Self-Regulation of the Okanagan Lake Kokanee Recreational Fishery: Dynamic Angler Effort Response to Varying Fish Abundance and Productivity
Bibliographic record
Abstract
Abstract The fishery for kokanee Oncorhynchus nerka in Okanagan Lake, British Columbia, exemplifies three common issues in the management of recreational fisheries: time-varying stock–recruit parameters, mixed stocks within a single fishing area, and open-access fishing effort. The aggregate kokanee stock (consisting of stream-spawning and shore-spawning stocks) declined 50-fold between 1971 and 1998 and then partially recovered in the most recent decade. This contrast in fish abundance provides an opportunity to gain insights into the predator–prey dynamics of anglers and fish in an open-access sport fishery. Structural variations in the Ricker stock–recruit function were fitted to the time series of spawner escapements and fishery data to estimate the relative role of nonstationary stock–recruit parameters versus angler harvest in the observed time series. The optimal model structures selected by Akaike's information criterion suggested that kokanee dynamics were driven by external factors (e.g., lake nutrient concentration or opossum shrimp Mysis diluviana abundance) as opposed to fishery harvest. The functional response of angler catch rates to fish abundance was well predicted by a type I function (i.e., no density dependence in catchability). Conversely, the angler effort numerical response to variation in kokanee density was sigmoid, with a steep transition at an average catch rate between 1 and 2 fish/d. The interaction of angler response functions with fish recruitment dynamics led to an open-access recreational fishery that never harvested the lower-productivity (stream-spawning) stock to a level below the optimal escapement abundance, despite dramatic declines in stock productivity (i.e., recruits per spawner at low abundance) over time. Standard management reactions to declining fish abundance (successive reductions in daily bag limits) were largely ineffective in altering harvest rate or effort dynamics. Our results show that the Okanagan Lake kokanee fishery was self-regulating over a large range in fish densities and that the declines in kokanee abundance resulted from time-varying stock productivity rather than from overharvest. Received January 15, 2013; accepted June 17, 2013
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".