Opposing trends in fisheries portfolio diversity at harvester and community scales signal opportunities for adaptation
Bibliographic record
Abstract
Understanding mechanisms that promote social-ecological resilience can inform future adaptation strategies. Among seafood dependent communities, these can be illuminated by assessing change among fisheries portfolios. Here, in collaboration with a Coast Salish Nation in British Columbia, Canada, we used expert Indigenous knowledge and network analyses to chronicle differences in fisheries portfolios pre and post a social-ecological regime shift. We then evaluated key drivers of change using semi-structured interviews. We found that while portfolios decreased in diversity of seafood types harvested and consumed among individuals overtime, portfolios increased in their diversification at the community level because more similar seafoods within less diverse individual portfolios were more commonly harvested and consumed by the Nation as a whole. Thus, diversity can operate simultaneously in opposing directions at different scales of organization. Experts identified four key mechanisms driving these changes, including commercial activities controlled by a centralized governance regime, intergenerational knowledge loss, adaptive learning to new ecological and economic opportunities, and the trading of seafood with other Indigenous communities. Unexpectedly, increased predation by marine mammals was also flagged as a key driver of change. Adaptation strategies that support access to and governance of diverse fisheries, exchange of seafoods among communities, and knowledge transfer among generations would promote social-ecological resilience, food security, and community well-being.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".