bioRxivEcologyPreprint
Towards a framework to unify the relationship between numerical abundance, biomass, and quantitative eDNA
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 StatementAquatic 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.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- The relationship between eDNA particle concentration and organism abundance in nature is strengthened by allometric scaling
M. C. Yates, D. Glaser, … A. M. Derry·bioRxiv·Jan 18, 2020
- Interspecific allometric scaling in eDNA production in fishes reflects physiological and surface area allometry
M. C. Yates, T. Wilcox, … D. Heath·bioRxiv·Apr 22, 2022
- eDNA provides accurate population abundance estimates with bioenergetics and particle mass-balance modelling
Julien Beaulieu, Matthew C. Yates, … Alison Margaret Derry·Molecular EcologyPeer-reviewed·Mar 31, 2026
- Read counts from environmental DNA (eDNA) metabarcoding reflect fish abundance and biomass in drained ponds
Cristina Di Muri, Lori Lawson Handley, … Bernd Hänfling·Metabarcoding and MetagenomicsPeer-reviewed·Oct 22, 2020
- A strong relationship between environmental DNA metabarcoding and rank-based abundance of fish
Joanne E. Littlefair, Lauren D. Hayhurst, … Melania E. Cristescu·Journal of Applied EcologyPeer-reviewed·Mar 1, 2026
- Quantitative environmental DNA metabarcoding shows high potential as a novel approach to quantitatively assess fish community
S. Tsuji, R. Inui, … Y. Akamatsu·bioRxiv·Apr 27, 2022