Principles for managing marine ecosystems prone to tipping points
Bibliographic record
Abstract
Abstract As climatic changes and human uses intensify, resource managers and other decision makers are taking actions to either avoid or respond to ecosystem tipping points, or dramatic shifts in structure and function that are often costly and hard to reverse. Evidence indicates that explicitly addressing tipping points leads to improved management outcomes. Drawing on theory and examples from marine systems, we distill a set of seven principles to guide effective management in ecosystems with tipping points, derived from the best available science. These principles are based on observations that tipping points (1) are possible everywhere, (2) are associated with intense and/or multifaceted human use, (3) may be preceded by changes in early‐warning indicators, (4) may redistribute benefits among stakeholders, (5) affect the relative costs of action and inaction, (6) suggest biologically informed management targets, and (7) often require an adaptive response to monitoring. We suggest that early action to preserve system resilience is likely more practical, affordable, and effective than late action to halt or reverse a tipping point. We articulate a conceptual approach to management focused on linking management targets to thresholds, tracking early‐warning signals of ecosystem instability, and stepping up investment in monitoring and mitigation as the likelihood of dramatic ecosystem change increases. This approach can simplify and economize management by allowing decision makers to capitalize on the increasing value of precise information about threshold relationships when a system is closer to tipping or by ensuring that restoration effort is sufficient to tip a system into the desired regime.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".