Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/gcb.13863 |
Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling | |
Feng, Xiaohui1; Uriarte, Maria1; Gonzalez, Grizelle2; Reed, Sasha3; Thompson, Jill4; Zimmerman, Jess K.4; Murphy, Lora1,5 | |
2018 | |
发表期刊 | GLOBAL CHANGE BIOLOGY
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ISSN | 1354-1013 |
EISSN | 1365-2486 |
出版年 | 2018 |
卷号 | 24期号:1页码:E213-E232 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate. |
英文关键词 | carbon flux climate change ecosystem demography model GPP NPP sensitivity analysis tropical forest variance decomposition |
领域 | 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000426506100018 |
WOS关键词 | NET PRIMARY PRODUCTION ; SOIL-MOISTURE DEFICIT ; AMAZON RAIN-FOREST ; HUMAN LAND-USE ; ELEVATED CO2 ; VEGETATION DYNAMICS ; LEAF RESPIRATION ; CARBON DYNAMICS ; GLOBAL CHANGE ; TREE GROWTH |
WOS类目 | Biodiversity Conservation ; Ecology ; Environmental Sciences |
WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/17416 |
专题 | 气候变化 资源环境科学 |
作者单位 | 1.Columbia Univ, Dept Ecol Evolut & Environm Biol, New York, NY USA; 2.US Forest Serv, Int Inst Trop Forestry, USDA, Rio Piedras, PR USA; 3.US Geol Survey, Southwest Biol Sci Ctr, Moab, UT USA; 4.Univ Puerto Rico, Dept Environm Sci, San Juan, PR 00936 USA; 5.Cary Inst Ecosyst Studies, Millbrook, NY USA |
推荐引用方式 GB/T 7714 | Feng, Xiaohui,Uriarte, Maria,Gonzalez, Grizelle,et al. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling[J]. GLOBAL CHANGE BIOLOGY,2018,24(1):E213-E232. |
APA | Feng, Xiaohui.,Uriarte, Maria.,Gonzalez, Grizelle.,Reed, Sasha.,Thompson, Jill.,...&Murphy, Lora.(2018).Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling.GLOBAL CHANGE BIOLOGY,24(1),E213-E232. |
MLA | Feng, Xiaohui,et al."Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling".GLOBAL CHANGE BIOLOGY 24.1(2018):E213-E232. |
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