There are an estimated 300,000+ terrestrial plant species on Earth exhibiting a profound diversity of form, function, and life history. Plant diversity strongly influences numerous ecological processes, as well as climate and the carbon cycle. Climate change projections and climate mitigation and carbon management policy are based on Earth System Models (ESMs), which represent terrestrial plant functional diversity using a handful of Plant Functional Type (PFTs) that aim to capture similarities in their effects on ecosystem processes and properties. However, the traditional PFT representation of vegetation suffers from a range of shortcomings, as models largely fail to reproduce historical spatial patterns of grass cover and productivity at regional to continental scales, and future predictions of forest productivity are highly divergent. Additionally, integrating trait data into PFTs is challenging. Next-generation models require more sophisticated representations of plant diversity to capture spatial and temporal ecosystem dynamics, including more complex representations of vegetation structure that include competition for light, nutrients, and water. These competitive interactions depend on plant traits. Emerging work suggests that many important plant traits and determinants of plant geography are phylogenetically conserved (closely related species tend to have related traits and climatic niches), and thus a new modeling framework should be consistent with evolutionary history. Similarly, many spectral properties of plant canopies appear to be phylogenetically conserved, thus enabling connections between remotely sensed hyperspectral data and plant functional traits. Our symposium will showcase new datasets and approaches that underpin a novel, integrative framework reorganizing vegetation functional types around phylogeny-driven functional diversity to better capture lineage-based trait coordination and distribution. The method is fundamentally different from previous approaches, as it uses phylogenetic relatedness to create ‘Lineage-based Functional Types’ (LFTs) which use a unified, scalable framework for representing plant functional diversity based on evolutionary relatedness. By parameterizing functional types based on evolutionary lineage rather than structural similarity, vegetation modelers could consistently modify the complexity of functional types for specific applications using phylogenetic relationships and thus anchor trait data in an evolutionary context. Developing and implementing LFTs should increase the accuracy of site-, regional-, and global-scale model predictions, providing a synthesis of plant functional ecology crucial for forecasting ecological responses to global change. This approach integrates three modern big data streams: burgeoning plant trait databases, hyperspectral remote sensing, and newly available molecular phylogenies. The LFT approach to vegetation modeling and macroecology is enabled by continually increasing computing power and the ecological data revolution.