Disentangling the effects of forage, social rank, and risk on movement autocorrelation of elephants using Fourier and wavelet analyses

Abstract

The internal state of an individual-as it relates to thirst, hunger, fear, or reproductive drive-can be inferred by referencing points on its movement path to external environmental and sociological variables. Using time-series approaches to characterize autocorrelative properties of step-length movements collated every 3 h for seven free-ranging African elephants, we examined the influence of social rank, predation risk, and seasonal variation in resource abundance on periodic properties of movement. The frequency domain methods of Fourier and wavelet analyses provide compact summaries of temporal autocorrelation and show both strong diurnal and seasonal based periodicities in the step-length time series. This autocorrelation is weaker during the wet season, indicating random movements are more common when ecological conditions are good. Periodograms of socially dominant individuals are consistent across seasons, whereas subordinate individuals show distinct differences diverging from that of dominants during the dry season. We link temporally localized statistical properties of movement to landscape features and find that diurnal movement correlation is more common within protected wildlife areas, and multiday movement correlations found among lower ranked individuals are typically outside of protected areas where predation risks are greatest. A frequency-related spatial analysis of movement-step lengths reveal that rest cycles related to the spatial distribution of critical resources (i.e., forage and water) are responsible for creating the observed patterns. Our approach generates unique information regarding the spatial-temporal interplay between environmental and individual characteristics, providing an original approach for understanding the movement ecology of individual animals and the spatial organization of animal populations.behavior ͉ foraging ͉ GPS data ͉ predation ͉ movement ecology C haracteristics of an animal's movement path offer insights into the external influences and internal states of an individual across time and space (1, 2). Scaled across a population or ecosystem, such information can offer new understanding of the salient factors driving the spatiotemporal structure of populations (3, 4). The methods used to garner this information have been the focus of research in the field of ecology for some time (5-7). Perhaps the most common of these methods involves analyzing correlations among consecutive displacements (i.e., step length or approximate velocity) and turning angles in the context of random walk models (4, 7). The strength of first-order autocorrelations in the directionality of movement has been used to categorize foraging strategy (8-10), whereas potential information contained in the autocorrelative structure of step lengths has largely been ignored. Given the existence of such structure in our data, as revealed by the analyses presented here, simple random walk models are clearly inadequate for characterizing movement in some animals. Our approach provides unique insight in...

Fractal reorientation clocks: Linking animal behavior to statistical patterns of search

“…32) or by learning processes occurring in predictable and static ecological conditions, which typically generate highly repetitive and cyclical movement behavior (e.g., ref. 33). …”

A movement ecology paradigm for unifying organismal movement research

“…It is, however, a logistical challenge to identify the key external and internal factors influencing movement in larger free-ranging organisms. Studies in this Special Feature exemplify how these challenges can be addressed: by using heart-rate transmitters attached to migrating vultures to assess their internal state (28); by raising individual butterflies from different source populations in common garden conditions and comparing their movement paths (34); by using time-series analyses to compare movement paths of individual elephants of known social status in different seasons and environmental conditions (27); and by assigning a seed aerodynamic trait and using the timing of seed release as proxies for motion and navigation capacities, respectively, in a study of wind-dispersed tree seeds (21).…”

A framework for generating and analyzing movement paths on ecological landscapes

“…We have not dealt in any detail with the problem of movement mode identification that is the key to the deconstruction component other than stressing that the frequency with which data are collected limits our ability to identify CAMs and their underlying FMEs (12). To undertake such an analysis is not a trivial problem: it requires computationally complex methods that can only be successfully applied to high-resolution data, but a start has been made (33)(34)(35).…”

“…Furthermore, stochastic differential equation methods (42) can be used to construct vector fields from data on the contemporaneous movement of many individuals, and then thin plate splines can be used to fit potential fields to these vector fields to identify regions on the landscape that are either repealing or attracting the individuals at a particular time of day (2). Recently, techniques new to the field of movement ecology, such as wavelet analysis (33) and artificial neural networks (34), are being applied to obtain insights into the effects that the internal and environmental states of a system have on movement paths. Beyond these, as the resolution of movement and landscape data improves dramatically over the next decade, we should expect to see the application of state-space estimation methods (32) that can take advantage of formulations such as ours, because our formulation permits the inclusion of detailed landscape information.…”