Genomics-based mixed-stock analysis reveals potential unsampled populations and population differences in intra-lake migration in walleye.
Bibliographic record
Abstract
Stock contributions to annual harvests provide key insights to conservation, especially in fish species that return to specific spawning sites and may establish genetically distinct populations. In this context, genetic stock identification (GSI) requires reference samples, yet sampling might be challenging as spawning sites could be in remote and/or unknown areas. Thus, any potential missing source population needs to be accounted for in management recommendations. Here, we (i) genotyped 1487 walleye (Sander vitreus) samples using a GT-seq panel of 336 single nucleotide polymorphisms and (ii) assessed individual migration distances from GPS records of fish harvested in two neighboring northern Quebec lakes (Mistassini and Mistasiniishish) important to the local Cree community. Samples were assigned to a source population using two methods, one requiring allele frequencies of known populations (RUBIAS) and the other without prior knowledge (STRUCTURE). Individual assignments to a known population reached 96% consistency between both methods. All five major source populations were identified in Mistassini Lake, but there was evidence of up to three small unsampled populations. Furthermore, Mistassini walleye populations were characterized by large differences in average migration distance with some remaining near their spawning rivers. In contrast, walleye in Mistasiniishish Lake were assigned with very high confidence to two populations with similar distribution throughout the lake. The complex population structure and migration patterns in the larger Mistassini Lake suggest a more heterogenous habitat and thus, greater potential for local adaptation. This study highlights the importance of combining analytical approaches to improve GSI studies for conservation practices.
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 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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".