Classification of African ground pangolin behaviour based on accelerometer readouts: validation of bio-logging methods

Abstract

Abstract Background Understanding how free-ranging animals behave can help in the design of optimal management strategies to both conserve species and enable individuals to express natural behaviours, maximising welfare. Animal-borne accelerometers passively collect data on body acceleration which can be interpreted to quantify behaviour. Accelerometers are increasingly used in behavioural research, however validation of accelerometer readouts to enable classification of discrete behaviours is required for each species. Pangolins are a heavily trafficked clade of mammals, all of which are considered vulnerable to extinction. They are also under-researched, with little known about their behaviour in the wild. In this study, we present the first validation of behavioural classification based on accelerometer readouts for a pangolin species; the ground pangolin (Smutsia temminckii). Results We present a standardised protocol for attaching accelerometers to pangolins to minimise the impact of devices on welfare. We match the readouts from accelerometers to behaviours defined through video observations. Using a random forest classification, we defined five discrete behaviours (walking, digging, feeding, investigating ground, and stationary, accuracy of 85%) and three activity levels (low, medium, and high, accuracy of 94%) from accelerometer readouts. We determine optimal sampling frequency and window length (50 Hz and five seconds for discrete behaviour, 10 Hz and seven seconds for activity level). We then deploy accelerometers and classify the behaviour of three free-ranging pangolins for between two and four days. We find considerable variation in peak daily activity between free-ranging pangolins with different individuals displaying nocturnal and crepuscular behaviour. We also find that pangolins spend the majority of their time (between 62 and 71%) at rest. Conclusion The methods we present will enable the quantification of ground pangolin behaviour in the wild to improve our understanding of the species’ ecology and help inform conservation efforts. This will also help to improve our fundamental understanding of animal behaviour and ecology.

Keywords

• Conservation
• Endangered species
• Machine learning
• Population monitoring
• Random forest
• Rehabilitation