DOI: 10.1111/gcb.13863
Scopus记录号: 2-s2.0-85030153161
论文题名: Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling
作者: Feng X. ; Uriarte M. ; González G. ; Reed S. ; Thompson J. ; Zimmerman J.K. ; Murphy L.
刊名: Global Change Biology
ISSN: 13541013
出版年: 2018
卷: 24, 期: 1 起始页码: e213
结束页码: e232
语种: 英语
英文关键词: carbon flux
; climate change
; ecosystem demography model
; GPP
; NPP
; sensitivity analysis
; tropical forest
; variance decomposition
Scopus关键词: Bayesian analysis
; carbon flux
; climate change
; community composition
; ecosystem modeling
; net primary production
; prediction
; sensitivity analysis
; tropical forest
; variance analysis
; Puerto Rico
; carbon
; Bayes theorem
; biological model
; biomass
; carbon cycle
; climate change
; forest
; plant leaf
; Puerto Rico
; tropic climate
; Bayes Theorem
; Biomass
; Carbon
; Carbon Cycle
; Climate Change
; Forests
; Models, Biological
; Plant Leaves
; Puerto Rico
; Tropical Climate
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. © 2017 John Wiley & Sons Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/110568
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Department of Ecology, Evolution & Environmental Biology, Columbia University, New York, NY, United States; International Institute of Tropical Forestry, United States Department of Agriculture Forest Service, Río Piedras, Puerto Rico; Southwest Biological Science Center, U.S. Geological Survey, Moab, UT, United States; Department of Environmental Science, University of Puerto Rico, San Juan, Puerto Rico; Cary Institute of Ecosystem Studies, Millbrook, NY, United States
Recommended Citation:
Feng X.,Uriarte M.,González G.,et al. Improving predictions of tropical forest response to climate change through integration of field studies and ecosystem modeling[J]. Global Change Biology,2018-01-01,24(1)