Practical implementation of cumulative‐effects management of marine ecosystems in western North America
Bibliographic record
Abstract
Abstract Globally, ecosystem structure and function have been degraded by the cumulative effects (CE) of multiple stressors. To maintain ecosystem resilience, there is an urgent need to better account for CE in management decision‐making at various scales. Current laws and regulations are supported by a multitude of frameworks and strategies that vary in application and terminology use across management agencies and geopolitical boundaries. We synthesized management frameworks that accounted for CE in marine ecosystems at the regional and national levels across western North America (Canada, United States, Mexico) to identify similarities and shared challenges to successful implementation. We examined examples of solutions to the identified challenges (e.g., interagency and cross‐border partnerships to overcome challenges of managing for ecologically relevant spatial scales). Management frameworks in general consisted of 3 phases: scoping and structuring the system; characterizing relationships; and evaluating management options. Challenges in the robust implementation of these phases included lack of interagency coordination, minimal incorporation of diverse perspectives, and data deficiencies. Cases that provided solutions to these challenges encouraged coordination at ecological rather than jurisdictional scales, enhanced involvement of stakeholders and Indigenous groups, and used nontraditional data sources for decision‐making. Broader implementation of these approaches, combined with increased interagency and international coordination and collaboration, should facilitate the rapid advancement of more effective CE assessment and ecosystem management in North America and elsewhere.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| 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".