Cryptic diversity revealed by <scp>DNA</scp> barcoding in Colombian illegally traded bird species
Bibliographic record
Abstract
Colombia is the country with the largest number of bird species worldwide, yet its avifauna is seriously threatened by habitat degradation and poaching. We built a DNA barcode library of nearly half of the bird species listed in the CITES appendices for Colombia, thereby constructing a species identification reference that will help in global efforts for controlling illegal species trade. We obtained the COI barcode sequence of 151 species based on 281 samples, representing 46% of CITES bird species registered for Colombia. The species analysed belong to nine families, where Trochilidae and Psittacidae are the most abundant ones. We sequenced for the first time the DNA barcode of 47 species, mainly hummingbirds endemic of the Northern Andes region. We found a correct match between morphological and genetic identification for 86-92% of the species analysed, depending on the cluster analysis performed (BIN, ABGD and TaxonDNA). Additionally, we identified eleven cases of high intraspecific divergence based on K2P genetic distances (up to 14.61%) that could reflect cryptic diversity. In these cases, the specimens were collected in geographically distant sites such as different mountain systems, opposite flanks of the mountain or different elevations. Likewise, we found two cases of possible hybridization and incomplete lineage sorting. This survey constitutes the first attempt to build the DNA barcode library of endangered bird species in Colombia establishing as a reference for management programs of illegal species trade, and providing major insights of phylogeographic structure that can guide future taxonomic research.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".