DNA barcoding of more than one million insect specimens indicates that at least one third of California's insect biodiversity remains undiscovered
Bibliographic record
Abstract
Insects are the most diverse terrestrial organisms, yet they are underrepresented in large-scale conservation assessments. To address this gap, the California Insect Barcoding Initiative is developing a publicly accessible, statewide DNA-barcode reference library for California insects to support scalable surveys, establish baseline biodiversity measurements, and generate potential distribution maps that inform conservation planning. Specimens are collected with a hybrid strategy that combines standardized Malaise trapping, to enable replicable sampling, and opportunistic collecting, to maximize taxonomic coverage. To date, we have barcoded over one million specimens; preliminary completeness analyses suggest that current sampling captures roughly 65–69% of the fauna, implying a conservative minimum of circa 61,000 insect species in California. Using all sequenced specimen records, we generated rule-based spatial range interpolations constrained by ecoregion and vegetation type, and used these to infer spatial patterns in insect species richness across the state. We identify areas of both high and low potential species richness, with current peaks in the Southern California Mountains ecoregion and the Mojave Basin and Range ecoregion. Our species richness estimates and spatial patterns are explicitly provisional and are expected to evolve as sampling gaps are addressed. Finally, we make all sequence data, specimen images, and occurrence records publicly available via the Barcode of Life Datasystem. This ongoing effort constitutes the first large-scale DNA barcode-based survey for California insects, providing an expandable foundation for tracking temporal change, testing drivers of insect diversity, and prioritizing regions for conservation and targeted inventory.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".