Explaining color pattern diversity through the dynamic analysis of display

通过显示的动态分析解释色彩模式的多样性

• 批准号: 2031105
• 负责人: Trevor Price
• 金额: $ 37.32万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031105&HistoricalAwards=false
• 关键词: Explaining; color; pattern; diversity; through

Humans have been fascinated by the diversity of color for centuries, and the field of color science spans across art, philosophy, psychology and biology. Different people and societies have different color experiences, and even the same person perceives color differently depending on the background. Our ability to perceive this variation is thanks to a sophisticated color perception system, especially relative to other mammals. Other animals, such as birds, have even more complex color perception, and are among some of the most brilliantly colored animals on the planet. What are the underlying causes of this diversity? How does an animal’s surroundings, such whether it lives in forests or out in the open shape the evolution and perception of colors? One reason for our lack of understanding may be that color signals are frequently presented to observers, such as rivals, mates or predators, using movements of various body parts. Recent developments in high-speed video technology and computer programing now make it possible to accurately describe these movements. This project will therefore study the association between animal displays, habitat, and color by comparing closely related species. The over-arching goal of this work is to understand the diversity of color in nature, and ultimately, the diversity of how color is perceived.While long recognized that display movements are integral component of animal signals, it is only in the past few years that technology (e.g. high speed video cameras, essential because many species can perceive movements much more finely than humans) and analytical techniques (e.g. deep learning methods to trace trajectories of movements) have become sufficiently advanced that they can be used to study displays in a comparative context, thereby linking them to differences in color and ecology across species. The goal of this project is to quantify display behavior, environmental and ecological features, social context, color, and color patterns across the Old World leaf warblers (genus Phylloscopus), chosen because previous work has shown qualitative associations between light environment, color, and displays in the group. Closely related, sympatric, species will be compared across a gradient of habitat types to ask how plumage color (quantified through spectrophotometry) and threat display (quantified by playback experiments and high-speed video) are associated with habitat, including background color and granularity, and prevailing light environments. New methods of analysis derived from comparisons of morphological shape will be used to place display movements on continuous scales, thereby enabling them to be ranked across species. The essential concept is that displays vary with environment, and thereby influence the evolution of both color and color patterns beyond those predicted from static analyses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

几个世纪以来，人类一直对色彩的多样性着迷，色彩科学的领域横跨艺术，哲学，心理学和生物学。不同的人和社会有不同的颜色体验，甚至同一个人对颜色的感知也因背景而异。我们能够感知这种变化是由于一个复杂的颜色感知系统，特别是相对于其他哺乳动物。其他动物，如鸟类，有更复杂的颜色感知，是地球上颜色最鲜艳的动物之一。造成这种多样性的根本原因是什么？动物所处的环境，比如它是生活在森林里还是生活在野外，是如何塑造颜色的进化和感知的？我们缺乏理解的一个原因可能是，颜色信号经常通过身体各部位的运动呈现给观察者，如竞争对手、配偶或捕食者。高速视频技术和计算机编程的最新发展现在使准确描述这些运动成为可能。因此，本项目将通过比较密切相关的物种来研究动物展示，栖息地和颜色之间的关联。这项工作的首要目标是了解自然界中颜色的多样性，并最终了解颜色如何被感知的多样性。虽然长期以来人们认识到展示运动是动物信号的组成部分，但直到最近几年，技术才开始（例如高速摄像机，因为许多物种可以比人类更精细地感知运动）和分析技术（例如，跟踪运动轨迹的深度学习方法）已经足够先进，可以用于在比较的背景下研究显示器，从而将它们与物种之间的颜色和生态差异联系起来。该项目的目标是量化显示行为，环境和生态特征，社会背景，颜色和颜色模式在旧大陆柳莺（属Phylloscopus），选择，因为以前的工作已经显示了光环境，颜色和显示组之间的定性关联。密切相关的，同域，物种将在栖息地类型的梯度进行比较，询问羽毛颜色（通过分光光度计量化）和威胁显示（通过回放实验和高速视频量化）与栖息地，包括背景颜色和粒度，以及当时的光环境。新的分析方法来自形态形状的比较，将被用来放置在连续的尺度上显示运动，从而使他们能够跨物种排名。基本概念是显示器随环境变化，从而影响颜色和颜色模式的演变，超出静态分析的预测。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。