Microsatellite DNA analysis of overwintering bull trout (<i>Salvelinus confluentus</i>) and its implications for harvest regulation and habitat management
Bibliographic record
Abstract
Abstract A mixed‐stock fishery occurs when multiple populations of a fish species are exploited together in a common area where they aggregate outside the breeding season (e.g. for feeding or overwintering), and the aggregation is known as a mixture. Recreational fishing often exploits such mixtures, and estimating the proportional contributions of populations to fisheries promotes more sustainable resource use. Ten DNA microsatellites were assayed in a mixture of bull trout Salvelinus confluentus Suckley overwintering in the Nechako River, upper Fraser River, British Columbia, and in baseline population samples from 14 tributaries putatively contributing to the overwintering mixture. A DNA microsatellite‐based mixed‐stock fishery analysis suggested that five populations together contributed 0.80 to the mixture. Most of the errors associated with the mixture estimates were attributable to uncertainty in baseline allele frequencies. Radiotracking data confirmed that tributary populations contributing to the mixture estimated by genetic analysis also contained individuals that moved between spawning tributaries and overwintering sites. The results better resolve habitat use by potadromous bull trout in the upper Fraser River and, in combination with assessments of baseline population‐specific spawning abundances and productivity, will better inform a decision of whether or not allowing some harvest within the current catch‐and‐release fishery is biologically sustainable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".