Fig. 2

Classification of time series data. First, noise-type distributions are established. Darker noise colors indicate increasing temporal dependence between time points. White noise suggests random processes without temporal structure that can be caused for instance by technical bias such as insufficient sampling density or too large measurement noise, or reflect an intrinsic lack of time structure. Second, the presence of neutral dynamics are tested. A positive test result (p value below the significance level) suggests a potential for interactions in the microbial community and can be followed up for instance by fitting community models that assume interactions, by network analysis or by causal model approaches. For high levels of external noise or too large sampling intervals, the neutrality test may yield a false positive outcome. In neutral communities, variation can be suitably analyzed within stochastic frameworks