Contrasting zooplankton communities in coastal rivers of eastern and western Guangdong, China: Relationships with environmental factors

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The study found significant differences in key environmental parameters (water temperature, pH, dissolved oxygen) between the rivers in eastern and western Guangdong. However, no corresponding geographical pattern was detected in the α-diversity indices of the zooplankton communities. Notably, zooplankton diversity in western Guangdong rivers was significantly positively correlated with salinity and total phosphorus (TP), with the Pielou evenness index showing particular sensitivity to TP. Self-organizing map (SOM) analysis demonstrated distinct geographical clustering patterns in zooplankton communities between the two regions. Furthermore, the random forest model identified 15 bioindicators whose abundances were closely associated with environmental factors such as water temperature and nutrient levels. The study highlights that rivers in eastern Guangdong are predominantly influenced by industrial pollution and hydrological regulation, whereas those in western Guangdong are more affected by agricultural non-point source pollution. These findings elucidate the differential regulatory mechanisms of abiotic factors and human activities on zooplankton communities, providing a scientific basis for the conservation and management of coastal river ecosystems in Guangdong Province. Coastal rivers in eastern and western Guangdong Zooplankton Community structure Geographical differences Environmental factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Global aquatic biodiversity and ecosystem functioning are increasingly threatened by climate change and anthropogenic activities (Maciej et al., 2021 ). As a vital component of aquatic ecosystems, zooplankton, which occupy the base of the aquatic food web, not only sustain energy flow and material cycling in rivers but also maintain the physical and chemical properties of rivers. These properties exert a decisive influence on the survival, reproduction, composition, density, and proportion of aquatic biotic communities (Khaksar et al., 2019 ). Serving as a key link in the aquatic food web, zooplankton represent the primary energy pathway from phytoplankton to fish; changes in the community structure of zooplankton thus have a profound impact on the stability and functionality of the entire aquatic ecosystem (Venkataramana et al., 2023 ). Zooplankton possess short life cycles and respond rapidly to environmental change. Their restricted dispersal also makes them valuable integrators of both abiotic and biotic influences over time (de et al., 2021). Zooplankton are highly sensitive to environmental changes and are therefore widely regarded as good indicators of ecosystem status. Changes in the physical, chemical, and biological parameters of aquatic systems lead to variations in the relative composition and abundance of plankton (Neila et al., 2022 ; Tartarotti et al., 2025 ). The coastal areas of eastern and western Guangdong serve as crucial ecological transition zones in the northern South China Sea, featuring dense river networks and unique brackish water environments that foster highly diverse zooplankton communities. As key carriers of secondary productivity in aquatic ecosystems, the distribution patterns and structural characteristics of these communities directly reflect changes in watershed environments and ecological responses (Lan & Du, 1996 ; Li et al., 2021 ). In recent years, with the development of the Guangdong-Hong Kong-Macao Greater Bay Area and the Beibu Gulf Economic Zone, human activities along the coast (such as land reclamation, sewage discharge, and waterway engineering) have increasingly disrupted estuarine ecosystems. However, distinct geographic, climatic, and anthropogenic pressures in eastern and western Guangdong likely drive divergent ecological responses in their respective zooplankton communities (N. Chen, 2014 ; Zheng et al., 2019 ). Investigating the heterogeneity of zooplankton communities in these two regions is essential for understanding the varying vulnerabilities of subtropical coastal ecosystems and formulating targeted conservation strategies. Although systematic investigations have been conducted on zooplankton communities in large estuaries such as the Pearl River Estuary (Yin et al., 2022 ), comparative studies on small and medium-sized river systems—including the Hanjiang and Rongjiang Rivers in eastern Guangdong and the Jianjiang and Moyangjiang Rivers in western Guangdong—remain insufficient. Existing literature predominantly focuses on single regions or specific seasons. For instance, (Wang et al., 2018 ) examined phytoplankton in the Hanjiang River, while Qiu et al. ( 2016 ) surveyed benthic diatoms in the Jianjiang River. However, these studies lack cross-regional comparative analyses and particularly neglect the differential response mechanisms of zooplankton communities to environmental gradients (e.g., salinity stratification, nutrient input patterns, and seasonal runoff variations) between the two regions. Such knowledge gaps hinder the precise formulation of regional biodiversity conservation strategies and impede the scientific assessment of ecosystem adaptive management under anthropogenic disturbances. This study focuses on typical coastal rivers in eastern Guangdong (Huanggang River, Hanjiang River, Rongjiang River, Lianjiang River, Longjiang River, Luohe River) and western Guangdong (Moyangjiang River, Jianjiang River, Lianjiang River), employing field surveys to address the following scientific questions: (1) What are the significant differences in zooplankton community structure between coastal rivers in eastern and western Guangdong? (2) How do the key environmental drivers influencing zooplankton distribution differ between the two regions? (3) How can zooplankton community characteristics be used to assess the ecological health status of these river systems? The findings will provide a scientific basis for ecological conservation in Guangdong’s coastal rivers and serve as a methodological reference for similar studies in other regions. Materials and methods Study site This study investigated coastal rivers in eastern Guangdong, including the Huanggang River, Hanjiang River, Rongjiang River, Lianjiang River, Longjiang River, and Luohe River, with the Rongjiang River watershed being the largest in drainage area. The study region is bounded by the Lianhua Mountain Range to the north and the South China Sea to the south. Influenced by maritime climate and topography, the area receives abundant rainfall with mean annual precipitation ranging from 1,400 to 2,400 mm. The annual runoff depth typically varies between 800 and 1,500 mm across the watersheds. These rivers are characterized by short courses, steep gradients, and relatively high peak flood discharges. The coastal rivers of western Guangdong include the Moyang River, Jian River, and Lian River. Among these, the Moyang River and Jian River have catchment areas exceeding 1,000 km². This region is bordered by the Pearl River Delta to the east, the Nanliu River of Guangxi to the west, and the Yunkaidashan and Yunwu Mountains to the north, which separate it from the Xijiang River system. To the south lies the South China Sea. The terrain slopes from north to south, characterized by short, steep rivers that generally flow north-to-south before draining into the South China Sea. We established 18 sampling sites across 9 rivers (Fig. 1 ) for aquatic ecological monitoring. Field Sampling and laboratory analysis In March 2021, zooplankton samples were collected from coastal rivers in eastern and western Guangdong. Following the methods described by Zhang & Huang ( 1995 ), qualitative samples were obtained by horizontal towing with a No. 25 plankton net (mesh size: 64 µm), while quantitative samples were collected using a 5L organic glass water sampler. For quantitative analysis, 30 L of surface water was filtered through the same plankton net and concentrated into 250 mL polyethylene bottles. All samples were preserved with formaldehyde solution to a final concentration of approximately 5%. Water samples for environmental analysis were collected at a depth of 0.5 meters at each sampling site. In-situ measurements of water temperature, dissolved oxygen (DO), salinity, and pH were immediately obtained using a YSI-556 multiparameter probe (YSI Inc., USA), while water transparency was determined using a Secchi disk. Additional water samples were field-filtered and transported to the laboratory for nutrient analysis, including total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH₃-N), and chemical oxygen demand (COD), performed using a Skalar-SA1100 continuous flow analyzer (Skalar Analytical, Netherlands). Chlorophyll-a (Chl-a) concentrations were determined through N, N-dimethylformamide (DMF) extraction followed by spectrophotometric analysis. Zooplankton were identified to the lowest possible taxonomic level, typically species or genus. The taxonomic identification of the rotifers was followed Koste ( 1978 ) and Wang ( 1961 ), while that of copepods and cladocerans was followed Institute of Zoology ( 1979 ) and Jiang and Du ( 1979 ). We used a microscope (BX-51, OLYMPUS, Tokyo, Japan) to identify the plankton in the samples. Zooplankton abundance was expressed as the number of individuals per liter (ind./L). Statistical analysis Wilcoxon rank-sum tests were employed to examine differences in nine environmental variables between coastal rivers entering the sea in eastern and western Guangdong, including pH, temperature, salinity, DO, transparency, TP, TN, COD, and Chl-a. Wilcoxon rank-sum tests were performed to compare three α diversity indices (Richness, Shannon diversity, and Pielou evenness index) in water bodies of coastal rivers entering the sea between eastern and western Guangdong. Spearman rank correlation analysis was performed to examine relationships between α-diversity indices and environmental variables. To control for Type I error inflation due to multiple testing, p-values were adjusted using the False Discovery Rate (FDR) method. Significant correlations are reported at FDR-adjusted *p* < 0.05. Results were visualized using the 'pheatmap' package in R v4.0.2. The self-organizing map (SOM) algorithm was applied to reveal distribution patterns of sampling sites across coastal rivers entering the sea in eastern and western Guangdong. Results were visualized using MATLAB R2014a (MathWorks, USA). We employed random forest analysis to classify zooplankton communities based on geographic regions. Bioindicator species were selected based on their importance scores in the random forest model. The top 15 taxa with the highest Mean Decrease Accuracy (MDA > 3.0) were identified as key bioindicators distinguishing eastern and western river communities. Spearman's rank correlation was then applied to examine relationships between bioindicator abundance and environmental variables. Results were visualized using the 'pheatmap' package in R v4.0.2 (R Core Team, 2020). Results Environmental variables Nine environmental variables were measured in the coastal rivers discharging into the sea in eastern and western Guangdong (in-situ parameters: pH, temperature, salinity, DO, transparency; nutrient data: TP, TN, COD, Chl-a), with results presented in Fig. 2 . Overall, the pH of the surveyed rivers was alkaline. Significant differences (wilcoxon test, p < 0.05) were observed in the mean pH and DO between eastern and western Guangdong’s coastal rivers, with eastern rivers exhibiting lower average pH and DO levels than western rivers. The average water temperature in eastern Guangdong’s rivers was significantly lower than in western rivers (wilcoxon test, p < 0.01). No significant difference in salinity was detected between the two regions. Transparency, TP, TN, and COD concentrations showed no notable variations between eastern and western rivers. However, Chl-a content in western Guangdong’s rivers was significantly higher than in eastern rivers (wilcoxon test, p < 0.05). The significantly lower pH and DO were observed in eastern rivers compared to western rivers (Fig. 2 ). For instance, acidifying effluents from e-waste recycling and textile industries likely drive pH reduction, while organic loads from municipal sewage depress DO. In contrast, the higher pH and DO in western rivers align with predominantly agricultural landscapes and higher forest cover, which enhance oxygen production and buffer against acidification. This clear dichotomy underscores the need for region-specific pollution control strategies. For instance, the Hanjiang River, a major watercourse in eastern Guangdong, exhibits water acidification in its downstream reaches due to urban sewage and industrial effluent discharges (Lin et al., 2023 ). Additionally, wastewater from e-waste recycling and textile dyeing industries in the Lianjiang River basin contains substantial acidic compounds, further reducing pH (Song et al., 2020 ). In contrast, western Guangdong rivers (e.g., Jianjiang and Moyangjiang) experience fewer pollution pressures, while higher forest coverage helps maintain alkaline conditions and elevated DO levels (Qiu et al., 2016 ). The significantly lower water temperature in eastern Guangdong rivers likely reflects regional climatic and hydrological differences. Eastern Guangdong’s subtropical coastal climate, with strong maritime influences, results in smaller temperature fluctuations, whereas western Guangdong’s longer sunshine duration and higher air temperatures lead to warmer river waters (W. Chen, 2014 ). Moreover, larger discharge volumes and faster flow velocities in eastern rivers (e.g., Hanjiang and Rongjiang) enhance air-water heat exchange, further cooling the water (Zhang & Shi, 2023 ). This could be attributed to higher nutrient inputs, for example, from intensive agricultural fertilizer use in the Moyangjiang Basin elevates nitrogen and phosphorus levels, stimulating phytoplankton growth (Cheng et al., 2016 ). Slower flow rates and longer hydraulic retention times in western rivers also favor phytoplankton accumulation (Gao et al., 2015 ). Conversely, stronger turbulence and pollution control measures (e.g., the Lianjiang River remediation project) in eastern rivers likely suppress algal proliferation (Liu et al., 2025 ). No significant interregional differences were observed in salinity or transparency, possibly due to similar tidal influences and seawater intrusion in both coastal areas (Pan et al., 2010 ). Comparable levels of TN, TP, and COD suggest analogous nutrient loads despite differing pollution sources. For example, both the Lianjiang (eastern Guangdong) and Jianjiang (western Guangdong) face agricultural runoff and municipal wastewater inputs, yielding similar nutrient concentrations (Chen et al., 2024 ; Li et al., 2021 ). Zooplankton community diversity Three α-diversity indices (Richness, Shannon, and Pielou) of zooplankton communities were calculated for 10 coastal rivers discharging into the sea in eastern and western Guangdong, and regional differences were compared (Fig. 3 ). The Richness, Shannon diversity, and Pielou evenness indices of zooplankton communities showed no significant differences between the coastal rivers of eastern and western Guangdong (Wilcoxon test, p > 0.05). These findings indicate that despite differing climatic, hydrological, and anthropogenic pressures, zooplankton diversity patterns in both eastern and western Guangdong may be regulated by common regional environmental drivers. For instance, both the Hanjiang River (eastern Guangdong) and Jianjiang River (western Guangdong) experience seasonal precipitation effects, leading to comparable nutrient inputs and zooplankton community structures (Lin et al., 2023 ; Qiu et al., 2016 ). Additionally, tidal influences in estuarine zones of both regions likely homogenize zooplankton communities through water mixing and salinity fluctuations (Huang et al., 2024 ). Notably, while alpha diversity indices showed no significant regional differences, community composition may exhibit location-specific characteristics. Such divergence could be attributed to distinct pollution sources (e.g., industrial effluents vs. agricultural runoff). Furthermore, zooplankton communities in western Guangdong rivers (e.g., Moyangjiang) may be more influenced by wave and tidal dynamics (Huang et al., 2024 ), whereas those in eastern rivers (e.g., Lianjiang) face unique stressors like e-waste recycling pollution (Song et al., 2020 ). Although these factors did not significantly alter diversity metrics, they may drive functional group variations. The observed similarities in alpha diversity between coastal rivers of eastern and western Guangdong may reflect common regional environmental pressures. However, future studies integrating community composition and functional traits are needed to elucidate underlying ecological distinctions. Relationship between zooplankton community diversity indices and environmental variables This study evaluated the relationships between environmental variables and zooplankton community diversity indices in coastal rivers discharging into the sea in eastern and western Guangdong (Fig. 4 ). In the eastern Guangdong rivers, no significant correlations were found between the Richness, Shannon diversity, and Pielou evenness indices of zooplankton communities and the measured environmental variables. In contrast, in the western Guangdong rivers, the Shannon diversity index exhibited positive correlations with both salinity and TP content, while the Pielou evenness index showed a positive correlation with salinity and a highly significant positive correlation with TP content. These findings indicate that water salinity and TP content have pronounced effects on zooplankton community diversity in western Guangdong's coastal rivers. Notably, the Pielou evenness index of zooplankton communities in western rivers demonstrated greater sensitivity to environmental variables compared to other α-diversity indices, particularly TP content, which showed the strongest statistical significance. Studies on rivers in western Guangdong, such as the Moyang River and Jian River, indicate that salinity and TP are key factors influencing zooplankton community diversity. Xu et al. ( 2024 ) used a random forest model to predict TP concentrations in the Moyang River basin and found a significant covariation between TP and rainfall, with TP levels rising markedly during the flood season. Furthermore, variance partitioning analyses revealed that TP concentration during the dry season was the predominant factor influencing the biomass of the plankton community (Yin et al., 2022 ). This may provide additional nutrient sources for zooplankton, thereby promoting increased community diversity. Additionally, sediment transport in the Moyang River estuary is jointly controlled by tidal currents and waves (Huang et al., 2024 ), and seasonal salinity variations may indirectly regulate zooplankton community structure by influencing water stratification and nutrient distribution (Sahwell et al., 2024 ). Similarly, research on benthic diatom diversity in the Jian River basin (Qiu et al., 2016 ) demonstrated that diatom community distribution is closely linked to water nutrient levels, further supporting the regulatory role of nutrients (e.g., TP) in aquatic biodiversity. In contrast, zooplankton communities in eastern Guangdong rivers, such as the Han River and Rong River, exhibit weaker responses to environmental variables. A study on zooplankton in the lower Han River (Lan & Du, 1996 ) revealed that species composition and abundance were primarily influenced by discharge and water temperature, with nutrients playing a relatively minor role. This may be attributed to water diversion projects in the Han River basin, such as the Han-Rong-Lian River Interbasin Transfer Project (Zhang, 2024 ), which enhances water exchange and dilutes nutrient concentrations, thereby reducing their direct impact on zooplankton communities. Furthermore, research on microplastic pollution in the lower Han River (Liang et al., 2022 ) indicated that pollutant inputs in densely populated areas were dominated by microplastics and heavy metals rather than nutrients, which may also explain the lower sensitivity of zooplankton diversity to TP in eastern Guangdong rivers. Notably, the Pielou evenness index of zooplankton communities in western Guangdong rivers showed the most pronounced response to TP levels, suggesting that evenness is a more sensitive indicator of nutrient effects than species richness. This finding aligns with Gao et al. ( 2015 )’s assessment of the Jian River basin using the Fish Index of Biotic Integrity, which found that pollution-tolerant species dominate in eutrophic conditions, leading to increased community evenness. Thus, the Pielou evenness index shows potential as a sensitive metric for monitoring aquatic ecosystem health in western Guangdong's coastal rivers, though further validation is needed. The divergent responses of zooplankton communities to environmental variables between the eastern and western rivers are consistent with the regional differences in hydrological regimes and anthropogenic pressures. Future studies incorporating long-term monitoring data could further elucidate the interactive effects of salinity and nutrients on zooplankton community dynamics. Zooplankton community structure Based on zooplankton abundance, the distribution patterns of sampling sites in coastal rivers discharging into the sea in eastern and western Guangdong were revealed using SOM. The distribution of 100 units on the SOM is shown in Fig. 5 . According to the similarity in zooplankton community composition among different neurons, the sites were clearly divided into two major clusters (I and II). Cluster I could be further subdivided into three subclusters (Ia, Ib, Ic), and Cluster II into three subclusters (IIa, IIb, IIc). These six subclusters (Ia, Ib, Ic, IIa, IIb, and IIc) consisted of 21, 24, 9, 24, 14, and 8 distribution units, respectively. Subcluster Ia comprised SR, PT, TY, CZ, and QY; Subcluster Ib included JK, LH, and YH; Subcluster Ic was represented by HG alone. Subcluster IIa consisted of CW, GM, YJ, HZ, JS, and SJ; Subcluster IIb included ZL and GZ; and Subcluster IIc was represented solely by HS. The results indicate a clear geographical clustering pattern of zooplankton communities in the coastal rivers of eastern and western Guangdong. Cluster Ia (SR, PT, TY, CZ, QY) and Ib (JK, LH, YH), predominantly in Guangdong eastern rivers, were associated with higher mean total phosphorus (TP: 0.26 mg/L) and chlorophyll-a (Chl-a: 2.75 µg/L), suggesting nutrient-enriched conditions likely linked to agricultural runoff. In contrast, Cluster IIa (CW, GM, YJ, HZ, JS, SJ) in western rivers showed lower nutrient levels but higher dissolved oxygen (DO: 8.47 mg/L), reflecting better water quality despite localized urban impacts. According to Qiu et al. ( 2016 ), the diversity of benthic diatoms in the Jian River basin is significantly influenced by river hierarchy and anthropogenic activities, with high dissimilarity observed between upstream and downstream communities—a pattern consistent with the distribution characteristics of zooplankton communities in this study. Additionally, water quality in western Guangdong rivers such as the Jian River is notably affected by agricultural and industrial pollution (Chen et al., 2024 ), which may contribute to regional variations in zooplankton community structure. Cluster Ic (HG) formed a distinct group, likely due to localized environmental conditions (e.g., estuarine salinity gradients) or unique pollution sources (e.g., e-waste dismantling), analogous to the heavy metal and organic pollution documented in the Lian River basin from e-waste recycling (Song et al., 2020 ). Clusters IIa (CW, GM, YJ, HZ, JS, SJ) and IIb (ZL, GZ) were mainly found in the Han River and Rong River basins of eastern Guangdong. The Han River basin generally exhibits better water quality, with phytoplankton communities dominated by diatoms (Lin et al., 2023 ), potentially providing a stable food source for zooplankton and shaping their distinct community structure. However, microplastic pollution from urban wastewater discharge in downstream sections (Liang et al., 2022 ) may partially alter zooplankton assemblages. Cluster IIc (HS) showed isolated clustering, possibly associated with unique hydrological conditions at the Rong River estuary, such as tidal dynamics and freshwater-saltwater mixing (Li et al., 2023 ), which can significantly modify zooplankton distribution patterns. In summary, the divergence in zooplankton communities between eastern and western Guangdong rivers reflects regional environmental heterogeneity. Western rivers are more impacted by non-point agricultural pollution and industrial discharges, whereas eastern rivers maintain relatively better water quality but face localized pressures from urban pollution and water diversion projects. Future studies incorporating additional environmental factors (e.g., nutrients, flow regimes) could further elucidate the drivers of community distribution. Geographical variation in dominant zooplankton species The random forest model identified 15 key zooplankton species that distinguish the communities between sampling sites in coastal rivers of eastern and western Guangdong: Trichocerca longiseta , Dicranophorus forcipatus , Trichocerca pusilla , Arcella hemisphaerica , Lecane luna , Diaphanosoma leuchtenbergianum , Arcella discoides , Brachionus budapestiensis , Difflugia corona , Brachionus calyciflorus , Brachionus ureeus , Brachionus caudatus , Polyarthra trigla , Moina micrura , and Bosmina longirostris (Fig. 6 ). These differences may be influenced by hydrological conditions, water quality characteristics, and habitat variations between the two regions. The correlation analysis between the abundance of key zooplankton species (the aforementioned 15 species) and environmental variables (Fig. 7 ) revealed that water temperature significantly influenced the abundance of several key zooplankton taxa ( B. calyciflorus , B. ureeus , D. forcipatus , A. discoides , B. calyciflorus , T. pusilla ). For example, temperature exerts a critical influence on the hatching conditions of zooplankton. Suitable hatching conditions vary by species and typically require sufficiently high temperature and appropriate oxygen levels (Gilbert, 2020 ; Schröder, 2005 ). The abundance of A. hemisphaerica and P. trigla showed significant correlations with pH, while A. hemisphaerica exhibited a negative correlation with DO. D. leuchtenbergianum and L. luna were negatively correlated with water transparency. In contrast, B. calyciflorus , B. ureeus , and A. discoides displayed positive correlations with TP content. Additionally, B. calyciflorus and B. ureeus were positively associated with the permanganate index, whereas B. calyciflorus and T. pusilla showed positive correlations with Chl-a. Temperature is the most consistently important variable structuring plankton communities (Gray et al., 2021 ).The influence of water temperature on zooplankton abundance has been corroborated by multiple studies (Beaugrand et al., 2002 ). Fluctuations in temperature affect algal growth due to changes in the rate of nutrient uptake (O'Connor et al., 2009 ). For instance, dynamic monitoring of phytoplankton in the Meixi section of the lower Han River revealed a significant correlation between water temperature and algal density (N. Chen, 2014 ). As phytoplankton serve as the primary food source for zooplankton, this finding indirectly supports the conclusion that water temperature affects zooplankton distribution through trophic interactions. Furthermore, chl-a concentrations at a monitoring station in the Han River basin exhibited significant fluctuations between dry and wet seasons due to temperature variations (Jin, 2023 ), aligning with the positive correlations observed in this study between B. calyciflorus / T. pusilla and chl-a. This suggests water temperature indirectly regulates zooplankton abundance by modulating primary productivity (Bopp et al., 2013 ). The impacts of pH and DO on zooplankton are equally noteworthy. Research on phytoplankton communities in the Chaozhou section of the Han River identified pH and temperature as key drivers of Cryptophyta distribution (Lin et al., 2023 ). Our study's finding of significant correlations between A. hemisphaerica / P. trigla abundance and pH further validates the direct regulatory role of water quality parameters in planktonic communities. The negative DO correlation may reflect differential zooplankton adaptation to hypoxic conditions. For example, microplastic pollution studies in the lower Han River noted substantial DO fluctuations in urban reaches due to pollutant discharge (Liang et al., 2022 ), potentially explaining A. hemisphaerica 's distribution patterns in low-DO environments. Negative transparency-zooplankton correlations (e.g., D. leuchtenbergianum and L. luna ) may indicate turbidity-induced suppression of filter-feeding species (Guo et al., 2025 ). Phytoplankton surveys in the Rong River estuary identified nitrate and inorganic phosphorus as critical factors governing community structure (Li et al., 2023 ), while our observed positive correlations between B. calyciflorus / B. ureeus and TP imply nutrient-mediated algal growth indirectly supports zooplankton. Moreover, water diversion projects in eastern Guangdong (e.g., Lian River rehabilitation) demonstrated that increased flow rates significantly improve water quality (Liu et al., 2025 ), providing empirical support for zooplankton responses to hydrological management. Zooplankton differences between eastern and western rivers could be influenced by watershed-scale human activities among other factors. Sediment analyses in the Lian River revealed extreme ecological risks from polybrominated diphenyl ethers (PBDEs) due to e-waste dismantling (Song et al., 2020 ), whereas the Han River exhibited milder microplastic pollution (Liang et al., 2022 ). This pollution gradient likely amplifies interregional zooplankton disparities. Additionally, non-point source pollution studies in the Han River basin identified negative correlations between forest coverage and pollutant loads (Zheng et al., 2019 ), suggesting land-use patterns indirectly regulate zooplankton via water quality mediation. Management implications Our findings suggest that zooplankton community metrics may serve as useful bioindicators for assessing river health in similar coastal river systems of Guangdong. Specifically: The Pielou evenness index, highly sensitive to TP in western rivers, could be integrated into routine monitoring programs to track eutrophication trends in agriculturally dominated basins. In eastern rivers, the relative insensitivity of diversity indices to nutrients highlights the need for complementary indicators (e.g., pollutant-tolerant species) to capture impacts from industrial and hydrological disturbances. The 15 key species identified by the random forest model (e.g., Brachionus calyciflorus , Trichocerca pusilla ) represent candidate indicators for targeted monitoring of specific stressors (temperature, nutrient enrichment). Limitations and future research This study provides a snapshot of zooplankton communities in March 2021. While revealing spatial patterns between eastern and western rivers, the single-season sampling limits our ability to capture seasonal dynamics. Future multi-seasonal surveys are recommended to validate these patterns and explore temporal variability in community-environment relationships. Conclusion This study compared the differences in zooplankton community structure between coastal rivers in eastern and western Guangdong Province and their relationship with environmental factors. The results showed that although there were no significant differences in the α-diversity indices (Richness, Shannon, Pielou) of zooplankton between the rivers in eastern and western Guangdong, the community structure exhibited distinct geographical clustering. In western Guangdong rivers, zooplankton diversity was significantly positively correlated with salinity and TP content, whereas the community structure in eastern Guangdong rivers showed a weaker response to environmental variables. A random forest model identified 15 key zooplankton species whose abundance was closely related to environmental factors such as water temperature, pH, and DO. The differences in zooplankton communities between the two regions were primarily influenced by regional hydrological characteristics, nutrient inputs, and human activity intensity. The findings provide evidence for the influence of abiotic factors and habitat characteristics on zooplankton communities, highlighting the potential regulatory roles of these variables, providing a scientific basis for the conservation and management of coastal river ecosystems in Guangdong Province. Declarations Author contributions Conceptualisation: Yuan Gao. Developing methods: Jianwei Liang, Mengfan Wang, Shanshan Yu, Qianfu Liu, Chao Wang, Yuan Gao. Data analysis: Jianwei Liang, Mengfan Wang. Preparation of figures and tables: Jianwei Liang, Xiaofeng Shao. Conducting the research, data interpretation, writing: Jianwei Liang, Mengfan Wang, Xiaofeng Shao, Shanshan Yu, Qianfu Liu, Chao Wang and Yuan Gao. Funding This work was supported by the Central Public-interest Scientific Institution Basal Research Fund, CAFS (grant numbers 2019XT07 and 2019XT0701). We are very grateful to all staff members of our team for their assistance during field work. Competing interests The authors declare no competing interests. References Beaugrand, G., Ibañez, F., Lindley, J. A., Philip, C., & Reid, P. C. (2002). Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Marine Ecology Progress Series , 232 , 179–195. https://doi.org/10.3354/meps232179 Bopp, L., Resplandy, L., Orr, J. C., Doney, S. C., Dunne, J. P., Gehlen, M., Halloran, P., Heinze, C., Ilyina, T., Séférian, R., Tjiputra, J., & Vichi, M. (2013). Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. 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Marine Pollution Bulletin , 190 , 114816–114816. https://doi.org/10.1016/J.MARPOLBUL.2023.114816 Wang, J. (1961). Rotifera Sinicarum Aquae Dulcis . Science Press. Wang, S., Chen, H., Liu, Q., XU, J., Zhang, G., & Wang, Z. (2018). Zooplankton community structure and its correlation with water quality in Hancheng Lake. Ecological Science , 37 (02), 114–123. https://doi.org/10.14108/j.cnki.1008-8873.2018.02.015 Xu, C., Liu, J., Xue, H.-t., Yu, X.-y., & Chen, W. (2024). Research on total phosphorus prediction in the Moyang ༲iver Basin based on machine learning models. Environmental Ecology , 6 (08), 23–28 + 38. Yin, T., Wang, Q., Yang, Y., & Cen, J. (2022). Comparative study on zooplankton community structure in Pearl River Estuary based on morphological and DNA identification Journal of Tropical Oceanography , 41 (03), 172–185. Zhang, C., & Shi, X. (2023). Analysis of Water Resources Regime in the Rongjiang River Basin, Eastern Guangdong. Pearl River , 44 (S2), 47–51. Zhang, W. (2024). Key Technical Issues and Solution Strategies for the Follow-up Optimization Project of Hanjiang-Rongjiang-Lianjiang Water System Connectivity. Sichuan Cement (12), 76–78. https://doi.org/10.20198/j.cnki.scsn.2024.12.038 Zhang, Z., & Huang, X. (1995). Studying Methods for Freshwater Plankton Research. Science Press . Zheng, Y., Cheng, X., Wang, Z., & Lai, C. (2019). Non-point source pollution in Hanjiang River Basin and its relation with landscape pattern. Water Resources Protection , 35 (05), 78–85. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 04 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 Editor assigned by journal 02 Jan, 2026 First submitted to journal 23 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8438446","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569039458,"identity":"733a73b4-8017-4187-91fa-a98df12921e2","order_by":0,"name":"Jianwei Liang","email":"","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Liang","suffix":""},{"id":569039459,"identity":"af658ea2-1256-4875-881d-d5d4f6bdbe44","order_by":1,"name":"Mengfan Wang","email":"","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mengfan","middleName":"","lastName":"Wang","suffix":""},{"id":569039460,"identity":"8a994212-63a1-4f13-b961-802a9f350c27","order_by":2,"name":"Xiaofeng Shao","email":"","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Shao","suffix":""},{"id":569039461,"identity":"1fe06584-36dc-48e6-83a3-f1def7a77da2","order_by":3,"name":"Shanshan Yu","email":"","orcid":"","institution":"Hebei Normal University of Science \u0026 Technology","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Yu","suffix":""},{"id":569039462,"identity":"aeb3be1e-8c39-4a40-bf3f-441dfd854050","order_by":4,"name":"Qianfu Liu","email":"","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qianfu","middleName":"","lastName":"Liu","suffix":""},{"id":569039463,"identity":"6e95b191-abee-47d2-b167-21ed845b891d","order_by":5,"name":"Chao Wang","email":"","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wang","suffix":""},{"id":569039464,"identity":"36a46c51-5482-4666-a36e-dcc40cca3e3b","order_by":6,"name":"Yuan Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBADOQYGxgcMCaRoMWZgYDYgTUtiA0gLUUDe/fAxaZ6aO+nbpQ+zfXjAYCfPwH72AF4thmfS0qR5jj3L3dmXzDwjgSHZsIEnD7/7DBtyzKR52A7nbjjDfxjoF+YEBgke/C407H8D1PLvcLrBGWZmoJZ6wlrkJYC28LYdToBqOUxYi4HEs2TLuX2HDXf2gLQYHDds48khYEt/8sEbb74dljfnYWZm/FFRLc/PfoaALQcYWCTADAYoyYZXPcgWYBR+QGgZBaNgFIyCUYAFAAD05TtXjSL/PwAAAABJRU5ErkJggg==","orcid":"","institution":"Pearl River Fisheries Research Institute, Chinese Academy of Fishery Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Gao","suffix":""}],"badges":[],"createdAt":"2025-12-24 03:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8438446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8438446/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-026-15302-4","type":"published","date":"2026-04-10T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":101397916,"identity":"b4e78b34-b10c-4777-9ada-6a287a91231c","added_by":"auto","created_at":"2026-01-29 09:38:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":969689,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of 18 sampling sites in coastal rivers of eastern and western Guangdong. The map was produced using ArcGIS software (version 10.2). Abbreviations for sampling sites: SR, Sanrao; HG, Huanggang; CZ, Chaozhou; YH, Yuhu; PT, Paotai; TY, Tongyu; LH, Lihu; QY, Qiaoyuan; JK, Jiaokeng; CW, Chunwan; GM, Gangmei; YJ, Yangjiang; ZL, Zhenlong; HZ, Huazhou; GZ, Gaozhou; SJ, Shijiao; HS, Hengshan; JS, Jishui\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/4c866a0179fade75585c1194.png"},{"id":101397736,"identity":"53bb956e-db8b-4f4c-b065-43968e19c7e3","added_by":"auto","created_at":"2026-01-29 09:36:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320079,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in aquatic environmental variables between eastern (ECR) and western (WCR) coastal rivers of Guangdong discharging to the sea. Asterisks (*) in each subplot indicate significant differences between groups (Wilcoxon rank-sum test, p \u0026lt; 0.05), while \"NS\" denotes non-significant differences (p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/17efdda0c726a749cb74647b.png"},{"id":101398037,"identity":"0aef8d94-6fc2-49c5-91ef-5eeccd5e7b31","added_by":"auto","created_at":"2026-01-29 09:39:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":197654,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in zooplankton community α-diversity indices between eastern (ECR) and western (WCR) coastal rivers. NS denotes non-significant differences (p \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/5a482f3cf122fbd38bc08c98.png"},{"id":101398069,"identity":"12081226-88fe-4fd2-ae8d-b4701291f38f","added_by":"auto","created_at":"2026-01-29 09:39:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125818,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of Spearman correlations between zooplankton α-diversity indices and environmental variables in coastal rivers discharging to the sea in eastern and western Guangdong. Correlation coefficients are represented by color gradients in each cell. Significant correlations are denoted as: *p \u0026lt; 0.05 (Bonferroni-adjusted), **p \u0026lt; 0.01 (Bonferroni-adjusted)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/9a4ef8f0e9c671bbb04196f3.png"},{"id":101321262,"identity":"086ec5eb-f28d-4a35-a027-25d6378725a1","added_by":"auto","created_at":"2026-01-28 13:02:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":406301,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of units on the SOM map\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/06c7ad3cfe387dd3e59f0691.png"},{"id":101321265,"identity":"580c5a4a-5983-4bbc-a296-1a5cf4519ef0","added_by":"auto","created_at":"2026-01-28 13:02:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":252495,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of Key Zooplankton Species Abundance Distribution in Coastal Rivers Entering the Sea in Eastern and Western Guangdong Using Random Forest Method\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/486d16fb578eb71c4181ffd2.png"},{"id":101397798,"identity":"9ddfcd0c-6619-4772-8864-1450e9548704","added_by":"auto","created_at":"2026-01-29 09:37:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":223373,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlations between key zooplankton abundance and environmental variables in coastal rivers entering the sea in eastern and western Guangdong\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/5bae19002073350b44698d34.png"},{"id":106809580,"identity":"11c25c89-4395-437b-b05e-f0c4efbe3b49","added_by":"auto","created_at":"2026-04-13 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3354530,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8438446/v1/89e405ab-478b-464b-b847-6f5c914f75a3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrasting zooplankton communities in coastal rivers of eastern and western Guangdong, China: Relationships with environmental factors","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal aquatic biodiversity and ecosystem functioning are increasingly threatened by climate change and anthropogenic activities (Maciej et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a vital component of aquatic ecosystems, zooplankton, which occupy the base of the aquatic food web, not only sustain energy flow and material cycling in rivers but also maintain the physical and chemical properties of rivers. These properties exert a decisive influence on the survival, reproduction, composition, density, and proportion of aquatic biotic communities (Khaksar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Serving as a key link in the aquatic food web, zooplankton represent the primary energy pathway from phytoplankton to fish; changes in the community structure of zooplankton thus have a profound impact on the stability and functionality of the entire aquatic ecosystem (Venkataramana et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Zooplankton possess short life cycles and respond rapidly to environmental change. Their restricted dispersal also makes them valuable integrators of both abiotic and biotic influences over time (de et al., 2021). Zooplankton are highly sensitive to environmental changes and are therefore widely regarded as good indicators of ecosystem status. Changes in the physical, chemical, and biological parameters of aquatic systems lead to variations in the relative composition and abundance of plankton (Neila et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tartarotti et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe coastal areas of eastern and western Guangdong serve as crucial ecological transition zones in the northern South China Sea, featuring dense river networks and unique brackish water environments that foster highly diverse zooplankton communities. As key carriers of secondary productivity in aquatic ecosystems, the distribution patterns and structural characteristics of these communities directly reflect changes in watershed environments and ecological responses (Lan \u0026amp; Du, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In recent years, with the development of the Guangdong-Hong Kong-Macao Greater Bay Area and the Beibu Gulf Economic Zone, human activities along the coast (such as land reclamation, sewage discharge, and waterway engineering) have increasingly disrupted estuarine ecosystems. However, distinct geographic, climatic, and anthropogenic pressures in eastern and western Guangdong likely drive divergent ecological responses in their respective zooplankton communities (N. Chen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Investigating the heterogeneity of zooplankton communities in these two regions is essential for understanding the varying vulnerabilities of subtropical coastal ecosystems and formulating targeted conservation strategies.\u003c/p\u003e \u003cp\u003eAlthough systematic investigations have been conducted on zooplankton communities in large estuaries such as the Pearl River Estuary (Yin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), comparative studies on small and medium-sized river systems\u0026mdash;including the Hanjiang and Rongjiang Rivers in eastern Guangdong and the Jianjiang and Moyangjiang Rivers in western Guangdong\u0026mdash;remain insufficient. Existing literature predominantly focuses on single regions or specific seasons. For instance, (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) examined phytoplankton in the Hanjiang River, while Qiu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) surveyed benthic diatoms in the Jianjiang River. However, these studies lack cross-regional comparative analyses and particularly neglect the differential response mechanisms of zooplankton communities to environmental gradients (e.g., salinity stratification, nutrient input patterns, and seasonal runoff variations) between the two regions. Such knowledge gaps hinder the precise formulation of regional biodiversity conservation strategies and impede the scientific assessment of ecosystem adaptive management under anthropogenic disturbances.\u003c/p\u003e \u003cp\u003eThis study focuses on typical coastal rivers in eastern Guangdong (Huanggang River, Hanjiang River, Rongjiang River, Lianjiang River, Longjiang River, Luohe River) and western Guangdong (Moyangjiang River, Jianjiang River, Lianjiang River), employing field surveys to address the following scientific questions: (1) What are the significant differences in zooplankton community structure between coastal rivers in eastern and western Guangdong? (2) How do the key environmental drivers influencing zooplankton distribution differ between the two regions? (3) How can zooplankton community characteristics be used to assess the ecological health status of these river systems? The findings will provide a scientific basis for ecological conservation in Guangdong\u0026rsquo;s coastal rivers and serve as a methodological reference for similar studies in other regions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy site\u003c/h2\u003e \u003cp\u003eThis study investigated coastal rivers in eastern Guangdong, including the Huanggang River, Hanjiang River, Rongjiang River, Lianjiang River, Longjiang River, and Luohe River, with the Rongjiang River watershed being the largest in drainage area. The study region is bounded by the Lianhua Mountain Range to the north and the South China Sea to the south. Influenced by maritime climate and topography, the area receives abundant rainfall with mean annual precipitation ranging from 1,400 to 2,400 mm. The annual runoff depth typically varies between 800 and 1,500 mm across the watersheds. These rivers are characterized by short courses, steep gradients, and relatively high peak flood discharges.\u003c/p\u003e \u003cp\u003eThe coastal rivers of western Guangdong include the Moyang River, Jian River, and Lian River. Among these, the Moyang River and Jian River have catchment areas exceeding 1,000 km\u0026sup2;. This region is bordered by the Pearl River Delta to the east, the Nanliu River of Guangxi to the west, and the Yunkaidashan and Yunwu Mountains to the north, which separate it from the Xijiang River system. To the south lies the South China Sea. The terrain slopes from north to south, characterized by short, steep rivers that generally flow north-to-south before draining into the South China Sea.\u003c/p\u003e \u003cp\u003eWe established 18 sampling sites across 9 rivers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for aquatic ecological monitoring.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField Sampling and laboratory analysis\u003c/h3\u003e\n\u003cp\u003eIn March 2021, zooplankton samples were collected from coastal rivers in eastern and western Guangdong. Following the methods described by Zhang \u0026amp; Huang (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), qualitative samples were obtained by horizontal towing with a No. 25 plankton net (mesh size: 64 \u0026micro;m), while quantitative samples were collected using a 5L organic glass water sampler. For quantitative analysis, 30 L of surface water was filtered through the same plankton net and concentrated into 250 mL polyethylene bottles. All samples were preserved with formaldehyde solution to a final concentration of approximately 5%.\u003c/p\u003e \u003cp\u003eWater samples for environmental analysis were collected at a depth of 0.5 meters at each sampling site. In-situ measurements of water temperature, dissolved oxygen (DO), salinity, and pH were immediately obtained using a YSI-556 multiparameter probe (YSI Inc., USA), while water transparency was determined using a Secchi disk. Additional water samples were field-filtered and transported to the laboratory for nutrient analysis, including total phosphorus (TP), total nitrogen (TN), ammonia nitrogen (NH₃-N), and chemical oxygen demand (COD), performed using a Skalar-SA1100 continuous flow analyzer (Skalar Analytical, Netherlands). Chlorophyll-a (Chl-a) concentrations were determined through N, N-dimethylformamide (DMF) extraction followed by spectrophotometric analysis.\u003c/p\u003e \u003cp\u003eZooplankton were identified to the lowest possible taxonomic level, typically species or genus. The taxonomic identification of the rotifers was followed Koste (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) and Wang (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), while that of copepods and cladocerans was followed Institute of Zoology (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) and Jiang and Du (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). We used a microscope (BX-51, OLYMPUS, Tokyo, Japan) to identify the plankton in the samples. Zooplankton abundance was expressed as the number of individuals per liter (ind./L).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWilcoxon rank-sum tests were employed to examine differences in nine environmental variables between coastal rivers entering the sea in eastern and western Guangdong, including pH, temperature, salinity, DO, transparency, TP, TN, COD, and Chl-a. Wilcoxon rank-sum tests were performed to compare three α diversity indices (Richness, Shannon diversity, and Pielou evenness index) in water bodies of coastal rivers entering the sea between eastern and western Guangdong. Spearman rank correlation analysis was performed to examine relationships between α-diversity indices and environmental variables. To control for Type I error inflation due to multiple testing, p-values were adjusted using the False Discovery Rate (FDR) method. Significant correlations are reported at FDR-adjusted *p* \u0026lt; 0.05. Results were visualized using the 'pheatmap' package in R v4.0.2. The self-organizing map (SOM) algorithm was applied to reveal distribution patterns of sampling sites across coastal rivers entering the sea in eastern and western Guangdong. Results were visualized using MATLAB R2014a (MathWorks, USA). We employed random forest analysis to classify zooplankton communities based on geographic regions. Bioindicator species were selected based on their importance scores in the random forest model. The top 15 taxa with the highest Mean Decrease Accuracy (MDA\u0026thinsp;\u0026gt;\u0026thinsp;3.0) were identified as key bioindicators distinguishing eastern and western river communities. Spearman's rank correlation was then applied to examine relationships between bioindicator abundance and environmental variables. Results were visualized using the 'pheatmap' package in R v4.0.2 (R Core Team, 2020).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental variables\u003c/h2\u003e \u003cp\u003eNine environmental variables were measured in the coastal rivers discharging into the sea in eastern and western Guangdong (in-situ parameters: pH, temperature, salinity, DO, transparency; nutrient data: TP, TN, COD, Chl-a), with results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the pH of the surveyed rivers was alkaline. Significant differences (wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed in the mean pH and DO between eastern and western Guangdong\u0026rsquo;s coastal rivers, with eastern rivers exhibiting lower average pH and DO levels than western rivers. The average water temperature in eastern Guangdong\u0026rsquo;s rivers was significantly lower than in western rivers (wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). No significant difference in salinity was detected between the two regions. Transparency, TP, TN, and COD concentrations showed no notable variations between eastern and western rivers. However, Chl-a content in western Guangdong\u0026rsquo;s rivers was significantly higher than in eastern rivers (wilcoxon test, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe significantly lower pH and DO were observed in eastern rivers compared to western rivers (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For instance, acidifying effluents from e-waste recycling and textile industries likely drive pH reduction, while organic loads from municipal sewage depress DO. In contrast, the higher pH and DO in western rivers align with predominantly agricultural landscapes and higher forest cover, which enhance oxygen production and buffer against acidification. This clear dichotomy underscores the need for region-specific pollution control strategies. For instance, the Hanjiang River, a major watercourse in eastern Guangdong, exhibits water acidification in its downstream reaches due to urban sewage and industrial effluent discharges (Lin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additionally, wastewater from e-waste recycling and textile dyeing industries in the Lianjiang River basin contains substantial acidic compounds, further reducing pH (Song et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, western Guangdong rivers (e.g., Jianjiang and Moyangjiang) experience fewer pollution pressures, while higher forest coverage helps maintain alkaline conditions and elevated DO levels (Qiu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The significantly lower water temperature in eastern Guangdong rivers likely reflects regional climatic and hydrological differences. Eastern Guangdong\u0026rsquo;s subtropical coastal climate, with strong maritime influences, results in smaller temperature fluctuations, whereas western Guangdong\u0026rsquo;s longer sunshine duration and higher air temperatures lead to warmer river waters (W. Chen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, larger discharge volumes and faster flow velocities in eastern rivers (e.g., Hanjiang and Rongjiang) enhance air-water heat exchange, further cooling the water (Zhang \u0026amp; Shi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This could be attributed to higher nutrient inputs, for example, from intensive agricultural fertilizer use in the Moyangjiang Basin elevates nitrogen and phosphorus levels, stimulating phytoplankton growth (Cheng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Slower flow rates and longer hydraulic retention times in western rivers also favor phytoplankton accumulation (Gao et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Conversely, stronger turbulence and pollution control measures (e.g., the Lianjiang River remediation project) in eastern rivers likely suppress algal proliferation (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). No significant interregional differences were observed in salinity or transparency, possibly due to similar tidal influences and seawater intrusion in both coastal areas (Pan et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Comparable levels of TN, TP, and COD suggest analogous nutrient loads despite differing pollution sources. For example, both the Lianjiang (eastern Guangdong) and Jianjiang (western Guangdong) face agricultural runoff and municipal wastewater inputs, yielding similar nutrient concentrations (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eZooplankton community diversity\u003c/h2\u003e \u003cp\u003eThree α-diversity indices (Richness, Shannon, and Pielou) of zooplankton communities were calculated for 10 coastal rivers discharging into the sea in eastern and western Guangdong, and regional differences were compared (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The Richness, Shannon diversity, and Pielou evenness indices of zooplankton communities showed no significant differences between the coastal rivers of eastern and western Guangdong (Wilcoxon test, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThese findings indicate that despite differing climatic, hydrological, and anthropogenic pressures, zooplankton diversity patterns in both eastern and western Guangdong may be regulated by common regional environmental drivers. For instance, both the Hanjiang River (eastern Guangdong) and Jianjiang River (western Guangdong) experience seasonal precipitation effects, leading to comparable nutrient inputs and zooplankton community structures (Lin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qiu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Additionally, tidal influences in estuarine zones of both regions likely homogenize zooplankton communities through water mixing and salinity fluctuations (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, while alpha diversity indices showed no significant regional differences, community composition may exhibit location-specific characteristics. Such divergence could be attributed to distinct pollution sources (e.g., industrial effluents vs. agricultural runoff). Furthermore, zooplankton communities in western Guangdong rivers (e.g., Moyangjiang) may be more influenced by wave and tidal dynamics (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), whereas those in eastern rivers (e.g., Lianjiang) face unique stressors like e-waste recycling pollution (Song et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although these factors did not significantly alter diversity metrics, they may drive functional group variations. The observed similarities in alpha diversity between coastal rivers of eastern and western Guangdong may reflect common regional environmental pressures. However, future studies integrating community composition and functional traits are needed to elucidate underlying ecological distinctions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRelationship between zooplankton community diversity indices and environmental variables\u003c/h3\u003e\n\u003cp\u003eThis study evaluated the relationships between environmental variables and zooplankton community diversity indices in coastal rivers discharging into the sea in eastern and western Guangdong (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the eastern Guangdong rivers, no significant correlations were found between the Richness, Shannon diversity, and Pielou evenness indices of zooplankton communities and the measured environmental variables. In contrast, in the western Guangdong rivers, the Shannon diversity index exhibited positive correlations with both salinity and TP content, while the Pielou evenness index showed a positive correlation with salinity and a highly significant positive correlation with TP content. These findings indicate that water salinity and TP content have pronounced effects on zooplankton community diversity in western Guangdong's coastal rivers. Notably, the Pielou evenness index of zooplankton communities in western rivers demonstrated greater sensitivity to environmental variables compared to other α-diversity indices, particularly TP content, which showed the strongest statistical significance.\u003c/p\u003e \u003cp\u003eStudies on rivers in western Guangdong, such as the Moyang River and Jian River, indicate that salinity and TP are key factors influencing zooplankton community diversity. Xu et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) used a random forest model to predict TP concentrations in the Moyang River basin and found a significant covariation between TP and rainfall, with TP levels rising markedly during the flood season. Furthermore, variance partitioning analyses revealed that TP concentration during the dry season was the predominant factor influencing the biomass of the plankton community (Yin et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This may provide additional nutrient sources for zooplankton, thereby promoting increased community diversity. Additionally, sediment transport in the Moyang River estuary is jointly controlled by tidal currents and waves (Huang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and seasonal salinity variations may indirectly regulate zooplankton community structure by influencing water stratification and nutrient distribution (Sahwell et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, research on benthic diatom diversity in the Jian River basin (Qiu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that diatom community distribution is closely linked to water nutrient levels, further supporting the regulatory role of nutrients (e.g., TP) in aquatic biodiversity. In contrast, zooplankton communities in eastern Guangdong rivers, such as the Han River and Rong River, exhibit weaker responses to environmental variables. A study on zooplankton in the lower Han River (Lan \u0026amp; Du, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) revealed that species composition and abundance were primarily influenced by discharge and water temperature, with nutrients playing a relatively minor role. This may be attributed to water diversion projects in the Han River basin, such as the Han-Rong-Lian River Interbasin Transfer Project (Zhang, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which enhances water exchange and dilutes nutrient concentrations, thereby reducing their direct impact on zooplankton communities. Furthermore, research on microplastic pollution in the lower Han River (Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated that pollutant inputs in densely populated areas were dominated by microplastics and heavy metals rather than nutrients, which may also explain the lower sensitivity of zooplankton diversity to TP in eastern Guangdong rivers. Notably, the Pielou evenness index of zooplankton communities in western Guangdong rivers showed the most pronounced response to TP levels, suggesting that evenness is a more sensitive indicator of nutrient effects than species richness. This finding aligns with Gao et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u0026rsquo;s assessment of the Jian River basin using the Fish Index of Biotic Integrity, which found that pollution-tolerant species dominate in eutrophic conditions, leading to increased community evenness. Thus, the Pielou evenness index shows potential as a sensitive metric for monitoring aquatic ecosystem health in western Guangdong's coastal rivers, though further validation is needed. The divergent responses of zooplankton communities to environmental variables between the eastern and western rivers are consistent with the regional differences in hydrological regimes and anthropogenic pressures. Future studies incorporating long-term monitoring data could further elucidate the interactive effects of salinity and nutrients on zooplankton community dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eZooplankton community structure\u003c/h3\u003e\n\u003cp\u003eBased on zooplankton abundance, the distribution patterns of sampling sites in coastal rivers discharging into the sea in eastern and western Guangdong were revealed using SOM. The distribution of 100 units on the SOM is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. According to the similarity in zooplankton community composition among different neurons, the sites were clearly divided into two major clusters (I and II). Cluster I could be further subdivided into three subclusters (Ia, Ib, Ic), and Cluster II into three subclusters (IIa, IIb, IIc). These six subclusters (Ia, Ib, Ic, IIa, IIb, and IIc) consisted of 21, 24, 9, 24, 14, and 8 distribution units, respectively. Subcluster Ia comprised SR, PT, TY, CZ, and QY; Subcluster Ib included JK, LH, and YH; Subcluster Ic was represented by HG alone. Subcluster IIa consisted of CW, GM, YJ, HZ, JS, and SJ; Subcluster IIb included ZL and GZ; and Subcluster IIc was represented solely by HS. The results indicate a clear geographical clustering pattern of zooplankton communities in the coastal rivers of eastern and western Guangdong.\u003c/p\u003e \u003cp\u003eCluster Ia (SR, PT, TY, CZ, QY) and Ib (JK, LH, YH), predominantly in Guangdong eastern rivers, were associated with higher mean total phosphorus (TP: 0.26 mg/L) and chlorophyll-a (Chl-a: 2.75 \u0026micro;g/L), suggesting nutrient-enriched conditions likely linked to agricultural runoff. In contrast, Cluster IIa (CW, GM, YJ, HZ, JS, SJ) in western rivers showed lower nutrient levels but higher dissolved oxygen (DO: 8.47 mg/L), reflecting better water quality despite localized urban impacts. According to Qiu et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the diversity of benthic diatoms in the Jian River basin is significantly influenced by river hierarchy and anthropogenic activities, with high dissimilarity observed between upstream and downstream communities\u0026mdash;a pattern consistent with the distribution characteristics of zooplankton communities in this study. Additionally, water quality in western Guangdong rivers such as the Jian River is notably affected by agricultural and industrial pollution (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which may contribute to regional variations in zooplankton community structure. Cluster Ic (HG) formed a distinct group, likely due to localized environmental conditions (e.g., estuarine salinity gradients) or unique pollution sources (e.g., e-waste dismantling), analogous to the heavy metal and organic pollution documented in the Lian River basin from e-waste recycling (Song et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Clusters IIa (CW, GM, YJ, HZ, JS, SJ) and IIb (ZL, GZ) were mainly found in the Han River and Rong River basins of eastern Guangdong. The Han River basin generally exhibits better water quality, with phytoplankton communities dominated by diatoms (Lin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), potentially providing a stable food source for zooplankton and shaping their distinct community structure. However, microplastic pollution from urban wastewater discharge in downstream sections (Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) may partially alter zooplankton assemblages. Cluster IIc (HS) showed isolated clustering, possibly associated with unique hydrological conditions at the Rong River estuary, such as tidal dynamics and freshwater-saltwater mixing (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can significantly modify zooplankton distribution patterns. In summary, the divergence in zooplankton communities between eastern and western Guangdong rivers reflects regional environmental heterogeneity. Western rivers are more impacted by non-point agricultural pollution and industrial discharges, whereas eastern rivers maintain relatively better water quality but face localized pressures from urban pollution and water diversion projects. Future studies incorporating additional environmental factors (e.g., nutrients, flow regimes) could further elucidate the drivers of community distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographical variation in dominant zooplankton species\u003c/h2\u003e \u003cp\u003eThe random forest model identified 15 key zooplankton species that distinguish the communities between sampling sites in coastal rivers of eastern and western Guangdong: \u003cem\u003eTrichocerca longiseta\u003c/em\u003e, \u003cem\u003eDicranophorus forcipatus\u003c/em\u003e, \u003cem\u003eTrichocerca pusilla\u003c/em\u003e, \u003cem\u003eArcella hemisphaerica\u003c/em\u003e, \u003cem\u003eLecane luna\u003c/em\u003e, \u003cem\u003eDiaphanosoma leuchtenbergianum\u003c/em\u003e, \u003cem\u003eArcella discoides\u003c/em\u003e, \u003cem\u003eBrachionus budapestiensis\u003c/em\u003e, \u003cem\u003eDifflugia corona\u003c/em\u003e, \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e, \u003cem\u003eBrachionus ureeus\u003c/em\u003e, \u003cem\u003eBrachionus caudatus\u003c/em\u003e, \u003cem\u003ePolyarthra trigla\u003c/em\u003e, \u003cem\u003eMoina micrura\u003c/em\u003e, and \u003cem\u003eBosmina longirostris\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These differences may be influenced by hydrological conditions, water quality characteristics, and habitat variations between the two regions.\u003c/p\u003e \u003cp\u003eThe correlation analysis between the abundance of key zooplankton species (the aforementioned 15 species) and environmental variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) revealed that water temperature significantly influenced the abundance of several key zooplankton taxa (\u003cem\u003eB. calyciflorus\u003c/em\u003e, \u003cem\u003eB. ureeus\u003c/em\u003e, \u003cem\u003eD. forcipatus\u003c/em\u003e, \u003cem\u003eA. discoides\u003c/em\u003e, \u003cem\u003eB. calyciflorus\u003c/em\u003e, \u003cem\u003eT. pusilla\u003c/em\u003e). For example, temperature exerts a critical influence on the hatching conditions of zooplankton. Suitable hatching conditions vary by species and typically require sufficiently high temperature and appropriate oxygen levels (Gilbert, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schr\u0026ouml;der, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The abundance of \u003cem\u003eA. hemisphaerica\u003c/em\u003e and \u003cem\u003eP. trigla\u003c/em\u003e showed significant correlations with pH, while \u003cem\u003eA. hemisphaerica\u003c/em\u003e exhibited a negative correlation with DO. \u003cem\u003eD. leuchtenbergianum\u003c/em\u003e and \u003cem\u003eL. luna\u003c/em\u003e were negatively correlated with water transparency. In contrast, \u003cem\u003eB. calyciflorus\u003c/em\u003e, \u003cem\u003eB. ureeus\u003c/em\u003e, and \u003cem\u003eA. discoides\u003c/em\u003e displayed positive correlations with TP content. Additionally, \u003cem\u003eB. calyciflorus\u003c/em\u003e and \u003cem\u003eB. ureeus\u003c/em\u003e were positively associated with the permanganate index, whereas \u003cem\u003eB. calyciflorus\u003c/em\u003e and \u003cem\u003eT. pusilla\u003c/em\u003e showed positive correlations with Chl-a.\u003c/p\u003e \u003cp\u003eTemperature is the most consistently important variable structuring plankton communities (Gray et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The influence of water temperature on zooplankton abundance has been corroborated by multiple studies (Beaugrand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Fluctuations in temperature affect algal growth due to changes in the rate of nutrient uptake (O'Connor et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For instance, dynamic monitoring of phytoplankton in the Meixi section of the lower Han River revealed a significant correlation between water temperature and algal density (N. Chen, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). As phytoplankton serve as the primary food source for zooplankton, this finding indirectly supports the conclusion that water temperature affects zooplankton distribution through trophic interactions. Furthermore, chl-a concentrations at a monitoring station in the Han River basin exhibited significant fluctuations between dry and wet seasons due to temperature variations (Jin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), aligning with the positive correlations observed in this study between \u003cem\u003eB. calyciflorus\u003c/em\u003e /\u003cem\u003eT. pusilla\u003c/em\u003e and chl-a. This suggests water temperature indirectly regulates zooplankton abundance by modulating primary productivity (Bopp et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impacts of pH and DO on zooplankton are equally noteworthy. Research on phytoplankton communities in the Chaozhou section of the Han River identified pH and temperature as key drivers of Cryptophyta distribution (Lin et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our study's finding of significant correlations between \u003cem\u003eA. hemisphaerica\u003c/em\u003e/\u003cem\u003eP. trigla\u003c/em\u003e abundance and pH further validates the direct regulatory role of water quality parameters in planktonic communities. The negative DO correlation may reflect differential zooplankton adaptation to hypoxic conditions. For example, microplastic pollution studies in the lower Han River noted substantial DO fluctuations in urban reaches due to pollutant discharge (Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), potentially explaining \u003cem\u003eA. hemisphaerica\u003c/em\u003e's distribution patterns in low-DO environments.\u003c/p\u003e \u003cp\u003eNegative transparency-zooplankton correlations (e.g., \u003cem\u003eD. leuchtenbergianum\u003c/em\u003e and \u003cem\u003eL. luna\u003c/em\u003e) may indicate turbidity-induced suppression of filter-feeding species (Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Phytoplankton surveys in the Rong River estuary identified nitrate and inorganic phosphorus as critical factors governing community structure (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), while our observed positive correlations between \u003cem\u003eB. calyciflorus\u003c/em\u003e/\u003cem\u003eB. ureeus\u003c/em\u003e and TP imply nutrient-mediated algal growth indirectly supports zooplankton. Moreover, water diversion projects in eastern Guangdong (e.g., Lian River rehabilitation) demonstrated that increased flow rates significantly improve water quality (Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), providing empirical support for zooplankton responses to hydrological management.\u003c/p\u003e \u003cp\u003eZooplankton differences between eastern and western rivers could be influenced by watershed-scale human activities among other factors. Sediment analyses in the Lian River revealed extreme ecological risks from polybrominated diphenyl ethers (PBDEs) due to e-waste dismantling (Song et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whereas the Han River exhibited milder microplastic pollution (Liang et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This pollution gradient likely amplifies interregional zooplankton disparities. Additionally, non-point source pollution studies in the Han River basin identified negative correlations between forest coverage and pollutant loads (Zheng et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), suggesting land-use patterns indirectly regulate zooplankton via water quality mediation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eManagement implications\u003c/h2\u003e \u003cp\u003eOur findings suggest that zooplankton community metrics may serve as useful bioindicators for assessing river health in similar coastal river systems of Guangdong. Specifically:\u003c/p\u003e \u003cp\u003eThe Pielou evenness index, highly sensitive to TP in western rivers, could be integrated into routine monitoring programs to track eutrophication trends in agriculturally dominated basins.\u003c/p\u003e \u003cp\u003eIn eastern rivers, the relative insensitivity of diversity indices to nutrients highlights the need for complementary indicators (e.g., pollutant-tolerant species) to capture impacts from industrial and hydrological disturbances.\u003c/p\u003e \u003cp\u003eThe 15 key species identified by the random forest model (e.g., \u003cem\u003eBrachionus calyciflorus\u003c/em\u003e, \u003cem\u003eTrichocerca pusilla\u003c/em\u003e) represent candidate indicators for targeted monitoring of specific stressors (temperature, nutrient enrichment).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and future research\u003c/h2\u003e \u003cp\u003eThis study provides a snapshot of zooplankton communities in March 2021. While revealing spatial patterns between eastern and western rivers, the single-season sampling limits our ability to capture seasonal dynamics. Future multi-seasonal surveys are recommended to validate these patterns and explore temporal variability in community-environment relationships.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study compared the differences in zooplankton community structure between coastal rivers in eastern and western Guangdong Province and their relationship with environmental factors. The results showed that although there were no significant differences in the α-diversity indices (Richness, Shannon, Pielou) of zooplankton between the rivers in eastern and western Guangdong, the community structure exhibited distinct geographical clustering. In western Guangdong rivers, zooplankton diversity was significantly positively correlated with salinity and TP content, whereas the community structure in eastern Guangdong rivers showed a weaker response to environmental variables. A random forest model identified 15 key zooplankton species whose abundance was closely related to environmental factors such as water temperature, pH, and DO. The differences in zooplankton communities between the two regions were primarily influenced by regional hydrological characteristics, nutrient inputs, and human activity intensity. The findings provide evidence for the influence of abiotic factors and habitat characteristics on zooplankton communities, highlighting the potential regulatory roles of these variables, providing a scientific basis for the conservation and management of coastal river ecosystems in Guangdong Province.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eConceptualisation: Yuan Gao. Developing methods: Jianwei Liang, Mengfan Wang, Shanshan Yu, Qianfu Liu, Chao Wang, Yuan Gao. Data analysis: Jianwei Liang, Mengfan Wang. Preparation of figures and tables: Jianwei Liang, Xiaofeng Shao. Conducting the research, data interpretation, writing: Jianwei Liang, Mengfan Wang, Xiaofeng Shao, Shanshan Yu, Qianfu Liu, Chao Wang and Yuan Gao.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was supported by the Central Public-interest Scientific Institution Basal Research Fund, CAFS (grant numbers 2019XT07 and 2019XT0701). We are very grateful to all staff members of our team for their assistance during field work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBeaugrand, G., Iba\u0026ntilde;ez, F., Lindley, J. A., Philip, C., \u0026amp; Reid, P. C. (2002). 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Non-point source pollution in Hanjiang River Basin and its relation with landscape pattern. \u003cem\u003eWater Resources Protection\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(05), 78\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\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":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coastal rivers in eastern and western Guangdong, Zooplankton, Community structure, Geographical differences, Environmental factors","lastPublishedDoi":"10.21203/rs.3.rs-8438446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8438446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study compared zooplankton community structure and its relationship with environmental factors in coastal rivers of eastern and western Guangdong, based on a survey of 18 sampling sites across 9 rivers in March 2021. The study found significant differences in key environmental parameters (water temperature, pH, dissolved oxygen) between the rivers in eastern and western Guangdong. However, no corresponding geographical pattern was detected in the α-diversity indices of the zooplankton communities. Notably, zooplankton diversity in western Guangdong rivers was significantly positively correlated with salinity and total phosphorus (TP), with the Pielou evenness index showing particular sensitivity to TP. Self-organizing map (SOM) analysis demonstrated distinct geographical clustering patterns in zooplankton communities between the two regions. Furthermore, the random forest model identified 15 bioindicators whose abundances were closely associated with environmental factors such as water temperature and nutrient levels. The study highlights that rivers in eastern Guangdong are predominantly influenced by industrial pollution and hydrological regulation, whereas those in western Guangdong are more affected by agricultural non-point source pollution. These findings elucidate the differential regulatory mechanisms of abiotic factors and human activities on zooplankton communities, providing a scientific basis for the conservation and management of coastal river ecosystems in Guangdong Province.\u003c/p\u003e","manuscriptTitle":"Contrasting zooplankton communities in coastal rivers of eastern and western Guangdong, China: Relationships with environmental factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 13:02:07","doi":"10.21203/rs.3.rs-8438446/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-04T14:14:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T22:46:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T22:46:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-12-24T03:20:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ee3bbd87-fb18-42fe-95ba-84224f9b412f","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:07:30+00:00","versionOfRecord":{"articleIdentity":"rs-8438446","link":"https://doi.org/10.1007/s10661-026-15302-4","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2026-04-10 15:57:18","publishedOnDateReadable":"April 10th, 2026"},"versionCreatedAt":"2026-01-28 13:02:07","video":"","vorDoi":"10.1007/s10661-026-15302-4","vorDoiUrl":"https://doi.org/10.1007/s10661-026-15302-4","workflowStages":[]},"version":"v1","identity":"rs-8438446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8438446","identity":"rs-8438446","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}