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.

The fossil record offers invaluable insights into the history of life on Earth, including species' responses to past climate changes, serving as a vital resource in addressing the contemporary biodiversity crisis. However, the fossil record is subject to multiple environmental, geological, and anthropogenic biases, which, if unaccounted for, may lead to misleading interpretations. Despite an increasing awareness of these biases among researchers, our ability to mitigate fossil record biases in analyses and account for the uncertainty produced remains limited. TransFoRM therefore seeks to develop a coherent framework to address inherent biases of the fossil record systematically. Specifically, I will (i) identify general biases of the fossil record through a systematic literature review; (ii) identify instances where researchers overlook biases in the fossil record through a statistical assessment of surveys (a many-analysts approach), (iii) develop a structural causal model that incorporates the data-generating process of the fossil record, to account for known biases and confounders; and (iv) strengthen the connections between paleontological research and conservation practice by equipping researchers with the tools, awareness, and knowledge necessary to navigate and communicate the complexities of the fossil record effectively. The transdisciplinary nature of the project and planned dissemination activities will lead to knowledge that is highly transferable into various fields. The outcomes will provide a comprehensive framework to alleviate biases in the fossil record, facilitating the propagation of fossil information to the contemporary ecological crises. TransFoRM is particularly timely, as confounder- and bias-corrected frameworks exist, with clear guidelines for their application to observational data in ecology, but are not yet integrated in paleontological research, prompting recent calls for their adoption.