DNA barcodes expose unexpected diversity in Canadian mites
Bibliographic record
Abstract
Mites (Arachnida: Acariformes, Parasitiformes) are the most abundant and species-rich group of arthropods in soil, but are also diverse in freshwater habitats, on plants, and as symbionts of larger animals. However, assessment of their diversity has been impeded by their small size and often cryptic morphology. As a consequence, published estimates of their species richness span more than two orders of magnitude (0.4-114 million). In this study we employ DNA barcoding and the Barcode Index Number (BIN) system to investigate mite diversity at over 1,800 sites across Canada, primarily from soil and litter habitats with smaller contributions from freshwater, plants, and animal hosts. Barcodes from 73,394 specimens revealed 7,077 BINs with representatives from all four orders (Ixodida, Mesostigmata, Sarcoptiformes, Trombidiformes) and 60% (186) of the known families. The BIN total is 2.4 times the number of species previously recorded from Canada (2,999), reflecting the unexpectedly high richness of several families. Richness projections suggest that more than 28,000 BINs occur at the sampled locations, indicating that the Canadian mite fauna almost certainly includes more than 30,000 species-a total similar to that for the most diverse insect order in Canada, Diptera. This unexpected diversity was partitioned into highly dissimilar, spatially-structured assemblages that likely reflect dispersal limitation and environmental heterogeneity. Further sampling of a greater diversity of habitats will refine understanding of mite diversity in Canada, but similar analyses in other geographic regions will be essential to ascertain their diversity at a global scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".