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J Exp Biol. 2009 Feb;212(Pt 4):471-82. doi: 10.1242/jeb.026377.

Estimating energy expenditure of animals using the accelerometry technique: activity, inactivity and comparison with the heart-rate technique.

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  • 1School of Biological Sciences, University of Liverpool, Crown Street, Liverpool, L69 7ZB, UK.

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  • J Exp Biol. 2009 Mar;212(Pt 5):745-6.


Several methods have been used to estimate the energy expenditure of free-ranging animals. A relatively new technique uses measures of dynamic body acceleration as a calibrated proxy for energy expenditure and has proved an excellent predictor of energy expenditure in active animals. However, some animals can spend much of their time inactive and still expend energy at varying rates for a range of physiological processes. We tested the utility of dynamic body acceleration to estimate energy expenditure during a range of active (locomotion, eating) and inactive (digesting, thermoregulating) behaviours exhibited by domestic chickens. We also compared this technique with the more established heart-rate method for estimating energy expenditure. During activity, the error of estimation using body acceleration was very similar to that from the heart-rate method. Importantly, our results also showed that body acceleration can be used to estimate energy expenditure when birds are inactive. While the errors surrounding these estimates were greater than those during activity, and those made using the heart-rate method, they were less than those made using interspecific allometric equations. We highlight the importance of selecting a methodology that is appropriate for the life-history of the subject animal. We suggest that, to achieve the greatest possible accuracy and precision when estimating energy expenditure in free-ranging animals, the two techniques should be combined, and both heart rate (f(H)) and dynamic body acceleration could be included as covariates in predictive models. Alternatively, measures of acceleration can be used to ascertain which behaviour is being exhibited at each moment and hence which predictive model should be applied.

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