The fishermen as predator : numerical responses of British Columbia gillnet fishermen to salmon abundance
Bibliographic record
Abstract
Fishermen are predators whose functional and numerical responses to prey can be studied. I review the fishery literature and discuss how much of it can be viewed in the context of a predator-prey system. I examined the numerical response, aggregation and movement of boats in response to fish abundance, in the British Columbia salmon gillnet fleet using catch data by area for the period 1979 to 1981. In any one year, for the whole coast, there was a strong relationship between the return (catch value per week per boat) and the number of gillnet boats fishing in the following week. I investigated three hypotheses to explain the within season movement of the gillnet fleet between areas (or sites) along the coast: fixed or traditional movement, which can be equated to an innate pattern of behaviour not substantially modified by learning; movement by fishermen to maximize individual return, which tends to equalize the return per unit time in all sites; and movement in which the drive to maximize individual return is modified by differential foraging costs and benefits between sites. At the individual area level, none of the hypotheses was sufficient to consistently explain the variation in boat numbers within a season. Fixed movement patterns do not adequately explain movement although they may be useful in the short term. Movement by fishermen to maximize individual returns did not consistently explain movement into and out of particular areas, and resultant area returns did not approach the provincial average return. Special features of each area appear to modify fishermen's attempts to maximize their indidividual returns such that each area tends to a characteristic return with respect to adjacent areas in any one year. However there is much variability in these returns between years. I compared the ability of the latter two hypotheses (equalization of return between sites and differential foraging costs) to predict boat numbers in particular areas in the following weeks. In some areas with identifiable features of location or boat type this approach worked well, but in most cases it was confounded by traditional and economic factors. Application of these hypotheses to components of the fleet, such as combination gillnetters and trollers, and pure gillnetters, may provide more insight into the mechanisms driving the numerical response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".