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Dickman AJ, Macdonald EA, Macdonald DW. A review of financial instruments to pay for predator conservation and encourage human-carnivore coexistence. Proc Natl Acad Sci. 2011;108:19836-6.
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Mladenoff, D. J., Sickley, T. A., & Wydeven, A. P. (1999). Predicting gray wolf landscape recolonization: logistic regression models vs. new field data. Ecol Appl, 9.
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Passilongo, D., Marchetto, M., & Apollonio, M. (2017). Singing in a wolf chorus: structure and complexity of a multicomponent acoustic behaviour. Hysterix, 28(2), 180–185.
Abstract: Wolf choruses ( Canis lupus ) are complex, multicomponent signals, composed by a series of different vocalizations emitted by a pack. Although howls, the main component, have been highly studied, poor attention has been drawn upon the other vocalizations of the chorus. In this study, we investigate the structure of the chorus by means of the analysis and the quantification of the different components, taking advantage both of the digital sound recording and analysis, and of the modern statistical methodologies. We provide for the first time a detailed, objective description of the types of call emitted during the wolf howlings, combining spectrographic examinations, spectral analyses and automated classifications, with the aim to identify different types of call. Our results show that wolf choruses have a rich, complex structure, that reveals six other types of call, to be added to those howls already described in literature. Wolf choruses are typically composed by other three different types of calls: the bark, i.e. relatively long calls characterized by low frequencies and the presence of harsh components (deterministic chaos); the whimper, characterized by a harmonic structure and a very short duration; and the growl, a call with a noisy structure, low frequencies but relative long duration. Although further investigations are necessary to understand the meaning of the different calls, this research provides a basis for those studies that aim to compare wolves and other canids vocal behaviour.
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Grönemann, K. (2015). Konfliktfeld Pferd und Wolf – Eine Untersuchung zu Einstellungen, Erwartungen und Befürchtungen von Pferdehaltern und Reitsportlern in Niedersachsen. Master's thesis, University Hildesheim, Hildesheim.
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Marr, I., Farmer, K., & Krueger, K. (2018). Evidence for Right-Sided Horses Being More Optimistic than Left-Sided Horses. Animals, 8(12), 219.
Abstract: An individual's positive or negative perspective when judging an ambiguous stimulus (cognitive bias) can be helpful when assessing animal welfare. Emotionality, as expressed in approach or withdrawal behaviour, is linked to brain asymmetry. The predisposition to process information in the left or right brain hemisphere is displayed in motor laterality. The quality of the information being processed is indicated by the sensory laterality. Consequently, it would be quicker and more repeatable to use motor or sensory laterality to evaluate cognitive bias than to perform the conventional judgment bias test. Therefore, the relationship between cognitive bias and motor or sensory laterality was tested. The horses (n = 17) were trained in a discrimination task involving a box that was placed in either a “positive” or “negative” location. To test for cognitive bias, the box was then placed in the middle, between the trained positive and negative location, in an ambiguous location, and the latency to approach the box was evaluated. Results indicated that horses that were more likely to use the right forelimb when moving off from a standing position were more likely to approach the ambiguous box with a shorter latency (generalized linear mixed model, p < 0.01), and therefore displayed a positive cognitive bias (optimistic).
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Pimenta, V., Barroso, I., Boitani, L., & Beja, P. (2018). Risks a la carte: Modelling the occurrence and intensity of wolf predation on multiple livestock species. Biol. Conserva., 228, 331–342.
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.
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Strien, A. J., Swaay, C. A. M., & Termaat, T. (2013). Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J Appl Ecol, 50(6), 1450–1458.
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.
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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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Podsakoff, P. M., MacKenzie, S. B., Lee, J. - Y., & Podsakoff, N. P. (2002). Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol., 85(5), 879–903.
Abstract: Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
$11.95
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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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