No abstract

A greater understanding of eDNA behavior in the environment is needed before it can be employed for ecosystem monitoring applications. The objectives of this study were to use autonomous sampling to conduct long‐term, high‐frequency monitoring of the eDNA of native salmonid species in a Californian coastal stream, describe temporal variation of eDNA on multiple scales and identify environmental factors that drive this variation, and evaluate the ability of the eDNA datasets to detect rare species and represent organismal abundance. Using high‐throughput autonomous environmental sample processors (ESPs) and qPCR, we enumerated eDNA concentrations from 674 water samples collected at subdaily intervals over 360 days at a single site. We detected eDNA from two imperiled salmonids (coho salmon Oncorhynchus kisutch and steelhead/rainbow trout O. mykiss) in most samples; O. kisutch eDNA was generally in lower concentration and more variable than O. mykiss eDNA. High‐frequency (i.e., subdaily and daily) variability in salmonid eDNA concentrations showed occasional patchiness (i.e., large differences between consecutive samples), while seasonal differences were observed consistent with the ecology of the species at this site. Salmonid eDNA concentrations were significantly associated with creek discharge, photoperiod, and whether the creek mouth was open or closed by a seasonal sandbar. The release of hatchery‐origin O. kisutch parr into the stream was associated with a significant increase in eDNA concentration for the remainder of the study. We compared eDNA signals with fish abundance data collected from traps located at the site. Fish were detected more often by eDNA than from trapping. Significant positive associations between fish abundance and eDNA concentrations were observed for O. mykiss; however, no such associations were observed for O. kisutch. This study adds to our knowledge on the occurrence and behavior of fish eDNA in lotic systems and informs future biomonitoring efforts using automated sampling technology.

To reduce the incidence of recreational waterborne illness, fecal indicator bacteria (FIB) are measured to assess water quality and inform beach management. Recently, predictive FIB models have been used to aid managers in making beach posting and closure decisions. However, those predictive models must be trained using rich historical data sets consisting of FIB and environmental data that span years, and many beaches lack such data sets. Here, we investigate whether water quality data collected during discrete short duration, high-frequency beach sampling events (e.g., samples collected at sub-hourly intervals for 24–48 h) are sufficient to train predictive models that can be used for beach management. We use data collected during six high-frequency sampling events at three California marine beaches and train a total of 126 models using common data-driven techniques. Tide, solar irradiation, water temperature, significant wave height, and offshore wind speed were found to be the most important environmental variables in the models. We validate the predictive performance of models using withheld data. Random forests are consistently the top performing model type. Overall, we find that data-driven models trained using high-frequency FIB and environmental data perform well at predicting water quality and can be used to inform public health decisions at beaches.

Coastal water quality is an important factor influencing public health and the quality of our nation’s beaches. In recent years, poor water quality has resulted in increased numbers of beach closures and corresponding negative impacts on tourism. This paper addresses some of the issues surrounding the management challenge of coastal water quality, in particular, beach water quality monitoring. For this effort, data on beach water quality monitoring activities conducted by states were assessed and synthesized. In total, 29 states were surveyed: 16 reported information for seawater; six reported for freshwater only; eight reported for both seawater and freshwater. Thresholds for advisories and closure vary nationally; however, all 29 states have established an online presence for their monitoring programs and display advisories and closures in real time, most often on spatial information (GIS) portals. Challenges in monitoring, prediction, and communication are assessed and discussed. Based on this assessment, the committee offers the following recommendations, as detailed in the text: • Standardization of water quality data and the distribution medium; • Enhanced public access to water quality monitoring data; • Consistent thresholds for swim advisories; • Water quality regulation reviews with stakeholder participation; • Enhanced predictive models incorporating rapid testing results; • Holistic water quality monitoring that includes indicators beyond fecal indicator bacteria; • Managing contaminants of emerging concern through identification, monitoring and control; and • Funding for water quality monitoring and reporting -- from federal, state, and local governments.

Forecasting environmental hazards is critical in preventing or building resilience to their impacts on human communities and ecosystems. Environmental data science is an emerging field that can be harnessed for forecasting, yet more work is needed to develop methodologies that can leverage increasingly large and complex data sets for decision support. Here, we design a data-driven framework that can, for the first time, forecast bacterial standard exceedances at marine beaches with 3 days lead time. Using historical data sets collected at two California sites, we train nearly 400 forecast models using statistical and machine learning techniques and test forecasts against predictions from both a naive “persistence” model and a baseline nowcast model. Overall, forecast models are found to have similar sensitivities and specificities to the persistence model, but significantly higher areas under the ROC curve (a metric distinguishing a model’s ability to effectively parse classes across decision thresholds), suggesting that forecasts can provide enhanced information beyond past observations alone. Forecast model performance at all lead times was similar to that of nowcast models. Together, results suggest that integrating the forecasting framework developed in this study into beach management programs can enable better public notification and aid in proactive pollution and health risk management.