Can DNA barcoding accurately discriminate megadiverse Neotropical freshwater fish fauna?
Bibliographic record
Abstract
BACKGROUND: The megadiverse Neotropical freshwater ichthyofauna is the richest in the world with approximately 6,000 recognized species. Interestingly, they are distributed among only 17 orders, and almost 80% of them belong to only three orders: Characiformes, Siluriformes and Perciformes. Moreover, evidence based on molecular data has shown that most of the diversification of the Neotropical ichthyofauna occurred recently. These characteristics make the taxonomy and identification of this fauna a great challenge, even when using molecular approaches. In this context, the present study aimed to test the effectiveness of the barcoding methodology (COI gene) to identify the mega diverse freshwater fish fauna from the Neotropical region. For this purpose, 254 species of fishes were analyzed from the Upper Parana River basin, an area representative of the larger Neotropical region. RESULTS: Of the 254 species analyzed, 252 were correctly identified by their barcode sequences (99.2%). The main K2P intra- and inter-specific genetic divergence values (0.3% and 6.8%, respectively) were relatively low compared with similar values reported in the literature, reflecting the higher number of closely related species belonging to a few higher taxa and their recent radiation. Moreover, for 84 pairs of species that showed low levels of genetic divergence (<2%), application of a complementary character-based nucleotide diagnostic approach proved useful in discriminating them. Additionally, 14 species displayed high intra-specific genetic divergence (>2%), pointing to at least 23 strong candidates for new species. CONCLUSIONS: Our study is the first to examine a large number of freshwater fish species from the Neotropical area, including a large number of closely related species. The results confirmed the efficacy of the barcoding methodology to identify a recently radiated, megadiverse fauna, discriminating 99.2% of the analyzed species. The power of the barcode sequences to identify species, even with low interspecific divergence, gives us an idea of the distribution of inter-specific genetic divergence in these megadiverse fauna. The results also revealed hidden genetic divergences suggestive of reproductive isolation and putative cryptic speciation in some species (23 candidates for new species). Finally, our study constituted an important contribution to the international Barcoding of Life (iBOL.org) project, providing barcode sequences for use in identification of these species by experts and non-experts, and allowing them to be available for use in other applications.
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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 | 0.002 |
| 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 | 0.001 |
| 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 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".