Macrozooplankton and the persistence of the deep chlorophyll maximum in a stratified lake
Bibliographic record
Abstract
Summary Deep chlorophyll maxima (DCM) are common in deep, oligotrophic stratified lakes. The DCM refer to the maximal chlorophyll a concentration found at depth, and not at the lake surface. While control of the DCM is thought to be via physicochemical factors in many lakes, a role for zooplankton grazing in epilimnetic waters remains a possibility. The occurrence and dynamics of DCM are poorly documented in smaller lakes, where zooplankton grazing is likely to have a stronger structuring effect. In small, shallow stratified lakes, biological control by grazing may be magnified by the short vertical gradient and overall higher water temperature. The respective contributions of several physical, chemical and biological parameters to the vertical distribution of phytoplankton biomass in a small stratified lake were examined. Associations between phytoplankton depth distribution and vertical gradients in temperature, light and nutrients and the density of herbivorous zooplankton were established through regressions and generalised linear models. Colimitation of the DCM by light from above and nutrients from below was detected. A threshold was detected at 3% incident light (100 μmol photon m−2 s−1), below which the DCM disappeared. Epilimnetic biomass was related to nutrient availability, with a threshold concentration at 4 μg P L−1, below which the DCM dominated. Greater stability of the water mass and more zooplankton were associated with higher phytoplankton biomass in the DCM. Stability is likely to have controlled vertical nutrient fluxes, which were intercepted by the metalimnetic phytoplankton. Zooplankton grazing of epilimnetic biomass could have increased incident light reaching the top of the metalimnion, thereby favouring proliferation of photosynthetic biomass in the DCM. Wind mixing events, as detected by a reduction in Lake number (LN, a measure of the influence of wind forcing on vertical structure), induced vertical intrusions of metalimnetic water, rich in nutrients and phytoplankton, into the epilimnion. We can infer that dominance of phytoplankton in the epilimnion would have occurred earlier during the summer if grazing by zooplankton had not removed epilimnetic phytoplankton. Our results suggest that, while stable stratification is necessary for initial DCM formation, zooplankton grazing may promote the persistence of a DCM.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".