Heat stress conditions affect the social network structure of free-ranging sheep

Extreme weather conditions, like heatwave events, are becoming more frequent with climate change. Animals often modify their behaviour to cope with environmental changes and extremes. During heat stress conditions, individuals change their spatial behaviour and increase the use of shaded areas to assist with thermoregulation. Here, we suggest that for social species, these behavioural changes and ambient conditions have the potential to influence an individual's position in its social network, and the social network structure as a whole. We investigated whether heat stress conditions (quantified through the temperature humidity index) and the resulting use of shaded areas, influence the social network structure and an individual's connectivity in it. We studied this in free-ranging sheep in the arid zone of Australia, GPS-tracking all 48 individuals in a flock. When heat stress conditions worsened, individuals spent more time in the shade and the network was more connected (higher density) and less structured (lower modularity). Furthermore, we then identified the behavioural change that drove the altered network structure and showed that an individual's shade use behaviour affected its social connectivity. Interestingly, individuals with intermediate shade use were most strongly connected (degree, strength, betweenness), indicating their importance for the connectivity of the social network during heat stress conditions. Heat stress conditions, which are predicted to increase in severity and frequency due to climate change, influence resource use within the ecological environment. Importantly, our study shows that these heat stress conditions also affect the animal's social environment through the changed social network structure. Ultimately, this could have further flow on effects for social foraging and individual health since social structure drives information and disease transmission.

Fission-fusion dynamics in sheep: the influence of resource distribution and temporal activity patterns

Fission-fusion events, i.e. changes to the size and composition of animal social groups, are a mechanism to adjust the social environment in response to short-term changes in the cost-benefit ratio of group living. Furthermore, the time and location of fission-fusion events provide insight into the underlying drivers of these dynamics. Here, we describe a method for identifying group membership over time and for extracting fission-fusion events from animal tracking data. We applied this method to high-resolution GPS data of free-ranging sheep (Ovis aries). Group size was highest during times when sheep typically rest (midday and at night), and when anti-predator benefits of grouping are high while costs of competition are low. Consistent with this, fission and fusion frequencies were highest during early morning and late evening, suggesting that social restructuring occurs during periods of high activity. However, fission and fusion events were not more frequent near food patches and water resources when adjusted for overall space use. This suggests a limited role of resource competition. Our results elucidate the dynamics of grouping in response to social and ecological drivers, and we provide a tool for investigating these dynamics in other species.

Sensor-Based Detection of Predator Influence on Livestock: A Case Study Exploring the Impacts of Wild Dogs (Canis familiaris) on Rangeland Sheep

Simple Summary

Sheep predation by wild dogs has serious production and animal welfare implications. By monitoring changes in the behaviour of sheep, on-animal sensors are an option for detecting wild dogs and alerting producers to their presence. This study identified differences in the daily distance travelled of sheep when in the presence and absence of a wild dog and highlights the potential for on-animal sensors to be used as a monitoring and management tool for wild dog detection.

Abstract

In Australia, wild dogs are one of the leading causes of sheep losses. A major problem with managing wild dogs in Australia’s rangeland environments is that sheep producers are often unaware of their presence until injuries or deaths are observed. One option for earlier detection of wild dogs is on-animal sensors, such as Global Positioning System (GPS) tracking collars, to detect changes in the behaviour of sheep due to the presence of wild dogs. The current study used spatio-temporal data, derived from GPS tracking collars, deployed on sheep from a single rangeland property to determine if there were differences in the behaviour of sheep when in the presence, or absence, of a wild dog. Results indicated that the presence of a wild dog influenced the daily behaviours of sheep by increasing the daily distance travelled. Differences in sheep diurnal activity were also observed during periods where a wild dog was present or absent on the property. These results highlight the potential for on-animal sensors to be used as a monitoring tool for sheep flocks directly impacted by wild dogs, although further work is needed to determine the applicability of these results to other sheep production regions of Australia.

Review: Precision Livestock Farming technologies in pasture-based livestock systems

Precision Livestock Farming (PLF) encompasses the combined application of single technologies or multiple tools in integrated systems for real-time and individual monitoring of livestock. In grazing systems, some PLF applications could substantially improve farmers' control of livestock by overcoming issues related to pasture utilisation and management, and animal monitoring and control. A focused literature review was carried out to identify technologies already applied or at an advanced stage of development for livestock management in pastures, specifically cattle, sheep, goats, pigs, poultry. Applications of PLF in pasture-based systems were examined for cattle, sheep, goats, pigs, and poultry. The earliest technology applied to livestock was the radio frequency identification tag, allowing the identification of individuals, but also for retrieving important information such as maternal pedigree. Walk-over-weigh platforms were used to record individual and flock weights. Coupled with automatic drafting systems, they were tested to divide the animals according to their needs. Few studies have dealt with remote body temperature assessment, although the use of thermography is spreading to monitor both intensively reared and wild animals. Global positioning system and accelerometers are among the most applied technologies, with several solutions available on the market. These tools are used for several purposes, such as animal location, theft prevention, assessment of activity budget, behaviour, and feed intake of grazing animals, as well as for reproduction monitoring (i.e., oestrus, calving, or lambing). Remote sensing by satellite images or unmanned aerial vehicles (UAVs) seems promising for biomass assessment and herd management based on pasture availability, and some attempts to use UAVs to monitor, track, or even muster animals have been reported recently. Virtual fencing is among the upcoming technologies aimed at grazing management. This system allows the management of animals at pasture without physical fences but relies on associative learning between audio cues and an electric shock delivered if the animal does not change direction after the acoustic warning. Regardless of the different technologies applied, some common constraints have been reported on the application of PLF in grazing systems, especially when compared with indoor or confined livestock systems. Battery lifespan, transmission range, service coverage, storage capacity, and economic affordability were the main factors. However, even if the awareness of the existence and the potential of these upcoming tools are still limited, farmers' and researchers' demands are increasing, and positive outcomes in terms of rangeland conservation, animal welfare, and labour optimisation are expected from the spread of PLF in grazing systems.

Feral dogs in Chitral gol national park, Pakistan: a potential threat to the future of threatened Kashmir Markhor (Capra falconeri cashmiriensis)

Feral dogs are well-organized hunters of ungulates in many parts of the world, causing great damage to wildlife populations and ultimately to the ecosystem. In Pakistan, the impacts of feral dogs on the wildlife have not been documented yet. In a period of fifteen years (2006-2020), feral dogs have killed hundreds of threatened markhor in Chitral gol national park (CGNP), Pakistan. Despite direct predation other impacts including disturbance and competition with other natural predators, could compromise conservation and management efforts. The population of feral dogs seems to have been increased with the increase of dumping sites by communities. Our findings suggest that there are pressing needs of controlling the feral dogs population and eradicating them from the core zone of CGNP and surrounding buffer communities. Conventional culling of dogs should be coupled with modern techniques like castration and sterilization. Communities should be educated regarding the clean environment, proper disposal of home wastes and, biodiversity conservation.

Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Simple Summary

Near-real-time monitoring of livestock using on-animal sensor technology has the potential to improve animal welfare and productivity through increased surveillance and improved decision-making capabilities. One potentially valuable application is for monitoring of lambing events in sheep. This research reports on the development of a machine learning classification algorithm for autonomous detection of lambing events. The algorithm uses data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data. Overall, four features of sheep behaviour were identified as having the greatest importance for lambing detection, including various measures of social distancing and frequency of posture change. Using these four features, the final algorithm was able to detect up to 91% of lambing events. This knowledge is intended to contribute to the development of commercially feasible lambing detection systems for improved surveillance of animals, ultimately improving methods of monitoring during critical welfare periods.

Abstract

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

Legal Complexities of Animal Welfare in Australia: Do On-Animal Sensors Offer a Future Option?

Simple Summary

‘Good animal welfare’ has evolved in recent decades to recognise behavioural, physiological and health factors, acknowledging that an animal may have good clinical health and be productive, though their welfare may be poor. The five freedoms and domains of animal welfare provide internationally recognised frameworks against which to evaluate practices to shape evidence-based standards which recognise both the physical and mental health needs of animals to provide a balanced view of an animal’s ability to cope in its environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements and technology improvements. Substantial literature discusses the use of technology for improving animal monitoring, management and productivity on and off farm, though little has been published in relation to using such technologies to support legislative compliance and drive overall improvements in animal welfare. This article discusses the current legislation around animal welfare (with a focus on the Australian red meat sector); the impact of public expectation of welfare standards and production practices; and the current and future opportunity for on-animal sensors to support animal welfare, monitoring, management and compliance.

Abstract

The five freedoms and, more recently, the five domains of animal welfare provide internationally recognised frameworks to evaluate animal welfare practices which recognise both the physical and mental wellbeing needs of animals, providing a balanced view of their ability to cope in their environment. Whilst there are many techniques to measure animal welfare, the challenge lies with how best to align these with future changes in definitions and expectations, advances in science, legislative requirements, and technology improvements. Furthermore, enforcement of current animal welfare legislation in relation to livestock in Australia and the reliance on self-audits for accreditation schemes, challenges our ability to objectively measure animal welfare. On-animal sensors have enormous potential to address animal welfare concerns and assist with legislative compliance, through continuous measurement and monitoring of an animal’s behavioural state and location being reflective of their wellbeing. As reliable animal welfare measures evolve and the cost of on-animal sensors reduce, technology adoption will increase as the benefits across the supply chain are realised. Future adoption of on-animal sensors by producers will primarily depend on a value proposition for their business being clear; algorithm development to ensure measures are valid and reliable; increases in producer knowledge, willingness, and trust in data governance; and improvements in data transmission and connectivity.

Can accelerometer ear tags identify behavioural changes in sheep associated with parturition?

On-animal sensor systems provide an opportunity to monitor ewes during parturition, potentially reducing ewe and lamb mortality risk. This study investigated the capacity of machine learning (ML) behaviour classification to monitor changes in sheep behaviour around the time of lambing using ear-borne accelerometers. Accelerometers were attached to 27 ewes grazing a 4.4 ha paddock. Data were then classified based on three different ethograms: (i) detection of grazing, lying, standing, walking; (ii) detection of active behaviour; and (iii) detection of body posture. Proportion of time devoted to performing each behaviour and activity was then calculated at a daily and hourly scale. Frequency of posture change was also calculated on an hourly scale. Assessment of each metric using a linear mixed-effects model was conducted for the 7 days (day scale) or 12 h (hour scale) before and after lambing. For all physical movements, regardless of the ethogram, there was a change in the days surrounding lambing. This involved either a decrease (grazing, lying, active behaviour) or peak (standing, walking) on the day of parturition, with most values returning to either pre-partum or near-pre-partum levels (all P < 0.001). Hourly changes also occurred for all behaviours (all P < 0.001), the most marked being increased walking behaviour and frequency of posture change. These findings indicate ewes were more restless around the time of parturition. Further application of this research should focus on development of algorithms that can be used to identify onset of lambing and/or time of parturition in pasture-based ewes.