Zooplankton seasonal vertical migration in an optimality-based plankton ecosystem model
Bibliographic record
Abstract
Abstract Several species from various zooplankton taxa perform seasonal vertical migrations (SVM) of typically several hundred meters between the surface layer and overwintering depths, particularly in high-latitude regions. We use OPtimality-based PLAnkton (OPPLA) ecosystem model) to simulate SVM behavior in zooplankton in the Labrador Sea. Zooplankton in OPPLA is a generic functional group without life cycle, which facilitates analyzing SVM evolutionary stability and interactions between SVM and the plankton ecosystem. A sensitivity analysis of SVM-related parameters reveals that SVM can amplify the seasonal variations of phytoplankton and zooplankton and enhance the reduction of summer surface nutrient concentrations. SVM is often explained as a strategy to reduce exposure to visual predators during winter. We find that species doing SVM can persist and even dominate the summer-time zooplankton community, even in the presence of Stayers, which have the same traits as the migrators, but do not perform SVM. The advantage of SVM depends strongly on the timing of the seasonal migrations, particularly the day of ascent. The presence of higher (visual) predators tends to suppress the Stayers in our simulations, whereas the SVM strategy can persist in the presence of non-migrating species even without higher predators.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".