Widespread admixture blurs population structure and confounds Lake Trout (Salvelinus namaycush) conservation even in the genomic era
Bibliographic record
Abstract
Intraspecific variation is important for species’ long-term persistence in changing environments. Conservation strategies targeting intraspecific variation often rely on the identification of management or policy units below the species level based on biological differences among populations. To identify management units, this paper examines intraspecific divergence of Lake Trout ( Salvelinus namaycush) in Great Slave Lake (GSL), Canada, using low-coverage whole-genome sequencing data. Specifically, we evaluate genetic differentiation and assess the relationship with morphological, mitochondrial, and putatively adaptive divergence. We show that at least three and up to five genetically distinct Lake Trout populations co-occur in GSL and exhibit differences in spatial distribution and body size, with signatures of selection. However, admixture was widespread (60% of the fish) and population structure was shallow (average F ST = 0.022). These findings highlight that, even in the era of whole genome sequencing, identifying discrete management units to implement conservation efforts and policy can remain challenging in systems where gene flow among genetically and ecologically distinct populations is ubiquitous. To give more recognition to this complexity, management efforts should also focus on the area where adaptive variation is evident, and evolutionary acts are at play, to better protect species’ resilience and adaptive potential in some natural systems.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".