A deep Generative Artificial Intelligence system to predict species coexistence patterns

Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict 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. The GAN accurately reproduces real patches' species composition and plant species' affinity 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. 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.
The authors thank the Yesaires team for making the fieldwork of quantification of species gypsum affinity possible. R.S.-M. was supported by the Ministry of Science and Innovations (FPU grant FPU17/00629). Financial support was provided by the projects RTI2018-099672-J-I00 and PID2020-113157GB-I00 (funded by MCIN/AEI/10.13039/501100011033 and ‘ERDF A way of making Europe’).