Selection at behavioral, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer
Bibliographic record
Abstract
Abstract What makes a species able to colonize novel environments? This question is key to understand the dynamics of adaptive radiations and ecological niche shifts, but the mechanisms that underlie expansion into novel habitats remain poorly understood at a genomic scale. Lizards from the genus Anolis are typically tropical and the green anole (Anolis carolinensis) constitutes an exception since it expanded into temperate North America from subtropical Florida. Thus, we used the green anole as a model to investigate signatures of selection associated with colonization of a new environment, namely temperate North America. To this end, we analyzed 29 whole genome sequences, representing the entire genetic diversity of the species. We used a combination of recent methods to quantify both positive and balancing selection in northern populations, including F ST outlier methods, machine learning and ancestral recombination graphs. We naively scanned for genes of interest and assessed the overlap between multiple tests. Strikingly, we identified many genes involved in behavior, suggesting that the recent successful colonization of northern environments may have been linked to behavioral shifts as well as physiological adaptation. These results were robust to recombination, gene length and clustering. Using a candidate genes strategy, we determined that genes involved in response to cold or behavior displayed more frequently signals of selection, while controlling for local recombination rate and gene length. In addition, we found signatures of balancing selection at immune genes in all investigated genetic groups, but also at genes involved in neuronal and anatomical development in Florida.
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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 | 0.000 |
| 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) | 0.001 | 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".