昆仑山北坡两种优势荒漠灌木的生物量预测模型  

Biomass estimation models for two dominant desert shrubs on the northern slopes of Kunlun Mountain

作　　者：张元梅 孙桂丽[1,2] 鲁艳 李利[3,4] 张志浩 张栋栋[5] ZHANG Yuanmei;SUN Guili;LU Yan;LI Li;ZHANG Zhihao;ZHANG Dongdong(College of Forestry and Langscape Architeture,Xinjiang Agricultural University,Urumqi 830052,Xinjiang,China;Key Laboratory of Forestry Ecology and Industrial Technology in Arid Areas,Urumqi 830052,Xinjiang,China;Xinjiang Desert Plant Roots Ecology and Vegetation Restoration Laboratory,Xinjiang Institute of Ecology and Geography,Chinese Academy of Sciences,Urumqi 830011,Xinjiang,China;Cele National Station of Observation and Research for Desert-Grassland Ecosystem,Cele 848300,Xinjiang,China;College of Life Sciences,Shihezi University,Shihezi 832003,Xinjiang,China)

机构地区：[1]新疆农业大学林学与风景园林学院,新疆乌鲁木齐830052 [2]干旱区林业生态与产业技术重点实验室,新疆乌鲁木齐830052 [3]中国科学院新疆生态与地理研究所,新疆荒漠植物根系生态与植被修复重点实验室,新疆乌鲁木齐830011 [4]新疆策勒荒漠草地生态系统国家野外科学观测研究站,新疆策勒848300 [5]石河子大学生命科学学院,新疆石河子832003

出　　处：《干旱区研究》2024年第2期284-292,共9页Arid Zone Research

基　　金：第三次新疆综合科学考察项目子课题(2021xjkk0304010);中国科学院“西部青年学者”项目(2021-XBQNXZ-018)。

摘　　要：构建数学模型是估算灌木生物量的重要方法之一。本研究以中昆仑山北坡山前荒漠带常见的两种荒漠灌木红砂(Reaumuria soongarica)和合头草(Sympegma regelii)为研究对象。采用全株收获法采集植株,分别以株高(H)、冠幅面积(S)、植株体积(V)为自变量,植株地上生物量(W_(1))、地下生物量(W_(2))、全株生物量(W_(3))为因变量,建立函数模型,选取决定系数(R^(2))、估计标准差(SEE)、回归检验显著水平(P值)为评价指标,以P<0.001为前提,选取R^(2)尽量大、SEE尽量小的模型为红砂和合头草生物量最优预测模型。结果显示:红砂和合头草的生物量最优预测模型均为二次函数模型,合头草全株最优预测模型为一次函数模型除外。红砂植株体积(V)与生物量的相关性最高,生物量最优预测模型R^(2)为0.820~0.920。合头草冠幅面积(S)与生物量相关性最高,生物量最优预测模型R^(2)为0.935~0.973。红砂和合头草生物量最优预测模型均通过(P<0.001)显著性检验,拟合率在84.1%~95.6%之间,可用于生物量估算,本研究为预测荒漠生态系统碳储量和评价碳汇潜力提供科学依据。Mathematical modeling is an important method for estimating shrub biomass.In this study,two desert shrubs,Reaumuria soongarica and Sympegma regelii,commonly found in the Piedmont belt of the northern slopes of the mid-Kunlun Mountains,were observed.The whole-plant harvesting method was employed,and plant height(H),canopy area(S),and plant volume(V)were used as independent variables.Plant above-ground biomass(W_(1)),below-ground biomass(W_(2)),and whole-plant biomass(W_(3))were used as dependent variables to establish the function model.The selection of optimal biomass estimation models for these two desert shrubs was based on the largest determination coefficient(R^(2)),smallest estimated standard deviation(SEE),and significance level(P<0.001).The results indicated that quadratic function models were optimal for estimating biomass in both R.soongarica and S.regelii,except for the whole-plant optimal prediction model of S.regelii,which followed a linear function.For R.soongarica,the highest correlation was observed between plant volume(V)and biomass,with R^(2) ranging from 0.820 to 0.920.For S.regelii,the highest correlation was between canopy area(S)and biomass,with R^(2) ranging from 0.935 to 0.973.All optimal models for biomass estimation in R.soongarica and S.regelii passed the significance test(P<0.001),with fit rates ranging from 84.1%to 95.6%.These models were deemed reliable for biomass estimation.The outcomes of this study can offer valuable insights for studying carbon stocks and evaluating carbon sink potential in desert ecosystems.

关 键 词：荒漠灌木 生物量 预测模型 昆仑山 

分 类 号：Q948[生物学—植物学]