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Animals living in human care for several generations face the risk of losing natural behaviors, which can lead to reduced animal welfare. The goal of this study is to demonstrate that meerkats (Suricata suricatta) living in zoos can assess potential danger and respond naturally based on acoustic signals only. This includes that the graded information of urgency in alarm calls as well as a response to those alarm calls is retained in captivity. To test the response to acoustic signals with different threat potential, meerkats were played calls of various animals differing in size and threat (e.g., robin, raven, buzzard, jackal) while their behavior was observed. The emitted alarm calls were recorded and examined for their graded structure on the one hand and played back to them on the other hand by means of a playback experiment to see whether the animals react to their own alarm calls even in the absence of danger. A fuzzy clustering algorithm was used to analyze and classify the alarm calls. Subsequently, the features that best described the graded structure were isolated using the LASSO algorithm and compared to features already known from wild meerkats. The results show that the graded structure is maintained in captivity and can be described by features such as noise and duration. The animals respond to new threats and can distinguish animal calls that are dangerous to them from those that are not, indicating the preservation of natural cooperative behavior. In addition, the playback experiments show that the meerkats respond to their own alarm calls with vigilance and escape behavior. The findings can be used to draw conclusions about the intensity of alertness in captive meerkats and to adapt husbandry conditions to appropriate welfare.
Locating a vocalizing animal can be useful in many fields of bioacoustics and behavioral research, and is often done in the wild, covering large areas. In zoos, however, the application of this method becomes particularly difficult, because, on the one hand, the animals are in a relatively small area and, on the other hand, reverberant environments and background noise complicate the analysis. Nevertheless, by localizing and analyzing animal sounds, valuable information on physiological state, sex, subspecies, reproductive state, social status, and animal welfare can be gathered. Therefore, we developed a sound localization software that is able to estimate the position of a vocalizing animal precisely, making it possible to assign the vocalization to the corresponding individual, even under difficult conditions. In this study, the accuracy and reliability of the software is tested under various conditions. Different vocalizations were played back through a loudspeaker and recorded with several microphones to verify the accuracy. In addition, tests were carried out under real conditions using the example of the giant otter enclosure at Dortmund Zoo, Germany. The results show that the software can estimate the correct position of a sound source with a high accuracy (median of the deviation 0.234 m). Consequently, this software could make an important contribution to basic research via position determination and the associated differentiation of individuals, and could be relevant in a long-term application for monitoring animal welfare in zoos.