Invasions, DNA barcodes, and rapid biodiversity assessment using ants of Mauritius
Bibliographic record
Abstract
BACKGROUND: Using an understudied taxon (Hymenoptera, Formicidae) found on a tropical island (Mauritius) where native flora and fauna have been threatened by 400 years of habitat modification and introduced species, we tested whether estimated incidences of diversity and complementarity were similar when measured by standard morphological alpha-taxonomy or phylogenetic diversity (PD) based on a standardized mitochondrial barcode and corroborating nuclear marker. RESULTS: We found that costs related to site loss (considered loss of evolutionary history measured as loss of barcode PD) were not significantly different from predictions made either a) using standard morphology-based taxonomy, or b) measured using a nuclear marker. Integrating morphology and barcode results permitted us to identify a case of initially morphologically-cryptic variation as a new and endemic candidate species. However, barcode estimates of the relative importance of each site or network of sites were dramatically affected when the species in question was known to be indigenous or introduced. CONCLUSION: This study goes beyond a mere demonstration of the rapid gains possible for diversity assessment using a standardized DNA barcode. Contextualization of these gains with ecological and natural history information is necessary to calibrate this wealth of standardized information. Without such an integrative approach, critical opportunities to advance knowledge will be missed.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| 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".