Angler Characteristics and Management Implications in a Large, Multistock, Spatially Structured Recreational Fishery
Bibliographic record
Abstract
Abstract Management of recreational fisheries involves understanding how anglers interact with the fishery resource. Managers must understand the source (spatial distribution), efficiency, and behavior of angler effort in order to develop optimal management strategies in a social–ecological framework. We interviewed anglers (n = 1,956) and assessed fish populations in 21 lakes that are part of a multistock, spatially structured fishery for Rainbow Trout Oncorhynchus mykiss in the interior of British Columbia, Canada. Our objective was to assess the spatial behavior, harvest behavior, and catch efficiency of anglers and to understand the strengths of interactions between anglers and fish populations in three regions within this large fishery. Our results suggest a diverse angler population that varied in its behavior and its impact on the fishery. Using a hierarchical cluster analysis, we identified four distinct angler groups based on three variables that directly described how anglers interacted with the fishery (spatial distribution, catchability, and harvest behavior). Angler characteristics varied between groups, and the relative proportions of the four discrete angler groups varied among management regions. Substantial variation in angler characteristics across groups and variation in the relative distribution of the groups across regions imply that a “one size fits all” management approach is not optimal for this fishery. Instead, strategies that are attuned to angler characteristics would constitute a better approach for managing this large, spatially structured fishery. Received April 18, 2012; accepted March 10, 2013
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".