![]() ![]() Epinephelus ongus did not move substantially during the second phase of routine behaviour the mean vector sum of the angular velocities in the second phase (Mean MG-2) of the routine behaviour was lower than those of the crab-eating, fish-eating, escape and intra-escape behaviours (ANOVA, P<0.01 Tukey–Kramer test, P<0.05 Fig. Using this paradigm, we achieved high true identification rates (87.5% for both feeding behaviours) with low false identification rates (4.4% for crab-eating and 5.6% for fish-eating) for both feeding behaviours (Figs 1, 2, Tables 1, 2).Įpinephelus ongus exhibited larger pitch motions to pick up crabs ( supplementary material Movies 1–6) the ratio of the range of pitch angular velocity to the range of yaw angular velocity in the first phase (Range Pitch–1/Range Yaw–1) of the crab-eating behaviour was larger than that of the fish-eating, escape, intra-attack and intra-escape behaviours (ANOVA, P<0.01 Tukey–Kramer test, P<0.05 Fig. Finally, each of the feeding behaviours was identified by a decision tree using specific parameters (Figs 1, 2). Secondly, the featured parameters were calculated ( supplementary material Table S2) after extracting the subsequent 5 s of data and then dividing into the first phase (2.1 s) and second phase (2.9 s) (see Materials and methods and supplementary material Fig. ongus behaviours recorded, 17 crab-eating, 34 fish-eating, 42 escape responses (escape), nine intraspecific attacks (intra-attack), 27 intraspecific escapes (intra-escape) and 16 routine movements (routine) were detected by a set threshold (2.0 g) ( supplementary material Table S1), from a total of 17 crab-eating, 34 fish-eating, 42 escape, 48 intra-attack and 48 intra-escape behaviours recorded by a video camera. ongus feeding behaviours on both crabs (crab-eating) and fish (fish-eating) using the gyroscope/acceleration data logger. The results of this study indicate that we can successfully identify E. However, as far as we are aware, no studies have been conducted using this method on distinguishing prey types. In addition, it was found that the identification accuracy was greater if the data were obtained from a data logger that incorporated a gyroscope and an accelerometer compared with data from only an accelerometer was used ( Noda et al., 2013). ![]() A recent study suggested that it would be possible to identify feeding strikes of predatory fish if the sampling frequency was sufficiently high (>100 Hz) ( Broell et al., 2013). Acceleration data-loggers are a useful tool to categorize behaviours in free-ranging animals ( Campbell et al., 2013 Nathan et al., 2012 Sakamoto et al., 2009), but only a few studies have applied this technique to identify feeding behaviours of predators ( Broell et al., 2013 Naito et al., 2013 Noda et al., 2013 Watanabe and Takahashi, 2013). metabolic rate, cognitive ability, etc.) of animals. ethogram) is an essential step toward the understanding of interactions between behaviours and internal states (e.g. ![]() Using decision trees with the parameters, high true identification rates (87.5% for both feeding behaviours) with low false identification rates (5% for crab-eating and 6.3% for fish-eating) were achieved for both feeding behaviours.Ĭataloguing discrete behaviours (i.e. Each feeding behaviour was then identified using a combination of parameters that were derived from the extracted data. For each data-logger record, we extracted 5 s of data when any of the three-axis accelerations exceeded absolute 2.0 g, to capture all feeding behaviours and other burst behaviours. ongus individuals using data loggers sampling at 200 Hz, and were validated by simultaneously recorded video images. Feeding behaviours and other burst behaviours, including escape responses, intraspecific interactions and routine movements, were recorded from six E. ![]() We examined whether we could identify the feeding behaviours of the trophic generalist fish Epinephelus ongus on different prey types (crabs and fish) using a data logger that incorporated a three-axis gyroscope and a three-axis accelerometer. ![]()
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