Improving herpetological surveys in eastern North America using the environmental DNA method
Bibliographic record
Abstract
Among vertebrates, herpetofauna has the highest proportion of declining species. Detection of environmental DNA (eDNA) is a promising method towards significantly increasing large-scale herpetological conservation efforts. However, the integration of eDNA results within a management framework requires an evaluation of the efficiency of the method in large natural environments and the calibration of eDNA surveys with the quantitative monitoring tools currently used by conservation biologists. Towards this end, we first developed species-specific primers to detect the wood turtle (Glyptemys insculpta) a species at risk in Canada, by quantitative PCR (qPCR). The rate of eDNA detection obtained by qPCR was also compared to the relative abundance of this species in nine rivers obtained by standardized visual surveys in the Province of Québec (Canada). Second, we developed multi-species primers to detect North American amphibian and reptile species using eDNA metabarcoding analysis. An occurrence index based on the distribution range and habitat type was compared with the eDNA metabarcoding dataset from samples collected in seven lakes and five rivers. Our results empirically support the effectiveness of eDNA metabarcoding to characterize herpetological species distributions. Moreover, detection rates provided similar results to standardized visual surveys currently used to develop conservation strategies for the wood turtle. We conclude that eDNA detection rates may provide an effective semiquantitative survey tool, provided that assay calibration and standardization is performed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".