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Article

Environmental DNA Reveals Ecologically Relevant Temporal and Spatial Variation of Fish Community in Silver Carp- and Bighead Carp-Dominant Drinking Water Reservoirs

by
Jinyu Tang
1,
Yangxin Dai
2,*,
Ming Li
3,
Lei Tian
3,
Bao Lou
1,
Fuyong Huang
1,
Zhigang Xie
1,
Yulai Dai
2 and
Wenfang He
3,*
1
Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
2
Hangzhou Academy of Agricultural Sciences, Hangzhou 310024, China
3
Jinhua Fisheries Technology Extension Station (Jinhua Aquatic Animal Disease Prevention and Control Center), Jinhua 321017, China
*
Authors to whom correspondence should be addressed.
Water 2025, 17(7), 1057; https://doi.org/10.3390/w17071057
Submission received: 6 February 2025 / Revised: 29 March 2025 / Accepted: 31 March 2025 / Published: 3 April 2025
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Abstract

:
Environmental DNA (eDNA) metabarcoding was utilized to compare the fish species composition, diversity, and their relationships with environmental factors in four medium-sized drinking water reservoirs (Tongjiqiao, Andi, Shafan, and Jinlan) within the Qiantang River Basin during both wet and dry seasons. A total of 44 fish species belonging to 6 orders, 15 families, and 40 genera were detected, with Cyprinidae being identified as the dominant family (68.2%). Silver carp (Hypophthalmichthys molitrix) and bighead carp (Hypophthalmichthys nobilis) were identified as the most abundant species, representing 81.94% and 99.98% of the relative eDNA abundance, respectively. The fish communities were characterized by river-resident species (59.1%), pelagic species (43.2%), and omnivorous (43.2%) and carnivorous (43.2%) feeding habits. Significant influences of seasonal and reservoir-specific variations on physicochemical parameters, species composition, and ecological traits were observed. However, no significant differences in community diversity (Chao1, Pielou_e, Shannon, and Simpson indices) or distribution patterns were detected between wet and dry seasons. In contrast, Jinlan Reservoir was found to exhibit distinct diversity and distribution patterns compared to the other three reservoirs, which was consistent with the relative eDNA abundance of H. molitrix and H. nobilis. Through a Spearman correlation analysis, the relative abundance of H. molitrix was revealed to be negatively correlated with community diversity, while H. nobilis was shown to have positive correlations (except with Chao1). This suggests that the excessive stocking of H. molitrix may lead to reduced diversity, whereas moderate stocking of H. nobilis could promote diversity restoration. Among physicochemical factors, Chao1 richness was found to be negatively correlated with conductivity, pH, phosphate–phosphorus (PO4-P), and the total nitrogen to total phosphorus ratio (TN/TP), while Shannon and Simpson diversity indices were negatively correlated with nitrate nitrogen (NO3-N) concentrations. A redundancy analysis (RDA) identified NO3-N and permanganate index (CODMn) as the primary physicochemical drivers of fish community structure, indicating that while physicochemical differences were found to influence species composition and diversity, their effects were considered relatively limited. These findings suggest that the overwhelming dominance of H. molitrix and H. nobilis in the reservoirs may reduce the influence of seasonal variations and cross-reservoir physicochemical disparities on fish community dynamics.

1. Introduction

Fish are crucial components participating in material cycling and energy fluxes in aquatic ecosystem [1]. Their community can serve as valuable ecological indicators, providing important insights into ecosystem health [2,3]. Established sampling methods like gillnetting, trawling, trapping, and electrofishing are relatively expensive, laborious, ineffective, taxonomically biased, and habitat destructive [4,5]. Other alternatives, such as hydroacoustics, visual census, and acoustic telemetry, also struggle with environmental interference and complex data processing [6,7]. Environmental DNA (eDNA) metabarcoding, which originally applied to detect invasive species [8], has been considered as an innovative technique for aquatic biodiversity biomonitoring [9], high detective efficiency, and broad species coverage. This technique analyzes DNA fragments from environmental samples (e.g., skin cells, mucus, or excreta) using PCR and high-throughput sequencing to provide a non-invasive, efficient, and comprehensive assessment of species composition and abundance [10,11]. Studies have shown that eDNA outperforms fishing or non-fishing methods at detecting species richness, estimating spatial and temporal patterns, and revealing the correlation between assemblage composition and physicochemical variables [5,12,13,14,15]. With the continuous development of eDNA technology and the improvement of genetic databases, its application in fishery resource surveys is becoming increasingly promising.
Drinking water reservoirs play an essential role in the urban–rural water supply as well as socio-economic development; therefore, safeguarding the security of water quality has become an important task in reservoir management [16]. Silver carp (Hypophthalmichthys molitrix) and bighead carp (Hypophthalmichthys nobilis), famous for their planktivorous characteristic, have been exceedingly introduced into reservoirs to control harmful algae, improve water quality, and maintain ecosystem health [17]. However, the transference of non-native species to new aquatic ecosystems is likely to change the original communities [18]. First, silver carp and bighead carp, which feed on plankton, will change the nutrient dynamics and biodiversity [17,19,20]. This in turn affects the fish community through a bottom-up effect. Second, these two fish species occupying the upper-middle niches may exert negative impacts on pelagic species. Beyond biotic factor, the temporal and spatial variations of physicochemical parameters are also relevant to the species composition and diversity of fish in aquatic ecosystems [21], which further complicate the key factor influencing community characteristics.
In this study, we applied eDNA metabarcoding to compare the fish community in four typical medium-sized drinking water reservoirs in Qiantang River Basin across two seasons. Specifically, the purpose of this study was to (1) assess species characteristics based on eDNA detections in silver carp- and bighead carp-dominant reservoirs, (2) compare species composition and community distribution across seasons and reservoirs, (3) and determine whether the abundant stocking of silver carp and bighead carp causes a certain tendency on seasonal or regional variations in the fish community. Our results will provide a scientific basis for aquatic germplasm resource conservation and the optimization of stock enhancement strategies in drinking water reservoirs.

2. Materials and Methods

2.1. Study Area

The Qiantang River Basin is located in the most economically developed region of southeastern China, spanning longitudes 117°30′ to 121°30′ E and latitudes 28°00′ to 30°30′ N. With a main stream length of 688 km and a basin area of approximately 55,558 km2 [22], it covers five provinces and municipalities: Anhui, Jiangxi, Fujian, Zhejiang, and Shanghai. The basin is rich in water resources, with an annual total water resource volume of approximately 38.9 billion m3. As a critical pillar for regional economic development and ecological conservation, the basin experiences a subtropical monsoon climate with distinct seasons. The average annual temperature is approximately 17 °C, featuring cold, dry winters and hot, humid summers. Annual precipitation averages 1600 mm, with 55–60% concentrated in the plum rain season (April–July), often causing floods. Precipitation is relatively low from July to September, while typhoons in late summer/autumn frequently bring heavy rainfall and downstream flooding. These climatic and hydrological characteristics significantly influence the water quality, quantity, and ecological functions of reservoirs within the basin.
The Tongjiqiao, Andi, Shafan, and Jinlan reservoirs, the focus of this study, are located in the upper reaches of the central tributaries of the Qiantang River Basin. Situated in sparsely populated mountainous areas, these reservoirs experience minimal human disturbance and share similar geographic locations, climatic conditions, and altitudes. They are stocked with comparable quantities of H. molitrix and H. nobilis fry annually to control water quality, making them ideal for studying the effects of season, region, and dominant populations on fish community characteristics. These four reservoirs also represent typical management practices for medium-sized drinking water reservoirs in the subtropical monsoon climate region of southeastern China (Figure 1). The four reservoirs share similar geographic locations, climatic conditions, and altitudes as well as highly comparable functions and management practices. The Tongjiqiao Reservoir has a catchment area of 4.77 km2, a total storage capacity of 80.76 million m3, and a normal storage capacity of 59.19 million m3. The Andi Reservoir has a catchment area of 3.42 km2, a total storage capacity of 70.97 million m3, and a normal storage capacity of approximately 50 million m3. The Shafan Reservoir has a catchment area of 2.52 km2, a total storage capacity of 85.55 million m3, and a normal storage capacity of approximately 60 million m3. The Jinlan Reservoir has a catchment area of 4.33 km2, a total storage capacity of 91.24 million m3, and a normal storage capacity of 68 million m3. These four reservoirs serve as major local water sources, providing critical functions such as water supply, flood control, power generation, and ecological water replenishment, thereby playing a vital role in regional socioeconomic development. The water quality of these reservoirs consistently meets or exceeds the Class II Environmental Quality Standard for Surface Water [23], maintained through stringent management practices under the Zhejiang Province Drinking Water Source Protection Regulations and the stocking of filter-feeding H. molitrix and H. nobilis to stabilize water quality.

2.2. Sample Collection and Water Quality/eDNA Analysis

Based on the morphological characteristics, surface area, and flow direction of the four reservoirs, four sampling sites were established in each reservoir (Figure 1). Sampling was conducted on July 18, 2023 (wet season) and December 6, 2023 (dry season). The geographic coordinates of each sampling site were recorded using a GPS device (G120BD, Unistrong, China). At each site, surface water (20 cm below the water surface) was analyzed in situ for water temperature (WT), conductivity (Con), dissolved oxygen (DO), and pH using a portable multiparameter water quality analyzer (YSI Pro Quatro, YSI Scientific Instrument, Yellow Springs, OH, USA). Water transparency (SD) was measured using a Secchi disk (20 cm in diameter). Water samples were collected at a depth of 50 cm below the surface using a 5 L water sampler. Three replicate samples were taken at each sampling site, with a minimum distance of 10 m between replicates to ensure spatial independence. The replicate samples were thoroughly mixed, and subsamples were transferred into a 500 mL glass bottle for water chemistry analysis and a 2000 mL sterile amber glass bottle for eDNA analysis. All samples were immediately stored in a cooling box and transported to the laboratory for further processing.
Water chemistry analysis included measurements of ammonia nitrogen (NH3-N, including NH4+ and NH3), nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), reactive phosphorus (PO4-P), total nitrogen (TN), total phosphorus (TP), and permanganate index (CODMn). The analytical methods followed established protocols as described in previous studies [24]. For eDNA analysis, water samples were vacuum-filtered through 0.22 µm polycarbonate membranes (47 mm diameter, Whatman, GE Healthcare Life Sciences, UK). The membranes were then stored at −80 °C until further processing. To prevent cross-contamination, all equipment was sterilized with 10% sodium hypochlorite solution after each filtration, and a distilled water filtration was included as a negative control. DNA was extracted from the filtered membranes using the E.Z.N.A.® Water DNA Kit (Omega Bio-Tek, Inc., Norcross, GA, USA). The concentration and purity of the extracted DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and 1% agarose gel electrophoresis. The diluted DNA was used as a template for PCR amplification with the universal fish primer pair MiFish-U (F: 5′-GTCGGTAAAACTCGTGCCAGC-3′; R: 5′-CATAGTGGGGTATCTAATCCYAGTTTG-3′). The PCR reaction mixture included 10 µL AmpliTaq gold 360 Master mix (Applied Biosystems, Inc., Carlsbad, CA, USA), 3.2 µg Bovine Serum Albumin (Thermo Fisher Scientific), 1 µL of each 5 µM forward and reverse tagged primers, 5.84 µL H2O, and 2 µL extracted DNA template (standardized to 5 ng/µL). The PCR amplification protocol consisted of an initial denaturation at 95 °C for 10 min, followed by 35 cycles of 94 °C for 1 min, 45 °C for 1 min, and 72 °C for 1 min, with a final extension at 72 °C for 5 min. For quality control, FastQC was used to assess read quality, retaining only those with alignment quality score > 40. Reads were filtered to select those with lengths of 303–323 bp (for COI) or 140–190 bp (for 12S) and free of ambiguous bases. Chimeras were removed using the uchime-denovo algorithm (implemented in VSEARCH). A minimum copy threshold of five reads per sample was applied to the COI data set on a sample-by-sample basis, and only MOTUs occurring in both PCR replicates were considered for the 12S data set to remove false positives [25]. The amplified products were quality-checked and subsequently sent to Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China), for high-throughput sequencing.
High-throughput sequencing reads were processed through filtering, denoising, assembly, and chimera removal to obtain high-quality sequences. Effective sequences were clustered into Operational Taxonomic Units (OTUs) at a 97% similarity threshold. Taxonomic annotation was performed by comparing the OTUs against the NCBI database (https://www.ncbi.nlm.nih.gov/). The valid scientific names of the identified species were verified using authoritative references, including the Fish Resources of Qiantang River, Fauna of Zhejiang: Freshwater Fishes, and Fishes of Taihu Lake [26,27,28]. Ecological traits, including ecological type, feeding habits, and habitat preference, were further refined based on the relevant literature [29]. Ecological types were classified based on habitat environment and migratory behavior into the following categories: river-resident, stream-resident, river–stream, anadromous, estuarine-resident, and river–coastal. Feeding habits were categorized as carnivorous, omnivorous, herbivorous, and planktivorous. Habitat preferences were classified as pelagic, demersal, and benthic following established ecological classifications.

2.3. Data Processing and Statistical Analysis

One-way analysis of variance (ANOVA) was used to examine differences in physicochemical parameters (WT, SD, Con, DO, pH, NH3-N, NO2-N, NO3-N, PO4-P, TIN, TN, TP, CODMn, and TN/TP), relative abundance of H. molitrix and H. nobilis, and diversity indices (Chao1 richness, Pielou’s evenness, Shannon, and Simpson diversity) among different seasons and reservoirs. Post hoc comparisons were performed using Tukey’s HSD test to identify specific differences between reservoirs. Non-metric Multidimensional Scaling (NMDS) and Analysis of Similarities (ANOSIM) were applied to assess the distribution patterns and group differences of fish communities based on species composition and sequence abundance. Spearman’s rank correlation heatmap analysis was used to evaluate the relationships between diversity indices and physicochemical parameters as well as dominant populations (top three species by relative abundance, including H. molitrix, H. nobilis, and Opsariichthys evolans). Redundancy analysis (RDA) was conducted to explore the relationships between common species (those present in all four reservoirs) and environmental factors. A significance level of p < 0.05 was applied for all statistical tests. Raw sequencing data were quality-controlled, annotated, and analyzed for diversity and correlation heatmaps using the Personal GeneCloud platform (https://www.genescloud.cn/). ANOVA and RDA were performed using SPSS 19.0 (IBM® SPSS® Statistics) and Canoco for Windows 4.5, respectively.

3. Results

3.1. Water Physicochemical Parameters

The physicochemical parameters of the four reservoirs are shown in Figure 2. During the wet season, parameters such as SD, Con, DO, NH3-N, NO3-N, TIN, TN, TP, and CODMn were significantly lower compared to the dry season (p < 0.01). In contrast, WT, pH, and TN/TP were significantly higher during the wet season (p < 0.01). Among the reservoirs, TP was the only parameter that showed a significant difference at p < 0.05, while all other physicochemical parameters exhibited highly significant differences (p < 0.01). Specifically, the Tongjiqiao Reservoir had higher values for all parameters except SD compared to the other three reservoirs (p < 0.05). Between the Andi and Shafan Reservoirs, significant differences were observed only for SD, Con, PO4-P, and CODMn (p < 0.05), while no significant differences were found for the remaining parameters (p > 0.05).

3.2. Fish Species Composition and Ecological Characteristics

Based on taxonomic annotation, a total of 44 fish species belonging to 40 genera, 15 families, and 6 orders were identified (Table 1). The order Cypriniformes was the most abundant, comprising 31 species (70.4%), followed by Perciformes with 9 species (20.4%). The orders Clupeiformes, Siluriformes, Mugiliformes, and Synbranchiformes were each represented by a single species (2.3%). At the family level, Cyprinidae was the most diverse, including 30 species across 9 subfamilies (68.2%), while all other families were represented by only 1 species (2.3%). Ecologically, the fish species were categorized into six ecological types: river-resident (26 species, 59.1%), stream-resident (9 species, 20.4%), river–stream (5 species, 11.4%), estuarine-resident (2 species, 4.5%), anadromous (1 species, 2.3%), and river–coastal (1 species, 2.3%). Feeding habits were classified as omnivorous (19 species, 43.2%), carnivorous (19 species, 43.2%), herbivorous (4 species, 9.1%), and planktivorous (2 species, 4.5%). In terms of habitat preference, the species were distributed across pelagic (19 species, 43.2%), demersal (10 species, 22.7%), and benthic (15 species, 34.1%) layers.
A total of 22 and 34 fish species were detected during the wet and dry seasons, respectively, with 12 species shared between the two seasons. The shared species were predominantly characterized by river-resident ecological types (9 species, 75.0%) and pelagic habitat preferences (7 species, 58.3%). The differences in fish community composition between the wet and dry seasons were primarily reflected in (1) ecological types, river-resident (7 vs. 11 species) and stream-resident (1 vs. 6 species) (Figure 3a); (2) feeding habits, omnivorous (3 vs. 11 species) and herbivorous (0 vs. 3 species) (Figure 3b); and (3) habitat preferences, pelagic (3 vs. 9 species) and demersal (1 vs. 7 species) (Figure 3c). Among the four reservoirs, the number of detected species ranged from 23 to 28, with the lowest diversity observed in the Tongjiqiao Reservoir and the highest in the Andi Reservoir. Fourteen species were common to all reservoirs, representing over 50% of the species detected in each reservoir. These shared species were predominantly characterized by river-resident ecological types (10 species, 71.4%), carnivorous feeding habits (7 species, 50.0%), and pelagic habitat preferences (7 species, 50.0%). The differences in fish community composition among the reservoirs were primarily reflected in (1) ecological types, where river-resident species showed the greatest variation (3 vs. 6 vs. 4 vs. 7 species) (Figure 3d); (2) feeding habits, where omnivorous (6 vs. 4 vs. 9 vs. 6 species) and carnivorous (1 vs. 9 vs. 3 vs. 6 species) species exhibited significant differences (Figure 3e); and (3) habitat preferences, where benthic species showed the greatest variation (1 vs. 7 vs. 5 vs. 5 species) (Figure 3f).

3.3. Relative Abundance of Dominant Fish Species

As shown in Figure 4, the relative abundance of H. molitrix and H. nobilis dominated the fish community, accounting for 81.94% to 99.98% of the total sequence reads. The next most abundant species was O. evolans, representing 0.12% to 1.93% of the total sequence reads. No significant differences were observed in the relative abundance of H. molitrix and H. nobilis between the wet and dry seasons (p > 0.05, Figure 4a). In contrast, their relative abundance varied significantly among reservoirs (p < 0.01). Specifically, H. molitrix abundance was significantly higher in the Jinlan Reservoir compared to the Andi and Shafan Reservoirs (p < 0.05), while H. nobilis abundance was significantly lower in the Jinlan Reservoir compared to the Tongjiqiao and Shafan Reservoirs (p < 0.05, Figure 4b). Among non-dominant species, only O. evolans (1.08%) during the dry season and Hemiculter tchangi (1.38%) during the wet season had relative abundances exceeding 1%. Among reservoirs, species with relative abundances > 1% included O. evolans (1.93%), Rhodeus sinensis (1.85%), Ctenopharyngodon idella (1.48%), and Micropterus salmoides (1.18%) in the Andi Reservoir as well as H. tchangi (2.28%) in the Shafan Reservoir.

3.4. Seasonal and Spatial Variations in Fish Diversity Indices

Seasonal and spatial variations in fish diversity indices are shown in Figure 5, and no significant differences were observed in fish diversity indices, including richness (Chao 1), evenness (Pielou_e), and diversity (Simpson and Shannon), between the wet and dry seasons (p > 0.05, Figure 5a). In contrast, significant spatial variations were detected among reservoirs. Specifically, the Jinlan Reservoir exhibited significantly lower Simpson and Shannon diversity indices compared to the Andi and Shafan Reservoirs (p < 0.05). Additionally, the Pielou_e evenness index in the Jinlan Reservoir was significantly lower than that in the other three reservoirs (p < 0.05, Figure 5b).

3.5. NMDS and ANOSIM Analysis of Fish Community Composition

The NMDS ordination results revealed minor differences in fish community composition between the wet and dry seasons (Figure 6a). Among reservoirs, the sampling sites in the Jinlan Reservoir were closely clustered (all located in the fourth quadrant), indicating high within-reservoir species reproducibility. The Tongjiqiao Reservoir showed moderate clustering, while the Andi and Shafan Reservoirs exhibited greater variability in community composition (Figure 6b). ANOSIM analysis further confirmed that seasonal differences in community composition were not significant (p = 0.534, R = −0.019, Figure 6c). In contrast, significant differences were observed among reservoirs (p = 0.003, R = 0.220). Specifically, the Jinlan Reservoir showed significant differences in community composition compared to the Tongjiqiao Reservoir (p < 0.05) and highly significant differences compared to the Andi and Shafan Reservoirs (p < 0.01, Figure 6d).

3.6. Environmental Drivers of Fish Community Diversity and Composition

The Spearman correlation analysis revealed that H. molitrix exhibited significant negative correlations with the Chao1 richness index (p < 0.05) as well as the Pielou_e evenness index, Shannon diversity index, and Simpson diversity index (p < 0.01). In contrast, H. nobilis showed significant positive correlations with the Pielou_e evenness index, Shannon diversity index, and Simpson diversity index (p < 0.01). Among the physicochemical variables, Con, pH, PO4-P, and TN/TP were significantly negatively correlated with the Chao1 richness index (p < 0.05). Additionally, NO3-N concentration was significantly negatively correlated with the Shannon and Simpson diversity indices (p < 0.05; Figure 7a). The relative abundance of species other than H. molitrix and H. nobilis was generally low. Therefore, a redundancy analysis (RDA) was conducted on the shared species (14 in total) across the four reservoirs. The first two RDA axes explained 38.0% and 3.8% of the variance in the shared species community, respectively (Figure 7b). Monte Carlo permutation tests indicated that NO3-N (F = 3.64, p = 0.044) and CODMn (F = 2.37, p = 0.038) had significant effects on the community composition of the shared species (p < 0.05).

4. Discussion

In this study, 44 fish species belonging to 6 orders, 15 families, and 40 genera were detected, of which 35 species (79.5%) matched records from the Updated Checklist of Freshwater Fishes in Zhejiang Province. This high detection rate demonstrates the applicability and reliability of eDNA technology for fish monitoring in the studied reservoirs. However, 20.5% (9 species) of the detected taxa were not previously recorded using traditional fishing methods. This discrepancy may arise from several factors: (1) the MiFish-U universal primers used in eDNA analysis have limited resolution for closely related species, potentially leading to misannotation [30,31,32]; (2) the reference database (NCBI) is incomplete, which may introduce false positives [16]; and (3) unregulated stocking and intentional releases, common practices in drinking water reservoirs, may introduce non-native species [33]. For example, Micropterus salmoides and Coptodon zillii likely entered the reservoirs through aquaculture escapes, natural dispersal, or human-mediated releases. These two invasive species, with high adaptability and reproductive capacity, may pose significant ecological threats to reservoirs ecosystems. Their presence may cause biodiversity decline by overgrazing on small species or fish larvae. As highlighted in the study on the Vltava River and its tributaries, reservoirs can enhance the spread of non-native species, which may alter the spatial distribution of native species and potentially lead to biotic homogenization [34]. Fortunately, the existing population of M. salmoides and C. zillii in four reservoirs are limited quantity according to the relative abundance, which positively correlates with fish biomass [35]. It is demonstrated that eDNA technology increases the likelihood of an early detection of invasive species, thereby reducing the control costs and ecological impacts associated with species invasions.
Understanding the composition and ecological characteristics of fish communities is essential for assessing changes in aquatic ecosystems [36]. The fish communities in the four drinking water reservoirs of the Qiantang River Basin exhibited the following features: (1) Cyprinidae dominated (68.2%), consistent with findings from the Jinhua and Lanxi Rivers, where Cyprinidae accounted for over 65% of the fish community; (2) H. molitrix and H. nobilis were overwhelmingly dominant, with relative eDNA abundances ranging from 81.94% to 99.98%, reflecting extensive stocking for water quality management; (3) river-resident species (59.1%) were predominant, as reservoir construction transformed flowing streams into lentic environments with reduced flow velocity and increased food availability, favoring sedentary species; (4) pelagic species (43.2%) were most common, likely due to the sampling of surface water, which aligns with the vertical distribution of eDNA; and (5) omnivorous (43.2%) and carnivorous (43.2%) species were equally prevalent, indicating abundant live prey resources and good water quality, consistent with the reservoirs meeting Class II standards for DO, CODMn, NH3-N, and TP under the Environmental Quality Standards for Surface Water (GB3838-2002).
The detection of fish eDNA is influenced by seasonal changes [37], among other factors. Fish reproduction typically peaks in late spring and early summer, leading to higher species richness during the wet season (summer and autumn) due to recruitment. However, contrary to this pattern, higher fish diversity during the dry season was observed in the coastal wetlands of the Pearl River Estuary [38]. In this study, species richness was significantly higher during the dry season than the wet season, with notable differences in ecological traits such as habitat preference (river-resident vs. stream-resident), vertical distribution (pelagic species vs. demersal species) [39], and feeding habits (omnivorous vs. herbivorous). The studied reservoirs, located in the upper reaches of Qiantang River tributaries, are relatively isolated due to dam construction, resulting in stable fish communities. The observed seasonal differences may be attributed to water-level fluctuations, as fish communities in deep reservoirs exhibit distinct vertical stratification with seasonal and regional variations [37,39]. In similar studies, such as research on fish distribution in the Three Gorges Reservoir and the Gezhouba Reservoir, further insights into the seasonal variations and spatial distribution patterns of fish communities in reservoir environments have been revealed [40]. In the Three Gorges Reservoir, fish are mainly found in static water areas and the middle to lower reaches, with higher species richness in summer and lower in autumn, influenced by exotic species invasions [41]. In the Gezhouba Reservoir, fish concentrate in static and slow-flowing areas and migratory corridors, with bottom-dwelling species lingering downstream due to migration barriers [42]. Over time, some of the bottom-dwelling fish abundance has changed, but the overall community remains dominated by static water and migratory fish. In this study, water samples were collected from the surface, and lower water levels during the dry season may have facilitated the detection of more species. Although species richness was similar across reservoirs, non-shared species and ecological traits (e.g., river-resident, omnivorous, carnivorous, and benthic species) varied significantly. This aligns with previous findings that even under similar abiotic conditions, differences in geographic location and ecological processes can lead to distinct fish community structures in tributaries of the same watershed [43].
The Shannon diversity index of fish communities in the reservoirs ranged from 0.13 to 1.22, below the 1.5–3.5 range proposed by Magurran, indicating low community complexity and potential threats to ecosystem stability [44]. This may result from excessive stocking of H. molitrix and H. nobilis, the degradation of spawning habitats for native species, and biological invasions. A significant positive correlation was observed between fish biomass and eDNA concentration, with H. molitrix and H. nobilis accounting for 81.94–99.98% of the relative eDNA abundance, confirming their dominance [35,45]. A Spearman correlation analysis revealed that the relative abundance of H. molitrix was negatively correlated with the Chao1 richness index, Pielou evenness index, Shannon diversity index, and Simpson diversity index (p < 0.05), suggesting its negative impact on community diversity and evenness. In contrast, H. nobilis showed positive correlations with the Pielou evenness, Shannon diversity, and Simpson diversity indices (p < 0.05), indicating a potential role in promoting community evenness, albeit with limited contributions to overall diversity. Seasonal variations and cross-reservoir physicochemical disparities significantly influence fish community structure and diversity, but the overwhelming dominance of H. molitrix and H. nobilis may diminish their effects. The "non-classical biomanipulation" theory, which utilizes the filter-feeding behavior of H. molitrix and H. nobilis to control cyanobacterial blooms, has gained widespread acceptance in China [46]. This theory posits that intensive stocking of these species can reduce phytoplankton biomass through direct grazing, thereby improving water clarity. However, excessive stocking can alter zooplankton communities, leading to smaller body sizes, reduced water transparency, and diminished algal control [47]. Recent studies have shown that excessive stocking can lead to zooplankton community shifts, favoring small-bodied cladocerans over large-bodied Daphnia, which may ultimately enhance algal resurgence. Moreover, long-term overstocking may simplify ecosystem structure, threatening native fish biodiversity [48]. In this study, fish community composition, ecological traits, and physicochemical parameters varied significantly across seasons and reservoirs, but differences in diversity and distribution patterns between wet and dry seasons were minimal. The Jinlan Reservoir exhibited lower Shannon and Simpson diversity indices and evenness compared to other reservoirs, consistent with the relative eDNA abundance of H. molitrix and H. nobilis, suggesting that the dominance of these species may overshadow the effects of seasonal and regional abiotic and biotic factors. This conclusion warrants further validation.
Physicochemical parameters can influence fish community composition and diversity through bottom-up effects [49,50]. However, the specific parameters affecting communities vary due to differences in latitude, altitude, season, geographic location, watershed area, water quality, and species-specific environmental requirements [51]. For example, CODMn has been shown to negatively impact native species richness in Beijing [52], while water temperature and NO3-N were found to be negatively correlated with fish distribution in the upper Yangtze River [51], and NH3-N and CODMn were reported to influence fish distribution in the middle and lower Yangtze. In this study, Chao1 richness was negatively correlated with Con, pH, PO4-P, and TN/TP, while Shannon and Simpson diversity indices were negatively correlated with NO3-N. These physicochemical parameters varied significantly across seasons and reservoirs (except for PO4-P), suggesting their limited influence on fish diversity. A redundancy analysis (RDA) revealed that CODMn and NO3-N were negatively correlated with herbivorous species but positively correlated with planktivorous species. Given the deep morphology of reservoirs, which facilitates particle sedimentation and minimizes sediment resuspension, the suspended particles in the water column are primarily planktonic organisms. Thus, increased concentrations of CODMn and NO3-N may promote the development of planktonic organisms, which shifts the fish community from being dominated by herbivorous species to being dominant by planktivorous ones [53]. This is consistent with the trend of increased nutrient enrichment driving lakes from macrophyte-dominated to phytoplankton-dominated states [54]. However, although CODMn and NO3-N were key drivers of plankton dynamics [55], the biomass of H. molitrix and H. nobilis biomass was not significantly correlated with CODMn or NO3-N concentrations. The result demonstrated that a stronger influence of non-environmental factors, such as targeted fish stocking or top-down trophic cascades over direct nutrient effects on fish community in the silver carp- and bighead carp-dominant reservoirs [56,57], which is consistent with the above conclusion.

5. Conclusions

Seasonal variations and reservoir characteristics significantly influence physicochemical parameters, fish species composition, and ecological traits. However, their effects on community diversity and distribution patterns may be overshadowed by the overwhelming biomass of H. molitrix and H. nobilis in the reservoirs. While physicochemical factors exert some influence on community structure and diversity, their impact is in-sufficient to alter the fundamental characteristics of the fish communities in these reservoirs. Future research will focus on long-term monitoring stocking/removal of silver carp and bighead carp to better understand their impacts on fish community structure and diversity. Comparative studies across multiple reservoirs and meta-analyses will help identify common patterns and unique features, informing comprehensive management strategies. Integrated management approaches that consider ecological, social, and economic factors are essential for sustainable ecosystem protection.

Author Contributions

Conceptualization, J.T. and M.L.; methodology, J.T.; software, Y.D. (Yangxin Dai); validation, L.T.; formal analysis, Y.D. (Yangxin Dai); investigation, J.T., M.L., L.T., Z.X. and Y.D. (Yulai Dai); resources, J.T.; data curation, J.T.; writing—original draft preparation, J.T.; writing—review and editing, Y.D. (Yangxin Dai), B.L. and W.H.; visualization, Y.D. (Yangxin Dai); supervision, F.H.; project administration, W.H.; funding acquisition, Y.D. (Yangxin Dai) and B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China, grant number LHZY24C190002, and the Jinhua Science and Technology Program Project, grant number 2022-2-012. The APC was funded jointly by the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China and the Jinhua Science and Technology Program Project.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

We would like to express our sincere gratitude to the Joint Funds of the Zhejiang Provincial Natural Science Foundation of China and the Jinhua Science and Technology Program Project for their financial and organizational support. Their contributions have been instrumental in enabling us to conduct this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. A map of the study area. Upper panel: The location of the Qiantang River Basin in Southeastern China. Lower panel: The four sampled reservoirs and the location of sampling sites in the study (the Tongjiqiao Reservoir, Andi Reservoir, Shafan Reservoir, and Jinlan Reservoir).
Figure 1. A map of the study area. Upper panel: The location of the Qiantang River Basin in Southeastern China. Lower panel: The four sampled reservoirs and the location of sampling sites in the study (the Tongjiqiao Reservoir, Andi Reservoir, Shafan Reservoir, and Jinlan Reservoir).
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Figure 2. Seasonal and inter-reservoir variation in water physicochemical parameters across four sampled reservoirs of the Qiantang River Basin (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; Different letters (e.g., a, b, c) indicate significant differences at the 0.05 level).
Figure 2. Seasonal and inter-reservoir variation in water physicochemical parameters across four sampled reservoirs of the Qiantang River Basin (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; Different letters (e.g., a, b, c) indicate significant differences at the 0.05 level).
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Figure 3. Seasonal and spatial variation in ecological traits of non-shared fish species in four sampled reservoirs of the Qiantang River Basin ((a–c): seasons; (d–f): reservoirs). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
Figure 3. Seasonal and spatial variation in ecological traits of non-shared fish species in four sampled reservoirs of the Qiantang River Basin ((a–c): seasons; (d–f): reservoirs). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
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Figure 4. Fish species relative abundance in four sampled reservoirs of the Qiantang River Basin across seasons (a) and reservoirs (b). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
Figure 4. Fish species relative abundance in four sampled reservoirs of the Qiantang River Basin across seasons (a) and reservoirs (b). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
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Figure 5. Fish diversity indices in the four sampled reservoirs of the Qiantang River Basin across seasons (a) and reservoirs (b). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; * indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
Figure 5. Fish diversity indices in the four sampled reservoirs of the Qiantang River Basin across seasons (a) and reservoirs (b). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; * indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
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Figure 6. The NMDS and ANOSIM analysis of fish community in four sampled reservoirs of the Qiantang River Basin across seasons (a,c) and reservoirs (b,d). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; * indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
Figure 6. The NMDS and ANOSIM analysis of fish community in four sampled reservoirs of the Qiantang River Basin across seasons (a,c) and reservoirs (b,d). (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir; * indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
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Figure 7. Environmental factors and fish community characteristics. (a) Spearman correlation coefficients between environmental factors and fish diversity indices. (b) Relationships between shared fish community and environmental variables. (* indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
Figure 7. Environmental factors and fish community characteristics. (a) Spearman correlation coefficients between environmental factors and fish diversity indices. (b) Relationships between shared fish community and environmental variables. (* indicates significance at the 0.05 level; ** indicates significance at the 0.01 level).
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Table 1. Composition and ecological characteristics of fish species in four sampled reservoirs of the Qiantang River Basin (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
Table 1. Composition and ecological characteristics of fish species in four sampled reservoirs of the Qiantang River Basin (TJ: Tongjiqiao Reservoir; AD: Andi Reservoir; SF: Shafan Reservoir; JL: Jinlan Reservoir).
Order/FamilySpeciesEcological CharacteristicsSeasonReservoir
Ecological TypesFeeding HabitHabitat HabitWetDryTJADSFJL
Clupeiformes
EngraulidaeCoilia nasusAnadromousCarnivorousPelagic−+−+−−
Cypriniformes
Cyprinidea
DanioninaeOpsariichthys uncirostrisStream-residentCarnivorousPelagic+−++++
Opsariichthys evolansStream-residentCarnivorousPelagic++++++
Rasbora steineri *Stream-residentOmnivorousDemersal−+−−−+
Zacco platypusStream-residentOmnivorousPelagic+++−+−
LeuciscinaeCtenopharyngodon idellaRiver-residentHerbivorousDemersal−+++++
Elopichthys bambusaRiver-residentCarnivorousPelagic−+−+−+
Mylopharyngodon piceusRiver-residentCarnivorousBenthic+−−+−−
CultrinaeAncherythroculter daovantieni *River-residentCarnivorousPelagic−+−++−
Ancherythroculter nigrocaudaRiver-residentCarnivorousPelagic++++++
Culter alburnusRiver-residentCarnivorousPelagic+−++++
Chanodichthys erythropterusRiver-residentCarnivorousPelagic−+−−−+
Hemiculter tchangi *River-residentOmnivorousPelagic++++++
Megalobrama amblycephalaRiver-residentHerbivorousDemersal++++++
Megalobrama pellegrini *River-residentOmnivorousPelagic−+−−−+
Parabramis pekinensisRiver-residentHerbivorousDemersal−++−−−
Pseudohemiculter disparRiver-residentOmnivorousPelagic−+−−−+
Sinibrama melrosei *River–streamOmnivorousPelagic+−+−+−
Sinibrama wuiRiver–streamOmnivorousPelagic−+−−−+
XenocyprinaeXenocypris davidiRiver-residentOmnivorousBenthic−+−−++
BarbinaeAcrossocheilus wenchowensisStream-residentHerbivorousPelagic−++++−
Spinibarbus hollandiRiver–streamOmnivorousDemersal−+−−+−
CyprininaeCarassius gibelioRiver-residentOmnivorousBenthic++++++
Cyprinus carpioRiver–streamOmnivorousBenthic−+−+−−
GobioninaeHemibarbus maculatusRiver–streamCarnivorousBenthic+−++++
Microphysogobio fukiensisStream-residentOmnivorousDemersal−++++−
Pseudorasbora parvaStream-residentOmnivorousPelagic−++++−
Squalidus chankaensis *River-residentOmnivorousDemersal−++−−−
AcheilognathinaeRhodeus sinensisRiver-residentOmnivorousPelagic++++++
HypophthalmichthyinaeHypophthalmichthys molitrixRiver-residentPlanktivorousPelagic++++++
Hypophthalmichthys nobilisRiver-residentPlanktivorousPelagic++++++
CobitidaeMisgurnus anguillicaudatusRiver-residentOmnivorousBenthic+−−−+−
Perciformes
CallionymidaeRepomucenus olidusEstuarine-residentOmnivorousBenthic−+−−+−
EleotridaeBostrychus sinensisEstuarine-residentCarnivorousBenthic++++++
OdontobutidaeOdontobutis obscura *River-residentCarnivorousBenthic−+−+−−
GobiidaeRhinogobius similis *Stream-residentCarnivorousBenthic−+++−+
MastacembelidaeMacrognathus aculeatusRiver-residentCarnivorousBenthic−+−+−−
SerranidaeSiniperca obscuraStream-residentCarnivorousDemersal−+−+++
ChannidaeChanna argusRiver-residentCarnivorousBenthic+−−−−+
CentrarchidaeMicropterus salmoidesRiver-residentCarnivorousDemersal++++++
CichlidaeCoptodon zillii *River-residentOmnivorousDemersal+−+−−−
Siluriformes
BagridaePelteobagrus fulvidracoRiver-residentCarnivorousBenthic+−−+−+
Mugiliformes
MugilidaeMugil cephalusRiver–coastalOmnivorousBenthic++−+++
Synbranchiformes
SynbranchidaeMonopterus albusRiver-residentCarnivorousBenthic+−−−+−
Note: *: unlisted in update checklist of freshwater fishes in Zhejiang Province.
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MDPI and ACS Style

Tang, J.; Dai, Y.; Li, M.; Tian, L.; Lou, B.; Huang, F.; Xie, Z.; Dai, Y.; He, W. Environmental DNA Reveals Ecologically Relevant Temporal and Spatial Variation of Fish Community in Silver Carp- and Bighead Carp-Dominant Drinking Water Reservoirs. Water 2025, 17, 1057. https://doi.org/10.3390/w17071057

AMA Style

Tang J, Dai Y, Li M, Tian L, Lou B, Huang F, Xie Z, Dai Y, He W. Environmental DNA Reveals Ecologically Relevant Temporal and Spatial Variation of Fish Community in Silver Carp- and Bighead Carp-Dominant Drinking Water Reservoirs. Water. 2025; 17(7):1057. https://doi.org/10.3390/w17071057

Chicago/Turabian Style

Tang, Jinyu, Yangxin Dai, Ming Li, Lei Tian, Bao Lou, Fuyong Huang, Zhigang Xie, Yulai Dai, and Wenfang He. 2025. "Environmental DNA Reveals Ecologically Relevant Temporal and Spatial Variation of Fish Community in Silver Carp- and Bighead Carp-Dominant Drinking Water Reservoirs" Water 17, no. 7: 1057. https://doi.org/10.3390/w17071057

APA Style

Tang, J., Dai, Y., Li, M., Tian, L., Lou, B., Huang, F., Xie, Z., Dai, Y., & He, W. (2025). Environmental DNA Reveals Ecologically Relevant Temporal and Spatial Variation of Fish Community in Silver Carp- and Bighead Carp-Dominant Drinking Water Reservoirs. Water, 17(7), 1057. https://doi.org/10.3390/w17071057

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