2004
DOI: 10.1016/j.ecolmodel.2003.10.020
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Simulation of alpine chamois Rupicapra r. rupicapra habitat use

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Cited by 12 publications
(8 citation statements)
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“…But goats seem to have a much higher proportion of browsing on woody species (up to 50%) than ungulates such as red deer (Stuber and Bü rgi 2001;Suter and others 2004). Additionally, reconstructed historic goat densities are much higher than modern average ungulate density in temperate forests (for example, Reimoser and Gossow 1996;Cô té and others 2004;Fankhauser and Enggist 2004). Therefore, the impact of woodland pasture on tree species composition might have been even more pronounced compared to browsing by ungulates.…”
Section: Discussionmentioning
confidence: 99%
“…But goats seem to have a much higher proportion of browsing on woody species (up to 50%) than ungulates such as red deer (Stuber and Bü rgi 2001;Suter and others 2004). Additionally, reconstructed historic goat densities are much higher than modern average ungulate density in temperate forests (for example, Reimoser and Gossow 1996;Cô té and others 2004;Fankhauser and Enggist 2004). Therefore, the impact of woodland pasture on tree species composition might have been even more pronounced compared to browsing by ungulates.…”
Section: Discussionmentioning
confidence: 99%
“…An individual-based spatially-explicit model simulating chamois movements (Fankhauser & Enggist 2004) suggested an influence of local topography and habitat structure on the altitudinal movements in our study area (P. Ingold et al, unpubl. data).…”
Section: Data Analysesmentioning
confidence: 92%
“…As chamois Rupicapra rupicapra are the most frequent large herbivore above the treeline in the eastern Alps, with particularly high population densities in the northeastern Calcareous Alps (Krause 1997), we considered this species to be the most important free ranging animal (long‐distance) dispersal vector in the region. We hence used telemetric data on chamois movement, taken from Fankhauser and Enggist (2004), to parameterise the random walk simulations. Essentially, we created a time series of activity states (following the categorization of states and the between‐state transition probabilities in Fankhauser and Enggist (2004)), and then simulated random walks based on a constant step duration of 3 min (or shorter, if the activity state was simulated to change earlier).…”
Section: Methodsmentioning
confidence: 99%