Integrating Artificial Intelligence and Environmental Metagenomics for Ecosystem Monitoring and Management

Abstract

Artificial Intelligence (AI) is driving a significant transformation in microbiology and environmental metagenomics, shifting the field from a descriptive framework towards a predictive and systems-level understanding. The advent of metagenomics has enabled direct analysis of microbial communities from environmental samples, overcoming limitations of culture-dependent approaches. However, rapid advances in high-throughput sequencing have generated unprecedented volumes of complex data, creating a critical need for advanced computational tools. In this context, AI has emerged as an essential approach for extracting meaningful biological insights. AI-based methods have enhanced microbial community analysis by enabling deeper understanding of community dynamics and functional interactions. Models such as cNODE predict community shifts based on initial species configurations, while Graph Neural Network approaches, including MicrobeGNN, estimate steady-state community structures using genomic relationships. Machine learning algorithms such as Random Forest are also widely applied to identify keystone species essential for ecosystem stability. In environmental DNA (eDNA) and metagenomic data analysis, machine learning improves tasks such as metagenome binning through tools like VAMB and SemiBin, while DeepMAsEd and ResMiCo detect assembly errors without reference genomes. Additionally, Natural Language Processing-based models such as DeepMicrobes and BERTax interpret DNA as structured language for accurate taxonomic classification. AI also contributes to predicting microbial functions, particularly in bioremediation, by identifying organisms capable of degrading pollutants using techniques such as Random Forest and Support Vector Machines. Reinforcement learning frameworks like SPAM-DFBA further model microbial metabolism as a decision-making system. In pathogen tracking, AI supports outbreak detection and source attribution, with applications including prediction of Salmonella enterica origins and real-time surveillance systems such as HealthMap. Future developments include microbial foundation models, tools like AlphaFold 3 and Evo, and digital twin systems, although challenges such as limited interpretability remain.