Vernal Pool Amphibian Inventories in Temperate Forests of Eastern North America: Can Environmental DNA Replace Traditional Methods?
Bibliographic record
Abstract
Amphibian populations have been globally declining since at least 1990. In temperate forests of eastern North America, vernal pools offer important breeding habitats to many amphibians but are usually not considered for forest management because of their small size and temporary status. Effective monitoring and management of amphibians rely on accurate knowledge of their spatiotemporal distributions, which is expensive to collect due to the amount of fieldwork re-quired. In this study, we tested whether eDNA metabarcoding could identify the same amphibian communities as traditional inventory protocols. We collected eDNA samples in twelve vernal pools in the spring of 2019 and identified their communities by metabarcoding. At each pool, three traditional amphibian inventory methods were used in May and June 2019: call surveys using acoustic recorders, trapping, and active search surveys. In total, 13 amphibian species were detected, most of them being detected by both eDNA and traditional methods. We found that species ecology and behavior are key factors of its detectability by a specific method. As eDNA metabarcoding is comparatively inexpensive and presents a higher repeatability, we conclude that eDNA sampling should be considered for integration as a standard monitoring tool, after an initial assessment of amphibian diversity.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| 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".