  1. Deciphering coexistence patterns is a current challenge to understanding
diversity maintenance, especially in rich communities where the complexity of
these patterns is magnified through indirect interactions that prevent their
approximation with classical experimental approaches. 2. We explore
cutting-edge Machine Learning techniques called Generative Artificial
Intelligence (GenAI) to decipher species coexistence patterns in vegetation
patches, training generative adversarial networks (GAN) and variational
AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind
community assemblage. 3. The GAN accurately reproduces the species composition
of real patches as well as the affinity of plant species to different soil
types, and the VAE also reaches a high level of accuracy, above 99%. Using the
artificially generated patches, we found that high order interactions tend to
suppress the positive effects of low order interactions. Finally, by
reconstructing successional trajectories we could identify the pioneer species
with larger potential to generate a high diversity of distinct patches in terms
of species composition. 4. Understanding the complexity of species coexistence
patterns in diverse ecological communities requires new approaches beyond
heuristic rules. Generative Artificial Intelligence can be a powerful tool to
this end as it allows to overcome the inherent dimensionality of this
challenge.
