Poster Presentation Australian Society of Fish Biology and Oceania Chondrichthyan Society Conference 2016

Correlations of metabolic rate and body acceleration in three coastal shark species under contrasting temperature regimes (#213)

Karissa Lear 1 , Nick Whitney 2 , Lauran Brewster 3 , Adrian Gleiss 1
  1. Centre for Fish and Fisheries Research, Murdoch University, Perth, WA, Australia
  2. Mote Marine Laboratory, Sarasota, FL, USA
  3. University of Hull, Hull, East Riding of Yorkshire, United Kingdom

The ability to produce estimates of the metabolic rate of free-ranging animals is fundamental to the study of their ecology. However, measuring the energy expenditure of animals in the field has proven difficult, especially for marine and aquatic taxa. Accelerometry is a relatively new technique that presents a means of translating metabolic rates measured in the laboratory to individuals studied in the field, pending appropriate laboratory calibrations. Here these calibrations are presented for three marine predators: nurse sharks (Ginglymostoma cirratum), lemon sharks (Negaprion brevirostris), and blacktip sharks (Carcharhinus limbatus), through simultaneously measuring body movement, as overall dynamic body acceleration (ODBA), and metabolic rate, through respirometry. Calibrations were made at a range of volitional swimming speeds and experimental temperatures. ODBA scaled linearly with oxygen consumption rate, maintaining strong correlations in all three study species, with R2>0.85. The best fit model used ODBA in combination with activity state (active or resting) and temperature to predict metabolic rate in lemon and nurse sharks, and ODBA and temperature to predict metabolic rate in blacktip sharks. The error associated with these predictions was less than 5% in all three species, indicating that accelerometry provides a powerful tool for predicting field metabolic rate in these animals. This study lays the groundwork for calculating the metabolic rate and time-energy budgets of these species in the wild using acceleration data alone, providing the opportunity for insight into how energetic demands drive behavioural decisions and ecological interactions.