This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: 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 and the proportions of the two main photosynthetic types of grasses at one site over a period of 25,000 years.
"Open grasslands are a relatively recent ecosystem in Earth's history, with open-habitat grasses present in the Eocene, about 40 million years ago," the researchers wrote in a report in the Proceedings of the National Academy of Sciences. "Grasses were potentially the first plants domesticated about 12,000 years ago and today include several of the world's most important staple foods, such as wheat, rice, maize, barley, sorghum and millet." But scientists face a massive challenge when trying to classify pollen fossils: Grass pollen grains tend to all look alike, said University of Illinois Urbana-Champaign plant biology professor Surangi Punyasena, who led the new research with former doctoral student Marc-Élie Adaimé, now a postdoctoral researcher at the Smithsonian's Office of Digital and Innovation. Unlike pollen from other flowering plants, which can be distinguished by their shapes, spikes, grooves or pore arrangements, pollen grains from different grass species look remarkably similar under a standard light microscope.
"As paleobotanists and paleontologists, we're restricted to working with the morphology of pollen grains, which are one of the main parts of the plant that can be fossilized," Punyasena said. The field is not lacking data; the problem lies in finding objective measures to classify it. "Within a small cubic centimeter of sediment, you could have thousands, potentially millions of pollen fossils," she said.
"But the level at which we were able to analyze it before machine learning was limited by human ability." Light microscopy could not distinguish characteristic features of a pollen grain's surface. Electron microscopy could detect more features, but in an expensive, labor-intensive manner. This limited scientists' ability to explore and understand the evolution and distribution of grasses, Punyasena said.
In earlier studies, she and her colleagues made advances in using super-resolution microscopy to reveal some of the hidden features of grass pollen. "Super-resolution microscopy works sort of like a confocal microscope, where you use a laser to illuminate one point at a time," Punyasena said. "But algorithmically, it's capturing all the scattered light and calculates it back to the point of origin.
You get close to electron microscopy quality, but the process is much faster, much easier." When analyzing the images, Adaimé "recognized that there were small differences in both the patterning and the complexity of the patterning on the surface of the pollen grains, and also the cell wall thickness," Punyasena said. Using images from several identifiable grass species, Adaimé trained a machine-learning model to recognize these differences. He then developed a statistical method that uses the patterns the model learned to estimate species diversity in samples containing pollen from multiple species.
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