DOI: 10.1016/j.foreco.2017.06.061
Scopus记录号: 2-s2.0-85024481929
论文题名: Productivity of Fagus sylvatica under climate change – A Bayesian analysis of risk and uncertainty using the model 3-PG
作者: Augustynczik A.L.D. ; Hartig F. ; Minunno F. ; Kahle H.-P. ; Diaconu D. ; Hanewinkel M. ; Yousefpour R.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2017
卷: 401 起始页码: 192
结束页码: 206
语种: 英语
英文关键词: Bayesian calibration
; European beech
; Forest management
; Risk
; Uncertainty
Scopus关键词: Bayesian networks
; Calibration
; Climate models
; Decision making
; Forestry
; Inference engines
; Productivity
; Risk analysis
; Risk assessment
; Risks
; Uncertainty analysis
; Alternative management
; Bayesian calibration
; Decision making process
; European beech
; Land expectation value
; Management interventions
; Parametric uncertainties
; Uncertainty
; Climate change
; Fagus sylvatica
英文摘要: To assess the long-term impacts of forest management interventions under climate change, process-based models, which allow to predict transient dynamics under environmental change, are arguably the most suitable tools available. A challenge for using these models for management decisions, however, is their higher parametric uncertainty, which propagates to predictions and thus into the decision-making process. Here, we demonstrate how this problem can be addressed through Bayesian inference. We first conduct a Bayesian calibration to generate an estimate of posterior parametric uncertainty for the process-based forest growth model 3-PG for Fagus sylvatica. The calibration uses data from twelve sites in Germany, together with a robust (Student's t) error model. We then propagate the estimated uncertainty together with economic uncertainty to forest productivity and Land Expectation Value (LEV), allowing us to evaluate alternative management regimes under climate change. Our results demonstrate that parametric and economic uncertainty have strong impacts on the variation of predicted forest productivity and profitability. Management regimes with increased thinning intensity were overall most robust to economic, climate change and parametric model uncertainty. We conclude that estimating and propagating economic and model uncertainty is crucial for developing robust adaptive management strategies for forests under climate change. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/64176
Appears in Collections: 影响、适应和脆弱性
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作者单位: Chair of Forestry Economics and Forest Planning, University of Freiburg, Tennenbacherstr, 4, Freiburg, Germany; Biometry and Environmental System Analysis, University of Freiburg, Tennenbacherstr. 4, Freiburg, Germany; Theoretical Ecology, Faculty of Biology and Pre-Clinical Medicine, University of Regensburg, Universitätsstra, ß, e 31, Regensburg, Germany; Department of Forest Sciences, University of Helsinki, Latokartanonkaari 7, Helsinki, Finland; Chair of Forest Growth and Dendroecology, University of Freiburg, Tennenbacherstr. 4, Freiburg, Germany
Recommended Citation:
Augustynczik A.L.D.,Hartig F.,Minunno F.,et al. Productivity of Fagus sylvatica under climate change – A Bayesian analysis of risk and uncertainty using the model 3-PG[J]. Forest Ecology and Management,2017-01-01,401