Evaluation of two different etorphine doses combined with azaperone in blesbok (Damaliscus pygargus phillipsi) immobilisation

Chemical immobilisation is essential for veterinarians to perform medical procedures in wild African ungulates. Potent opioids combined with neuroleptic drugs are most often used for this purpose. The present study aimed at comparing the quality of immobilisation and effects on physiological variables between a high (high etorphine-azaperone [HE]: 0.09 mg kg–1) and low etorphine dose (low etorphine-azaperone [LE]: 0.05 mg kg–1), both combined with azaperone (0.35 mg kg–1), in 12 adult female boma-acclimatised blesbok. It was hypothesised that a reduction in etorphine’s dose in combination with azaperone would result in less cardiorespiratory impairment but likely worsen the quality of immobilisation. Both treatments resulted in rapid induction and recovery times. Overall inter-treatment differences occurred in pulse rate (HE and LE: 52 ± 15 and 44 ± 11 beats minute–1, p 0.0001), respiratory rate (HE and LE: 15 ± 4 and 17 ± 4 breaths minute–1, p 0.006), partial pressure of exhaled carbon dioxide (HE and LE: 62.0 ± 5.0 and 60.0 ± 5.6 millimetre of mercury [mmHg], p 0.028) and arterial carbon dioxide (HE and LE: 58.0 ± 4.5 and 55.0 ± 3.9 mmHg, p 0.002). Both HE and LE led to bradycardia, hypertension and marked hypoxia to a similar extent. Furthermore, quality of induction, immobilisation and recovery were similar in both treatments. The role of azaperone in the development of cardiorespiratory compromise and gas exchange impairment that occurred when these combinations were used is still unclear. Further studies are recommended to elucidate drug- and dose-specific physiological effects in immobilised antelope.

A Scoping Review of Viral Diseases in African Ungulates

(1) Background: Viral diseases are important as they can cause significant clinical disease in both wild and domestic animals, as well as in humans. They also make up a large proportion of emerging infectious diseases. (2) Methods: A scoping review of peer-reviewed publications was performed and based on the guidelines set out in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for scoping reviews. (3) Results: The final set of publications consisted of 145 publications. Thirty-two viruses were identified in the publications and 50 African ungulates were reported/diagnosed with viral infections. Eighteen countries had viruses diagnosed in wild ungulates reported in the literature. (4) Conclusions: A comprehensive review identified several areas where little information was available and recommendations were made. It is recommended that governments and research institutions offer more funding to investigate and report viral diseases of greater clinical and zoonotic significance. A further recommendation is for appropriate One Health approaches to be adopted for investigating, controlling, managing and preventing diseases. Diseases which may threaten the conservation of certain wildlife species also require focused attention. In order to keep track of these diseases, it may be necessary to consider adding a “Wildlife disease and infection” category to the World Organisation for Animal Health-listed diseases.

Deep learning based evaluation of the nightly behaviour of African ungulates in zoos

1. The description and analysis of animal behaviour over long periods of time is one of 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 forwardlooking approach to analyse even large amounts of video data in an adequate time frame. 2. In this study we present amulti-step convolutional neural network system for detecting animal behaviour states, which works with high accuracy. An important aspect of our approach is the introduction of model averaging and post-processing 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 performs 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 images and conduct behavioural studies of wildlife. 4. The use of a multi-step convolutional neural network for fast and accurate classification of wildlife behaviour 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 post-processing rules are a suitable way to make species-specific adjustments and substantially increase the accuracy of the description of single behavioural 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.