A new machine learning method will scan hundreds of thousands of butterfly, moth and mammal images to measure how often evolution repeats itself. Evolutionary convergence—when unrelated species independently evolve similar features, like the streamlined bodies of dolphins and extinct ichthyosaurs—offers a window into whether evolution follows predictable patterns or produces unpredictable novelty. Until now, no study has systematically measured how much convergence actually occurs across entire groups of organisms, or how it varies with evolutionary distance. This fellowship will use deep learning to embed photographs and 3D skull scans into multidimensional spaces, quantifying similarity across entire visible bodies for the first time. If successful, the research will provide quantitative answers to a fundamental question: is evolution predictable in practice? The methods developed could eventually allow researchers to forecast evolutionary outcomes—for example, how quickly a pathogen might evolve resistance to a new antibiotic, or what traits might emerge under specific environmental pressures. This is primarily fundamental science, but the predictive frameworks it creates could open new avenues in biomedical science and biotechnology, much as earlier work on evolutionary theory underpinned modern vaccine design and drug development.
View original technical description
Is evolution predictable? If I asked you whether a new antibiotic might face resistance, would you say yes? What about larger changes? Which animal species will be extinct in a million years, or ten? What will new ones look like? How will human culture itself have evolved? The answers to questions like these matter deeply for the resilience of our ecosystem and society. But it is not yet known to what degree such questions are answerable. Is evolution predictable in theory and practice? From what? To what extent? This Future Leaders Fellowship will empower me to lead an international network of researchers to give concrete answers to fundamental questions of evolutionary predictability. A key to their answers is evolutionary convergence, the repeated evolution of similar phenotypes, or overall phenomes, like the suggested (but also disputed) repetition of body forms in placental and marsupial mammals. Convergence offers to tell us what we can expect from evolution, more of the same, or its alternative, something entirely new? But, while many examples of convergence have been hypothesised, no study has measured the full extent to which they vary with evolutionary distance and, therefore, just how predictable they are. For the first time, new computational methods of machine learning will allow us to measure the extent of evolutionary convergence across entire visible phenomes. This fellowship will undertake the most comprehensive tests of evolutionary convergence ever performed, across two parallel research programs on diverse evolutionary groups, the butterflies and moths and the mammals, our own evolutionary clade. This will compare the extent and evolutionary patterns of convergence in all phenotype visible among hundreds of thousands of lepidopteran photographs and images from 3D mammal skull scans. This will test the extent to which evolution is ultimately, or only locally, predictable across real-world macroevolutionary diversifications. This will give quantitative answers to questions of predictability that have long fascinated humanity. Beyond this, the answers will tell us how far we can expect to predict new evolutionary events. These new insights, and the methods developed to gain them, will provide new avenues for practical evolutionary prediction, with potential applications from biomedical science to technological innovation. To deliver these insights, as a Future Leaders Fellow, I will deploy and extend three key breakthroughs in machine learning. First, new applications of deep-learning methods will embed images in multidimensional spaces, measuring their similarity, leading their first applications to evolution (Hoyal Cuthill et al., 2019, Science Advances; 2020, Nature). Second, I will develop new machine learning applications to directly measure the predictability of one phenotype from another with evolutionary distance. The third innovation will then develop avenues for phenotypic image prediction given evolutionary distance, incorporating generative machine learning methods. To achieve these aims, this fellowship will build an international network of researchers around the Fellow, evolutionary biologist Dr Jennifer Hoyal Cuthill, hosted by the School of Life Sciences at the University of Essex and mentored by leading evolutionary, ecological and data scientists. Collaborative Project Partners and Co-I will include industrial computer scientists at Cross Labs, Cross Compass, Japan and experts on world-leading collections at the Natural History Museum and University of Cambridge Zoology Museum. This fellowship will, thereby, resource a sea-change in evolutionary research, developing and applying cutting-edge technology to provide new answers to the fundamental scientific question, is evolution predictable? Integration of the insights, enabled by the exceptional scope of the Future Leaders Fellowship, will provide new theoretical and predictive frameworks for evolutionary science.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know