Winter plankton dynamics in a boreal lake: community structure, vertical distribution and reproduction under ice
Bibliographic record
Abstract
Abstract Lakes are understudied during ice-covered periods; yet physical, hydrodynamical and biological processes continue under the ice. Ice cover reorganizes lake ecosystems by altering thermal stratification and solar radiation, creating conditions that support different organisms, food webs, and potential trophic cascades. We investigated seasonal shifts in biomass and vertical distribution of heterotrophic and autotrophic micro-organisms (heterotrophic bacteria, picoautotrophs, colorless and pigmented nanoflagellates) and zooplankton across winter in relation to limnological characteristics and hydrodynamics. Boreal Lake Simoncouche was sampled at five depths on six dates between autumn overturn (November 2020) and spring overturn (April 2021) for temperature, dissolved oxygen, chlorophyll-a, and plankton. Low chlorophyll-a and high heterotroph-to-autotroph biomass ratios indicated dominance of the heterotrophic energy pathway under ice. Heterotrophic micro-organisms also dominated during well-lit overturns, emphasizing the role of the microbial loop in all seasons. Zooplankton richness remained high under ice (18–22 taxa), with most species favoring deeper, warmer layers. Several species of rotifer and cladoceran sustained egg production through winter, ceasing only briefly in February. These findings highlight strong vertical and seasonal heterogeneity in winter-active plankton communities, shaped by stratification, light, and resources, and support the view that winter is biologically dynamic, with consequences for the subsequent open-water season.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".