Drivers of recreational fisher compliance in temperate marine conservation areas: A study of Rockfish Conservation Areas in British Columbia, Canada
Bibliographic record
Abstract
Overfishing has impacted marine species over the last century, with many large-bodied and long-lived species declining to critical levels. Marine conservation areas are a popular management tool to protect and recover marine species and their habitats from intensive fishing pressure and human caused marine degradation. However, many marine conservation areas are thought to have low levels of compliance from diverse fishing populations. Little research exists that quantifies recreational fisher compliance and its drivers within marine conservation areas. We used the Rockfish Conservation Areas (RCAs) in British Columbia as a case study to investigate drivers of compliance. Our objectives were to (1) assess levels of recreational fisher RCA knowledge and compliance, (2) explore factors influencing fisher RCA knowledge and compliance, (3) quantitatively assess levels of fisher rockfish bycatch and release rates, (4) elicit fisher perceptions of RCAs, and (5) obtain fishers’ suggestions for improving rockfish conservation. We conducted 325 structured dockside interviews with recreational fishers in 16 locations. Intentional noncompliance was reported by seven percent of recreational fishers, and accidental noncompliance by 16%. The main reason for noncompliance was lack of knowledge. Recreational fishers were almost uniformly unknowledgeable of RCAs and their regulations across fishing experience levels. We found that 25.5% of recreational fishers had never heard of RCAs and ∼60% were unsure of RCA locations. However, 77% of fishers believed that rockfish conservation is necessary. The high recreational noncompliance rate in RCAs–primarily accidental fishing–is likely compromising the ability of these marine conservation areas to protect inshore rockfish. The ecological usefulness of marine conservation areas hinges upon users knowing about, and understanding, conservation area rules and regulations. We recommend managers implement a public outreach and education campaign to address the high levels of noncompliance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".