欢迎访问《生态学杂志》官方网站,今天是

生态学杂志 ›› 2026, Vol. 45 ›› Issue (10): 3219-3229.doi: 10.13292/j.1000-4890.202610.030

• ·生态水文过程与系统韧性专栏·(专栏组稿专家:董建志、孙守琴、薛宝林) • 上一篇    下一篇

不同水文连通性湖泊湿地土壤微生物群落对水位变化的响应

田佳鹰,赵睿思,王梦丽,姬思雨,刘思琨,任爱天*   

  1. (长江大学园艺园林学院, 湖北荆州 434025)

  • 出版日期:2026-10-10 发布日期:2027-04-12

Response of soil microbial communities to waterlevel fluctuations in lake wetlands with differential hydrological connectivity.

TIAN Jiaying, ZHAO Ruisi, WANG Mengli, JI Siyu, LIU Sikun, REN Aitian*   

  1. (College of Horticulture and Landscape Architecture, Yangtze University, Jinzhou 434025, Hubei, China).

  • Online:2026-10-10 Published:2027-04-12

摘要: 湿地水位变化影响着土壤微生物群落组成和多样性。然而,不同连通性湖泊湿地土壤微生物如何响应水位波动的研究相对匮乏。本研究选取长江中下游浅水湖泊洪湖(通江闸控)和崇湖(不通江)为对象,分析两类湖泊湿地不同水位高程下(常淹、未淹)土壤理化性质、植被以及土壤微生物群落,并结合径流量、径流深度与水位3项水文指标,揭示不同连通性湖泊水位高程变化对湿地土壤微生物群落的影响以及微生物的响应机制。结果表明:(1)洪湖植被丰富度指数、崇湖地上生物量以及两湖泊微生物丰富度指数均随水位升高而增加;崇湖植被优势度指数则随水位升高而下降。(2)NMDS分析结果表明,水位变化显著改变了洪湖植被群落以及两类湖泊微生物群落组成;同时,两湖泊未淹区微生物网络节点数目和边数量显著高于常淹区。(3)洪湖日均径流量和径流深度、硝态氮和速效磷含量以及微生物边平均度是细菌群落组成和丰富度重要预测因子;崇湖日均水位、有机碳和铵态氮含量是细菌群落组成和丰富度重要预测因子。(4)结构方程模型表明,水文不仅可以直接影响地上植被群落和微生物群落,同时还通过土壤理化性质和植被群落间接影响微生物群落。本研究从微生物方面视角为不同连通性湖泊湿地科学管理和保护提供了依据。


关键词: 植被, 土壤微生物, 水位变化, 湖泊连通性

Abstract: Water-level fluctuations are a key driver of the composition and diversity of soil microbial communities in wetlands. However, few studies have focused on the responses of soil microorganisms to waterlevel changes in lake wetlands with different hydrological connectivity. We examined the soil physicochemical properties, vegetation, and soil microbial communities at different water levels (frequently flooded areas and unflooded area) in these two types of lake wetlands, including Honghu Lake (a shallow river-connected and gate-controlled lake) and Chong Lake (a disconnected lake) in the middle and lower reaches of the Yangtze River. We further elucidated the effects of water level fluctuations on soil microbial communities and their response mechanisms in lakes with different hydrological connectivity based on hydrological indicators including runoff volume, runoff depth, and water level. The results showed that: (1) The vegetation richness index of Honghu Lake increased with rising water levels, while Simpson index showed the opposite trend. In addition, the aboveground biomass of Chong Lake and the microbial richness indices of the two lakes increased as water levels increased. (2) The results of the NMDS analysis showed that water level changes significantly altered the vegetation community in Honghu Lake and microbial community composition in two types of lakes. Meanwhile, the number of nodes and edges in the microbial network of the unflooded area in both lakes was significantly higher than in the frequently flooded areas. (3) Runoff volume, runoff depth, nitrate, available phosphorus content, and the average degree of microbial network edges were the important predictors of bacterial community composition and richness in Honghu Lake. By contrast, daily mean water level, organic carbon content, and ammonium content were important predictors of bacterial community composition and richness in Chong Lake. (4) Structural equation modeling showed that hydrology not only directly affected aboveground vegetation communities and microbial communities, but also indirectly affected microbial communities by changing soil physicochemical properties and vegetation communities. This study can provide a microbial perspective for the scientific management and protection of wetlands with varying connectivity.


Key words: vegetation, soil microorganism, water level fluctuation, lake connectivity