Allometric scaling of eDNA production in stream‐dwelling brook trout (<i>Salvelinus fontinalis</i>) inferred from population size structure
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Bibliographic record
Abstract
Abstract Environmental DNA (eDNA) concentration exhibits a positive correlation with organism abundance in nature, but modeling this relationship could be substantially improved by incorporating the biology of eDNA production. A recent model (Molecular Ecology, 10.1111/mec.15543) extended models of physiological allometric scaling to eDNA production, hypothesizing that brook trout eDNA production scales nonlinearly with mass as a power function with scaling coefficients <1 in lakes. To validate this hypothesis, we reanalyzed previously published data (Biological Conservation, 10.1016/j.biocon.2015.12.023) that examined the correlation between eDNA concentration and brook trout abundance in streams. We found that allometrically scaled mass (ASM) (e.g., ∑(individual mass 0.36 ) best described patterns of eDNA concentration across streams ( r 2 = 0.43). ASM exhibited substantially improved model fit relative to biomass ( r 2 = 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 ( r 2 = 0.40, ∆AIC = 1.25). Additionally, the optimal scaling coefficient estimated from the data (0.36) was substantially lower than that found in the previous study. 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it