Modelling phase shifts in a rocky subtidal ecosystem
Bibliographic record
Abstract
The rocky subtidal ecosystem of the Atlantic coast of Nova Scotia is characterised by 2 community states, kelp beds and urchin barrens that alternate on a decadal time scale. While the shift from barrens to a kelp bed requires a large perturbation, namely a disease outbreak causing urchin mass mortality, the reverse shift is more gradual and mediated by the formation and propagation of destructive grazing aggregations (or fronts) of sea urchins Strongylocentrotus droebachiensis. We have developed a spatial model for the re-establishment of kelp beds and the formation of a feeding front of urchins in order to describe transitions (phase shifts) between kelp bed and barrens states following an urchin mass mortality event. Our model includes size-specific movement, growth, and survival of urchins, as well as growth of kelp and grazing by urchins. The position of the kelp bed-barrens interface (or grazing front) predicted by our model is in general agreement with field observations at 2 sites and for up to 7 yr after a mass mortality event. Elasticity analysis showed that daily foraging movements by individual urchins have the greatest effect on the position of the feeding front and the density of the urchins there. Survival and recruitment rates of urchins also have large effects on urchin density at the front. The density of urchin populations in deep-water thermal refuges (from a disease agent) and the distance between these source populations and remnant or emergent kelp beds have large effects on the time required for the re-formation of a feeding front after an event of urchin mass mortality. These findings suggest that site-specific characteristics should be taken into account in the development of sustainable harvesting strategies and habitat-based management of the urchin fishery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".