The ability to define and quantify the behaviour and energetic costs of different activities is fundamental to a full understanding of fish ecology and movement, but monitoring activity and measuring energy expenditure in fish in the field is problematic. New telemetry methods using data loggers that incorporate tri-axial accelerometers promise to provide a method for simultaneously recording the behaviour and activity-specific energy use in both the laboratory and field. Using electronic data loggers equipped with tri-axial accelerometers we have measured dynamic body acceleration (DBA) during aerobic exercise in European sea bass Dicentrarchus labrax whilst swimming in a swim-tunnel respirometer at ambient water temperatures of between 5.5 and 17.5°C. For all individuals, dynamic body acceleration (both vectorial dynamic body acceleration [VeDBA] and overall dynamic body acceleration [ODBA]) scaled linearly with oxygen consumption and as a function of ambient temperature. When the 2 DBA metrics were compared, VeDBA was not significantly different from ODBA, though the value for Akaike's information criterion was lower for VeDBA (indicating a better fit for the VeDBA model). In this paper, we provide further evidence to support the use of acceleration as a means to quantify the activity-specific energetic costs of swimming in teleosts and highlight some of the problems associated with monitoring the activity and metabolic rate of fish in restricted laboratory conditions.
Summary
Monitoring and measuring the behaviour and movement of aquatic animals in the wild is typically challenging, though micro-accelerometer (archival or telemetry) tags now provide the means to remotely identify and quantify behavioural states and rates such as resting, swimming, and migrating, and to estimate activity and energy budgets. Most studies use low frequency (≤32 Hz) accelerometer sampling due to battery and data-archiving constraints. In this study we assessed the effect of sampling frequency (aliasing) on activity detection probability using the great sculpin (Myoxocephalus polyacanthoceaphalus) as a model species. Feeding strikes and escape responses (fast-start activities) and spontaneous movements among 7 different great sculpin were triggered, observed and recorded using a tri-axial accelerometer sampling at 100 Hz and video records. We demonstrate that multiple parameters in the time and probability domains can statistically differentiate between activities with high detection (90%) and identification (80%) probabilities. Detection probability for feeding and escape activities decreased by 50% when sampling at <10 Hz. Our analyses illustrate additional problems associated with aliasing and how activity and energy-budget estimates can be compromised and misinterpreted. We recommend that high-frequency (>30 Hz) accelerometer sampling be used in similar lab and field studies. If battery and (or) data storage is limited, we also recommend archiving the events via an on-board algorithm that determines the highest likelihood and subsequent archiving of the various event-classes of interest.
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