TY  - JOUR
AU  - Hirn, J.
AU  - Garcia, J. E.
AU  - Montesinos-Navarro, A.
AU  - Sanchez-Martin, R.
AU  - Sanz, V.
AU  - Verdu, M.
PY  - 2022
DA  - 2022//
TI  - A deep Generative Artificial Intelligence system to predict species coexistence patterns
T2  - Methods Ecol. Evol.
JO  - Methods in Ecology and Evolution
SP  - 1052
EP  - 1061
VL  - 13
PB  - Wiley
KW  - artificial intelligence
KW  - direct interactions
KW  - generative adversarial networks
KW  - indirect interactions
KW  - species coexistence
KW  - variational AutoEncoders
AB  - 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.
SN  - 2041-210x
UR  - https://arxiv.org/abs/2107.06020
UR  - https://doi.org/10.1111/2041-210X.13827
DO  - 10.1111/2041-210X.13827
LA  - English
N1  - WOS:000765239700001
ID  - Hirn_etal2022
ER  -