Mixed-stock analysis in the age of genomics: Rapture genotyping enables evaluation of stock-specific exploitation in a freshwater fish population with weak genetic structure
Bibliographic record
Abstract
Abstract Mixed-stock analyses using genetic markers have informed fisheries management in cases where strong genetic differentiation occurs among local spawning populations, yet many fisheries are supported by multiple spawning stocks that are weakly differentiated. Freshwater fisheries exemplify this problem, with many harvested populations supported by multiple stocks of young evolutionary age that are isolated across small spatial scales. As a result, attempts to conduct genetic mixed-stock analyses of inland fisheries have often been unsuccessful. Advances in genomic sequencing now offer the ability to discriminate among populations with weak population structure by providing the necessary resolution to conduct mixed-stock assignment among previously indistinguishable stocks. We demonstrate the use of genomic data to conduct a mixed-stock analysis of Lake Erie’s commercial and recreational walleye ( Sander vitreus ) fisheries and estimate the relative harvest of weakly differentiated stocks (pairwise F ST < 0.01). We used RAD-capture (Rapture) to sequence and genotype individuals at 12,081 loci that had been previously determined to be capable of discriminating between western and eastern basin stocks with 95% reassignment accuracy, which was not possible in the past with microsatellite markers. Genetic assignment of 1,075 fish harvested from recreational and commercial fisheries in the eastern basin indicated that western basin stocks constituted the majority of individuals harvested during peak walleye fishing season (July – September). Composition of harvest changed seasonally, with eastern basin fish comprising much of the early season harvest (May – June). Clear spatial structure in harvest composition existed; more easterly sites contained more individuals of east basin origin than did westerly sites. Our study provides important stock contribution estimates for Lake Erie fishery management and demonstrates the power of genomic data to facilitate mixed-stock analysis in exploited fish populations with weak population structure or limited existing genetic resources.
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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 | 0.001 | 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".