资源环境科技发展态势分析平台

Harmful algal blooms (HABs), in particular those consisting of the cyanobacteria Microcystis, are becoming increasingly more common across the globe. Despite the growing body of evidence that suggests vertical heterogeneity of Microcystis can be a precursor to HAB formation, the abiotic drivers of vertical distribution of Microcystis are poorly understood in the field environment. The prediction of subsurface cyanobacteria is also pertinent because subsurface concentrations are not easily recognizable to the public or lake system managers, creating a risk of exposure to harmful algal toxins. High-frequency temporal and vertical data were collected from a research station anchored in a stratified and eutrophic lake for five months. Using a combination of dimensional analysis and machine learning approaches, data show that the magnitude of the subsurface Microcystis concentration peak and the center of gravity of the deep cyanobacteria layer are statistically significantly mediated by the thermal structure of the lake. The peak subsurface cyanobacteria biovolume is related to the thermocline depth and temperature, whereas the center of gravity of the subsurface cyanobacteria biovolume is related to the mixed layer depth and temperature. Furthermore, our data suggest there is a seasonal evolution of the subsurface cyanobacteria center of gravity that could potentially indicate timing of HAB onset. Based on easily measured parameters associated with the vertical lake temperature profile and meteorological conditions, we provide evidence of predictable trends in subsurface cyanobacteria variables.

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