The creation of artificial snags, so-called high stumps, within forest management operations is a recently established tool to enrich standing deadwood as a habitat for saproxylic species. In this study, we analysed the impact of active high stump management on saproxylic beetle species. We selected 63 high stumps in six Bavarian forest districts (Germany), which were felled and subjected to close examination, focusing on beetle (Coleoptera) colonization. We identified 63 emerged coleopteran species belonging to 29 families; a further 10 taxa were identified only at the genus or family level, respectively. Moreover, 17% of the obtained taxa are listed in the German Red List of Coleoptera. Furthermore, 32% of the examined high stump trunks, predominantly broad-leaved tree species, harboured Red List beetle taxa. In particular, trembling aspen (Populus tremula) showed a disproportionately high number of Red List beetle species. The total species richness of beetles was independent of the height, diameter and decay stage of the snags. High stumps (snags) containing Red List beetle species tended to have higher amounts of deadwood in their surroundings, but the difference was not significant. According to the results of our study, actively creating high stumps proved to be a suitable method for creating habitats and serve as stepping-stones for endangered saproxylic species. Proactive high stump management during harvest can be a valuable component of deadwood management and biodiversity protection in forests.

The thickness and composition of forest floors plays an essential role for the efficiency and resilience of mountain forests to store carbon, water and nutrients. Up to now, the distribution of particularly thick organic forest floors (TOFF) in the Bavarian Calcareous Alps is poorly known and their ecosystem services deserve increased consideration under climate change. We wanted to improve the knowledge of the TOFF-distribution and to investigate the forcing processes and ecological functions of TOFF. We aimed to quantify their carbon storage potential and to model areas in which humus management is mandatory for sustainable forest use. We drew a stratified sample of soil profiles. Through the combination of relief and soil parameters, we identified crucial control variables and modelled actual and potential (without human disturbance) forest floor thickness in the Bavarian Calcareous Alps based on quantile regression and Generalized Additive Models (GAM). TOFF were predicted to occur on approximately 10% of the forest area of the Bavarian Alps. A decisive condition for the development of TOFF was the absence or only shallow development of mineral fine soil. Contrary to conventional wisdom, these TOFF were found across a wide range of (montane to subalpine) elevations. C-storage of TOFF amounts to ca. 6.9 t C/ha per cm of humus depth and ca. 5.2 Mt C in the study area, resulting in C accumulations comparable to peatlands. TOFF are decisive for the delivery of ecosystem services, especially in the protection forests of the Bavarian Calcareous Alps. Due to the absence or ephemeral depth of mineral soil, all ecological functions depend solely on the forest floor. Therefore, the careful handling of the humus stock is mandatory for a sustainable management in these forests.

Bislang war es weitgehend unklar, welche Rolle die Humuspflege in der Bewirtschaftung der Bergwälder in den Bayerischen Alpen spielt. Eine Umfrage im Rahmen des Projektes «Alpenhumus als klimasensitiver C-Speicher und entscheidender Standortfaktor im Bergwald» sollte den aktuellen Wissensstand in der forstlichen Praxis klären und herausfinden, ob und welche unterstützenden Massnahmen notwendig sind, um die Humuspflege und den Humusaufbau in den Gebirgswäldern der Bayerischen Alpen zu verbessern. Die Rückmeldungen aus 29 Fragebögen zeigten, dass die Försterinnen und Förster den Anteil der Tangelhumusstandorte mit mehr als 15 cm Humusmächtigkeit in ihrem Zuständigkeitsgebiet auf knapp 20% schätzen. Hochgerechnet auf die Waldfläche der Bayerischen Alpen ergibt dies einen Anteil von rund 48 250 ha. Wichtigster Indikator des Forstpersonals für die Lokalisation mächtiger Humusauflagen ist die Bodenvegetation. Etwa 20% der Befragten geben einen sichtbaren Rückgang von Tangelhumus an. Als Hauptgefährdung für Tangelhumus nennen die Befragten die Bodenerosion, die Klimaerwärmung, das Ausbleiben der Verjüngung und die Kronennutzung. Als relevante Massnahmen zur Humuspflege geben sie das Belassen von Kronenmaterial im Bestand, die Einleitung und/oder Sicherstellung der Verjüngung sowie die Jagd an. Zur Verbesserung des Humusaufbaus wünschen sich die Befragten gezieltere Planungen, eine Änderung des Schalenwildmanagements und Schulungen für die Waldbesitzer. Verbesserte Karten mit Geodaten, eine Förderung der natürlichen Waldentwicklung und ein Leitfaden zur Humuspflege sollen dabei zusätzlich unterstützen.

At two forest sites in Germany (Pfaffenwinkel, Pustert) stocked with mature Scots pine (Pinus sylvestris L.), we investigated changes of topsoil chemistry during the recent 40 years by soil inventories conducted on replicated control plots of fertilization experiments, allowing a statistical analysis. Additionally, we monitored the nutritional status of both stands from 1964 until 2019 and quantified stand growth during the monitoring period by repeated stand inventories. Moreover, we monitored climate variables (air temperature and precipitation) and calculated annual climatic water balances from 1991 to 2019. Atmospheric nitrogen (N) and sulfur (S) deposition between 1964 and 2019 was estimated for the period 1969–2019 by combining annual deposition measurements conducted in 1985–1987 and 2004 with long‐term deposition records from long‐term forest monitoring stations. We investigated interrelations between topsoil chemistry, stand nutrition, stand growth, deposition, and climate trends. At both sites, the onset of the new millennium was a turning point of important biogeochemical processes. Topsoil acidification turned into re‐alkalinization, soil organic matter (SOM) accumulation stopped, and likely turned into SOM depletion. In the new millennium, topsoil stocks of S and plant‐available phosphorus (P) as well as S and P concentrations in Scots pine foliage decreased substantially; yet, age‐referenced stand growth remained at levels far above those expected from yield table data. Tree P and S nutrition as well as climate change (increased temperature and drought stress) have replaced soil acidification as major future challenges for both forests. Understanding of P and S cycling and water fluxes in forest ecosystems, and consideration of these issues in forest management is important for successfully tackling the new challenges. Our study illustrates the importance of long‐term forest monitoring to identify slow, but substantial changes of forest biogeochemistry driven by natural and anthropogenic global change.

Questions Although thick forest floors overlying unweathered bedrock are important resources for mountain forests' functioning, their actual distribution is poorly known and difficult to delimit in the field. We therefore asked: (a) What is the specific composition of vegetation growing on Folic Histosols; (b) can indicator plants be used to detect Folic Histosols in mountain forests; (c) what do functional traits of plant indicators tell about the ecological properties of Folic Histosols? Location Northern Calcareous Alps, south Germany. Methods Based on representative stratified sampling of joint vegetation plots and soil profile descriptions, we estimated the frequency and thickness of Folic Histosols, determined the proportion of compositional variation specifically attributable to forest floor thickness using ordination, applied Indicator Species Analysis and searched for typical traits and ecological requirements of indicator species. Results The co‐existence of acidophilic and calciphytic plants is typical for the tessellated occurrence and the successional origin of Folic Histosols. In the study region, the detection of Folic Histosols on pure limestone or dolomite by ground vegetation works very well. Particularly acidophilic plants are suitable indicators for thick forest floors. The indicator value of bryophytes and Ericaceae for Folic Histosols is likely related to the colonization of rotten wood. Folic Histosol indicator species are widely spread in the allocation to sociology group, which ranges from open landscapes to dark forests and reflects successional origin. Conclusions In mountain forests on carbonate bedrock, thick humus layers often occur next to bare rock. This tessellated structure can also be detected in the ground vegetation, where acidophilic and calciphytic plants occur side by side. Thick Folic Histosols in late successional forests are dominated by acidophilic plants colonizing rotten wood. Thus, the detection of Folic Histosols by understorey species is an easy and cost‐effective possibility and one key to protect these vulnerable forest sites.

Utilisation of forest biomass for power and heat production plays an important role in Bavarian forestry. On sites with low levels of available nutrients, sustainability might be impaired when whole crowns and forest residues are extracted. Harvesting can be adapted by roughly delimbing coniferous crowns, leaving most branches and needles on site. During field trials, biomass exports were reduced by 17 w-%. The additional work needed amounted to 38.4 min/ODT (motor-manual variant) or 4.2 min/ODT (fully mechanised variant). Forwarding productivity was increased and could in favourable conditions (i.e. when driving longer distances) compensate the extra costs. The method can be used as an economically and ecologically feasible alternative for energy wood utilisation on sites with low levels of available nutrients.

Because of some land-use practices (such as overstocking with wild ungulates, historical clear-cuts for mining, and locally persisting forest pasture), protective forests in the montane vegetation belt of the Northern Limestone Alps are now frequently overaged and poorly structured over large areas. Windthrow and bark beetle infestations have generated disturbance areas in which forests have lost their protective functions. Where unfavorable site conditions hamper regeneration for decades, severe soil loss may ensue. To help prioritize management interventions, we developed a geographic information system-based model for assessing sensitivity to site degradation and applied it to 4 test areas in the Northern Limestone Alps of Austria and Bavaria. The model consists of (1) analysis of site conditions and forest stand structures that could increase sensitivity to degradation, (2) evaluation of the sensitivity of sites and stands, and (3) evaluation and mapping of mountain forests' sensitivity to degradation. Site conditions were modeled using regression algorithms with data on site parameters from pointwise soil and vegetation surveys as responses and areawide geodata on climate, relief, and substrate as predictors. The resulting predictor–response relationships were applied to test areas. Stand structure was detected from airborne laser scanning data. Site and stand parameters were evaluated according to their sensitivity to site degradation. Sensitivities of sites and stands were summarized in intermediate-scale sensitivity maps. High sensitivity was identified in 3 test areas with pure limestone and dolomite as the prevailing sensitivity level. Moderately sensitive forests dominate in the final test area, Grünstein, where the bedrock in some strata contains larger amounts of siliceous components (marl, mudstone, and moraines); degraded and slightly sensitive forests were rare or nonexistent in all 4 test areas. Providing a comprehensive overview of site and forest stand structure sensitivity to site degradation, our sensitivity maps can serve as a planning instrument for the management and protection of mountain forests.

Many mountain forests of the Bavarian Alps are found on sites with low supply of N, P and K and, in fewer cases, of Mg and Ca. In order to identify these sites, which are sensitive to biomass extraction, in the field, plant species of the soil vegetation can be used as indicators. The aim of our study was to identify indicator species of nutrient-poor forest sites in the Bavarian Alps by analysing a comprehensive database of coincident vegetation plots and soil profiles. A total of 745 vascular plant species and the moss genus Sphagnum were tested for their relevance in this regard by indicator-species analysis. For the analysis, a total of 1,496 forest sites were classified according to their nutrient supply and analysed statistically. Potentilla erecta, Vaccinium vitis-idaea, Homogyne alpina and Huperzia selago were identified as general indicator species of nutrient-poor sites. Occurrences of Vaccinium myrtillus (cover >= 5%) as well as of Juncus effusus, Luzula sylvatica and Luzula pilosa indicate nutrient-poor, acid mineral soils whereas Calamagrostis varia, Sesleria albicans, Melampyrum sylvaticum, Aster bellidiastrum and Anthoxanthum odoratum are closely connected to nutrient-poor calcareous sites on limestone and dolomite. The presented indicator species were specifically compiled for the nutrient-poor forest sites of the Bavarian Alps. They allow on-site recognition of nutrient-poor forest sites without labor-intensive and time-consuming effort. The method is particularly useful, as site maps allow only a broad categorisation of nutrient supply.

Due to advances in spatial modeling and improved availability of digital geodata, traditional mapping of potential natural vegetation (PNV) can be replaced by ecological modeling approaches. We developed a new model to map forest types representing the potential natural forest vegetation in the Bavarian Alps. The TRM model is founded on a three-dimensional system of the ecological gradients temperature (T), soil reaction (R), and soil moisture (M). Within such a "site cube" forest types are defined as homogenous site units that give rise to forest communities with comparable species composition, structure, production and protective functions. The three gradients were modeled using regression algorithms with area-wide, high resolution geodata on climate, relief and soil as predictors and average Ellenberg indicator values for temperature, acidity and moisture of vegetation plots as dependent variables summarizing plant responses to ecological gradients. The resulting predictor-response relationships allowed us to predict gradient positions of each raster cell in the region from geodata layers. The three-dimensional system of gradients was partitioned into 26 forest types, which can be mapped for the whole region. TRM-based units are supplemented by 22 forest types of special sites defined by other ecological factors such as geomorphology, for which individual GIS rules were developed. The application of our model results in an intermediate-scale map of potential natural forest vegetation, which is based on an explicit function of temperature, reaction and moisture and is therefore consistent and repeatable in contrast to traditional PNV maps.

We present an approach to produce maps of Ellenberg values for soil reaction (R-value) in the Bavarian Alps. Eleven meaningful environmental predictors covering GIS-derived information on climatic, topographic and soil conditions were used to predict R-values. As dependent variables, Ellenberg indicator values for soil reaction were queried from plot records in the vegetation database WINALPecobase. We used an additive georegression model, which combines complex prediction models and the increased prediction accuracy of a boosting algorithm. In addition to environmental predictors we included spatial effects into the model to account for spatial autocorrelation. As we were particularly interested in the usefulness of averaged R-values for spatial prediction, we applied two different models: (1) a geo-additive regression model that estimates mean R-values and (2) a proportional odds model predicting the probability distribution over R-values 1 to 9. We found meaningful dependencies between the R-value and our predictors. Both models produced the same spatial pattern of predictions. Spatial effects had an impact only in the first model. The main drawback of mean R-values is the oversimplification of complex conditions of soil reaction, which is entailed by averaging and regression to mean values. Therefore, regionalized average indicator values provide only limited information on site-ecological characteristics. Model 1 failed to predict the range and shapes of original indicator spectra precisely. In contrast, the second model provided a more sophisticated picture of soil reaction. To make the multivariate output of model 2 comparable to that of model 1, we propose to plot the distribution in a three-dimensional color-space. In addition, comparison of both models based on a multiple linear regression model resulted in a R 2 of 0.93. The proportional odds model is a promising approach also for other indicator values and different regions as well as for other ordinal-scaled ecological parameters.

Can forest site characteristics be used to predict Ellenberg indicator values for soil moisture? Which is the best averaged mean value for modelling? Does the distribution of soil moisture depend on spatial information? Bavarian Alps, Germany. We used topographic, climatic and edaphic variables to model the mean soil moisture value as found on 1505 forest plots from the database WINALPecobase. All predictor variables were taken from area-wide geodata layers so that the model can be applied to some 250 000 ha of forest in the target region. We adopted methods developed in species distribution modelling to regionalize Ellenberg indicator values. Therefore, we use the additive georegression framework for spatial prediction of Ellenberg values with the R-library mboost, which is a feasible way to consider environmental effects, spatial autocorrelation, predictor interactions and non-stationarity simultaneously in our data. The framework is much more flexible than established statistical and machine-learning models in species distribution modelling. We estimated five different mboost models reflecting different model structures on 50 bootstrap samples in each case. Median R 2 values calculated on independent test samples ranged from 0.28 to 0.45. Our results show a significant influence of interactions and non-stationarity in addition to environmental covariates. Unweighted mean indicator values can be modelled better than abundance-weighted values, and the consideration of bryophytes did not improve model performance. Partial response curves indicate meaningful dependencies between moisture indicator values and environmental covariates. However, mean indicator values <4.5 and >6.0 could not be modelled correctly, since they were poorly represented in our calibration sample. The final map represents high-resolution information of site hydrological conditions. Indicator values offer an effect-oriented alternative to physically-based hydrological models to predict water-related site conditions, even at landscape scale. The presented approach is applicable to all kinds of Ellenberg indicator values. Therefore, it is a significant step towards a new generation of models of forest site types and potential natural vegetation.

WINALPecobase (GIVD ID EU-DE-003) is an ecological database of mountain forest plots in the Bavarian Alps (Germany).Created in 2009, the database features the following characteristics: (1) 1,505 georeferenced forest relevés with concomitant soil profile descriptions, (2) placement across the whole study area (ca.4,600 km²) according to a design that combines systematic and stratified sampling, (3) consistent standards for vegetation and soil inventory, and (4) extensive quality control of the database.The database is available for collaborative research.

Nach dem Prinzip „vom Punkt auf die Fläche zum Anw e der“ wurde ein praxisorientiertes Waldinformationssystem für d ie Bergwälder der Nordalpen aufgebaut. In einem Geographischen Informationssyst em (GIS) wurden alle verfügbaren Punktdaten (Bodenprofile, Vegetationsaufna hmen, Forstinventuren) und flächendeckenden Geodaten (Gestein, Boden, Relief, Klima, Vegetation) vereint und forstlich relevante Standortsdaten mit Hilfe vo n neuesten GIS-Techniken abgeleitet. Forstleute finden konkrete Hinweise auf d ie potentiell natürliche Waldzusammensetzung und die vorherrschenden Standortbedin gungen der Bergwälder sowie Abschätzungen zur Baumarteneignung, Empfindli chkeit gegenüber Biomassenutzung und zum Wuchspotenzial.

Question: Which thermal climate model performs best in predicting the combined effects of temperature and radiation on forest vegetation in the Bavarian Alps?Location: Bavarian Alps, Germany.Methods: In order to find the best model for effective thermal climate for the Bavarian Alps, we analysed models using the following predictors derived from climate data and/or a digital elevation model: (a) temperature variables only, (b) temperature plus slope aspect and inclination, and (c) temperature plus potential global solar radiation. Models were tested by linear regression against four response variables based on average Ellenberg indicator values for temperature (cover weighted/unweighted, with/without bryophytes), which were computed for 2280 georeferenced releves from the vegetation database BERGWALD. We optimized (b) by empirically searching for thermally most favourable slope aspect and inclination.Results: Closest model fit was achieved for unweighted temperature values based on vascular plants without bryophytes. Model fit (adj. R(2)) increased from using temperature alone to temperature-radiation, to temperature-aspect-inclination as predictors. The best spatially explicit model for predicting temperature values (adj. R(2) = 0.57) was based on the variable combination mean temperature in the growing season (May to September), slope aspect (optimal aspect 195 degrees) and inclination (optimal slope 30 degrees).Conclusion: Combining mean temperatures and relief variables in GIS allows creation of predictive maps of mountain forest response to thermal climate. Applied to climate change scenarios, our model can forecast potential vegetation distribution in the future. The superiority of simple empirical relief factors over a widely used model of potential radiation casts doubt on the meaningfulness of the latter for vegetation studies.

Question: What are the main drivers for tree species distribution in the Bavarian Alps? What are the species-specific habitat requirements? Are predictions in accordance with expert knowledge? Location: Bavarian Alps (Southern Germany). Methods: To describe tree species–environment relationships, we established species distribution models for the 14 most common tree species of the region. We combined tree species occurrence data from forest inventories and a vegetation database with environmental data from a digital elevation model, climate maps and soil maps. For modelling, we used generalized additive models (GAM) combined with techniques to account for spatial autocorrelation and uneven coverage of environmental gradients. We developed parsimonious models to judge whether statistical models correspond to models based on expert knowledge. Results: Conceptual models were generally in accordance with expectations. Variables based on average temperatures were the most important predictors in most models. Proxies for soil properties such as water and nutrient availability were statistically significant and generally plausible, but appeared largely redundant for model performance. Altitudinal limits of tree species were generally well represented by models. Most species responded differently to summer and January temperatures, indicating that temperature variables are proxies not only for energy balance, but also for frost damage and drought. Although model building benefits considerably from collation with expert knowledge, there are limitations. Conclusions: Meaningful species distribution models can be obtained from noisy data sets covering only a small fraction of species ranges. Models calibrated with such data sets benefit from hypothesis-driven model building rather than strict data-driven model building. Hence, misleading explanations and predictions can be avoided and uncertainties identified. Nevertheless, projections based on climate scenarios can be substantially improved only with models calibrated on a wider data set. Ideally, environmental gradients should cover the whole niche space of a species, or at least include regions with analogous climate.

Question: What are the main drivers for tree species distribution in the Bavarian Alps? What are the species-specific habitat requirements? Are predictions in accordance with expert knowledge?