Adaptive evolution of flowers to optimize pollen transfer by animal pollinators is considered a key driver of plant speciation and the success of flowering plants. Plant-pollinator interactions are embedded in complex abiotic (climatic) and biotic (other plants/pollinators) contexts, and the structure and function of interaction networks changes across such contexts. To date, however, we lack a broad-scale perspective on how these contexts affect which flower phenotypes are ‘fit’, and how flowers evolve to adapt to these contexts. This knowledge gap limits our understanding of the processes that generate and maintain biodiversity, critically important in light of current global change. In MountBuzz, I aim at developing a novel context-dependent ecological perspective on the processes structuring the evolution of flower diversity by linking the commonly separated fields of community ecology and macroevolutionary modelling. First, to determine which flower phenotypes are ‘fit’ (high reproductive success) in different a-/biotic environmental contexts, my team and I will analyze plant-pollinator interactions and flower and pollinator trait data along four elevational gradients across the tropics. We will combine empirical field observations with pollination experiments to pinpoint context-dependent changes in phenotype-fitness relationships. Second, synthesizing across these results, we will test whether patterns of flower macroevolution follow predictable, context-dependent trajectories by employing machine-learning based predictive modelling and phylogenetic comparative methods. The results of MountBuzz will deliver a new perspective on the relative importance of pollinator-mediated selection and environment-dependent processes in driving flower evolution and plant diversification. My study set-up (cross-continental, cross-environmental, cross-lineages) further allows for identifying generalities in patterns, thereby delivering novel hypotheses for future research.