TY - JOUR
T1 - Unravelling the origins of anomalous diffusion
T2 - From molecules to migrating storks
AU - Vilk, Ohad
AU - Aghion, Erez
AU - Avgar, Tal
AU - Beta, Carsten
AU - Nagel, Oliver
AU - Sabri, Adal
AU - Sarfati, Raphael
AU - Schwartz, Daniel K.
AU - Weiss, Matthias
AU - Krapf, Diego
AU - Nathan, Ran
AU - Metzler, Ralf
AU - Assaf, Michael
N1 - Publisher Copyright:
© 2022 authors. Published by the American Physical Society.
PY - 2022/7
Y1 - 2022/7
N2 - 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 nonstationarity ("Moses effect"). We show that such a decomposition of real-life data allows us to infer nontrivial behavioral predictions and to resolve open questions in the fields of single-particle tracking in living cells and movement ecology.
AB - 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 nonstationarity ("Moses effect"). We show that such a decomposition of real-life data allows us to infer nontrivial behavioral predictions and to resolve open questions in the fields of single-particle tracking in living cells and movement ecology.
UR - http://www.scopus.com/inward/record.url?scp=85135912088&partnerID=8YFLogxK
U2 - 10.1103/PhysRevResearch.4.033055
DO - 10.1103/PhysRevResearch.4.033055
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AN - SCOPUS:85135912088
SN - 2643-1564
VL - 4
JO - Physical Review Research
JF - Physical Review Research
IS - 3
M1 - 033055
ER -