Abstract• Existing growth and yield plots of pure and mixed stands of Norway spruce (Picea abies (L.) H. Karst.) and European beech (Fagus sylvatica L.) were aggregated in order to unify the somewhat scattered sources of information currently available, as well as to develop a sound working hypothesis about mixing effects. The database contains information from 23 long-term plots, covering an ecological gradient from nutrient poor and dry to nutrient rich and moist sites throughout Central Europe.• An empirically formed interaction model showed, that depending on the site conditions, dry mass growth in mixed stands can range from −46% to +138 % of the growth yielded by a scaled combination of pure stands at equal mixing proportions.• Drawing from the interaction model, overyielding of the mixed stands appears to be triggered by two separate mechanisms. On poor sites, where significant overyielding is commonly found, facilitation by beech offsets nutrient-related growth limitations in spruce. In contrast, overyielding of mixed stands occurs less frequently on rich sites, and appears to be based on an admixture effect, with spruce reducing the severe intra-specific competition common in pure beech stands.• It was concluded that silviculture can accelerate growth of spruce by beech admixtures on poor sites, while growth of beech can be promoted by admixture of spruce, particularly on excellent sites.

No abstract

The impact of climate change on the soil microbiome potentially alters the biogeochemical cycle of terrestrial ecosystems. In semi-arid environments, water availability is a major constraint on biogeochemical cycles due to the combination of high summer temperatures and low rainfall. Here, we explored how 10 years of irrigation of a water-limited pine forest in the central European Alps altered the soil microbiome and associated ecosystem functioning. A decade of irrigation stimulated tree growth, resulting in higher crown cover, larger yearly increments of tree biomass, increased litter fall and greater root biomass. Greater amounts of plant-derived inputs associated with increased primary production in the irrigated forest stands stimulated soil microbial activity coupled with pronounced shifts in the microbiome from largely oligotrophic to more copiotrophic lifestyles. Microbial groups benefitting from increased resource availabilities (litter, rhizodeposits) thrived under irrigation, leading to enhanced soil organic matter mineralization and carbon respired from irrigated soils. This unique long-term study provides new insights into the impact of precipitation changes on the soil microbiome and associated ecosystem functioning in a water-limited pine forest ecosystem and improves our understanding of the persistency of long-term soil carbon stocks in a changing climate.

No abstract

For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems’ behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding.