The hydrodynamic disturbances of two species of krill: implications for aggregation structure

Abstract

Swimming capacity is dependent on size (Johnson and Tarling, 2008) and organism size is directly related to the spatial extent of the hydrodynamic disturbance, which potentially provides a sensory field for prey, predators and conspecifics (Abrahamsen et al., 2010 Accepted 15 February 2011 SUMMARY Krill aggregations vary in size, krill density and uniformity depending on the species of krill. These aggregations may be structured to allow individuals to sense the hydrodynamic cues of neighboring krill or to avoid the flow fields of neighboring krill, which may increase drag forces on an individual krill. To determine the strength and location of the flow disturbance generated by krill, we used infrared particle image velocimetry measurements to analyze the flow field of free-swimming solitary specimens (Euphausia superba and Euphausia pacifica) and small, coordinated groups of three to six E. superba. Euphausia pacifica individuals possessed shorter body lengths, steeper body orientations relative to horizontal, slower swimming speeds and faster pleopod beat frequencies compared with E. superba. The downward-directed flow produced by E. pacifica has a smaller maximum velocity and smaller horizontal extent of the flow pattern compared with the flow produced by E. superba, which suggests that the flow disturbance is less persistent as a potential hydrodynamic cue for E. pacifica. Time record analysis reveals that the hydrodynamic disturbance is very weak beyond two body lengths for E. pacifica, whereas the hydrodynamic disturbance is observable above background level at four body lengths for E. superba. Because the nearest neighbor separation distance of E. superba within a school is less than two body lengths, hydrodynamic disturbances are a viable cue for intraspecies communication. The orientation of the position of the nearest neighbor is not coincident with the orientation of the flow disturbance, however, which indicates that E. superba are avoiding the region of strongest flow.

Biogenic inputs to ocean mixing

“…The conflicting conclusions from these studies illustrate the variability and challenges faced when characterizing biogenic mixing. It is clear that schools of swimming animals create measurable changes in fluid disturbances, and recent laboratory studies of schooling krill confirm this (Catton et al, 2011). However, in terms of generating mixing by disrupting stratification in the ocean, vertical migration behavior may result in more effective mixing than mostly horizontally swimming animals.…”

Measurement of sardine-generated turbulence in a large tank

“…This anisotropic turbulence may be caused by the anisotropic swimming pattern produced by the feeding school. Catton et al (2011) conducted a small tank experiment to study fluid motion around swimming krill using laser sheet illumination. They reported that krill ag gregations actively transferred water parcels down to successive group members, consequently disturbing the water column at a scale of aggregations.…”

“…Although these characteristics appear to be critical for determining the intensity of the induced turbulence (Huntley & Zhou 2004), the previous observations did not consider swimming behavior but used acoustic backscatter merely to estimate population density. However, in a tank experiment, Catton et al (2011) reported that individual (not aggregated) krill were able to transfer a water parcel by a distance of 1 body length, while aggregated krill actively transferred water parcels down to successive group members and disturbed the water column at a scale of aggregations. They suggested that different biological characteristics (i.e.…”

Simultaneous measurement of 3D zooplankton trajectories and surrounding fluid velocity field in complex flows

“…and even fish schools. With its capability to identify and track multiple organisms and measure complex flow variations in a volumetric domain, this technique can provide measurement means for researchers interested in biogenic mixing (Katija and Dabiri, 2009), in situ characterization of organisms and flow (Sutherland et al, 2011), krill schooling (Catton et al, 2011), plankton aggregation in oceanic thin layers (Durham and Stocker, 2012) or predator-prey interactions (Buskey et al, 2002;Adhikari, 2013;Rothschild and Osborn, 1988). Thus, the application of this technique can extend from investigating organism movement within benthic boundary layers to even wind-driven seed dispersal in terrestrial systems.…”