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Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments

Citation

Wiesner-Friedman, Corinne; Beattie, Rachelle E.; Stewart, Jill R.; Hristova, Krassimira R.; & Serre, Marc L. (2021). Microbial Find, Inform, and Test Model for Identifying Spatially Distributed Contamination Sources: Framework Foundation and Demonstration of Ruminant Bacteroides Abundance in River Sediments. Environmental Science & Technology, 55(15), 10451-10461.

Abstract

Microbial pollution in rivers poses known ecological and health risks, yet causal and mechanistic linkages to sources remain difficult to establish. Host-associated microbial source tracking (MST) markers help to assess the microbial risks by linking hosts to contamination but do not identify the source locations. Land-use regression (LUR) models have been used to screen the source locations using spatial predictors but could be improved by characterizing transport (i.e., hauling, decay overland, and downstream). We introduce the microbial Find, Inform, and Test (FIT) framework, which expands previous LUR approaches and develops novel spatial predictor models to characterize the transported contributions. We applied FIT to characterize the sources of BoBac, a ruminant Bacteroides MST marker, quantified in riverbed sediment samples from Kewaunee County, Wisconsin. A 1 standard deviation increase in contributions from land-applied manure hauled from animal feeding operations (AFOs) was associated with a 77% (p-value <0.05) increase in the relative abundance of ruminant Bacteroides (BoBac-copies-per-16S-rRNA-copies) in the sediment. This is the first work finding an association between the upstream land-applied manure and the offsite bovine-associated fecal markers. These findings have implications for the sediment as a reservoir for microbial pollution associated with AFOs (e.g., pathogens and antibiotic-resistant bacteria). This framework and application advance statistical analysis in MST and water quality modeling more broadly.

URL

http://dx.doi.org/10.1021/acs.est.1c01602

Reference Type

Journal Article

Year Published

2021

Journal Title

Environmental Science & Technology

Author(s)

Wiesner-Friedman, Corinne
Beattie, Rachelle E.
Stewart, Jill R.
Hristova, Krassimira R.
Serre, Marc L.

Article Type

Regular

Continent/Country

United States of America

State

Nonspecific

ORCiD

Stewart, J - 0000-0002-3474-5233