An analytical framework for estimating aquatic species density from environmental DNA

Abstract

Environmental DNA (eDNA) analysis of water samples is on the brink of becoming a standard monitoring method for aquatic species. This method has improved detection rates over conventional survey methods and thus has demonstrated effectiveness for estimation of site occupancy and species distribution. The frontier of eDNA applications, however, is to infer species density. Building upon previous studies, we present and assess a modeling approach that aims at inferring animal density from eDNA. The modeling combines eDNA and animal count data from a subset of sites to estimate species density (and associated uncertainties) at other sites where only eDNA data are available. As a proof of concept, we first perform a cross‐validation study using experimental data on carp in mesocosms. In these data, fish densities are known without error, which allows us to test the performance of the method with known data. We then evaluate the model using field data from a study on a stream salamander species to assess the potential of this method to work in natural settings, where density can never be known with absolute certainty. Two alternative distributions (Normal and Negative Binomial) to model variability in eDNA concentration data are assessed. Assessment based on the proof of concept data (carp) revealed that the Negative Binomial model provided much more accurate estimates than the model based on a Normal distribution, likely because eDNA data tend to be overdispersed. Greater imprecision was found when we applied the method to the field data, but the Negative Binomial model still provided useful density estimates. We call for further model development in this direction, as well as further research targeted at sampling design optimization. It will be important to assess these approaches on a broad range of study systems.

Environmental DNA analysis as a non‐invasive quantitative tool for reproductive migration of a threatened endemic fish in rivers

“…Since the depth gradient makes visual detection difficult in the lake, and unpredictable water current and eDNA diffusion may have made eDNA analysis inaccurate in the lake (Dunker et al, ; Eichmiller, Bajer, & Sorensen, ; Ghosal, Eichmiller, Witthuhn, & Sorensen, ), this difference is not surprising. Researchers should pay attention to the unreliability of conventional data as well as to that of eDNA data (Chambert et al, ). It is also notable that both positive and negative visual detection results were obtained in the range of eDNA concentration widely from 10 2 to 10 3.7 copies/L (see Figure ), probably because of an uneven detection efficiency of both methods, depending on the environmental heterogeneity within the river.…”

“…Researchers should pay attention to the unreliability of conventional data as well as to that of eDNA data (Chambert et al, 2018). It is also notable that both positive and negative visual detection results were obtained in the range of eDNA concentration widely from 10 2 to 10 3.7 copies/L (see Figure 3), probably because of an uneven detection efficiency of both methods, depending on the environmental heterogeneity within the river.…”

“…Poor correlation, which can compromise the reliability of eDNA analysis for estimating animal abundance, may be explained by overdispersion of data generated by both eDNA analysis and conventional methods. Chambert, Pilliod, Goldbeerg, Doi, and Takahara () listed several factors that result in the overdispersion of eDNA data, including variation in individual shedding rates (Klymus et al, ; Maruyama et al, ; Mizumoto et al, ), uneven distribution of animals in the environment (Lacoursière‐Roussel et al, ; Laramie, Pilliod, & Goldberg, ; Pilliod et al, ), environmental disturbance (Barnes & Turner, ) and sampling methods and environmental conditions (Goldberg, Pilliod, Arkle, & Waits, ; Lacoursière‐Roussel et al, ; Pilliod, Goldberg, Arkle, & Waits, ).…”

Meta‐analysis supports further refinement of eDNA for monitoring aquatic species‐specific abundance in nature

“…New studies should take advantage of the increasing number of methodological papers being published on eDNA collection techniques, extraction techniques, and the statistical analysis of eDNA data (e.g., Lacoursière-Roussel, Rosabal, et al, 2016;Hinlo et al, 2017;Chambert, Takahara, Pilliod, Goldberg, & Doi, 2018) to optimize and standardize field, laboratory, and analytical practices. New studies should take advantage of the increasing number of methodological papers being published on eDNA collection techniques, extraction techniques, and the statistical analysis of eDNA data (e.g., Lacoursière-Roussel, Rosabal, et al, 2016;Hinlo et al, 2017;Chambert, Takahara, Pilliod, Goldberg, & Doi, 2018) to optimize and standardize field, laboratory, and analytical practices.…”

Downstream transport and seasonal variation in freshwater pearl mussel (Margaritifera margaritifera) eDNA concentration

“…Downstream transport of eDNA does at the same time hamper analysis of local presence and abundance of target species because any sample may hold a mixture of eDNA shed both locally and further upstream (Shogren et al, 2017). Knowledge on how environmental variables affect eDNA decay and transport may help to predict eDNA concentrations in dependence of species abundance (Carraro, Hartikainen, Jokela, Bertuzzo, & Rinaldo, 2018;Chambert, Pilliod, Goldberg, Doi, & Takahara, 2018;Shogren et al, 2017;Wilcox et al, 2016). However, only a few field studies have explored the relationship between the spatial distributions of individuals and eDNA concentrations in natural lotic systems (Doi et al, 2017;Spear, Groves, Williams, & Waits, 2015;Tillotson et al, 2018;Wilcox et al, 2016).…”