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European beech (Fagus sylvatica L.) is the most important tree species in Central Europe and is considered to be relatively resistant to climate warming. However, dry summers in the last five years led to considerable damage in beech stands in Southern Germany (SG). Assisted migration of drought resistant beech provenances including those of Oriental beech (Fagus orientalis Lipsky) may help to stabilise Central European beech forests under climate change. The focus of this study is to compare the climatic ranges of F. sylvatica and F. orientalis using quantile distribution of climatic variables based on WorldClim data at forest sites within their natural distribution area. Temperature, precipitation, and aridity quantile ranges showed that F. orientalis is better adapted to warmer and drier climate compared to F. sylvatica. The quantile distribution method was applied to the whole range of the species to map the habitat suitability for both species at marginal sites in the target region (SG) in the current climatic scenario (1970-2000) and in a warmer scenario (+2°C) using the climate marginality index (CMI), i.e., the distance of sites to the xeric edge at low-latitude and low-altitude distribution limits for the species. To this purpose we applied the simple BIOCLIM algorithm using annual temperature and precipitation as climatic variables. According to our results, F. orientalis seems a promising species with a high potential for future afforestation activities in Southern Germany, especially at marginal sites of European beech forests. However, before introducing F. orientalis on a larger scale in the study area, further research on the species ecology and genetics are needed. For further application of the quantile range method, we produced tables of the vigintiles of the climatic range for both species, which can be used for estimating CMI based on WorldClim data in other regions. [ABSTRACT FROM AUTHOR]

Based on macroecological data, we test the hypothesis whether European tree species of temperate and boreal distribution maintain their water and nutrient supply in the more arid southern margin of their distribution range by shifting to more fertile soils with higher water storage than in their humid core distribution range (cf. soil compensatory effects). To answer this question, we gathered a large dataset with more than 200,000 plots that we related to summer aridity (SA), derived from WorldClim data, as well as soil available water capacity (AWC) and soil nutrient status, derived from the European soil database. The soil compensatory effects on tree species distribution were tested through generalized additive models. The hypothesis of soil compensatory effects on tree species distribution under limiting aridity was supported in terms of statistical significance and plausibility. Compared to a bioclimatic baseline model, inclusion of soil variables systematically improved the models’ goodness of fit. However, the relevance measured as the gain in predictive performance was small, with largest improvements for P. sylvestris, Q. petraea and A. alba. All studied species, except P. sylvestris, preferred high AWC under high SA. For F. sylvatica, P. abies and Q. petraea, the compensatory effect of soil AWC under high SA was even more pronounced on acidic soils. Soil compensatory effects might have decisive implications for tree species redistribution and forest management strategies under anthropogenic climate change. Therefore, soil compensatory effects deserve more intensive investigation, ideally, in studies combining different spatial scales to reduce the uncertainty associated with the precision of soil information. [ABSTRACT FROM AUTHOR]

Many uncertainties emerge dealing with future climate conditions and their possible impacts on forests. In this paper we suggest a probabilistic approach extending existing ensemble species distribution modeling concepts by addressing important sources of uncertainty. We exemplify our approach using European beech as the target tree species and bioclimatic predictors derived from WorldClim data. Model parameter uncertainty is represented by 1000 parameter samples from a Bayesian generalized linear model. Climate change impact ( CCI ) is based on 63 different climate model outputs using four RCP-scenarios ( R epresentative C oncentration P athways) in addition to the parameter uncertainty. The proposed difference of the probability of occurrence p occ to a predefined threshold, allows for evaluation of parameter uncertainty as well as for the uncertainty of future climate and describes the changing niche position. Further, we suggest the probability that the probability of occurrence exceeds a predefined threshold ( p exc ) as a metric for the distance of a site to the niche edge. These metrics are unambiguously determinable, intuitive and evident. Stands at a central, a marginal and an intermediate niche position are taken to exemplify deduction and application of p occ and p exc . A regional exercise shows how a map of p exc may support forest management planning and decision making under severe uncertainty. A key advantage of our novel metrics is the reformulation of common species distribution model (SDM) outputs in terms of risk thereby accounting for two important sources of uncertainty. [ABSTRACT FROM AUTHOR]

Questions: How can SDMs be adopted as a tool for forest management planning? Based on presence-absence data, which modelling techniques are appropriate to determine species potential distribution for forest management planning under climate change? Do species distribution models (SDMs) agree with expert knowledge about species distribution and species traits? How can forest researcher deal with distribution data of a species whose distribution is heavily affected by human impacts? Location: Bavaria (Southern Germany). Methods: We used SDMs based on the Second National Forest Inventory from 2002 (4 × 4 km grid) containing presence-absence data of tree species to identify species environment relationships ('Grinnellian niche'). As an example, the distribution of silver fir ( Abies alba Mill.) was modelled. Site condition data of the plots were derived from solar radiation, climate and soil maps. Models applied were boosted regression trees (BRT) and generalised additive models (GAM). Model predictions were compared with an expert based evaluation of the potential natural vegetation and were run with a climate change scenario (WETTREG B1) to project future distribution of silver fir. Results: Both models discriminated well between presence and absence of silver fir but underestimated the potential distribution. The BRT model was more sensitive to local site conditions in the present data, but the GAM showed more generality. The truncated response curves and high uncertainties of predictions at the edge of the site spectrum indicated a low data density and that the data did not cover the whole niche space of silver fir. As indicated by validation with expert knowledge, the model output approached potential distribution by optimizing true positive predictions. The classification of SDMs output into risk classes allowed model evaluation and interpretation. Predictions of GAM and BRT under the climate change scenario showed high accordance and therefore, low uncertainty. Finally, large areas of Bavaria are described to have a high risk of silver fir cultivation in future. Conclusions: SDMs are especially interesting as a decision basis for forest management because some of the general limitations of static modelling approaches are not relevant in this context. Limitations of forest inventory data can be partially overcome by using information on the potential distribution of species. The transferability of the models to future scenarios strongly depends on the spectrum and range of the training data sets and the depicted functional relationships. In order to improve the models and reduce the uncertainties, we need to improve the soil data and cover the whole niche space of silver fir. [ABSTRACT FROM AUTHOR]

Climate change may reduce forest growth and increase forest mortality, which is connected to high carbon costs through reductions in gross primary production and net ecosystem exchange. Yet, the spatiotemporal patterns of vulnerability to both short‐term extreme events and gradual environmental changes are quite uncertain across the species' limits of tolerance to dryness. Such information is fundamental for defining ecologically relevant upper limits of species tolerance to drought and, hence, to predict the risk of increased forest mortality and shifts in species composition. We investigate here to what extent the impact of short‐ and long‐term environmental changes determines vulnerability to climate change of three evergreen conifers (Scots pine, silver fir, Norway spruce) and two deciduous hardwoods (European beech, sessile oak) tree species at their southernmost limits of distribution in the Mediterranean Basin. Finally, we simulated future forest growth under RCP 2.6 and 8.5 emission scenarios using a multispecies generalized linear mixed model. Our analysis provides four key insights into the patterns of species' vulnerability to climate change. First, site climatic marginality was significantly linked to the growth trends: increasing growth was related to less climatically limited sites. Second, estimated species‐specific vulnerability did not match their a priori rank in drought tolerance: Scots pine and beech seem to be the most vulnerable species among those studied despite their contrasting physiologies. Third, adaptation to site conditions prevails over species‐specific determinism in forest response to climate change. And fourth, regional differences in forests vulnerability to climate change across the Mediterranean Basin are linked to the influence of summer atmospheric circulation patterns, which are not correctly represented in global climate models. Thus, projections of forest performance should reconsider the traditional classification of tree species in functional types and critically evaluate the fine‐scale limitations of the climate data generated by global climate models. Current and forecasted trends in tree growth from temperate and boreal tree species growing at the southernmost limit of their natural distribution in Europe. The tree growth projections by 2100 revealed a generalized decrease in growth under the climatic conditions derived from the RCP 8.5 emission scenario. [ABSTRACT FROM AUTHOR]