BA, Yale University
SM, The University of Chicago
PhD, The University of Chicago
IB 105 ONL, Environmental Biology (Online)
IB 439, Biogeography
Palynology, paleoecology, paleoclimate, and computer vision applications
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 seek to re-imagine the field of paleoecology and expand the range of ecological and evolutionary hypotheses that can be addressed by increasing the throughput, reproducibility, and taxonomic resolution of an unrecognized source of “big data”– the microfossil record. 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. Our long-term goal is to transform the paleoecological analysis workflow, from imaging to classification to interpretation.
Mander, L., and Punyasena, S. W. (2018). Fossil pollen and spores in paleoecology. In Methods in Paleoecology (pp. 215-234). Springer.
Sivaguru, M., Urban, M. A., Fried, G., Wesseln, C. J., Mander, L., and Punyasena, S. W. (2018). Comparative performance of airyscan and structured illumination superresolution microscopy in the study of the surface texture and 3D shape of pollen. Microscopy research and technique, 81(2), 101-114.
Mander, L., Li, M., Mio, W., Fowlkes, C. C., and Punyasena, S. W. (2013). Classification of grass pollen through the quantitative analysis of surface ornamentation and texture. Proceedings of the Royal Society B: Biological Sciences, 280(1770), 20131905.
Punyasena, S. W., Tcheng, D. K., Wesseln, C., and Mueller, P. G. (2012). Classifying black and white spruce pollen using layered machine learning. New Phytologist, 196(3), 937-944.