One of humankind's grand environmental challenges is anticipating and understanding how biodiversity change and loss will impact ecosystems and human well-being. Trait-based approaches offer much promise. However, to do so will require access to trait data across vast spatial, temporal, and taxonomic scales and big, bold, general theory. While big data can provide a wealth of information, to quote Sydney Brenner “we are drowning in a sea of data and thirsting for some theoretical framework with which to understand it…”. We argue that effective and rapid biodiversity forecasting using functional traits requires (i) integrating theory and (ii) integrating different big data streams using an (iii) OpenScience biodiversity and functional diversity workflow. Such integration clarifies essential data needed and can rapidly leverage and build strength across datasets. We emphasize how big theory needs to guide the proliferation of big trait data to extract meaningful insights from complex ecological and biological systems. We give several examples of how trait-based theory can guide the collection and standardization of trait data. This process will enable researchers to develop more robust and generalizable theoretical models, which can inform conservation and management strategies and ultimately help address pressing global environmental challenges. We also highlight how we can rapidly progress in trait-based studies and biodiversity science through data integration. Integrating differing biodiversity observation records (including trait data) can help bridge gaps in trait knowledge. Combining data from different sources within an open workflow can reveal gaps in our understanding of ecosystem processes and highlight areas where further research is needed. In turn, this can inform management decisions aimed at conserving biodiversity and mitigating the effects of global environmental change. We show specifics of the BIEN workflow where Open Science practices have facilitated the integration of diverse datasets, allowing researchers to leverage observation and trait data and build strength. Full integration across databases will require tackling the major impediments to data integration: taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives. Open Science practices can facilitate such integration. We highlight how the Biodiversity Forecasting Initiative (BioFI), newly funded by NSF, will address the dual challenges of BigData and BigTheory to enhance our knowledge of the Earth's biodiversity and advance our ability to forecast the biosphere functioning. BioFI provides the data and computational scaffolding to integrate biological and environmental disciplines to produce the best-available predictions of future biodiversity trajectories using trait-based theory.
