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Spatial and temporal dynamics of a freshwater eukaryotic plankton community revealed via 18S rRNA gene metabarcoding.通过18S rRNA基因宏条形码揭示的淡水真核浮游生物群落的时空动态
Hydrobiologia. 2018;818(1):71-86. doi: 10.1007/s10750-018-3593-0.
2
Reduced forms of nitrogen are a driver of non-nitrogen-fixing harmful cyanobacterial blooms and toxicity in Lake Erie.氮的还原形式是驱动安大略湖非固氮有害蓝藻水华和毒性的因素之一。
Harmful Algae. 2019 Jan;81:86-93. doi: 10.1016/j.hal.2018.11.003. Epub 2019 Jan 2.
3
Ammonium recycling supports toxic Planktothrix blooms in Sandusky Bay, Lake Erie: Evidence from stable isotope and metatranscriptome data.铵的再循环支持伊利湖桑达斯基湾有毒束丝藻水华的形成:稳定同位素和宏转录组数据的证据。
Harmful Algae. 2019 Jan;81:42-52. doi: 10.1016/j.hal.2018.11.011. Epub 2018 Dec 1.
4
Enhanced real-time cyanobacterial fluorescence monitoring through chlorophyll-a interference compensation corrections.通过叶绿素 a 干扰补偿校正增强实时蓝藻荧光监测。
Water Res. 2019 Jan 1;148:86-96. doi: 10.1016/j.watres.2018.10.034. Epub 2018 Oct 15.
5
Relationship between Photosynthetic Capacity and Microcystin Production in Toxic under Different Iron Regimes.不同铁处理条件下有毒蓝藻光合能力与微囊藻毒素产生的关系。
Int J Environ Res Public Health. 2018 Sep 7;15(9):1954. doi: 10.3390/ijerph15091954.
6
Microcystin interferes with defense against high oxidative stress in harmful cyanobacteria.微囊藻毒素会干扰有害蓝藻抵御高氧化应激的能力。
Harmful Algae. 2018 Sep;78:47-55. doi: 10.1016/j.hal.2018.07.008. Epub 2018 Aug 10.
7
Random forest versus logistic regression: a large-scale benchmark experiment.随机森林与逻辑回归:大规模基准实验。
BMC Bioinformatics. 2018 Jul 17;19(1):270. doi: 10.1186/s12859-018-2264-5.
8
Interkingdom microbial consortia mechanisms to guide biotechnological applications.跨界微生物联合体机制指导生物技术应用。
Microb Biotechnol. 2018 Sep;11(5):833-847. doi: 10.1111/1751-7915.13300. Epub 2018 Jul 16.
9
Microcystin Content in Phytoplankton and in Small Fish from Eutrophic Nyanza Gulf, Lake Victoria, Kenya.富营养化的维多利亚湖尼安萨湾浮游植物和小鱼中的微囊藻毒素含量。
Toxins (Basel). 2018 Jul 3;10(7):275. doi: 10.3390/toxins10070275.
10
Temporal and spatial distribution of Microcystis biomass and genotype in bloom areas of Lake Taihu.太湖蓝藻水华区微囊藻生物量和基因型的时空分布。
Chemosphere. 2018 Oct;209:730-738. doi: 10.1016/j.chemosphere.2018.06.141. Epub 2018 Jun 26.

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.

DOI:10.1016/j.hal.2019.05.004
PMID:31358280
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7877229/
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。