Comparison of eDNA metabarcoding to camera trapping for terrestrial vertebrate monitoring highlights the importance of substrate type, frequency of sampling and animal size
Bibliographic record
Abstract
Fauna biodiversity assessments often rely on traditional biomonitoring techniques such as camera traps, which may have biases that lead to gaps in biodiversity data. Environmental DNA (eDNA) has emerged as a new source of biodiversity data that may account for these gaps. However, eDNA biodiversity assessment remains relatively untested in terrestrial environments. We compared vertebrate detections using two independent monitoring methods: camera traps and eDNA (n = 160), across two sites in south-western Australia. We also investigated the suitability of tree hollow sediment as a source of eDNA, and the effect of other factors (visitation frequency and timing, animal size) on vertebrate species detectability. We detected 31 taxa with eDNA and 47 with camera traps of which 14 overlapped (12 mammals and 2 birds). Tree hollow sediment detected a wider range of biodiversity than did soil at the entrance of the hollow. By comparing camera trap data with eDNA sequence reads, we were able to detect animals with eDNA that had visited the area up to two months prior to sample collection, with a negative correlation between sequence read amount and days since last recorded detection via camera. “Large” animals (>3kg) detected via camera were associated with significantly higher sequence read amounts than smaller animals. Our results show the effect of substrate selection, frequency of sampling and animal size, on eDNA based surveys. If the aim is to detect broad taxon diversity eDNA based approaches need to be complemented by traditional vertebrate survey methods.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".