东北温带森林树种和功能多样性对生态系统多功能性的影响

doi:10.17521/cjpe.2023.0098

摘要/Abstract

摘要：

生物多样性对维持多种生态系统功能和提高群落抵抗扰动能力具有重要意义。选择效应和生态位互补效应是被广泛讨论的维持生态系统功能的两种机制。然而, 对于两种机制在气候变化背景下如何维持森林生态系统多功能性(EMF)的理解还不充分。该研究基于分布在中国东北寒温带和中温带天然森林的样地, 以功能性状多样性(FD_q_{= 0})、单个和多维性状功能分散指数(FDis)代表生态位互补效应, 群落加权平均性状值(CWM)代表选择效应, 使用多元线性模型和偏最小二乘法路径模型(结构方程模型), 探究气候变化背景下不同气候区内EMF的主要影响因子和驱动机制。主要结果有: (1)在中温带森林中, 生物多样性的两种属性(树种多样性(SR)和FD_q_{= 0})都对EMF有显著的正效应, 但FD_q_{= 0}比SR更重要。在寒温带森林中, 没有识别到生物多样性与生态系统多功能性之间的显著关系(BEMF)。(2)中温带森林群落SR对EMF的正效应被性状差异和群落加权平均最大树高(CWM_H_max)介导, 选择效应和生态位互补效应两种机制同时维持EMF, 选择效应略高于互补效应。CWM_H_max是影响寒温带森林EMF的主要生物因素, 选择效应是寒温带森林EMF的主要维持机制, SR对EMF的促进作用不显著, 性状差异与EMF无关。(3)由于生物多样性的“保险效应”, 中温带森林抵抗气候变化的能力更强, 气候因素对SR、性状差异、CWM_H_max和EMF的影响都不显著。寒温带森林对气候变化敏感, 气候是影响EMF的重要非生物因素。更高的年平均气温和降水量显著改变了群落的性状组成(CWM_H_max), 稀释了具有高竞争力和适应力性状(例如, 最大树高(H_max))的物种对生态系统功能的贡献, 降低了选择效应。该研究结果强调了生物多样性对维持森林EMF的重要性, 证明了选择效应和生态位互补效应都是中国东北温带森林EMF的驱动机制, 并表明气候变化可能会通过改变寒温带地区森林群落性状组成(例如, CWM_H_max)间接影响EMF。

关键词: 生态系统多功能性, 树种多样性, 选择效应, 生态位互补效应, 功能性状组成, 功能性状差异, 气候变化

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

李杰, 郝珉辉, 范春雨, 张春雨, 赵秀海. 东北温带森林树种和功能多样性对生态系统多功能性的影响. 植物生态学报, 2023, 47(11): 1507-1522. DOI: 10.17521/cjpe.2023.0098

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. Chinese Journal of Plant Ecology, 2023, 47(11): 1507-1522. DOI: 10.17521/cjpe.2023.0098

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0098

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

图/表 8

图1 东北温带森林209个森林样地的地理分布。CBS, 中温带长白山; DXA, 寒温带大兴安岭。

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.

图2 东北温带森林地理位置、气候和树种多样性的主成分(PC)分析。CBS, 中温带长白山; DXA, 寒温带大兴安岭。bio1, 年平均气温; bio13, 最湿月份降水量; bio16, 最干季度降水量; bio19, 最冷季度降水量; Chao’s richness, 树种多样性; latitude, 纬度; longitude, 经度。

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.

图3 东北温带森林生物多样性与生态系统多功能性(EMF)之间的关系。Chao’s richness, 树种多样性; FDq = 0, 功能性状多样性; $R_{\text{adj}}^{2}$, 模.型调整R2。灰色区域表示模型95%置信区间。CBS, 中温带长白山; DXA, 寒温带大兴安岭。

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.

图4 东北温带森林群落功能性状变量与生态系统多功能性(EMF)之间的关系。A, 群落加权平均性状值(CWM)。B, 功能分散指数(FDis)。Hmax, 最大树高; LC:LN, 叶片碳氮比; LCC, 叶片碳含量; LPC, 叶片磷含量; multi, 多维性状功能; SLA, 比叶面积。$R_{\text{adj}}^{2}$, 模型调整R2。蓝色实线代表整个数据集上的趋势线, 灰色区域代表模型95%置信区间。CBS, 中温带长白山; DXA, 寒温带大兴安岭。

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.

图5 东北温带森林群落功能性状变量与生态系统多功能性(EMF)之间的双变量关系。CWM, 群落加权平均性状值; FDis, 功能分散指数; FDq = 0, 功能性状多样性; Hmax, 最大树高; LCC:LNC, 叶片碳氮比; LCC, 叶片碳含量; LPC, 叶片磷含量; multi, 多维性状; SLA, 比叶面积。圆圈和短线分别代表标准化回归系数估计值和95%置信区间。

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.

图6 东北温带森林生态系统多功能性(EMF)的最佳预测变量。bio1, 年平均气温; bio13, 最湿月份降水量; bio16, 最干季度降水量; bio19, 最冷季度降水量; CWMHmax, 群落加权平均最大树高; CWMSLA, 群落加权平均比叶面积; FDisHmax, 最大树高功能分散指数; FDisLC:LN, 叶片碳氮比功能分散指数; FDisSLA, 比叶面积功能分散指数; FDisLCC, 叶片碳含量功能分散指数; FDisLPC, 叶片磷含量功能分散指数; FDismulti, 多维性状功能分散指数; FDq = 0, 功能性状多样性; Lat, 纬度; Lon, 经度; R2adj, 模型调整R2。圆点和短线分别代表标准化回归系数估计值和95%置信区间。

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.

图7 东北温带森林生物和非生物因素对生态系统多功能性(EMF)影响的结构方程模型图。Chao’s richness, 树种多样性; Climate, 气候因素; CWMHmax, 群落加权平均最大树高; Geography, 地理因素; GoF, 模型拟合优度; rho, Dillon-Goldstein’s rho值; Trait differences, 性状差异。黑色、红色实线和虚线表示路径正、负和不显著。箭头线附近的数值表示标准化路径系数(β), 并给出因变量R2。*, p < 0.05; ***, p < 0.001。

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.

图8 东北温带森林生物和非生物因素对生态系统多功能性(EMF)的直接和间接效应。Chao’s richness, 树种多样性; Climate, 气候因素(潜在变量); CWMHmax, 群落加权平均最大树高; Geography, 地理因素(潜在变量); Trait differences, 性状差异(潜在变量)。深色区域表示直接效应, 透明区域表示间接效应。

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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