Sensory strategies and behavioural algorithms in predator-prey-interactions

动物的感知和行为形成了一个紧密结合的环路。感官系统为动物提供有关其周围环境的信息，而运动系统执行行为动作。行为算法是将感觉输入映射到自适应运动输出的规则。捕食者-猎物-猎物相互作用在自然界中无处不在，对捕食者和猎物都构成了很大的选择压力。感觉和行为策略是捕食者-猎物-猎物相互作用的关键部分，特别是在视觉系统中有很好的记录，包括伪装和视觉引导的追逐和逃跑。追逐和逃跑是研究捕食者和猎物的感觉策略和行为算法的很好的模型，因为它们服务于一个明确可识别的目标，并且可以通过实验跟踪和数学描述。在这里，我提出了一种经验和理论相结合的方法来研究捕食者和猎物的感觉策略和行为算法，这些捕食者和猎物由听觉信息引导，即回声定位蝙蝠和耳蛾。将听觉引导的捕食者-猎物相互作用与更知名的视觉策略进行比较，对于全面理解行为算法至关重要，包括共同原理以及感官系统之间的根本差异。我将开发、校准和描述先进的技术，用于自由飞行蝙蝠的3D跟踪，以及基于虚拟现实和仿生机器人猎物的飞行蝙蝠实时闭环实验。我将经验性地测量飞蛾提供的感官声学线索，包括飞行声音和时变回声信息，并量化多少尺度对声学伪装的贡献。在野外和实验室用自由飞行的回声定位蝙蝠进行的实验中，我将经验性地测试不同反捕食者策略的有效性(声学伪装、停止翼拍和降落到地面)。此外，我将分离回声定位蝙蝠依靠猎物产生的声学线索来检测和选择自由飞行的飞蛾，它们如何追逐躲避的飞蛾，并对它们攻击猎物的控制规则进行建模。在建模方法中，我将把负趋音性分为三个连续的行为算法，负趋音性是防御耳蛾的第一道防线。我将使用社区范围内的听力阈值、飞行运动学和蝙蝠叫声参数数据来模拟相互检测距离，并将蝙蝠搜索和躲避飞蛾的模型参数化。在不同的建模方法中，我将检验负趋声的恒定缓冲假说，预测蛾成功逃逸所需的最低听力阈值，并开发一个大规模的感觉-运动-模型来描述蛾逃避飞行的效果和成本。

Sensing and acting of animals form a tightly integrated loop. Sensory systems provide animals with information about their surroundings, and motor systems implement the behavioural actions. Behavioural algorithms are the rules that map the sensory input to adaptive motor output. Predator-prey-interaction occur everywhere in nature and pose a strong selection pressure on both predators and prey. Sensory and behavioural strategies are crucial part of predator-prey-interactions, particularly well documented in the visual system, including camouflage and visually-guided pursuit and escape. Pursuit and escape are great models to study the sensory strategies and behavioural algorithms of predators and prey, as they serve a clearly identifiable goal and can be experimentally tracked and mathematically described. Here, I propose a combined empirical and theoretical approach to investigate sensory strategies and behavioural algorithms in predators and prey that are guided by auditory information, namely echolocating bats and eared moths. Investigating auditory-guided predator-prey-interactions in comparison to the better-known visual strategies is crucial for obtaining a general understanding of behavioural algorithms including common principles as well as fundamental differences between sensory systems.I will develop, calibrate and describe advanced technologies for the 3D-tracking of free-flying bats and for real-time closed-loop experiments with flying bats based on virtual realities and biomimetic robotic prey. I will empirically measure sensory acoustic cues provided by moths, including flight sounds, and time-variant echo information, and quantify how much scales contribute to acoustic camouflage. In experiments with free-flying echolocating bats in the field and lab, I will empirically test the effectiveness of different antipredator strategies (acoustic camouflage, cessation of wingbeats, and dropping to the ground). Furthermore, I will isolate the prey-generated acoustic cues that echolocating bats rely on to detect and select free-flying moths, how they pursue evading moths, and I will model the control rules underlying their attacks on prey.In a modelling approach, I will divide negative phonotaxis, which is the first line of evasive defence of eared moths, into three consecutive behavioural algorithms. I will use community-wide data of hearing thresholds, flight kinematics and bat call parameters to model mutual detection distances, and to parametrize models of bat search and moth evasive flight. In different modelling approaches, I will test the constant-buffer-hypothesis of negative phonotaxis, predict the minimally required hearing thresholds of moths for successful escape, and develop a large-scale sensory-motor-model to delineate the effectiveness and costs of moth evasive flight.