The effect of the native bacterial community structure on the predictability ofE. coliO157:H7 survival in manure-amended soil

Abstract

The survival capability of pathogens like Escherichia coli O157:H7 in manure-amended soil is considered to be an important factor for the likelihood of crop contamination. The aim of this study was to reveal the effects of the diversity and composition of soil bacterial community structure on the survival time (ttd) and stability (irregularity, defined as the intensity of irregular dynamic changes in a population over time) of an introduced E. coli O157:H7 gfp-strain were investigated for 36 different soils by means of bacterial PCR-DGGE fingerprints.Bacterial PCR-DGGE fingerprints made with DNA extracts from the different soils using bacterial 16S-rRNA-gene-based primers were grouped by cluster analysis into two clusters consisting of six and 29 soils and one single soil at a cross-correlation level of 16% among samples per cluster. Average irregularity values for E. coli O157:H7 survival in the same soils differed significantly between clusters (P = 0.05), whereas no significant difference was found for the corresponding average ttd values (P = 0.20). The irregularity was higher for cluster 1, which consisted primarily of soils that had received liquid manure and artificial fertilizer and had a significant higher bacterial diversity and evenness values (P < 0.001).Bacterial PCR-DGGE fingerprints of 36 manure-amended soils revealed two clusters which differed significantly in the stability (irregularity) of E. coli O157 decline. The cluster with the higher irregularity was characterized by higher bacterial diversity and evenness.The consequence of a high temporal irregularity is a lower accuracy of predictions of population behaviour, which results in higher levels of uncertainty associated with the estimates of model parameters when modelling the behaviour of E. coli O157:H7 in the framework of risk assessments. Soil community structure parameters like species diversity and evenness can be indicative for the reliability of predictive models describing the fate of pathogens in (agricultural) soil ecosystems.