2011
DOI: 10.1111/j.1365-2672.2011.05028.x
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Persistence and diversity of faecal coliform and enterococci populations in faecally polluted waters

Abstract: Aim:  To assess the persistence and diversity of faecal bacterial populations (faecal coliforms and enterococci) that have recently been included in microbial source tracking (MST) predictive models. Methods and Results:  The analysed bacterial populations included members of the enterococci group (ENT) [Enterococcus faecium (FM), Enterococcus faecalis (FS) and Enterococcus hirae (HIR)] and the faecal coliform group (FC) [diverse Escherichia coli phenotypes (ECP) and cellobiose‐negative faecal coliforms (CNFC)… Show more

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Cited by 3 publications
(2 citation statements)
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“…However, since sampling was performed only in December, we cannot rule out the influence of seasonal variations not only on the amounts of enterococci in water but also on the relative proportions of the various enterococcal species (23). In a recent study, persistence of enterococci in winter months in water was dependent upon the bacterial species (24). In particular, E. hirae was able to replicate in the environment at a high rate even in winter.…”
Section: Discussionmentioning
confidence: 93%
“…However, since sampling was performed only in December, we cannot rule out the influence of seasonal variations not only on the amounts of enterococci in water but also on the relative proportions of the various enterococcal species (23). In a recent study, persistence of enterococci in winter months in water was dependent upon the bacterial species (24). In particular, E. hirae was able to replicate in the environment at a high rate even in winter.…”
Section: Discussionmentioning
confidence: 93%
“…The system also estimated the importance of each indicator in the training matrix at different concentration levels, generating a heat map that presents all MST indicators sorted by the frequency with which they were selected by the best predictive models at each level of aging and dilution. Since persistence in the environment is dependent on season, the importance of indicators and MST methods in two different seasons (summer and winter) was taken into account (Ballesté and Blanch, 2010;Bonjoch et al, 2011Bonjoch et al, , 2009Muniesa et al, 1999).…”
Section: Machine Learning Methods: Ichnaea Modelingmentioning
confidence: 99%