Abstract
Species distribution models (SDM) assess and predict how species spatial distributions depend on the environment, due to species ecological preferences. These models are used in many different scenarios such as conservation plans or monitoring of invasive species. The choice of a model and of environmental data have strong impact on the model’s ability to capture important ecological information. Specifically, state-of-the-art models generally rely on local, punctual environmental information, and do not take into account environmental variation in surrounding landscape. Here we use a convolutional neural network model to analyze and predict species distributions depending on high resolution data including remote sensing images, land cover and altitude. We show that the model unravel the functional response of vegetation to both local and large-scale environmental variation. To demonstrate the ecological significance of the results, we propose an original statistical analysis of t-SNE nonlinear dimension reduction. We illustrate and test the traits-species-environment relationships learned by the model and expressed in t-SNE dimensions.
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Test occurrences are contained in \(5 \times 5\) km quadrats with no train occurrences and represent 2.5% of the overall set.
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Acknowledgement
This project has received funding from the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 and from the European Union’s Horizon 2020 research and innovation program under grant agreement No 863463 (Cos4Cloud project).
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Deneu, B., Joly, A., Bonnet, P., Servajean, M., Munoz, F. (2021). How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12666. Springer, Cham. https://doi.org/10.1007/978-3-030-68780-9_15
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