March 5-6, 2009, Bangkok, ThailandIn this study, the relationship between the breathing frequency and the dynamic body acceleration (DBA) of one hatchery-reared loggerhead turtle Carretta carretta was examined using acceleration data loggers. Two acceleration data loggers (M190L-D2GT, W1000-3MPD3GT, Little Leonard, Japan) were attached on the lower-beak and carapace of a hatchery-reared loggerhead turtle, respectively. Breathing was successfully detected from the angle and depth of the beak-attached data logger and DBA, which has been used as an index of activity levels (Wilson et al., 2006), was calculated from the forward acceleration of the carapace-attached logger. There was a positive correlation between the DBA in the previous dive and the breathing frequency; the relationship was exponential. The result suggests that the number of breaths increased exponentially after a more active dive

ARAI, NOBUAKI

KAWABATA, YUUKI

KOBAYASHI, MASATO

NAITO, YASUHIKO

OKUYAMA, JUNICHI

OKUZAWA, KOICHI

Kyoto University Research Information Repository

Title Preliminary result of the relationship between the breathingfrequency and dynamic body accelerationAuthor(s)KAWABATA, YUUKI; OKUYAMA, JUNICHI; NAITO,YASUHIKO; ARAI, NOBUAKI; KOBAYASHI, MASATO;OKUZAWA, KOICHICitationProceedings of the 5th International Symposium onSEASTAR2000 and Asian Bio-logging Science (The 9thSEASTAR2000 workshop) (2010): 11-14Issue Date2010-02URL http://hdl.handle.net/2433/107344RightType Conference PaperTextversionpublisherKyoto University11  Preliminary result of the relationship between the breathing frequency and dynamic body acceleration (DBA) of the hatchery-reared loggerhead turtle Carretta carretta  YUUKI KAWABATA1, JUNICHI OKUYAMA1, YASUHIKO NAITO2, NOBUAKI ARAI1, MASATO KOBAYASHI3, AND KOICHI OKUZAWA3 1Graduate School of Informatics, Kyoto University, 606-8501 Kyoto, Japan 2National Institute of Polar Research, 1-9-10, Kaga, Itabashi, Tokyo 173-8515, Japan 3Ishigaki Tropical Station, Seikai National Fisheries Research Institute, FRA, 907-0451 Okinawa, Japan  ABSTRACT In this study, the relationship between the breathing frequency and the dynamic body acceleration (DBA) of one hatchery-reared loggerhead turtle Carretta carretta was examined using acceleration data loggers. Two acceleration data loggers (M190L-D2GT, W1000-3MPD3GT, Little Leonard, Japan) were attached on the lower-beak and carapace of a hatchery-reared loggerhead turtle, respectively. Breathing was successfully detected from the angle and depth of the beak-attached data logger and DBA, which has been used as an index of activity levels (Wilson et al., 2006), was calculated from the forward acceleration of the carapace-attached logger. There was a positive correlation between the DBA in the previous dive and the breathing frequency; the relationship was exponential. The result suggests that the number of breaths increased exponentially after a more active dive.  KEYWORDS: acceleration data logger, breath, Carretta carretta, metabolic INTRODUCTION For air-breathing aquatic animals, breathing at the water surface is a limiting factor in all underwater activities such as foraging, resting, mating and predator-avoidance. Therefore, it is essential from the ecological point of view to understand dive-induced breathing patterns in aquatic animals. Dive-induced breathing patterns of sea turtles have been studied in captivity. So far, the relationship between the breathing frequency and the dive duration has been elucidated (Lutcavage and Lutz, 1991). In addition, the difference of breathing frequencies between active dives and resting dives has been understood (Lutz et al., 1989); however, no studies have been conducted on how the breathing frequency changes in accordance with activity levels. The information could provide a novel insight into the estimation of the energy expenditure and metabolic rate of the sea turtles, which can be used as key currencies in behavioral ecology (Krebs and Davies, 1993). The objective of this study was to understand how the breathing frequency changes with activity levels of loggerhead turtles Carretta carretta. To achieve the objective, we measured breathing and activity levels of the turtle simultaneously, using acceleration data loggers.       MATERIALS AND METHODS Sample turtle One immature hatchery-reared loggerhead turtle was used in this experiment. Standard carapace length and body weight of the turtle were 633 mm and 35.4 kg, respectively. The turtle was reared in Yaeyama Station of the Seikai National Fisheries Research Institute, Fisheries Research Agency.   Attachment of acceleration data loggers and experimental protocol Two acceleration data loggers (M190L-D2GT and W1000-3MPD3GT; Little Leonardo Co., Tokyo, Japan) were affixed to the lower beak and carapace of the loggerhead turtle respectively, using epoxy putty (Konishi Co., Ltd. Osaka, Japan) and two-component epoxy resin (ITW Industry Co., Ltd. Osaka, Japan). Acceleration data loggers can record depth and temperature at 1 s intervals, and two- or three-axis accelerations at 1/32 s intervals.   We released the logger-attached turtle in the experimental tank (10 m x 10 m x 2.2 m) on 22 April in 2008 and recovered the data loggers in the next day.   Extraction of breathing and activity level from acceleration data  From the acceleration data, we could extract static and dynamic components of the acceleration (Tanaka et al., 2001, Okuyama et al., in press). The static components (or low frequency) of the acceleration 12  represent angles of the body parts; the dynamic components (or high frequency) of the acceleration represent the animal movements such as flipper strokes. Sea turtles complete a single exhalation and inhalation with each respiration; they extend their neck upward when they breathe at the surface. Therefore, we detected the time of breathing when the turtle was at the surface and angle of the head was upward. In this study, breathing was defined when the head angle was greater than 40o and the swimming depth (which was obtained from the pressure data of carapace-attached logger) was less than 0.2 m (for more details, see Okuyama et al., in press), which was the most appropriate definition to a allow high detection rate and low false detection rate.   Recent studies suggest that dynamic body acceleration (DBA) can serve as an index of activity levels of free-living animals (Wilson et al., 2006, Halsey et al., 2008, Yasuda and Arai, 2009). Therefore, in this study, we calculated the DBA as an index of activity level during each dive. Dynamic components of the forward acceleration from the carapace-attached data-logger were converted into absolute positive units, and the resulting values were averaged during each dive.   For the extractions of these behaviors, we used IGOR Pro ver. 5, IFDL (WaveMatrics, Inc., USA) and Ethographer (Sakamoto et al., 2009).    Data analyses Diving was defined as the period when no breathing was recorded for over one minute. The number of breaths was then counted in each inter-dive interval and these values were used in regression of the previous dive durations versus the number of breaths. This regression analysis was conducted in order to reaffirm that there is a positive correlation between previous dive durations and number of breaths as is revealed by the previous study using visual observation (Lutcavage and Lutz, 1991).   Breathing frequency was determined as number of breaths divided by the previous dive duration. These values were used in regressions of DBA during the previous dive versus the breathing frequency. Breathing frequency and averaged DBA per dive were used at this time in order to exclude the effect of the previous dive durations on the number of breaths. To understand how the breathing frequency changes with the activity levels, we tested three regression models (linear, exponential and inverse) to estimate the breath frequency from the DBA during the previous dive. The generalized linear model (GLM) and Akaike information criterion (AIC) were used to investigate the effect of the DBA and which regression model was the most predictable.   Statistical analyses were conducted using R 2.8.0 (The R foundation for Statistical Computing, Vienna, Austria) with R library “MASS”.   RESULTS AND DISCUSSION In the analysis investigating the effect of the previous dive duration on the number of breaths, there was a significant positive correlation between these values (Fig. 1; Pearson‟s correlation test; R=0.62, p<0.01). This result was consistent with the previous study on the relationship between the previous dive durations and the number of breaths conducted by the visual observation (Lutcavage and Lutz, 1991).  previous dive duration (s)Number of breaths  Fig. 1 The relationship between previous dive duration and number of breaths. There was a positive correlation between these values (Pearson‟s correlation test, R=0.62, p<0.01).  In the analysis investigating the effect of the DBA during the previous dive on the breath frequency, the lowest AIC was obtained for the exponential regression model which included the effect of the DBA (Table 1); the effect of the DBA was also significant (G-test, p<0.01). The model indicated that there was a significant positive correlation between the DBA during the previous dive and the breathing frequency (Fig. 2), and that the relationship was exponential. These results suggest that the number of breaths increased exponentially after a more active dive.   Table 1 Model fitting procedure for generalized linear models (GLMs). The best fit model was chosen according to the lowest Akaike information criterion (AIC). k is the number of model parameters. Parameter Regression k AIC Null  1 470.1  DBA Inverse 2 410.6   Linear 2 400.0    exponential 2 397.5   13  0.1 0.2 0.3 0.4DBA  in the previous dive (G; x 9.8 m/s2 )2468100Breathing frequency (min-1) Fig. 2 The relationship between the DBA in the previous dive and the breathing frequency. The line in the figure represents the exponential regression model in GLM.    Understanding the whole mechanism of the relationship between activity levels and oxygen intake is more complex. In addition to the breathing frequency, the tidal volume in each breath and O2-CO2 exchange efficiency are related to the oxygen intake (Lutz and Bentley, 1985, Lutcavage et al., 1987, Lutcavage and Lutz, 1991). Therefore, we have to understand how the tidal volume and O2-CO2 exchange efficiency change in accordance with the increase of activity levels and breathing frequency, for further elucidating the dive-induced breathing patterns of sea turtles.  Oxygen intake can be measured using a swim chamber with oxygen meter, and Okuyama et al., (in press) suggested the possibility of measurement of tidal volume using the period of the neck-upward motion during the breath using an acceleration data logger. Therefore, simultaneous measurement of activity levels, breathing frequency, tidal volume and oxygen intake would be possible in the swim chamber, clarifying the dive-induced breathing patterns and metabolic rates.  Recently, categorization of underwater behaviors such as foraging, resting, swimming and mating has been conducted using acceleration data (Yoda et al., 2001, Tsuda et al., 2006, Nadimi et al., 2008, Okuyama et al., in press). If we obtain the data of behavioral categories and breathing patterns simultaneously, we could understand which behavior costs and benefits to sea turtle at a given period. The information leads to the elucidation of the time-to-time decision making of sea turtles.    ACKNOWLEDGEMENTS We would like to express our great thanks to the participants in the study for their kind cooperation. We especially thank the staff working at Yaeyama Station, of the Seikai National Fisheries Research Institute, FRA. We thank the staff and students of the Graduate School of Informatics at Kyoto University for their constructive comments.   REFERENCES Halsey, L. G., Shepard, E. L. C., Hulston, C. J., Venables, M. C., White, C. R., Jeukendrup, A. E. and Wilson, R. P. (2008). Acceleration versus heart rate for estimating energy expenditure and speed during locomotion in animals: Tests with an easy model species, Homo sapiens. Zoology 111, 231-241.  Krebs, J. R. & Davies, N. B. (1993). An introduction to behavioural ecology. pp. 420. Blackwell Publishing, Oxford. Lutcavage, M. E. and Lutz, P. L. (1991). Voluntary Diving Metabolism and Ventilation in the Loggerhead Sea-Turtle. J. Exp. Mar. Biol. Ecol. 147, 287-296.  Lutcavage, M. E., Lutz, P. L. and Baier, H. (1987). Gas-Exchange in the Loggerhead Sea-Turtle Caretta-Caretta. J. Exp. Biol. 131, 365-372.  Lutz, P. L. and Bentley, T. B. (1985). Respiratory Physiology of Diving in the Sea Turtle. Copeia 671-679.  Lutz, P. L., Bergey, A. and Bergey, M. (1989). Effects of Temperature on Gas-Exchange and Acid-Base-Balance in the Sea Turtle Caretta-Caretta at Rest and During Routine Activity. J. Exp. Biol. 144, 155-169.  Nadimi, E. S., Sogaard, H. T. and Bak, T. (2008). ZigBee-based wireless sensor networks for classifying the behaviour of a herd of animals using classification trees. Biosyst. Eng. 100, 167-176.  Okuyama, J., Kawabata, Y., Naito, Y., Arai, N. and Kobayashi, M. (in press). Monitoring beak movements with an acceleration data logger: a useful technique for assessing the feeding and breathing behaviors of sea turtles. Endangered Species Research  Sakamoto, K. Q., Sato, K., Ishizuka, M., Watanuki, Y., Takahashi, A., Daunt, F. and Wanless, S. (2009). Can ethograms be automatically generated using body acceleration data from free-ranging birds? PLos one 4, 1-12.  Tanaka, H., Takagi, Y. and Naito, Y. (2001). Swimming speeds and buoyancy compensation of migrating adult chum salmon Oncorhynchus keta revealed by speed/depth/acceleration data logger. J. Exp. Biol. 204, 3895-3904.  Tsuda, Y., Kawabe, R., Tanaka, H., Mitsunaga, Y., Hiraishi, T., Yamamoto, K. and Nashimoto, K. (2006). Monitoring the spawning behaviour of chum salmon with an acceleration data logger. Ecol. Fresh. Fish 15, 264-274.  14  Wilson, R. P., White, C. R., Quintana, F., Halsey, L. G., Liebsch, N., Martin, G. R. and Butler, P. J. (2006). Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant. J. Animal Ecol. 75, 1081-1090.  Yasuda, T. and Arai, N. (2009). Changes in flipper beat frequency, body angle and swimming speed of female green turtles Chelonia mydas. Mar. Ecol. Prog. Ser. 386, 275-286.  Yoda, K., Naito, Y., Sato, K., Takahashi, A., Nishikawa, J., Ropert-Coudert, Y., Kurita, M. and Le Maho, Y. (2001). A new technique for monitoring the behaviour of free-ranging Adelie penguins. J. Exp. Biol. 204, 685-6

Preliminary result of the relationship between the breathing frequency and dynamic body acceleration

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Preliminary result of the relationship between the breathing frequency and dynamic body acceleration

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