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 and diversity. The rise of trait-based ecology has led to an increased focus on the distribution and dynamics of traits in communities. 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 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 Drivers Theory " or TDT. It shows that the shape and dynamics of trait distributions can be uniquely 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, recent empirical studies spanning local and biogeographic gradients. Further, we analyze how the shift in trait distributions influences ecosystem productivity across an elevational gradient and a 140-year long ecological experiment. We argue that our general 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 'scale up' to influence the dynamics of species assemblages across climatic gradients and how shifts in functional composition influences ecosystem functioning. Further, it offers a novel framework to integrate trait, metabolic/allometric, and species-richness based 4 approaches in order to build a more predictive functional biogeography to show how assemblages of species have and will respond to climate change.