A review of adaptation options in fisheries management to support resilience and transition under socio-ecological change
Bibliographic record
Abstract
Abstract Social-ecological systems dependent on fisheries must be resilient or adapt to remain viable in the face of change. Here, we identified possible interventions (termed “adaptation options”) from published literature, aimed at supporting social or ecological resilience and/or aiding adaptation to changes induced by environmental or social stressors. Our searches centered on nations/regions across North America, Europe, and the South Pacific, encompassing fisheries literature with and without a climate change focus, to compare how, when, and by whom interventions are currently or potentially implemented. We expected that adaptation options within a climate change context would have a greater focus on enhancing social resilience due to a connection with climate change adaptation assessment methodology. Instead, we found a greater focus on ecological resilience, likely indicating a focus on management adaptation. This pattern, along with the more extensive use of social adaptation options responsively and outside the context of climate change, along with an importance in bottom-up influences in implementing them, suggests a general lack of centralized planning and organization with regards to adaptation of stakeholders. Determining how adaptation options are created, chosen, and implemented is a crucial step within or external to ecosystem-based management, especially if planned stakeholder adaption is the goal.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".