Elephants in space and time

Abstract

2005. Elephants in space and time. Á/ Oikos 109: 331 Á/341.Autocorrelation in animal movements can be both a serious nuisance to analysis and a source of valuable information about the scale and patterns of animal behavior, depending on the question and the techniques employed. In this paper we present an approach to analyzing the patterns of autocorrelation in animal movements that provides a detailed picture of seasonal variability in the scale and patterns of movement. We used a combination of moving window Mantel correlograms, surface correlation and crosscorrelation analysis to investigate the scales and patterns of autocorrelation in the movements of three herds of elephants in northern Botswana. Patterns of autocorrelation of elephant movements were long-range, temporally complicated, seasonally variable, and closely linked with the onset of rainfall events. Specifically, for the three elephant herds monitored there was often significant autocorrelation among locations up to lags of 30 days or more. During many seasonal periods there was no indication of decreasing autocorrelation with increasing time between locations. Over the course of the year, herds showed highly variable and complex patterns of autocorrelation, ranging from random use of temporary home ranges, periodic use of focal areas, and directional migration. Even though the patterns of autocorrelation were variable in time and quite complex, there were highly significant correlations among the autocorrelation patterns of the different herds, indicating that they exhibited similar patterns of movement through the year. These major patterns of autocorrelation seem to be related to patterns of rainfall. The strength of correlation in movement patterns of the different herds decreased markedly at the cessation of major rain events. Also, there was a strong crosscorrelation between strength of autocorrelation of movement and rainfall, peaking at time lags of between three and four weeks. Overall, these approaches provide a powerful way to explore the scales and patterns of autocorrelation of animal movements, and to explicitly link those patterns to temporally variable environmental attributes, such as rainfall or vegetation phenology.

Temporal autocorrelation functions for movement rates from global positioning system radiotelemetry data

“…Cross-correlation of steplength data with environmental data (e.g. rainfall) has been used to unravel some of the underlying causes of autocorrelation structure in radiotelemetry data (Cushman et al 2005;Wittemyer et al 2008). ACFs and related time-series methods such as spectral analysis only point to patterns in the data.…”

Area utilization patterns of humpback dolphins (Sousa plumbea) in Richards Bay, KwaZulu-Natal, South Africa

“…(see Hurlbert 1984;Legendre et al 2002;Millar and Anderson 2004;Crawley 2007;Corkeron et al 2011;. Subsampling of datasets is often used to reduce autocorrelation, although De Solla et al (1999) finds this to be less effective than alternative approaches, and Cushman et al (2005) and indicate that trying to attain statistical independence through subsampling ''incurs heavy costs in terms of information loss''. Mixed effect models are said to be a better choice when dealing with lack of independence in the data (Millar and Anderson 2004;Dormann et al 2007;Chaves 2010;Hegel et al 2010), and the random factor structure accounts better for autocorrelated error variances (Økland 2007) and reduces overall variance (Hegel et al 2010).…”

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

“…Treating cyclic (strongly repetitive) patterns of movement as behavioral signals can offer new insight into the factors impacting population organization and the spatial ecology of animals (12). Relating these movement cycles, detectable through autocorrelation in step lengths or other movement properties, to the ecological context in which they occur can provide insights into the internal states of individuals and serve to identify salient motivations of recognizable canonical activity modes (e.g., foraging vs. heading for water) (1,2).…”