Résumé
In a context of rapidly declining biodiversity, knowing the distribution of endangered species is critical to ensure the protection of the areas they occupy. To achieve this, species distribution models (SDMs) typically use a range of variables to determine the suitability of an area to a given species, which enables scientists to produce species distribution maps.Most SDMs are statistically limited in the number of predictors they can take into account, which leads to using summary variables such as temperature yearly average or bathymetry extrema. Deep learning-based SDMs have been proposed to tackle this limitation by bringing powerful implicit feature extractors. Here, we describe a new model to advance the adaptation of such Deep-SDMs to marine environments and take advantage of the knowledge of the environmental seascape around surveyed points.We used data from the Reef Life Survey data set to predict the presence of 1,796 fish species around all of Australia, in a wide range of climates. The environmental seascape around each point was encoded as a raster image populated with 15 environmental variables, 4 human activity variables, and completed by a 10-year time series of temperature anomaly.The model performance was highly dependent on the species, with a 0.95 F1 score for the best performing species, Notolabrus parilus, but rapidly decreased, with the 100th best species having a 0.38 F1 score.