The duality of ocean acidification as a resource and a stressor
Bibliographic record
Abstract
Abstract Ecologically dominant species often define ecosystem states, but as human disturbances intensify, their subordinate counterparts increasingly displace them. We consider the duality of disturbance by examining how environmental drivers can simultaneously act as a stressor to dominant species and as a resource to subordinates. Using a model ecosystem, we demonstrate that CO2‐driven interactions between species can account for such reversals in dominance; i.e., the displacement of dominants (kelp forests) by subordinates (turf algae). We established that CO2 enrichment had a direct positive effect on productivity of turfs, but a negligible effect on kelp. CO2 enrichment further suppressed the abundance and feeding rate of the primary grazer of turfs (sea urchins), but had an opposite effect on the minor grazer (gastropods). Thus, boosted production of subordinate producers, exacerbated by a net reduction in its consumption by primary grazers, accounts for community change (i.e., turf displacing kelp). Ecosystem collapse, therefore, is more likely when resource enrichment alters competitive dominance of producers, and consumers fail to compensate. By recognizing such duality in the responses of interacting species to disturbance, which may stabilize or exacerbate change, we can begin to understand how intensifying human disturbances determine whether or not ecosystems undergo phase shifts.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".