Towards a framework to unify the relationship between numerical abundance, biomass, and quantitative eDNA
Bibliographic record
Abstract
Abstract Does environmental DNA (eDNA) correlate more closely with numerical abundance (N) or biomass in aquatic organisms? We hypothesize that the answer is ‘neither’: eDNA production likely scales allometrically, reflecting key physiological rates and surface area-to-body mass relationships. Building on individual-level frameworks developed from the Metabolic Theory of Ecology, we derive a framework through which quantitative eDNA data can be transformed to simultaneously reflect both population-level N and biomass. We then validated our framework using data from two previously published studies: (i) a marine eDNA metabarcoding dataset; and (ii) a freshwater single-species qPCR dataset. Using a Bayesian modeling approach, we estimated the value of the allometric scaling coefficient that jointly optimized the relationship between N, biomass, and ‘corrected’ eDNA data to be 0.82 and 0.77 in Case Studies (i) and (ii), respectively. These estimates closely match expected scaling coefficients estimated in previous work on Teleost fish metabolic rates. We also demonstrate that correcting quantitative eDNA can significantly improve correspondence between eDNA- and traditionally-derived quantitative community biodiversity metrics (e.g., Shannon index and Bray-Curtis dissimilarity) under some circumstances. Collectively, we show that quantitative eDNA data is unlikely to correspond exactly to either N or biomass, but can be ‘corrected’ to reflect both through our unifying joint modelling framework. This framework can also be further expanded to include other variables that might impact eDNA pseudo-steady-state concentrations in natural ecosystems (e.g., temperature, pH, and phenology), and is flexible enough to model these relationships across trophic levels. Significance Statement Aquatic animals release DNA (from shed cells, mucous, faeces, etc.) into water, which can be detected via environmental DNA (eDNA) sampling. What is less clear is whether we can estimate numerical abundance ( N ) or biomass from eDNA concentrations. We hypothesize that eDNA production scales allometrically; that is, large animals release less DNA per unit mass than smaller animals. Building from the Metabolic Theory of Ecology, we derived a framework through which eDNA data can be transformed to simultaneously reflect both N and biomass. We then validated the framework using two case studies in marine and freshwater systems. This framework unifies discrepancies between eDNA, N , and biomass data, unlocking the potential of eDNA to monitor population abundance/biomass and quantify biodiversity.
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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 | 0.031 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".