What makes an effective warning signal?

许多动物表现出伪装模式，帮助它们隐藏在背景中，不太容易被捕食者发现。但有些动物有鲜艳的色彩，使它们在环境中脱颖而出。虽然已知一些引人注目的图案与吸引配偶有关，但其他图案已经进化成警告捕食者该动物有毒或不好吃。这些警告模式被描述为“警示”，通常在昆虫中发现，例如帝王蝶、瓢虫和黄蜂。虽然警示性图案看起来可能会吸引捕食者，但实际上它们起到了威慑作用。食肉动物对红色或黄色等带有警示色彩的猎物非常警惕，它们很快就能意识到警告色是危险的信号，并避开带有警示色彩的猎物。尽管像伪装图案一样的警告信号已经吸引了生物学家150多年，但对于是什么特征使这些图案与众不同并有效地对抗捕食者，或者它们是如何被设计成利用捕食者看待世界的方式，仍然没有明确的认识。事实上，警示语模式的定义是松散的描述性的，例如，它们被描述为“引人注目的”、“引人注目的”或“与众不同的”。但是，是什么使这些模式与自然界的其他模式区别开来：是什么构成了有效的警告信号？在这个项目中，我们建议通过数学建模和对小鸡和人类的实验来解决这个重要的问题。我们将首次测量警讯和非警讯物种的模式，并使用图像处理技术量化警讯模式的特征。我们将从博物馆藏品中收集蝴蝶、飞蛾和甲虫的照片，使用的技术使我们能够像觅食的鸟类一样“看到”这些图案。例如，我们知道鸟类的视觉系统对什么颜色和图案最敏感，并且可以计算警告信号如何刺激它们的视觉系统。一旦模型做出了预测，我们就可以用行为实验来测试它们，测量鸟类对警告模式的反应，以及哪些特征能提高猎物的存活率。我们还计划测试一个关于为什么警告模式作为警告信号的新假设。人类对特定种类的图案（例如特定大小和排列的条纹或斑点）感到厌恶或不舒服。有人认为，这些模式可能会使大脑“超负荷”，并使这些信号令人反感。我们将建立大脑视觉处理早期阶段的计算模型，并测试警告模式是否会产生过度反应。然后，我们将通过选择应该使人类视觉超载的模式来测试模型，并采取经典的视觉不适措施。该项目将使我们能够理解警告模式的形式和功能，并最终给我们一个精确和有效的定义。这项工作将广泛吸引公众，并有可能提高视觉警报和鸟类威慑的功效。

Many animals exhibit camouflage patterns that help them remain hidden against their background and be less visible to predators. But some animals have vivid, bright colouring that makes them stand out in their environment. Whilst some conspicuous patterns are known to be involved in attracting a mate, others have evolved to warn a predator that the animal is poisonous or unpalatable. These warning patterns are described as being 'aposematic', and are commonly found in insects, for example, monarch butterflies, ladybirds and wasps. Although aposematic patterns look like they might attract predators, in fact they act as a deterrent. Predators are wary towards prey that are warningly coloured, such as being red or yellow, and are quick to learn that warning colours signal danger and avoid aposematically coloured prey. Although warning signals, like camouflage patterns, have fascinated biologists for more than 150 years, there is still no clear understanding of what features make these patterns distinctive and effective against predators, or how they are designed to exploit the ways in which predators see the world. Indeed, definitions of aposematic patterns are loosely descriptive, for example, they are described as 'striking', 'conspicuous' or 'distinctive'. But what is it that sets these patterns apart from others in the natural world: what makes an effective warning signal?In this project, we propose to tackle this important question through mathematical modeling, and experiments with chicks and humans. For the first time, we will measure the patterns of aposematic and non-aposematic species, and use image processing techniques to quantify the characteristics of aposematic patterns. We will collect photographs of butterflies, moths and beetles from museum collections using techniques that allow us to 'see' the patterns as a foraging bird would. For example, we know what colours and patterns avian visual systems are most sensitive to, and can calculate how warning signals stimulate their visual systems. Once the models have made their predictions we can test them using behavioural experiments that measure how birds react to the aposematic patterns and what features enhance prey survival.We also plan to test a novel hypothesis for why aposematic patterns act as warning signals. Humans find particular classes of pattern (e.g. stripes or spots of specific sizes and arrangements) aversive or uncomfortable. It has been suggested that these patterns could 'overload' the brain, and make these signals aversive. We will build a computational model of the early stages of visual processing in the brain, and test if aposematic patterns do deliver excessive responses. We will then test the model by choosing patterns that should visually overload humans, and taking classic visual discomfort measures.The project will allow us to understand the form and function of aposematic patterns, and finally give us a precise and working definition. The work will broad appeal to the general public, and potentially improve the efficacy of visual alerts and avian deterrents.