ArticleThe ISME journal2017
Characterising and predicting cyanobacterial blooms in an 8-year amplicon sequencing time course.
Article in The ISME journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 122 citations in OpenAlex.
- Bacterial community shifts in response to arsenic and cadmium contamination in aquatic ecosystems: a microcosm study.Scientific reports · 2026Article
- Time series of environment and plankton during Raphidiopsis raciborskii blooms in two subtropical Chinese reservoirs.Scientific data · 2026Article
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- Direct Evidence of Microbial Sunscreen Production by Scum-Forming Cyanobacteria in the Baltic Sea.Environmental microbiology reports · 2025Article
- Bacimethrin, an allelopathic vitamin B1 antagonist, is linked with microbial gene expression patterns in a hypereutrophic watershed.PloS one · 2025Article
- Response of particle-attached and free-living bacterial communities to Microcystis blooms.Applied microbiology and biotechnology · 2024Article
- Effects of salinity and nutrient stress on a toxic freshwater cyanobacterial community and its associated microbiome: An experimental study.Environmental microbiology reports · 2024Article
- CyanoStrainChip: A Novel DNA Microarray Tool for High-Throughput Detection of Environmental Cyanobacteria at the Strain Level.Environmental science & technology · 2024Article
- Toward a Predictive Understanding of Cyanobacterial Harmful Algal Blooms through AI Integration of Physical, Chemical, and Biological Data.ACS ES&T water · 2024Review
- Biological and Chemical Approaches for Controlling Harmful Microcystis Blooms.Journal of microbiology (Seoul, Korea) · 2024Review
- Selection of photosynthetic traits by turbulent mixing governs formation of cyanobacterial blooms in shallow eutrophic lakes.The ISME journal · 2024Article
- Quantitative relationships among high-throughput sequencing, cyanobacteria toxigenic genotype abundance and microcystin occurrence in bathing waters.The Science of the total environment · 2023Article
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- Coexistence ofEnvironmental science & technology · 2023Article
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- Spatio-temporal connectivity of the aquatic microbiome associated with cyanobacterial blooms along a Great Lake riverine-lacustrine continuum.Frontiers in microbiology · 2023Article
- Rapid on-site detection of harmful algal blooms: real-time cyanobacteria identification using Oxford Nanopore sequencing.Frontiers in microbiology · 2023Article
- Impact of Stagnation on the Diversity of Cyanobacteria in Drinking Water Treatment Plant Sludge.Toxins · 2022Article
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- Infection by a eukaryotic gut parasite in wild Daphnia sp. associates with a distinct bacterial community.FEMS microbiology ecology · 2022Article
Corrections and comments
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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, bloom events are predictable. We used probabilistic assemblages of OTUs to characterise the bloom-associated community, and to classify samples into bloom or non-bloom categories, achieving up to 92% classification accuracy (86% after excluding cyanobacterial sequences). Finally, using symbolic regression, we were able to predict the start date of a bloom with 78-92% accuracy (depending on the data used for model training), and found that sequence data was a better predictor than environmental variables.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.