Ongoing developments in phylogenetics coupled with emerging cyber-infrastructure and new data sources provide unparalleled opportunities for mobilizing and integrating massive amounts of information from organismal biology, ecology, genetics, climate, and other areas such that patterns in complex data will emerge, yielding new hypotheses for further study. Two case studies that attempt to integrate heterogeneous data across spatial and temporal scales reveal challenges and opportunities for our understanding of plant evolution.  In the first study, our analysis of Florida plants has led to new methods for high-throughput use of museum locality data in modeling species distributions, both now and under models of climate change. These analyses, which rely on integrating public data from taxonomy, distributions, climate, and predictions of global change through novel algorithms and workflows, have demonstrated the capacity for data-driven science for discovery of new biodiversity patterns, with fundamental implications for the conservation and management of Florida’s ecosystems. Ongoing work to couple species distribution models with an evolutionary tree of Florida plants promises further insight to the origins, maintenance, and conservation of biodiversity. This test case sets the stage for further integration of phylogenetic, distributional, temporal, and environmental data for discovering patterns of biodiversity and the processes that shape its distribution, assembly into communities, and interactions in ecosystems.  The second study integrates microbial diversity with the evolutionary history, ecological function, and genetic diversity of plants across an ancient floristic disjunction (Eastern North America and Eastern Asia). This project focuses on the relative roles of ancient evolutionary history and recent ecological interactions in shaping current patterns of biodiversity, providing new insights on interactions involving plant diversity, microbial diversity, and ecosystem function, with important implications for global climate-carbon cycle models that inform national and international climate and energy policies.  Integrating diverse data, including phylogenies, genes, museum specimen records, metagenomic sequences, microbial function, plant functional traits, and climate models, presents enormous challenges but also opportunities for new perspectives on old problems.  Although numerous questions and specific hypotheses may be addressed through integrated analyses of biodiversity and environmental data, perhaps the greatest value of such data-enabled science will lie in the unanticipated patterns that emerge.