Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914637279133696 |
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| author | Estopinan, Joaquim Bonnet, Pierre Servajean, Maximilien Munoz, François Joly, Alexis |
| author_facet | Estopinan, Joaquim Bonnet, Pierre Servajean, Maximilien Munoz, François Joly, Alexis |
| contents | The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International Union for Conservation of Nature (IUCN) measures the extinction risk of species. Automatic methods have been developed to provide information on the IUCN status of under-assessed taxa. However, these compensatory methods are based on current species characteristics, mainly geographical, which precludes their use in future projections. Here, we evaluate a novel method for classifying the IUCN status of species benefiting from the generalisation power of species distribution models based on deep learning. Our method matches state-of-the-art classification performance while relying on flexible SDM-based features that capture species' environmental preferences. Cross-validation yields average accuracies of 0.61 for status classification and 0.78 for binary classification. Climate change will reshape future species distributions. Under the species-environment equilibrium hypothesis, SDM projections approximate plausible future outcomes. Two extremes of species dispersal capacity are considered: unlimited or null. The projected species distributions are translated into features feeding our IUCN classification method. Finally, trends in threatened species are analysed over time and i) by continent and as a function of average ii) latitude or iii) altitude. The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America. Furthermore, the proportion of threatened species is predicted to peak around the two Tropics, at the Equator, in the lowlands and at altitudes of 800-1,500 m. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_05470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts Estopinan, Joaquim Bonnet, Pierre Servajean, Maximilien Munoz, François Joly, Alexis Populations and Evolution Machine Learning Applications The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International Union for Conservation of Nature (IUCN) measures the extinction risk of species. Automatic methods have been developed to provide information on the IUCN status of under-assessed taxa. However, these compensatory methods are based on current species characteristics, mainly geographical, which precludes their use in future projections. Here, we evaluate a novel method for classifying the IUCN status of species benefiting from the generalisation power of species distribution models based on deep learning. Our method matches state-of-the-art classification performance while relying on flexible SDM-based features that capture species' environmental preferences. Cross-validation yields average accuracies of 0.61 for status classification and 0.78 for binary classification. Climate change will reshape future species distributions. Under the species-environment equilibrium hypothesis, SDM projections approximate plausible future outcomes. Two extremes of species dispersal capacity are considered: unlimited or null. The projected species distributions are translated into features feeding our IUCN classification method. Finally, trends in threatened species are analysed over time and i) by continent and as a function of average ii) latitude or iii) altitude. The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America. Furthermore, the proportion of threatened species is predicted to peak around the two Tropics, at the Equator, in the lowlands and at altitudes of 800-1,500 m. |
| title | Modelling Species Distributions with Deep Learning to Predict Plant Extinction Risk and Assess Climate Change Impacts |
| topic | Populations and Evolution Machine Learning Applications |
| url | https://arxiv.org/abs/2401.05470 |