Predicting Risk to Estuary Water Quality and Patterns of Benthic Environmental DNA in Queensland, Australia using Bayesian…

Predictive modeling can inform natural resource management by demonstrating stressor-response pathways and quantifying the effects on selected endpoints. This study develops a risk assessment model using the Bayesian network-relative risk model (BN-RRM) approach, and, for the first time, incorporates eukaryote environmental DNA data as a measure of benthic community structure into an ecological risk assessment context. Environmental DNA sampling is a relatively new technique for biodiversity measurements that involves extracting DNA from environmental samples, sequencing a region of the 18s rDNA gene, and matching the sequences to organisms. Using a network of probability distributions, the BN-RRM model predicts risk to water quality objectives and also the richness of benthic taxa in the Noosa, Pine, and Logan Estuaries in South East Queensland (SEQ), Australia. The model is more accurate at predicting Dissolved Oxygen than it is the Chlorophyll-a water quality endpoint, and it predicts photosynthesizing benthos more accurately than heterotrophs. Results of BN-RRM modeling indicate that the water quality and benthic assemblages of the Noosa are relatively homogenous across all sub risk regions, and that the Noosa has a high probability (73 - 92% probability) of achieving water quality objectives, which indicates low relative risk. On the other hand, the Middle Logan, Middle Pine, and Lower Pine regions are much less likely to meet objectives (15 - 55% probability), indicating a relatively high risk to water quality in those regions. The benthic community richness patterns associated with low relative risk in the Noosa are high Diatom relative richness and low Green Algae richness. The only benthic pattern consistently associated with high relative risk to water quality is the high Fungi richness state. The BN-RRM predicts current conditions in SEQ based on available monitoring data, and provides a basis for future predictions and adaptive management at the direction of resource managers. As new data are made available or more questions are asked, this BN-RRM model can be updated and improved.