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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".