Non‐fatal removal of ground pangolin (<i>Smutsia temminckii</i> Smuts, 1832) tracking devices by predators

Carroll, Daire; Harvey‐Carroll, Jessica; Trivella, Cara‐Marie; Connelly, Ellen

doi:10.1111/aje.13225

Cited by 3 publications

References 25 publications

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

Harvey-Carroll,

Carroll,

Trivella

et al. 2024

Anim Biotelemetry

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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.

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

Harvey-Carroll,

Carroll,

Trivella

et al. 2024

Anim Biotelemetry

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Hematological and Biochemical Parameters as Prognostic Indicators for Successful Rehabilitation in Confiscated Temminck's Pangolins (Smutsia Temminckii)

Bentley,

Chimaliro,

Ashpole

et al. 2024

Journal of Zoo and Wildlife Medicine

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

Harvey-Carroll,

Carroll,

Trivella

et al. 2024

Preprint

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Background Quantifying animal behaviour is important to many areas of ecology and conservation. 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. This is particularly important for endangered species for which a high level of human intervention is needed to counteract population declines and extinctions. Animal-borne bio-logging devices, such as accelerometers, present the opportunity to passively collect data on the behaviour of free ranging animals. Accelerometers are increasingly used in behavioural research. Validation of accelerometer readouts to enable classification of discrete behaviours, however, has only been carried out for a small number of species. This presents a barrier to the meaningful interpretation of data. 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 attachment protocol to minimize the impact of devices on pangolin welfare. We match the readouts from accelerometers to behaviours defined through video observations. Using a Random Forest (RF) classification, we defined five discreet 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 smoothing window (50 hz and 5 seconds for full behaviour, 10 Hz and 7 seconds for activity level). We then deploy accelerometers and classify the behaviour of three free ranging pangolins for between two and four days. 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.

show abstract

Non‐fatal removal of ground pangolin (Smutsia temminckii Smuts, 1832) tracking devices by predators

Cited by 3 publications

References 25 publications

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

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

Hematological and Biochemical Parameters as Prognostic Indicators for Successful Rehabilitation in Confiscated Temminck's Pangolins (Smutsia Temminckii)

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

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