Animal population sizes are often estimated using aerial sample counts by human observers, both for wildlife and livestock. The associated methods of counting remained more or less the same since the 1970s, but suffer from low precision and low accuracy of population estimates. Aerial counts using cost‐efficient Unmanned Aerial Vehicles or microlight aircrafts with cameras and an automated animal detection algorithm can potentially improve this precision and accuracy. Therefore, we evaluated the performance of the multi‐class convolutional neural network RetinaNet in detecting elephants, giraffes and zebras in aerial images from two Kenyan animal counts. The algorithm detected 95% of the number of elephants, 91% of giraffes and 90% of zebras that were found by four layers of human annotation, of which it correctly detected an extra 2.8% of elephants, 3.8% giraffes and 4.0% zebras that were missed by all humans, while detecting only 1.6 to 5.0 false positives per true positive. Furthermore, the animal detections by the algorithm were less sensitive to the sighting distance than humans were. With such a high recall and precision, we posit it is feasible to replace manual aerial animal count methods (from images and/or directly) by only the manual identification of image bounding boxes selected by the algorithm and then use a correction factor equal to the inverse of the undercounting bias in the calculation of the population estimates. This correction factor causes the standard error of the population estimate to increase slightly compared to a manual method, but this increase can be compensated for when the sampling effort would increase by 23%. However, an increase in sampling effort of 160% to 1,050% can be attained with the same expenses for equipment and personnel using our proposed semi‐automatic method compared to a manual method. Therefore, we conclude that our proposed aerial count method will improve the accuracy of population estimates and will decrease the standard error of population estimates by 31% to 67%. Most importantly, this animal detection algorithm has the potential to outperform humans in detecting animals from the air when supplied with images taken at a fixed rate.

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We describe an outbreak of rabies in a pack of African wild dogs (Lycaon pictus) in the Limpopo-Lipadi Private Game and Wilderness Reserve in the Tuli region of south-eastern Botswana. We define the pack's behavioural response to the disease, clinical signs, and management interventions undertaken and make recommendations to mitigate against future disease outbreaks of this nature. The outbreak, which occurred in late 2014 and early 2015, resulted in the death or disappearance of 29 individuals out of a pack of 35 wild dogs. The disruption to the social structure within the pack, the behaviour of the animals and clinical signs were similar to that documented during previous rabies outbreaks amongst African wild dogs in Southern and East Africa in recent years. Management interventions taken during the outbreak were aimed at preventing extirpation of the pack and reducing the risk of further disease spread to other mammals in the reserve.

Free-living stages of ticks on a commercial game farm in the Thabazimbi District, Limpopo Province, South Africa, were collected by drag-sampling with flannel strips during the period September 2003 to August 2004. A total of 5 tick species was collected from 4 sites. Boophilus decoloratus was the most abundant species, followed by Amblyomma hebraeum. Seasonal abundance of the ticks was quantified and an optimum time to implement control measures against the ticks is proposed

Despite the large number of collection records, there are no recent collections of ixodid ticks of this magnitude in the Waterberg area, Limpopo Province, South Africa. Free-living ticks on a commercial game farm were obtained by a total of 432 drag-samples in eight sample sites from September 2003 to August 2008. The ticks were collected to establish the difference between tick species and densities associated with acaricide-controlled (semi-intensive) and controlfree areas on a game farm in the Thabazimbi District, Limpopo Province, South Africa. A total of eight tick species were collected, namely Amblyomma hebraeum, Rhipicephalus (Boophilus) decoloratus, Haemaphysalis elliptica, Hyalomma rufipes, Rhipicephalus appendiculatus, Rhipicephalus evertsi evertsi, Rhipicephalus zambeziensis and Rhipicephalus spp. The most abundant tick species collected was Rhipicephalus (Boophilus) decoloratus. The difference in species and numbers reflects the effectiveness of acaricide treatment against ticks and its relevance to tick numbers on a game farm.

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Samenvatting (Dutch) Samevatting (Afrikaans) Biography -Bradley Schroder List of publications PE&RC Training and Education Statement Acknowledgement of financial support

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