Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models
Article first published online: 25 JAN 2013
© 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society
Journal of Animal Ecology
Volume 82, Issue 3, pages 572–586, May 2013
How to Cite
Gautestad, A. O., Loe, L. E., Mysterud, A. (2013), Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models. Journal of Animal Ecology, 82: 572–586. doi: 10.1111/1365-2656.12027
- Issue published online: 15 APR 2013
- Article first published online: 25 JAN 2013
- Manuscript Accepted: 19 OCT 2012
- Manuscript Received: 21 MAR 2012
- Research Council of Norway. Grant Number: 179370/I10
- fractal geometry;
- Lévy walk;
- memory map;
- multi-scaled random walk;
- site fidelity
- Increased inference regarding underlying behavioural mechanisms of animal movement can be achieved by comparing GPS data with statistical mechanical movement models such as random walk and Lévy walk with known underlying behaviour and statistical properties.
- GPS data are typically collected with ≥1 h intervals not exactly tracking every mechanistic step along the movement path, so a statistical mechanical model approach rather than a mechanistic approach is appropriate. However, comparisons require a coherent framework involving both scaling and memory aspects of the underlying process. Thus, simulation models have recently been extended to include memory-guided returns to previously visited patches, that is, site fidelity.
- We define four main classes of movement, differing in incorporation of memory and scaling (based on respective intervals of the statistical fractal dimension D and presence/absence of site fidelity). Using three statistical protocols to estimate D and site fidelity, we compare these main movement classes with patterns observed in GPS data from 52 females of red deer (Cervus elaphus).
- The results show best compliance with a scale-free and memory-enhanced kind of space use; that is, a power law distribution of step lengths, a fractal distribution of the spatial scatter of fixes and site fidelity.
- Our study thus demonstrates how inference regarding memory effects and a hierarchical pattern of space use can be derived from analysis of GPS data.