Alpha and beta diversity of functional traits in subtropical evergreen broad-leaved secondary forest communities

Introduction Intra-speciic variation is the main source of functional trait diversity and has similar ecological effects as inter-speciic variation.Methods We studied 79 species and 3546 individuals from 50 ixed monitoring plots in subtropical evergreen broad - leaved secondary forests in Zhejiang Province, China. Using trait gradient analysis, we examined nine traits (speciic leaf area, leaf dry matter content, wood density, leaf area, chlorophyll content, leaf nitrogen content, leaf phosphorus content, leaf potassium content, and nitrogen-phosphorus ratio) by decomposing species functional traits into alpha (within-community) and beta (among-communities) measure the impact of environmental gradients and the presence of other species on the variation of traits.Result All nine functional traits showed some degree of differentiation in the forest communities, with a greater range of variation in alpha values than in beta values . Correlations were signiicantly different between the trait differences in the communities. The alpha values of each trait showed a higher correlation with other components than the beta values. The factors affecting intra-speciic trait variation were relatively complex. The alpha component had a more signiicant and stronger effect on intra-speciic trait variation compared to the beta component. Abiotic factors, such as soil nutrient content, soil nitrogen-phosphorus content, directly affected the beta component. In contrast, biotic factors, such as tree height variation, had a direct and stronger effect on the alpha component.Discussion Our results demonstrate that alpha and beta components, as independent differentiation axes among coexisting species, have different sensitivities to different environmental factors and traits in different ecological strategies and spatial scales. Trait gradient analysis can more clearly reveal the variation patterns of species traits in communities, which will help to understand the scale effects and potential mechanisms of trait relationships.