Mind the gap! Integrating taxonomic approaches to assess ant diversity at the southern extreme of the Atlantic Forest
Bibliographic record
Abstract
Understanding patterns of species diversity relies on accurate taxonomy which can only be achieved by long-term natural history research and the use of complementary information to establish species boundaries among cryptic taxa. We used DNA barcoding to characterize the ant diversity of Iguazú National Park (INP), a protected area of the Upper Paraná Atlantic Forest ecoregion, located at the southernmost extent of this forest. We assessed ant diversity using both cytochrome c oxidase subunit 1 (COI) sequences and traditional morphological approaches, and compared the results of these two methods. We successfully obtained COI sequences for 312 specimens belonging to 124 species, providing a DNA barcode reference library for nearly 50% of the currently known ant fauna of INP. Our results support a clear barcode gap for all but two species, with a mean intraspecific divergence of 0.72%, and an average congeneric distance of 17.25%. Congruently, the library assembled here was useful for the discrimination of the ants of INP and allowed us to link unidentified males and queens to their worker castes. To detect overlooked diversity, we classified the DNA barcodes into Molecular Operational Taxonomic Units (MOTUs) using three different clustering algorithms, and compared their number and composition to that of reference species identified based on morphology. The MOTU count was always higher than that of reference species regardless of the method, suggesting that the diversity of ants at INP could be between 6% and 10% higher than currently recognized. Lastly, our survey contributed with 78 new barcode clusters to the global DNA barcode reference library, and added 36 new records of ant species for the INP, being 23 of them new citations for Argentina.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".