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Miyata, H., Gajdon, G. K., Huber, L., & Fujita, K. (2011). How do keas (Nestor notabilis) solve artificial-fruit problems with multiple locks? Anim. Cogn., 14(1), 45–58.
Abstract: Keas, a species of parrots from New Zealand, are an interesting species for comparative studies of problem solving and cognition because they are known not only for efficient capacities for object manipulation but also for explorative and playful behaviors. To what extent are they efficient or explorative, and what cognitive abilities do they use? We examined how keas would solve several versions of artificial-fruit box problems having multiple locks. After training keas to remove a metal rod from over a Plexiglas lid that had to be opened, we exposed the birds to a variety of tasks having two or more locks. We also introduced a preview phase during which the keas had extended opportunity to look at the tasks before the experimenter allowed the birds to solve them, to examine whether the preview phase would facilitate the birds' performance on the tasks. In a large number of tests, the keas showed a strong trend to solve the tasks with no positive effect of previewing the tasks. When the tasks became complex, however, the keas corrected inappropriate responses more quickly when they had had chance to preview the problems than when they had not. The results suggest that the keas primarily used explorative strategies in solving the lock problems but might have obtained some information about the tasks before starting to solve them. This may reflect a good compromise of keas' trial-and-error tendency and their good cognitive ability that result from a selection pressure they have faced in their natural habitat.
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Henry, S., Fureix, C., Rowberry, R., Bateson, M., & Hausberger, M. (2017). Do horses with poor welfare show 'pessimistic' cognitive biases? Sci. Nat., 104(1), 8.
Abstract: This field study tested the hypothesis that domestic horses living under putatively challenging-to-welfare conditions (for example involving social, spatial, feeding constraints) would present signs of poor welfare and co-occurring pessimistic judgement biases. Our subjects were 34 horses who had been housed for over 3 years in either restricted riding school situations (e.g. kept in single boxes, with limited roughage, ridden by inexperienced riders; N = 25) or under more naturalistic conditions (e.g. access to free-range, kept in stable social groups, leisure riding; N = 9). The horses' welfare was assessed by recording health-related, behavioural and postural indicators. Additionally, after learning a location task to discriminate a bucket containing either edible food ('positive' location) or unpalatable food ('negative' location), the horses were presented with a bucket located near the positive position, near the negative position and halfway between the positive and negative positions to assess their judgement biases. The riding school horses displayed the highest levels of behavioural and health-related problems and a pessimistic judgment bias, whereas the horses living under more naturalistic conditions displayed indications of good welfare and an optimistic bias. Moreover, pessimistic bias data strongly correlated with poor welfare data. This suggests that a lowered mood impacts a non-human species' perception of its environment and highlights cognitive biases as an appropriate tool to assess the impact of chronic living conditions on horse welfare.
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Iliopoulos, Y., Sgardelis, S., Koutis, V., & Savaris, D. (2009). Wolf depredation on livestock in central Greece. Mamm. Reas., 54(1), 11–22.
Abstract: We studied wolfCanis lupus Linnaeus, 1758 -- livestock conflict in central Greece by investigating patterns of 267 verified wolf attacks on livestock for 21 months. Wolves attacked adult goats 43% and cattle 218% more than expected, whereas sheep 41% less than expected from their availability. Wolves killed less than four sheep or goats in 79%, and one cow or calf in 74% of depredation events, respectively. We recorded higher attack rates during wolf post-weaning season. Wolf attacks on strayed, or kept inside non predator-proof enclosures, sheep and goats, were on average two to four times respectively more destructive than those when livestock was guarded by a shepherd. Sheepdog use reduced losses per attack. Optimal sheepdog number ranged from 3 to 9 animals depending on flock size. Losses per attack were positively related to the number of wolves involved. Total losses per farm were positively correlated with the size of livestock unit but percentage losses per capita increased with decreasing flock size. Management implications to mitigate livestock depredation are discussed.
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Genov, P. W., & Kostava, V. (1993). Untersuchungen zur zahlenmäßigen Stärke des Wolfes und seiner Einwirkung auf die Haustierbestände in Bulgarien. Zeitschrift für Jagdwissenschaft, 39(4), 217–223.
Abstract: Die Untersuchung wurde in der Zeitspanne von 1984 bis 1988 durchgeführt. Es wurden die Protokolle des Staatlichen Versicherungsinstituts benutzt, die Angaben für Raubüberfälle von Wölfen auf Haustiere beinhalten (Tabelle 1). Außerdem wurden Angaben über die während dieser Zeitspanne erlegten Wölfe zusammengefaßt. Die Abschußzahlen lauten: 1984 – 163, 1985 – 147, 1986 – 179, 1987 – 211 und 1988 – 220 Tiere. Die Anzahl der in den einzelnen Gebirgen lebenden Wölfe wurde nach einer Umfrage festgestellt. Für die in Betracht kommenden Gebirge werden folgende Bestandszahlen angenommen: Rhodopen -- 60-80 Individuen, 189 bis 264 km2 pro Tier, Rila- und Piringebirge -- 60-80 Tiere, 109 bis 145 km2 pro Tier, Ossogowo-Belassiza Gebirgssystem -- 40-50 Individuen, 57-70 km2 pro Tier, West- und Mittelbalkan -- 35-38 Wölfe, 200 km2 pro Tier. Dazu kommen noch 10-15 Wölfe im Flußbecken von Beli Lom und etwa 20 Exemplare in Strandscha- und Sakargebirge. Insgesamt lebten in Bulgarien im Jahre 1988 etwa 260-330 Wölfe (Abb. 1).
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Ruid, D. B., Paul, W. J., Roell, B. J., Wydeven, A. P., Willging, R. C., Jurewicz, R. L., et al. (2009). Wolf-Human Conflicts and Management in Minnesota, Wisconsin, and Michigan. In A. P. Wydeven, T. R. Van Deelen, & E. J. Heske (Eds.), Recovery of Gray Wolves in the Great Lakes Region of the United States: An Endangered Species Success Story (pp. 279–295). New York, NY: Springer New York.
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Irving-Pease, E. K., Ryan, H., Jamieson, A., Dimopoulos, E. A., Larson, G., & Frantz, L. A. F. (2019). Paleogenomics of Animal Domestication. In C. Lindqvist, & O. P. Rajora (Eds.), Paleogenomics: Genome-Scale Analysis of Ancient DNA (pp. 225–272). Cham: Springer International Publishing.
Abstract: Starting with dogs, over 15,000 years ago, the domestication of animals has been central in the development of modern societies. Because of its importance for a range of disciplines – including archaeology, biology and the humanities – domestication has been studied extensively. This chapter reviews how the field of paleogenomics has revolutionised, and will continue to revolutionise, our understanding of animal domestication. We discuss how the recovery of ancient DNA from archaeological remains is allowing researchers to overcome inherent shortcomings arising from the analysis of modern DNA alone. In particular, we show how DNA, extracted from ancient substrates, has proven to be a crucial source of information to reconstruct the geographic and temporal origin of domestic species. We also discuss how ancient DNA is being used by geneticists and archaeologists to directly observe evolutionary changes linked to artificial and natural selection to generate a richer understanding of this fascinating process.
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Boyce, P. N., & McLoughlin, P. D. (2021). Ecological Interactions Involving Feral Horses and Predators: Review with Implications for Biodiversity Conservation. Jour. Wild. Mgmt., n/a(n/a).
Abstract: ABSTRACT For many ecosystems, feral horses are increasingly becoming an important if not dominant component of ungulate biomass and hence influence on community dynamics. Yet we still know little of how horses contribute to key ecological interactions including predator-prey and indirect competitive relationships at a community level. Notably, feral species like horses can exhibit life-history traits that differ from that of native (mainly artiodactyl) herbivore competitors. Artificial selection for traits like increased, early, or extended reproduction that have yet to be reversed by natural selection, coupled with naturally selected differences in anatomy and behavior, in addition to unique management objectives for horses compared to other species, means that the dynamics of feral horse populations are not likely to align with what might be expected of other large herbivores. Unexpected population dynamics and inherent biological asymmetries between native ungulates and feral horses may therefore influence the former via direct competition for shared resources and through enemy-mediated interactions like apparent competition. In several localities feral horses now co-exist with multiple native prey species, some of which are in decline or are species at risk. Compounding risks to native species from direct or indirect competitive exclusion by horses is the unique nature and socio-political context of feral horse management, which tends towards allowing horse populations to be limited largely by natural, density-dependent factors. We summarize the inherent asymmetries between feral horse biology and that of other ungulate prey species with consequences for conservation, focusing on predator-prey and emerging indirect interactions in multi-prey systems, and highlight future directions to address key knowledge gaps in our understanding of how feral horses may now be contributing to the (re)structuring of food webs. Observations of patterns of rapid growth and decline, and associated skews in sex ratios of feral horse populations, indicate a heightened potential for indirect interactions among large ungulate prey species, where there is a prevalence of feral horses as preferred prey, particularly where native prey are declining. In places like western North America, we expect predator-prey interactions involving feral horses to become an increasingly important factor in the conservation of wildlife. This applies not only to economically or culturally important game species but also at-risk species, both predators (e.g., wolves [Canis lupus], grizzly bears [Ursus arctos]) and prey (e.g., woodland caribou [Rangifer tarandus caribou]), necessitating an ecological understanding of the role of horses in natural environments that goes beyond that of population control. ? 2021 The Wildlife Society.
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Hofmeester, T. R., Cromsigt, J. P. G. M., Odden, J., Andrén, H., Kindberg, J., & Linnell, J. D. C. (2019). Framing pictures: A conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison. Ecol Evol, .
Abstract: Abstract Obtaining reliable species observations is of great importance in animal ecology and wildlife conservation. An increasing number of studies use camera traps (CTs) to study wildlife communities, and an increasing effort is made to make better use and reuse of the large amounts of data that are produced. It is in these circumstances that it becomes paramount to correct for the species- and study-specific variation in imperfect detection within CTs. We reviewed the literature and used our own experience to compile a list of factors that affect CT detection of animals. We did this within a conceptual framework of six distinct scales separating out the influences of (a) animal characteristics, (b) CT specifications, (c) CT set-up protocols, and (d) environmental variables. We identified 40 factors that can potentially influence the detection of animals by CTs at these six scales. Many of these factors were related to only a few overarching parameters. Most of the animal characteristics scale with body mass and diet type, and most environmental characteristics differ with season or latitude such that remote sensing products like NDVI could be used as a proxy index to capture this variation. Factors that influence detection at the microsite and camera scales are probably the most important in determining CT detection of animals. The type of study and specific research question will determine which factors should be corrected. Corrections can be done by directly adjusting the CT metric of interest or by using covariates in a statistical framework. Our conceptual framework can be used to design better CT studies and help when analyzing CT data. Furthermore, it provides an overview of which factors should be reported in CT studies to make them repeatable, comparable, and their data reusable. This should greatly improve the possibilities for global scale analyses of (reused) CT data.
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McGreevy, P., & Yeates, J. (2018). Horses (Equus caballus). In Companion Animal Care and Welfare. Companion Animal Care and Welfare.
Abstract: Summary Domestic horses are equid members of the class Mammalia, order Perissodactyla, and family Equidae. Horses are obligate herbivores, with nutritional requirements as listed in a table. Adequate space is necessary for exercise, exploration, flight, sharing resources, play, and rolling. Company is essential for all horses, including stallions. Company provides opportunities for mutual grooming and play and allows horses to stand head-to-tail to remove flies. Unhandled horses may respond to humans as they would to predators, whereas handled horses' responses depend on their previous interactions with humans. Horses can suffer from several diseases as listed in another table. The best method of euthanasia of horses is usually sedation followed by either cranial shooting or the injection of an overdose of pentobarbitone into the jugular vein. Behavioural signs of distress can include increased locomotory activity, vigilance behaviours, neighing, snorting, pawing, nibbling walls and buckets, defaecation, rearing, kicking stable walls or doors, and high-stepping 'prancing'.
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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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