Indigenous knowledge as data for modern fishery management: a case study of Dungeness crab in Pacific Canada
Bibliographic record
Abstract
ABSTRACT Introduction: Fisheries management is often data-limited, and conducted at spatial scales that are too large to address the needs of Indigenous peoples, whose cultures depend upon the local availability of marine resources. Outcomes: We combined Indigenous ecological knowledge with simulation modelling to inform modern fishery management. Semi-structured interviews with Indigenous fishers in coastal British Columbia, Canada, uncovered severe declines in the abundance and catches of Dungeness crab ( Cancer magister ) since the 1990s. We modelled the current probability of “successful“ crab harvesting trips—as defined by expectations from past catches by Indigenous fishers—using fishery-independent data from nine sites. These probabilities were very low (<20%) for all sites except one. Discussion: Our study highlights that local depletions, which Indigenous fishers attribute to commercial and recreational fisheries, have been widespread and undetected by federal managers who manage Dungeness crab at regional scales without fishery-independent data. Further, local depletions impacted the ability of Indigenous fishers to access traditional foods. Conclusion: Integrating Indigenous knowledge with scientific research is crucial to inform locally-relevant fisheries management and conservation.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".