Testing the effectiveness of environmental <scp>DNA</scp> (<scp>eDNA</scp>) to quantify larval amphibian abundance
Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) monitoring is rapidly becoming an established approach for detecting the presence of aquatic organisms and may also be useful for indexing or estimating species abundance. However, the link between eDNA concentration and abundance of individuals (i.e., density or biomass) remains tenuous and may vary widely across species and environmental conditions. We investigated the relationship between eDNA concentration and abundance in two common and closely related amphibians in eastern North America, the wood frog (Rana sylvatica), and northern leopard frog (R. pipiens). We manipulated tadpole density in 80‐L mesocosms and documented the relationship between tadpole density and biomass and eDNA concentration through time. The two species differed in the amount of detectible genetic material produced, despite having comparable biomass. Concentration of eDNA increased with tadpole numbers and was primarily correlated with tadpole density in wood frogs and biomass in leopard frogs. eDNA degradation rates were rapid and comparable between species, with tadpoles becoming indetectable within 5 days post‐removal from the mesocosm, irrespective of tadpole density. Overall, our findings support that eDNA concentration has potential for tracking amphibian abundance in wetlands, but that indices of abundance are likely to be coarse and species‐specific calibration will be required. Future research should address how biotic and abiotic factors influence eDNA production, degradation, and recovery across species and through time before relying on eDNA for monitoring amphibian abundance in nature.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".