Distinguishing the distributions of two cryptic frogs (Anura: Discoglossidae) using molecular data and environmental modeling
Bibliographic record
Abstract
Currently, the identification of two cryptic Iberian amphibians, Discoglossus galganoi Capula, Nascetti, Lanza, Bullini and Crespo, 1985 and Discoglossus jeanneae Busack, 1986, relies on molecular characterization. To provide a means to discern the distributions of these species, we used 385-base-pair sequences of the cytochrome b gene to identify 54 Spanish populations of Discoglossus. These data and a series of environmental variables were used to build up a logistic regression model capable of probabilistically designating a specimen of Discoglossus found in any Universal Transverse Mercator (UTM) grid cell of 10 km × 10 km to one of the two species. Western longitudes, wide river basins, and semipermeable (mainly siliceous) and sandstone substrates favored the presence of D. galganoi, while eastern longitudes, mountainous areas, severe floodings, and impermeable (mainly clay) or basic (limestone and gypsum) substrates favored D. jeanneae. Fifteen percent of the UTM cells were predicted to be shared by both species, whereas 51% were clearly in favor of D. galganoi and 34% were in favor of D. jeanneae, considering odds of 4:1. These results suggest that these two species have parapatric distributions and allow for preliminary identification of potential secondary contact areas. The method applied here can be generalized and used for other geographic problems posed by cryptic species.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".