Abstract
AbstractMeasures of environmental DNA (eDNA) concentrations in water samples have the potential to be both a cost‐efficient and a nondestructive method to estimate fish population abundance. However, the inherent temporal and spatial variability in abiotic and biotic conditions in aquatic systems have been suggested to be a major obstacle to determine relationships between fish eDNA concentrations and fish population abundance. Moreover, once water samples are collected, methodological biases are common, which introduces additional sources of variation to potential relationships between eDNA concentrations and fish population abundance. Here, we evaluate the performance of applying the droplet digital PCR (ddPCR) method to estimate fish population abundance in experimental enclosures. Using large‐scale enclosure ecosystems that contain populations of nine‐spined stickleback (Pungitius pungitius), we compared the concentrations of fish eDNA (COI mitochondrial region, 134 bp) obtained with the ddPCR method with high precision estimates of fish population abundance (i.e., number of individuals) and biomass. To evaluate the effects of contrasted concentrations of humic substances (potential PCR inhibitors) on the performance of ddPCR assays, we manipulated natural dissolved organic carbon (DOC) concentrations (range 4–11 mg/L) in the enclosures. Additionally, water temperature (+2°C) was manipulated in half of the enclosures. Results showed positive relationships between eDNA concentration and fish abundance and biomass estimates although unexplained variation remained. Still and importantly, fish eDNA estimates from high DOC enclosures were not lowered by potential inhibitory effects with our procedure. Finally, water temperature (although only 2°C difference) was neither detected as a significant factor influencing fish eDNA estimates. Altogether, our work highlights that ddPCR‐based eDNA is a promising method for future quantification of fish population abundance in natural systems.
Highlights
Measures of species-specific environmental DNA could be a cost-efficient and nondestructive method compared to traditional methods to estimate population abundances in aquatic ecosystems (Barnes & Turner, 2016; Coble et al, 2019)
Our work, aiming to test the use of a droplet digital PCR (ddPCR)-based environmental DNA (eDNA) method to quantify fish population abundances, showed positive and significant correlations between nine-spined stickleback population estimates and stickleback eDNA concentrations. Such findings are in line with many recent studies (Klobucar, Rodgers, & Budy, 2017; Klymus, Richter, Chapman, & Paukert, 2015; Lacoursière-Roussel, Côté, Leclerc, Bernatchez, & Cadotte, 2016; Nevers et al, 2018; Takahara et al, 2012; Wilcox et al, 2016) highlighting that eDNA may be a promising tool for estimating fish population abundance and biomass in aquatic systems
We further aimed to evaluate whether fish eDNA concentrations obtained by the ddPCR method are not biased due to the presence of high amounts of organic matter, as relatively high concentrations of dissolved organic carbon (DOC) can be found in lakes worldwide (Seekell et al, 2015)
Summary
Measures of species-specific environmental DNA (eDNA) could be a cost-efficient and nondestructive method compared to traditional methods to estimate population abundances in aquatic ecosystems (Barnes & Turner, 2016; Coble et al, 2019). Results are not always straightforward and current knowledge highlights both the potentials and limits of eDNA methods to quantify the abundance of fish populations (e.g., Capo, Spong, Norman, et al, 2019; Levi et al, 2019; Nevers et al, 2018; Wilcox et al, 2016; Wilcox, Young, et al, 2018; Yates, Fraser, & Derry, 2019) Both abiotic and biotic factors are known to influence eDNA persistence and degradation in the water column (e.g., water retention time, temperature, light, oxygen, pH, salinity, microbial activity; see Hansen et al, 2018 for review). High concentrations of terrestrial DOC indirectly affect most of the aforementioned environmental conditions (temperature, light, oxygen, pH, microbial activity; Solomon et al, 2015)
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