Experimental animals

Two fish of each species Lepomis gibbosus L., Colomesus psittacus (Bloch & Schneider) and Thysochromis ansorgii (Boulenger), were obtained from commercial dealers and kept in standard aquaria according to the instructions in Riehl and Baensch (1991).

Experimental set-up and fish training

Two experimental tanks were used. Tank 1 had a floor surface of 100 cm×100 cm to give ample space for a lateral spread of the fish's wake. Tank 2 had a floor surface of 40 cm×100 cm to facilitate the laser illumination of the particles in the field of view and thus improve image quality. Image quality was highly dependent on the number of seeding particles that the laser light had to pass before it reached the area of interest. In both tanks the water level was set to 40±3 cm.

Individual fish were trained to swim on a straight line through the centre of the experimental tank to reach a goal compartment at the opposite wall where they received a food reward. Before a measurement the fish was kept in a small compartment at the start of the swimming route to allow the water in the experimental tank to calm down for at least 5 min. In training sessions the fish were conditioned to swim to a green light (a bright LED flashing at approximately 2 Hz). The light was mounted in the goal compartment at the lower end of a feeding tube, through which the fish was rewarded with a mosquito larva. As soon as the fish reached the goal compartment, it was locked out from the measurement area in the middle of the tank by shutting a sliding door.

To monitor the altitude of a swimming fish in side view, a third camera (camera 3) was positioned beside the experimental tank. The experimental tank was illuminated with green light to aid fish orientation. To avoid impairing the pictures taken by cameras 1 and 2, their object lenses were equipped with red filters. In initial experiments, a dim halogen lamp illuminated the fish while it was in the field of view, allowing to regain its kinematics from camera 1. In later experiments, an additional camera with a green filter (camera 4) was mounted above the experimental tank to improve image quality for kinematic analysis. Camera 4 was also synchronised with the light sheet illumination. Cameras 1, 2 and 4 were type DMK803 (The Imaging Source, Bremen, Germany); camera 3 was a surveillance camera (Conrad Electronic, Hirschau, Germany).

Flow measurements

A custom-made PIV device was used to measure the flow around and behind the freely swimming fish. Tracer particles (Vestosint 1101, Degussa AG, Marl, Germany) were seeded into the water and illuminated in six horizontal light sheets generated with modulated laser diodes (wavelength 650 nm, output power 50 mW) (Fig. 1). Vestosint is a synthetic material of density 1.02 g mm^–2. Median particle size was 74±5 μm (95%<147±5 μm). The light sheets were spaced equidistantly (vertical distance 10 mm for Colomesus and 12 mm for Lepomis and Thysochromis). The laser diodes were modulated using a micro-controller (Motorola 68HC05, Schaumburg, IL, USA) so that only one of the six planes was illuminated at a time, and the illuminated plane was switched to scan the water volume once in 12 video frames (equivalent to once per 0.48 s). Particle images were taken with one or two CCD cameras (termed cameras 1 and 2) mounted above the tank. Each camera had a chip size 768×582 pixels, frame rate was CCIR standard (50 half frames s^–1). The beginning of each half frame taken by the cameras was synchronised with the light sheet illumination. There was no need to adjust the focus of the cameras as light sheets were switched because the distance between the cameras and the light sheets exceeded tenfold the longest distance between light sheets. Pictures were stored on S-VHS video recorders (Philips VR1000, Eindhoven, The Netherlands) together with a synchronisation signal that served to determine the light sheet for each picture. Video frames were digitised using an MJPEG computer board (Miro Video DC30, Pinnacle Systems, Mountain View, CA, USA). The MJPEG board was set to a compression factor of 1:12, which does not affect the PIV results (Freek et al., 1999).

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Fig. 1.

Experimental set-up in (A) top view and (B) front view. Fish were trained to swim on a straight line through the middle of the experimental tank to reach a goal compartment where they received a food reward. Before a trial, an individual fish was kept in a starting compartment for at least 5 min. Six horizontal light sheets (thin illuminated layers of laser light) were installed in the middle of the tank. The water was seeded with neutrally buoyant particles. Cameras 1 and 2 recorded the movements of the seeding particles from above the tank. Cameras 3 and 4 recorded the fish's movements.

Data analysis

Since the cameras could only be synchronised to the start of a half frame, not a full frame, half frames were, if necessary, rearranged using Delphi 2.0 (Borland, Scotts Valley, CA, USA). Particle images were analysed using custom-made programs in MatLab 5.1 (The Mathworks, Natick, MA, USA). Analysis of particle displacement followed the principles of digital particle image velocimetry (Willert and Gharib, 1991), but was improved using the spurious vector detection technique described by Hart (2000). The interrogation area (the subimage that is subjected to the correlation procedure) was 32×32 pixels with an overlap of 50%, resulting in vector fields of 43×31 vectors. The images of the fish's silhouette from cameras 1 and 3 or cameras 4 and 3, respectively, were analysed manually using Scion Image (Scion Corporation, Frederick, MD, USA).

The analysis of fish-generated water disturbances is complicated by several specific problems. Firstly, water flow may be highly divergent and vortex structures may be small compared to the field of view. This makes the application of standard data post-processing procedures (e.g. Høst-Madsen and McCluskey, 1994; Westerweel, 1997; Hanke et al., 2000) undesirable. Where image quality was sufficient, which was the case in the narrow water tank (tank 2), we discarded post-processing of our velocity data. Velocity vectors were validated using three criteria. (1) The peak position criterion suggested by Hart (2000). Vectors were not used when the position of the first correlation peak in the product of two adjacent correlation planes differed from its position in each of the planes by more than 0.1 times the width of the interrogation area. (2) A peak height criterion. Vectors were assigned a status depending on the height of the correlation peak relative to its surrounding. (3) A velocity criterion. Velocity vectors that corresponded to a particle displacement of more than 0.3 times the width of an interrogation area and were out of the velocity range expected from comparison with similar measurements were discarded.

Secondly, the dynamic velocity range is very broad. The ratio of the highest to the lowest water velocities at a given point in time may easily reach an order of several hundred. This problem was solved by multiple-time-scale processing. Different time scales were not only used for different stages of the ageing of the water disturbances (cf. Hanke and Brücker, 1998), but also for different locations at a given point in time. Analysis started with a time spacing of 12 frames for each interrogation area, followed by a time spacing of 1.0 and 0.5 frames, depending on velocity and vector status information. Time spacing between 12 frames and 1 frame was not possible because of the scanning procedure (see above).

Thirdly, the fish moving through the camera's field of view is a foreign body, which can lead to false velocity information when using correlation techniques. This problem was solved by manually removing false vectors caused by the fish or its shadow using custom-made programs in Delphi 2.0 (Borland). Because our study focused on the long term development of the wake, the fish was in the field of view in less than 2% of the images.

Fourthly, in still water the fish's movements are generally not reproducible from trial to trial. This problem was overcome by the scanning technique, which allowed derivation of three-dimensional information from a single trial.

Statistical analysis

We compared the width of the trails caused by the three fish species using a U-test (e.g. Sachs, 1997) and the distribution of water velocities in the trails by comparing two trail indices TI1 and TI2, following the principles of discriminant analysis (e.g. Deichsel and Trampisch, 1985).

Selection of tank and tank width

Animal-generated flow is often difficult to study because it is usually neither reproducible nor stationary or two-dimensional. To achieve reproducibility, fish were forced to swim in a flow tank (for references, see Drucker and Lauder, 2003). This method is appropriate if one is interested in the structure of the wake measured close to the fish. However, if one is interested in the ageing of fish-generated wakes, flow tanks are usually too short. Furthermore, they add a certain degree of turbulence to the water that can override weak flow structures. The use of S-DPIV in still water copes with these problems.

All of our measurements were therefore done in still water tanks. To improve image quality (see Materials and methods) and thus reduce the number of unreliable vectors, which in our wide tank could be as high as 25% compared to 1% in the narrow tank, most measurements were performed in the narrow tank (tank 2). The disadvantage of the narrow tank is that its walls may have restricted the lateral spread of the fish's wake or changed its structure. In Lepomis, a portion of the wake usually reached the border of the field of view, equivalent to two thirds of the width of tank 2, in a short time (Fig. 5A). However, the clear vortex structures of the fish wakes even after 60 s in Lepomis that can be seen in Fig. 3 and the lateral spread of the wakes of Thysochromis (Fig. 5C, narrow tank) compared with the data from the broad tank (Table 1) led us to believe that the wall effect can be small in some swimming manoeuvres. On the other hand, a lateral spread of a goldfish's wake that exceeded 50 cm has been reported elsewhere (Hanke et al., 2000). Hence tank width should always be adapted to the specific question asked.

Hydrodynamic wake detection

Our data show that even small fish produce wakes that can indicate the presence of the wake generator for at least 0.5–5 min, depending on body length and swimming style. This makes fish-generated wakes a potential source of information for piscivorous fish (cf. Pohlmann et al., 2001) and mammals (cf. Dehnhardt et al., 2001). While wake-following behaviour in the viscous length scale of planktonic organisms has been attributed to chemoreception (Yen et al., 1998), in the swimming fish range of Reynolds number the mechanical component of the wake is an additional cue that fish cannot easily avoid.

Why should the detection of hydrodynamic trails with mechanosensory systems be comparable or even superior to the use of other sensory systems operating in the dark, especially the acoustic system? Consider a prey fish that swims in a manner similar to the Lepomis in our trial 2 (Table 1). With an average speed of 44.6 cm s^–1 (5.2 body length s^–1; Table 1), it can cover a range of more than 25 m within 1 min. Thehydrodynamic trail after 1 min still contains velocities higher than 1.7 mm s^–1 (Table 1), which are above the detection threshold of the lateral line (Görner, 1963). To estimate the acoustic field produced by a small fish 25 m away, we substitute the tail fin by a dipole and use the dipole equations quoted by Kalmijn (1988). This approximation is justified as long as the fish does not change its volume, i.e. the monopole moment is zero; the quadrupole and higher moments fall off with the distance much faster than the dipole moment. Assuming a dipole radius of 10 mm, an oscillation amplitude of 10 mm and an oscillation frequency of 5 Hz, the water velocity produced by the dipole at a distance of 25 m is as small as 1.8×10^–11 m s^–1, and the corresponding acceleration amplitude is 3.1×10^–10 m s^–2. The hearing threshold of a typical piscivorous predator, the perch Perca fluviatilis, is in the order of 10^–4 m s^–2 (Karlsen, 1992). Thus while the hydrodynamic sensory system most probably responds to the hydrodynamic trail, acoustic perception of the prey is impossible in this example. The same calculation with the Thysochromis data from trial 12 (see Table 1, lowest average swimming speed of all trials presented – this may be closer to the fish's routine speed than the Lepomis speeds are) leads to a distance of more than 4 m covered by the fish in 1 min. The dipole in our model causes an acceleration amplitude of 1.5×10^–7 m s^–2 at a distance of 4 m, which is again well below the hearing threshold of the predator. The hydrodynamic trail contained water velocities of 0.96 mm s^–1 and would most probably be sensed.

We do not know how often the high swimming speeds of Lepomis observed in our experiments (average up to 5.2 BL s^–1, where BL is body length) occur in nature. Due to experimental constraints Lepomis, like all other fish, was trained to swim to a flashing light where it expected a food reward. In this situation, high swimming speeds represented the spontaneous behaviour of our Lepomis. In nature the upper range of swimming speeds may well be reached in various situations including defending a breeding or feeding territory, reproductive interactions or predator–prey interactions. A 10 cm goldfish Carassius auratus can reach 11.4 BL s^–1 for 0.1 min (Tsukamoto et al., 1975). In addition, the results from the fast-swimming Lepomis give a first estimate for the wakes of fast-swimming fish that have not been investigated yet. Prolonged speeds in a 25 cm herring Clupea harengus were 5.5 BL s^–1 for 3 min (He and Wardle, 1988; Videler, 1993).

It has not been shown that the hydrodynamic wake of a prey fish unequivocally reveals the species or the swimming style to a predator. However, in the examples presented here, wake patterns were diverse (Figs 3, 5, 6), while vortex structures in the length scale of the flow-generating structures were observed (Fig. 3). Since many hydrodynamic sensory systems measure water flow in multiple points (Bleckmann, 1994), it is likely that a predator can extract information beyond the mere presence of a wake and learn to interpret such flow structures to a certain degree.