Tipping points and interactive effects of chronic human disturbance and acute heat stress on coral diversity
Bibliographic record
Abstract
Multiple anthropogenic stressors co-occur ubiquitously in natural ecosystems. However, multiple stressor studies often produce conflicting results, potentially because the nature and direction of stressor interactions depends upon the strength of the underlying stressors. Here, we first examine how coral α- and β-diversities vary across sites spanning a gradient of chronic local anthropogenic stress before and after a prolonged marine heatwave. Developing a multiple stressor framework that encompasses non-discrete stressors, we then examine interactions between the continuous and discrete stressors. We provide evidence of additive effects, antagonistic interactions (with heatwave-driven turnover in coral community composition diminishing as the continuous stressor increased), and tipping points (at which the response of coral Hill-richness to stressors changed from additive to near synergistic). We show that community-level responses to multiple stressors can vary, and even change qualitatively, with stressor intensity, underscoring the importance of examining complex, but realistic continuous stressors to understand stressor interactions and their ecological impacts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".