<scp>DNA</scp> barcoding of Chinese snakes reveals hidden diversity and conservation needs
Bibliographic record
Abstract
DNA barcoding has greatly facilitated studies of taxonomy, biodiversity, biological conservation, and ecology. Here, we establish a reliable DNA barcoding library for Chinese snakes, unveiling hidden diversity with implications for taxonomy, and provide a standardized tool for conservation management. Our comprehensive study includes 1638 cytochrome c oxidase subunit I (COI) sequences from Chinese snakes that correspond to 17 families, 65 genera, 228 named species (80.6% of named species) and 36 candidate species. A barcode gap analysis reveals gaps, where all nearest neighbour distances exceed maximum intraspecific distances, in 217 named species and all candidate species. Three species-delimitation methods (ABGD, sGMYC, and sPTP) recover 320 operational taxonomic units (OTUs), of which 192 OTUs correspond to named and candidate species. Twenty-eight other named species share OTUs, such as Azemiops feae and A. kharini, Gloydius halys, G. shedaoensis, and G. intermedius, and Bungarus multicinctus and B. candidus, representing inconsistencies most probably caused by imperfect taxonomy, recent and rapid speciation, weak taxonomic signal, introgressive hybridization, and/or inadequate phylogenetic signal. In contrast, 43 species and candidate species assign to two or more OTUs due to having large intraspecific distances. If most OTUs detected in this study reflect valid species, including the 36 candidate species, then 30% more species would exist than are currently recognized. Several OTU divergences associate with known biogeographic barriers, such as the Taiwan Strait. In addition to facilitating future studies, this reliable and relatively comprehensive reference database will play an important role in the future monitoring, conservation, and management of Chinese snakes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".