Falcon Study
Transcript of Falcon Study
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2014. Published by The Company of Biologists Ltd | The Journal of Experimental Biology (2014) 217, 225-234 doi:10.1242/jeb.092403
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ABSTRACT
This study reports on experiments on falcons wearing miniature
videocameras mounted on their backs or heads while pursuing flying
prey. Videos of hunts by a gyrfalcon (Falco rusticolus), gyrfalcon (F.
rusticolus)/Saker falcon (F. cherrug) hybrids and peregrine falcons (F.
peregrinus) were analyzed to determine apparent prey positions on
their visual fields during pursuits. These video data were then
interpreted using computer simulations of pursuit steering laws
observed in insects and mammals. A comparison of the empirical and
modeling data indicates that falcons use cues due to the apparent
motion of prey on the falcons visual field to track and capture flying
prey via a form of motion camouflage. The falcons also were foundto maintain their preys image at visual angles consistent with using
their shallow fovea. These results should prove relevant for
understanding the co-evolution of pursuit and evasion, as well as the
development of computer models of predation and the integration of
sensory and locomotion systems in biomimetic robots.
KEY WORDS: Predation, Pursuit-evasion, Avian vision, Falcon,
Motion camouflage
INTRODUCTION
How predators track and capture their prey poses a fundamental
problem in animal behavior that combines sensory perception,
neural computation and locomotion. Empirical studies in
combination with computational modeling (Pais and Leonard, 2010;Reddy et al., 2006; Srinivasan and Davey, 1995) have identified
pursuit strategies used by insects (Olberg, 2012), bats (Ghose et al.,
2006), dogs (Shaffer et al., 2004), fish (Lanchester and Mark, 1975)
and humans (Fajen and Warren, 2004; McBeath et al., 1995).
However, no empirical studies have addressed how falcons and
other birds pursue flying prey, mostly due to the difficulty of
recording their 3D flight trajectories in the field. The pursuit
strategies used by birds have evolved in the context of their unique
flight capabilities, as well as their need to pursue rapid, erratically
moving prey in complex environments. Understanding their
methods for tracking and following rapidly moving objects should
provide inspiration for the design of unmanned aerial vehicles
(UAVs) and other biomimetic robots.
Stereoscopic video methods successfully used to study flocking(Ballerini et al., 2008a; Cavagna et al., 2010; Cavagna et al., 2008)
are challenging to apply to this problem because of the wide
geographic areas covered and the rapid, unpredictable motion of
both predator and prey. However, bird-mounted sensors offer new
opportunities for this field. While miniaturized, bird-mounted GPS
sensors have been used to study navigation, flocking energetics and
RESEARCH ARTICLE
Physics Department, Haverford College, Haverford, PA 19041, USA.
*Author for correspondence ([email protected])
Received 17 June 2013; Accepted 16 September 2013
decision making in bird flocks (Biro et al., 2006; Nagy et al., 2010;
Steiner et al., 2000; Usherwood et al., 2011), the 10 Hz data
collection rate and 0.3 m spatial resolution of the GPS are of limited
use in the present context. By contrast, miniaturized bird-mounted
cameras have proved effective in studying the aerodynamics of
avian flight (Carruthers et al., 2007; Gillies et al., 2011) and bird
behavior in other contexts (Bluff and Rutz, 2008; Moll et al., 2007;
Rutz and Bluff, 2008).
Here, we consider how animal-borne video data can distinguish
between different proposed pursuit strategies that use perceptual
cues from the environment. The simplest strategy is classical pursuit,
in which the pursuer (the predator) always flies directly toward theevading prey (Nahin, 2012) (Fig. 1A), so the evaders image is
centered on the pursuers visual field. Chases consistent with
classical pursuit have been observed for honeybees (Zhang et al.,
1990), flies (Land, 1973; Land, 1993; Trischler et al., 2010) and
tiger beetles (Gilbert, 1997).
In contrast, dogs (Shaffer et al., 2004), humans (Fajen and
Warren, 2004; McBeath et al., 1995), hoverflies (Collett and Land,
1975) and teleost fish (Lanchester and Mark, 1975) have been
observed to use constant bearing decreasing range (CBDR)
(Fig. 1B). To describe this strategy, it is useful to first define a
baseline vector oriented along the pursuer-to-evader line-of-sight
and with a length equal to the pursuerevader distance; in general,
either the magnitude or direction of the baseline vector can change
during a pursuit. In CBDR, the optimal bearing angle, o (definedas the angle between the pursuers velocity and the baseline vector)
is fixed at:
where vp and ve are the speeds of the pursuer and evader, and is
the angle between the evader velocity and baseline vector. This
strategy gives the minimum interception time for constant evader
and pursuer velocity if vpve sin, and this steering law can be
realized by the pursuers maneuvering to maintain the evader at a
constant angle in its visual field.
In another strategy, termed motion camouflage (Justh and
Krishnaprasad, 2006), the pursuer maneuvers to reduce parallax-based cues on the evaders visual field (Reddy et al., 2006), a
behavior observed in dragonflies (Olberg, 2012), hoverflies
(Srinivasan and Davey, 1995), bats (Ghose et al., 2006) and humans
(Anderson and McOwan, 2003). The pursuer can achieve motion
camouflage by maneuvering to keep the evaders apparent position
on the pursuers visual field at a fixed angle (Fig. 1C); here, we
assume that the pursuers head is maintained at a constant angle with
respect to its velocity. For an evader that moves in approximately
linear trajectories with occasional changes in speed or direction, this
angle agrees with the value of o from Eqn 1 and CBDR for each
region of constant evader velocity. Eqn 1 sets the pursuers velocity
v
vsin
sin, (1)o
1 e
p
=
Falcons pursue prey using visual motion cues: new perspectives
from animal-borne camerasSuzanne Amador Kane* and Marjon Zamani
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perpendicular to the baseline equal to that of the evader, thus
ensuring that the baseline vector always points in the same direction,
as shown by the constant (absolute) value of , the constant target
angle. For this reason, this strategy is also called constant absolute
target direction (CATD). Consequently, the CATD version of motion
camouflage can be considered an extension of CBDR to the case of
maneuvering prey. CATD results in shorter times to capture than
classical pursuit for occasionally erratic prey motion.
The complexity of avian vision must be considered in
understanding pursuit strategies used by birds. So far, empirical
studies of how birds visually perceive their surroundings during flight
(Erichsen et al., 1989; Land, 1999; Martin, 2011) have not explored
how the apparent motion of prey on avian predators visual fields
influences pursuit strategies. However, research on the interactionbetween vision and flight in several avian species has provided
evidence that optical flow cues are used by budgerigars navigating
their environment (Bhagavatula et al., 2011), zebra finches avoiding
obstacles (Eckmeier et al., 2008), hummingbirds feeding (Delafield-
Butt et al., 2010; Lee et al., 1991) and Harris hawks and pigeons
landing on a perch (Davies and Green, 1990; Lee et al., 1993).
Raptors, in particular, have large, forward-facing eyes with such
a limited range of eye motion (25 deg) (Jones et al., 2007) that they
must move their heads to scan their field of view (ORourke et al.,
2010; Wallman and Pettigrew, 1985). Their effective visual fields
are complicated by the fact that each raptor eye has two foveae
(retinal regions with enhanced visual acuity) oriented at different
angles with respect to the head axis (Fig. 1D). The shallow, or
temporal, fovea has a line of sight oriented at 916 deg from theheads forward direction and is used primarily for visualizing objects
at close range (30 deg to the head axis (Frost et al.,
1990; Lord, 1956; Tucker, 2000; Wood, 1917).
As a result, a flying falcon cannot view distant prey at an optimal
angle for acute vision with its deep fovea and also face its head
forward; however, flying with its head turned to favor the deep
fovea significantly increases drag. Tucker and colleagues have
argued for a pursuit strategy in which falcons can view prey at the
optimal visual angle while keeping their heads facing forward by
flying along trajectories that resemble logarithmic spirals (Tucker et
al., 2000) (Fig. 1E). In this model, the falcon maintains a constant
angle, determined by the orientation of its deep foveal field, between
the line of sight to the prey and the falcon head axis. The falcon is
assumed to fly with its head oriented along its velocity, so its bearing
angle is fixed at f>30 deg. Using a telescopic tracking device,
Tucker and colleagues established that falcon pursuit trajectories are
indeed curved, although their results could not quantitativelydistinguish between the possible theoretical pursuit models (Tucker
et al., 2000). Walking flies have been shown to approach a stationary
blackwhite edge using a logarithmic spiral trajectory, consistent
with using a fixed bearing angle (Osorio et al., 1990).
In the optimal visual angle model, the value of fshould be fixed
at a constant value (e.g. ~30 deg or 916 deg for the deep or shallow
foveal fields, respectively), independent of predator and prey
velocities. By contrast, in CATD the value of o depends explicitly
on vp/ve and , while for classical pursuit, we always have =0 deg.
Thus, one can distinguish between these three strategies by
measuring the behavior of during pursuits: for optimal visual angle
and classical pursuit, the value of is held at the corresponding
constant values for all times, while for CATD the value of should
be constant (and equal to o) as long as the prey does not maneuver,but assumes a new constant value once the prey changes its velocity.
Even though each strategy considered here predicts the predator
maintains a constant bearing angle, it is important to note that
constant bearing angle does not necessarily correspond to a spiraling
predator trajectory. Because both CATD and CBDR result in both a
constant bearing angle and a constant baseline direction, the
predators trajectory is locally straight, not spiral.
Here, the question of which pursuit strategy is used by falcons
was addressed using the results of a study of three species of falcons
hunting avian prey in the field while wearing miniature
videocameras on their heads or backs. This approach overcomes the
naturally low pay-off ratio of filming predation in the field through
collaboration with several skilled falconers who routinely hunt with
falcons. The empirical findings were interpreted using computersimulations of a model predator pursuing flying prey using each of
the proposed pursuit strategies.
RESULTS
Computer simulations
Computer simulations were performed to model classical pursuit,
optimal visual angle and CATD, but not CBDR because the falcons
avian prey maneuvers frequently during chases. The results were
displayed as a simulated video image of the prey as it would appear
on a camera mounted on the head of the pursuing predator (see
details in Materials and methods). Prey positions on the video are
displayed as a plot of the preys horizontal () and vertical () angles
with respect to the cameras optical axis, defined as (0 deg, 0 deg)
(Fig. 2A). Typical results are shown in Fig. 2B for all times duringsample chases for each strategy considered. Constant values of
ve=7.4 m s1, vp=10ms
1 were used, and initial distance between
falcon and prey (z) was fixed at 40 m; the direction of prey velocity
was initially 6.8 deg and 90 time steps between prey maneuvers
were chosen to conform with video data discussed below. For
classical pursuit, the preys simulated (, ) was maintained at
(0 deg, 0 deg) except at very low values ofz(not shown). There,
perspective effects shifted the apparent prey location to lower
values for all models, due to the cameras location above the body
and head axes. We did not model CBDR as a distinct case, as its
predicted predator motion via Eqn 1 is already incorporated into the
RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403
List of symbols and abbreviationsCATD constant absolute target direction
CBDR constant bearing decreasing range
f focal length in pixels
I prey image size in pixels
n number of video frames analyzed
O prey actual size in m
t time
TR response timeve prey (evader) speed
vp predator (pursuer) speed
z distance between falcon and prey
constant target angle
angle between the evader velocity and the baseline
(pursuerevader distance) vector
t simulation time step
angle of the apparent prey position along the horizontal
direction
bearing angle
f optimal visual angle determined by high acuity fovea
orientation
o optimal bearing angle defined by Eqn 1
angle of the apparent prey position along the vertical direction
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algorithm used here for CATD for a maneuvering prey. For optimal
visual angle and CATD, the preys apparent location remained at
fixed angles (, ), although the non-zero response time resulted in
excursions from the optimal angles when the prey abruptly
maneuvered. Thus, for CATD, the simulations yielded a series of
apparent prey positions at values of (, ) that depended on prey and
predator velocity via Eqn 1, while for optimal visual angle at 45 deg
(Tucker et al., 2000), these values were independent of prey
maneuvering. For each choice of prey as it maneuvered, a differentaverage value (, ) was achieved after a small response time lag.
Using a realistic response time resulted in a small spread of angles
about the optimal values due to the time lag between the preys
maneuvers and the model predators response. (A more realistic
model for how the falcon accelerated during maneuvers would have
added to this variation.)
Pursuit analysis
The results of pursuit sequences recorded by bird-mounted cameras
were analyzed to measure the preys apparent motion on the video
image, its distance with respect to the falcon and the falcons roll
angle versus time. Here, a chase was defined as a sequence in which
the falcon initiated a pursuit and pursued the prey until losing it,
capturing it or attempting to capture it. The falcons studied here
[peregrine falcon (Falco peregrinus Tunstall) and gyrfalcon (Falco
rusticolus L.)/Saker falcon (Falco cherrugGray), hereafter termed
hybrid falcon] used the most common tactics observed in studies of
falcon attacks on individual birds (Dekker, 1980; Dekker and Lange,
2001) and flocks (Zoratto et al., 2010): the stoop, a steep, rapid dive
from above the prey; and the tail chase, in which the falcon usespowered level flight to pursue prey.
The head-mounted videos of the hybrid falcon recorded 15 chases
of carrion crows (Corvus corone) over fields in Belgium, while
those for the gyrfalcon recorded five chases of Houbara bustards
(Chlamydotis undulata) in the desert in Dubai. A total of 28 back-
mounted videos of peregrine falcons hunting crows and other birds
in a variety of terrains in the UK, Belgium and the USA were also
analyzed. The prey (, ) versus time data fell into three motifs: (1)
optical fixation with the preys image held at approximately constant
angles (, ) was present in 78% of the head-mounted and 54% of
back-mounted videos (Fig. 3); (2) constant angular rate of change
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403
A D
B E
C
DS S
D
ve
=0 deg
vp
ve
ve(t3)
ve(t2)vp(t3)
vp(t2)
(t3)
(t2)
(t1)
vp
vp
ve(t1)
vp(t1)
Fig. 1. Trajectories resulting from alternative pursuit
strategies. (A) Classical pursuit; (B) constant bearing
decreasing range (CBDR); and (C) motion camouflage with
the baseline held at a constant absolute angle (after Ghose
et al., 2006). In B and C, the instantaneous baseline vector,
which points from the pursuer to the evader, is indicated by a
dashed line. (D) Orientation of the shallow (S) and deep (D)
visual fields and head axis (dashed line) in raptors (adapted
from Tucker, 2000). The effect of refraction by the cornea is
not shown. (E) Logarithmic spiral trajectory resulting fromkeeping the prey at optimal visual angle. , bearing angle;
, constant target angle; , angle between evader velocity
and baseline; ve, prey (evader) speed; vp, predator (pursuer)
speed; t, time. See List of symbols and abbreviations.
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along either or was observed in 22% of the remaining head-
mounted and 29% of the remaining back-mounted videos (Fig. 3B);
and (3) erratic motion of the prey on the image was observed in theremaining videos, which occurred at close distances when prey
accelerations resulted in large angular excursions. The video data
did not indicate that falcons kept the preys image fixed with respect
to distant objects.
The hybrid falcon made saccadic head motions during two chases;
head saccades were distinguishable from prey maneuvering or
acceleration by the falcon by the very rapid (0.1 s) motions of the
entire visual field and by its rapid return to the original position in
only 0.330.12 s (mean s.d.). For one chase video that recorded
six head saccades, it was possible to extrapolate (, ) for the prey
when it was off-camera by using the distant landscapes angular
movement (, ) added to the preys values of (, ) immediately
before and after the saccade (Fig. 4A). Most of the saccadic motion
was along , with a change in the vertical direction of only=0.211.8 deg (mean s.d.). The preys extrapolated values were
=363 deg (mean s.d.) during these events; values extrapolated
immediately before and after the falcon turned its head agreed
within experimental uncertainties.
The back-mounted video could sometimes image the falcons
head motion directly during foraging for prey and pursuits (Fig. 4B).
These videos showed that peregrine falcons usually oriented their
heads in the forward direction. However, they did regularly turn
their heads from side to side to scan the world during foraging
flights, looking downward to scan the ground with either their left
or right eyes with equal frequency.
In three chases recorded by a camera on a hybrid falcon, the video
recorded a second hybrid falcon intercepting prey birds in the air.
These videos showed that the second hybrid falcon approached thecrows with a velocity directed to an angle with that of their prey
(Fig. 4C). At the pursuits conclusion, the falcon needs to have its
final velocity vector oriented directly toward the prey so it can strike
the prey with its feet (Fox, 1995; Goslow, 1971). In our videos,
230 ms before impact, the falcon was observed to splay its tail and
spread its wings in anticipation of contact; the falcons feet went
from fully retracted to fully extended in 67 ms. In addition, we
obtained similar values for the times at which raptors spread their
wings and extend their feet before impact from our analysis of high
speed video of a red-tailed hawk and peregrine falcon attacking
falconry lures (Destin, 2012).
Lateralization of vision
The head-mounted video also allowed determination of the preys
position relative to the head axis angle during pursuits. To determinewhether falcons use a preferred eye while chasing prey, we
performed a statistical analysis of the distribution of and values
during pursuits. We verified that the small (25 deg) full range of
RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403
A B
50 40 30 20 10 0 5040302010
10
5
0
120
5
(deg)
(
deg)
23.0 deg, 11 deg
(+23.0 deg, +11 deg)
(x, y)
(0 deg, 0 deg)
Fig. 2. Computer-simulated bird-mounted video images of prey during pursuits. (A) Schematic and (B) simulated prey position on the image for classical
pursuit (red circles), motion camouflage (blue circles) and optimal visual angle (black squares). In B, black dashed circles enclose regions with different prey
velocities, while vertical lines at =23 deg indicate the edges of the video images. Shaded regions indicate the range of peak visual angles of the left deep
(red) and shallow (gray) foveae for other raptor species. , angle of the apparent prey position along the vertical direction; , angle of the apparent prey position
along the horizontal direction.
A
B
Time (s)
Visualangle(deg)
0 5 10 15 20
20
10
0
10
20
Fig. 3. Prey tracking data from bird-mounted videos. (A) Tracked prey
positions (red circles and lines) superimposed on a video image of a crow
(arrow) during pursuit by a hybrid gyrfalcon/Saker falcon. Black circles
indicate prey positions in regions of approximately constant bearing angle.
(B) Plots of camera angles (black squares and lines) and (red circles and
lines) versus time for the entire chase sequence for which an excerpt is
shown in A. The gray shaded region indicates the peak visual angles for the
left raptor shallow fovea for other raptor species.
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eye motion measured for other raptor species also applies to falconsby examining close-up videos of gyrfalcon and gyrfalcon/Saker
falcon hybrids posted online. As a result, we estimate that and
should agree with the predators actual bearing angles within
12.5 deg for the head-mounted video. Thus, by determining the
fraction of prey images recorded for negative or positive , we also
could determine whether the falcon favored its left or right eye for
viewing prey. We analyzed these data for head-mounted video from
the hybrid falcon and the gyrfalcon and from back-mounted videodata from the peregrine falcons.
Fig. 5 shows the resulting distributions of prey and for the
hybrid falcon and for the gyrfalcon. These results show the majority
of prey positions at negative , strongly suggesting that falcons prefer
to view prey using their left visual fields. The hybrid falcon data for
distant chases (defined here asz>8 m) had a mean =8.80.2 deg
(n=1277) with 94% of the prey images in the left visual field. For
distant chases, the gyrfalcon data had 79% of the prey images in the
left visual field, and an estimated mean of =7.60.3 deg (n=517,
where n=number of video frames analyzed), where the large error bars
are due to limitations from the limited camera calibration information
available.
By contrast, for pursuits by the hybrid falcon where the prey was
8 m away, the distribution had a lower mean value of2.80.6 deg (n=231) and 72% of measured values were 0 deg.
For the gyrfalcon, average =1.10.8 deg for close-up chases
(n=201).
For the hybrid falcon data, the vertical angles were distributed
about the horizontal, with the mean of the distribution of for
chases >8 m away at 2.40.1 deg, and forz
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along the horizontal and vertical directions. Constant prey angle
with 0 deg could be consistent with classical pursuit if the
videocamera were oriented with its optical axis at a non-zero angle
to the falcons head axis. In case of such misalignment, for classical
pursuit the preys image would be stationary at a non-zero angle
because it would be at the focus of expansion on the optical flow
field, which would be offset on the cameras field of view. As
mentioned earlier, we ruled out this effect by measuring the optical
flow field to rule out such offsets in the cameras mounting angle.Thus, the apparent prey motion captured on video does not agree
with classical pursuit.
We now consider the extent to which the finding that falcons
maintain the prey at a constant bearing angle for long intervals of
time during pursuits is consistent with either CATD or optimal
visual angle. For distant chases, was peaked at values of
8.80.2 deg for the hybrid falcon and 7.60.3 deg for the
gyrfalcon. Although the shallow foveas optimal angle has not been
measured for these species, values for other raptors range from 9 to
15 deg. The measured range disagrees with the range of 3045 deg
found in other raptors for the deep foveas line of sight. Thus, the
range of observed is consistent with optimal visual angle strategy
using the shallow, but not the deep, fovea.
However, the measured constant values were also consistentwith those predicted by CATD for plausible values of predator and
prey velocity and prey maneuvering. The definitive test for CATD
is constancy of the baseline vectors orientation (Fig. 6C), but this
measurement was not feasible because of the difficulty of
performing 3D imaging of the wide-ranging birds. Instead, computer
simulations and animal-borne video were combined to provide an
alternative way to distinguish between these two strategies. As
previously noted, for optimal visual angle, the preys position should
remain at a fixed value of independent of predator and prey
velocity and set by the foveal optimal line of sight. By contrast, the
values of constant predicted by CATD vary when the prey
maneuvers, in agreement with what is seen in the video data. For
example, compare Fig. 2B and Fig. 3A: while the optimal visual
angle predictions always correspond to the same value, CATDresults in clusters of different constant values as the prey
maneuvers in the simulations, just as observed in the actual videos.
We might be able to reconcile the optimal visual angle strategy
with the video data if the falcons eyes could undergo saccades of
the angular magnitude observed, as this would create a mismatch
between the measured and the actual angle on the retinal field.
However, this discrepancy should be at most 12.5 deg, based on
observed raptor eye motions in other species and our own survey of
videos showing eye motion in gyrfalcons and gyrfalcon/Saker falcon
hybrids. Instead, we observed multiple cases where the change in (,
) values between apparent optical fixes was 10 deg. Thus, the
observed heterogeneity in the measured constant values supports
CATD over optimal visual angle, assuming that falcon eye motions
this large are unlikely.Overall, these data provide strong evidence that the falcons
studied use visual motion-based steering laws while pursuing flying
prey. The video data indicate that the falcon maneuvers so that the
preys constant (, ) values do not contribute to non-zero flow
vectors on the optical flow field. We suggest that the actual strategy
used may be a synthesis of both CATD and optimal visual angle.
Recall that the falcon can control the value of with which it
pursues the prey by varying its speed (Eqn 1). These birds can fly
fast enough to pursue their prey with values of not in agreement
with their shallow foveal fields. Instead, the falcons studied adjusted
their velocities during pursuits so as to utilize CATD while
maintaining the preys at values near-optimal for its shallow fovea.
This has an additional advantage, as the range of most observed
horizontal prey angles was consistent with the measured binocular
overlap region of 1018 deg measured in other raptor species
(Martin and Katzir, 1999; ORourke et al., 2010).
When not sustaining constant bearing angles, the falcons often
maintained the apparent prey motion at a constant angular rate of
change. For small falconprey distances, the falcon pursues its prey
at smaller average visual and bearing angles before interception andcapture. While data for the hybrid falcon and gyrfalcon showed a
strong preponderance of prey fixes in the left visual field, data for
the peregrine falcon from back-mounted video did not show any
evidence of lateralization. Other non-raptor bird species have been
found to favor the left or right eye for specific tasks, presumably due
to brain lateralization (Rogers, 2012). Further work will attempt to
discern the extent to which the observed lateralized prey positions
are found in other birds.
These results indicate that the falcons only occasionally view
distant prey using their deep fovea during pursuits. By instead
viewing the prey using its shallow fovea, the falcon can take
advantage of higher acuity vision, (possibly) binocular vision and a
shorter interception path while reducing drag by keeping its head
oriented forward. The hybrid falcon was observed to perform headsaccades consistent with imaging the prey using its deep fovea in
one chase. The falcons also occasionally tilted their heads while
foraging, presumably to scan the ground for prey. These results
agree with findings of an earlier study of head motions used by
flying gull-billed terns (Gelochelidon nilotica, another bifoveate
species) foraging for and capturing prey (Land, 1999). The resulting
eye orientation was consistent with their using the deep fovea for
this purpose, without any evident lateral preference. Given these
results, it would be worth examining results for helical raptor
soaring trajectories for evidence of a preferred handedness (Akos et
al., 2008). Finally, the preference for viewing prey with an average
RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403
A B
C
or
Videocamera
Prey imagez f
O
h
v
Prey
I
Fig. 6. Bird-mounted camera mounting geometries. (A) Schematic
diagram showing the relative orientation of the camera optical axis and body
axis in flight on the back-mounted videocameras. (B) Head-mountedvideocamera on gyrfalcon/Saker falcon hybrid. (C) Pinhole optics geometry
for measuring angles (, ) of the prey from its image, and z, the distance
from the falcon to its prey (not to scale). O, prey actual size (m); f, focal
length (pixels); I, prey image size (pixels); h, distance above the eyes.
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0 deg agrees well with the fact that raptors have a high acuity
horizontal retinal streak (Inzunza et al., 1991; Jones et al., 2007).
In the one instance of prey capture recorded on video, we found
that the falcons began to spread their wings and brake for impact
with the prey at 230 ms and that their feet went from fully retracted
to fully extended in 67 ms, consistent with our analysis of high
speed videos of raptor strikes (results not shown) (Destin, 2012) and
with published values of 210 ms [the time at which Harris hawks
were found to begin preparing to land on a perch (Davies and Green,1990)] and 60100 ms (the time for foot extension for peregrine
falcons attacking birds on the wing (Goslow, 1971)].
These results have implications for the co-evolution of pursuit-
evasion mechanisms. Most prior research on optimal evasive
responses by birds has focused on locomotion rather than sensory
inputs (Hedenstrom and Rosen, 2001; Howland, 1974; Van den
Hout et al., 2010; Weihs and Webb, 1984). A recent review of prey
escape strategies has commented on the tendency of prey to double
back and move toward the pursuing predator (Domenici et al.,
2011), a strategy found to result in lower capture rates for owls
(Shifferman and Eilam, 2004). Such prey maneuvers may be
attempts to move out of the predators high acuity visual field. One
study allowed pursuer and evader visual systems to evolve
computationally in response to the outcomes of simulated pursuitsand captures; this work found that the angle for optimal vision for
pursuers agreed with the observed orientation of the shallow or the
deep fovea (Cliff and Miller, 1996). In the absence of empirical data,
computational models of predation on flocks (Ballerini et al., 2008a;
Ballerini et al., 2008b; Lee, 2006; Lee et al., 2006; Mecholsky et al.,
2010) have assumed classical pursuit. These results should be
revisited to model how likely pursuit strategies used by falcons on
single birds influence their attacks on flocks.
The emerging evidence indicates that a wide variety of animals
utilize visual motion-based cues and CATD/motion camouflage-based
strategies for the pursuit and capture of individual prey. It also
reinforces the importance of using a sensory ecology approach
(Martin, 2012) in understanding problems such as bird collisions with
man-made objects (Martin and Shaw, 2010) and antipredatorsurveillance (Fernndez-Juricic, 2012). The complexity and extreme
speed of the falcons attack and its avian preys evasive response raise
important, unresolved questions. Raptor pursuit strategies have
evolved under the constraints of avian flight aerodynamics, prey
behavior and environment. How optimal are the actual pursuit
strategies used by falcons? How do their hunts adapt to different
hunting tactics? What import do these results hold for the study of
attacks on flocks? How do prey evasive strategies relate to the falcons
hunting methods? Our ongoing experimental and modeling efforts
will explore these issues by extending pursuit-evasion models into
three dimensions and adding in realistic predator-maneuvering
capabilities, as well as exploring different ways to model the fitness
of pursuit strategies.
MATERIALS AND METHODS
Animals
This study used six peregrine falcons (F. peregrinus) and two gyrfalcon (F.
rusticolus)/Saker falcon (F. cherrug) hybrids (hybrid falcons) flown and
housed by participating falconers who were provided with videocameras,
mounts, data storage media and instructions for using the equipment and
conducting and documenting the field studies. The experiments explored
predation by free-flying falcons in the field in all cases. All prey were wild,
free-living birds encountered by the raptors during hunting flights. Most
videos analyzed here were recorded in collaboration with four falconers who
hunted with six different peregrine falcons and two hybrid falcons, in the
USA (Pennsylvania, Arizona and Wyoming) and in the UK, The
Netherlands and Belgium. Filming occurred during autumn and winter
20092013. All falconers were licensed and had all necessary permits; all
personnel involved followed the relevant regulations and laws of each
country in question, as well as those of Haverford Colleges Committee on
Animal Care and the ARRIVE guidelines (NC3Rs, 2010). All back-mounted
videos were filmed with peregrine falcons. The head-mounted camera was
used only with a hybrid falcon because of that species greater mass. The
male hybrid falcons hunted in a pair while one of the birds wore a video
camera. Only one of the hybrid falcons was habituated to wearing the head-
mounted camera, so the video data presented here are from one individual.In two chases, the hybrid falcon wearing the camera recorded the other
hybrid falcon pursuing and capturing a prey bird. In the remaining chases,
the falcon wearing the camera recorded the prey bird during a solo pursuit.
In a few cases, videos were drawn from publically available archives on the
internet where they had been posted by falconers, including five chases with
a gyrfalcon wearing a head-mounted camera hunting Houbara bustards
(Chlamydotis undulata) in the desert in Dubai (Abu Dhabi Sports Council,
2011), and two pursuits of ring-necked doves (Streptopelia capicola) by a
peregrine falcon wearing a back-mounted camera in Arizona (Gonzales,
2010).
Video methods
The back-mounted cameras and battery packs for the head-mounted cameras
were mounted on unanesthetized birds using Marshall Radio Telemetry
(North Salt Lake, UT, USA) Trackpack backpacks designed to secure radiotelemetry transmitters on falcons and hawks (Fig. 6A). These mounts use
in Teflon ribbons to hold the cameras stably between the falcons wings,
just above and to the rear of the birds head. In a few cases, we were able to
obtain video recorded from videocameras mounted in specially made
falconry hoods (Fig. 6B). Falconers experienced in hood-making prepared
hoods customized for each falcon and acclimated the birds to wearing them
before their actual use in hunting.
We used commercially available miniature (mass 14 g) model 808
camcorders (Toplanter, Huizhou, China). Including mounting hardware, the
total mass was 20 g,
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vertical axis was oriented along the birds ventraldorsal axis (Fig. 6A). As
a consequence, objects in the environment rotated on the back-mounted
video when the falcons body rolled from side to side. By separately
measuring roll using the orientation of a distant, level horizon, we were able
to correct for this by rotating the resulting images to compensate.
Because the videocameras used have pinhole optics, we can estimate
distances,z, to objects of known size, O, using the relationship:
z= Of/I , (2)
wherefis the camera focal length in pixels andIis the image size in pixels(Fig. 6C) for objects not foreshortened due to linear perspective. The angle
of the apparent prey image located at a distance (x,y) in pixels from the
image center is at angles and with respect to the camera defined by
(Fig. 2A):
If the falcons eyes are oriented forward in their primary position of gaze [as
is typical for raptors (Wallman and Pettigrew, 1985) and as we have found
to be true by examining videos of different falcon species], then (, )
constitute the horizontal and vertical components of . The resulting preypositions on the video are shown plotted as angles (, ) on the cameras
visual plane, with 0 deg corresponding to both the image center and the
falcons forward head axis (for head-mounted video) or forward velocity
direction (for back-mounted video) at large distances,z(Fig. 2A). Positive
values of are on the birds right visual field, negative angles on its left
visual field; positive (negative) values of indicate that the prey was located
above (below) the forward axis. The prey images were displaced downward
before interception and capture at very low zdue to the mounting of the
camera above the head and body axis. Video frames show the cameras total
field of view in the coronal plane of the falcons head or body within the
cameras field of view: 2323 deg and 10.5510.55 deg. Although
these cameras cannot image all objects visible to the falcon, because its deep
foveal retinal field extends to higher angles, by tracking background objects
visible immediately before and after the prey moved off-camera, we were
able to infer its position at higher angles. Finally, we checked that thevideocamera was not oriented at an angle to the head forward axis by
computing optical flow fields during prey pursuits for approximately linear
falcon velocity; this established that the focus of expansion was indeed along
the center of the cameras field of view.
Image processing and data analysis
We performed calibrations of the bird-mounted videocameras focal length,
distortion parameters and other optical parameters (Bradski and Kaehler,
2012; Cyganek and Siebert, 2009) using OpenCV version 2.3.1 programs
written in Python 2.7 (http://www.python.org). The results indicated that the
pinhole optics resulted in only small barrel distortions with a standard
deviation of 0.8 pixels and a maximum range of deviation of 1.5 pixels
over the entire 1280720 pixel image. The resulting focal lengths also were
measured to 0.5%, using filming of objects of known length as an
additional check. For the head-mounted video of a gyrfalcon from anothersource, wide-angle lens distortions limited our ability to perform analyses
of the angles (, ) (Abu Dhabi Sports Council, 2011) beyond approximate
values near the preys average apparent position.
To analyze the tracks of birds and other objects on video, we used the
image analysis program ImageJ (National Institutes of Health, Bethesda,
MD, USA). Videos were first converted into image stacks using VirtualDub
(http://www.virtualdub.org), checking that no frames were dropped or added.
Separate checks were performed by filming a fast clock to make sure that
the videocameras did not skip or add frames. The image stacks were then
converted to grayscale, the background subtracted when possible, and an
image intensity threshold was applied to obtain a binary image of only
moving prey birds. Tracking was performed using one of two ImageJ
plugins: MTrack2 (for automatic tracking) or MTrackJ (for manual
x
f
y
f
tan ,
tan . (3)
1
1
=
=
tracking). [Particle image velocimetry methods were attempted, but did not
yield useful results because of the falcons erratic motion and the resulting
unstable rotating and moving high-contrast background.] Fitting, statistical
analysis, Fourier transforms, correlation functions, and other data analysis
was carried out in Origin 8.6 (OriginLab, Northampton, MA, USA). All
error bars reported here are s.e. unless noted otherwise. The estimated error
in tracked prey angles was 0.2 deg.
To measure roll angle from the bird camera video, we used the
orientation of the distant level horizon when visible. The horizons
orientation was automatically detected using an Opencv 3.2 programwritten in Python 2.7. Each image frame was individually converted to
grayscale and smoothed using a Gaussian filter, then subjected to an
adaptive threshold to compensate for non-uniform illumination and five
rounds of dilation/erosion filters to remove speckle noise while preserving
large features. Finally, a Canny filter and probabilistic Hough Line
transform were used to find the orientation of the horizon lines, which
were inspected by hand to confirm only distant horizon lines were
analyzed (Bradski and Kaehler, 2012).
Computer simulations
Computer simulations of birdcamera videos generated by each pursuit
model considered were written in Python 2.7. All simulations assumed the
chase was confined to a horizontal plane because this sufficed to model
the behavior of interest here. Simulation parameters were drawn whenever
possible from empirical data for flight speeds and aerodynamicparameters. Maximum flight speeds were unavailable for carrion crows,
but values from 7.4 to 15.6 m s1 have been reported for other species of
crows (Broun and Goodwin, 1943; Schnell and Hellack, 1978; Verbeek
and Caffrey, 2002). For peregrine falcons, empirical data for maximum
speeds in level flapping flight ranged from 10 to 31 m s1 (Alerstam, 1987;
Cochran and Applegate, 1986; Pennycuick, 1997; Pennycuick et al.,
1994). Measured flight speeds for diving and stooping falcons range from
3 0 m s1 for diving peregrine falcons (Alerstam, 1987) to 58 m s1 for
stooping gyrfalcons (Tucker et al., 1998). Roll angles were computed
using the balanced turning approximation (Pennycuik, 2008) and
aerodynamic parameter estimates for falcon lift coefficient=0.53 to 1.65
at Reynolds number=105 and wing area=0.132 m2 (Tucker, 1998). The
simulations assumed no limitations on turning radius or other maneuvers
required to implement each strategy, because birds can separately move
and shape each wing (and their tails) to achieve low radius turns, abruptrolls and other maneuvers not permitted by fixed wing aerodynamics
(Carruthers et al., 2007; Pennycuik, 2008; Warrick et al., 2002).
Birds, including raptors, can process visual information at rates 90Hz
(Fox, 1995; Jones et al., 2007). Consequently, the simulation time step was
fixed at t=1/(329.97 Hz)=11.1 ms1/90 Hz to agree with this limit; this
value was also close to an integral multiple of the video frame capture rate.
Simulated video images were generated every three time steps. The
projective geometry for simulating images corresponded to that for the
empirical videocamera mounting geometry. The timing and direction of prey
accelerations were drawn from approximate values observed on video. In
the simulations, capture was defined as a falconprey distance of 0.25 m,
and events during the actual capture were not simulated.
The model pursuer moved at a constant speed, so only its bearing and roll
angles changed in response to perceived evader motion. To account for
predator response time, TR, to prey maneuvers, the program computed themotion of the pursuer at time tusing the preys position and velocity at tTR.
The time lag between perceived prey motion and predator response was set
at TR=6t, assuming the falcon requires at least three flicker fusion periods
to detect prey acceleration and a 38 ms reaction time once the stimulus is
received (Pomeroy and Heppner, 1977; Potts, 1984); this value for TRis also
consistent with the 6724 ms for birds in a flock to respond to the initiation
of turning (Potts, 1984).
AcknowledgementsWe wish to thank the falconers who participated in this study: Eddy de Mol and
Francois Lorrain, Dennis Decaluw, Troy Morrs and Stephen Lea, and other
falconers who contributed valuable advice: Robert Giroux, Jack Hubley and Patrick
Miller. Robert Musters fabricated the hoods used to hold the videocameras, and
RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403
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Bruce Boyes helped to design and adapt the backpack mounts. Emily
Cunningham played a key role in the early conception of this project. We would
like to acknowledge helpful conversations with Peter J. Love (Haverford College),
Charlotte Hemelrijk and Hanno Hildebrandt (University of Groningen). We wish to
express our appreciation for the helpful comments and suggestions by two
anonymous reviewers.
Competing interestsThe authors declare no competing financial interests.
Author contributionsS.A.K. and M.Z. jointly digitized, analysed and interpreted the video data received
from cooperating falconers. S.A.K. conceived of the experimental and
computational study design, provided video equipment and instructions to the
falconers, performed the computer modelling, and wrote the paper with input from
M.Z. during the initial drafting and revisions.
FundingThis research was supported in part by a grant to Haverford College from the
Howard Hughes Medical Institute and by a Special Projects Award from the Marion
E. Koshland Integrated Natural Sciences Center of Haverford College.
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RESEARCH ARTICLE The Journal of Experimental Biology (2014) doi:10.1242/jeb.092403