Background: The use of accelerometers in bio-logging devices has proved to be a powerful tool for the quantification of animal behaviour. While bio-logging techniques are being used on wide range of species, to date they have only been seldom used with non-human primates. This is likely due to three main factors: the long tradition of direct field observations, a difficulty of attaching bio-logging devices to wild primates and the challenge of deciphering acceleration signals in species' with remarkable locomotor and behavioural diversity. Here, we overcome these aforementioned obstacles and provide methodology for identification of behaviours from accelerometer data of wild chacma baboons (Papio ursinus) in Cape Town, South Africa. Results:We apply machine learning techniques to process complex accelerometer data, collected by bespoke tracking collars to quantify a range of behaviours (focusing on locomotion and foraging behaviour). We successfully identify six broad state behaviours that represent 93.3% of the time budget of the baboons. Resting, walking, running and foraging were all identified with high recall and precision representing the first classification of multiple behavioural states from accelerometer data for a wild primate. Conclusion:Our 'end to end' process-from collar design and build to the collection and quantification of acceleration data-provides advantages over gathering data by traditional observation, not least because it affords data collection without the presence of an observer which may affect an animal's behaviour. Furthermore, our methodology and findings open new possibilities for the fine-scale study of movement and foraging ecology in wild primates, and in particular our baboon study population which is in conflict with people.
Biologging devices are used ubiquitously across vertebrate taxa in studies of movement and behavioural ecology to record data from organisms without the need for direct observation. Despite the dramatic increase in the sophistication of this technology, progress in reducing the impact of these devices to animals is less obvious, notwithstanding the implications for animal welfare. Existing guidelines focus on tag weight (e.g. the ‘5% rule'), ignoring aero/hydrodynamic forces in aerial and aquatic organisms, which can be considerable. Designing tags to minimize such impact for animals moving in fluid environments is not trivial, as the impact depends on the position of the tag on the animal, as well as its shape and dimensions. We demonstrate the capabilities of computational fluid dynamics (CFD) modelling to optimize the design and positioning of biologgers on marine animals, using the grey seal (Halichoerus grypus) as a model species. Specifically, we investigate the effects of (a) tag form, (b) tag size, and (c) tag position and quantify the impact under frontal hydrodynamic forces, as encountered by seals swimming at sea. By comparing a conventional versus a streamlined tag, we show that the former can induce up to 22% larger drag for a swimming seal; to match the drag of the streamlined tag, the conventional tag would have to be reduced in size by 50%. For the conventional tag, the drag induced can differ by up to 11% depending on the position along the seal's body, whereas for the streamlined tag this difference amounts to only 5%. We conclude by showing how the CFD simulation approach can be used to optimize tag design to reduce drag for aerial and aquatic species, including issues such as the impact of lateral currents (unexplored until now). We also provide a step‐by‐step guide to facilitate the implementation of CFD in biologging tag design.
Background Fine-scale data on animal position are increasingly enabling us to understand the details of animal movement ecology and dead-reckoning, a technique integrating motion sensor-derived information on heading and speed, can be used to reconstruct fine-scale movement paths at sub-second resolution, irrespective of the environment. On its own however, the dead-reckoning process is prone to cumulative errors, so that position estimates quickly become uncoupled from true location. Periodic ground-truthing with aligned location data (e.g., from global positioning technology) can correct for this drift between Verified Positions (VPs). Yet relatively few bio-logging studies have adopted this approach due to an apparent inaccessibility of the complex analytical processes involved. We present step-by-step instructions for implementing Verified Position Correction (VPC) dead-reckoning in R using the tilt-compensated compass method, accompanied by the mathematical protocols underlying the code and improvements and extensions of this technique to reduce the trade-off between VPC rate and dead-reckoning accuracy. These protocols are all built into a user-friendly, fully-annotated VPC dead-reckoning R function; Gundog.Tracks, with multi-functionality to reconstruct animal movement paths across terrestrial, aquatic, and aerial systems, provided within the supplementary information as well as online (GitHub). Results The Gundog.Tracks function is demonstrated on three contrasting model species (the African lion Panthera leo, the Magellanic penguin Spheniscus magellanicus, and the Imperial cormorant Leucocarbo atriceps) moving on land, in water and in air, respectively. We show the effect of uncorrected errors in speed estimations, heading inaccuracies and infrequent VPC rate and demonstrate how these issues can be addressed. Conclusions The function provided will allow anyone familiar with R to dead-reckon animal tracks readily and accurately, as the key complex issues are dealt with by Gundog.Tracks. This will help the community to consider and implement a valuable, but often overlooked method of reconstructing high-resolution animal movement paths across diverse species and systems without requiring a bespoke application.
The presence of wildlife adjacent to and within urban spaces is a growing phenomenon globally. When wildlife’s presence in urban spaces has negative impacts for people and wildlife, nonlethal and lethal interventions on animals invariably result. Recent evidence suggests that individuals in wild animal populations vary in both their propensity to use urban space and their response to nonlethal management methods. Understanding such interindividual differences and the drivers of urban space use could help inform management strategies. We use direct observation and high-resolution GPS (1 Hz) to track the space use of 13 adult individuals in a group of chacma baboons (Papio ursinus) living at the urban edge in Cape Town, South Africa. The group is managed by a dedicated team of field rangers, who use aversive conditioning to reduce the time spent by the group in urban spaces. Adult males are larger, more assertive, and more inclined to enter houses, and as such are disproportionately subject to “last resort” lethal management. Field rangers therefore focus efforts on curbing the movements of adult males, which, together with high-ranking females and their offspring, comprise the bulk of the group. However, our results reveal that this focus allows low-ranking, socially peripheral female baboons greater access to urban spaces. We suggest that movement of these females into urban spaces, alone or in small groups, is an adaptive response to management interventions, especially given that they have no natural predators. These results highlight the importance of conducting behavioral studies in conjunction with wildlife management, to ensure effective mitigation techniques.
The use of animal‐attached data loggers to quantify animal movement has increased in popularity and application in recent years. High‐resolution tri‐axial acceleration and magnetometry measurements have been fundamental in elucidating fine‐scale animal movements, providing information on posture, traveling speed, energy expenditure, and associated behavioral patterns. Heading is a key variable obtained from the tandem use of magnetometers and accelerometers, although few field investigations have explored fine‐scale changes in heading to elucidate differences in animal activity (beyond the notable exceptions of dead‐reckoning). This paper provides an overview of the value and use of animal heading and a prime derivative, angular velocity about the yaw axis, as an important element for assessing activity extent with potential to allude to behaviors, using “free‐ranging” Loggerhead turtles ( Caretta caretta ) as a model species. We also demonstrate the value of yaw rotation for assessing activity extent, which varies over the time scales considered and show that various scales of body rotation, particularly rate of change of yaw, can help resolve differences between fine‐scale behavior‐specific movements. For example, oscillating yaw movements about a central point of the body's arc implies bouts of foraging, while unusual circling behavior, indicative of conspecific interactions, could be identified from complete revolutions of the longitudinal axis. We believe this approach should help identification of behaviors and “space‐state” approaches to enhance our interpretation of behavior‐based movements, particularly in scenarios where acceleration metrics have limited value, such as for slow‐moving animals.
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