Biology subjects

Weller, D. L.

Publications and source records attributed to Weller, D. L..

4 recordsLinked to original sources

bioRxiv2022

Structural equation models suggest that on-farm non-crop vegetation removal is not associated with improved food safety outcomes but is linked to impaired water quality

While growers have reported pressures to minimize wildlife intrusion into produce fields through non-crop vegetation (NCV) removal, NCV provides key ecosystem services. To model food safety and environmental tradeoffs associated with NCV removal, published and publicly available food safety and water quality data from the Northeastern USA were obtained. Because data on NCV removal are not widely available, forest-wetland cover was used as a proxy, consistent with previous studies. Structural equation models (SEMs) were used to quantify the effect of forest-wetland cover on (i) food safety outcomes (e.g., detecting pathogens in soil) and (ii) water quality (e.g., nutrient levels). Based on the SEMs, NCV was not associated with or had a protective effect on food safety outcomes (more NCV was associated with a reduced likelihood of pathogen detection). The probabilities of detecting Listeria spp. in soil (Effect Estimate [EE]=-0.17; P=0.005) and enterohemorrhagic Escherichia coli in stream samples (EE=-0.27; P<0.001) were negatively associated with the amount of NCV surrounding the sampling site. Higher amounts of NCV were also associated with lower nutrient, salinity, and sediment levels and higher dissolved oxygen levels. Total phosphorous levels were negatively associated with the amount of NCV in the upstream watershed (EE=-0.27; P<0.001). Similar negative associations (P<0.05) were observed for other physicochemical parameters, such as nitrate (EE=-0.38). Our findings suggest that NCV should not be considered an inherent produce safety risk or result in farm audit demerits. This study also provides a framework for evaluating environmental trade-offs associated with using specific preharvest food safety strategies. ImportanceCurrently, on-farm food safety decisions are typically made independently of conservation considerations, often with detrimental impacts on agroecosystems. Co-managing agricultural environments to simultaneously meet conservation and food safety aims is complicated because farms are closely linked to surrounding environments, and management decisions can have unexpected environmental, economic, and food safety consequences. Thus, there is a need for research on the conservation and food safety trade-offs associated with implementing specific preharvest food safety practices. Understanding these trade-offs is critical for developing adaptive co-management strategies and ensuring the short and long-term safety, sustainability, and profitability of agricultural systems. This study quantifies tradeoffs and synergies between food safety and environmental aims, and outlines a framework for modeling trade-offs and synergies between management aims that can be used to support future co-management research.

ecology↗

bioRxiv2022

Conditional forest models built using metagenomic data could accurately predict Salmonella contamination in Northeastern streams

The use of water contaminated with Salmonella for produce production contributes to foodborne disease burden. To reduce human health risks, there is a need for novel, targeted approaches for assessing the pathogen status of agricultural water. We investigated the utility of water microbiome data for predicting Salmonella contamination of streams used to source water for produce production. Grab samples were collected from 60 New York streams in 2018 and tested for Salmonella. Separately, DNA was extracted from the samples and used for Illumina shotgun metagenomic sequencing. Reads were trimmed and used to assign taxonomy with Kraken2. Conditional forest (CF), regularized random forest (RRF), and support vector machine (SVM) models were implemented to predict Salmonella contamination. Model performance was determined using 10-fold cross-validation repeated 10 times to quantify area under the curve (AUC) and Kappa score. Taxa identified as the most informative for accurately predicting Salmonella contamination based on conditional variable importance were compared to taxa identified by ALDEx2 as being differentially abundant between Salmonella-positive and - negative samples. CF models outperformed the other two algorithms based on AUC (0.82 - CF, 0.76 - RRF, 0.67 - SVM) and Kappa score (0.41- CF, 0.38 - RRF, 0.19 - SVM). CF and differential abundance tests both identified Aeromonas (VI = 0.32) and Tabrizicola (VI = 0.12) as the two most informative taxa for predicting Salmonella contamination. The taxa identified in this study warrant further investigation as indicators of Salmonella contamination in Northeastern freshwater streams. IMPORTANCEUnderstanding the associations between surface water microbiome composition and the presence of foodborne pathogens, such as Salmonella, can facilitate the identification of novel indicators of Salmonella contamination. This study assessed the utility of microbiome data and three machine learning algorithms for predicting Salmonella contamination of Northeastern streams. The research reported here both expanded the knowledge on the microbiome composition of surface waters and identified putative novel indicators (i.e., Aeromonas and Tabrizicola) for Salmonella in Northeastern streams. These putative indicators warrant further research to assess whether they are consistent indicators of Salmonella for regions, waterways, and years not represented in the dataset used in this study.

microbiology↗

bioRxiv2022

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

While fecal indicator bacteria (FIB) testing is used to monitor surface waters for potential health hazards, recent studies report substantial variation in FIB levels and that observed variation appeared dependent on scale of analysis (SOA). Citizen science data and random effects models were used to quantify variance in FIB levels attributable to spatial versus temporal factors. Separately, Bayesian models were used to quantify the ratio of spatial to nonspatial variance in FIB levels, and identify associations between environmental factors and FIB levels. Separate analyses were performed for each stream, lake, and watershed as well as at the state-level (three SOAs). 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, were constant across SOA, the effect of land cover was both highly dependent on SOA and consistently smaller than the effect of outfalls. This study demonstrates the importance of scale when designing monitoring programs or using FIB monitoring to inform management. Moreover, these data represent a comprehensive survey of water quality in Upstate NY, and the study highlights a valuable publicly available database.

microbiology↗

bioRxiv2020

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments, and because environmental conditions affect the likelihood of pathogen contamination and the relationship between indicator organism levels (e.g., E. coli) and pathogen presence. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between E. coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR]=2.18), and eaeA-stx codetection (OR=6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR=50.2) and NY (OR=18.4), and Salmonella detection in AZ (OR=4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of the food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

microbiology↗