Collaborative Research: BirdFlow: Learning Bird Population Flows from Citizen Science Data

合作研究：BirdFlow：从公民科学数据中学习鸟类种群流动

• 批准号: 2210980
• 负责人: Adriaan Dokter
• 金额: $ 67.46万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210980&HistoricalAwards=false
• 关键词: Collaborative; Research; BirdFlow; Learning; Bird

Billions of birds migrate each year in journeys that are largely hidden from human observation, yet critical to the success of bird populations. To understand and monitor migratory species, data and methods are needed that can capture the movements of bird populations across the globe. The eBird citizen science project receives millions of bird observations throughout the year and uses these data to produce detailed weekly abundance maps for hundreds of migratory species around the world. Despite this rich information about bird distributions, scientists lack widespread, detailed data about the migratory routes that link bird populations and their habitats throughout the year. In the BirdFlow project, a team of computer scientists and ornithologists will use citizen science data to create models and algorithms to infer population movements of migratory birds. The models will allow inferences currently unavailable to ecologists at the scale of full populations and flyways, including simulated migration routes and movement forecasts. The resulting data will help address urgent needs in ecology, conservation, and industry, including understanding connectivity between populations and links between migration and evolution, as well as applications to disease spread and aviation safety. Visualizations and educational material will be created to inspire the public and raise awareness about biodiversity and ecosystem health. The BirdFlow project will develop models and algorithms to infer bird movements from citizen science data. Data products from the eBird Status and Trends project will provide information about the weekly distributions of bird populations, and optimization problems will be formulated to infer population movements that are consistent with the weekly distributions and approximately minimize energetic costs. Individual tracking data and other evidence will be used to validate and improve models. Technically, the work will build on an emerging line of research that uses probabilistic graphical models to learn about probability distributions over many variables from partial information, such as noisy estimates of the distributions of individual variables. Software and data products will be created that will allow scientists to use pre-fitted BirdFlow models to simulate synthetic migration routes and create movement forecasts for species of interest. The project team will use BirdFlow to conduct ecological research about patterns and drivers of migration in the Western Hemisphere. Project information can be found at https://birdflow-science.github.io/.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

每年有数十亿只鸟类迁徙，这些迁徙在很大程度上隐藏在人类观察之外，但对鸟类种群的成功至关重要。为了了解和监测迁徙物种，需要能够捕捉到鸟类种群在地球仪上移动的数据和方法。eBird公民科学项目全年接收数百万次鸟类观测数据，并利用这些数据为世界各地数百种迁徙物种绘制详细的每周丰度图。尽管有关鸟类分布的信息如此丰富，但科学家们缺乏有关全年鸟类种群及其栖息地的迁徙路线的广泛、详细的数据。在BirdFlow项目中，一个由计算机科学家和鸟类学家组成的团队将使用公民科学数据来创建模型和算法，以推断候鸟的种群移动。这些模型将允许生态学家在整个种群和飞行路线的规模上进行目前无法进行的推断，包括模拟迁移路线和运动预测。由此产生的数据将有助于解决生态，保护和工业的迫切需求，包括了解人口之间的联系以及迁移和进化之间的联系，以及疾病传播和航空安全的应用。将制作可视化和教育材料，以激励公众并提高对生物多样性和生态系统健康的认识。 BirdFlow项目将开发模型和算法，从公民科学数据中推断鸟类运动。来自eBird状态和趋势项目的数据产品将提供有关鸟类种群每周分布的信息，并将制定优化问题，以推断与每周分布一致的种群移动，并近似最小化能量成本。个人跟踪数据和其他证据将用于验证和改进模型。从技术上讲，这项工作将建立在一个新兴的研究路线上，该研究使用概率图形模型从部分信息中了解许多变量的概率分布，例如对单个变量分布的噪声估计。将创建软件和数据产品，使科学家能够使用预先安装的BirdFlow模型来模拟合成迁移路线，并为感兴趣的物种创建移动预测。该项目团队将利用BirdFlow对西半球的迁移模式和驱动因素进行生态研究。项目信息可以在www.example.com上找到https://birdflow-science.github.io/.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。