The Amazon rainforest acts as a significant carbon store and plays a crucial role in partially offsetting global anthropogenic CO2 emissions. Climate change and rising CO2 levels are altering ecosystem functions and this is projected to continue into the future. However, uncertainties remain on how individual trees and the hyperdiverse forest community will respond. To address this, the first FACE (free-air CO2 enrichment) experiment in the tropics is being installed in the Brazilian Amazon. Due to its high biodiversity, many dominant tree species occur at < 1 individual/ha; following standard practice to attempt full species replication would require experimenting in over 10 ha of forest, which is impractical. Here, we show how functional space-diversity is used to solve this challenge by assessing the main morphological, anatomical and hydraulic functional traits of the tree community identified at the experimental site. We measured traits across 414 species to create a functional group framework which represents a gradient from acquisitive (fast-growing) to conservative (slow-growing) species in terms of carbon, nutrient, and water uses, allowing us to elucidate mechanisms in a species-independent way. We found a separation emerged between acquisitive (high SLA, low CN) and conservative species, alongside a hydraulic axis distinguishing high stomatal density (higher gas exchange) from high wood density (more drought resistant). Moreover, we compared the functional space occupied by the AmazonFACE tree community to that of the Amazon basin to assess representativeness at a large scale.

This study highlights the challenges and opportunities posed by biodiversity in conducting ecosystem-level experiments in tropical forests and underscores the potential of a functional trait approach to interpret findings regarding broader ecological contexts. Filling this knowledge gap is key to assessing the confidence in the projections of the future of the global carbon cycle in Earth System Models, and ultimately improving their accuracy and robustness.