Speciation and extinction in the tropics: insights from Asian barbets
Bibliographic record
Abstract
Biodiversity in the tropics is disproportionately high compared to other habitats, and also under disproportionate threat from human impact. It is necessary to understand how this diversity evolved and how it is partitioned across space in order to preserve it. In this thesis I construct phylogenies of tropical forest dependent vertebrates from Southeast Asia and the islands of the Sunda shelf, a region referred to as Sundaland. I focus on the tree squirrels (genus Sundasciurus) and Asian barbets (Aves: Family Megalaimidae), two taxa with similar ecological characteristics. I use these phylogenies to test hypotheses that have been put forward to explain high levels of tropical diversity including the Pleistocene pump and museum hypotheses. I also use phylogenies to elucidate phylogeographic patterns within the region. I find no evidence for an increase in speciation in the Pleistocene, but I do find within species structure that dates to this period. Common phylogeographic patterns were identified between many forest dependent vertebrates that suggest that populations on the island of Sumatra are generally more closely related to Malay Peninsula populations than to populations on Borneo. From a methodological viewpoint we propose careful usage of universal primers in ancient DNA studies because of our finding of increased risk of amplifying pseudogenes of the mtDNA.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".