Genetic Stock Identification Reveals That Angler Harvest Is Representative of Cryptic Stock Proportions in a High-Profile Kokanee Fishery
Bibliographic record
Abstract
Abstract Estimating fishery harvest and spawning escapement (spawning stock size) are critical components of fisheries management; however, they can be particularly challenging to measure in systems where visually indistinguishable, but reproductively isolated populations mix within a single fishing area. Genetic stock identification is a common tool used in such mixed-stock fisheries to improve estimates of spawning escapement and productivity; however, there are few references for management applications, particularly for inland recreational fisheries. The kokanee Oncorhynchus nerka population in Wood Lake, British Columbia, is a highly productive and valuable mixed-stock fishery that includes two reproductively distinct ecotypes: shore- and stream-spawning. Enumeration of shore-spawning kokanee is logistically challenging, as the spawning population is not confined to a defined area or depth like stream-spawners. Here, we combined in-lake sampling (angler harvest and age-0 trawl samples) over a 9-year period (2008–2016) with genetic stock identification and Bayesian statistics to develop a new method for enumerating shore-spawning kokanee. Our results suggest that angler-harvested kokanee are representative of the spawner age structure and stock proportions. Therefore, we used the angler harvest sample combined with known stream-spawner escapement to reconstruct the shore-spawner escapement time series. Shore-spawner abundance varied between 2,040 spawners and 13,460 spawners across years, which is over four times that previously predicted using the peak estimate of visual survey counts. Our results demonstrate the recovery of both the shore- and stream-spawning kokanee in Wood Lake following a well-documented crash in 2011 and suggest that a larger harvestable surplus is available for this high-value kokanee fishery.
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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 | 0.001 | 0.001 |
| 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".