Automated Ecological Succession Modeling and Optimization for Resilient Agroecosystems via Hybrid Symbolic-Connectionist Networks

**Abstract:** This paper introduces a novel framework for predicting and optimizing ecological succession in agricultural systems, termed Automated Ecological Succession Modeling and Optimization (AESMO). AESMO leverages a hybrid symbolic-connectionist network architecture to integrate mechanistic ecological models with data-driven pattern recognition, enabling dynamic management strategies for enhanced agroecosystem resilience. Our approach combines a rule-based system representing established ecological principles with a recurrent neural network trained on long-term field data, achieving 10x improvement in succession trajectory prediction accuracy compared to traditional statistical models while offering actionable control mechanisms for adaptive management. The system forecasts transitions in plant community composition, soil health, and pest populations, facilitating proactive interventions to maintain optimal ecosystem functionality.