Bibliographic record
Abstract
Resilient kelp forests provide foundation habitat for marine ecosystems and are indicators of the ecosystems’ sustainable natural capital. Loss of resilience and imperfectly reversible catastrophic shifts from kelp forests to urchin barrens, due to pollution or loss of a top predator, are part of an ecological tipping point phenomenon, and involve a loss in sustainable natural capital. Management controls to prevent or reverse these shifts and losses are classified in a number of ways. Systemic controls eliminate the cause of the problem. Symptomatic controls use leverage points for more direct control of the populations affected, urchin harvesting or culling, or kelp enhancement. There is a distinction between ongoing structural (press) controls versus temporary or intermittent perturbation (pulse) controls, and one between shift preventing versus shift reversing or restorative controls. Adaptive management and the options it creates both focus on reductions in uncertainty and control policies with the flexibility to take advantage of those reductions. The various management distinctions are most easily understood by modeling the predator-urchin-kelp marine ecosystem. This paper develops a mathematical model of the ecosystem that has the potential for two different catastrophic shifts between equilibria. Pulse disturbances, originating from exogenous abiotic factors or population dynamics elsewhere in the metacommunity, can activate shifts. A measure of probabilistic resilience is developed and used as part of an assessment of the ecosystem’s sustainable stock of natural capital. With perturbation outcomes clustered around the originating equilibrium, hysteresis is activated, resulting imperfect reversibility of catastrophic shifts, and a loss in natural capital. The difficulty of reversing a shift from kelp forest to urchin barren, with an associated loss in sustainable natural capital, is an example. Management controls are modeled. I find that systemic and symptomatic, and press and pulse, controls can be complementary. Restorative controls tend to be more difficult or costly than preventative ones. Adaptive management, favoring flexible, often preventative, controls, creates option value, lowering control costs and/or losses in sustainable natural capital. Two cases are used to illustrate, Tasmania, Australia and Haida Gwaii, Canada.
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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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".