昆仑山北坡两种优势荒漠灌木的生物量预测模型

doi:10.13866/j.azr.2024.02.11

干旱区研究 ›› 2024, Vol. 41 ›› Issue (2): 284-292.doi: 10.13866/j.azr.2024.02.11

昆仑山北坡两种优势荒漠灌木的生物量预测模型

张元梅¹^,²(), 孙桂丽¹^,², 鲁艳³^,⁴(), 李利³^,⁴, 张志浩³^,⁴, 张栋栋⁵

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

收稿日期:2023-06-11 修回日期:2023-09-08 出版日期:2024-02-15 发布日期:2024-03-11
通讯作者: 鲁艳. E-mail: luyan@xjb.ac.cn
作者简介:张元梅（1998-），女，硕士研究生，主要从事荒漠化防治研究. E-mail: zhangyuanmeicj@163.com
基金资助:
第三次新疆综合科学考察项目子课题(2021xjkk030401);中国科学院“西部青年学者”项目(2021-XBQNXZ-018)

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

ZHANG Yuanmei¹^,²(), SUN Guili¹^,², LU Yan³^,⁴(), LI Li³^,⁴, ZHANG Zhihao³^,⁴, ZHANG Dongdong⁵

1. College of Forestry and Langscape Architeture, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
2. Key Laboratory of Forestry Ecology and Industrial Technology in Arid Areas, Urumqi 830052, Xinjiang, China
3. Xinjiang Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
4. Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Cele 848300, Xinjiang, China
5. College of Life Sciences, Shihezi University, Shihezi 832003, Xinjiang, China

Received:2023-06-11 Revised:2023-09-08 Online:2024-02-15 Published:2024-03-11

摘要/Abstract

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(W₁)、地下生物量(W₂)、全株生物量(W₃)为因变量，建立函数模型，选取决定系数(R²)、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R²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R²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R²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。

关键词: 荒漠灌木, 生物量, 预测模型, 昆仑山

Abstract:

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 (W₁), below-ground biomass (W₂), and whole-plant biomass (W₃) 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 (R²), 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 R² ranging from 0.820 to 0.920. For S. regelii, the highest correlation was between canopy area (S) and biomass, with R² 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.

Key words: desert shrub, biomass, estimation model, Kunlun Mountain

张元梅, 孙桂丽, 鲁艳, 李利, 张志浩, 张栋栋. 昆仑山北坡两种优势荒漠灌木的生物量预测模型[J]. 干旱区研究, 2024, 41(2): 284-292.

ZHANG Yuanmei, SUN Guili, LU Yan, LI Li, ZHANG Zhihao, ZHANG Dongdong. Biomass estimation models for two dominant desert shrubs on the northern slopes of Kunlun Mountain[J]. Arid Zone Research, 2024, 41(2): 284-292.

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名称	H/cm	S/cm²	V/cm³	W₁/g	W₂/g	W₃/g
红砂	21.92±7.37	2264.42±1535.76	18327.49±17394.53	109.97±74.99	59.44±51.13	169.41±122.67
合头草	24.80±7.32	1279.61±1014.76	12571.02±13741.84	29.89±32.44	107.07±100.83	136.72±132.00

名称	因变量	变量	模型	R²	SEE
红砂	W₁	H	W₁=0.0486H²+5.0658H-26.934	0.548	52.653
		S	W₁=-3E-06S²+0.0597S-5.9616	0.831	32.242
		V	W₁=-4E-08V²+0.0062V+17.884	0.909	23.591
合头草	W₁	H	W₁=0.1307H²-3.6823H+34.018	0.684	18.906
		S	W₁=4E-06S²+0.0166S-0.849	0.935	8.55
		V	W₁=5E-09V²+0.002V+2.7985	0.922	9.387

名称	因变量	变量	模型	R²	SEE
红砂	W₂	H	W₂=0.0914H²＋0.017H+10.41	0.468	38.918
		S	W₂=3E-06S²＋0.0116S+13.487	0.694	29.523
		V	W₂=2E-08V²＋0.0012V+23.284	0.820	22.676
合头草	W₂	H	W₂=0.302H²-5.0247H+30.243	0.741	53.216
		S	W₂=-2E-06S²+0.1056S-22.589	0.964	19.739
		V	W₂=-6E-08V²+0.0103V+0.015	0.955	22.143

名称	因变量	变量	模型	R²	SEE
红砂	W₃	H	W₃=0.1399H²+5.0827H-16.524	0.541	86.784
		S	W₃=0.1088S^0.94	0.693	0.505
		V	W₃=-1E-08V²+0.0074V+41.168	0.902	40.211
合头草	W₃	H	W₃=0.4358H²-8.8662H+65.938	0.741	69.607
		S	W₃=0.1283S-27.455	0.973	22.134
		V	W₃=-6E-08V²+0.0123V+2.454	0.962	26.479

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

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