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 general framework through which quantitative eDNA data can be transformed to simultaneously reflect both population-level N and biomass. Notably, our framework demonstrates that even under circumstances where eDNA is assumed to scale linearly with either numerical abundance or biomass (e.g. the allometric scaling coefficient (b) is equal to 0 or 1, respectively), deriving the other metric necessitates a correction based on population/species mean mass if size-structure variation exists among populations. We then validated our general 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 framework, 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).

Introduction to special issue: Advancing disease ecology through eDNA monitoring of infectious agents

“…Further advances in application have allowed for a more robust understanding of when and how eDNA detections are useful for routine monitoring applications. For example, some studies have been able to move from presence absence in detection to a unified model for accurately estimating population abundance (Yates et al., 2023) and measuring the temporal (Levi et al., 2019) and spatial inference of presence in a land‐ or sea‐scape (Allan et al., 2021; Carraro et al., 2020). Thus, eDNA biodiversity monitoring is moving from proof‐of‐concept to implementation across a broad set of applications.…”