Morphological and genomic responses to hurricanes arise and persist during a biological invasion
Bibliographic record
Abstract
Hurricanes can be a source of strong, episodic natural selection, especially for coastal and island populations. In Anolis lizards, selection favors morphological traits that enhance clinging performance under hurricane-force winds. However, we know little about the longer-term persistence of morphological and genomic responses to these pulse-like events. To address this limitation, we capitalized on the well-documented history of hurricanes and spread of the invasive brown anole lizard, Anolis sagrei , over the past 130 y in the southeastern United States. We used 30 sites with estimates of the number of hurricanes experienced since population establishment. We found that hurricane frequency is consistently related to morphological trait values that increase clinging performance—longer limbs and larger toepads. In contrast, traits with no known connection to clinging ability were not related to hurricane frequency. Our genomic results show that despite a complex genetic architecture for most traits, populations retain a signature of hurricane-mediated selection, with several loci being strongly associated with both hurricane frequency and longer limbs. Further, we found that hurricanes are a more persistent driver of among-population genomic differentiation than other environmental variables. These results solidify hurricanes as a major force shaping morphological and genomic variation in Anolis lizards. They also highlight how the evolutionary trajectories of populations will likely be altered as climate change modifies historical patterns of natural selection, such as those involving extreme weather events.
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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) | 0.001 | 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".