Paleo-Drainage Basin Connectivity Predicts Evolutionary Relationships across Three Southeast Asian Biodiversity Hotspots
Bibliographic record
Abstract
Understanding factors driving diversity across biodiversity hotspots is critical for formulating conservation priorities in the face of ongoing and escalating environmental deterioration. While biodiversity hotspots encompass a small fraction of Earth's land surface, more than half the world's plants and two-thirds of terrestrial vertebrate species are endemic to these hotspots. Tropical Southeast (SE) Asia displays extraordinary species richness, encompassing four biodiversity hotspots, though disentangling multiple potential drivers of species richness is confounded by the region's dynamic geological and climatic history. Here, we use multilocus molecular genetic data from dense multispecies sampling of freshwater fishes across three biodiversity hotspots, to test the effect of Quaternary climate change and resulting drainage rearrangements on aquatic faunal diversification. While Cenozoic geological processes have clearly shaped evolutionary history in SE Asian halfbeak fishes, we show that paleo-drainage re-arrangements resulting from Quaternary climate change played a significant role in the spatiotemporal evolution of lowland aquatic taxa, and provide priorities for conservation efforts.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".