A Bayesian Coalescent Model of the DNA Barcode Gap
Bibliographic record
Abstract
A simple statistical model of the DNA barcode gap is outlined in both frequentist and Bayesian contexts. Here, accuracy of recently introduced nonparametric metrics, inspired by coalescent theory, is used to characterize the extent of proportional overlap/separation in maximum and minimum pairwise genetic distances within and among species, respectively. Using a straightforward binomial count of overlapping specimen records, probabilities of taxon distance distribution overlap/separation are directly estimated. The mean and variance are derived for edge cases of no and full overlap, and are shown to have good asymptotic properties. Further, a new way to visualize distances and the DNA barcode gap estimators via the empirical cumulative distribution function (ECDF) appears revealing. Using R and the probabilistic programming language, Stan, the proposed maximum likelihood estimators (MLEs) and Bayesian model are demonstrated on cytochrome b (CYTB) gene sequences from two Agabus diving beetle species, A. bipustulatus and A. nevadensis ( N = 701 and N = 2 individuals, respectively). Analyses clearly expose problems, showing much uncertainty in parameter estimates, particularly under the frequentist paradigm, and when specimen sample sizes for target species are small. Findings herein highlight the promise of the Bayesian approach using a conjugate beta prior for reliable posterior estimation over classical inference when available data are sparse. Obtained results can help shed light on foundational and applied research questions concerning DNA-based specimen identification and species delineation for studies in evolutionary biology and ecology, as well as biodiversity conservation, forensics, management and restoration of wide-ranging taxa.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".