Using genomics to quantify population structure and genetic diversity of the Arctic char (Salvelinus alpinus) in the Lower Northwest Passage, Nunavut, Canada
Bibliographic record
Abstract
The contemporary genetic diversity and population structure of a species reflect past and present demographic changes, and the combined effects of microevolutionary forces on its populations. Understanding the factors that shape geographical distributions and intraspecific genetic diversity is an important first step in the formulation of conservation and management plans. Because of their ecological, evolutionary, cultural, and economic significance, many efforts have been dedicated to genetic studies of wild populations of salmonid species like the Arctic char (Salvelinus alpinus). Despite this, patterns of genetic diversity remain uncharacterized in the King William Island (KWI) region of the Lower Northwest Passage (LNWP), Nunavut. Given rapid climate warming in the Arctic, food insecurity issues among Indigenous populations, and the success of a char fishery in Cambridge Bay, the Gjoa Haven community in KWI is eager to establish its own science-based, ecologically-sound, and sustainable char fishery. Key to this aspiration is the delineation of distinct char populations (stocks) to guide fisheries management. This thesis centres on quantifying and understanding the patterns of genetic diversity in char in the LNWP using two genome-wide panels of Single Nucleotide Polymorphism markers (SNPs) obtained from DNA and RNA sequencing (DNAseq and RNAseq). Using DNAseq data comprising 3,074 SNPs for 413 individuals and a series of population genetics approaches, I found deep genetic differentiation between char populations in the KWI and those on the mainland around Chantrey Inlet, ~200km to the south. I also found 22 SNPs with possible signatures of selection, and associations between genetic distance, geographic distance, and environmental variables. I then focused on the two clusters to infer their demographic histories, and found support for an isolation-with-migration demographic scenario over a strict-isolation model. The RNAseq datasets agreed on an overarching genetic division between KWI and Chantrey Inlet, and the presence of a mixed-stock site in Murchison River, but overall lower genetic differentiation (lower FST) compared to DNAseq data. Finally, I developed a new genetic tool for accurate and fast stock composition identification. Collectively, this work adds to salmonid population genetic studies, and brings new insights for the establishment of a char commercial fishery in the LNWP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".