Multi-media eDNA Metabarcoding Uncovers Habitat-Specific Fish Community and Monitoring Efficiency: Implications for Ecosystem Management in a River-Lake System (Honghu Lake Basin, China)

doi:10.21203/rs.3.rs-8183469/v1

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Multi-media eDNA Metabarcoding Uncovers Habitat-Specific Fish Community and Monitoring Efficiency: Implications for Ecosystem Management in a River-Lake System (Honghu Lake Basin, China)

https://doi.org/10.21203/rs.3.rs-8183469/v1

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Environmental DNA (eDNA) metabarcoding is widely used for monitoring aquatic biodiversity, yet its efficiency across different ecosystems and sample types requires further evaluation. This study assessed fish diversity using eDNA from water and sediment in the river-lake system of China’s Honghu Basin. Results demonstrated that: (1) Water samples from rivers yielded the highest taxonomic richness (7 orders, 37 species), significantly surpassing that of lake water (7 orders, 17 species), river sediment (3 orders, 9 species), and lake sediment (3 orders, 7 species). (2) Sediment eDNA consistently detected a higher proportion of benthic/demersal species than water eDNA in both ecosystems.(3) Riverine communities exhibited greater α-diversity and more complex structure than those in lakes. (4)Linear Discriminant Analysis (LDA) identified Pseudorasbora parva and Carassius auratus as key species in the river, whereas Hypophthalmichthys nobilis was the key species in the lake. (5)Redundancy Analysis (RDA) showed that fish communities in river water were significantly correlated with Total Nitrogen (TN), Nitrate-Nitrogen (NO₃-N), and Total Phosphorus (TP), while communities in lake water were primarily associated with Turbidity (Turb), pH, Chlorophyll-a (Chl.a), and Chemical Oxygen Demand (COD). These findings offer a theoretical basis for optimizing eDNA-based biodiversity monitoring and support the conservation of freshwater ecosystems.

Environmental DNA (eDNA)

Honghu Lake Basin

Fish Diversity

Environmental Factors

River-Lake System

Fish, as key consumers in aquatic food webs, are crucial indicator organisms for assessing ecosystem health, water quality, and aquatic ecological security, as their distribution patterns and population dynamics effectively reflect the status of aquatic ecosystems(Qian et al., 2023; Zou et al., 2020). Traditional fish monitoring techniques, primarily including visual surveys, trawling, and hydroacoustic surveys (Lyu et al., 2024), rely heavily on capture-based methods to obtain information on species composition and abundance. These conventional approaches often require manual capture, counting, and identification, which are not only time-consuming and costly but also cause direct disturbance and damage to ecosystems. Since the implementation of the "Ten-Year Fishing Ban" in the Yangtze River Basin in 2021, traditional monitoring methods dependent on trawling have been strictly limited by policy, creating an urgent need for more gentle and efficient monitoring technologies.

Environmental DNA (eDNA) technology offers a novel paradigm for fish-based ecological assessment. By extracting DNA directly from environmental samples (Rieder et al., 2024), it fundamentally minimizes harm to individual organisms and the ecological environment (Wang et al., 2021) and proves more efficient and convenient than traditional methods (Minamoto et al., 2012). This technology has achieved remarkable success in monitoring aquatic community composition (Takahashi et al., 2023) and is widely applied in rare species detection、invasive species tracking (Adrian-Kalchhauser and Burkhardt-Holm, 2016; Robson et al., 2016), and biodiversity assessment (Cheng et al., 2023; Stat et al., 2019). For instance, Perry et al.(2024) used aquatic eDNA to investigate the spatiotemporal patterns of river fish diversity, while Carvalho et al.(2024) revealed seasonal dynamics of fish community composition, demonstrating the utility of eDNA. However, challenges remain in its application for fish monitoring. Most existing studies focus on eDNA from water samples, despite evidence suggesting that sedimentary eDNA may have a longer persistence time and higher local concentration than aquatic eDNA, which degrades rapidly (Corinaldesi et al., 2008; Turner et al., 2015). Studies by Shao et al.(2024)and Sakata et al.(2021) further confirmed that sedimentary eDNA is more effective for detecting benthic fish species; for example, Shao et al.(2024) found nine benthic species exclusively in sediment samples. Conversely, Zhu et al.(2023) reported fewer fish species detected in sediment samples (187 species) than in water samples (258 species) in Tokyo Bay using eDNA metabarcoding. Therefore, whether sedimentary eDNA outperforms aquatic eDNA in monitoring efficiency, and whether this potential difference relates to fish ecological niches (e.g., benthic vs. pelagic), remains unclear, hindering the standardization and broad application of eDNA technology, particularly in complex river-lake systems.

River-lake interconnected systems are vital components of freshwater ecosystems. Classical theory suggests that rivers, due to strong hydrological dynamics and high habitat heterogeneity, generally support higher fish diversity (Oberdoff et al., 1995). However, some studies argue that large connected lakes can act as regional "species sinks," maintaining or even exceeding the fish species richness of individual connected rivers(Ru, 2008; Zhang, 2015). Notably, existing research exhibits a spatial bias, predominantly focusing on mountainous lakes or connected river-lake systems with relatively simple habitats(Baxter et al., 2005; Eloranta et al., 2015; Ru, 2008; Su, 2016; YZ, 2007), while studies on hydrologically fragmented floodplain lakes—whose ecological processes are profoundly altered by dams and sluices and are more complex due to intense human influence—remain scarce. The distribution patterns and maintenance mechanisms of fish communities in these fragmented floodplain lakes urgently need clarification.

Lake Honghu, the largest freshwater lake in Hubei Province and a Ramsar Wetland of International Importance, represents an ideal model for studying such systems. It is a typical fragmented floodplain lake in the middle Yangtze River that forms a unique river-lake system with upstream channels, playing a significant role in regional and global biodiversity conservation. However, Lake Honghu has experienced a dramatic decline in fish resources, with species numbers plummeting from 73 in the mid-20th century to 32 in 2021 (Li, 2023; Melaku et al., 2023), posing severe challenges to biodiversity conservation.

Based on this research background and identified gaps, this study aims to systematically analyze the fish distribution patterns and environmental driving mechanisms in the Lake Honghu basin. We hypothesize that: (1) sedimentary eDNA exhibits higher detection efficiency for benthic fish than aquatic eDNA, but may have lower overall species detection rates; (2) fish diversity in the river channels is significantly higher than in the lake due to greater habitat heterogeneity; and (3) fish community structures in the river and lake are driven by distinct environmental factors.Addressing these hypotheses, our specific objectives are to: 1) compare the efficiency of aquatic and sedimentary eDNA in detecting fish species and explore its association with fish ecological traits; 2) analyze the composition and diversity differences of fish communities between the riverine and lacustrine habitats of the Lake Honghu system; and 3) identify the key environmental factors influencing fish distribution and reveal the drivers of diversity. The findings will provide a valuable regional case study for eDNA technology application and community ecology in global analogous ecosystems, while offering scientific evidence and practical guidance for the precise conservation of fish resources and aquatic ecological restoration in the Lake Honghu basin.

Study Area and Sampling Sites

The study area is located within the Honghu Lake Basin (111°57′–114°05′E, 29°26′–31°02′N) in the south-central part of Hubei Province, China. Situated in the heart of the Jianghan Plain, the basin is flanked by the Yangtze River to its east and west, serving as a crucial fish reproduction and habitat area. Honghu Lake (113°12′–113°26′E, 29°49′–29°58′N) is situated in the lower reaches of the Four-Lake Basin of the Jianghan Plain, on the northern bank of the middle Yangtze River, spanning Honghu City and Jianli City. With a water surface area of approximately 400 km², it is the seventh largest freshwater lake in China and a typical shallow lake in the middle-lower Yangtze River basin (Yang et al., 2023). Designated as a Ramsar Wetland of International Importance in 2008, the Honghu Wetland was further elevated to a National Nature Reserve in 2014(Wang et al., 2017; Yang et al., 2023), underscoring its significant ecological value.

Field sampling was conducted in June 2022. A total of 27 sampling sites were established, comprising 10 sites in the inflowing rivers and 17 sites within the lake area. At each site, paired environmental samples were collected: one set for water quality analysis and another for fish eDNA analysis. Specifically, within the lake area, sediment samples were collected from sites LS01 to LS08, and water samples were collected from sites LW01 to LW16. From the inflowing rivers, sediment samples were obtained from sites RS01 to RS07, and water samples from sites RW01 to RW10. The geographical distribution of all sampling sites is presented in Figure 1.

Sample Collection and Physicochemical Analysis

Water samples were collected from the surface layer (0–15 cm depth) using a water sampler. Six parameters—temperature (T), dissolved oxygen (DO), pH, electrical conductivity (EC), chlorophyll‑a (Chl‑a), and turbidity (Turb)—were measured on-site using a YSI ProDSS multiparameter water quality probe. Additional physicochemical parameters, including total phosphorus (TP), total nitrogen (TN), nitrate nitrogen (NO₃‑N), ammonia nitrogen (NH₃‑N), chemical oxygen demand (COD), and permanganate index (COD_Mn), were determined in the laboratory following standard methods (Rice et al., 2012). Sediment samples were collected using a 1/32 m² Petersen grab sampler at an approximate depth of 10 cm. Sediment pH was measured immediately with a soil pH meter. Other sediment properties, including concentrations of nickel (Ni), mercury (Hg), copper (Cu), manganese (Mn), arsenic (As), cadmium (Cd), lead (Pb), redox potential (Eh), TP, phosphate‑phosphorus (PO₄‑P), NH₃‑N, and TN, were analyzed using established standard procedures (Rice et al., 2012).

eDNA Sample Processing and Analysis

For aquatic eDNA, 1.5 L of water was collected per sample. All water samples were transported to the laboratory under cold conditions within 24 hours and filtered through 0.22 μm Millipore polycarbonate membranes. The filters were then placed in sterile centrifuge tubes and stored at –80 °C until DNA extraction. To monitor potential contamination, ultrapure water was used as a negative control during filtration.DNA was extracted from material collected on water filters using the cetyltrimethylammonium bromide (CTAB) method. Sediment eDNA was extracted using the OMEGA Soil DNA Kit (M5635‑02, Omega Bio‑Tek, Norcross, GA, USA) following the manufacturer’s protocol (Lueders et al., 2004). Polymerase chain reaction (PCR) amplification was performed using universal fish‑specific 12S rRNA primers (Forward:GTYGGTAAAWCTCGTGCCAG;Reverse:CATAGTGGGGTATCTAATCCYAGTTTGG) (Sato et al., 2018). Each 25 μL PCR reaction contained: 0.25 μL Q5 high‑fidelity DNA polymerase, 5 μL 5× Reaction Buffer, 5 μL 5× High GC Buffer, 2 μL dNTPs (10 mM), 2 μL template DNA, 1 μL each of forward and reverse primers (10 μM), and 8.75 μL ddH₂O. The thermal cycling conditions were: initial denaturation at 98 °C for 30 s; 30 cycles of denaturation at 98 °C for 30 s, annealing at 59.5 °C for 30 s, and extension at 72 °C for 45 s; followed by a final extension at 72 °C for 5 min. PCR products were checked for concentration, purity, and integrity using a NanoDrop NC2000 spectrophotometer and agarose gel electrophoresis. Target bands were excised and purified using magnetic bead‑based clean‑up. Equimolar pools of PCR amplicons were sequenced on an Illumina MiSeq platform following the standard sequencing protocol (Callahan et al., 2017).

Raw sequencing reads were quality‑filtered by requiring a Q30 score threshold > 85% (i.e., > 85% of bases with a Phred score ≥ 30, representing an error rate ≤ 0.1%) (Bolyen et al., 2019; Ewing and Green, 1998). To ensure sufficient species coverage and detection sensitivity, the clean reads for each sample need to be greater than 50,000(Callahan et al., 2017). Taxonomic assignment of amplicon sequence variants (ASVs) was performed by comparing sequences against the NCBI （BLAST: Basic Local Alignment Search Tool）NT database using BLASTn. The best hit for each sequence was selected as the annotation result. All annotations were manually verified against the FishBase database（FishBase : A Global Information System on Fishes） and historical fish records from the Comprehensive Scientific Survey Report of Hubei Honghu National Nature Reserve(Li, 2023) to exclude non‑freshwater or implausible species. Final fish ASV tables and sequence data were compiled in Excel for subsequent analysis.

Statistical Analysis

Fish alpha diversity within each habitat was evaluated using the Shannon diversity index (Barnes, 1993). Beta diversity was employed to examine compositional differences in fish communities among sampling environments. Correlation analysis of ASV annotations was conducted with a significance threshold set at p < 0.001 and |r| > 0.60 (Xu et al., 2024). Fish community co‑occurrence networks were visualized and analyzed for topological properties using Gephi. Linear discriminant analysis (LDA) was performed with the R package microeco(v4.5.1) to identify taxa distinguishing river and lake communities. Redundancy analysis (RDA) was carried out using the veganpackage (v4.5.1) to evaluate the influence of environmental factors on fish community structure. Fish species were categorized into pelagic (upper and middle water layers) and benthic/demersal (lower and bottom layers) groups based on their ecological traits (Xu et al., 2024). All statistical analyses and graphics were generated using R.

Fish Species Composition and Diversity Across Different Media

The results revealed significant differences in taxonomic annotations depending on the habitat and medium. In the riverine habitat, water samples yielded the highest taxonomic richness among all habitats, with a total of 35 species belonging to 7 orders (Fig. 2a). At the order level, Cypriniformes was the absolutely dominant group, comprising 23 species and accounting for a relative abundance of 96.76%. This was followed by Gobiiformes (1.04%), Siluriformes (1.09%), Synbranchiformes (0.04%), Cyprinodontiformes (1.01%), and Anabantiformes (0.05%). At the species level, Carassius auratus was the primary dominant species (dominance = 0.43, relative abundance = 47.77%), with Cyprinus carpio, Rhodeus sinensis, and Pseudobrama simoni collectively forming the secondary dominant species (Fig. 2a). In contrast, river sediment samples yielded a considerably lower diversity, with only 9 species from 3 orders annotated (Fig. 2a). Cypriniformes remained dominant (7 species) but represented a lower proportion of relative abundance (34.29%). Gobiiformes and Cichliformes were each represented by one species, with relative abundances of 33.78% and 31.91%, respectively. The dominant species in river sediment included Carassius auratus(dominance = 0.22, relative abundance = 22.18%), Coptodon zillii, Rhinogobius giurinus, and Hypophthalmichthys nobilis (Fig. 2a).

Within the lacustrine habitat, water samples contained 17 species from 7 orders (Fig. 2a). Cypriniformes again dominated overwhelmingly, represented by 11 species and a relative abundance of 97.65%, with a 100% detection frequency. Other orders, such as Clupeiformes and Beloniformes, were each represented by only a single species. At the species level, Hypophthalmichthys nobilis was the sole absolute dominant species, exhibiting a very high dominance index (0.87), a relative abundance of 93.03%, and a detection frequency of 93.75% (Fig. 2a). Sediment samples from the lake were even less diverse, with annotations for only 7 species from 3 orders (Fig. 2a). Cypriniformes (5 species) maintained absolute dominance, constituting 99.99% of the relative abundance with 100% detection frequency. Synbranchiformes and Osteoglossiformes were each represented by one species. Hypophthalmichthys nobilis remained the primary dominant species (dominance = 0.81, relative abundance = 81.42%), and Ctenopharyngodon Idella acted as a secondary dominant species (Fig. 2a).

To investigate the relationship between eDNA monitoring efficiency and fish ecological niches, all annotated species were categorized into pelagic and benthic/demersal groups based on their life habits (Fig. 2c). The results demonstrated that benthic/demersal species numerically dominated in all habitats. Crucially, the proportion of benthic/demersal species was higher in sediment eDNA than in water eDNA for both ecosystems (Lake: 71.43% in sediment vs. 58.82% in water; River: 88.89% in sediment vs. 74.29% in water), indicating a higher detection sensitivity of sediment eDNA for bottom-dwelling fish. Venn diagram analysis further illustrated that combined water-sediment sampling in the lake habitat detected one additional benthic species, Monopterus albus, which was not found in water samples alone (Fig. 2b), confirming that sediment eDNA effectively complements detection gaps associated with aquatic eDNA.

Analysis of alpha diversity, based on the Shannon diversity index, showed significant differences (P < 0.01) among the fish communities detected in different environments. The order of diversity was: River Water > Lake Water > River Sediment > Lake Sediment (Fig. 2d). Furthermore, NMDS analysis revealed that fish community structures differed significantly between river and lake habitats, regardless of whether water or sediment eDNA was used (Fig. 2e), underscoring habitat type as a key factor shaping fish assemblage patterns in the Honghu Basin.

Spatial Distribution Characteristics of Fish in the Lake Area

Analysis based on eDNA from both water and sediment revealed distinct spatial heterogeneity in fish distribution within Lake Honghu (Fig. 3a-b). Aquatic eDNA data indicated an uneven spatial distribution of fish Amplicon Sequence Variant (ASV) abundance, with significantly higher values observed in the northeastern bay areas of the lake (e.g., sites LW03 and LW02). Furthermore, regions with high fish species richness were primarily concentrated in lake zones connected to upstream river estuaries.

In contrast, the distribution patterns of fish ASV abundance and species richness revealed by sedimentary eDNA exhibited notable differences compared to the results from aquatic eDNA. This disparity in distribution patterns between the two media suggests that eDNA analyses from different environmental matrices (water vs. sediment) likely reflect distinct spatial dimensions of fish distribution and ecological preferences within the lake area.

Fish Community Networks and Inter-Habitat Differences

To gain deeper insights into the organizational patterns of fish communities in the Honghu Basin, co-occurrence networks were constructed separately for river and lake habitats based on eDNA annotations derived from both water and sediment samples (Fig. 4a-b). Topological analysis of these networks revealed significant differences in complexity between the river and lake fish communities.

In terms of overall structure, the river fish community network was more complex, possessing a greater number of edges (66) and nodes (161) compared to the lake network (Edges: 42; Nodes: 109). Regarding metrics associated with network stability, the river network exhibited higher values for both average path length (0.99 vs. 0.94) and modularity (0.84 vs. 0.77) than the lake network. This indicates that the river community possesses more distinct modular subdivisions and greater structural stability. Conversely, for metrics reflecting connection tightness, the lake fish community network demonstrated higher network density (0.13 vs. 0.08) and higher average degree (5.19 vs. 4.88) than the river network, suggesting that species within the lake community are more tightly interconnected. Synthesizing these indicators, the river fish community can be characterized as a complex, highly modular, and stable “modular-loose network”, whereas the lake fish community constitutes a simpler, tightly connected, and highly integrated "tight-simple network".

To further identify the key biological taxa driving the differences between these two habitat communities, we performed Linear Discriminant Analysis (LDA) Effect Size (LEfSe) analysis (Fig. 4c). At the family level, the key discriminant taxa for the river fish community were Gobionidae, Acheilognathidae, and Cyprinidae, while Xenocyprididae was the key family for the lake community. At the genus level, Pseudorasbora and Carassius were identified as the key genera in the river, whereas Hypophthalmichthys was the key genus in the lake. Ultimately, at the species level, the analysis confirmed Pseudorasbora parva and Carassius auratus as the key species in the river fish community, and Hypophthalmichthys nobilis as the key species in the lake community. These results further confirm, from a taxonomic perspective, the significant divergence in fish community structure between the river and lake habitats.

Influence of Environmental Factors

Redundancy analysis (RDA) was employed to elucidate the relationships between fish communities and environmental variables across different habitats and media. The RDA for the water environment (Fig. 5a) showed that the first two axes collectively explained 96.8% of the cumulative variance, with RDA1 and RDA2 accounting for 81.1% and 15.7%, respectively. Fish communities detected in the river water environment were significantly correlated with total nitrogen (TN, p=0.015), nitrate nitrogen (NO₃-N, p=0.018), and total phosphorus (TP, p=0.027). In contrast, fish communities in the lake water environment were primarily associated with turbidity (Turb, p=0.002), pH (p=0.002), chlorophyll-a (Chl.a, p=0.003), and chemical oxygen demand (COD, p=0.011).

For the sediment environment (Fig. 5b), the first two RDA axes explained a cumulative variance of 78.1%, with RDA1 contributing 63.2% and RDA2 contributing 14.9%. Fish communities annotated from river sediments showed a significant correlation with sediment phosphate-phosphorus (PO₄-P, p=0.010). No significant correlations were found between lake sediment communities and any of the measured sediment factors.

Furthermore, the relationships between key species identified by LDA analysis (Fig.4c) and environmental factors were examined. The lake key species, Hypophthalmichthys nobilis, exhibited significant positive correlations with Turb, Chl.a, COD, and pH in the water environment, but significant negative correlations with TN, TP, and NO₃-N. In sediments, it showed positive correlations with Cd and Pb, and a negative correlation with TP. Conversely, the river key species, Carassius auratus, was positively correlated with TN and NO₃-N in water, and negatively correlated with Turb, Chl.a, and pH. In sediments, it was positively correlated with TP, and negatively correlated with Eh, Cd, and Pb.

These findings collectively demonstrate that fish communities in rivers and lakes are influenced by distinct environmental drivers. River communities are primarily associated with nutrient concentrations (TN, NO₃-N, TP), whereas lake communities are more closely linked to factors related to eutrophication and water clarity (Turb, Chl.a, COD, pH).

Reliability of eDNA-Based Methods for Assessing Fish Diversity

This study detected a total of 18 fish species in Lake Honghu using environmental DNA (eDNA) metabarcoding. This result shows a certain discrepancy with the 26–35 species recorded by Li et al. (2023) in 2021 using traditional survey methods, indicating a slightly lower species detection rate for eDNA in this specific study area. This disparity reflects the general uncertainty observed in comparisons between eDNA and traditional methods: some studies demonstrate the significant advantages of eDNA, such as Jiang et al.(2023)reporting higher species richness in the Pearl River Estuary compared to bottom trawling, while others, like Qian et al(2023) in the Yangtze River and He et al.(2024)in floodplain lakes, found lower eDNA detection rates, consistent with the trend observed here.

Several factors may contribute to this uncertainty: 1) Environmental characteristics: The high-water period in Lake Honghu from May to August likely dilutes eDNA concentrations, reducing detection probability (Curtis et al., 2020). 2) Sampling protocol: The absence of a standardized eDNA sampling protocol (Wang et al., 2021) leads to vast differences in sampled water volumes across studies, ranging from 400 mL (Gibson et al., 2024) to 40 L (Couton et al., 2023). Larger volumes are more likely to capture eDNA molecules, and the 1.5 L collected per site in this study may be insufficient to fully represent the lake's fish diversity. 3) Technical limitations: Potential errors can arise from database completeness(Karabanov et al., 2022; Liu et al., 2025a; Mu et al., 2023; Zhu et al., 2023b) and primer bias (Hao et al., 2024; Jiang et al., 2023b; Klindworth et al., 2013; Sanchez et al., 2022). Although taxonomic assignments in this study were verified against FishBase, the misidentification of marine species remains possible. Notably, eDNA technology exhibits high sensitivity for detecting cryptic and invasive species (Takahashi et al., 2023), as evidenced by the successful identification of Coptodon zillii and Gambusia affinisin this study. From a historical perspective, the number of fish species in Lake Honghu has drastically declined from 87 species in the 1950s to approximately 30 species in 1993 (Song, 1999), and 26–35 species in 2021(Li, 2023). The detection of 18 species via eDNA in this study aligns with this long-term declining trend, supporting the historical consistency of eDNA monitoring.

Regarding medium specificity, this study found that aquatic eDNA detected a significantly higher number of species than sedimentary eDNA, consistent with findings by Zhu et al.(2023) in Tokyo Bay. Despite differences in total species richness, the community structure characteristics revealed by the two media were highly similar: β-diversity analysis showed no significant difference between fish communities detected in water and sediment (Fig. 2e), and benthic/demersal fish dominated in both media (Fig. 2c). However, sedimentary eDNA demonstrated unique value: firstly, it detected benthic species not found in water samples (e.g., Monopterus albus); secondly, the proportion of dominant species was significantly higher in sediment (river sediment: 44.44% vs. water: 11.43%; lake sediment: 28.57% vs. water: 5.88%), and the proportion of benthic/demersal fish was also higher (Fig. 2c), aligning with conclusions by Sakata et al. (2020) and Shao et al. (2024)regarding the advantage of sediment for detecting benthic fish. This medium-specific difference stems from eDNA transport characteristics: aquatic eDNA disperses widely, more readily reflecting the regional species pool (Corinaldesi et al., 2011; Dunker et al., 2016; Sakata et al., 2020), whereas sedimentary eDNA, while more stably recording local dominant and benthic species, may provide incomplete information due to degradation during transport.

Based on these findings, this study proposes optimized eDNA monitoring strategies: if the goal is to rapidly obtain a general overview of fish diversity, sampling only the water environment offers the best balance of efficiency and cost. However, for comprehensive and precise monitoring, particularly targeting benthic fish, rare species, or specific functional groups, a combined water and sediment sampling strategy is essential. This approach leverages the complementary nature of the two media to minimize species omission and enhance the completeness and accuracy of assessments.

Fish Distribution Patterns and Underlying Mechanisms in the Honghu Basin

This study revealed a clear river-lake differentiation in fish community composition within the Honghu Basin using eDNA technology. Specifically, the river environment exhibited higher fish species richness (α-diversity) and a more complex community network structure. This network showed "modular and low-connectivity" characteristics (with higher average path length and modularity), indicating it comprises multiple relatively independent and structurally stable sub-modules. In contrast, the lake community had lower diversity and a simpler network structure, characterized by "integrated and high-connectivity" features (higher network density and average degree), reflecting a community dominated by a few widespread species with more homogeneous interspecific associations. This "higher in rivers, lower in lakes" diversity pattern aligns with observations by He et al. (2022) in the Yangtze River Basin, but its underlying drivers require in-depth exploration from the perspectives of habitat heterogeneity and environmental pressures.

Firstly, habitat filtering is the fundamental cause of these differences. The hydrological dynamics and higher habitat heterogeneity (e.g., diverse flow velocities, substrate types) in the river corridor provide abundant niches for fish with different ecological requirements, thereby supporting higher species coexistence. In contrast, the relatively homogeneous environment of the lake water body tends to form a community structure dominated by a few superior species.

More critically, the significant differences observed in this study are likely amplified by the unique ecological context of Honghu Lake as a "river-lake fragmented" water body. Unlike naturally connected lakes such as Poyang Lake and Dongting Lake, which maintain and aggregate high diversity by acting as "species sinks" for the Yangtze River(Ru, 2008), Honghu Lake's direct hydrological connectivity to the Yangtze River has been severed since the construction of sluice gates in the 1950s. This geographical isolation has fundamentally altered the lake's ecological functions, not only cutting off the migration routes of potamodromous fish but also significantly reducing species recruitment from the main Yangtze River channel. Consequently, the lake community has become increasingly isolated and more susceptible to local environmental pressures.

Against this backdrop of isolation, strong environmental filtering, particularly eutrophication, has become the dominant force simplifying the lake community. Results from RDA and LDA analyses in this study provide evidence: the abundance of the key lake species, Hypophthalmichthys nobilis, was negatively correlated with eutrophication indicators such as Total Nitrogen (TN) and Total Phosphorus (TP), while turbidity (Turb) and chlorophyll-a (Chl.a) were key environmental factors influencing the lake fish community. This suggests that long-term nutrient enrichment has significantly homogenized the lake habitat by triggering algal blooms, deteriorating underwater light conditions (increased turbidity), and degrading submerged macrophytes, depriving many fish species (particularly those that spawn on vegetation) of breeding and shelter habitats, ultimately leading to a decline in species numbers. Notably, as a filter-feeding fish, Hypophthalmichthys nobili can benefit from increased phytoplankton biomass, explaining its dominance in eutrophic lakes and its positive correlation with turbidity—a finding consistent with Feng et al. (2023). Furthermore, the positive correlation between heavy metals (e.g., Cd, Pb) in sediments and key species (Hypophthalmichthys nobili, Carassius auratus) reveals the potential threat of environmental pollution to aquatic communities through food chain bioaccumulation.

Finally, the spatial heterogeneity of fish distribution provides important insights for habitat management. This study found that within the lake, fish ASV abundance was significantly higher in the northeastern bay areas and river inflow zones (e.g., sites LW03, LW02) than in other regions. These areas typically have better water exchange conditions, more suitable water quality (e.g., higher dissolved oxygen, DO), and serve as key nodes connecting remnant waterways, likely providing local high-quality habitats for fish refuge, feeding, and reproduction. This pattern resembles the "higher fish diversity in inflow river mouth areas" observed in other Yangtze River basin lakes (Ru et al., 2008), even though Honghu Lake is overall isolated. Therefore, in future eDNA monitoring optimization and habitat conservation practices, prioritizing and increasing sampling efforts in these ecological hotspots will more effectively assess current fish diversity status and provide precise spatial guidance for implementing ecological restoration measures focused on enhancing habitat connectivity and heterogeneity (e.g., aquatic vegetation restoration, ecological water level regulation).

Ecological Implications and Research Limitations

Ecological Significance and Management Implications

This study elucidates the fish community structure and environmental drivers in the Honghu river-lake system, providing a scientific basis for developing differentiated ecosystem management strategies. At the community scale, the findings support targeted conservation measures. For riverine habitats, the significant correlation between fish communities and NO₃-N/TN concentrations indicates that controlling external nitrogen input is critical for mitigating eutrophication pressure and maintaining suitable fish habitats. For lacustrine habitats, pH and turbidity are key factors influencing fish distribution. Thus, restoring submerged macrophytes to regulate the underwater light environment and pH could create more suitable conditions for dominant taxa like Cypriniformes and benthic species detected via sedimentary eDNA (e.g., Rhinogobius giurinus). Additionally, the higher fish ASV abundance in northeastern lake bays and estuary areas identifies these as critical habitats. Prioritizing habitat heterogeneity restoration (e.g., artificial reefs, underwater topography modification) in these ecological hotspots can support spawning, feeding, and refuge needs for diverse fish species, enhancing local and overall community diversity. Meanwhile, maintaining and restoring residual river-lake hydrological connectivity is essential for completing fish life cycles, promoting species exchange, mitigating community divergence, and strengthening ecosystem stability and resilience.

At the ecosystem scale, these management strategies could synergistically enhance multiple ecological functions. Integrating water quality improvement with wetland restoration would strengthen key services of the Honghu floodplain ecosystem, including hydrological regulation(e.g., flood peak attenuation, groundwater recharge), water purification(via nutrient and pollutant removal by plants and sediment adsorption), and habitat provision. This approach not only directly promotes fish resource recovery but may also drive synergistic succession of planktonic and benthic communities through bottom-up effects, ultimately fostering a virtuous cycle of "water quality improvement – biodiversity restoration – habitat optimization" (Bouloy et al., 2024; Mihaljević et al., 2024). This integrated model offers a valuable demonstration for the conservation and restoration of freshwater ecosystems in the mid-Yangtze River and global subtropical floodplain systems.

The structural differences between river and lake fish communities further clarify distinct conservation priorities. The dynamic, heterogeneous river environment fosters a complex, disturbance-resistant "modular" community stabilized by keystone species (e.g., Pseudorasbora parva, Carassius auratus). Thus, river conservation should focus on maintaining natural hydrological regimes and habitat complexity, avoiding channelization, hardening, and disruptive hydraulic engineering. In contrast, the static, homogeneous lake environment forms a simple, vulnerable, integrated community dominated by filter-feeding species like Hypophthalmichthys nobilis. Lake management must prioritize safeguarding overall ecosystem health, strictly controlling nutrient inputs, and preventing local issues (e.g., algal blooms) from escalating into systemic ecological crises.

Research Limitations and Future Perspectives

Despite providing critical insights, the conservation strategies derived from this study have limitations requiring future attention. First, the temporal scale of data limits strategy generalizability. Relying on eDNA data from a single sampling event (June 2022) fails to capture seasonal dynamics across high-water (May–August) and dry periods. Hydrological fluctuations significantly influence fish distribution and eDNA transport/degradation (Eros et al., 2024; Wang et al., 2019), so point-in-time data may yield management parameters (e.g., water quality thresholds) lacking dynamic adaptability. Future monitoring should incorporate multi-season sampling.

Second, medium-specific eDNA detection efficiency may lead to incomplete conservation coverage. Aquatic eDNA detected more species (e.g., 35 in rivers) than sedimentary eDNA (e.g., 9 in rivers), yet sediment detected exclusive benthic taxa. This efficiency gap, combined with inherent technical uncertainties (e.g., database false positives, undetected low-biomass species), risks underestimating rare or niche-specific taxa (e.g., benthic fish). Strategies based on species inventories must account for these limitations.

Third, the synergy and feasibility of implementation require further validation. While the proposed strategies span pollution control to habitat restoration, their implementation priorities (e.g., sequencing source control versus in-situ restoration) and synergistic mechanisms (e.g., coupling macrophyte restoration with fish habitat protection) remain unclarified. Moreover, strategies must better integrate complex anthropogenic constraints (e.g., urban discharges from Jingzhou, agricultural non-point source pollution). Large-scale projects (e.g., dyke removal, channel restoration) face socio-economic challenges like land-use conflicts and funding sustainability. Future studies should incorporate cost-benefit analysis and establish a multi-stakeholder governance framework involving government, communities, and NGOs to enhance strategy operability and effectiveness(Liu et al., 2025b; Liu et al., 2025c).

This study clarifies the monitoring efficacy of eDNA technology and its environmental drivers by comparing water and sedimentary eDNA in the Honghu river-lake system. Results confirm that aquatic eDNA yields higher total species detection, while sedimentary eDNA offers unique advantages for characterizing benthic fish and dominant species, underscoring the necessity of combined water-sediment sampling for comprehensive monitoring. River habitats exhibited higher fish diversity and more complex community networks than lakes, a divergence driven by distinct environmental factors: river communities correlated strongly with nitrogen and phosphorus nutrients, whereas lake communities were influenced by turbidity(turb), chlorophyll-a(chl.a), and other eutrophication-related indicators, revealing an ecological filtering effect compounded by river-lake fragmentation and eutrophication.

Additionally, estuary and lake bay areas were identified as biodiversity hotspots. These findings provide a scientific basis for implementing "priority protection of key habitats and differentiated management of environmental pressures" in the Honghu Basin. Furthermore, this study offers a theoretical foundation and practical framework for standardizing eDNA-based monitoring and ecological management in comparable global freshwater systems.

Acknowledgements Thanks to the guidance of the teacher, the help of fellow students, and the financial support provided by the National Key Research and Development Program (2023YFC3304301).

Author contribution Ge Li: Data curation, Methodology, Visu alization, Writing – original draft. Borui Zou: Writing – original draft, Methodology. Cong Wang: Writing – original draft. Lu Zhang: Writing – original draft. Xi Liu: Data curation, Investigation, Funding acquisition, Writing – review & editing. Zhi Wang: Writing – review & editing, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

Funding This research was funded by National Key research and development program of China (2023YFC3304301)

Data availability Data are available from the corresponding author upon request via email.

Declarations All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.

Competing interests The authors declare no competing interests.

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Multi-media eDNA Metabarcoding Uncovers Habitat-Specific Fish Community and Monitoring Efficiency: Implications for Ecosystem Management in a River-Lake System (Honghu Lake Basin, China)

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Abstract

Figures

Introduction

Materials and Methods

Study Area and Sampling Sites

Sample Collection and Physicochemical Analysis

eDNA Sample Processing and Analysis

Statistical Analysis

Results

Fish Species Composition and Diversity Across Different Media

Spatial Distribution Characteristics of Fish in the Lake Area

Fish Community Networks and Inter-Habitat Differences

Influence of Environmental Factors

Discussion

Reliability of eDNA-Based Methods for Assessing Fish Diversity

Fish Distribution Patterns and Underlying Mechanisms in the Honghu Basin

Ecological Implications and Research Limitations

Conclusion

Declarations

References

Additional Declarations

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