Cyanobacteria in winter: Seasonal dynamics of harmful algal blooms and their driving factors in boreal lakes
Bibliographic record
Abstract
Lake cyanobacteria can overgrow and form blooms, often releasing life-threatening toxins. Harmful algal blooms (HABs) are typically caused by excess nutrients and high temperatures, but recent observations of cyanobacteria beneath the ice in boreal lakes suggest that the dynamics are more complex. This study investigates the seasonal dynamics of HABs in boreal lakes and identifies their driving factors. We study cyanobacteria assemblages in two boreal lakes in Abitibi-Témiscamingue (Quebec, Canada): Lake Fortune, noted for its under-ice cyanobacteria, and Lake Beauchamp, which has experienced recurrent summer-only cyanobacterial blooms. From June 2021 to July 2022, we identified monthly cyanobacterial communities and estimated water nutrients, organic carbon, temperature, oxygen, and pH. Cyanobacterial communities were dominated by the genus Planktothrix in Lake Fortune, and this genus was in a bloom state for each month of the year. Cyanobacterial abundance was highest (210 000 cells/mL) in November and lowest (28 000 cells/mL) in March. The abundance of Planktothrix correlated with total nitrogen and phosphorus and dissolved organic carbon concentrations. Planktothrix dominated even under ice cover, because of its ability to thrive in low-light and low phosphorus conditions. In Lake Beauchamp, Aphanothece was found throughout the year, highest (27 800 cells/mL) in August and lowest (2100 cells/mL) in March. In Lake Beauchamp, cyanobacterial blooms correlated with total dissolved phosphorus, nitrogen and organic carbon concentrations during summer and fall. The dominance of Aphanothece was especially pronounced during the summer and fall. Our study provides new knowledge about the seasonal dynamics of cyanobacterial blooms to help guide the future management of HABs in boreal lakes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".