Global trait–environment relationships of plant communities

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 question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait–environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions. Although plant functional trait combinations reflect ecological trade-offs at the species level, little is known about how this translates to whole communities. Here, the authors show that global trait composition is captured by two main dimensions that are only weakly related to macro-environmental drivers.

Similarly, within-plot trait variation does not vary systematically with macro-environment. 226 Our results indicate that, at fine spatial grain, macro-environmental drivers are much less 227 important for functional trait composition than has been assumed from floristic analyses 228 restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be  Table 2). We selected these 18 traits because they affect 265 different key ecosystem processes and are expected to respond to macroclimatic drivers 266 ( Table 1). In addition, they were sufficiently measured across all species globally to allow for 267 imputation of missing values (see Methods). All analyses were confined to vascular plant 268 species and included all vegetation layers in a community, from the canopy to the herb layer 269 (see Methods). 270 We used this unprecedented fine-resolution dataset to test the hypothesis (Hypothesis 1) that  Table   295 1 for expected relationships and Supplementary

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Consistent with Hypothesis 1 and as illustrated in Figure 1, global variation in plot-level trait 301 means was much higher than expected by chance: all traits had positive standardized effect 302 sizes (SESs), which were significantly > 0 for 17 out of 18 traits based on gap-filled data 303 (mean SES = 8.06 standard deviations (SD), Table 2). This suggests that environmental or 304 biotic filtering is a dominant force of community trait composition globally. Also as predicted 305 by Hypothesis 1, within-plot trait variance was typically lower than expected by chance 306 (mean SES = -1.76 SD, significantly < 0 for ten traits but significantly > 0 for three traits; 307 Table 2). Thus, trait variation within communities may also be constrained by filtering. opposing trait values can co-exist in the same community. In combination with our finding of 323 strong trait convergence, these results reveal a strong parallel of present-day community 324 assembly to individual species' evolutionary histories.
325 Surprisingly, we found only limited support for Hypothesis 2. Community-level trait 326 composition was poorly captured by global climate and soil variables. None of the 30 327 environmental variables accounted individually for more than 10% of the variance in the traits 328 defining the main dimensions in Fig. 2 (Supplementary Fig. 2). The coefficients of 329 determination were not improved when testing for non-linear relationships (see Methods).     We note that the strongest community-level correlations with environment were found for 360 traits not linked to the leaf economics spectrum. Mean stem specific density increased with 361 potential evapotranspiration (PET, r 2 =15.6%; Fig. 4a, b), reflecting the need to produce 362 denser wood with increasing evaporative demand. Leaf N:P ratio increased with growing-363 season warmth (growing degree days above 5°C, GDD5, r 2 =11.5%; Fig. 4d), indicating strong 364 phosphorus limitation 29 in most plots in the tropics and subtropics (Fig. 4c, d). This pattern 365 was not brought about by a parallel increase in the presence of legumes, which tend to have 366 relatively high N:P ratios; excluding all species of Fabaceae resulted in a very similar 367 relationship with GDD5 (r 2 =10.0%). The global N:P pattern is consistent with results based 368 on traits of single species related to mean annual temperature 30 . We assume that the main 369 underlying mechanism is the high soil weathering rate at high temperatures and humidity, databases. Prior to name matching, we ran a series of string manipulation routines in R, to 468 remove special characters and numbers, as well as standardized abbreviations in names.

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Taxon names were parsed and resolved using Taxonomic Name Resolution Service version   Table 1 Table 1). 523 We are aware that using species mean values for traits excludes the possibility to account for 524 intraspecific variance, which can also strongly respond to the environment 39 . Thus, using one 525 single value for a species is a source of error in calculating trait means and variances.  Table 2). We refer to these climate and 541 soil data as "environmental data". CWV is equal to functional dispersion as described by Rao (Fig. 4). We also tested for non-linear 581 relationships with environment by including an additional quadratic term in the linear model 582 and then report coefficients of determination. As in the linear relationships of CWM with 583 environment, the highest r 2 values in models with an additional quadratic term were 584 encountered between stem specific density and PET (r 2 =0.156) and leaf N:P ratio and 585 growing degree days above 5°C (GDD5, r 2 =0.118). These were not substantially different 586 from the linear CWM-environment relationships, which had r 2 =0.156 and r 2 =0.115, 587 respectively (Fig. 4, Supplementary Fig. 2). Similarly, including a quadratic term in the  above.

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The forest plots, in particular, confirmed the overall patterns, with respect to variation in 639 CWM explained by the first two PCA axes (60.5%) and the two orthogonal continua from 640 small to large size and the leaf economics spectrum (Supplementary Fig. 6). The mean PCA loadings across these 100 subsets (summarized in Supplementary Fig. 10) 716 were fully consistent with those of the full data set in Fig. 2 RDAs across all 100 runs) was considerably larger than that for the total dataset, which is also 723 reflected in consistently higher correlations between traits and environmental variables 724 ( Supplementary Fig. 11). The highest mean correlation was encountered for plant height and    are also based on log e -transformed values. Stem specific density is stem dry mass per stem fresh volume, specific leaf area is leaf area per leaf dry 901 mass, leaf C, N and P are leaf carbon, nitrogen and phosphorus content, respectively, per leaf dry mass, leaf dry matter content is leaf dry mass per 902 leaf fresh mass, leaf delta 15 N is the leaf nitrogen isotope ratio, stem conduit density is the number of vessels and tracheids per unit area in a cross 903 section, conduit element length refers to both vessels and tracheids. SESs were calculated by randomizing trait values across all species globally 100 904 times and calculating CWM and CWV with random trait values, but keeping all species abundances in plots (see Fig. 1   Variance in CWM explained by the first and second axis was 29.7% and 20.1%, respectively.

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The vegetation sketches schematically illustrate the size continuum (short vs. tall) and the leaf 933 economics continuum (low vs. high LDMC and leaf N content per area in light and dark green 934 colours, respectively). See Table 2 and Supplementary Table 2 Table 2 and Supplementary Table 2