Sampling of seafood and dairy processing facilities in the north-eastern USA produced 18 isolates of Listeria spp. that could not be identified at the species-level using traditional phenotypic and genotypic identification methods. Results of phenotypic and genotypic analyses suggested that the isolates represent two novel species with an average nucleotide blast identity of less than 92 % with previously described species of the genus Listeria . Phylogenetic analyses based on whole genome sequences, 16S rRNA gene and sigB gene sequences confirmed that the isolates represented by type strain FSL M6-0635^T and FSL A5-0209 cluster phylogenetically with Listeria cornellensis . Phylogenetic analyses also showed that the isolates represented by type strain FSL A5-0281^T cluster phylogenetically with Listeria riparia . The name Listeria booriae sp. nov. is proposed for the species represented by type strain FSL A5-0281^T ( = DSM 28860^T = LMG 28311^T), and the name Listeria newyorkensis sp. nov. is proposed for the species represented by type strain FSL M6-0635^T ( = DSM 28861^T = LMG 28310^T). Phenotypic and genotypic analyses suggest that neither species is pathogenic.

While rain and irrigation events have been associated with an increased prevalence of foodborne pathogens in produce production environments, quantitative data are needed to determine the effects of various spatial and temporal factors on the risk of produce contamination following these events. This study was performed to quantify these effects and to determine the impact of rain and irrigation events on the detection frequency and diversity of Listeria species (including L. monocytogenes ) and L. monocytogenes in produce fields. Two spinach fields, with high and low predicted risks of L. monocytogenes isolation, were sampled 24, 48, 72, and 144 to 192 h following irrigation and rain events. Predicted risk was a function of the field's proximity to water and roads. Factors were evaluated for their association with Listeria species and L. monocytogenes isolation by using generalized linear mixed models (GLMMs). In total, 1,492 (1,092 soil, 334 leaf, 14 fecal, and 52 water) samples were collected. According to the GLMM, the likelihood of Listeria species and L. monocytogenes isolation from soil samples was highest during the 24 h immediately following an event (odds ratios [ORs] of 7.7 and 25, respectively). Additionally, Listeria species and L. monocytogenes isolates associated with irrigation events showed significantly lower sigB allele type diversity than did isolates associated with precipitation events ( P = <0.001), suggesting that irrigation water may be a point source of L. monocytogenes contamination. Small changes in management practices (e.g., not irrigating fields before harvest) may therefore reduce the risk of L. monocytogenes contamination of fresh produce.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future.

Environmental (i.e., meteorological and landscape) factors and management practices can affect the prevalence of foodborne pathogens in produce production environments. This study was conducted to determine the prevalence of Listeria monocytogenes, Listeria species (including L. monocytogenes), Salmonella, and Shiga toxin-producing Escherichia coli (STEC) in produce production environments and to identify environmental factors and management practices associated with their isolation. Ten produce farms in New York State were sampled during a 6-week period in 2010, and 124 georeferenced samples (80 terrestrial, 33 water, and 11 fecal) were collected. L. monocytogenes, Listeria spp., Salmonella, and STEC were detected in 16, 44, 4, and 5% of terrestrial samples, 30, 58, 12, and 3% of water samples, and 45, 45, 27, and 9% of fecal samples, respectively. Environmental factors and management practices were evaluated for their association with terrestrial samples positive for L. monocytogenes or other Listeria species by univariate logistic regression; analysis was not conducted for Salmonella or STEC because the number of samples positive for these pathogens was low. Although univariate analysis identified associations between isolation of L. monocytogenes or Listeria spp. from terrestrial samples and various water-related factors (e.g., proximity to wetlands and precipitation), multivariate analysis revealed that only irrigation within 3 days of sample collection was significantly associated with isolation of L. monocytogenes (odds ratio = 39) and Listeria spp. (odds ratio = 5) from terrestrial samples. These findings suggest that intervention at the irrigation level may reduce the risk of produce contamination.

A total of 27 Listeria isolates that could not be classified to the species level were obtained from soil samples from different locations in the contiguous United States and an agricultural water sample from New York. Whole-genome sequence-based average nucleotide identity blast (ANIb) showed that the 27 isolates form five distinct clusters; for each cluster, all draft genomes showed ANI values of <95 % similarity to each other and any currently described Listeria species, indicating that each cluster represents a novel species. Of the five novel species, three cluster with the Listeria sensu stricto clade and two cluster with sensu lato. One of the novel sensu stricto species, designated L. cossartiae sp. nov., contains two subclusters with an average ANI similarity of 94.9%, which were designated as subspecies. The proposed three novel sensu stricto species (including two subspecies) are Listeria farberi sp. nov. (type strain FSL L7-0091^T=CCUG 74668^T=LMG 31917^T; maximum ANI 91.9 % to L. innocua ), Listeria immobilis sp. nov. (type strain FSL L7-1519^T=CCUG 74666^T=LMG 31920^T; maximum ANI 87.4 % to L. ivanovii subsp. londoniensis ) and Listeria cossartiae sp. nov. [subsp. cossartiae (type strain FSL L7-1447^T=CCUG 74667^T=LMG 31919^T; maximum ANI 93.4 % to L. marthii ) and subsp. cayugensis (type strain FSL L7-0993^T=CCUG 74670^T=LMG 31918^T; maximum ANI 94.7 % to L. marthii ). The two proposed novel sensu lato species are Listeria portnoyi sp. nov. (type strain FSL L7-1582^T=CCUG 74671^T=LMG 31921^T; maximum ANI value of 88.9 % to L. cornellensis and 89.2 % to L. newyorkensis ) and Listeria rustica sp. nov. (type strain FSL W9-0585^T=CCUG 74665^T=LMG 31922^T; maximum ANI value of 88.7 % to L. cornellensis and 88.9 % to L . newyorkensis ). L. immobilis is the first sensu stricto species isolated to date that is non-motile. All five of the novel species are non-haemolytic and negative for phosphatidylinositol-specific phospholipase C activity; the draft genomes lack the virulence genes found in Listeria pathogenicity island 1 (LIPI-1), and the internalin genes inlA and inlB, indicating that they are non-pathogenic.