Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
Bibliographic record
Abstract
Aim: Species with wide distributions spanning the African Guinean and Congolian rainforests are often composed of genetically distinct populations or cryptic species with geographic distributions that mirror the locations of the remaining forest habitats. We used phylogeographic inference and demographic model testing to evaluate diversification models in a widespread rainforest species, the African Foam-nest Treefrog (Chiromantis rufescens). Location: Guinean and Congolian rainforests, West and Central Africa. Taxon: Chiromantis rufescens. Methods: We collected mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data for 130 samples of Chiromantis rufescens. After estimating population structure and inferring species trees using coalescent methods, we tested demographic models to evaluate alternative population divergence histories that varied with respect to gene flow, population size change, and periods of isolation and secondary contact. Species distribution models were used to identify regions of climatic stability that could have served as forest refugia since the Last Interglacial. Results: Population structure within Chiromantis rufescens resembles the major biogeographic regions of the Guinean and Congolian forests. Coalescent-based phylogenetic analyses provide strong support for an early divergence between the western Upper Guinean forest and the remaining populations. Demographic inferences support diversification models with gene flow and population size changes even in cases where contemporary populations are currently allopatric, which provides support for forest refugia and barrier models. Species distribution models suggest that forest refugia were available for each of the populations throughout the Pleistocene. Main conclusions: Considering historical demography is essential for understanding population diversification, especially in complex landscapes such as those found in the Guineo-Congolian forest. Population demographic inferences help connect patterns of genetic variation to diversification model predictions. The diversification history of Chiromantis rufescens was shaped by a variety of processes, including vicariance from river barriers, forest fragmentation, and adaptive evolution along environmental gradients.
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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 | 0.003 | 0.007 |
| 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) | 0.004 | 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".