Genetic Stock Structure of Anadromous Arctic Char in Canada's Central Arctic: Potential Implications for the Management of Canada's Largest Arctic Char Commercial Fishery
Bibliographic record
Abstract
Abstract The Arctic Char Salvelinus alpinus is widely considered the most important subsistence fish species in the Canadian Arctic. Throughout the species’ range, commercial fisheries for Arctic Char also exist, the management of which primarily follows river-specific harvest strategies. Such an approach, however, may not be appropriate if the management unit or stock does not accurately reflect a demographically independent population or if mixtures of populations are being harvested. We assayed microsatellite DNA variation among 744 Arctic Char from the Cambridge Bay region, Nunavut, where the largest commercial fishery for the species exists, in order to identify the most appropriate spatial scale at which these stocks should be managed. Our sampling design specifically mirrored that of the commercial fishery in order to describe patterns of genetic structure and genetic variation within and among the harvested component. We also included Arctic Char from more geographically distant sampling locations to provide a spatial context for genetic stock structuring in the region. Overall, we found moderate but significant structure across the entire study area. In contrast, commercially harvested stocks were weakly differentiated, especially among the stocks that are considered part of the Wellington Bay complex. We propose several hypotheses for this weak differentiation, including (1) our sampling design that mirrored the commercial harvest, (2) high rates of potential gene flow, and (3) large effective population sizes. Our results may have important implications for commercial and subsistence fisheries management, including the notion that there are several potential units of conservation below the species level. Received September 29, 2015; accepted August 17, 2016 Published online November 28, 2016
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".