Conditional Generative Adversarial Network for the simulation of seabird trajectories based on environmental data

Seabirds are impacted worldwide by ongoing and emerging threats, such as habitat loss, unsustainable fishing and climate change. Within this context, models able to simulate and predict seabird behaviour depending on their environment would be valuable for decision making. In this study, we present the Spatial GAN (Generative Adversarial Network), able to simulate seabird foraging trajectories based on a spatial environmental condition. It is illustrated using GPS-tracking for Northern Gannets ( Morus bassanus ) from five different colonies. We demonstrate that the model simulates central-place foraging trajectories coherent with field data for each colony. The ability of the Spatial GAN to simulate trajectories with realistic global characteristics of length and duration consistently outperforms those of other existing models, including classic approaches such as HMM-informed Random Walks, and deep learning approaches such as VAE and the GAN without spatiality. The Spatial GAN also correctly simulates trajectories for birds from sites not included in the training dataset. While it may simulate erroneous trajectories on land when bathymetric configurations are new, the inclusion of only 10 to 20 GPS-trajectories from a new site greatly improves model simulations. As such, the architecture of the Spatial GAN we developed is a good starting point for a trajectory simulator using environmental maps as input data. The specificity of this approach compared to existing ones is its capacity to approximate the relationship between seabird movements and the environment structure by itself, without any explicit constraint.