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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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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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Galaverni, M., Palumbo, D., Fabbri, E., Caniglia, R., Greco, C., & Randi, E. (2012). Monitoring wolves (Canis lupus) by non-invasive genetics and camera trapping: A small-scale pilot study. Eur J Wildl Res, 58.
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Stenglein, J. L., Waits, L. P., Ausband, D. E., Zager, P., & Mack, C. M. (2011). Estimating gray wolf pack size and family relationships using non invasive genetic sampling at rendezvous sites. J Mammal, 92.
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Core Development Team, R. (2011). R: a language and environment for statistical computing. Vienna, Austria: R foundation for statistical computing.
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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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Morgan, T. W., & Elliott, C. L. (2011). Comparison of remotely-triggered cameras vs. howling surveys for estimating coyote (Canis latrans) Abundance in central Kentucky. J Ky Acad Science, 72.
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Primack, R. B. (2010). Essentials of conservation biology. Fifth: Edition.
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Huron, D. (2010). Voice Denumerability of Homogeneous Timbres. Music Percept Interdiscip J, 6.
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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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