When adaptation is slowed down: Genomic analysis of evolutionary stasis in thermal tolerance during biological invasion in a novel climate
Bibliographic record
Abstract
Research conducted during the past two decades has demonstrated that biological invasions are excellent models of rapid evolution. Even so, characteristics of invasive populations such as a short time for recombination to assemble optimal combinations of alleles may occasionally limit adaptation to new environments. Here, we investigated such genetic constraints to adaptation in the invasive brown anole (Anolis sagrei)-a tropical ectotherm that was introduced to the southeastern United States, a region with a much colder climate than in its native Caribbean range. We examined thermal physiology for 30 invasive populations and tested for a climatic cline in cold tolerance. Also, we used genomics to identify mechanisms that may limit adaptation. We found no support for a climatic cline, indicating that thermal tolerance did not shift adaptively. Concomitantly, population genomic results were consistent with the occurrence of recombination cold spots that comprise more than half of the genome and maintain long-range associations among alleles in invasive populations. These genomic regions overlap with both candidate thermal tolerance loci that we identified using a standard genome-wide association test. Moreover, we found that recombination cold spots do not have a large contribution to population differentiation in the invasive range, contrary to observations in the native range. We suggest that limited recombination is constraining the contribution of large swaths of the genome to adaptation in invasive brown anoles. Our study provides an example of evolutionary stasis during invasion and highlights the possibility that reduced recombination occasionally slows down adaptation in invasive populations.
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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 | 0.000 | 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".