Studies of “evolution-in-action†have revealed much about the causes of evolutionary change, including why it sometimes fails. However, it is not always obvious when these causes are also responsible for extinction and adaptation over million-year timescales--the timescale primarily relevant to the evolution and maintenance of biodiversity. With increasing rates of global change, it is vital to understand how and why species either adapt and survive, or fail to adapt and perish. This project builds a bridge between the causes of evolution studied over short timescales and the long-term outcomes evident from existing evolutionary diversity with a new set of computational tools and resources for biology research and education. New models will integrate field, genetic and experimental studies with patterns of trait change from across the tree of life. The research will apply these models to comprehensive datasets in mammals and fishes to better understand the causes of trait change over million-year timescales. The research will also develop and implement freely available classroom resources that specifically address issues of scale and causation over short and long evolutionary timescales--educating the next generation of citizens and scientists to the pressing challenge of predicting how current global change will affect the long-term outlook of biodiversity.
Recent controversies suggest strong limits on what inferences can be made from macroevolutionary data alone. One solution to these limitations is to synthesize what we know about the causes and limits of evolution from field and experimental studies into macroevolutionary methods. This project identifies three "injection sites" where such information can be integrated into comparative models to elucidate the causes of macroevolution. The research will develop new models that integrate measurements of genetic variation, natural selection and population data with macroevolutionary scale data. It will also enable integration of biomechanical models based on knowledge of trait functions. By uniting macroevolutionary models with knowledge and data on how microevolutionary data affect the evolutionary process, this research will open new paths for studying the causes of macroevolution. These models will be further connected to the field of causal inference--which has revolutionized artificial intelligence by rethinking how statistical methods represent causation. Finally, the research will address unfilled gaps in biology pedagogy by developing and investigating how to make the non-intuitive shift from Mendelian genetics to macroevolution in biology curricula. This will be accomplished by developing and implementing novel, software-based Open Education resources across multiple institutions.
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.
