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  - 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  -