The Role of DNA Barcodes in Understanding and Conservation of Mammal Diversity in Southeast Asia
Bibliographic record
Abstract
BACKGROUND: Southeast Asia is recognized as a region of very high biodiversity, much of which is currently at risk due to habitat loss and other threats. However, many aspects of this diversity, even for relatively well-known groups such as mammals, are poorly known, limiting ability to develop conservation plans. This study examines the value of DNA barcodes, sequences of the mitochondrial COI gene, to enhance understanding of mammalian diversity in the region and hence to aid conservation planning. METHODOLOGY AND PRINCIPAL FINDINGS: DNA barcodes were obtained from nearly 1900 specimens representing 165 recognized species of bats. All morphologically or acoustically distinct species, based on classical taxonomy, could be discriminated with DNA barcodes except four closely allied species pairs. Many currently recognized species contained multiple barcode lineages, often with deep divergence suggesting unrecognized species. In addition, most widespread species showed substantial genetic differentiation across their distributions. Our results suggest that mammal species richness within the region may be underestimated by at least 50%, and there are higher levels of endemism and greater intra-specific population structure than previously recognized. CONCLUSIONS: DNA barcodes can aid conservation and research by assisting field workers in identifying species, by helping taxonomists determine species groups needing more detailed analysis, and by facilitating the recognition of the appropriate units and scales for conservation planning.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".