干旱区研究

干旱区研究 ›› 2020, Vol. 37 ›› Issue (2): 462-469.doi: 10.13866/j.azr.2020.02.19

• 生物资源 • 上一篇    下一篇

乌拉特荒漠草原红砂生物量预测模型

李香云1,2，岳平1，程欢1，3，郭新新1,2，赵生龙1,2，张森溪1,2，王少昆1,2，左小安1   

  1. （1. 中国科学院西北生态环境资源研究院，乌拉特荒漠草原研究站，甘肃 兰州730000； 2. 中国科学院大学，北京100049； 3. 四川农业大学林学院，四川 成都610000）
  • 收稿日期:2019-05-22 修回日期:2019-06-04 出版日期:2020-03-26 发布日期:2020-04-21
  • 通讯作者: 左小安.E-mail: zuoxa@lzb.ac.cn
  • 作者简介:李香云（1997-），女，博士，主要从事干旱区生态恢复研究.E-mail: lixiangyun19@mails.ucas.ac.cn
  • 基金资助:
    国家自然科学基金（41622103, 41571106）；中科院青年创新促进会项目（1100000036）；中国科学院大学生创新实践训练计划

Biomass prediction model for Reaumuria soongorica in the Urat desert steppe in Inner Mongolia

LI Xiangyun1,2,YUE Ping1,CHENG Huan1,3,GUO Xinxin1,2,ZHAO Shenglong1,2,ZHANG Senxi1,2,WANG Shaokun1,2,ZUO Xiaoan1   

  1. 1. Urat Desertgrassland Research Station,Northwest Institute of EcoEnvironment and Resources,CAS,Lanzhou 730000,Gansu,China; 2. University of Chinese Academy of Sciences,Beijing 100049,China;3. Sichuan Agricultural University,Chengdu 610000,Sichuan,China)
  • Received:2019-05-22 Revised:2019-06-04 Online:2020-03-26 Published:2020-04-21

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摘要： 红砂(Reaumuria soogorica)是一种广泛分布在中国半荒漠地区的多年生半灌木，是干旱荒漠区分布最广的植物种之一，具有固沙、固土的优良特性。其生物量估算对评价荒漠草原红砂的生态功能和荒漠草原经营管理具有重要作用，红砂生物量模型是估测红砂生物量的重要方法之一。本研究采用全挖法，以乌拉特荒漠草原优势种之一红砂为研究对象，基于对红砂地上、地下和整株生物量及株高(H)、冠幅(C)、基径(D)等的测定，通过数理统计的回归分析方法，利用相关生长模型(幂函数Ｗ=aXb)，分别构建了地上部分(W1)、地下部分(W2)和全株生物量(W)的预测模型。通过对比判别系数R2的大小，挑选最佳生物量估测模型。结果表明：① 以冠幅(C)为指标的估测模型W1=0.555×C1.867(R2=0.866)能较好地反映红砂单株地上生物量累计特征。② 以复合因子基径×基径×株高(D2H)为指标的估测模型W2=2.259×(D2H)0.762(R2=0.769)能较好地反映红砂单株地下生物量累计特征。③ 以复合因子基径×基径×株高(D2H)为指标的估测模型W=7.057×(D2H)0.813(R2=0.859)能较好地反映红砂总生物量的累计特征。利用此类方法建立的生物量模型，精度高，简便易行，为评价乌拉特荒漠草原红砂的生态功能和准确测定其生物量提供科学依据。

关键词: 荒漠草原, 红砂, 生物量, 预测模型, 乌拉特, 内蒙古

Abstract: Reaumuria soongorica is a perennial semishrub that is widely distributed in the semidesert regions of China.It is one of the most typical plant species in arid deserts and shows excellent characteristics of soil fixation wind erosion prevention.Estimation of Reaumuria soongorica biomass is important for evaluating its ecological function in the desert steppe and managing desert grasslands.In this context,a predictive model is one of the important methods to estimate R.soongorica biomass.Therefore,the full digging method was used to obtain the biomass of aboveground parts and belowground roots of R.soongorica in Urat desert steppe.Regression analysis with the relevant growth model(power function W=aXb) was applied to construct the predictive models for biomass of the aboveground parts(W1),underground parts(W2),and whole plant(W) based on plant height(H),crown width(C),and base diameter(D).Optimal biomass estimation models were then screened by comparing size of the discriminant coefficient R2.The results showed that (1) the estimation model W1= 0.555×C1.867(R2=0.866) better reflected cumulative characteristics of the aboveground biomass of a single R.soongorica plant;(2) the estimation model W2=2.259×(D2H)0.762(R2=0.769) better reflected cumulative characteristics of the underground biomass of R.soongorica;and (3) the estimation model W=7.057×(D2H)0.813(R2=0.859) better reflected cumulative characteristics of the total biomass of R.soongorica.These biomass models showed high precision and were easy to implement.Our data provide a scientific basis for evaluating the ecological function of R.soongorica in Urat desert steppe and for accurately measuring the biomass of this species.

Key words: desert steppe, Reaumuria soongorica, biomass, predictive model, Urat, Inner Mongolia