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ABSTRACT Unconventional oil and gas (UOG) extraction is increasing exponentially around the world, as new technological advances have provided cost-effective methods to extract hard-to-reach hydrocarbons. While UOG has increased the energy output of some countries, past research indicates potential impacts in nearby stream ecosystems as measured by geochemical and microbial markers. Here, we utilized a robust data set that combines 16S rRNA gene amplicon sequencing (DNA), metatranscriptomics (RNA), geochemistry, and trace element analyses to establish the impact of UOG activity in 21 sites in northern Pennsylvania. These data were also used to design predictive machine learning models to determine the UOG impact on streams. We identified multiple biomarkers of UOG activity and contributors of antimicrobial resistance within the order Burkholderiales . Furthermore, we identified expressed antimicrobial resistance genes, land coverage, geochemistry, and specific microbes as strong predictors of UOG status. Of the predictive models constructed ( n  = 30), 15 had accuracies higher than expected by chance and area under the curve values above 0.70. The supervised random forest models with the highest accuracy were constructed with 16S rRNA gene profiles, metatranscriptomics active microbial composition, metatranscriptomics active antimicrobial resistance genes, land coverage, and geochemistry ( n  = 23). The models identified the most important features within those data sets for classifying UOG status. These findings identified specific shifts in gene presence and expression, as well as geochemical measures, that can be used to build robust models to identify impacts of UOG development. IMPORTANCE The environmental implications of unconventional oil and gas extraction are only recently starting to be systematically recorded. Our research shows the utility of microbial communities paired with geochemical markers to build strong predictive random forest models of unconventional oil and gas activity and the identification of key biomarkers. Microbial communities, their transcribed genes, and key biomarkers can be used as sentinels of environmental changes. Slight changes in microbial function and composition can be detected before chemical markers of contamination. Potential contamination, specifically from biocides, is especially concerning due to its potential to promote antibiotic resistance in the environment. Additionally, as microbial communities facilitate the bulk of nutrient cycling in the environment, small changes may have long-term repercussions. Supervised random forest models can be used to identify changes in those communities, greatly enhance our understanding of what such impacts entail, and inform environmental management decisions. 

ABSTRACT Production of unconventional oil and gas continues to rise, but the effects of high-density hydraulic fracturing (HF) activity near aquatic ecosystems are not fully understood. A commonly used biocide in HF, 2,2-dibromo-3-nitrilopropionamide (DBNPA), was studied in microcosms of HF-impacted (HF+) versus HF-unimpacted (HF−) surface water streams to (i) compare the microbial community response, (ii) investigate DBNPA degradation products based on past HF exposure, and (iii) compare the microbial community response differences and similarities between the HF biocides DBNPA and glutaraldehyde. The microbial community responded to DBNPA differently in HF-impacted versus HF-unimpacted microcosms in terms of the number of 16S rRNA gene copies quantified, alpha and beta diversity, and differential abundance analyses of microbial community composition through time. The differences in microbial community changes affected degradation dynamics. HF-impacted microbial communities were more sensitive to DBNPA, causing the biocide and by-products of the degradation to persist for longer than in HF-unimpacted microcosms. A total of 17 DBNPA by-products were detected, many of them not widely known as DBNPA by-products. Many of the brominated by-products detected that are believed to be uncharacterized may pose environmental and health impacts. Similar taxa were able to tolerate glutaraldehyde and DBNPA; however, DBNPA was not as effective for microbial control, as indicated by a smaller overall decrease of 16S rRNA gene copies/ml after exposure to the biocide, and a more diverse set of taxa was able to tolerate it. These findings suggest that past HF activity in streams can affect the microbial community response to environmental perturbation such as that caused by the biocide DBNPA. IMPORTANCE Unconventional oil and gas activity can affect pH, total organic carbon, and microbial communities in surface water, altering their ability to respond to new environmental and/or anthropogenic perturbations. These findings demonstrate that 2,2-dibromo-3-nitrilopropionamide (DBNPA), a common hydraulic fracturing (HF) biocide, affects microbial communities differently as a consequence of past HF exposure, persisting longer in HF-impacted (HF+) waters. These findings also demonstrate that DBNPA has low efficacy in environmental microbial communities regardless of HF impact. These findings are of interest, as understanding microbial responses is key for formulating remediation strategies in unconventional oil and gas (UOG)-impacted environments. Moreover, some DBNPA degradation by-products are even more toxic and recalcitrant than DBNPA itself, and this work identifies novel brominated degradation by-products formed.