Anomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in processes ranging from microscopic movement of molecules to macroscopic, large-scale paths of migrating birds. Using data from multiple empirical systems, spanning 12 orders of magnitude in length and 8 orders of magnitude in time, we employ a method to detect the individual underlying origins of anomalous diffusion and transport in the data. This method decomposes anomalous transport into three primary effects: long-range correlations (Joseph effect), fat-tailed probability density of increments (Noah effect), and non-stationarity (Moses effect). We show that such a decomposition of real-life data allows to infer nontrivial behavioral predictions, and to resolve open questions in the fields of single particle cell tracking and movement ecology.
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