Behavioral and phenotypic constraint belie deep genomic divergence and seasonal adaptation in a widespread desert lizard
Bibliographic record
Abstract
Cryptic species offer opportunities to reveal the mechanisms that constrain phenotypic divergence during speciation. We integrated whole-genome sequencing, morphological, micro- and macro-climatic, and behavioral data to investigate divergence across a well-documented genetic break in the desert-adapted side-blotched lizard, Uta stansburiana, on the Baja California peninsula. Despite deep genomic differentiation, clades show remarkable similarity in morphology, habitat use, and thermal biology. Nearly all genetic differentiation (87%) is explained by isolation by distance and seasonal variation in precipitation, with almost no effect of temperature. Behavioral thermoregulation and changes in activity time accommodate strong macro- and micro-climatic differences, buffering against selection that would otherwise drive morphological and physiological divergence. In contrast, genomic signatures of selection and divergence in genes associated with the nervous system, sensory perception, and biomolecule metabolism indicate adaptation to differences in rainfall seasonality. The results show behavioral flexibility can constrain phenotypic divergence, yielding cryptic species-level genetic divergence despite strong eco-climatic disparities and selection pressures. More broadly, this study shows how rigorous statistical integration of multiple data types can disentangle competing eco-climatic drivers that can decouple phenotype from genotype during speciation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 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".