{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127277"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127277","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Decoding evolutionary and ecological information from pollen morphology: Deep learning for phylogenetic and environmental reconstructions","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Adaime, Marc-Elie"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Plant Biology","degree_department":null,"school":null,"contributors":["Punyasena, Surangi W","Heath, Katy D","Leslie, Andrew B","Tan, Milton","Kong, Shu"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-06","date_published":"2024-12-06","updated_at":"2026-07-22T22:25:03Z","subjects":["Pollen","Evolution","Deep Learning","Phylogenetics","Paleontology","Image Processing","Computational Biology","Paleoecology"],"languages":["en","eng"],"rights":["Copyright 2024 Marc-Elie Adaime"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127277","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Punyasena, Surangi W","Heath, Katy D","Leslie, Andrew B","Tan, Milton","Kong, Shu"]},{"key":"dc:creator","label":"Author","values":["Adaime, Marc-Elie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-06","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Plant Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pollen","Evolution","Deep Learning","Phylogenetics","Paleontology","Image Processing","Computational Biology","Paleoecology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Marc-Elie Adaime"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127277"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Marc-Elie Adaime, accepted the attached license on 2024-12-06 at 13:30.","The student, Marc-Elie Adaime, submitted this Dissertation for approval on 2024-12-06 at 16:04.","This Dissertation was approved for publication on 2024-12-06 at 17:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21533 on 2025-03-28 at 14:28:28","The fossil pollen record provides a valuable archive for exploring plant evolutionary history and ecological dynamics. Traditional palynology is limited by conserved pollen across several plant lineages, which restricts identification to higher taxonomic levels. Recent advances in imaging and machine learning have provided new insights, yet most focus on species identification rather than broader evolutionary and ecological questions. This dissertation introduces new methodologies integrating deep learning, image processing, and traditional analyses to advance palynology. Chapter 2 uses phylogenetically-informed neural networks to identify potentially extinct pollen types and place them within a reference phylogeny, revealing the wealth of evolutionary information encoded in pollen morphology. Chapter 3 quantifies Poaceae community diversity and distinguishes C3 from C4 grass pollen using CNN-derived features, revealing physiological adaptations and providing, for the first time, the ability to accurately estimate grass diversity and C3:C4 ratios in paleo-grasslands using pollen morphology alone. Chapter 4 reconstructs the evolutionary history of Podocarpus pollen morphology in relation to environmental variability, showing adaptive responses to temperature and solar radiation. Moreover, it emphasizes the importance of incorporating fossil data in reconstructing shifts in morphospace and ancestral states. Together, these chapters illustrate how abstract pollen morphological features derived from neural networks can be used to resolve evolutionary relationships, quantify community diversity, and reveal physiological and environmental adaptations in plants. This dissertation shows that deep learning, combined with traditional approaches, can decode complex morphological features embedded in pollen grains, addressing unexplored questions in ecology and evolution."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Decoding evolutionary and ecological information from pollen morphology: Deep learning for phylogenetic and environmental reconstructions"]}]}],"canonical_facts":{"dc:contributor":["Punyasena, Surangi W","Heath, Katy D","Leslie, Andrew B","Tan, Milton","Kong, Shu"],"dc:creator":["Adaime, Marc-Elie"],"dc:date":["2024-12-06","2024-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Marc-Elie Adaime, accepted the attached license on 2024-12-06 at 13:30.","The student, Marc-Elie Adaime, submitted this Dissertation for approval on 2024-12-06 at 16:04.","This Dissertation was approved for publication on 2024-12-06 at 17:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21533 on 2025-03-28 at 14:28:28","The fossil pollen record provides a valuable archive for exploring plant evolutionary history and ecological dynamics. Traditional palynology is limited by conserved pollen across several plant lineages, which restricts identification to higher taxonomic levels. Recent advances in imaging and machine learning have provided new insights, yet most focus on species identification rather than broader evolutionary and ecological questions. This dissertation introduces new methodologies integrating deep learning, image processing, and traditional analyses to advance palynology. Chapter 2 uses phylogenetically-informed neural networks to identify potentially extinct pollen types and place them within a reference phylogeny, revealing the wealth of evolutionary information encoded in pollen morphology. Chapter 3 quantifies Poaceae community diversity and distinguishes C3 from C4 grass pollen using CNN-derived features, revealing physiological adaptations and providing, for the first time, the ability to accurately estimate grass diversity and C3:C4 ratios in paleo-grasslands using pollen morphology alone. Chapter 4 reconstructs the evolutionary history of Podocarpus pollen morphology in relation to environmental variability, showing adaptive responses to temperature and solar radiation. Moreover, it emphasizes the importance of incorporating fossil data in reconstructing shifts in morphospace and ancestral states. Together, these chapters illustrate how abstract pollen morphological features derived from neural networks can be used to resolve evolutionary relationships, quantify community diversity, and reveal physiological and environmental adaptations in plants. This dissertation shows that deep learning, combined with traditional approaches, can decode complex morphological features embedded in pollen grains, addressing unexplored questions in ecology and evolution."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127277"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Marc-Elie Adaime"],"dc:subject":["Pollen","Evolution","Deep Learning","Phylogenetics","Paleontology","Image Processing","Computational Biology","Paleoecology"],"dc:title":["Decoding evolutionary and ecological information from pollen morphology: Deep learning for phylogenetic and environmental reconstructions"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Plant Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}