RESUMO
BACKGROUND: The Amur tiger (Panthera tigris altaica) is the largest and one of the most endangered cats in the world. In wild and captive cats, communication is mainly dependent on olfaction. However, vocal communication also plays a key role between mother and cubs during the breeding period. How cubs express their physiological and psychological needs to their mother and companions by using acoustic signals is little known and mainly hindered by the difficult process of data collection. Here, we quantitatively summarized the vocal repertoire and behavioral contexts of captive Amur tiger cubs. The aim of the present work was to investigate the behavioral motivations of cub calls by considering influential factors of age, sex, and rearing experiences. RESULTS: The 5335 high-quality calls from 65 tiger cubs were classified into nine call types (Ar-1, Ar-2, Er, eee, Chuff, Growl, Hiss, Haer, and Roar) produced in seven behavioral contexts. Except for Er, eight of the nine call types were context-specific, related to Play (Ar-2, eee, and Roar), Isolation (Ar-1), Offensive Context (Haer, Growl, and Hiss), and a friendly context (Chuff). CONCLUSIONS: The results suggest that cubs are not quiet, but instead they express rich information by emitting various call types, which are probably crucial for survival in the wild. We herein provide the first detailed spectrogram classification to indicate vocal repertoires of calls and their coding with respect to behavioral contexts in Amur tiger cubs, and we pave the steps for revealing their social communication system, which can be applied for conservation of populations. These insights can help tiger managers or keepers to improve the rearing conditions by understanding the feline cubs' inner status and needs by monitoring their vocal information expressions and exchanges.
RESUMO
The automatic individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. Most existing research primarily relies on manual identification, which does not scale well to large datasets. In this paper, the deep convolution neural networks algorithm is constructed to implement the automatic individual identification for large numbers of Amur tiger images. The experimental data were obtained from 40 Amur tigers in Tieling Guaipo Tiger Park, China. The number of images collected from each tiger was approximately 200, and a total of 8277 images were obtained. The experiments were carried out on both the left and right side of body. Our results suggested that the recognition accuracy rate of left and right sides are 90.48% and 93.5%, respectively. The accuracy of our network has achieved the similar level compared to other state of the art networks like LeNet, ResNet34, and ZF_Net. The running time is much shorter than that of other networks. Consequently, this study can provide a new approach on automatic individual identification technology in the case of the Amur tiger.
