by regulating climate, carbon, nitrogen and water cycles, and sustaining macroflora and fauna biodiversity through providing diverse niches and continuous environmental gradients (Battin et al., 2016; Myers et al., 2000; Wang et al., 2022). Due to dramatic changes in climatic, biological and abiotic features along elevation, mountain ecosystems serve as an ideal 'natural laboratory' for investigating biodiversity patterns and their driving forces (Wang et al., 2022). Although the diversity and distribution of plants and animals in these ecosystems have been extensively studied, the patterns and ecological roles of soil microbial communities remain poorly understood. Soil microorganisms represent nearly 60% of Earth's biodiversity and are fundamental to soil ecosystem functioning (Anthony et al., 2023; Sokol et al., 2022; Yao et al., 2022). As key drivers of ecosystem multifunctionality (EMF), they mediate essential processes, such as nutrient cycling, organic matter decomposition, water regulation, plant-soil interactions and primary productivity (Ni et al., 2020; Semeraro et al., 2022; Wang et al., 2020). Understanding the spatial distribution of soil microbial communities and their functional relationship with the above-ground ecosystem is crucial to unravelling the mechanisms of biodiversity maintenance in mountain ecosystems. Such knowledge is critical for predicting how these sensitive ecosystems will respond to ongoing climate change.

Furthermore, while plant and animal communities typically exhibit hump-shaped or decreasing diversity patterns along elevational gradients, microbial communities display more complex trends—including hump-shaped, U-shaped, monotonically decreasing or inconsistent patterns—depending on environmental factors, such as location, climate and pH (Shen et al., 2013, 2020; Singh et al., 2012).

However, most studies have focused on bacteria and fungi, leaving archaea and protists understudied despite their ecological importance. Archaea, representing some of Earth's most abundant and metabolically versatile organisms, thrive in diverse environments, from moderate to extreme conditions (Baker et al., 2020; Chaban et al., 2006). They drive key biogeochemical processes, including methanogenesis, anaerobic methane oxidation and ammonia oxidation, significantly influencing greenhouse gas emissions and nutrient cycling (Offre et al., 2013; Tourna et al., 2011; Weber et al., 2015). Similarly, protists exhibit remarkable functional diversity through various trophic strategies, such as phototrophy, phagotrophy, saprotrophy and parasitism, making them a diverse and ecologically significant component of global ecosystems (Anthony et al., 2023; Geisen et al., 2018). Photoautotrophic protists contribute ~25% of global photosynthesis and play an important role in primary production and soil carbon fixation, while phagotrophic and saprotrophic protists participate widely in organic matter decomposition and nutrient mineralization (Geisen et al., 2018, 2020). As keystone consumers in microbial food webs, phagocytic protists also regulate bacterial and fungal communities (Geisen et al., 2018). Despite their ecological significance, the elevational distribution patterns of archaea and protists in mountain ecosystems remain poorly understood compared with those of bacteria and fungi. While bacterial and fungal diversity is known to play critical roles in sustaining EMF and often exhibit positive correlations with it (Delgado-Baquerizo et al., 2016; Wagg et al., 2014), the influence of archaea and protists on EMF in mountain ecosystems remains largely unexplored. Investigating these microbial groups across elevation gradients would provide a more holistic view of soil biodiversity and its ecological drivers in mountainous ecosystems (Geisen et al., 2018; Weber et al., 2015). Such insights are essential for predicting microbial
community responses to climate change and their subsequent im-
pacts on ecosystem stability.

The Qinghai-Tibetan Plateau, often referred to as the 'roof of
the world' (Yao et al., 2015), has forests predominantly distributed
in the Hengduan Mountains area along its south-eastern edge (Gao
et al., 2023; Hua & Wang, 2018). The unique geographic position
and orientation of the Hengduan Mountains create exceptional cli-
matic and topographic variation, with environments ranging from
dry-hot river valleys at lower elevations to alpine climates at higher
elevations (Su et al., 2019). These steep environmental gradients es-
tablish the region as both a global biodiversity hotspot and a climate-
sensitive zone (Myers et al., 2000), making it particularly valuable
for studying microbial distribution patterns across elevations.
Therefore, in this study, we conducted a quadrat survey of plant
communities and soil archaeal and protistan communities along
elevational gradients in three mountains (Baima, Meili and Haba)
within the Hengduan Mountains. We assessed EMF by integrating
variables related to soil nutrient cycling, microbial activity and plant
performance. Our study aimed to determine the elevational pat-
terns and drivers of archaeal and protistan communities under the
combined influence of climate, soil properties and vegetation, and
to evaluate their contributions to EMF across vegetation succession
and elevation gradients. We tested the following hypotheses: (1) The
assembly of archaeal and protistan communities across alpine ele-
vational gradients is strongly governed by vegetation-climate-soil
interactions, with vegetation playing a dominant role. Vegetation
succession creates distinct microenvironments that selectively filter
microbial taxa with specialized functional traits, shaping their eleva-
tional patterns. We predict that archaea would be more dominant
in nutrient-poor, dry shrub and cold timberline zones due to their
extremophilic adaptations, while protists would prevail in moderate
conditions (e.g. mid-elevation forests) where resources and climate
are less constrained. (2) Compositional shifts of archaeal and pro-
tistan communities along elevational gradients would influence EMF
more strongly than their alpha diversity. This is because environ-
mental filtering selects elevation-specific functional groups, which
in turn modulate distinct ecosystem processes (e.g. nutrient cycling
and organic matter decomposition). Consequently, their elevational
turnover is expected to have cascading impacts on EMF. As a biodi-
versity hotspot, the Hengduan Mountains provide an ideal system
to explore microbial diversity-EMF relationships. This research is of
significance for understanding how above-ground-below-ground
biodiversity interactions sustain ecosystem functions and offers in-
sights into their resilience under climate change.

2 | MATERIALS AND METHODS

2.1 | Study area and soil sampling

Three typical mountains, Mount Baima, Meili and Haba in the
Hengduan Mountains area, located in the northwest of Yunnan
Province, Southwest China, each separated by ≥50 km (22°-32.1° N,

97°-103° E), were selected for this study (Figure S1). The Hengduan
Mountains area is characterized by a cold temperate mountain
monsoon climate with distinct dry and wet seasons, with the mean
annual precipitation (MAP) ranging from 659.4 to 884.5 mm, of
which approximately 85% is concentrated in June, July and August.
No fieldwork permits were required for this study.

Vegetation profiling and soil collection were conducted on the
south-facing slopes of Mount Baima, Meili and Haba during the
growing season (July to August) of 2019 and 2020. Survey quad-
rats were established along altitudinal transects from near the valley
bottoms to the alpine timberline ecotone at intervals of approxi-
mately 100-300m elevation. Consequently, 9 sites on Mount Baima
(2276-4312m), 13 sites on Mount Meili (2382-4185m) and 9 sites
on Mount Haba (1707-3930m) were included, each encompassing
similar vegetation types at a given elevation. At each site, three inde-
pendent quadrats (5m×5m for shrubs and 30m×30m for forests)
were randomly selected, with a distance between adjacent quadrats
≥30m. Three soil cores were randomly collected from 0 to 15cm
depth in each quadrat after the litter layer was removed. Before ho-
mogenizing the three soil cores into one composite sample, visible
plant residue and gravel were removed. A total of 93 samples were
collected and then transported to the laboratory on ice. Fresh soils
were sieved through a 2-mm mesh, with sub-samples stored at 4°C
and -80°C for subsequent soil physicochemical properties and mo-
lecular analyses, respectively.

Plant species, crown width, shrub height, diameter at breast
height (DBH) and tree height were recorded for each quadrat.
Vegetation types along the elevational transects displayed dis-
tinct changes: shrub (Shrub, 1707-2687m), Quercus spinosa shrub
(QuercusS, 2538-2820m), Pine forest (PineF, 2732-3477m), Fabri
forest (FabriF, 3325-3730m) and Abies georgei forest (AbiesF, 3636-
4185m) successively dominating the elevational transects (Table S1).
More information on climate and vegetation is provided in Table S1.

2.2 | Determination of soil physicochemical
properties

Soil water content (SWC) was determined by drying soil at 105°C
to constant weight, and soil pH was determined with a soil-to-
water ratio of 1: 2.5 (FE20 FiveEasy pH meter, Mettler Toledo,
Germany). Particle size distribution (sand: 0.02–2mm, silt: 0.002–
0.02mm, clay: <0.002mm) was determined by laser particle size
analysis (Mastersizer 2000, Malvern Instruments Ltd., Britain)
(Yao et al., 2021). Soil organic matter (SOM) was determined using
the K₂Cr₂O₇·H₂SO₄ oxidation-reduction colorimetric method
(Lu, 2000). Soil total carbon (TC) and total nitrogen (TN) were deter-
mined with an elemental analyser (Vario MAX, Elementar, Germany)
(Lu, 2000). Soil available phosphorus (AP) was determined using
the molybdenum antimony anti-colorimetric method (Lu, 2000).
Soil inorganic N (NH₄⁺-N, NO₃⁻-N) concentrations were measured
with a continuous flow analyser (SEAL AutoAnalyzer 3, Germany)
after extraction with 2 M KCl solution in a soil water ratio of 1:5 (Jiao
et al., 2019). Selected soil physicochemical properties are presented in Table S2.

## 2.3 | DNA extraction and sequencing of archaeal 16S rRNA and protistan 18S rRNA genes

DNA was extracted from 0.50g of soil (fresh weight) using the DNeasy PowerSoil Kit (QIAGEN GmbH, Germany) according to the manufacturer's instructions. The concentration and purity of extracted DNA were determined using a NanoDrop ND-2000 spectrophotometer (NanoDrop Technologies, USA). The extracted DNA was stored at -40°C. Archaeal 16S rRNA and protistan 18S rRNA genes were amplified using the primer sets Arch519F (CAGCCGCCGCGGTAA)/ Arch915R (GTGCTCCCCCGCCAATTCCT) (Jiao et al., 2019) and FW-TAReuk454FWD1 (CCAGCASCYGCGGTAATTCC)/REV-TAReukREV3 (ACTTTCGTTCTTGATYRA) (Zhao et al., 2019), respectively. Individual PCRs (25µL) contained 12.5µL Premix Taq™ DNA polymerase (TaKaRa Bio, Shiga, Japan), 0.5µL each primer (10µM), 2µL template DNA (10ngµl⁻¹) and 9.5µL PCR grade water. Triplicate PCRs were performed for each sample (see Table S3 for thermocycling conditions). PCR products were purified using a QIAquick purification kit (Qiagen) and then pooled in equimolar ratios. Paired-end sequencing of amplicons was performed using the NovaSeq 6000 platform (Illumina, San Diego, USA) with flow cell by Majorbio BioPharm Technology (Majorbio, Shanghai, China).

## 2.4 | Bioinformatic analysis

Amplicon sequencing data were processed using the standard operation procedure of Microbial Ecology (QIIME 2). High-quality sequences were processed using DADA2 for denoising and clustering into amplicon sequence variants (ASVs). Taxonomic assignment was conducted using the classify-sklearn method, with archaea sequences blasting against the SILVA v132 database and protist sequences blasting against the Protist Ribosomal Reference v4.12.0 database. For downstream analyses, non-relevant ASVs (including Chloroplast, Mitochondrial, Rhodophyta, Streptophyta, Fungi, Opisthokonta, X, Metazoans and unknown eukaryotes) were removed, and quality filtering, 2381 archaeal ASVs and 16,612 protistan ASVs were retained at 100% identity. Raw reads were deposited in the NCBI Sequence Read Archive (SRA) database (Accession Number PRJNA1148298).

## 2.5 | Climate data extraction and statistical analysis

Meteorological background data (MAT and MAP) and the normalized difference vegetation index (NDVI) were extracted for each sample quadrat using ArcGIS 10.8 software from the Data Center

for Resources and Environmental Sciences, Chinese Academy of Sciences, based on each sample quadrat's latitude and longitude. The MAT and MAP data were sourced from the China Meteorological Data Set, while the NDVI was obtained from the China Long Term Vegetation Index (NDVI) Spatial Distribution DataSet. Pearson correlation analysis was employed to assess linear relationships among environmental heterogeneity, the alpha- and beta diversity of archaeal and protistan communities, the EMF index, and relationships between the EMF index and environmental factors. On the contrary, Spearman correlation analysis was utilized to examine non-linear relationships between community composition (i.e. the relative abundance of a specific taxon) and both EMF and environmental factors. The beta diversity of the microbial communities was calculated based on the weighted UniFrac distance matrix and the unweighted Bray-Curtis distance matrix, and then ordinated using non-metric multidimensional scaling (NMDS). The relative contribution of different factors in shaping archaeal and protistan communities' dissimilarity, including vegetation type, elevation and mountain, was examined by permutation multivariate analysis of variance (PERMANOVA) (packages: vegan, adonis2, RStudio 4.2.2.). To quantify the relative importance of deterministic and stochastic processes in the construction of different communities, the numerical distribution of normalized stochasticity ratio (NST, with a value between 0 and 1) between sample pairs in different groups was retrieved using the packages NST, R.Studio 4.2.2. (Ning et al., 2019). NST >0.5 is defined as stochastic processes and <0.5 as deterministic processes (Ning et al., 2019).

The EMF index was assessed based on 10 variables representing soil multiple ecosystem functional categories, including soil nutrient cycling and fertility, microbial activity and plant performance (Delgado-Baquerizo et al., 2016; Ma et al., 2022). Specifically, soil nutrient cycle and fertility were characterized using soil parameters, including SOM, AP, available potassium (AK), ammonium nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), urease activity (SUE), sucrase activity (SSC) and the microbial activity was characterized by soil basal respiration (SBR). The above-ground vegetation biomass (AVB) and NDVI were used to represent plant performance. The EMF index was calculated using an averaging approach, and each parameter was Z-score transformed and then averaged to obtain the EMF index for each site (Hu et al., 2021).

A structural equation model (SEM) was constructed to further quantify the direct and indirect effects of vegetation, climate and edaphic factors on archaeal and protistan diversities and EMF, using the 'piecewiseSEM' package in R (RStudio 4.2.2). Random forest analysis was conducted using the 'randomForest' package in R (RStudio 4.2.2) to identify the most important environmental factors in predicting the archaeal and protistan diversities and the most important dominant taxa in predicting the EMF (Liw & Wiener, 2002). The importance of each variable was computed for the average of 1000 trees and determined by an increase in the mean square error (InMSE).
3 | RESULTS

3.1 | Alpha and beta diversities of archaeal and protistan communities along elevational gradients

Both the alpha diversity of archaeal and protistan communities
substantially changed in response to vegetation types and
elevational gradients (n=93, p<0.01; **Figure 1a,b**). Generally,
protistan alpha diversity across the three mountains showed a
hump-shape distribution pattern across the vegetation types and
elevation increase (**Figure 1a,b; Figure S2**), coinciding with the
pattern observed for plant alpha diversity (**Figure 1c**). In contrast,
archaeal alpha diversity exhibited a 'U'-shape pattern (**Figure 1a,b;
Figure S2**). Pearson correlation analysis showed that plant alpha
diversity was positively correlated (R=0.42, correlation coefficient)
with protistan alpha diversity, but had a negative correlation
(R=-0.44) with archaeal alpha diversity (**Table S4, p<0.01**).

The beta diversity of both archaeal and protistan communities
showed consistent patterns of variation along the elevational gradi-
ents across the three mountains (Figure 2a,b). Archaeal communities
clustered into three main groups corresponding to shrub (Shrub and
QuercusS), pine forest (PineF) or broad-leaved and coniferous for-
ests (FabriF and AbiesF), showing greater within-group similarity in
low- (<2820 m, Shrub and QuercusS) and high-elevation (>3477 m,
FabriF and AbiesF) zones (Figure 2a). In contrast, protistan communi-
ties showed a more steady transition associated with changes in veg-
etation and elevation (Figure 2b). PERMANOVA (Adonis2) analysis
indicated that both archaeal and protistan community structure was
primarily explained by vegetation type (21.16% for archaea; 17.91%
for protist; p<0.001), followed by elevation (7.93% for archaea;
10.88% for protist; p<0.001), and mountain (3.46% for archaea;
7.54% for protist; p<0.001) (Table 1). Random forest and Mantel
test correlation analyses further predicted that vegetation (R²=0.72
for archaea and 0.79 for protist, coefficient of determination), eleva-
tion (R²=0.61 for archaea and 0.74 for protist) and MAT (R²=0.52
for archaea and 0.64 for protist) were the most important factors
influencing archaeal and protistan communities, followed by soil
physicochemical properties, such as SWC, SOM, pH, SOC and TN

(p < 0.01; Figure 2c,d; Table S5). Variance Partitioning Analysis (VPA) further showed that the variation in archaeal and protistan communities was primarily explained by vegetation (22.51% of variation for archaea and 19.74% for protist), followed by elevation-dependent edaphic (16.17% for archaea and 12.63% for protist) and climatic (10.28% for archaea and 11.34% for protist) factors (Table 2).

3.2 | Changes in archaeal and protistan community structure across elevational gradients

Both archaeal and protistan community compositions showed
consistent patterns among the three mountains with dominant
taxa distinctly changing with vegetation types and increasing el-
evation (Figure 3a-c). Specifically, archaeal communities were
dominated by ammonia-oxidizing archaea (AOA) of the order
Nitrososphaerales (synonymous with Nitrososphaerota group I.1b,
relative abundance >90%) at low-elevation zones (<2820 m, Shrub
and QuercusS) and changed to heterotrophic, non-AOA of the phy-
lum Gagatemarchaeota (synonymous with Nitrososphaerota group
I.1c, >80%) at high-elevation zones dominated with FabriF and
AbiesF (>3477 m) (Figure 3a) (Sheridan et al., 2023; Stieglmeier et al.,
2014). The two groups showed a negative and positive correlation
with the elevation, respectively, (p < 0.01; Figure S3). Woesearchaea
within the Nanoarchaeota phylum and Thermoplasmata within the
Euryarchaeota phylum were only present at the middle and high-
elevation zones (2820-3926 m) with a proportion of 7.3% and 6.3%,
respectively (Figure 3a).

In comparison with archaea, protistan community composition
showed a more distinct and consecutive shift along the elevation and
vegetation types (Figure 3b,c). The phylum *Cercozoa* and *Ciliophora*
belonging to phagotrophic protists were abundant in all quadrats
and together accounted for >60% of the protistan community in
each quadrat, with the highest relative abundance in the middle
elevation zones (PineF) (R²=0.55; *p*<0.01; Figure 3b,c; Figure S3).
Interestingly, photoautotrophic (*Chlorophyta* and *Dinoflagellata*) and
parasitic protists (*Apicomplexa*) had contrasting patterns, with their
relative abundance decreasing and increasing with the vegetation

<figure><img src="image_5.png" /><figcaption>FIGURE 1 Chao1 alpha diversity patterns of archaeal, protistan and plant communities across (a) vegetation types and (b, c) along elevational gradients of three mountains in the south-eastern Tibetan Plateau. AbiesF, Abies georgei and Rhododendron platyphyllum; FabriF, Fabri forest (Tsuga Dumosa and Abies fabri); PineF, Pine forests and shrub; QuercusS, Quercus aquifolioides shrub. Further vegetation information is shown in Table S1.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>FIGURE 2 Beta diversity of archaeal and protistan communities in three mountains in the south-eastern Tibetan Plateau. Nonmetric multidimensional (NMDS) ordinations of (a) archaeal and (b) protistan communities across vegetation types. The relative importance of different factors predicted to explain variation of (c) archaeal and (d) protistan community structures based on random forest model analysis. AK, available potassium; AP, available phosphorus; AVB, above-ground vegetation biomass; C/N, total carbon to nitrogen ratio; ELE, elevation; MAP, mean annual precipitation; MAT, mean annual temperature; NH₄⁺-N, ammonium nitrogen; NO₃⁻-N, nitrate nitrogen; P-Chao1, plant chao1; P-Shannon, plant Shannon; SIN, soil inorganic nitrogen; SOC, soil organic carbon; SOM, soil organic matter; SWC, soil water content; TC, total carbon; TN, total nitrogen; VEG, vegetation types. Vegetation, climate and soil in mosaic diagram (c, d) represent the comprehensive index of plant factors (VEG, AVB, NDVI, P-Chao1, P-Shannon), climate factors (MAT and MAP) and edaphic factors (pH, SWC, TC, TN, C/N, SOC, AP, AK, NH₄⁺-N, NO₃⁻-N and SIN).</figcaption></figure>

<table><caption>TABLE 1 Predicted relative effect of vegetation type, elevation and mountain on archaeal and protistan communities based on PERMANOVA (adonis2) analysis.</caption><thead><tr><th>Adonis2</th><th>VEG</th><th>Elevation</th><th>Mountain</th><th>VEG &amp; elevation</th><th>VEG &amp; mountain</th><th>Elevation &amp; mountain</th><th>VEG &amp; elevation &amp; mountain</th><th>Others</th></tr></thead><tbody><tr><td>Archaea</td><td>21.16%</td><td>7.93%</td><td>3.46%</td><td>16.87%</td><td>9.07%</td><td>4.32%</td><td>11.56%</td><td>25.63%</td></tr><tr><td>Protist</td><td>17.91%</td><td>10.88%</td><td>7.54%</td><td>12.92%</td><td>10.63%</td><td>8.12%</td><td>7.93%</td><td>24.07%</td></tr></tbody></table>

Abbreviation: VEG, vegetation types.

types and elevation increase, respectively (R²=0.44 for phototrophy and R²=0.61 for parasitic; *p* < 0.01; Figure 3b,c; Figure S3).

Random forest and Pearson correlation analyses were further conducted to assess the influence of elevation-dependent climatic and edaphic factors on the dominant microbial taxa. Results

suggested that elevation-dependent climatic and edaphic factors had opposite effects on the relative abundance of *Nitrososphaerales* and *Gagatemarchaeaceae* (*p* < 0.01; Figure S4). Among these factors, NH₄⁺-N and SOM correlated most strongly with the greatest variation in *Nitrososphaerales* and *Gagatemarchaeaceae* among samples,
<table><caption>TABLE 2 Predicted relative effect of vegetation, climate and soil factors on archaeal and protistan communities based on VPA analysis.</caption><thead><tr><th>VPA</th><th>Vegetation</th><th>Climate</th><th>Soil</th><th>Vegetation &amp; climate</th><th>Vegetation &amp; soil</th><th>Climate &amp; soil</th><th>Vegetation &amp; climate &amp; soil</th><th>Others</th></tr></thead><tbody><tr><td>Archaea</td><td>22.51%</td><td>10.28%</td><td>16.17%</td><td>3.78%</td><td>2.83%</td><td>1.69%</td><td>3.25%</td><td>38.95%</td></tr><tr><td>Protist</td><td>19.74%</td><td>11.34%</td><td>12.63%</td><td>2.23%</td><td>2.45%</td><td>4.88%</td><td>3.47%</td><td>43.26%</td></tr></tbody></table>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /><figcaption>FIGURE 3 Composition of archaeal and protistan communities along elevational gradients and across vegetation types in three mountains in south-eastern Tibetan Plateau. (a, b) Archaeal and protistan community composition along elevation and across vegetation types. (c) Classification of protistan metabolic types along elevation and between vegetation types. (d) Predicted relative importance of top six soil factors explaining the variation of the dominant taxa based on random forest model analysis.</figcaption></figure>

respectively, followed by SWC (Figure 3d). The dominant protistan taxa correlated mainly with soil nutrients, including carbon, nitrogen, phosphorus and potassium contents (Figure 3d; Figure S4).

## 3.3 | Factors driving the elevational patterns of archaeal and protistan communities

Bray-Curtis dissimilarity analysis was conducted to further examine the key factors and processes driving the assembly of archaeal and protistan communities. The dissimilarity of both archaeal and protistan communities increased significantly with the heterogeneity of plant communities, climate and soil properties ($p < 0.01$), with a higher slope for archaea (0.60, plant; 0.44, climate; 0.33, soil) than for protists (0.45, plant; 0.40, climate; 0.28, soil) (Figure 4a-c). The heterogeneity of plant communities accounted for the highest

proportion of variance in archaeal (38%) and protistan (41%) community dissimilarity (Figure 4d).

Normalized stochasticity ratio analysis was conducted to quantify the contribution of deterministic and stochastic processes in shaping archaeal and protistan communities. The NST values for both archaeal and protistan communities showed a hump-shaped trend with increasing elevation, and the deterministic processes accounted for a higher proportion in Shrub (low-elevation zones) and AbiesF (high-elevation zones) for both archaeal and protistan communities (Figure 5a,b), suggesting stronger environmental selection pressures on microbial communities at the bottom and top of the mountains than in the middle. Furthermore, the relative contribution of stochastic processes was generally greater in protistan communities (50%-79%) than in archaeal communities (29%-73%) (Figure 5b), indicating that protists may possess a higher adaptive capacity towards various environments than archaea.
<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

FIGURE 4 Decay patterns of archaeal and protistan communities associated with plant heterogeneity, climate heterogeneity and soil heterogeneity. The relationship between archaeal and protistan community heterogeneity and plant heterogeneity (a), climate heterogeneity (b) and soil heterogeneity (c). Indexes were estimated based on Bray-Curtis dissimilarity of parameters listed in Tables S1 and S2. (d) The relative contribution of each factor in explaining the variation of archaeal and protistan communities.

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

FIGURE 5 Ecological processes driving the assembly of soil archaeal and protistan communities along the elevation in three mountains. (a, b) The relative contribution of deterministic and stochastic processes to the assembly of archaeal and protistan communities along the elevation and across vegetation types. Stochasticity in ecological processes was quantified using the normalized stochasticity ratio (NST), with 0.5 as the cut-off point. NST > 0.5 is considered as being dominated by the stochastic process and NST < 0.5 as being dominated by the deterministic process.
## 3.4 | The correlation between archaeal and protistan communities and ecosystem multifunctionality

The EMF index, based on soil and plant variables, was lowest under Shrub and QuercusS vegetation at low elevation and highest under FabriF, and positively correlated with the beta diversity of archaeal and protistan communities ($R^2=0.67$ for archaea and $R^2=0.69$ for protists; all *p* < 0.01), but showed no significant correlation with their alpha diversity (*p* > 0.05) (Figure 6). The EMF index of each category function, that is soil nutrient and fertility, microbial activity and plant performance, also showed a similar pattern as the average EMF index, that is increased with vegetation types and elevation increase and then declined at high-elevation zones (Figure S5). Furthermore, random forest analysis was conducted to predict the importance of different archaeal and protistan taxa in predicting EMF (Figure S6). The results demonstrated that archaeal *Nitrososphaerales*, *Gagatemarchaeaceae*, *Woesearchaea Thermoplasmata* and protistan *Chlorophyta*, *Ciliophora*, *Cercozoa*, *Apicomplexa* and *Lobosa* phyla

were the most important taxa for EMF (Figure S6), among which *Gagatemarchaeaceae*, *Thermoplasmata*, *Cercozoa* and *Apicomplexa* were positively correlated with EMF while other taxa were negatively correlated (Table S6).

SEM was constructed to evaluate the direct and indirect influences of vegetation, climate, and edaphic factors on EMF and archaeal and protistan communities (Figure 6g). The analysis showed that the climate, vegetation type and soil physicochemical properties were largely influenced by elevation, which directly or indirectly explained 27% and 42% of the variation in alpha diversity, and 79% and 83% of the variation in beta diversity for archaea and protists, respectively (Figure 6g). The elevation, and elevation-dependent vegetation and soil properties had a significant influence on EMF directly and indirectly via regulating archaeal and protistan community composition, with the greatest effect from elevation ($STE_{elevation}=0.63$) followed by vegetation type ($STE_{vegetation}=0.37$) and physicochemical properties ($STE_{soil properties}=0.34$). Additionally, beta diversity of archaeal and protistan communities ($STE_{archaeal beta}=0.34$; $STE_{protistan beta}=0.42$) showed a significant effect on EMF (Figure 6h).

<figure><img src="image_15.png" /><figcaption>**FIGURE 6** Relationships between soil ecosystem multifunctionality function (EMF) index and different environmental and microbial factors. (a, b) The distribution pattern of EMF across vegetation types and elevation in three mountains. (c–f) Relationships between EMF and archaeal and protistan diversity. (g) Structural equation modelling (SEM) predicting direct and indirect effects of environmental factors on archaeal and protistan diversities and EMF. The red and black arrows represent the direct and indirect effects, respectively, and the dotted arrows indicate a non-significant effect. The thickness and value on the arrow represent the size of the path coefficient, and $R^2$ represents the variance that can be explained by endogenous variables. The low ratio of chi-squared to degrees of freedom ($\chi^2/df < 3$), non-significant probability level (*p* > 0.05), high goodness-of-fit index (GFI > 0.90), low Akaike information criteria (AIC), and low root-mean-square errors of approximation (RMSEA < 0.05) listed below the SEMs indicate that our data matches the hypothetical models. Significance level: \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001. (h) The standard total effect (including direct and indirect effect) of different factors on the alpha and beta diversities of archaea and protists and EMF based on the SEM model.</figcaption></figure>
Interestingly, protistan beta diversity had the greatest influence on both the alpha (STE=-0.63) and beta diversity (STE=0.74) of the archaeal community (Figure 6h).

# 4 | DISCUSSION

## 4.1 | The contrasting elevational distribution pattern of archaeal and protistan communities in the Hengduan mountains

As important components of soil microbiomes, archaea and protists play vital roles in contributing to the biogeochemical cycling of carbon and nitrogen, influencing greenhouse gas production and modulating the soil microbial food webs (Baker et al., 2020; Geisen et al., 2018; van Wolferen et al., 2022). However, the diversity and assembly processes of soil archaeal and protistan communities in mountain ecosystems remain poorly understood. Based on an intensive survey of vegetation, soil properties and archaeal and protistan communities along elevational gradients from three typical mountains in the south-eastern Tibetan Plateau, our study revealed a hump-shaped alpha diversity pattern for protists and plants, contrasting with a U-shaped pattern for archaea along the elevation (Figure 1). Consistent with the observation patterns of protistan and plant diversities, the unimodal pattern of bacterial diversity along the elevation gradient was also observed in the Shegyla mountain (3106-4479 m) in the south-eastern Tibetan Plateau (Wang et al., 2015), and in Mount Fuji (1000-3700 m) (Singh et al., 2012). These could be explained by the mid-domain effect, that is the increasing overlap of different species in the centre of a shared geographic domain due to boundary restrictions on the distribution of species, leading to a mid-domain diversity peak (Colwell & Lees, 2000). In this study, the low-elevation zones of the three investigated mountains are typical dry-hot valleys with low soil nutrient contents (e.g. SOC and TN), while high-elevation zones near the timberline are very cold and wet with a low pH range of 4.2-5.6, representing extreme environmental conditions. In contrast, the mid-elevation zones had more suitable climatic and edaphic conditions for biome, supporting higher plant and protistan diversity. Higher plant diversity at the middle elevation zones not only provides diverse and abundant nutrient resources for microbial communities, but also creates suitable microclimate, such as soil moisture, soil temperature and oxygen conditions (Acosta-Mercado & Lynn, 2005; Cesarz et al., 2022). The observed positive correlation between protistan and plant diversity indices further supports this. However, the archaeal alpha diversity showed an opposite trend to protistan and plant communities, which may be attributed to the remarkable ability of certain archaeal members to thrive in extreme environments (Offre et al., 2013).

Our study further showed that the stochastic process dominated archaeal and protistan community construction in the mid-elevation zones, while the deterministic processes accounted for a higher proportion in low- (Shrub) and high-elevation zones (AbiesF) for both archaea and protists (Figure 5a,b). This could be explained by the

extreme environmental conditions in the low- and high-elevation zones exerting stronger environmental selection pressures on soil archaea and protists, thereby leading to greater deterministic processes in these elevation zones. Consistently, a meta-analysis-based study has shown that stochastic processes are important drivers in shaping mountain microbial communities, while deterministic processes mainly play a dominant role at high-elevation zones with harsh environmental conditions (Wang et al., 2022). Stegen et al. (2012) also show that deterministic processes are more important under extreme environmental conditions, leading to a greater similarity in habitat preferences among closely related microbes.

## 4.2 | Vegetation plays a dominant role in shaping archaeal and protistan communities

Our PERMANOVA and random forest analyses revealed that archaeal and protistan communities were dominantly influenced by vegetation type over elevation-dependent climatic and edaphic factors (Table 1; Figure 2). Decay pattern analysis further indicated that both archaeal and protistan community dissimilarities were primarily driven by plant heterogeneity, followed by climate and soil heterogeneity (Figure 4). These consistent findings highlight vegetation as a dominant factor shaping soil microbial communities in our study system. This contrasts with the previous observation that regional-scale climate factors (e.g. temperature) or soil properties (e.g. pH) dominantly structure mountain ecosystem microbial communities, particularly for bacteria (Shen et al., 2013; Singh et al., 2012, 2014; Wang et al., 2015). We suggest this discrepancy may stem from insufficient consideration of vegetation in those studies. Mounting evidence indicates that plants can exert strong host selection on microbial assembly through the secretion of root exudates and litter to recruit, filter and enrich microbial taxa with specific functions in different plant species (Martin et al., 2017; Müller et al., 2016). This strong host selection explains both the higher similarity of archaeal or protistan communities within vegetation types (Figure 2) and greater correlation between microbial dissimilarity and plant heterogeneity (Figure 4) in our study.

In addition to its direct biotic effects, vegetation significantly modifies microhabitat conditions, such as soil moisture, pH and nutrient levels through canopy effects and affecting nutrient cycling (Prober et al., 2015; Tedersoo et al., 2016). Our study corroborates these findings, demonstrating distinct variations in soil properties, including soil pH, SWC, nutrients and respiration rates, across different vegetation types (Table S2). While edaphic factors, such as SWC, available nitrogen and phosphorus (AP), are known drivers of protistan communities (Geisen et al., 2014; Geisen et al., 2018; Zhao et al., 2019), our results indicate these factors (SWC, ammonia and AP), though statistically significant, exerted a secondary influence compared with vegetation effects (Figure 2). This observation aligns with growing evidence that vegetation type acts as a key environmental filter, overriding elevation-dependent climatic factors through modifying microhabitat conditions and nutrient
inputs along elevational gradients. For instance, Li et al. (2018) documented that abrupt vegetation transitions in vegetation types (e.g. from broadleaf to coniferous forests) drove stair-step patterns in bacterial diversity via associated shifts in soil pH and organic matter. Similarly, Zhang et al. (2015) reported strong correlations between plant diversity and bacterial community structure independent of temperature gradients. These findings are further supported by Veen et al. (2017), who demonstrated that vegetation-specific root exudates and litter chemistry coordinated taxon-specific responses of soil microbes and nematodes to elevational changes.

Collectively, these studies demonstrate that vegetation integrates climatic and edaphic influences by both directly selecting for specific microbial taxa and indirectly shaping biophysical micro-environments that regulate community assembly. By integrating above-ground vegetation, our study provides empirical evidence that in the alpine forest ecosystem of the south-eastern Tibetan Plateau, vegetation type exerts stronger control over soil archaeal and protistan community assembly than either climatic or edaphic factors alone. Future studies should employ multi-omics approaches to unravel the mechanistic basis of plant-microbe specificity, while long-term elevation transect studies could help disentangle the relative contributions of vegetation succession versus climate change in shaping these microbial communities.

## 4.3 | The shift of archaeal and protistan community composition along the elevation and their association with ecosystem multifunctionality

Soil microorganisms serve multiple ecosystem functions, such as nutrient cycling, promoting plant growth and adaptation to biotic and abiotic stresses, which represent the ‘supplying’ and ‘supporting’ functions of ecosystems (Hu et al., 2021; Wang et al., 2020). Specifically, archaea, well known for their tolerance to extreme environments, play key roles in carbon and nitrogen cycling, as key drivers of methanogenesis, anaerobic methane oxidation and ammonia oxidation processes in both aquatic and terrestrial ecosystems (Baker et al., 2020; Weber et al., 2015). Further, archaea account for 1%–10% of the total soil prokaryotes, which greatly contribute to soil microbial biomass and nutrient pools (Bates et al., 2011). Similarly, protists, with diverse morphological and metabolic features, offer multiple ecosystem functions, such as participating in animal and plant residue decomposition and carbon fixation as saprotrophs and phototrophs, regulating microbial food webs as predators, and influencing plant growth as parasites or symbionts (Anthony et al., 2023; Geisen et al., 2018). Despite their ecological significance, the contributions of archaea and protists to EMF remain less evaluated compared with bacteria and fungi.

Our study showed that the average EMF index increased significantly with vegetation type and elevation increase but declined at high-elevation zones, and was co-influenced by elevation, vegetation, soil properties and microbial community composition, exhibiting a similar pattern as each category function of soil nutrient

and fertility, microbial activity and plant performance (Figure 6; Figure S5). This further supports the previous findings that the category function of ecosystem, that is soil nutrient and fertility, microbial activity and plant performance, has a synergistic effect on EMF (Prober et al., 2015; Wagg et al., 2014; Wang et al., 2020). The lowest EMF index under Shrub and Quercus S vegetation could be caused by low nutrient turnover, microbial activity and plant-derived carbon input under the harsh environments of dry-hot valley (Shen et al., 2020; Wardle et al., 2004). Intriguingly, our study showed that the EMF index was positively correlated with the beta diversity of archaeal and protistan communities but showed negative or non-significant correlations with their alpha diversity. A number of studies have shown that the contribution of microbial diversity to EMF varies among different functional groups and different ecosystems. For instance, it was found that fungal diversity often regulates soil EMF more strongly than bacterial diversity in temperate croplands, boreal forests and semi-arid grasslands (Li et al., 2022; Zheng et al., 2019), while bacterial communities play dominant roles in alpine meadows and forests (Schmidt et al., 2014; Wang et al., 2021). A stronger positive correlation of EMF with fungal diversity rather than with bacteria and protists under changing C inputs was also observed in a semi-arid grassland (Feng et al., 2024). The negative or weak correlation between archaeal and protistan alpha diversity and EMF observed in this study might be due to the complexity in alpine mountain ecosystems with abrupt changes in vegetation, climatic and soil environment conditions, in which different microbial groups may alternatively and complementarily function among various ecological processes.

Meanwhile, an increasing body of evidence shows that microbial composition plays a vital role in maintaining multiple ecosystem functions (Hu et al., 2021; Jing et al., 2015; Yang et al., 2021; Zeng et al., 2024). This is further corroborated by our observation of a positive correlation between soil EMF index and beta diversity of archaeal and protistan communities. Correspondingly, both archaeal and protistan community compositions clearly shifted with increasing elevation, with their potential ecological functions varying accordingly. Specifically, the archaeal communities were dominated by AOA *Nitrososphaerales* at low- and mid-elevation zones but exclusively predominated by heterotrophic *Gagatemarchaeaceae* at high-elevation zones in which soils were strongly acidic (Figure 3). Coinciding with our observation, AOA have been frequently observed as the dominant archaeal taxa in a range of habitats, including soil, freshwater and marine sediments, and play an essential role in N cycling as the main ammonia oxidizers in terrestrial ecosystems (Gubry-Rangin et al., 2011; Tourna et al., 2011; Zhang et al., 2017). In contrast, *Gagatemarchaeaceae* are mainly found in acidic environments (Lehtovirta et al., 2009; Nicol & Schleper, 2006), and can account for a considerable proportion in archaeal communities, for example, up to 29% in a mixed deciduous forest with a pH range of 3.8–4.3 (Catão et al., 2013; Kemnitz et al., 2007). The pine needles of coniferous trees are generally considered to be more acidic than the leaves of deciduous trees, and there is generally
<figure><img src="image_16.png" /></figure>

higher humic acid accumulation (Acosta-Mercado & Lynn, 2005; Burgess-Conforti et al., 2019), which well explains the enrichment of *Gagatemarchaeaceae* in high-elevation zones with FabriF and AbiesF vegetation. *Gagatemarchaeaceae* are thought to be heterotrophic, and their growth is not associated with ammonia oxidation (Sheridan et al., 2023; Weber et al., 2015). Our previous observations of higher NH₄⁺-N accumulation, extremely low nitrification rate, and archaeal amoA gene abundance in high-elevation zones, while the opposite pattern was observed in low-elevation zones (Liu et al., 2024). These could be the consequence of the predominance of non-AOA *Gagatemarchaeaceae* at high elevations and ammonia-oxidizing *Nitrososphaerales* at low elevations. Furthermore, *Gagatemarchaeaceae* are supposed to play an important role in organic matter degradation processes (Weber et al., 2015), and their organic carbon metabolism is confirmed by genomic and metabolic analyses in a recent study (Sheridan et al., 2023). These findings align with the highest SOC contents observed in FabriF and AbiesF vegetation zones. Collectively, our study revealed that the shift from a *Nitrososphaerales*-dominated to a *Gagatemarchaeaceae*-dominated archaeal community, driven by vegetation type from broad-leaved forest to coniferous forest, profoundly influences soil ecosystem functions, particularly N transformation and SOC accumulation in alpine forests in the Tibetan Plateau.

Similarly, protistan community composition significantly changed along the elevation and vegetation types, with photoautotrophic *Chlorophyta* declining while parasitic *Apicomplexa* increased along the elevation (Figure 3). The high proportion of photoautotrophic protists in low-elevation zones could be attributed to more light availability under Shrub and Quercus vegetation than in broad-leaved and coniferous forests in mid- and high-elevation zones. Contrastingly, parasitic protists can parasitize diverse hosts (animals, plants and other organisms) (Geisen et al., 2014, 2018). High nutrient availability and soil moisture in FabriF and AbiesF vegetation zones might select for more hosts of parasitic protists and thus benefit their enrichment. Our results further showed that the phyla *Cercozoa* and *Ciliophora* were prevalent across all sites and were significantly correlated with EMF (Figure 3; Table S6). This aligns with previous studies which identify *Cercozoa* as a key predictor of soil EMF in agro-ecosystems (Zeng et al., 2024) and show a positive correlation between *Cercozoa* richness and soil EMF at a global scale (Delgado-Baquerizo et al., 2020). Both *Cercozoa* and *Ciliophora* are phagotrophic protists, commonly detected in various environments (Geisen et al., 2020; Zhao et al., 2020). They are supposed to play central roles in nutrient cycling and plant health by preying on microbes, or activating bacteria that produce pathogen-suppressive secondary metabolites (Geisen et al., 2018; Xiong et al., 2020). Furthermore, *Cercozoa*, characterized by their diverse lineages and reproductive capabilities, have pseudopodia for movement and preparation, and a hard calcium carbonate shell for protection (Geisen et al., 2018). *Ciliophora*, highly diverse, can adapt to extreme conditions by reducing volume or forming cysts, making them tolerant to soil moisture and environmental changes (Geisen et al., 2014, 2018). These adaptations well explain the wide distribution of phagotrophic

protists across elevation gradients and vegetation types in this study. Together, all these suggest that protists play a crucial role in determining soil EMF in alpine forest ecosystems, by directly participating in nutrient cycling or indirectly regulating soil microbial community structure and function. However, this study did not incorporate bacterial and fungal communities to further elucidate the interactions between protists, bacteria and fungi, as well as their potential cascading effects on EMF along the elevational gradient. Future research should aim to comprehensively assess soil microfood webs by integrating the entire soil microbiome (e.g. bacteria, fungi, archaea, protists and soil fauna), which would provide deeper insights into their complex interactions and collective contributions to EMF in alpine mountain ecosystems.

## 5 | CONCLUSIONS

Our study revealed contrasting elevational patterns in the alpha diversity of archaea and protists, as well as distinct shifts in their community composition, in the alpine forests of the south-eastern Tibetan Plateau. We found that stochastic processes predominantly governed community assembly for both groups, while deterministic processes became increasingly important in low- and high-elevation zones. Notably, vegetation emerged as the primary driver of archaeal and protistan community assembly, exerting a stronger influence than climatic and edaphic factors. We identified systematic elevational shifts in archaeal and protistan taxa with distinct metabolic properties and revealed significant associations between soil EMF and the beta diversity of both microbial groups, as well as specific functional guilds within them. These findings highlight the critical roles of archaea and protists in maintaining alpine forest ecosystem functioning through direct participation in nitrogen and carbon cycling and sustaining underground biodiversity. Our study also sheds light on the distinct ecophysiological traits of archaea and protists, which are integral components of the soil microbiome. By integrating above-ground-below-ground interactions, this study advances our mechanistic understanding of microbial biogeography in alpine forest ecosystems, offering valuable insights for predicting the future responses of below-ground biodiversity to climate change and its potential feedback on ecosystem functions. This is particularly relevant in regions of high biodiversity and climate sensitivity, such as the Tibetan Plateau.

## AUTHOR CONTRIBUTIONS

Li-Mei Zhang and Bao-Min Yao designed the study. Bao-Min Yao, Si-Yi Liu, Lin Zhang, Zhi-Ming Zhang, Cong-Cong Shen and Fang He collected the samples. Bao-Min Yao, Fang He and Chuan-Hong Li conducted the laboratory analyses. Bao-Min Yao, Chuan-Hong Li, Yu Shi, Li-Li Han and Li-Mei Zhang performed the data processes. Bao-Min Yao, Yuan Ge, Yu Shi, Yuan Ge, Guo-Xin Sun, Bao-Dong Chen, Graeme W. Nicol, Christina Hazard and Li-Mei Zhang wrote the manuscript. All authors contributed critically to the drafts and gave final approval for publication.
## ACKNOWLEDGEMENTS

We would like to thank Guang-Shuai Cui, Liu Yang, Bo-Jian Li, Bing Han and Xiong Chao for their cooperation in field survey and sampling, and Jun-Tao Wang and Ju-Pei Shen for their assistance and suggestion in data analyses. This work was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2019QZKK0306 and 2019QZKK0308).

## CONFLICT OF INTEREST STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## DATA AVAILABILITY STATEMENT

Raw reads were deposited in the NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA1148298).

## ORCID

Lin Zhang https://orcid.org/0000-0002-3252-0751

Yuan Ge https://orcid.org/0000-0003-0234-5638

Bao-Dong Chen https://orcid.org/0000-0002-1790-7800

Li-Mei Zhang https://orcid.org/0000-0002-7383-8475

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## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

**Table S1.** Vegetation, climate information and geographic location of sampling sites in Mts Baima, Haba, and Meili in the Southeastern Tibetan Plateau.

**Table S2.** Soil physicochemical properties used for soil heterogeneity estimation and SEM construction.

**Table S3.** The primers and PCR amplification profiles used in this study.

**Table S4.** Pearson's correlations between archaeal and protistan alpha diversity and environmental factors.

**Table S5.** Mantel test results of archaeal and protistan communities and environmental factors.

**Table S6.** Spearman's correlations between soil EMF index and the dominant archaeal and protistan taxa.

**Figure S1.** The landscape and map of sampling sites in Mts Baima, Haba, and Meili in the Southeastern Tibetan Plateau.

**Figure S2.** The alpha diversity patterns of archaeal and protistan communities along elevational gradients in three mountains in the Southeastern Tibetan Plateau.

**Figure S3.** Correlations between elevation and the relative abundance of dominant archaeal and protistan taxa.

**Figure S4.** Correlations between archaeal and protistan dominant communities and environmental factors.

**Figure S5.** Correlations between EMF index of different category function and elevation gradient.

**Figure S6.** The importance of dominant archaeal and protistan taxa in predicting EMF based on random forest model.

**How to cite this article:** Yao, B.-M., Nicol, G. W., Shi, Y., Hazard, C., Zhang, Z.-M., Zhang, L., Han, L.-L., He, F., Liu, S.-Y., Sun, G.-X., Ge, Y., Chen, B.-D., Shen, C.-C., Li, C.-H., & Zhang, L.-M. (2025). Vegetation dominantly shapes the elevational patterns of archaeal and protistan communities and their ecological functions in the Hengduan Mountains, Tibetan Plateau. *Functional Ecology*, 39, 2327–2341. https://doi.org/10.1111/1365-2435.70114