Food-chemical discrimination and correlated evolution between plant diet and plant-chemical discrimination in lacertiform lizards
Bibliographic record
Abstract
Lizards use chemical cues to locate and identify prey and plant food, assess the nutritional quality of food, and detect plant toxins. Among insectivorous lizards, all actively foraging species studied respond strongly to prey chemicals sampled lingually, but ambush foragers do not. Much recent research has been devoted to assessing differential responses to food and nonfood chemicals (i.e., food-chemical discrimination) by omnivorous and herbivorous species and determining whether correlated evolution has occurred between plant diet and plant-chemical discrimination. We conducted experimental studies of food-chemical discrimination by two species of teiid lizards, the omnivorous Cnemidophorus murinus and the actively foraging insectivorous Ameiva ameiva. The omnivore distinguished both prey and plant chemicals from control substances. The insectivore exhibited prey-chemical, but not plant-chemical, discrimination, as indicated by tongue-flicking and biting. A comparative analysis using concentrated-changes tests showed that correlated evolution has occurred between plant consumption and plant-chemical discrimination in a major lizard taxon, Lacertiformes. These results extend and strengthen previous findings of similar correlated evolution to a new group and add to a growing database indicating that omnivorous lizards use chemical cues to assess both prey and plant foods.
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 | 0.001 |
| 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".