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Tree recruitment models are important for predicting the dynamics of uneven-aged forests. Previous studies of recruitment of European beech, Norway spruce and silver fir have shown different ecological amplitudes of these species. However, recruitment in uneven-aged stands and the values of environmental factors at which the greatest and poorest recruitment can be expected remain poorly explained. The main objectives of this study were to 1) explain how 39 stand, site and climatic factors and their interactions influence the number of recruited trees in uneven-aged forests; 2) determine the optimal and critical ranges of influential factors, including stand basal area, number of trees, proportion of tree species, shade casting, soil pH, site productivity, temperature and precipitation; and 3) estimate the maximum expected response of recruitment to changes in stand density while controlling for the effect of other limiting factors. A Tobit censored regression model was used to consider that the observed range of the number of recruited trees is censored at zero. The models were parametrized and validated using 30,963 forest inventory plots (200 m2 each) in uneven-aged forests in Slovenia. The models, which used 9 stand, 6 site and 3 climatic factors, explained 15 %, 10 % and 8 % of the total variation of the number of recruited spruce, fir and beech, respectively. Stand structure was the most important factor, with stand basal area (BA) and the proportion of the studied species having the greatest effect. Site factors including soil pH and rockiness were important for fir recruitment. The number of recruited beech and spruce was positively influenced by decadal precipitation. Higher temperatures decreased recruitment of spruce. Beech was the only species sensitive to shade casting. Recruitment of beech was higher if shade was imposed by tree species other than beech. The optimal and critical ranges of limiting factors differ between species. The model suggests that the optimal stand basal area for recruitment of beech is ≤ 19 m2/ha, which is higher than that for spruce (≤ 16 m2/ha) but lower than that for fir (≤ 28 m2/ha). The maximum predicted response of the studied species to changes in stand basal area shows that stand density control is efficient for regulating recruitment of spruce and beech, but not for fir. The suggested sensitivities and threshold values may be used in individual tree growth models or simulation–optimization studies in support of forest management decisions. Nasl. z nasl. zaslona. Opis vira z dne 5. 12. 2022. Bibliografija: str. 11-13.

Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to projecting the development of forest carbon sink capacity in a future climate. Forest management capabilities are important to include in dynamic vegetation models to account for the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the implementation of a forest management module containing even-age and clear-cut and uneven-age and continuous-cover management alternatives in the dynamic vegetation model LPJ-GUESS. Different age and species structure initialisation strategies and harvest alternatives are introduced. The model is applied at stand and European scales. Different management alternatives are applied in simulations of European beech (Fagus sylvaticus) and Norway spruce (Picea abies) even-aged monoculture stands in central Europe and evaluated against above-ground standing stem volume and harvested volume data from long-term experimental plots. At the European scale, an automated thinning and clear-cut strategy is applied. Modelled carbon stocks and fluxes are evaluated against reported data at the continent and country levels. Including wood harvest in regrowth forests increases the simulated total European carbon sink by 32%% in 1991%2015 and improves the fit to the reported European carbon sink, growing stock, and net annual increment (NAI). Growing stock (156%m3%ha%1) and NAI (5.4%m3%ha1%yr1) densities in 2010 are close to reported values, while the carbon sink density in 2000%2007 (0.085%kg%C%m%2%yr1) equates to 63%% of reported values, most likely reflecting uncertainties in carbon fluxes from soil given the unaccounted for forest land-use history in the simulations. The fit of modelled and reported values for individual European countries varies, but NAI is generally closer to reported values when including wood harvest in simulations.

Global forests are the main component of the land carbon sink, which acts as a partial buffer to CO2 emissions into the atmosphere. Dynamic vegetation models offer an approach to projecting the development of forest carbon sink capacity in a future climate. Forest management capabilities are important to include in dynamic vegetation models to account for the effects of age and species structure and wood harvest on carbon stocks and carbon storage potential. This article describes the implementation of a forest management module containing even-age and clear-cut and uneven-age and continuous-cover management alternatives in the dynamic vegetation model LPJ-GUESS. Different age and species structure initialisation strategies and harvest alternatives are introduced. The model is applied at stand and European scales. Different management alternatives are applied in simulations of European beech (Fagus sylvaticus) and Norway spruce (Picea abies) even-aged monoculture stands in central Europe and evaluated against above-ground standing stem volume and harvested volume data from long-term experimental plots. At the European scale, an automated thinning and clear-cut strategy is applied. Modelled carbon stocks and fluxes are evaluated against reported data at the continent and country levels. Including wood harvest in regrowth forests increases the simulated total European carbon sink by 32 % in 1991–2015 and improves the fit to the reported European carbon sink, growing stock, and net annual increment (NAI). Growing stock (156 m3 ha−1) and NAI (5.4 m3 ha1 yr1) densities in 2010 are close to reported values, while the carbon sink density in 2000–2007 (0.085 kg C m−2 yr1) equates to 63 % of reported values, most likely reflecting uncertainties in carbon fluxes from soil given the unaccounted for forest land-use history in the simulations. The fit of modelled and reported values for individual European countries varies, but NAI is generally closer to reported values when including wood harvest in simulations.