Dissertation Research: Revealing the spatial distribution of risk in animal groups

论文研究：揭示动物群体风险的空间分布

• 批准号: 1701289
• 负责人: Daniel Rubenstein
• 金额: $ 1.32万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1701289&HistoricalAwards=false
• 关键词: Dissertation; Research; Revealing; spatial; distribution

Non-technical AbstractAnimal groups are remarkable for their ability to interact with the environment in a way that individuals are unable to, such as through migration, collective intelligence, or predator avoidance. For example, predators attacking highly coordinated fish schools have little success due to how efficient information transmission is in the group. The technological challenges of filming and tracking large groups of animals have limited our understanding of how they are so effective at predator avoidance, but recent advances in computer vision and high-speed filming now allow us to accurately recreate these information transmission networks and show how information moves through animal groups. Insights into these networks have put forth predictions that are at odds with long-established hypotheses on where in the group animals are safest: classic behavioral ecology theory suggests the center, while new network data suggests the edge of the group. The research carried out here will for the first time test these competing hypotheses. Live interactions between a northern pike predator and schools of golden shiners will be filmed in the laboratory and then recreated from a sensory network perspective using sophisticated computer vision software to better understand how information is transferred and the ramifications of individual location in the group. The experiment will provide insights into the fields of behavioral ecology, sensory ecology, game theory, and network science. It will provide scientific and statistical training to graduate and undergraduate students, and findings will be disseminated at scientific conferences, through blogging, and through science outreach to local high schools.Technical AbstractFor decades, Hamilton's Selfish Herd theory has served as the expectation for the spatial distribution of predation risk in animal groups. In this model, cohesive grouping emerges as animals move to position other individuals between themselves and a potential hidden predator, hence minimizing their "domain of danger". Support for this theory has been mixed for mobile animal groups such as fish schools, however, because the Selfish Herd theory does not allow for prey to respond to the predator. Real predator-prey interactions, on the other hand, are dynamic. Until very recently, directly testing Hamilton's Selfish Herd theory in fish schools has been impossible due to technological limitations on the quality and quantity of behavior data. Recent advances in computer vision and high-speed cameras, however, now allow for accurate measures of the fine-scale movements of all members of fish schools, as well as estimations of the visual information available to them. Networks constructed from this visual information are an accurate predictor of how movement decisions transfer through schools of fish. Here research will directly test the non-exclusive hypotheses of whether spatial positioning or network structure is a more accurate predictor of mortality risk by filming predator-prey interactions between golden shiners and a northern pike predator in the laboratory.

动物群体以个体无法做到的方式与环境相互作用的能力而引人注目，例如通过迁移，集体智慧或捕食者回避。例如，捕食者攻击高度协调的鱼群几乎没有成功，这是由于群体中信息传递的效率。拍摄和跟踪大型动物群体的技术挑战限制了我们对它们如何有效地避免捕食者的理解，但计算机视觉和高速拍摄的最新进展现在使我们能够准确地重建这些信息传输网络，并显示信息如何在动物群体中移动。对这些网络的深入研究提出了与长期以来关于动物群体中哪里最安全的假设不一致的预测：经典的行为生态学理论认为是中心，而新的网络数据则认为是群体的边缘。本研究将首次检验这些相互竞争的假设。一只北方梭子鱼捕食者和一群金色发光鱼之间的实时互动将在实验室中拍摄，然后使用复杂的计算机视觉软件从感觉网络的角度进行重建，以更好地了解信息是如何传递的，以及群体中个人位置的影响。该实验将提供对行为生态学，感官生态学，博弈论和网络科学领域的见解。它将为研究生和本科生提供科学和统计培训，研究结果将在科学会议上传播，通过博客，并通过科学推广到当地的高中。技术摘要几十年来，汉密尔顿的自私羊群理论一直担任的期望在动物群体中的捕食风险的空间分布。在这个模型中，当动物移动到自己和潜在的隐藏捕食者之间的位置时，就会出现内聚群体，从而最小化它们的“危险域”。然而，对于像鱼群这样的移动的动物群体来说，支持这一理论的观点并不一致，因为自私的羊群理论不允许猎物对捕食者做出反应。另一方面，真实的捕食者与被捕食者之间的互动是动态的。直到最近，直接测试汉密尔顿的自私羊群理论在鱼群一直是不可能的，由于技术限制的质量和数量的行为数据。然而，计算机视觉和高速摄像机的最新进展现在允许精确测量鱼群所有成员的精细运动，以及估计它们可用的视觉信息。从这些视觉信息构建的网络是一个准确的预测器，可以预测鱼群的运动决策是如何传递的。在这里，研究将直接测试的非排他性的假设，是否空间定位或网络结构是一个更准确的预测死亡率的风险，通过拍摄捕食者之间的相互作用的黄金发光体和北方梭子鱼捕食者在实验室。