Counting animal species with DNA barcodes: Canadian insects
Bibliographic record
Abstract
Recent estimates suggest that the global insect fauna includes fewer than six million species, but this projection is very uncertain because taxonomic work has been limited on some highly diverse groups. Validation of current estimates minimally requires the investigation of all lineages that are diverse enough to have a substantial impact on the final species count. This study represents a first step in this direction; it employs DNA barcoding to evaluate patterns of species richness in 27 orders of Canadian insects. The analysis of over one million specimens revealed species counts congruent with earlier results for most orders. However, Diptera and Hymenoptera were unexpectedly diverse, representing two-thirds of the 46 937 barcode index numbers (=species) detected. Correspondence checks between known species and barcoded taxa showed that sampling was incomplete, a result confirmed by extrapolations from the barcode results which suggest the occurrence of at least 94 000 species of insects in Canada, a near doubling from the prior estimate of 54 000 species. One dipteran family, the Cecidomyiidae, was extraordinarily diverse with an estimated 16 000 species, a 10-fold increase from its predicted diversity. If Canada possesses about 1% of the global fauna, as it does for known taxa, the results of this study suggest the presence of 10 million insect species with about 1.8 million of these taxa in the Cecidomyiidae. If so, the global species count for this fly family may exceed the combined total for all 142 beetle families. If extended to more geographical regions and to all hyperdiverse groups, DNA barcoding can rapidly resolve the current uncertainty surrounding a species count for the animal kingdom. A newly detailed understanding of species diversity may illuminate processes important in speciation, as suggested by the discovery that the most diverse insect lineages in Canada employ an unusual mode of reproduction, haplodiploidy.This article is part of the themed issue 'From DNA barcodes to biomes'.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".