Statistical tools for learning about trait evolution across species

Research on evolutionary biology has been centered on understanding trait variation across species and its contribution to the extraordinary biodiversity observed on Earth. However, this venture is complicated by a deficiency in computational tools that can effectively navigate evolutionary relationships among species or incorporate large and diverse trait datasets generated by modern experimental studies.

Hence, the objective of this project is to expand existing toolkit through the design of a suite of novel statistical and machine learning methods for studying trait evolution across species. These tools will be thoroughly tested through computer simulations, compared to alternative state-of-the-art approaches, and applied to publicly available datasets to address specific evolutionary problems.

All tools and datasets generated by the project will be freely and widely disseminated to the scientific community, providing researchers with a powerful framework in which to answer a variety of exciting questions about trait evolution across the tree of life. The project will also advance the participation of underrepresented groups in science and engineering through recruitment of female Hispanic high school and undergraduate students to the research team.

Additionally, the project leaders will design and teach hands-on courses in evolutionary genomics and bioinformatics for retired senior citizens in the local community and for Native American communities across the country.

Elucidating the processes underlying trait variation across species is a fundamental problem in evolutionary biology, and one for which existing tools lag far behind modern datasets. The current project will address this issue through the design of statistical and supervised machine learning approaches for robustly and accurately predicting the general and specific evolutionary mechanisms by which traits evolve across species.

In particular, the tools developed will properly account for species phylogenetic relationships, integrate diverse omics and other trait data, and create new avenues for testing specialized evolutionary hypotheses. Availability of these methods will facilitate studies of associations between traits related by a phylogeny, processes and forces driving the evolution of such traits, and adaptive trait evolution arising from different types of structural variations.

Moreover, findings from applications of these tools to empirical datasets will illuminate connections across different levels of biological organization and diverse biological systems. Finally, the developed tools will be applicable to a wide range of ever-increasing genomic, transcriptomic, and other modern omics and trait data, promoting future research in the processes shaping the distribution of traits across species and their roles in evolutionary innovation. Results from the project will be available at http://assisgroup.fau.edu/.

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.