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.
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: 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? Location: Bavarian Alps, Germany.Methods: 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.Results: 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 nonstationarity 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. Conclusions:Indicator values offer an effect-oriented alternative to physicallybased 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.
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