Assessing hydrodynamic space use of brown trout, Salmo trutta, in a complex flow environment: a return to first principles.

Abstract

It is commonly assumed that stream-dwelling fish should select positions where they can reduce energetic costs relative to benefits gained and enhance fitness. However, the selection of appropriate hydrodynamic metrics that predict space use is the subject of recent debate and a cause of controversy. This is for three reasons: (1) flow characteristics are often oversimplified, (2) confounding variables are not always controlled and (3) there is limited understanding of the explanatory mechanisms that underpin the biophysical interactions between fish and their hydrodynamic environment. This study investigated the space use of brown trout, Salmo trutta, in a complex hydrodynamic flow field created using an array of different sized vertically oriented cylinders in a large open-channel flume in which confounding variables were controlled. A hydrodynamic drag function (D) based on single-point time-averaged velocity statistics that incorporates the influence of turbulent fluctuations was used to infer the energetic cost of steady swimming. Novel hydrodynamic preference curves were developed and used to assess the appropriateness of D as a descriptor of space use compared with other commonly used metrics. Zones in which performanceenhancing swimming behaviours (e.g. Kaŕmań gaiting, entraining and bow riding) that enable fish to hold position while reducing energetic costs (termed 'specialised behaviours') were identified and occupancy was recorded. We demonstrate that energy conservation strategies play a key role in space use in an energetically taxing environment with the majority of trout groups choosing to frequently occupy areas in which specialised behaviours may be adopted or by selecting low-drag regions.

Turbulent flow reduces oxygen consumption in the labriform swimming shiner perch,Cymatogaster aggregata

“…Most studies of fish swimming and respirometry have used flowstraightening devices that result in low-turbulence flow conditions within the experimental setup, similar to the LTF condition described in this study. The natural habitat of most fishes is more turbulent than that created by such laboratory conditions, but most studies seeking to create more natural flows have focused on fish behaviour in coherent vortex structures, which represent only a small portion of the flows encountered by fish in their natural habitats (Lacey et al, 2012;Kerr et al, 2016). In predictable vortexdominated flows, it is common for fish to display energy-saving behaviour (e.g.…”

“…Few studies have explicitly tested the effect of aperiodic, more randomized flow on fish swimming energetics and kinematics. Of these, many have suggested that fish should expend more energy in unsteady flows (Enders et al, 2003;Lupandin, 2005;Tritico and Cotel, 2010) or show greater variation among individuals (Kerr et al, 2016), as each fish must accommodate the unique flow structures it encounters.…”

“…Fish have been shown to reduce the energetic costs of swimming (or otherwise adapt to changes in ambient flow) in specific circumstances by employing behaviours such as (1) gait switching (Korsmeyer et al, 2002), (2) Kármán gaiting (Liao, 2003), (3) entraining (Webb, 1998), (4) bow riding (Newman and Wu, 1975;Taguchi and Liao, 2011), (5) tail holding (Kerr et al, 2016) and (6) wall holding (Kerr et al, 2016). Below, the present results are discussed in the context of expected fish behaviour under these different energy-reducing strategies to identify potential mechanisms behind the reduced metabolic costs measured in this study.…”

“…Following Kerr et al (2016), we visualized the 3D position of all fish in each flow and speed condition with heatmaps, to determine whether fish were consistent in their positions in HTF or LTF, perhaps to take advantage of regional flow conditions or 'dead spots'. To aid interpretation of the results, we plotted the standard deviation of position per fish, flow condition and speed and fitted a rudimentary linear model per fish and flow condition (Script 1).…”

“…Overall space use in the tank was visualized using heatmaps, following Kerr et al (2016), which did not suggest consistent positioning in the tank between fish and in different flow and speed conditions (Fig. 6).…”