The role of past climate change in driving novelties in Sky Island ant populations
Bibliographic record
Abstract
It is undeniable that climatic changes have affected species distributions and adaptations, and considerable effort has been deployed in the last decades to understand their consequences on genetic variation. However, little is known about how climate changes affect evolution of developmental systems within species. To explore this question, I studied populations of the ant Monomorium emersoni in five Arizona Sky Islands. These populations have experienced a climatic warm-up following the last glaciation, which is predicted to further continue under anthropogenic climate change scenarios. Queens of this species have two morphs, winged and wingless, where they each represent a different life history. First, I determined that the wingless queens phenotype has evolved in parallel on each of the Sky Islands and emerged in response to an inherent developmental propensity in combination with changes in the habitat following the last glaciation. I discovered some variations across populations in the developmental genetic processes that underlie the wingless phenotype, and that these variations reflect a demographic split between populations that occurred in the past. I also found that some other changes are shared among all populations, revealing a deterministic evolution of some part of the wing patterning gene network, independent of population history. Furthermore, I identified in the M. emersoni genome a signature of divergent selection on several outlier loci suggesting that populations are also adapting to temperature gradients within each Sky Island following the climatic warm up. This adaptive genomic divergence appears to con- strain gene flow among populations from divergent thermal environments within the same Sky Island. In general, my results show that environmental change can lead to adaptive divergence modifying population demographic parameters, and that it can also facilitate the evolution of variations in developmental systems that can fuel to further phenotypic diversification. Past climatic changes are therefore generating novelties across levels of biological organization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".