Abstract: Wolves (Canis lupus) can kill domestic livestock resulting in intense conflicts with humans. Damage to livestock should be reduced to facilitate human-wolf coexistence and ensure positive outcomes of conservation efforts. Current knowledge on the effectiveness of livestock protection measures from wolves is limited and scattered in the literature. In this study, we compiled a dataset of 30 cases describing the application of 11 measures of protecting cattle and smaller livestock against wolves, estimated their effectiveness as a relative risk of damage, and identified the best measures for damage reduction. We found that: (1) lethal control and translocation were less effective than other measures, (2) deterrents, especially fladry which is a fence with ropes marked by hanging colored flags that sway in the wind and provide a visual warning signal, were more effective than guarding dogs; (3) deterrents, fencing, calving control and herding were very effective, but the last two measures included only one case each; and (4) protection of cattle was more effective than that of small stock (sheep and goats, or sheep only) and mixed cattle and small stock. In all of these cases, the relative risk of damage was reduced by 50-100%. Considering Germany as an example of a country with a recovering wolf population and escalating human-wolf conflicts, we suggest electric fences and electrified fladry as the most promising measures, which under suitable conditions can be accompanied by well-trained livestock guarding dogs, and the temporary use of deterrents during critical periods such as calving and lambing seasons. Further research in this field is of paramount importance to efficiently mitigate human-wolf conflicts.

Abstract: As a unique species of equine, the donkey has certain specific variations from the horse. This review highlights the origins of the donkey and how this impacts on its behavior, physiology, and propensity to disease. The donkey is less of a flight animal and has been used by humans for pack and draught work, in areas where their ability to survive poorer diets, and transboundary disease while masking overt signs of pain and distress has made them indispensable to human livelihoods. When living as a companion animal, however, the donkey easily accumulates adipose tissue, and this may create a metabolically compromised individual prone to diseases of excess such as laminitis and hyperlipemia. They show anatomic variations from the horse especially in the hoof, upper airway, and their conformation. Variations in physiology lead to differences in the metabolism and distribution of many drugs. With over 44 million donkeys worldwide, it is important that veterinarians have the ability to understand and treat this equid effectively.

Abstract: Horses’ ability to adapt to new environments and to acquire new information plays an important role in handling and training. Social learning in particular would be very adaptive for horses as it enables them to flexibly adjust to new environments. In the context of horse handling, social learning from humans has been rarely investigated but could help to facilitate management practices. We assessed the impact of human demonstration on the spatial problem-solving abilities of horses during a detour task. In this task, a bucket with a food reward was placed behind a double-detour barrier and 16 horses were allocated to two test groups of 8 horses each. One group received a human demonstration of how to solve the spatial task while the other group received no demonstration. We found that horses did not solve the detour task more often or faster with human demonstration. However, both test groups improved rapidly over trials. Our results suggest that horses prefer to use individual rather than social information when solving a spatial problem-solving task

Abstract: Summary Reliable assessment of animal populations is a long-standing challenge in wildlife ecology. Technological advances have led to widespread adoption of camera traps (CTs) to survey wildlife distribution, abundance and behaviour. As for any wildlife survey method, camera trapping must contend with sources of sampling error such as imperfect detection. Early applications focused on density estimation of naturally marked species, but there is growing interest in broad-scale CT surveys of unmarked populations and communities. Nevertheless, inferences based on detection indices are controversial, and the suitability of alternatives such as occupancy estimation is debatable. We reviewed 266 CT studies published between 2008 and 2013. We recorded study objectives and methodologies, evaluating the consistency of CT protocols and sampling designs, the extent to which CT surveys considered sampling error, and the linkages between analytical assumptions and species ecology. Nearly two-thirds of studies surveyed more than one species, and a majority used response variables that ignored imperfect detection (e.g. presence?absence, relative abundance). Many studies used opportunistic sampling and did not explicitly report details of sampling design and camera deployment that could affect conclusions. Most studies estimating density used capture?recapture methods on marked species, with spatially explicit methods becoming more prominent. Few studies estimated density for unmarked species, focusing instead on occupancy modelling or measures of relative abundance. While occupancy studies estimated detectability, most did not explicitly define key components of the modelling framework (e.g. a site) or discuss potential violations of model assumptions (e.g. site closure). Studies using relative abundance relied on assumptions of equal detectability, and most did not explicitly define expected relationships between measured responses and underlying ecological processes (e.g. animal abundance and movement). Synthesis and applications. The rapid adoption of camera traps represents an exciting transition in wildlife survey methodology. We remain optimistic about the technology's promise, but call for more explicit consideration of underlying processes of animal abundance, movement and detection by cameras, including more thorough reporting of methodological details and assumptions. Such transparency will facilitate efforts to evaluate and improve the reliability of camera trap surveys, ultimately leading to stronger inferences and helping to meet modern needs for effective ecological inquiry and biodiversity monitoring.

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.