Predicting changes in eDNA benthic communities due to changes in water quality.

What is it about?

Predictive modeling can inform natural resource management by representing stressor–response pathways in a logical way and quantifying the effects on selected endpoints. This study demonstrates a risk assessment model using the Bayesian network relative risk model (BN‐RRM) approach to predict water quality and, for the first time, eukaryote environmental DNA (eDNA) data as a measure of benthic community structure. Environmental DNA sampling is a technique for biodiversity measurements that involves extracting DNA from environmental samples, amplicon sequencing a targeted gene, in this case the 18s rDNA gene (which targets eukaryotes), and matching the sequences to organisms. Using a network of probability distributions, the BN‐RRM model predicts risk to water quality objectives and the relative richness of benthic taxa groups in three estuaries of varying ecological condition. The model predicts Dissolved Oxygen more accurately than the chlorophyll a water quality endpoint and photosynthesizing benthos more accurately than heterotrophs. The BN‐RRM model provides a basis for future predictions and adaptive management at the direction of resource managers.

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Why is it important?

Anthropogenic activities are deteriorating the ecological, chemical and physical conditions of estuaries worldwide. Consequently, new tools are required to both monitor their condition and predict how changes in drives will ultimately be expressed in their ecological communities. Here we provide a new approach which predicts how water quality alters the composition of eDNA derived benthic communities.