Abstract
Animal movement exhibits self-similarity across a range of both spatial and temporal scales reminiscent of statistical fractals. Stressors are known to induce changes in these statistical patterns of behavior, although the direction and interpretation of such changes are not always clear. We examined whether the imposition of known hydrodynamic disruptors, bio-logging devices and flipper bands, induces changes in the temporal organization (complexity) of foraging sequences in two penguin species, little penguins (Eudyptula minor) and Adelie penguins (Pygoscelis adeliae). Detrended fluctuation analysis showed that foraging sequences produced by little penguins carrying larger loggers were more complex, i.e., were more erratic tending toward greater stochasticity, than those carrying smaller loggers. However, logger size did not affect complexity in foraging sequences of Adelie penguins. Logger position was associated only weakly with altered complexity in little penguins, with individuals carrying loggers in the middle of their backs displaying slightly more complex dive sequences than those carrying loggers lower on their backs. Finally, despite their known negative effects on penguin fitness, flipper bands were not associated with dive sequence complexity in little penguins. Despite that externally attached devices can disrupt certain behavioral parameters in diving seabirds, we found mixed evidence in support of the hypothesis that such devices significantly disrupt the time-structured organizational properties of foraging sequences in the two penguin species investigated. However, smaller species carrying larger loggers, and perhaps those positioned higher on their backs, may experience an added element of noise in their behavioral sequences that may indicate a departure from foraging behavior observed under normal, unburdened conditions.
Highlights
Animal movement exhibits self-similarity across a range of both spatial and temporal scales reminis‐ cent of statistical fractals
Adélie penguins exhibited higher mean values of αDFA ranging between 0.91 and 0.98. These values indicate that dive sequences are long-range dependent and resemble persistent fractional Gaussian noise, as shown previously [30, 37]
We observed a significant difference between dive sequences produced by little penguins carrying loggers of different sizes (Table 1; General Linear Mixed effects models (GLMM): αDFA, df = 21, t = 2.22, p = 0.04; mean αDFA large logger = 0.85 ± 0.008; mean αDFA small logger = 0.94 ± 0.008): little penguins carrying larger loggers exhibited lower values of αDFA, reflecting greater stochasticity in dive sequences than those carrying smaller loggers
Summary
Animal movement exhibits self-similarity across a range of both spatial and temporal scales reminis‐ cent of statistical fractals. Such patterns are known to emerge in spatial and temporal sequences of animal movement, which exhibits. Physiological or behavioral changes can impact the complexity observed in time series data collected from diverse systems [18]. These deviations from normal behavioral patterns in nonlinear systems, known as ‘complexity loss’, were first observed in physiological systems producing heart rate variability [22], stride patterns [23] and neural activity [24]: pathological systems produce times series with altered complexity signatures. Complexity loss, as far as it has been detected in altered behavior sequences, is predicted to reduce an animal’s fitness long term
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