Spatial Versus Nonspatial Variance in Fecal Indicator Bacteria Differs Within and Between Ponds

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

Even though differences in methodology (e.g., sample volume and detection method) have been shown to affect observed microbial water quality, multiple sampling and laboratory protocols continue to be used for water quality monitoring. Research is needed to determine how these differences impact the comparability of findings to generate best management practices and the ability to perform meta-analyses. This study addresses this knowledge gap by compiling and analyzing a data set representing 2,429,990 unique data points on at least one microbial water quality target (e.g., Salmonella presence and Escherichia coli concentration). Variance partitioning analysis was used to quantify the variance in likelihood of detecting each pathogenic target that was uniquely and jointly attributable to non-methodological versus methodological factors. The strength of the association between microbial water quality and select methodological and non-methodological factors was quantified using conditional forest and regression analysis. Fecal indicator bacteria concentrations were more strongly associated with non-methodological factors than methodological factors based on conditional forest analysis. Variance partitioning analysis could not disentangle non-methodological and methodological signals for pathogenic Escherichia coli, Salmonella, and Listeria. This suggests our current perceptions of foodborne pathogen ecology in water systems are confounded by methodological differences between studies. For example, 31% of total variance in likelihood of Salmonella detection was explained by methodological and/or non-methodological factors, 18% was jointly attributable to both methodological and non-methodological factors. Only 13% of total variance was uniquely attributable to non-methodological factors for Salmonella, highlighting the need for standardization of methods for microbiological water quality testing for comparison across studies.IMPORTANCEThe microbial ecology of water is already complex, without the added complications of methodological differences between studies. This study highlights the difficulty in comparing water quality data from projects that used different sampling or laboratory methods. These findings have direct implications for end users as there is no clear way to generalize findings in order to characterize broad-scale ecological phenomenon and develop science-based guidance. To best support development of risk assessments and guidance for monitoring and managing waters, data collection and methods need to be standardized across studies. A minimum set of data attributes that all studies should collect and report in a standardized way is needed. Given the diversity of methods used within applied and environmental microbiology, similar studies are needed for other microbiology subfields to ensure that guidance and policy are based on a robust interpretation of the literature.

Scale and detection method impacted Salmonella prevalence and diversity in ponds

Site-specific approaches for managing food safety hazards in agricultural water require an understanding of foodborne pathogen ecology. This study identified factors associated with Salmonella contamination in Virginia ponds. Grab samples (250 mL, N = 600) were collected from 30 sites across nine ponds. Culture- and culture-independent (CIDT)-based methods were used to detect Salmonella in each sample. Salmonella isolated by culture-based methods were serotyped by Kauffman-White classification. Environmental data were collected for each sample. McNemar's χ2 was used to determine if Salmonella detection differed by testing method. Separate mixed effect models were used to identify environmental factors associated with culture and CIDT-based Salmonella detection. Separate models were built for each pond, and for all ponds combined. Salmonella detection differed significantly (p < 0.001) between CIDT (31 %; 183/600)- and culture (13 %; 77/600)-based methods. Culture-based methods yielded 11 different serovars. All cultured Salmonella samples were confirmed by CIDT; 42.1 % of CIDT Salmonella-positive samples could be cultured. Associations between environmental factors and Salmonella detection also varied substantially by pond and detection method. In the all-pond model, associations were observed for five factors (total coliforms, Escherichia coli, air temperature, UV, rain) for both culture- and CIDT-based Salmonella detection. Rain prior to sampling (24 h) increased odds of Salmonella detection for culture (OR = 5.09) and CIDT (OR = 3.62) in the all-pond model. When all the pond data were used, models masked associations at the individual pond level, as there were noticeable differences between ponds and the odds of isolating Salmonella by environmental factors. Ponds were within a 187-ha area in this study, emphasizing water management needs to be individualized (i.e., assess hazards/risks by pond). Results also highlight detection methods and scale strongly affect observed water quality and should be considered when developing monitoring programs to develop guidance for growers.

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

AIMS

While fecal indicator bacteria (FIB) testing is used to monitor surface water for potential health hazards, observed variation in FIB levels may depend on the scale of analysis (SOA). Two decades of citizen science data, coupled with random effects models, were used to quantify the variance in FIB levels attributable to spatial versus temporal factors.

METHODS AND RESULTS

Separately, Bayesian models were used to quantify the ratio of spatial to non-spatial variance in FIB levels and identify associations between environmental factors and FIB levels. Separate analyses were performed for three SOA: waterway, watershed, and statewide. As SOA increased (from waterway to watershed to statewide models), variance attributable to spatial sources generally increased and variance attributable to temporal sources generally decreased. While relationships between FIB levels and environmental factors, such as flow conditions (base versus stormflow), were constant across SOA, the effect of land cover was highly dependent on SOA and consistently smaller than the effect of stormwater infrastructure (e.g. outfalls).

CONCLUSIONS

This study demonstrates the importance of SOA when developing water quality monitoring programs or designing future studies to inform water management.