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.

Abstract: Monitoring of the wolf Canis lupus is a demanding task as it lives in low densities, utilizes vast home ranges and disperses over large areas. These factors make obtaining accurate data about population parameters over the whole distribution area of the species impossible. Thus detailed local studies on socio-spatial organization are essential to calibrate information obtained over a larger area. We applied GPS/GSM telemetry, non-invasive genetic sampling, year-round tracking, camera trapping and howling stimulations to determine the number of family groups, population density and home-range sizes of wolves in the Drawa Forest (DF, western Poland, 2500 km2), an area recently recolonized by the species. Home ranges of three collared male wolves ranged from 321.8 to 420.6 km2 (MCP 100%) and from 187.5 to 277.5 km2 (Kernel 95%), but core areas had a size of 30.5-84.7 km2 (MCP50%) and 35.0-88.8 km2 (Kernel 50%). Mean near neighbour distance between centres of 6 tracked pack homesites was 15.3 km. The number of wolves in DF increased from 14 individuals in 2013/2014 to 30 in 2016/2017. The annual rate of increase varied from 43% in 2014/2015 to 7% in the final year. Population density for the whole study area was relatively low (1.2 indiv./100 km2 in 2016/2017), but densities within territories of two packs studied with telemetry were 1.9 and 1.5 indiv./100 km2. Mean pack size varied between 3.5 and 5.6 individuals, with the largest pack comprising 8 wolves. Mean number of pups observed in summers (June-August) was 4.5. Differences in home range sizes between wolves in western and eastern Poland indicate that results of regional studies cannot be freely extrapolated despite close genetic relationships. Thus, decisions related to management of wolf habitats should be based on intensive local studies.

Abstract: The effects of hyperflexion on the welfare of dressage horses have been debated. This study aimed to investigate acute stress responses of dressage horses ridden in three different Head-and-Neck-positions (HNPs). Fifteen dressage horses were ridden by their usual rider in a standardised 10-min dressage programme in either the competition frame (CF), hyperflexion (“Low-Deep-and-Round”; LDR) or a looser frame (LF) in a balanced order on three separate test days. Heart rate (HR), heart rate variability parameters (HRV), behaviour and rein tension were recorded during the test. Salivary cortisol concentrations were measured 60min before and 0, 5, 15 and 30min after the test. Rein tension was significantly lower in LF and did not differ between CF and LDR; however approx. 15% of recordings in CF and LDR were above the sensor detection limit of 5kg. The horses had significantly higher cortisol concentrations directly after LDR compared to LF. In addition, the horses showed more distinctive head movements, including head waving, during LDR. There were no significant treatment effects on HR and HRV. In conclusion, the results indicate that LDR may be more stressful to these horses during riding.