Collaborative Research: BoCP-Implementation: BioFI- Biodiversity Forecasting Initiative to Understand Population, Community and Ecosystem Function Under Global Change

合作研究：BoCP-实施：BioFI-生物多样性预测倡议，以了解全球变化下的人口、社区和生态系统功能

• 批准号: 2225079
• 负责人: Amy Frazier
• 金额: $ 49.95万
• 依托单位: Arizona State University
• 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: 2022-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225079&HistoricalAwards=false
• 关键词: Collaborative; Research; BoCP; Implementation; BioFI

There is an urgent need to understand how biodiversity on the planet will change. This requires predicting which plant species are at risk of extinction. It also requires understanding the responses of as many species as possible to future climate conditions. This knowledge is essential to ensure the continuation of ecosystem services that contribute to human well-being. This project- the Biodiversity Forecasting Initiative (BioFI)- increases our knowledge of Earth's biodiversity and advances our ability to forecast biodiversity and biosphere functioning almost anywhere on the planet. In light of emerging environmental challenges and the rise of big data, demand for programming skills and data literacy will continue to increase. This project enhances the future of biodiversity science by training the next generation of scientists in tools for analyzing global biodiversity data sources. One education and training outcome is the development and delivery of an open-source, multi-modal, multi-level curriculum in the emerging field of Biodiversity Data Science. Additionally, this project provides direct training and mentoring for several graduate students and postdoctoral researchers. BioFI focuses on changes in plant species' abundance, traits, and geographic distribution which impact ecosystem function. This project brings together ecologists, environmental engineers, data scientists, and conservation stakeholders to integrate a variety of biodiversity data sources. BioFI provides the data and computational scaffolding to integrate biological and environmental disciplines to produce accurate predictions of future biodiversity trajectories. BioFI enables scientists to plan how to respond to disasters such as droughts and fires. BioFI leverages immense biodiversity and Earth observation data that provide global coverage and wide taxonomic scope. BioFI allows scientists to: (i) forecast the short-term and long-term consequences of biodiversity change; (ii) understand how potential changes in biodiversity will impact human well-being (ecosystem services that impact forestry, water supply, flood prevention, agriculture, and disease risk) and (iii) expedite our ability to plan for effective management.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.

迫切需要了解地球上的生物多样性将如何改变。这需要预测哪些植物物种有灭绝的风险。它还需要了解尽可能多的物种对未来气候条件的反应。这些知识对于确保促进人类福祉的生态系统服务的继续至关重要。该项目 - 生物多样性预测计划（Biofi） - 提高了我们对地球生物多样性的了解，并提高了我们预测几乎在地球上任何地方运作的生物多样性和生物圈的能力。鉴于新兴的环境挑战和大数据的增加，对编程技能和数据素养的需求将继续增加。该项目通过培训下一代科学家来分析全球生物多样性数据源的工具，从而增强了生物多样性科学的未来。一个教育和培训结果是在生物多样性数据科学新兴领域的开源，多模式，多层次课程的开发和交付。此外，该项目为几位研究生和博士后研究人员提供直接培训和指导。 Biofi专注于影响生态系统功能的植物物种丰度，性状和地理分布的变化。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，以整合各种生物多样性数据源。 Biofi提供数据和计算脚手架，以整合生物学和环境学科，以产生对未来生物多样性轨迹的准确预测。 Biofi使科学家能够计划如何应对干旱和火灾等灾难。 Biofi利用了巨大的生物多样性和地球观察数据，这些数据提供了全球覆盖范围和广泛的分类范围。 Biofi允许科学家：（i）预测生物多样性变化的短期和长期后果； （ii）了解生物多样性的潜在变化将如何影响人类的福祉（影响林业，供水，预防洪水，农业和疾病风险的生态系统服务），以及（iii）加快我们计划有效管理的能力。该奖项反映了NSF的法规任务，并被认为是通过基金会的知识优点和广泛的criitia criter criteria criter criteria criteria criter criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria criteria crietia均值得一提。

项目成果

Improved prediction of tree species richness and interpretability of environmental drivers using a machine learning approach

• DOI: 10.1016/j.foreco.2023.120972
• 发表时间: 2023-04-21
• 期刊: FOREST ECOLOGY AND MANAGEMENT
• 影响因子: 3.7
• 作者: Brugere，Lian;Kwon，Youngsang;Kedron，Peter
• 通讯作者: Kedron，Peter

How to Improve the Reproducibility, Replicability, and Extensibility of Remote Sensing Research

• DOI: 10.3390/rs14215471
• 发表时间: 2022-10
• 期刊: Remote. Sens.
• 作者: P. Kedron;Amy E. Frazier