Red River barrier and Pleistocene climatic fluctuations shaped the genetic structure of<i>Microhyla fissipes</i>complex (Anura: Microhylidae) in southern China and Indochina
Bibliographic record
Abstract
South China and Indochina host striking species diversity and endemism. Complex tectonic and climatic evolutions appear to be the main drivers of the biogeographic patterns. In this study, based on the geologic history of this region, we test 2 hypotheses using the evolutionary history of Microhyla fissipes species complex. Using DNA sequence data from both mitochondrial and nuclear genes, we first test the hypothesis that the Red River is a barrier to gene flow and dispersal. Second, we test the hypothesis that Pleistocene climatic cycling affected the genetic structure and population history of these frogs. We detect 2 major genetic splits that associate with the Red River. Time estimation suggests that late Miocene tectonic movement associated with the Red River drove their diversification. Species distribution modeling (SDM) resolves significant ecological differences between sides of the Red River. Thus, ecological divergence also probably promoted and maintained the diversification. Genogeography, historical demography, and SDM associate patterns in southern China with climate changes of the last glacial maximum (LGM), but not Indochina. Differences in geography and climate between the 2 areas best explain the discovery. Responses to the Pleistocene glacial–interglacial cycling vary among species and regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".