Abstract: The numbers of horses grazing at pasture are increasing in developed countries, so a proper understanding of their feeding selectivity and of the tactics they use for extracting nutrients from swards is essential for the management of horses and grasslands. Resource acquisition in herbivores can be optimised through the modulation of their intake and patch selection, both being strongly dependent on the characteristics of swards. However, the principles by which horses adjust their grazing behaviour in response to variations in sward features are not completely understood. The aim of this study was to determine whether the behaviour of horses conforms to optimal foraging models. We hypothesized that, faced with binary choices between vegetative swards of a good and similar quality at three different heights, horses would select the taller sward, i.e. that allowing a higher reward in terms of dry matter intake rate. Three groups of three 2-year-old saddle horses were grazed on a semi-natural pasture that was managed to produce three contrasting sward heights at 6, 11 and 17 cm, in a Latin-square design. The instantaneous intake rate was determined from bite rate measured at pasture on the three sward heights, and bite mass estimated from measurements using swards offered indoors in experimental trays. Daily dry matter intake was estimated individually by total faecal collection and an estimation of digestibility from faecal nitrogen. Short-term (first 30 min) and daily preferences were assessed from the time spent grazing each sward offered in pair-wise tests at pasture. The results show that daily voluntary intake (an average of 21 g DM kg LW-1 day-1) and total grazing time (an average of 14 h day-1) were independent of sward height and of the choice of patches offered. In choice situations, the animals spent more time grazing on the taller sward, both during the first 30 min and at the daily scale. These results show that horses choose between vegetative patches of a good and similar quality according to the predictions from optimal foraging models, and select the one that they can ingest faster. Further research will now have to explore how the horses will adapt their feeding behaviour when they face a trade-off between sward height and quality.

Abstract: Rollkur, the usually coercively obtained hyperflexion of the horse's neck, is employed as a training method by some dressage riders; however, its use is controversial as it may cause discomfort and adversely affect the horse's welfare. The objectives of this study were to determine: (1) if horses showed differences in stress, discomfort and fear responses as measured by heart rate and behaviour when ridden in Rollkur (R) obtained by pressure on the reins compared to regular poll flexion (i.e. with the nose-line being at or just in front of the vertical; N), and (2) if they showed a preference between the two riding styles when given the choice. Fifteen riding horses were ridden 30 times through a Y-maze randomly alternating between sides. Riding through one arm of the Y-maze was always followed by a short round ridden in R, whereas riding through the other arm was followed by a short round ridden in N. Immediately after the conditioning phase, horses were again repeatedly ridden into the maze; however, riders left it to the horse to decide which arm of the maze to enter. During R, horses moved slower and showed more often behavioural signs of discomfort, such as tail-swishing, head-tossing or attempted bucks (P < 0.05), and 14 of the 15 horses chose significantly (P < 0.05) more often the maze-arm associated with N rather than R. Subsequently, eight of the horses were also subjected to two fear tests following a short ride in N as well as a ride in R. During R, horses tended to react stronger (P = 0.092) to the fear stimuli and to take longer (P = 0.087) to approach them. These findings indicate that a coercively obtained Rollkur position may be uncomfortable for horses and that it makes them more fearful and therefore potentially more dangerous to ride. Further studies need to assess horses' reaction to gradual training of Rollkur, as opposed to a coercively obtained hyperflexion, in order to decide whether the practice should be banned.

Abstract: A large body of theoretical and empirical research suggests that kinship influences the development and maintenance of social bonds among group-living female mammals, and that human females may be unusual in the extent to which individuals form differentiated social relationships with nonrelatives. Here we combine behavioral observations of party association, spatial proximity, grooming, and space use with extensive molecular genetic analyses to determine whether female chimpanzees form strong social bonds with unrelated individuals of the same sex. We compare our results with those obtained from male chimpanzees who live in the same community and have been shown to form strong social bonds with each other. We demonstrate that party association is as good a predictor of spatial proximity and grooming in females as it is in males, that the highest party association indices are consistently found between female dyads, that the sexes do not differ in the long-term stability of their party association patterns, and that these results cannot be explained as a by-product of the tendency of females to selectively range in particular areas of the territory. We also show that close kin (i.e. mother-daughter and sibling dyads) are very rare, indicating that the vast majority of female dyads that form strong social bonds are not closely related. Additional analyses reveal that “subgroups” of females, consisting of individuals who frequently associate with one another in similar areas of the territory, do not consist of relatives. This suggests that a passive form of kin-biased dispersal, involving the differential migration of females from neighboring communities into subgroups, was also unlikely to be occurring. These results show that, as in males, kinship plays a limited role in structuring the intrasexual social relationships of female chimpanzees.

Abstract: Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a behavior space spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research.

Abstract: Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network.