Mengshu Han

Baihua Reservoir, a typical large waterbody in the karst region of southwestern China and an essential drinking water source, is characterized by a high carbonate buffering capacity that profoundly shapes the structure and function of its phytoplankton community. This study systematically elucidates the multi-dimensional driving mechanisms underlying seasonal phytoplankton community assembly in karst reservoirs by integrating multiple analytical models—including the Neutral Community Model, β-diversity decomposition, co-occurrence network analysis, XGBoost-SHAP machine learning, and Partial Least Squares Path Modeling—based on monthly sampling at five sites from 2020 to 2024. The results revealed that: (1) Stochastic processes dominated community assembly across all four seasons, while deterministic processes played a crucial role in local species turnover. (2) The co-occurrence network structure showed significant seasonal dynamics, with the composition of keystone species adaptively shifting in response to changing environmental conditions. (3) The key environmental factors influencing the phytoplankton community exhibited clear seasonal patterns, primarily pH, NH₃-N, and COD_Mn in spring; water temperature, COD_Mn, and NH₃-N in summer; TN, TP, and pH in autumn; and pH, water temperature, and DO in winter. To support the sustainable management of karst reservoirs, we propose seasonally differentiated strategies derived from our phytoplankton community analysis: target COD_Mn reduction in spring and summer, focus on TN and TP load control in autumn, prioritize water column stability in winter, and maintain hydrological connectivity and pH monitoring year-round. This approach enhances phytoplankton community stability, safeguards drinking water safety, and provides a targeted management model for similar reservoir ecosystems globally.

Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within the Chayuan watershed, a representative karst region in southwest China. Our findings revealed significant spatial divergence in water-quality variables (including water temperature, pH, total nitrogen, total phosphorus, permanganate index, dissolved oxygen, chlorophyll-a, and ammonia nitrogen) between habitats. Twenty-nine dominant zooplankton species were identified in reservoir and river communities, with only eight shared between the two habitats. The mechanisms underlying the corresponding zooplankton community structures showed distinct segregation between habitats, with deterministic processes predominating in reservoir communities (explaining 25.1% of the variation) and stochastic processes predominating in river communities (3.4% of the variation explained). Environmental drivers differed substantially between habitats: reservoir communities were primarily influenced by total nitrogen, dissolved oxygen, and chlorophyll-a concentrations, whereas river communities responded predominantly to ammonia nitrogen levels. This study provides novel insights into the divergent mechanisms governing zooplankton community assembly in lentic versus lotic systems within a shared karst watershed, offering theoretical foundations for ecosystem-specific management strategies in fragile karst environments. Future research should focus on key climatic variables (e.g., extreme precipitation) and hydrological dynamics (such as flow velocity and water residence time) to further elucidate the mechanisms behind zooplankton community assembly, providing deeper insights to facilitate effective ecosystem management in karst environments.

This study investigated the pollution characteristics of heavy metals (Ni, Cu, Zn, As, Cd, Hg, Pb) in the sediments of the Hongfeng Reservoir in Guizhou. The fuzzy comprehensive evaluation method was used to evaluate the pollution level of heavy metals in the study area. The toxicity risk index method determined the biological harmful toxicity of heavy metals and the principal component analysis method determined the source of pollution of the heavy metals. The results show that the pollution level of heavy metals in the study area ranges from no pollution to strong pollution, and Ni and Zn have strong biological toxicity. According to the principal component analysis, domestic and industrial sewage and agricultural activities are the main pollution sources of Cu, Ni and Zn. The pollution sources of Cd, Pb and As are mainly the discharge of pollutants around the reservoir, industrial activities and human activities.

Rivers are an important mediator between human activities and the natural environment. They provide multiple functions, including irrigation, transportation, food supply, recreation, and water supply. Therefore, evaluations of water quality and pollution sources are of great significance for ecological restoration and management of rivers. In this study, the improved “vušekriterijumska optimizacija i kompromisno rješenje” (VIKOR in Serbian; in English: Multicriteria Optimization and Compromise Solution), and a geodetector were used to analyze the water quality characteristics and pollution sources of the Maotiao River Basin (Gizhou province, SW China). The results showed that the water quality of the Maotiao River Basin deteriorated significantly during the summer drought period, as was evident in the reservoirs and lakes. It improved in the wet season (i.e., during the summer period) due to runoff dilution. Water quality decreased along the river’s course, from upstream to downstream sections. The results of the geographic detector analysis showed that agricultural areas were the primary factor affecting the spatial distribution of water quality in the river basin. In July, August, and November 2020, the influence of agricultural land was 0.72, 0.60, or 0.80, respectively, and the interactions among urban, industrial, agricultural, and forested areas explained 99.2%, 83.2%, or 99.9% of the spatial differentiation of water quality, respectively. Due to the influence of spatial scale, settlements have a small influence on the spatial distribution of water quality. Their impact factors were 0.38, −0.24, and −0.05, respectively. Notably, the negative relationship of water quality and forested areas reflects that topography, types of landscapes, and soil thickness have considerable influences on the Maotiao River Basin’s water quality. Based on the findings, we infer that good farmland water conservancy projects and comprehensive management of different types of landscapes, such as forests, agriculture, and urban area and water bodies, are of great significance for improving water quality.