Global regime shift dynamics of catastrophic sea urchin overgrazing
Bibliographic record
Abstract
A pronounced, widespread and persistent regime shift among marine ecosystems is observable on temperate rocky reefs as a result of sea urchin overgrazing. Here, we empirically define regime-shift dynamics for this grazing system which transitions between productive macroalgal beds and impoverished urchin barrens. Catastrophic in nature, urchin overgrazing in a well-studied Australian system demonstrates a discontinuous regime shift, which is of particular management concern as recovery of desirable macroalgal beds requires reducing grazers to well below the initial threshold of overgrazing. Generality of this regime-shift dynamic is explored across 13 rocky reef systems (spanning 11 different regions from both hemispheres) by compiling available survey data (totalling 10 901 quadrats surveyed in situ ) plus experimental regime-shift responses (observed during a total of 57 in situ manipulations). The emergent and globally coherent pattern shows urchin grazing to cause a discontinuous ‘catastrophic’ regime shift, with hysteresis effect of approximately one order of magnitude in urchin biomass between critical thresholds of overgrazing and recovery. Different life-history traits appear to create asymmetry in the pace of overgrazing versus recovery. Once shifted, strong feedback mechanisms provide resilience for each alternative state thus defining the catastrophic nature of this regime shift. Importantly, human-derived stressors can act to erode resilience of desirable macroalgal beds while strengthening resilience of urchin barrens, thus exacerbating the risk, spatial extent and irreversibility of an unwanted regime shift for marine ecosystems.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".