Are genetic differences between eastern and western Golden-crowned Kinglets populations correlated with environmental variation?
Bibliographic record
Abstract
Species with broad distributions often inhabit different habitats across their range, and studying how the observed environmental variation influences genetic variation can provide insights into the forces that drive population differentiation. Examining the relationship between genetic and environment variation is of importance given the effect climate change has on natural populations. Here we use high-throughput sequencing to assess population structure and examine the relationship between genetic and environment variation in eastern and western populations of Golden-crowned Kinglets ( Regulus satrapa Lichtenstein, 1823) with 14 438 single nucleotide polymorphisms from 41 individuals. Our analyses revealed that eastern and western populations of Golden-crowned Kinglets are differentiated from each other ( F ST = 0.08; p < 0.001), matching results from previous studies, and show low to moderate ecological variation (Cohen’s D = 0.52–2.59) for the three environmental variables we measured. The variable examining precipitation showed a moderate correlation with genetic variation ( r = 0.47; p < 0.001) and partial-redundancy and latent factor mixed models identified loci associated with precipitation. These results indicate the potential for environment to drive genetic differences between genetically distinct populations, although isolation by geographic distance during the last glacial maximum appears to have had a stronger effect on population structure. Overall, our study demonstrates the importance of examining genetic and ecological variation in unison to gain greater insights into how environmental variation contributes to genetic variation.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".