Resource selection functions are commonly employed to evaluate animal′s habitat selection, e.g. the disproportionate use of habitats relative to their availability. While environmental conditions or animal motivations may vary over time, sometimes in an unknown manner, studying changes in habitat selection usually requires a priori time discretization. This limits our ability to precisely answer the question: when is an animal-habitat selection changing? Here, we present a straightforward and flexible alternative approach based on fitting dynamic logistic models to used/available data. First, using simulated dataset, we demonstrate that dynamic logistic models performed well to recover temporal variations in habitat selection. We then show real-world applications for studying diel, seasonal, and post-release changes in habitat selection of blue wildebeest (Connochaetes taurinus). Finally, we provide the relevant R scripts to facilitate the adoption of the method by ecologists. Dynamic logistic models allow to study temporal changes in habitat selection in a framework consistent with resource selection functions, but without the need to discretize time, which can be a difficult task when little is known about the process studied, or may obscure inter-individual variability in timing of change. These models should undoubtedly find their place in the movement ecology toolbox.