Dataset Information

From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.

ABSTRACT: High-resolution animal location data are increasingly available, requiring analytical approaches and statistical tools that can accommodate the temporal structure and transient dynamics (non-stationarity) inherent in natural systems. Traditional analyses often assume uncorrelated or weakly correlated temporal structure in the velocity (net displacement) time series constructed using sequential location data. We propose that frequency and time-frequency domain methods, embodied by Fourier and wavelet transforms, can serve as useful probes in early investigations of animal movement data, stimulating new ecological insight and questions. We introduce a novel movement model with time-varying parameters to study these methods in an animal movement context. Simulation studies show that the spectral signature given by these methods provides a useful approach for statistically detecting and characterizing temporal dependency in animal movement data. In addition, our simulations provide a connection between the spectral signatures observed in empirical data with null hypotheses about expected animal activity. Our analyses also show that there is not a specific one-to-one relationship between the spectral signatures and behavior type and that departures from the anticipated signatures are also informative. Box plots of net displacement arranged by time of day and conditioned on common spectral properties can help interpret the spectral signatures of empirical data. The first case study is based on the movement trajectory of a lion (Panthera leo) that shows several characteristic daily activity sequences, including an active-rest cycle that is correlated with moonlight brightness. A second example based on six pairs of African buffalo (Syncerus caffer) illustrates the use of wavelet coherency to show that their movements synchronize when they are within approximately 1 km of each other, even when individual movement was best described as an uncorrelated random walk, providing an important spatial baseline of movement synchrony and suggesting that local behavioral cues play a strong role in driving movement patterns. We conclude with a discussion about the role these methods may have in guiding appropriately flexible probabilistic models connecting movement with biotic and abiotic covariates.

SUBMITTER: Polansky L

PROVIDER: S-EPMC3032889 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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Publications

From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.

Polansky Leo L Wittemyer George G Cross Paul C PC Tambling Craig J CJ Getz Wayne M WM

Ecology 20100501 5

High-resolution animal location data are increasingly available, requiring analytical approaches and statistical tools that can accommodate the temporal structure and transient dynamics (non-stationarity) inherent in natural systems. Traditional analyses often assume uncorrelated or weakly correlated temporal structure in the velocity (net displacement) time series constructed using sequential location data. We propose that frequency and time-frequency domain methods, embodied by Fourier and wav ...[more]

PMID: 20503882

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Project description:BackgroundAnimal movement is a behavioral trait shaped by the need to find food and suitable habitat, avoid predators, and reproduce. Using high-resolution tracking data, it is possible to describe movement in greater detail than ever before, which has led to many discoveries about the behavioral strategies of particular species. Recently, enough data been become available to enable a comparative approach, which has the potential to uncover general causes and consequences of variation in movement patterns, but which must be scale specific.MethodsHere we introduce a new multi-scale movement syndrome (MSMS) framework for describing and comparing animal movements and use it to explore the behavior of four sympatric mammals. MSMS incorporates four hierarchical scales of animal movement: (1) fine-scale movement steps which accumulate into (2) daily paths which then, over weeks or months, form a (3) life-history phase. Finally, (4) the lifetime track of an individual consists of multiple life-history phases connected by dispersal or migration events. We suggest a series of metrics to describe patterns of movement at each of these scales and use the first three scales of this framework to compare the movement of 46 animals from four frugivorous mammal species.ResultsWhile subtle differences exist between the four species in their step-level movements, they cluster into three distinct movement syndromes in both path- and life-history phase level analyses. Differences in feeding ecology were a better predictor of movement patterns than a species' locomotory or sensory adaptations.ConclusionsGiven the role these species play as seed dispersers, these movement syndromes could have important ecosystem implications by affecting the pattern of seed deposition. This multiscale approach provides a hierarchical framework for comparing animal movement for addressing ecological and evolutionary questions. It parallels scales of analyses for resource selection functions, offering the potential to connect movement process with emergent patterns of space use.

Dataset Information

From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.

Publications

From moonlight to movement and synchronized randomness: Fourier and wavelet analyses of animal location time series data.

Similar Datasets

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