Fish diversity and environmental relationships in the Jinsha River during the initial phases of the 10-year fishing ban: A metabarcoding approach

Fish diversity and environmental relationships in the Jinsha River during the initial phases of the 10-year fishing ban: A metabarcoding approach | 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 Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 20 June 2025 V1 Latest version Share on Fish diversity and environmental relationships in the Jinsha River during the initial phases of the 10-year fishing ban: A metabarcoding approach Authors : yan zhao 0009-0000-0321-458X , zhongyuan wang , Feifei HU , Zhibin Guo , Jinling Gong , Xuemei Li , Deguo Yang , and Tingbing Zhu [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175041921.19377427/v1 Published Ecology and Evolution Version of record Peer review timeline 320 views 168 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fish diversity is essential for maintaining the balance of aquatic ecosystems, particularly in rivers impacted by overfishing and hydropower projects, such as the Jinsha River, the upstream segment of the Yangtze River. During initial phases (August and November, 2023) of the 10-year fishing ban in the Yangtze River basin, we investigated fish diversity, seasonal variations, and their correlation with environmental factors in the Jinsha River using environmental DNA (eDNA) metabarcoding. Utilizing two pairs of 12S rRNA primers, MiFish-U and AcMDB07, we identified 61 fish species across 5 orders, 17 families, and 52 genera, including 4 national protected and 7 invasive alien fish. Among them, Cypriniformes constituted the predominant group within the fish community, accounting for 65.6%. This finding aligns with the results from a recent fish catch study, which recorded 68 species of fish belonging to 4 orders, 15 families and 48 genera, including 4 national protected species and 8 invasive alien fish. The alpha diversity analysis revealed compositional differences in the fish community across various regions and seasons. Furthermore, key environmental factors, such as water temperature, dissolved oxygen, nitrate nitrogen, total suspended solids and conductivity, were found to be highly correlated with the fish diversity in the Jinsha River. Consequently, we provided detailed seasonal data on fish diversity and its correlations with environmental factors, which will aid in the systematic management and restoration of fishery resources and the assessment of the 10-year fishing ban in the Jinsha River. Fish diversity and environmental relationships in the Jinsha River during the initial phases of the 10-year fishing ban: A metabarcoding approach Yan Zhao 1,2# , Zhongyuan Wang 1# , Feifei HU 1 , Zhibin Guo 1 , Jinling Gong 1 , Xuemei Li 1 , Deguo Yang 1 , Tingbing Zhu 1* Key Laboratory of Freshwater Biodiversity Conservation, Ministry of Agriculture and Rural Affairs of China, Yangtze River Fisheries Research Institute, Chinese Academy of Fisheries Science, Wuhan 430223, China Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi 214081, China. # These authors contributed equally to this work. *Corresponding author: [email protected] . Abstract Fish diversity is essential for maintaining the balance of aquatic ecosystems, particularly in rivers impacted by overfishing and hydropower projects, such as the Jinsha River, the upstream segment of the Yangtze River. During initial phases (August and November, 2023) of the 10-year fishing ban in the Yangtze River basin, we investigated fish diversity, seasonal variations, and their correlation with environmental factors in the Jinsha River using environmental DNA (eDNA) metabarcoding. Utilizing two pairs of 12S rRNA primers, MiFish-U and AcMDB07, we identified 61 fish species across 5 orders, 17 families, and 52 genera, including 4 national protected and 7 invasive alien fish. Among them, Cypriniformes constituted the predominant group within the fish community, accounting for 65.6%. This finding aligns with the results from a recent fish catch study, which recorded 68 species of fish belonging to 4 orders, 15 families and 48 genera, including 4 national protected species and 8 invasive alien fish. The alpha diversity analysis revealed compositional differences in the fish community across various regions and seasons. Furthermore, key environmental factors, such as water temperature, dissolved oxygen, nitrate nitrogen, total suspended solids and conductivity, were found to be highly correlated with the fish diversity in the Jinsha River. Consequently, we provided detailed seasonal data on fish diversity and its correlations with environmental factors, which will aid in the systematic management and restoration of fishery resources and the assessment of the 10-year fishing ban in the Jinsha River. Keywords Fish diversity, eDNA, Jinsha River, 10-year fishing ban, environmental factors 1 Introduction Fish communities, as apex consumers in freshwater ecosystems, serve as vital ecological indicators that reflect the overall balance and stability of these environments. Consequently, fish diversity plays a crucial role in maintaining the health and integrity of aquatic ecosystems (Magurran, Khachonpisitsak, & Ahmad, 2011). However, in recent years, a range of anthropogenic factors—such as overfishing, captive breeding, water pollution, the invasion of non-native species, and the construction of hydropower stations and dams—have led to the degradation and loss of fish habitats, resulting in a sharp decline in fish diversity across the Yangtze River basin (Qian et al., 2023). This decline is particularly notable in the dramatic reduction and depletion of rare and endemic fish populations (Esguícero & Arcifa, 2010; Gao TH, 2013; Zhang P, 2018). As a result, the erosion of fish diversity in the Yangtze River basin threatens the ecological integrity and long-term sustainability of freshwater ecosystems. Moreover, the decline of native fish populations has disrupted the food web, adversely affected other aquatic species and undermined the overall health of these ecosystems. The Jinsha River basin, situated in the upper reaches of the Yangtze River, is ecologically rich in biodiversity and abundant in fish resources. It also possesses significant potential as a hydropower source. As China’s largest hydropower base, the Jinsha River basin serves as a critical source of runoff and nutrients for the Yangtze River. However, with nearly 40 cascade dams either constructed or planned, the basin’s fish habitats—both native and non-native—have been significantly altered or even severely damaged (Sun, He, Sui, & Chen, 2020). Additionally, the construction of these cascade hydropower stations has led to a continuous decline in the concentrations and fluxes of total phosphorus (TP), dissolved total carbon (DTC), and total silicon in the reservoirs, potentially destabilizing river ecosystems and disrupting the food chain (Zhao et al., 2024). In this context, fish community structures in the upper reaches of the Jinsha River have experienced moderate to severe disturbances, characterized by an increase in exotic species and a trend toward smaller sizes and younger age classes (Yan et al., 2022). However, the removal of a low-head dam and the installation of a fish passage in a tributary of the Jinsha River have significantly increased upstream fish abundance and species richness, helping to reduce disparities between upstream and downstream fish communities (Tan et al., 2024). In 2020, the Yangtze River Basin implemented a 10-year fishing ban to protect the biodiversity and natural habitats of rare and endemic fish species (WX, 2022). As a result, continuous, long-term surveys are now essential for assessing the variation, recovery and diversity of fishery resources in this region. Traditional fishing survey methods (e.g., multi-layer gillnets and ground cages) suffer from several limitations, including gear selectivity, physical damage to specimens, and high labor intensity. Additionally, certain species may be underestimated or entirely missed. While environmental DNA (eDNA) metabarcoding has emerged as a powerful method that integrates traditional ecological techniques with next-generation sequencing, enabling the detection of species from a single water sample, including those that are otherwise difficult to identify through conventional methods such as microscopy (Deiner et al., 2017; Hanfling et al., 2016). Fish eDNA metabarcoding involves the analysis of DNA fragments released into the water by fish throughout their life cycle, which may originate from cell shedding, excrement, germ cells, or decomposed tissues following death (Jerde, Wilson, & Dressler, 2019). This technique can detect trace amounts of DNA, allowing for the identification of invasive and elusive species (Thomsen et al., 2012). For example, eDNA has been successfully employed to track the invasive aquatic plant Elodea canadensis in Norway, with changes in DNA concentration closely aligning with the plant’s growth cycle (Angles d’Auriac, Strand, Mjelde, Demars, & Thaulow, 2019). Additionally, Balasingham et al . detected three at-risk species—Eastern Sand Darter ( Ammocrypta pellucida ), Northern Madtom ( Noturus stigmosus ), and Silver Shiner ( Notropis photogenis )—alongside the invasive Round Goby ( Neogobius melanostomus ) in two tributaries of the Great Lakes in Ontario, Canada (Balasingham, Walter, Mandrak, & Heath, 2018). As a rapidly emerging tool, eDNA is increasingly utilized to monitor multiple species across diverse ecosystems, including freshwater environments, over varying temporal scales. The structure and diversity of fish communities are intricately linked to the ecological characteristics of their habitats, exhibiting distinct response patterns to environmental variables (Heino, Soininen, Alahuhta, Lappalainen, & Virtanen, 2015). Key environmental factors influencing species diversity and abundance include altitude, water temperature, stream width and depth, pH and turbidity (Yan et al., 2022). Furthermore, various other factors—such as chlorophyll-a, ammonium nitrogen (NH3-N), dissolved oxygen (DO), total nitrogen (TN), chemical oxygen demand (COD), and altitude—collectively impact the α-diversity of fish populations in the Yangtze River (Qian et al., 2023). Additionally, Cheng et al . demonstrated that, alongside temperature and DO, reservoir age significantly influences fish community diversity in the Wujiang River, a tributary of the Yangtze River (Cheng et al., 2024). Similarly, Shen et al . found that fish communities in the tributaries of the upper Yangtze River were more strongly influenced by key environmental factors—such as water temperature, DO, electrical conductivity and ammonia nitrogen—than those in the mainstem (Shen et al., 2024). The heterogeneity of these environmental factors across spatiotemporal scales plays a crucial role in shaping the dynamics of fish composition and community structure, thereby influencing biodiversity patterns and species interactions within aquatic ecosystems. This variability leads to fluctuations in species abundance, distribution and community composition, which are essential for understanding ecological processes and managing aquatic habitats. Current research on fish diversity in the Jinsha River is impeded by fragmented data and inadequate species identification, particularly concerning rare and endangered species. In the summer (August 2023) and autumn (November 2023), eDNA metabarcoding technology was utilized to assess fish diversity at four sites along the river, while also investigating the relationships between fish communities and environmental factors. The findings may offer valuable insights for future ecological research and conservation initiatives in the Jinsha River. 2 Materials and Methods 2.1 Study area and eDNA metabarcoding sampling Four sampling sites along the main stream of the Jinsha River were systematically selected for this study: Shigu (SG), Panzhihua (PZH), Qiaojia (QJ) and Suijiang (SJ) (Fig. 1). Water samples were collected during two sampling periods: summer (August 2023) and autumn (November 2023). At each site, a total of 6 L of water was collected, with 2 L per sample obtained during three separate collection events. The water was gathered using a plexiglass water collector. Prior to sampling, all equipment—including the water collector and storage containers—was disinfected by soaking in 10% sodium hypochlorite for 30 minutes, followed by thorough rinsing three times with on-site environmental water. During the collection process, disposable, sterilized latex gloves were worn and replaced immediately between samples to prevent contamination. The collected water samples were kept cool until filtration, which was carried out using a vacuum suction pump and a 0.45 μm pore size mixed cellulose membrane (Whatman, UK). After filtration, the filter membranes were transferred into sterile tubes, rapidly frozen in liquid nitrogen, and stored at -80 ℃ until DNA extraction. The study was conducted with the approval of the relevant fishery authorities and adhered to the 10-year fishing ban implemented in the Yangtze River basin. Fig.1 eDNA sampling site map of Jinsha River mainstream 2.2 DNA extraction and PCR amplification DNA was extracted from the filtration membranes using the Qiagen DNeasy Blood&Tissue Kit (Germany) according the manufacturer’s instructions. The concentration and quality of the extracted DNA were assessed using a NanoDrop 2000 spectrophotometer and 1% agarose gel electrophoresis, respectively. A blank filter membrane moistened with distilled water was used as a negative control to minimize potential contamination and experimental errors. Two primer pairs, MiFish-U and AcMDB07, targeting the 12S rRNA region(Bylemans, Gleeson, Hardy, & Furlan, 2018; Miya et al., 2015), were selected for amplification (Table 1). The total PCR reaction volume is 50 μL, consisting of 25 μL Taq DNA polymerase, 2 μL of each forward and reverse primer (10 μM), 5 μL DNA template, and 16 μL ddH 2 O. The PCR cycling conditions were as follows: initial denaturation at 95℃ for 5 min; 37 cycles of denaturation at 95℃ for 30 s, annealing at 54℃ for 45 s, and extension at 72℃ for 40 s; followed by a final extension at 72℃ for 7 min and storage at 4℃. After amplification, the PCR products were analyzed using 2% agarose gel electrophoresis. Mixed PCR products were then purified through gel extraction using the OMEGA Gel Extraction Kit (USA). Target DNA fragments were eluted with TE buffer. The purified products were subsequently sent to Guangdong Meige Gene Technology Co., Ltd. for high-throughout sequencing. Table 1 eDNA primer MiFish-U F: GTCGGTAAAACTCGTGCCAGC R: CATAGTGGGGTATCTAATCCCAGTTTG 180 (Miya et al., 2015) AcMDB07 F: GCCTATACCGCCGTCG R: GTACACTTACCATGTTACGACTT 300 (Bylemans et al., 2018) 2.3 Data processing and statistical analyses The raw sequencing data obtained from high-throughput sequencing were subjected to initial quality control using Fastp (v0.12.4), which included adapter trimming and removal of low-quality reads(Chen, Zhou, Chen, & Gu, 2018). The quality of the reads before and after filtering was assessed using FastQC (v0.11.9). The filtered paired-end reads were subsequently merged using Usearch (v11.0.667, http://drive5.com/uparse), with primer sequences removed based on alignment to both ends of the reads(Edgar, 2010). Merged reads shorter than 150 bp were discarded. All merged reads from individual samples were pooled, and VSEARCH (v2.15.2) was used to dereplicate the sequences and remove those with an abundance of fewer than four reads. Operational taxonomic units (OTUs) were clustered at 98% sequence similarity. Chimeric sequences were identified and removed using the UCHIME3 algorithm, resulting in a final OTU sequence set. The merged sample reads were mapped to the OTU sequence set to generate the final OTU table. Representative OTU sequences were then aligned against reference sequences for the 12S rRNA gene metabarcoding region, including databases such as MitoFish (http://mitofish.aori.u-tokyo.ac.jp/), NCBI (https://www.ncbi.nlm.nih.gov/) and BOLD (https://v4.boldsystems.org), using BLAST (v2.2.31)(Sato, Miya, Fukunaga, Sado, & Iwasaki, 2018). Sequences with alignment lengths shorter than 90% were discarded. Taxonomic assignments were made based on a similarity threshold of ≥98% (McClenaghan et al., 2020). Additionally, OTUs assigned to the same taxonomic unit were merged, and non-fish sequences were excluded from the dataset. 2.4 Environmental factors and diversity indices In this study, physical environmental factors, including water temperature (WT), pH, turbidity (NTU), conductivity (EC), and dissolved oxygen (DO), were measured in situ using a HACH HQ40D multiparameter water quality analyzer (USA) in accordance with former study(Meng et al., 2023). Additionally, nutrient factors such as total nitrogen (TN), ammonium nitrogen (NH 4 + -N), nitrate nitrogen (NO 3 - -N), nitrite nitrogen (NO 2 - -N), total phosphate (TP), phosphate (PO 4 3- -P) and total suspended solids (TSS) were conducted with a portable multi-parameter spectrophotometer (Hach DR1990, Hach, USA) according to previous studies (Meng et al., 2022; Meng, Hu, Xiang, Fu, & Li, 2025). The Chao1, Shannon, Pielou, Maragalef and Simpson diversity indices were used to assess the alpha diversity of the fish community structure. Principal Coordinate Analysis (PCoA) was performed using the vegan package (version 2.4.3) in R (version 3.3.1) to visualize community composition patterns. To further explore differences among groups, Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted. The coefficient of determination (R 2 ) and statistical significance ( P <0.05) were used to interpret seasonal variation in fish community structure. Additionally, Spearman correlation analysis was applied to investigate the relationships between fish diversity indices and environmental factors in the mainstream of the Jinsha River. 3. Results 3.1 Fish species composition by eDNA metabarcoding A total of 878 OTUs were obtained, and taxonomic annotation was performed on the raw data. After excluding non-fish species and marine fish, 61 freshwater fish species were identified in the mainstem of the Jinsha River using eDNA metabarcoding. These species belonged to 52 genera, 17 families and 5 orders. The order Cypriniformes dominated the fish community, comprising 40 species from 35 genera and 4 families, accounting for 65.6% of the total species. This group included 34 species from the family Cyprinidae, 3 from Cobitidae, 2 from Balitoridae, and 1 from Catostomidae. The second most abundant group was Siluriformes, which included 10 species from 9 genera and 6 families, representing 16.4% of the total species. Among these were 3 species from Sisoridae and Bagridae, as well as 1 species each from Loricariidae, Ictaluridae, Amblycipitidae, and Siluridae. Perciformes accounted for 14.8%, comprising 9 species from 6 genera and 5 families, including 3 species from Gobiidae, 3 from Cichlidae, and 1 species each from Percidae, Centrarchidae, and Channidae. Both Salmoniformes and Synbranchiformes were represented by 1 species each, together contributing 3.2% of the total (Table 2). Among the identified species, four are listed as nationally protected: Myxocyprinus asiaticus , Liobagrus kingi , Procypris rabaudi , and Euchiloglanis kishinouyei . Seasonally, 35 species were detected in summer, distributed across 4 orders, 11 families, and 28 genera. In contrast, 52 species were identified in autumn, covering 5 orders, 16 families, and 46 genera. A total of 25 species were shared between the two seasons (Fig. 2). An analysis of species composition across the four sampling sites revealed that all locations consistently detected 52 fish species. These included Acanthorhodeus chankaensis , Carassius auratus , Ctenopharyngodon idella , Culter alburnus , Cyprinus carpio , Pseudorasbora parva , Hemiculter leucisculus , and Hemiculter tchangi , resulting in an overall detection rate of 85.2%. Additionally, seven invasive alien species were identified through eDNA analysis, namely Rhynchocypris lagowskii , Micropterus salmoides , Coptodon zillii , Oreochromis aureus , Oreochromis niloticus , Sander lucioperca , and Pterygoplichthys pardalis . Table 2. List of fish species detected in the Jinsha River by eDNA metabarcoding Cypriniformes Cyprinidae Acanthorhodeus chankaensis √ √ √ √ Ancherythroculter kurematsui √ √ Carassius auratus √ √ √ √ Ctenopharyngodon idella √ √ √ √ Culter alburnus √ √ √ √ Cyprinus carpio √ √ √ √ Pseudorasbora parva √ √ √ √ Hemiculter leucisculus √ √ √ √ Hemiculter tchangi √ √ √ √ Hemiculter bleekeri √ Rhodeus sinensis √ √ √ √ Saurogobio dabryi √ √ √ √ Xenocypris argentea √ √ √ √ Zacco platypus √ √ √ √ Acrossocheilus yunnanensis √ √ √ √ Hypophthalmichthys molitrix √ √ √ √ Aristichys nobilis √ √ √ √ Schizothorax kozlovi √ √ √ √ Abbottina obtusirostris √ √ √ √ Abbottina rivularis √ √ √ √ Anabarilius liui √ √ √ √ Ochetobius elongatus √ √ √ √ Coreius heterodon √ Megalobrama amblycephala √ √ √ √ Procypris rabaudi √ √ Opsariichthys bidens √ Pseudolaubuca engraulis √ √ √ √ Ptychobarbus kaznakovi √ √ √ √ Rhinogobio typus √ √ √ √ Sinibrama taeniatus √ √ √ √ Spinibarbus sinensis √ √ √ √ Schizopygopsis malacanthus √ √ √ √ Rhynchocypris lagowskii √ √ √ √ Myxocyprinus asiaticus √ √ Catostomidae Schistura fasciolata √ √ √ √ Cobitidae Triplophysa orientalis √ √ √ √ Triplophysa stenura √ √ √ √ Misgurnus anguillicaudatus √ √ √ √ Balitoridae Jinshaia sinensis √ √ √ √ Jinshaia abbreviata √ √ √ √ Perciformes Centrarchidae Micropterus salmoides √ Channidae Channa gachua √ √ √ √ Cichlidae Coptodon zillii √ √ √ √ Oreochromis aureus √ √ Oreochromis niloticus √ √ √ √ Gobiidae Rhinogobius cliffordpopei √ √ √ √ Rhinogobius giurinus √ √ √ √ Rhinogobius brunneus √ √ √ √ Percidae Sander lucioperca √ √ √ √ Siluriformes Sisoridae Euchiloglanis kishinouyei √ √ √ Pareuchiloglanis anteanalis √ √ √ √ Glyptothorax sinensis √ Siluridae Silurus asotus √ √ √ √ Bagridae Tachysurus fulvidraco √ √ √ √ Tachysurus vachellii √ √ √ √ Pseudobagrus pratti √ √ √ √ Loricariidae Pterygoplichthys pardalis √ √ √ √ Ictaluridae Ictalurus punctatus √ √ √ √ Amblycipitidae Liobagrus kingi √ √ √ √ Salmoniformes Salangidae Neosalanx taihuensis √ √ √ √ Synbranchiformes Synbranchidae Monopterus albus √ √ √ √ Note: √ indicates detection of the species. Fig.2 Comparison of fish species detected in the Jinsha River during summer and autumn 3.2 Relative sequence abundance analyses Further analysis of relative sequence abundance revealed variations in fish community composition at both the genus and species levels across the four sampling sites (Fig. 3A, 3B). The top 20 most abundant genera were identified at each site (Fig. 3A). Among these, Ctenopharyngodon and Coptodon were consistently detected across all four locations, with Ctenopharyngodon showing the highest relative abundance, reaching up to 65%. At the genus level, the five most dominant genera at each site were as follows: in PZH, Ctenopharyngodon , Coptodon , Hemiculter , Hypophthalmichthys , and Rhinogobius ; In QJ, Schizothorax , Hemiculter , Ctenopharyngodon , Coptodon , and Cyprinus ; In SG, Ctenopharyngodon , Coptodon , Schizothorax , Hemiculter , and Cyprinus ; in SJ, Rhinogobius , Ctenopharyngodon , Hypophthalmichthys , Neosalanx , and Coptodon. At the species level, the top 20 most abundant species were also recoded at each site (Fig. 3B). Notably, Ctenopharyngodon idella and Coptodon zillii consistently ranked among the top five species across all locations. At the PZH site, the five most abundant species were Ctenopharyngodon idella , Coptodon zillii , Hemiculter leucisculus , Aristichthys nobilis , and Hemiculter tchangi . The QJ site exhibited a similar composition, with Ctenopharyngodon idella , Coptodon zillii , Hemiculter leucisculus , Hemiculter tchangi , and Cyprinus carpio . At the SG station, the top five species were Ctenopharyngodon idella , Coptodon zillii , Cyprinus carpio , Hemiculter tchangi , and Triplophysa orientalis . Similarly, at the SJ site, the dominant species were Ctenopharyngodon idella , Rhinogobius cliffordpopei , Neosalanx taihuensis , Rhinogobius giurinus , and Coptodon zillii . Overall, based on relative abundance across all sites, the dominant fish species identified through eDNA monitoring were Ctenopharyngodon idella , Coptodon zillii , Hemiculter leucisculus , Aristichthys nobilis , Hemiculter tchangi , Cyprinus carpio , Rhinogobius cliffordpopei , and Neosalanx taihuensis . A B Fig.3 Relative abundance of fish genus (A) and species (B) (OTU similarity is greater than 98%) in each sampling site of the Jinsha River. 3.3 Fish species coverage rate based on eDNA Previous studies using conventional fishing methods, including gillnets and cast nets, have documented between 60 and 98 fish species in the mainstream of Jinsha River (Shao, 2017; Wang, 2024; H. Yang, Shen, L., He, YF., Tian, HW., Gao, L., Wu, JM., et al, 2023; Z. Yang, Tang, HY., Zhu, D., Gao, SB., Xu, W., Wan, L., Gong, YT., Qiao, Y., 2014) (Table 3). In the present study, conducted from 2022 to 2023, similar gear-based sampling was performed, yielding a total of 68 fish species, which were classified into 4 orders, 15 families, and 48 genera. Among these, the order Cypriniformes was predominant, comprising 51 species and accounting for 75.0% of the total species richness. Additionally, five species were identified as nationally protected, and seven species were classified as non-native (Wang, 2024). A comparative analysis of conventional capture methods and eDNA metabarcoding revealed a total of 91 fish species detected by the two approaches combined, belonging to 5 orders, 19 families, and 62 genera. The eDNA approach detected 61 species, slightly fewer than those recorded by conventional fishing. A total of 38 species were commonly detected by both methods, whereas eDNA metabarcoding uniquely identified 23 species that were not recorded by traditional capture-based surveys (Table 4). Table 3 Historical investigation results of fish species in the Jinsha River 2009-2012 Panzhihua Yalong river estuary to Geliping river section 60 (Z. Yang, Tang, HY., Zhu, D., Gao, SB., Xu, W., Wan, L., Gong, YT., Qiao, Y., 2014) 2013-2017 Panzhihua section 65 (Shao, 2017) 2017-2021 Mainstream of Jinsha river 98 (H. Yang, Shen, L., He, YF., Tian, HW., Gao, L., Wu, JM., et al, 2023) 2022-2023 Mainstream of Jinsha river 68 (Wang, 2024) Table 4 Species composition in the Jinsha River mainstream based on conventional fishing and eDNA metabarcoding 1 S. sinensis * * 47 R. brunneus - * 2 S. taeniatus * * 48 X. argentea - * 3 A. rivularis * * 49 A. yunnanensis - * 4 C. alburnus * * 50 A. liui - * 5 H. leucisculus * * 51 O. elongatus - * 6 H. bleekeri * * 52 C. heterodon - * 7 H. tchangi * * 53 M. amblycephala - * 8 C. idella * * 54 P. engraulis - * 9 C. auratus * * 55 R. typus - * 10 C. carpio * * 56 M. albus - * 11 H. molitrix * * 57 G. sinensis - * 12 S. kozlovi * * 58 P. pratti - * 13 Z. platypus * * 59 P. pardalis - * 14 S. malacanthus * * 60 I. punctatus - * 15 O. bidens * * 61 L. kingi - * 16 P. parva * * 62 Onychostom sima * - 17 R. sinensis * * 63 Ancherythroculter nigrocauda * - 18 S. dabryi * * 64 Cyprinus carpio specularis * - 19 P. kaznakovi * * 65 Schizothorax wangchiachii * - 20 A. nobilis * * 66 Schizothorax prenanti * - 21 P. rabaudi * * 67 Schizothorax malacanthus * - 22 J. sinensis * * 68 Schizothorax chongi * - 23 M. anguillicaudatus * * 69 Schizothorax davidi * - 24 T. stenura * * 70 Schizothorax dolichonema * - 25 M. asiaticus * * 71 Schizothorax longibarbus * - 26 R. giurinus * * 72 Percocypris pingi * - 27 S. lucioperca * * 73 Garra pingi pingi * - 28 C. zillii * * 74 Dsicogobio yunnanensis * - 29 O. niloticus * * 75 Rhodeus lighti * - 30 O. aureus * * 76 Rhodeus ocellatus * - 31 C. gachua * * 77 Pseudogyrinocheilus prochilus * - 32 M. salmoides * * 78 Acheilognathus macropterus * - 33 P. fulvidraco * * 79 Cultrichthys erythropterus * - 34 P. vachelli * * 80 Schistura dabryi dabryi * - 35 P. anteanalis * * 81 Trilophysa bleekeri * - 36 E. kishinouyei * * 82 Trilophysa stoliczkae * - 37 S. asotus * * 83 Trilophysa huidongensis * - 38 R. lagowskii * * 84 Trilophysa anterodorsalis * - 39 A. obtusirostris - * 85 Paramisgurnus dabryanus * - 40 A. kurematsui - * 86 Paramisgurnus potanini * - 41 A. chankaensis - * 87 Neosalanx tangkahkeii * - 42 J. abbreviata - * 88 Micropercops swinhonis * - 43 S. fasciolatus - * 89 Pelteobaggrus nitidus * - 44 T. orientalis - * 90 Hystus macropterus * - 45 N. taihuensis - * 91 Silurus meridionalis * - 46 R. cliffordpopei - * Note: - indicates no detection of the species. * indicates detection of the species. 3.4 Fish diversity analysis The Alpha diversity indices of fish in the Jinsha River were calculated as follows: the Chao1 index ranged from 731.383 to 827.161, the ACE index from 712.245 to 820.722, the Shannon diversity index from 3.028 to 3.663, and the Simpson diversity index from 0.905 to 0.955 (Table 5). Both the Chao1 and ACE indices, which reflect species richness based on OUT abundance, were highest at the SJ site and lowest at the PZH site. Regarding the Shannon diversity index, the highest community diversity was observed at the SG site, while the lowest was recorded at QJ. In contrast, the trend of Simpson’s index was opposite to that of Shannon’s index, with QJ showing the highest community evenness and SG the lowest (Fig. 4). Spatial and temporal variations in all four inices—Chao1, ACE, Shannon and Simpson—were statistically significant ( P <0.05) (Fig. 5) Table 5 Alpha diversity indices of fish across seasons and sampling sites in the Jinsha River PZH 731.383 712.245 3.271 0.930 QJ 815.269 786.801 3.028 0.905 SG 783.316 761.390 3.663 0.955 SJ 827.161 820.722 3.565 0.954 Fig. 4 Box plot of fish alpha diversity indices at each sampling site in the Jinsha River. Fig. 5 Box plot of fish alpha diversity indices across seasons in the Jinsha River. Principal Coordinate Analysis (PCoA) of species-level sequence abundance revealed pronounced seasonal variation in fish community composition, with a clear distiction observed between summer and autumn (Fig. 6). Permutational Multivariate Analysis of Variance (PERMANOVA) further confirmed this pattern, showing a significant seasonal effect on community structure (R² = 0.446, P = 0.001). These results indicate a high degree of heterogeneity and a statistically significant difference in species composition between the two seasons. Fig. 6 PCoA of fish community composition in the Jinsha river 3.5 Relationship between fish community and environmental factors We conducted Spearman correlation analysis between environmental factors and fish community Alpha diversity in the Jinsha River main stem. The Maragalef index was significantly positively correlated with NO 3 - -N (R=0.520, P =0.009) and WT (R=0.469, P =0.001), while negatively correlated with TSS (R=-0.693, P =0.000) and EC (R=-0.629, P =0.001). (Fig. 7). Fig. 7 Spearman correlation analysis of environmental factors and fish alpha diversity in the Jinsha River. The legend on the right represents different R value intervals. *0.01< P <0.05，**0.001< P <0.01，***0.0001< P <0.001 3.5 Relationship between fish community structure and environmental factors DCA results indicated that the length of the primary ordination axis was less than 3, suggesting that Redundancy Analysis (RDA) was appropriate for this dataset. To minimize the influence of rare species, only dominant species and key environmental factors from each season and sampling site were included in the analysis. RDA, based on the eDNA survey results from the mainstem of the Jinsha River, revealed that environmental factors had a significant effect on fish community structure (F=4.3, P =0.006) (Fig. 8). Axis I and Axis II explained 25.25% and 13.64% of the total variance, respectively. Environmental variables varied significantly across river sections and had a marked impact on the abundance of dominant fish species. Among them, WT, NO 3 — N, TSS and DO were identified as key influencing factors. The RDA results indicate that WT and NO 3 - -N content positively influence the abundance of Coptodon zillii and Carassius auratus . The abundance of Pelteobagrus vachelli and Hemiculter leucisculus is positively influenced by NO 3 - -N and DO content, while Schizothorax dolichonema and Schizothorax wangchiachii abundance is positively influenced by TN and TSS content. Fig. 8 RDA ordination of fish community structure based on environmental factors and eDNA data from the Jinsha River. S1-S11 correspond to the following species, respectively: Hypophthalmichthys molitrix , Coptodon zillii , Schizothorax dolichonema , Rhodeus sinensis , Schizothorax wangchiachii , Hemiculter tchangi , Pelteobagrus vachelli , Aristichys nobilis , Carassius auratus , Hemiculter leucisculus , Cyprinus carpio . Rhodeus sinensis , Pelteobagrus vachelli and Hemiculter leucisculus 4 Discussion 4.1 Overview of eDNA-based fish monitoring This study demonstrates the effectiveness of eDNA metabarcoding as a reliable, non-invasive tool for assessing fish diversity in the Jinsha River. A total of 61 fish species were detected using eDNA technology, including seven invasive alien species and several dominant native species. Relative to conventional fishing methods (68 species), 38 species were commonly detected by both methods, whereas eDNA metabarcoding uniquely identified 23 species not recorded (Wang, 2024). These results underscore the high sensitivity of eDNA in detecting both native and non-native taxa, as well as capturing seasonal and spatial variations in community composition. The accuracy of eDNA-based surveys is influenced by various biological and technical factors, including DNA shedding and degradation rates, which are shaped by species traits, environmental conditions, and molecular properties (Stewart, 2019). Additionally, primer specificity and methodological consistency are crucial for accurate species identification and reliable amplification (Dejean et al., 2011). The findings align with the growing body of literature that positions eDNA as a powerful tool for monitoring fish communities in large river systems, further validating its application in ecological research and biodiversity conservation. 4.2 Composition and dominant species of fish communities The eDNA data revealed that Cypriniformes accounted for 65.6% of all detected species, confirming the taxonomic dominance of this group in the Jinsha River, consistent with findings from traditional fishing methods (Wang, 2024; Yan et al., 2022). Among the dominant species, Ctenopharyngodon idella , Coptodon zillii , Hemiculter leucisculus , Aristichthys nobilis , Hemiculter tchangi , Cyprinus carpio , Rhinogobius cliffordpopei , and Neosalanx taihuensis were the most abundant and widely distributed, with OTUs corresponding to these species detected across nearly all sampling sites. This widespread detection may reflect their high population sizes, greater biomass, and enhanced environmental adaptability. Of particular concern is the identification of seven non-native invasive species, including Coptodon zillii , which was consistently observed at high relative abundance. Coptodon zillii has become a dominant species in the Jiulong River Basin of Southeast China, posing significant ecological risks to the native fish community (Feng et al., 2025). The presence of such species highlights ongoing ecological invasion pressures in the Jinsha River and underscores the need for early-warning monitoring strategies based on eDNA. 4.3 Temporal and spatial patterns in fish diversity PCoA revealed significant seasonal differences in fish community composition, with 25 species common to both summer and autumn samples, and a greater number of fish species detected in autumn. In contrast, eDNA metabarcoding of the Chongqing section of the upper Yangtze River indicated seasonal variations in fish composition, with the richness index being higher in summer compared to other seasons (Shen et al., 2023). These differences likely reflect environmental influences on eDNA distribution, such as changes in water temperature, flow velocity, and turbidity. Additionally, fish behavior—including reproductive activity, seasonal migration, and overwintering, may also affect the eDNA shedding rate, thereby contributing to seasonal patterns in species detectability. Furthermore, spatial variation in diversity was evident across sampling sites. Fluctuations in fish species detection and eDNA relative sequence abundance at various sampling locations may indicate changes in habitat utilization and distribution (Stoeckle, Soboleva, & Charlop-Powers, 2017). Notably, the SJ site, located in a sheltered cove, exhibited higher Chao1 and ACE index values. This may be attributed to the relatively stagnant water conditions and the input of bait from recreational fishing, which together promote local fish aggregation and increase the likelihood of eDNA detection. In contrast, sites with stronger currents may experience faster eDNA dispersion or dilution, reducing the likelihood of local detection. 4.4 Environmental drivers of community structure Statistical analyses revealed significant correlations between fish diversity indices and environmental variables, particularly WT and DO, NO 3 - —N and TSS. RDA further indicated that WT was a key factor influencing community structure, which aligns with findings from previous studies (Cao, 2025; Lai et al., 2024; Qian et al., 2023). In this study, Coptodon zillii , Hemiculter tchangi and Carassius auratus were associated with higher WT, whereas Schizothorax dolichonema and Schizothorax wangchiachii preferred lower WT, this pattern is closely linked to the regulatory role of WT in shaping fish life-history traits. As WT of river increases, DO levels tend to decline, and excessively low DO can impose physiological stress on fish. For example, low DO has been shown to reduce species richness and alter community composition, whereas maintaining adequate DO levels is essential for sustaining fish popupations and preserving ecosystem health, as highlighted in the Pearl River Estuary study (Lai et al., 2024). In the present study, species such as Rhodeus sinensis , Pelteobagrus vachelli and Hemiculter leucisculus exhibited strong positive correlations with DO levels, suggesting their preference for well-oxygenated environments. Additionally, seasonal decreases in DO, often associated with elevated summer temperatures, may further exacerbate stress in sensitive species. Moreover, elevated levels of NO 3 - —N and TSS are widely recognized as key indicators of anthropogenic pollution in aquatic ecosystems. Species such as Hypophthalmichthys molitrix , Coptodon zillii , Rhodeus sinensis and Aristichys nobilis were predominantly found in warm, well-oxygenated waters with low nutrient concentrations, indicating a preference for clean water environments. In contrast, species including Schizothorax dolichonema , Schizothorax wangchiachii , Hemiculter tchangi , Carassius auratus , Hemiculter leucisculus and Cyprinus carpio . were commonly observed in polluted waters, exhibiting tolerance to high levels of organic matter, elevated nitrogen and phosphorus concentrations, and increased turbidity. These species may serve as potential indicators of eutrophication. These species-specific responses to environmental variables highlight the complex ecological dynamics in the Jinsha River and contribute to the observed heterogeneity in fish community structure. Our findings align with eDNA-based studies in other ecosystems. For example, Dong et al . identified DO, water level, and flow velocity as key factors shaping fish communities in freshwater lakes and upstream rivers (Dong, 2023). Furthermore, watershed-scale variables such as elevation, river width, and surrounding land cover also influence fish community structure. While this study focused on in-stream environmental parameters, future research should incorporate broader landscape-level variables to gain a more comprehensive understanding of the multi-scalar drivers of fish diversity. 4.5 Implications and future research This study demonstrates the utility of eDNA metabarcoding as a powerful tool for assessing biodiversity in large river systems. The fish species richness and abundance detected in this study were found to closely align with those observed using traditional survey methods, indicating its ability to detect a wide range of native and invasive species, capture seasonal patterns, and quantify environmental correlations underscores its potential for ecological monitoring and resource management in the Jinsha River Basin during the “Ten-Year Fishing Ban” period. Future research should focus on integrating eDNA monitoring with traditional survey methods and hydrological modeling to enhance spatial accuracy and ecological inference. Additionally, incorporating watershed-scale environmental and land-use variables will yield a more comprehensive understanding of the factors driving of biodiversity in the Jinsha River and beyond. Such integrative approaches are crucial for supporting long-term conservation efforts and adaptive management strategies in dynamic riverine ecosystems. Acknowledgements This work was funded by Finance Special Fund of Chinese Ministry of Agriculture and Rural Affairs of the People’s Republic of China: Routine Monitoring Program for the Yangtze River Post-Fishing Ban, and the Central Public-Interest Scientific Institution Basal Research Fund, CAFS (No. 2023TD61). Author contributions Y. Zhao, Z.Y. Wang, D.G. Yang, and T.B. Zhu conceived the study. Z.Y. Wang, F.F. Hu, and Z.B. Guo collected the samples. J.L. Gong, and X.M. Li extracted the DNA and performed sequencing. Y. Zhao, Z.Y. Wang, and T.B. Zhu wrote the manuscript. All authors have read and approved the final manuscript. Competing interests The authors declare no competing interests. Data Availability Statement All the required data are uploaded as supplementary material References Angles d’Auriac, M. B., Strand, D. A., Mjelde, M., Demars, B. O. L., & Thaulow, J. (2019). Detection of an invasive aquatic plant in natural water bodies using environmental DNA. PLoS One, 14 (7), e0219700. doi:10.1371/journal.pone.0219700 Balasingham, K. D., Walter, R. P., Mandrak, N. E., & Heath, D. D. (2018). Environmental DNA detection of rare and invasive fish species in two Great Lakes tributaries. Mol Ecol, 27 (1), 112-127. doi:10.1111/mec.14395 Bylemans, J., Gleeson, D. M., Hardy, C. M., & Furlan, E. (2018). Toward an ecoregion scale evaluation of eDNA metabarcoding primers: A case study for the freshwater fish biodiversity of the Murray-Darling Basin (Australia). Ecol Evol, 8 (17), 8697-8712. doi:10.1002/ece3.4387 Cao, L., Wang, YP., Ge, K., Xu, SY., Liu, K. . (2025). Fish community diversity and environmental drivers in Shijiu Lake during the early phase of the Ten⁃Year Fishing Ban on the Yangtze River. Asian journal of ecotoxicology, 20 (1), 109-122. Chen, S., Zhou, Y., Chen, Y., & Gu, J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34 (17), i884-i890. doi:10.1093/bioinformatics/bty560 Cheng, R., Zhou, X., Zhang, Y., Li, Q., Zhang, J., Luo, Y., . . . Shen, Y. (2024). eDNA reveals spatial homogenization of fish diversity in a mountain river affected by a reservoir cascade. J Environ Manage, 361 , 121248. doi:10.1016/j.jenvman.2024.121248 Deiner, K., Bik, H. M., Machler, E., Seymour, M., Lacoursiere-Roussel, A., Altermatt, F., . . . Bernatchez, L. (2017). Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol Ecol, 26 (21), 5872-5895. doi:10.1111/mec.14350 Dejean, T., Valentini, A., Duparc, A., Pellier-Cuit, S., Pompanon, F., Taberlet, P., & Miaud, C. (2011). Persistence of environmental DNA in freshwater ecosystems. PLoS One, 6 (8), e23398. doi:10.1371/journal.pone.0023398 Dong, Z., Chen, SS., Lv, HS., Zhang, LB., Yao, WZ., He, WP. (2023). Study on fish diversity in Chongqing Jiangbei Section of upper reaches of Yangtze River based on environmental DNA technology. Asian journal of ecotoxicology, 18 (6), 1-15. Edgar, R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26 (19), 2460-2461. doi:10.1093/bioinformatics/btq461 Esguícero, A. L. H., & Arcifa, M. (2010). Fragmentation of a Neotropical migratory fish population by a century-old dam. Hydrobiologia, 638 (1), 41-53. doi:10.1007/s10750-009-0008-2 Feng, S., Pan, X., Wang, J., Liu, W., Hui, Y., Wang, G., . . . Ji, F. (2025). Risk Screening of the Non-Native Fish in the Jiulong River Basin of Southeast China. Animals (Basel), 15 (4). doi:10.3390/ani15040461 Gao TH, T. H., Ye C, Duan XB. (2013). Fish composition and diversity in the main stream of the national nature reserve of rare and endemic fishes in the upper reaches of the Yangtze River. Freshwater Fishery, 36 (2), 36-42. Hanfling, B., Lawson Handley, L., Read, D. S., Hahn, C., Li, J., Nichols, P., . . . Winfield, I. J. (2016). Environmental DNA metabarcoding of lake fish communities reflects long-term data from established survey methods. Mol Ecol, 25 (13), 3101-3119. doi:10.1111/mec.13660 Heino, J., Soininen, J., Alahuhta, J., Lappalainen, J., & Virtanen, R. (2015). A comparative analysis of metacommunity types in the freshwater realm. Ecol Evol, 5 (7), 1525-1537. doi:10.1002/ece3.1460 Jerde, C. L., Wilson, E. A., & Dressler, T. L. (2019). Measuring global fish species richness with eDNA metabarcoding. Mol Ecol Resour, 19 (1), 19-22. doi:10.1111/1755-0998.12929 Lai, H., Bi, S., Yi, H., Li, H., Wei, X., Wang, G., . . . Li, G. (2024). Characteristics of demersal fish community structure during summer hypoxia in the Pearl River Estuary, China. Ecol Evol, 14 (7), e11722. doi:10.1002/ece3.11722 Magurran, A. E., Khachonpisitsak, S., & Ahmad, A. B. (2011). Biological diversity of fish communities: pattern and process. J Fish Biol, 79 (6), 1393-1412. doi:10.1111/j.1095-8649.2011.03091.x McClenaghan, B., Fahner, N., Cote, D., Chawarski, J., McCarthy, A., Rajabi, H., . . . Hajibabaei, M. (2020). Harnessing the power of eDNA metabarcoding for the detection of deep-sea fishes. PLoS One, 15 (11), e0236540. doi:10.1371/journal.pone.0236540 Meng, Z., Chen, K., Hu, F., Liu, L., Yang, D., Li, X. J. F. i. E., & Evolution. (2023). Environmental and spatial factors play different roles in phytoplankton community assembly in different hydrological seasons in Lake Wuchang, China. 11 , 1154695. Meng, Z., Chen, K., Liu, L., Hu, F., Zhu, Y., Li, X., & Yang, D. J. F. i. E. S. (2022). Niche and interspecific association with respect to the dominant phytoplankton species in different hydrological periods of Lake Wuchang, China. 10 , 985672. Meng, Z., Hu, F., Xiang, M., Fu, X., & Li, X. J. A. (2025). Spatiotemporal Dynamics of Fish Density in a Deep-Water Reservoir: Hydroacoustic Assessment of Aggregation Patterns and Key Drivers. 15 (7), 1068. Miya, M., Sato, Y., Fukunaga, T., Sado, T., Poulsen, J. Y., Sato, K., . . . Iwasaki, W. (2015). MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R Soc Open Sci, 2 (7), 150088. doi:10.1098/rsos.150088 Qian, M. M., Wang, Z. Y., Zhou, Q., Wang, J., Shao, Y., Qiao, Q., . . . Yan, Z. G. (2023). Environmental DNA unveiling the fish community structure and diversity features in the Yangtze River basin. Environ Res, 239 (Pt 1), 117198. doi:10.1016/j.envres.2023.117198 Sato, Y., Miya, M., Fukunaga, T., Sado, T., & Iwasaki, W. (2018). MitoFish and MiFish Pipeline: A Mitochondrial Genome Database of Fish with an Analysis Pipeline for Environmental DNA Metabarcoding. Mol Biol Evol, 35 (6), 1553-1555. doi:10.1093/molbev/msy074 Shao, K., Yang, Z., Tang, HY., Que, YF., Li, WT., Xiong, MH. (2017). Changes of fish community structures in Panzhihua section of the Jinsha River before and after impoundment of the Guanyinyan hydropower station. Resources and Environment in the Yangtze Basin, 29 (11), 2417-2426. Shen, Y., Zhang, Y., Cheng, R., Wang, W., Duan, C., Liu, Z., . . . Luo, Y. (2023). Diversity and spatiotemporal dynamics of fish communities in the Chongqing section of the upper Yangtze River based on eDNA metabarcoding. Ecol Evol, 13 (11), e10681. doi:10.1002/ece3.10681 Shen, Y., Zhou, X., Zhang, Y., Zhang, J., Li, Q., Chen, Q., . . . Luo, Y. (2024). Important fish diversity maintenance status of the tributaries in a hotspot fish conservation area in the upper Yangtze River revealed by eDNA metabarcoding. Sci Rep, 14 (1), 24128. doi:10.1038/s41598-024-75176-9 Stewart, K. (2019). Understanding the effects of biotic and abiotic factors on sources of aquatic environmental DNA. Biodiversity and Conservation, 28 (5), 983-1001. Stoeckle, M. Y., Soboleva, L., & Charlop-Powers, Z. (2017). Aquatic environmental DNA detects seasonal fish abundance and habitat preference in an urban estuary. PLoS One, 12 (4), e0175186. doi:10.1371/journal.pone.0175186 Sun, H., He, D., Sui, X., & Chen, Y. (2020). Predicting impacts of future climate change and hydropower development towards habitats of native and non-native fishes. Sci Total Environ, 707 , 135419. doi:10.1016/j.scitotenv.2019.135419 Tan, J., Sun, J., Wang, Y., Tian, H., Cheng, B., Qing, J., . . . Shi, X. (2024). Fish community dynamics following the low-head dam removal and newly installed fish passage in a headstream tributary of Jinsha River, Southwest China. Sci Total Environ, 954 , 176774. doi:10.1016/j.scitotenv.2024.176774 Thomsen, P. F., Kielgast, J., Iversen, L. L., Wiuf, C., Rasmussen, M., Gilbert, M. T., . . . Willerslev, E. (2012). Monitoring endangered freshwater biodiversity using environmental DNA. Mol Ecol, 21 (11), 2565-2573. doi:10.1111/j.1365-294X.2011.05418.x Wang, Z., Zhu, TB., Hu, FF., Gong, JL., Guo, ZB., Li, XM., Yang, DG. . (2024). Fish community structure and species diversity in the main stream of the Jinsha River in the early ten-year fishing ban. Journal of Fishery Sciences of China, 31 (3), 286-300. WX, C. (2022). The 10-year fishing ban is an important measure to protect the Yangtze River. Acta Hydrobiologica Sinica, 46 (1), 1. Yan, T., He, J., Yang, D., Ma, Z., Chen, H., Zhang, Q., . . . He, Z. (2022). Fish Community Structure and Biomass Particle-Size Spectrum in the Upper Reaches of the Jinsha River (China). Animals (Basel), 12 (23). doi:10.3390/ani12233412 Yang, H., Shen, L., He, YF., Tian, HW., Gao, L., Wu, JM., et al. (2023). Status of aquatic organisms resources and their environments in the Yangtze River system (2017-2021). Journal of Fisheries of China, 47 (2), 3-30. Yang, Z., Tang, HY., Zhu, D., Gao, SB., Xu, W., Wan, L., Gong, YT., Qiao, Y. (2014). Temporal distribution patterns of fish community structure and species composition in the Panzhihua section of the Jinsha River. Journal of Hydroecology, 35 (5), 43-51. Zhang P, Y. Z., Cao L, Qiao Y, Chen XJ, Chang JB. (2018). Effects of upstream and downstream dam operation on the spawning habitat suitability of Coreius guichenoti in the middle reach of the Jinsha River. Ecological Engineering, 120 , 198-208. Zhao, B., Zeng, Q., Wang, J., Jiang, Y., Yan, L., Hou, J., . . . Hu, P. (2024). Influence of cascade reservoirs on the distribution, transport, and retention patterns of biogenic elements in the Jinsha River. Sci Total Environ, 951 , 175535. doi:10.1016/j.scitotenv.2024.175535 Information & Authors Information Version history V1 Version 1 20 June 2025 Peer review timeline Published Ecology and Evolution Version of Record 14 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords ecosystem ecosystem ecology freshwater molecular genetics sequencing vertebrate Authors Affiliations yan zhao 0009-0000-0321-458X Yangtze River Fisheries Research Institute, Chinese Academy of Fisheries Science View all articles by this author zhongyuan wang Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Feifei HU Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Zhibin Guo Yangtze River Fisheries Research Institute, Chinese Academy of Fisheries Science View all articles by this author Jinling Gong Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Xuemei Li Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Deguo Yang Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Tingbing Zhu [email protected] Chinese Academy of Fishery Sciences Yangtze River Fisheries Research Institute View all articles by this author Metrics & Citations Metrics Article Usage 320 views 168 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation yan zhao, zhongyuan wang, Feifei HU, et al. Fish diversity and environmental relationships in the Jinsha River during the initial phases of the 10-year fishing ban: A metabarcoding approach. Authorea . 20 June 2025. DOI: https://doi.org/10.22541/au.175041921.19377427/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175041921.19377427/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffe7c3f884c06e3',t:'MTc3OTQ4MDg0Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();