Introduction history and natural selection jointly shape evolution of a signaling trait during biological invasion
Bibliographic record
Abstract
Introductions of invasive species to new environments often result in rapid rates of trait evolution. While in some cases these evolutionary transitions are adaptive and driven by natural selection, they can also result from non-adaptive processes associated with the invasion history. Here, we examined the role of adaptive and non-adaptive evolutionary processes in the brown anole (Anolis sagrei), a widespread invasive lizard for which genetic data have helped trace the sources of non-native populations. We focused on the dewlap, a signaling trait known to be subject to multiple selective pressures. We measured dewlap reflectance, pattern, and size in non-native populations across the southeastern United States. We combine these trait measurements with quantification of environmental variables known to influence dewlap signal effectiveness, such as canopy openness. Further, we use genome-wide data to estimate ancestry and to perform association mapping for dewlap traits. We found that among-population variation in dewlap characteristics is best explained by ancestry, as contributed by invasion history. This result was supported by genome-wide association mapping, which identified several ancestry-specific loci associated with dewlap traits. Despite the strong imprint of invasion history on dewlap variation, we also detect significant relationships between dewlap traits and local environmental conditions. Thus, our results are also consistent with natural selection influencing trait evolution during the brown anole invasion. Our study clarifies the importance of ancestry and admixture in shaping phenotypes during biological invasion, while also showing that some traits can respond adaptively to conditions encountered in the invasive range despite potential constraints imposed by invasion history.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".