Changes in the network temperature of posttraumatic stress disorder symptoms among children and adolescents following earthquakes.

The ability to match physiological supply to demand sets the limits of animal performance. From the minimum rates of energetic consumption in hibernating bears () to the maximum metabolic rates of hovering hummingbirds (), oxygen (O) supply must vary in response to the mass-specific metabolic requirements of tissues, which fluctuate with body size, activity state, and a wide range of environmental factors. For air-breathing vertebrates, the simple act of underwater submersion activates the dive response, lowering and redistributing cardiac output (). This innate response results in a rapid decrease in heart rate (i.e., bradycardia) and peripheral vasoconstriction, functioning to slow O depletion and preserve endogenous O for hypoxia-intolerant organs like the brain and the heart (–). How, and if, divers modulate dive heart rate to meet the metabolic demands of underwater exercise has long been discussed. For diving birds and mammals adapted to maximize effective foraging during breath-hold dives, theoretical models suggest the dive response should fluctuate to support energetically demanding prey search, chase, and capture (). Empirical data from animals in controlled settings and a few free-ranging scenarios demonstrate that heart rate increases with increasing swimming effort ensuring adequate O delivery to active muscles (–). Consequently, a flexible dive response that optimizes the use of lung, muscle, and blood-bound O has replaced the idea of a fixed diving bradycardia, but data as to how this flexibility supports multiple, distinct kinds of underwater exercise in diving birds and mammals remain limited. Among diving mammals, lunge-feeding rorquals epitomize the physiological challenge of modulating O supply to support extreme exercise during breath-hold diving. Rorquals (Balaenopteridae), including blue whales () and humpback whales (), feed by performing repeated high-power lunges at tens to hundreds of meters below the surface, a behavior described as the “largest biomechanical event on Earth” (). Each feeding lunge requires rapid acceleration of a 10 to 10 kg animal up to 4 m s (), effective maneuvering, including up to 360° barrel rolls and variable, often steep, pitch angles for optimal body positioning (), and precisely timed opening and closure of the mouth within seconds of peak approach speed (). During a lunge, feeding rorquals must meet the concurrent metabolic demands of thrust- and maneuver-generating muscles () as well as those of the sensory organs that support visual processing or other means of perceiving prey fields. As turning incurs a higher energetic cost than straight-line travel (), energy demand is also expected to increase for lunges with increasing amounts of accumulated maneuvering. Rorquals offset the high power output of lunges with long periods of unpowered filtering, accounting for up to 80% of the lunge cycle in the largest blue whales (). This results in an intermittent “lunge-and-filter” swimming strategy that is unique among large, air-breathing vertebrate filter feeders. Thus, despite brief periods of very high power output during lunges, rorquals are afforded low metabolic rates during foraging dives (, ). At the cardiovascular level, how rorquals meet the extreme biomechanical demands of lunge feeding during the dive response remains unclear. The only successful study to measure heart rate in a free-ranging diver without prior restraint was in a single blue whale (). Digital electrocardiogram (ECG) signals and low-resolution dive behavior (only time and depth data sampled at 1 Hz) showed a typical dive response with heart rates during dives as low as 2 beats min (bpm) followed by high heart rates, up to 37 bpm, at the sea surface (). Despite the extreme bradycardia (i.e., a heart rate below resting) observed during dives, heart rate increased 2.5 fold during presumed high-power lunges and gradually decreased during filtering though the absence of high-resolution motion sensors (i.e., accelerometers and magnetometers) and small sample size precluded a complete investigation of this pattern.