A multi-realm perspective on applying potential tipping points to environmental decision-making
Bibliographic record
Abstract
Ecosystems experiencing pressures are at risk of rapidly transitioning (“tipping”) from one state to another. Identifying and managing these so-called tipping points continue to be a challenge in marine, freshwater, and terrestrial ecosystems, particularly when multiple potentially interacting drivers are present. Knowledge of tipping points, the mechanisms that cause them, and their implications for management practices are evolving, but often in isolation within specific ecological realms. Here, we summarize current knowledge of tipping points in marine, freshwater, and terrestrial realms and provide a multi-realm perspective of the challenges and opportunities for applying this knowledge to ecosystem management. We brought together conservation practitioners and global experts in marine, freshwater, and terrestrial tipping points and identified seven challenges that environmental policymakers and managers contend with including (1) predictability, (2) spatiotemporal scales, (3) interactions, (4) reversibility, (5) socio-ecological context, (6) complexity and heterogeneity, and (7) selecting appropriate action. We highlight opportunities for cross-scalar and cross-realm knowledge production and provide recommendations for enabling the management of tipping points. Although knowledge of tipping points is imperfect, we stress the need to continue working toward incorporating tipping points perspectives in environmental management across all realms.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.043 |
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; both teacher heads agree on what is shown here.
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".