Microbial community-level physiological profiles: Considering whole data set and integrating dynamics of colour development

Abstract

Abstract Community Level Physiological Profiles (CLPPs) have been used as ecological indicators from long ago as they determine physiologic and metabolic potential of microbial communities and indicate the capability to adapt their metabolism to anthropogenic stressors or to changes in environmental conditions. Also, these profiles are proxies for functional diversity or functional traits of a microbial community, usually linked to the ecosystem functioning. However, statistical data analysis of CLPPs has turned out to be the main handicap in these studies. Specifically, the interference of inoculum density, the need to choose an optimal incubation time and the different profiles obtained depending on the incubation time-point limits its usefulness. To this aim, we have developed a new approach for CLPP data analysis that considers the whole data set and dynamics of colour development along the incubation period. This approach is based on compositional data (CoDa) transformation, which reflects relative rather than absolute information, followed by repeated measures multivariate analysis of variance and data representation in a canonical variates plot. The approach has been tested by using CLPPs from real fluvial biofilm samples affected by seasonality, flow stress and different sediment depth conditions, and it has been compared to the most classic applied approaches until today (incubation fixed point or curve integration, followed by a Principal Components Analysis). Our results highlight that the proposed approach (i) makes ecological interpretation of CLPPs more sensitive, (ii) clarifies differences between CLPPs even in situations of marked seasonality while classical approaches provided confounding results, (iii) produces ecologically meaningful results (i.e. the proposed approach showed significant differences in organic matter use capabilities among flow conditions and depth), and (iv) unifies the analysis methodology for CLPPs data. With this work we provide an R-script to be used in future studies.