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.
Palynology, microscopy, machine learning, evolution
BA, Yale University
SM, The University of Chicago
PhD, The University of Chicago
Additional Campus Affiliations
Associate Professor, Plant Biology
Associate Professor, Center for Latin American and Caribbean Studies
Affiliate, Carl R. Woese Institute for Genomic Biology
Affiliate, Geography and Geographic Information Science
Hornick, T., Richter, A., Harpole, W. S., Bastl, M., Bohlmann, S., Bonn, A., Bumberger, J., Dietrich, P., Gemeinholzer, B., Grote, R., Heinold, B., Keller, A., Luttkus, M. L., Mäder, P., Motivans Švara, E., Passonneau, S., Punyasena, S. W., Rakosy, D., Richter, R., ... Dunker, S. (2022). An integrative environmental pollen diversity assessment and its importance for the Sustainable Development Goals. Plants People Planet, 4(2), 110-121. https://doi.org/10.1002/ppp3.10234
Punyasena, S. W., Haselhorst, D. S., Kong, S., Fowlkes, C. C., & Moreno, J. E. (2022). Automated identification of diverse Neotropical pollen samples using convolutional neural networks. Methods in Ecology and Evolution, 13(9), 2049-2064. https://doi.org/10.1111/2041-210X.13917
Lakeram, S., Punyasena, S., Sivaguru, M., & Elrick, S. (2021). Visualizing ecological data in Pennsylvanian coal balls using computer tomography (CT). In Geological Society of America, 2021 annual meeting; GSA connects 2021 Geological Society of America (GSA), Boulder, CO, United States. https://doi.org/10.1130/abs/2021AM-368634
Mander, L., Parins-Fukuchi, C., Dick, C. W., Punyasena, S. W., & Jaramillo, C. (2021). Phylogenetic and ecological correlates of pollen morphological diversity in a Neotropical rainforest. Biotropica, 53(1), 74-85. https://doi.org/10.1111/btp.12847
Haselhorst, D. S., Moreno, J. E., & Punyasena, S. W. (2020). Assessing the influence of vegetation structure and phenological variability on pollen-vegetation relationships using a 15-year Neotropical pollen rain record. Journal of Vegetation Science, 31(4), 606-615. https://doi.org/10.1111/jvs.12897