Algal blooms as a reactive dynamic response to seasonal perturbation in an experimental system
Bibliographic record
Abstract
Abstract Algal blooms are typical of many aquatic freshwater ecosystems in seasonal environments. Such blooms could derive from transient reactive dynamics of algae and limiting nutrients following seasonal perturbation events. Linking parameter estimates derived from previously published lab experiments with empirical estimates of algal density dependence, we modeled dynamic interactions between nutrients and the green algal species Chlorella vulgaris and tested model predictions in a dozen 140 L mesocosms supplied with bi-weekly inputs of liquid fertilizer. Consistent with the reactive nutrient-driven model, Chlorella populations exhibited an initial surge in abundance over the first month followed by collapse as they rapidly converged on stable equilibria. The reactive model suggests that the magnitude of transient blooms is positively related to the augmentation of nutrients and depression of algae over the winter period. The magnitude of both algal peaks and equilibrium abundance was positively related to fertilizer loading, as predicted by the reactive model. Our results suggest that transient reactive responses to climate-driven perturbation events can be an important contributor to seasonal algal blooms observed in many temperate freshwater ecosystems. Controlled experimental studies such as ours may be helpful in understanding and potentially mediating the impact of fertilizer run-off on freshwater systems in temperate agricultural landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".