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First published online January 8, 2007
Journal of Experimental Biology 210, 311-324 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.02646
The predictive start of hunting archer fish: a flexible and precise motor pattern performed with the kinematics of an escape C-start
Universität Erlangen-Nürnberg, Institut für Zoologie II, Staudtstrasse 5, D-91058 Erlangen, Germany
* Author for correspondence (e-mail: sschuste{at}biologie.uni-erlangen.de)
Accepted 9 November 2006
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Summary |
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Key words: sensorimotor integration, reticulospinal system, C-start, fast-start, prediction, cognition, fish, archer fish
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Introduction |
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TOPSummaryIntroductionMaterials and methodsResultsDiscussionReferences |
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;
Webb, 1975). Numerous
neurophysiological and neuroethological studies (reviewed in
Faber et al., 1989;
Nissanov and Eaton, 1989;
Domenici and Blake, 1997;
Zottoli and Faber, 2000;
Eaton et al., 2001;
Hale et al., 2002;
Fetcho and Bhatt, 2004) have
contributed to making the underlying network of identified reticulospinal
neurons perhaps the best approachable vertebrate model system for sensorimotor
integration. The escape network of fish has been studied with a wide range of
techniques, from chronic recordings in the behaving animal (e.g.
Zottoli, 1977;
Deliagina et al., 2000) to a
steadily increasing wealth of novel optical and genetic techniques
(Fetcho and O'Malley, 1997;
O'Malley et al., 1996;
Fetcho and Bhatt, 2004;
Fetcho and Higashijima, 2004;
Hale et al., 2004;
Miesenböck, 2004).
Furthermore, although some of the cellular components of the escape network
are already found in lamprey (e.g.
McClellan and Grillner, 1983;
Buchanan, 1993) the network is
by no means conservative but appears capable of rapid evolutionary changes
(Westneat et al., 1998;
Hale et al., 2002;
O'Steen et al., 2002;
Tytell and Lauder, 2002).
Previously, C-starts were not noticed in precisely aimed feeding strikes
(Fig. 1). A reason for this
could be that in an escape the purpose is simply to get away from the source
of danger, which demands a motor pattern optimized in terms of acceleration
but not necessarily in terms of precision (e.g.
Bennett, 1984). While several
studies have shown that the mean escape direction can well be set in relation
to the direction of a stimulus, the scatter around this mean direction is
large (Eaton and Emberley,
1991; Domenici and Blake,
1993; Foreman and Eaton,
1993; Domenici and Blake,
1997; Domenici and Batty,
1997; Tytell and Lauder,
2002) and it is not known whether take-off speed is tunable in
escapes. Most importantly, the angular scatter appears too large for these
responses to be used to precisely strike at a target over larger distances.
Evidently, for an escape a large scatter around a mean direction would seem to
be of clear survival advantage: it prevents predators from adjusting to the
regularities in their prey's escape pattern. For escapes that require higher
precision - for instance when collisions with school members must be avoided -
a much slower C-start is used (Domenici and
Batty, 1997; Domenici and
Blake, 1997). This, again, supports the view that evolution has
shaped the fast C-starts to be fast but not very precise. When both top speed
and high precision are needed, as in predatory strikes, fish use a
kinematically (Webb, 1976;
Harper and Blake, 1990;
Spierts and van Leeuwen, 1999;
Hale, 2002;
Schriefer and Hale, 2004) and
probably also neuronally (Hale,
2002), different fast-start pattern, the S-type start. In this,
the fish's body first bends not into a C but an S-shape. These starts usually
do not involve large turns (Domenici and
Blake, 1997; Hale,
2002) but are directed in or close to the initial pre-start
orientation of the fish. Interestingly, a detailed comparison of S-type
escapes and strikes of pike suggests that more complex neuronal circuitry is
used to drive the precisely aimed strikes, which require a longer initial
bending phase than do the escapes
(Schriefer and Hale,
2004).
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In summary, all previous evidence suggests that fish use distinct high-speed circuitry optimized to serve distinct purposes: either to (1) rapidly drive large turns, but at compromised precision, or (2) drive precisely aimed starts in a restricted angular range and at lesser performance. Here we provide an example that challenges this view: the predictive start of archer fish.
In their impressive hunting behavior (e.g.
Lüling, 1963;
Dill, 1977;
Schuster et al., 2004;
Schuster et al., 2006;
Schlegel et al., 2006), archer
fish dislodge prey by precisely aimed shots of water and catch and devour
their prey as it hits the water surface. Dislodged prey usually attains
horizontal speed and falls on a ballistic path towards the water surface. As
soon as the prey has started falling, both shooter and bystanding school
members can predict the later point of prey impact. A rapid turn aligns them
precisely to the later point of catch and the fish take off with a speed
matched to the distance they will have to cover
(Rossel et al., 2002;
Wöhl and Schuster, 2006).
The precision of the response (about 6°) is remarkable because the fish
must rapidly select turn size and take-off speed from a broad range, based
solely on sensory information sampled within less than 100 ms of the prey's
motion (Fig. 2). Therefore, the
underlying motor network must be able to combine extreme speed with both
precision and flexibility. To narrow down our search for the underlying
neuronal substrate we studied the kinematics of the predictive starts and
compared them both with the wealth of fast start patterns known in other
teleost fish (Fig. 1) as well
as directly with archer fish fast C-type escapes. Strikingly, the predictive
starts of archer fish show all the hallmarks of a fast C-start and are
kinematically equivalent to archer fish escape C-starts. Therefore the
findings imply that the same elements of reticulospinal circuitry are
recruited to drive both motor patterns.
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Materials and methods |
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Kinematic analysis
Responses were recorded from above at 500 frames s-1 using a
high-speed digital video system (NAC Hotshot 1280, NAC Europe, Stuttgart,
Germany; resolution 1280x1024 pixels, shutter speed set to 1/500 s, lens
Nikkor 35 mm 1:1.4) that monitored a sufficiently large area (about 40
cmx50 cm) in which the school initiated responses. The system allowed
recordings under normal room illumination, but the bottom of the tank was
diffusely illuminated from below (100 W lamp with diffuser) for better
contrast. For the analyses of this study coordinates of the snout, caudal
peduncle and a third point on the midline of the fish's rigid anterior body
part were manually digitized using Object Image 2.12 (by Norbert Vischer,
University of Amsterdam, based on NIH Image 1.63)
(Fig. 3). The third point was
selected as distant as possible from the snout so that its connection to the
tip of the mouth coincided with the midline of the anterior body. Two
additional points on the midline of the fish's posterior body part were
digitized when the fish were bent but were not used in the present
analysis.
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Onset of a response was easy to detect in all responses and was defined as
the first frame in which movement occurred. The end of kinematic stage 1 could
be unequivocally defined as the instant in which the fish's body was maximally
bent (Fig. 3). The end of
kinematic stage 2 and start of the subsequent take-off phase (stage 3)
(Wöhl and Schuster, 2006)
was defined as the time when the tail bent maximally in a direction opposite
to that assumed previously at the end of stage 1
(Fig. 3). Again, defining the
end of stage 2 in this way seemed appropriate and posed no problems in any of
the responses.
In our analysis a simple variable turned out to be useful: We quantified
the course of bending (stage 1) and straightening (stage 2) by means of the
instantaneous `chord length', the shortest distance from the snout to the
posterior tip of the caudal peduncle normalized to its distance in the
stretched-straight fish (Fig.
4A). Furthermore, we measured the course of angular changes in the
anterior body part, approximately from the snout to the center of mass. This
can be done accurately, as illustrated in
Fig. 4B, because the anterior
part of archer fish is relatively stiff and the stretched-straight center of
mass is placed rather posteriorly. Angular readings were sufficiently precise
(about 2°, see above) to allow determination of angular speed and
acceleration (Fig. 5) by means
of 5-point moving linear regression analyses (Origin 7.5). Complete time
courses of angular speed and angular acceleration could be reliably analyzed
for most of the 110 responses analyzed in detail in this study. The maximum of
angular speed (and angular acceleration) during the response could
unequivocally be determined; in two-peaked curves with two distinct maxima the
larger one was taken. Note that the turning rates of the head and of the CM
are expected to be related (Domenici,
2001; Domenici et al.,
2004), particularly because the anterior part of archer fish is
rigid and because the CM-to-head direction is in line with the anterior part
both initially before the start as well as during the take-off, as is perhaps
best seen in the silhouettes shown in Fig.
3.
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Domenici and Blake, 1997) and
(2) only if the frame rate is large enough that CM displacement between frames
is small compared to body length (which was the case in our recordings).
However, the error introduced by the digitizing procedure was intolerably high
for a precise determination of speed and acceleration. It could be efficiently
reduced in a simple but laborious manner: each frame of a given sequence was
independently digitized 3 times and the respective coordinates were averaged
over the independent digitizations of the frame. This worked very well, as
evident in the acceleration plots shown in
Fig. 6 and the course of
accumulated distance in Fig.
7C. Rare cases, in which reflected water droplets of the shots
caused surface waves and correspondingly large frame-to-frame deviations in
the apparent position of corresponding points on the fish's body, were not
analyzed. The actual (large) numbers of speed and acceleration profiles that
were available for analysis are reported in
Table 1 and in the text.
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Predictive starts
Predictive starts to dislodged prey items were elicited as described
previously (Rossel et al.,
2002; Wöhl and Schuster,
2006). Briefly, a dead fly (Lucilia sp.), in a few cases
a food pellet (a Sera Cichlid stick, Sera, Heinsberg, Germany), was wetted and
stuck on the lower side of a transparent cylindrical disk (Plexiglas) that was
rigidly mounted so that the prey initially was at a height h=30 cm
above the water surface. The full time course of prey motion and the point of
impact were monitored in each of the responses. Besides the requirement of an
initial inclination of the responding fish of less than 45°, the following
criteria were applied to select the most informative responses for further
analysis. (1) To ensure that only predictive starts were analyzed, responding
fish clearly had to have their turns completed and to start their take-off
while prey was still falling. (2) The area around the responding fish had to
be free of obstacles in the interval from turn-onset to the first 20 ms after
take-off. (3) To exclude the possibility that a fish could simply follow the
target's motion, a minimum angle of 10° was required between the initial
direction in which the fish's length axis was pointing and the direction in
which the target took off. A set of N=76 responses was thus obtained.
These showed all hallmarks of archer fish predictive starts
(Rossel et al., 2002;
Wöhl and Schuster, 2006):
turns of various angles had to be made that accurately aligned the fish
towards the later point of prey impact. This alignment was already achieved
right at the beginning of the prey's falling motion and 109±62 ms (mean
± s.d.; N=76) before its impact. 17 of the reponses came from
the shooters that had actually dislodged the prey, 59 came from bystanders.
The mean error of the aim taken in the responses [sign convention as used
previously (Wöhl and Schuster,
2006)] was not significantly different from zero (average
-0.5°, s.d.=11.9°, N=76). Moreover, the fish that made the
catch took off with a speed matched to both distance and time until prey
impact in the way described previously [correlation between take-off speed and
distance/remaining time: r2=0.31, P<0.002 (see
Wöhl and Schuster,
2006)]. Turn-sizes ranged from 4° to 156° and take-off
speed ranged from 0.4 to 2.1 m s-1 (see
Table 1). Because it does not
affect any of the conclusions reached, no attempts were made to remove a weak
correlation (r2=0.104, P<0.005, N=76)
that existed between the distance the responding fish had to cover and the
size of the aligning turn it had to make towards the later point of
impact.
Escape starts
Escape starts were difficult to elicit and fish habituated rapidly. A
maximum of 10 escape-stimuli were given per day and a recovery period of at
least 30 min was allowed between stimuli. Two different techniques yielded a
total of 34 escape C-starts for direct comparison with predictive starts.
Escape starts were experimentally elicited either by sudden ejection of air
bubbles from a tube mounted on the bottom of the tank below the school (15
responses) or by releasing a white styrofoam sphere (diameter 10 cm) from an
initial height of 30 cm above the water surface (19 responses). In either
case, the responding fish were close to the water surface, patrolling for
prey, much as they were when predictive starts were elicited. The analyzed
responses were selected according to various criteria: (1) a less than 45°
initial inclination with respect to the water surface was required; (2) only
sequences that were completed (including the first 20 ms of take-off) within
the field of view of the imaging system were selected; (3) as with the
predictive starts, no obstacles were allowed around the responding fish from
onset of the turn till the first 20 ms after take-off; (4) responding fish had
to be stationary prior to their escape start. Within the set of escapes
recorded, the ranges of turning angle and of take-off speed were roughly
comparable to those in the set of predictive responses. Turn-size ranged from
1.4° to 128° and take-off speed varied from 0.3 to 2.2 m
s-1 (Table 1).
Turn-size and subsequent take-off speed were not correlated
(r2=0.000, P=0.994).
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), is located
rather posteriorly, immediately behind the stiff anterior body part, at about
half (47±1%; mean ± s.d.) of the fish's body length. Hence, the
rigid anterior body part can be used as a convenient indicator of turning
angle, and separating the rotational and translational shifts that occurred in
the 2 ms between subsequent images could be done simply in the way illustrated
in Fig. 4. In the start shown
in Fig. 3, the typical bending
of the fish's body into a C-shape is clearly evident, as are the two kinematic
stages. In this start, the final aim was already reached by the end of
kinematic stage 1 and was adhered to, while large angular changes occurred on
the posterior body part of the fish as it straightened in kinematic stage 2.
The time courses of body bending, accumulated angle and accumulated distance
of this start are shown in Fig.
4 (filled circles). For comparison,
Fig. 4 also shows the
respective time courses for a second predictive start that required a larger
angle of turning (open circles).
All predictive starts were of the single-bend type (sensu
Domenici and Blake, 1997),
i.e. the sense of turning was constant within each of the two stages. By the
end of stage 2 the posterior part of the midline was usually (i.e. in 73 of 76
responses) very slightly bent in a direction opposing that assumed at the end
of stage 1.
Performance
Although archer fish predictive starts followed the classic C-start
pattern, we expected them to be either of the slow type (cf.
Fig. 1) or to be compromised in
other ways compared to fast C-starts of other teleost fish because of the high
demands on accuracy and complex sensorimotor integration involved in the
behavior. However, not only is the performance uncompromised but archer fish
predictive starts are among the fastest C-type starts known: the bending into
the C-shape takes less than 60 ms, in some cases only 10 ms. Also the
subsequent straightening occurs within less than 88 ms, in some cases within
only 12 ms. The total duration of the maneuver was always below 146 ms and
could occur in as little as 30 ms. A detailed look at further kinematic
variables underscores the striking performance
(Table 1). Maximum angular
speed was attained in the initial bending phase, on average 13±5
msbefore onset of stage 2, and ranged from about 400 deg. s-1 to
over 4500 deg. s-1. The peak angular acceleration was reached very
rapidly (3.0±0.7 ms; mean ± s.d., N=67) after response
onset and 21.1±6.5 ms (mean ± s.d.) before the maximal bending
was achieved. Angular acceleration reached impressive values of up to 450 000
deg. s-2. Peak linear speed was attained 20.2±14.6 ms (mean
± s.d.) after onset of stage 2 and reached levels of up to 2.1 m
s-1, or 24.4 BL s-1. The linear acceleration
was up to 120 m s-2, i.e. about 12x g
(acceleration due to gravity).
Variability matched to the predictive task
The turns the fish had to make as well as the required speed of take-off
varied substantially among our recorded responses. Turns had to be chosen in a
range from 4° to 156° and take-off speed (measured as the speed
attained in the first 20 ms that follow the fast-start, see Materials and
methods) in a range of 0.4 to 2.1 m s-1, depending on the fish's
initial orientation and distance from the prey's later impact and the
remaining time till impact. As expected, the kinematics of the fast-start
pattern must somehow reflect this need for variability. To show the range of
kinematic variations among our recorded predictive starts,
Fig. 7 reports the full
spectrum of time courses of three basic kinematic characteristics of the
fast-starts: the bending and straightening of the fish's body
(Fig. 7A), the accumulated
angle of turning (Fig. 7B), and
the accumulated displacement (Fig.
7C). Within the pattern of variations the following general
features are worth noting: (1) the durations of the two kinematic phases are
weakly correlated (r2=0.266, P<0.0001)
(Fig. 8A, red circles). (2) The
rate of changes in `chord length' (see Fig.
4A) usually tends to be symmetrical in the two kinematic stages,
i.e. rapid bending of the fish's body tends to be followed by rapid
straightening. However, the coupling of bending rate and straightening rate is
weak (r2=0.288, P<0.0001)
(Fig. 8B, red circles). (3)
Although the major variation in the courses of accumulated angle occurs in
stage 1, stage 2 also can introduce some angular variation. This was
particularly evident in the large turns. In these, turning usually continued
during kinematic stage 2. The stage 2 angular changes (i.e. angular deviation
between end of stage 1 and end of stage 2) were in the range
5.7±16.6° (mean ± s.d., N=76). (4) The course of
displacement is variable in both kinematic stages. The basic underlying
pattern is a two-peaked acceleration with one peak in each stage. The height
and timing of the two peaks is variable, but the peak in stage 1 is usually
the larger one.
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The pattern of significant correlations between kinematics and take-off speed is far less clear. Expectedly, maximal linear speed attained (r2=0.382, P<0.0001, N=72) as well as the maximal acceleration (r2=0.382, P<0.0001, N=72) correlated well with take-off speed. These apparently trivial correlations are still important to note, simply because they show that the fast-start kinematics is indeed of relevance for the subsequent take-off and that this is not solely determined, for instance, by fin strokes after the end of stage 2. Among the nontrivial fast-start kinematic variables that determine take-off speed the rate of straightening in stage 2 was the best predictor (r2=0.216, P<0.0001, N=76) (Fig. 10, filled circles); however, the correlation is weak. The rate of straightening is virtually unrelated to the maximum bending achieved by the end of stage 1 (r2=0.079, P<0.02, N=76), which was the best predictor of turn size. The correlation between the rate of straightening and the previous rate of bending (Fig. 8B) may explain two other weak correlations that existed between take-off speed and two stage 1 kinematic parameters: the rate of bending (r2=0.156, P<0.001, N=76) and the maximum angular velocity in stage 1 (r2=0.138, P<0.01, N=72).
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In summary, turn size of the predictive starts seems to be set by how much the fish bends into the C-shape. The rate at which the bending is then subsequently released tends to set the speed of the subsequent take-off. But a number of other yet unclear effects are likely to contribute to take-off speed and to making speed independent of the size of the preceding turn.
Direct comparison: archer fish escape-starts
The complex and precisely aimed archer fish predictive start is evidently a
typical C-type start. Comparison with C-starts of other teleost fish shows
that is a top performance fast C-start (see summary of teleost fast-start
patterns shown in Fig. 1). This
would imply that the Mauthner-associated reticulospinal network underlies the
response and, in turn, that this network is fully capable of driving a complex
behavior in which flexible matching to sensory data, top speed and precision
must be achieved at the same time. However, such a conclusion would be
premature because two major critical issues must first be resolved: it is not
known whether (1) archer fish C-type escapes do follow the pattern found in
other teleost fish at all and (2) their escapes, even if they were of the
C-type, would still be much faster than their predictive starts and their
speed would be related to the speed of predictive starts in the same way as is
the speed of a slow versus a fast C-start in other teleost fish. In
the latter case, archer fish predictive starts, though fast compared to other
teleosts, would still have to be considered as slow C-starts by archer fish
standards, and this would imply that they recruit more complex circuitry than
the escapes. Therefore, this section aims specifically at testing two
hypotheses: (1) archer fish C-type escapes deviate from the pattern typical
for teleost Mauthner-driven C-type escapes, and (2) the speed of archer fish
escapes relates to that of their predictive starts in the same way as the
speeds of fast and slow C-starts do in other teleost fish.
To test these hypotheses we analyzed a large number of archer fish escape starts in parallel to the characterization of their predictive responses. This allowed us to compare, in the same group under the same conditions and with same techniques of recording and analysis, the kinematics, performance and degree of variability in archer fish predictive starts and escapes. The first hypothesis is readily falsified: all escape responses were of the C-type. No S-type escapes were observed.
Archer fish escapes share with their predictive starts a high degree of kinematic variability. The spectrum of variations in escape kinematics is documented in Fig. 11 in the same way as it was for the predictive starts in Fig. 7. The escapes reveal similar relations among stage 1 and stage 2 kinematic variables as did the predictive responses. For instance, the durations of kinematic stage 1 and 2 were weakly related (r2=0.120, P<0.05, N=34; Fig. 8A, blue circles) and the rates of bending and subsequent straightening correlated well (r2=0.418, P<0.0001, N=34; Fig. 8B, blue circles). Furthermore, the variations in escape kinematics could also be linked to the variations in their take-off speed and turn size. The best predictors of these variables were the same as those found for the predictive starts: turn size correlated well with the maximum bending reached by the end of stage 1 (r2=0.473, P<0.0001, N=34) (Fig. 9, blue circles) and take-off speed correlated well with the rate of straightening in stage 2 (r2=0.484, P<0.0001, N=34) (Fig. 10, blue circles). In none of the regression lines shown in Figs 8, 9, 10 were the slopes and y-axis intercepts different in the escapes (blue lines) and in the predictive starts (red lines).
The kinematics of archer fish C-type escapes closely followed the pattern described in all other teleost fish studied so far and particularly that of the archer fish predictive starts described above. This is illustrated, for instance, in Fig. 12 in which a predictive start (A) and an escape start (B) are shown that both involved a similar degree of turning. The performance characteristics of archer fish C-type escapes are reported in Table 1 together with those of the predictive starts. Clearly, the performance of archer fish escapes is not superior to that of the predictive starts and, most importantly, is not related to it in the way a teleost slow C-start would be to a fast C-start. The most important conclusion that must be drawn from Table 1 is that the respective kinematic variables fully overlap. Besides their range, the table reports means ± s.e.m. for easy comparison with reports on other teleost fast starts. However, it must be emphasized that much care is needed in interpreting these averages, because the responses are variably matched to the task (which requires a certain turn and speed). Consider, for instance, the average total duration of the escapes, which was about 10 ms longer in the escapes. However, this does not mean that the total duration of archer fish escapes is in general slightly longer than that of the predictive starts. Though the ranges of turning were comparable, the average turn was slightly larger in the escapes (by about 13°) and this could account for their apparently longer average total duration. Nevertheless, even when the apparent difference in total duration - or any other apparent difference in the variables of Table 1 - was meaningful then clearly these differences are small and do not support the classification of predictive starts as slow C-starts compared to fast C-start escapes.
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Performance of escapes and predictive starts
Despite the fine-tuning of the kinematics to the turn size and speed
required in the predictive task, archer fish C-starts are among the fastest
known in teleost fish. Their top linear speed reaching more than 24
BL s-1 and maximum linear acceleration of up to 120 m
s-2 (Table 1) are
perhaps paralleled only by the fast-starts of pike, another acceleration
specialist. In pike, direct accelerometric measurements during escapes yielded
peak linear acceleration of up to 120 m s-2
(Harper and Blake, 1991). For
comparison, trout show peak linear acceleration of about 40 m s-2
(Webb, 1978b). The C-type
escapes of pike took about twice as long as archer fish C-starts. The pike's
top linear specific speed of 10.5 BL s-1
(Harper and Blake, 1991) is
readily surpassed by archer fish. Part of the outstanding performance of
archer fish, when compared to other acceleration specialists, might seem
attributable to the higher temperatures enjoyed by archer fish. However, this
seems unlikely, because a rather low Q10 of only 1.2 describes the
temperature-dependence, for instance, of maximum speed [in trout (e.g.
Johnson et al., 1996)]. At any
rate, the most important point can safely be made: archer fish fast-starts are
among the fastest C-type starts known in teleost fish.
The angular performance of the predictive starts is equally impressive. It
is perhaps best appreciated by comparing it with the impressive performance of
dipteran flies, such as Drosophila, whose body saccades can rotate
the fly by 90° in only 50 ms (e.g. Fry
et al., 2003). This implies an angular speed of 1800 deg.
s-1 or an angular acceleration of up to about 36 000 deg.
s-2. By comparison, an archer fish fast start rotates the anterior
body part of the fish at an angular speed up to 4500-5000 deg. s-1
and tops the impressive angular acceleration of the fly by at least an order
of magnitude (see Table 1).
Angular speed and acceleration unfortunately have not comprehensively been
analyzed for many teleost fast starts. However, the evidence available
suggests that archer fish predictive turns are among the fastest known in
teleost fish. For the C-type escapes of an acceleration specialist, the
muskellunge, Hale reports maximum angular speed values of about 2500 deg.
s-1 and a maximum angular acceleration of 200 000 deg.
s-2 [fig. 5 in Hale
(Hale, 2002)]. For the bichir
Polypterus, a mean maximum angular speed of 3600 deg. s-1
has been found (Tytell and Lauder,
2002) and a similar maximum angular speed was also in reported in
goldfish (Eaton et al.,
1982).
An interesting side aspect of the remarkable performance of archer fish is
that their fast-starts are initiated directly beneath the water surface where
the fish patrol, looking for aerial prey. It has been argued that starts
performed close to the air-water interface should be energetically costly
because of the energy lost to the production of surface waves (e.g.
Hertel, 1966;
Webb et al., 1991). In case
that Hertel's original estimate of an up to fivefold drag increase close to
the surface is correct, then the fast-start performance of archer fish would
be even more remarkable and would seem to imply that archer fish have found
efficient ways to diminish the costly surface waves during their fast-starts.
The body form of archer fish seems perfectly optimized for producing powerful
accelerations even against large drag. As is common among acceleration
specialists, in archer fish large body depth is placed posteriorly
(Webb, 1984) and the dorsal
and anal fins are fully erected after stage 1, thus maximizing the amount of
water that is accelerated backwards.
Using C-start circuitry to drive archer fish predictive starts
Previous work has suggested various distinct fast-start patterns that each
appear to be optimized in a particular aspect (see
Fig. 1). Fish could select the
appropriate motor pattern depending on whether high accuracy, top acceleration
or a wide range of turning angles is required. The underlying notion that
seems to be supported by the range of distinct patterns is that a single
program is unable to fulfil all requirements. This is very reasonable because
a trade-off is likely to exist between achieving top acceleration, accuracy, a
broad range of output variables (i.e. angle and speed at take-off) and a large
amount of variability required in matching the motor program according to
actual sensory information. Taking this perspective, the present
classification of fast-starts (Fig.
1)(Domenici and Blake,
1997; Schriefer and Hale,
2004) could be understood as follows. (1) The S-start offers speed
and accuracy but only limited angular range. It has therefore previously been
considered the only fast-start used in hunting. (2) C-starts offer speed and a
large angular range but limited accuracy. Previously no predator has been
reported to use a C-type fast start and this seemed fitting with the
apparently large scatter around a mean escape direction chosen for a fixed
stimulus position. (3) Most tellingly, fish that do perform fast C-starts have
been found to resort to a slower form when high accuracy is required. This
slower form probably involves more neurones of the reticulospinal network
(Domenici and Batty, 1997;
Domenici and Blake, 1997) and
has been described as a slow C-type start that combines large angular range
and accuracy but allows only reduced speed.
The particular situation faced by hunting archer fish is not easily
fulfilled by any of these three known patterns. The wide angular range
required of the response seems to exclude the precise S-type pattern and,
combined with the precision needed, would only allow for a slow C-type start.
However, the predictive starts must be fast. This is because of (1) the
competition within their own school, (2) the risk of an escape of the downed
insect and (3) the heavy pressure from other surface-feeding fish whose
mechanosensory system is immediately alarmed (e.g.
Bleckmann, 1993) as soon as the
prey actually impacts the surface. In fact, juvenile (surface-feeding) belonid
fish are usually found together with archer fish in various biotopes in
Thailand, outnumbering the archer fish so greatly (S. Schuster, unpublished)
that these would appear to have little chance of making a catch unless they
are already on their way long before the belonids are alarmed.
Matching the motor pattern to the initial values of prey movement
The advent of digital high-speed video has enabled researchers to analyze a
large number of fast-starts in detail and this has disposed of the earlier
views (e.g. Webb, 1976;
Eaton et al., 1977) that
fast-starts are partly stereotypic. Our results, together with those of others
(e.g. Tytell and Lauder,
2002), clearly demonstrate how extremely variable a C-type fast
start can be in both of its two kinematic stages. The defined neuroethological
context of archer fish predictive starts (see
Fig. 2) may be helpful in
showing that these variations are not simply `noise' within the motor system,
but rather are the adjustments by which the fish tune fast-start kinematics to
the task, i.e. set specific initial speed levels and turn angles. The findings
may contribute to the understanding that characterizing an `average'
fast-start performance may be of little use unless the `desired' motor output
(i.e. the turning angle and take-off speed) is known.
An interesting detail of archer fish predictive starts is that the angle of turning must not be coupled to the take-off speed. How can this be realized in a C-type start? The problem here is that a large bend is needed for a large turn. But kicking out of strong bending should then lead to high acceleration and high take-off speed so that larger turns would also lead to large take-off speed. How are the two variables decoupled? To do this archer fish appear to modulate the speed at which a given degree of bending is released. Lower take-off speed from a given state of bending is achieved by lowering the rate of straightening.
Implications for reticulospinal control
The finding that predictive starts are fast C-starts implies that the
underlying small network of paired identified reticulospinal neurons is in
principle capable of the rather complex high-speed processing required to
drive the predictive responses. At present it is a wide open question which
specific neuronal computations contribute to the required precision and tuning
and how the input structure (or the recruitment of different parts of the
system) would decide the obviously different directionality of escapes and
feeding starts. It comes as no surprise, however, that the network does hold
an enormous computational potential that could be used in the task. Of these
we would like to emphasize three aspects.
(1) Dendritic integration. The wide dendritic integration fields of the Mauthner cells and their serial homologues are suited to integrating a multiplicity of inputs in yet unknown ways. Moreover, each cell receives inputs via both chemical and electrical synapses, which adds to the richness of possible computations.
(2) Distributed processing. While firing of the Mauthner cell usually
appears to elicit the C-type start in intact fish, it has long been known that
the start can also be elicited in the absence of the Mauthner cell. This
suggests distributed processing that also involves the other cells and in
which missing cells can be partly substituted by the remaining ones. The first
study to suggest this was, to our knowledge, by Kimmel et al.
(Kimmel et al., 1980). Using
developmental deletions in embryonal zebrafish this study showed that
zebrafish without Mauthner cells are capable of fast-start with no reduction
in latency but with a slightly reduced performance. In goldfish, Eaton et al.
(Eaton et al., 1982) found
fast-starts of equal performance but longer latency after the Mauthner cells
had been destroyed. Perhaps most telling are results of in vivo laser
ablation in larval zebrafish (Liu and
Fetcho, 1999). This study showed significant increases in response
latency and a striking decrease in performance only if the Mauthner cells are
killed together with the two largest segmentally related (Mauthner like)
hindbrain cells (called MiD2cm and MiD3cm)
(Liu and Fetcho, 1999) (see
also Fetcho and Higashijima,
2004). It is therefore clear that distributed processing occurs in
the segmentally related hindbrain cells but the various functional roles of
the distributed processing remain to be seen.
(3) Downstream adjustments. A third aspect of potential relevance is the
fine-tuning that can probably be done downstream at the level of the
motoneurones. Even after dendritic integration of sensory inputs and
distributed processing in the network has led to issuing a fast-start command
there is still plenty of room for complex adjustments. An interesting
possibility, raised by the findings of an early study
(Aljure et al., 1980), is that
other synaptic inputs to the motoneurons could preset their state to achieve
precise control over angle and speed.
Conclusions
The findings presented here have two major implications. (1) It is clear
that the teleost C-start network of identified reticulospinal neurons is
capable of driving even rather complex behaviors, such as the archer fish
predictive start, that require a high degree of precision and complex
sensomotor integration performed accurately and at top speed. (2) The
accessibility of this network, in turn, offers the fascinating perspective of
disclosing in the future how a defined vertebrate network of a small number of
identified neurons performs an impressively complex sensomotor integration
task and is tuned to do so.
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Aljure, E., Day, J. W. and Bennett, M. V. L. (1980). Postsynaptic depression of Mauthner cell-mediated startle reflex, a possible contributor to habituation. Brain Res. 188,261 -268.[CrossRef][Medline]
Bennett, M. V. L. (1984). Escapism: some startling revelations. In Neural Mechanisms of Startle Behavior (ed. R. C. Eaton), pp. 353-363. New York: Plenum.
Bleckmann, H. (1993). Role of the lateral line in fish behavior. In Behavior of Teleost Fishes (ed. T. J. Pitcher), pp. 201-246. London: Chapman & Hall.
Buchanan, J. T. (1993). Electrophysiological
properties of identified classes of lamprey spinal neurons. J.
Neurophysiol. 70,2313
-2325.
Deliagina, T. G., Zelenin, P. V., Fagerstedt, P., Grillner, S.
and Orlovsky, G. N. (2000). Activity of reticulospinal
neurons during locomotion in the freely behaving lamprey. J.
Neurophysiol. 83,853
-863.
Dill, L. M. (1977). Refraction and the spitting behavior of the archerfish (Toxotes chatareus). Behav. Ecol. Sociobiol. 2,169 -184.
Domenici, P. (2001). Scaling the locomotor performance in predator-prey interactions: from fish to killer whales. Comp. Biochem. Physiol. 131A,169 -182.[CrossRef]
Domenici, P. and Batty, R. S. (1997). The escape behavior of solitary herring and comparisons with schooling individuals. Mar. Biol. 128, 29-38.[CrossRef]
Domenici, P. and Blake, R. W. (1993). Escape trajectories in angelfish (Pterophyllum eimekei). J. Exp. Biol. 177,253 -272.[Abstract]
Domenici, P. and Blake, R. W. (1997). The kinematics and performance of fish fast-start swimming. J. Exp. Biol. 200,1165 -1178.[Abstract]
Domenici, P., Standen, E. M. and Levine, R. P.
(2004). Escape manoeuvres in the spiny dogfish (Squalus
acanthias). J. Exp. Biol.
207,2339
-2349.
Eaton, R. C. and Emberley, D. S. (1991). How
stimulus direction determines the trajectory of the Mauthner-initiated escape
response in a teleost fish. J. Exp. Biol.
161,469
-487.
Eaton, R. C., Bombardieri, R. and Meyer, D. L.
(1977). The Mauthner-initiated startle response in teleost fish.
J. Exp. Biol. 66,65
-81.
Eaton, R. C., Lavender, W. A. and Wieland, C. M. (1982). Alternative neural pathways initiate fast-start responses following lesions of the Mauthner neuron in goldfish. J. Comp. Physiol. 145,485 -496.[CrossRef]
Eaton, R. C., Lee, R. K. K. and Foreman, M. B. (2001). The Mauthner cell and other identified neurons of the brainstem escape network of fish. Prog. Neurobiol. 63,467 -485.[CrossRef][Medline]
Faber, D. S., Fetcho, J. R. and Korn, H. (1989). Neuronal networks underlying the escape response in goldfish: general implications for motor control. Ann. N. Y. Acad. Sci. 563,11 -33.[Medline]
Fetcho, J. R. and Bhatt, D. H. (2004). Genes and photons: new avenues into the neuronal basis of behavior. Curr. Opin. Neurobiol. 14,707 -714.[CrossRef][Medline]
Fetcho, J. R. and Higashijima, S. I. (2004).
Optical and genetic approaches toward understanding neuronal circuits in
zebrafish. Integr. Comp. Biol.
44, 57-70.
Fetcho, J. R. and O'Malley, D. M. (1997). Imaging neuronal networks in behaving animals. Curr. Opin. Neurobiol. 7,832 -838.[CrossRef][Medline]
Foreman, M. B. and Eaton, R. C. (1993). The direction change concept for reticulospinal control of goldfish escape. J. Neurosci. 13,4101 -4133.[Abstract]
Fry, S. N., Sayaman, R. and Dickinson, M. D.
(2003). The aerodynamics of free-flight maneuvers in Drosophila.
Science 300,495
-498.
Hale, M. E. (2002). S- and C-start escape responses of the muskellunge (Esox masquinongy) require alternative neuromotor mechanisms. J. Exp. Biol. 205,2005 -2016.
Hale, M. E., Long, J. H., McHenry, M. J. and Westneat, M. W. (2002). Evolution of behavior and neural control of the fast-start escape response. Evolution 56,993 -1007.[CrossRef]
) and take-off (speed v). The match that
the responding fish must make is illustrated by showing three ways (in
different colors) in which a given shot might dislodge the target insect. The
correspondingly different impact points P1-P3 (shown in
the respective colors) would require speed levels
v1-v3 and turning angles
