Genetic structure in relation to movements in wild European grayling (<i>Thymallus thymallus</i>) in three Norwegian rivers
Bibliographic record
Abstract
The relationship among genetic structure, movements, and barriers was studied in European grayling (Thymallus thymallus). Gene flow is closely related to movement patterns, and genetic differentiation may be negatively correlated with movement. Grayling movements in the Glomma River (8149 tagged) and the neighboring Trysilelva River (4070 tagged) were different among sampling sites. Most individuals were stationary (±0.5 km), but some individuals moved long distances (≤153 km). The movement data indicated potential for gene flow between the two upstream sampling sites in the Glomma River and between the two midstream sites in the Trysilelva River. Allele frequencies at seven microsatellite loci indicated pairwise differentiation across sampling sites. Genetic structuring was generally consistent with the observed movement pattern. Isolation-by-linear-distance or number of potential barriers (dams, lakes) was not significant among sites within streams or among sites within the two southern streams. They were significant, however, if two outlying northern populations in the Pasvik River were included. Our results suggest that although grayling may move long distances, they also exhibit genetic structuring within large continuous river systems. Fish movement, often the result of flexible and adaptive behavioral strategies, is the ecological mechanism for gene flow. Movements may be the adaptive link explaining how environmental conditions affect genetic structuring.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".