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

doi:10.17520/biods.2024230

Abstract

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

Xingyu Wang, Jinghui Meng, Siyuan Ren, Yan Zhu. Relationship between biodiversity and aboveground biomass in the warm temperate deciduous broad-leaved forest of Donglingshan, Beijing[J]. Biodiv Sci, 2024, 32(12): 24230.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2024230

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

Figures/Tables 5

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

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.

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.

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.

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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