Collaborative Research: Near Term Forecasts of Global Plant Distribution, Community Structure, and Ecosystem Function

合作研究：全球植物分布、群落结构和生态系统功能的近期预测

• 批准号: 1934389
• 负责人: Laura Duncanson
• 金额: $ 29.61万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934389&HistoricalAwards=false
• 关键词: Collaborative; Research; Near; Term; Forecasts

This project is the first to explore how plant species distributions across the entire globe may respond to global change. The project brings together ecologists, environmental engineers, data scientists, and conservation stakeholders to determine optimal ways to integrate these data sources to make near term forecasts for all plants globally by addressing changes in (1) species' abundance and geographic distribution, (2) community structure, and (3) ecosystem function. This three-pronged approach is designed to span a range of approaches to understand the spectrum of possible futures consistent with current knowledge while integrating knowledge across scales of biological organization. These forecasts will be used along with input from conservation stakeholders to assess how differing conservation decisions can minimize the impacts of global change responses. An ultimate goal of the project is to automate a pipeline to ingest new incoming data, update forecasts, and serve these to end-users to enable a near-real time forecasting workflow to provide best-available predictions at any given time to inform conservation decisions. A key aspect of these forecasts is their reliance on novel environmental information that better characterize the conditions that influence plant performance, including soil moisture and extreme weather events based on NASA satellite observations. These species-level predictions will be linked to community demography models that integrate a variety of relatively untapped data sources for understanding global change, including plant trait data, community plot data across the globe, highly detailed plot data from National Ecological Observatory Network (NEON) and Long Term Ecological Research (LTER) sites, and global biomass data from NASA's Global Ecosystem Dynamics Investigation (GEDI) mission. By integrating this wide variety of data sources, the mechanistic understanding needed to make robust near term forecasts can be made, to understand ecosystem properties like Net Primary productivity, Carbon stock, and resilience. Based on workshops with conservation stakeholders, researchers will determine how best to use this unique suite of forecasts to best inform different conservation questions in different regions of the world. The project will also result in an open, cleaned and curated database on global plant distributions. This will aid others in exploring data and predictions by delivering and visualizing complex future scenarios in an easy to use portal. All results of the project can be found at the website for the Biodiversity Informatics and Forecasting Institute or BIFI, at https://enquistlab.github.io/BIFI .This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity.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.

该项目是第一个探索全球植物物种分布如何应对全球变化的项目。该项目汇集了生态学家、环境工程师、数据科学家和保护利益相关者，以确定整合这些数据源的最佳方式，通过应对(1)物种丰度和地理分布、(2)群落结构和(3)生态系统功能的变化，对全球所有植物做出短期预测。这种三管齐下的方法旨在跨越一系列方法，以了解与当前知识相一致的可能未来的光谱，同时整合跨生物组织规模的知识。这些预测将与保护利益攸关方的意见一起使用，以评估不同的保护决定如何将全球变化反应的影响降至最低。该项目的最终目标是自动化一条管道，以获取新的传入数据，更新预测，并将这些数据提供给最终用户，以实现近乎实时的预测工作流程，以便在任何给定时间提供最佳预测，为保护决策提供信息。这些预报的一个关键方面是它们依赖于新的环境信息，这些信息更好地描述了影响植物表现的条件，包括基于NASA卫星观测的土壤湿度和极端天气事件。这些物种水平的预测将与群落人口模型相联系，该模型集成了各种相对未开发的数据源来了解全球变化，包括植物特征数据、全球各地的群落地块数据、来自国家生态观测网络(NEON)和长期生态研究(LTER)站点的高度详细的地块数据，以及来自NASA全球生态系统动态调查(GEDI)任务的全球生物量数据。通过整合这些种类繁多的数据源，可以从机械上理解做出可靠的短期预测所需的机制，从而理解生态系统的属性，如净初级生产力、碳储量和弹性。基于与保护利益相关者的研讨会，研究人员将确定如何最好地利用这套独特的预测来最好地为世界不同地区的不同保护问题提供信息。该项目还将建立一个关于全球植物分布的开放、清洁和精选的数据库。这将通过在易于使用的门户中交付和可视化复杂的未来场景来帮助其他人探索数据和预测。该项目的所有结果都可以在生物多样性信息学和预测研究所的网站上找到，网址是https://enquistlab.github.io/BIFI。该项目是国家科学基金会利用数据革命大创意活动的一部分。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。