@article{VilkAghionNathanetal.2022,
  author    = {Vilk, Ohad and Aghion, Erez and Nathan, Ran and Toledo, Sivan and Metzler, Ralf and Assaf, Michael},
  title     = {Classification of anomalous diffusion in animal movement data using power spectral analysis},
  series = {Journal of physics : A, Mathematical and theoretical},
  volume    = {55},
  journal   = {Journal of physics : A, Mathematical and theoretical},
  number    = {33},
  publisher = {IOP Publishing},
  address   = {Bristol},
  issn      = {1751-8113},
  doi       = {10.1088/1751-8121/ac7e8f},
  pages     = {16},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{VilkCamposMendezetal.2022,
  author    = {Vilk, Ohad and Campos, Daniel and M{\´e}ndez, Vicen{\c{c}} and Lourie, Emmanuel and Nathan, Ran and Assaf, Michael},
  title     = {Phase transition in a non-Markovian animal exploration model with preferential returns},
  series = {Physical review letters},
  volume    = {128},
  journal   = {Physical review letters},
  number    = {14},
  publisher = {American Physical Society},
  address   = {College Park},
  issn      = {0031-9007},
  doi       = {10.1103/PhysRevLett.128.148301},
  pages     = {7},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@article{NathanMonkArlinghausetal.2022,
  author    = {Nathan, Ran and Monk, Christopher T. and Arlinghaus, Robert and Adam, Timo and Al{\´o}s, Josep and Assaf, Michael and Baktoft, Henrik and Beardsworth, Christine E. and Bertram, Michael G. and Bijleveld, Allert and Brodin, Tomas and Brooks, Jill L. and Campos-Candela, Andrea and Cooke, Steven J. and Gjelland, Karl O. and Gupte, Pratik R. and Harel, Roi and Hellstrom, Gustav and Jeltsch, Florian and Killen, Shaun S. and Klefoth, Thomas and Langrock, Roland and Lennox, Robert J. and Lourie, Emmanuel and Madden, Joah R. and Orchan, Yotam and Pauwels, Ine S. and Riha, Milan and R{\"o}leke, Manuel and Schl{\"a}gel, Ulrike and Shohami, David and Signer, Johannes and Toledo, Sivan and Vilk, Ohad and Westrelin, Samuel and Whiteside, Mark A. and Jaric, Ivan},
  title     = {Big-data approaches lead to an increased understanding of the ecology of animal movement},
  series = {Science},
  volume    = {375},
  journal   = {Science},
  number    = {6582},
  publisher = {American Assoc. for the Advancement of Science},
  address   = {Washington},
  issn      = {0036-8075},
  doi       = {10.1126/science.abg1780},
  pages     = {734 -- +},
  year      = {2022},
  abstract  = {Understanding animal movement is essential to elucidate how animals interact, survive, and thrive in a changing world. Recent technological advances in data collection and management have transformed our understanding of animal "movement ecology" (the integrated study of organismal movement), creating a big-data discipline that benefits from rapid, cost-effective generation of large amounts of data on movements of animals in the wild. These high-throughput wildlife tracking systems now allow more thorough investigation of variation among individuals and species across space and time, the nature of biological interactions, and behavioral responses to the environment. Movement ecology is rapidly expanding scientific frontiers through large interdisciplinary and collaborative frameworks, providing improved opportunities for conservation and insights into the movements of wild animals, and their causes and consequences.},
  language  = {en}
}
@article{VilkAghionAvgaretal.2022,
  author    = {Vilk, Ohad and Aghion, Erez and Avgar, Tal and Beta, Carsten and Nagel, Oliver and Sabri, Adal and Sarfati, Raphael and Schwartz, Daniel K. and Weiß, Matthias and Krapf, Diego and Nathan, Ran and Metzler, Ralf and Assaf, Michael},
  title     = {Unravelling the origins of anomalous diffusion},
  series = {Physical Review Research},
  volume    = {4},
  journal   = {Physical Review Research},
  number    = {3},
  publisher = {American Physical Society},
  address   = {College Park, MD},
  issn      = {2643-1564},
  doi       = {10.1103/PhysRevResearch.4.033055},
  pages     = {033055-1 -- 033055-16},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}