The Maximum Entropy Theory of Ecology (METE) predicts the shapes of macroecological metrics in relatively static ecosystems using constraints imposed by static state variables. In disturbed ecosystems, however, with time-varying state variables, its predictions often fail. We extend macroecological theory from static to dynamic by combining the MaxEnt inference procedure with explicit mechanisms governing disturbance. In the static limit, the resulting theory, DynaMETE, reduces to METE but also predicts new scaling relationships among static state variables. Under disturbances, expressed as shifts in demographic, ontogenic growth, or migration rates, DynaMETE predicts the time trajectories of the state variables as well as the time-varying shapes of macroecological metrics such as the species abundance distribution and the distribution of metabolic rates over individuals. An iterative procedure for completely solving the dynamic theory is presented. In a lowest-order iteration, characteristic signatures of the deviation from static predictions of macroecolgoical patterns are shown to result from different kinds of disturbance. Because DynaMETE combines MaxEnt inference with explicit dynamical mechanisms, but does not assume any specific trait distributions over species or individuals, it is widely applicable across diverse ecosystems. This makes it a promising theory of macroecology for ecosystems responding to anthropogenic or natural disturbances.