This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed by comparison with manually scoring, before being applied to seven nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data were used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in behavioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.
This study investigates the possibility of using machine learning models created in DeepLabCut and Create ML to automate aspects of behavioral coding and aid in behavioral analysis. Two models with different capabilities and complexities were constructed and compared to a manually observed control period. The accuracy of the models was assessed before being applied to 7 nights of footage of the nocturnal behavior of two African elephants (Loxodonta africana). The resulting data was used to draw conclusions regarding behavioral differences between the two elephants and between individually observed nights, thus proving that such models can aid researchers in be-havioral analysis. The models were capable of tracking simple behaviors with high accuracy, but had certain limitations regarding detection of complex behaviors, such as the stereotyped behavior sway, and displayed confusion when deciding between visually similar behaviors. Further expansion of such models may be desired to create a more capable aid with the possibility of automating behavioral coding.
The social- and self-directed behaviours of two golden lion tamarins (Leontopithecus rosalia) was investigated while having residence in the tropical house of Aalborg Zoo, Denmark. The behaviour of the female had been distinct from the male since 2018. Despite several diagnostic examinations and medicinal treatments, the cause of the aberrant and apparently stereotypical behavioural patterns remained undetermined, which initiated this study. The pair were recorded throughout a predetermined observation period and these recordings were analysed to illuminate the behaviours of the individuals. The results showed that there were differences in the type, frequency, and durations of both social- and self-directed behaviours between the sick female and the healthy male. The female expressed both social- and self-directed behaviours more frequently and for longer durations, while the male were more socially reserved and did not engage in social behaviours as frequently. Furthermore, subsequent necropsy of the female revealed uro-genital disease.