TY - JOUR A1 - Hindes, Jason A1 - Assaf, Michael A1 - Schwartz, Ira B. T1 - Outbreak size distribution in stochastic epidemic models JF - Physical review letters N2 - Motivated by recent epidemic outbreaks, including those of COVID-19, we solve the canonical problem of calculating the dynamics and likelihood of extensive outbreaks in a population within a large class of stochastic epidemic models with demographic noise, including the susceptible-infected-recovered (SIR) model and its general extensions. In the limit of large populations, we compute the probability distribution for all extensive outbreaks, including those that entail unusually large or small (extreme) proportions of the population infected. Our approach reveals that, unlike other well-known examples of rare events occurring in discrete-state stochastic systems, the statistics of extreme outbreaks emanate from a full continuum of Hamiltonian paths, each satisfying unique boundary conditions with a conserved probability flux. Y1 - 2022 U6 - https://doi.org/10.1103/PhysRevLett.128.078301 SN - 0031-9007 SN - 1079-7114 SN - 1092-0145 VL - 128 IS - 7 PB - American Physical Society CY - College Park, Md. ER - TY - JOUR A1 - Nathan, Ran A1 - Monk, Christopher T. A1 - Arlinghaus, Robert A1 - Adam, Timo A1 - Alós, Josep A1 - Assaf, Michael A1 - Baktoft, Henrik A1 - Beardsworth, Christine E. A1 - Bertram, Michael G. A1 - Bijleveld, Allert A1 - Brodin, Tomas A1 - Brooks, Jill L. A1 - Campos-Candela, Andrea A1 - Cooke, Steven J. A1 - Gjelland, Karl O. A1 - Gupte, Pratik R. A1 - Harel, Roi A1 - Hellstrom, Gustav A1 - Jeltsch, Florian A1 - Killen, Shaun S. A1 - Klefoth, Thomas A1 - Langrock, Roland A1 - Lennox, Robert J. A1 - Lourie, Emmanuel A1 - Madden, Joah R. A1 - Orchan, Yotam A1 - Pauwels, Ine S. A1 - Riha, Milan A1 - Röleke, Manuel A1 - Schlägel, Ulrike A1 - Shohami, David A1 - Signer, Johannes A1 - Toledo, Sivan A1 - Vilk, Ohad A1 - Westrelin, Samuel A1 - Whiteside, Mark A. A1 - Jaric, Ivan T1 - Big-data approaches lead to an increased understanding of the ecology of animal movement JF - Science N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1126/science.abg1780 SN - 0036-8075 SN - 1095-9203 VL - 375 IS - 6582 SP - 734 EP - + PB - American Assoc. for the Advancement of Science CY - Washington ER - 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 - GEN 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 T2 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1303 Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-577643 SN - 1866-8372 IS - 1303 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 - 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 -