DOI: 10.1016/j.foreco.2014.06.026
Scopus记录号: 2-s2.0-84904512231
论文题名: A critical review of forest biomass estimation models, common mistakes and corrective measures
作者: Sileshi G.W.
刊名: Forest Ecology and Management
ISSN: 0378-1127
出版年: 2014
卷: 329 起始页码: 237
结束页码: 254
语种: 英语
英文关键词: Allometry
; Confirmation bias
; Freedman paradox
; Hypothetico-deductive
; Isometry
; Uncertainty
Scopus关键词: Biology
; Forestry
; Statistical tests
; Uncertainty analysis
; Allometry
; Confirmation bias
; Freedman paradox
; Hypothetico-deductive
; Isometry
; Uncertainty
; Biomass
; Akaike information criterion
; allometry
; biomass
; carbon flux
; ecological modeling
; error analysis
; forest ecosystem
; model test
; uncertainty analysis
; Biology
; Biomass
; Forestry
; Statistical Analysis
英文摘要: The choice of biomass estimation models (BEMs) is one of the most important sources of uncertainty in quantifying forest biomass and carbon fluxes. This review was motivated by many mistakes and pitfalls I encountered in the recent literature regarding BEMs. The most common mistakes were the arbitrary choice of analytical methods, model dredging and inadequate model diagnosis, ignoring collinearity, uncritical use of model selection criteria and uninformative reporting of results. Sometimes, errors in parameter estimates were not checked and model uncertainty was ignored when interpreting and reporting results. Consequently, biologically implausible and statistically dubious equations such as ln(M)=ln(a)+b(lnD)+c(lnD)2+d(lnD)3+e(lnρ) have been published as allometric models. These are perpetuated in the literature, databases and field manuals and will pose a serious threat to the integrity of future forest biomass estimates. Through worked examples, I also illustrate that (1) allometric coefficients can be biased by the choice of analytical procedures and methodological artefacts; (2) collinearity of predictors can result in coefficients with unacceptable levels of error (3) the R2 and Akaike information criterion (AIC) have been misused and have resulted in the selection of implausible BEMs; and (4) differences in the definition of model "bias" has sometimes led to contradictory reports. I propose corrective measures for most of these problems and provide suggestions for prospective authors on how to avoid pitfalls in interpretation and reporting of results. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/65798
Appears in Collections: 影响、适应和脆弱性
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作者单位: World Agroforestry Centre, Chitedze Research Station, P.O. Box 30798, Lilongwe, Malawi
Recommended Citation:
Sileshi G.W.. A critical review of forest biomass estimation models, common mistakes and corrective measures[J]. Forest Ecology and Management,2014-01-01,329