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Abstract Environmental DNA (eDNA) has revolutionized ecological research, particularly for biodiversity assessment in various environments, most notably aquatic media. Environmental DNA analysis allows for non‐invasive and rapid species detection across multiple taxonomic groups within a single sample, making it especially useful for identifying rare or invasive species. Due to dynamic hydrological processes, eDNA samples from running waters may represent biodiversity from broad contributing areas, which is convenient from a biomonitoring perspective but also challenging, as hydrological knowledge is required for meaningful biological interpretation. Hydrologists could also benefit from eDNA to address unsolved questions, particularly concerning water movement through catchments. While naturally occurring abiotic tracers have advanced our understanding of water age distribution in catchments, for example, current geochemical tracers cannot fully elucidate the timing and flow paths of water through landscapes. Conversely, biological tracers, owing to their immense diversity and interactions with the environment, could offer more detailed information on the sources and flow paths of water to the stream. The informational capacity of eDNA as a tracer, however, is determined by the ability to interpret the complex biological heterogeneity at a study site, which arguably requires both biological and hydrological expertise. As eDNA data has become increasingly available as part of biomonitoring campaigns, we argue that accompanying eDNA surveys with hydrological observations could enhance our understanding of both biological and hydrological processes; we identify opportunities, challenges, and needs for further interdisciplinary collaboration; and we highlight eDNA's potential as a bridge between hydrology and biology, which could foster both domains. This article is categorized under:Science of Water > Hydrological ProcessesScience of Water > MethodsWater and Life > Nature of Freshwater Ecosystems 

The goods and services provided by riverine systems are critical to humanity, and our reliance increases with our growing population and demands. As our activities expand, these systems continue to degrade throughout the world even as we try to restore them, and many efforts have not met expectations. One way to increase restoration effectiveness could be to explicitly design restorations to promote microbial communities, which are responsible for much of the organic matter breakdown, nutrient removal or transformation, pollutant removal, and biomass production in river ecosystems. In this paper, we discuss several design concepts that purposefully create conditions for these various microbial goods and services, and allow microbes to act as ecological restoration engineers. Focusing on microbial diversity and function could improve restoration effectiveness and overall ecosystem resilience to the stressors that caused the need for the restoration. Advances in next-generation sequencing now allow the use of microbial ‘omics techniques (e.g., metagenomics, metatranscriptomics) to assess stream ecological conditions in similar fashion to fish and benthic macroinvertebrates. Using representative microbial communities from stream sediments, biofilms, and the water column may greatly advance assessment capabilities. Microbes can assess restorations and ecosystem function where animals may not currently be present, and thus may serve as diagnostics for the suitability of animal reintroductions. Emerging applications such as ecological metatranscriptomics may further advance our understanding of the roles of specific restoration designs towards ecological services as well as assess restoration effectiveness. 

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical featurescolloquially termed a chemical fingerprintthat can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92− 100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment. 

This record includes software and data used to compute shared information between streamwater microbial taxa and hydrologic metrics for the article: URycki, D. R., Bassiouni, M., Good, S. P., Crump, B. C., & Li, B. (2022). The streamwater microbiome encodes hydrologic data across scales. Science of The Total Environment, 157911. https://doi.org/10.1016/j.scitotenv.2022.157911 Abstract: Many fundamental questions in hydrology remain unanswered due to the limited information that can be extracted from existing data sources. Microbial communities constitute a novel type of environmental data, as they are comprised of many thousands of taxonomically and functionally diverse groups known to respond to both biotic and abiotic environmental factors. As such, these microscale communities reflect a range of macroscale conditions and characteristics, some of which also drive hydrologic regimes. Here, we assess the extent to which streamwater microbial communities (as characterized by 16S gene amplicon sequence abundance) encode information about catchment hydrology across scales. We analyzed 64 summer streamwater DNA samples collected from subcatchments within the Willamette, Deschutes, and John Day river basins in Oregon, USA, which range 0.03–29,000 km2 in area and 343–2334 mm/year of precipitation. We applied information theory to quantify the breadth and depth of information about common hydrologic metrics encoded within microbial taxa. Of the 256 microbial taxa that spanned all three watersheds, we found 9.6 % (24.5/256) of taxa, on average, shared information with a given hydrologic metric, with a median 15.6 % (range = 12.4–49.2 %) reduction in uncertainty of that metric based on knowledge of the microbial biogeography. All of the hydrologic metrics we assessed, including daily discharge at different time lags, mean monthly discharge, and seasonal high and low flow durations were encoded within the microbial community. Summer microbial taxa shared the most information with winter mean flows. Our study demonstrates quantifiable relationships between streamwater microbial taxa and hydrologic metrics at different scales, likely resulting from the integration of multiple overlapping drivers of each. Streamwater microbial communities are rich sources of information that may contribute fresh insight to unresolved hydrologic questions. This record can also be found on Github: https://github.com/uryckid/Microbial-Mutual-Information 

This Python script queries the USGS StreamStats Service API for a list of available basin characteristics, and the values for those characteristics, for each input site. The script takes as input a matrix of site identifiers and location coordinates and returns 1) a matrix of values for available basin characteristics obtained from StreamStats for each input location and 2) a matrix of basin characteristic variable names and definitions. To run this script exactly as written, create 3 columns of data in comma-separated format: 1) 'Site,' which are the study site identifiers, 2) 'lonSS,' the longitudinal coordinates, and 3) 'latSS,' the latitudinal coordinates (in decimal degrees). Name the input file 'ssLocs.csv' and store it in a subfolder named 'Data.' Otherwise, the pathnames for input and output files can be modified within the script. The output files 'ssDats.csv' and 'Descriptions.csv' will also be saved to the subfolder 'Data'. Multiple code runs may be necessary to obtain information for all sites; as long as the output file 'ssDats.csv' remains in the 'Data' folder, the script will only query for sites with missing information. If the program returns an error or is unable to obtain data for a site after several attempts, it may be that the input coordinates do not point to a cell defined as water in the StreamStats application. A solution is to check the coordinates manually in the StreamStats web application (http://streamstats.usgs.gov). This script was developed as part of the analysis described in: URycki DR, Good SP, Crump BC, Chadwick J and Jones GD (2020) River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams. Front. Water 2:574728. doi: 10.3389/frwa.2020.574728 

This entry contains the data and code for the analysis described in URycki DR, Good SP, Crump BC, Chadwick J and Jones GD (2020) River Microbiome Composition Reflects Macroscale Climatic and Geomorphic Differences in Headwater Streams. Frontiers in Water 2:574728. doi: 10.3389/frwa.2020.574728 Abstract: Maintaining the quality and quantity of water resources in light of complex changes in climate, human land use, and ecosystem composition requires detailed understanding of ecohydrologic function within catchments, yet monitoring relevant upstream characteristics can be challenging. In this study, we investigate how variability in riverine microbial communities can be used to monitor the climate, geomorphology, land-cover, and human development of watersheds. We collected streamwater DNA fragments and used 16S rRNA sequencing to profile microbiomes from headwaters to outlets of the Willamette and Deschutes basins, two large watersheds prototypical of the U.S. Pacific Northwest region. In the temperate, north-south oriented Willamette basin, microbial community composition correlated most strongly with geomorphic characteristics (mean Mantel test statistic r = 0.19). Percentage of forest and shrublands (r = 0.34) and latitude (r = 0.41) were among the strongest correlates with microbial community composition. In the arid Deschutes basin, however, climatic characteristics were the most strongly correlated to microbial community composition (e.g., r = 0.11). In headwater sub-catchments of both watersheds, microbial community assemblages correlated with catchment-scale climate, geomorphology, and land-cover (r = 0.46, 0.38, and 0.28, respectively), but these relationships were weaker downstream. Development-related characteristics were not correlated with microbial community composition in either watershed or in small or large sub-catchments. Our results build on previous work relating streamwater microbiomes to hydrologic regime and demonstrate that microbial DNA in headwater streams additionally reflects the structural configuration of landscapes as well as other natural and anthropogenic processes upstream. Our results offer an encouraging indication that streamwater microbiomes not only carry information about microbial ecology, but also can be useful tools for monitoring multiple upstream watershed characteristics.