Large-scale integrative taxonomy of the smallest insects reveals astonishing temperate diversity
Bibliographic record
Abstract
Abstract Fairyflies (Hymenoptera: Chalcidoidea: Mymaridae) are a diverse but taxonomically neglected group of parasitoid wasps that attack eggs of other insects. Being among the very smallest of all insects, they are often overlooked in biodiversity surveys despite being one of the most abundant microhymenoptera in many habitats. The traditional approach of morphological sorting for species delimitation can be challenging due to their minute size and meticulous slide-mounting technique. Ways to accelerate their discovery are needed. We conducted the first large-scale study of Mymaridae in temperate forests, combining DNA megabarcoding and the Large-scale Integrative Taxonomy (LIT) workflow to describe their diversity. We obtained COI barcodes from 2,098 specimens and used ASAP and RESL for species delimitation. Between 42 and 114 molecular clusters were delimited. Reducing morphological validation to only 9% of the sample enabled accurate determination while limiting time and effort. We confirmed the presence of 55 species, including many potentially new to science. The LIT workflow was effective for Mymaridae, although cryptic diversity remains unresolved in some large clusters, especially in the genera Alaptus and Anagrus , where high haplotype diversity and morphological ambiguity suggest additional hidden species. DNA reference databases proved unreliable, with less than 1% correct species matches, highlighting the taxonomic gap for this group. Nonetheless, we contributed 16 new identified reference barcodes to public databases and added new provincial and national species records for Canada. Our results demonstrate the value of combining molecular and morphological data in a standardized workflow and underscore the importance of improving reference databases for effective biodiversity assessments of dark taxa like microhymenoptera.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".