Principal component analysis of global within-plot trait variances (CWVs)
The plots (n = 1,098,015) are shown by coloured dots, with shading indicating plot density on a logarithmic scale, ranging from yellow with 1–2 plots at the same position to dark red with 631–1,281 plots. Post hoc correlations of PCA axes with climate and soil variables are shown in blue and magenta, respectively. Arrows are enlarged in scale to fit the size of the graph; thus, their lengths show only differences in variance explained relative to each other. Variance in CWV explained by the first and second axes was 24.9% and 13.4%, respectively. CWV values of all traits increase from the left to the right, which reflects increasing species richness (r² = 0.116 between scores of the first axis and number of species in the communities for which traits were available). The vegetation sketches schematically illustrate low and high variation in the plant size and leaf economics continua. See Table 2 and Supplementary Table 2 for the description of traits and environmental variables.

Principal component analysis of global within-plot trait variances (CWVs) The plots (n = 1,098,015) are shown by coloured dots, with shading indicating plot density on a logarithmic scale, ranging from yellow with 1–2 plots at the same position to dark red with 631–1,281 plots. Post hoc correlations of PCA axes with climate and soil variables are shown in blue and magenta, respectively. Arrows are enlarged in scale to fit the size of the graph; thus, their lengths show only differences in variance explained relative to each other. Variance in CWV explained by the first and second axes was 24.9% and 13.4%, respectively. CWV values of all traits increase from the left to the right, which reflects increasing species richness (r² = 0.116 between scores of the first axis and number of species in the communities for which traits were available). The vegetation sketches schematically illustrate low and high variation in the plant size and leaf economics continua. See Table 2 and Supplementary Table 2 for the description of traits and environmental variables.

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Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key r...

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... However, plant species primarily represent and survive as part of a plant community instead of as separate species or individuals. Therefore, the variation in traits among species cannot represent the characteristics of plant communities or vegetation, nor the ecological processes at the ecosystem level [5,6]. ...
... The community-weighted mean (CWM) trait, which aggregates plant functional traits at the community level using the weighted-mean approach, is a commonly used indicator for the study of vegetation-environment relationships [7]. The CWM trait is typically calculated as the mean of plant functional trait values at the species level weighted by the relative abundance of taxa [5]. However, measuring CWM traits through field surveys is time-consuming and labour-intensive, while using trait data from published databases is hindered by differences in sampling and measurement criteria and usually ignores intraspecific variation of traits [11]. ...
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