Investigating climate impacts in the Gulf of Mexico with Dynamic Bayesian Networts

The Gulf of Mexico is an ecologically and economically important marine ecosystem that is affected by a variety of natural and anthropogeni c pressures. These complex and interacting pressures, together with the dynamic environment of the Gulf, present challenges for the effective management of its resources. The recent adoption of Bayesian networks to ecology allows for the discovery and quan tification of complex interactions from data after making only a few assumptions about observations of the system. In this study, we apply Bayesian network models, with different levels of structural complexity and a varying number of hidden variables to account for uncertainty when modelling ecosystem dynamics. From these models, we predict focal ecosystem components within the Gulf of Mexico. The predictive ability of the models varied with their structure. The model that performed best was parameterized through data- driven learning techniques and accounted for multispecies associations and their interactions with human and natural pressures over time. Then, we altered sea surface temperature in the best performing model to explore the response of different variables to increased temperature. The magnitude and even direction of predicted responses varied by ecosystem components due to heterogeneity in driving factors and their spatial overlap. Our findings suggest that due to varying species sensitivity to drivers, changes in temperature will potentially lead to trade- offs in terms of population productivity. We were able to discover meaningful interactions between ecosystem components and their environment and show how sensitive these relationships are to climate perturbations, which increases our understanding of the potential future response of the system to increasing temperature. Our findings demonstrate that accounting for additional sources of variation, by incorporating multiple interactions and press ures in the model layout, has the potential for gaining deeper insights into the structure and dynamics of ecosystems.