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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2602.11771 |
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| _version_ | 1866914324907294720 |
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| author | Gigot--Léandri, Sébastien Morand, Gaétan Joly, Alexis Munoz, François Mouillot, David Botella, Christophe Servajean, Maximilien |
| author_facet | Gigot--Léandri, Sébastien Morand, Gaétan Joly, Alexis Munoz, François Mouillot, David Botella, Christophe Servajean, Maximilien |
| contents | Species distribution models (SDMs) commonly produce probabilistic occurrence predictions that must be converted into binary presence-absence maps for ecological inference and conservation planning. However, this binarization step is typically heuristic and can substantially distort estimates of species prevalence and community composition. We present MaxExp, a decision-driven binarization framework that selects the most probable species assemblage by directly maximizing a chosen evaluation metric. MaxExp requires no calibration data and is flexible across several scores. We also introduce the Set Size Expectation (SSE) method, a computationally efficient alternative that predicts assemblages based on expected species richness. Using three case studies spanning diverse taxa, species counts, and performance metrics, we show that MaxExp consistently matches or surpasses widely used thresholding and calibration methods, especially under strong class imbalance and high rarity. SSE offers a simpler yet competitive option. Together, these methods provide robust, reproducible tools for multispecies SDM binarization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11771 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How to Optimize Multispecies Set Predictions in Presence-Absence Modeling ? Gigot--Léandri, Sébastien Morand, Gaétan Joly, Alexis Munoz, François Mouillot, David Botella, Christophe Servajean, Maximilien Artificial Intelligence Species distribution models (SDMs) commonly produce probabilistic occurrence predictions that must be converted into binary presence-absence maps for ecological inference and conservation planning. However, this binarization step is typically heuristic and can substantially distort estimates of species prevalence and community composition. We present MaxExp, a decision-driven binarization framework that selects the most probable species assemblage by directly maximizing a chosen evaluation metric. MaxExp requires no calibration data and is flexible across several scores. We also introduce the Set Size Expectation (SSE) method, a computationally efficient alternative that predicts assemblages based on expected species richness. Using three case studies spanning diverse taxa, species counts, and performance metrics, we show that MaxExp consistently matches or surpasses widely used thresholding and calibration methods, especially under strong class imbalance and high rarity. SSE offers a simpler yet competitive option. Together, these methods provide robust, reproducible tools for multispecies SDM binarization. |
| title | How to Optimize Multispecies Set Predictions in Presence-Absence Modeling ? |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2602.11771 |