Wind‐driven physical processes and sediment characteristics affect the distribution and nutrient limitation of nearshore phytoplankton in a stratified low‐productivity lake
Bibliographic record
Abstract
Lay Abstract Wind‐driven physical processes are expected to affect the spatial distribution and composition of algae in lakes and reservoirs, and to determine their access to nearshore nutrients. We used nutrient addition bioassays to detect changes in the nutrient status of phytoplankton, which indicate changes in nutrient availability in the water. We examined these effects at offshore and nearshore sites and at different times of the year, under different stratification conditions (prestratification, early and late stratification) and with different phytoplankton communities. Phytoplankton accumulated downwind, but their growth rate was usually higher at upwind than downwind sites. This suggests that the quantity and quality of algal food sources for higher trophic levels may vary in predictable but opposite ways. Wind‐driven surface waves and upwelling activity were associated with changes in phytoplankton nutrient limitation in nearshore areas, but these differences were site specific. Our results suggest that wind‐driven physical processes and sediment characteristics are both important in determining internal nutrient loading and phytoplankton nutrient limitation in nearshore areas. On windy days, nutrient limitation of offshore phytoplankton at the lake surface was always related to the conditions found upwind, suggesting rapid exchanges between nearshore and offshore areas. Wind‐driven physical processes affect the distribution and nutrient limitation of phytoplankton in lakes, and are likely to influence the efficiency of energy transfers through planktonic food webs. These wind‐driven processes should be included more specifically into food web models.
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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) | 0.001 | 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".