Effect of tree species and functional diversity on ecosystem multifunctionality in temperate forests of northeast China

doi:10.17521/cjpe.2023.0098

Abstract

Abstract:

Aims Biodiversity is important for maintaining multiple ecosystem functions and enhancing community resilience to disturbance. Selection effect and niche complementarity effect are two widely discussed mechanisms for maintaining ecosystem function, but the understanding of how these two mechanisms maintain forest ecosystem multifunctionality (EMF) under climate change is still limited. It is essential to deepen our understanding of these mechanisms, particularly in assessing whether there are differences in their effectiveness across different climatic zones.

Methods Based on plots distributed in natural forests of middle temperate and cold temperate zones in northeastern China, we used functional trait diversity (FD_q_{= 0}), single and multidimensional trait functional dispersion indices (FDis) to represent the niche complementarity effect, and community weighted mean trait values (CWM) to represent the selection effect. We also explored the driving force of EMF to climate change by using multivariate linear models and partial least squares path modeling (PLS-PM; structural equation model).

Important findings (1) In middle temperate forests, two attributes of biodiversity (tree species diversity (SR) and FD_q_{= 0}) had significant positive effects on EMF, and FD_q_{= 0} had stronger effects than SR. In cold temperate forests, no significant relationship between biodiversity and EMF (BEMF) was found. (2) In middle temperate forest communities, the effects of SR on EMF were mediated by trait differences and community weighted mean maximum tree height (CWM_H_max) value. Both selection effect and niche complementarity effect simultaneously maintained EMF in middle temperate forests, with selection effect slightly higher than complementarity effect. CWM_H_max was the main biotic factor influencing cold temperate forest EMF, and selection effect was the main driving force on EMF in these forests. SR and trait differences did not have a significant promoting effect on EMF. (3) Due to the “insurance effect” of biodiversity, middle temperate forests had a stronger resistance to climate change. Climate factors had no significant impact on SR, trait differences, CWM_H_max and EMF. However, cold temperate forests were sensitive to climate change, and climatic factors were important abiotic factors affecting EMF. Higher annual mean air temperature and precipitation significantly altered community trait composition (e.g., CWM_H_max), diluting the contribution of species with high competitiveness and fitness traits (e.g., maximum tree height (H_max) trait) to ecosystem functions, and reducing the strength of the selection effect. This study highlights the importance of biodiversity for maintaining forest EMF, and demonstrates that both selection effect and complementarity effect are driving forces for temperate forest EMF in northeastern China.

Key words: ecosystem multifunctionality, tree species diversity, selection effect, niche complementarity effect, functional trait composition, functional trait differences, climate change

LI Jie, HAO Min-Hui, FAN Chun-Yu, ZHANG Chun-Yu, ZHAO Xiu-Hai. Effect of tree species and functional diversity on ecosystem multifunctionality in temperate forests of northeast China[J]. Chin J Plant Ecol, 2023, 47(11): 1507-1522.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2023.0098

https://www.plant-ecology.com/EN/Y2023/V47/I11/1507

Figures/Tables 8

Fig. 1 Geographic distribution of the 209 forest plots in temperate forests of northeast China. CBS, middle temperate Changbai Mountains; DXA, cold temperate Da Hinggan Mountains.

Fig. 2 Results of principal component (PC) analysis with geography, climate, and tree species diversity for the forest plots in temperate forests of northeast China. CBS, middle temperate Changbai Mountains; DXA, cold temperate Da Hinggan Mountains. bio1, annual mean air temperature; bio13, precipitation of the wettest month; bio16, precipitation of the driest quarter; bio19, precipitation of the coldest quarter; Chao’s richness, tree species diversity.

Fig. 3 Bivariate relationships between biodiversity and ecosystem multifunctionality (EMF) in temperate forests of northeast China.Chao’s richness, tree species diversity; FDq = 0, functional trait diversity;$R_{\text{adj}}^{2}$, the adjusted R2 of the model. The grey area represents the 95% confidence interval of the model. CBS, middle temperate Changbai Mountain; DXA, cold temperate Da Hinggan Mountains.

Fig. 4 Bivariate relationships between forest community trait variables and ecosystem multifunctionality (EMF) in temperate forests of northeast China. A, Community weighted mean trait values (CWM). B, Functional dispersion indices (FDis). Hmax, maximum tree height; LC:LN, leaf carbon nitrogen content ratio; LCC, leaf carbon content; LPC, leaf phosphorus content; multi, multidimensional trait; SLA, specific leaf area. R2adj, the adjusted R2 of the model. Blue solid line represents the trend line in the entire dataset, the grey area represents the 95% confidence interval of the model. CBS, middle temperate Changbai Mountains; DXA, cold temperate Da Hinggan Mountains.

Fig. 5 Bivariate relationships between forest community trait variables and ecosystem multifunctionality (EMF) in temperate forests of northeast China. CBS, Changbai Mountains; DXA, Da Hinggan Mountains. CWM, community weighted mean trait values; FDis, functional dispersion indices; FDq = 0, functional trait diversity; Hmax, maximum tree height; LC:LN, leaf carbon nitrogen content ratio; LCC, leaf carbon content; LPC, leaf phosphorus content; multi, multidimensional trait; SLA, specific leaf area. The dots and dashes represent the estimated values of standardized regression coefficients and the 95% confidence interval, respectively.

Fig. 6 Optimal predictors of ecosystem multifunctionality (EMF) in temperate forests of northeast China. CBS, Changbai Mountains. DXA, Da Hinggan Mountains. bio1, annual mean air temperature; bio13, precipitation of the wettest month; bio16, precipitation of the driest quarter; bio19, precipitation of the coldest quarter; CWMHmax, maximum tree height community weighted mean trait value; CWMSLA, specific leaf area community weighted mean trait value; FDisHmax, maximum tree height functional dispersion index; FDisLC:LN, leaf carbon nitrogen content ratio functional dispersion index; FDisSLA, specific leaf area functional dispersion index; FDisLCC, leaf carbon content functional dispersion index; FDisLPC, leaf phosphorus content functional dispersion index; FDismulti, multidimensional trait functional dispersion index; FDq = 0, functional trait diversity; Lat, latitude; Lon, longitude. R2adj, the adjusted R2 of the model. The dots and dashes represent the estimated values of standardized regression coefficients and the 95% confidence interval, respectively.

Fig. 7 Structural equation model about the impact of biotic and abiotic factors on ecosystem multifunctionality (EMF) in northeast temperate forests. CBS, Changbai Mountains. DXA, Da Hinggan Mountains. Chao’s richness, tree species diversity; CWMHmax, community weighted mean maximum tree height value; GoF, model goodness-of-fit value; rho, Dillon-Goldstein’s rho value. Solid black, red, and dashed lines represent positive, negative, and non-significant paths, respectively. Standardized path coefficients (β) are indicated near the arrow lines, and response variables’ R2 are provided. *, p < 0.05; ***, p < 0.001.

Fig. 8 Direct and indirect effects of biotic and abiotic factors on ecosystem multifunctionality (EMF) in temperate forests of northeast China. CBS, the Changbai Mountain; DXA, the Da Hinggan Mountains. Chao’s richness, tree species diversity; Climate, climate factor (latent variable); CWMHmax, community weighted mean maximum tree height value; Geography, geography factor (latent variable); Trait differences, trait differences (latent variable). The dark and transparent bars represent direct and indirect effects, respectively.

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