590 Tiere (Zoologie)
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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.
In recent decades, zoos have been increasingly transformed into education centers with the goal of raising awareness about environmental issues and providing environmental education. Probably the simplest and most widespread environmental education program in the zoo is the guided tour. This study therefore aims to test whether a one hour zoo tour has an influence on the participants’ connection to nature and attitude towards species conservation. For this purpose, 269 people who had voluntarily registered for a zoo tour were surveyed before and after the tour. In addition to the regular zoo tour, special themed tours and tours with animal feedings were included. The results show a positive increase in connection to nature and a strengthening of positive attitudes towards species conservation for all tour types. For nature connectedness, in particular, people with an initial high connection to nature benefitted from the special themed tours and the tours, including animal feedings. For attitudes towards species conservation, no difference was found between the tour types. The results prove the positive influence of a very simple environmental education program, even for people with a preexisting high level of connection to nature and positive attitude towards species conservation.
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.
1.Thedescriptionandanalysisofanimalbehavioroverlongperiodsoftimeisoneof the most important challenges in ecology. However, most of these studies are limited due to the time and cost required by human observers. The collection of data via video recordings allows observation periods to be extended. However, their evaluation by human observers is very time-consuming. Progress in automated evaluation, using suitable deep learning methods, seems to be a forward-looking approach to analyze even large amounts of video data in an adequate time frame.
2. In this study, we present a multistep convolutional neural network system for detecting three typical stances of African ungulates in zoo enclosures which works with high accuracy. An important aspect of our approach is the introduction of model averaging and postprocessing rules to make the system robust to outliers.
3. Our trained system achieves an in-domain classification accuracy of >0.92, which is improved to >0.96 by a postprocessing step. In addition, the whole system per- forms even well in an out-of-domain classification task with two unknown types, achieving an average accuracy of 0.93. We provide our system at https://github. com/Klimroth/Video-Action-Classifier-for-African-Ungulates-in-Zoos/tree/main/ mrcnn_based so that interested users can train their own models to classify im- ages and conduct behavioral studies of wildlife.
4. The use of a multistep convolutional neural network for fast and accurate clas- sification of wildlife behavior facilitates the evaluation of large amounts of image data in ecological studies and reduces the effort of manual analysis of images to a high degree. Our system also shows that postprocessing rules are a suitable way to make species-specific adjustments and substantially increase the accuracy of the description of single behavioral phases (number, duration). The results in the out-of-domain classification strongly suggest that our system is robust and achieves a high degree of accuracy even for new species, so that other settings (e.g., field studies) can be considered.
Zoos attract millions of visitors every year, many of whom are schoolchildren. For this reason, zoos are important institutions for the environmental education of future generations. Empirical studies on the educational impact of environmental education programs in zoos are still rare. To address this issue, we conducted two studies: In study 1, we investigated students’ interests in different biological topics, including zoos (n = 1,587). Data analysis of individual topics revealed large differences of interest, with advanced students showing less interest in zoos. In study 2, we invited school classes of this age group to visit different guided tours at the zoo and tested connection to nature before and after each educational intervention (n = 608). The results showed that the guided tours are an effective tool to raise students’ connection to nature. Add-on components have the potential to further promote connection to nature. The education programs are most effective with students with a low initial nature connection.