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Recent temperature observations suggest a general warming trend that may be causing the range of tree species to shift to higher latitudes and altitudes. Since biotic interactions such as herbivory can change tree species composition, it is important to understand their contribution to vegetation changes triggered by climate change. To investigate the response of forests to climate change and herbivory by wild ungulates, we used the forest gap model ForClim v2.9.6 and simulated forest development in three climatically different valleys in the Swiss Alps. We used altitudinal transects on contrasting slopes covering a wide range of forest types from the cold (upper) to the dry (lower) treeline. This allowed us to investigate (1) altitudinal range shifts in response to climate change, (2) the consequences for tree species composition, and (3) the combined effect of climate change and ungulate herbivory. We found that ungulate herbivory changed species composition and that both basal area and stem numbers decreased with increasing herbivory intensity. Tree species responded differently to the change in climate, and their ranges did not change concurrently, thus causing a succession to new stand types. While climate change partially compensated for the reductions in basal area caused by ungulate herbivory, the combined effect of these two agents on the mix of the dominant species and forest type was non-compensatory, as browsing selectively excluded species from establishing or reaching dominance and altered competition patterns, particularly for light. We conclude that there is an urgent need for adaptive forest management strategies that address the joint effects of climate change and ungulate herbivory. [ABSTRACT FROM AUTHOR]

Abstract: To examine the speed of tree regeneration in lower subalpine forests (1300–1700m a.s.l.) of the European Alps after blowdown events, we developed a spatially explicit simulation model. Calibrated with data from 10 years of observation on extensive blowdown areas in Switzerland, the model incorporates the interactions between the changing herb layer and the growth of Norway spruce (Picea abies (L.) Karst.). The model consists of a vegetation model and a tree model: the vegetation model simulates spatial changes in the herb layer using a cellular automaton combined with a Markov transition matrix. Herb layer changes include the dispersal of tall herbs and raspberry (Rubus idaeus) on different microsite types, the growth of pioneer tree species (Sorbus aucuparia), the decay of fallen trunks, and their colonization by plants. The tree model simulates growth and demographic processes of spruce trees (P. abies) with an individual-based approach, including seed dispersal, germination, establishment, growth and mortality as a function of herb layer properties. The model was evaluated through sensitivity analysis along with stepwise multiple rank regression. As a measure of the goodness of fit, we compared the model results with independent field data. The sensitivity analysis showed that seed availability, seedling survival and the presence of advance regeneration are the key processes for successful reforestation. The model reliably simulated the observed tree height distributions after 8 and 10 years (R 2 between 0.81 and 0.93). Long-term simulations yielded initially sparse spruce regeneration, which is retarded by unfavourable microsite conditions. The results indicate that the recovery of the forest to protect against avalanches and landslides may not be achieved for several decades on blowdown sites with large distances to the nearest forest edge, and thus low seed supply. On sites where less than 10% of the surface was covered by logs and with higher seed supply, the protection against avalanches and landslides may be sufficient again about 50 years after a blowdown event. [Copyright &y& Elsevier]

Abstract: Forest gap models have been applied widely to examine forest development under natural conditions and to investigate the effect of climate change on forest succession. Due to the complexity and parameter requirements of such models a rigorous evaluation is required to build confidence in the simulation results. However, appropriate data for model assessment are scarce at the large spatial and temporal scales of successional dynamics. In this study, we explore a data source for the evaluation of forest gap models that has been used only little in the past, i.e., large-scale National Forest Inventory data. The key objectives of this study were (a) to examine the potentials and limitations of using large-scale forest inventory data for evaluating the performance of forest gap models and (b) to test two particular models as case studies to derive recommendations for their future improvement. We used data from the first Swiss National Forest Inventory to examine the species basal area and tree numbers in different diameter classes simulated by the gap models ForClim (version 2.9.3) and PICUS (version 1.4) for forest types that are typical of mountain forests in Switzerland. The results showed the potential of data from large-scale forest inventories for evaluating model performance. Since this type of data is typically based on a large number of samples across environmental gradients, they are particularly suited for investigations at the general level of the dominant species based on stand basal area. However, the surprisingly small variability of juvenile trees (trees <12cm diameter at breast height; dbh) indicated limitations of the data used. Insufficient representativeness due to small sample plot size and uncertainty regarding past management limit an evaluation of structural forest aspects such as species diversity, and number of small trees (dbh<12cm). The examined models reproduced the observed species composition satisfactorily. However, there were clear model deficiencies in the simulation of successional patterns and of juvenile tree numbers. We identified priorities for future model development. We conclude that large-scale forest inventory data can be valuable for model evaluation, particularly when they cover large environmental gradients and do not come from intensively managed forests. Due to their limitations, they must, however, be complemented by other data such as from a full cruise. [Copyright &y& Elsevier]