DNA barcoding for effective biodiversity assessment of a hyperdiverse arthropod group: the ants of Madagascar
Bibliographic record
Abstract
The role of DNA barcoding as a tool to accelerate the inventory and analysis of diversity for hyperdiverse arthropods is tested using ants in Madagascar. We demonstrate how DNA barcoding helps address the failure of current inventory methods to rapidly respond to pressing biodiversity needs, specifically in the assessment of richness and turnover across landscapes with hyperdiverse taxa. In a comparison of inventories at four localities in northern Madagascar, patterns of richness were not significantly different when richness was determined using morphological taxonomy (morphospecies) or sequence divergence thresholds (Molecular Operational Taxonomic Unit(s); MOTU). However, sequence-based methods tended to yield greater richness and significantly lower indices of similarity than morphological taxonomy. MOTU determined using our molecular technique were a remarkably local phenomenon-indicative of highly restricted dispersal and/or long-term isolation. In cases where molecular and morphological methods differed in their assignment of individuals to categories, the morphological estimate was always more conservative than the molecular estimate. In those cases where morphospecies descriptions collapsed distinct molecular groups, sequence divergences of 16% (on average) were contained within the same morphospecies. Such high divergences highlight taxa for further detailed genetic, morphological, life history, and behavioral studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".