Scientists used machine-learning and super-resolution microscopy to overcome a challenge that has stymied research into the natural history of grasses for decades. Their method allowed them to detect subtle differences among grass pollen grains and trace changes in grass diversity at one site over a period of 25,000 years.
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Pollen study reveals grass history
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Pollen grains from Croton hirtus, a flowering herb; Mabea occidentalis, a tropical evergreen tree; and Agropyron repens, a perennial grass, provide a glimpse of the extraordinary morphological variety of pollen and the relative simplicity of grass pollen.
From left, plant biology professor Surangi Punyasena, former University of Illinois doctoral student Marc-Élie Adaimé and their colleagues developed deep-learning methods for establishing the composition and diversity of fossil grass pollen samples. Adaimé is now a postdoctoral researcher at the Smithsonian’s Office of Digital and Innovation.
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