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Automatic animal identification from drone camera based on point pattern analysis of herd behaviour

This study investigated the accuracy of animal identification based on herd behaviour from drone camera footage. We evaluated object detection algorithms and point pattern analysis, using footage from drone altitudes ranging from 15 m to 130 m. We applied transfer learning to state-of-the-art lightweight object detection algorithms (Tensorflow and YOLO) based on feature extraction. In the point pattern analysis, we treated each animal as a point and identified them by the behavioural pattern of those points. The five animal species

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Seismic savanna: machine learning for classifying wildlife and behaviours using ground-based vibration field recordings

We develop a machine learning approach to detect and discriminate elephants from other species, and to recognise important behaviours such as running and rumbling, based only on seismic data generated by the animals. We demonstrate our approach using data acquired in the Kenyan savanna, consisting of 8000 h seismic recordings and 250 k camera trap pictures. Our classifiers, different convolutional neural networks trained on seismograms and spectrograms, achieved 80%–90% balanced accuracy in detecting elephants up to 100 m away, and

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Quantifying the ecological success of a community-based wildlife conservation area in Tanzania

In Tanzania, community-based natural resource management of wildlife occurs through the creation of Wildlife Management Areas (WMAs). WMAs consist of multiple villages designating land for wildlife conservation, and sharing a portion of subsequent tourism revenues. Nineteen WMAs are currently operating, encompassing 7% of Tanzania’s land area, with 19 more WMAs planned. The ecological success or failure of WMAs for wildlife conservation has yet to be quantified. We defined ecological success in this case as significantly greater densities of wildlife and

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