北京东灵山暖温带落叶阔叶林群落生物多样性与地上生物量的关系

doi:10.17520/biods.2024230

生物多样性 ›› 2024, Vol. 32 ›› Issue (12): 24230. DOI: 10.17520/biods.2024230 cstr: 32101.14.biods.2024230

北京东灵山暖温带落叶阔叶林群落生物多样性与地上生物量的关系

王兴煜¹^,², 孟京辉¹^,^*(), 任思远³^,^*(), 祝燕²^,^*()()

1.北京林业大学林学院, 北京 100083
2.中国科学院植物研究所植被与环境变化重点实验室, 北京 100093
3.中国自然资源航空物探遥感中心, 北京 100083

收稿日期:2024-06-11 接受日期:2024-08-11 出版日期:2024-12-20 发布日期:2025-01-24
通讯作者: *E-mail: jmeng@bjfu.edu.cn; rsy9999ml@163.com; zhuyan@ibcas.ac.cn
基金资助:
国家自然科学基金(32271614);国家自然科学基金(31870408);中国科学院战略生物资源计划和中国科学院植被与环境变化国家重点实验室资助项目(Y7206F1016)

Relationship between biodiversity and aboveground biomass in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing

Xingyu Wang¹^,², Jinghui Meng¹^,^*(), Siyuan Ren³^,^*(), Yan Zhu²^,^*()()

1. College of Forestry, Beijing Forestry University, Beijing 100083, China
2. Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
3. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China

Received:2024-06-11 Accepted:2024-08-11 Online:2024-12-20 Published:2025-01-24
Contact: *E-mail: jmeng@bjfu.edu.cn; rsy9999ml@163.com; zhuyan@ibcas.ac.cn
Supported by:
National Natural Science Foundation of China(32271614);National Natural Science Foundation of China(31870408);Biological Resources Programme, Chinese Academy of Sciences and the State Key Laboratory of Vegetation and Environmental Change, Chinese Academy of Sciences(Y7206F1016)

1. 附录.pdf(557KB)

摘要/Abstract

摘要： 森林生物多样性-地上生物量关系在全球物种丧失加剧的背景下备受关注。但目前的研究主要聚焦在单一方面的多样性, 鲜少同时探究物种、功能和结构三个方面的多样性, 生物多样性哪方面更能影响地上生物量变化尚不清楚。本研究基于北京东灵山1 ha次生林样地清查数据, 使用线性回归模型分析了物种多样性、功能多样性、结构多样性和生物因素(林分密度)、非生物因素(地形)与地上生物量的相关性, 并通过结构方程模型综合比较物种、功能和结构多样性对地上生物量影响的相对重要性。结果表明: (1)多元回归结果表明Pielou均匀度指数、比叶面积群落加权平均值、胸径Shannon指数和林分密度对地上生物量有显著正影响, 而功能离散度和海拔对地上生物量有显著负影响。(2)结构方程结果表明, 林分密度和以胸径Shannon指数为主的结构多样性对地上生物量的总效应最大, 其次是海拔、物种多样性(Pielou均匀度指数)、功能多样性(功能离散度和比叶面积群落加权平均值)。研究认为生态位互补和质量比假说共同解释暖温带落叶阔叶林地上生物量, 其中林分密度和胸径Shannon指数导致的生态位互补效应起主导作用。研究结果为解释生物多样性-生态系统功能关系提供了有力支撑, 为暖温带森林的可持续经营和管理提供了理论依据, 具有重要的现实意义。

关键词: 物种多样性, 功能多样性, 群落结构, 森林地上生物量

Abstract

Aims: The relationship between biodiversity and aboveground biomass in forests has attracted wide attention under the background of the backdrop of intensified global species loss. However, current research primarily focuses on single aspect of biodiversity, rarely considering species, functional, and structural diversity simultaneously. It still remains unclear which aspects of biodiversity influence changes in aboveground biomass.

Methods: Based on the census data of 1 ha secondary forest plot from Donglingshan in Beijing, we analyzed the correlation between biotic and abiotic factors and aboveground biomass using linear regression model. Additionally, structural equation modeling (SEM) was employed to comprehensively compare the relative impacts of species, functional, and structural diversity on aboveground biomass.

Results: (1) The multiple regression results indicated that plots with higher Pielou evenness index, community weighted mean trait value of specific leaf area (CWM.SLA), DBH Shannon index and stand density had a significant positive impact on aboveground biomass, while functional dispersion (FDis) and elevation had a significant negative impact on aboveground biomass. (2) Structural equation model indicated that stand density and structural diversity, primarily represented by DBH Shannon index, had the greatest total effect on aboveground biomass, followed by elevation, species diversity (Pielou evenness index), and functional diversity (functional dispersion and community weighted mean trait value of specific leaf area).

Conclusion: The study suggests that both the niche complementarity hypothesis and the mass ratio hypothesis jointly explain the aboveground biomass in warm temperate deciduous broad-leaved forests, with the niche complementarity effect caused by stand density and DBH Shannon index. The findings provide strong support for explaining the biodiversity-ecosystem functioning relationship and offer a theoretical basis for the sustainable management and conservation of warm temperate forests, holding significant practical implications.

Key words: species diversity, functional diversity, community structure, forest aboveground biomass

王兴煜, 孟京辉, 任思远, 祝燕 (2024) 北京东灵山暖温带落叶阔叶林群落生物多样性与地上生物量的关系. 生物多样性, 32, 24230. DOI: 10.17520/biods.2024230.

Xingyu Wang, Jinghui Meng, Siyuan Ren, Yan Zhu (2024) Relationship between biodiversity and aboveground biomass in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing. Biodiversity Science, 32, 24230. DOI: 10.17520/biods.2024230.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2024230

https://www.biodiversity-science.net/CN/Y2024/V32/I12/24230

图/表 5

表1 北京东灵山次生林样地内预测变量信息统计表

Table 1 The statistical information of predictor variables in the 1 ha plot of secondary forest in Donglingshan, Beijing

变量 Variables	范围 Range	平均值 Mean	标准偏差 Standard deviation
凹凸度 Convexity	-26.72至30.55	0.06	11.78
海拔 Elevation (m)	1,335.97-1,406.54	1,380.11	18.56
林分密度 Stand density	56-176	115.48	32.83
物种丰富度 Species richness	6-13	9.00	2.06
Pielou均匀度指数 Pielou evenness index	0.50-0.93	0.75	0.10
功能丰富度 FRic	0.11-7.83	3.41	3.26
功能离散度 FDis	1.38-2.57	2.04	0.34
比叶面积群落加权平均值 CWM.SLA	200.46-258.34	233.11	31.84
木材密度群落加权平均值 CWM.WD	0.59-0.69	0.63	0.03
胸径Shannon指数 DBH Shannon index	0.66-0.69	0.68	0.01

图1 北京东灵山次生林Pielou均匀度指数、功能离散度(FDis)、比叶面积群落加权平均值(CWM.SLA)、胸径Shannon指数、林分密度(Stand.density)、海拔(Elevation)与地上生物量的双变量线性回归关系。实线代表线性回归模型下得到的拟合直线, 灰色区域代表模型95%置信区间。R2表示调整后的拟合系数, P值表示整个模型的显著度。

Fig. 1 Bivariate linear regression relationship between Pielou evenness index, functional dispersion (FDis), community weighted mean trait value of specific leaf area (CWM.SLA), DBH Shannon index, stand density (Stand.density), elevation (Elevation) and aboveground biomass (AGB) in the secondary forest in Donglingshan, Beijing. The solid line represents the fitted straight line obtained under the linear regression model, and the gray area represents the 95% confidence interval of the model. R2 and P are the adjusted R2 and significance of the whole regression model, respectively.

图2 北京东灵山次生林预测变量对地上生物量的相对影响。基于逐步回归分析筛选预测因子纳入模型, 5个维度的预测因子包括: 物种多样性、功能多样性、结构多样性、林分密度、地形。Pielou均匀度指数(Species.evenness)代表物种多样性; 功能离散度(FDis)、比叶面积群落加权平均值(CWM.SLA)代表功能多样性; 胸径Shannon指数(DBH.Shannon.index)代表结构多样性; 林分密度(Stand.density)代表样地林分分布情况; 海拔(Elevation)代表地形。R2是调整后R2。* P < 0.05; **P < 0.01; *** P < 0.001。

Fig. 2 Relative effects of predictors on aboveground biomass of the secondary forest plot in Donglingshan, Beijing. Based on stepwise regression analysis, the predictors were selected for inclusion in the model, and the predictors of five dimensions included: species diversity, functional diversity, structural diversity, stand density, and topography. Pielou evenness index (Species.evenness) represents species diversity, functional dispersion (FDis) and community weighted mean trait value of specific leaf area (CWM.SLA) represent functional diversity; DBH Shannon index (DBH.Shannon.index) represents structural diversity; stand density (Stand.density) represents the distribution of forest stands within the plot; elevation represents topography. The R2 was estimated based on adjusted R2.

图3 生物和非生物因子与地上生物量的结构方程模型。模型考虑了海拔(Elevation)、Pielou均匀度指数(Pielou evenness index)、功能离散度(FDis)、比叶面积群落加权平均值(CWM.SLA)、胸径Shannon指数(DBH Shannon index)和林分密度(Stand density)对地上生物量(AGB)的直接和间接影响。箭头方向表示因果关系, 实线和虚线表示作用强弱, 蓝色表示正相关, 红色表示负相关。线上的数值表示具有相应统计意义的标准化路径系数。模型评价采用比较拟合指数(CFI)、标准化残差均方和平方根(SRMR)。* P < 0.05; ** P < 0.01; *** P < 0.001。

Fig. 3 Structural equation models (SEM) of biotic and abiotic factors and aboveground biomass. The model accounting for the direct and indirect effects of elevation, pielou evenness index, functional dispersion (FDis), community weighted mean trait value of specific leaf area (CWM.SLA), DBH Shannon index and stand density on aboveground biomass (AGB). Arrows represent the flow of causality, solid and dashed lines indicate the strength of the effect, blue indicates a positive correlation, and red indicates a negative correlation. Number and its associated asterisks are standardized path coefficient and significant level. The model was evaluated using the comparative fit index (CFI) and the standardized residual mean square root (SRMR). * P < 0.05; ** P < 0.01; *** P < 0.001.

表2 结构方程模型中预测变量对森林地上生物量的直接、间接和总标准化影响效应

Table 2 Direct, indirect, and total standardized effects of predictor variables on the aboveground biomass in our structural equation model (SEM). FDis, Functional dispersion; CWM.SLA, Community weighted mean trait value of specific leaf area.

解释变量 Explanatory variable	对地上生物量的作用路径 Pathway to productivity	结构方程模型 SEM
解释变量 Explanatory variable	对地上生物量的作用路径 Pathway to productivity	影响效应 Impact effect	P
海拔 Elevation	直接效应 Direct effect	-0.24	< 0.001
	间接效应 Indirect effect	-0.26	< 0.01
	总效应 Total effect	-0.50	< 0.001
林分密度 Stand density	直接效应 Direct effect	0.41	< 0.001
	间接效应 Indirect effect	-0.19	< 0.01
	总效应 Total effect	0.22	< 0.001
Pielou均匀度指数 Pielou evenness index	直接效应 Direct effect	0.46	< 0.001
	间接效应 Indirect effect	-0.23	< 0.001
	总效应 Total effect	0.23	< 0.001
功能离散度 FDis	直接效应 Direct effect	-0.36	< 0.001
功能离散度 FDis	总效应 Total effect	-0.36	< 0.001
比叶面积群落加权平均值 CWM.SLA	直接效应 Direct effect	0.51	< 0.001
比叶面积群落加权平均值 CWM.SLA	总效应 Total effect	0.51	< 0.001
胸径Shannon指数 DBH Shannon index	直接效应 Direct effect	0.44	< 0.001
胸径Shannon指数 DBH Shannon index	总效应 Total effect	0.44	< 0.001

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北京东灵山暖温带落叶阔叶林群落生物多样性与地上生物量的关系

Relationship between biodiversity and aboveground biomass in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing

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