Biodiversity assessments in the 21st century: the potential of insect traps to complement environmental samples for estimating eukaryotic and prokaryotic diversity using high-throughput DNA metabarcoding
Bibliographic record
Abstract
The rapid loss of biodiversity, coupled with difficulties in species identification, call for innovative approaches to assess biodiversity. Insects make up a substantial proportion of extant diversity and play fundamental roles in any given ecosystem. To complement morphological species identification, new techniques such as metabarcoding make it possible to quantify insect diversity and insect-ecosystem interactions through DNA sequencing. Here we examine the potential of bulk insect samples (i.e., containing many non-sorted specimens) to assess prokaryote and eukaryote biodiversity and to complement the taxonomic coverage of soil samples. We sampled 25 sites on three continents and in various ecosystems, collecting insects with SLAM traps (Brazil) and Malaise traps (South Africa and Sweden). We then compared our diversity estimates with the results obtained with biodiversity data from soil samples from the same localities. We found a largely different taxonomic composition between the soil and insect samples, testifying to the potential of bulk insect samples to complement soil samples. Finally, we found that non-destructive DNA extraction protocols, which preserve insect specimens for morphological studies, constitute a promising choice for cost-effective biodiversity assessments. We propose that the sampling and sequencing of insect samples should become a standard complement for biodiversity studies based on environmental DNA.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".