Chemosensory species recognition may reduce the frequency of hybridization between native and introduced lizards
Bibliographic record
Abstract
The introduction of alien species to islands by human activity can cause catastrophic consequences for small populations of island endemics. Hybridization between the endangered and endemic insular lizard Podarcis atrata (Boscá, 1916) from the Columbretes Islands (Mediterranean, Spain) and the common mainland lizard Podarcis hispanica (Steindachner, 1870) could potentially occur because mainland haplotypes have already been detected in the islands, the two species are closely genetically related, and the frequency of visitors to these islands is increasing. However, reproductive decisions of lizards are often mediated by species recognition mechanisms based on chemical cues. On the basis of this observation, even if some mainland P. hispanica lizards were introduced to the islands, interspecific recognition might make rare an eventual hybridization with the insular P. atrata. We examined interspecific chemical recognition between the insular P. atrata and the mainland P. hispanica. Our results showed that lizards of both sexes responded more strongly (i.e., directed a significantly higher number of tongue flicks) to scents of conspecific individuals than to scents of heterospecifics. Chemical recognition of conspecifics by endemic island P. atrata lizards may reduce the occurrence of hybridization with introduced mainland P. hispanica lizards and protect the insular gene pool.
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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) | 0.002 | 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".