Metachronal swimming in Antarctic krill: gait kinematics and system design

Murphy, David; Webster, D. R.; Kawaguchi, So; King, Robert A.; Yen, Jeannette

doi:10.1007/s00227-011-1755-y

Cited by 55 publications

(169 citation statements)

References 39 publications

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“…Furthermore, long-tailed crustaceans exhibit the distinct tail-to-head stroke pattern in a variety of swimming modes, including forward swimming and hovering, in which the limbs experience different hydrodynamic forces (29). These observations suggest that the intersegmental phase differences between swimmerets result primarily from neural input to the muscles rather than from the interaction between hydrodynamic forces and passive body mechanics.…”

Section: Discussionmentioning

confidence: 99%

“…Hence, the fluid dynamics of cilia beating are significantly different from the fluid dynamics of crustacean swimming. Relatively few studies have examined metachronal limb paddling for the range of Re under which crustaceans operate (28)(29)(30). Recently, a model based on drag forces alone predicted a slight mechanical advantage of metachronal wave in krill swimming (31).…”

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confidence: 99%

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Neural mechanism of optimal limb coordination in crustacean swimming

Zhang¹,

Guy²,

Mulloney

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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A fundamental challenge in neuroscience is to understand how biologically salient motor behaviors emerge from properties of the underlying neural circuits. Crayfish, krill, prawns, lobsters, and other long-tailed crustaceans swim by rhythmically moving limbs called swimmerets. Over the entire biological range of animal size and paddling frequency, movements of adjacent swimmerets maintain an approximate quarter-period phase difference with the more posterior limbs leading the cycle. We use a computational fluid dynamics model to show that this frequency-invariant stroke pattern is the most effective and mechanically efficient paddling rhythm across the full range of biologically relevant Reynolds numbers in crustacean swimming. We then show that the organization of the neural circuit underlying swimmeret coordination provides a robust mechanism for generating this stroke pattern. Specifically, the wave-like limb coordination emerges robustly from a combination of the half-center structure of the local central pattern generating circuits (CPGs) that drive the movements of each limb, the asymmetric network topology of the connections between local CPGs, and the phase response properties of the local CPGs, which we measure experimentally. Thus, the crustacean swimmeret system serves as a concrete example in which the architecture of a neural circuit leads to optimal behavior in a robust manner. Furthermore, we consider all possible connection topologies between local CPGs and show that the natural connectivity pattern generates the biomechanically optimal stroke pattern most robustly. Given the high metabolic cost of crustacean swimming, our results suggest that natural selection has pushed the swimmeret neural circuit toward a connection topology that produces optimal behavior. locomotion | coupled oscillators | phase locking | metachronal waves I t is widely believed that neural circuits have evolved to optimize behavior that increases reproductive fitness. Despite this belief, few studies have clearly identified the neural mechanisms producing optimal behaviors. The complexity of behaviors generally makes it difficult to assess their optimality, and neural circuits are often too complicated to concretely link neural mechanisms to the overt behavior. Energy-intensive locomotion such as steady swimming, walking, and flying provides important model systems for studying optimality because the goal of the behavior is clear and it is likely to have been optimized for efficiency (1). For example, the kinematics of locomotion has been shown to be optimal in the cases of the undulatory motion of the sandfish lizard and the lamprey (2, 3). On the other hand, the neural circuits underlying locomotion in most organisms are not sufficiently characterized to understand how they give rise to the optimal motor behavior. Because of the distinct frequency-invariant stroke pattern and the relative simplicity of the neuronal circuit, limb coordination of long-tailed crustaceans during steady swimming provides an ideal model system ...

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Neural mechanism of optimal limb coordination in crustacean swimming

Zhang¹,

Guy²,

Mulloney

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…As seen in the first frame, this motion creates a highly localized impulsive jet directly behind the animal, with flow speeds up to 50 mm s by other leg pairs (Murphy et al 2011). The jet immediately forms into a toroidal wake vortex (indicated by the filled arrows) as the fluid curls forward to fill the space evacuated by the animal.…”

Section: Flow Measurementsmentioning

confidence: 93%

A high‐speed tomographic PIV system for measuring zooplanktonic flow

Murphy

Webster

Yen

2012

Limnology & Ocean Methods

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Quantification of the flow produced by zooplankton as they swim and feed yields insight into locomotion techniques, social aggregations, feeding strategies, and energetics. For instance, various feeding strategies employed by zooplankton have been illuminated by considering the fluid mechanics involved. Fields and Yen (1993) mapped the three-dimensional feeding current of a filter-feeding calanoid copepod (Pleuromamma xiphias) to quantify the volumetric extent of its signature that might be visible to prey or predators. Fields and Yen (1997) also showed how vorticity in the feeding current of the copepod Euchaeta rimana orients the entrained prey such that an escape attempt will transmit positional information to the predator's antennal sensory array. Similarly, Malkiel et al. (2003) showed, using three-dimensional velocity measurements, that filter-feeding copepods must occasionally jump to avoid re-sampling the same fluid volume. Kiørboe et al. (2009) investigated ambush-feeding copepods such as Acartia tonsa and Oithona davisae and showed, using fluid simulations, how these predators can rapidly accelerate toward their prey without developing a significant flow disturbance that would push the prey away. Kiørboe et al. (2010a) also demonstrated with flow measurements how ambush feeding, as opposed to filter-or cruise-feeding, generates a smaller timeaveraged flow signal (and thus a lower predation risk) for copepods smaller than 1 mm, whereas the opposite is true for larger copepods. Further, quantification of zooplankton-generated flow fields can provide energetic estimates of the cost of propulsion or feeding through the viscous dissipation of kinetic energy, a calculation that involves the spatial gradients of the velocity field (Catton et al. 2007). For example, van Duren and Videler (2003) showed that the rate of energy dissipation in tethered Temora longicornis was two orders of magnitude higher for escape responses than for feeding. These examples indicate that key aspects of zooplankton behavior and interactions can be understood only by quantifying the flows that they produce and to which they respond.Various AbstractQuantification of the hydrodynamic disturbance of free-swimming zooplankton provides insight into propulsion as well as organism sensory interactions. Current flow measurement techniques, such as planar particle image velocimetry (PIV), are limited by their two-dimensional nature. These techniques also are challenged by the small spatial scale of zooplankton, by the high speeds achieved by many zooplankton species, and by zooplankton photosensitivity to a broad range of wavelengths. We present a high-speed tomographic PIV system using near-infrared laser illumination that is capable of measuring three-dimensional velocity vectors in a volume surrounding a plankter. This technique is assessed by recording and analyzing the time-resolved flow field created by a high-speed escape of a copepod (Calanus finmarchicus). Persistent body and wake vortices are created by the impulsive momentum ...

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“…This has been suggested for a small crustacean (Cypridopsis vidua Muller, Cyprididae, Ostracoda (Meisch, 2000)), using the antennae and antennulae in a similar manner, as well (Hunt et al, 2007). Even more complicated swimming kinematics allows for multiple modes of swimming, as described for pelagic Antarctic krill (Murphy et al, 2011). However, the pea crab s parasitic life cycle within mussel beds likely has less need for distinct swimming modes and swimming with four pereiopods suffices.…”

Section: Kinematicsmentioning

confidence: 97%

“…Considering the Re of mature male pea crabs, their hydrodynamic regime will involve similar aspects as those of the water boatman (Notonectidae) and other small aquatic arthropods (see also Catton et al, 2007;Stamhuis & Videler, 1995;Videler et al, 2002;Yen, 2000;Murphy et al, 2011). At this Re, both pressure drag and frictional drag are important factors (Vogel, 1994).…”

Section: Introductionmentioning

confidence: 99%

Swimming of the pea crab (Pinnotheres pisum)

Versteegh¹,

Müller²

2014

Animal Biol

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Aquatic organisms have to deal with different hydrodynamic regimes, depending on their size and speed during locomotion. The pea crab swims by beating the third and fourth pereiopod on opposite sides as pairs. Using particle tracking velocimetry and high-speed video recording, we quantify the kinematics and vortices in the wake of the pea crab. Where the proximal parts of the pereiopods beat in antiphase, their distal parts show an overlapping beat period. By using four instead of two limbs for propulsion, an uninterrupted forward movement is established, reducing the influence of the acceleration reaction. Before body speed is maximal, force generation of the pereiopods seems most active when passing an orthogonal position with the body.

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Metachronal swimming in Antarctic krill: gait kinematics and system design

Cited by 55 publications

References 39 publications

Neural mechanism of optimal limb coordination in crustacean swimming

Neural mechanism of optimal limb coordination in crustacean swimming

A high‐speed tomographic PIV system for measuring zooplanktonic flow

Swimming of the pea crab (Pinnotheres pisum)

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