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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.11900 |
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| _version_ | 1866916737275920384 |
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| author | Harrell, Lauren Kaeser-Chen, Christine Ayan, Burcu Karagol Anderson, Keith Conserva, Michelangelo Kleeman, Elise Neumann, Maxim Overlan, Matt Chapman, Melissa Purves, Drew |
| author_facet | Harrell, Lauren Kaeser-Chen, Christine Ayan, Burcu Karagol Anderson, Keith Conserva, Michelangelo Kleeman, Elise Neumann, Maxim Overlan, Matt Chapman, Melissa Purves, Drew |
| contents | Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_11900 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Heterogeneous graph neural networks for species distribution modeling Harrell, Lauren Kaeser-Chen, Christine Ayan, Burcu Karagol Anderson, Keith Conserva, Michelangelo Kleeman, Elise Neumann, Maxim Overlan, Matt Chapman, Melissa Purves, Drew Machine Learning Populations and Evolution 92B20 (Primary) 68T07, 92D40 (Secondary) I.2.1; J.3 Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with environmental factors. We introduce a novel presence-only SDM with graph neural networks (GNN). In our model, species and locations are treated as two distinct node sets, and the learning task is predicting detection records as the edges that connect locations to species. Using GNN for SDM allows us to model fine-grained interactions between species and the environment. We evaluate the potential of this methodology on the six-region dataset compiled by National Center for Ecological Analysis and Synthesis (NCEAS) for benchmarking SDMs. For each of the regions, the heterogeneous GNN model is comparable to or outperforms previously-benchmarked single-species SDMs as well as a feed-forward neural network baseline model. |
| title | Heterogeneous graph neural networks for species distribution modeling |
| topic | Machine Learning Populations and Evolution 92B20 (Primary) 68T07, 92D40 (Secondary) I.2.1; J.3 |
| url | https://arxiv.org/abs/2503.11900 |