Effects of increased specialization on revenue of Alaskan salmon fishers over four decades
Bibliographic record
Abstract
Abstract Theory and previous studies have shown that commercial fishers with a diversified catch across multiple species may experience benefits such as increased revenue and reduced variability in revenue. However, fishers can only increase the species diversity of their catch if they own fishing permits that allow multiple species to be targeted, or if they own multiple single‐species permits. Individuals holding a single permit can only increase catch diversity within the confines of their permit (e.g. by fishing longer or over a broader spatial area). Using a large dataset of individual salmon fishers in Alaska, we build a Bayesian variance function regression model to understand how diversification impacts revenue and revenue variability, and how these effects have evolved since the 1970s. Applying these models to six salmon fisheries that encompass a broad geographic range and a variety of harvesting methods and species, we find that the majority of these fisheries have experienced reduced catch diversity through time and increasing benefits of specialization on mean individual revenues. One factor that has been hypothesized to reduce catch diversity in salmon fisheries is large‐scale hatchery production. While our results suggest negative correlations between hatchery returns and catch diversity for some fisheries, we find little evidence for a change in variability of annual catches associated with increased hatchery production. Synthesis and applications . Despite general trends towards more specialization among commercial fishers in Alaska, and more fishers exclusively targeting salmon, we find that catching fewer species can have positive effects on revenue. With increasing specialization, it is important to understand how individuals buffer against risk, as well as any barriers that prevent diversification. In addition to being affected by environmental variability, fishers are also affected by economic factors including demand and prices offered by processors. Life‐history variation in the species targeted may also play a role. Individuals participating in Alaskan fisheries with high contributions of pink salmon — which have the shortest life cycles of all Pacific salmon — also have the highest variability in year‐to‐year revenue.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".