The predictive start of hunting archer fish: a flexible and precise motor pattern performed with the kinematics of an escape C-start.
Once their shots have successfully dislodged aerial prey, hunting archer fish monitor the initial values of their prey's ballistic motion and elicit an adapted rapid turning maneuver. This allows these fish to head straight towards the later point of catch with a speed matched to the distance to be covered. To make the catch despite severe competition the fish must quickly and yet precisely match their turn and take-off speed to the initial values of prey motion. However, the initial variables vary over broad ranges and can be determined only after prey is dislodged. Therefore, the underlying neuronal circuitry must be able to drive a maneuver that combines a high degree of precision and flexibility at top speed. To narrow down which neuronal substrate underlies the performance we characterized the kinematics of archer fish predictive starts using digital high-speed video. Strikingly, the predictive starts show all hallmarks of Mauthner-driven teleost C-type fast-starts, which have previously not been noted in feeding strikes and were not expected to provide the high angular accuracy required. The high demands on flexibility and precision of the predictive starts do not compromise their performance. On the contrary, archer fish predictive starts are among the fastest C-starts known so far among teleost fish, with peak linear speed beyond 20 body lengths s(-1), angular speed over 4500 deg. s(-1), maximum linear acceleration of up to 12 times gravitational acceleration and peak angular acceleration of more than 450 000 deg. s(-2). Moreover, they were not slower than archer fish escape C-starts, elicited in the same individuals. Rather, both escapes and predictive starts follow an identical temporal pattern and all kinematic variables of the two patterns overlap. This kinematic equivalence strongly suggests that archer fish recruit their C-start escape network of identified reticulospinal neurons, or elements of it, to drive their predictive starts. How the network drives such a rather complex behavior without compromising speed is a wide open question.
Wöhl
Saskia
S
Universität Erlangen-Nürnberg, Institut für Zoologie II, Staudtstrasse 5, D-91058 Erlangen, Germany.
Schuster
Stefan
S
D000054:Acceleration; D000818:Animals; D001696:Biomechanical Phenomena; D004924:Escape Reaction / Q000502:physiology; D009039:Motion Perception / Q000502:physiology; D009048:Motor Skills / Q000502:physiology*; D010473:Perciformes / Q000502:physiology*; D011235:Predatory Behavior / Q000502:physiology*; D013550:Swimming / Q000502:physiology*; D013997:Time Factors; D014741:Video Recording