浑善达克沙地6种灌木生物量模拟
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摘要
在干旱半干旱区,乔木比较稀疏或难以存活,灌木往往在植被群落中占有很大的优势地位,其生态功能及生态学意义尤其值得重视。浑善达克沙地疏林草地是沙地顶级植物群落,其中乔木稀疏分布,而灌木在沙甸以及沙陇背风坡呈密集分布。灌木在固定沙丘、改良土壤、提供栖息地等方面具有重要的生态意义,其生物量组成也在沙地植被群落中占有很大比重。已往的研究中,灌木相比乔木通常处于次要地位,对灌木的研究尚不充分,灌木生物量的模拟方法亦多采用乔木生物量的模拟方法。然而灌木形态结构与乔木有明显差异,专门针对灌木的生物量模拟方法研究尚不多见。以6种沙地灌木为样本,基于异速生长模型,对比了若干地表测量指标对灌木生物量的预测能力,其中设计了一种更贴近灌木实际形态的圆台体积作为新的预测指标。研究结果表明:(1)在单因素指标中,相比高度和地径,冠幅与灌木生物量的相关性更强。(2)相比单因素指标,复合指标与灌木生物量之间的相关性更强。其中冠幅相关的复合指标更优于地径相关的复合指标。这预示着冠幅以及冠幅相关的复合指标对灌木生物量具有较好的预测能力。(3)圆台体积能进一步提高对灌木生物量的预测能力。相关分析和拟合评价结果显示,圆台体积与灌木生物量的相关性更强,拟合误差较小,并且对于不同的灌木种类,其相关性和拟合精度表现出较高的稳定性。这意味着圆台体积对于不同的灌木种类,均具有较好的生物量预测效果。因此建议,在灌木属性测量较为充分的情况下,圆台体积是更为理想的预测指标,而在测量不充分情况下,冠幅及其相关复合指标更适宜进行灌木生物量预测。研究结果最终建立了6种沙地灌木的圆台体积-生物量的异速生长模拟方程,为进一步研究沙地灌木的碳储量以及灌木在半干旱植物群落中的生态意义提供科学基础。
In arid and semi-arid regions,trees are sparse or have difficulty surviving,and shrubs often occupy a dominant position in vegetation communities. The ecological functions and ecological value of shrubs are particularly worthy of attention in these regions. The sparse-elm grassland in Otindag Sandy Land is the climatic climax community; the trees are sparsely distributed,while the shrubs are densely distributed between the dunes or on the leeward slopes of the dunes.Shrubs have significant ecological functions in fixing sand dunes,improving soils,providing habitats,and increasing vegetation biodiversity. Shrub biomass accounts for a large proportion of the sandy land vegetation community. According to previous studies,shrubs are usually less important than trees,but the research on shrubs is not yet sufficient. The present shrub biomass simulation method is mostly adapted from the methods used for trees. However, the morphological characteristics of shrubs are obviously different from those of trees. A biomass prediction model specific for shrubs urgently needs to be developed. In this study,six dominant shrub species were investigated. Based on an allometric model,seven shrub measurements were used as predictors and compared to assess their abilities to predict shrub biomass. Among the seven measurements,a circular platform volume model,which has a structure close to the actual shape of a shrub,was designed as a new predictor. In this study,correlation coefficients and three model-fitting accuracy evaluation indices[including determination of the R2 coefficient,significance index p-values,and standard error of the estimate( SEE) ]were used to assess the prediction ability of the seven measurements. The results showed that:( 1) Among the single measurements,the correlation coefficient between the crown diameter and biomass was highest,compared to those between height or ground diameter and biomass.( 2) The correlation between the composite indices and shrub biomass was much stronger than that between the single measurements and biomass. In addition,the crown-related composite indices had stronger correlations with biomass than the ground diameter-related composite index did. This indicates that the crown diameter or crown-related composite indices may be better in predicting shrub biomass.( 3) The circular platform volume model further improved the ability to predict shrub biomass. The results of correlation analysis and fitting analysis showed that the correlation between the circular platform volume and biomass was stronger and the fitting error was smaller compared with other prediction indicators. The correlation and fitting accuracy between circular platform volume and biomass were similar across different shrub species. This means that the circular platform volume model has better and more-stable prediction ability than other models for many shrub species,which implies that it is also more scalable. Therefore,we suggest that the circular platform volume is an ideal predictor when shrubs are adequately measured in the field. Otherwise,if the shrubs are not sufficiently measured,the crown diameter and crown-related composite indices are more suitable for the prediction of shrub biomass. In sum,this study established a biomass prediction model based on the circular platform volume of the six sandy shrub species,which provides a scientific basis for further study of the shrub carbon sink in sandy land and the ecological significance of shrubs in semi-arid and arid regions.
In arid and semi-arid regions,trees are sparse or have difficulty surviving,and shrubs often occupy a dominant position in vegetation communities. The ecological functions and ecological value of shrubs are particularly worthy of attention in these regions. The sparse-elm grassland in Otindag Sandy Land is the climatic climax community; the trees are sparsely distributed,while the shrubs are densely distributed between the dunes or on the leeward slopes of the dunes.Shrubs have significant ecological functions in fixing sand dunes,improving soils,providing habitats,and increasing vegetation biodiversity. Shrub biomass accounts for a large proportion of the sandy land vegetation community. According to previous studies,shrubs are usually less important than trees,but the research on shrubs is not yet sufficient. The present shrub biomass simulation method is mostly adapted from the methods used for trees. However, the morphological characteristics of shrubs are obviously different from those of trees. A biomass prediction model specific for shrubs urgently needs to be developed. In this study,six dominant shrub species were investigated. Based on an allometric model,seven shrub measurements were used as predictors and compared to assess their abilities to predict shrub biomass. Among the seven measurements,a circular platform volume model,which has a structure close to the actual shape of a shrub,was designed as a new predictor. In this study,correlation coefficients and three model-fitting accuracy evaluation indices[including determination of the R2 coefficient,significance index p-values,and standard error of the estimate( SEE) ]were used to assess the prediction ability of the seven measurements. The results showed that:( 1) Among the single measurements,the correlation coefficient between the crown diameter and biomass was highest,compared to those between height or ground diameter and biomass.( 2) The correlation between the composite indices and shrub biomass was much stronger than that between the single measurements and biomass. In addition,the crown-related composite indices had stronger correlations with biomass than the ground diameter-related composite index did. This indicates that the crown diameter or crown-related composite indices may be better in predicting shrub biomass.( 3) The circular platform volume model further improved the ability to predict shrub biomass. The results of correlation analysis and fitting analysis showed that the correlation between the circular platform volume and biomass was stronger and the fitting error was smaller compared with other prediction indicators. The correlation and fitting accuracy between circular platform volume and biomass were similar across different shrub species. This means that the circular platform volume model has better and more-stable prediction ability than other models for many shrub species,which implies that it is also more scalable. Therefore,we suggest that the circular platform volume is an ideal predictor when shrubs are adequately measured in the field. Otherwise,if the shrubs are not sufficiently measured,the crown diameter and crown-related composite indices are more suitable for the prediction of shrub biomass. In sum,this study established a biomass prediction model based on the circular platform volume of the six sandy shrub species,which provides a scientific basis for further study of the shrub carbon sink in sandy land and the ecological significance of shrubs in semi-arid and arid regions.
引文
[1]曾伟生.国内外灌木生物量模型研究综述.世界林业研究,2015,28(1):31-36.
[2]Zandler H,Brenning A,Samimi C.Quantifying dwarf shrub biomass in an arid environment:comparing empirical methods in a high dimensional setting.Remote Sensing of Environment,2015,158:140-155.
[3]李钢铁,秦富仓,贾守义,董锦兰,吉日格勒,王满贵.旱生灌木生物量预测模型的研究.内蒙古林学院学报,1998,20(2):26-32.
[4]Conti G,Enrico L,Casanoves F,Díaz S.Shrub biomass estimation in the semiarid Chaco forest:a contribution to the quantification of an underrated carbon stock.Annals of Forest Science,2013,70(5):515-524.
[5]卢振龙,龚孝生.灌木生物量测定的研究进展.林业调查规划,2009,34(4):37-40,45-45.
[6]Yang H T,Wang Z R,Tan H J,Gao Y H.Allometric models for estimating shrub biomass in desert grassland in northern China.Arid Land Research and Management,2017,31(3):283-300.
[7]万五星,王效科,李东义,冯学全,张千千,于小红.暖温带森林生态系统林下灌木生物量相对生长模型.生态学报,2014,34(23):6985-6992.
[8]林伟,李俊生,郑博福,郭建明,胡理乐.井冈山自然保护区12种常见灌木生物量的估测模型.武汉植物学研究,2010,28(6):725-729.
[9]赵蓓,郭泉水,牛树奎,孙武,张志旭.大岗山林区几种常见灌木生物量估算与分析.东北林业大学学报,2012,40(9):28-33.
[10]何列艳,亢新刚,范小莉,高延,冯启祥.长白山区林下主要灌木生物量估算与分析.南京林业大学学报:自然科学版,2011,35(5):45-50.
[11]仇瑶,常顺利,张毓涛,王文栋,何平,王慧杰,谢锦.天山林区六种灌木生物量的建模及其器官分配的适应性.生态学报,2015,35(23):7842-7851.
[12]罗永开,方精云,胡会峰.山西芦芽山14种常见灌木生物量模型及生物量分配.植物生态学报,2017,41(1):115-125.
[13]Ludwig J A,Reynolds J F,Whitson P D.Size-biomass relationships of several Chihuahuan desert shrubs.The American Midland Naturalist,1975,94(2):451-461.
[14]史宇飞,金永焕,金兰淑.国内榆树疏林研究现状.水土保持应用技术,2011,(2):32-35.
[15]赵云杰,田赟,王晓.典型榆树疏林碳氮储量及其分配特征.中国水土保持科学,2014,12(6):82-89.
[16]马媛,李钢铁,潘羿壅,麻云霞,王强,张驰.浑善达克沙地3种灌木生物量的预测模型.干旱区资源与环境,2017,31(6):198-201.
[17]Rojo V,Arzamendia Y,Pérez C,Baldo J,ViláB.Double sampling methods in biomass estimates of Andean shrubs and tussocks.Rangeland Ecology&Management,2017,70(6):718-722.
[18]罗红,李百炼.异速生长模型研究概述.生态学杂志,2011,30(9):2060-2065.
[19]Vora R S.Predicting biomass of five shrub species in northeastern California.Journal of Range Management,1988,41(1):63-65.
[20]曾珍英,刘琪璟,曾慧卿.江西千烟洲几种灌木生物量模型的研究.福建林业科技,2005,32(4):68-72.
[21]Huff S,Ritchie M,Temesgen H.Allometric equations for estimating aboveground biomass for common shrubs in northeastern California.Forest Ecology and Management,2017,398:48-63.
[22]Zhang L,Cui G S,Shen W,Liu X S.Cover as a simple predictor of biomass for two shrubs in Tibet.Ecological Indicators,2016,64:266-271.
[2]Zandler H,Brenning A,Samimi C.Quantifying dwarf shrub biomass in an arid environment:comparing empirical methods in a high dimensional setting.Remote Sensing of Environment,2015,158:140-155.
[3]李钢铁,秦富仓,贾守义,董锦兰,吉日格勒,王满贵.旱生灌木生物量预测模型的研究.内蒙古林学院学报,1998,20(2):26-32.
[4]Conti G,Enrico L,Casanoves F,Díaz S.Shrub biomass estimation in the semiarid Chaco forest:a contribution to the quantification of an underrated carbon stock.Annals of Forest Science,2013,70(5):515-524.
[5]卢振龙,龚孝生.灌木生物量测定的研究进展.林业调查规划,2009,34(4):37-40,45-45.
[6]Yang H T,Wang Z R,Tan H J,Gao Y H.Allometric models for estimating shrub biomass in desert grassland in northern China.Arid Land Research and Management,2017,31(3):283-300.
[7]万五星,王效科,李东义,冯学全,张千千,于小红.暖温带森林生态系统林下灌木生物量相对生长模型.生态学报,2014,34(23):6985-6992.
[8]林伟,李俊生,郑博福,郭建明,胡理乐.井冈山自然保护区12种常见灌木生物量的估测模型.武汉植物学研究,2010,28(6):725-729.
[9]赵蓓,郭泉水,牛树奎,孙武,张志旭.大岗山林区几种常见灌木生物量估算与分析.东北林业大学学报,2012,40(9):28-33.
[10]何列艳,亢新刚,范小莉,高延,冯启祥.长白山区林下主要灌木生物量估算与分析.南京林业大学学报:自然科学版,2011,35(5):45-50.
[11]仇瑶,常顺利,张毓涛,王文栋,何平,王慧杰,谢锦.天山林区六种灌木生物量的建模及其器官分配的适应性.生态学报,2015,35(23):7842-7851.
[12]罗永开,方精云,胡会峰.山西芦芽山14种常见灌木生物量模型及生物量分配.植物生态学报,2017,41(1):115-125.
[13]Ludwig J A,Reynolds J F,Whitson P D.Size-biomass relationships of several Chihuahuan desert shrubs.The American Midland Naturalist,1975,94(2):451-461.
[14]史宇飞,金永焕,金兰淑.国内榆树疏林研究现状.水土保持应用技术,2011,(2):32-35.
[15]赵云杰,田赟,王晓.典型榆树疏林碳氮储量及其分配特征.中国水土保持科学,2014,12(6):82-89.
[16]马媛,李钢铁,潘羿壅,麻云霞,王强,张驰.浑善达克沙地3种灌木生物量的预测模型.干旱区资源与环境,2017,31(6):198-201.
[17]Rojo V,Arzamendia Y,Pérez C,Baldo J,ViláB.Double sampling methods in biomass estimates of Andean shrubs and tussocks.Rangeland Ecology&Management,2017,70(6):718-722.
[18]罗红,李百炼.异速生长模型研究概述.生态学杂志,2011,30(9):2060-2065.
[19]Vora R S.Predicting biomass of five shrub species in northeastern California.Journal of Range Management,1988,41(1):63-65.
[20]曾珍英,刘琪璟,曾慧卿.江西千烟洲几种灌木生物量模型的研究.福建林业科技,2005,32(4):68-72.
[21]Huff S,Ritchie M,Temesgen H.Allometric equations for estimating aboveground biomass for common shrubs in northeastern California.Forest Ecology and Management,2017,398:48-63.
[22]Zhang L,Cui G S,Shen W,Liu X S.Cover as a simple predictor of biomass for two shrubs in Tibet.Ecological Indicators,2016,64:266-271.