Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, CA 93106, USA
Southwest Fisheries Science Center, National Oceanographic and Atmospheric Administration, Santa Cruz, CA 95060, USA.
Philos Trans R Soc Lond B Biol Sci. 2018 May 19;373(1746). doi: 10.1098/rstb.2017.0005.
Mobile animal groups provide some of the most compelling examples of self-organization in the natural world. While field observations of songbird flocks wheeling in the sky or anchovy schools fleeing from predators have inspired considerable interest in the mechanics of collective motion, the challenge of simultaneously monitoring multiple animals in the field has historically limited our capacity to study collective behaviour of wild animal groups with precision. However, recent technological advancements now present exciting opportunities to overcome many of these limitations. Here we review existing methods used to collect data on the movements and interactions of multiple animals in a natural setting. We then survey emerging technologies that are poised to revolutionize the study of collective animal behaviour by extending the spatial and temporal scales of inquiry, increasing data volume and quality, and expediting the post-processing of raw data.This article is part of the theme issue 'Collective movement ecology'.
移动动物群体提供了自然界中最引人注目的自组织范例之一。虽然对鸟类群体在天空中盘旋或凤尾鱼群逃避捕食者的实地观察激发了人们对集体运动机制的浓厚兴趣,但在现场同时监测多个动物的挑战历史上限制了我们精确研究野生动物群体集体行为的能力。然而,最近的技术进步现在为克服许多这些限制提供了令人兴奋的机会。在这里,我们回顾了现有的方法,用于收集关于自然环境中多个动物的运动和相互作用的数据。然后,我们调查了新兴技术,这些技术有望通过扩展研究的空间和时间尺度、增加数据量和质量以及加快原始数据的后处理,彻底改变对集体动物行为的研究。本文是主题为“集体运动生态学”的一部分。