USING AUTOMATED, QUANTITATIVE IMAGING ANALYSIS OF FOSSIL GRASS SILICA PHYTOLITH TO ELUCIDATE EVOLUTION OF TEMPERATE GRASSLANDS
To better understand the ecological role of pooids in early grass-dominated habitats, we use quantitative analysis of GSSCP 3-D morphology of modern and Oligo-Miocene pooid GSSCP. Our work has previously demonstrated that this approach can robustly classify fossil phytoliths into grass subclades (e.g., subtribe), although precision varies depending on subclade. To do so, we have created a database of nearly 2,400 3-D GSSCP models from 45 Pooideae species and 70 species early-diverging grasses, Bambusoideae and Oryzoideae. The models were developed from confocal images of GSSCP stained with fluorescent dye, which were converted to 3-D surface models that can be quantified and analyzed using 3-D geometric morphometrics. We then use machine learning and linear discriminant analysis to develop a framework in which we can place fossil GSSCP in Oligo-Miocene samples from the Great Plains of North America and Turkey, allowing us to better circumscribe the ecologies of the grasses that made up the earliest grassland communities.