Unveiling South African insect diversity: DNA barcoding’s contribution to biodiversity data
Bibliographic record
Abstract
Insects are one of the most species-rich groups on Earth. They comprise much of animal diversity and play vital roles in ecosystems, including pollination, pest control, and decomposition. However, only a fraction of this diversity has been formally described. South Africa is recognised as one of the most biologically diverse countries globally, with an estimated 44 000 insect species. Many crops rely on insect pollinators, including canola, apples, oranges, and sunflowers. A shortage of wild pollinators currently threatens crop yields, yet our knowledge of insect diversity within South Africa is sparse. There are few taxonomic specialists relative to South Africa’s biodiversity, and the methods used for insect identification can be time-consuming and expensive. DNA barcoding provides an important research tool to accelerate insect biodiversity research. In this review, we queried the public DNA barcoding BOLD (Barcode of Life Data System) database for records of “Insecta” within South Africa, and 416 211 published records assigned to 28 239 unique BINs (Barcode Index Numbers) were returned. We identified five taxonomic orders with more BINs than known species in southern Africa (Hymenoptera, Diptera, Thysanoptera, Plecoptera, and Strepsiptera). Most of the barcoded records were derived from Malaise trap sampling in Gauteng, Mpumalanga and Limpopo, while the rest of South Africa remains poorly sampled. We suggest that there is a need for a comprehensive national sampling effort alongside increased investment in taxonomic expertise to generate critical baseline data on insect biodiversity before species are lost to extinction.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".