Acoustic localization of terrestrial wildlife: Current practices and future opportunities

被引:63
|
作者
Rhinehart, Tessa A. [1 ]
Chronister, Lauren M. [1 ]
Devlin, Trieste [1 ]
Kitzes, Justin [1 ]
机构
[1] Univ Pittsburgh, Dept Biol Sci, Pittsburgh, PA 15260 USA
来源
ECOLOGY AND EVOLUTION | 2020年 / 10卷 / 13期
关键词
acoustic localization system; autonomous recording units; bioacoustics; conservation; microphone array; wildlife monitoring; ROBOT AUDITION SYSTEM; JAPANESE HOUSE BATS; MICROPHONE-ARRAY; TIME-DIFFERENCES; LOCATION SYSTEM; SAGE-GROUSE; PIPISTRELLUS-ABRAMUS; ECHOLOCATION CALLS; MOVEMENT BEHAVIOR; FLIGHT BEHAVIOR;
D O I
10.1002/ece3.6216
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
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.
引用
收藏
页码:6794 / 6818
页数:25
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