Implementing Adaptive Management within a Fisheries Management Context: A Systematic Literature Review Revealing Gaps, Challenges, and Ways Forward
Bibliographic record
Abstract
Adaptive management acknowledges uncertainty and complexity in socio–ecological systems, providing a structured approach for learning and for making the needed management adjustments. Despite its utility, there are few examples of how adaptive management has been applied. To identify the extent to which implementation aligns with theory, we conducted a systematic literature review of adaptive management in a fisheries management context to compare how adaptive management was defined, applied and what was deemed important for implementation. Following the PRISMA approach for meta-synthesis, 20 papers were identified and reviewed against the eight key components of adaptive management. Across the case studies, we found ambiguity in the definitions of adaptive management, a varying emphasis on the different components of adaptive management and barriers to adaptive management that stemmed from both outside the process and as part of the iterative cycle. Our analysis suggests that for adaptive management to be implemented in other natural resource management situations, consideration should be given to the active and ongoing participation of those outside management, integrating socio–economic values into decision-making, and ensuring a monitoring plan is implemented. Additionally, attention should be paid to having the time and ability to detect the effects of management actions against a broader background of change. This analysis offers insights into how management support can lead to more effective objective-based decisions, thereby improving management over time.
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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 | 0.060 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".