Habitat-Linked Population Genetic Differentiation in the Blue Tit Cyanistes caeruleus
Bibliographic record
Abstract
Although the recent emergence of the field of landscape genetics has led to several studies investigating the effects of habitat composition between populations on genetic differentiation, much less is known on the impact of within-habitat ecological characteristics on levels of gene flow and genetic differentiation among populations. Using data on 840 individuals sampled in 8 sites in Corsica and 1 in southern France and analyzed at 10 microsatellite loci, we assessed the spatial and temporal population genetic structure of blue tits (Cyanistes caeruleus) in Corsica and identified the ecological factors, both between- and within-sampling sites, responsible for the observed genetic structure. We found temporally stable fine-scale genetic structure within Corsica, with genetic differentiation values among populations corresponding to ~25% of levels observed between Corsica and the mainland. This structure was not explained by the geographic distance among populations or by the presence of physical barriers but was instead related to local habitat types (deciduous or evergreen oaks). Our results are thus consistent with previously documented phenotypic differences among habitats in morphological and reproductive traits. These findings suggest that although individuals have high dispersal ability, local adaptation might reduce gene flow among populations located in different habitats.
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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 | 0.000 | 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 | 0.000 |
| 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".