Genomics and telemetry suggest a role for migration harshness in determining overwintering habitat choice, but not gene flow, in anadromous Arctic Char
Bibliographic record
Abstract
Abstract Migration is a ubiquitous life history trait with profound evolutionary and ecological consequences. Recent developments in telemetry and genomics, when combined, can bring significant insights on the migratory ecology of nonmodel organisms in the wild. Here, we used this integrative approach to document dispersal, gene flow and potential for local adaptation in anadromous Arctic Char from six rivers in the Canadian Arctic. Acoustic telemetry data from 124 tracked individuals indicated asymmetric dispersal, with a large proportion of fish (72%) tagged in three different rivers migrating up the same short river in the fall. Population genomics data from 6,136 SNP markers revealed weak, albeit significant, population differentiation (average pairwise F ST = 0.011) and asymmetric dispersal was also revealed by population assignments. Approximate Bayesian computation simulations suggested the presence of asymmetric gene flow, although in the opposite direction to that observed from the telemetry data, suggesting that dispersal does not necessarily lead to gene flow. These observations suggested that Arctic Char home to their natal river to spawn, but may overwinter in rivers with the shortest migratory route to minimize the costs of migration in nonbreeding years. Genome scans and genetic–environment associations identified 90 outlier markers putatively under selection, 23 of which were in or near a gene. Of these, at least four were involved in muscle and cardiac function, consistent with the hypothesis that migratory harshness could drive local adaptation. Our study illustrates the power of integrating genomics and telemetry to study migrations in nonmodel organisms in logistically challenging environments such as the Arctic.
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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 | 0.000 |
| 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 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".