Abstract: 1. The recent recovery of the wolf in southern Europe has not yet removed the risk
of local extinction. Wolf populations are fragmented and often comprise fewer than
500 individuals. In North America, northern and eastern Europe, wolves feed maiiily
on wild herbivores. In southern Europe, this canid has apparently adapted to feed
also on fruit, rubbish, livestock, small and medium-size mammals.
2. The main conservation problem lies with predation o n domestic ~ingulates,w liich
leads to extensive killing of wolves. The reintroduction of wild large herbivores has
been advocated as a means of reducing attacks on livestock, but predatiori on the
latter may remain high if domestic ungulates are locally abundant.
3. Our synthesis of 15 studies, published in the last 15 years, on food habits of the
wolf in southern Europe, has shown that ungulates have been the main diet component
overall. A significant inverse correlation was found between the occurrence (%) of
wild and domestic ungulates in the diet. The presence of relatively few wild ungulate
species was necessary to reduce predation on livestock.
4. Selection of wild and domestic ungulate prey was influenced mainly by their local
abundance, but also by their accessibility. Feeding dependence on rubbish was local
and rare. In Italy, the consumption of riibbish/fruit and that of ungulates was significantly
negatively correlated. Diet breadth increased as the presence of large prey
in tlie diet decreased.
5. The simultaneous reintroduction of severa1 wild ungulate species is likely to reduce
predation on livestock and may prove to be one of the most effective conservation
measures.

Abstract: Learning through the observation of others allows the transfer of information without the costs incurred during individual trial and error learning. Horses (Equus caballus) are a highly social species, which might be expected to be capable of learning from others, but experimental findings are inconsistent, and potentially confounded by social facilitation effects not related directly to the learning of the task. We refined the methods used in previous equine social learning studies, to examine and distinguish specific social influences on learning of a task: we used predefined group leaders rather than agonistically dominant individuals to demonstrate a detour task to familiar conspecific observers; in addition we had two control groups: a non-observer (true control) and a group with the demonstrator simply present at the goal (social facilitation control). 44 socially kept horses were allocated to one of the three test conditions and took part in five trials each. Success rate, latency and detour direction were recorded. There was no significant difference between the three groups in the likelihood of them succeeding in the task nor latency to succeed; however there was a significant difference in the route chosen by the groups, with the true control choosing the side with the entrance gate significantly more than either the observer group or social facilitation group. Both of the latter two groups chose to go in the same direction relative to themselves, regardless of which side the gate was. Seven out of nine horses in the observer group chose the same direction as their demonstrator every time. Our results show a significant role of social facilitation on detour behaviour and highlight the importance of including adequate controls for simpler cognitive influences on behaviour before claims can be made about the specific learning of motor actions or goal directed behaviour. Social cues may be important to horses if the task is sufficiently challenging and motivationally important, so future work should consider more demanding, but ecologically relevant situations, in order to maximise the potential revelation of social learning effects which do not depend on simple local or stimulus enhancement effects.

Abstract: Understanding the factors that influence horse learning is critical to ensure horse welfare and rider safety. In this study, data were obtained from horses (n=96) training to step backwards through a corridor in response to bit pressure. Following training, learning ability was determined by the latency to step backwards through the corridor when handled on the left and right reins. Additionally, horse owners were questioned about each horse's management, training, behavior and signalment (such as horse breed, age and sex). Factors from these four broad domains were examined using a multiple logistic regression (MLR) model, following an Information Theoretic approach, for associations between horses' behavioral attributes and their ability to learn the task. The MLR also included estimates of the rider's ability and experience as well as owner's perceptions of their horse's trainability and temperament. Results revealed several variables including explanatory variables that correlated significantly with rate of learning. Horses were faster at backing, a behavioral trait, when handled on the right (t = 3.65, df = 94, P < 0.001) than the left side. Thoroughbred horses were slower at completing the tests than other breeds of horses when handled on the left side (LM, F1,48=4.5, P=0.04) and right side (LM, F1,45=6.0, P=0.02). Those in regular work, a training factor, did not learn faster than their unworked counterparts on the right rein but completed the task faster on the left rein (F1,44=5.47, P=0.02). This may reflect differences in laterality and habituation effects. In contrast, more anxious horses were faster at completing the test when handled from the right (Spearman, r=-0.22, P=0.04). It is possible that these horses have an increased arousal level when interacting with handlers, resulting in more engagement with the lesson, accounting for the improved performance results. The findings of this study will help clarify how horse behavior, training and management may influence learning and how their application may optimize learning outcomes. Future equine behavior assessment and research questionnaires should include items that assess these qualities.

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.

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.