Cryptic Genetic Diversity Is Paramount in Small-Bodied Amphibians of the Genus Euparkerella (Anura: Craugastoridae) Endemic to the Brazilian Atlantic Forest
Bibliographic record
Abstract
Morphological similarity associated to restricted distributions and low dispersal abilities make the direct developing "Terrarana" frogs of the genus Euparkerella a good model for examining diversification processes. We here infer phylogenetic relationships within the genus Euparkerella, using DNA sequence data from one mitochondrial and four nuclear genes coupled with traditional Bayesian phylogenetic reconstruction approaches and more recent coalescent methods of species tree inference. We also used Bayesian clustering analysis and a recent Bayesian coalescent-based approach specifically to infer species delimitation. The analysis of 39 individuals from the four known Euparkerella species uncovered high levels of genetic diversity, especially within the two previously morphologically-defined E. cochranae and E. brasiliensis. Within these species, the gene trees at five independent loci and trees from combined data (concatenated dataset and the species tree) uncovered six deeply diverged and geographically coherent evolutionary units, which may have diverged between the Miocene and the Pleistocene. These six units were also uncovered in the Bayesian clustering analysis, and supported by the Bayesian coalescent-based species delimitation (BPP), and Genealogical Sorting Index (GSI), providing thus strong evidence for underestimation of the current levels of diversity within Euparkerella. The cryptic diversity now uncovered opens new opportunities to examine the origins and maintenance of microendemism in the context of spatial heterogeneity and/or human induced fragmentation of the highly threatened Brazilian Atlantic forest hotspot.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".