DOI: 10.1016/j.foreco.2012.12.022
Scopus记录号: 2-s2.0-84872508090
论文题名: Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model
作者: Csilléry K. ; Seignobosc M. ; Lafond V. ; Kunstler G. ; Courbaud B.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 292 起始页码: 64
结束页码: 74
语种: 英语
英文关键词: Conifers
; Disturbance
; Forest dynamics
; Forest management data
; Growing stock volume
Scopus关键词: Annual volumes
; Anthropogenic disturbance
; Complex Processes
; Conifers
; Data sets
; Dead volumes
; Demographic parameters
; Disturbance
; Field data
; Forest dynamics
; Forest stand
; French Alps
; Growing stocks
; Individual-based
; Informative measures
; Managed forest
; Management scheme
; Mortality rate
; Spatially explicit forest dynamics
; Spatiotemporal variability
; State-space models
; Temperate forests
; Tree mortality
; Computer simulation
; Population statistics
; Time series
; Forestry
; anthropogenic effect
; Bayesian analysis
; coniferous forest
; data set
; demography
; disturbance
; estimation method
; forest dynamics
; forest inventory
; forest management
; harvesting
; mortality
; periodicity
; spatiotemporal analysis
; time series
; Biological Populations
; Data Processing
; Forest Management
; France
; Growing Season
; Mountains
; Softwoods
; Statistics
; Time Series Analysis
; Volume
; Alps
; France
; Coniferophyta
英文摘要: Tree mortality is a complex process that exhibits great spatio-temporal variability. Long term mortality data is needed to understand this demographic parameter and how it is related to biotic, climatic, and anthropogenic disturbances. Here, we propose a Bayesian state-space model to estimate tree mortality time series in managed forests, where tree mortality is expressed as the annual proportion of the dead volume over the growing stock volume in a forest stand (subsequently, volume mortality rate). We argue that the volume mortality rate is an informative measure of tree mortality; and our simulations and field data suggests that the volume mortality rate is a good proxy of the demographic sense annual mortality rate (i.e. based on the number of trees), though the quality of the approximation depends on the particular management scheme. The proposed Bayesian state-space model combines two types of data: annual dead volumes from harvest reports and total growing stock volumes from periodic inventories. We illustrate the performance of the Bayesian state-space model using data simulated with an individual based and spatially explicit forest dynamic model (Samsara2). Then, we apply the Bayesian state-space model to field data from four forests in the French Alps and recover, unprecedented, century long, time series of annual volume mortality rate at the scale of a forest stand (10. ha) within each forest. We advocate the use of forest management data for future research, since temperate forests are managed in many countries since decades, thus many other unexploited data sets must exist. © 2012 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66732
Appears in Collections: 影响、适应和脆弱性
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作者单位: Irstea, Centre de Grenoble, EMGR Ecosystèmes montagnards, 2 rue de la papeterie, F-38402 Saint Martin d'Hères, France; Ecologie des Forts Mèditerranèennes, UR 629, INRA, F-84914 Avignon cedex 9, France
Recommended Citation:
Csilléry K.,Seignobosc M.,Lafond V.,et al. Estimating long-term tree mortality rate time series by combining data from periodic inventories and harvest reports in a Bayesian state-space model[J]. Forest Ecology and Management,2013-01-01,292