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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.