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Collaborative research: A mechanistic approach to assess the impacts of hurricanes on tropical forests

Collaborative research: A mechanistic approach to assess the impacts of hurricanes on tropical forests
合作研究:评估飓风对热带森林影响的机械方法
批准号:
2028688
负责人:
Maria Uriarte
金额:
$26.31万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-03-01 至 2024-02-29

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中文摘要
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英文摘要
The intensity and frequency of hurricanes in the Atlantic Ocean have been increasing. Hurricanes change forest structure and can increase carbon emissions to the atmosphere. Severe hurricanes favor light-loving tree species that are particularly vulnerable to wind damage, making forests more vulnerable to subsequent hurricanes. More severe hurricanes may also lead to the development of shorter forests dominated by wind-resistant species. In the short term, tree damage from severe hurricanes will reduce forest carbon storage and through accelerated decomposition, much of the carbon in dead biomass will return to the atmosphere as carbon dioxide. However, the long-term effects of more severe hurricanes on carbon dynamics are not well understood and depend on the severity of the hurricane and post-disturbance tree establishment, growth, and death. The research will use data collected after Hurricane María in Puerto Rico to improve understanding of these long-term effects. Society will benefit from the development of modeling schemes that can be used to predict forest resilience to climate change. Results will be shared with scientists and managers working in the U.S Forest Service and 28 young scientists will gain valuable experience in ecological research.The proposed research studies the unique situation caused by the passage of Hurricane María over Puerto Rico in 2017 to gain important, new knowledge about the effects of extreme wind on forests and to develop a realistic approach to representing forest responses to wind damage in earth system models. María was the strongest hurricane to make direct landfall in Puerto Rico since 1928 and may reflect changes in North Atlantic hurricanes. This project completes the seventh 5-year census of the 16 hectare Luquillo Forest Dynamics Plot to characterize post-disturbance tree recruitment, growth, and delayed mortality. The project combines data on tree damage and forest recovery after Hurricane María and two weaker storms, hurricanes Hugo (1989) and Georges (1998), with aerial laser-derived representation of tree biomechanical properties. This information will be used to characterize the relationship between canopy structure of tree species and their response to a range of hurricane severities. The data will then be used in the Ecosystem Demography model, allowing assessment of the impacts of various climate change scenarios on forest and carbon dynamics.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.
期刊论文(1)
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会议论文
Chronic Winds Reduce Tropical Forest Structural Complexity Regardless of Climate, Topography, or Forest Age
无论气候、地形或森林年龄如何,慢性风都会降低热带森林结构的复杂性
DOI: 10.1007/s10021-024-00900-5
发表时间: 2024
期刊: Ecosystems
影响因子: 3.7
作者: [Ankori-Karlinsky, Roi, Hall, Jazlynn, Murphy, Lora, Muscarella, Robert, Martinuzzi, Sebastián, Fahey, Robert, Zimmerman, Jess K., Uriarte, María]
通讯作者: Uriarte, María
Collaborative Research: Impacts of severe drought on tropical forest post-disturbance recovery
  • 批准号:
    2140580
  • 项目类别:
    Standard Grant
  • 资助金额:
    $100.52万
  • 财政年份:
    2022
  • 负责人:
    Maria Uriarte
  • 依托单位:
RAPID: Hurricane Maria: ASSESSING LANDSCAPE RESILIENCE TO A CHANGING DISTURBANCE REGIME
  • 批准号:
    1801315
  • 项目类别:
    Standard Grant
  • 资助金额:
    $17.48万
  • 财政年份:
    2018
  • 负责人:
    Maria Uriarte
  • 依托单位:
A NEIGHBORHOOD APPROACH TO THE BIOGEOGRAPHY OF PUERTO RICAN TREES
  • 批准号:
    1753810
  • 项目类别:
    Standard Grant
  • 资助金额:
    $56.2万
  • 财政年份:
    2018
  • 负责人:
    Maria Uriarte
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  • 批准号:
    1311367
  • 项目类别:
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  • 资助金额:
    $1.64万
  • 财政年份:
    2013
  • 负责人:
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