Mind/Brain Institute, Johns Hopkins University, Baltimore, Maryland, USA.
Mechanical Engineering Department, Johns Hopkins University, Baltimore, Maryland, USA.
Sci Rep. 2018 Apr 11;8(1):5830. doi: 10.1038/s41598-018-24035-5.
The study of animal behavior has been revolutionized by sophisticated methodologies that identify and track individuals in video recordings. Video recording of behavior, however, is challenging for many species and habitats including fishes that live in turbid water. Here we present a methodology for identifying and localizing weakly electric fishes on the centimeter scale with subsecond temporal resolution based solely on the electric signals generated by each individual. These signals are recorded with a grid of electrodes and analyzed using a two-part algorithm that identifies the signals from each individual fish and then estimates the position and orientation of each fish using Bayesian inference. Interestingly, because this system involves eavesdropping on electrocommunication signals, it permits monitoring of complex social and physical interactions in the wild. This approach has potential for large-scale non-invasive monitoring of aquatic habitats in the Amazon basin and other tropical freshwater systems.
通过先进的方法学,动物行为的研究已经发生了革命性的变化,这些方法学可以在视频记录中识别和跟踪个体。然而,对于包括生活在浑浊水中的鱼类在内的许多物种和栖息地来说,行为的视频记录是具有挑战性的。在这里,我们提出了一种基于个体产生的电信号,以亚秒级时间分辨率识别和定位厘米级弱电鱼类的方法。这些信号是通过网格电极记录的,并使用两部分算法进行分析,该算法首先识别每个个体鱼类的信号,然后使用贝叶斯推理估计每条鱼的位置和方向。有趣的是,由于该系统涉及对电通信信号的偷听,因此它允许在野外监测复杂的社会和物理相互作用。这种方法有可能对亚马逊盆地和其他热带淡水系统中的水生栖息地进行大规模的非侵入性监测。