Data from: Allometric scaling of eDNA production in stream-dwelling brook trout (Salvelinus fontinalis) inferred from population size structure
Bibliographic record
Abstract
Environmental DNA (eDNA) concentration exhibits a positive correlation with organism abundance in nature, but modelling this relationship could be substantially improved by incorporating the biology of eDNA production. A recent model (Yates et al. 2020) extended models of physiological allometric scaling to eDNA production, hypothesizing that brook trout eDNA production scales non-linearly with mass as a power-function with scaling coefficients < 1 in lakes. To validate this hypothesis, we re-analysed data from Wilcox et al. (2016) that examined the correlation between eDNA concentration and brook trout abundance in streams. We found that allometrically scaled mass (ASM) (e.g. ∑(individual mass0.36) best described patterns of eDNA concentration across streams (r2 = 0.43). ASM exhibited substantially improved model fit relative to biomass (r2 = 0.31, ∆AIC = 5.19), indicating that eDNA production did not scale linearly with biomass. However, the explanatory power of ASM was comparable to density (r2 = 0.40, ∆AIC = 1.25). Additionally, the optimal scaling coefficient estimated from the data (0.36) was substantially lower than that found in Yates et al. (2020) (0.72). Discrepancies between datasets could be attributable to ecological differences between study habitats (streams vs lakes) or due to the exclusion of juveniles (i.e. individuals < 75 mm) that can be abundant in stream environments. Nevertheless, this study adds to the growing body of literature demonstrating that individual eDNA production does not scale linearly with biomass.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".