ABI Innovation: Informatics Tools for Population-level Movement Dynamics

ABI 创新：人口层面运动动态的信息学工具

• 批准号: 1062411
• 负责人: Thomas Mueller
• 金额: $ 82.99万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1062411&HistoricalAwards=false
• 关键词: ABI; Innovation; Informatics; Tools; Population

A grant is awarded to University of Maryland, College Park to develop informatics tools that allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population level. Technological advances such as GPS tracking devices have facilitated much recent progress in understanding the movements of individual animals, but scientists' understanding of the emergent spatial dynamics at the population level has not kept pace, in large part due to an absence of appropriate tools for data handling and statistical analysis. To bridge this key gap and study such processes as spatial learning, social interactions vis-à-vis aggregation, and population level movement patterns (e.g., migration, nomadism), detailed analyses of individual movement paths are not sufficient. Researchers must, in addition, attend to the relationships that exist between moving animals. This project will develop new and innovative data management and analysis tools focusing on the interrelationship of multiple moving individuals. These include measures that calculate 1) realized mobility (quantifying the relationship of individual to population ranges), 2) population dispersion (quantifying the spatial relationship among individuals), 3) movement coordination (quantifying the coordination of movements among individuals), and 4) intra-individual concordance (quantifying the spatial relationship of relocations of individuals over time). These innovative ways of treating animal movement data will allow researchers to investigate a broad range of new research questions. For example, by statistically analyzing the interrelationships of relocation data among individuals, it will be possible to distinguish and quantify population-level movement patterns such as migration, range residency, and nomadism. The same tools can be used to analyze interrelationships of relocation data among individuals but across time, thereby examining how animal movements change as individuals age and gain experience. Finally these same tools may be applied to analyze social networks and use animal relocations to understand fission-fusion dynamics of grouping behavior and characterize the timing and consistency of aggregations. Using existing data, they will develop and test these new tools using datasets on Mongolian gazelles, whooping cranes, and blacktip sharks. These species represent not only different types of movement (on land, in air, in water) but also different types of relocation data (from visual observations of individually marked animals to GPS relocations to relocations obtained from networked sensor arrays). They will focus on spatial learning and changes in migratory patterns in whooping cranes, nomadic long-distance movement in gazelles, and group formation in sharks. The project will develop an analysis package in the open-source language R and complement it with a step-by-step hands-on manual to make tools available to a broad, international user community that includes academics, scientists working for governments and non-governmental organizations, and professionals directly engaged in conservation practice and land management. The software package will be made publicly available under http://www.clfs.umd.edu/biology/faganlab/movement/. Efforts will also include a major emphasis on graduate and undergraduate research and training, through assistantships for PhD students and undergraduates. Additional broader impacts will emerge from analyses of the whooping crane dataset. Through collaborations with endangered species biologists in the US Geological Survey, these analyses will have direct relevance to specific management actions for the whooping crane, such as the timing, group size, and composition of crane reintroductions and potentially their training with ultra-light aircraft.

一笔赠款授予马里兰州大学帕克分校，以开发信息学工具，使科学家和保护管理人员能够使用动物搬迁和跟踪数据来研究人口一级的运动过程。全球定位系统跟踪装置等技术进步促进了最近在了解单个动物的运动方面取得的进展，但科学家对种群水平上出现的空间动态的理解并没有跟上步伐，这在很大程度上是由于缺乏适当的数据处理和统计分析工具。为了弥合这一关键差距，并研究空间学习、社会互动维斯聚集以及人口水平移动模式等过程（例如，移徙、游牧），对个别迁移路径的详细分析是不够的。 此外，研究人员还必须注意运动动物之间存在的关系。 该项目将开发新的和创新的数据管理和分析工具，重点是多个移动个人的相互关系。这些措施包括计算1）已实现的流动性（量化个人与人口范围的关系），2）人口分散（量化个人之间的空间关系），3）运动协调（量化个人之间的运动协调），以及4）个人内部一致性（量化个人随时间迁移的空间关系）。这些处理动物运动数据的创新方法将使研究人员能够调查广泛的新研究问题。 例如，通过统计分析个人之间重新安置数据的相互关系，将有可能区分和量化人口一级的移动模式，如迁移，范围居住和游牧。同样的工具可以用来分析个体之间的迁移数据的相互关系，但随着时间的推移，从而研究动物运动如何随着个体年龄的增长和经验的积累而变化。最后，这些相同的工具可以应用于分析社交网络，并使用动物重新定位来理解群体行为的裂变-融合动力学，并表征聚合的时间和一致性。 利用现有数据，他们将使用蒙古瞪羚、高鸣鹤和黑鳍鲨的数据集开发和测试这些新工具。 这些物种不仅代表不同类型的移动（在陆地上，在空中，在水中），而且还代表不同类型的重新定位数据（从单独标记的动物的视觉观察到GPS重新定位到从网络传感器阵列获得的重新定位）。他们将专注于空间学习和高鸣鹤迁徙模式的变化，瞪羚的游牧长距离运动和鲨鱼的群体形成。 该项目将用开放源码语言R开发一个分析包，并辅之以一个逐步操作手册，向广泛的国际用户社区提供工具，其中包括学术界、为政府和非政府组织工作的科学家以及直接从事养护实践和土地管理的专业人员。该软件包将在http://www.clfs.umd.edu/biology/faganlab/movement/上公开提供。 努力还将包括通过为博士生和本科生提供助学金，重点关注研究生和本科生的研究和培训。 通过对高鸣起重机数据集的分析，还将出现其他更广泛的影响。 通过与美国地质调查局濒危物种生物学家的合作，这些分析将直接关系到对鸣禽起重机的具体管理行动，例如重新引入起重机的时机、群体大小和组成，以及可能的超轻型飞机训练。