Extreme diversity of tropical parasitoid wasps exposed by iterative integration of natural history, DNA barcoding, morphology, and collections
Bibliographic record
Abstract
We DNA barcoded 2,597 parasitoid wasps belonging to 6 microgastrine braconid genera reared from parapatric tropical dry forest, cloud forest, and rain forest in Area de Conservación Guanacaste (ACG) in northwestern Costa Rica and combined these data with records of caterpillar hosts and morphological analyses. We asked whether barcoding and morphology discover the same provisional species and whether the biological entities revealed by our analysis are congruent with wasp host specificity. Morphological analysis revealed 171 provisional species, but barcoding exposed an additional 142 provisional species; 95% of the total is likely to be undescribed. These 313 provisional species are extraordinarily host specific; more than 90% attack only 1 or 2 species of caterpillars out of more than 3,500 species sampled. The most extreme case of overlooked diversity is the morphospecies Apanteles leucostigmus. This minute black wasp with a distinctive white wing stigma was thought to parasitize 32 species of ACG hesperiid caterpillars, but barcoding revealed 36 provisional species, each attacking one or a very few closely related species of caterpillars. When host records and/or within-ACG distributions suggested that DNA barcoding had missed a species-pair, or when provisional species were separated only by slight differences in their barcodes, we examined nuclear sequences to test hypotheses of presumptive species boundaries and to further probe host specificity. Our iterative process of combining morphological analysis, ecology, and DNA barcoding and reiteratively using specimens maintained in permanent collections has resulted in a much more fine-scaled understanding of parasitoid diversity and host specificity than any one of these elements could have produced on its own.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".