Monitoring animal behaviour and environmental interactions : GPS collars and satellite remote sensing within a satellite remote sensing context

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Monitoring animal behaviour and environmental interactions using wireless sensor networks, GPS collars and satellite remote sensing

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.

Animal behaviour understanding using wireless sensor networks

This paper presents research that is being conducted by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with the aim of investigating the use of wireless sensor networks for automated livestock monitoring and control. It is difficult to achieve practical and reliable cattle monitoring with current conventional technologies due to challenges such as large grazing areas of cattle, long time periods of data sampling, and constantly varying physical environments. Wireless sensor networks bring a new level of possibilities into this area with the potential for greatly increased spatial and temporal resolution of measurement data. CSIRO has created a wireless sensor platform for animal behaviour monitoring where we are able to observe and collect information of animals without significantly interfering with them. Based on such monitoring information, we can identify each animal's behaviour and activities successfully

Animal behaviour and cancer

Scientists are increasingly coming to realize that oncogenic phenomena are both frequent and detrimental for animals, and must therefore be taken into account when studying the biology of wildlife species and ecosystem functioning. Here, we argue that several behaviours that are routine in an individual's life can be associated with cancer risks, or conversely prevent/cure malignancies and/or alleviate their detrimental consequences for fitness. Although such behaviours are theoretically expected to be targets for natural selection, little attention has been devoted to explore how they influence animal behaviour. This essay provides a summary of these issues as well as an overview of the possibilities offered by this research topic, including possible applications for cancer prevention and treatments in humans.

Using accelerometer, high sample rate GPS and magnetometer data to develop a cattle movement and behaviour model

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

Animal Control - What constitutes a reliable cue to stop animal movement ?

Controlling free-ranging livestock requires low-stress cues to alter animal behaviour. Recently modulated sound and electric shock were demonstrated to be effective in controlling free-ranging cattle. In this study the behaviour of 60, 300 kg Belmont Red heifers were observed for behavioural changes when presented cues designed to impede their movement through an alley. The heifers were given an overnight drylot shrink off feed but not drinking water prior to being tested. Individual cattle were allowed to move down a 6.5 m wide alley towards a pen of peers and feed located 71 m from their point of release. Each animal was allowed to move through the alley unimpeded five times to establish a basal behavioural pattern. Animals were then randomly assigned to treatments consisting of sound plus shock, vibration plus shock, a visual cue plus shock, shock by itself and a control. The time each animal required to reach the pen of peers and feed was recorded. If the animal was prevented from reaching the pen of peers and feed by not penetrating through the cue barrier at set points along the alley for at least 60 sec the test was stopped and the animal was returned to peers located behind the release pen. Cues and shock were manually applied from a laptop while animals were observed from a 3.5 m tower located outside the alley. Electric shock, sound, vibration and Global Position System (GPS) hardware were housed in a neck collar. Results and implications will be discussed.

Poster : Understanding interactions between autonomous animal control and temperament when cattle are subjected to virtual fencing applications

Virtual fencing has the potential to control grazing livestock. Understanding and refi ning the cues that can alter behaviour is an integral part of autonomous animal control. A series of tests have been completed to explore the relationship between temperament and control. Prior to exposure to virtual fencing control the animals were scored for temperament using fl ight speed and a sociability index using contact logging devices. The behavioural response of 30, Belmont Red steers were observed for behavioural changes when presented with cues prior to receiving an electrical stimulation. A control and four treatments designed to interrupt the animal’s movement down an alley were tested. The treatments consisted of sound plus electrical stimulation, vibration plus electrical stimulation, a visual cue plus electrical stimulation and electrical stimulation by itself. The treatments were randomly applied to each animal over fi ve consecutive trials. A control treatment in which no cues were applied was used to establish a basal behavioural pattern. A trial was considered completed after each animal had been retained behind the cue barrier for at least 60 sec. All cues and electrical stimulation were manually applied from a laptop located on a portable 3.5 m tower located immediately outside the alley. The electric stimulation consisted of 1.0 Kv of electricity. Electric stimulation, sound and vibration along with the Global Position System (GPS) hardware to autonomously record the animal’s path within the alley were recorded every second.

An objective measure of reactive behaviour in horses

Several tests have been devised in an attempt to detect behaviour modification due to training, supplements or diet in horses. These tests rely on subjective observations in combination with physiological measures, such as heart rate (HR) and plasma cortisol concentrations, but these measures do not definitively identify behavioural changes. The aim of the present studies was to develop an objective and relevant measure of horse reactivity. In Study 1, HR responses to auditory stimuli, delivered over 6 days, designed to safely startle six geldings confined to individual stalls was studied to determine if peak HR, unconfounded by physical exertion, was a reliable measure of reactivity. Both mean (±SEM) resting HR (39.5 ± 1.9 bpm) and peak HR (82 ± 5.5 bpm) in response to being startled in all horses were found to be consistent over the 6 days. In Study 2, HR, plasma cortisol concentrations and speed of departure from an enclosure (reaction speed (RS)) in response to a single stimulus of six mares were measured when presented daily over 6 days. Peak HR response (133 ± 4 bpm) was consistent over days for all horses, but RS increased (3.02 ± 0.72 m/s on Day 1 increasing to 4.45 ± 0.53 m/s on Day 6; P = 0.005). There was no effect on plasma cortisol, so this variable was not studied further. In Study 3, using the six geldings from Study 1, the RS test was refined and a different startle stimulus was used each day. Again, there was no change in peak HR (97.2 ± 5.8 bpm) or RS (2.9 ± 0.2 m/s on Day 1 versus 3.0 ± 0.7 m/s on Day 6) over time. In the final study, mild sedation using acepromazine maleate (0.04 mg/kg BW i.v.) decreased peak HR in response to a startle stimulus when the horses (n = 8) were confined to a stall (P = 0.006), but not in an outdoor environment when the RS test was performed. However, RS was reduced by the mild sedation (P = 0.02). In conclusion, RS may be used as a practical and objective test to measure both reactivity and changes in reactivity in horses.