Depth-Dependent Spatiotemporal Dynamics of Overwintering Pelagic Microcystis in a Temperate Water Body
Bibliographic record
Abstract
Cyanobacteria in the genus Microcystis are dominant components of many harmful algal blooms worldwide. Their pelagic–benthic life cycle helps them survive periods of adverse conditions and contributes greatly to their ecological success. Many studies on Microcystis overwintering have focused on benthic colonies and suggest that sediment serves as the major inoculum for subsequent summer blooms. However, the contemporaneous overwintering pelagic population may be important as well but is understudied. In this study, we investigated near-surface and near-bottom pelagic population dynamics of both microcystin-producing Microcystis and total Microcystis over six weeks in winter at Dog Lake (South Frontenac, ON, Canada). We quantified relative Microcystis concentrations using real-time PCR. Our results showed that the spatiotemporal distribution of overwintering pelagic Microcystis was depth dependent. The abundance of near-bottom pelagic Microcystis declined with increased depth with no influence of depth on near-surface Microcystis abundance. In the shallow region of the lake (<10 m), most pelagic Microcystis was found near the lake bottom (>90%). However, the proportion of near-surface Microcystis rose sharply to over 60% as the depth increased to approximately 18 m. The depth-dependent distribution pattern was found to be similar in both microcystin-producing Microcystis and total Microcystis. Our results suggest the top of the water column may be a more significant contributor of Microcystis recruitment inoculum than previously thought and merits more attention in early CHAB characterization and remediation.
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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 | 0.000 | 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 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".