Stochastic Assembly Drives Community Structure Over Environmental Filtering in a Fragmented Floodplain Lake: Multi-Scale Evidence from eDNA and Hydroacoustic Data

Stochastic Assembly Drives Community Structure Over Environmental Filtering in a Fragmented Floodplain Lake: Multi-Scale Evidence from eDNA and Hydroacoustic Data | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 2 May 2025 V1 Latest version Share on Stochastic Assembly Drives Community Structure Over Environmental Filtering in a Fragmented Floodplain Lake: Multi-Scale Evidence from eDNA and Hydroacoustic Data Authors : Hui Wang 0000-0001-5619-0198 , Zijun Wu , Yanping Zhang , Jinfeng He , Guodong Ding , Chenhong Li 0000-0003-3075-1756 , and Haixin Zhang [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174619290.06359467/v1 259 views 173 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The drivers of community dynamics during dry seasons remain poorly understood in many aquatic ecosystems, especially in highly dynamic floodplain lakes. We integrated hydroacoustic profiling and environmental DNA (eDNA) metabarcoding to investigate winter fish community assembly mechanisms in Poyang Lake, China’s largest freshwater lake. Vertical stratification partitioned species by body size, with larger taxa (>70 cm total length, TL) dominating benthic zones and smaller individuals (<30 cm TL) aggregating in surface waters. Despite this vertical niche differentiation, horizontal homogenization of species composition (NMDS: ANOSIM R2 = 0.065, p = 0.2) reflected ecological drift due to resource competition (βNTI |RC| < 0.95). A strong correlation between water depth variation (CV) and fish density fluctuation (R² = 0.62; p < 0.01) highlighted habitat fragmentation effects. Furthermore, low phylogenetic turnover revealed phylogenetic niche conservatism within the regional species pool. Winter habitat fragmentation confined communities to profundal refugia, intensifying stochastic assembly (undominated processes: 72.97%; homogenizing dispersal: 26.15%) via niche contraction, which drove elevated taxonomic β-diversity (βSør= 0.634; βJacc= 0.776). These findings underscore the necessity of maintaining bathymetric heterogeneity and hydrological connectivity to mitigate stochastic dominance in Poyang Lake’s winter fishery management. Introduction Community assembly, which refers to species colonization processes from regional metacommunities to local ecosystems, elucidates mechanistic determinants of biodiversity patterns (Hardy et al., 2012). Consequently, the assembly of local community requires successful traversal through historical constraints (eg. dispersal barriers) and ecological thresholds (eg. biotic interactions and abiotic gradients) (Pereira et al., 2018). The mechanisms of community construction have been summarised in various aspects by various theories. Niche-based theory usually describes process such as environmental filtering and species interactions, which emphasizes species-specific adaptations to ecological gradients (Whitfield, 2002). Meanwhile, neutral theory views all species as randomly dispersal, which emphasizes spatial dynamics and ecological drift over niche differentiation (Whitfield, 2002). The metacommunity theory integrates different scales, and view interplay between local factors (eg. environmental factors, species association) and regional process (eg. dispersal) as key of community aggregation (Leibold et al., 2004), in which mainly be divided into four fundamental processes: selection, drift, speciation, and dispersal (Vellend, 2010). In fact, niche-based deterministic filters (environmental selection, interspecific competition, evolutionary tradeoffs) interact synergistically with neutral stochastic processes (dispersal barriers, colonization dynamics, phylogeographic constraints) to collectively regulate biodiversity configuration over ecological timescales (Leibold et al., 2004; Márquez & Kolasa, 2013; Pereira et al., 2018). The relative dominance of assembly drivers manifests different patterns of predictability in community ecology (Shi et al., 2021). Community β-diversity, serving as a critical nexus between α- and γ-diversity, reflects spatial variation in species composition and quantifies the interplay of ecological processes shaping biodiversity patterns (Chalmandrier et al., 2019; Mori et al., 2018). By decomposing β-diversity into turnover (species replacement) and nestedness (subsampling from richer communities), this metric provides mechanistic insights into community assembly across spatial scales (Baselga, 2010). A taxonomically rich regional pool enhances β-diversity through intensified biotic interactions and environmentally filtered species sorting, where deterministic processes like environmental filtering (eg. climate) and niche-based interspecific interactions (e.g., asymmetric competition) drive habitat-specific community divergence (Márquez & Kolasa, 2013; Pereira et al., 2018). Conversely, stochastic processes modulate β-diversity by either amplifying spatial aggregation or reducing compositional variation (Huang et al., 2023; Veach et al., 2016). Since, β-diversity patterns ultimately encapsulate the hierarchical interplay of regional species availability, localized ecological filters, and stochastic demographic dynamics (Bannar-Martin et al., 2018; Guan et al., 2025; Peterson et al., 2021). Poyang Lake, the largest freshwater lake in China, serves as a critical ecotone within the Yangtze River floodplain system, characterized by dynamic river-lake interactions that drive pronounced seasonal water-level fluctuations (Li et al., 2019). These fluctuations historically sustained expansive floodplain wetlands, providing essential habitats for diverse aquatic biota, including threatened species (eg. Neophocaena asiaeorientalis ). However, intensified anthropogenic pressures-notably extensive sand mining in northern outflow channels-have profoundly altered hydrological regimes, particularly during winter dry seasons (November–March) (Yan et al., 2022). Bathymetric modifications have deepened and widened discharge channels, accelerating water outflow and exacerbating extreme low-water conditions, with lake levels declining to record lows in recent decades (Sun & Ma, 2020). Thus, clarifying how hydrological fluctuations affect multi-scale community assembly processes plays a key role in conserving fish species diversity in Poyang Lake. Hydroacoustic methods provide high-resolution, spatiotemporal data on fish density, size ranges and habitat use, but face challenges in species identification (Guo et al., 2019; Liu et al., 2023). Conversely, eDNA metabarcoding allows sensitive, taxon-specific detection of rare or cryptic species through trace genetic material, with lacking of quantitative density estimates (Sahu et al., 2023). Hence, incorporating eDNA metabarcoding (species presence/richness) with hydroacoustic data (density distribution) offers an opportunity to gain advances mechanistic understanding of community assembly of Poyang Lake’s wintering fish communities. In this study, we revealed fish composition and distribution pattern of Poyang Lake during dry season through hydroacoustic assessment and eDNA metabarcoding, respectively. We also explored the correlation between environment factors and fish community, and further investigated the mechanism of dry season fish community assembly by combining data from different scale. Materials and methods 1. Study area, collection of water samples and hydroacoustic data Poyang Lake (28°22’–29°45’N, 115°47’–116°45’E), China’s largest seasonal freshwater lake and a crucial Asian wetland ecosystem, exhibits dramatic hydrological fluctuations (expanding to >4,000 km² during flood seasons while contracting to <1,000 km² in dry periods). We collected eDNA samples from 34 systematically distributed stations across the lake during the winter dry season (6 th – 13 th , December 2023). Sampling stations were clustered into 8 hydrological groups based on spatial proximity and connectivity (FIGURE 1). At each station, 2L integrated water sample was obtained through vertical column sampling (surface to 1.5m depth) using an upright water collector, with thorough homogenization of water column strata. All sampling equipment was UV-sterilized and pre-treated with 10% bleach solution. Filtration through 0.45 µm mixed cellulose ester filter membrane (Merck Millipore) was conducted in situ. Membranes were immediately preserved in CTAB buffer (Solarbio, China) at 4°C during fieldwork and transferred to -80°C storage within 12 hours post-collection. Hydroacoustic assessment utilized a Biosonics DT-X split-beam scientific echosounder (200 kHz; BioSonics Inc., USA) with standardized ”Z”-transect sampling design (FIGURE 1). The transducer (6.8° nominal beam angle) was deployed at 0.5 m depth via starboard-mounted stabilization frame, maintaining vessel speed ≤10 km/h throughout surveys. System calibration followed Foote et al. protocols using 38.1mm tungsten carbide reference spheres (K. G. Foote, 1980). Acoustic parameters included: 5 pings/s repetition rate, 0.4 ms pulse duration, and -130 dB TS threshold. Raw data acquisition utilized BioSonics Visual Acquisition 5.0 software with automatic noise filtration (threshold: -70 dB). Chlorophyll-a concentrations were derived from Landsat8 OLI/TIRS imagery accessed via USGS EarthExplorer (https://earthexplorer.usgs.gov/) by using a machine learning algorithm (Cao et al., 2020). FIGURE Sampling sites at December in Poyang Lake. 2. Molecular experiment Given the complex composition of eDNA and potential inhibitors (e.g., humic acids), DNA extraction was performed using a modified phenol-chloroform protocol under UV-sterilized laminar flow conditions. Filter membranes were digested with 20 μL proteinase K (20 mg/mL; Yeasen Biotech, China) at 56°C for 2 hours, followed by organic phase separation via centrifugation (16,000 × g, 10 min) with 2 mL chloroform. The aqueous supernatant was precipitated using 500 μL ice-cold isopropanol and 250 μL 5 M NaCl, pelleted by centrifugation (14,000 × g, 15 min), and washed twice with 70% ethanol. Purified DNA was resuspended in 50 μL TE buffer (10 mM Tris-HCl, 1 mM EDTA, pH 8.0) and quantified via Qubit 4.0 Fluorometer (Thermo Fisher Scientific). A 171-bp fragment of the mitochondrial 12S rRNA gene was amplified using tele02 primers (tele02-F: 5′-AAACTCGTGCCAGCCACC-3′; tele02-R: 5′-GGGTATCTAATCCCAGTTTG-3′) (Taberlet et al., 2018) following the thermocycling protocol from S. Zhang et al. 2023. PCR products were purified using AMPure XP magnetic beads (Beckman Coulter) and eluted in 20 μL TE buffer. Library preparation included dual-indexed Illumina adaptor ligation during a second PCR (8 cycles), followed by size selection via 2% agarose gel electrophoresis. Target bands were extracted using the FastPure Gel DNA Extraction Mini Kit (Vazyme Biotech, China) and sequenced on an Illumina NovaSeq 6000 platform (2×150 bp paired-end reads). 3. Hydroacoustic data analysis Echo signal processing was conducted in Visual Analyzer 4.1 (BioSonics Inc.) with the following thresholds: single echo detection threshold of -70 dB; echo length range from 0.75 to 3.0; time-varied gain of 40 lgR, minimum target spacing of 2 pings and minimum target count per track of 3 pings. Survey coverage (D) was calculated as\(D=\frac{L}{\sqrt{}A}\), where L represents transect length (m), and A represents survey area (m²) (S. L. Foote & Morrison, 1987). Target Strength (TS)-to-Length conversion using\(TS=20\log{TL-71.9}\), where TS means target strength (dB) and TL means total fish length (cm). To estimate fish density (per 3,000 pings/sampling unit; n=38), the density of each sampling unit is used to calculate the average density for the entire survey area. This is combined with the volume of the water body in the survey area to estimate fish density and resources by\(\rho_{i}=\frac{S_{i}\times 1000}{V_{i}}\), where \(\rho_{i}\)represents fish density in i th unit (ind./1000 m³), \(S_{i}\)represents fish count per unit and \(V_{i}\) means volume in in i th unit. Vertical stratification divided the water column into three layers, upper (0–33% depth), middle (33–66% depth) and lower (66–100% depth). Statistical analyses (ANOVA, spatial autocorrelation) were performed in SPSS 25.0 (IBM Corp.) and ArcGIS 10.2 (ESRI), with density distributions visualized via inverse distance weighting (IDW) interpolation (power=2, search radius=500 m). 4. eDNA data analysis Raw paired-end sequencing data underwent quality control through Fastp v0.23.2 (S. Chen et al., 2018) with adapter trimming and removal of low-quality reads (Phred score <20, length <50 bp). Overlapping reads were merged using PANDAseq v2.11 (Masella et al., 2012) with a minimum overlap of 20 bp and 95% sequence identity. Demultiplexing was performed via sample-specific dual-index barcodes, followed by length filtering (retaining sequences within ±10% of expected 171-bp target). Chimeric sequences and PCR repeat amplification templates were removed using VSEARCH v2.18.0 with the UNOISE3 algorithm, generating amplicon sequence variants (ASVs) at 100% identity (Rognes et al., 2016). Taxonomic assignment of ASVs was conducted via BLASTn v2.13.0 against a curated reference database combining NCBI nt, MitoFish database and self-sequenced mitochondrial genomes (Sato et al., 2018; Zhu et al., 2023). Matches were filtered at ≥97% identity over ≥90% query coverage, with hierarchical classification resolving ambiguous assignments. To reveal ecological patterns of biodiversity among different sampling sites, non-metric multidimensional scaling (NMDS) ordination (based Jaccard presence-absence matrix) implemented in vegan v2.6.4 followed by an ANOSIM (999 permutations) testing significance of group variation. Generalized additive models (GAM; mgcv v1.9.0) related species richness to hydroacoustic densities (ind./1000 m³), with thin-plate splines accounting for non-linear relationships (k=3 basis functions). Furthermore, correlation between density coefficient of variation (density CV), average density and water depth coefficient of variation (water depth CV) were explored through Spearman correlation analysis with hydroacoustic data after uniform grouping. Both taxonomic and phylogenetic β-diversity calculated using betapart v1.6 with both Sørensen’s index (β Sør and β phylo-Sør ) and Jaccard index (β jacc and β phylo-jacc ) (Baselga & Orme, 2012). Phylogenetic relationship of detected species was constructed from whole mitochondrion genome. For a better understanding of the assembly process, stochastic/deterministic processes assessed via βNTI (β-nearest taxon index) in iCAMP v1.5.1 (Ning et al., 2020). \(\beta NTI>2\)indicates heterogeneous selection while \(\beta NTI<-2\) indicates homogeneous selection, both represent deterministic processes. Since\(|\text{βNTI}|\leq 2\) indicates that the community is primarily influenced by stochastic processes and further partitioned by Raup-Crick index (RC) (Raup & Crick, 1979). \(RC>0.95\) indicates dispersal limitation, \(RC<-0.95\) indicates homogeneous dispersal, and\(|RC|\leq 0.95\) represents that community is influenced by undominated processes such as ecological drift. Joint Species Distribution Models (jSDMs) are statistical methods used in ecology to simultaneously analyse the distribution of multiple species in relation to environmental factors. Therefore, response matrix (presence/absence), predictors (chlorophyll-a (μg/L), water depth (m), channel width (m) and spatial coordinates) and latent variables were incorporated for understanding community-scale distribution patterns through sjSDM (Pichler & Hartig, 2021). Figures generated using ggplot2 v3.4.2 (Wickham, 2016) adhered to ColorBrewer 2.0 perceptually uniform palettes. 1.Fish Community Composition and Threatened Species Distribution FIGURE (A) Fish proportion detected in each group classified by family, and (B) proportion of total fish in class, order and family, respectively. From inside to outside of figure B, they are classified by class, order and family. Figure A and B legend consistent. High-throughput eDNA metabarcoding (97% similarity threshold) identified 65 vertebrate species across Poyang Lake’s winter fish assemblage, including one mammal, Neophocaena asiaeorientalis , dominated by Cypriniformes (69.2%, n=45) (FIGURE 2). Xenocyprididae (Cyprinidae) (29.2%, n=19) and Gobionidae (16.9%, n=11) comprised the dominant families, reflecting the lake’s role as a cyprinid biodiversity hotspot. (FIGURE 2). TheYangtze finless porpoise ( Neophocaena asiaeorientalis ) was detected in every group. Meanwhile, two non-indigenous cichlids ( Oreochromis niloticus and Oreochromis aureus ) exhibited restricted distributions (Group 5 exclusively). In total, four threatened species were documented, including the critically endangered Yangtze finless porpoise ( Neophocaena asiaeorientalis ) and Chinese sturgeon ( Acipenser sinensis ), and endemic cyprinids under provincial protection, Rhynchocypris oxycephalus (Jiangxi Province Grade II protected wildlife) and Liobagrus anguillicauda (Jiangxi Province Grade II protected wildlife). 2. Body Size-Driven Vertical Habitat Stratification FIGURE (A) Body-length range in different layer and (B) body-length proportion of different groups. Water depth of each group as displayed in FIGURE S2. The water layers were divided into lower (33%), middle (33%~66%) and upper (66%~100%) according to acoustic data layer. The red dash line in figure A represents the body-length range of Neophocaena asiaeorientalis (70~180 cm). Body-length was classified into 4 groups (0~30cm, 30~80cm, 80~130cm, 130cm~) and the proportion of each class were displayed in figure B. Body-size structured vertical partitioning emerged as a key ecological driver. Usually, species with longer body-length were detected at the lower or middle water layer, except group 8. Most of the species with longer body-length in Group 8 were found in the middle and upper water layer (FIGURE 3). Body length of Neophocaena asiaeorientalis range from 70 to 180 cm (Amano & Kasuya, 2025), thus, species with body-length exceed 180 cm could be Acipenser sinensis (CABI, 2022). In total 11 species were detected in group 1, with body-length range from 0 ~ 30 cm, which represents a miniaturized fish community. Hydroacoustic density profiles revealed divergent size-density relationships (FIGURE S1), groups 4, 5, 6, and 7 have a similar distribution of body length and density. Meanwhile, group 2 had the opposite length-density distribution, with the species in the lower water layer having the longest body lengths but the lowest densities (FIGURE 3, S2). Groups 1 and 3 had similar distributions of species length and species density, with the lowest densities in the middle water layer. Among the groups, the total density of Group 7 was dominant with the largest range of body lengths and the highest densities of the species in its lower layer. Finally, group 8 owes the lowest total density, with long body-length species mostly distributed at middle and upper layer. 3. Environmental Drivers of Biodiversity Patterns FIGURE (A) The generalized additive model fitting between richness and density. And (B) NMDS analysis between different sampling group and the polygon represents different groups. Stress value of NMDS model is 0.182. (C) Correlation between density CV and average density, and (D) correlation between density CV and water depth CV. The generalized additive model fitting showed that richness from eDNA was mildly negatively correlated with density (FIGURE 4). To further reveal difference between sampling groups, NMDS analysis and ANONISM were implemented based on Jaccard matrix. Most sampling sites were scattered and did not form distinct clusters. Sampling sites from group1 and group6 was slightly differentiated from others. The ANOSIM analysis showed intergroup differences only explained 6.5% total variation, which means no statistically significant difference among groups. For obtain detailed information on the explanatory power of each environmental variable, the relationship between fish species abundance (Richness), density and environmental factors in Poyang Lake was analyzed by generalized additive model with water depth significantly correlated with species richness (Individual effect: 45.34%, p = 0.01) and density (spearman’s rho: 0.14; p<0.01) (FIGURE S2; S3, Supplementary Table 1). Furthermore, water depth CV was significantly negative correlated with density CV (R 2 =0.62; p<0.01), while slightly positive correlated with average density (FIGURE 4). 4. Taxonomic and Phylogenetic β-Diversity Partitioning FIGURE β diversity partition with (A) Sørensen’s index, (B) Jaccard index, (C) Phylogenetic Sørensen’s index and (D) Phylogenetic Jaccard index through R package betapart. For Sørensen (A) and phylogenetic Sørensen index (C), β sne demonstrates ‘nestedness’, β sim represents ‘turnover’. For Jaccard index and phylogenetic Jaccard index, β jne demonstrates ‘nestedness’, β jtu represents ‘turnover’. Similarity shows overall similarity values (similarity = 1 – overall β diversity). The decomposition of β-diversity into turnover and nestedness with and without phylogenetic relationship included were carried out for further understand the mechanism of species community assembly (FIGURE 5). As shown, turnover is the dominant process under most situations, except with phylogenetic Sørensen index (β Sør turnover:0.225; β Sør nestedness:0.259). The weight of turnover decreased and nestedness increased for both β-Sørensen and β-Jaccard after the introduction of phylogenetic relationship. In parallel, the weight of turnover decreased from 0.416 (β Sør ) to 0.225 (β phylo-Sør ), while the weight of turnover decreased from 0.588 (β jacc ) to 0.367 (β phylo-jacc ), which indicating phylogenetic niche conservatism. The Sørensen index (considering species presence/absence) shows nestedness dominance, whereas the Jaccard index (considering only species substitutions) places more emphasis on turnover, suggesting that the filtering role of regional species pools coexists with localized random substitutions (FIGURE 5). 5. Stochastic Dominance in Multiscale Community Assembly FIGURE Calculation and visualization of community assembly process and variation partitioning of environment, species associations and spatial autocorrelation. (A) described the composition of community assembly process in each group according to null model; and (B) the Veen plot displayed variation explained by three components, respectively. High turnover rates indicated that the variations between communities mainly stem from species replacement. Null model analyses revealed stochasticity as the dominated process in community assembly (FIGURE 6). Undominated process (eg. weak dispersal, weak selection, drift) and homogenizing dispersal were main processes and contributed 72.97% and 26.15% to fish community assembly in Poyang Lake, respectively. Determinism (Heterogeneous selection and homogeneous selection) accounted for only 0.1% community variation. However, joint species distribution modeling revealed the individual effect of environmental factors (eg. water depth, surface width and concentration of chlorophyll a) was 28.9%, species association and spatial autocorrelation explained 29.7% and 5.6% variations, separately, while 40.2% remained unexplained. Discussion In this study, integrated eDNA metabarcoding and hydroacoustic surveys were implemented to investigate the fish composition of Poyang Lake. A total of 65 species were detected, including 4 protected species, Acipenser sinensis, Neophocaena asiaeorientalis, Rhynchocypris oxycephalus and Liobagrus anguillicauda , and 2 non-indigenous species, Oreochromis niloticus and Oreochromis aureus . Consistent with previous studies (Jiang et al., 2023; Kong et al., 2025; Y. Zhang et al., 2024), fishes from Cypriniformes accounted for the major component. The Order-level is dominated by Cypriniformes, while the family-level is dominated by Cyprinidae and Cobitidae, sedentary species such as C. carpio and C. auratus remain the major economic species in Poyang Lake (Fu et al., 2003; Yang et al., 2024). Hydroacoustic profiling demonstrated significant vertical stratification in fish assemblages, with larger-bodied taxa (>70 cm total length) predominantly occupying benthic zones, while smaller individuals (<30 cm) aggregated in surface waters. Species coexistence is central to maintaining biodiversity, isolating species to diverse ecological niches allows them to allocate resources in a way that reduces overlapping demands (Turnbull et al., 2013). In addition, scarcity of overwintering resources (food or habitat) exacerbates ecological niche differentiation among species, the coexistence of species when resources are limited will leading to resource-use partitioning (Niella et al., 2025). Bathymetric heterogeneity can provide a wide variety of micro-habitats for fishes in different niches. However, the negative correlation between water depth CV and density CV suggests that habitat homogenization, such as winter refuges, may exacerbate density fluctuations through resource limitation, which indicates habitat fragmentation. Furthermore, low intergroup variations (ANOSIM R 2 = 0.065, p-value: 0.2) reflecting stochastically localised fish extinctions due to resource competition resulting in small compositional differences (Nunn et al., 2020). Taxonomic β-diversity refers to the dissimilarity of species composition between communities in different habitats (Qian et al., 2021). Phylogenetic β-diversity connected contemporary assembly processes to regional evolutionary legacies via mean pairwise phylogenetic distance metrics (Douda et al., 2018; Graham & Fine, 2008). Winter hydrological contraction fragments Poyang Lake into semi-isolated sub-basins, creating bathymetry-defined microrefugia that drive elevated species turnover (β jtu = 0.588) (X. Chen et al., 2022; Sun & Ma, 2020). Furthermore, low phylogenetic turnover indicates environmental gradients (e.g., climate) structure regional species pools and reflects evolutionary conservatism within the Yangtze ichthyofauna (e.g., Cypriniformes constituting 69.2% of fish assemblages), with clade-specific niche conservatism constraining novel species colonization (Fu et al., 2003; Qian et al., 2021; Vaudo et al., 2018; Xiong et al., 2024). The phylogenetic-taxonomic β-diversity decoupling demonstrates that regional environmental filters select evolutionarily conserved lineages from the species pool, while local communities assemble stochastically from these pre-filtered taxa (Qin et al., 2022). The βNTI partitioning reflects the shaping of community structure by historical evolutionary processes (e.g. environmental filtering) at larger spatial scales, with jSDM reflects the immediate effects of current environmental conditions, spatial processes and interspecific interactions on species coexistence (Pichler & Hartig, 2021; Stegen et al., 2012). The βNTI partitioning revealed stochastic dominance (homogenizing dispersal: 26.15%; undominated processes: 72.97%) in local community assembly, with environmental effect evidenced by correlations between species density/richness and water depth, high taxonomic turnover and high environmental interpretation according to jSDM. Niche contraction caused by habitat fragmentation reinforced the dominance of stochastic processes (Wilson et al., 2016). Environmental selection delineates the boundaries of species presence, while ecological drift led to random differentiation in localized community composition in Poyang Lake, driving high turnover and beta diversity (Le Moigne et al., 2023). Hydrological connectivity between secondary channels and mainstem systems mediates metacommunity persistence, sustaining ecological processes across aquatic-terrestrial interfaces (Crites et al., 2012; Kume et al., 2014). However, habitat compression due to retreating water levels confining fish populations to fragmented deep-water refugia, dramatically reducing available niche space, shortage of resources and even causing extinction debt in the taxonomic and phylogenetic diversity due to loss of floodplain connectivity, intensifying stochastic processes (e.g., homogenizing dispersal, ecological drift) in community assembly (Catano et al., 2025; Yuan et al., 2024). Hence, deterministic selection and phylogenetic conservatism shape regional species pools, winter habitat fragmentation forced fish to confine to local refugia, and niche space contraction led to increased competition for resources, which in turn counteract the influence of environmental factors on community assembly mechanisms. Conclusions Integration of eDNA metabarcoding and hydroacoustic approaches confirmed that fish community assembly during overwintering in Poyang Lake is governed by the interplay of stochastic and deterministic processes. Phylogenetic niche conservatism shaped regional species pool, while niche contraction induced by habitat fragmentation intensified ecological drift and thereby driving high turnover and β diversity, which ultimately reflects the hierarchical coupling of regional deterministic filtering and localized stochastic assembly. 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Author Contributions Li Chenhong and Zhang Haixin conceptualised this study, reviewed and edited draft manuscript; Li Chenhong, Zhang Haixin, Wang Hui and Wu Zijun designed research; Wang Hui and Wu Zijun performed research, analyzed data and wrote the original manuscript; Zhang Yanping, He Jinfeng and Ding Guo Dong performed investigation, molecular experiment and data curation. Tables and Figures (with captions) FIGURE 1 Sampling sites at December in Poyang Lake. FIGURE 2 (A) Fish proportion detected in each group classified by family, and (B) proportion of total fish in class, order and family, respectively. From inside to outside of figure B, they are classified by class, order and family. Figure A and B legend consistent. FIGURE 3 (A) Body-length range in different layer and (B) body-length proportion of different groups. Water depth of each group as displayed in Fig. S2. The water layers were divided into lower (33%), middle (33%~66%) and upper (66%~100%) according to acoustic data layer. The red dash line in figure A represents the body-length range of Neophocaena asiaeorientalis (70~180 cm). Body-length was classified into 4 groups (0~30cm, 30~80cm, 80~130cm, 130cm~) and the proportion of each class were displayed in figure B. FIGURE 4 (A) The generalized additive model (GAM) fitting between richness and density. And (B) NMDS analysis between different sampling group and the polygon represents different groups. Stress value of NMDS model is 0.182. (C) Correlation between density coefficient of variation (density CV) and average density, and (D) correlation between density coefficient of variation (density CV) and water depth coefficient of variation (water depth CV). FIGURE 5 β diversity partition with (A) Sørensen’s index, (B) Jaccard index, (C) Phylogenetic Sørensen’s index and (D) Phylogenetic Jaccard index through R package betapart. For Sørensen (A) and phylogenetic Sørensen index (C), β sne demonstrates ‘nestedness’, β sim represents ‘turnover’. For Jaccard index and phylogenetic Jaccard index, β jne demonstrates ‘nestedness’, β jtu represents ‘turnover’. Similarity shows overall similarity values (similarity = 1 – overall β diversity). FIGURE 6 Calculation and visualization of community assembly process and variation partitioning of environment, species associations and spatial autocorrelation. (A) described the composition of community assembly process in each group according to null model; and (B) the Veen plot displayed variation explained by three components, respectively. FIGURE S Fish density in different water layers according to hydroacoustic assessment. FIGURE S Water depth in Poyang Lake during winter dry season according to hydroacoustic assessment. FIGURE S Fish density in Poyang Lake during winter dry season according to hydroacoustic assessment. Supplementary Material File (figure1.pdf) Download 77.31 MB File (figures2.pdf) Download 38.98 MB File (figures3.pdf) Download 39.05 MB Information & Authors Information Version history V1 Version 1 02 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords community assembly environmental dna fisheries management hydroacoustic assessment lake poyang winter dry seasons Authors Affiliations Hui Wang 0000-0001-5619-0198 Shanghai Ocean University View all articles by this author Zijun Wu Fisheries Research Institute of Jiangxi Province View all articles by this author Yanping Zhang Fisheries Research Institute of Jiangxi Province View all articles by this author Jinfeng He Shanghai Ocean University View all articles by this author Guodong Ding Jiangxi Fishery Resources and Ecological Environment Monitoring Center View all articles by this author Chenhong Li 0000-0003-3075-1756 Shanghai Ocean University View all articles by this author Haixin Zhang [email protected] Fisheries Research Institute of Jiangxi Province View all articles by this author Metrics & Citations Metrics Article Usage 259 views 173 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hui Wang, Zijun Wu, Yanping Zhang, et al. Stochastic Assembly Drives Community Structure Over Environmental Filtering in a Fragmented Floodplain Lake: Multi-Scale Evidence from eDNA and Hydroacoustic Data. Authorea . 02 May 2025. DOI: https://doi.org/10.22541/au.174619290.06359467/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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