ArticleProceedings. Biological sciences2013
Classification of grass pollen through the quantitative analysis of surface ornamentation and texture.
Article in Proceedings. Biological sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Quantifying pollen and spore morphology using a language model.Biology letters · 2026Article
- A 23-million-year record of morphological evolution within Neotropical grass pollen.The New phytologist · 2025Article
- The evolutionary history evident in grass pollen morphology.The New phytologist · 2025Article
- Automated graptolite identification at high taxonomic resolution using residual networks.iScience · 2024Article
- Deductive automated pollen classification in environmental samples via exploratory deep learning and imaging flow cytometry.The New phytologist · 2023Article
- Historical anthropogenic disturbances explain long-term moorland vegetation dynamics.Ecology and evolution · 2023Article
- Palyno-Morphological Characteristics as a Systematic Approach in the Identification of Halophytic Poaceae Species from a Saline Environment.Plants (Basel, Switzerland) · 2022Article
- Phylogenetic, ecological and intraindividual variability patterns in grass phytolith shape.Annals of botany · 2022Article
- Article
- Pollen wall patterns as a model for biological self-assembly.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2021Article
- Grass-SpecificThe Plant cell · 2020Article
- Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains.Analytical and bioanalytical chemistry · 2020Article
- The Persistent Homology Mathematical Framework Provides Enhanced Genotype-to-Phenotype Associations for Plant Morphology.Plant physiology · 2018Article
- Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace.Frontiers in plant science · 2018Article
- Pollen-vegetation richness and diversity relationships in the tropics.Vegetation history and archaeobotany · 2018Article
- A morphometric analysis of vegetation patterns in dryland ecosystems.Royal Society open science · 2017Article
- A combinatorial approach to angiosperm pollen morphology.Proceedings. Biological sciences · 2016Article
- Towards a morphological metric of assemblage dynamics in the fossil record: a test case using planktonic foraminifera.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2016Article
- Poaceae Pollen from Southern Brazil: Distinguishing Grasslands (Campos) from Forests by Analyzing a Diverse Range of Poaceae Species.Frontiers in plant science · 2016Article
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5 authors.
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Abstract
Taxonomic identification of pollen and spores uses inherently qualitative descriptions of morphology. Consequently, identifications are restricted to categories that can be reliably classified by multiple analysts, resulting in the coarse taxonomic resolution of the pollen and spore record. Grass pollen represents an archetypal example; it is not routinely identified below family level. To address this issue, we developed quantitative morphometric methods to characterize surface ornamentation and classify grass pollen grains. This produces a means of quantifying morphological features that are traditionally described qualitatively. We used scanning electron microscopy to image 240 specimens of pollen from 12 species within the grass family (Poaceae). We classified these species by developing algorithmic features that quantify the size and density of sculptural elements on the pollen surface, and measure the complexity of the ornamentation they form. These features yielded a classification accuracy of 77.5%. In comparison, a texture descriptor based on modelling the statistical distribution of brightness values in image patches yielded a classification accuracy of 85.8%, and seven human subjects achieved accuracies between 68.33 and 81.67%. The algorithmic features we developed directly relate to biologically meaningful features of grass pollen morphology, and could facilitate direct interpretation of unsupervised classification results from fossil material.
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