2008
DOI: 10.1038/ismej.2008.36
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Characterizing mixed microbial population dynamics using time-series analysis

Abstract: Due to a general shortage of temporal population data, dynamic structures in microbial communities remain largely unexplored. Knowledge of community dynamics is, however, essential for understanding the mechanisms by which microbes interact. Here, we have used a computational approach for quantification of bacteria in multispecies populations, generating data for time-series modeling. Moreover, we have used online FR-IR spectroscopy to monitor the main metabolic processes. The approach enabled us to provide a … Show more

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Cited by 38 publications
(40 citation statements)
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“…The time-series data were analyzed using generalized additive models (GAM) implemented in R (Trosvik et al 2008). Differences among treatments were analyzed using one-way analysis of variance (ANOVA) and the least significant difference (LSD) in SPSS 19.0 for Windows (SPSS, Inc., Chicago, IL, USA).…”
Section: Discussionmentioning
confidence: 99%
“…The time-series data were analyzed using generalized additive models (GAM) implemented in R (Trosvik et al 2008). Differences among treatments were analyzed using one-way analysis of variance (ANOVA) and the least significant difference (LSD) in SPSS 19.0 for Windows (SPSS, Inc., Chicago, IL, USA).…”
Section: Discussionmentioning
confidence: 99%
“…Indeed, balance between populations was strictly governed by interaction among community members, each of them might be affected positively or negatively by each other (Trosvik et al, 2008). It should be the case for the most impacted fungi S. depressa, only present in CYP79A1 transgenic plants, that is a parasite of another fungi.…”
Section: Discussionmentioning
confidence: 99%
“…Our study reports, for the first time, the application of GAM models to elucidate the microbial ecology of specific functional traits in mangrove sediments. These niche-based species distribution models [76] have been applied to a wide range of plants, macroorganisms and microorganisms [14,[77][78][79] and consist of observations of species over a gradient of environmental parameters (or "predictors") that can have a direct or indirect effect on the establishment or survival of the species at a limited time period. GAM models assume pseudo-equilibrium between the environmental parameters and the species spatial pattern underlying the realized niche for the species studied [76].…”
Section: Discussionmentioning
confidence: 99%