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.

Past human influences on Amazonian forest: The marks of prehistoric human societies on tropical forests can still be detected today. Levis et al. performed a basin-wide comparison of plant distributions, archaeological sites, and environmental data. Plants domesticated by pre-Columbian peoples are much more likely to be dominant in Amazonian forests than other species. Furthermore, forests close to archaeological sites often have a higher abundance and richness of domesticated species. Thus, modern-day Amazonian tree communities across the basin remain largely structured by historical human use. Science , this issue p. 925

Species distribution models (SDMs) are widely used in ecology and conservation. Presence-only SDMs such as MaxEnt frequently use natural history collections (NHCs) as occurrence data, given their huge numbers and accessibility. NHCs are often spatially biased which may generate inaccuracies in SDMs. Here, we test how the distribution of NHCs and MaxEnt predictions relates to a spatial abundance model, based on a large plot dataset for Amazonian tree species, using inverse distance weighting (IDW). We also propose a new pipeline to deal with inconsistencies in NHCs and to limit the area of occupancy of the species. We found a significant but weak positive relationship between the distribution of NHCs and IDW for 66% of the species. The relationship between SDMs and IDW was also significant but weakly positive for 95% of the species, and sensitivity for both analyses was high. Furthermore, the pipeline removed half of the NHCs records. Presence-only SDM applications should consider this limitation, especially for large biodiversity assessments projects, when they are automatically generated without subsequent checking. Our pipeline provides a conservative estimate of a species’ area of occupancy, within an area slightly larger than its extent of occurrence, compatible to e.g. IUCN red list assessments.