Evaluating patterns and drivers of spatial change in the recreational guided fishing sector in Alaska
Bibliographic record
Abstract
Understanding the impacts of recreational fishing on habitats and species, as well as the social and ecological importance of place to anglers, requires information on the spatial distribution of fishing activities. This study documented long-term changes in core fishing areas of a major recreational fishery in Alaska and identified biological, regulatory, social, and economic drivers of spatial fishing patterns by charter operators. Using participatory mapping and in-person interviews, we characterized the spatial footprint of 46 charter operators in the communities of Sitka and Homer since the 1990s. The spatial footprint differed between Homer and Sitka respondents, with Homer operators consistently using larger areas for Pacific halibut than Sitka operators. Homer and Sitka showed opposite trends in core fishing location area over time, with an overall decrease in Homer and an overall increase in Sitka. For both Sitka and Homer respondents, the range of areas fished was greater for Pacific halibut than for rockfish/lingcod or Pacific salmon. Spatial patterns were qualitatively different between businesses specializing in single species trips and those that operated multispecies trips and between businesses with one vessel and those with multiple vessels. In Homer, the most frequently cited reasons for changes in the location and/or extent of fishing were changes in trip type and the price of fuel, while in Sitka, the most frequently cited reasons for spatial shifts were changes to Pacific halibut regulations and gaining experience or exploring new locations. The diversity of charter fishing strategies in Alaska may allow individual charter operators to respond differently to perturbations and thus maintain resilience of the industry as a whole to social, environmental, and regulatory change. This research also highlights the importance of understanding fishers' diverse portfolio of activities to effective ecosystem-based management.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".