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: 1934759
• 负责人: Amy Frazier
• 金额: $ 29.44万
• 依托单位: Arizona State University
• 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=1934759&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，以确定将这些数据源整合到全球所有植物的最佳方法，通过解决（1）物种的丰度和地理分布的变化，（2）社区结构和（3）生态系统功能。这种三管齐下的方法旨在跨越各种方法，以了解与当前知识一致的可能未来的范围，同时跨越生物组织范围的知识。这些预测将与保护利益相关者的投入一起使用，以评估不同的保护决策如何最大程度地减少全球变化响应的影响。该项目的最终目标是自动化管道，以获取新的传入数据，更新预测，并将其提供给最终用户，以启用近乎真实的时间预测工作流程，以在任何给定时间提供最合适的预测，以提供保存决策。这些预测的一个关键方面是它们依赖新的环境信息，这些信息可以更好地表征影响植物性能的条件，包括基于NASA卫星观测的土壤水分和极端天气事件。这些物种水平的预测将与社区人口统计模型相关联，这些模型整合了各种相对尚未开发的数据源，以了解全球变化，包括植物特征数据，全球范围内的社区情节数据，来自国家生态观测网络（NEON）的高度详细的情节数据和长期的生态研究（LTER）站点（LTER）站点（LTER）站点以及NASA全球生物量数据的全球生物学动物学调查（GEDI Dynemits（Gedi）的全球生物量数据。通过整合了各种各样的数据源，可以对进行稳健的近期预测所需的机制理解，以了解诸如净初级生产力，碳库存和弹性之类的生态系统特性。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使用这套独特的预测套件，以最好地为世界各个地区的不同保护问题提供信息。该项目还将导致有关全球工厂分布的开放，清洁和策划的数据库。这将通过在易于使用的门户中提供和可视化复杂的未来场景来帮助其他人探索数据和预测。 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影响审查标准。

项目成果

An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones

使用无人机在干旱地区绘制入侵植被测绘的集成光谱结构工作流程

• DOI: 10.3390/drones5010019
• 发表时间: 2021
• 期刊: Drones
• 影响因子: 4.8
• 作者: Kedia, Arnold Chi;Kapos, Brandi;Liao, Songmei;Draper, Jacob;Eddinger, Justin;Updike, Christopher;Frazier, Amy E.
• 通讯作者: Frazier, Amy E.

Scope and its role in advancing a science of scaling in landscape ecology

• DOI: 10.1007/s10980-022-01403-1
• 发表时间: 2022-01-23
• 期刊: LANDSCAPE ECOLOGY
• 影响因子: 5.2
• 作者: Frazier, Amy E.

Exploring changes in landscape ecological risk in the Yangtze River Economic Belt from a spatiotemporal perspective

• DOI: 10.1016/j.ecolind.2022.108744
• 发表时间: 2022-04
• 期刊: Ecological Indicators
• 影响因子: 6.9
• 作者: Penglai Ran;Shougeng Hu;Amy E. Frazier;S. Qu;De-Qing Yu;Luyi Tong

A review of methods for scaling remotely sensed data for spatial pattern analysis

• DOI: 10.1007/s10980-022-01449-1
• 发表时间: 2022-10
• 期刊: Landscape Ecology
• 影响因子: 5.2
• 作者: Katherine Markham;Amy E. Frazier;Kunwar K. Singh;M. Madden

Spatial and temporal predictions of whooping crane ( Grus americana ) habitat along the US Gulf Coast

美国墨西哥湾沿岸美洲鹤栖息地的时空预测

• DOI: 10.1111/csp2.12696
• 发表时间: 2022
• 期刊: Conservation Science and Practice
• 影响因子: 3.1
• 作者: Golden, Katherine E.;Hemingway, Benjamin L.;Frazier, Amy E.;Scholtz, Rheinhardt;Harrell, Wade;Davis, Craig A.;Fuhlendorf, Samuel D.
• 通讯作者: Fuhlendorf, Samuel D.