DOI: 10.1016/j.quascirev.2016.01.012
Scopus记录号: 2-s2.0-84959018989
论文题名: Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data
作者: Dawson A. ; Paciorek C.J. ; McLachlan J.S. ; Goring S. ; Williams J.W. ; Jackson S.T.
刊名: Quaternary Science Reviews
ISSN: 2773791
出版年: 2016
卷: 137 起始页码: 156
结束页码: 175
语种: 英语
英文关键词: Bayesian
; Calibration
; Dispersal
; Expert elicitation
; Forest
; Fossil
; Modelling
; Pollen
; Prediction
; Sediment
; Vegetation
Scopus关键词: Calibration
; Climate change
; Ecosystems
; Forestry
; Land use
; Models
; Plants (botany)
; Productivity
; Sediments
; Uncertainty analysis
; Vegetation
; Bayesian
; Dispersal
; Expert elicitation
; Forest
; Fossil
; Pollen
; Forecasting
; Potato virus M
英文摘要: Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns. © 2016 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/59641
Appears in Collections: 过去全球变化的重建
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作者单位: Department of Statistics, University of California, Berkeley, CA, United States; Department of Geosciences, The University of Arizona, Tucson, AZ, United States; Department of Biological Sciences, University of Notre Dame, South Bend, IN, United States; Department of Geography, University of Wisconsin-Madison, Madison, WI, United States; Department of the Interior Southwest Climate Science Center, U.S. Geological Survey, Tucson, AZ, United States
Recommended Citation:
Dawson A.,Paciorek C.J.,McLachlan J.S.,et al. Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data[J]. Quaternary Science Reviews,2016-01-01,137