The Whitney Laboratory for Marine Bioscience, Department of Biology, University of Florida, 9505 Ocean Shore Blvd, St Augustine, FL 32080, USA.
Department of Wildlife, Fish, and Conservation Biology, University of California, Davis, Davis, CA 95616, USA.
J Exp Biol. 2018 Oct 29;221(Pt 21):jeb180877. doi: 10.1242/jeb.180877.
The architecture of the cephalic lateral line canal system, with distinct lines for the supraorbital, infraorbital and mandibular canals, is highly conserved among fish species. Because these canals lie on a cranial platform, the sensory input they receive is expected to change based on how flow interacts with the head and how the canal pores are spatially distributed. In this study, we explored how head width, a trait that can vary greatly between species and across ontogeny, affects flow sensing. We inserted pressure sensors into physical fish head models of varying widths (narrow, intermediate and wide) and placed these models in steady and vortical flows. We measured sensory performance in terms of detecting flow parameters (flow speed, vortex shedding frequency and cylinder diameter), sensitivity (change in pressure gradient as a function of flow speed) and signal-to-noise ratio (SNR; strength of vortex shedding frequency with respect to background). Our results show that in all model heads the amount of hydrodynamic information was maximized at the anterior region regardless of what metric we used to evaluate the sensory performance. In addition, we discovered that all model heads had the highest SNR for vortices at the intermediate flow speeds but that each head width passively optimized the SNR for different sized vortices, which may have implications for refuge and prey seeking. Our results provide insight into the sensory ecology of fishes and have implications for the design of autonomous underwater vehicles.
头部侧线系统的结构,包括明显的眶上、眶下和下颌骨通道,在鱼类物种中高度保守。由于这些通道位于颅平台上,因此预计它们接收到的感觉输入会根据流动与头部的相互作用以及通道孔的空间分布方式而发生变化。在这项研究中,我们探讨了头宽(一种在物种间和个体发育过程中差异很大的特征)如何影响流动感测。我们将压力传感器插入具有不同宽度(窄、中、宽)的物理鱼类头部模型中,并将这些模型置于稳定和涡旋流动中。我们根据检测到的流参数(流速、涡脱落频率和圆柱直径)、灵敏度(压力梯度随流速的变化)和信噪比(相对于背景的涡脱落频率强度)来衡量感觉性能。我们的研究结果表明,在所有模型头部中,无论我们使用哪种指标来评估感觉性能,在前部区域都能获得最大的水动力信息量。此外,我们发现,所有模型头部在中等流速下对涡旋的 SNR 最高,但每个头部宽度都被动地优化了不同大小涡旋的 SNR,这可能对避难和寻找猎物有影响。我们的研究结果为鱼类的感觉生态学提供了深入的了解,并对自主水下机器人的设计具有重要意义。