Predation experiments were performed with adult zebrafish predators (1.49 ± 0.43 cm, standard length, n = 38) and larval zebrafish prey (5-13 days-post-fertilization, n = 31). After 10 min in an experimental predation arena (figure S1), a divider between the fish was removed and recording proceeded until the predator either captured or otherwise ceased pursuing the prey. Using a high-speed video camera (500 frames s-1), we recorded the predator and prey from a dorsal perspective. Numerous (n=31) active chase sequences were recorded over variable duration. The prey's centroid position and the predator's body midline were acquired through automated image processing of our recordings. This procedure, and all data analyses, were performed by custom programming within MATLAB (v.2014b, MathWorks, Natick, MA, USA, figure S2c, see manuscript for details). For each active chase, we identified every burst and coast phase from the rate of change in the predator's heading (figure S2b). To control for variation in chase duration, we randomly selected a single burst from each sequence for further analysis (see figure S3 for complete dataset). To assess the predator's pursuit strategy, we considered how the heading changes during tail beats were related to the tail excursion and the bearing (figure 1a) by geometric mean regression. 

The control of a predator's locomotion is critical to its ability to capture prey. Flying animals adjust their heading continuously with control similar to guided missiles. However, many animals do not move with rapid continuous motion, but rather interrupt their progress with frequent pauses. To understand how such intermittent locomotion may be controlled during predation, we examined the kinematics of zebrafish (Danio rerio) as they pursued larval prey of the same species. Like many fishes, zebrafish move with discrete burst-and-coast swimming. We found that the change in heading and tail excursion during the burst phase was linearly related to the prey's bearing. These results suggest a strategy, which we call intermittent pure pursuit, that offers advantages in sensing and control. This control strategy is similar to perception and path-planning algorithms required in the design of some autonomous robots and may be common to a diversity of animals.

The data plotted in Figure 1 of the manuscript and supplemental materials are contained within the Data_tidy directory. Running the Matlab file zfPred_tidyData.m will generate unformatted timeseries figures for each sequence and unformatted versions of the scatterplots in Fig. 1c-d.