| 项目编号: | 1457767
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| 项目名称: | Collaborative Research: Developing integrated trait-based scaling theory to predict community change and forest function in light of global change |
| 作者: | Gregory Asner
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| 承担单位: | Carnegie Institution of Washington
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| 批准年: | 2014
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| 开始日期: | 2015-08-01
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| 结束日期: | 2018-07-31
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| 资助金额: | USD214944
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| 资助来源: | US-NSF
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| 项目类别: | Continuing grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Biological Sciences - Environmental Biology
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| 英文关键词: | change
; forest change
; plant functional trait
; forest
; researcher
; project
; metabolic scaling theory
; drought
; functional trait
; tropical forest productivity
; ecosystem function
; forest canopy structure
; forest dieback
; forest response
; trait driver theory
; tropical forest drought response
; several post-doctoral researcher
; tropical forest
; tdt model function
; novel scaling theory
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| 英文摘要: | Tropical forests store an enormous amount of carbon, with the Amazon alone accounting for 10% of the Earth's primary productivity. Changes in tropical forest productivity in response to drought are an important feedback in the carbon cycle; yet, we currently have a very incomplete understanding of how biomass, productivity, and species composition of these forests respond to changes in temperature and water availability. This project will take a new approach to understanding tropical forest drought responses by focusing on the relationships between plant functional traits, metabolic scaling theory, and climate drivers. Functional traits are easily measureable metrics that allow us to better predict plant growth, reproduction, and forest change. Metabolic scaling theory describes the relationships between the size of an organism, its growth rate, and temperature. This project will attempt to significantly advance our understanding of how tropical ecosystems respond to changes in temperature and precipitation. Researchers will use scaling theory to provide a predictive framework that links forest responses to drought with well understood plant traits measured using novel ground and remote sensing technology.
This project will assess changes in productivity in response to drought, as well as tree mortality and forest dieback. This will be accomplished using both field measurements as well as pre-existing LIDAR and hyperspectral remote sensing data from forests across an elevation gradient in the Peruvian Amazon. Specifically, researchers will use a suite of plant functional traits to provide detailed, 3D maps of forest canopy structure and the spatial distribution of traits. The novel scaling theory developed with these data (Trait Driver Theory, TDT) will then be used to predict ecosystem function from changes in trait distributions over time in response to drought. The project will also involve a field experiment to simulate drought with throughfall collectors to help parameterize TDT model functions. The TDT results will also be compared to predictions from the ecosystem demography model ED2. Model code, images, and algorithms will be made available in public repositories, and any new plant functional trait data will be added to global databases. The project will provide training for several post-doctoral researchers, undergraduate students, and K-12 science teachers and will use the GEM Network Geoweb Portal for outreach to the general public. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/93776
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| Appears in Collections: | 影响、适应和脆弱性
气候减缓与适应
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| Recommended Citation: | |
Gregory Asner. Collaborative Research: Developing integrated trait-based scaling theory to predict community change and forest function in light of global change. 2014-01-01.
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