Swimming Energy Economy in Bottlenose Dolphins Under Variable Drag Loading

Abstract

Instrumenting animals with tags contributes additional resistive forces (weight, buoyancy, lift, and drag) that may result in increased energetic costs; however, additional metabolic expense can be moderated by adjusting behavior to maintain power output. We sought to increase hydrodynamic drag for near-surface swimming bottlenose dolphins, to investigate the metabolic effect of instrumentation. In this experiment, we investigate whether (1) metabolic rate increases systematically with hydrodynamic drag loading from tags of different sizes or (2) whether tagged individuals modulate speed, swimming distance, and/or fluking motions under increased drag loading. We detected no significant difference in oxygen consumption rates when four male dolphins performed a repeated swimming task, but measured swimming speeds that were 34% (>1 m s −1) slower in the highest drag condition. To further investigate this observed response, we incrementally decreased and then increased drag in six loading conditions. When drag was reduced, dolphins increased swimming speed (+1.4 m s −1 ; +45%) and fluking frequency (+0.28 Hz; +16%). As drag was increased, swimming speed (−0.96 m s −1 ; −23%) and fluking frequency (−14 Hz; 7%) decreased again. Results from computational fluid dynamics simulations indicate that the experimentally observed changes in swimming speed would have maintained the level of external drag forces experienced by the animals. Together, these results indicate that dolphins may adjust swimming speed to modulate the drag force opposing their motion during swimming, adapting their behavior to maintain a level of energy economy during locomotion.

Whistling is metabolically cheap for communicating bottlenose dolphins (Tursiops truncatus)

“…For each whistle, the directivity index (DI) was estimated from F c using a modified relationship between directivity and frequency for dolphin whistles (DI=0.20F c +4.3; Branstetter et al, 2012). The DI was subtracted from the calculated RMS source level to convert it into the equivalent omni-directional sound source radiating the same acoustic power (Madsen and Wahlberg, 2007;Urick, 1983). The energy flux density (EFD) was calculated by adding 10log(T ) to the equivalent omni-directional RMS source level, where T is the duration of the 95% energy window in seconds (Madsen, 2005).…”

“…The signal was integrated, and the flow was determined assuming a linear response between differential pressure and flow (Fahlman et al, 2015). Breath-by-breath respiratory flow analysis using a pneumotachometer provides very similar results to conventional flow-through respirometry (Fahlman et al, 2015), and is capable of detecting both transient and cumulative increases in oxygen consumption rates (Fahlman et al, 2019;van der Hoop et al, 2018).…”

Minimizing the impact of biologging devices: Using computational fluid dynamics for optimizing tag design and positioning

“…Despite this, most studies using tags have so far largely failed to take advantage of technological advancements to reduce the impact of tags on animals (Portugal & White, 2018). Crucially, for projects involving tags on aerial and aquatic animals, the focus on weight by most existing tag guidelines -for example the 3% or 5% rule (Casper, 2009) -ignores aero/hydrodynamic impacts (most notably drag) which are key in modulating energy expenditure and behaviour during swimming (Cornick, Inglis, Willis, & Horning, 2006;Culik & Wilson, 1991;Rosen, Gerlinsky, & Trites, 2017;van der Hoop et al, 2018) and flight (Bowlin et al, 2010;Pennycuick, Fast, Ballerstädt, & Rattenborg, 2012; but see Tomotani, Bil, Jeugd, Pieters, & Muijres, 2019). This may lead to biased data which are not representative of freely moving animals (Barron, Brawn, & Weatherhead, 2010;Lear, Gleiss, & Whitney, 2018;Ropert-Coudert et al, 2000), as well as raising important ethical concerns for the animal being tagged (Wilson & McMahon, 2006).…”

“…These approaches are beneficial insofar as during live experiments, it is possible to observe how animals react to tags under real operational conditions (cf. Pavlov & Rashad, 2012;van der Hoop et al, 2018), as well as assessing animal energetics, kinetics and biomechanics, and changes in these over time (Geertsen, Teilmann, Kastelein, Vlemmix, & Miller, 2004;van der Hoop et al, 2018;Ropert-Coudert, Knott, Chiaradia, & Kato, 2007;Rosen et al, 2017). However, experimental approaches are limited in that they are very time-consuming and labour-intensive, wind or flume tunnels are not always accessible, and the use of live animals raises ethical concerns and requires appropriate licensing (Kyte, Pass, Pemberton, Sharman, & McKnight, 2018).…”

Automated peak detection method for behavioral event identification: detecting Balaenoptera musculus and Grampus griseus feeding attempts

“…Studying animal behavior while minimizing levels of invasiveness is a challenge many biologists face [1][2][3]. Difficulty also arises while attempting to observe animals in environments and during time periods that are relatively inaccessible to humans [4].…”