Pollen analysis is completely dependent on morphological comparisons and the consistent classification of shape. An extensive vocabulary describes the diversity of pollen form, but quantitative shape metrics, common in invertebrate and vertebrate paleontology, have not had a place in palynology. Morphometric methods such as outline, developed for hard skeletal parts, are not applicable to small, deformable grains lacking homologous landmarks. As a result, pollen identifications are still based largely on qualitative descriptions.
Point clouds representing many modern species will be used to develop shape measures capable of classifying deformable, variable, shape data. Point cloud data will be generated from 3D Z-stacks of in-focus fluorescence images. Shape features will be correlated with known categories of taxonomy, pollination mechanism, and habitat/climatic preferences.
The ultimate goal is to transform palynology from a primarily qualitative, descriptive discipline into a quantitative one. Potential advances that will be made possible include: (1) species-level identifications, bridging the current taxonomic divide between paleoecology and ecology and allowing tests of ecological hypotheses with pollen data; (2) automation of pollen identification, allowing an unprecedented increase in data collection and accuracy; (3) identification of novel functional characteristics that can be used to infer classifications beyond taxonomy, e.g. pollination mechanism. With the isolation and identification of functional morphological characters, hypotheses such as the loss of animal-pollinated ecosystems following the Cretaceous-Paleocene mass extinction can be quantitatively tested for the first time.
Mander, L., J. Rodriguez, P.G. Mueller,
S.T. Jackson, and S.W. Punyasena. Identifying the pollen of an extinct spruce species in the Late Quaternary sediments of the Tunica Hills region, southeastern United States. Journal of Quaternary Science.
Mander, L. and S.W. Punyasena (in press).
On the taxonomic resolution of pollen and spore records of Earth's vegetation. International Journal of Plant Sciences.
Han, J.G, H. Cao, A. Barb, S.W. Punyasena,
C. Jaramillo, and C-R. Shyu. 2014. A Neotropical Miocene pollen database employing image-based search and semantic modeling. Applications in Plant Sciences. 2(8): 1400030.
Mander, L., S.J. Baker, C.M. Belcher, D.S. Haselhorst, J. Rodriguez, J.L. Thorn, S. Tiwari, D.H. Urrego, C.J. Wesseln and S.W. Punyasena. 2014. The accuracy and consistency of grass pollen identification by human analysts compared to recent computational methods. Applications in Plant Sciences. 2(8): 1400031.
Mander, L., M. Li, W. Mio, C.C. Fowlkes and S.W. Punyasena. 2013. Identification of grass pollen through the quantitative analysis of surface ornamentation and texture. Proceedings of the Royal Society B 280(1770): 20131905
Belcher, C.M, S.W. Punyasena, and M. Sivaguru.
2013. Novel application of confocal laser scanning microscopy and 3D volume rendering toward improving the resolution of the fossil record of charcoal. PLoS ONE 8(8):e72265.
Johnsrud, S., H. Yang, A. Nayak, and S.W.
Punyasena. 2013. Semi-automated segmentation of pollen grains in microscopic images: A tool for three imaging modes. Grana 52(3): 181-191.
Sivaguru M., L. Mander, G. Fried, and S.W.
Punyasena. 2012. Capturing the surface texture and shape of pollen: a comparison of microscopy techniques. PLoS ONE 7(6): e39129.
Punyasena, S.W., D.K. Tcheng, C. Wesseln,
and P. G. Mueller. 2012. Classifying black and white spruce pollen using layered machine learning. New Phytologist 196(3): 937-944.
Pang B, Schlessman D, Kuang X, Zhao N, Shyu
D, Korkin D, Shyu CR, "An Integrated Approach to Sequence-Independent Local Alignment of Protein Binding Sites", IEEE/ACM Trans. Comput. Biology Bioinform., 2014
Project Members:
Dr. Surangi PunyasenaDr. Chi-Ren Shyu
Dr. Charless Fowlkes
Dr. Washington Mio
Project Description:
Pollen analysis is completely dependent on morphological comparisons and the consistent classification of shape. An extensive vocabulary describes the diversity of pollen form, but quantitative shape metrics, common in invertebrate and vertebrate paleontology, have not had a place in palynology. Morphometric methods such as outline, developed for hard skeletal parts, are not applicable to small, deformable grains lacking homologous landmarks. As a result, pollen identifications are still based largely on qualitative descriptions.Point clouds representing many modern species will be used to develop shape measures capable of classifying deformable, variable, shape data. Point cloud data will be generated from 3D Z-stacks of in-focus fluorescence images. Shape features will be correlated with known categories of taxonomy, pollination mechanism, and habitat/climatic preferences.
The ultimate goal is to transform palynology from a primarily qualitative, descriptive discipline into a quantitative one. Potential advances that will be made possible include: (1) species-level identifications, bridging the current taxonomic divide between paleoecology and ecology and allowing tests of ecological hypotheses with pollen data; (2) automation of pollen identification, allowing an unprecedented increase in data collection and accuracy; (3) identification of novel functional characteristics that can be used to infer classifications beyond taxonomy, e.g. pollination mechanism. With the isolation and identification of functional morphological characters, hypotheses such as the loss of animal-pollinated ecosystems following the Cretaceous-Paleocene mass extinction can be quantitatively tested for the first time.
Dataset:
View Whole DataSource Codes:
View Source CodeTools:
Pollen Search EnginePublications:
New Projects:
1. National Science Foundation, DBI-1262351, Punyasena (PI, UIUC), Mio (PI, FSU), Fowlkes (PI, UC Irvine). Collaborative Research: ABI Innovation: Breaking Through the Taxonomic Barrier of Fossil Pollen Identification Using Bioimage Informatics2. Collaborative Research: ABI Innovation: Breaking through the taxonomic barrier of the fossil pollen record using bioimage informatics Award Number:1262547; Principal Investigator:Charless Fowlkes; Co-Principal Investigator:; Organization:University of California-Irvine;NSF Organization:DBI
3. Collaborative Research: ABI Innovation: Breaking through the taxonomic barrier of the fossil pollen record using bioimage informatics Award Number:1262561; Principal Investigator:Surangi Punyasena; Co-Principal Investigator:; Organization:University of Illinois at Urbana-Champaign;NSF Organization:DBI