Regional and Longitudinal Dynamics of Cyanobacterial Blooms/Cyanobiome and Cyanotoxin Production in the Great Lakes Area
Bibliographic record
Abstract
Cyanobacteria (blue-green algae) are a diverse group of prokaryotic microorganisms that impact global biogeochemical cycles. Under eutrophic conditions, cyanobacterial species can produce cyanotoxins, resulting in harmful algal blooms (cHABs) that degrade water quality and result in economic and recreational losses. The Laurentian Great Lakes, a key global freshwater source, are increasingly affected by these blooms. To understand the underlying mechanisms in cHAB formation, we investigated microcystin levels, cyanotoxin genes/transcripts, and taxonomic/microcystin metabarcoding across three sampling locations in the Canadian Great Lakes region, including Hamilton Harbour, Bay of Quinte, and Three Mile Lake (Muskoka), to observe the regional and longitudinal cyanobacterial dynamics. The results revealed a positive correlation between microcystin levels, the occurrence of cyanobacterial taxonomic/cyanotoxin molecular markers, and the relative widespread abundance of specific dominant cyanobacterial taxa, including Planktothrix, Microcystis, and Dolichospermum. The Cyanobium genus was not observed in Hamilton Harbor samples during late summer (August to September), while it was consistently observed in the Three Mile Lake and Bay of Quinte samples. Notably, Dolichospermum and saxitoxin genes were predominantly higher in Three Mile Lake (an inland lake), suggesting site-specific characteristics influencing saxitoxin production. Additionally, among the potential microcystin producers, in addition to Microcystis, Hamilton Harbour and Bay of Quinte samples showed consistent presence of less dominant microcystin-producing taxa, including Phormidium and Dolichospermum. This study highlights the complexity of cHAB formation and the variability in cyanotoxin production in specific environments. The findings highlight regional and site-specific factors that can influence cyanobacterial taxonomic and molecular profiles, necessitating the integration of advanced molecular technologies for effective monitoring and targeted management strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".