Widespread parallel population adaptation to climate variation across a radiation: implications for adaptation to climate change
Bibliographic record
Abstract
Global warming will impact species in a number of ways, and it is important to know the extent to which natural populations can adapt to anthropogenic climate change by natural selection. Parallel microevolution within separate species can demonstrate natural selection, but several studies of homoplasy have not yet revealed examples of widespread parallel evolution in a generic radiation. Taking into account primary phylogeographic divisions, we investigate numerous quantitative traits (size, shape, scalation, colour pattern and hue) in anole radiations from the mountainous Lesser Antillean islands. Adaptation to climatic differences can lead to very pronounced differences between spatially close populations with all studied traits showing some evidence of parallel evolution. Traits from shape, scalation, pattern and hue (particularly the latter) show widespread evolutionary parallels within these species in response to altitudinal climate variation greater than extreme anthropogenic climate change predicted for 2080. This gives strong evidence of the ability to adapt to climate variation by natural selection throughout this radiation. As anoles can evolve very rapidly, it suggests anthropogenic climate change is likely to be less of a conservation threat than other factors, such as habitat loss and invasive species, in this, Lesser Antillean, biodiversity hot spot.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".