Environmental DNA surveys can underestimate amphibian occupancy and overestimate detection probability: implications for practice
Bibliographic record
Abstract
Abstract Environmental DNA analysis of water samples has recently emerged as a powerful tool for studying the distribution, ecology, and conservation of many amphibian species. Validating efficacy through comparison with established methods of detection is important for any new method. We used multi‐method and single‐method occupancy models to explore the effectiveness of 3 techniques for detecting northern leopard frogs (Lithobates pipiens) and boreal chorus frogs (Pseudacris maculata). We conducted surveys using automated recording units, environmental DNA sampling, and visual encounter surveys in spring (30 Apr–3 Jun) and summer (23 Jul–27 Aug) of 2014 at 68 sites in southern Alberta, Canada. The multi‐method analysis incorporated data from all 3 survey methods while accounting for the lack of independence of detections within a sampling occasion, and we compared this to single‐method analyses to explore occupancy biases associated with each method and differences in method‐specific detection probability. Occupancy and detection probabilities estimated from environmental DNA analyses were biased for northern leopard frogs, overestimating probability of detection and underestimating occupancy. We could not assess bias for boreal chorus frogs because of overdispersion present in in the models. Although no single covariate explained this bias, it was ameliorated (albeit by reducing precision) by including a raw visual count of individuals as a proxy for abundance during the breeding but not the post‐metamorphic season. These results emphasize the need for careful consideration of temporal and spatial aspects of sampling design, conducting pilot studies, and external validation of eDNA‐only occupancy monitoring schemes prior to widespread implementation.
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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 | 0.132 | 0.398 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".