TY  - JOUR
A1  - Schlägel, Ulrike E.
A1  - Signer, Johannes
A1  - Herde, Antje
A1  - Eden, Sophie
A1  - Jeltsch, Florian
A1  - Eccard, Jana
A1  - Dammhahn, Melanie
T1  - Estimating interactions between individuals from concurrent animal movements
JF  - Methods in ecology and evolution : an official journal of the British Ecological Society
N2  - Animal movements arise from complex interactions of individuals with their environment, including both conspecific and heterospecific individuals. Animals may be attracted to each other for mating, social foraging, or information gain, or may keep at a distance from others to avoid aggressive encounters related to, e.g., interference competition, territoriality, or predation. With modern tracking technology, more datasets are emerging that allow to investigate fine‐scale interactions between free‐ranging individuals from movement data, however, few methods exist to disentangle fine‐scale behavioural responses of interacting individuals when these are highly individual‐specific.
In a framework of step‐selection functions, we related movements decisions of individuals to dynamic occurrence distributions of other individuals obtained through kriging of their movement paths. Using simulated data, we tested the method's ability to identify various combinations of attraction, avoidance, and neutrality between individuals, including asymmetric (i.e. non‐mutual) behaviours. Additionally, we analysed radio‐telemetry data from concurrently tracked small rodents (bank vole, Myodes glareolus) to test whether our method could detect biologically plausible behaviours.
We found that our method was able to successfully detect and distinguish between fine‐scale interactions (attraction, avoidance, neutrality), even when these were asymmetric between individuals. The method worked best when confounding factors were taken into account in the step‐selection function. However, even when failing to do so (e.g. due to missing information), interactions could be reasonably identified. In bank voles, responses depended strongly on the sexes of the involved individuals and matched expectations.
Our approach can be combined with conventional uses of step‐selection functions to tease apart the various drivers of movement, e.g. the influence of the physical and the social environment. In addition, the method is particularly useful in studying interactions when responses are highly individual‐specific, i.e. vary between and towards different individuals, making our method suitable for both single‐species and multi‐species analyses (e.g. in the context of predation or competition).
KW  - attraction-avoidance
KW  - fine-scale interactions
KW  - individual variability
KW  - inter-specific interactions
KW  - movement behaviour
KW  - occurrence estimates
KW  - social environment
KW  - step-selection function
Y1  - 2019
U6  - https://doi.org/10.1111/2041-210X.13235
SN  - 2041-210X
SN  - 2041-2096
VL  - 10
IS  - 8
SP  - 1234
EP  - 1245
PB  - Wiley
CY  - Hoboken
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  -