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.

非技术摘要动物群体因其以个体无法做到的方式与环境互动的能力而引人注目，例如通过迁徙、集体智慧或躲避捕食者。例如，由于鱼群中信息传输的效率，捕食者攻击高度协调的鱼群几乎没有成功。拍摄和跟踪大群动物的技术挑战限制了我们对它们如何如此有效地躲避捕食者的理解，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，并展示信息如何在动物群中传播。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相矛盾的预测：经典的行为生态学理论表明了中心，而新的网络数据表明了群体的边缘。这里进行的研究将首次检验这些相互竞争的假设。北梭鱼捕食者与金光鱼群之间的现场互动将在实验室中拍摄，然后使用先进的计算机视觉软件从感觉网络的角度进行重建，以更好地了解信息如何传递以及群体中个体位置的影响。该实验将为行为生态学、感官生态学、博弈论和网络科学领域提供见解。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、通过博客以及向当地高中进行科学宣传传播。技术摘要几十年来，汉密尔顿的自私群体理论一直是对动物群体捕食风险空间分布的期望。在这个模型中，当动物移动到将其他个体置于自己和潜在的隐藏捕食者之间时，就会出现有凝聚力的群体，从而最大限度地减少它们的“危险范围”。然而，对于鱼群等移动动物群体，对这一理论的支持不一，因为自私群体理论不允许猎物对捕食者做出反应。另一方面，真正的捕食者与猎物的相互作用是动态的。直到最近，由于行为数据质量和数量的技术限制，在鱼群中直接测试汉密尔顿的自私群体理论一直是不可能的。然而，计算机视觉和高速摄像机的最新进展现在可以准确测量鱼群所有成员的精细运动，并估计它们可获得的视觉信息。根据这些视觉信息构建的网络可以准确预测鱼群的运动决策如何传递。这项研究将通过在实验室中拍摄金光鱼和白斑狗鱼捕食者之间的捕食者与猎物之间的相互作用，直接测试空间定位或网络结构是否能更准确地预测死亡风险的非排他性假设。