{"paper_id":"4585d02d-36f4-45da-b01b-1f2f3fb92956","body_text":"Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China Jiannan Zhao, Yunni Gao, Jingxiao Zhang, Yongli Li, Xiaofei Gao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5048078/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The eco-health assessment of regional reservoirs is important for ensuring the sustainable utilization of water resources and maintenance of water security, particularly in regions facing water scarcity. The present study aimed to construct a B-IBI based on the community characteristics of macrobenthos in ten large and medium-sized reservoirs across four major river basins in Henan Province, China. The results revealed the identification of 92 species belonging to 3 phyla, 6 classes, 18 orders, 47 families. The B-IBI was established based on five key metrics, namely the number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a strong capability to distinguish the impaired sites from the reference sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province. Macrobenthos Index of Biotic Integrity Reservoirs Nitrogen Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Reservoirs play important roles in maintaining regional social, economic and environmental functions, such as flood control, agricultural irrigation, water supply, sediment reduction, hydroelectricity generation, tourism, climate regulation, navigation and transportation, biodiversity, and environmental flows (Hou et al., 2022 ). Human water demands are becoming more reliant on reservoir storage (Guo et al., 2021 ). Reservoirs contribute approximately 10% to the global natural freshwater storage capacity in lakes, and the role of reservoirs in global water security and sustainable development will become more critical, particularly in regions where water scarcity and variability prevail (Guo et al., 2021 ; Lehner et al., 2011 ). However, the intensification of anthropogenic activities and climate change has led to varying degrees of decline in water quality and ecological degradation in reservoirs(Leigh et al., 2010 ; Liu et al., 2019 ; Mendes et al., 2022 ; Park et al., 2018 ). Therefore, it is increasingly crucial to assess the ecosystem health of reservoirs for the utilization of water resources and maintenance of water security. The ecosystem health of reservoirs is characterized by an intricate and dynamic equilibrium of diverse biotic and abiotic components that synergistically uphold the system’s integrity and resilience (Costanza et al., 1992 ; Molozzi et al., 2012 ; Han et al., 2014 ; Banerjee et al., 2017 ). The assessment of reservoirs’ environmental status has predominantly relied on indices that are based on multiple physicochemical parameters, i.e. , the trophic state index and water quality index (Uddin et al., 2021 ), where biological indexes are less used. Due to the growing prevalence of eutrophication and algal bloom issues in reservoirs worldwide, plankton community-based approaches were developed to indicate eco-environmental status in reservoirs (Liu et al., 2019 ; Park et al., 2018 ; Qin et al., 2023 ; Te and Gin, 2011 ; Xiong et al., 2022 ). Some earlier studies have developed bioindicators based on fish community in reservoirs (Terra and Araújo, 2011 ) . Comparatively speaking, macrobenthos are the most commonly used assemblages among freshwater organisms in bioassessment worldwide due to their bottom-dwelling lifestyle, a relatively long life-span, high diversity and sensitivity to various disturbances (Bonada et al., 2006 ; Jun et al., 2012 ; Resh, 2008 ). Macrobenthos have become indispensable for river ecological monitoring and evaluation in numerous countries, including China (Hargett et al., 2007 ; Hu et al., 2022 ; Sarrazin-Delay et al., 2014 ; Szoszkiewicz et al., 2006 ; Zhang et al., 2019 ). Due to their relatively long lifespan and bottom-dwelling lifestyle, they also offer certain advantages in assessing the ecological status of reservoirs in comparison to other aquatic organisms. But biological assessment based on macrobenthos in reservoirs requires further endeavors (Molozzi et al., 2012 ). The index of biotic integrity (IBI) approach is currently used worldwide to assess ecological health of various waterbodies (Davy-Bowker et al., 2006 ; Molozzi et al., 2012 ). It compares the integrity of communities found in test sites with those in reference sites while minimizing human disturbances (Bonada et al., 2006 ). The index of biotic integrity, first introduced by Karr in (1981), is one comprehensive bioassessment tool that integrates the responses of multiple metrics to various types of human impact and relies on reference conditions (Astin, 2007 ; Beck and Hatch, 2009 ; De-La-Ossa-Carretero et al., 2016 ; Wang et al., 2023 ). The IBI is created by combining a range of individual measurements that encompass various aspects of a community, including its diversity and composition in terms of taxonomy, species adaptability and functional groups (Kaboré et al., 2022 ). The first IBI approach based on macrobenthos were used to assess eco-health of rivers (Dj et al., 2003 ; Macedo et al., 2016 ), and have been widely used to judge biological conditions in lotic waters such as rivers and streams more than in lentic waters including reservoirs. Henan Province is the only province that spans the Yangtze, Huai, Yellow and Hai Rivers in China. However, the regional and temporal water scarcity in Henan Province is serious. Hence, more than 2500 reservoirs have been constructed to provide uninterrupted water supply and maintain region's water security. The aquatic environment of the province has been significantly influenced by human activities and rapid socio-economic development. In addition to the ecological monitoring and evaluation of individual reservoirs (Qin et al., 2023 ), the ecological environment quality of most reservoirs lacks operative monitoring and evaluation, and relevant work is urgently needed. Hence, ten large and medium-sized reservoirs distributed across four basins in Henan Province were selected, to develop a B-IBI based on the macrobenthos community characteristics to assess their eco-health. It will provide a suitable method for evaluating relatively long-term impacts of human disturbance on reservoir ecology. The present study is believed to be the pioneering effort in developing a B-IBI for the purpose of monitoring and comparing eco-health of reservoirs across four major river basins. Results 2. Macrobenthos community characteristics A total of 90 species belonging to 3 phyla, 6 classes, 17 orders, 45 families, and 81 genera were identified in the 10 reservoirs. Fifty-seven species belonging to the Class Insecta were identified, primarily including families Heptageniidae、Libellulidae and Chironomidae. Nineteen species mainly belonging to families Planorbidae, Lymnaeidae, Bithyniidae of the Class Gastropoda were found. There were 6 species, 4 species, 4 species, and 2 species belonging to the Classes Bivalvia, Crustacea, Oligochaeta and Clitellata, respectively. The families Tubificidae (Class Oligochaeta), Chironomidae (Class Insecta), and Palaemonidae (Class Crustacea) exhibited widespread dominance in the investigated reservoirs. The reservoirs situated within the Huai and Yangtze River basins exhibited a relatively higher overall species richness compared to those in the Yellow and Hai River basins. Specifically, the QP in the Huai River basin and the YHK in the Yangtze River basin exhibited particularly high species richness, with up to 17 taxa identified at specific sampling sites. The XX, also located in the Yangtze River basin, displayed a relatively diverse overall taxonomic composition. In contrast, the reservoirs in the Yellow and Hai River basins demonstrated a less diverse taxonomic composition, with some sites only harboring two identified taxa (Figure. 1a). At the family level, across all 44 sampling sites within the 10 reservoirs, Chironomidae, Palaemonidae, and Tubificidae emerged as the dominant taxonomic groups, with Chironomidae being the most widely distributed, present at 35 of the sampling locations (Figure. 1b). The abundance of Phylum Arthropoda and Mollusca was higher in all reservoirs compared to Phyla Annelida, which was absent specifically in XLD Reservoir (Fig. 2 ). The Class Insecta belonging to Phyla Arthropoda was dominant in Reservoirs XX and YHK within the Yangtze River basin, QTH within the Yellow River basin, CSD within the Huai River basin and BQ within the Hai River basin. The Class Crustacea belonging to Phyla Arthropoda was dominant in Reservoirs GX and XLD within the Yellow River basin. The Classes Insecta (Phyla Arthropoda) and Gastropoda (Phyla Mollusca) exhibited dominance in Reservoirs QP, SYH and CSD within the Huai River basin (Figure. 2). 2.2. Development of B-IBI Among the 30 candidate metrics of macrobenthos, eight metrics demonstrated a robust discriminatory capability between 11 reference and 33 impaired sites (IQ ≥ 2, Figure. 3). They are Number of taxa (M1), Number of Crustacean and Mollusca taxa (M6), Number of Intolerant taxa (M13), Intolerant % (M15), Tolerant % (M16), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The results of Spearman correlation analyses showed strong correlation between M1 and M27, M13 and M15, M16 and M17. The metrics M1, M13 and M16 were excluded due to their stronger correlations with other parameters. The five metrics M6, M15, M17, M18, M27 were selected for the construction of B-IBI due to their low correlation (r < 0.75) with each other. The B-IBI index demonstrated a normal distribution across all sampling sites, and significant differences (IQ ≥ 2) in the B-IBI scores were observed between the reference and impaired sites, thus confirming the reliability of the assessment index (Figure. 4). 2.3. Assessment of reservoir health The B-IBI scores of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated that there were 12 sites classified as excellent, 11 sites classified as good, 12 sites classified as fair, 6 sites classified as poor and 3 sites classified as very poor (Figure. 5a). According to the median B-IBI score of sites for each reservoir, only one reservoir (QP in Huai River basin) achieved an excellent classification, four reservoirs (GX and XLD in the Yellow River basin, XX in the Yangtze River basin, CSD in the Huai River basin) were classified as good. Three reservoirs (SYH in the Huai River basin, YHK in the Yangtze River basin, BQ in the Hai River basin) received a fair classification. Two reservoirs (QTH and HKC in the Yellow River basin) were categorized as poor. No reservoir was rated as very poor (Figure. 5b). According to the median B-IBI scores of sites within each basin, the reservoirs in the Huai and Yangtze River basins were classified as good, while those in the Yellow and Hai River basins were categorized as fair (Figure. 5c). The B-IBI negatively correlated with TN and EC ( p < 0.05). TN demonstrated significant negative correlations with the B-IBI metrics M15, M18, and M27 ( p < 0.05). Specifically, M15 positively correlated with pH, while negatively correlated with EC and TN ( p < 0.05). Furthermore, M18 displayed negative correlations with temperature (TEMP) and TN, while showing positive correlations with turbidity (TURB) and ammonia nitrogen (NH 4 + -N) Lastly, M27 exhibited a positive correlation with pH, but was negatively correlated with TN (Figure. 6). Discussion 3.1 Community characteristics of macrobenthos The macrobenthos in the investigated reservoirs primarily consisted to the phyla Arthropoda and Mollusca, which are also commonly found in other freshwater lotic and lentic ecosystems (Yan Li et al., 2023 ; Shi et al., 2017 ). The intolerant taxa identified in the reservoirs all belonged to the aforementioned phyla. Species belonging to the Phylum Annelida are characterized by high pollution tolerance (Blocksom et al., 2002; Castellanos Romero et al., 2017 ; Hilsenhoff, 1987 ). The absence of the Phylum Annelida in Reservoir XLD, and the high richness and/or relative abundance of the intolerant taxa in Reservoirs GX, XLD, XX, and QP collectively suggest that these reservoirs may experience minimal pollution pressure. The B-IBI index assessed their health status as good or excellent, indicating a strong alignment between the community characteristics of macrobenthos and the health assessment. Crustacea dominated the reservoirs in the Yellow River Basin, while Insecta dominated the Yangtze and Hai River basins. In addition, both Insecta and Gastropoda dominated the Huai River basin. It indicated that the community structure exhibited obvious differences in the investigated reservoirs across the four basins at the higher taxa level. However, we did not observe statistically significant differences at the lower taxa level. The B-IBI metrics selected in this study are derived from the taxonomic richness and relative abundance of lower taxa levels. The similarity of benthic animal community structures across various water bodies at lower taxonomic levels should serve as a crucial criterion for the development and implementation of an identical evaluation system(Yanli Li et al., 2023 ; Poikane et al., 2016 ; Zhang et al., 2019 ). Therefore, we propose that the constructed B-IBI evaluation system can be applied to reservoirs in multiple river basins within Henan Province, even throughout the Central Plains in China. 3.2 Construction of B-IBI The utilization of multi-metric indices of biological condition has been widely implemented on a global scale due to their practicality in incorporating diverse biological metrics across various levels of ecological organization. The primary challenge lies in distinguishing natural variability from anthropogenic impacts when developing and applying these multi-metric indices. Reference sites is crucial in the construction of a useful IBI. However, finding anthropogenically undisturbed sites is rare. Therefore, the least-disturbed reference condition is most commonly utilized. Water quality and land use variables are the predominant criteria employed to determine the reference condition (Ruaro et al., 2020 ). A distinct disparity was observed between the reference and repaired sites, despite being situated in four different river basins with varying hydrology, geomorphology, and biogeochemistry. Five metrics including number of Crustacean and Mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27), were screened and combined into B-IBI index to assess the health conditions of the 10 reservoirs in Henan Province. The metrics, particularly M6, M15, M17 and M27, are widely utilized in multi-metric indices based on macroinvertebrates such as B-IBI (Ruaro et al., 2020 ). The typical responses of macrobenthos to environmental stress encompass a reduction in species richness, an augmentation in the abundance of tolerant taxa, a decline in the diversity and density of sensitive taxa, as well as a simplification of food webs (Burdon et al., 2016 ; Graeber et al., 2017 ). The species belonging to Crustacea and Mollusca are predominantly distributed in habitats with lower and intermediate levels of disturbance. The number of Crustacean and Mollusca taxa (M6) is highly responsive to change in water quality, making it widely incorporated into assessment methodologies (Yanli Li et al., 2023 ). The number of Crustacean and Mollusca taxa (M6) in the reference sites were much higher than that in the repaired sites, indicating that M6 serves as a highly sensitive indicator for assessing environmental stress in the current study. The Shannon-Wiener diversity index (M27) is commonly employed in biological assessments involving macroinvertebrates (Kaboré et al., 2022 ; You et al., 2021 ). It quantifies diversity by considering both the number and relative abundance of the present species (Fierro et al., 2018 ; Ndatimana et al., 2023 ). In the current study, the Shannon-Wiener diversity index (M27) is significantly correlated with taxa richness (r = 0.827, p < 0.01). but taxa richness was less included in bioassessment approaches, as maximum richness might occur at intermediate disturbance in most ecosystems(Svensson et al., 2007 ). We observed a significantly lower Shannon-Wiener diversity index (M27) at the repaired sites. Furthermore, the species composition at the repaired sites was predominantly comprised of pollution-tolerant species such as Famliy Tubificida, while pollution-intolerant sensitive species like Ephemerotera and Trichoptera were considerably less abundant. Sensitivity and /or tolerance indices were the most reliable metric category(Poikane et al., 2016 ). Another three retained metrics pertain to the richness and relative abundance of tolerant or intolerant taxa in response to environmental pollution. Intolerance % (M15) denotes the proportion of more sensitive benthic species relative to the total abundance of macrobenthos at each sampling site. The BMWP index (M18) incorporates the sensitivity values of taxonomic units at the family level and is less stringent for species classification (Castellanos Romero et al., 2017 ). The BI index (M17), however, takes into account the tolerance values of the species and imposes higher requirements on species classification, which exhibited the most robust associations with environmental factors (Liu et al., 2024 ). The correlation coefficients of the three metrics are lower than 0.75, and the significant differences were observed between the reference sites and repaired sites. 3.3 Eco-health of Reservoirs in Henan Province The B-IBI index demonstrates a robust discriminatory capacity between reference and impaired sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province. Furthermore, it can effectively discriminate among different sites within an individual reservoir. Notably, all reservoirs except for HKC contains sites classified as excellent or good grades. Interestingly, the eco-health status of the four reservoirs in the Yellow River basin ranges from poor to good levels, highlighting their relative dependence. The reason for this could be that, with the exception of XLD, which is a channel-type reservoir located on the Yellow River, the remaining reservoirs lack direct connectivity with the main water body of the basin(Zhao et al., 2022 ). Compared with the reservoirs in central and southern parts of Henan Province, the ecological status of the investigated reservoirs in the north, such as HKC, QTH and BQ were much worser. The water scarcity in the north was more serious than that in the other parts of Henan Province(Zhou et al., 2023 ). Hence, the water quality management in the reservoirs of the north is urgent. The TN concentrations was negatively correlated with scores of the B-IBI index, and several core metrics including M15, M18, and M27. In the Jincheng region of Qin River, specifically in the upstream area of the HKC reservoir, nitrogen and ammonium were identified as key drivers influencing macrobenthos community characteristics (Yanli Li et al., 2023 ). Increased concentrations of nitrogen favored tolerant species (Su et al., 2019 ). The average TN concentration in HKC, QTH and BQ was 4.06 mg L − 1 , much higher than that in the other six reservoirs (1.87 mg L − 1 ). Responsiveness to anthropogenic pressures is considered a crucial factor for evaluating method performance. The B-IBI index and its core metrics exhibited significant correlations with environmental variables, particularly for TN, thereby validating the robustness of the B-IBI. Materials and methods 4.1. Study area Henan Province is situated in the east-central region of China, spanning between 110.35°-116.65°E and 31.38°-36.37°N, covers a total area of 167,000 km 2 . The majority of the province is characterized by a temperate monsoon climate. Henan Province is a major agricultural region with a population exceeding 99 million. The effective management of reservoirs are of utmost importance to both human livelihoods and agricultural productivity. We sampled 44 sites from 10 large and medium-sized reservoirs in Henan Province during August 2021 (Table 1 ). The sampling sites mainly represent the inlet, center, and outlet of each reservoir, ensuring a comprehensive coverage of the reservoir's water body (Figure. 7). Table 1 The basic information of the 10 reservoirs in Henan Province Reservoir Abbreviation River basin Site No. Storage capacity (10 8 m 3 ) Functions Baoquan BQ Hai River S1-S5 6.3 (2, 3, 5) Qingtianhe QTH Yellow River S6-S10 0.2 (1, 5, 6) Hekoucun HKC Yellow River S11-S14 3.17 (1, 3) Guxian GX Yellow River S21-S23 11.75 (2, 3, 5) Xiaolangdi XLD Yellow River S24-S26 51 (1, 2, 3, 4) Xixia XX Yangtze River S27-S30 0.9 (5) Yahekou YHK Yangtze River S31-S35 13.39 (1,2,3) Qianping QP Huai River S15-S20 5.93 (1,2,3,5) Suyahu SYH Huai River S36-S39 16 (1,2,3) Chushandian CSD Huai River S40-S44 12.51 (1,2,3) Notes: 1, water supply; 2, agricultural irrigation; 3, flood control; 4, sediment reduction; 5, hydroelectric power generation; 6, tourism 4.2. Field sampling and data collection Three subsamples at each site were collected using a modified Peterson grab sampler (0.0625 m 2 ). The collected mud samples were washed on-site using a 60-mesh sieve, and the remaining net contents containing invertebrates were immediately placed into labeled plastic bags containing 95% ethanol. The sieved samples were emptied into white porcelain plates to pick out all macrobenthos in laboratory, which were preserved in 100 mL plastic bottles with 95% ethanol before identification. The identification of benthos was carried out using a dissecting and a compound microscope (SMZ800N and Ci-L, Olympus, Japan). The benthos collected at each site were meticulously enumerated in order to calculate the density of each taxonomic unit of macrobenthos per unit area. Data of physicochemical parameters were also collected for each sampling site. water temperature (TEMP), pH, dissolved oxygen (DO), and conductivity (EC) were measured in situ using a portable water quality analyzer (HACH). Additional water samples were collected for further laboratory analysis of turbidity (TURB), total nitrogen (TN), ammonia nitrogen (NH 4 + -N), permanganate index (COD Mn ), and total phosphorus (TP). The water samples were stored below 4°C and transported to the laboratory within 24 hours to maintain sample integrity. The analysis of these indicators followed the guidelines specified in the \"Environmental Quality of Surface Water in the People's Republic of China\" (GB3838-2002). 4.3. Development of the B-IBI 4.3.1. Selection of reference sites Under ideal conditions, reference sites should have experienced little disturbance from anthropogenic activities(Steedman, 1994 ; Davy-Bowker et al., 2006 ; Zhang et al., 2019 ). Given its high population density and role as a major agricultural production area and significant province for mineral resources in Henan Province, it presents challenges in identifying reference sites that strictly adhere to the definition of being undisturbed. Here water quality and local habitat status were mainly used to filter reference sites. The water quality, as determined by the levels of TN, TP, NH 4 + -N and COD Mn , should meet or exceed Class III standards according to China’s Surface Water Environmental Quality Standard. Secondly, the site should be free from any dams, agricultural land, residential areas, and roads in its vicinity. The construction of the B-IBI involved screening 11 reference sites (Figure. 7). 4.3.2 Metric selection The study compiled a total of thirty candidate metrics for benthic macroinvertebrate assemblages, which were further categorized into seven distinct groups: species composition, community composition, pollution tolerance, tropic status, nutritional structure, habitat and diversity index (Table S1 )(You et al., 2021 ; Zhu et al., 2023 ). The candidate metrics were subjected to box and whisker plot-tests. The degree of inter-quartile overlap (IQ) in the boxplots was utilized to assess the discriminatory power of each metric (Figure S1 ). Metrics were retained if they exhibited a significant discriminative ability (IQ ≥ 2) between the reference and impaired sites. The retained metrics were used to conduct a Spearman correlation analysis to examine the redundancy of metrics, with a criterion set at a minimum correlation coefficient of 0.75 ( p < 0.05, Table S2). The Kruskal-Wallis test was subsequently employed to assess the discriminatory power of the redundant metrics more accurately between reference and impaired sites. Only the metric with the higher chi-squared value was retained (You et al., 2021 ). 4.3.3. Standardization of core metrics The key measurements displayed a wide range of raw values, necessitating the standardization of each metric as a score using the ratio technique(Ka et al., 2002 ). The score values ranged from 0 to 1, and any metric value exceeding 1 was capped at a maximum of 1(Table S3). For metrics that decreased with disturbance, the anticipated value (V95%) for all samples was assigned as the standardized value based on the 95th percentile of the assessment metric, following Eq. (1). Conversely, for metrics that increased with disruption, the anticipated value (V5%) was determined by considering the standardized value derived from the 5th percentile of the evaluation metric using Eq. ( 2 ). Herein, BIn represents the calculated standardized value of an assessment metric, Vn denotes its actual measured value, and Vmax signifies the maximum among all samples considered in the study. The final B-IBI score at each site is obtained by summing up the calculated index scores, and the 25th percentile of the B-IBI value at the reference sites is defined as excellent. The B-IBI values below this threshold are categorized into four equal grades, resulting in four additional grades. The B-IBI score for each reservoir and river basin is derived by computing the median value of B-IBI scores across all monitoring sites of each reservoir and each river basin. 4.4. Statistical analyses The distribution map of sampling sites and the result map were created using ArcMap 10.8 software. RStudio software was utilized for data analysis and spatial distribution plotting. Origin 2023 software was employed for graphical and descriptive analysis of other data. IBM SPSS Statistics 26 software was used to conduct Spearman correlation analysis. Conclusions The community characteristics of macrobenthos were investigated in ten large and medium-sized reservoirs across four basins (the Hai, Yellow, Yangtze and Huai Rivers) in Henan Province during August 2021, followed by the establishment of the B-IBI index incorporating into five core metrics including number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). 92 species were identified in 3 phyla, 6 classes, 18 orders, 47 families in 44 sites of the 10 reservoirs. The dominant class in the reservoirs across the four basins was different, but the community structure at lower taxa level did not exhibit significant difference. The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated that 12, 11, 12, 6 and 3 sites were categorized as excellent, good, fair, poor, and very poor levels, respectively. Two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a robust capacity to discriminate between reference and impaired sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province. A significant inverse correlation was observed between total nitrogen (TN) levels and the B-IBI scores, suggesting that the B-IBI effectively reflects the impact of nitrogen pollution. The management of reservoirs, particularly those located in the northern region of Henan Province, should prioritize the reduction of nitrogen pollution input. Declarations Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Acknowledgements This work was funded by the Project of Yellow River Fisheries Resources and Environment Investigation from the MARA, P. R. China, the Major Science and Technology Program in Henan Province (232102320250), and the Breeding Project of Henan Normal University (HNU2021PL05). Author contributions J.N.Z was responsible for data processing, image production, and the primary composition of the manuscript. Y.N.G contributed to data collection, drafting of the manuscript, and subsequent revisions. J.X.Z., Y.L.L., X.F.G., H.T.Y., J.D., and X.J.L. participated in the survey and data gathering efforts. Competing interests The authors declare no competing interests. References Astin, L.A.E., 2007. Developing biological indicators from diverse data: The Potomac Basin-wide Index of Benthic Integrity (B-IBI). Ecol Indic 7, 895–908. https://doi.org/10.1016/j.ecolind.2006.09.004. Banerjee, A., Chakrabarty, M., Rakshit, N., Mukherjee, J., Ray, S., 2017. Indicators and assessment of ecosystem health of Bakreswar reservoir, India: An approach through network analysis. Ecol Indic 80, 163–173. https://doi.org/10.1016/j.ecolind.2017.05.021. Beck, M.W., Hatch, L.K., 2009. A review of research on the development of lake indices of biotic integrity. Environmental Reviews 17, 21–44. https://doi.org/10.1139/A09-001. Bonada, N., Prat, N., Resh, V.H., Statzner, B., 2006. Developments in aquatic insect biomonitoring: A comparative analysis of recent approaches. Annu Rev Entomol 51, 495–523. https://doi.org/10.1146/annurev.ento.51.110104.151124. Burdon, F.J., Reyes, M., Alder, A.C., Joss, A., Ort, C., Räsänen, K., Jokela, J., Eggen, R.I.L., Stamm, C., 2016. Environmental context and magnitude of disturbance influence trait-mediated community responses to wastewater in streams. Ecol. Evol. 6, 3923–3939. https://doi.org/10.1002/ece3.2165. Castellanos Romero, K., Pizarro Del Río, J., Cuentas Villarreal, K., Costa Anillo, J.C., Pino Zarate, Z., Gutierrez, L.C., Franco, O.L., Arboleda Valencia, J.W., 2017. Lentic water quality characterization using macroinvertebrates as bioindicators: An adapted BMWP index. Ecol. Indic. 72, 53–66. https://doi.org/10.1016/j.ecolind.2016.07.023. Costanza, R., Norton, B., Haskell, B., 1992. Ecosystem health:new goals for environmental management. Washington D.C.:Island Press. Davy-Bowker, J., Clarke, R.T., Johnson, R.K., Kokes, J., Murphy, J.F., Zahrádková, S., 2006. A comparison of the European Water Framework Directive physical typology and RIVPACS-type models as alternative methods of establishing reference conditions for benthic macroinvertebrates. Hydrobiologia 566, 91–105. https://doi.org/10.1007/s10750-006-0068-5. De-La-Ossa-Carretero, J.A., Lane, M.F., Llansó, R.J., Dauer, D.M., 2016. Classification efficiency of the B-IBI comparing water body size classes in Chesapeake Bay. Ecol Indic 63, 144–153. https://doi.org/10.1016/j.ecolind.2015.12.010. Dj, K., Ka, B., Fa, F., At, H., Rm, H., Pr, K., Dv, P., Jl, S., Wt, T., Mb, G., Ws, D., 2003. Development and evaluation of a Macroinvertebrate Biotic Integrity Index (MBII) for regionally assessing Mid-Atlantic Highlands Streams. Environ. Manage. 31. https://doi.org/10.1007/s00267-002-2945-7. Fierro, P., Arismendi, I., Hughes, R.M., Valdovinos, C., Jara-Flores, A., 2018. A benthic macroinvertebrate multimetric index for Chilean Mediterranean streams. Ecol. Indic. 91, 13–23. https://doi.org/10.1016/j.ecolind.2018.03.074. Graeber, D., Jensen, T.M., Rasmussen, J.J., Riis, T., Wiberg-Larsen, P., Baattrup-Pedersen, A., 2017. Multiple stress response of lowland stream benthic macroinvertebrates depends on habitat type. Sci. Total Environ. 599–600, 1517–1523. https://doi.org/10.1016/j.scitotenv.2017.05.102. Guo, Z., Boeing, W.J., Borgomeo, E., Xu, Y., Weng, Y., 2021. Linking reservoir ecosystems research to the sustainable development goals. Science of the Total Environment 781, 146769. https://doi.org/10.1016/j.scitotenv.2021.146769. Han, J.H., Kim, B., Kim, C., An, K.G., 2014. Ecosystem health evaluation of agricultural reservoirs using multi-metric lentic ecosystem health assessment (LEHA) model. Paddy and Water Environment 12, 7–18. https://doi.org/10.1007/s10333-014-0444-0. Hargett, E.G., ZumBerge, J.R., Hawkins, C.P., Olson, J.R., 2007. Development of a RIVPACS-type predictive model for bioassessment of wadeable streams in Wyoming. Ecol Indic 7, 807–826. https://doi.org/10.1016/j.ecolind.2006.10.001. Hilsenhoff, W.L., 1987. An Improved Biotic Index of Organic Stream Pollution. Gt. Lakes Entomol. 20. https://doi.org/10.22543/0090-0222.1591. Hou, J., Van Dijk, A.I.J.M., Beck, H.E., Renzullo, L.J., Wada, Y., 2022. Remotely sensed reservoir water storage dynamics (1984-2015) and the influence of climate variability and management at a global scale. Hydrol Earth Syst Sci 26, 3785–3803. https://doi.org/10.5194/hess-26-3785-2022. Hu, X., Zuo, D., Xu, Z., Huang, Z., Liu, B., Han, Y., Bi, Y., 2022. Response of macroinvertebrate community to water quality factors and aquatic ecosystem health assessment in a typical river in Beijing, China. Environ Res 212, 113474. https://doi.org/10.1016/j.envres.2022.113474. Jun, Y.C., Won, D.H., Lee, S.H., Kong, D.S., Hwang, S.J., 2012. A multimetric benthic macroinvertebrate index for the assessment of stream biotic integrity in Korea. Int J Environ Res Public Health 9, 3599–3628. https://doi.org/10.3390/ijerph9103599. Ka, B., Jp, K., Dj, K., Fa, F., Sm, C., 2002. Development and evaluation of the Lake Macroinvertebrate Integrity Index (LMII) for New Jersey lakes and reservoirs. Environ. Monit. Assess. 77. https://doi.org/10.1023/a:1016096925401. Kaboré, I., Ouéda, A., Moog, O., Meulenbroek, P., Tampo, L., Bancé, V., Melcher, A.H., 2022. A benthic invertebrates-based biotic index to assess the ecological status of West African Sahel Rivers, Burkina Faso. J. Environ. Manage. 307. https://doi.org/10.1016/j.jenvman.2022.114503. Karr, J.R., 1981. Assessment of Biotic Integrity Using Fish Communities. Fisheries (Bethesda) 6, 21–27. https://doi.org/10.1577/1548-8446(1981)006<0021:aobiuf>2.0.co;2. Lehner, B., Liermann, C.R., Revenga, C., Vörömsmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., Rödel, R., Sindorf, N., Wisser, D., 2011. High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management. Front Ecol Environ 9, 494–502. https://doi.org/10.1890/100125. Leigh, C., Burford, M.A., Roberts, D.T., Udy, J.W., 2010. Predicting the vulnerability of reservoirs to poor water quality and cyanobacterial blooms. Water Res 44, 4487–4496. https://doi.org/10.1016/j.watres.2010.06.016. Li, Yan, He, Y., Liu, M., Uddin, K.B., Zhao, Y., Wang, Haijun, Cui, Y., Wang, Hongzhu, 2023. Benthic macroinvertebrate assemblages in relation to high ammonia loading: A 5-year fertilization experiment in 5 subtropical ponds. Environ. Pollut. 337, 122587. https://doi.org/10.1016/j.envpol.2023.122587. Li, Yanli, Li, X., Liu, Q., Xu, Z., Wang, M., 2023. Community characteristics of macroinvertebrates and ecosystem health assessment in Qin River, a main tributary of the Yellow River in China. Environ. Sci. Pollut. Res. 30, 56410–56424. https://doi.org/10.1007/s11356-023-26314-9. Liu, G., Qi, X., Lin, Z., Lv, Y., Khan, S., Qu, X., Jin, B., Wu, M., Oduro, C., Wu, N., 2024. Comparison of different macroinvertebrates bioassessment indices in a large near-natural watershed under the context of metacommunity theory. Ecol. Evol. 14, 1–15. https://doi.org/10.1002/ece3.10896. Liu, L., Chen, H., Liu, M., Yang, J.R., Xiao, P., Wilkinson, D.M., Yang, J., 2019. Response of the eukaryotic plankton community to the cyanobacterial biomass cycle over 6 years in two subtropical reservoirs. ISME Journal 13, 2196–2208. https://doi.org/10.1038/s41396-019-0417-9. Macedo, D.R., Hughes, R.M., Ferreira, W.R., Firmiano, K.R., Silva, D.R.O., Ligeiro, R., Kaufmann, P.R., Callisto, M., 2016. Development of a benthic macroinvertebrate multimetric index (MMI) for Neotropical Savanna headwater streams. Ecol. Indic. 64, 132–141. https://doi.org/10.1016/j.ecolind.2015.12.019. Macedo, D.R., Hughes, R.M., Ferreira, W.R., Firmiano, K.R., Silva, D.R.O., Ligeiro, R., Kaufmann, P.R., Callisto, M., 2016. Development of a benthic macroinvertebrate multimetric index (MMI) for Neotropical Savanna headwater streams. Ecol. Indic. 64, 132–141. https://doi.org/10.1016/j.ecolind.2015.12.019. Mendes, C.F., dos Santos Severiano, J., Moura, G.C. de, dos Santos Silva, R.D., Monteiro, F.M., Barbosa, J.E. de L., 2022. The reduction in water volume favors filamentous cyanobacteria and heterocyst production in semiarid tropical reservoirs without the influence of the N:P ratio. Science of the Total Environment 816. https://doi.org/10.1016/j.scitotenv.2021.151584. Molozzi, J., Feio, M.J., Salas, F., Marques, J.C., Callisto, M., 2012. Development and test of a statistical model for the ecological assessment of tropical reservoirs based on benthic macroinvertebrates. Ecol Indic 23, 155–165. https://doi.org/10.1016/j.ecolind.2012.03.023. Ndatimana, G., Arimoro, F.O., Chukwuemeka, V.I., Assie, F.A.G.J., Action, S., Nantege, D., 2023. Development of lake macroinvertebrate-based multimetric index for monitoring ecological health in North Central Nigeria. Environ. Monit. Assess. 195, 1–21. https://doi.org/10.1007/s10661-023-12036-5. Park, B.S., Li, Z., Kang, Y.H., Shin, H.H., Joo, J.H., Han, M.S., 2018. Distinct Bloom Dynamics of Toxic and Non-toxic Microcystis (Cyanobacteria) Subpopulations in Hoedong Reservoir (Korea). Microb Ecol 75, 163–173. https://doi.org/10.1007/s00248-017-1030-y. Poikane, S., Johnson, R.K., Sandin, L., Schartau, A.K., Solimini, A.G., Urbanič, G., Arbačiauskas, K. stutis, Aroviita, J., Gabriels, W., Miler, O., Pusch, M.T., Tim, H., Böhmer, J., 2016. Benthic macroinvertebrates in lake ecological assessment: A review of methods, intercalibration and practical recommendations. Sci. Total Environ. 543, 123–134. https://doi.org/10.1016/j.scitotenv.2015.11.021. Qin, M., Fan, P., Li, Y., Wang, H., Wang, W., Liu, H., Messyasz, B., Goldyn, R., Li, B., 2023. Assessing the Ecosystem Health of Large Drinking-Water Reservoirs Based on the Phytoplankton Index of Biotic Integrity (P-IBI): A Case Study of Danjiangkou Reservoir. Sustainability 15, 5282. https://doi.org/10.3390/su15065282. Resh, V.H., 2008. Which group is best? Attributes of different biological assemblages used in freshwater biomonitoring programs. Environ Monit Assess 138, 131–138. https://doi.org/10.1007/s10661-007-9749-4. Ruaro, R., Gubiani, É.A., Hughes, R.M., Mormul, R.P., 2020. Global trends and challenges in multimetric indices of biological condition. Ecol. Indic. 110, 105862. https://doi.org/10.1016/j.ecolind.2019.105862. Sarrazin-Delay, C.L., Somers, K.M., Bailey, J.L., 2014. Using Test Site Analysis and two Nearest Neighbor Models, ANNA and RDA, to Assess Benthic Communities with Simulated Impacts. Freshwater Science 33, 1249–1260. https://doi.org/10.1086/678702. Shi, X., Liu, J., You, X., Bao, K., Meng, B., Chen, B., 2017. Evaluation of river habitat integrity based on benthic macroinvertebrate-based multi-metric model. Ecol. Model. 353, 63–76. https://doi.org/10.1016/j.ecolmodel.2016.07.001. Steedman, R.J., 1994. Ecosystem Health as a Management Goal. J. North Am. Benthol. Soc. 13, 605–610. https://doi.org/10.2307/1467856. Su, P., Wang, X., Lin, Q., Peng, J., Song, J., Fu, J., Wang, S., Cheng, D., Bai, H., Li, Q., 2019. Variability in macroinvertebrate community structure and its response to ecological factors of the Weihe River Basin, China. Ecol. Eng. 140, 1–13. https://doi.org/10.1016/j.ecoleng.2019.105595. Svensson, J.R., Lindegarth, M., Siccha, M., Lenz, M., Molis, M., Wahl, M., Pavia, H., 2007. Maximum species richness at intermediate frequencies of disturbance: Consistency among levels of productivity. Ecology 88, 830–838. https://doi.org/10.1890/06-0976. Szoszkiewicz, K., Buffagni, A., Davy-Bowker, J., Lesny, J., Chojnicki, B.H., Zbierska, J., Staniszewski, R., Zgola, T., 2006. Occurrence and variability of River Habitat Survey features across Europe and the consequences for data collection and evaluation. Hydrobiologia 566, 267–280. https://doi.org/10.1007/s10750-006-0090-7. Te, S.H., Gin, K.Y.H., 2011. The dynamics of cyanobacteria and microcystin production in a tropical reservoir of Singapore. Harmful Algae 10, 319–329. https://doi.org/10.1016/j.hal.2010.11.006. Terra, B.D.F., Araújo, F.G., 2011. A preliminary fish assemblage index for a transitional river-reservoir system in southeastern Brazil. Ecol Indic 11, 874–881. https://doi.org/10.1016/j.ecolind.2010.11.006. Uddin, M.G., Nash, S., Olbert, A.I., 2021. A review of water quality index models and their use for assessing surface water quality. Ecol Indic 122, 107218. https://doi.org/10.1016/j.ecolind.2020.107218. Wang, Yixia, Wu, N., Liu, G., Mu, H., Gao, C., Wang, Yaochun, Wu, Y., Zeng, Y., Yan, Y., 2023. Incorporating functional metrics into the development of a diatom-based index of biotic integrity (D-IBI) in Thousand Islands Lake (TIL) catchment, China. Ecol Indic 153. https://doi.org/10.1016/j.ecolind.2023.110405. Whittier, T., Stoddard, J., Larsen, D., Herlihy, A., 2007. Selecting Reference Sites for Stream Biological Assessments: Best Professional Judgment or Objective Criteria. J. North Am. Benthol. Soc. 26. https://doi.org/10.1899/0887-3593(2007)26[349:SRSFSB]2.0.CO;2. Whittier, T., Stoddard, J., Larsen, D., Herlihy, A., 2007. Selecting Reference Sites for Stream Biological Assessments: Best Professional Judgment or Objective Criteria. J. North Am. Benthol. Soc. 26. https://doi.org/10.1899/0887-3593(2007)26[349:SRSFSB]2.0.CO;2. Xi, Y.-L., 2023. Assessing the ecological health of the Qingyi River Basin using multi-community indices of biotic integrity. Ecol. Indic. 156, 111160. https://doi.org/10.1016/j.ecolind.2023.111160. Xiong, M., Li, R., Zhang, T., Liao, C., Yu, G., Yuan, J., Liu, J., Ye, S., 2022. Zooplankton Compositions in the Danjiangkou Reservoir, a Water Source for the South-to-North Water Diversion Project of China. Water (Switzerland) 14. https://doi.org/10.3390/w14203253. You, Q., Yang, W., Jian, M., Hu, Q., 2021. A comparison of metric scoring and health status classification methods to evaluate benthic macroinvertebrate-based index of biotic integrity performance in Poyang Lake wetland. Sci. Total Environ. 761, 144112. https://doi.org/10.1016/j.scitotenv.2020.144112. Zhang, J., Ma, J., Zhang, Z., He, B., Zhang, Y., Su, L., Wang, B., Shao, J., Tai, Y., Zhang, X., Huang, H., Yang, Y., Dai, Y., 2022. Initial ecological restoration assessment of an urban river in the subtropical region in China. Sci. Total Environ. 838, 156156. https://doi.org/10.1016/j.scitotenv.2022.156156. Zhang, Y., Cheng, L., Kong, M., Li, W., Gong, Z., Zhang, L., Wang, X., Cai, Y., Li, K., 2019. Utility of a macroinvertebrate-based multimetric index in subtropical shallow lakes. Ecol. Indic. 106, 105527. https://doi.org/10.1016/j.ecolind.2019.105527. Zhao, Q., Ding, S., Lu, X., Liang, G., Hong, Z., Lu, M., Jing, Y., 2022. Water-sediment regulation scheme of the Xiaolangdi Dam influences redistribution and accumulation of heavy metals in sediments in the middle and lower reaches of the Yellow River. Catena 210, 105880. https://doi.org/10.1016/j.catena.2021.105880. Zhou, Y., Tong, X., Gan, R., Liu, P., Guo, L., Zhao, S., 2023. Distribution characteristics and influencing factors of water resources in Henan Province. Hydrol. Res. 54, 508–522. https://doi.org/10.2166/nh.2023.096. Zhu, H., Zhang, Y.-Z., Peng, Y.-C., Shi, B.-C., Liu, T., Dong, H.-B., Wang, Y., Ren, Y.-C., Xi, Y.-L., 2023. Assessing the ecological health of the Qingyi River Basin using multi-community indices of biotic integrity. Ecol. Indic. 156, 111160. https://doi.org/10.1016/j.ecolind.2023.111160. Additional Declarations No competing interests reported. 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3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":5847790,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe performance of the eight metrics with IQ≥2 between the reference and impaired sites.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure.3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5048078/v1/30fccecd0ace9346730e48f5.png\"},{\"id\":69934038,\"identity\":\"03c32147-f37b-4eb3-a851-e34c73daeff4\",\"added_by\":\"auto\",\"created_at\":\"2024-11-26 18:25:36\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":276408,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifference of the B-IBI scores between the reference and impaired 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6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2195947,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSpearman correlation of B-IBI metrics and water quality parameters in 10 Reservoirs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure.6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5048078/v1/58236b9eb86138fb404708cc.png\"},{\"id\":69934042,\"identity\":\"a2e68f82-267e-46bc-9333-33e2592adfc6\",\"added_by\":\"auto\",\"created_at\":\"2024-11-26 18:25:36\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":7742659,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLocations of 44 sites in 10 reservoirs in Henan Province, China. Circles and triangles indicate impaired and reference sites, respectively. The corresponding detailed information of each reservoir can be found in Table 1\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure.7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5048078/v1/55ba20121b0d13b4025465f4.png\"},{\"id\":72640391,\"identity\":\"e12c115f-6b52-4059-88c1-35790a5755ef\",\"added_by\":\"auto\",\"created_at\":\"2024-12-30 16:05:42\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":19031315,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5048078/v1/a8431da8-a8ef-4393-91c0-f53847c7ef32.pdf\"},{\"id\":69934304,\"identity\":\"19c72c9d-801d-461a-bd8e-857060289ba0\",\"added_by\":\"auto\",\"created_at\":\"2024-11-26 18:33:36\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":567187,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterialScientificReports.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5048078/v1/0de92d594868e87318e397cf.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eReservoirs play important roles in maintaining regional social, economic and environmental functions, such as flood control, agricultural irrigation, water supply, sediment reduction, hydroelectricity generation, tourism, climate regulation, navigation and transportation, biodiversity, and environmental flows (Hou et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Human water demands are becoming more reliant on reservoir storage (Guo et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Reservoirs contribute approximately 10% to the global natural freshwater storage capacity in lakes, and the role of reservoirs in global water security and sustainable development will become more critical, particularly in regions where water scarcity and variability prevail (Guo et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Lehner et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). However, the intensification of anthropogenic activities and climate change has led to varying degrees of decline in water quality and ecological degradation in reservoirs(Leigh et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Liu et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Mendes et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Park et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Therefore, it is increasingly crucial to assess the ecosystem health of reservoirs for the utilization of water resources and maintenance of water security.\\u003c/p\\u003e \\u003cp\\u003eThe ecosystem health of reservoirs is characterized by an intricate and dynamic equilibrium of diverse biotic and abiotic components that synergistically uphold the system\\u0026rsquo;s integrity and resilience (Costanza et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e; Molozzi et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Han et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Banerjee et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The assessment of reservoirs\\u0026rsquo; environmental status has predominantly relied on indices that are based on multiple physicochemical parameters, \\u003cem\\u003ei.e.\\u003c/em\\u003e, the trophic state index and water quality index (Uddin et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), where biological indexes are less used. Due to the growing prevalence of eutrophication and algal bloom issues in reservoirs worldwide, plankton community-based approaches were developed to indicate eco-environmental status in reservoirs (Liu et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Park et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Qin et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Te and Gin, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Xiong et al., \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Some earlier studies have developed bioindicators based on fish community in reservoirs (Terra and Ara\\u0026uacute;jo, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) .\\u003c/p\\u003e \\u003cp\\u003eComparatively speaking, macrobenthos are the most commonly used assemblages among freshwater organisms in bioassessment worldwide due to their bottom-dwelling lifestyle, a relatively long life-span, high diversity and sensitivity to various disturbances (Bonada et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Jun et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Resh, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). Macrobenthos have become indispensable for river ecological monitoring and evaluation in numerous countries, including China (Hargett et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Hu et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Sarrazin-Delay et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Szoszkiewicz et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Due to their relatively long lifespan and bottom-dwelling lifestyle, they also offer certain advantages in assessing the ecological status of reservoirs in comparison to other aquatic organisms. But biological assessment based on macrobenthos in reservoirs requires further endeavors (Molozzi et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe index of biotic integrity (IBI) approach is currently used worldwide to assess ecological health of various waterbodies (Davy-Bowker et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Molozzi et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). It compares the integrity of communities found in test sites with those in reference sites while minimizing human disturbances (Bonada et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). The index of biotic integrity, first introduced by Karr in (1981), is one comprehensive bioassessment tool that integrates the responses of multiple metrics to various types of human impact and relies on reference conditions (Astin, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Beck and Hatch, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; De-La-Ossa-Carretero et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Wang et al., \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The IBI is created by combining a range of individual measurements that encompass various aspects of a community, including its diversity and composition in terms of taxonomy, species adaptability and functional groups (Kabor\\u0026eacute; et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The first IBI approach based on macrobenthos were used to assess eco-health of rivers (Dj et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Macedo et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), and have been widely used to judge biological conditions in lotic waters such as rivers and streams more than in lentic waters including reservoirs.\\u003c/p\\u003e \\u003cp\\u003eHenan Province is the only province that spans the Yangtze, Huai, Yellow and Hai Rivers in China. However, the regional and temporal water scarcity in Henan Province is serious. Hence, more than 2500 reservoirs have been constructed to provide uninterrupted water supply and maintain region's water security. The aquatic environment of the province has been significantly influenced by human activities and rapid socio-economic development. In addition to the ecological monitoring and evaluation of individual reservoirs (Qin et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), the ecological environment quality of most reservoirs lacks operative monitoring and evaluation, and relevant work is urgently needed. Hence, ten large and medium-sized reservoirs distributed across four basins in Henan Province were selected, to develop a B-IBI based on the macrobenthos community characteristics to assess their eco-health. It will provide a suitable method for evaluating relatively long-term impacts of human disturbance on reservoir ecology. The present study is believed to be the pioneering effort in developing a B-IBI for the purpose of monitoring and comparing eco-health of reservoirs across four major river basins.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2. Macrobenthos community characteristics\\u003c/h2\\u003e \\u003cp\\u003eA total of 90 species belonging to 3 phyla, 6 classes, 17 orders, 45 families, and 81 genera were identified in the 10 reservoirs. Fifty-seven species belonging to the Class Insecta were identified, primarily including families Heptageniidae、Libellulidae and Chironomidae. Nineteen species mainly belonging to families Planorbidae, Lymnaeidae, Bithyniidae of the Class Gastropoda were found. There were 6 species, 4 species, 4 species, and 2 species belonging to the Classes Bivalvia, Crustacea, Oligochaeta and Clitellata, respectively. The families Tubificidae (Class Oligochaeta), Chironomidae (Class Insecta), and Palaemonidae (Class Crustacea) exhibited widespread dominance in the investigated reservoirs.\\u003c/p\\u003e \\u003cp\\u003eThe reservoirs situated within the Huai and Yangtze River basins exhibited a relatively higher overall species richness compared to those in the Yellow and Hai River basins. Specifically, the QP in the Huai River basin and the YHK in the Yangtze River basin exhibited particularly high species richness, with up to 17 taxa identified at specific sampling sites. The XX, also located in the Yangtze River basin, displayed a relatively diverse overall taxonomic composition. In contrast, the reservoirs in the Yellow and Hai River basins demonstrated a less diverse taxonomic composition, with some sites only harboring two identified taxa (Figure. 1a).\\u003c/p\\u003e \\u003cp\\u003eAt the family level, across all 44 sampling sites within the 10 reservoirs, Chironomidae, Palaemonidae, and Tubificidae emerged as the dominant taxonomic groups, with Chironomidae being the most widely distributed, present at 35 of the sampling locations (Figure. 1b).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe abundance of Phylum Arthropoda and Mollusca was higher in all reservoirs compared to Phyla Annelida, which was absent specifically in XLD Reservoir (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The Class Insecta belonging to Phyla Arthropoda was dominant in Reservoirs XX and YHK within the Yangtze River basin, QTH within the Yellow River basin, CSD within the Huai River basin and BQ within the Hai River basin. The Class Crustacea belonging to Phyla Arthropoda was dominant in Reservoirs GX and XLD within the Yellow River basin. The Classes Insecta (Phyla Arthropoda) and Gastropoda (Phyla Mollusca) exhibited dominance in Reservoirs QP, SYH and CSD within the Huai River basin (Figure. 2).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Development of B-IBI\\u003c/h2\\u003e \\u003cp\\u003eAmong the 30 candidate metrics of macrobenthos, eight metrics demonstrated a robust discriminatory capability between 11 reference and 33 impaired sites (IQ\\u0026thinsp;\\u0026ge;\\u0026thinsp;2, Figure. 3). They are Number of taxa (M1), Number of Crustacean and Mollusca taxa (M6), Number of Intolerant taxa (M13), Intolerant % (M15), Tolerant % (M16), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The results of Spearman correlation analyses showed strong correlation between M1 and M27, M13 and M15, M16 and M17. The metrics M1, M13 and M16 were excluded due to their stronger correlations with other parameters. The five metrics M6, M15, M17, M18, M27 were selected for the construction of B-IBI due to their low correlation (r\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.75) with each other.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe B-IBI index demonstrated a normal distribution across all sampling sites, and significant differences (IQ\\u0026thinsp;\\u0026ge;\\u0026thinsp;2) in the B-IBI scores were observed between the reference and impaired sites, thus confirming the reliability of the assessment index (Figure. 4).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Assessment of reservoir health\\u003c/h2\\u003e \\u003cp\\u003eThe B-IBI scores of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated that there were 12 sites classified as excellent, 11 sites classified as good, 12 sites classified as fair, 6 sites classified as poor and 3 sites classified as very poor (Figure. 5a). According to the median B-IBI score of sites for each reservoir, only one reservoir (QP in Huai River basin) achieved an excellent classification, four reservoirs (GX and XLD in the Yellow River basin, XX in the Yangtze River basin, CSD in the Huai River basin) were classified as good. Three reservoirs (SYH in the Huai River basin, YHK in the Yangtze River basin, BQ in the Hai River basin) received a fair classification. Two reservoirs (QTH and HKC in the Yellow River basin) were categorized as poor. No reservoir was rated as very poor (Figure. 5b). According to the median B-IBI scores of sites within each basin, the reservoirs in the Huai and Yangtze River basins were classified as good, while those in the Yellow and Hai River basins were categorized as fair (Figure. 5c).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe B-IBI negatively correlated with TN and EC (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). TN demonstrated significant negative correlations with the B-IBI metrics M15, M18, and M27 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Specifically, M15 positively correlated with pH, while negatively correlated with EC and TN (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05). Furthermore, M18 displayed negative correlations with temperature (TEMP) and TN, while showing positive correlations with turbidity (TURB) and ammonia nitrogen (NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e-N) Lastly, M27 exhibited a positive correlation with pH, but was negatively correlated with TN (Figure. 6).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Community characteristics of macrobenthos\\u003c/h2\\u003e \\u003cp\\u003eThe macrobenthos in the investigated reservoirs primarily consisted to the phyla Arthropoda and Mollusca, which are also commonly found in other freshwater lotic and lentic ecosystems (Yan Li et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Shi et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The intolerant taxa identified in the reservoirs all belonged to the aforementioned phyla. Species belonging to the Phylum Annelida are characterized by high pollution tolerance (Blocksom et al., 2002; Castellanos Romero et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Hilsenhoff, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1987\\u003c/span\\u003e). The absence of the Phylum Annelida in Reservoir XLD, and the high richness and/or relative abundance of the intolerant taxa in Reservoirs GX, XLD, XX, and QP collectively suggest that these reservoirs may experience minimal pollution pressure. The B-IBI index assessed their health status as good or excellent, indicating a strong alignment between the community characteristics of macrobenthos and the health assessment.\\u003c/p\\u003e \\u003cp\\u003eCrustacea dominated the reservoirs in the Yellow River Basin, while Insecta dominated the Yangtze and Hai River basins. In addition, both Insecta and Gastropoda dominated the Huai River basin. It indicated that the community structure exhibited obvious differences in the investigated reservoirs across the four basins at the higher taxa level. However, we did not observe statistically significant differences at the lower taxa level. The B-IBI metrics selected in this study are derived from the taxonomic richness and relative abundance of lower taxa levels. The similarity of benthic animal community structures across various water bodies at lower taxonomic levels should serve as a crucial criterion for the development and implementation of an identical evaluation system(Yanli Li et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Poikane et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Therefore, we propose that the constructed B-IBI evaluation system can be applied to reservoirs in multiple river basins within Henan Province, even throughout the Central Plains in China.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Construction of B-IBI\\u003c/h2\\u003e \\u003cp\\u003eThe utilization of multi-metric indices of biological condition has been widely implemented on a global scale due to their practicality in incorporating diverse biological metrics across various levels of ecological organization. The primary challenge lies in distinguishing natural variability from anthropogenic impacts when developing and applying these multi-metric indices. Reference sites is crucial in the construction of a useful IBI. However, finding anthropogenically undisturbed sites is rare. Therefore, the least-disturbed reference condition is most commonly utilized. Water quality and land use variables are the predominant criteria employed to determine the reference condition (Ruaro et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). A distinct disparity was observed between the reference and repaired sites, despite being situated in four different river basins with varying hydrology, geomorphology, and biogeochemistry.\\u003c/p\\u003e \\u003cp\\u003eFive metrics including number of Crustacean and Mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27), were screened and combined into B-IBI index to assess the health conditions of the 10 reservoirs in Henan Province. The metrics, particularly M6, M15, M17 and M27, are widely utilized in multi-metric indices based on macroinvertebrates such as B-IBI (Ruaro et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The typical responses of macrobenthos to environmental stress encompass a reduction in species richness, an augmentation in the abundance of tolerant taxa, a decline in the diversity and density of sensitive taxa, as well as a simplification of food webs (Burdon et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Graeber et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The species belonging to Crustacea and Mollusca are predominantly distributed in habitats with lower and intermediate levels of disturbance. The number of Crustacean and Mollusca taxa (M6) is highly responsive to change in water quality, making it widely incorporated into assessment methodologies (Yanli Li et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The number of Crustacean and Mollusca taxa (M6) in the reference sites were much higher than that in the repaired sites, indicating that M6 serves as a highly sensitive indicator for assessing environmental stress in the current study.\\u003c/p\\u003e \\u003cp\\u003eThe Shannon-Wiener diversity index (M27) is commonly employed in biological assessments involving macroinvertebrates (Kabor\\u0026eacute; et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; You et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). It quantifies diversity by considering both the number and relative abundance of the present species (Fierro et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Ndatimana et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In the current study, the Shannon-Wiener diversity index (M27) is significantly correlated with taxa richness (r\\u0026thinsp;=\\u0026thinsp;0.827, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01). but taxa richness was less included in bioassessment approaches, as maximum richness might occur at intermediate disturbance in most ecosystems(Svensson et al., \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). We observed a significantly lower Shannon-Wiener diversity index (M27) at the repaired sites. Furthermore, the species composition at the repaired sites was predominantly comprised of pollution-tolerant species such as Famliy Tubificida, while pollution-intolerant sensitive species like Ephemerotera and Trichoptera were considerably less abundant.\\u003c/p\\u003e \\u003cp\\u003eSensitivity and /or tolerance indices were the most reliable metric category(Poikane et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Another three retained metrics pertain to the richness and relative abundance of tolerant or intolerant taxa in response to environmental pollution. Intolerance % (M15) denotes the proportion of more sensitive benthic species relative to the total abundance of macrobenthos at each sampling site. The BMWP index (M18) incorporates the sensitivity values of taxonomic units at the family level and is less stringent for species classification (Castellanos Romero et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The BI index (M17), however, takes into account the tolerance values of the species and imposes higher requirements on species classification, which exhibited the most robust associations with environmental factors (Liu et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The correlation coefficients of the three metrics are lower than 0.75, and the significant differences were observed between the reference sites and repaired sites.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e3.3 Eco-health of Reservoirs in Henan Province\\u003c/h3\\u003e\\n\\u003cp\\u003eThe B-IBI index demonstrates a robust discriminatory capacity between reference and impaired sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province. Furthermore, it can effectively discriminate among different sites within an individual reservoir. Notably, all reservoirs except for HKC contains sites classified as excellent or good grades. Interestingly, the eco-health status of the four reservoirs in the Yellow River basin ranges from poor to good levels, highlighting their relative dependence. The reason for this could be that, with the exception of XLD, which is a channel-type reservoir located on the Yellow River, the remaining reservoirs lack direct connectivity with the main water body of the basin(Zhao et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Compared with the reservoirs in central and southern parts of Henan Province, the ecological status of the investigated reservoirs in the north, such as HKC, QTH and BQ were much worser. The water scarcity in the north was more serious than that in the other parts of Henan Province(Zhou et al., \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Hence, the water quality management in the reservoirs of the north is urgent.\\u003c/p\\u003e \\u003cp\\u003eThe TN concentrations was negatively correlated with scores of the B-IBI index, and several core metrics including M15, M18, and M27. In the Jincheng region of Qin River, specifically in the upstream area of the HKC reservoir, nitrogen and ammonium were identified as key drivers influencing macrobenthos community characteristics (Yanli Li et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Increased concentrations of nitrogen favored tolerant species (Su et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The average TN concentration in HKC, QTH and BQ was 4.06 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e, much higher than that in the other six reservoirs (1.87 mg L\\u003csup\\u003e\\u0026minus;\\u0026thinsp;1\\u003c/sup\\u003e). Responsiveness to anthropogenic pressures is considered a crucial factor for evaluating method performance. The B-IBI index and its core metrics exhibited significant correlations with environmental variables, particularly for TN, thereby validating the robustness of the B-IBI.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1. Study area\\u003c/h2\\u003e \\u003cp\\u003eHenan Province is situated in the east-central region of China, spanning between 110.35°-116.65°E and 31.38°-36.37°N, covers a total area of 167,000 km\\u003csup\\u003e2\\u003c/sup\\u003e. The majority of the province is characterized by a temperate monsoon climate. Henan Province is a major agricultural region with a population exceeding 99\\u0026nbsp;million. The effective management of reservoirs are of utmost importance to both human livelihoods and agricultural productivity.\\u003c/p\\u003e \\u003cp\\u003eWe sampled 44 sites from 10 large and medium-sized reservoirs in Henan Province during August 2021 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The sampling sites mainly represent the inlet, center, and outlet of each reservoir, ensuring a comprehensive coverage of the reservoir's water body (Figure. 7).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThe basic information of the 10 reservoirs in Henan Province\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eReservoir\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAbbreviation\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRiver basin\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSite No.\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eStorage capacity (10\\u003csup\\u003e8\\u003c/sup\\u003e m\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eFunctions\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBaoquan\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBQ\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHai River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS1-S5\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.3\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(2, 3, 5)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQingtianhe\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQTH\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYellow River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS6-S10\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.2\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1, 5, 6)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHekoucun\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHKC\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYellow River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS11-S14\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.17\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1, 3)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGuxian\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGX\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYellow River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS21-S23\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e11.75\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(2, 3, 5)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eXiaolangdi\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eXLD\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYellow River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS24-S26\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e51\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1, 2, 3, 4)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eXixia\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eXX\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYangtze River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS27-S30\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(5)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYahekou\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYHK\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eYangtze River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS31-S35\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e13.39\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1,2,3)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQianping\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQP\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHuai River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS15-S20\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.93\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1,2,3,5)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSuyahu\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSYH\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHuai River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS36-S39\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e16\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1,2,3)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChushandian\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCSD\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHuai River\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eS40-S44\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12.51\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e(1,2,3)\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eNotes: 1, water supply; 2, agricultural irrigation; 3, flood control; 4, sediment reduction; 5, hydroelectric power generation; 6, tourism\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003cp\\u003e\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Field sampling and data collection\\u003c/h2\\u003e \\u003cp\\u003eThree subsamples at each site were collected using a modified Peterson grab sampler (0.0625 m\\u003csup\\u003e2\\u003c/sup\\u003e). The collected mud samples were washed on-site using a 60-mesh sieve, and the remaining net contents containing invertebrates were immediately placed into labeled plastic bags containing 95% ethanol. The sieved samples were emptied into white porcelain plates to pick out all macrobenthos in laboratory, which were preserved in 100 mL plastic bottles with 95% ethanol before identification. The identification of benthos was carried out using a dissecting and a compound microscope (SMZ800N and Ci-L, Olympus, Japan). The benthos collected at each site were meticulously enumerated in order to calculate the density of each taxonomic unit of macrobenthos per unit area.\\u003c/p\\u003e \\u003cp\\u003eData of physicochemical parameters were also collected for each sampling site. water temperature (TEMP), pH, dissolved oxygen (DO), and conductivity (EC) were measured in situ using a portable water quality analyzer (HACH). Additional water samples were collected for further laboratory analysis of turbidity (TURB), total nitrogen (TN), ammonia nitrogen (NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e-N), permanganate index (COD\\u003csub\\u003eMn\\u003c/sub\\u003e), and total phosphorus (TP). The water samples were stored below 4°C and transported to the laboratory within 24 hours to maintain sample integrity. The analysis of these indicators followed the guidelines specified in the \\\"Environmental Quality of Surface Water in the People's Republic of China\\\" (GB3838-2002).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3. Development of the B-IBI\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e4.3.1. Selection of reference sites\\u003c/h2\\u003e \\u003cp\\u003eUnder ideal conditions, reference sites should have experienced little disturbance from anthropogenic activities(Steedman, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e; Davy-Bowker et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Zhang et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Given its high population density and role as a major agricultural production area and significant province for mineral resources in Henan Province, it presents challenges in identifying reference sites that strictly adhere to the definition of being undisturbed. Here water quality and local habitat status were mainly used to filter reference sites. The water quality, as determined by the levels of TN, TP, NH\\u003csub\\u003e4\\u003c/sub\\u003e\\u003csup\\u003e+\\u003c/sup\\u003e-N and COD\\u003csub\\u003eMn\\u003c/sub\\u003e, should meet or exceed Class III standards according to China’s Surface Water Environmental Quality Standard. Secondly, the site should be free from any dams, agricultural land, residential areas, and roads in its vicinity. The construction of the B-IBI involved screening 11 reference sites (Figure. 7).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3.2 Metric selection\\u003c/h2\\u003e \\u003cp\\u003eThe study compiled a total of thirty candidate metrics for benthic macroinvertebrate assemblages, which were further categorized into seven distinct groups: species composition, community composition, pollution tolerance, tropic status, nutritional structure, habitat and diversity index (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e)(You et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Zhu et al., \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The candidate metrics were subjected to box and whisker plot-tests. The degree of inter-quartile overlap (IQ) in the boxplots was utilized to assess the discriminatory power of each metric (Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Metrics were retained if they exhibited a significant discriminative ability (IQ ≥ 2) between the reference and impaired sites. The retained metrics were used to conduct a Spearman correlation analysis to examine the redundancy of metrics, with a criterion set at a minimum correlation coefficient of 0.75 (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05, Table S2). The Kruskal-Wallis test was subsequently employed to assess the discriminatory power of the redundant metrics more accurately between reference and impaired sites. Only the metric with the higher chi-squared value was retained (You et al., \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3.3. Standardization of core metrics\\u003c/h2\\u003e \\u003cp\\u003eThe key measurements displayed a wide range of raw values, necessitating the standardization of each metric as a score using the ratio technique(Ka et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). The score values ranged from 0 to 1, and any metric value exceeding 1 was capped at a maximum of 1(Table S3). For metrics that decreased with disturbance, the anticipated value (V95%) for all samples was assigned as the standardized value based on the 95th percentile of the assessment metric, following Eq.\\u0026nbsp;(1). Conversely, for metrics that increased with disruption, the anticipated value (V5%) was determined by considering the standardized value derived from the 5th percentile of the evaluation metric using Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e\\u003cimg src=\\\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732644582.png\\\"\\u003e\\u003c/p\\u003e\\u003cp\\u003eHerein, \\u003cem\\u003eBIn\\u003c/em\\u003e represents the calculated standardized value of an assessment metric, \\u003cem\\u003eVn\\u003c/em\\u003e denotes its actual measured value, and \\u003cem\\u003eVmax\\u003c/em\\u003e signifies the maximum among all samples considered in the study. The final B-IBI score at each site is obtained by summing up the calculated index scores, and the 25th percentile of the B-IBI value at the reference sites is defined as excellent. The B-IBI values below this threshold are categorized into four equal grades, resulting in four additional grades. The B-IBI score for each reservoir and river basin is derived by computing the median value of B-IBI scores across all monitoring sites of each reservoir and each river basin.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4. Statistical analyses\\u003c/h2\\u003e \\u003cp\\u003eThe distribution map of sampling sites and the result map were created using ArcMap 10.8 software. RStudio software was utilized for data analysis and spatial distribution plotting. Origin 2023 software was employed for graphical and descriptive analysis of other data. IBM SPSS Statistics 26 software was used to conduct Spearman correlation analysis.\\u003c/p\\u003e \\u003c/div\\u003e \"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThe community characteristics of macrobenthos were investigated in ten large and medium-sized reservoirs across four basins (the Hai, Yellow, Yangtze and Huai Rivers) in Henan Province during August 2021, followed by the establishment of the B-IBI index incorporating into five core metrics including number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). 92 species were identified in 3 phyla, 6 classes, 18 orders, 47 families in 44 sites of the 10 reservoirs. The dominant class in the reservoirs across the four basins was different, but the community structure at lower taxa level did not exhibit significant difference. The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated that 12, 11, 12, 6 and 3 sites were categorized as excellent, good, fair, poor, and very poor levels, respectively. Two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a robust capacity to discriminate between reference and impaired sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province. A significant inverse correlation was observed between total nitrogen (TN) levels and the B-IBI scores, suggesting that the B-IBI effectively reflects the impact of nitrogen pollution. The management of reservoirs, particularly those located in the northern region of Henan Province, should prioritize the reduction of nitrogen pollution input.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eData availability\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was funded by the Project of Yellow River Fisheries Resources and Environment Investigation from the MARA, P. R. China, the Major Science and Technology Program in Henan Province (232102320250), and the Breeding Project of Henan Normal University (HNU2021PL05).\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor contributions\\u003c/p\\u003e\\n\\u003cp\\u003eJ.N.Z was responsible for data processing, image production, and the primary composition of the manuscript. Y.N.G contributed to data collection, drafting of the manuscript, and subsequent revisions. \\u0026nbsp;J.X.Z., Y.L.L., X.F.G., H.T.Y., J.D., and X.J.L. participated in the survey and data gathering efforts.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAstin, L.A.E., 2007. Developing biological indicators from diverse data: The Potomac Basin-wide Index of Benthic Integrity (B-IBI). Ecol Indic 7, 895\\u0026ndash;908. https://doi.org/10.1016/j.ecolind.2006.09.004. \\u003c/li\\u003e\\n\\u003cli\\u003eBanerjee, A., Chakrabarty, M., Rakshit, N., Mukherjee, J., Ray, S., 2017. Indicators and assessment of ecosystem health of Bakreswar reservoir, India: An approach through network analysis. Ecol Indic 80, 163\\u0026ndash;173. https://doi.org/10.1016/j.ecolind.2017.05.021. \\u003c/li\\u003e\\n\\u003cli\\u003eBeck, M.W., Hatch, L.K., 2009. A review of research on the development of lake indices of biotic integrity. Environmental Reviews 17, 21\\u0026ndash;44. https://doi.org/10.1139/A09-001. \\u003c/li\\u003e\\n\\u003cli\\u003eBonada, N., Prat, N., Resh, V.H., Statzner, B., 2006. Developments in aquatic insect biomonitoring: A comparative analysis of recent approaches. Annu Rev Entomol 51, 495\\u0026ndash;523. https://doi.org/10.1146/annurev.ento.51.110104.151124. \\u003c/li\\u003e\\n\\u003cli\\u003eBurdon, F.J., Reyes, M., Alder, A.C., Joss, A., Ort, C., R\\u0026auml;s\\u0026auml;nen, K., Jokela, J., Eggen, R.I.L., Stamm, C., 2016. Environmental context and magnitude of disturbance influence trait-mediated community responses to wastewater in streams. Ecol. Evol. 6, 3923\\u0026ndash;3939. https://doi.org/10.1002/ece3.2165. \\u003c/li\\u003e\\n\\u003cli\\u003eCastellanos Romero, K., Pizarro Del R\\u0026iacute;o, J., Cuentas Villarreal, K., Costa Anillo, J.C., Pino Zarate, Z., Gutierrez, L.C., Franco, O.L., Arboleda Valencia, J.W., 2017. Lentic water quality characterization using macroinvertebrates as bioindicators: An adapted BMWP index. Ecol. Indic. 72, 53\\u0026ndash;66. https://doi.org/10.1016/j.ecolind.2016.07.023. \\u003c/li\\u003e\\n\\u003cli\\u003eCostanza, R., Norton, B., Haskell, B., 1992. Ecosystem health:new goals for environmental management. Washington D.C.:Island Press. \\u003c/li\\u003e\\n\\u003cli\\u003eDavy-Bowker, J., Clarke, R.T., Johnson, R.K., Kokes, J., Murphy, J.F., Zahr\\u0026aacute;dkov\\u0026aacute;, S., 2006. A comparison of the European Water Framework Directive physical typology and RIVPACS-type models as alternative methods of establishing reference conditions for benthic macroinvertebrates. Hydrobiologia 566, 91\\u0026ndash;105. https://doi.org/10.1007/s10750-006-0068-5. \\u003c/li\\u003e\\n\\u003cli\\u003eDe-La-Ossa-Carretero, J.A., Lane, M.F., Llans\\u0026oacute;, R.J., Dauer, D.M., 2016. Classification efficiency of the B-IBI comparing water body size classes in Chesapeake Bay. Ecol Indic 63, 144\\u0026ndash;153. https://doi.org/10.1016/j.ecolind.2015.12.010. \\u003c/li\\u003e\\n\\u003cli\\u003eDj, K., Ka, B., Fa, F., At, H., Rm, H., Pr, K., Dv, P., Jl, S., Wt, T., Mb, G., Ws, D., 2003. Development and evaluation of a Macroinvertebrate Biotic Integrity Index (MBII) for regionally assessing Mid-Atlantic Highlands Streams. Environ. Manage. 31. https://doi.org/10.1007/s00267-002-2945-7. \\u003c/li\\u003e\\n\\u003cli\\u003eFierro, P., Arismendi, I., Hughes, R.M., Valdovinos, C., Jara-Flores, A., 2018. A benthic macroinvertebrate multimetric index for Chilean Mediterranean streams. Ecol. Indic. 91, 13\\u0026ndash;23. https://doi.org/10.1016/j.ecolind.2018.03.074. \\u003c/li\\u003e\\n\\u003cli\\u003eGraeber, D., Jensen, T.M., Rasmussen, J.J., Riis, T., Wiberg-Larsen, P., Baattrup-Pedersen, A., 2017. Multiple stress response of lowland stream benthic macroinvertebrates depends on habitat type. Sci. Total Environ. 599\\u0026ndash;600, 1517\\u0026ndash;1523. https://doi.org/10.1016/j.scitotenv.2017.05.102. \\u003c/li\\u003e\\n\\u003cli\\u003eGuo, Z., Boeing, W.J., Borgomeo, E., Xu, Y., Weng, Y., 2021. Linking reservoir ecosystems research to the sustainable development goals. Science of the Total Environment 781, 146769. https://doi.org/10.1016/j.scitotenv.2021.146769. \\u003c/li\\u003e\\n\\u003cli\\u003eHan, J.H., Kim, B., Kim, C., An, K.G., 2014. Ecosystem health evaluation of agricultural reservoirs using multi-metric lentic ecosystem health assessment (LEHA) model. Paddy and Water Environment 12, 7\\u0026ndash;18. https://doi.org/10.1007/s10333-014-0444-0. \\u003c/li\\u003e\\n\\u003cli\\u003eHargett, E.G., ZumBerge, J.R., Hawkins, C.P., Olson, J.R., 2007. Development of a RIVPACS-type predictive model for bioassessment of wadeable streams in Wyoming. Ecol Indic 7, 807\\u0026ndash;826. https://doi.org/10.1016/j.ecolind.2006.10.001. \\u003c/li\\u003e\\n\\u003cli\\u003eHilsenhoff, W.L., 1987. An Improved Biotic Index of Organic Stream Pollution. Gt. Lakes Entomol. 20. https://doi.org/10.22543/0090-0222.1591. \\u003c/li\\u003e\\n\\u003cli\\u003eHou, J., Van Dijk, A.I.J.M., Beck, H.E., Renzullo, L.J., Wada, Y., 2022. Remotely sensed reservoir water storage dynamics (1984-2015) and the influence of climate variability and management at a global scale. Hydrol Earth Syst Sci 26, 3785\\u0026ndash;3803. https://doi.org/10.5194/hess-26-3785-2022. \\u003c/li\\u003e\\n\\u003cli\\u003eHu, X., Zuo, D., Xu, Z., Huang, Z., Liu, B., Han, Y., Bi, Y., 2022. Response of macroinvertebrate community to water quality factors and aquatic ecosystem health assessment in a typical river in Beijing, China. Environ Res 212, 113474. https://doi.org/10.1016/j.envres.2022.113474. \\u003c/li\\u003e\\n\\u003cli\\u003eJun, Y.C., Won, D.H., Lee, S.H., Kong, D.S., Hwang, S.J., 2012. A multimetric benthic macroinvertebrate index for the assessment of stream biotic integrity in Korea. Int J Environ Res Public Health 9, 3599\\u0026ndash;3628. https://doi.org/10.3390/ijerph9103599. \\u003c/li\\u003e\\n\\u003cli\\u003eKa, B., Jp, K., Dj, K., Fa, F., Sm, C., 2002. Development and evaluation of the Lake Macroinvertebrate Integrity Index (LMII) for New Jersey lakes and reservoirs. Environ. Monit. Assess. 77. https://doi.org/10.1023/a:1016096925401. \\u003c/li\\u003e\\n\\u003cli\\u003eKabor\\u0026eacute;, I., Ou\\u0026eacute;da, A., Moog, O., Meulenbroek, P., Tampo, L., Banc\\u0026eacute;, V., Melcher, A.H., 2022. A benthic invertebrates-based biotic index to assess the ecological status of West African Sahel Rivers, Burkina Faso. J. Environ. Manage. 307. https://doi.org/10.1016/j.jenvman.2022.114503. \\u003c/li\\u003e\\n\\u003cli\\u003eKarr, J.R., 1981. Assessment of Biotic Integrity Using Fish Communities. Fisheries (Bethesda) 6, 21\\u0026ndash;27. https://doi.org/10.1577/1548-8446(1981)006\\u0026lt;0021:aobiuf\\u0026gt;2.0.co;2. \\u003c/li\\u003e\\n\\u003cli\\u003eLehner, B., Liermann, C.R., Revenga, C., V\\u0026ouml;r\\u0026ouml;msmarty, C., Fekete, B., Crouzet, P., D\\u0026ouml;ll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J.C., R\\u0026ouml;del, R., Sindorf, N., Wisser, D., 2011. High-resolution mapping of the world\\u0026rsquo;s reservoirs and dams for sustainable river-flow management. Front Ecol Environ 9, 494\\u0026ndash;502. https://doi.org/10.1890/100125. \\u003c/li\\u003e\\n\\u003cli\\u003eLeigh, C., Burford, M.A., Roberts, D.T., Udy, J.W., 2010. Predicting the vulnerability of reservoirs to poor water quality and cyanobacterial blooms. Water Res 44, 4487\\u0026ndash;4496. https://doi.org/10.1016/j.watres.2010.06.016. \\u003c/li\\u003e\\n\\u003cli\\u003eLi, Yan, He, Y., Liu, M., Uddin, K.B., Zhao, Y., Wang, Haijun, Cui, Y., Wang, Hongzhu, 2023. Benthic macroinvertebrate assemblages in relation to high ammonia loading: A 5-year fertilization experiment in 5 subtropical ponds. Environ. Pollut. 337, 122587. https://doi.org/10.1016/j.envpol.2023.122587. \\u003c/li\\u003e\\n\\u003cli\\u003eLi, Yanli, Li, X., Liu, Q., Xu, Z., Wang, M., 2023. Community characteristics of macroinvertebrates and ecosystem health assessment in Qin River, a main tributary of the Yellow River in China. Environ. Sci. Pollut. Res. 30, 56410\\u0026ndash;56424. https://doi.org/10.1007/s11356-023-26314-9. \\u003c/li\\u003e\\n\\u003cli\\u003eLiu, G., Qi, X., Lin, Z., Lv, Y., Khan, S., Qu, X., Jin, B., Wu, M., Oduro, C., Wu, N., 2024. Comparison of different macroinvertebrates bioassessment indices in a large near-natural watershed under the context of metacommunity theory. Ecol. Evol. 14, 1\\u0026ndash;15. https://doi.org/10.1002/ece3.10896. \\u003c/li\\u003e\\n\\u003cli\\u003eLiu, L., Chen, H., Liu, M., Yang, J.R., Xiao, P., Wilkinson, D.M., Yang, J., 2019. Response of the eukaryotic plankton community to the cyanobacterial biomass cycle over 6 years in two subtropical reservoirs. ISME Journal 13, 2196\\u0026ndash;2208. https://doi.org/10.1038/s41396-019-0417-9. \\u003c/li\\u003e\\n\\u003cli\\u003eMacedo, D.R., Hughes, R.M., Ferreira, W.R., Firmiano, K.R., Silva, D.R.O., Ligeiro, R., Kaufmann, P.R., Callisto, M., 2016. Development of a benthic macroinvertebrate multimetric index (MMI) for Neotropical Savanna headwater streams. Ecol. Indic. 64, 132\\u0026ndash;141. https://doi.org/10.1016/j.ecolind.2015.12.019. \\u003c/li\\u003e\\n\\u003cli\\u003eMacedo, D.R., Hughes, R.M., Ferreira, W.R., Firmiano, K.R., Silva, D.R.O., Ligeiro, R., Kaufmann, P.R., Callisto, M., 2016. Development of a benthic macroinvertebrate multimetric index (MMI) for Neotropical Savanna headwater streams. Ecol. Indic. 64, 132\\u0026ndash;141. https://doi.org/10.1016/j.ecolind.2015.12.019. \\u003c/li\\u003e\\n\\u003cli\\u003eMendes, C.F., dos Santos Severiano, J., Moura, G.C. de, dos Santos Silva, R.D., Monteiro, F.M., Barbosa, J.E. de L., 2022. The reduction in water volume favors filamentous cyanobacteria and heterocyst production in semiarid tropical reservoirs without the influence of the N:P ratio. Science of the Total Environment 816. https://doi.org/10.1016/j.scitotenv.2021.151584. \\u003c/li\\u003e\\n\\u003cli\\u003eMolozzi, J., Feio, M.J., Salas, F., Marques, J.C., Callisto, M., 2012. Development and test of a statistical model for the ecological assessment of tropical reservoirs based on benthic macroinvertebrates. Ecol Indic 23, 155\\u0026ndash;165. https://doi.org/10.1016/j.ecolind.2012.03.023. \\u003c/li\\u003e\\n\\u003cli\\u003eNdatimana, G., Arimoro, F.O., Chukwuemeka, V.I., Assie, F.A.G.J., Action, S., Nantege, D., 2023. Development of lake macroinvertebrate-based multimetric index for monitoring ecological health in North Central Nigeria. Environ. Monit. Assess. 195, 1\\u0026ndash;21. https://doi.org/10.1007/s10661-023-12036-5. \\u003c/li\\u003e\\n\\u003cli\\u003ePark, B.S., Li, Z., Kang, Y.H., Shin, H.H., Joo, J.H., Han, M.S., 2018. Distinct Bloom Dynamics of Toxic and Non-toxic Microcystis (Cyanobacteria) Subpopulations in Hoedong Reservoir (Korea). Microb Ecol 75, 163\\u0026ndash;173. https://doi.org/10.1007/s00248-017-1030-y. \\u003c/li\\u003e\\n\\u003cli\\u003ePoikane, S., Johnson, R.K., Sandin, L., Schartau, A.K., Solimini, A.G., Urbanič, G., Arbačiauskas, K. stutis, Aroviita, J., Gabriels, W., Miler, O., Pusch, M.T., Tim, H., B\\u0026ouml;hmer, J., 2016. Benthic macroinvertebrates in lake ecological assessment: A review of methods, intercalibration and practical recommendations. Sci. Total Environ. 543, 123\\u0026ndash;134. https://doi.org/10.1016/j.scitotenv.2015.11.021. \\u003c/li\\u003e\\n\\u003cli\\u003eQin, M., Fan, P., Li, Y., Wang, H., Wang, W., Liu, H., Messyasz, B., Goldyn, R., Li, B., 2023. Assessing the Ecosystem Health of Large Drinking-Water Reservoirs Based on the Phytoplankton Index of Biotic Integrity (P-IBI): A Case Study of Danjiangkou Reservoir. Sustainability 15, 5282. https://doi.org/10.3390/su15065282. \\u003c/li\\u003e\\n\\u003cli\\u003eResh, V.H., 2008. Which group is best? Attributes of different biological assemblages used in freshwater biomonitoring programs. Environ Monit Assess 138, 131\\u0026ndash;138. https://doi.org/10.1007/s10661-007-9749-4. \\u003c/li\\u003e\\n\\u003cli\\u003eRuaro, R., Gubiani, \\u0026Eacute;.A., Hughes, R.M., Mormul, R.P., 2020. Global trends and challenges in multimetric indices of biological condition. Ecol. Indic. 110, 105862. https://doi.org/10.1016/j.ecolind.2019.105862. \\u003c/li\\u003e\\n\\u003cli\\u003eSarrazin-Delay, C.L., Somers, K.M., Bailey, J.L., 2014. Using Test Site Analysis and two Nearest Neighbor Models, ANNA and RDA, to Assess Benthic Communities with Simulated Impacts. Freshwater Science 33, 1249\\u0026ndash;1260. https://doi.org/10.1086/678702. \\u003c/li\\u003e\\n\\u003cli\\u003eShi, X., Liu, J., You, X., Bao, K., Meng, B., Chen, B., 2017. Evaluation of river habitat integrity based on benthic macroinvertebrate-based multi-metric model. Ecol. Model. 353, 63\\u0026ndash;76. https://doi.org/10.1016/j.ecolmodel.2016.07.001. \\u003c/li\\u003e\\n\\u003cli\\u003eSteedman, R.J., 1994. Ecosystem Health as a Management Goal. J. North Am. Benthol. Soc. 13, 605\\u0026ndash;610. https://doi.org/10.2307/1467856. \\u003c/li\\u003e\\n\\u003cli\\u003eSu, P., Wang, X., Lin, Q., Peng, J., Song, J., Fu, J., Wang, S., Cheng, D., Bai, H., Li, Q., 2019. Variability in macroinvertebrate community structure and its response to ecological factors of the Weihe River Basin, China. Ecol. Eng. 140, 1\\u0026ndash;13. https://doi.org/10.1016/j.ecoleng.2019.105595. \\u003c/li\\u003e\\n\\u003cli\\u003eSvensson, J.R., Lindegarth, M., Siccha, M., Lenz, M., Molis, M., Wahl, M., Pavia, H., 2007. Maximum species richness at intermediate frequencies of disturbance: Consistency among levels of productivity. Ecology 88, 830\\u0026ndash;838. https://doi.org/10.1890/06-0976. \\u003c/li\\u003e\\n\\u003cli\\u003eSzoszkiewicz, K., Buffagni, A., Davy-Bowker, J., Lesny, J., Chojnicki, B.H., Zbierska, J., Staniszewski, R., Zgola, T., 2006. Occurrence and variability of River Habitat Survey features across Europe and the consequences for data collection and evaluation. Hydrobiologia 566, 267\\u0026ndash;280. https://doi.org/10.1007/s10750-006-0090-7. \\u003c/li\\u003e\\n\\u003cli\\u003eTe, S.H., Gin, K.Y.H., 2011. The dynamics of cyanobacteria and microcystin production in a tropical reservoir of Singapore. Harmful Algae 10, 319\\u0026ndash;329. https://doi.org/10.1016/j.hal.2010.11.006. \\u003c/li\\u003e\\n\\u003cli\\u003eTerra, B.D.F., Ara\\u0026uacute;jo, F.G., 2011. A preliminary fish assemblage index for a transitional river-reservoir system in southeastern Brazil. Ecol Indic 11, 874\\u0026ndash;881. https://doi.org/10.1016/j.ecolind.2010.11.006. \\u003c/li\\u003e\\n\\u003cli\\u003eUddin, M.G., Nash, S., Olbert, A.I., 2021. A review of water quality index models and their use for assessing surface water quality. Ecol Indic 122, 107218. https://doi.org/10.1016/j.ecolind.2020.107218. \\u003c/li\\u003e\\n\\u003cli\\u003eWang, Yixia, Wu, N., Liu, G., Mu, H., Gao, C., Wang, Yaochun, Wu, Y., Zeng, Y., Yan, Y., 2023. Incorporating functional metrics into the development of a diatom-based index of biotic integrity (D-IBI) in Thousand Islands Lake (TIL) catchment, China. Ecol Indic 153. https://doi.org/10.1016/j.ecolind.2023.110405. \\u003c/li\\u003e\\n\\u003cli\\u003eWhittier, T., Stoddard, J., Larsen, D., Herlihy, A., 2007. Selecting Reference Sites for Stream Biological Assessments: Best Professional Judgment or Objective Criteria. J. North Am. Benthol. Soc. 26. https://doi.org/10.1899/0887-3593(2007)26[349:SRSFSB]2.0.CO;2. \\u003c/li\\u003e\\n\\u003cli\\u003eWhittier, T., Stoddard, J., Larsen, D., Herlihy, A., 2007. Selecting Reference Sites for Stream Biological Assessments: Best Professional Judgment or Objective Criteria. J. North Am. Benthol. Soc. 26. https://doi.org/10.1899/0887-3593(2007)26[349:SRSFSB]2.0.CO;2. \\u003c/li\\u003e\\n\\u003cli\\u003eXi, Y.-L., 2023. Assessing the ecological health of the Qingyi River Basin using multi-community indices of biotic integrity. Ecol. Indic. 156, 111160. https://doi.org/10.1016/j.ecolind.2023.111160. \\u003c/li\\u003e\\n\\u003cli\\u003eXiong, M., Li, R., Zhang, T., Liao, C., Yu, G., Yuan, J., Liu, J., Ye, S., 2022. Zooplankton Compositions in the Danjiangkou Reservoir, a Water Source for the South-to-North Water Diversion Project of China. Water (Switzerland) 14. https://doi.org/10.3390/w14203253. \\u003c/li\\u003e\\n\\u003cli\\u003eYou, Q., Yang, W., Jian, M., Hu, Q., 2021. A comparison of metric scoring and health status classification methods to evaluate benthic macroinvertebrate-based index of biotic integrity performance in Poyang Lake wetland. Sci. Total Environ. 761, 144112. https://doi.org/10.1016/j.scitotenv.2020.144112. \\u003c/li\\u003e\\n\\u003cli\\u003eZhang, J., Ma, J., Zhang, Z., He, B., Zhang, Y., Su, L., Wang, B., Shao, J., Tai, Y., Zhang, X., Huang, H., Yang, Y., Dai, Y., 2022. Initial ecological restoration assessment of an urban river in the subtropical region in China. Sci. Total Environ. 838, 156156. https://doi.org/10.1016/j.scitotenv.2022.156156. \\u003c/li\\u003e\\n\\u003cli\\u003eZhang, Y., Cheng, L., Kong, M., Li, W., Gong, Z., Zhang, L., Wang, X., Cai, Y., Li, K., 2019. Utility of a macroinvertebrate-based multimetric index in subtropical shallow lakes. Ecol. Indic. 106, 105527. https://doi.org/10.1016/j.ecolind.2019.105527. \\u003c/li\\u003e\\n\\u003cli\\u003eZhao, Q., Ding, S., Lu, X., Liang, G., Hong, Z., Lu, M., Jing, Y., 2022. Water-sediment regulation scheme of the Xiaolangdi Dam influences redistribution and accumulation of heavy metals in sediments in the middle and lower reaches of the Yellow River. Catena 210, 105880. https://doi.org/10.1016/j.catena.2021.105880. \\u003c/li\\u003e\\n\\u003cli\\u003eZhou, Y., Tong, X., Gan, R., Liu, P., Guo, L., Zhao, S., 2023. Distribution characteristics and influencing factors of water resources in Henan Province. Hydrol. Res. 54, 508\\u0026ndash;522. https://doi.org/10.2166/nh.2023.096. \\u003c/li\\u003e\\n\\u003cli\\u003eZhu, H., Zhang, Y.-Z., Peng, Y.-C., Shi, B.-C., Liu, T., Dong, H.-B., Wang, Y., Ren, Y.-C., Xi, Y.-L., 2023. Assessing the ecological health of the Qingyi River Basin using multi-community indices of biotic integrity. Ecol. Indic. 156, 111160. https://doi.org/10.1016/j.ecolind.2023.111160. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Macrobenthos, Index of Biotic Integrity, Reservoirs, Nitrogen\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5048078/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5048078/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe eco-health assessment of regional reservoirs is important for ensuring the sustainable utilization of water resources and maintenance of water security, particularly in regions facing water scarcity. The present study aimed to construct a B-IBI based on the community characteristics of macrobenthos in ten large and medium-sized reservoirs across four major river basins in Henan Province, China. The results revealed the identification of 92 species belonging to 3 phyla, 6 classes, 18 orders, 47 families. The B-IBI was established based on five key metrics, namely the number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a strong capability to distinguish the impaired sites from the reference sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-11-26 18:17:31\",\"doi\":\"10.21203/rs.3.rs-5048078/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-11-06T05:48:37+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-10-30T17:18:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-10-28T07:58:48+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"105666434067675075645729175860464538322\",\"date\":\"2024-10-21T13:44:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"288902734377164138166310777543834125889\",\"date\":\"2024-10-16T20:29:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-10-16T20:19:43+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-10-16T13:23:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-09-18T02:15:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-09-17T10:28:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Scientific Reports\",\"date\":\"2024-09-07T08:53:53+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"scientific-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"scirep\",\"sideBox\":\"Learn more about [Scientific Reports](http://www.nature.com/srep/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Scientific Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"78f967ae-e2fb-4263-86a6-814a18738717\",\"owner\":[],\"postedDate\":\"November 26th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-12-30T15:58:57+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-5048078\",\"link\":\"https://doi.org/10.1038/s41598-024-83236-3\",\"journal\":{\"identity\":\"scientific-reports\",\"isVorOnly\":false,\"title\":\"Scientific Reports\"},\"publishedOn\":\"2024-12-28 15:57:04\",\"publishedOnDateReadable\":\"December 28th, 2024\"},\"versionCreatedAt\":\"2024-11-26 18:17:31\",\"video\":\"\",\"vorDoi\":\"10.1038/s41598-024-83236-3\",\"vorDoiUrl\":\"https://doi.org/10.1038/s41598-024-83236-3\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5048078\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5048078\",\"identity\":\"rs-5048078\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}