Motion camouflage in three dimensions

116

107

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 falcon's visual field to track and capture flying prey via a form of motion camouflage. The falcons also were found to maintain their prey's 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 INTRODUCTIONHow 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 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 ...

Section: Research Articlementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Falcons pursue prey using visual motion cues: new perspectives from animal-borne cameras

Kane

Zamani

2014

116

107

“…CATD is used by dragonflies (Combes et al, 2012;Olberg, 2012), bats (Ghose et al, 2006; Ghose et al, 2009) and humans (Fajen and Warren, 2004). CATD is a motion camouflage strategy (Justh and Krishnaprasad, 2006;Reddy et al, 2006) because the predator perceives no prey motion on its visual field and vice versa; the only motion cue is looming (retinal expansion) (Reddy et al, 2007). However, camouflage of the predator's motion on the prey's visual field may be a by-product of its sensory implementation, given that data are mixed on whether prey respond more strongly to predators on a tangential approach or collision course (Fernández-Juricic et al., 2005;Stankowich and Blumstein, 2005).…”

Section: Pursuit-evasion In Biologymentioning

confidence: 99%

“…2D. (Ghose et al, 2006;Reddy et al, 2006). The predator can implement CATD by maneuvering to keep the prey's image at constant visual angle (determined by the instantaneous value of ϕ ο ) for fixed predator head orientation (CATD in Fig.…”

mentioning

confidence: 99%

When hawks attack: animal-borne video studies of goshawk pursuit and prey-evasion strategies

Kane

Fulton

Rosenthal

2015

114

Video filmed by a camera mounted on the head of a Northern Goshawk (Accipiter gentilis) was used to study how the raptor used visual guidance to pursue prey and land on perches. A combination of novel image analysis methods and numerical simulations of mathematical pursuit models was used to determine the goshawk's pursuit strategy. The goshawk flew to intercept targets by fixing the prey at a constant visual angle, using classical pursuit for stationary prey, lures or perches, and usually using constant absolute target direction (CATD) for moving prey. Visual fixation was better maintained along the horizontal than vertical direction. In some cases, we observed oscillations in the visual fix on the prey, suggesting that the goshawk used finite-feedback steering. Video filmed from the ground gave similar results. In most cases, it showed goshawks intercepting prey using a trajectory consistent with CATD, then turning rapidly to attack by classical pursuit; in a few cases, it showed them using curving non-CATD trajectories. Analysis of the prey's evasive tactics indicated that only sharp sideways turns caused the goshawk to lose visual fixation on the prey, supporting a sensory basis for the surprising frequency and effectiveness of this tactic found by previous studies. The dynamics of the prey's looming image also suggested that the goshawk used a tau-based interception strategy. We interpret these results in the context of a concise review of pursuit-evasion in biology, and conjecture that some prey deimatic 'startle' displays may exploit tau-based interception.KEY WORDS: Pursuit-evasion, Predator-prey, Avian vision, Northern Goshawk, Visual guidance, Sensory ecology, Accipiter gentilis, Looming, Antipredator behavior, Startle effect INTRODUCTIONMany problems in the study of animal behavior require an integrated biomechanical and sensory ecology approach that considers the organism's locomotor and perceptual capabilities, its sensory cues and the dynamics of its behavioral responses (Fernández-Juricic, 2012;Martin, 2012). Animal-borne video methods (Rutz and Troscianko, 2013) now make it possible to measure the visual cues received by organisms in the field, revealing new information about, for example, how they move through their environment (BBC, 2009), interact with conspecifics (Takahashi et al., 2004) and use tools (Rutz et al., 2007). Recent studies have used the stable video recorded by cameras mounted on the heads of birds (headcams) to explore the visual strategies used by falcons chasing prey (Kane and Zamani, 2014) and peafowl detecting model predators (Yorzinski and Platt, 2014). Here, we report using headcam video to explore for the first time pursuit-evasion and landing behavior in the Northern Goshawk, Accipiter gentilis (Linnaeus 1758) (hereafter, goshawk), a large diurnal raptor (Fig. 1). Biologically derived models have inspired robotic algorithms for swarming, following and collision avoidance (Mischiati and Krishnaprasad, 2012;Srinivasan, 2011), and the goshawk is of special inte...

“…when the follower overtook the leading bat. Two cost functions,  and , were computed to examine how closely the leader-follower relationship matched, respectively, the CP and CATD strategies (Justh and Krishnaprasad, 2005;Reddy et al, 2006;Reddy, 2007;Wei et al, 2009). The cost function  is the cosine of the angle between the paired bats' separation vector and the vector representing the velocity of the follower.…”

Section: Bat-bat Pursuit Strategiesmentioning

confidence: 99%

Effects of competitive prey capture on flight behavior and sonar beam pattern in paired big brown bats,Eptesicus fuscus

Chiu

Reddy

Xian

et al. 2010