A comprehensive <scp>DNA</scp> barcode database for Central European beetles with a focus on Germany: adding more than 3500 identified species to BOLD
Bibliographic record
Abstract
Beetles are the most diverse group of animals and are crucial for ecosystem functioning. In many countries, they are well established for environmental impact assessment, but even in the well-studied Central European fauna, species identification can be very difficult. A comprehensive and taxonomically well-curated DNA barcode library could remedy this deficit and could also link hundreds of years of traditional knowledge with next generation sequencing technology. However, such a beetle library is missing to date. This study provides the globally largest DNA barcode reference library for Coleoptera for 15 948 individuals belonging to 3514 well-identified species (53% of the German fauna) with representatives from 97 of 103 families (94%). This study is the first comprehensive regional test of the efficiency of DNA barcoding for beetles with a focus on Germany. Sequences ≥500 bp were recovered from 63% of the specimens analysed (15 948 of 25 294) with short sequences from another 997 specimens. Whereas most specimens (92.2%) could be unambiguously assigned to a single known species by sequence diversity at CO1, 1089 specimens (6.8%) were assigned to more than one Barcode Index Number (BIN), creating 395 BINs which need further study to ascertain if they represent cryptic species, mitochondrial introgression, or simply regional variation in widespread species. We found 409 specimens (2.6%) that shared a BIN assignment with another species, most involving a pair of closely allied species as 43 BINs were involved. Most of these taxa were separated by barcodes although sequence divergences were low. Only 155 specimens (0.97%) show identical or overlapping clusters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".