title: Classifying black and white spruce pollen using layered machine learning.
abstract: Pollen is among the most ubiquitous of terrestrial fossils, preserving an extended record of vegetation change. However, this temporal continuity comes with a taxonomic tradeoff. Analytical methods that improve the taxonomic precision of pollen identifications would expand the research questions that could be addressed by pollen, in fields such as paleoecology, paleoclimatology, biostratigraphy, melissopalynology, and forensics. We developed a supervised, layered, instance-based machine-learning classification system that uses leave-one-out bias optimization and discriminates among small variations in pollen shape, size, and texture. We tested our system on black and white spruce, two paleoclimatically significant taxa in the North American Quaternary. We achieved > 93% grain-to-grain classification accuracies in a series of experiments with both fossil and reference material. More significantly, when applied to Quaternary samples, the learning system was able to replicate the count proportions of a human expert (R(2) = 0.78, P = 0.007), with one key difference - the machine achieved these ratios by including larger numbers of grains with low-confidence identifications. Our results demonstrate the capability of machine-learning systems to solve the most challenging palynological classification problem, the discrimination of congeneric species, extending the capabilities of the pollen analyst and improving the taxonomic resolution of the palynological record.
authors.affiliation: Department of Plant Biology, University of Illinois, 505 S. Goodwin Avenue, Urbana, IL, 61801, USA.|Illinois Informatics Institute, University of Illinois, 1205 West Clark Street, Urbana, IL, 61801, USA.
authors.forename: Surangi W
authors.initials: SW
authors.lastname: Punyasena
authors.affiliation: Illinois Informatics Institute, University of Illinois, 1205 West Clark Street, Urbana, IL, 61801, USA.|National Center for Supercomputing Applications, 1205 West Clark Street, Urbana, IL, 61801, USA.
authors.forename: David K
authors.initials: DK
authors.lastname: Tcheng
authors.affiliation: Department of Plant Biology, University of Illinois, 505 S. Goodwin Avenue, Urbana, IL, 61801, USA.
authors.forename: Cassandra
authors.initials: C
authors.lastname: Wesseln
authors.affiliation: Illinois State Museum, Research and Collections Center, 1011 E. Ash Street, Springfield, IL, 62703, USA.
authors.forename: Pietra G
authors.initials: PG
authors.lastname: Mueller
mesh_terms: D001185:Artificial Intelligence; D005580:Fossils; D007091:Image Processing, Computer-Assisted / Q000379:methods; D020407:Internet; D028222:Picea / Q000033:anatomy & histology / Q000502:physiology*; D011058:Pollen / Q000033:anatomy & histology / Q000145:classification* / Q000502:physiology; D015203:Reproducibility of Results; D012984:Software