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Sonja Vospernik (email)

Probability of bark stripping damage by red deer (Cervus elaphus) in Austria

Vospernik S. (2006). Probability of bark stripping damage by red deer (Cervus elaphus) in Austria. Silva Fennica vol. 40 no. 4 article id 316. https://doi.org/10.14214/sf.316

Abstract

Bark stripping by red deer (Cervus elaphus) causes considerable damage to Austrian forests, however, the incidence of bark stripping was never examined from large scale survey data. In this manuscript we present a logistic regression model for bark stripping damage (static model) and a model for recent (5-year period) bark stripping damage to previously undamaged trees (dynamic model) developed from Austrian National Forest Inventory data. Both models showed bark stripping damage to be most frequent in core red deer habitat areas and less frequent in less suitable habitat. Damage was concentrated at elevations of 400–1200 m and in alluvial forests (only static model). Norway spruce (Picea abies), European ash (Fraxinus excelsior), Sweet chestnut (Castanea sativa) and Sorbus spp. had 11–12 times more injuries than all the other species. Red deer preferred the smallest trees with a breast height diameter of 5 cm for bark stripping and damage probability decreased rapidly for trees with a breast height diameter greater than 25 cm. Our static model showed a maximum of bark stripping damage in stands with a mean height of 20 m. In the dynamic model the probability for bark stripping damage decreased with decreasing mean height. Also, in the static model the probability for bark stripping damage increased with increasing spruce proportion and with increasing stand density whereas in the dynamic model the proportion of previous bark stripping damage was a good predictor. Goodness of fit and discrimination of both models were good. In combination with forest growth models, the bark stripping models can be used to predict the risk of damage associated with different forest and habitat management options.

Keywords
bark stripping; red deer; logistic regression

Author Info
  • Vospernik, University of Natural Resources and Applied Life Sciences, Institute of Forest Growth Research, Department of Forest and Soil Sciences, Peter-Jordan-Stra§e 82, A-1190 Vienna, Austria ORCID ID:E-mail sonja.vospernik@boku.ac.at (email)

Received 17 January 2006 Accepted 11 August 2006 Published 31 December 2006

Available at https://doi.org/10.14214/sf.316 | Download PDF

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