Impacts of Quaternary glaciation, geological history and geography on animal species history in continental East Asia: A phylogeographic review
Bibliographic record
Abstract
Continental East Asia has a mild Pleistocene climate and a complex recent geological history. Phylogeographic studies of animals over the last 30 years have produced several distinctive patterns. Glaciation refugia are numerous and are not restricted to any particular regions. Most of them are localized and species-specific, although several large refugia, for example the mountains of SW China, are shared by multiple species and have refugia-within-refugia. Furthermore, postglaciation range expansion events vary greatly in time, scale and direction. Large-scale south-to-north post-LGM expansions are few and mostly occurred in the northern regions. Additionally, several unique geographic features, including the three-step terrain of China and the northern arid belt, have significant impacts on many species histories. Overall, the impacts of Pleistocene glaciations, particularly the LGM, on species history vary drastically from nondetectable to significant. The impacts are the least for species from the southwestern region and are most dominant for species from the north. Geological events play a more significant role in shaping species history than Pleistocene climatic changes. Phylogeographic patterns among animals species are highly consistent with those of plants. Future phylogeographic endeavour in East Asia should be hypothesis-driven and seek processes that underlie common patterns. The wide use of genomic data allow accurate estimates of historical population processes and exploration of older history beyond the Pleistocene.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".