环境DNA(eDNA)技术是一种水生态系统生物检测手段，通过捕获水体中的游离DNA片段，在分子水平上高效解析水生生物群落的结构特征。该研究应用eDNA宏条形码技术，结合β多样性分析、线性判别分析效应大小(LEfSe)、共现网络及随机森林模型等方法，系统评估了金沙江下游向家坝水电站对冬季浮游动物多样性时空分布及群落演替的影响。结果表明，向家坝水电站显著改变了浮游动物α多样性特征：坝下干流的物种丰富度(Chao1)和系统发育多样性(PD)显著高于上游区域，且沿大坝方向呈递减趋势；支流的功能多样性显著高于干流。优势类群在不同水体类型中的分布差异显著：坝下干流中原生动物相对丰度最高，而坝上支流中以桡足类占优。坝下干流浮游动物群落的共现网络复杂度及稳定性更高。在河段尺度上，冬季浮游动物的β多样性由周转组分驱动，坝上支流因空间隔离和栖息地异质性较高，β多样性显著高于干流。水动力和水质条件是影响向家坝河段冬季浮游动物α多样性的关键环境因子，而营养和水质条件的变化则是驱动β多样性及其组分变化的关键环境因子，在向家坝河段的冬季浮游动物群落构建中发挥关键作用。

Objective: Environmental DNA (eDNA) technology is an emerging tool for the biological monitoring of aquatic ecosystems. It enables an efficient molecular analysis of the structural characteristics of aquatic communities by capturing DNA fragments freely present in water. This study focuses on the following objectives: (1) analyzing the spatial distribution patterns of winter zooplankton diversity and community composition in the Xiangjiaba section of the lower Jinsha River; (2) exploring the assembly processes and driving factors of winter zooplankton communities; (3) assessing the potential impacts of hydropower development on zooplankton diversity in this section during winter; and (4) identifying key environmental factors affecting zooplankton community structure and elucidating the regulatory mechanisms involved. Methods: This study applied eDNA metabarcoding to investigate winter zooplankton communities in the Xiangjiaba section of the lower Jinsha River. Species, phylogenetic, and functional diversity indices were calculated to evaluate the α-diversity. β-diversity was partitioned into species turnover and nestedness components. Community composition variations were assessed by applying Bray-Curtis distance, principal coordinates analysis (PCoA), and permutational multivariate analysis of variance (PerMANOVA). Key taxa were identified using linear discriminant analysis effect size (LEfSe). Co-occurrence networks were constructed utilizing sparse correlations for compositional data (SparCC) to evaluate community structure and stability. Random forest models were employed to identify crucial environmental drivers shaping zooplankton diversity patterns. Results: The Xiangjiaba Hydropower Station significantly impacted the α-diversity of winter zooplankton in the lower Jinsha River. Specifically, the downstream area of the mainstream river exhibited markedly higher Chao1 richness (Chao1) and phylogenetic diversity than the upstream regions. Additionally, both indices gradually declined toward the dam, suggesting a "homogenization effect" caused by reservoir regulation. Regarding functional traits, functional richness (FRic) and functional divergence (FDiv) were markedly greater in the tributaries than in the mainstream, reflecting a greater ecological niche differentiation in less-regulated habitats. The composition of dominant zooplankton groups varied across different water body types. Protozoans dominated the downstream region of the mainstream, whereas copepods were predominant in the upstream tributaries. The upstream region of the mainstream exhibited moderate protozoan abundance levels but lacked a single dominant group. Co-occurrence network analysis revealed that the downstream area of the mainstream had a more complex and robust network structure, with higher connectivity and lower vulnerability, indicating enhanced community stability. At the river section scale, the β-diversity of the winter zooplankton was primarily driven by species turnover, with species replacement being the primary community assembly process. Tributaries exhibited significantly enhanced β-diversity compared to the mainstream, largely due to spatial isolation and heterogeneous environmental conditions. The upstream area of the mainstream, functioning as a transition zone between the tributaries and downstream, demonstrated greater environmental homogenization and reduced community dissimilarity. Conclusions: Hydrological dynamics (e.g., water depth, flow velocity, and water level fluctuations) and water quality (e.g., temperature and turbidity) are the main environmental factors influencing α-diversity patterns of winter zooplankton. Variations in nutrient levels (e.g., chlorophyll a) and water quality (e.g., conductivity and water temperature) are the key drivers of β-diversity and its components, particularly species turnover. These findings suggest that developing hydropower stations and associated environmental changes notably influence zooplankton community structure and assembly processes. Tributary inflow and dam-induced habitat modifications are critical in shaping spatial biodiversity patterns in regulated river systems.