Abstract
Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.
Highlights
Up-to-date information concerning harmful invasive species and pathogens is critical for minimizing negative outcomes to ecosystem and human health (Stohlgren and Schnase, 2006; Bohan et al, 2017; Cordier et al, 2020)
We present the constituent parts of a cloud-based, data science pipeline for using Bayesian hierarchical occupancy models to analyze relationships between the high frequency data streams produced by autonomous robotic eDNA samplers and the high frequency data streams produced by automated sensor networks tracking abiotic conditions (Figure 1)
We used data from the Yellowstone River, Montana, and the Snake River, Idaho, to demonstrate how real-time data collected by autonomous samplers and automated sensors could be integrated into a data science pipeline to provide timely information about aquatic ecosystem health
Summary
Up-to-date information concerning harmful invasive species and pathogens is critical for minimizing negative outcomes to ecosystem and human health (Stohlgren and Schnase, 2006; Bohan et al, 2017; Cordier et al, 2020). Marine harmful algal bloom alert bulletins and early warning systems require integrating information about phytoplankton, toxin concentrations within shellfish, water temperature and wind speeds, and ocean or lake circulation forecasts (e.g., Glibert et al, 2018) Automated sensor networks, such as the U.S Geological Survey’s (USGS) streamgage network and National Oceanic and Atmospheric Administration’s (NOAA) weather station network, have made it much easier to track changing abiotic conditions with both high and broad spatiotemporal resolution (Sepulveda et al, 2015; Al-Chokhachy et al, 2017; Kovach et al, 2019), but automated, collection of comparable biological data remains a challenge (Sugai, 2020). Biological data collection at relevant scales is no longer the bottleneck
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