Evaluating the utility of an accelerometer and urinary hormone analysis as indicators of estrus in a Zoo‐housed koala (<i>Phascolarctos cinereus</i>)

Takahashi, Maressa Q.; Tobey, Jennifer R.; Pisacane, Corinne Bacon; Andrus, Chris Hamlin

doi:10.1002/zoo.20212

Cited by 23 publications

(11 citation statements)

References 19 publications

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“…Koalas were observed from a distance of approximately 15 m, at which the behavior of the koala was not influenced by the observer. Behaviors were recorded in 1-s intervals and coded according to an ethogram (adapted from [30]; see Table 1). …”

Section: Methodsmentioning

confidence: 99%

Activity Patterns of Free-Ranging Koalas (Phascolarctos cinereus) Revealed by Accelerometry

et al. 2013

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An understanding of koala activity patterns is important for measuring the behavioral response of this species to environmental change, but to date has been limited by the logistical challenges of traditional field methodologies. We addressed this knowledge gap by using tri-axial accelerometer data loggers attached to VHF radio collars to examine activity patterns of adult male and female koalas in a high-density population at Cape Otway, Victoria, Australia. Data were obtained from 27 adult koalas over two 7-d periods during the breeding season: 12 in the early-breeding season in November 2010, and 15 in the late-breeding season in January 2011. Multiple 15 minute observation blocks on each animal were used for validation of activity patterns determined from the accelerometer data loggers. Accelerometry was effective in distinguishing between inactive (sleeping, resting) and active (grooming, feeding and moving) behaviors. Koalas were more active during the early-breeding season with a higher index of movement (overall dynamic body acceleration [ODBA]) for both males and females. Koalas showed a distinct temporal pattern of behavior, with most activity occurring from mid-afternoon to early morning. Accelerometry has potential for examining fine-scale behavior of a wide range of arboreal and terrestrial species.

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Section: Methodsmentioning

confidence: 99%

Activity Patterns of Free-Ranging Koalas (Phascolarctos cinereus) Revealed by Accelerometry

et al. 2013

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“…Typically, authors used accelerometry to monitor behavioral changes associated with reproduction [67,68] or behavioral responses to veterinary or husbandry practices [69][70][71][72][73][74]. In studying welfare of free-ranging wild species, mortality sensors are a common feature of telemeters and typically provide a special signal to alert researchers to the animal's demise [75].…”

Section: Survey Of Questions Currently Served By Accelerometry: Body mentioning

confidence: 99%

Observing the unwatchable through acceleration logging of animal behavior

et al. 2013

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Behavior is an important mechanism of evolution and it is paid for through energy expenditure. Nevertheless, field biologists can rarely observe animals for more than a fraction of their daily activities and attempts to quantify behavior for modeling ecological processes often exclude cryptic yet important behavioral events. Over the past few years, an explosion of research on remote monitoring of animal behavior using acceleration sensors has smashed the decades-old limits of observational studies. Animal-attached accelerometers measure the change in velocity of the body over time and can quantify fine-scale movements and body postures unlimited by visibility, observer bias, or the scale of space use. Pioneered more than a decade ago, application of accelerometers as a remote monitoring tool has recently surged thanks to the development of more accessible hardware and software. It has been applied to more than 120 species of animals to date. Accelerometer measurements are typically collected in three dimensions of movement at very high resolution (>10 Hz), and have so far been applied towards two main objectives. First, the patterns of accelerometer waveforms can be used to deduce specific behaviors through animal movement and body posture. Second, the variation in accelerometer waveform measurements has been shown to correlate with energy expenditure, opening up a suite of scientific questions in species notoriously difficult to observe in the wild. To date, studies of wild aquatic species outnumber wild terrestrial species and analyses of social behaviors are particularly few in number. Researchers of domestic and captive species also tend to report methodology more thoroughly than those studying species in the wild. There are substantial challenges to getting the most out of accelerometers, including validation, calibration, and the management and analysis of large quantities of data. In this review, we illustrate how accelerometers work, provide an overview of the ecological questions that have employed accelerometry, and highlight the emerging best practices for data acquisition and analysis. This tool offers a level of detail in behavioral studies of free-ranging wild animals that has previously been impossible to achieve and, across scientific disciplines, it improves understanding of the role of behavioral mechanisms in ecological and evolutionary processes. AbstractResumen: El comportamiento es un mecanismo importante de la evolución y que se paga a través del gasto de energía. Sin embargo, los biólogos de campo raramente observan los animales durante más de una fracción de sus actividades y los intentos de cuantificar el comportamiento para el modelado de los procesos ecológicos a menudo excluyen eventos crípticos pero importantes. En los últimos años se produjeron avances importantes en el monitoreo remoto del comportamiento de los animales, utilizando sensores de telemétro de aceleración (acelerómetros) que empujan los límites tradicionales de los estudios observacionales. Acelerómetros uni...

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“…At its most basic, accelerometers discriminate between two behavioural states (e.g. activity vs. resting or swimming vs. prey capture) and can do so accurately [5,35]. Adding more categories for the model to discriminate increases complexity, but reduces the uniqueness of the model, thus decreasing its overall accuracy [12,13].…”

Section: Number Of Behaviour Categories: Less Is More?mentioning

confidence: 99%

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

Ladds

Thompson²,

Kadar

et al. 2017

Anim Biotelemetry

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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.

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Evaluating the utility of an accelerometer and urinary hormone analysis as indicators of estrus in a Zoo‐housed koala (Phascolarctos cinereus)

Cited by 23 publications

References 19 publications

Activity Patterns of Free-Ranging Koalas (Phascolarctos cinereus) Revealed by Accelerometry

Activity Patterns of Free-Ranging Koalas (Phascolarctos cinereus) Revealed by Accelerometry

Observing the unwatchable through acceleration logging of animal behavior

Super machine learning: improving accuracy and reducing variance of behaviour classification from accelerometry

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