Ecological networks are complex systems forming hierarchical structures in which energy and matter is transferred between the network’s compartments. Predicting energy flows in food webs usually involves complex parameter-rich models. In this thesis, the application of the principle of maximum entropy (MaxEnt) to obtain least biased probability distributions based on prior knowledge is proposed as an alternative to predict the most likely energy flows in food webs from the network topology alone. This approach not only simplifies the characterisation of food web flow patterns based on little empirical knowledge but can also be used to investigate the role of bottom-up and top-down controlling forces in ecosystems resulting from the emergent phenomena based on the complex interactions on the level of species and individuals. The integrative measure of “flow extent”, incorporating both bottom- up and top-down controlling forces on ecosystems, is proposed as a principle behind ecosystem evolution and evaluated against empirical data on food web structure. It could be demonstrated that the method of predicting energy flow with the help of MaxEnt is very flexible, applicable to many different setting and types of questions in ecology, and therefore providing a powerful tool for modelling the energy transfer in ecosystems. Further research has to show in how far the most likely flow patterns are realised in real-word ecosystems. The concept of flow extent maximisation as a selection principle during ecosystem evolution can enhance the understanding of emergent phenomena in complex ecosystems and maybe help to draw a link between thermodynamics and ecology.