Driving effects of forest stand structure of a subtropical evergreen broad-leaved forest on species composition variation: From local to regional scales

doi:10.17520/biods.2022139

Abstract

Abstract:

Aim: The variation of species composition can be partitioned into two components which are explained by environment and space, and can be used to further explore the niche process and neutral process of community assembly. Forest stand structure characterizes the light availability and heterogeneity under forest canopies, and it is a major driving factor in the variation of species composition for forest communities. However, few studies have applied forest stand structure to explain the variation of species composition within forest communities. In this study, we sampled 19 separate 1-ha forest dynamics plots established among grids of 1 km² (regional scale) near the 20 ha subtropical mid-mountain moist evergreen broad-leaved forest dynamics plot (local scale, less than 1 km²) in Ailao Mountains as a research platform. Using forest stand structure, environmental factors, and spatial structure variables as the explanatory variables for the species composition variation. We resolved the driving forces of species composition variation at local and regional scales, with an emphasis on the role of forest stand structure in driving the variation of species composition.

Methods: Based on a long-term community survey and airborne LiDAR data, we used redundancy analysis (RDA) and variance partitioning to analyze how environment, spatial structure, and forest stand structure were related to species composition variation in a subtropical evergreen broad-leaved forest at both local and regional scales. To explore the main factors driving the variation of species composition, we analyzed the relative importance of individual explanatory variables using commonality analysis and hierarchical partitioning method.

Results: Inclusion of forest stand structure as an explanatory variable increased the response rate of the variation of species composition at both local and regional scales. However, inclusion of forest stand structure as an explanatory variable resulted in a significant decrease in the rate of spatial structure at the local level. The cumulative contribution of forest stand structure and environment to the variation in species composition was 41.0% at the local level. At the regional scale, forest stand structure and environment contributed a cumulative explanatory rate in species composition was 23.0%.

Conclusions: This study confirms that, for subtropical broad-leaved evergreen forests, forest stand structure characterizing the light environment is an important driver of the variation in species composition, which deepens our understanding of the role of environmental filtering in driving the variation in species composition and remedies the traditional under-consideration of environmental factors. From local to regional scales, the role of purely spatial structure declines sharply to negligible levels. However, the relative importance of environmental filtering is significantly enhanced from the local to the regional scale. Light availability may have a strong effect shaping species composition below the forest canopies. This new relationship between forest stand structure on species composition warrants future studies to explore the causal mechanisms of how forest stand structure drives species composition variation in subtropical broad-leaved evergreen forests.

Key words: forest stand structure, scale effect, LiDAR, light availability, Ailao Mountains

Xin Yang, Zhiliang Yao, Bin Wang, Handong Wen, Yun Deng, Min Cao, Zhiming Zhang, Zhenghong Tan, Luxiang Lin. Driving effects of forest stand structure of a subtropical evergreen broad-leaved forest on species composition variation: From local to regional scales[J]. Biodiv Sci, 2023, 31(2): 22139.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2022139

https://www.biodiversity-science.net/EN/Y2023/V31/I2/22139

Figures/Tables 4

Fig. 1 Distance decay of species compositional similarity between plots. (a) 20 1-ha plots divided by the 20 ha subtropical mid-mountain moist evergreen broad-leaved forest dynamics plot in Ailao Mountains; (b) 19 1-ha forest dynamics plots established among grids of 1 km2 near the 20 ha forest dynamics plot in Ailao Mountains.

Fig. 2 The Venn diagram of variation partitioning explained by spatial structures and environmental factors (a, b) and by spatial structures, environmental factors and forest stand structures (c, d) of 20 1-ha plots divided by the 20 ha subtropical mid-mountain moist evergreen broad-leaved forest dynamics plot in Ailao Mountains and 19 1-ha forest dynamics plots established among grids of 1 km2 near the 20 ha forest dynamics plot in Ailao Mountains (values < 0 not shown)

Fig. 3 The relative importance of individual variables of spatial structures, environmental factors and forest stand structures in 20 1-ha plots divided by the 20 ha subtropical mid-mountain moist evergreen broad-leaved forest dynamics plot in Ailao Mountains by rdacca.hp. FS_PC1 and FS_PC2 represent the first and the second principal components of forest stand structures, respectively. Env_PC1, Env_PC2 and Env_PC3 represent the first, the second and the third principal components of topographic environmental factors, respectively. MEM3, MEM4, MEM5, MEM6, MEM8, x and y represent the spatial structures, where, x and y are linear trends of spatial structures.

Fig. 4 The relative importance of individual variables of spatial structures, environmental factors and forest stand structures in 19 1 ha forest dynamics plots established among grids of 1 km2 near the 20 ha forest dynamics plot in Ailao Mountains by rdacca.hp. FS_PC1 and FS_PC2 represent the first and the second principal components of forest stand structures, respectively. Env_PC1, Env_PC2 and Env_PC3 represent the first, the second and the third principal components of environmental factors, respectively. MEM1 represents the spatial structures.

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