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.
翻译:1. 消化共存模式是当前理解多样性维系的一个挑战,特别是在富裕社区,这些模式的复杂性通过间接互动而放大,防止与古典实验方法接近。 2. 我们探索尖端机器学习技术,称为 " 创造人工智能(GenAI) ",以破译植被补丁中的物种共存模式,培训基因对抗网络和变异自动进化器(VAE),然后用来拆解社区组合背后的一些机制。 3. GAN准确地复制了真实的补丁的物种构成以及植物物种与不同类型土壤的亲近性。 VAE也达到高度精确度,超过99%。我们发现,高端互动利用人工生成的补丁,往往抑制低序互动的积极效应。最后,通过重建继承轨迹,我们可以确定具有较大潜力的先驱物种,在物种构成方面产生高度多样性。 4. 理解不同生态系统物种共存模式的复杂性,要求采取超越超常态规则的新方法。