Acquisition of food in many animal species depends on the pursuit and capture of moving prey. Among modern humans, the pursuit and interception of moving targets plays a central role in a variety of sports, such as tennis, football, Frisbee, and baseball. Studies of target pursuit in animals, ranging from dragonflies to fish and dogs to humans, have suggested that they all use a constant bearing (CB) strategy to pursue prey or other moving targets. CB is best known as the interception strategy employed by baseball outfielders to catch ballistic fly balls. CB is a time-optimal solution to catch targets moving along a straight line, or in a predictable fashion—such as a ballistic baseball, or a piece of food sinking in water. Many animals, however, have to capture prey that may make evasive and unpredictable maneuvers. Is CB an optimum solution to pursuing erratically moving targets? Do animals faced with such erratic prey also use CB? In this paper, we address these questions by studying prey capture in an insectivorous echolocating bat. Echolocating bats rely on sonar to pursue and capture flying insects. The bat's prey may emerge from foliage for a brief time, fly in erratic three-dimensional paths before returning to cover. Bats typically take less than one second to detect, localize and capture such insects. We used high speed stereo infra-red videography to study the three dimensional flight paths of the big brown bat, Eptesicus fuscus, as it chased erratically moving insects in a dark laboratory flight room. We quantified the bat's complex pursuit trajectories using a simple delay differential equation. Our analysis of the pursuit trajectories suggests that bats use a constant absolute target direction strategy during pursuit. We show mathematically that, unlike CB, this approach minimizes the time it takes for a pursuer to intercept an unpredictably moving target. Interestingly, the bat's behavior is similar to the interception strategy implemented in some guided missiles. We suggest that the time-optimal strategy adopted by the bat is in response to the evolutionary pressures of having to capture erratic and fast moving insects.

This paper describes measurements of the sonar beam pattern of flying echolocating bats, Eptesicus fuscus, performing various insect capture tasks in a large laboratory flight room. The beam pattern is deduced using the signal intensity across a linear array of microphones. The positions of the bat and insect prey are obtained by stereoscopic reconstruction from two camera views. Results are reported in the form of beam-pattern plots and estimated direction of the beam axis. The bat centers its beam axis on the selected target with a standard deviation (sigma) of 3 degrees. The experimental error is +/- 1.4 degrees. Trials conducted with two targets show that the bat consistently tracks one of the targets with its beam. These findings suggest that the axis of the bat sonar beam is a good index of selective tracking of targets, and in this respect is analogous to gaze in predominantly visual animals.

SUMMARYEcholocation allows bats to orient and localize prey in complete darkness. The sonar beam of the big brown bat, Eptesicus fuscus, is directional but broad enough to provide audible echo information from within a 60-90 deg. cone. This suggests that the big brown bat could interrogate a natural scene without fixating each important object separately. We tested this idea by measuring the directional aim and duration of the bat's sonar beam as it performed in a dual task, obstacle avoidance and insect capture. Bats were trained to fly through one of two openings in a fine net to take a tethered insect at variable distances behind the net. The bats sequentially scanned the edges of the net opening and the prey by centering the axis of their sonar beam with an accuracy of ~5 deg. The bats also shifted the duration of their sonar calls, revealing sequential sampling along the range axis. Changes in duration and directional aim were correlated, showing that the bat first inspected the hole, and then shifted its gaze to the more distant insect, before flying through the net opening. Contrary to expectation based on the sonar beam width, big brown bats encountering a complex environment accurately pointed and shifted their sonar gaze to sequentially inspect closely spaced objects in a manner similar to visual animals using saccades and fixations to scan a scene. The findings presented here from a specialized orientation system, echolocation, offer insights into general principles of active sensing across sensory modalities for the perception of natural scenes. Supplementary material available online at

The human reference genome serves as the foundation for genomics by providing a scaffold for alignment of sequencing reads, but currently only reflects a single consensus haplotype, which impairs read alignment and downstream analysis accuracy. Reference genome structures incorporating known genetic variation have been shown to improve the accuracy of genomic analyses, but have so far remained computationally prohibitive for routine large-scale use. Here we present a graph genome implementation that enables read alignment across 2,800 diploid genomes encompassing 12.6 million SNPs and 4.0 million indels. Our Graph Genome Pipeline requires 6.5 hours to process a 30x coverage WGS sample on a system with 36 CPU cores compared with 11 hours required by the GATK Best Practices pipeline.Using complementary benchmarking experiments based on real and simulated data, we show that using a graph genome reference improves read mapping sensitivity and produces a 0.5% increase in variant calling recall, or about 20,000 additional variants being detected per sample, while variant calling specificity is unaffected. Structural variations (SVs) incorporated into a graph genome can be genotyped accurately under a unified framework. Finally, we show that iterative augmentation of graph genomes yields incremental gains in variant calling accuracy. Our implementation is a significant advance towards fulfilling the promise of graph genomes to radically enhance the scalability and accuracy of genomic analyses.

Adaptive behaviors require sensorimotor computations that convert information represented initially in sensory coordinates to commands for action in motor coordinates. Fundamental to these computations is the relationship between the region of the environment sensed by the animal (gaze) and the animal's locomotor plan. Studies of visually guided animals have revealed an anticipatory relationship between gaze direction and the locomotor plan during target-directed locomotion. Here, we study an acoustically guided animal, an echolocating bat, and relate acoustic gaze (direction of the sonar beam) to flight planning as the bat searches for and intercepts insect prey. We show differences in the relationship between gaze and locomotion as the bat progresses through different phases of insect pursuit. We define acoustic gaze angle, gaze , to be the angle between the sonar beam axis and the bat's flight path. We show that there is a strong linear linkage between acoustic gaze angle at time t [ gaze (t)] and flight turn rate at time t ϩ into the future [ . flight (t ϩ )], which can be expressed by the formula . flight (t ϩ ) ϭ k gaze (t). The gain, k, of this linkage depends on the bat's behavioral state, which is indexed by its sonar pulse rate. For high pulse rates, associated with insect attacking behavior, k is twice as high compared with low pulse rates, associated with searching behavior. We suggest that this adjustable linkage between acoustic gaze and motor output in a flying echolocating bat simplifies the transformation of auditory information to flight motor commands.