Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Abstract

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

Methodological differences between studies confound one-size-fits-all approaches to managing surface waterways for food and water safety

“…Environmentally derived factors based on ecoregion ( N = 9), habitat type (terrestrial = 6, aquatic = 2), climate ( N = 1), or hydrologic region ( N = 1) were the top-ranked regional features for 14 of the 17 forests. This is consistent with past studies that have repeatedly associated microbial water quality with environmental parameters, including weather and hydrologic characteristics ( 8 – 11 , 167 – 172 ). Here, region and water type were strongly associated with several of the microbial water quality parameters considered in the present study.…”

Have genetic targets for faecal pollution diagnostics and source tracking revolutionized water quality analysis yet?

“…5 ). This is contrary to expectations, as environmental data, such as data on catchment hydrology and land use (GIS, mapping), have proven essential for an improved application, understanding and interpretation of GFPD in water quality research (Reischer et al 2008 , Peed et al 2011 , Bambic et al 2015 , Verhougstraete et al 2015 , Frick et al 2020 , Green et al 2021 , Diedrich et al 2023 ). Without a doubt, there is significant potential to better utilize and integrate environmental data in GFPD analysis in future HRWM research (Fig.…”

“…MST methods have also been used to more generally identify factors and features that promote or reduce watershed faecal pollution rather than just identifying pollution sources. As an example, Green et al ( 2021 ) used MST and cultivation-based FIO in an investigation of 68 streams in New York State, USA, to identify stream features, land use practices and meteorological patterns that drive faecal pollution levels from multiple sources.…”

Scale of analysis drives the observed ratio of spatial to non-spatial variance in microbial water quality: insights from two decades of citizen science data

“…For example, a survey of New York streams proposed using sediment levels (e.g., turbidity) as a supplementary indicator of microbial water quality (13). The repeated identification of a positive association between sediment levels and FIB levels, and between sediment levels and enteric pathogen presence suggests that these associations are reproducible and robust to study design (e.g., location of study, sampling method, laboratory methods (13,26,(30)(31)(32)(33)). However, before incorporating sediment monitoring into existing FIB monitoring programs, an improved understanding of spatial and temporal variation in sediment and FIB levels is also needed (e.g., to inform sampling frequency).…”