DOI: 10.1016/j.foreco.2013.09.047
Scopus记录号: 2-s2.0-84888137831
论文题名: Propagating uncertainty to estimates of above-ground biomass for Kenyan mangroves: A scaling procedure from tree to landscape level
作者: Cohen R. ; Kaino J. ; Okello J.A. ; Bosire J.O. ; Kairo J.G. ; Huxham M. ; Mencuccini M.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2013
卷: 310 起始页码: 968
结束页码: 982
语种: 英语
英文关键词: Above-ground biomass
; Allometric equations
; Kenya
; Mangrove
; Mixed-effects models
; Uncertainty propagation
Scopus关键词: Above ground biomass
; Allometric equations
; Kenya
; Mangrove
; Mixed-effects models
; Uncertainty propagation
; Biomass
; Deforestation
; Developing countries
; Estimation
; Forecasting
; Plants (botany)
; Uncertainty analysis
; aboveground biomass
; allometry
; climate change
; data set
; deforestation
; ecological modeling
; estimation method
; landscape
; mangrove
; prediction
; uncertainty analysis
; Biomass
; Deforestation
; Developing Countries
; Equations
; Forecasts
; Kenya
; Mangrove
; Models
; Plants
; Kenya
; Rhizophoraceae
英文摘要: Mangroves are globally important carbon stores and as such have potential for inclusion in future forest-based climate change mitigation strategies such as Reduced Emissions from Deforestation and Degradation (REDD+). Participation in REDD+will require developing countries to produce robust estimates of forest above-ground biomass (AGB) accompanied by an appropriate measure of uncertainty. Final estimates of AGB should account for known sources of uncertainty (measurement and predictive) particularly when estimating AGB at large spatial scales. In this study, mixed-effects models were used to account for variability in the allometric relationship of Kenyan mangroves due to species and site effects. A generic biomass equation for Kenyan mangroves was produced in addition to a set of species-site specific equations. The generic equation has potential for broad application as it can be used to predict the AGB of new trees where there is no pre-existing knowledge of the specific species-site allometric relationship: the most commonly encountered scenario in practical biomass studies. Predictions of AGB using the mixed-effects model showed good correspondence with the original observed values of AGB although displayed a poorer fit at higher AGB values, suggesting caution in extrapolation. A strong relationship was found between the observed and predicted values of AGB using an independent validation dataset from the Zambezi Delta, Mozambique (R2=0.96, p= <0.001). The simulation based approach to uncertainty propagation employed in the current study produced estimates of AGB at different spatial scales (tree - landscape level) accompanied by a realistic measure of the total uncertainty. Estimates of mangrove AGB in Kenya are presented at the plot, regional and landscape level accompanied by 95% prediction intervals. The 95% prediction intervals for landscape level estimates of total AGB stocks suggest that between 5.4 and 7.2 megatonnes of AGB is currently held in Kenyan mangrove forests. © 2013 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/66323
Appears in Collections: 影响、适应和脆弱性
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作者单位: School of Geosciences, University of Edinburgh, Crew Building, West Mains Road, Edinburgh EH9 3JN, United Kingdom; Kenya Marine and Fisheries Research Institute, P.O. Box 81651, Mombasa, Kenya; Department of Plant Biology and Nature Management (APNA), Vrije Universiteit Brussels, Pleinlaan 2, 1050 Brussels, Belgium; School of Life, Sport and Social Sciences, Edinburgh Napier University, Edinburgh, United Kingdom
Recommended Citation:
Cohen R.,Kaino J.,Okello J.A.,et al. Propagating uncertainty to estimates of above-ground biomass for Kenyan mangroves: A scaling procedure from tree to landscape level[J]. Forest Ecology and Management,2013-01-01,310