Explaining and modeling species communities is more than ever a central goal of ecology. Recently, joint species distribution models (JSDMs), which extend species distribution models (SDMs) by considering correlations among species, have been proposed to improve species community analyses and rare species predictions while potentially inferring species interactions. Here, we illustrate the mathematical links between SDMs and JSDMs and their ecological implications and demonstrate that JSDMs, just like SDMs, cannot separate environmental effects from biotic interactions. We provide a guide to the conditions under which JSDMs are (or are not) preferable to SDMs for species community modeling. More generally, we call for a better uptake and clarification of novel statistical developments in the field of biodiversity modeling. In an era of global changes, developing reliable biodiversity models has become an important research area.Species distribution models are the common tools to understand and predict the distributions of species across space and time. However, they fail to explicitly account for species interactions.To this aim, joint species distribution models were introduced to tease apart the effect of the environment from that of species interactions, to improve rare species modeling, to account for functional traits, and to improve the predictive power of biodiversity models.Nevertheless, most announced advantages have remained unfulfilled, and there is still a need to better integrate the effect of species interactions in the response of species to environmental change.