Towards a sustainable Arctic fishery: Population genomics of lake whitefish in a hybrid species complex
Bibliographic record
Abstract
Genetic variation is an important predictor of population persistence under changing or stressful environmental conditions. Consequently, efforts to preserve genetic variation by identifying genetically distinct populations and delineating management units are primary goals of conservation genetics and fisheries management. Accelerated melting of sea ice in the lower Northwest Passage (LNWP) in Nunavut has recently opened the passage to shipping, providing an opportunity for fishery establishment. Nunavut communities have some of the highest rates of food insecurity across Canada, so the commercial harvest of profitable fish species could help to alleviate this crisis. However, sustainable fishery management in the LNWP requires the characterization of genetic structure in focal species to determine the best practices for managing demographically independent populations and, over the longer term, conserving genetic variation. The lake whitefish (Coregonus clupeaformis) is an important commercial species across Canada, is abundant in the LNWP, and is valued by the local people in Nunavut. However, lake whitefish have only recently expanded their range into this area, and the distribution of their genetic variation across the LNWP is unknown. Using genome-wide panels of single nucleotide polymorphisms, my results suggest one genetic population of lake whitefish in the LNWP. However, using putatively adaptive markers, I find weak evidence for two to three units of lake whitefish in the area. Further, I uncover genetic indication of hybridization between lake whitefish, Arctic cisco (C. autumnalis), and sardine cisco (C. sardinella) in the LNWP. Admixture among these species may make setting sustainable catch limits for lake whitefish challenging, as fishing pressures will likely decrease the abundance and genetic diversity of each species. Thus, my work aims to inform fishing regulations that reduce the relative exploitation of genetically distinct units of lake whitefish and minimize impacts of harvest on hybrids and their parental species. Setting reasonable fishing limits and preserving the genetic diversity of lake whitefish in the LNWP will reduce the likelihood of a fishery collapse, and increase probability for the species to become a sustainable resource for the people of Nunavut.
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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 | 0.000 |
| 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 | 0.000 |
| 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".