Abstract: Recently, training horses within round-pens has increased in popularity. Practitioners often maintain that the responses they elicit from horses are similar to signals used with senior conspecifics. To audit the responses of horses to conspecifics, 6 mare-young-horse dyads, this study introduced them to each other in a round-pen and videoed them for 8 min. These dyads spent significantly more time farther than 10 m apart than they did less than 1 m apart (p < .001). The time they spent less than 1 m apart decreased over the 8-min test period (p = .018). Mares occupied the center of the round-pen and chased youngsters for 0.73% of the test period (p < .001). Mares made all agonistic approaches (p < .001), and youngsters (p = .018) made all investigative approaches. Head lowering and licking-and-chewing were exhibited most when the youngsters were facing away from the mares (p < .001). The frequency of head lowering increased during the test period (p = .027), whereas the frequency of licking-and-chewing did not change. The results bring into question the popular interpretation and ethological relevance of equine responses commonly described in round-pen training and show that mares did not condition young horses to remain in close proximity to them.

Abstract: In sport horses, equestrian competitions may lead to increased cortisol release. The competition itself but also a new environment or transport to the events can be considered as stressors. In this study, warmblood sport horses (4-16 years, 4 mares, 2 stallions, 18 geldings) were transported by road for one (t1), 3.5 (t3.5) and 8 hours (t8), (n=8 per group). Heart rate (HR) is an indicator of stress and correlates with cortisol release. Heart rate was recorded with a mobile recording system (f810i, Polar, Kempele, Finland), fixed to a girth around the thorax of the horse and was monitored the day before transport, during transport and 12 hours thereafter. In addition, cortisol metabolite concentrations in faeces were determined in order to analyse adrenocortical activation during transport. Faecal samples were collected three times per day. Cortisol metabolite concentrations in faeces are increased approximately 24 hours after an elevation in plasma cortisol concentrations. Basal HR before transport did not differ between groups (45±2 (SEM) beats/min). During the first 30 min of transport, HR increased significantly (84±6, p<0.001, Kruskal-Wallis-H-test) but did not differ between groups. During the remaining part of the transport, HR was 61±6 beats/min (p<0.001 vs. first 30 min). When HR was analysed for the last 30 min of transport, values for groups t1 and t8 were significantly (p<0.05) higher than in group t3.5. HR decreased to baseline values within less than 30 min after transport and did not differ between groups (30-60 min after transport: 44±3 beats/min). Faecal cortisol metabolite concentrations on the day after transport were elevated compared to pre-transport baseline values in all three groups of horses. Significant differences existed between groups (e.g. 18 hours after transport t1: 91±5, t3.5: 110±18, t8: 136±27 ng/g; p<0.05). Based on HR analysis, horses of group t3.5 had adapted to transport, while the prolonged transport in group t8 again was a stressor. In group t1, which was transported only for one hour, the higher heart during the last 3 min of transport was still part of the initial rise in HR. After an initial increase, heart decreases indicating adaptation of the horse to transport. Cortisol metabolite concentrations in faeces indicate that transport causes adrenocortical activation with the amount of cortisol released depending on the duration of transport. Transport over medium distances appears to no major stressor and a negative impact on the performance of the horse is unlikely. Transport over 8 hours caused the most pronounced cortisol release and HR increases again during the last hours of an 8-hour transport. Transports exceeding this time may temporarily affect the subsequent performance of the horse in equestrian sports.
Supported by Stiftung Forschung für das Pferd

Abstract: Social animals live and interact together, forming complex relationships and social structure. These relationships can have important fitness consequences, but most studies do not explicitly measure those relationships. An approach that explicitly measures relationships will further our understanding of social complexity and the consequences of both direct and indirect interactions. Social network analysis is the study of social groups as networks of nodes connected by social ties. This approach examines individuals and groups in the context of relationships between group members. Application of social network analysis to animal behaviour can advance the field by identifying and quantifying specific attributes of social relationships, many of which are not captured by more common measures of sociality, such as group size. Sophisticated methods for network construction and analysis exist in other fields, but until recently, have seen relatively little application to animal systems. We present a brief history of social network analysis, a description of basic concepts and previous applications to animal behaviour. We then highlight relevance and constraints of some network measures, including results from an original study of the effect of sampling on network parameter estimates, and we end with promising directions for research. By doing so, we provide a prospective overview of social network analysis' general utility for the study of animal social behaviour.

Abstract: I develop guidelines for assessing the precision and power of statistical techniques that are frequently used to study nonhuman social systems using observed dyadic associations. Association indexes estimate the proportion of time that two individuals are associated. Binomial approximation and nonparametric bootstrap methods produce similar estimates of the precision of association indexes. For a mid-range (0.4-0.9) association index to have a standard error of less than 0.1 requires about 15 observations of the pair associated, and for it to be less than 0.05, this rises to 50 observations. The coefficient of variation among dyads of the proportion of time that pairs of individuals are actually associated describes social differentiation (S), and this may be estimated from association data using maximum likelihood. With a poorly differentiated population (S~0.2), a data set needs about five observed associations per dyad to achieve a correlation between true and estimated association indexes of r=~0.4. It requires about 10 times as much data to achieve a representation with r=~0.8. Permutation tests usually reject the null hypothesis that individuals have no preferred associates when S2H>5, where H is the mean number of observed associations per individual. Thus most situations require substantial numbers of observations of associations to give useful portrayals of social systems, and sparse association data inform only when social differentiation is high.

Abstract: Objective: In the context of the ongoing discussion about keeping horses in individual stable boxes vs. in herds the lack of relaxation of the horse as a flight animal is an argument often put forward against individual housing. The long-term objective of our investigations is to determine the sleep phases in various housing systems in order to find a substantiated answer to that issue. For that purpose, the sleep waves measured by EEG have to be defined beforehand and allocated to the individual stages of sleep. The experiments described here are intended to support this effort. The objective is to establish a method which by means of a portable polysomnograph allows to capture the sleeping behaviour of horses for the purpose of defining the individual stages of sleep. It was investigated which stages of sleep horses undergo, and to what extent they may be comparable to those of humans.
Animals/materials/methods: Given the high technical effort involved, somnographic examination of large animals has so far been difficult and mostly required the animals to be sedated. Meanwhile, however, instruments such as the Somnoscreen by Somnomedics have become available. This is a completely portable polysomnograph with up to 28 channels and wireless online signal transmission and synchronous video transmission to a PC.
Using this instrument, the sleep profile of 5 Icelandic ponies and 10 horses (different race) aged 5-10 years was recorded and evaluated for 4 or 5 nights per horse. The following parameters were assessed: EEG (electroencephalogram), EOG (electrooculogram), EMG (electromyogram), ECG (electrocardiogram), thoracic and abdominal breathing motions as well as identification of the body posture. Synchronous video recordings were made. EEG recordings were obtained through gold-coated disk electrodes with long flexible cables, applied and secured to the scalp.
Results: As with humans, various stages of sleep can also be defined for horses using the above methods of recording. The waking condition is characterised by alpha waves, which just like in humans are within a range of 8-12 Hz. Typical REM phases as in humans were also detected, although not only when stretched completely on their side, as has hitherto always been described, but also when lying on their chest. Phases of deep sleep (stage 4) can also be measured, with the animals mostly in a standing position. The multi-stage human sleeping pattern, which is made up of 4-6 repeat phases of sleep (waking stage eyes open – waking stage eyes closed – REM phase – stage 1 – stage 2 – stage 3 – stage 4 – return to REM phase etc.) was found to be similar in horses in individual sequences. However, the sleep phases are shorter and more frequently interrupted by waking phases.
Conclusions: Horses are flight animals, which is why they have to be “on eye” in every situation so as to be able to flee in the face of danger. In a natural herd lying positions are only assumed if one or more members watch over the herd. In some publications the REM phase is treated as equivalent to the deep sleep phase. Although the REM phase is a phase of total muscle relaxation it is at the same time the dream phase and due to the high frequencies and the low amplitudes in the EOG resembles Stage I. This means that the sleeping horse can be awakened very quickly from this REM phase so as to be able to react to any dangerous situation. It therefore makes sense for the horses to assume a lying position during REM phases as the muscles are relaxed, yet a waking condition can be reached very quickly. A standing position seems to be preferred during deep sleep phases, where waking takes rather long, so that at least the position will not have to be changed. Whether the sleeping behaviour changes depending on age and race has yet to be investigated.