ABSTRACT

The timescale of life, delineating when species diverged from common ancestors, serves as a framework for understanding biology and is fundamental context for other fields of science such as geology, chemistry, and biomedicine. The advancements in genomics and computing have refined this timescale, resulting in the publication of thousands of evolutionary trees scaled to time, known as timetrees. This project supports the development and expansion of TimeTree, an open-source and unified database that synthesizes these data into a global timetree of life. Users can obtain the divergence time of any two species, a timeline showing all evolutionary splits back in time, and exportable timetrees of any group or from a custom list of species. Events in geological and astronomical history, such as asteroid impacts, oxygen levels, and solar luminosity, are integrated into the same timescale with species timetrees and timelines, facilitating interdisciplinary research and applications in areas like molecular biology, conservation, and medicine. The new TimeTree resource will implement new algorithms, tools, and application programming interfaces to speed, improve, and increase the efficiency of timetree curation and data mining. It will have a new online educational component designed for K-12 and college students, including assignments that demonstrate the principles, models, and methods for building timetrees. Its new graphical interface will be enhanced specifically for use by non-scientists and will include training tools to engage stakeholder communities, allowing for more user feedback. This expanded TimeTree resource aims to stimulate and accelerate research and discovery across diverse scientific disciplines while also serving as a simple-to-use tool for education and outreach.

The enhancements in TimeTree will enable researchers worldwide to query, retrieve, visualize, and explore evolutionary patterns, thus aiding hypothesis formulation and augmenting both scientific and societal returns on investments in basic biological research. The newly developed approaches and computational infrastructure will have greater efficiency that will result in the largest timetree of life ever assembled. The incorporation of a novel super-timetree estimation method will yield a more rapid and precise synthesis, affording greater and frequent automated assembly of the super-timetree and updates of the database. For example, a deep-learning approach will be employed to identify and incorporate newly published timetree studies quickly into the database. Beyond these core functionalities, TimeTree aims to bridge scientific disciplines by developing and integrating new tools for data analytics and mining, streamlining the use of complex timetree data. A specialized tool will allow users to access all associated study data for the node of interest, perform analyses on those data, and retrieve the data and results for further offline analyses. Advances in the application programming interface will offer a broader array of functionalities, such as serving commonly used biodiversity metrics. Innovative widgets will interface with other databases to provide them with divergence times and derivative metrics. TimeTree also will also include analytical utilities, such as lineages-through-time and diversification plots, so researchers may immediately see the results for any group. To ensure long-term sustainability while limiting maintenance costs, automated tools for processing and curating published timetrees will be integrated. TimeTree will remain fully open access, with the super-timetree, associated study data, and source code being readily downloadable.

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.

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