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.

社区水平的生理概况（CLPPs）早已被用作生态指标，因为它们确定了微生物群落的生理和代谢潜能，并表明了使其代谢适应人为压力源或环境条件变化的能力。同样，这些概况是微生物群落功能多样性或功能特征的代理，通常与生态系统功能相关。但是，CLPPs的统计数据分析已成为这些研究的主要障碍。具体而言，接种密度的干扰，选择最佳孵育时间的需求以及根据孵育时间点获得的不同配置文件限制了其用途。为此，我们开发了一种用于CLPP数据分析的新方法，该方法考虑了整个数据集以及整个潜伏期的颜色发展动态。此方法基于成分数据（CoDa）转换，该转换反映相对信息而不是绝对信息，然后重复测量方差的多变量分析和标准变量图中的数据表示。该方法已通过使用受季节，流量应力和不同沉积物深度条件影响的实际河流生物膜样品中的CLPPs进行了测试，并且已与迄今为止最经典的应用方法进行了比较（孵育固定点或曲线积分，然后进行原理分析）。组件分析）。我们的结果表明，建议的方法（i）使对CLPP的生态学解释更加敏感，（ii）澄清了CLPP之间的差异，即使在明显的季节性情况下也是如此，而经典方法提供了令人困惑的结果；（iii）产生了具有生态意义的结果（即，所提出的方法显示出流量条件和深度之间有机物利用能力的显着差异），以及（iv ）统一了CLPP数据的分析方法。通过这项工作，我们提供了R脚本供将来的研究中使用。