Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course

The ISME Journal
Nicolas TromasB Jesse Shapiro

Abstract

Cyanobacterial blooms occur in lakes worldwide, producing toxins that pose a serious public health threat. Eutrophication caused by human activities and warmer temperatures both contribute to blooms, but it is still difficult to predict precisely when and where blooms will occur. One reason that prediction is so difficult is that blooms can be caused by different species or genera of cyanobacteria, which may interact with other bacteria and respond to a variety of environmental cues. Here we used a deep 16S amplicon sequencing approach to profile the bacterial community in eutrophic Lake Champlain over time, to characterise the composition and repeatability of cyanobacterial blooms, and to determine the potential for blooms to be predicted based on time course sequence data. Our analysis, based on 135 samples between 2006 and 2013, spans multiple bloom events. We found that bloom events significantly alter the bacterial community without reducing overall diversity, suggesting that a distinct microbial community-including non-cyanobacteria-prospers during the bloom. We also observed that the community changes cyclically over the course of a year, with a repeatable pattern from year to year. This suggests that, in principle, bloo...Continue Reading

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Citations

Jan 31, 2020·Environmental Microbiology·Nicolas TromasAlessandra Giani
Aug 6, 2019·Frontiers in Microbiology·Seong-Jun ChunChi-Yong Ahn
Apr 11, 2018·Frontiers in Microbiology·Nicolas TromasB Jesse Shapiro
Nov 26, 2020·Toxins·Saber MoradinejadMichèle Prévost
May 11, 2021·Journal of Applied Statistics·Alex Paynter, Amy D Willis
Jan 12, 2020·The Science of the Total Environment·Thomas A EdgeJames Macklin

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