Abstract: Summary The Ridden Horse Pain Ethogram (RHpE) comprises 24 behaviours, the majority of which are at least 10 times more likely to be seen in lame horses compared with non-lame horses. The observation of >=8/24 behaviours is likely to reflect the presence of musculoskeletal pain, although some lame horses score <8/24 behaviours. A marked reduction in RHpE scores after resolution of lameness using diagnostic anaesthesia proves a causal relationship between pain and RHpE scores. Horses should be assessed for approximately 10?min in walk, trot (including 10?m diameter circles), canter and transitions. The validity of the RHpE has been verified for use in horses which perform dressage-type movements, and which have been trained to work with the front of the head in a vertical position. It has not, as yet, been used in horses while jumping, racehorses, western performance or endurance horses. The RHpE provides a valuable tool for riders, trainers, veterinarians and other equine professionals to recognise the presence of musculoskeletal pain, even if overt lameness cannot be recognised. Riders with a higher skill-level may improve gait quality, but cannot obscure behavioural signs of pain, although specific behaviours may change. Tight saddle tree points, the rider sitting on the caudal third of the saddle and rider weight may influence RHpE scores. Accurate application of the RHpE requires training and practice. The RHpE is a powerful tool for the assessment of ridden horses and the identification of likely musculoskeletal pain. Such pain merits further investigation and treatment, to improve equine welfare and performance. The RHpE provides an additional means of evaluating the response to diagnostic anaesthesia. It provides a mechanism for client education and a diplomatic way of communicating with clients about equine discomfort related to saddle-fit, rider size, their position in the saddle and ability to ride in balance.

Abstract: Predation on livestock is a source of human-wildlife conflicts and can undermine the conservation of large carnivores. To design effective mitigation strategies, it is important to understand the determinants of predation across livestock species, which often differ in husbandry practices, vulnerability to predators and economic value. Moreover, attention should be given to both predation occurrence and intensity, because these can have different spatial patterns and predictors. We used spatial risk modelling to quantify factors affecting wolf predation on five livestock species in Portugal. Within the 1619 parishes encompassing the entire wolf range in the country, the national wolf compensation scheme recorded 17,670 predation events in 2009-2015, each involving one or more livestock species: sheep (31.7%), cattle (27.7%), goats (26.8%), horses (14.8%) and donkeys (3.2%). Models built with 2009-2013 data and validated with 2014-2015 data, showed a shared general pattern of predation probability on each species increasing with its own density and proximity to wolf packs. For some species there were positive relations with the density of other livestock species, and with habitat variables such as altitude, and land cover by shrubland and natural pastures. There was also a general pattern for predation intensity on each species increasing with its own density, while proximity to wolf packs had no significant effects. Predation intensity on goats, cattle and horses increased with the use of communal versus private pastures. Our results suggest that although predation may occur wherever wolves coexist with livestock species, high predation intensity is mainly restricted to particular areas where husbandry practices increase the vulnerability of animals, and this is where mitigation efforts should concentrate.

Abstract: The innovation process is currently undergoing significant change in many industries. The World Wide Web has created a virtual world of collective intelligence and helped large groups of people connect and collaborate in the innovation process [1]. Von Hippel [2], for instance, states that a large number of users of a given technology will come up with innovative ideas. This process, originating in business, is now also being observed in science. Discussions around “Citizen Science” [3] and “Science 2.0” [4] suggest the same effects are relevant for fundamental research practices. “Crowdsourcing” [5] and “Open Innovation” [6] as well as other names for those paradigms, like Peer Production, Wikinomics, Swarm Intelligence etc., have become buzzwords in recent years. However, serious academic research efforts have also been started in many disciplines. In essence, these buzzwords all describe a form of collective intelligence that is enabled by new technologies, particularly internet connectivity. The focus of most current research on this topic is in the for-profit domain, i.e. organizations willing (and able) to pay large sums to source innovation externally, for instance through innovation contests. Our research is testing the applicability of Crowdsourcing and some techniques from Open Innovation to the scientific method and basic science in a non-profit environment (e.g., a traditional research university). If the tools are found to be useful, this may significantly change how some research tasks are conducted: While large, apriori unknown crowds of “irrational agents” (i.e. humans) are used to support scientists (and teams thereof) in several research tasks through the internet, the usefulness and robustness of these interactions as well as scientifically important factors like quality and validity of research results are tested in a systematic manner. The research is highly interdisciplinary and is done in collaboration with scientists from sociology, psychology, management science, economics, computer science, and artificial intelligence. After a pre-study, extensive data collection has been conducted and the data is currently being analyzed. The paper presents ideas and hypotheses and opens the discussion for further input.