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

基本信息

批准号：
2210979
负责人：
Daniel Sheldon
金额：
$ 82.7万
依托单位：
University of Massachusetts Amherst
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=2210979&HistoricalAwards=false
关键词：
Collaborative Research BirdFlow Learning Bird

项目摘要

Billions of birds migrate each year in journeys that are largely hidden from human observation, yet are 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在很大程度上被人类观察隐藏的旅程中迁移，但对于鸟类种群的成功至关重要。为了了解和监测迁徙物种，需要数据和方法，以捕获鸟类种群在全球范围内的运动。 ebird Citizen Science项目全年都接受了数百万个鸟类的观察，并使用这些数据为世界上数百种迁徙物种生成了详细的每周丰富地图。尽管有有关鸟类分布的丰富信息，但科学家仍缺乏有关全年将鸟类种群及其栖息地联系起来的迁徙路线的广泛，详细的数据。在Birdflow项目中，一组计算机科学家和鸟类学家将使用公民科学数据来创建模型和算法来推断迁徙鸟类的人口运动。这些模型将允许在完全人口和飞行的规模（包括模拟迁移路线和运动预测）的规模上对生态学家目前无法进行的推论。最终的数据将有助于满足生态，保护和行业的紧急需求，包括了解人群与移民与进化之间的联系，以及疾病传播和航空安全的应用。将创建可视化和教育材料，以激发公众并提高人们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出鸟类的运动。来自ebird状态和趋势项目的数据产品项目将提供有关鸟类种群每周分布的信息，并将提出优化问题，以推断与每周分布一致的人口运动，并大致最小化了能量成本。单个跟踪数据和其他证据将用于验证和改善模型。从技术上讲，这项工作将建立在新兴的研究线上，该研究使用概率图形模型来从部分信息中了解许多变量的概率分布，例如单个变量分布的嘈杂估计。将创建软件和数据产品，该产品将允许科学家使用预先安装的鸟类模型来模拟合成迁移路线并为感兴趣的物种创建运动预测。该项目团队将使用鸟类进行有关西半球迁移模式和驱动因素的生态研究。可以在https://birdflow-science.github.io/.This奖上找到项目信息，反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估标准，认为值得通过评估来获得支持。