Survival Strategies of Specialists and Generalists in Maintaining Ecological Networks of the eastern Himalayan river basin

Survival Strategies of Specialists and Generalists in Maintaining Ecological Networks of the eastern Himalayan river basin | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 16 February 2026 V1 Latest version Share on Survival Strategies of Specialists and Generalists in Maintaining Ecological Networks of the eastern Himalayan river basin Authors : Lingsu Bu 0009-0006-6248-5324 , Peipei Wei , Shengxian Yang , Xin Chao , Huiqiu Liu , Jiajie Xu , Guochun Zhang , Longyang Dian , and Sang Ba [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177122893.31651417/v1 138 views 68 downloads Contents Abstract Figures Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Microeukaryote generalists and specialists are vital for community dynamics and ecosystem functioning, but their distribution patterns, assembly mechanisms, and mutual transformation are poorly studied in the extreme Tibetan Plateau environment. This investigation examines diversity patterns, community assembly, species interactions, and evolutionary dynamics of microeukaryotic communities, including subcommunities (of generalists and specialists, across seasonal gradients in the eastern Himalayan river basin. Generalist and specialist subcommunities exhibited distinct seasonal distribution patterns and ecological functions. Contrary to predictions, specialists had higher abundance and richness in both seasons. Diversity indices (Shannon, Simpson, richness) were elevated in dry season, while phylogenetic diversity fluctuated. β-diversity was primarily driven by species replacement, with specialists showing higher turnover. Community assembly became more stochastic from dry to wet season, especially for specialists, whereas deterministic forces dominated for generalists. Geographic factors, particularly latitudinal gradients, were key drivers of seasonal composition differences. Co-occurrence networks revealed that while both subcommunities are crucial for stability, specialists were disproportionately responsible for complexity and robustness. These findings expand understanding of specialists’ and generalists’ survival strategies in ecological networks. Specialists create intricate, fragile networks and drive microeukaryotic diversification by transforming into generalists. This distinct mechanism in plateau river ecosystems highlights specialists’ pivotal role in maintaining ecosystem stability and resilience amid harsh environments. Ecology and Evolution ARTICLE Survival Strategies of Specialists and Generalists in Maintaining Ecological Networks of the eastern Himalayan river basin Lingsu Bu 1,2 | Peipei Wei 1,2 | Sh engxian Yang 1,2 | Xin Chao 1,2 | Huiqiu Liu 1,2 | Jiajie Xu 1,2 | Guochun Zhang 1,2 | Longyang Dian 3 | Ba Sang 1,2 1 Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China | 2 Provincial Level of Mitika Wetland Ecosystem Observation and Research Station in Tibet Autonomous Region, Nagqu, 852000, China | 3 State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China Correspondence Ba Sang Email: [email protected] Open Research Statement The original contributions presented in the study are included in the supplementary material. 18S rDNA;Generalist; Specialist; Survival strategy; The eastern Himalayan river basin Abstract Microeukaryote generalists and specialists are vital for community dynamics and ecosystem functioning, but their distribution patterns, assembly mechanisms, and mutual transformation are poorly studied in the extreme Tibetan Plateau environment. This investigation examines diversity patterns, community assembly, species interactions, and evolutionary dynamics of microeukaryotic communities, including subcommunities (of generalists and specialists, across seasonal gradients in the eastern Himalayan river basin. Generalist and specialist subcommunities exhibited distinct seasonal distribution patterns and ecological functions. Contrary to predictions, specialists had higher abundance and richness in both seasons. Diversity indices (Shannon, Simpson, richness) were elevated in dry season, while phylogenetic diversity fluctuated. β-diversity was primarily driven by species replacement, with specialists showing higher turnover. Community assembly became more stochastic from dry to wet season, especially for specialists, whereas deterministic forces dominated for generalists. Geographic factors, particularly latitudinal gradients, were key drivers of seasonal composition differences. Co-occurrence networks revealed that while both subcommunities are crucial for stability, specialists were disproportionately responsible for complexity and robustness. These findings expand understanding of specialists’ and generalists’ survival strategies in ecological networks. Specialists create intricate, fragile networks and drive microeukaryotic diversification by transforming into generalists. This distinct mechanism in plateau river ecosystems highlights specialists’ pivotal role in maintaining ecosystem stability and resilience amid harsh environments. 1 | Introduction Eastern Himalayan rivers on the Qinghai-Tibet Plateau are key systems for biodiversity and ecological protection, with the plateau often referred to as Earth’s third polar ecosphere. The unique hydroclimatic and geomorphological conditions of the plateau support diverse species and regulate the climate of Tibet, Asia, and the Northern Hemisphere. However, the fragile alpine river ecosystem is highly sensitive to global climate change, making the study of its organic and inorganic components vital for understanding its ecological dynamics. Eukaryotic microorganisms, which include primary producers, decomposers and parasites, play key roles in maintaining food web trophic stability and significantly modulate microbial net productivity and nutrient cycling (Ma et al., 2022; Shao et al., 2023). Eukaryotic microorganisms are fundamental components for the river ecosystems, Illuminating their community dynamics can deepen our understanding of the ecological processes within the eastern Himalayan river basin’s water environment. Investigating eukaryotic microorganism communities might be the key to understanding plateau river ecosystems under global change. Species diversity is determined by both physical (niche) and biological (biotic interactions) environments. Understanding the composition and formation mechanisms of microbial communities is crucial for revealing the sustainability of ecosystems. In survival strategies under different environmental conditions, microorganisms increase their viability by becoming generalists and specialists (Sriswasdi et al., 2017). Generalists are widely distributed and show a wide range of environmental tolerance, while specialists have very specific and narrow habitat preferences (Pandit et al., 2009). Previous research on generalist and specialist species reveals that most prokaryotes exhibit one of these ecological strategies (Qi et al., 2022; Yu et al., 2023). Several studies demonstrate that the relative contributions of stochastic and deterministic ecological processes to community assembly can differ significantly between microbial generalists and specialists (Lindstrom & Langenheder, 2012; szenjokely & Langenheder, 2014). Species with different niche widths have different survival abilities due to their biochemical and physiological characteristics as well as their ecological (Yu et al., 2023; O ’Gorman et al., 2012). However, how differences in niche width affect ecological processes and ecological molecular networks is largely unknown. in the context of global biological homogenization, the importance of generalists and specialists for ecological mechanisms is increasing. In this study, we investigated the ecological processes of microeukaryotic communities focusing on the generalists and specialists in the eastern Himalayan river basin on the Qinghai-Tibet Plateau. The distribution characteristics of microeukaryotic communities and niches in different seasons (wet and dry) were analyzed by 18S rDNA amplicon sequencing. A variety of models and analytical methods were used to explore the effects of niche width on the ecology of eukaryotic microorganisms, and to analyze the contributions of generalist and specialist taxa to the ecological network. The study’s findings will deliver novel insights into preserving microeukaryotic community diversity within extreme environments, while also providing valuable insights to enhance our understanding and prediction of how these communities in plateau water ecosystems will respond to global warming. 2 | Materials and Methods 2.1 | Study Region and Sample Collection The eastern Himalayan river basin (HR) is in the southeast of Tibet, spanning mountainous subtropical and tropical climates, mainly covered by alpine forests, Most of the precipitation falls from June to September, accounting for about 60%-90% of the total annual precipitation, which is greater than 2,000 mm per year (Li et al, 2013; Liu al, 2019).The topography and geomorphologic structure in this area are complex, and the river channel drop is very large, which contains rich water energy resources. Especially after flowing through the Grand Canyon, the altitude drop reaches 2000 m. The Grand Canyon makes the upper and lower reaches form completely different climates, habitats and vegetation types, which is an important basis for the formation of biodiversity in the region, and also a natural barrier distribution of organisms (Ma et al, 2022). The HR study area extends west to east from Gongbo’ gyamda County, Nyingchi City, Tibet Autonomous Region,China, Tibet to Basu County,Qamdo City, Tibet Autonomous Region,China, with an average elevation of 2,909 m, ranging from 4540 m(west) to 644 m(east) and spanning coordinates 29°15 ’–29°31’ N, 92°20 ’–96°38’ E.Surface water samples were collected in May 2022 (dry season) and July 2023 (wet season) in different habitats, with a total of 34 sample points distributed throughout the HR (Figure 1). Approximately 9 L of surface (approximate depth: 20 cm) water was collected from each sample site, which included water samples for 3 replicate samples at each site. Water underwent on-site filtration through a 200-μm nylon filter, aimed at eliminating insoluble impurities along with organisms and plant tissues.The filter did not remove eukaryotic microorganisms, thereby preserving them in the collected water samples. Overall, 102 water samples were obtained during each water period (34 sites × 3 replicates). Within the laboratory setting, water samples were filtered under vacuum through a sterile 0.22-µm polycarbonate membrane. (Millipore, Inc., Burlington, Massachusetts, USA). The DNA in each water sample was collected on a membrane by filtering 350 mL of water. During filtration, the pressure was maintained at less than 0.03 MPa, with filtration time limited to 2 h. The DNA membranes were carefully placed into sterile freezing tubes, completely wrapped in tin foil, and promptly preserved in liquid nitrogen. These frozen DNA membranes were then transported to NovoHorizon (Beijing, China) for high-throughput sequencing. 2.2 | Measurement of environmental characteristics Water pH, temperature (WT), and electrical conductivity (EC) were measured by portable multiparameter water quality tester (HI98195, Hanna, Veneto Region, Province of Padua, Italy). Dissolved oxygen (DO) was determined by portable dissolved oxygen tester (HI98193, Hanna). Water turbidity (TUR) was measured by high-precision data turbidity meter (HI98703, Hanna). A multiparameter water quality detector (HI83399, Hanna) was used to measure ammonia nitrogen (NH4+-N). Water flow speed (WS) was measured by direct-reading current meter (FP-111, Global Water, Amalgam Way, Gold River, USA). Longitude (LON), latitude (LAT), and altitude (ALT) of the sample points were recorded by a global positioning System 104 (Explorist 500, Magellan, Santa Clara, California, USA). Field measurements captured these physical and chemical parameters, with three replicate readings taken. Water samples were meticulously collected, stored, and transported in strict compliance with GB3838-2002 ”Environmental Quality Standards for Surface Water” (Ministry of Ecology and Environment of the People’s Republic of China, 2002). The Tibet Boyuan Testing Company (Lhasa, Tibet Autonomous Region, China) determined water total phosphorus (TP) and total nitrogen (TN) contents and chemical oxygen demand (COD). Total phosphorus was determined according to the ammonium molybdate spectrophotometric method in GB1183-1989 (Water quality- Determination of total phosphorus-Ammonium molybdate spectrophotometric method); total nitrogen (TN) was determined according to the alkaline potassium persulfate digestion ultraviolet spectrophotometric method in HJ636-2012 (Water quality-Determination of total nitrogen-Alkaline potassium persulfate digestion UV spectrophotometric method); and chemical oxygen demand (COD) was determined using the potassium dichromate method, following standard HJ828-2017 (Water Quality - Determination of Chemical Oxygen Demand - Dichromate Method). 2.3 | The extraction of DNA and high-throughput sequencing techniques Environmental DNA was meticulously extracted using a Power Soil DNA Isolation Kit (Carlsbad, California, USA), with DNA quality assessed through standard 1% agarose gel electrophoresis and purity verified via a high-precision Nano Drop 2000 spectrophotometer (Waltham, Massachusetts, USA). We used PCR to amplify the high-variation V9 region of 18S rDNA using the primers 1391f (5’-GTACACACCGCCCGTC-3’) and 1510r (5’-TGATCCTTCTGCAGGTTCACCTAC-3’) (Medlin et al., 1988; Lane, 1991). The PCR products were sequenced by Novogene Sequencing (Beijing, China) using the PE250 strategy on an Illumina (San Diego, USA) Novaseq 6000 platform. 2.4 | Amplicon data and statistical analysis Amplicon sequence variants (ASVs) were obtained using the DADA2 plug-in in QIIME2 software (Bolyen et al., 2019) and using the SILVA database (version 138) (Quast et al., 2013) to annotate. Deletion of ASVs of non-eukaryotic microorganisms and low abundance ASVs (< 8 reads), and then the ASVs table was flattened and diluted according to the smallest sequence in the sample for downstream analysis (Liu et al, 2020). The ’spaa’ package was used to calculate the niche breadth of species (Levins, 1968). The Null model was used to quantify the proportion of different ecological processes in community construction. Spearman correlation analysis was performed using the ”psyc ”package in R for co-occurrence network construction, and correlation was selected (|R| > 0.6, P <0.05) nodes were substituted for analysis, calculated by Gephi (V0.9.7) software, and colored by module, niche and phyla respectively. A detailed description of the statistical analysis is as follows: The ”spaa” package was used to calculate the niche breadth of species (Levins, 1968), followed by 1000 random rearrangements of the occurrence frequency of ASVs by the substitution method of the ”EcolUtils” package (Salazar, 2015), and the zero distribution of the niche breadth index was calculated. When the actual niche width index exceeds the upper 95% confidence interval of the 1000 permutation zero distribution, the ASV is defined as a generalist. ASVs below the lower bound of the 95% confidence interval of the zero distribution are defined as specialists (Zhang et al., 2018). The Null model was used to quantify the proportion of different ecological processes in community construction. The model is based on the Bray-Curtis based Raup-Crick distance (RCbray) and the beta-nearest taxon index (βNTI), which categorizes community building processes into deterministic processes (homogeneous selection and heterogeneous selection) and stochastic processes (dispersal limitation, homogenizing dispersal, and undominated processes) (Stegen et al., 2015). Firstly, the observed beta-mean nearest taxon distance (βMNTD) and null βMNTD values are calculated using the ”picante” package in R. The standard deviation between the observed β-MNTD and the mean distribution of the null βMNTD is then calculated as β-NTI. When β-NTI < -2 or β-NTI ≥ 2, it is determined as homogeneous selection and heterogeneous selection, respectively. When |βNTI| < 2, the ”vegan” package in R is used to calculate RCbray. When RCbray < -0.95, it means homogeneous dispersal; when RCbray ≥ -0.95, it means limited dispersal; when |RCbray| < 0.95, it indicates undominated processes, including drift, diversification, weak selection, and weak dispersal (Stegen et al., 2012; FitzJohn, 2012). ASVs with relative abundance ≥ 0.02% and occurrence greater than 1/5 of the total sample size were selected on different seasons to construct the eukaryotic microbial community co-occurrence network. Spearman correlation analysis was performed using the ”psych” package in R for co-occurrence network construction, and correlations with (|R| > 0.6, P 0.4 indicates that the network has a modular structure (Zhang et al., 2022). By calculating the intra-module connectivity (Zi) and inter-module connectivity (Pi) of nodes and constructing the Z-P graph, network nodes can be divided into module hubs (Zi > 2.5 and Pi 2.5 and Pi > 0.62), connectors (Zi 0.62), and peripheral nodes (Zi < 2.5 and Pi < 0.62) (Wang et al., 2020; Cui et al., 2019). Among network topological attributes, negative cohesion and positive cohesion are indicators to describe community stability through the characterization of interactions in the network (Cui et al., 2019); total cohesion is the absolute sum of positive cohesion and negative cohesion, and is an indicator of the complexity of community symbiotic network (Hernandez et al., 2021). The network robustness is defined as the proportion of remaining nodes (ASVs) after randomly removing 50% of nodes in this network. The node vulnerability measures its relative contribution to the global efficiency, and the network vulnerability is expressed by the maximum node vulnerability among all network nodes. In addition, to further explore the characteristics of interactions within eukaryotic microbial networks, network cohesion was chosen to quantify the complexity of the community and network robustness was chosen to quantify the stability of the community. Positive cohesion can reflect the degree of cooperative behavior in eukaryotic microbial communities, while negative cohesion can indicate the severity of competitive behavior among eukaryotic microbial communities. The α diversity index (including richness index, Shannon diversity index, Pielou evenness index, Simpson diversity index) was calculated using the ”vegan” package in R. The α diversity of eukaryotic microbial communities and subcommunities in different seasons was examined by Wilcoxon rank-sum test. β diversity and its component decomposition were calculated using the ”adespatial” package in R according to the Jaccard difference index. The attenuation characteristics of community Bray-Curtis similarity with geographical distance were analyzed using the ”vegan” and ”geosphere” packages in R software. Canonical correspondence analysis (CCA) and random forest model were used to calculate the correlations between eukaryotic microbial communities, α diversity and subcommunities with seasonal abundance and distance from environmental factors. CCA and random forest model were performed in the ”vegan” package in R and the ”randomForest” package in R. All of the above analyses were completed in R-4.2.3 and Origin2021(Vienna, Austria). 3 | Results 3.1 | Seasonal Dynamics of Generalist and Specialist Microbial Eukaryotes in Arid and Rainy Seasons A total of 14,828 high-quality ASVs were obtained after quality control and sequence filtering. In the dry season, 10,240 ASVs were detected, among which 146 ASVs were classified as generalist species, accounting for 6.29% of the abundance of microeukaryotes community, while 933 ASVs were classified as specialists, accounting for 73.18% of the abundance. In the wet season, 8,737 ASVs were detected, among which 526 ASVs were classified as generalist species, accounting for 4.45% of the abundance, and 1,420 ASVs were classified as specialists, accounting for 77.49% of the abundance. Overall, the abundance of specialist sub-community was higher than that of generalist sub-community in both seasons. The phylum-level compositional proportion of Ochrophyta was the highest in both the dry and wet seasons, 31.98% and 35.95%, respectively. This was followed by Deatomeae with 9.95% in the dry season and 13.17% in the wet season. Besides, Cercozoa (5.14%) and Ciliophora (4.29%) dominated during the dry season,whereas, Chlorophyta (7.92%) and Phragmoolastophyta (4.62%) dominated during the wet season. Cercozoa and Ochrophyta were the dominant phyla among the generalist. Cercozoa (20.45%), Ochrophyta (18.16%), Bsidiomycota (3.99%), and Ciliophora (3.9%) dominated in the dry season; Ochrophyta (28.07%), Cercozoa (13.08%), Ascomycota (8.66%) and Diatomta (7.95%) dominated in the wet season. Ochrophyta and Diatomta were the dominant phylum among the specialists. The dry season was dominated by Ochrophyta (37.3%), Diatomta (10.58%), Arthropoda (8.19%) and Cryptophyceae (4.87%); the wet season was dominated by Ochrophyta (39.24%), Diatomta (12.69%), Chlorophyta (8.94%) and Arthropoda (8.08%) dominated. The distribution characteristics of eight most abundant 8 taxonomic groups in different seasons with different sub-communities are illustrated in Fig. 2. Shannon and Simpson diversity indices were markedly higher in the dry season than in the wet season for assemblages (Wilcoxon, p < 0.01). The richness index was also higher in the dry season for the microeukaryotic communities and for generalist taxa (Wilcoxon, p < 0.01). In contrast, the mean phylogenetic indices were significantly higher during the dry season for microeukaryote communities. (Wilcoxon, p < 0.01), while the opposite trend was observed among the spesialist taxa. The results showed that the difference in the composition of assemlbages was mainly determined by the process of species replacement, and the proportion of species replacement among specialist was higher than that among generalist (Fig 3). The proportion of total β-diversity and nesting process in all assembages was significantly higher in wet season than in dry season, and the proportion of species replacement of the entire assemblage was higher in dry season than in wet season. Both stochastic and deterministic processes were important in the assembly of microeukaryotic communities in both seasons (Fig.4). During the dry season, stochastic processes accounted for 59.53% and deterministic processes accounted for 40.46% of the microbial community assembly. Generalist assembalges showed 95.19% stochastic processes and 4.81% deterministic processes. Specialist assembalges exhibited 96.07% stochastic processes and 3.92% deterministic processes. In the wet season, stochastic processes accounted for 74.87% and deterministic processes accounted for 25.13% of microbial community assembly. Generalist assembalges showed 65.95% stochasticity and 34.04% determinism. Specialist sub-community exhibited 97.86% stochasticity and 2.14% determinism. An increase in stochastic processes in the microeukaryotic community and among the specialist taxa was detected from the dry to wet season, while generalist taxa were more controlled by deterministic processes. The regression analysis (based on Bray-Curtis distance) (Fig.4b and e) revealed geospatial decay in both seasons. Notably, the specialist sub-community (dry season: R 2 = 0.27, P < 0.01; wet season: R 2 = 0.12, P < 0.01) exhibited a more pronounced decay trend than the generalist sub-community (dry season: R 2 = 0.19, P < 0.01; wet season: R 2 = 0.05, P < 0.01). Furthermore, this decay trend was significantly stronger during the dry season compared to the wet season. 3.2 | Influence of Geographic and Environmental Factors on microeukaryotic communities According to the canonical correspondence analysis (CCA) (Fig. 5a and b), geographic and environmental factors explained more of the compositional differences for microeukaryotic communities and sub-communities in the dry season than in the wet season (sum of CCA axes 1 and 2:dry season: 35.99%; wet season: 26.49%). Moreover, geographic and environmental factors had different effects on the composition of microeukaryotic communities and sub-communities in time. In the dry season, the ordering axes explained 23.03% and 12.96%, respectively, and LAT, DO and COD were the most influential parameters on microeukaryotes community dynamics; In the wet season, the explanation rates of the first and second ordering axes were 14.39% and 12.1%, respectively, and LAT, TP and TUR were the most influential parameters on the dynamics of microeukaryotic communities. were the most influential parameters on microeukaryotes community dynamics. Among the factors of influence, the geographic factor LAT was the most important cause of differences in community composition in different seasons. Correlations and best multiple regression models were used to assessed the importance of geographic and environmental factors affecting the on the α-diversity of generalist and specialist subcommunities. In the dry season, water body physicochemical properties and geographic factors were mainly negatively correlated with microeukaryotes sub-community α-diversity (Fig. 5c) and explained more of the generalist sub-community α-diversity, in which LAT, LON, and COD were the important variables predicting the changes in generalist sub-community α-diversity with significant effects ( P <0.05). ALT, DO, and LAT were the strong influences ( P <0.05) in predicting changes in the α-diversity of the specialist sub-community; In the wet season, the physicochemical properties of the water body and geographic factors were mainly positively correlated with the α-diversity of the microeukaryotes sub-community (Fig. 5d) and explained more for the specialist sub-community, with LAT, PH, and WT being the strong influences ( P <0.05) on the α-diversity of the generalist sub-community. TUR, ALT and LAT were strong influences ( P <0.05) on the alpha diversity of the specialist subcommunity. Overall, ALT, DO and EC only significantly influenced the alpha diversity of specialized subcommunities. Combined with CCA analysis and multiple regression analysis, geographic factor (LAT) was the most important variable in the distribution of microeukaryotic communities and subcommunities in the HR. Seasonal dynamics made geographic factors and water body environment show different effects on the distribution pattern of diversity of microeukaryotes sub-communities. 3.3 | Impact of Generalist and Specialist on the Complexity of Microeukaryotes Community Table 1. Key topological features of eukaryotic microbial symbiotic networks. Topological Parameters Dry season Wet season Generalist-Specialist Eukaryotic microorganism Generalist-Specialist Eukaryotic microorganism Average Degree 52.886 68.806 11.177 14.887 Number of Nodes 623 892 474 663 Number of Edges 16474 44387 2649 4935 Graph Density 0.085 0.112 0.024 0.022 Average Path Length 2.477 2.321 3.893 3.769 Average Clustering Coefficient 0.602 0.604 0.626 0.599 Modularity 1.132 0.828 0.737 0.67 Positive correlation (%) 74.60 71.37 92.30 93.94 Negative correlation (%) 25.40 28.63 7.70 6.06 The microeukaryotes community co-occurrence network in the dry season consisted of 892 nodes and 44387 edges with an average connectivity of 68.806(Table 1). Ochrophyta (18.5%), Cercozoa (10.09%), Ciliophora (6.84%), Diatomea (5.04%), Chlorophyta (4.6%) and Arthropoda (3.36%) occupied nearly half of the links in the network (total = 49.55%). Compared to a mixed distribution of phyla in the network topology, there was greater segregation between generalist and specialist sub-communities. Moreover, generalist and specialist tend to exist in different modules (Fig. 6d and h), with Module I and Module IV consisting of 54.1% and 47.2% of specialists, respectively, except for Module II, which contains 22.6% of generalist nodes and 26.9% of specialist nodes. In contrast, Module III contains 45.32% of the specialized talent clusters. This topological separation was confirmed when constructing a new network containing only generalist and specialist (Fig. 6). The co-occurring network of microeukaryotic communities in the wet season consisted of 663 nodes and 4935 edges, with an average connectivity of 14.89. The topology of the network of microeukaryotic communities and sub-communities in the wet season was similar to that of the dry season, with nodes of seven dominant clades occupying almost half of the links in the network (49.32%), and generalists and specialists being present in the different modules. Among them, the generalist subcolony is concentrated in module I (35.8%) (Fig. 6g and h), and the specialist tends to be present in module II (79.3%) versus the remaining modules (53.4%). Specialists were more likely to be connectors (dry season: 20, wet season: 13) and were six times more numerous than generalists (dry season: 3, wet season: 3). Network hub was not detected in the network in either season (Fig. 7a and b). There were more keystone species (module hubs and connectors) in the dry season than in the wet season, mainly generalists and specialists (dry season 23 : 20; wet season 22 : 11) Removal of either generalists or specialists from the original microeukaryotes community resulted in a decrease in network complexity and network stability, but the reduction of network stability in subcommunities with the removal of specialists was higher than the decrease with the removal of generalist communities, in both seasons (Fig. 7b-f). This suggests that the removal of specialists resulted in a simpler, more fragile network compared to the removal of generalists. 4 | Discussion 4.1 | Distinct Distribution Patterns of Generalist versus Specialist Species Microorganisms have crucial roles in global biogeochemical processes, with different species employing diverse survival strategies, such as generalists and specialists. Generalists tend to have broader niches, allowing them to better withstand environmental filtering, leading to wider and more abundant distributions. In contrast, specialists have narrower niches and are more sensitive to environmental fluctuations, resulting in more limited distributions (Inceoglu et al., 2015; Pandit et al., 2009). However, the distribution of generalists and specialists in the studied region challenges this expectation, contradicting previous studies (Jun et al., 2023; Xu et al., 2022).possibly because specialists, though occupying narrower niches, may be richer and more diverse than generalists (Wan et al., 2021). Another contributing factor could be the presence of numerous tributaries and significant altitude variations in the studied region. Habitat differences across tributaries, with most sampling points located in tributaries due to safety concerns, This environment likely fosters a diminished presence of generalists alongside a surge in specialists, as the dominant watercourse is defined by a high-mountain canyon characterized by swift, turbulent currents. Seasonal dynamics also influence the diversity and composition of microeukaryotic communities. Community diversity tends to decrease in the wet season, likely due to increased water flow, reduced river stability, and higher turbidity from surface runoff, which blocks photosynthesis in some eukaryotic microorganisms. This leads to reduced resource acquisition and living space (Li et al., 2023). In summer, declining dissolved oxygen levels and increased competition for oxygen further inhibit growth and survival, leading to the decline of some eukaryotic microorganisms. 4.2 | Environmental Adaptation of Generalist and Specialist in Dry and Wet Seasons We categorized the overall β-diversity of microeukaryotic communities and subcommunities at each sampled site into two components: species turnover (replacement of community species) and nesting (loss or gain of community species) (Si et al., 2017). The two components refer to the replacement of individuals of certain species at one site by a different species of the same number and the disappearance of certain individuals from one site to another site, respectively. The turnover components accounted for a larger proportion of β-diversity in the HR than the nested components in both seasons, which is consistent with the results on β-diversity of microeukaryotic communities in the middle reaches of the HR (Yang et al., 2023a). The differences in microeukaryotic species composition were mainly due to spatial substitutions, and those changes were random and related to speciation, diffusion history, and competition. The turnover components of the specialist species were higher than those of the generalist species, which may be related to the higher speciation rate of the specialist species subcommunity. The greater competitiveness of specialist than generalist species also increased the turnover of species. Nested components occupied a larger proportion in generalist than in specialist species and were more susceptible to richness differences. In seasonal dynamics, the loss or gain of microeukaryotic species may lead to changes in community richness. Compared with the dry season, the wet season led to an increase in nested components of microeukaryotic communities and subcommunities and species loss. Differences in the distribution patterns of microeukaryotic communities and subcommunities across different seasons were correlated with environmental heterogeneity and geographical factors (Zhang et al., 2018; Xu et al., 2021a). According to CCA and correlation and best multiple regression model results, the spatial factors LAT and ALT had the greatest effects on the abundance of microeukaryotic communities, consistent with the dominance of stochastic processes in community assembly. Increasing latitude was a limiting effect on generalist subcommunity α diversity and a facilitating effect on specialist subcommunity α diversity in both seasons. This result deviates from those in a study of planktonic bacteria in lake reservoirs (Jun, 2023). The increase in elevation was a significant limiting effect on the α diversity of specialists in both seasons, consistent with the decrease in eukaryotic microorganism diversity in the middle reaches of the HR with the increase in elevation (Yang et al., 2023a). Among environmental factors, increases in TUR, DO, and EC significantly increased the specialists in different seasons, but DO and EC decreased in the wet season, which inhibited the α diversity of specialists to a certain extent. Water pH and elevation had inhibitory effects on the α diversity of generalists in the different seasons, inhibition was strongest during the wet season. Water pH and elevation inhibited the α diversity of generalists; this inhibition was strongest during the wet season. The composition and diversity of microeukaryotic communities and subcommunities were influenced by the water environment factors under seasonal dynamics. This slows down mutual transformation of specialized and generalist species. The results of this study are the first to be found. The possible reason is that the HR receives more precipitation in the wet season, and the rainwater carries sediment and dead wood into the river, resulting in poor river stability, and consequently, the stability and complexity of ecological networks in the wet season. Under environmental disturbances, specialists were more affected by environmental factors than generalists, and in the regression analysis, environmental factors had a greater significance effect on specialists in the wet season, with a greater explanatory rate. Specialists and generalists compete for resources and space (Bono et al., 2020), and the community productivity of generalists is relatively less responsive to environmental fluctuations than that of specialists (Matias et al., 2013), and the results of the regression analysis proved that the significance of the environmental factors in the wet season had a reduced explanatory rate for generalists. Thus, specialists that are more exposed to environmental disturbances may adapt to the environment by reducing the rate of reproductive propagule formation and transformation to ensure their reproduction and reduce energy consumption. Compared to the dry season, the abundance of specialized subcommunities in the wet season was greater than that in the dry season (dry season: 73.18%; wet season: 77.49%). The metabolism of microeukaryotic generalists may be more flexible than that of specialists (Chen et al., 2021), and generalists may need to consume more energy to maintain the same rate of environmental adaptation as that of specialists (Villalba et al., 2022). In addition, generalists, because of their broader niches than those of specialists, have greater chances of encountering stressors exerted by abiotic environments and biotic interactions, and thus, evolution may be more dependent on abiotic and biotic controls (Aguilée et al., 2018; Škaloud et al., 2019). Thus, it is possible that generalists rely more on the transformation of specialists to better adapt to their environment. 4.3 | Impacts of Generalists and Specialists within Co-occurrence Networks Interactions among species shape community composition, governing the stability and spatial arrangements of microbial ecosystems. Interactions such as predation, competition, and mutualistic symbiosis are of significant importance for community assembly, responses to disturbances, and ecosystem functioning (Ren et al., 2023). The properties of co-occurrence networks, including nodes, average clustering coefficients, average path lengths, and modules, showed seasonal variation. In this study, modularization coefficients were greater than 0.4 in both temporal and ecological dimensions, indicating a significant ”small-world” or ”small-habitat” phenomenon (Deng et al., 2012). Eukaryotic microbes are closely correlated across phyla, with overlapping and dissimilar niches, resulting in the coexistence of species in the same environment and maintaining the stability of the community structure. However, the edges, average degree and graph density values were lower in the wet season than in the dry season, which may be due to the fact that the microbial community in the wet season was more affected by the environmental stress and complex microbial interactions (high cohesion), the adaptation to the environment was reduced, and microorganisms that were intensified by competition were filtered to death (Mei et al, 2013). Whereas, in terms of positive correlation edges and positive cohesion, wet season species may cooperate to counteract unstable ecological networks due to precipitation in their strongly collaborative manner. Key taxa can have important roles in maintaining network structure, and their loss may lead to network instability or even disintegration (Yang et al., 2022). In this study, key taxa were more abundant in the dry season, but more than half of the key taxa were specialists and generalists in both dry and wet seasons (Figure 7a and d), and there were more specialists than generalists. Thus, specialists and generalists have roles in maintaining in network structural stability. Removing either generalist or specialist nodes from the network led to a decrease in network cohesion and robustness (Figure 7b, c, e and f), as well as a decrease in network complexity and stability. The important roles of specialists and generalists in maintaining the complexity of the network structure are consistent with the findings of Xu et al. (2021b). In particular, removing nodes of specialists led to negative cohesion and reduced positive cohesion and robustness of the netwssssork (Figure 7b, c, e and f). Overall, the specialist subcommunity contributed the most to the stability and complexity of the overall microeukaryotic network. 4.4 | Evolutionary potential and stochastic processes of generalist and specialist subcommunities across seasons At a large spatial scale, evolutionary processes, dispersal, and environmental filtering play fundamental roles in shaping regional species composition. In this study, speciation, extinction, and turnover rates of specialist subcommunities in both dry and wet seasons were higher than those of generalist subcommunities. In narrow niches, specialists face increased competition and thus increase their competitive abilities (higher negative cohesion than that of generalists). Resource constraints promote speciation because specialists can precisely allocate limited niche space and resources, thereby increasing their advantage in terms of species diversification. In addition, diversification rates and dispersal constraints of specialists are higher than those of generalists, making them more likely to encounter ecological or geographic barriers, such as watershed constraints and elevational gradients, resulting in potentially higher rates of speciation (Rolland & Salamin, 2016; Xu et al., 2022) and a more diffuse phylogeny. This was also confirmed in multiple regression analyses, where ALT was a significant factor limiting specialist diversity across seasons. Specialist subcommunities with high species number and abundance could colonize a given environment and continuously transform into generalists because of the high speciation rate. The evolutionary transition between specialists and generalists suggests that in the HR, microbial diversity is maintained predominantly through the continual presence of specialist taxa rather than the continual replenishment of generalist taxa supporting specialist taxa. This result is in contrast to those in studies on bacteria in basin and soil (Craig Maclean, 2005; Sriswasdi et al., 2017). Seasonal changes from dry to wet seasons slow the evolutionary processes of generalist and specialist subcommunities by decreasing speciation, extinction, and turnover rates. One possible reason for this slowing of evolutionary processes is the increase in homogenization of rivers and environmental perturbations with increases in human activities and rainfall during the wet season. Specialists can increase colonization to maintain the stability and complexity of ecological networks in highland rivers and counteract environmental fluctuations. By contrast, generalists are influenced by a substantial nestedness component and may experience genetic drift or species loss, resulting in an extinction rate higher than the speciation rate. However, the ongoing transition of specialists to generalists ensured relatively stable generalist subcommunities. Stochastic ecological processes dominated the assembly of microeukaryotic communities and generalist and specialist subcommunities in the HR, in contrast to findings in the middle reaches of the HR (Yang et al., 2023a). One possible explanation for this discrepancy is that the lower altitude of the lower than the middle reaches of the river, coupled with extensive pastoral lands, roads, and farmlands surrounding the basin, leads to frequent human activities. Water flow washes nutrients generated from agricultural and pastoral activities into the rivers, increasing nutrient availability within the basin. In nutrient-rich environments, communities shaped by stochastic processes tend to emerge, whereas in nutrient-poor environments, communities shaped by deterministic processes are most prevalent (Chase, 2010). In the different seasons, dispersal limitation had a greater effect on specialist subcommunities, whereas undominated processes had a greater effect on generalist subcommunities. The wet season increased stochasticity in the assembly of microeukaryotic communities and subcommunities, especially by increasing the contribution of undominated processes and reducing that of dispersal limitation of specialist subcommunities. This result can be explained by increasing precipitation during the wet season leading to higher runoff, resulting in habitat homogenization and increased river connectivity, thereby increasing similarity of microeukaryotic communities among sampling points. By contrast, in the dry season, the relatively weak river connectivity limits plankton dispersal, increasing the distance-decay effect. The increase in undominated processes may be due to significantly greater diversification, which implies that diversification has a crucial role in aquatic community aggregation (Hanson et al., 2012; Milke et al., 2022). Alternatively, the increase may be due to increased wet season precipitation leading to high flow velocities and sediment disturbances that force ecological drift (Huber et al., 2020). Homogeneous selection is due to selection pressures formed by similar abiotic environments and interactions among organisms in space and time, which can lead to convergence among microbial communities (Stegen et al., 2015). Hence, the decline in alpha diversity of generalist species during the wet season was likely due to the increase in homogenizing effects of homogeneous selection processes (dry season: 1.43%; wet season: 34.05%). 5 | Conclusion This study revealed distinct distribution and ecological roles of generalist and specialist subcommunities in HR microeukaryotic communities across seasons. Specialists showed higher abundance and richness than generalists in both seasons, contrary to expectations, likely due to habitat heterogeneity from tributaries and altitude variations. Seasonal dynamics influenced diversity: Shannon, Simpson, and richness indices were higher in the dry season, while phylogenetic diversity varied between communities and seasons. β-diversity of microeukaryotes was primarily driven by species replacement, with specialists showing higher turnover than generalists. Stochastic processes increasingly dominated community assembly from dry to wet season, particularly for specialists, while generalists remained more deterministic. Geospatial decay was consistent in both seasons, stronger for specialists. Geographic factors, especially latitude, were the main drivers of community composition differences across seasons. Environmental adaptation strategies varied significantly between specialists and generalists. Specialists were more sensitive to environmental fluctuations, with α-diversity affected by altitude, dissolved oxygen, and conductivity. Generalists had broader tolerance but were more inhibited by pH and elevation during the wet season. The wet season increased nestedness and species loss, reducing network complexity and stability compared to the dry season. Co-occurrence network analysis showed that both generalists and specialists were key to network stability, but specialists contributed more to complexity and robustness. Removing specialist nodes caused greater stability loss than removing generalist nodes, indicating specialists formed more complex and fragile networks. Over half of keystone taxa were specialists and generalists, with specialists being more prevalent. Evolutionary analyses showed higher speciation, extinction, and turnover rates in specialist subcommunities than in generalists across both seasons. Specialists maintained high diversification rates despite narrow niches, continuously transforming into generalists, thereby sustaining generalist subcommunities at stable levels. This transition represented a unique mechanism for maintaining microbial diversity in the HR system, differing from patterns in bacterial communities in basins and soils. Seasonal shifts from dry to wet conditions slowed these processes through increased environmental homogenization and perturbation. Advancing further, multi-omics approaches can overcome the taxonomic limitations inherent in amplicon sequencing. Experimental validation will be essential to corroborate inferred coevolutionary relationships between generalist and specialist taxa. Extensive spatial sampling across heterogeneous alpine ecosystems will assess the generalizability of these findings, while predictive modeling will project the trajectories of microeukaryotic communities under ongoing environmental change. Collectively, these initiatives will enhance our understanding of microbial assembly mechanisms and their ecological responses in plateau river ecosystems. Author contributions Ba Sang、Longyang Dian: Conceptualization, methodology, funding acquisition, investigation, Project administration, Writing - Review & Editing; Lingsu Bu: Conceptualization, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - Original Draft. Peipei Wei: Conceptualization, Formal analysis, Methodology, Visualization, Editing. Shengxian Yang、Huiqiu Liu、Xin Chao: Investigation, Supervision, Methodology, Validation, Editing. Jiajie Xu: Investigation, Supervision, Methodology, Visualization. Guochun Zhang: Formal analysis, Investigation , Validation. All authors have read and agreed to the published version of the manuscript. Funding This research was financially supported by the Supported by Science and Technology Projects of Xizang Autonomous Region,China(XZ202501ZY0018),2022 Special Fund for Supporting the Reform & Development of Local Universities by Central Financial Allocation ([2022] No. 1), Supported by the Taishan Young Scholars Program (tsqn202103019) and the Natural Science Foundation of Shandong Province (2021QB004), The Overseas Outstanding Young Scientists Fund Program of Shandong Province (Grant No. 2022HWYQ003) and the State Key Laboratory of Microbial Technology Open Project Fund (Project No. M2024-01). Competing financial interests The authors declare no competing financial interests. Data availability statement The original contributions presented in the study are included in the supplementary material. Reference Aguilée, R., F. Gascuel, A. Lambert, and R. 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Figure.2 Classification and composition of generalist and specialist microeukaryotes sub-communities in different seasons in the eastern Himalayan river basin.(a) The niche width, relative abundance of generalists and specialists, as measured by their occurrence frequency in the dry season.(b) The niche width and relative abundance of generalist and specialist species, as determined by their occurrence frequency during the wet season.(c) Taxonomic distribution of bacterial generalists and specialists at the phylum level during the dry season.(d) Distribution of generalist and specialist taxonomic classifications at the phylum level during the wet season. Figure.3 β-diversity decomposition of microeukaryotes communities in two different seasons in the downstream of the eastern Himalayan river basin.(a) β-diversity decomposition of microeukaryotes communities in the dry season. (b) β-diversity decomposition of microeukaryotes communities in the wet season. 1-βis the inverse of beta diversity, which indicates the similarity between communities. A value of 1 means no difference (identical communities), and a value of 0 means complete difference (no species in common).A higher value of Repl indicates a greater proportion of species being replaced between the two conditions, suggesting a higher degree of species turnover. Figure.4 Community assembly processes of microeukaryotes communities and generalist and specialist taxa in the dry and wet seasons; (a) Spatial decay patterns of microeukaryotes sub-communities in the dry season; (b) Relative contributions of ecological assembly processes in the dry season based on basis; (c) Changes in nearest taxon index (βNTI) of microeukaryotes communities and sub-communities in the dry season; (d) Spatial decay patterns of microeukaryotes sub-communities in the wet season; (e) Relative contributions of ecological assembly processes in the wet season based on basis; (f) Changes in nearest taxon index (βNTI) of microeukaryotes communities and sub-communities in the wet season. Figure. 5 Correlation analysis of microeukaryotes communities and generalist and specialist sub-communities of the eastern Himalayan river basin with environmental factors; (a) CCA (canonical correspondence analysis) of microeukaryotes communities in the dry season; (b) CCA (canonical correspondence analysis) of microeukaryotes communities in the wet season; (c) (d) correlation and best multiple regression model of microeukaryotes sub-communities in the dry season and in the wet season, with the size of the circles representing the significance of the variables (i.e. proportion of variance explained calculated by correlation and best multiple regression model and ANOVA decomposition analysis, comprised: Data Collection: Environmental variables (TN, PH, NH 4 , LAT, LON, EC, DO, COD, ALT) and microbial data (microeukaryote, generalist, specialists). PCA: CCA components explained 23.03% and 12.1% variance. Variance Decomposition: ANOVA quantified environmental impacts on diversity indices (Shannon, Simpson, PD, Richness), with correlation analysis. Statistical Analysis: Significance testing identified key environmental drivers of microbial community structure.Visualization: Scatter plots for CCA, bar charts for explained variance, and heatmaps for variable correlations.), and explanatory factors screened for significance ( P <0.05) are presented in the figure by means of circles; where. EC: Electrical conductivity; WT: Water temperature; DO: Dissolved oxygen; TUR: Turbidity; TN: Total Nitrogen; TP: Total Phosphorus; NH4 : Ammonium Nitrogen; COD: Chemical Oxygen Demand; WS: Water flow WS: Water Speed; ALT: Altitude; LAT: Latitude; LON: Longitude.PD:Pielou’s Evenness Index. Figure.6 The co-occurrence networks, network properties, and key species of the microeukaryotes communities and sub-communities in the downstream region of the eastern Himalayan river basin. These networks are constructed based on the entire community and are represented with different colors to depict the distribution of phyla and ecotypes in both the dry and wet seasons, as well as the networks composed of generalist and specialist, and the distribution of species modules. (a) and (e) display the distribution of phyla in the dry and wet seasons; (b) and (f) demonstrate the distribution of ecotypes in the dry and wet seasons; (c) and (g) the networks composed of generalist and specialist are depicted; (d) and (h) showcase the distribution of species modules. Figure.7 The key species of the microeukaryotes sub-communities in the eastern Himalayan river basin. (a) and (d) the classification of key species in the dry and wet seasons is shown. Specifically; (b) and (e) respectively depict the network cohesion of the microeukaryotes communities in the dry and wet seasons, and when the generalist or specialist sub-communities are removed; (c) and (f) demonstrate the total cohesion in the dry and wet seasons, where the total cohesion represents the sum of the absolute values of positive and negative cohesions. Figure.8 The figure for the graphical table of contents and considered for the publication cover. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Information & Authors Information Version history V1 Version 1 16 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords freshwater microbial population ecology sequencing Authors Affiliations Lingsu Bu 0009-0006-6248-5324 Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University View all articles by this author Peipei Wei Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Shengxian Yang Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Xin Chao Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Huiqiu Liu Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Jiajie Xu Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Guochun Zhang Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Longyang Dian State Key Laboratory of Microbial Technology, Institute of Microbial Technology, Shandong University, No. 72 Binhai Avenue, Qingdao, 266237, P. R. China View all articles by this author Sang Ba [email protected] Laboratory of Wetland and Watershed Ecosystems of Tibetan Plateau, School of Ecology and Environment, Xizang University, Lhasa 850000, China View all articles by this author Metrics & Citations Metrics Article Usage 138 views 68 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lingsu Bu, Peipei Wei, Shengxian Yang, et al. Survival Strategies of Specialists and Generalists in Maintaining Ecological Networks of the eastern Himalayan river basin. Authorea . 16 February 2026. DOI: https://doi.org/10.22541/au.177122893.31651417/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 . 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