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Influenced by natural geographical conditions and intensity of human activities, the water environment of different areas in Erhai Lake exhibits significant differentiation. To analyze the spatiotemporal variation characteristics of the water environment in Erhai Lake, this study constructed water quality correlation networks at overall, annual, and quarterly scales for three lake regions (the north, middle, and south) based on 19 water physicochemical indicators monitored quarterly at the lake center and corresponding bays (Shaping Bay, Wase Bay, and Xiangyang Bay) from 2017 to 2024. The results indicate that: (1) The water quality network at the center of Erhai Lake is sparser and structurally simpler than that in the lake bays. This low connectivity and high modularity structure can localize disturbances such as pollution, thereby endowing higher resistance. In contrast, the bay networks are more complex and densely connected, making them more prone to chain fluctuations under external disturbances and having relatively weaker resistance; (2) The core nodes quality in the center lake network water are TN, TP, SD, and Chl.a, with TP serving as a common key driver. Its regulation can produce an efficient lever effect. Although water quality indicators in the bays have more connections, they possess fewer key nodes and rely more on random connected paths for buffering disturbances, thus having relatively lower stability; (3) The network structure of water quality in the bays undergoes seasonal evolution, gradually becoming more complex and compact from spring to winter, which may be jointly driven by seasonal fluctuations in rainfall runoff and aquatic plants. Therefore, by regulating the aquatic plant communities in the bays, the local network density can be reduced and modularity enhanced, thereby providing a key pathway toward simplifying and stabilizing the overall lake water quality network. This study is the first to incorporate the network stability framework into water quality assessment of plateau lakes. The proposed “water quality network structure characteristics” offer novel early-warning indicators (such as modularity and density) for zonal management of Erhai Lake and indicate the precise governance approach of reducing external loads and regulating aquatic vegetation to drive the network towards a more favorable configuration.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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