Multiscale effects of wind‐induced hydrodynamics on lake plankton distribution
Bibliographic record
Abstract
Abstract In this study, we used a combination of high‐intensity sampling technologies, and a 3D hydrodynamic model of a medium‐sized lake in southern Ontario, Canada to investigate physical–biological relationships at spatial scales from 100 m to 6 km and temporal scales from hours to months. At the scale of the whole study basin, we predicted that stronger winds would lead to higher zooplankton biomass downwind relative to upwind. The hydrodynamic model suggests rapid downwind displacement of progressively deeper surface mixed layers with increasing winds, and we found a statistically higher downwind biomass of small‐bodied zooplankton on windy days, but not large zooplankton. At a fine spatial scale (hundreds of meters), we predicted that zooplankton patchiness would decrease with increasing wind mixing of the upper water column and confirmed this for small‐bodied but not large‐bodied zooplankton. At this fine‐scale cross‐correlations of zooplankton biomass with water temperature and chlorophyll fluorescence suggested that zooplankton are not simply moved passively by water masses. We also found a clear change in the cross‐correlation between large‐ and small‐bodied zooplankton biomass, with out‐of‐phase spatial distributions during calm periods becoming in‐phase with increasing winds. Overall these results indicate that the response of zooplankton to wind‐driven physical forces is strongly dependent on an interaction between their body size, which determines their swimming speed and capacity to position themselves vertically in the water column, and the spatial scale and intensity of the wind‐generated physical forces. We discuss the implications for food web interactions.
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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 | 0.000 | 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".