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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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Molecular species identification of bushmeat recovered from the Serengeti ecosystem in Tanzania

Bushmeat harvesting and consumption represents a potential risk for the spillover of endemic zoonotic pathogens, yet remains a common practice in many parts of the world. Given that the harvesting and selling of bushmeat is illegal in Tanzania and other parts of Africa, the supply chain is informal and may include hunters, whole-sellers, retailers, and individual resellers who typically sell bushmeat in small pieces. These pieces are often further processed, obscuring species-identifying morphological characteristics, contributing to incomplete or mistaken knowledge

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Where is the game? Wild meat products authentication in South Africa: a case study

Background: Wild animals’ meat is extensively consumed in South Africa, being obtained either from ranching, farming or hunting. To test the authenticity of the commercial labels of meat products in the local market, we obtained DNA sequence information from 146 samples (14 beef and 132 game labels) for barcoding cytochrome c oxidase subunit I and partial cytochrome b and mitochondrial fragments. The reliability of species assignments were evaluated using BLAST searches in GenBank, maximum likelihood phylogenetic analysis and the character-based

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Three-gene PCR and high-resolution melting analysis for differentiating vertebrate species mitochondrial DNA for biodiversity research and complementing forensic surveillance

Reliable molecular identification of vertebrate species from morphologically unidentifiable tissue is critical for the prosecution of illegally-traded wildlife products, conservation-based biodiversity research, and identification of blood-meal hosts of hematophagous invertebrates. However, forensic identification of vertebrate tissue relies on sequencing of the mitochondrial cytochrome oxidase I (COI) ‘barcode’ gene, which remains costly for purposes of screening large numbers of unknown samples during routine surveillance. Here, we adapted a rapid, low-cost approach to differentiate 10 domestic and 24 wildlife species that are

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