Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions 1-3 , but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear 4 . Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits-wood density, specific leaf area and maximum height-consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies 5 . Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our traitbased approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.Phenotypic traits are considered fundamental drivers of community assembly and thus species diversity 1,6 . The effects of traits on individual plant physiologies and functions are increasingly understood, and have been shown to be underpinned by well-known and globally consistent trade-offs 1-3 . For instance, traits such as wood density and specific leaf area capture trade-offs between the construction cost and longevity or strength of wood and leaf tissues 2,3 . By contrast, we still have a limited understanding of how such trait-based trade-offs translate into competitive interactions between species, particularly for long-lived organisms such as trees. Competition is a key filter through which ecological and evolutionary success is determined 4 . A long-standing hypothesis is that the intensity of competition decreases as two species diverge in trait values 7 (trait dissimilarity). The few studies [8][9][10][11][12][13] that have explored links between traits and competition have shown that linkages were more complex than this, as particular trait values may also confer competitive advantage independently from trait dissimilarity 9,13,14 . This distinction is fundamental for species coexistence and the local mixture of traits. If neighbourhood competition is driven mainly by trait dissimilarity, this will favour a wide spread of trait values at a local scale. By contrast, if neighbourhood interactions are mainly driven by the c...

Abstract. The classical environmental control model assumes that species distribution is determined by the spatial variation of underlying habitat conditions. This niche-based model has recently been challenged by the neutral theory of biodiversity which assumes that ecological drift is a key process regulating species coexistence. Understanding the mechanisms that maintain biodiversity in communities critically depends on our ability to decompose the variation of diversity into the contributions of different processes affecting it. Here we investigated the effects of pure habitat, pure spatial, and spatially structured habitat processes on the distributions of species richness and species composition in a recently established 24-ha stem-mapping plot in the subtropical evergreen broad-leaved forest of Gutianshan National Nature Reserve in East China. We used the new spatial analysis method of principal coordinates of neighbor matrices (PCNM) to disentangle the contributions of these processes. The results showed that (1) habitat and space jointly explained ;53% of the variation in richness and ;65% of the variation in species composition, depending on the scale (sampling unit size); (2) tree diversity (richness and composition) in the Gutianshan forest was dominantly controlled by spatially structured habitat (24%) and habitat-independent spatial component (29%); the spatially independent habitat contributed a negligible effect (6%); (3) distributions of richness and species composition were strongly affected by altitude and terrain convexity, while the effects of slope and aspect were weak; (4) the spatial distribution of diversity in the forest was dominated by broad-scaled spatial variation; (5) environmental control on the one hand and unexplained spatial variation on the other (unmeasured environmental variables and neutral processes) corresponded to spatial structures with different scales in the Gutianshan forest plot; and (6) five habitat types were recognized; a few species were statistically significant indicators of three of these habitats, whereas two habitats had no significant indicator species. The results suggest that the diversity of the forest is equally governed by environmental control (30%) and neutral processes (29%). In the finescale analysis (10 3 10 m cells), neutral processes dominated (43%) over environmental control (20%).

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Global change is impacting forests worldwide, threatening biodiversity and ecosystem services including climate regulation. Understanding how forests respond is critical to forest conservation and climate protection. This review describes an international network of 59 long-term forest dynamics research sites (CTFS-ForestGEO) useful for characterizing forest responses to global change. Within very large plots (median size 25 ha), all stems ≥1 cm diameter are identified to species, mapped, and regularly recensused according to standardized protocols. CTFS-ForestGEO spans 25°S-61°N latitude, is generally representative of the range of bioclimatic, edaphic, and topographic conditions experienced by forests worldwide, and is the only forest monitoring network that applies a standardized protocol to each of the world's major forest biomes. Supplementary standardized measurements at subsets of the sites provide additional information on plants, animals, and ecosystem and environmental variables. CTFS-ForestGEO sites are experiencing multifaceted anthropogenic global change pressures including warming (average 0.61°C), changes in precipitation (up to AE30% change), atmospheric deposition of nitrogen and sulfur compounds (up to 3.8 g N m À2 yr À1 and 3.1 g S m À2 yr À1), and forest fragmentation in the surrounding landscape (up to 88% reduced tree cover within 5 km). The broad suite of measurements made at CTFS-ForestGEO sites makes it possible to investigate the complex ways in which global change is impacting forest dynamics. Ongoing research across the CTFSForestGEO network is yielding insights into how and why the forests are changing, and continued monitoring will provide vital contributions to understanding worldwide forest diversity and dynamics in an era of global change.

The theory of metabolic ecology predicts specific relationships among tree stem diameter, biomass, height, growth and mortality. As demographic rates are important to estimates of carbon fluxes in forests, this theory might offer important insights into the global carbon budget, and deserves careful assessment. We assembled data from 10 oldgrowth tropical forests encompassing censuses of 367 ha and > 1.7 million trees to test the theory's predictions. We also developed a set of alternative predictions that retained some assumptions of metabolic ecology while also considering how availability of a key limiting resource, light, changes with tree size. Our results show that there are no universal scaling relationships of growth or mortality with size among trees in tropical forests. Observed patterns were consistent with our alternative model in the one site where we had the data necessary to evaluate it, and were inconsistent with the predictions of metabolic ecology in all forests.