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利用代谢组学和环境数据评估微囊藻素时空动态的潜在生态驱动因素。

Evaluating putative ecological drivers of microcystin spatiotemporal dynamics using metabarcoding and environmental data.

机构信息

US Environmental Protection Agency, Cincinnati, OH, 45268, USA.

US Environmental Protection Agency, Cincinnati, OH, 45268, USA.

出版信息

Harmful Algae. 2019 Jun;86:84-95. doi: 10.1016/j.hal.2019.05.004. Epub 2019 May 31.

Abstract

Microcystin is a cyanobacterial hepatotoxin of global concern. Understanding the environmental factors that cause high concentrations of microcystin is crucial to the development of lake management strategies that minimize harmful exposures. While the literature is replete with studies linking cyanobacterial production of microcystin to changes in various nutrients, abiotic stressors, grazers, and competitors, no single biotic or abiotic factor has been shown to be reliably predictive of microcystin concentrations in complex ecosystems. We performed random forest regression analyses with 16S and 18S rRNA gene sequencing data and environmental data to determine which putative ecological drivers best explained spatiotemporal variation in total microcystin and several individual congeners in a eutrophic freshwater reservoir. Model performance was best for predicting concentrations of the congener MC-LR, with ca. 88% of spatiotemporal variance explained. Most of the variance was associated with changes in the relative abundance of the cyanobacterial genus Microcystis. Follow-up RF regression analyses revealed that factors that were the most important in predicting MC-LR were also the most important in predicting Microcystis population dynamics. We discuss how these results relate to prevailing ecological hypotheses regarding the function of microcystin.

摘要

微囊藻毒素是一种具有全球意义的蓝藻肝毒素。了解导致微囊藻毒素浓度升高的环境因素对于制定湖泊管理策略至关重要,这些策略可以最大限度地减少有害暴露。尽管文献中充斥着将微囊藻毒素的产生与各种营养物质、非生物胁迫因子、食草动物和竞争者的变化联系起来的研究,但没有单一的生物或非生物因素被证明能够可靠地预测复杂生态系统中微囊藻毒素的浓度。我们使用 16S 和 18S rRNA 基因测序数据和环境数据进行随机森林回归分析,以确定哪些假定的生态驱动因素最能解释富营养化淡水水库中总微囊藻毒素和几种单一同系物的时空变化。预测 MC-LR 浓度的模型性能最佳,约 88%的时空方差得到解释。大部分方差与蓝藻属微囊藻的相对丰度变化有关。后续的 RF 回归分析表明,在预测 MC-LR 浓度方面最重要的因素也是预测微囊藻种群动态方面最重要的因素。我们讨论了这些结果与关于微囊藻毒素功能的流行生态假设的关系。

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