In the course of climate change, natural regeneration of oaks (Quercus spp.) is gaining in importance for forest conversion to climate-adapted mixed forests. In order to predict areas in which natural oak regeneration could establish, variables influencing the occurrence and density of oak regeneration were identified using geostatistical zero-altered negative binomial generalized linear models (ZANB). For this purpose, large-scale inventory data from the state forest of Saxony were analysed. The dataset was derived from 6060 permanent plots. The results show that the occurrence of oak regeneration depends on a number of environmental variables. In addition to seed availability, the establishment environment, especially with regard to the light ecology of oak regeneration, was important. High basal area of pine increased the probability for oak regeneration occurrence. The most important variables for the regeneration density of oak have similarly been found to be those describing the seed availability. The highest regeneration densities are predicted within oak stands, with an optimum relationship at 25 m2 ha-1 of oak basal area. The results further show that a high regeneration density was achieved on sites with low fertility and favourable light conditions. Oak regeneration density increased with increasing browsing percent on rowan, indicating that browsing on oak can be reduced if other palatable species are available. Using the identified variables, the occurrence and density of oak regeneration can be predicted in space with high accuracy. The statistical tool developed can be used for planning forest conversion incorporating natural regeneration.
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Citation
Axer M, Martens S, Schlicht R, Eisenhauer D-R, Wagner S (2023). Modelling natural regeneration of Oak in Saxony, Germany: identifying factors influencing the occurrence and density of regeneration. iForest 16: 47-52. - doi: 10.3832/ifor4064-015
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Marco Borghetti
Paper history
Received: Jan 11, 2022
Accepted: Dec 22, 2022
First online: Feb 16, 2023
Publication Date: Feb 28, 2023
Publication Time: 1.87 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2023
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