BA, Yale University
SM, The University of Chicago
PhD, The University of Chicago
IB 105 ONL, Environmental Biology (Online)
IB 439, Biogeography
IB 546B, Intro to Graduate Studies
Palynology, microscopy, machine learning, evolution
Our lab studies the influence of climate on the composition, structure, and long-term evolution of lowland Neotropical plant communities. We use the fossil pollen record to document plant response to past climate variability. Because pollen and spores are widespread in the terrestrial sediment record, we are able to use these microscopic fossils to study long-term trends in plant ecology and evolution.
We are interested in improving all aspects of palynological research. The focus of our current work is on developing microscopy and computer automation methods to improve the quantity and quality of pollen and spore counts. We are exploring different microscopy techniques, image analysis, and machine learning. With these new tools, we aim to develop larger and more comprehensive data sets that will expand the scope of paleoecological research.
Romero, I. C., S. Kong, C. C. Fowlkes, C. Jaramillo, M. A. Urban, F. Oboh-Ikuenobe, C. D’Apolito, and S. W. Punyasena. 2020. Improving the taxonomy of fossil pollen using convolutional meural networks. Proceedings of the National Academy of Sciences. 117(45): 28496-28505
Romero, I. C., M. A. Urban, and S. W. Punyasena. 2020. Airyscan superresolution microscopy: A high-throughput alternative to electron microscopy for the visualization and analysis of fossil pollen. Review of Palaeobotany and Palynology. 276: 104192
Urban, M. A., I. C. Romero, M. Sivaguru, and S. W. Punyasena. 2018. Nested cell strainers: an alternative method of preparing palynomorphs and charcoal. Review of Palaeobotany and Palynology. 253: 101-109.
Mander, L. and S. W. Punyasena. 2014. On the taxonomic resolution of pollen and spore records of Earth’s vegetation. International Journal of Plant Sciences. 175(8): 931-945.
Mander, L., M. Li, W. Mio, C. C. Fowlkes and S. W. Punyasena. 2013. Classification of grass pollen through the quantitative analysis of surface ornamentation and texture. Proceedings of the Royal Society B 280(1770): 20131905.
Punyasena, S. W., D. K. Tcheng, C. Wesseln, and P. G. Mueller. 2012. Classifying black and white spruce pollen using layered machine learning. New Phytologist 196(3): 937-944.
McElwain, J. C., and S. W. Punyasena. 2007. Mass extinction events and the plant fossil record. Trends in Ecology & Evolution 22(10): 548-557.