Computational Tools for Quantifying Poaceae Pollen Diversity and Photosynthetic Pathway Composition (C3/C4) from Superresolution Images
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2024
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| _version_ | 1866902284494962688 |
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| author | Adaimé, Marc-Élie Kong, Shu Punyasena, Surangi W. |
| author_facet | Adaimé, Marc-Élie Kong, Shu Punyasena, Surangi W. |
| contents | <p>This release includes the codebase and models supporting the study titled "<em>Reconstructing the diversity dynamics of paleo-grasslands using deep learning on superresolution images of fossil Poaceae pollen.</em>" </p> <p>The pipeline enables: </p> <ul> <li>Classification of superresolution images of grass (Poaceae) pollen grains using convolutional neural networks (CNNs) trained on 60 extant species. </li> <li>Extraction of high-level morphological features that serve as proxies for taxonomic diversity, quantified <em>via</em> Shannon entropy over the probability distribution of morphological variability within each community.</li> <li>Discrimination between C3 and C4 grass species based solely on pollen morphology, using a random forest classifier. </li> <li>Temporal reconstruction of past diversity and C3 <em>vs</em>. C4 composition across different time periods, and their comparison to independent environmental records, including atmospheric CO2, temperature, precipitation, and fire history. </li> </ul> <p>The use of Shannon entropy as a diversity proxy is validated through ecological simulations, which are also included in this repository. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_13756262 |
| institution | Zenodo |
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| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Computational Tools for Quantifying Poaceae Pollen Diversity and Photosynthetic Pathway Composition (C3/C4) from Superresolution Images Adaimé, Marc-Élie Kong, Shu Punyasena, Surangi W. Poaceae Pollen Morphology Deep Learning Machine Learning Random Forest Convolutional Neural Networks Photosynthetic Pathway Superresolution Microscopy Shannon Entropy Grassland Grass Evolution Biological Community Simulations Computational Biology <p>This release includes the codebase and models supporting the study titled "<em>Reconstructing the diversity dynamics of paleo-grasslands using deep learning on superresolution images of fossil Poaceae pollen.</em>" </p> <p>The pipeline enables: </p> <ul> <li>Classification of superresolution images of grass (Poaceae) pollen grains using convolutional neural networks (CNNs) trained on 60 extant species. </li> <li>Extraction of high-level morphological features that serve as proxies for taxonomic diversity, quantified <em>via</em> Shannon entropy over the probability distribution of morphological variability within each community.</li> <li>Discrimination between C3 and C4 grass species based solely on pollen morphology, using a random forest classifier. </li> <li>Temporal reconstruction of past diversity and C3 <em>vs</em>. C4 composition across different time periods, and their comparison to independent environmental records, including atmospheric CO2, temperature, precipitation, and fire history. </li> </ul> <p>The use of Shannon entropy as a diversity proxy is validated through ecological simulations, which are also included in this repository. </p> |
| title | Computational Tools for Quantifying Poaceae Pollen Diversity and Photosynthetic Pathway Composition (C3/C4) from Superresolution Images |
| topic | Poaceae Pollen Morphology Deep Learning Machine Learning Random Forest Convolutional Neural Networks Photosynthetic Pathway Superresolution Microscopy Shannon Entropy Grassland Grass Evolution Biological Community Simulations Computational Biology |
| url | https://doi.org/10.5281/zenodo.13756262 |