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Hampson, B. A., Zabek, M. A., Pollitt, C. C., & Nock, B. (2011). Health and behaviour consequences of feral horse relocation. Rangel. J., 33(2), 173–180.
Abstract: Despite ongoing projects involving the breeding and release of equids into semi-wild and wild environments, insufficient information is available in the literature that describes strategies used by equids to adapt and survive in a novel environment. The aim of this study was to assess the ability of naïve, feral Equus caballus (horse) mares to cope in a novel feral horse environment and investigate possible reasons why some may not survive this challenge. Four mares taken from a semi-arid desert environment remained in good health but significantly changed their movement behaviour pattern when surrounded by prime grazing habitat in a mesic temperate grassland. Three of the four mares captured from the prime grazing habitat and released in the semi-arid desert habitat died, apparently due to stress and/or starvation, within 8 weeks of release. The fourth mare survived 4 months but lost considerable weight.The group of mares relocated to the semi-arid desert environment had difficulty adapting to relocation and did not take up the movement behaviour strategy of local horses, which required long distance treks from a central water hole to distant feeding areas at least 15 km away. The movement behaviour, range use and health consequences of relocating equids may be of interest to wildlife ecologists, animal behaviourists and horse welfare groups. The observations may be used to guide those intending on relocating managed domestic and native horses to novel habitats.
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Bates, D. (2005). Fitting linear mixed models in R. R News, 5.
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Price, E. O. (1984). Behavioral aspects of animal domestication. Q Rev Biol, 59.
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Gardner, E. L., & Engel, D. R. (1971). Imitational and social facilitatory aspects of observational learning in the laboratory rat. Psychon. Sci., 25(1), 5–6.
Abstract: Rats acquired a food-motivated leverpressing response by “observational learning” or by trial-and-error learning under conditions of social facilitation or isolation. Both the observational learning and social facilitation Ss learned faster than did the isolated trial-and-error Ss. There was no difference in speed of learning between the observational learning and social facilitation groups. It is suggested that some previous studies purporting to demonstrate observational learning may have demonstrated socially facilitated trial-and-error learning instead.
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Meddock, T., & Osborn, D. (1968). Neophobia in wild and laboratory mice. Psychol Sci, 12.
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Zentall, T. R., Sutton, J. E., & Sherburne, L. M. (1996). True imitative learning in pigeons. Psychol Sci, 7.
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Liker, A., & Bókony, V. (2009). Larger groups are more successful in innovative problem solving in house sparrows. Proc Natl Acad Sci USA, 106(19), 7893–7898.
Abstract: Group living offers well-known benefits to animals, such as better predator avoidance and increased foraging success. An important additional, but so far neglected, advantage is that groups may cope more effectively with unfamiliar situations through faster innovations of new solutions by some group members. We tested this hypothesis experimentally by presenting a new foraging task of opening a familiar feeder in an unfamiliar way to house sparrows in small and large groups (2 versus 6 birds). Group size had strong effects on problem solving: sparrows performed 4 times more and 11 times faster openings in large than in small groups, and all members of large groups profited by getting food sooner (7 times on average). Independently from group size, urban groups were more successful than rural groups. The disproportionately higher success in large groups was not a mere consequence of higher number of attempts, but was also related to a higher effectiveness of problem solving (3 times higher proportion of successful birds). The analyses of the birds' behavior suggest that the latter was not explained by either reduced investment in antipredator vigilance or reduced neophobia in large groups. Instead, larger groups may contain more diverse individuals with different skills and experiences, which may increase the chance of solving the task by some group members. Increased success in problem solving may promote group living in animals and may help them to adapt quickly to new situations in rapidly-changing environments.
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Morand-Ferron, J., & Quinn, J. L. (2011). Larger groups of passerines are more efficient problem solvers in the wild. Proc Natl Acad Sci USA, 108(38), 15898–15903.
Abstract: Group living commonly helps organisms face challenging environmental conditions. Although a known phenomenon in humans, recent findings suggest that a benefit of group living in animals generally might be increased innovative problem-solving efficiency. This benefit has never been demonstrated in a natural context, however, and the mechanisms underlying improved efficiency are largely unknown. We examined the problem-solving performance of great and blue tits at automated devices and found that efficiency increased with flock size. This relationship held when restricting the analysis to naive individuals, demonstrating that larger groups increased innovation efficiency. In addition to this effect of naive flock size, the presence of at least one experienced bird increased the frequency of solving, and larger flocks were more likely to contain experienced birds. These findings provide empirical evidence for the “pool of competence” hypothesis in nonhuman animals. The probability of success also differed consistently between individuals, a necessary condition for the pool of competence hypothesis. Solvers had a higher probability of success when foraging with a larger number of companions and when using devices located near rather than further from protective tree cover, suggesting a role for reduced predation risk on problem-solving efficiency. In contrast to traditional group living theory, individuals joining larger flocks benefited from a higher seed intake, suggesting that group living facilitated exploitation of a novel food source through improved problem-solving efficiency. Together our results suggest that both ecological and social factors, through reduced predation risk and increased pool of competence, mediate innovation in natural populations.
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Sabou, M., Bontcheva, K., & Scharl, A. (2012). Crowdsourcing Research Opportunities: Lessons from Natural Language Processing. In Proceedings of the 12th International Conference on Knowledge Management and Knowledge Technologies (pp. 1–18). i-KNOW '12. New York, NY, USA: Acm.
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Bücheler, T., & Sieg, J. H. (2011). Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method. Proceedings of the 2nd European Future Technologies Conference and Exhibition 2011 (FET 11), 7, 327–329.
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.
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