Assessment of long-range correlation in animal behavior time series: The temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus)

Abstract

Assessment of long-range correlation in animal behaviour time series: the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus). AbstractThe aim of this study was to evaluate the performance of a classical method of fractal analysis, Detrended Fluctuation Analysis (DFA), in the analysis of the dynamics of animal behavior time series. In order to correctly use DFA to assess the presence of long-range correlation, previous authors using statistical model systems have stated that different aspects should be taken into account such as: 1) the establishment by hypothesis testing of the absence of short term correlation, 2) an accurate estimation of a straight line in the log-log plot of the fluctuation function, 3) the elimination of artificial crossovers in the fluctuation function, and 4) the length of the time series. Taking into consideration these factors, herein we evaluated the presence of long-range correlation in the temporal pattern of locomotor activity of Japanese quail (Coturnix coturnix) and mosquito larva (Culex quinquefasciatus). In our study, modeling the data with the general ARFIMA model, we rejected the hypothesis of short range correlations (d=0) in all cases. We also observed that DFA was able to distinguish between the artificial crossover observed in the temporal pattern of locomotion of Japanese quail, and the crossovers in the correlation behavior observed in mosquito larvae locomotion. Although the test duration can slightly influence the parameter estimation, no qualitative differences were observed between different test durations.

The fractal organization of ultradian rhythms in avian behavior

“…64 and is described in detail elsewhere 18, 55 and in section SI Detrended Fluctuation Analysis. Briefly, DFA estimates the self-similarity parameter α that measures the autocorrelation structure of the time series.…”

C. elegans episodic swimming is driven by multifractal kinetics

“…Fractal geometry provides a mathematical framework for characterizing scale-free and self-similar patterns 3 – 5 . Fractal temporal patterns of behavior and physiology have been reported in the locomotion of birds, mosquito larvae, and flies 6 – 8 , crawling of cultured C. elegans worms 9 , clicking sounds produced by feeding sea horses 10 , and swimming of zooplankton 11 , 12 . In humans, temporal fractal patterns have been observed in wrist movements during habitual sleep/wake cycles 13 , gait 14 , 15 , heartbeats 16 , 17 , and brain activity 18 , 19 .…”

C. elegansepisodic swimming is driven by multifractal kinetics

“…A scale-free distribution of actively-moving and inactive residence times has been reported for episodic Drosophila behavior 9 ; modeling suggested that the scale-free nature of these residence times contributes to maximizing the food exploitation area while optimizing food intake time. Previous analyses have shown long-range temporal correlations in scale-free properties of the behaviors of many species, including fractal (but not multifractal) analysis of C. elegans crawling 7,8,10–13 . The fractal nature of C. elegans crawling in agar (Alves et al, 2017) is consistent with our long-range memory and clusterization finding.…”

“…Fractal geometry provides a mathematical framework for characterizing scale-free and self-similar patterns 4–6 . Fractal temporal patterns of behavior and physiology have been reported in the locomotion of birds, mosquito larvae, and flies 7–9 , crawling of cultured C. elegans worms 10 , clicking sounds produced by feeding sea horses 11 , and swimming of zooplankton 12,13 . In humans, temporal fractal patterns have been observed in wrist movements during habitual sleep/wake cycles 14 , gait 15,16 , heartbeats 17,18 , and brain activity 19,20 .…”