Abstract: Since the first sporadic occurrences of grey wolves (Canis lupus) west of the Polish border in 1996, wolves have shown a rapid population recovery in Germany. Wolves are known to avoid people and wolf attacks on humans are very rare worldwide. However, the subjectively perceived threat is considerable, especially as food-conditioned habituation to humans occurs sporadically. Lower Saxony (Germany) has an exceedingly higher human population density than most other regions with territorial wolves; thus, the potential for human-wolf conflicts is higher. Using hunters' wildlife survey data from 455 municipalities and two years (2014-2015) and data from the official wolf monitoring (557 confirmed wolf presences and 500 background points) collected between 2012-2015, grey wolf habitat selection was modelled using generalized additive models with respect to human population density, road density, forest cover and roe deer density. Moreover, we tested whether habitat use changed in response to human population and road density between 2012/2013 and 2014/2015. Wolves showed a preference for areas of low road density. Human population density was less important as a covariate in the model of the survey data. Areas with higher prey abundance (5-10 roe deer/km2) and areas with >20% forest cover were preferred wolf habitats. Wolves were mostly restricted to areas with the lowest road and human population densities. However, between the two time periods, avoidance of human density decreased significantly. Recolonization of Germany is still in its early stages and it is unclear where this process will halt. To-date authorities mainly concentrate on monitoring measures. However, to avoid conflict, recolonization will require more stringent management of wolf populations and an improved information strategy for rural populations.

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 social and spatial organization of urban fox groups prior to and during an outbreak of sarcoptic mange was compared with predictions derived from the resource dispersion hypothesis (RDH). We investigated the availability of three key resources. Neither daytime rest sites nor breeding sites appeared to be limited in availability. The availability of food deliberately supplied by local householders was examined by questionnaire surveys. The daily and weekly amount of food supplied was greatly in excess of the minimum requirements of a pair of foxes, but was consistent between territories. The availability of this food source increased markedly as a result of more people feeding the foxes. In agreement with the RDH, group size prior to the outbreak of mange increased from 2.25 animals (N=4) to 6.57 animals (N=7). Before the outbreak of mange, two territories were divided. Increased scavenge availability on smaller territories may have promoted these changes. Excluding these spatial changes, territories were very stable between years. After the outbreak of mange, group size declined as a direct result of mange-induced mortality. Surviving animals increased their ranges only after neighbouring groups had died out. Ranges did not increase in size in response to a decline in food availability. Nor were the increases in range size associated with the relinquishment of parts of the existing territory. These postmange changes are contrary to the RDH. Three factors may have promoted these changes: the elimination of interstitial space, the forced dispersal of young or future division of the territory.

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: Summary Many publications documenting large-scale trends in the distribution of species make use of opportunistic citizen data, that is, observations of species collected without standardized field protocol and without explicit sampling design. It is a challenge to achieve reliable estimates of distribution trends from them, because opportunistic citizen science data may suffer from changes in field efforts over time (observation bias), from incomplete and selective recording by observers (reporting bias) and from geographical bias. These, in addition to detection bias, may lead to spurious trends. We investigated whether occupancy models can correct for the observation, reporting and detection biases in opportunistic data. Occupancy models use detection/nondetection data and yield estimates of the percentage of occupied sites (occupancy) per year. These models take the imperfect detection of species into account. By correcting for detection bias, they may simultaneously correct for observation and reporting bias as well. We compared trends in occupancy (or distribution) of butterfly and dragonfly species derived from opportunistic data with those derived from standardized monitoring data. All data came from the same grid squares and years, in order to avoid any geographical bias in this comparison. Distribution trends in opportunistic and monitoring data were well-matched. Strong trends observed in monitoring data were rarely missed in opportunistic data. Synthesis and applications. Opportunistic data can be used for monitoring purposes if occupancy models are used for analysis. Occupancy models are able to control for the common biases encountered with opportunistic data, enabling species trends to be monitored for species groups and regions where it is not feasible to collect standardized data on a large scale. Opportunistic data may thus become an important source of information to track distribution trends in many groups of species.