Estimating the position of animals over time provides useful additional information for understanding animal behavior and for ecology studies in general. A common approach for this task is to deploy microphone arrays (nodes) and use the acoustic signals to estimate the direction of arrival (DOA) of the sound source. DOAs from different nodes are then intersected to find the source's position. However, when multiple sources are active, the DOA association problem (AP) arises as it becomes unclear which DOAs correspond to the same source. This problem is further exacerbated in bioacoustical scenarios where large distances increase the error in the DOA estimates, and sounds often overlap in both time and frequency. In this paper, we propose a method to tackle the DOA AP in such challenging environments. In particular, we beamform to each of the estimated DOAs and extract features that characterize each of the detected sources, then, we associate features from different nodes based on their similarity, resulting in groups of DOAs that belong to the same source. Preliminary simulations suggest the potential of the proposed method for scenarios with missed detections and unknown number of sources, even when the number of microphones available at each node is limited.