Contact Information
Biography
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.
Research Interests
Palynology, microscopy, machine learning, evolution
Education
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
External Links
Recent Publications
Feng, J. T., Satheesan, S. P., Kong, S., Donders, T. H., & Punyasena, S. W. (2025). Addressing the “open world”: detecting and segmenting pollen on palynological slides with deep learning. https://doi.org/10.1101/2025.01.05.631390
Punyasena, S. W. (Accepted/In press). The evolutionary history evident in grass pollen morphology. New Phytologist. https://doi.org/10.1111/nph.20387
Adaïmé, M. É., Kong, S., & Punyasena, S. W. (2024). Deep learning approaches to the phylogenetic placement of extinct pollen morphotypes. PNAS Nexus, 3(1), Article pgad419. https://doi.org/10.1093/pnasnexus/pgad419
Lloyd, B. A., Barclay, R. S., Dunn, R. E., Currano, E. D., Mohamaad, A. I., Skersies, K., & Punyasena, S. W. (2024). CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology. Applications in Plant Sciences, 12(1), Article e11566. https://doi.org/10.1002/aps3.11566
Lakeram, S. R., Elrick, S., & Punyasena, S. W. (2023). Review of the cellulose acetate peel method and the physical and digital curation of coal balls. Applications in Plant Sciences, 11(6), Article e11556. https://doi.org/10.1002/aps3.11556