IntBIO COLLABORATIVE RESEARCH: Integrating fossils, genomics, and machine learning to reveal drivers of Cretaceous innovations in flowering plants

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The Tree of Life is marked by short periods of rapid innovation where groups emerge with dramatically altered forms and diversify quickly. This has happened multiple times throughout geologic history including with the rise of birds, mammals, the transition of plants from water to land, and the origination of flowering plants. The rapid emergence and diversification of flowering plants in particular represents one of the most remarkable episodes in the history of life on earth. However, while fundamental to understanding the ecology and evolution of modern ecosystems, this episode remains unexplained and leads to one of the grand challenges in the biological sciences – determining what processes may be responsible for such rapid changes in form and function across the Tree of Life. A major impediment to addressing this question is the availability of data and methods for analyzing those data. The goal of this study is to use and develop new machine learning approaches to gathering data for both fossil and living plant species and to use these data to help develop new techniques. These techniques will help identify what contributed to the rapid change in plants that resulted in their dominance in the environment today. This project will train undergraduates, graduate students, and postdoctoral fellows in machine learning methods, evolutionary biology, and techniques for working with both fossil and living specimens. The project will also include resource development and training for middle schoolers, high schoolers, undergraduates, and the broader research community.This project aims to evaluate the evolutionary processes underlying the emergence of innovation, using flowering plants as a case study. Specifically, the project will examine Cretaceous radiations of flowering plants characterized by rapid evolution, a rich fossil record, and the origin of innovations and lineages of great ecological significance. The central goals of the proposed research are to (a) generate a large morphological dataset for flowering plants using novel machine learning methods, (b) develop new statistical methods for modeling evolution, and (c) use these advances in data collection and methods to identify the processes that led to the episodic and rapid emergence of novelty across the Tree of Life. Collectively, these new developments in machine learning techniques, morphological data collection, and analytical techniques for addressing evolutionary processes will be potentially transformative to several fields including the biological sciences, computational biology, and machine learning. The large scope and scale of this project, together with its highly integrative nature, creates the potential to address one of the most important standing questions in evolutionary biology.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.