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Zaccaroni, M., Passilongo, D., Buccianti, A., Dessi-Fulgheri, F., Facchini, C., & Gazzola, A. (2012). Group specific vocal signature in free- ranging wolf packs. Ethol Ecol Evol, 24.
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Blanco, J. C., & Yolanda, C. (2012). Surveying wolves without snow: a critical review of the methods used in Spain. Hystrix. Ital J Mammal, 23.
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Passilongo, D., Dessi-Fulgheri, F., Gazzola, A., Zaccaroni, M., & Apollonio, M. (2012). Wolf counting and individual acoustic discrimination by spectrographic analysis [Abstract]. Bioacoustics, 21.
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Marescot, L., Pradel, R., Duchamp, C., Cubaynes, S., Mrboutin, E., & Choquet, R. (2011). Capture – recapture population growth rate as a robust tool against detection heterogeneity for population management. Ecol Appl, 21.
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Berger, K. M. (2006). Carnivore-Livestock conflicts: effects of subsidized predator control and economic correlates on the sheep industry. Conserv Biol, 20.
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Passilongo, D., Buccianti, A., Dessi-Fulgheri, F., Gazzola, A., Zaccaronii, M., & Apollonio, M. (2010). The Acoustic Structure Of Wolf Howls In Some Eastern Tuscany (Central Italy) Free Ranging Packs. Bioacoustics, 19(3), 159–175.
Abstract: Italian wolf howls are described for the first time from observations between 2003–2008 of a population living in eastern Tuscany, central Italy. A sample of 37 howls selected among single responses and 128 howls included in the choruses of 7 free ranging packs was recorded and analysed. The mean fundamental frequency of the howls ranged between 274–908 Hz. Two main structures recognised by means of multivariate explorative analysis, in particular Principal Component and Cluster Analysis, were ascribed to breaking and flat howls. Discriminant Function Analysis was applied to the recognised groups with the aim to find a general rule for classification. Howls with different features were correctly assigned to the groups obtained by explorative analysis in 95.8% of cases. The analysis of the variables characterising the structure of the howls suggests that maximum frequency and range of fundamental frequency are the most important parameters for classification, while duration does not appear to play any significant role.
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Sueur, J., Aubin, T., & Simonis, C. (2008). Seewave: a free modular tool for sound analysis and synthesis. Bioacoustics, 18.
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Kleiven, J., Bjerke, T., & Kaltenborn, B. P. (2004). Factors influencing the social acceptability of large carnivore behaviours. Biodivers Conserv, 13.
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Passilongo, D., Mattioli, L., Bassi, E., Szabó, L., & Apollonio, M. (2015). Visualizing sound: counting wolves by using a spectral view of the chorus howling. Front. Zool., 12(1), 22.
Abstract: Monitoring large carnivores is a central issue in conservation biology. The wolf (Canis lupus) is the most studied large carnivore in the world. After a massive decline and several local extinctions, mostly due to direct persecutions, wolves are now recolonizing many areas of their historical natural range. One of the main monitoring techniques is the howling survey, which is based on the wolves' tendency to use vocalisations to mark territory ownership in response to howls of unknown individuals. In most cases wolf howling sessions are useful for the localisation of the pack, but they provide only an aural estimation of the chorus size.
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Walpole, M. J., & Leader-Williams, N. (2002). Tourism and flagship species in conservation. Biodivers Conserv, 11.
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