Fly‐derived DNA and camera traps are complementary tools for assessing mammalian biodiversity
Bibliographic record
Abstract
Abstract Background Metabarcoding of vertebrate DNA found in invertebrates (iDNA) represents a potentially powerful tool for monitoring biodiversity. Preliminary evidence suggests fly iDNA biodiversity assessments compare favorably with established approaches such as camera trapping or line transects. Aims and Methods To assess whether fly‐derived iDNA is consistently useful for biodiversity monitoring across a diversity of ecosystems, we compared metabarcoding of the mitochondrial 16S gene of fly pool‐derived iDNA (range = 49–105 flies/site, N = 784 flies) with camera traps (range = 198–1,654 videos of mammals identified to the species level/site) at eight sites, representing different habitat types in five countries across tropical Africa. Results We detected a similar number of mammal species using fly‐derived iDNA (range = 8–15 species/site) and camera traps (range = 8–27 species/site). However, the two approaches detected mostly different species (range = 6%–43% of species detected/site were detected with both methods), with fly‐derived iDNA detecting on average smaller‐bodied species than camera traps. Despite addressing different phylogenetic components of local mammalian communities, both methods resulted in similar beta‐diversity estimates across sites and habitats. Conclusion These results support a growing body of evidence that fly‐derived iDNA is a cost‐ and time‐efficient tool that complements camera trapping in assessing mammalian biodiversity. Fly‐derived iDNA may facilitate biomonitoring in terrestrial ecosystems at broad spatial and temporal scales, in much the same way as water eDNA has improved biomonitoring across aquatic ecosystems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".