Scaling from Traits to Ecosystems: Developing a General Trait Driver Theory via Integrating Trait-Based and Metabolic Scaling Theories

Aim: More powerful tests of biodiversity theories need to move beyond species richness and explicitly focus on mechanisms generating diversity via trait composition. The rise of trait-based ecology has led to an increased focus on the distribution and dynamics of traits across broad geographic and climatic gradients and how these distributions influence ecosystem function. However, a general theory of trait-based ecology, that can apply across different scales (e.g. species that differ in size) and gradients (e.g. temperature), has yet to be formulated. While research focused on metabolic and allometric scaling theory provides the basis for such a theory, it does not explicitly account for differences in traits within and across taxa, such as variation in the optimal temperature for growth. Here we synthesize trait-based and metabolic scaling approaches into a framework that we term ‘Trait Driver Theory’ or TDT. It shows that the shape and dynamics of trait and size distributions can be linked to fundamental drivers of community assembly and how the community will respond to future drivers. To assess predictions and assumptions of TDT, we review several theoretical studies and recent empirical studies spanning local and biogeographic gradients. Further, we analyze how the shift in trait distributions influences ecosystem processes across an elevational gradient and a 140-year-long ecological experiment. We show that TDT provides a baseline for (i) recasting the predictions of ecological theories based on species richness in terms of the shape of trait distributions and (ii) integrating how specific traits, including body size, and functional diversity then ‘scale up’ to influence ecosystem functioning and the dynamics of species assemblages across climate gradients. Further, TDT offers a novel framework to integrate trait, metabolic/allometric, and species-richness-based approaches to better predict functional biogeography and how assemblages of species have and may respond to climate change.