Accelerometer-based predictions of behaviour elucidate factors affecting the daily activity patterns of spotted hyenas

Animal activity patterns are highly variable and influenced by internal and external factors, including social processes. Quantifying activity patterns in natural settings can be challenging, as it is difficult to monitor animals over long time periods. Here, we developed and validated a machine-learning-based classifier to identify behavioural states from accelerometer data of wild spotted hyenas (Crocuta crocuta), social carnivores that live in large fission-fusion societies. By combining this classifier with continuous collar-based accelerometer data from five hyenas, we generated a complete record of activity patterns over more than one month. We used these continuous behavioural sequences to investigate how past activity, individual idiosyncrasies, and social synchronization influence hyena activity patterns. We found that hyenas exhibit characteristic crepuscular-nocturnal daily activity patterns. Time spent active was independent of activity level on previous days, suggesting that hyenas do not show activity compensation. We also found limited evidence for an effect of individual identity on activity, and showed that pairs of hyenas who synchronized their activity patterns must have spent more time together. This study sheds light on the patterns and drivers of activity in spotted hyena societies, and also provides a useful tool for quantifying behavioural sequences from accelerometer data.

Energy expenditure of southern right whales varies with body size, reproductive state and activity level

Quantifying the energy expenditure of animals is critical to understand the cost of anthropogenic disturbance relative to their overall energy requirements. We used novel drone focal follows (776 follows, 185 individuals) and aerial photogrammetry (5,372 measurements, 791 individuals) to measure the respiration rate and body condition loss of southern right whales (Eubalaena australis) on a breeding ground in Australia. Respiration rates were converted to oxygen consumption and field metabolic rates (FMR) using published bioenergetic models. The intra-seasonal loss in body condition of different reproductive classes (calves, juveniles, adults, pregnant and lactating) was converted to blubber energy loss and total energy expenditure (TEE). Using these two metrics, we tested the effects of body size, reproductive state and activity level on right whale energy expenditure. Respiration rates and mass-specific FMR decreased exponentially with an increase in body size, as expected based on allometric scaling. FMR increased curvilinearly with an increase in swim speed, likely as a result of increased drag and increased locomotion costs. Respiration rates and FMR were 44% higher for pregnant and lactating females compared to adults, suggesting significant costs of foetal maintenance and milk production, respectively. The estimated FMR of adults based on their respiration rates, corresponded well with the estimated TEE based on body condition loss. The rate of decline in body condition of pregnant and lactating females was considerably higher than expected based on respiration rates, which likely reflects the milk energy transfer from mothers to calves, which is not reflected in their FMR.

Methylation levels assessment with Methylation-Sensitive High-Resolution Melting (MS-HRM)

Testing for disease-related DNA methylation changes provides clinically relevant information in personalized patient care. Methylation-Sensitive High-Resolution Melting (MS-HRM) is a method used for measuring methylation changes and has already been used in diagnostic settings. This method utilizes one set of primers that initiate the amplification of both methylated and non-methylated templates. Therefore, the quantification of the methylation levels using MS-HRM is hampered by the PCR bias phenomenon. Some approaches have been proposed to calculate the methylation level of samples using the high-resolution melting (HRM) curves. However, limitations of the methylation calculation using MS-HRM have not been evaluated systematically and comprehensively. We used the Area Under the Curve (AUC), a derivative of the HRM curves, and least square approximation (LSA) to establish a procedure that allowed us to infer methylation levels in an MS-HRM experiment and assess the limitations of that procedure for the assays’ specific methylation level measurement. The developed procedure allowed, with certain limitations, estimation of the methylation levels using HRM curves.