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.
Surface water monitoring and microbial
source tracking (MST) are
used to identify host sources of fecal pollution and protect public
health. However, knowledge of the locations of spatial sources and
their relative impacts on the environment is needed to effectively
mitigate health risks. Additionally, sediment samples may offer time-integrated
information compared to transient surface water. Thus, we implemented
the newly developed microbial find, inform, and test framework to
identify spatial sources and their impacts on human (HuBac) and bovine (BoBac) MST markers, quantified from
both riverbed sediment and surface water in a bovine-dense region.
Dairy feeding operations and low-intensity developed land-cover were
associated with 99% (p-value < 0.05) and 108%
(p-value < 0.05) increases, respectively, in the
relative abundance of BoBac in sediment, and with
79% (p-value < 0.05) and 39% increases in surface
water. Septic systems were associated with a 48% increase in the relative
abundance of HuBac in sediment and a 56% increase
in surface water. Stronger source signals were observed for sediment
responses compared to water. By defining source locations, predicting
river impacts, and estimating source influence ranges in a Great Lakes
region, this work informs pollution mitigation strategies of local
and global significance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.