Key principles of marine ecosystem-based management
Bibliographic record
Abstract
Ecosystem-Based Management (EBM) has gained international popularity in recent years, but the lack of consensus on its definition has precluded the use of a universal implementation framework. The large number and variety of principles that make up EBM, and the diversity in perspectives among key management players, has impeded the practical application of EBM. Agreement on a list of the essential ingredients of EBM is vital to successful application. A frequency analysis of EBM principles was conducted to identify the Key Principles that currently define EBM, from a list of twenty-six principles extracted from a subset of the EBM theoretical/conceptual literature (covering a range of published sources across disciplines and application types). Fifteen Key Principles were identified (in descending frequency of appearance in the literature): Consider Ecosystem Connections, Appropriate Spatial & Temporal Scales, Adaptive Management, Use of Scientific Knowledge, Integrated Management, Stakeholder Involvement, Account for Dynamic Nature of Ecosystems, Ecological Integrity & Biodiversity, Sustainability, Recognise Coupled Social-Ecological Systems, Decisions reflect Societal Choice, Distinct Boundaries, Interdisciplinarity, Appropriate Monitoring, and Acknowledge Uncertainty. This paper also examines the development of EBM principles over time, leading to predictions on the directions EBM will take in the future. The frequency analysis methodology used here can be replicated to update the Key Principles of EBM in the future. Indeed, further research on potential emerging Key Principles such as ‘Consider Cumulative Impacts’, ‘Apply the Precautionary Approach’ and ‘Explicitly Acknowledge Trade Offs’ will help shape EBM and its successful application in the management of marine activities.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".