A Deep Generative Artificial Intelligence system to decipher species coexistence patterns

TL;DR

This paper demonstrates that Generative Artificial Intelligence, specifically GANs and VAEs, can effectively model and analyze complex species coexistence patterns, revealing insights into community assembly and succession in ecological communities.

Contribution

The study introduces the application of GANs and VAEs to decipher species coexistence patterns, achieving high accuracy and uncovering mechanisms behind community assemblage.

Findings

GAN accurately reproduces species composition and soil affinity.

VAE reaches above 99% accuracy in modeling patches.

High order interactions suppress positive effects of low order interactions.

Abstract

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.