Two weeks ago we looked at the use of eDNA in monitoring for the presence or absence of aquatic species. While our post was not a comprehensive review of this subject, we did include some of the most promising aspects — and some of the challenges — associated with using this new technology.
In short, eDNA has been found to be accurate in detecting aquatic species such as fish (Levi et al. 2018, Goldberg et al. 2016; Rees et al. 2014) and can be a cost-effective, low impact method that can be supported by volunteer efforts. However, there are also limits to eDNA methods, such as temporal and spatial constraints that impact the quality of the DNA after it is shed and thus the likelihood of accurate results.
Despite these limitations to eDNA methods, newer research by Levi et al. (2018) and others such as Tillotson et al. (2018) have found that, given the correct sampling regime, eDNA can be effective at estimating salmonid abundance, not just presence/absence.
The work by Levi et al. (2018) examined water samples from just upstream of the Auke Creek research weir on Auke Creek in Alaska. Water samples were collected for three years, from 2014-2016, after each day’s salmon enumeration, which provided an accurate abundance estimate against which to compare the eDNA estimation.
In addition, river height was recorded daily and converted to streamflow (cubic feet per second) using an established rating curve. Lab analyses were then completed on the water samples to enumerate salmonid eDNA in the samples. Finally, the eDNA was flow-corrected using each day’s streamflow.
The study found that flow-corrected eDNA rates reflected the abundance of both coho and sockeye adults and sockeye smolts that had passed the water sampling site the previous day. Levi et al. (2018) concluded that
“eDNA thus promises accurate and efficient enumeration, but to deliver the most robust numbers will need higher-resolution stream-flow data, at-least-daily sampling, and a focus on species with simple life histories, since shedding rate varies amongst jacks, juveniles, and adults.”
The data needs required for eDNA to be able to enumerate salmonid populations are thus very high. At least daily eDNA sampling, accurate and consistent stream flow measurements, and a clear knowledge of how eDNA differs among different life history stages, are high orders for at least the potential for accurate eDNA results. These requirements could certainly be met in certain well-studied systems, like Auke Creek, and look at salmonids with less diverse life histories, such as coho and sockeye.
Unfortunately, O. mykiss are not one of those salmonids with a simple or predictable life history which researchers could use to guide and design their sampling regime. Most likely, daily eDNA sampling and the other data needs to estimate fish abundance make eDNA methods more expensive than more conventional methods, at least for steelhead.
Wild Steelheaders United and Trout Unlimited support continued research to better determine the DNA shedding rates of the different salmonid species (especially O. mykiss!) and their different life history stages — key things to know if we want to be able to use eDNA methods to estimate population abundances of O. mykiss and other salmonids with confidence in the future.
Goldberg, C.S., C.R. Turner, K. Deiner, et al. 2016. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods in ecology and evolution. 7:1299-1307.
Levi, T., J. M. Allen, D. Bell, J. Joyce, J. R. Russell, D. A. Tallmon, S. C. Vulstek, C. Yang, and D. W. Yu. 2018. Environmental DNA for the enumeration and management of Pacific salmon. Molecular Ecology Resources:597–608.
Rees, H.C., B.C. Maddison, D.J. Middleditch, J.R. Patmore, and K.C. Gough. 2014. The detection of aquatic animal species using environmental DNA–a review of eDNA as a survey tool in ecology. Journal of Applied Ecology. 51:1450-1459.
Tillotson, M.D., R.P. Kelly, J.J. Duda, et al. 2018. Concentrations of environmental DNA (eDNA) reflect spawning salmon abundance at fine spatial and temporal scales. Biological Conservation. 220:1-11.