Methods for assessing movement path recursion with application to African buffalo in South Africa

Abstract

Recent developments of automated methods for monitoring animal movement, e.g., global positioning systems (GPS) technology, yield high-resolution spatiotemporal data. To gain insights into the processes creating movement patterns, we present two new techniques for extracting information from these data on repeated visits to a particular site or patch ("recursions"). Identification of such patches and quantification of recursion pathways, when combined with patch-related ecological data, should contribute to our understanding of the habitat requirements of large herbivores, of factors governing their space-use patterns, and their interactions with the ecosystem.We begin by presenting output from a simple spatial model that simulates movements of largeherbivore groups based on minimal parameters: resource availability and rates of resource recovery after a local depletion. We then present the details of our new techniques of analyses (recursion analysis and circle analysis) and apply them to data generated by our model, as well as two sets of empirical data on movements of African buffalo (Syncerus caffer): the first collected in Klaserie Private Nature Reserve and the second in Kruger National Park, South Africa.Our recursion analyses of model outputs provide us with a basis for inferring aspects of the processes governing the production of buffalo recursion patterns, particularly the potential influence of resource recovery rate. Although the focus of our simulations was a comparison of

Recursive movement patterns: review and synthesis across species

“…These include bees (Thomson et al 1997, Williams andThomson 1998), birds (Gill 1988, Garrison andGass 1999), bats (Lemke 1984), primates (Watts 1998), ungulates (Edwards et al 1996, Bar-David et al 2009), large felines (Laundre 2010), and elephants (English et al 2014). Nevertheless, the wide scope and generality of this phenomenon may be still considerably underestimated by the scientific community.…”

Mining Periodicity from Dynamic and Incomplete Spatiotemporal Data

“…While period detection in 1-D time series has been long studied, with standard techniques such as fast Fourier transform (FFT) and auto-correlation existing in the literature, solution to the problem of detecting periods in 2-D spatiotemporal data remains largely unknown until the recent work [2]. In this work, the authors first describe an intuitive approach to identify recursions in movement data, and then propose an extension of the 1-D Fourier Transform, named complex Fourier transform (CFT), to detect circular movements from the input sequence.…”

“…In order to define a closed path, or a recursion, one needs to divide the landscape into a grid of patches (a 105 × 105 matrix is used in [2]). Then, a close path exists in the movement sequence if an exact (to the resolution of landscape discretization) recursion to a previous location at a later time is found.…”

“…It can be shown that Z( f ) provides an indication of the trend of circular motion, and can also be used to distinguish clockwise from counterclockwise patterns. Interested readers are referred to [2] for detailed illustrations and results of CFT.…”

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“…Recently, mining periodic patterns from mobility data has also received attention [162,168,169]. Use of signal processing techniques such as Fourier and wavelet transform was proposed in [174,175]. As shown in [162], such forms of signal processing approaches perform weakly in presence of noise which make them inapplicable on raw mobility data.…”