Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,o...Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,only few prediction models are available for aboveground biomass in rangelands,as compared with forests.In addition to the development of prediction models,we tested whether such prediction models vary with plant growth forms and life spans,and with the inclusion of site and/or quadrat-specific factors.Methods We collected dataset of aboveground biomass from destructive harvesting of 8088 individual plants belonging to 79 species in 735 quadrats across 35 sites in semi-steppe rangelands in Iran.A logarithmic transformation of the power-law model was used to develop simple prediction models for the easy estimation of above-ground biomass using plant coverage and vegetation density as predictors for the species-specific model,multispecies and plants of different growth forms and life spans.In addition,additive and multiplicative linear regression models were developed by using plant coverage and one categorical variable from the site and/or quadrat-specific factors.Important Findings The log-transformed power-law model based on plant coverage pre-cisely predicted aboveground biomass across the whole dataset for ei-ther most of the species-specific model,multispecies or plants of the same growth forms(shrubs,forbs or graminoids)and life spans(annuals,biennials or perennials).The addition of vegetation density as a single or in a compound predictor variable had relatively poor performance com-pared with the model having plant coverage only.Although generalizing at the levels of plant group forms and/or life spans did not substantially enhance the model-fit and validation of the plant coverage-based mul-tispecies model,the inclusion of plant growth forms or life spans as a categorical predictor variable had performed well.Generalized models in this study will greatly contribute to the accurate and easy predic-tion of aboveground biomass in the studied rangelands and will be also useful to rangeland practitioners and ecological modellers interested in the global relationship between biodiversity and aboveground biomass productivity across space and time in natural rangelands.展开更多
基金This work was supported by the University of Tehran,Iran(grant No.3870306)We would like to thank Mr.Mohsen Hosseini,Drs.Esmaeil Alizadeh and Azad Rastegar for their contributions to this work.A.A.is financially supported by Guangdong Provincial Government(grant No.205588)for conducting ecological research at South China Normal University.
文摘Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,only few prediction models are available for aboveground biomass in rangelands,as compared with forests.In addition to the development of prediction models,we tested whether such prediction models vary with plant growth forms and life spans,and with the inclusion of site and/or quadrat-specific factors.Methods We collected dataset of aboveground biomass from destructive harvesting of 8088 individual plants belonging to 79 species in 735 quadrats across 35 sites in semi-steppe rangelands in Iran.A logarithmic transformation of the power-law model was used to develop simple prediction models for the easy estimation of above-ground biomass using plant coverage and vegetation density as predictors for the species-specific model,multispecies and plants of different growth forms and life spans.In addition,additive and multiplicative linear regression models were developed by using plant coverage and one categorical variable from the site and/or quadrat-specific factors.Important Findings The log-transformed power-law model based on plant coverage pre-cisely predicted aboveground biomass across the whole dataset for ei-ther most of the species-specific model,multispecies or plants of the same growth forms(shrubs,forbs or graminoids)and life spans(annuals,biennials or perennials).The addition of vegetation density as a single or in a compound predictor variable had relatively poor performance com-pared with the model having plant coverage only.Although generalizing at the levels of plant group forms and/or life spans did not substantially enhance the model-fit and validation of the plant coverage-based mul-tispecies model,the inclusion of plant growth forms or life spans as a categorical predictor variable had performed well.Generalized models in this study will greatly contribute to the accurate and easy predic-tion of aboveground biomass in the studied rangelands and will be also useful to rangeland practitioners and ecological modellers interested in the global relationship between biodiversity and aboveground biomass productivity across space and time in natural rangelands.
