An extended mtDNA phylogeography for the alpine newt illuminates the provenance of introduced populations
Bibliographic record
Abstract
Abstract Many herpetofauna species have been introduced outside of their native range. MtDNA barcoding is regularly used to determine the provenance of such populations. The alpine newt has been introduced across the Netherlands, the United Kingdom and Ireland. However, geographical mtDNA structure across the natural range of the alpine newt is still incompletely understood and certain regions are severely undersampled. We collect mtDNA sequence data of over seven hundred individuals, from both the native and the introduced range. The main new insights from our extended mtDNA phylogeography are that 1) haplotypes from Spain do not form a reciprocally monophyletic clade, but are nested inside the mtDNA clade that covers western and eastern Europe; and 2) haplotypes from the northwest Balkans form a monophyletic clade together with those from the Southern Carpathians and Apuseni Mountains. We also home in on the regions where the distinct mtDNA clades meet in nature. We show that four out of the seven distinct mtDNA clades that comprise the alpine newt are implicated in the introductions in the Netherlands, United Kingdom and Ireland. In several introduced localities, two distinct mtDNA clades co-occur. As these mtDNA clades presumably represent cryptic species, we urge that the extent of genetic admixture between them is assessed from genome-wide nuclear DNA markers. We mobilized a large number of citizen scientists in this project to support the collection of DNA samples by skin swabbing and underscore the effectiveness of this sampling technique for mtDNA barcoding.
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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 | 0.001 |
| 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 | 0.000 |
| 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".