Genetic monitoring informs conservation status and trend of Arctic grayling at the southern edge of their distribution
Bibliographic record
Abstract
The number of effective breeders (Nb) has been touted as a means to monitor freshwater fishes, but the realized application of Nb has been limited. Using genetic monitoring data for two Arctic grayling (Thymallus arcticus) populations of conservation concern, we describe temporal trends in genetic variation and Nb, determine how sampling and variance in reproductive success influence estimates of Nb, and quantify the relationship among Nb, effective population size (Ne), and adult abundance (Nc). Temporal trends in allelic richness (AR) and Nb tracked known or suspected population trajectories. Nb increased in one population where there has been extensive conservation action, and both Nb and AR tracked a decline in the other population where harsh winter conditions have resulted in overwinter mortality events. After accounting for population demography, Ne estimates for each population were 190.7 and 308.8. Overall, this study demonstrates that temporal genetic data effectively resolve demographic and evolutionary status and trend in Arctic grayling, provides insight into the demographic factors that influence genetic variation, and emphasizes the value of temporal genetic data for conservation and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".