Safeguarding marine protected areas from cumulative effects: a review of methods, best practices, and applications
Bibliographic record
Abstract
Marine Protected Areas (MPAs) are key ocean conservation tools that can safeguard the diversity and function of marine ecosystems in the face of an increasing footprint and intensity of human activities. To be effective, MPA design, implementation, and management must consider not only individual, but also cumulative effects of historical, current and foreseeable future activities both within and outside MPA boundaries. Cumulative effects are seldom incorporated into MPA management as it can be challenging for MPA practitioners to select appropriate methods of assessment and integration. This paper examines two aspects of cumulative effects related to MPAs: a review of how cumulative effects are currently considered in MPA management worldwide, and a review of the primary and grey literature addressing cumulative effects knowledge and application in MPA contexts. The review of 646 global MPA management plans revealed that 36% did not contain any cumulative effects-related search terms and therefore likely lacked any provisions for, or even mentions of, cumulative effects. The review of cumulative effects knowledge found that few projects included all cumulative effects steps: scope and structure, assessment, and decision-making. Although significant advances have occurred in risk-based and spatial cumulative effects assessment methods over time, decision-making is rarely included in any cumulative effects projects. To bridge the gap between theory and practice, we propose a framework that embeds cumulative effects within the MPA designation and adaptive management process which will enable comprehensive scoping, meaningful assessments, and clear and transparent decision-making with respect to cumulative effects.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".