Phytoplankton growth and succession driven by topography and hydrodynamics in seasonal ice-covered lakes

IF 7.3 2区 环境科学与生态学 Q1 ECOLOGY Ecological Informatics Pub Date : 2025-05-01 Epub Date: 2025-01-27 DOI:10.1016/j.ecoinf.2025.103053
Ziyue Zhao , Yanfeng Wu , Y. Jun Xu , Yexiang Yu , Guangxin Zhang , Dehua Mao , Xuemei Liu , Changlei Dai
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Abstract

Understanding how underwater topography affects phytoplankton succession by influencing hydrodynamics is crucial for maintaining the ecological health of lakes. However, there is a lack of in-depth research that accurately depicts underwater topography and coupleing hydrodynamics to establish the reproduction and migration mechanisms of phytoplankton, especially in seasonal ice-covered lakes. A typical seasonally ice-covered lake, Lake Chagan, was selected, and 164 water column and plankton samples were collected in 2023. An integrated underwater topographic-hydrodynamic model was constructed based on topographic data from 597 exploration points and long-term hydrological and meteorological observational data. The dominant algal species and their three-dimensional distribution and succession processes during different periods were studied in detail. The effects of topographic factors (relief, surface curvature, water depth, slope gradient, roughness, and slope aspect) on the hydrodynamic field and phytoplankton distribution were discussed. The results showed that the phytoplankton species diversity was higher in the bottom water column during the non-ice-covered period (March to October). The dominant species of phytoplankton varied with seasons, with diatoms dominating in the ice-covered period and harmful phytoplankton such as cyanobacteria in the non-ice-covered period. The biomass and biomass density of cyanobacteria were also higher than those of other phytoplankton. Phytoplankton species diversity and richness indices in the surface water column had a significant combined effect on the entire lake ecosystem. Surface curvature and slope gradient were the main factors affecting flow velocity during the non-ice-covered period (p  0.05, r = −0.58 and − 0.62), directly affecting the spatial distribution of cyanobacterial biomass (p  0.05, r = 0.65; p  0.01, r = −0.71). Therefore, attention should be paid to the surface curvature and slope of the sediment when controlling cyanobacterial blooms via by sediment dredging. These studies explored the behavior of phytoplankton in response to their fluid environment from a combined biological and physical-dynamic perspective and provided an effective reference for the water environment management of seasonal ice-covered lakes with harmful algal blooms.

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季节性冰雪覆盖湖泊中地形和水动力驱动的浮游植物生长和演替
了解水下地形如何通过影响水动力学影响浮游植物演替,对于维持湖泊生态健康至关重要。然而,目前还缺乏准确描绘水下地形和耦合水动力的深入研究,以建立浮游植物的繁殖和迁移机制,特别是在季节性冰雪覆盖的湖泊中。选取典型的季节性冰湖查干湖,于2023年采集了164个水柱和浮游生物样本。基于597个测点的地形资料和长期水文气象观测资料,构建了水下地形-水动力一体化模型。详细研究了不同时期的优势藻种及其三维分布和演替过程。讨论了地形因子(地形起伏、地表曲率、水深、坡度、粗糙度和坡向)对水动力场和浮游植物分布的影响。结果表明:无冰期(3 ~ 10月)底水柱浮游植物物种多样性较高;浮游植物的优势种随季节变化而变化,冰期以硅藻为主,非冰期以蓝藻等有害浮游植物为主。蓝藻的生物量和生物量密度也高于其他浮游植物。表层水体浮游植物物种多样性和丰富度指数对整个湖泊生态系统具有显著的综合效应。地表曲率和坡度是影响非冰雪覆盖期流速的主要因素(p≤0.05,r = - 0.58和- 0.62),直接影响蓝藻生物量的空间分布(p≤0.05,r = 0.65;P≤0.01,r = - 0.71)。因此,在疏浚河道控制藻华时,应注意沉积物的表面曲率和坡度。这些研究从生物学和物理动力学相结合的角度探讨了浮游植物对其流体环境的响应行为,为季节性冰覆盖湖有害藻华的水环境管理提供了有效参考。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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