Modeling of Spatially Referenced Environmental and Meteorological Factors Influencing the Probability ofListeriaSpecies Isolation from Natural Environments

Abstract

Many pathogens have the ability to survive and multiply in abiotic environments, representing a possible reservoir and source of human and animal exposure. Our objective was to develop a methodological framework to study spatially explicit environmental and meteorological factors affecting the probability of pathogen isolation from a location. Isolation of Listeria spp. from the natural environment was used as a model system. Logistic regression and classification tree methods were applied, and their predictive performances were compared. Analyses revealed that precipitation and occurrence of alternating freezing and thawing temperatures prior to sample collection, loam soil, water storage to a soil depth of 50 cm, slope gradient, and cardinal direction to the north are key predictors for isolation of Listeria spp. from a spatial location. Different combinations of factors affected the probability of isolation of Listeria spp. from the soil, vegetation, and water layers of a location, indicating that the three layers represent different ecological niches for Listeria spp. The predictive power of classification trees was comparable to that of logistic regression. However, the former were easier to interpret, making them more appealing for field applications. Our study demonstrates how the analysis of a pathogen's spatial distribution improves understanding of the predictors of the pathogen's presence in a particular location and could be used to propose novel control strategies to reduce human and animal environmental exposure.The transmission cycle of many pathogens involves biotic hosts and abiotic environments. After infection of a host with a pathogen like Listeria monocytogenes, Bacillus anthracis, enterohemorrhagic Escherichia coli, Salmonella spp., or Toxoplasma gondii, large numbers of the pathogen may be shed into the environment where, under favorable conditions, they may survive, multiply, and infect new hosts, including humans (6,11,13,30,37). It is important to identify spatially explicit environmental and meteorological factors that favor a pathogen's presence in a particular environmental location. That information could be used to design novel measures to reduce the presence of the pathogen in the environment and prevent exposure and infection of animal and human hosts. For analysis of pathogens' spatial distribution in the environment, geographic information systems (GIS) integrated with standard statistical and epidemiological methods provide tremendous opportunities (5).Detection of pathogens in environmental samples is usually based on culturing methods without enumeration, resulting in presence/absence data. For such data, a standard statistical approach to predict microbial presence as influenced by covariates would be logistic regression (LR). However, classification trees (CT) have recently been suggested as a powerful yet simple alternative to LR in ecological studies (7,48). It is therefore of interest to contrast the performance of the CT with that of the standard LR approach in predict...

Permanent colonization of creek sediments, creek water and limnic water plants by fourListeriaspecies in low population densities

“…Moreover, slope gradients have been reported to impact the detection of Listeria in water samples [35,37,67]. We found no indication that pelagic cell counts of Listeria are higher than cell counts on water plants and in sediment at all areas sampled.…”

“…This was somewhat surprising since R. fluitans and sediment are vastly different habitats with respect to oxygen and nutrient availability, substrates, predators and species interaction [35]. However, it is known that L. monocytogenes and L. innocua harbor an unusually large regulatory gene repertoire in their genomes, including more than 200 transcriptional regulators (ca.…”

“…L. monocytogenes has been isolated from different natural surroundings including soil [31,[33][34][35][36][37][38], vegetation [24,36,37], surface water [38][39][40][41][42][43][44] and wild animals [45][46][47][48]. In general, systematic studies concerning the occurrence of L. monocytogenes in soil and fresh water are rare.…”

Multifactorial Effects of Ambient Temperature, Precipitation, Farm Management, and Environmental Factors Determine the Level of Generic Escherichia coli Contamination on Preharvested Spinach

“…Additionally, we created 14 period variables describing the mean levels of weather characteristics between the day of sample collection (day 0) and days 1,2,3,4,5,6,7,8,9,10,15,20,25, and 29 prior to sample collection. The reason for these point and period measures was to assess if weather influences E. coli count on spinach instantly or cumulatively (29). For brevity, in Table 1, we define only variables significant at a P value of 0.2 in univariable analyses.…”

“…Collection and manipulation of weather and local landscape variables were performed following the general procedure suggested by Ivanek et al (29) and described in detail in our previous study (13). Briefly, weather and landscape information of interest were obtained from the National Resource Information (NRI) databases based on the GPS coordinates recorded for the individual spinach sampling locations.…”