Partial migration is a crucial mobility pattern in animal ecology. Unlike complete migrations that take place when all the individuals in a population migrate with a clear separation of ranges, partial migrations are migrations that often follow modalities and times that vary from animal to animal. As a result the distinction between migratory and nonmigratory behavior becomes less defined. In this paper, we present an interdisciplinary effort geared to evaluate whether a recent time-aware, density-based clustering technique, called SeqScan, relying on the novel concept of object’s presence can be effectively applied to the study of partial migrations. To that end, we propose an extended framework centered on SeqScan, comprising a noise model for the detection of fine-grained movement patterns, i.e. excursions and inter-cluster transitions, and an internal time-aware validity index, for clustering evaluation. Furthermore, we contrast SeqScan with a recent technique developed in the context of animal ecology and grounded on statistical methods. For the study, we use real trajectories from two large herbivorous species located in Bavaria. We argue that the classification capabilities of SeqScan are comparable to those of the reference method. Moreover, the SeqScan framework overcomes important limitations of more conventional techniques, offering, in particular, the opportunity of quantifying the mobility behavior of individuals.