SummaryPollen 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.

The visualization of taxonomically diagnostic features of individual pollen grains can be a challenge for many ecologically and phylogenetically important pollen types. The resolution of traditional optical microscopy is limited by the diffraction of light (250 nm), while high resolution tools such as electron microscopy are limited by laborious preparation and imaging workflows. Airyscan confocal superresolution and structured illumination superresolution (SR-SIM) microscopy are powerful new tools for the study of nanoscale pollen morphology and three-dimensional structure that can overcome these basic limitations. This study demonstrates their utility in capturing morphological details below the diffraction limit of light. Using three distinct pollen morphotypes (Croton hirtus, Dactylis glomerata, and Helianthus sp.) and contrast-enhancing fluorescent staining, we were able to assess the effectiveness of the Airyscan and SR-SIM. We further demonstrate that these new superresolution methods can be easily applied to the study of fossil pollen material.

Expertise in the broader impacts of scientific research is an increasingly important aspect of professional development, particularly because federal grant proposals are commonly reviewed using both the Intellectual Merit and the Broader Impacts Criteria. Unfortunately, training in broader impacts, such as science communication and outreach, is not typically part of undergraduate or graduate curricula. We initiated one of the first graduate-level biology courses on broader impacts, focusing on giving graduate students firsthand, authentic experiences with grant writing, science communication, and educational outreach. Students in this interdisciplinary course learned from experts, wrote for a broad audience about their own research, and proposed and implemented outreach in collaboration with local organizations. We outline our approach, discuss outcomes from each activity, assess our impact, and finally consider how future programs might expand on this model.

• Premise of the study: Humans frequently identify pollen grains at a taxonomic rank above species. Grass pollen is a classic case of this situation, which has led to the development of computational methods for identifying grass pollen species. This paper aims to provide context for these computational methods by quantifying the accuracy and consistency of human identification.• Methods: We measured the ability of nine human analysts to identify 12 species of grass pollen using scanning electron microscopy images. These are the same images that were used in computational identifications. We have measured the coverage, accuracy, and consistency of each analyst, and investigated their ability to recognize duplicate images.• Results: Coverage ranged from 87.5% to 100%. Mean identification accuracy ranged from 46.67% to 87.5%. The identification consistency of each analyst ranged from 32.5% to 87.5%, and each of the nine analysts produced considerably different identification schemes. The proportion of duplicate image pairs that were missed ranged from 6.25% to 58.33%.• Discussion: The identification errors made by each analyst, which result in a decline in accuracy and consistency, are likely related to psychological factors such as the limited capacity of human memory, fatigue and boredom, recency effects, and positivity bias.