Echolocation and flight strategy of Japanese house bats during natural foraging, revealed by a microphone array system

Fujioka, Emyo; Mantani, Shigeki; Hiryu, Shizuko; Riquimaroux, Hiroshi; Watanabe, Yoshiaki

doi:10.1121/1.3523300

Cited by 32 publications

(38 citation statements)

References 33 publications

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“…abramus during natural foraging emits long (9-11 ms) shallowsweeping frequency-modulated (FM) sounds, with energy concentrated in the terminal sweep frequency of the fundamental component around 40 kHz (15,25). The bat decreases the pulse duration and interpulse interval (IPI) while approaching target prey (16,30) and slightly extends the constant-frequency (CF) portion of the pulse just before approaching a prey item (16). When the bat successfully captures an insect, it completes the interception with an easily recognized brief burst of sounds emitted at a high rate of about 150 Hz (the "feeding buzz"), followed by a silent interval (the "postbuzz pause") (31).…”

Section: Methodsmentioning

confidence: 99%

“…The model assumes two prey items, because the microphone-array measurement showed that bats sometimes capture two prey items consecutively and rarely capture more than two insects within short time intervals. Bats changed their flight paths much more acrobatically in the horizontal plane than in the vertical plane during natural foraging (15,16). Thus, we separately model flight dynamics in the horizontal and vertical planes as follows:…”

Section: Methodsmentioning

confidence: 99%

“…3D). We also defined the approach phase as the interval between the extension of CF duration and capture (15,16). Intervals between two successive captures within 1.5 s and over 3 s were defined as shortinterval and long-interval successive captures, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The 3D flight path and pulse direction of echolocating Pipistrellus abramus (Vespertilionidae, ∼15 cm wingspan, 5-8 g body mass) (16,25) in the wild were reconstructed using differences in arrival time and sound pressure between the microphones (Fig. 3A).…”

Section: Methodsmentioning

confidence: 99%

“…During natural foraging, the sonar attention of bats alternately shifts from their flying direction to the side (15). Additionally the wild bats are capable of capturing two prey items within the short interval of 1 s (16).…”

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confidence: 99%

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Echolocating bats use future-target information for optimal foraging

Fujioka

Aihara

Sumiya

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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When seeing or listening to an object, we aim our attention toward it. While capturing prey, many animal species focus their visual or acoustic attention toward the prey. However, for multiple prey items, the direction and timing of attention for effective foraging remain unknown. In this study, we adopted both experimental and mathematical methodology with microphone-array measurements and mathematical modeling analysis to quantify the attention of echolocating bats that were repeatedly capturing airborne insects in the field. Here we show that bats select rational flight paths to consecutively capture multiple prey items. Microphone-array measurements showed that bats direct their sonar attention not only to the immediate prey but also to the next prey. In addition, we found that a bat's attention in terms of its flight also aims toward the next prey even when approaching the immediate prey. Numerical simulations revealed a possibility that bats shift their flight attention to control suitable flight paths for consecutive capture. When a bat only aims its flight attention toward its immediate prey, it rarely succeeds in capturing the next prey. These findings indicate that bats gain increased benefit by distributing their attention among multiple targets and planning the future flight path based on additional information of the next prey. These experimental and mathematical studies allowed us to observe the process of decision making by bats during their natural flight dynamics.bat sonar | aerial capture | microphone array | mathematical modeling | flight dynamics S electively focusing attention on a particular target allows us to effectively extract information (1-4). Animals spatially focus their attention toward prey for suitable foraging (5, 6). During prey pursuit, most animals direct their visual attention toward it [e.g., tiger beetle (7), dragonfly (8), and falcon (9)]. Specifically, for example, dragonflies maintain a prey item at a constant retinal position during approaching it so that they steer an interception flight path (interception strategy) (10). However, for multiple prey items, the direction and timing of attention for effective foraging remain unknown.Echolocating bats actively emit sonar signals to obtain surrounding information and adaptively change the characteristics of the emissions depending on the situation (11). Therefore, their attention in terms of the sonar (sonar attention) is characterized as the direction to which bats emit their sonar beams (12-14). When echolocating bats approach an airborne insect, the sonar attention directs toward the prey (12, 13). During natural foraging, the sonar attention of bats alternately shifts from their flying direction to the side (15). Additionally the wild bats are capable of capturing two prey items within the short interval of 1 s (16).We predicted that bats localize multiple prey items by distributing their attention between them and then select an efficient flight path to successively capture the multiple prey. To test this prediction, ...

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Echolocating bats use future-target information for optimal foraging

Fujioka

Aihara

Sumiya

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Acoustic localization of terrestrial wildlife: Current practices and future opportunities

Rhinehart

Chronister

Devlin

et al. 2020

Ecology and Evolution

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Autonomous acoustic recorders are an increasingly popular method for low‐disturbance, large‐scale monitoring of sound‐producing animals, such as birds, anurans, bats, and other mammals. A specialized use of autonomous recording units (ARUs) is acoustic localization, in which a vocalizing animal is located spatially, usually by quantifying the time delay of arrival of its sound at an array of time‐synchronized microphones. To describe trends in the literature, identify considerations for field biologists who wish to use these systems, and suggest advancements that will improve the field of acoustic localization, we comprehensively review published applications of wildlife localization in terrestrial environments. We describe the wide variety of methods used to complete the five steps of acoustic localization: (1) define the research question, (2) obtain or build a time‐synchronizing microphone array, (3) deploy the array to record sounds in the field, (4) process recordings captured in the field, and (5) determine animal location using position estimation algorithms. We find eight general purposes in ecology and animal behavior for localization systems: assessing individual animals' positions or movements, localizing multiple individuals simultaneously to study their interactions, determining animals' individual identities, quantifying sound amplitude or directionality, selecting subsets of sounds for further acoustic analysis, calculating species abundance, inferring territory boundaries or habitat use, and separating animal sounds from background noise to improve species classification. We find that the labor‐intensive steps of processing recordings and estimating animal positions have not yet been automated. In the near future, we expect that increased availability of recording hardware, development of automated and open‐source localization software, and improvement of automated sound classification algorithms will broaden the use of acoustic localization. With these three advances, ecologists will be better able to embrace acoustic localization, enabling low‐disturbance, large‐scale collection of animal position data.

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