Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.
Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order...
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Public Library of Science (PLoS)
2025-03-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012887 |
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| author | Russell Dinnage Marian Kleineberg |
| author_facet | Russell Dinnage Marian Kleineberg |
| author_sort | Russell Dinnage |
| collection | DOAJ |
| description | Data on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organize and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a 'decoder' function, that allows the transformation from this vector space to the original 3D morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply. The trained model has been made publicly available and can be used by the community, including for finetuning on new data, representing an early step toward developing shared, reusable AI models for analyzing organismal morphology. |
| format | Article |
| id | doaj-art-5342456b728747f3bbf85dea5d38d085 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS Computational Biology |
| spelling | doaj-art-5342456b728747f3bbf85dea5d38d0852025-08-20T03:47:27ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-03-01213e101288710.1371/journal.pcbi.1012887Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills.Russell DinnageMarian KleinebergData on the three dimensional shape of organismal morphology is becoming increasingly available, and forms part of a new revolution in high-throughput phenomics that promises to help understand ecological and evolutionary processes that influence phenotypes at unprecedented scales. However, in order to meet the potential of this revolution we need new data analysis tools to deal with the complexity and heterogeneity of large-scale phenotypic data such as 3D shapes. In this study we explore the potential of generative Artificial Intelligence to help organize and extract meaning from complex 3D data. Specifically, we train a deep representational learning method known as DeepSDF on a dataset of 3D scans of the bills of 2,020 bird species. The model is designed to learn a continuous vector representation of 3D shapes, along with a 'decoder' function, that allows the transformation from this vector space to the original 3D morphological space. We find that approach successfully learns coherent representations: particular directions in latent space are associated with discernible morphological meaning (such as elongation, flattening, etc.). More importantly, learned latent vectors have ecological meaning as shown by their ability to predict the trophic niche of the bird each bill belongs to with a high degree of accuracy. Unlike existing 3D morphometric techniques, this method has very little requirements for human supervised tasks such as landmark placement, increasing it accessibility to labs with fewer labour resources. It has fewer strong assumptions than alternative dimension reduction techniques such as PCA. Once trained, 3D morphology predictions can be made from latent vectors very computationally cheaply. The trained model has been made publicly available and can be used by the community, including for finetuning on new data, representing an early step toward developing shared, reusable AI models for analyzing organismal morphology.https://doi.org/10.1371/journal.pcbi.1012887 |
| spellingShingle | Russell Dinnage Marian Kleineberg Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. PLoS Computational Biology |
| title | Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. |
| title_full | Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. |
| title_fullStr | Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. |
| title_full_unstemmed | Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. |
| title_short | Generative AI extracts ecological meaning from the complex three dimensional shapes of bird bills. |
| title_sort | generative ai extracts ecological meaning from the complex three dimensional shapes of bird bills |
| url | https://doi.org/10.1371/journal.pcbi.1012887 |
| work_keys_str_mv | AT russelldinnage generativeaiextractsecologicalmeaningfromthecomplexthreedimensionalshapesofbirdbills AT mariankleineberg generativeaiextractsecologicalmeaningfromthecomplexthreedimensionalshapesofbirdbills |