A Framework to Unify the Relationship Between Numerical Abundance, Biomass, and Environmental <scp>DNA</scp>
Bibliographic record
Abstract
ABSTRACT Does environmental DNA (eDNA) concentration correlate with numerical abundance ( N ) or biomass in aquatic organisms? We hypothesize that eDNA can be adjusted to simultaneously reflect both. Building on frameworks developed from the Metabolic Theory of Ecology, we derive two equations to adjust eDNA data to simultaneously reflect both N and biomass using population size structure data and allometric scaling coefficients. We also demonstrate that these equations share model parameters, necessitating the joint estimation of regressions between adjusted eDNA, N , and biomass. Furthermore, our framework can be extended to model how other variables (temperature, taxa, diet, trophic level, etc.) might impact relationships between eDNA, N , and biomass in natural ecosystems. We applied our framework to data from two previously published studies correlating eDNA to Brook Trout ( Salvelinus fontinalis ) N and biomass. In both case studies, point estimates of the scaling coefficient ( b ) reflected allometric processes ( b = 0.51 and 0.37 for Case Study 1 and 2, respectively), with credible intervals indicating that b likely differed from zero (i.e., eDNA scales with N ) and one (i.e., eDNA scales with biomass). Directly estimating the value of b improved estimates of N and biomass relative to assuming b equals 0, which particularly affected the capacity to estimate biomass. However, models assuming eDNA production scaled with biomass (i.e., b = 1) were largely similar to estimating b , implying that assuming eDNA scales linearly with biomass might be a sufficient approximation for some systems. Nevertheless, the framework demonstrates that correlating eDNA directly with either N or biomass (as is commonly done in many studies) inherently necessitates an adjustment to infer the other metric if populations exhibit size structure variation. Collectively, we demonstrate that quantitative eDNA data is unlikely to correspond exactly to either population N or biomass but can be adjusted to simultaneously reflect both.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".