Environmental Filtering Drives Microbial Niche Shift from Generalists to Specialists During the Aging of Proglacial Lakes on the Qinghai-Tibet Plateau

Environmental Filtering Drives Microbial Niche Shift from Generalists to Specialists During the Aging of Proglacial Lakes on the Qinghai-Tibet Plateau | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 November 2025 V1 Latest version Share on Environmental Filtering Drives Microbial Niche Shift from Generalists to Specialists During the Aging of Proglacial Lakes on the Qinghai-Tibet Plateau Authors : Xiaodong Li , Bingya Zhang , Qing Yang , Zhao Xue , Guangli Mu , Meiqi Huang , Qianggong Zhang , Jinrong Huang , Yingxin Zhao , and Yindong Tong 0000-0002-0503-6585 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176407769.96901351/v1 246 views 121 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Global warming is reshaping material cycles in cryospheric regions and aquatic ecosystems globally worldwide. Concurrently, accelerated glacier ablation has facilitated the formation of numerous newly emerging proglacial lakes—systems that serve as ideal natural laboratories for investigating primary ecological succession during the initial stages of lake development. However, the mechanisms driving microbial succession during proglacial lake aging, especially the shift in microbial niche strategies and the underlying community assembly process, remain inadequately understood. To establish a proglacial lake development gradient, this study investigates 12 such lakes at distinct developmental stages in the central Qinghai-Tibet Plateau, with formation dates spanning from before the 1980s to 2021. Employing 16S/18S rRNA gene sequencing, network analysis, and null models, we analyzed changes in the structure, assembly, and function of prokaryotic and eukaryotic microbial communities, in conjunction with the variations in aquatic physicochemical factors. Our findings revealed clear patterns of microbial change across proglacial lake development. As lakes aged, microbial α-diversity decreased, whereas β-diversity increased; this β-diversity shift was driven primarily by species turnover, highlighting the dominant role of environmental filtering. Concurrently, microbial niche breadth contracted systematically, co-occurrence networks became structurally simplified and overall community stability declined. Notably, assembly processes diverged between microbial groups: bacterial community assembly shifted from stochastic to deterministic processes, while the eukaryotic communities remained governed by stochastic processes throughout lake aging. Functionally, bacteria transitioned from investing in high-cost stress-response trait (e.g., cold shock proteins) to prioritizing efficient nutrient biosynthesis (e.g., histidine, lysine). In contrast, fungal communities showed an increase in pathogenic taxa. This study clarifies how environmental filtering drives microbial successions, specifically, transition from generalist to specialist strategies, via niche-related processes. These findings provide a critical framework for predicting potential ecological trajectories in glacier retreat regions under ongoing climate change. Environmental Filtering Drives Microbial Niche Shift from Generalists to Specialists During the Aging of Proglacial Lakes on the Qinghai-Tibet Plateau Abstract Global warming is reshaping material cycles in cryospheric regions and aquatic ecosystems globally worldwide. Concurrently, accelerated glacier ablation has facilitated the formation of numerous newly emerging proglacial lakes—systems that serve as ideal natural laboratories for investigating primary ecological succession during the initial stages of lake development. However, the mechanisms driving microbial succession during proglacial lake aging, especially the shift in microbial niche strategies and the underlying community assembly process, remain inadequately understood. To establish a proglacial lake development gradient, this study investigates 12 such lakes at distinct developmental stages in the central Qinghai-Tibet Plateau, with formation dates spanning from before the 1980s to 2021. Employing 16S/18S rRNA gene sequencing, network analysis, and null models, we analyzed changes in the structure, assembly, and function of prokaryotic and eukaryotic microbial communities, in conjunction with the variations in aquatic physicochemical factors. Our findings revealed clear patterns of microbial change across proglacial lake development. As lakes aged, microbial α-diversity decreased, whereas β-diversity increased; this β-diversity shift was driven primarily by species turnover, highlighting the dominant role of environmental filtering. Concurrently, microbial niche breadth contracted systematically, co-occurrence networks became structurally simplified and overall community stability declined. Notably, assembly processes diverged between microbial groups: bacterial community assembly shifted from stochastic to deterministic processes, while the eukaryotic communities remained governed by stochastic processes throughout lake aging. Functionally, bacteria transitioned from investing in high-cost stress-response trait (e.g., cold shock proteins) to prioritizing efficient nutrient biosynthesis (e.g., histidine, lysine). In contrast, fungal communities showed an increase in pathogenic taxa. This study clarifies how environmental filtering drives microbial successions, specifically, transition from generalist to specialist strategies, via niche-related processes. These findings provide a critical framework for predicting potential ecological trajectories in glacier retreat regions under ongoing climate change. Global warming; niche differentiation; microbial community succession; aging of proglacial lakes; glacier retreat. 1. Introduction Global warming is driving glacier ablation at an unprecedented rate (Korneva et al., 2024) . Observational data showed significantly accelerated global glacier mass loss since the beginning of the 21st century, with retreat rates of glaciers on the Qinghai-Tibet Plateau (QTP) being particularly remarkable (Hugonnet et al., 2021) . The rapid melting of glaciers has led to the formation of numerous newly emerged proglacial lakes at their termini, rendering these water bodies one of the most prominent geomorphological features within the glacier retreat zones (Gu et al., 2023; Milner et al., 2017) . As rapidly expanding freshwater ecosystems globally, proglacial lakes are not only highly responsive to regional climatic and environmental shifts, but also serve as unique natural “laboratories” for investigating the early developmental processes of nascent ecosystems (Xu et al., 2025) . The QTP, known as the “Asian Water Tower,” experiences glacier retreat with profound implications for regional water cycles and ecological security (Yao et al., 2022) , rendering it an ideal region for investigating aquatic ecosystem succession in the cryosphere under climate change. Proglacial lakes formed by glacier retreat typically exhibit unique environmental characteristics: high altitude, low water temperature, low nutrient concentrations, and high turbidity influenced by glacial meltwater inputs (Peoples et al., 2025; Stibal et al., 2020) . As these lakes develop, systematic changes in hydrological connectivity, light conditions and organic matter input drive the transition of water bodies from oligotrophic to mesotrophic states (Kleinteich et al., 2022) . Compared to polar regions, proglacial lakes on the QTP are subjected to more intense ultraviolet radiation and more complex topography, thus shaping their distinct biogeochemical cycling pattern (Wang et al., 2014) . In this process, microorganisms, functioning as pioneer colonizers and key ecosystem engines, shape the developmental trajectories and functional characteristics of nascent ecosystems. They achieve this by driving the biogeochemical cycles of essential elements such as carbon, nitrogen, and sulfur (Battin et al., 2016) . For instance, recent studies suggest that proglacial lakes on the QTP are active sources of greenhouse gases (e.g., carbon dioxide and methane), whose fluxes are closely related to the structures of microbial community (Huang et al., 2025; Liu et al., 2025a) . Simultaneously, microbial-mediated mercury methylation processes also occur in these lakes, potentially impacting the safety of downstream ecosystems (Mu et al., 2025) . Therefore, elucidating the succession patterns of microbial communities in proglacial lakes is essential for accurately predicting the functional evolution of ecosystems in glacier retreat zones. As the material foundation and energy hub of aquatic food webs, microbial community succession directly determines the complexity and functionality of aquatic ecosystems (Stibal et al., 2020) . Existing research has predominantly focused on describing the community compositions of microorganisms in specific proglacial lakes or their geographical distribution patterns (Kohler et al., 2024) . For example, studies in the Alps showed that bacterial community structure in proglacial lakes is largely influenced by water temperature and turbidity (Peter and Sommaruga, 2016) . In Greenland, different aquatic habitats (including glacial ice, meltwater, and lakes) host microbial groups with unique metabolic characteristics (Bomberg et al., 2019) , and in the Himalayas, sediments from lakes closer to glaciers exhibit a higher abundance of functional genes related to nitrogen cycling (Kumar et al., 2021) . However, most of these studies are based on discrete spatial and temporal sampling, thus limiting a systematic characterization of community succession trajectories along ecosystem development gradients. More critically, there remains a lack of systematic understanding of the internal mechanisms driving this succession process, particularly how microbial niche strategies shift and how the relative importance of deterministic versus stochastic community assembly processes changes along environmental gradients. To address these research gaps, this study investigated 12 proglacial lakes in a typical glacier retreat area of the central QTP (4,800-6,200 m a. s. l.). These lakes span a formation period from before the 1980s to 2021, thus establishing a complete lake developmental sequence. By integrating 16S/18S rRNA gene amplicon sequencing with co-occurrence network analysis, null models, and environmental correlation analysis, we aimed to elucidate the succession patterns and underlying assembly mechanisms of microbial communities along this proglacial lake gradient. This study focuses on the following key scientific questions: (1) How do the structure, diversity, and stability of microbial communities change with lake aging? (2) How do microbial niche breadth and community assembly processes (deterministic versus stochastic processes) respond to lake development? (3) Do the functional traits of microbial communities (e.g., carbon/nitrogen metabolism, stress response) show a transition from “high-cost” generalist strategy to “high-efficiency” specialist strategies? (4) Do bacterial and eukaryotic microorganisms exhibit synchronous or asynchronous ecological patterns during their succession? This research ultimately aims to lay a critical scientific foundation for understanding the microbial community composition, ecosystem stability, and evolutionary trajectories of the high-altitude emerging proglacial lakes under global warming. In turn, this foundation would support the construction of a theoretical framework for predicting ecological evolution in glacier retreat regions. 2. Materials and methods 2.1 Study area and field sampling Located within the Nyainqêntanglha Mountain, a well-documented hotspot for rapid glacier retreat under global warming, the study area provides an ideal setting for investigating proglacial lake dynamics. Specifically, we examine the Lhasa Valley Glacier No. 1 (90.184° E, 29.851° N; China Glacier Inventory No. 5O270B0143) on the southern slope of this range (Figure 1a). Characterized by a semi-arid climate with limited annual precipitation (only 460 mm), the glacier serves as a vital water source, making its meltwater-dominated hydrological regime particularly sensitive to climatic changes (Huang et al., 2025) . As a classic continental-type glacier, it has carved a glacial trough extending from the southwest to the northeast, characterized by higher terrain in the south and lower terrain in the north. The glacier spans an elevation range from 4,800 to 6,200 meters above sea level and covers an area of 6.0 km², accounting for 19% of the regional total (An et al., 2021) . In recent decades, climate warming has accelerated the melting of glaciers in this region, with a mass decrease rate of -9.6 Gt per year during 2000 ‒ 2019 (Hugonnet et al., 2021) , leading to the formation of diverse proglacial lakes, including glacial margin lakes, moraine lakes, and glacial erosion lakes (Liu et al., 2024a) . To systematically investigate microbial functional succession and community assembly processes, we selected 12 proglacial lakes along a developmental gradient in the foreland of Lhasa Valley Glacier No. 1 (Figure S1a). We determined the formation time of each proglacial lake and measured the straight-line distance from its centroid to the glacier front using high-resolution remote sensing imagery from Google Earth (www.google.com/intl/en/earth/). Based on these measurements (Table S1), combined with an assessment of geomorphological characteristics and hydrological conditions, the lakes were classified into four successive developmental stages (Figure S1b): Recently Formed Lakes (RFL: L1‒L3): Characterized by the most recent formation, these lakes have maintained direct contact with the glacier terminus. Their hydrology is dominated by direct glacial melt-water input, with basin morphology actively shaped by glacial processes and sediments mainly consisting of glacial till. Young Proglacial Lakes (YPL: L4 ‒ L6): Representing an early developmental stage, these lakes have separated from direct glacier contact but maintain strong hydrological connectivity. Water supply remains predominantly glacial meltwater, with initial contributions from seasonal snowmelt. Developing Proglacial Lakes (DPL: L7 ‒ L9): At an intermediate developmental stage, these lakes show substantially reduced glacial meltwater influence. Hydrological inputs transition to a mixed regime of glacial meltwater and seasonal snowmelt, reflecting progressive isolation from the glacier. Mature Proglacial Lakes (MPL: L10 ‒ L12): As the endpoints of the developmental gradient, these lakes have severed all direct glacial connections. Their hydrology is now sustained primarily by seasonal snowmelt and groundwater, thereby fostering autonomous and stable lacustrine ecosystems. 2.2 Sample collection and processing For each lake, we established three sampling sites along a transect from the center to the shoreline. At each site, water samples were collected from three stratified layers: surface (0–15 cm), middle (mid-depth), and bottom (0–15 cm above the sediment-water interface). All environmental and microbiological sampling was conducted in August 2024. Water samples were obtained from the surface, middle, and bottom layers in each proglacial lake for every sampling site utilizing a 5-liter deep water sampler (JC-800, Juchuang Environment, China). To eliminate insoluble impurities, water samples underwent initial filtration through a 200 μm nylon filter. Subsequently, for the microbiological assessment, the filtered water was transferred into 2-liter polyethylene plastic bottles, which had been pre-wetted thrice with the in-situ water. For nutrient analyses, similarly, samples were gathered in 500-milliliter Teflon-coated polyethylene bottles, similarly pre-wetted three times with in-situ water. A total of 108 microbial samples were gathered (i.e., 3 sites×3 layers×12 lakes). Within 3 hours of collection, the water samples were transported to the laboratory for processing. In the laboratory, the water samples underwent filtration by vacuum extraction unit employing a 0.22 μm pore size polycarbonate membrane (Millipore Corporation, Billerica, USA). Polycarbonate membranes, containing the sample’s DNA, were deposited in sterile cryotube. These cryotubes were shielded by aluminum foil and promptly preserved in liquid nitrogen for freezing. Subsequently, they were kept under cold-chain conditions and forwarded to Majorbio (Shanghai, China) for further DNA extraction and high-throughput sequencing. In the field, key water parameters—including water temperature (WT), pH, electrical conductivity (EC), and total dissolved solids (TDS)—were measured across different lake layers using a portable multiparameter water quality analyzer (HI 98195, Hanna, Italy). Turbidity (TUR) was determined with a high-precision turbidity meter (HI98703, Hanna, Italy). All water samples were collected, stored, and transported following the guidelines of China’s Environmental Quality Standards for Surface Water (issued by the Ministry of Ecology and Environment). In the laboratory, nutrient concentrations were analyzed with a UV-visible spectrophotometer (UV-4800, Unico, China). Total phosphorus (TP), total dissolved phosphorus (TDP), and orthophosphate (PO₄³⁻) were quantified via phosphomolybdenum heteropoly acid spectrophotometry. Total nitrogen (TN) was determined by alkaline potassium persulfate oxidation, nitrate-nitrogen (NO₃⁻-N) by phenol disulfonic acid photometry, and ammonia nitrogen (NH₄⁺-N) by hypobromite oxidation. All analyses were performed in triplicate, and the results for these environmental factors are detailed in Table S2. 2.3 DNA Extraction, PCR, and Sequencing DNA extraction from the filter membrane was performed using the Mobio Power Soil DNA Isolation Kit (MoBio Laboratories, Carlsbad, USA) in strict accordance with the manufacturer’s protocol. The quality of the extracted DNA was assessed by agarose gel electrophoresis using a 1% concentration gel, while DNA concentration and purity were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). The hypervariable region V3-V4 of 16S rRNA gene were amplified with primer pairs 338F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’ -GGACTACHVGGGTWTCTAAT-3’) by T100 Thermal Cycler PCR thermocycler (BIO-RAD, USA) (Cambiaghi et al., 2016) . For the 18S rDNA amplification, the V9 hypervariable region was targeted using the primers 1391F (5’-GTACA CACCGCCCGTC-3’) and 1510R (5’-TGATCCT TCTGCAGGTTCACCTAC-3’), as shown by Amaral-Zettler et al. (2009) . Each primer included a unique barcode sequence at the 5’ end to facilitate multiplex sequencing. A polymerase chain reaction (PCR) was conducted under the following conditions: initial denaturation at 95 °C for 3 minutes, followed by 30 cycles of denaturation at 95 °C for 30 seconds, annealing at 55 °C for 30 seconds, and extension at 72 °C for 45 seconds, with a final extension at 72 °C for 10 minutes. PCR products were visualized on a 2% agarose gel to confirm successful amplification. Subsequently, the products were purified using the GeneJet Gel Recovery Kit and further refined on a 1×TAE agarose gel (2% concentration) to ensure high-quality DNA sequencing. For library construction, the TruSeq DNA PCR-Free Sample Preparation Kit (Illumina) was used to prepare sequencing libraries according to the manufacturer instruction. The libraries were then sequenced on an Illumina NovaSeq 6000 platform (Beijing, China) using paired-end 250 bp sequencing to generate high-resolution data for downstream analysis. 2.4 Bioinformatic and Statistical Analysis Raw FASTQ files were de-multiplexed using an in-house perl script, and then quality-filtered by fastp (version 0.19.6) (Chen et al., 2018) and merged by FLASH (version 1.2.7) (Magoč and Salzberg, 2011) . Then, the optimized sequences were clustered into operational taxonomic units (OTUs) using UPARSE (version 7.1) with 97% sequence similarity level (Edgar, 2013) . The most abundant sequence for each OTU was selected as a representative sequence. To minimize the effects of sequencing depth on alpha and beta diversity measure, the number of 16S/18S rRNA gene sequences from each sample were rarefied to 20,000, which still yielded an average Good’s coverage of 99.09%, respectively. The taxonomy of each OTU representative sequence was analyzed by RDP Classifier (version 2.2) against the 16S/18S rRNA gene database (Silva, version 138) using confidence threshold of 0.7 (Quast et al., 2013) . A total of 13,473 bacterial OTUs and 6,728 eukaryotic OTUs were identified. For subsequent analysis focusing on eukaryotic micro-organisms (e.g., fungi, protists), we had retained 3,027 OTUs after excluding metazoan and plant sequences. The putative metabolic functions of the bacterial communities were predicted using PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) based on 16S rRNA gene sequences (Douglas et al., 2020) . The functional guilds and trophic modes of fungi were annotated using FUNGuild based on the 18S rRNA gene data (Nguyen et al., 2015) . Unless otherwise specified, all statistical analyses were conducted using R software (version 4.4.3, http://cran.r-project.org/).α-diversity (Shannon-Wiener diversity index) of microbial communities were computed using the “vegan” package. Principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) were performed using the “vegan” package. Mantel tests were used to evaluate correlation between environmental variables and selected characteristics of microorganism employing the “linkET” package. Using the “vegan” package, the Bray-Curtis distance was calculated to represent the β-diversity of microbial communities. The “betapart” package was used to calculate and partition the total β-diversity for all the sampling sites, evaluating the relative contributions of species replacement (Turnover) and richness difference (Nestedness) to overall β-diversity, as defined by Baselga (2009) . The β-nearest taxon index (βNTI) was calculated using the “NST” package, with the |βNTI| > 2 means deterministic processes dominate microbial community assembly, while |βNTI| < 2 suggests stochastic processes prevail (Zhou and Ning, 2017a) . The fitting of the neutral community model ( R 2 ) was determined using the “Hmisc” package to explore the magnitudes of the contribution of dispersal limitation to community assembly (Chen et al., 2019) . Additionally, we constructed microbial co-occurrence networks based on Spearman’s correlation coefficient results (| R | > 0.75; p < 0.05) to investigate the relationships among microbial taxa in the package “microeco”, and the resulting network was visualized using Gephi (version 0.9.1) . Using the “vegan” package, the average variation degree (AVD) of microbial community was calculated. Typically, a lower AVD value implies a higher community stability (Xun et al., 2021) . Partial Least Squares Structural Equation Modeling (PLS-SEM) was conducted using the “plspm” package. Random Forest analysis was performed using the “randomForest” package to screen the importance of environmental variables on microbial community functional traits. Sampling sites were mapped using ArcMap (version 10.8). 3. Results 3.1 Microbial community succession and dominance of species turnover as proglacial lakes aged Bacterial and eukaryotic microbial communities displayed significant, synchronous successional trends in diversity as proglacial lakes aged, with structural shifts primarily driven by the species turnover. Specifically, both microbial groups exhibited significant differences in α-diversity (Wilcoxon rank-sum test, p < 0.05) and β-diversity (PERMANOVA, p < 0.05) across distinct developmental stages (Figures S2b, e). As the proglacial lakes matured, α-diversity declined consistently in both groups, while the β-diversity increased significantly (Figures 1b–c, Table S3). In terms of community structure, principal coordinate analysis (PCoA) and analysis of similarities (ANOSIM) showed clear within-group clustering of both bacterial and eukaryotic communities across the gradient (Figures S2c, f), further confirming systematic structural differentiation along the developmental gradient ( p < 0.001). At the taxonomic composition level, the relative abundances of dominant bacterial phyla—Proteobacteria (45.47%), Bacteroidota (17.98%), and Actinobacteriota (14.32%)—shifted significantly with the lake development: Proteobacteria and Bacteroidota decreased, while Actinobacteriota increased (Figure S2a). Similarly, among eukaryotic microorganisms, the dominant phyla Chlorophyta (36.27%), Ciliophora (14.50%), and Dinoflagellata (12.40%) showed differential responses, with Chlorophyta and Dinoflagellata increasing in abundance, while Ciliophora declined (Figure S2d, Table S5). Further decomposition of microbial β-diversity (Figures S2g–h) demonstrated that species turnover dominated both bacterial (89.26%) and eukaryotic (90.54%) communities, whereas the nestedness component contributed only marginally (10.74% and 9.46%, respectively). These results suggest that the community reorganization along the proglacial lake development gradient is primarily driven by species replacement rather than net species loss, highlighting the crucial roles of environmental filtering and niche-based processes in community assembly. 3.2 Niche differentiation and network simplification as proglacial lakes aged Microbial co-occurrence networks exhibited systematic structural simplification along the proglacial lake development gradient, coupled with significant niche differentiation among species. Co-occurrence network analysis revealed distinct microbial interaction patterns along the proglacial lake development gradient (Figure 2a). Cross-domain comparison suggested that bacterial networks exhibited higher average degree (32.50) than eukaryotic microbial networks (15.06), while demonstrating lower values for both average path length (3.05 vs. 3.26) and modularity (0.05 vs. 0.09) (Table S6). Systematic variations in network topology were observed along the proglacial lake development gradient within each microbial domain. Both bacterial and eukaryotic microbial networks showed decreasing average degree but increasing average path length and modularity along the gradient (Figure 2b), reflecting a trend toward structural simplification and enhanced modularization. Taxonomic analysis of high-centrality OTUs (top 25%) identified Proteobacteria and Bacteroidota as keystone taxa in bacterial networks, with Cercozoa and Chlorophyta serving analogous roles in eukaryotic microbial communities. The richness of these keystone taxa declined with increasing proglacial lake development (Figure S3a-b). Robustness analysis along the gradient confirmed that networks from more developed lakes had lower initial efficiency and connectivity (Figure 2c), indicating reduced resistance to disturbance. Furthermore, targeted removal of highly connected nodes caused more rapid deterioration of these parameters compared to random removal, underscoring the crucial role of keystone taxa in maintaining network stability. Niche breadth analysis (Levins’ index) revealed broader niche width for bacteria (mean = 4.92) than for eukaryotic microbial communities (mean = 3.67), with both groups showing progressive niche narrowing along the development gradient (Figure 2d). Community stability assessment using AVD showed higher stability in bacterial communities (AVD = 0.65) than in eukaryotic microbial communities (AVD = 0.68). Notably, AVD values increased along the development gradient for both communities (Figure 2e), reflecting a consistent decline in community stability during proglacial lake development. 3.3 Microbial community assembly in proglacial lakes Bacterial and eukaryotic microbial communities exhibited contrasting assembly mechanisms along the proglacial lake development gradient. Based on phylogenetic analysis of microbial communities (Figure 3a), β-nearest taxon index (βNTI) values were computed to evaluate the assembly processes of bacterial and eukaryotic microbial communities ( Figure 3b). Quantification of βNTI distributions show that deterministic processes dominated bacterial community assembly (53.71%) while stochastic process prevailed in eukaryotic microbial communities (92.24%). Furthermore, bacterial community assembly shifted from stochastic to deterministic processes along the proglacial lake development gradient (Figure 3c). To explore relationships between microbial diversity and community assembly, we performed linear regression between |βNTI| and the Shannon-Wiener index. A significant negative correlation has been observed for both bacterial and eukaryotic microbial communities ( p < 0.001), with |βNTI| decreasing as Shannon diversity increased (Figure 3d), suggesting that microbial communities with lower diversity are more strongly influenced by deterministic processes. We applied a neutral community model to quantify the relative impact of stochastic processes (Figure 3e). Across the proglacial lake development gradient, over 75% of microbial taxa fell within the 95% confidence interval, indicating that the neutral model effectively described the relationship between OTU occurrence frequency and relative abundance. Comparison of dispersal rate coefficients (m-values) revealed stronger stochastic influences on eukaryotic microorganisms (m = 0.003) than on bacteria (m = 0.069). As the proglacial lakes matured, m-values decreased in both communities, suggesting elevated dispersal limitation in more developed lakes. 3.4 Distinct assembly pathways mediated by abiotic factors in proglacial lakes Environmental filtering exerted distinct selective pressures on bacterial and eukaryotic microbial communities, with WT and TUR emerging as the predominant drivers of community structure and function (Figure 4). Analysis of environmental heterogeneity revealed that WT was the only factor showing statistically significant variation across the proglacial lake development gradient (Wilcoxon rank-sum test, p < 0.05; Figure S4). Mantel tests identified WT, TUR, and NO₃⁻-N as significant drivers of bacterial community structure ( p < 0.05), whereas eukaryotic communities were primarily shaped by WT and TUR, with turbidity exhibiting a particularly strong effect ( p < 0.001; Figure 4a). A variance partitioning analysis (VPA) quantified the individual explanatory power of these factors, identifying WT as the dominant predictor for bacterial communities (11.05%; Figure 4b) and TUR as the primary driver for eukaryotic communities (17.69%; Figure 4c). To further elucidate how these environmental factors influence community functional traits, we performed the Random Forest analysis (Figures 4d–e), which demonstrated that WT served as the principal regulator, showing the highest percentage increase in mean squared error (%IncMSE) for both niche width (bacteria: 14.02; eukaryotic: 17.66) and AVD (bacteria: 22.18; eukaryotic: 16.94). PLS-SEM elucidated the distinct pathways through which environmental factors mediate community assembly (Figures 4f–g). The models exhibited strong explanatory power for key ecological attributes ( R² : 0.396–0.883 for bacteria; 0.443–0.747 for eukaryotes; Table S7) and excellent goodness-of-fit (GOF = 0.854 for bacteria; GOF = 0.779 for eukaryotes). For bacterial communities, WT (loading = -0.869) and PO₄³⁻ (loading = -0.845) served as primary environmental mediators, directly influencing composition and diversity, which subsequently drove changes in niche characteristics (loading = 0.912) and stability (loading = -0.838). Eukaryotic communities were predominantly shaped by WT (loading = -0.827) and TUR (loading = 0.701), with NO₃⁻-N (loading = -0.794) playing an additional significant role. Effect decomposition analysis confirmed that water properties exerted stronger direct effects on bacterial communities, while their influence on eukaryotic communities was primarily mediated through niche and diversity pathways (Figure S5). 3.5 Successional shifts in bacterial and fungal functional traits in proglacial lakes Bacterial communities exhibited a functional reprogramming along the proglacial lake developmental gradient, transitioning from stress response to nutrient biosynthesis. Functional potential of bacterial communities was predicted from 16S rRNA gene sequences using PICRUSt2, based on which we calculated functional α- and β-diversity to assess variations in functional profile (Figure 5a). Both functional α-diversity and β-diversity changed significantly along the proglacial lake developmental gradient, with α-diversity decreasing (Wilcoxon rank-sum test, p < 0.01) and β-diversity increasing (Kruskal-Wallis test, p < 0.01), indicating a pronounced functional divergence as lakes aged. Principal coordinate analysis revealed clear separation of bacterial functional structures across developmental stages (Figure 5b), supported by ANOSIM ( R = 0.39, p < 0.001). Among the top 20 most abundant KEGG orthologs (KOs) that varied significantly along the gradient (Kruskal–Wallis test, p < 0.05; Figure 5c), the cold shock protein gene K03704 was most abundant in the RFL and declined thereafter (Table S9), indicating stronger cold stress in early developmental stages of proglacial lakes. In contrast, K01952(phosphoribosyllformylglycinamidine synthase) and K00004(butanediol dehydro- genase) increased from RFL to MPL, while K00029 (malic enzyme) decreased. KEGG pathway analysis further indicated successional shifts in core metabolic pathways (Figure 5d). Broad-scope metabolic pathways (e.g., ko01100, ko01110) were dominant across all stages but most pronounced in RFL (Figure 5e), reflecting highly active and versatile metabolism in nascent proglacial lakes. Similarly, quorum sensing (ko02024) and two-component system (ko02020) pathways were most abundant in RFL, underscoring the importance of efficient microbial communication under the early-stage stressful conditions. In contrast, amino acid biosynthesis (ko01230) and ABC transporter (ko02010) pathways decreased along the developmental gradient (Table S10). Consistent with these trends, predicted enzyme activities, including phosphoenolpyruvate carboxylase (EC 4.1.1.31) and cellulase (EC 3.2.1.4) were highest in RFL (Table S11). Stress response modules such as the GABA shunt (M00027) and proline biosynthesis (M00015) were also enriched in early-stage lakes (Figure 5f; Table S12), whereas histidine (M00026) and lysine (M00016) biosynthetic pathways were significantly upregulated in mid–late developmental stages (DPL and MPL), highlighting a progressive shift in nutrient cycling potential. Spearman correlation analysis identified SAL, NH₄⁺-N, and TP as key environmental drivers of these functional shifts (Figure S6). Analysis of fungal functional traits based on 18S rRNA sequencing using FUNGuild revealed significant successional shifts in trophic modes and functional guilds along the proglacial lake developmental gradient. Trophic mode analysis demonstrated relatively high predicted abundances of pathotrophs (36.64%) and pathotroph-saprotrophs (33.28%) in these lakes (Figure S7a). Pathotroph abundance showed significant variation across developmental stages and increased consistently with lake maturity (Kruskal-Wallis test, p < 0.05; Figure S7c). At the functional guild level (Figure S7b), plant pathogens displayed the highest predicted relative abundance (35.57%), primarily represented by Pythium , followed by the composite guilds Animal Endosymbiont-Animal Pathogen-Endophyte-Plant Pathogen-Undefined Saprotroph and Fungal Parasite-Undefined Saprotroph, mainly composed of Rhodotorula and Tremellales , respectively. Both plant pathogens and fungal parasite-undefined saprotroph guilds varied significantly across developmental stages and increased with lake development ( p < 0.05; Figure S7d). Spearman correlation analysis identified WT and NH₄⁺-N as positive correlates of both saprotrophic and pathotrophic trophic modes ( p < 0.05; Figure S7e), while TUR showed negative correlation with the pathotroph-saprotroph-symbiotroph complex. Furthermore, NH₄⁺-N and PO₄³⁻ concentrations were positively correlated with several pathogen-related functional guilds, confirming their roles as key environmental driver shaping fungal functional succession along the proglacial lake developmental gradient. 4. Discussion 4.1 Environmental filtering drives microbial community assembly through niche-based processes This study systematically confirms that during proglacial lake development, environmental filtering drives deterministic succession of microbial communities through multidimensional niche-based processes. As lakes progress from initial formation to mature stages, both bacterial and eukaryotic microorganisms show significant decreases in α-diversity ( Figures 1b), while β-diversity increases largely and is primarily driven by species turnover (contribution >89%) (Figures S2g–h). This pattern contrasts sharply with the classical succession theory that predicts increasing diversity over time, indicating that under rapid glacier retreat, strong abiotic filtering continuously screens the regional species pool through niche selection, leading to systematic replacement by adaptive species (Zhou and Ning, 2017b) . This process highlights the principle that environmental filtering predominates over biotic interactions in early primary succession stages (Baecher, 2024) . The mechanisms driving niche processes are manifested across multiple dimensions. Water temperature and turbidity act as core environmental filters, systematically reshaping microbial niche space by altering the habitat physicochemical characteristics (Figures 4a–c). In early-stage lakes, low temperature (50 NTU) limits light penetration depth and alters particle adsorption properties, collectively forming intense environmental stress (Figure S4). This multifaceted stress selects generalist species with broad niches: Proteobacteria and Bacteroidota, as typical r-strategists, dominate this stage through high metabolic plasticity and rapid growth strategies (Liu et al., 2024b) . Their dominance coincides with high expression of cold shock proteins and ABC transporter genes in early lakes, reflecting adaptive evolution to harsh glacial environments. As glaciers further retreat and lakes develop to intermediate stages, niche structure undergoes fundamental restructuring. Rising water temperature (5–10°C) has significantly improves microbial metabolic efficiency, while decreased turbidity (<20 NTU) opens up new photic niches. This shift promotes gradual replacement of generalists by specialist through competitive exclusion: Actinobacteriota, as K-strategists, establish a competitive advantage through the synthesis of antibiotics and extracellular enzymes (Garzke et al., 2019) ; Their relative abundance displays a significant positive correlation with lake development. Eukaryotic microorganisms respond more complexly: the significant increase in Chlorophyta relative abundance marks successful occupation of photosynthetic niches, while Dinoflagellata achieve cross-trophic resource utilization through mixotrophic strategies, demonstrating niche differentiation diversity under climate change (Stoecker et al., 2017) . Quantitative analysis of community assembly mechanisms provides deeper evidence for niche processes (Figures 3b–c). The gradient shift in bacterial community assembly from stochastic to deterministic processes (proportion of |βNTI| > 2 increasing from 38% to 72%) clearly reflects intensifying niche selection. This transition shows distinct stage characteristics: in early development, environmental stress from glacial meltwater input acts as the main selective pressure; whereas in the late stages, the importance of biotic interactions (such as competitive exclusion and mutualism) significantly increases (Wang et al., 2022) . In contrast, persistent dispersal limitation (m = 0.003) in eukaryotic microbial communities reflects spatial barriers in niche occupation, possibly related to their larger body size and more complex life history strategies (Liu et al., 2025b) . Gradient changes in microbial functional traits further support the driving role of niche processes. High expression of stress response genes (e.g., cold shock protein K03704) and quorum sensing systems in early lakes reflects niche strategies for maintaining population stability in glacier-affected uncertain environment, but upregulation of amino acid biosynthesis pathways in late lakes reflects niche specialization for optimizing resource use efficiency in climate-warming stabilized environments (Liang et al., 2025) . This continuous functional transition confirms, at the molecular level, the systematic screening of microbial traits by environmental filtering. These findings collectively construct a comprehensive niche-driven framework: proglacial lake development is essentially a systematic restructuring of niche architecture. Climate change drives deterministic succession of microbial communities from opportunistic r-strategists to competitive K-strategists by altering environmental gradients that affect niche dimensions and intensity. This framework not only deepens our understanding of primary succession mechanisms in high-altitude lake ecosystems but also provides a new theoretical basis for predicting ecological trajectories in glacier retreat regions under global warming. 4.2 Niche breadth contraction and co-evolution of species interaction networks Our research shows that along the proglacial lake development gradient, microbial niche breadth exhibits systematic contraction, with Levin’s index decreasing from 5.42 and 4.13 to 3.87 and 2.95 for bacteria and eukaryotic microorganisms, respectively (Figure 2d). This change closely correlates with network topology parameters: average degree decreases from 38.6 to 24.3, while modularity index increases from 0.04 to 0.11 (Figure 2b). Niche breadth contraction directly leads to simplified interspecies interactions, manifested as reduced network connection density and enhanced modularity (Philipp et al., 2025) . This structural transformation reflects the fundamental pattern that environmental filtering promotes niche specialization in climate change-driven ecosystem development (Mouillot et al., 2012) . Changes in keystone taxa further reveal cascading effects of niche contraction. In early-development lakes, Proteobacteria and Cercozoa serve as keystone taxa in networks, maintaining highly connected network structures through their broad niche widths. However, as glaciers retreat and lakes develop, the dominance of these generalists is gradually replaced by specialists, leading to significantly decreased network connectivity (Figures S3a–b). Network robustness analysis shows that networks in late-development lakes exhibit faster collapse when disturbed—after removing 30% of nodes, network efficiency decreases by ~62%, compared to only 38% in early lakes (Figure 2c). This phenomenon confirms the core ecological function of generalists as network stabilizers in climate change contexts (Gilarranz et al., 2017) . From a metabolic perspective, niche breadth contraction closely relates to microbial functional strategy shifts. High expression of broad-spectrum metabolic pathways (e.g., ko01100) and quorum sensing systems (ko02024) in early lakes reflects generalist strategies maintaining diverse metabolic capabilities to cope with environmental uncertainty under glacial influences. In mature lakes, specialists achieve optimized resource utilization in climate-warming stabilized environments by upregulating specific amino acid synthesis pathways (e.g., histidine M00026 and lysine M00016). This functional transformation co-evolves with network structure simplification, embodying microbial adaptive transition from energy-intensive generalist strategies to efficiency-optimized specialist strategies (Iram et al., 2025; Mafa‐Attoye et al., 2022) . Cross-domain comparisons reveal differential strategies of bacteria and eukaryotic microorganisms in responding to niche contraction. Bacterial networks show higher connectivity (average degree 32.50 vs 15.06) and lower modularity (0.05 vs 0.09), indicating their buffering of climate change-induced niche variations through maintaining higher functional redundancy. In contrast, stronger modularity in eukaryotic microbial networks reflects their strategy of reducing interspecific competition through niche partitioning. This difference may stem from their fundamental distinctions in metabolic plasticity and life history strategies responding to climate change (Hernandez et al., 2021) . Community stability assessment (AVD) provides quantitative evidence for ecological consequences of niche contraction. AVD values for bacteria and eukaryotic microorganisms increase from 0.58 and 0.61 to 0.72 and 0.75, respectively (Figure 2e), indicating significantly reduced community stability with niche breadth contraction. This finding supports the niche breadth-stability hypothesis, suggesting that communities with broader niche generalists have stronger resistance to climate change and other environmental disturbances (Landi et al., 2018) . Notably, the sharp network efficiency decline (up to 85%) caused by targeted removal of highly connected nodes further confirms the crucial role of keystone taxa in maintaining ecosystem stability under glacier retreat. 4.3 Functional metabolic strategies in response to niche transformation Against the macro-background of global climate change driving glacier retreat, systematic restructuring of microbial functional metabolic characteristics reflects deep adaptation to niche transformation. Significant decreases in functional α-diversity ( p < 0.05) and synchronous increases in functional β-diversity along the proglacial lake development gradient (Figure 5a) mark the strategic transition of microorganisms from metabolic generalization to metabolic specialization. This functional reorganization directly responds to climate change-driven niche structure alterations: in early development, harsh niche conditions (low temperature, high turbidity, nutrient limitations) under glacial influence select high-cost stress response strategies; whereas in late stages, stable niche environments promoted by climate warming facilitate high-efficiency growth optimization strategies (Chen et al., 2022) . At the molecular level, the key functional gene expression patterns reveal microbial adaptive strategies to niche stress under glacial influences. High expression of cold shock protein genes (K03704) in early lakes reflects microbial maintenance of protein synthesis homeostasis at low temperatures by regulating RNA secondary structure and ribosomal function (Barria et al., 2013) . Simultaneously, significant enrichment of ABC transporter pathways (ko02010) reflects survival strategies for maintaining nutrient uptake under nutrient-limited conditions caused by glacial meltwater by increasing substrate affinity. These high energy-cost metabolic characteristics align with niche features of high turbidity and low water temperature under glacial influence. As climate warming promotes lake development, improved niche conditions drive fundamental metabolic strategy changes. Significant upregulation of phosphoribosylformy- lglycinamidine synthase (K01952) and butanediol dehydrogenase (K00004) marks a transition from stress metabolism to growth metabolism. These enzymes’ key roles in purine synthesis and organic acid metabolism reflect microbial metabolic investment in growth and reproduction under climate-improved environmental conditions (Diankristanti and Ng, 2023) . Notably, specific upregulation of histidine (M00026) and lysine (M00016) biosynthetic pathways in late-development lakes further confirms the metabolic shift from exogenous nutrient dependence to endogenous synthesis capacity, a process closely related to ecosystem development stages after glacier retreat. Microbial communication system changes provide new perspectives for understanding niche adaptation. High expression of quorum sensing (ko02024) and two-component system (ko02020) pathways in early lakes reflect survival strategies where microorganisms coordinate population behaviors through intercellular communication in the glacier-affected uncertain environments. These systems enhance microbial community survival competitiveness in harsh niches by regulating biofilm formation, antibiotic production, and stress responses (Hense and Schuster, 2015) . After niche conditions improve due to climate warming, this high-cost communication strategy is gradually replaced by more economical metabolic regulation mechanisms. Fungal functional trait transformation reveals ecological impacts of niche transformation from another dimension. Pathogen relative abundance increases from 28.3% to 45.6% (Figure S7c), reflecting host niche emergence and organic matter composition changes as glaciers retreat and lakes develop. Increases in plant pathogens (represented by Pythium ) and fungal parasite-saprotroph composite functional groups (represented by Rhodotorula and Tremellales , respectively) mark fungal niche evolution from simple saprotrophic nutrition to complex parasitic-saprotrophic networks (Tanunchai et al., 2022) . This refinement in trophic strategy shows significant correlation with WT increase ( R 2 = 0.72, p < 0.05) and NH 4 ⁺-N concentration rise ( R 2 = 0.66, p < 0.01) caused by climate warming confirms climate change’s key role in driving fungal niche differentiation. Functional module analysis further reveals microbial metabolic networks’ overall response to niche conditions. Enrichment of GABA shunt (M00027) and proline synthesis (M00015) modules in early lakes reflects mechanisms by which microorganisms cope with glacial environmental stress by maintaining intracellular redox balance and osmotic regulation (Braga et al., 2024) . In developed lakes, relative abundance of these stress response modules significantly decreases, replaced by upregulated metabolic pathways related to growth and reproduction. This functional module reprogramming demonstrates, at the systems level, microbial communities’ metabolic adaptation to climate change-driven niche transformation, providing important molecular ecological evidence for understanding ecosystem evolution in glacier retreat regions under global warming. 5. Conclusion and limitations This study establishes that environmental filtering governs microbial ecological succession in QTP proglacial lakes through coordinated niche-based processes. We demonstrate that water temperature and turbidity serve as primary environmental filters, driving a deterministic shift in community assembly toward deterministic processes while promoting species turnover from r-strategist generalists to K-strategist specialists. This niche restructuring directly reduces microbial interaction network complexity and stability, as evidenced by decreased connectivity and increased modularity. At the functional level, microbial communities undergo metabolic reprogramming from high-cost stress response strategies to efficient nutrient biosynthesis pathways. Our findings reveal the predominant role of environmental filtering in controlling early ecosystem development under glacier retreat, providing new insights into microbial succession patterns. Our findings enable improved prediction of ecological trajectories in high-altitude glacier retreat zones, informing management strategies under climate change. It should be noted that the functional traits analyzed in this study were predicted from 16S/18S rRNA gene sequences. Future research should integrate metagenomic and metatranscriptomic approaches to empirically validate these functional shifts and elucidate their underlying molecular regulatory mechanisms. References Amaral-Zettler, L.A., McCliment, E.A., Ducklow, H.W., Huse, S.M., 2009. A method for studying protistan diversity using massively parallel sequencing of V9 hypervariable regions of small-subunit ribosomal RNA genes. PLOS ONE. https://doi.org/10.1371/journal.pone.0006372An, B., Yao, T., Guo, Y., Wang, W., Li, J., Li, X., Wang, Z., 2021. Protection, restoration, and governance technology demonstration system in the typical regions of the Lhasa River Basin. Chinese Science Bulletin (Chinese Version). https://doi.org/10.1360/tb-2021-0022Baecher, J.A., 2024. The environmental filtering paradigm and non‐filtering community assembly processes. J. Biogeogr. https://doi.org/10.1111/jbi.14973Barria, C., Malecki, M., Arraiano, C.M., 2013. Bacterial adaptation to cold. Microbiology. https://doi.org/10.1099/mic.0.052209-0Baselga, A., 2009. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. https://doi.org/10.1111/j.1466-8238.2009.00490.xBattin, T.J., Besemer, K., Bengtsson, M.M., Romani, A.M., Packmann, A.I., 2016. The ecology and biogeochemistry of stream biofilms. Nat. Rev. Microbiol. https://doi.org/10.1038/nrmicro.2016.15Bomberg, M., Claesson Liljedahl, L., Lamminmäki, T., Kontula, A., 2019. Highly Diverse Aquatic Microbial Communities Separated by Permafrost in Greenland Show Distinct Features According to Environmental Niches. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01583Braga, J.D., Thongngam, M., Kumrungsee, T., 2024. Gamma-aminobutyric acid as a potential postbiotic mediator in the gut–brain axis. npj Sci. Food. https://doi.org/10.1038/s41538-024-00253-2Cambiaghi, A., Ferrario, M., Masseroli, M., 2016. Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration. Brief. Bioinform. https://doi.org/10.1093/bib/bbw031Chen, Huaihai, Ma, K., Lu, C., Fu, Q., Qiu, Y., Zhao, J., Huang, Y., Yang, Y., Schadt, C.W., Chen, Hao, 2022. Functional Redundancy in Soil Microbial Community Based on Metagenomics Across the Globe. Front. Microbiol. https://doi.org/10.3389/fmicb.2022.878978Chen, S., Zhou, Y., Chen, Y., Gu, J., 2018. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty560Chen, W., Ren, K., Isabwe, A., Chen, H., Liu, M., Yang, J., 2019. Correction to: Stochastic processes shape microeukaryotic community assembly in a subtropical river across wet and dry seasons. Microbiome. https://doi.org/10.1186/s40168-019-0763-xDiankristanti, P.A., Ng, I.-S., 2023. Microbial itaconic acid bioproduction towards sustainable development: Insights, challenges, and prospects. Bioresource Technol. https://doi.org/10.1016/j.biortech.2023.129280Douglas, G.M., Maffei, V.J., Zaneveld, J.R., Yurgel, S.N., Brown, J.R., Taylor, C.M., Huttenhower, C., Langille, M.G.I., 2020. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0548-6Edgar, R.C., 2013. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat. Methods. https://doi.org/10.1038/nmeth.2604Garzke, J., Connor, S.J., Sommer, U., O’Connor, M.I., 2019. Trophic interactions modify the temperature dependence of community biomass and ecosystem function. PLOS Biol. https://doi.org/10.1371/journal.pbio.2006806Gilarranz, L.J., Rayfield, B., Liñán-Cembrano, G., Bascompte, J., Gonzalez, A., 2017. Effects of network modularity on the spread of perturbation impact in experimental metapopulations. Science. https://doi.org/10.1126/science.aal4122Gu, Y., Li, Z., Lei, P., Wang, R., Xu, H., Friman, V., 2023. Phylogenetic distance–decay patterns are not explained by local community assembly processes in freshwater lake microbial communities. Environ. Microbiol. https://doi.org/10.1111/1462-2920.16437Hense, B.A., Schuster, M., 2015. Core principles of bacterial autoinducer systems. Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/mmbr.00024-14Hernandez, D.J., David, A.S., Menges, E.S., Searcy, C.A., Afkhami, M.E., 2021. Environmental stress destabilizes microbial networks. ISME J. https://doi.org/10.1038/s41396-020-00882-xHuang, M., Mu, G., Mai, F., Zhou, Y., Li, X., Yang, Q., Shao, B., Wang, J., Tong, Y., 2025. Methane cycling in typical emerging proglacial lakes on the Tibetan Plateau: Insights into the metabolic mechanisms mediated by microorganisms. Water Res. https://doi.org/10.1016/j.watres.2025.123533Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., Kääb, A., 2021. Accelerated global glacier mass loss in the early twenty-first century. Nature. https://doi.org/10.1038/s41586-021-03436-zIram, N., Ren, Y., Zhao, R., Zhao, S., Dong, C., Han, Y., Zhang, Y., 2025. Deciphering Soil Keystone Microbial Taxa: Structural Diversity and Co-Occurrence Patterns from Peri-Urban to Urban Landscapes. Microorganisms. https://doi.org/10.3390/microorganisms13081726Kleinteich, J., Hanselmann, K., Hildebrand, F., Kappler, A., Zarfl, C., 2022. Glacier melt-down changes habitat characteristics and unique microbial community composition and physiology in Alpine lakes sediments. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiac075Kohler, T.J., Bourquin, M., Peter, H., Yvon-Durocher, G., Sinsabaugh, R.L., Deluigi, N., Styllas, M., Styllas, M., Schön, M., Tolosano, M., de Staercke, V., Battin, T.J., null, null, 2024. Global emergent responses of stream microbial metabolism to glacier shrinkage. Nat. Geosci. https://doi.org/10.1038/s41561-024-01393-6Korneva, I.A., Toropov, P.A., Muraviev, A.Ya., Aleshina, M.A., 2024. Climatic factors affecting Kamchatka glacier recession. Int. J. Climatol. https://doi.org/10.1002/joc.8328Kumar, V., Kumar, S., Singh, D., 2021. Metagenomic insights into Himalayan glacial and kettle lake sediments revealed microbial community structure, function, and stress adaptation strategies. Extremophiles. https://doi.org/10.1007/s00792-021-01252-xLandi, P., Minoarivelo, H.O., Brännström, Å., Hui, C., Dieckmann, U., 2018. Complexity and stability of ecological networks: a review of the theory. Population Ecology. https://doi.org/10.1007/s10144-018-0628-3Liang, X., Zhang, Yong, Wan, S., Xia, L., Li, P., Wang, S., Yang, Y., Zhu, W., Zhou, M., Wang, M., Zhang, Yu, 2025. Impacts of sulfur application on microbial communities and functional attributes in rubber plantation soil. BMC Microbiol. https://doi.org/10.1186/s12866-025-03971-zLiu, K., Liu, Y., Yan, Q., Guo, X., Wang, W., Zhang, Z., Hu, A., Xiao, X., Ji, M., Liu, P., 2024a. Temperature-driven shifts in bacterioplankton biodiversity: Implications for cold-preferred species in warming Tibetan proglacial lakes. Water Res. https://doi.org/10.1016/j.watres.2024.122263Liu, K., Liu, Y., Yan, Q., Guo, X., Wang, W., Zhang, Z., Hu, A., Xiao, X., Ji, M., Liu, P., 2024b. Temperature-driven shifts in bacterioplankton biodiversity: Implications for cold-preferred species in warming Tibetan proglacial lakes. Water Res. https://doi.org/10.1016/j.watres.2024.122263Liu, S., Mai, F., Li, X., Huang, M., Yang, Q., Mu, G., Lu, L., Zhang, Q., Liu, Y., Tong, Y., 2025a. Glacier-fed lakes produce lower methane fluxes than non-glacier-fed lakes on the Tibetan Plateau. Communications Earth & Environment. https://doi.org/10.1038/s43247-025-02464-1Liu, S., Mai, F., Li, X., Huang, M., Yang, Q., Mu, G., Lu, L., Zhang, Q., Liu, Y., Tong, Y., 2025b. Glacier-fed lakes produce lower methane fluxes than non-glacier-fed lakes on the Tibetan Plateau. Communications Earth & Environment. https://doi.org/10.1038/s43247-025-02464-1Mafa‐Attoye, T.G., Borden, K.A., Alvarez, D.O., Thevathasan, N., Isaac, M.E., Dunfield, K.E., 2022. Roots alter soil microbial diversity and interkingdom interactions in diversified agricultural landscapes. Oikos. https://doi.org/10.1111/oik.08717Magoč, T., Salzberg, S.L., 2011. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. https://doi.org/10.1093/bioinformatics/btr507Milner, A.M., Khamis, K., Battin, T.J., Brittain, J.E., Barrand, N.E., Füreder, L., Cauvy-Fraunié, S., Gíslason, G.M., Jacobsen, D., Hannah, D.M., Hodson, A.J., Hood, E., Lencioni, V., Ólafsson, J.S., Robinson, C.T., Tranter, M., Brown, L.E., 2017. Glacier shrinkage driving global changes in downstream systems. Proc. Natl. Acad. Sci. U.S.A. https://doi.org/10.1073/pnas.1619807114Mouillot, D., Graham, N.A.J., Villéger, S., Mason, N.W.H., Bellwood, D.R., 2012. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2012.10.004Mu, G., Huang, M., Wu, Z., Cui, H., Yang, Q., Li, X., Cui, X., Tong, Y., 2025. Characteristics of nutrients and microbial communities in proglacial lakes on the Tibetan Plateau and their potential linkages associated with mercury. J. Hazard. Mater. https://doi.org/10.1016/j.jhazmat.2025.138117Nguyen, N.H., Song, Z., Bates, S.T., Branco, S., Tedersoo, L., Menke, J., Schilling, J.S., Kennedy, P.G., 2015. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. https://doi.org/10.1016/j.funeco.2015.06.006Peoples, L.M., Giersch, J.J., Tappenbeck, T.H., Vanderwall, J.W., Ranieri, J.M., Vick-Majors, T.J., Elser, J.J., Church, M.J., 2025. Microbial communities in glacial lakes of Glacier National Park, MT, USA. FEMS Microbiol. Ecol. https://doi.org/10.1093/femsec/fiaf060Peter, H., Sommaruga, R., 2016. Shifts in diversity and function of lake bacterial communities upon glacier retreat. ISME J. https://doi.org/10.1038/ismej.2015.245Philipp, L., Klauschies, T., Guill, C., 2025. Metacommunity connectance and emergent patterns drive diversity via spatial eco‐evolutionary feedback. Oikos. https://doi.org/10.1002/oik.11039Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. https://doi.org/10.1093/nar/gks1219Stibal, M., Bradley, J.A., Edwards, A., Hotaling, S., Zawierucha, K., Rosvold, J., Lutz, S., Cameron, K.A., Mikucki, J.A., Kohler, T.J., Šabacká, M., Anesio, A.M., 2020. Glacial ecosystems are essential to understanding biodiversity responses to glacier retreat. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-1163-0Stoecker, D.K., Hansen, P.J., Caron, D.A., Mitra, A., 2017. Mixotrophy in the Marine Plankton. Annu. Rev. Mar. Sci. https://doi.org/10.1146/annurev-marine-010816-060617Tanunchai, B., Ji, L., Schroeter, S.A., Wahdan, S.F.M., Hossen, S., Delelegn, Y., Buscot, F., Lehnert, A.-S., Alves, E.G., Hilke, I., Gleixner, G., Schulze, E.-D., Noll, M., Purahong, W., 2022. FungalTraits vs. FUNGuild: Comparison of Ecological Functional Assignments of Leaf- and Needle-Associated Fungi Across 12 Temperate Tree Species. Microb. Ecol. https://doi.org/10.1007/s00248-022-01973-2Wang, M., Hou, J., Lei, Y., 2014. Classification of Tibetan lakes based on variations in seasonal lake water temperature. Sci. Bull. https://doi.org/10.1007/s11434-014-0588-8Wang, Y., Wang, J., Zou, X., Qu, M., Li, J., 2022. Groundwater depth regulates assembly processes of abundant and rare bacterial communities across arid inland river basin. J. Environ. Manag. https://doi.org/10.1016/j.jenvman.2022.115767Xu, J., Zhu, J., Zhou, Y., Liu, Y., Ba, S., 2025. Glacial microbial-environmental interactions on the Tibetan Plateau: A review. Water Biology and Security. https://doi.org/10.1016/j.watbs.2025.100366Xun, W., Liu, Y., Li, W., Ren, Y., Xiong, W., Xu, Z., Zhang, N., Miao, Y., Shen, Q., Zhang, R., 2021. Specialized metabolic functions of keystone taxa sustain soil microbiome stability. Microbiome. https://doi.org/10.1186/s40168-020-00985-9Yao, T., Bolch, T., Chen, D., Gao, J., Immerzeel, W., Piao, S., Su, F., Thompson, L., Wada, Y., Wang, L., Wang, T., Wu, G., Xu, B., Yang, W., Zhang, G., Zhao, P., 2022. The imbalance of the Asian water tower. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-022-00299-4Zhou, J., Ning, D., 2017a. Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/mmbr.00002-17Zhou, J., Ning, D., 2017b. Stochastic Community Assembly: Does It Matter in Microbial Ecology? Microbiol. Mol. Biol. Rev. https://doi.org/10.1128/mmbr.00002-17 Supplementary Material File (fig.1.tif) Download 15.76 MB File (fig.2.tif) Download 12.66 MB File (fig.3.tif) Download 15.18 MB File (fig.4.tif) Download 13.91 MB File (fig.5.tif) Download 17.33 MB Information & Authors Information Version history V1 Version 1 25 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords aging of proglacial lakes glacier retreat global warming microbial community succession niche differentiation Authors Affiliations Xiaodong Li Tibet University View all articles by this author Bingya Zhang Tibet University View all articles by this author Qing Yang Tianjin University View all articles by this author Zhao Xue Tibet University View all articles by this author Guangli Mu Tianjin University View all articles by this author Meiqi Huang Tianjin University View all articles by this author Qianggong Zhang CAS Institute of Tibetan Plateau Research View all articles by this author Jinrong Huang Tibet University View all articles by this author Yingxin Zhao Tianjin University View all articles by this author Yindong Tong 0000-0002-0503-6585 [email protected] Tianjin University View all articles by this author Metrics & Citations Metrics Article Usage 246 views 121 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Xiaodong Li, Bingya Zhang, Qing Yang, et al. Environmental Filtering Drives Microbial Niche Shift from Generalists to Specialists During the Aging of Proglacial Lakes on the Qinghai-Tibet Plateau. Authorea . 25 November 2025. DOI: https://doi.org/10.22541/au.176407769.96901351/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.176407769.96901351/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffbda9e7ad141e2',t:'MTc3OTQ1MzI1NQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();