TY  - JOUR
A1  - Vilk, Ohad
A1  - Aghion, Erez
A1  - Avgar, Tal
A1  - Beta, Carsten
A1  - Nagel, Oliver
A1  - Sabri, Adal
A1  - Sarfati, Raphael
A1  - Schwartz, Daniel K.
A1  - Weiß, Matthias
A1  - Krapf, Diego
A1  - Nathan, Ran
A1  - Metzler, Ralf
A1  - Assaf, Michael
T1  - Unravelling the origins of anomalous diffusion
BT  - from molecules to migrating storks
JF  - Physical Review Research
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.
Y1  - 2022
U6  - https://doi.org/10.1103/PhysRevResearch.4.033055
SN  - 2643-1564
VL  - 4
IS  - 3
SP  - 033055-1
EP  - 033055-16
PB  - American Physical Society
CY  - College Park, MD
ER  - 
TY  - JOUR
A1  - Vilk, Ohad
A1  - Campos, Daniel
A1  - Méndez, Vicenç
A1  - Lourie, Emmanuel
A1  - Nathan, Ran
A1  - Assaf, Michael
T1  - Phase transition in a non-Markovian animal exploration model with preferential returns
JF  - Physical review letters
N2  - We study a non-Markovian and nonstationary model of animal mobility incorporating both exploration and memory in the form of preferential returns. Exact results for the probability of visiting a given number of sites are derived and a practical WKB approximation to treat the nonstationary problem is developed. A mean-field version of this model, first suggested by Song et al., [Modelling the scaling properties of human mobility, Nat. Phys. 6, 818 (2010)] was shown to well describe human movement data. We show that our generalized model adequately describes empirical movement data of Egyptian fruit bats (Rousettus aegyptiacus) when accounting for interindividual variation in the population. We also study the probability of visiting any site a given number of times and derive a mean-field equation. Our analysis yields a remarkable phase transition occurring at preferential returns which scale linearly with past visits. Following empirical evidence, we suggest that this phase transition reflects a trade-off between extensive and intensive foraging modes.
Y1  - 2022
U6  - https://doi.org/10.1103/PhysRevLett.128.148301
SN  - 0031-9007
SN  - 1079-7114
VL  - 128
IS  - 14
PB  - American Physical Society
CY  - College Park
ER  - 
TY  - JOUR
A1  - Vilk, Ohad
A1  - Aghion, Erez
A1  - Nathan, Ran
A1  - Toledo, Sivan
A1  - Metzler, Ralf
A1  - Assaf, Michael
T1  - Classification of anomalous diffusion in animal movement data using power spectral analysis
JF  - Journal of physics : A, Mathematical and theoretical
N2  - The field of movement ecology has seen a rapid increase in high-resolution data in recent years, leading to the development of numerous statistical and numerical methods to analyse relocation trajectories. Data are often collected at the level of the individual and for long periods that may encompass a range of behaviours. 

Here, we use the power spectral density (PSD) to characterise the random movement patterns of a black-winged kite (Elanus caeruleus) and a white stork (Ciconia ciconia). The tracks are first segmented and clustered into different behaviours (movement modes), and for each mode we measure the PSD and the ageing properties of the process. 

For the foraging kite we find 1/f noise, previously reported in ecological systems mainly in the context of population dynamics, but not for movement data. We further suggest plausible models for each of the behavioural modes by comparing both the measured PSD exponents and the distribution of the single-trajectory PSD to known theoretical results and simulations.
KW  - diffusion
KW  - anomalous diffusion
KW  - power spectral analysis
KW  - ecological
KW  - movement data
Y1  - 2022
U6  - https://doi.org/10.1088/1751-8121/ac7e8f
SN  - 1751-8113
SN  - 1751-8121
VL  - 55
IS  - 33
PB  - IOP Publishing
CY  - Bristol
ER  -