Inferred support for disturbance‐recovery hypothesis of <scp>N</scp>orth <scp>A</scp>tlantic phytoplankton blooms
Bibliographic record
Abstract
Abstract Analyses of satellite‐derived chlorophyll data indicate that the phase of rapid phytoplankton population growth in the North Atlantic (the “spring bloom”) is actually initiated in the winter rather than the spring, contradicting Sverdrup's critical depth hypothesis. An alternative disturbance‐recovery hypothesis (DRH) has been proposed to explain this discrepancy, in which the rapid deepening of the mixed layer reduces zooplankton grazing rates sufficiently to initiate the bloom. We use Bayesian parameter inference on a simple Nutrient‐Phytoplankton‐Zooplankton (NPZ) model to investigate the DRH and also investigate how well the model can capture the multiyear and spatial dynamics of phytoplankton concentrations and population growth rates. Every parameter in our NPZ model was inferred as a probability distribution given empirical constraints, which provides a more objective method to identify a model parameterization given available empirical evidence, rather than fixing or tuning individual parameter values. Our model explains around 75% of variation in the seasonal dynamics of phytoplankton concentrations, 30% of variation in their population rates of change, and correctly predicts the phases of population growth and decline. Our parameter‐inferred model supports the DRH, revealing the sustained reduction of grazing due to mixed‐layer deepening as the driving mechanism behind bloom initiation, with the relaxation of nutrient limitation being another contributory mechanism. Our results also show that the continuation of the bloom is caused in part by the maintenance of phytoplankton concentrations below a level that can support positive zooplankton population growth. Our approach could be employed to formally assess alternative hypotheses for bloom formation.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".