Physiological and nutritional constraints on zooplankton productivity due to eutrophication and climate change predicted using a resource-based modeling approach
Bibliographic record
Abstract
Emerging evidence suggests that zooplankton production is affected by physiological and nutritional constraints due to climate change and eutrophication, which in turn could have broad implications for food-web dynamics and fisheries production. In this study, we developed a resource-based zooplankton production dynamics model that causally links freshwater cladoceran and copepod daily production-to-biomass (P/B) ratios with water temperature, phytoplankton biomass and community composition, and zooplankton feeding selectivity. This model was used to evaluate constraints on zooplankton growth under four hypothetical scenarios: involving natural plankton community seasonal succession; lake fertilization to enhance fisheries production; eutrophication; and climatic warming. Our novel modeling approach predicts zooplankton production is strongly dependent on seasonal variation in resource availability and quality, which results in more complex zooplankton dynamics than predicted by simpler temperature-dependent models. For mesotrophic and hypereutrophic lakes, our study suggests that the ultimate control over zooplankton P/B ratios shifts from physiological control during colder periods to strong resource control during warmer periods. Our resource-based model provides important insights into the nature of biophysical control of zooplankton under a changing climate that has crucial implications for food web energy transfer and fisheries production.
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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 | 0.001 | 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".