Relationship between variation of plant functional traits and individual growth at different vertical layers in a subtropical evergreen broad-leaved forest of Dinghushan

doi:10.17520/biods.2021205

Abstract

Abstract:

Aims: Establishing the relationship between plant functional traits and community dynamics is one of the core issues of functional ecology. Previous studies have found weak evidence of a relationship between functional traits and growth rate relationships in tree communities, highlighting the evidence that functional traits often fail to predict growth rate. It is therefore essential to advance a predictive approach to functional ecology. Since functional traits vary both among species and within species, linking functional traits at the individual level with plant growth will provide a path towards a more rigorous trait-based community ecology.
Methods: Based on the data from two survey of a 1.44-ha plot and the data of six plant functional traits from 4,142 individuals in Dinghushan, the effects of plant functional traits on plant growth were analyzed at the individual level and species level. First, the varied trends of different functional traits at different vertical layers (shrub layer, subcanopy layer and forest canopy layer) were explored. Second, the intraspecific and interspecific variations of each functional trait under different vertical layers were analyzed. Finally, the effects of plant functional traits, light competition and underground competition on tree growth were quantified using a structural equation model.
Results: Results suggest that: (1) Plant functional traits at different vertical layers exhibited significant differentiation. From the shrub layer to the canopy layer, leaf area (LA), specific leaf area (SLA) and energy supply-demand index (R) decreased significantly, while leaf thickness (LT) and leaf dry matter content (LDMC) increased significantly. (2) The interspecific variation of plant functional traits at different vertical layers is greater than the intraspecific variation, and the intraspecific variation of functional traits in the canopy layer is greater than that in the shrub layer and subcanopy layer. (3) The structural equation model based on plant functional traits at an individual level produces a higher degree of explanation for growth than at the species level. Furthermore, the introduction of plant functional traits at the individual level is more conducive to improving the ability to predict individual growth at the shrub layer. (4) Light competition and underground competition indirectly affect plant growth by affecting functional traits. From the shrub layer to the canopy layer, the interactions between conspecific individual gradually weakened and the interactions between different species gradually increased.
Conclusion: Our study highlights that analyzing plant functional traits at an individual level could help to better understand plant community structure and dynamics.

Key words: vertical layer, variation of functional trait, biotic interactions, growth dynamic, structural equation model

Yanpeng Li, Yunlong Ni, Han Xu, Juyu Lian, Wanhui Ye. Relationship between variation of plant functional traits and individual growth at different vertical layers in a subtropical evergreen broad-leaved forest of Dinghushan[J]. Biodiv Sci, 2021, 29(9): 1186-1197.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2021205

https://www.biodiversity-science.net/EN/Y2021/V29/I9/1186

Figures/Tables 6

Fig. 1 A structural equation model illustrating the effects of plant functional traits, underground competition and light competition on the annual growth rate of individuals. Trait, Plant functional trait; LA, Leaf area; LDMC, Leaf dry matter content; SLA, Specific leaf area; LT, Leaf thickness; R, Energy supply-demand index; H, Height; LC-c, Neighborhood indices of competition for light for conspecific neighbors; LC-h, Neighborhood indices of competition for light for heterospecific neighbors; UC-c, Neighborhood indices of underground competition for conspecific neighbors; UC-h, Neighborhood indices of underground competition for heterospecific neighbors; AGR, Annual growth rate.

Fig. 2 Differences of plant functional traits at species level with different vertical layers. Different lowercase letters indicate significant differences of plant functional traits with different vertical layers at 5% level. The meanings of LA, LT, LDMC, SLA and R are the same as in Fig. 1.

Fig. 3 Intraspecific variation and interspecific variation of plant functional traits at different vertical layers. The meaning of LA, LT, LDMC, SLA, R and H are the same as in Fig. 1, and the factor Mean represents the average variation level of all plant functional traits.

Fig. 4 The structural equation models (SEM) for the effects of plant functional traits at individual (A) and species level (B), underground competition and light competition on the annual growth rate of individuals in the shrub layer. Arrows represent the hypothesized causal relationships between variables. Arrow width indicates the strength of the relationship. Red color indicates negative relationships. Green color indicates positive relationships. Values next to the arrows are path coefficients (standardized partial regression coefficients) with associated statistical significance, *** P < 0.001; ** P < 0.01; * P < 0.05; ns, not significant. Values at outside of the variables represent the percentage of variance explained by the model. CFI, Comparative fit index; RMSEA, Root mean square error of approximation; SRMR, Standardized root mean square residual; χ2, Goodness-of-fit statistic assesses. The meaning of Trait, LA, R, LC-c, LC-h, UC-c, UC-h and AGR are the same as in Fig. 1.

Fig. 5 The structural equation models (SEM) for the effects of plant functional traits at individual (A) and species level (B), underground competition and light competition on the annual growth rate of individuals in the subcanopy layer. The frame structure of SEM is the same as in Fig. 4. The meaning of Trait, LA, R, LC-c, LC-h, UC-c, UC-h and AGR are the same as in Fig. 1.

Fig. 6 The structural equation models (SEM) for the effects of plant functional traits at individual (A) and species level (B), underground competition and light competition on the annual growth rate of individuals in the canopy layer. The frame structure of SEM is the same as in Fig. 4. The meaning of Trait, LDMC, R, LC-c, LC-h, UC-c, UC-h and AGR are the same as in Fig. 1.

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