Hydrodynamic handicaps and organizational complexity in the foraging behavior of two free-ranging penguin species

Abstract

Background: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 Adélie penguins (Pygoscelis adeliae).Results: 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 Adélie 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. Conclusions: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.

Oceanic thermal structure mediates dive sequences in a foraging seabird

“…We observed a strong correlation between the two DFA results at each breeding stage (incubation: r = .95, p < .001; guard: r = .98, p < .001; postguard: r = .98, p < .001). Thus, both methods converged to indicate that dive sequences from foraging little penguins are best characterized as persistent, long‐range dependent fractional Gaussian noise (Figure 3), in accordance with previous studies on penguins (Cottin et al., 2014; Le Guen et al., 2018; MacIntosh et al., 2013; Meyer et al., 2015, 2017). The observed best‐scaling regions were similar for the three stages (incubation, guard and postguard) and included the scales 2 7 –2 11 , or 128–2048 s.…”

“…Previous studies have shown that dive sequences from foraging penguins are best characterized as persistent, long‐range dependent fractional Gaussian noise (Cottin et al., 2014; Le Guen et al., 2018; MacIntosh, Pelletier, Chiaradia, Kato, & Ropert‐Coudert, 2013; Meyer et al., 2017; Meyer, MacIntosh, Kato, Chiaradia, & Ropert‐Coudert, 2015). In other words, dive and postdive durations of a given length are typically followed by dive and postdive durations of a similar length, with such patterns of fluctuation between these two behavioral states persisting across a range of measurement scales.…”

Reproductive performance and diving behaviour share a common sea‐ice concentration optimum in Adélie penguins (Pygoscelis adeliae)

“…Concerning the fractal analysis, values of α DFA were similar to those reported previously for Adélie penguins at 0.94 ± 0.005 (Meyer et al., ), indicating that foraging time series were long‐range dependent and persistent ( α DFA >0.5, i.e. dive and post‐dive times of a given length were more likely to be followed by dive and postdive times of similar lengths).…”

“…() and applied to Adélie penguins in Cottin et al. () and Meyer, MacIntosh, Kato, Chiaradia, and Ropert‐Coudert (), we used Detrended Fluctuation Analysis (DFA; Peng et al., ) to measure long‐range dependence in the sequential distribution of dives and surface times as an indicator of complexity in individual diving sequences. We performed DFA using the “fractal” package (Constantine & Percival, ) in R to estimate the scaling exponents ( α DFA ) of these sequences (Peng et al., ), which measures the degree to which time series are long‐range dependent and statistically self‐similar (Taqqu, Teverovsky, & Willinger, ).…”

Recent prey capture experience and dynamic habitat quality mediate short-term foraging site fidelity in a seabird

“…Penguins were equipped with a GPS logger (CatTrack, South Carolina, USA) recording a position every 2 -6 s, and an accelerometer data logger (G6aþ, CEFAS Technology Pty Ltd, Suffolk, UK) recording tri-axial acceleration and depth at a frequency of 30 Hz during dives greater than 1.5 m. The combined tag weight was 62 g in air, less than 6% of the mean bodyweight (1038.7 g + 8.9 in this study) and the proportional cross-sectional area was less than 3.5%, an important consideration for logger effects on penguins due to the influence of drag [25,26]. For further details regarding tag specifications and attachment protocols see Carroll et al [20,21].…”