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Research Spending & Results

Award Detail

Doing Business As Name:University of Maryland College Park
  • Laura Duncanson
  • (202) 271-0917
Award Date:09/15/2019
Estimated Total Award Amount: $ 296,104
Funds Obligated to Date: $ 163,260
  • FY 2019=$163,260
Start Date:09/01/2019
End Date:08/31/2021
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:Collaborative Research: Near Term Forecasts of Global Plant Distribution, Community Structure, and Ecosystem Function
Federal Award ID Number:1934389
DUNS ID:790934285
Parent DUNS ID:003256088
Program:HDR-Harnessing the Data Revolu
Program Officer:
  • Peter McCartney
  • (703) 292-8470

Awardee Location

Street:3112 LEE BLDG 7809 Regents Drive
County:College Park
Awardee Cong. District:05

Primary Place of Performance

Organization Name:University of Maryland College Park
Street:1151 Lefrak Hall
City:College Park
County:College Park
Cong. District:05

Abstract at Time of Award

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 . 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.

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