Entropy maximization (EM, also known as MaxEnt) is a general inference procedure that originated in statistical mechanics. It has been applied recently to predict ecological patterns, such as species abundance distributions and species-area relationships. It is well known in physics that the EM result strongly depends on how elementary configurations are described. Here we argue that the same issue is also of crucial importance for EM applications in ecology. To illustrate this, we focus on the EM prediction of species-level spatial abundance distributions. We show that the EM outcome depends on (1) the choice of configuration set, (2) the way constraints are imposed, and (3) the scale on which the EM procedure is applied. By varying these choices in the EM model, we obtain a large range of EM predictions. Interestingly, they correspond to spatial abundance distributions that have been derived previously from mechanistic models. We argue that the appropriate choice of the EM model assumptions is nontrivial and can be determined only by comparison with empirical data.