Collaborative Research: BIO AI: Is asexual reproduction an evolutionary dead end? Leveraging AI and the fossil record to compare evolutionary outcomes across reproductive modes

Reproduction in most plants and animals combines genetic material from two parents and results in offspring that differ from each other. However, some organisms instead reproduce as clones, where offspring are genetically identical to each other and to their parent. This clonal reproduction has long been known to be more efficient, but strictly clonal reproduction is rare and scattered across the tree of life. This presents a quandary – if clonal reproduction is more efficient, why isn’t it more common? The key to resolving this puzzle is thought to relate to the variation generated by typical reproduction. The more limited variability of clonal populations should make them less able to adapt to changing conditions and therefore be especially prone to extinction. This prediction has been surprisingly difficult to test. Extinction is best studied in the fossil record, but it is usually not possible to determine how reproduction occurred from fossil remains. The researchers overcome this challenge by using the uniquely informative fossil record of small crustaceans called ostracodes to test for evolutionary differences between clonal and non-clonal species. The shells of these ostracodes differ between males and females and previous work has documented the presence of both in most ostracode species, indicating typical reproduction. Some species, however, are known to be comprised of females only, indicating clonal reproduction. This project tests if species with clonal reproduction show more limited trait evolution and elevated extinction risk when compared to species with typical reproduction. The researchers will develop an AI-based tool that will be shared with the scientific community to automatically measure the size and shape of fossil remains. They will also create and evaluate AI-based approaches for extracting additional information from images of fossils, which has potential to significantly increase the efficiency of imaged-based data collection. This project applies novel paleontological data to a central question in evolutionary biology and will train post-doctoral fellows and several undergraduate students at the intersection of biology, paleontology, and AI, while engaging museum audiences about the scientific process. The research will document reproductive mode in fossil ostracode species from the Late Cretaceous through Paleogene, ~86 – 23 million years ago, from the U.S. Coastal Plain, with data acquisition via newly developed AI tools. Mixed-effect linear models will be used to quantify evolutionary variation in traits, which can then be compared across species with different reproductive mode. Occurrences of these same species will be documented from the published literature, existing databases, and new study of previously unpublished samples from natural history collections. From these occurrences, capture-mark-recapture analysis will be used to measure and compare extinction rates across reproductive modes. 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.