Deep barcode divergence in Brazilian freshwater fishes: the case of the São Francisco River basin
Bibliographic record
Abstract
BACKGROUND AND AIMS: The application of DNA barcoding as a global standard for fish identification is probing diverse worldwide realms (Nearctic, Australian and the Neotropics) and environments (e.g. marine and freshwater). Comparing the patterns of sequence divergence among conspecific and congeneric taxa between realms can provide valuable information on recent evolutionary histories of lineages as barcode data accumulates. MATERIALS AND METHODS: Herein, we have analyzed over 100 species (around 50%) of the Neotropical fish fauna from the São Francisco River, in southeast Brazil. Our aims were to test the performance of DNA barcoding in this biodiversity-rich region, and to compare patterns of genetic divergence with previous studies. RESULTS: The mean Kimura two-parameter distances within species, genera, families, orders, and classes were 0.5, 10.6, 21.0, 22.7, and 24.4%, respectively, with 100% of the species examined successfully differentiated by barcoding. With the exception of Astyanax bimaculatus lacustris, Piabina argentea, and Bryconamericus stramineus, all other species yield a single, cohesive cluster of barcode sequences. The average 'nearest-neighbor distance' was 11.12%, 21-fold higher than the mean within species distance of around 0.54%. In a few instances, deep lineage divergences among conspecifics (up to 10%) and congenerics (up to 22.9%) taxa were revealed. CONCLUSIONS: Reflecting possible cases of cryptic speciation and the deeper phylogeographic history of São Francisco fish fauna, with some higher clades extending back into the late Cretaceous and Cenozoic (90 mya), when much of the diversification of the Neotropical region apparently took place. In addition, barcodes also highlighted misidentifications and helped to document range extensions for known species.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".