Final Report Summary - MIOVAT (Miocene Vegetation of the African Tropics (Project MioVAT))
Project MioVAT has led to a new method of classifying pollen grains that is based on a combination of high-resolution imaging and computational image analyses. Experiments have been undertaken comparing these computational classifications with classifications produced human analysts. Computational methods achieve between 77.5% and 85.8% classification accuracy. Human analysts examining the same specimens achieve coverage of between 87.5% and 100% and identification accuracy of between 46.67% and 87.5%. The identification consistency of each human analyst ranged from 32.5% to 87.5%, and the proportion of duplicate image pairs that analysts missed ranged from 6.25% to 58.33%. Comparative microscopy work using Quercus, Picea and Poaceae pollen has shown that the taxonomic resolution of the pollen and spore fossil record can be increased considerably by using a combination of microscopy techniques that aim to recover morphological information from below the diffraction limit of light and computational image analyses. This in turn increases the range and depth of hypotheses that can be tested using the fossil pollen and spore record. These core methodological results that have already been published have provided a new computational approach to the description of 2-dimensional biological shapes, which has utility beyond the confines of the discipline of palynology. The results of this project have also highlighted and explored an emerging tension between the classification of biological objects by such computational methods, and classifications produced in a more traditional manner by human analysts.