Wed, Aug 17, 2022: 2:00 PM-2:15 PM
520C
Background/Question/MethodsA multitude of studies have demonstrated consistent, positive correlations between organism abundance and the concentration of environmental DNA (eDNA) in aquatic environments. Strong relationships observed in laboratory experiments provide substantial evidence for a functional link between the two variables. However, in natural ecosystems correlations between eDNA and organism abundance tend to be weaker due to the myriad of biotic and abiotic factors that can influence pseudo-steady-state eDNA concentrations, thus (partially) decoupling its direct functional link with abundance. Among these factors, the physiology of eDNA production remains relatively understudied. Both biotic factors (e.g. life-history stage, body mass) and abiotic factors (e.g. temperature) can affect key physiological rates linked to eDNA production. We propose that frameworks developed in the field of Metabolic Ecology can be applied to model the effect of physiology on eDNA production. Using natural ecosystems inhabited by Brook Charr, we investigated if integrating allometry improved correlations between eDNA and Brook Charr abundance. We similarly extended allometric models to a previously published eDNA metabarcoding dataset to evaluate if allometric processes affected fish eDNA production at an inter-specific scale in a marine ecosystem. Finally, we propose the extension of bioenergetics frameworks to model the effect of temperature on eDNA production.
Results/ConclusionsWe found that integrating allometric scaling significantly improved correlations between organism abundance and eDNA concentrations in natural ecosystems. In our Brook Trout study, both density and biomass were significantly and positively correlated with eDNA (adj. r-squared = 0.59 and 0.63, respectively). However, integrating allometry into metrics of abundance significantly improved model fit (adj. r-squared = 0.78). We similarly found that integrating allometry significantly improved correlations between metabarcoding read count and marine fish abundance (adj. r-squared = 0.45) relative to traditional metrics of abundance (density and biomass, adj. r-squared = 0.14 and 0.33, respectively). We also highlight emergent research supporting the application of bioenergetics frameworks to model the effect of temperature on eDNA production in poikilothermic organisms. Collectively, these results illustrate the potential utility of extending well-developed frameworks from the Metabolic Theory of Ecology and bioenergetics to model eDNA production. Our results further highlight that operationalizing eDNA to infer abundance will likely require more than simple correlations with organism biomass/density. Nevertheless, the future is promising – models that integrate eDNA dynamics in nature could represent an effective means to infer abundance, particularly when traditional methods are considered too ‘costly’ or difficult to obtain.
Results/ConclusionsWe found that integrating allometric scaling significantly improved correlations between organism abundance and eDNA concentrations in natural ecosystems. In our Brook Trout study, both density and biomass were significantly and positively correlated with eDNA (adj. r-squared = 0.59 and 0.63, respectively). However, integrating allometry into metrics of abundance significantly improved model fit (adj. r-squared = 0.78). We similarly found that integrating allometry significantly improved correlations between metabarcoding read count and marine fish abundance (adj. r-squared = 0.45) relative to traditional metrics of abundance (density and biomass, adj. r-squared = 0.14 and 0.33, respectively). We also highlight emergent research supporting the application of bioenergetics frameworks to model the effect of temperature on eDNA production in poikilothermic organisms. Collectively, these results illustrate the potential utility of extending well-developed frameworks from the Metabolic Theory of Ecology and bioenergetics to model eDNA production. Our results further highlight that operationalizing eDNA to infer abundance will likely require more than simple correlations with organism biomass/density. Nevertheless, the future is promising – models that integrate eDNA dynamics in nature could represent an effective means to infer abundance, particularly when traditional methods are considered too ‘costly’ or difficult to obtain.