How does one best evaluate the cost of swimming in turbulent flows Traditional metrics gathered from swimming kinematics, such as tail-beat frequency, slip and Strouhal number, have been used to assess the performance of propulsive, thrust-based locomotion in uniform flows, but are inappropriate in unsteady flows. As a first approximation, simply observing if fish prefer or avoid turbulent habitats may indicate if an energetic benefit exists. However, care must be exercised since cover complexity, which is intimately linked to turbulence in current-swept environments, offers other advantages such as visual isolation from conspecifics and predators in addition to hydrodynamic benefits. Oxygen consumption is the most direct measure of metabolic cost required during fish locomotion, yet few studies have assessed the energetic expenditure of fishes swimming in turbulent flow. One exception showed that juvenile Atlantic salmon swimming at a constant average flow velocity increase their energy expenditure when exposed to turbulence, generated as wide fluctuations of flow velocity around a mean flow value. This increase in cost with turbulence was especially dramatic for trials conducted at higher mean flow velocities. Migratory fishes commonly encounter high levels of turbulence and show difficulty maintaining proper orientation. These fish exhibit increased respiratory rates and a decreased ability to perform escape behaviours after experimental turbulence trials. Thus, a picture has emerged where fish choose habitats not only based on average flow velocity but also on the degree of variation in flow velocity. These
studies make it clear that models need to take velocity fluctuations into consideration when estimating the costs of locomotion in natural flow environments. The nature of the datamakes it difficult to determine exactly what flow features are responsible for the increase in swimming costs. Oxygen consumption is a timeaveraged measurement and thus cannot provide the temporal resolution necessary to determine the proximate hydrodynamic mechanisms that affect physiology.
If combined with quantitative flow visualization, the opportunity to correlate metabolic swimming costs to
specific turbulent features will be possible for the first time. Complementary data, such as kinematics and muscle activity
How does one best evaluate the cost of swimming in turbulent flows Traditional metrics gathered from swimming kinematics, such as tail-beat frequency, slip and Strouhal number, have been used to assess the performance of propulsive, thrust-based locomotion in uniform flows, but are inappropriate in unsteady flows. As a first approximation, simply observing if fish prefer or avoid turbulent habitats may indicate if an energetic benefit exists. However, care must be exercised since cover complexity, which is intimately linked to turbulence in current-swept environments, offers other advantages such as visual isolation from conspecifics and predators in addition to hydrodynamic benefits. Oxygen consumption is the most direct measure of metabolic cost required during fish locomotion, yet few studies have assessed the energetic expenditure of fishes swimming in turbulent flow. One exception showed that juvenile Atlantic salmon swimming at a constant average flow velocity increase their energy expenditure when exposed to turbulence, generated as wide fluctuations of flow velocity around a mean flow value. This increase in cost with turbulence was especially dramatic for trials conducted at higher mean flow velocities. Migratory fishes commonly encounter high levels of turbulence and show difficulty maintaining proper orientation. These fish exhibit increased respiratory rates and a decreased ability to perform escape behaviours after experimental turbulence trials. Thus, a picture has emerged where fish choose habitats not only based on average flow velocity but also on the degree of variation in flow velocity. Thesestudies make it clear that models need to take velocity fluctuations into consideration when estimating the costs of locomotion in natural flow environments. The nature of the datamakes it difficult to determine exactly what flow features are responsible for the increase in swimming costs. Oxygen consumption is a timeaveraged measurement and thus cannot provide the temporal resolution necessary to determine the proximate hydrodynamic mechanisms that affect physiology.If combined with quantitative flow visualization, the opportunity to correlate metabolic swimming costs tospecific turbulent features will be possible for the first time. Complementary data, such as kinematics and muscle activity
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