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AccelNet: International Tropical Forest Science Alliance (ITFSA): A global multi-network science and training
项目编号2020424
Stuart Davies
项目主持机构Smithsonian Institution
开始日期2021-01-01
结束日期12/31/2025
英文摘要Tropical forests are vital to the functioning of Earth, providing essential services to society by storing half of all terrestrial biomass and carbon, absorbing a quarter of human-caused carbon emissions, and helping to stabilize climate and water cycles. Global changes, including deforestation, forest degradation, and climatic and atmospheric changes, are greatly affecting tropical forests. How these changes will impact tropical forests in the future remains highly uncertain. This AccelNet project establishes a global alliance of tropical forest research networks designed to accelerate the research and training that are needed to improve understanding of how tropical forests function and to predict how they will respond to and influence future global changes. The International Tropical Forest Science Alliance (ITFSA) includes 11 tropical forest research networks that manage 11,656 forest research plots in 56 countries that will coordinate through in-person workshops, virtual meetings, public webinars, and international fellowships and internships. By leveraging the vast and complementary knowledge and resources within the individual networks, ITFSA will rapidly advance tropical forest science, develop data management and analysis tools, and help build a new generation of scientists with the global networking and interdisciplinary science skills required to achieve a more complete understanding of how tropical forests contribute to a healthy, functioning Earth.

Understanding the diversity, structure, functioning, and dynamics of tropical forests involves multiple domains including biogeography, ecology, remote sensing, and modeling. Despite operating at different spatial and temporal scales, progress in all these areas depends on long-term, field-based observations of tropical forests across the globe. The ITFSA will integrate these science domains to provide critical insights into: (i) forest diversity and distributions by resolving taxonomic issues and providing improved understanding of the ecology and evolution of tropical tree species; (ii) drivers of tropical forest dynamics through synthetic analyses of vast long-term demographic datasets; (iii) scaling-up ground-based observations of forest biomass, diversity, and dynamics with remote sensing to provide a global assessment of forests; and (iv) predicting tropical forest structure and function through improved process-based knowledge and benchmark data for developing and testing Earth System Models. This multi-network science and training program will foster science synthesis and innovation, capacity-building in science and international networking, and the development of analytical and bioinformatics tools. By accelerating science and expanding capacity for international collaboration, ITFSA will transform global-scale understanding of tropical forests in both fundamental and applied ways that will advance scientific knowledge for the public and policy makers.

The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.

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.
资助机构US-NSF
项目经费$1,999,818.00
项目类型Standard Grant
国家US
语种英语
文献类型项目
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/213159
推荐引用方式
GB/T 7714
Stuart Davies.AccelNet: International Tropical Forest Science Alliance (ITFSA): A global multi-network science and training.2021.
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