A new tool facilitates nationwide environmental DNA surveys

Photo Credit

USDA Forest Service photo.

A researcher pours collected stream water into a filtration apparatus where small particulates, including eDNA, collect on a fine membrane filter.

Large-scale wildlife monitoring initiatives are an increasingly popular way to document trends in at-risk and invasive species over time and space. Sampling environmental DNA (eDNA)—or DNA released by species into the environment—is a cost-effective and sensitive option for collecting occurrence data. Until now, to develop lab tests, or “assays”, to detect a particular species, scientists needed to find and test tissue from all other closely related species that could be present. This ensured that they monitored their intended target, but it could also pose a serious bottleneck at larger spatial scales when the sheer number of closely related species to test is insurmountably high. Because of this, assays are typically only approved for local or regional use.

Scientists have created a machine learning tool, eDNAssay, to circumvent this. The tool uses publicly available DNA sequences to predict the outcome of tissue testing and has been tested across 46 assays targeting invasive species—including an amphibian, crustaceans, fishes, mammals, mollusks, plants, and a reptile—against all closely related species in the continental United States, to create 4,206 total predictions. The eDNAssay tool was found to be 96 percent accurate in 649 predictions when paired with lab testing, which is a massive improvement over other approaches. 

This method and the new invasive species assays are poised to have a big impact, both for scientists and wildlife managers engaged in large-scale eDNA surveys. Accurate predictions using specificity rather than finding and testing tissue for each nontarget species (which could easily number in the hundreds) has the potential to save hundreds of thousands of dollars and years of development time.

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