Modeling Marine Ecosystem Dynamics using Markov Networks

Comprehensive modeling faces major obstacles when dealing with marine ecosystems due to their intricate and interdependent ecological components. With a focus on species interactions, nutrient cycling, and responses to external stimuli, this study uses Markov networks to disentangle the complex dynamics of marine ecosystems. By utilizing a probabilistic approach, we examine the ecosystem’s stability and resilience, exposing significant species interactions and uncovering trends in the dynamics of nutrient cycling. Our model offers important insights into vulnerabilities related to climate change by simulating responses to external influences. Sensitivity analysis pinpoint crucial variables for ecosystem management, and validation of the model against actual data shows its accuracy. Future conditions of the marine ecosystem can be inferred from long-term forecasts that are based on dynamic probabilities. Through the contribution of this research, a more detailed understanding of the dynamics of marine ecosystems is gained, enabling informed decision-making for conservation and sustainable resource management.