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Background: Semi-automating the analyses of accelerometry data makes it possible to synthesize large data sets. However, when constructing activity budgets from accelerometry data, there are many methods to extract, analyse and report data and results. For instance, machine learning is a robust approach to classifying data. We used a new method, super learning, that combines base learners (different machine learning methods) in an optimal manner to achieve overall improved accuracy. Other facets of super learning include the number of behavioural categories to predict, the number of epochs (sample window size) used to split data for training and testing and the parameters on which to train the models. Results: The super learner accurately classified behaviour categories with higher accuracy and lower variance than comparative models. For all models tested, using four behaviours, in comparison with six, achieved higher rates of accuracy. The number of epochs chosen also affected the accuracy with smaller epochs (7 and 13) performing better than longer epochs (25 and 75). Conclusions: Correct model selection, training and testing are imperative to creating reliable and valid classification models. To do so means model fitting must use a wide array of selection criteria. We evaluated a number of these including model, number of behaviours to classify and epoch length and then used a parameter grid search to implement the models. We found that all criteria tested contributed to the models' overall accuracies. Fewer behaviour categories and shorter epoch length improved the performance of all models tested. The super learner classified behaviours with higher accuracy and lower variance than other models tested. However, when using this model, users need to consider the additional human and computational time required for implementation. Machine learning is a powerful method for classifying the behaviour of animals from accelerometers. Care and consideration of the modelling parameters evaluated in this study are essential when using this type of statistical analysis. [ABSTRACT FROM AUTHOR]

Background: Numerous factors like continuous habitat reduction or fragmentation for free-ranging giraffes (Giraffa camelopardalis) as well as e.g. suboptimal housing conditions for animals in captivity might lead to behavioural alterations as part of the overall adaptation process to the changing living conditions. In order to facilitate current and future studies on giraffe behaviour, a comprehensive ethogram was compiled based on existing literature, as well as observations on giraffes in the wild (Hwange National Park, Zimbabwe; Entabeni Game Reserve, South Africa), and in captivity (National Zoological Gardens of South Africa, Pretoria). Findings: The resulting ethogram lists 65 different behavioural patterns, which were described and grouped into seven categories: General activities, Abnormal repetitive behaviours, General interactions, Bull-Cow behaviour, Bull- Bull behaviour, Cow-Bull behaviour, Maternal behaviours, and Interactions by calves. The behaviours were further described regarding a presumed purpose, particularly with respect to social interactions and sexual behaviour. Contradictory descriptions from previous studies were considered and discussed in comparison with our own observations. Conclusions: This ethogram provides a basis for current and future studies by suggesting a terminology which can be used for harmonizing behavioural observations, thus helping to facilitate comparability of future results. Subsequently, a better understanding of the behavioural ecology of giraffes in the wild as well as in captivity could aid future conservation efforts. [ABSTRACT FROM AUTHOR]