Collaborative Research: MRA: Modeling and forecasting phenology across spatiotemporal and taxonomic scales using ecological observatory and mobilized digital herbarium data

合作研究：MRA：利用生态观测站和移动数字植物标本室数据对跨时空和分类尺度的物候进行建模和预测

基本信息

批准号：
2242804
负责人：
Sydne Record
金额：
$ 31.77万
依托单位：
University of Maine
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242804&HistoricalAwards=false
关键词：
Collaborative Research MRA Modeling forecasting

项目摘要

Environmental change of all kinds – including climate change, urbanization, and wildfire – affects the seasonal timing of life cycle events in plants worldwide. Most notable are the effects of environmental conditions on the seasonal onset and duration of flowering. The timing of flowering within and among species is important for the persistence of natural populations because it affects interactions between plants, and the availability of flowers and fruits for the animals that depend on them. But the effects of environmental change on flowering differ among species and regions. This project aims to understand and forecast changes in flowering and fruiting among thousands of different plant species across the continental U.S.A. This project takes full advantage of millions of observations of flowering times collected by scientists working with the National Ecological Observation Network (NEON) and citizen-scientists contributing observations from their homes, neighborhoods, and public lands to the National Phenology Network (NPN). The researchers will augment these records of flowering times with the data from millions more herbarium specimens that are available on-line to detect the responses of flowering times to the past century of climate change. These observations will be combined with soil quality, plant cover, land use history, climate, and disturbance data to better understand how different environmental conditions influence species-specific and regional flowering times. Finally, the researchers will use statistical models to forecast short and long-term changes in future flowering times. The combined dataset will be a valuable resource available to other researchers examining the effects of environmental change on plant species and community traits. In addition, the research will provide educational opportunities for K-12, undergraduate and graduate students, and postdoctoral researchers. The project will also engage citizen-scientists who will contribute to a database of flowering times observed from herbarium collections through the CrowdCurio crowdsourcing platform.Plant phenology–the seasonal timing of key developmental events–is essential for species’ reproductive success. However, critical gaps remain in our understanding of phenology across space, time, and taxa. Increasingly, online herbaria and associated data are being mobilized to address these knowledge gaps because they provide extensive data that can be used to detect phenological responses to climatic change within and among biomes, functional groups, and taxonomic groups. In this project, the standardized, replicated, and focused phenological observations provided by NEON and the USA National Phenology Network will be harmonized for the first time with the taxonomic, spatial, and geographic breadth of herbarium data. First, flowering times derived from herbarium specimens will be assembled and augmented to include 4400 plant species that collectively span much of the continental US, with specific attention to key regions that have been digitized but overlooked: prairie, alpine, and urban biomes. Second, sources of variation in phenology within and among species, geographic regions, and higher taxa, and the effects of numerous understudied extrinsic factors (e.g., fire history, soil quality, disturbance) will be modeled. Third, forecasts of near- and long-term changes in the phenological behavior of populations, species, and communities will be modeled to better understand phenological responses at multiple ecological, phylogenetic, and temporal scales. Collectively, these efforts will help to elucidate plausible mechanistic responses to climatic and geographic factors that will determine species’ future phenology.This project is jointly funded by the Division of Environmental Biology/Macrosystems Biology and NEON-enabled Science Program and the Division of Biological Infrastructure/Capacity: Cyberinfrastructure Program.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.

各种环境变化（包括气候变化、城市化和野火）都会影响全世界植物生命周期事件的季节性时间，最显着的是环境条件对开花季节和持续时间的影响。物种间的变化对于自然种群的持续存在很重要，因为它影响植物之间的相互作用，以及依赖它们的动物的花朵和果实的可用性，但环境变化对开花的影响因物种和地区而异。了解并预测数千个开花和结果的变化该项目充分利用了与国家生态观测网络 (NEON) 合作的科学家收集的数百万个开花时间观测数据，以及公民科学家将其在家中、社区和公共土地上的观测数据贡献给国家物候网络（NPN）。研究人员将利用数百万个在线植物标本的数据来扩充这些开花时间记录，以检测开花时间对过去一个世纪气候变化的反应。将与土壤质量、植物覆盖、土地利用历史、气候和干扰数据相结合，以更好地了解不同的环境条件如何影响物种特定和区域的开花时间。最后，研究人员将使用统计模型来预测短期和长期的开花时间。该数据集将成为其他研究人员研究环境变化对植物物种和群落特征的影响的宝贵资源。此外，该研究还将为 K-12、本科生和研究生提供教育机会。该项目还将吸引公民科学家。他们将通过 CrowdCurio 众包平台为从植物标本馆收集到的开花时间数据库做出贡献。植物物候学（关键发育事件的季节时间）对于物种的繁殖成功至关重要。然而，我们对跨空间物候学的理解仍然存在重大差距。、时间和类群越来越多地利用在线植物标本馆和相关数据来解决这些知识差距，因为它们提供了广泛的数据，可用于检测生物群落内部和之间气候变化的物候反应，在该项目中，NEON 和美国国家物候网络提供的标准化、可复制和集中的物候观测将首次与植物标本室数据的分类、空间和地理广度相协调。，来自植物标本馆标本的开花时间将被汇集和扩充，以包括涵盖美国大陆大部分地区的 4400 种植物物种，特别关注已数字化但被忽视的关键区域：其次，将对物种、地理区域和高等分类群内部和之间物候变化的来源以及许多未充分研究的外部因素（例如火灾历史、土壤质量、干扰）的影响进行建模。第三，将对种群、物种和群落物候行为的近期和长期变化进行建模，以更好地理解多个生态、系统发育和时间尺度的物候反应。总的来说，这些努力将有助于阐明对气候和地理因素的合理机械反应，这些因素将决定物种未来的物候。该项目由环境生物学/宏观系统生物学和 NEON 科学计划司以及生物基础设施司共同资助/能力：网络基础设施计划。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。