Neotropical Bats: Estimating Species Diversity with DNA Barcodes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
DNA barcoding using the cytochrome c oxidase subunit 1 gene (COI) is frequently employed as an efficient method of species identification in animal life and may also be used to estimate species richness, particularly in understudied faunas. Despite numerous past demonstrations of the efficiency of this technique, few studies have attempted to employ DNA barcoding methodologies on a large geographic scale, particularly within tropical regions. In this study we survey current and potential species diversity using DNA barcodes with a collection of more than 9000 individuals from 163 species of Neotropical bats (order Chiroptera). This represents one of the largest surveys to employ this strategy on any animal group and is certainly the largest to date for land vertebrates. Our analysis documents the utility of this tool over great geographic distances and across extraordinarily diverse habitats. Among the 163 included species 98.8% possessed distinct sets of COI haplotypes making them easily recognizable at this locus. We detected only a single case of shared haplotypes. Intraspecific diversity in the region was high among currently recognized species (mean of 1.38%, range 0-11.79%) with respect to birds, though comparable to other bat assemblages. In 44 of 163 cases, well-supported, distinct intraspecific lineages were identified which may suggest the presence of cryptic species though mean and maximum intraspecific divergence were not good predictors of their presence. In all cases, intraspecific lineages require additional investigation using complementary molecular techniques and additional characters such as morphology and acoustic data. Our analysis provides strong support for the continued assembly of DNA barcoding libraries and ongoing taxonomic investigation of bats.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it