Alternative tree species under climate warming in managed European forests

Besim Balic

Forest Ecology and Management 430 (2018) 485–497 Contents lists available at ScienceDirect Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco Alternative tree species under climate warming in managed European forests T Eric Andreas Thurma, , Laura Hernandezb, Andri Baltensweilerc, Szegin Ayand, Ervin Rasztovitse, Kamil Bielakf, Tzvetan Mladenov Zlatanovg, David Hladnikh,i, Besim Balicj, Alexandra Freudenschussk, Richard Büchsenmeisterk, Wolfgang Falka ⁎ a Bavarian State Institute of Forestry LWF, Dep. Soil and Climate, Germany INIA-CIFOR, Spain c Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Dep. Forest Resources and Management, Switzerland d Kastamonu University, Faculty of Forestry, Dep. of Silviculture, Turkey e NARIC – Forest Research Institute, Hungary f Warsaw University of Life Sciences, Dep. of Silviculture, Poland g Institute of Biodiversity and Ecosystem Research, Bulgarian Academy of Sciences, Bulgaria h University of Ljubljana, Biotechnical Faculty, Dep. of Forestry and Renewable Forest Resources, Slovenia i Slovenian Forestry Institute, Slovenia j Faculty of Forestry in Sarajevo, Forest Management and Urban Greenery, Bosnia and Herzegovina k Federal Research and Training Centre for Forests, Dep. Natural Hazards and Landscape (BFW), Austria b A R T I C LE I N FO A B S T R A C T Keywords: Thermophilic Rare species Species distribution models Climate-soil models Site index models Winners & losers Northward shift Biogeographical regions This study estimates the present and future distribution potential of 12 thermophilic and rare tree species for Europe based on climate-soil sensitive species distribution models (SDMs), and compares them to the two major temperate and boreal tree species (Fagus sylvatica and Picea abies). We used European national forest inventory data with 1.3 million plots to predict the distribution of the 12 + 2 tree species in Europe today and under future warming scenarios of +2.9 and +4.5 °C. The SDMs that were used to calculate the distributions were in a ﬁrst step only given climate variables for explanation. In a second step, deviations which could not be explained by the climate models were tested in an additional soil variable-based model. Site-index models were applied to the found species distribution to estimate the growth performance (site index) under the given climate. We ﬁnd a northward shift of 461 km and 697 km for the thermophilic species over the regarded time period from 2060 to 2080 under a warming scenario of 2.9 °C and 4.5 °C, respectively. Potential winners of climatic warming have their distribution centroid below 48°N. Fagus sylvatica and Picea abies will lose great parts of their potential distribution range (approx. 55 and 60%, respectively). An index of area gain and growth performance revealed Ulmus laevis, Quercus rubra, Quercus cerris and Robinia pseudoacacia as interesting alternatives in managed temperate forests currently dominated by F. sylvatica and P. abies. The 12 investigated species are already in focus in forestry and it has been shown that the changing climate creates conditions for a targeted promotion in European forests. Nevertheless, area winners exhibited lower growth performances. So, forest conversion with these warm-adapted species goes hand in hand with loss of overall growth performance compared to current species composition. So, the results are a premise for a further discussion on the ecological consequences and the consistency with forest socio-economic goals and conservation policies. 1. Introduction Forests cover 33% of the European land area and 96% of this area is managed and contributes to the supply of the resource wood (Forest ⁎ Europe, 2015). Fagus sylvatica L. and Picea abies (L.) Karst. are two of the most common tree species in boreal and temperate European forests (Köble and Seufert, 2001) with high economic relevance especially for the timber industry (Eurostat, 2018). Nevertheless, several studies Corresponding author. E-mail address: thurm@lrz.tum.de (E.A. Thurm). https://doi.org/10.1016/j.foreco.2018.08.028 Received 20 April 2018; Received in revised form 10 August 2018; Accepted 13 August 2018 0378-1127/ © 2018 Elsevier B.V. All rights reserved. Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. step would be to use genetic adaptability of species and to promote drought stress tolerant characteristics or choose drought tolerant populations for planting (Chakraborty et al., 2015; Fady et al., 2016; Montwé et al., 2016). Nonetheless, the mentioned strategies can only mitigate the risk to a certain degree, and in many regions of Europe the increasing temperatures will force a change of tree species composition (Bonan, 2008; Hanewinkel et al., 2013). In order to prevent a successional-driven permanence of the currently dominant forests species and to ensure the social and economic beneﬁts of this sector, it is important to formulate recommendations for the introduction of alternative tree species. Therefore the question arises which species are able to form the next generation of high-resistance forest and can comply with the qualitative and quantitative demands of the wood market. Traditionally, large cultivation trials generated recommendations for new non-native or rare species (a detailed description for several species can be found in Pâques, 2013). However, the long time span of these trials is contrasted by the rapidity of climatic change, and forces us to seek faster methodical ways to recommend species ad hoc. Species distribution models (SDMs) are able to generate such information (Elith and Leathwick, 2009). Modern computation and high quality occurrence data sets make it relatively easy to generate such models. They have therefore become a popular tool in macroecology over the last decades to understand global distribution patterns of species. However, SDMs have scarcely been used as a tool in the forest sector or for predict that their proportion will decrease under climate change in European forests (e.g. Hanewinkel et al., 2013; Kellomaki et al., 2001; Ruiz-Labourdette et al., 2013) and they will be replaced by species with a more thermophilic character (Kullman, 2008). Since timber producing tree species typically have rotation periods of 60 years and more, forest managers must know already today which and where such thermophilic species can replace or enrich the current tree species compositions. Hanewinkel et al. (2013) forecasted a shift in wood assortment in Central Europe from productive species, mainly P. abies and Pinus sylvestris L., to slow-growing species like Mediterranean oaks. This transformation led to lower productivity and land expectation values. However, adaptive forest management provides strategies to mitigate such impacts. Over the last two decades the research on adaption to climate change has greatly increased. Some measures aim at increasing forest stability based on the current tree species spectrum, for instance by decreasing the forest stand density (Aussenac and Granier, 1988; Kohler et al., 2010; Rais et al., 2014). Also, the shortening of the rotation period reduces the risk of unexpected mortality (Seidl et al., 2011). Another strategy is to regenerate or foster suitable tree species mixtures. Several studies have shown that compensatory and facilitative eﬀects between species are able to improve the stability of tree growth (del Río et al., 2017; Lebourgeois et al., 2013; Pretzsch et al., 2013; Thurm et al., 2016) and also the water status of the trees (Schäfer et al., 2018; Schume et al., 2004) by reducing drought stress. A next Fig. 1. Annual temperature and precipitation range of the 12 investigated species plus the two comparative species Fagus sylvatica and Picea abies. The data represents the occurrences of the species in the inventory data in Europe. Climate data derives from WorldClim 1.4 dataset (Hijmans et al., 2005). The species are ranked by their median temperature. 486 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. conservation strategies because of the uncertainty of these models (see similar debate in Pearson and Dawson, 2003) and the often coarse spatial resolution of macroecological studies. SDMs have to meet the spatial and temporal needs of decision-makers and practitioners (Guisan et al., 2013). Therefore it is important to give species-speciﬁc statements in a ﬁne spatial resolution (Falk and Mellert, 2011) when considering model outcomes in tree species choices. European managed forests are anthropogenically driven (e.g. 27% of European forest stands were aﬀorested or regenerated by planting or seeding, 32% are mono-speciﬁc; Forest Europe, 2015), so dispersal limitations or species interactions like competition are not constraints for large areas. Nowadays, it is important to describe species’ climatic requirements to evaluate the species out of their natural range (Booth, 2017). Our study investigated 12 rare and thermophilic tree species (Fig. 1) that might be able to replace or enrich important economic tree species in Central Europe under drier and warmer conditions (e.g. Kölling, 2017; Albrecht and Avila, 2018). The species are not among the major tree species in Europe (UN-ECE/ICP Forests Level I). The small amount of data for these species makes it diﬃcult to model them and so far only a few studies have dealt with their distribution in Europe (see literature review in appendix of Dyderski et al. (2018)). To ﬁll the gap, we investigate which of these species will be winners or losers under two scenarios of changing climate. To facilitate the presentation and understanding of the results we stratify them according to the European biogeographical regions in Roekaerts (2002). As the distribution of forest areas in Europe also inﬂuences the expansion or reduction of a tree species’ area, we include this topic in our investigations. We formulate the above considerations into three hypotheses: (I) the northward shift of Picea abies and Fagus sylvatica is comparable to the 12 investigated thermophilic or non-native species. (II) The potential distribution area of the 12 investigated thermophilic or nonnative species will increase in the European temperate and boreal zone, whereas the area of P. abies and F. sylvatica will decrease. (III) The further south the species is distributed, the greater is its potential area expansion under climatic warming. While the focus is clearly on the changes in the potential distribution of the 12 + 2 species, we add to the ﬁndings the maximum growth performance of each species as an important criterion in assessing suitable alternative species for the European forest sector. 2. Materials & method 2.1. Tree dataset For the study, we analyzed 12 thermophilic tree species (see Fig. 1 for the species list) plus Picea abies and Fagus sylvatica as representatives of one major coniferous and broadleaf timber species in Central Europe. We deﬁned the species as thermophilic (Giesecke, 2016; Kullman, 2008) because of their warmer temperature amplitude compared to tree species P. abies and F. sylvatica (see Fig. 1). There is a long list of species which are under discussion to substitute P. abies and F. sylvatica in Central Europe (e.g. Kölling, 2017; Albrecht and Avila, 2018). Based on growth performance and drought resistance, we selected species which have the potential to increase their importance for forestry in the future (Hanewinkel et al., 2013; Rigling et al., 2013). The species are well known to forestry and present in European inventory data, but at the same time there is limited knowledge on their traits, distribution limits and growth patterns. The climatic requirements of some species, like Abies grandis (Dougl. ex D. Don) Lindl., Castanea sativa Mill., Larix kaempferi (Lamb.) Carr., Prunus avium L., Pinus nigra Arnold., Quercus rubra L. and Robinia pseudoacacia L. are closer to Central European forests, whereas the importance of others like Quercus pubescens Mild. and Quercus cerris L. might increase in the future due to climate change. We aimed not to focus on species that were part of a previous study (Walentowski et al., 2017) that used distribution models, vegetation analysis and tree ring data to assess future suitability of tree species under climate change in southern Germany (e.g. A. campestre, A. platanoides, A. pseudoplatanus, Carpinus betulus, Q. robur, Q. petrea, Sorbus and Tilia species). Our species list includes native and non-native species in Europe (see Table 1). We used the most up-to-date national forest inventory data and forest management plans (see Fig. 2; data description in Supplementary material 1) from 12 European countries, representing a wide range of diﬀerent climatic zones. National forest inventory data originated from Spain, France, Switzerland, Austria, Italy, Germany, Poland, Slovenia and Bosnia-Herzegovina and the forest management plans came from Hungary and Bulgaria, and large parts of Turkey. These data contained occurrences and biometric parameters (diameter at breast height 1.3 m – DBH, tree height, age (for most countries), social tree classes by Kraft (1884)) of the investigated species which were used to rank the species Table 1 Overview of the model quality for the 14 tree species with the applied abbreviation of the tree species (Abb.) and the classiﬁcation if the species is native (Nat.) in Europe (Y – Native, No – Non-Native); separated according to pure climate model and climate & soil model. Quality indices are sensitivity (SE), speciﬁty (SP) and the true skill statistic (TSS). N presents the underlying number of data points for the ﬁnal SDM (presence – absence; 50:50). Columns ‘Site Index Models’ include model quality of the site index models with the coeﬃcient of determination R2 and the number of data points NG. Species Names Abies grandis Acer monspessulanum Castanea sativa Fagus sylvatica Larix kaempferi Picea abies Pinus nigra Prunus avium Pyrus pyraster Quercus cerris Quercus pubescens Quercus rubra Robinia pseudoacacia Ulmus laevis Average Explanatory variables Abb. AB AM CS FS LK PA PN PV PP QC QP QR RP UL Distribution models Nat. No Y Y Y No Y Y Y Y Y Y No No Y Climate Bio1 – Conti1 – Bio18 – DR – PARMADO1 Bio5 – Bio6 – Bio12 – STR_SUB – PARMADO1 Bio1 – Conti1 – Bio12 – PARMADO1 Bio5 – Bio6 – Bio18 – Bio5 : Bio18 – DIMP – PARMADO1 Bio1 – Conti1 – Bio18 Bio10 – Conti1 – Bio18 – VS – BS_TOP Bio1 – Conti1 – Bio18 – DR – PARMADO1 Bio5 – Bio6 – Bio18 – Bio5 : Bio18 – DIMP Bio1 – Conti1 – Bio18 Bio1 – Conti1 – Bio18 – STR_TOP – NUTRI1 Bio1 – Conti1 – Bio12 – TEXT – NUTRI1 Bio1 – Bio6 – Bio18 – DR – PH Bio10 – Bio6 – Bio18 – NUTRI1 Bio10 – Bio6 – Bio18 – PARMADO1 487 Site index models Climate & Soil SE SP TSS SE SP TSS N R2 NG 0.93 0.90 0.89 0.88 0.95 0.92 0.86 0.85 0.86 0.88 0.91 0.88 0.89 0.86 0.89 0.76 0.76 0.81 0.77 0.87 0.75 0.67 0.73 0.70 0.79 0.76 0.69 0.82 0.84 0.77 0.69 0.66 0.70 0.65 0.82 0.68 0.53 0.57 0.57 0.67 0.67 0.57 0.70 0.70 0.66 0.95 0.92 0.89 0.90 – 0.91 0.87 0.86 – 0.91 0.92 0.90 0.89 0.86 0.90 0.76 0.76 0.81 0.79 – 0.79 0.71 0.72 – 0.79 0.76 0.71 0.82 0.84 0.77 0.72 0.68 0.71 0.69 – 0.69 0.58 0.58 – 0.70 0.68 0.61 0.70 0.71 0.67 876 803 5004 10,836 2429 17,607 4984 7709 1926 3089 3825 3737 6086 819 4981 0.37 0.13 0.18 0.04 0.06 0.07 0.02 0.11 0.25 0.12 0.12 0.20 0.06 0.16 0.13 450 70 1149 12050 757 12292 23731 1229 156 122138 18875 4366 177548 455 26804 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. data (mean age = 54.6 years, standard deviation = 29.7). We included the following six climate variables in our SDMs because they were strong predictors in previous studies (e.g. Dyderski et al., 2018; Walentowski et al., 2017): annual mean temperature (Bio1), maximum temperature of warmest month (Bio5), minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12) and precipitation of the warmest quarter (Bio18) (see also Supplementary material 3.1). In addition, we calculated a continentality index (Conti1) (Conrad, 1946), Conti1 = 1.7A −14 sin(φ + 10) (1) where A is the diﬀerence of Bio5 and Bio 6 and φ represented geographic latitude. The predictions for the future climate were calculated for the time period 2061–2080 (also WorldClim 1.4 data, below abbreviated as ‘2070’). Models were applied for emission scenarios RCP 4.5 and RCP 8.5. Within the same emission scenario, there is a large deviation between the diﬀerent global climate models (Goberville et al., 2015). Therefore, we used three global climate models per emission scenario (HadGEM2-ES, MPI-ESM-LR, NorESM1-M) from European institutes. The distribution models were predicted with the diﬀerent global climate models and the outputs averaged to scenario- and species-speciﬁc future prediction. The applied climate models have been used extensively in previous studies in Europe (e.g. Dyderski et al., 2018; Schüler et al., 2014) and they seem to capture well the range of global climate models in the respective RCPs (Supplementary material 4). The ensemble of the three climate models represents an annual temperature increase in Europe of 2.9 °C for RCP 4.5 and 4.5 °C for RCP 8.5. An overview of the temperature increase in individual biogeographical regions (Roekaerts, 2002) can be found in Supplementary material 4. Europe was deﬁned as the area within longitude 15°W to 45°E and latitude from 35° to 70°N. Fig. 2. Species plots of compiled dataset. according to growth characteristics. We complemented the data with presence/absence information of the ICP Level I plot data on crown condition and with a pan-European tree occurrence dataset (Mauri et al., 2017). The Mauri et al. (2017) dataset provides presence information from national forest inventories of 30 countries. In the end, our data set encompassed 1,352,113 species plots represented at a spatial resolution of 1 km. Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.foreco.2018.08.028. 2.2. Tree data preparation and processing 2.4. Soil dataset For the calibration of the SDMs, we carried out a species-speciﬁc selection of the data. For each species, the data points were grouped into a 16 × 16 km raster (see schematic ﬁgure of method in Supplementary material 2) and only one randomly selected data point per 16 × 16 km raster cell was used for the modeling. Raster cells which were occupied by data points of the analyzed species were preferentially selected. Presences were combined with the equal number of absences (see Table 1 for sample size N of each species). We sorted the absences randomly within each 16 × 16 km raster cell and then reduced the number of absences per cell to one and ﬁnally sorted the cells according to their x and y coordinates and selected each ½ * N (number of absences that equals the number of presences) cell. This ensures that absences are randomly chosen within each selected cell and that cells are homogeneously distributed over the study area (Supplementary material S1.2). Rasterizing to a 16 × 16 km raster eliminated doubling of species plots (e.g. plots were included in the national forest inventory of Germany and in the pan-European tree occurrence dataset) because only one data point per cell was used. Additionally, the ﬁltering to a coarser raster reduced autocorrelation in the data while at the same time taking the rareness of species into account. There was no quantitative consideration of spatial autocorrelation in addition to the rasterization to 16 km. The importance of soil for species distribution models has been shown in many studies (e.g. Boulangeat et al., 2012; Dolos et al., 2015; Piedallu et al., 2016). Nevertheless, the use of soil variables is always a trade-oﬀ between spatial resolution and coverage of the data. We decided to use the European Soil Database (ESDB, Panagos et al., 2012) because its extent covers most of our analyzed data points (whole of Europe and Russia without Turkey). We selected 11 variables from the ESDB dataset: dominant parent material (PARMADO1), topsoil available water capacity (AWC_TOP), depth to rock (DR), volume of stones (VS), depth to impermeable layer (DIMP), topsoil organic carbon content, texture (TEXT), top- and subsoil structure (STR_TOP, STR_SUB) and presence of a raw peaty topsoil horizon (PEAT). The selection was oriented according to hypotheses on water and nutrient availability as well as on chemical properties that could weaken species’ vitality (Mellert et al., 2011). Additionally, we used a map of nutrient supply (NUTRI1). The map is a classiﬁcation of the ESDB soil type map into nine diﬀerent nutrient types (Kolb, 2017; Supplementary material 3.1) and has the same geographical extent as ESDB. These soil variables were included in the statistical analyses as nominal predictors. In addition, a numeric pHvalue map was used with a resolution of 5 * 5 km (Reuter et al., 2008) The pH-value map covers 26 member states of the European Union (Bulgaria and Romania are not included) plus Norway, Switzerland and Albania. 2.3. Climatic data set To describe the climatic conditions, we used climate data from the WorldClim 1.4 datasets, with a cell resolution of 1 × 1 km (accessed May 2017 – Hijmans et al., 2005). The data represent climate means for 1961–1990, which coincides well with the age of the trees in the used 2.5. Statistical analyses SDMs were created without dispersal limitation as most forests in Europe are managed and adaptation of forests to climate change needs 488 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. chemistry (E)). Soil models were only considered if the improvement of TSS was greater than 0.005. To falsify our research hypothesis, we calculated the potential area shift for each individual species. The potential distribution areas were derived using the prevalence as a probability threshold. Guisan et al. (2006) recommended a balanced dataset for the analysis of rare species. To ensure comparability of the results, we also applied the equal weighting of the data to the two major tree species F. sylvatica and P. abies. The prevalence is 0.5 for all species due to the balanced dataset of 50:50 presences and absences. To estimate how species gain or loss would behave under a nonchanging land-use assumption, we cropped the species distribution today and in the future by the forest land based on forest-type mapping of Kempeneers et al. (2011). We applied the distribution to all raster cells with a proportion of forest land above 10%. This deﬁnition overestimates the forest area (Forest Europe, 2015). We use this threshold as the reference area because it describes the potential habitats of the categories ‘forest’ and ‘other wooded land’ area (deﬁned in FAO, 2001) in the Southern European region. These regions are highly important for the investigated thermophilic species. In order to refer to the actual forest cover of Forest Europe (2015), one would have to multiply each raster cell area of the SDM output with the proportion of forest land in this cell. To investigate the distribution shifts of the species in diﬀerent biogeographical regions, we applied the spatial classiﬁcation of the biogeographical regions map of Europe (Roekaerts, 2002). The distance of the potential distribution shift was calculated by the centroid of the current and the future distribution. The direction results from the vector of the two points. A logarithmic linear model was used to show the relationship between the latitude of the centroid C and the forecasted area gain or loss G with the equation: management assistance due to the high speed of the expected temperature rise until the end of century. Species distribution models were performed with generalized additive models (gam) and linear regression models (lm). Overall, we used 19 diﬀerent environmental variables within these models. The 19 variables were grouped in ﬁve ‘site characteristic’ classes (warmth (A), chilliness (B), drought (C), soil physics (D) and soil chemistry (E)). To avoid an overﬁtting of the models, we made a species-speciﬁc variable selection and restricted models to a maximum of ﬁve variables (so that from each group one variable is represented) and a minimum of three variables (from the three climate classes A–C only one variable is represented from each). SDMs were created in two steps because some of the soil property variables are highly correlated with the climate variables at the European scale (e.g. dominant parent material and temperature). In the ﬁrst step, a pure climate model (Eq. (2)) was generated with climate variables of the site characteristic classes A-C (VarA−C ). We followed Franklin and Miller (2010) and restricted the number of variables and the degrees of freedom within our approach. We applied a generalized additive model (binomial distribution, logit link function) with three variables of the respective classes A, B and C. We generated a set of models with all possible variable combinations and tested them against each other (multiple regressions with constant number of variables in the models). Models with highly correlated variables (Pearson’s r2 > 0.7) (Aguirre-Gutiérrez et al., 2013) were excluded from the selection process. The best model was chosen by means of the Akaike information criterion (Burnham and Anderson, 1998) and biological plausibility of the results (partial eﬀects in accordance with biological principles, no obvious regional misclassiﬁcation). Standard smoothing terms were used with three degrees of freedom (3 knots) to restrict SDMs to only sigmoid or optimum response functions. The smoothing functions fn of the individual climate variables were ﬁtted by thin plate splines. Pc = a + f1 (Var A) + f2 (Var B ) + f3 (Var C ) + εc log(G ) = a1 + a2 ·(logC ) + ε The applied log-log transformation allows the calculation of the ratio of the areal loss to a complete loss of a species in Europe (−100%). The suitability index Sai (Eq. (6)) should relate the potential area gain or loss with the maximum growth potential of a species. It was determined as the product of the area gain Ag and the site indices of the species Si within their range (species with the lowest min and the greatest max unit). The Si is the mean site index of a species at the reference age of 80 years within the frame of the respective species distribution. The product of Ag and Si was used to avoid the overestimation of outliers. The greatest Sai represents the best suitability. (2) ε always represents the residual error of the respective model. For the residuals of the climate model (Eq. (2)) εc was deﬁned separately, because it was used in the second step to describe the inﬂuence of the soil on the distribution of the species. The soil models (Eqs. (3) and (4)) ﬁt the residuals of climate models by the soil variables VarD&E for physical and chemical properties, respectively, with ordinary least squares regression models (lm). an represents the coeﬃcients of the ﬁxed effects. εc = a1 + a2 ·(Var D) + ε (3) εc = a1 + a2 ·(Var E ) + ε (4) (5) Sai = We built two diﬀerent models because of diﬀerent spatial coverings of the physical and chemical soil parameters. The probability of species occurrence (Pc + s ) was composed by the sum of the climatic and the soil models Pc + s = Pc + εc . If no soil information for a spatial area was given only the prediction of the climate model (Eq. (2)) was calculated.εc is the sum of the outcomes of Eqs. (3) and (4). Whether the soil models were chosen (see ‘Explanatory Variables’ in Table 1) depends on whether they improved the overall model performance – measured by the true skill statistic (TSS) (Allouche et al., 2006) – compared to the pure climate model. The TSS is a sum of sensitivity (proportion of correctly predicted presences) and speciﬁcity (proportion of correctly predicted absences) minus one. It reaches values between −1 and +1, with 1 representing a perfect ﬁt. To derive characteristics of model performances bootstrapping was done with 100 runs and a data split of 90:10. The TSS was calculated for each 10% test data in each bootstrap run and averaged over all runs. Diﬀerences in TSS between the pure climatic model and the combined climate and soil models were used to select the soil models that maximize TSS (one from class soil physics (D) and one from class soil Agmax −Agmin Simax −Simin · Agi −Agmin Sii−Simin (6) To predict the growth performances of the individual species, we applied site index models. The site index of an inventory point was calculated by the ratio of maximum dominant tree height to the maximum site index curve. The species-speciﬁc maximum site index curves (see Supplementary material 7.4) were derived with a ChapmanRichards (Richards, 1959) function based on the predominant and dominant individual trees (Kraft social class 1 and 2). The adaption of Chapman-Richards curves was made with a nonlinear 95% quantile regression (Koenker and Park, 1996) (detailed description in Brandl et al. (2018)). In a further step, the species-speciﬁc site indices were put in relation to the climate variables with a generalized additive model, with a gamma error distribution and a log-link function. The climate variables of individual site models were identical to the climate variables which were already selected for the species distribution models. Sii = a + f1 (Var A) + f2 (Var B ) + f3 (Var C ) + ε (7) All analyses were performed in the software environment of R 3.3.2 (R Core Team, 2016) with the R packages raster (Hijmans, 2016), mgcv 489 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. (Wood, 2011) for the generalized additive models (gam) and quantreg (Koenker, 2018) for the nonlinear quantile regression (nlrq). Table 2 Area of the analyzed species in Europe today and in the future under two climate change scenarios for the currently wooded regions. Area (million km2) 3. Results The summary results of model performances are shown in Table 1. The applied method generated an average performance (true skill statistics) of 0.67 for the species distribution models. Average sensitivity attained a value of 0.90 and average speciﬁcity was 0.77. All site index models were signiﬁcant and had an average R2 of 0.13 (see also Supplementary material S7). Variable selection showed no speciﬁc pattern for broadleaf or coniferous species. The most frequently selected variable in the SDMs was mean precipitation in the warmest quarter (Bio18 – 11 times), followed by mean annual temperature (Bio1 – 8 times) as well as continentality (Conti1 – 8 times) and dominant parent material (PARMADO1 – 6 times). The majority of the climatic curves show a bell-shaped course; a few have an S-shaped course (most important curves in Supplementary material 3.2). Only P. nigra was nearly linearly negatively related to summer precipitation (Bio18), resulting from a high amount of presences with summer precipitations close to zero. The soil models reduced or raised the climatic probability by −0.15 to 0.15. The most frequently selected soil variable PARMADO1 increased the probability mainly on the younger soils (unconsolidated or eolian deposition, organic materials) and decreased the probability on older soils (igneous or metamorphic rocks). No soil variables were selected for P. pyraster and L. kaempferi. In general, soil variables did not signiﬁcantly improve the TSS of the climate models (mean +0.02 TSS). The greatest improvement was achieved for P. nigra and Q. rubra. Distribution maps of individual species are presented in Supplementary material 5 (an example is shown in Fig. 3). The mean potential species distribution area of the 12 thermophilic species was 1.2 million km2 calculated with a probability of occurrence > 0.5 (Table 2). P. abies and F. sylvatica reached a potential area of 2.52 m km2 and 1.38 m km2. P. nigra showed the greatest potential range of the thermophilic species with1.64 m km2. Area gain and loss (%) Latin name Today 2070 RCP 4.5 2070 RCP 8.5 2070 RCP 4.5 2070 RCP 8.5 Abies grandis Acer monspessulanum Castanea sativa Fagus sylvatica Larix kaempferi Picea abies Pinus nigra Prunus avium Pyrus pyraster Quercus cerris Quercus pubescens Quercus rubra Robinia pseudoacacia Ulmus laevis Average Including FS & PA 0.71 1.00 1.09 1.38 0.50 2.52 1.64 1.41 1.62 1.32 1.31 1.59 1.32 0.94 1.20 1.31 0.40 1.57 1.41 0.61 0.22 0.92 2.29 1.41 2.59 2.70 2.23 2.54 3.02 2.50 1.91 1.74 0.29 1.84 1.43 0.41 0.19 0.49 2.35 1.29 2.59 3.22 2.88 2.45 3.12 3.08 2.06 1.83 −44 58 30 −56 −57 −63 39 0 60 105 70 60 129 165 51.33 35.46 −60 85 32 −70 −63 −81 43 −9 60 144 120 55 137 227 64.27 44.30 Generally, the climate change scenarios RCP 4.5 and RCP 8.5 caused a drastic reduction or increase of the potential area of some species. For example, under the assumption of the RCP 4.5 scenarios U. laevis beneﬁted most from climate change. Its potential distribution area increased by 165% from 0.94 m km2 to 2.50 m km2. Great gains are also found for R. pseudoacacia (+129%) and Q. cerris (+105%). These area changes become even more extreme under the warmer scenario RCP 8.5. The greatest (potential) area losses under the scenario RCP 4.5 were found for P. abies (−63%), L. kaempferi (−57%), F. sylvatica (−56%) and A. grandis (−44%). Under the RCP 8.5 scenario, these losses increased further. The inﬂuence of the climatic warming on individual biogeographic regions can be seen in Table 3. The distribution area gain of the thermophilic species due to climate change is obvious for Alpine region Fig. 3. Example of the modeled distribution of Castanea sativa (a) today and (b) in 2070 under a temperature increase of approx. 2.9 °C. The area of occurrence (probability > 0.5) is colored with a red to orange color ramp (from 1.0 to 0.5 in 0.1 probability steps), while non-occurrence (probability < 0.5) is colored grey to white (also in 0.1 probability steps). 490 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. Table 3 Percentage share of distribution for the 12 thermophilic species within each biogeographic region assuming the current land-use distribution. Italic numbers in the last row include the comparative species F. sylvatica and P. abies. Area of the biogeographical regions summarizes the potentially occupied area of a region within the given extent of Europe (see also Fig. 5; a table which summarizes all investigated species can be found in Supplementary material 6). Biogeographical regions Alpine Anatolian Arctic Atlantic Black Sea Boreal Continental Mediterranean Pannonian Steppic Averageb Including FS & PA a b c Areaa (million km2) of biogeographical regions Average share of biogeographical region’s area (%) Today 2070 RCP 4.5 2070 RCP 8.5 18.7 14.0 0.0 43.2 33.4 0.4 37.3 29.5 67.0 39.4 22.2 24.0 40.2 19.7 0.0 37.4 28.8 18.2 57.1 19.2 47.2 41.6 34.1 31.4 43.7 20.1 0.0 29.8 25.0 34.2 51.1 12.7 31.6 27.5 36.2 32.5 0.5 0.1 0.0 0.4 0.1 1.9 1.5 0.6 0.1 0.2 5.4c Area gain and loss (%) 2070 RCP 4.5 2070 RCP 8.5 21.5 5.8 0.0 −5.8 −4.6 17.9 19.7 −10.4 −19.8 2.2 11.9 7.4 24.9 6.1 0.0 −13.4 −8.4 33.9 13.8 −16.9 −35.4 −11.9 14.0 8.4 Based on currently wooded areas. Average weighted by area of the biogeographical regions Sum. Fig. 4. Shifts of centroids of the species distribution (a, map) for the 14 analyzed species under the RCP 4.5 scenario and (b, barplot) the species-speciﬁc distribution area gains and losses in the respective biogeographical regions, assuming the current land-use distribution. Points in the map represent the current centroid of distribution, arrowhead shows the future centroid. Y-axes in the barplots depict an area of 200,000 km2. Abbreviation: A. grandis (AG), A. monspessulanum (AM), C. sativa (CS), F. sylvatica (FS), L. kaempferi (LK), P. abies (PA), P. avium (PV), P. nigra (PN), P. pyraster (PP), Q. cerris (QC), Q. pubescens (QP), Q. rubra (QR), R. pseudoacacia (RP), U. laevis (UL). (RCP 8.5 in Supplementary material 6). 491 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. Table 4 Shift of the centroids of the distribution areas. Standard deviation of average in brackets. The species are sorted according to increasing northward shift of RCP 4.5. Shift distance (km) Species name RCP 4.5 RCP 8.5 Castanea sativa Acer monspessulanum Fagus sylvatica Picea abies Ulmus laevis Quercus cerris Prunus avium Quercus pubescens Pyrus pyraster Pinus nigra Robinia pseudoacacia Larix kaempferi Abies grandis Quercus rubra Average 303 332 393 434 435 445 450 454 457 496 523 554 567 616 461 (83.2) 596 610 616 525 613 667 706 791 709 796 790 727 771 842 697 (91.4) (RCP 4.5 = 21.5%; RCP 8.5 = 24.9%). Also, the boreal (RCP 4.5 = 17.9%; RCP 8.5 = 33.9%) and the continental region (RCP 4.5 = 19.7%; RCP 8.5 = 13.8%) show a potential improvement of climate conditions for nearly all thermophilic species. A great loss of potential distribution area was modeled for Southern Europe, in particular for the Pannonian (RCP 4.5 = −19.8%; RCP 8.5 = −35.4%) and the Mediterranean region (RCP 4.5 = −10.4%; RCP 8.5 = −16.9%). Unexpectedly, the Atlantic region also lost (potential) distribution area for the target species. Fig. 4 shows the area gain and loss for the scenario RCP 4.5 for the 12 + 2 analyzed species (RCP 8.5 in Supplementary material 6.3). Great distribution shifts into the boreal region were calculated for species which originate from Central and Eastern Europe like U. laevis, P. pyraster and P. avium, as well as the non-native species R. pseudoacacia and Q. rubra. P. abies, one of the most important boreal species, suﬀered a severe loss of area in this bioregion. The continental region exhibited a diﬀerent picture: some species beneﬁted from climate warming, mainly the Mediterranean species Q. cerris, Q. pubescens, P. nigra as well as non-native R. pseudoacacia. F. sylvatica as dominant species of Central Europe and the investigated conifers see a considerable decline in their distribution area in the continental region. The centroid of the species distribution and how it would shift under changing climate for the diﬀerent climatic scenarios is shown in Fig. 4a. The average shift is 461 km under RCP 4.5 and 697 km under RCP 8.5 (see also Table 4). The shift of Q. rubra, A. grandis and L. kaempferi is higher compared to C. sativa, A. monspessulanum and F. sylvatica. The mean direction was north-northeast (27°). The species-speciﬁc diﬀerences in direction are small and mainly driven by the land-water distribution, continentality and the soil conditions. The shift of F. sylvatica (393 and 616 km, respectively) and P. abies (434 and 525 km, respectively) is within the range of the other species. In Fig. 5, we depicted the relationship between the latitudinal center of the current distribution and the potential area gain or loss under RCP scenarios 4.5 and 8.5. The regression is signiﬁcant both for RCP 4.5 (p > 0.001, R2 = 0.62) and for RCP 8.5 (p > 0.001, R2 = 0.70). Species with a distribution above the 48° latitude seem to lose potential distribution under climatic warming. The calculated site index models showed a mean site index of 21.5 m over all species and averaged for their respective species distribution areas (see Table 5). A. grandis (33.0 m) and L. kaempferi (29.6 m) reach the greatest site indices today and A. monspessulanum (8.7 m) and Q. pubescens (11.9 m) the smallest site indices (speciesspeciﬁc growth curves and site indices predicted to distribution can be seen in Supplementary material 7). The average site index of all 14 species is constant under climate warming. Nevertheless, the Fig. 5. Potential area gain of the investigated species under a temperature warming of +2.9 °C (RCP 4.5 – orange curve) and +4.5 °C (RCP 8.5 – red curve). Asterisks (***) mean that the underlying linear models are highly signiﬁcant (p > 0.001, respective model parameter in Supplementary material 6). To aid orientation, the latitudes of four cities are depicted. (For interpretation of the references to colour in this ﬁgure legend, the reader is referred to the web version of this article.) Table 5 Mean site index at the tree age of 80 years for the diﬀerent climate scenarios, based on the species-speciﬁc distribution areas of the respective scenarios. The species are sorted according to decreasing mean site index today. Mean site index [80 years] Species name Today RCP 4.5 RCP 8.5 Abies grandis Larix kaempferi Quercus rubra Picea abies Fagus sylvatica Prunus avium Ulmus laevis Castanea sativa Quercus cerris Pinus nigra Robinia pseudoacacia Pyrus pyraster Quercus pubescens Acer monspessulanum Average 33.0 29.6 27.6 26.7 26.3 22.7 21.7 21.4 20.9 20.4 18.1 12.5 11.9 8.7 21.5 34.1 28.7 25.5 28.6 25.9 21.8 23.0 20.2 21.4 20.7 18.5 12.4 11.0 9.4 21.5 35.5 28.8 25.0 30.3 25.6 21.6 24.5 20.2 21.7 20.5 18.8 12.1 11.0 9.4 21.8 investigation exhibited that under climate warming some species will ﬁnd enhanced growth conditions in their expected distribution (P. abies, U. laevis, A. grandis) and for some species the conditions will deteriorate (Q. rubra, C. sativa). Despite the almost constant average growth performance of the distribution area in climate change, the growth performance in the individual regions might change in the future. The suitability of the thermophilic species as alternatives for P. abies and F. sylvatica under climate change is shown in Fig. 6 combining area gain and site index at 80 years. The best suitability would be located in the upper right corner of the ﬁgure and it decreases with increasing distance from this corner. U. laevis combined best growth performance with potential area gain under climate warming, followed by Q. rubra, Q. cerris and R. pseudoacacia. L. kaempferi, Q. pubescens and A. 492 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. average shift of 156 m per year−1 for the A1ﬁ emission scenario (comparable with RCP 8.5) and 164 m per year−1 for the B1 (comparable to RCP 4.5). These rates are much slower than our results, and come from the consideration of density-dependent population processes and seed dispersal. A study of Kullman (2008) used long-term, systematic monitoring data of tree line vegetation records in Sweden from the early 20th century to 2005. He could show that thermophilic tree species (Quercus robur, Ulmus glabra, Acer platanoides, Alnus glutinosa and Betula pendula) had expanded their range in the order of 50–300 km northwards. These drastic shifts documented from ﬁeld data in a relatively short period with a comparably moderate warming rate suggest that our results of 461–697 km could be realistic. 4.1.1. Natural migration rate Before we discuss the variable selection of the species distribution models, we brieﬂy address an important assumption in our study. We presumed that the investigated species were mainly planted by humans so the natural dispersal speed is not a limitation factor. In contrast, researchers such as Feurdean et al. (2013), who investigated post-glacial migration rates with the Northern Eurasian Plant Macrofossil Database, have come to the result that (natural unassisted) migration from south to north occurred at a substantially lower pace (60–260 m yr−1) than the potential migration in our results for modern climate change. Saltré et al. (2013), however, provided evidence that maximum postglacial migration rates could have reached 630 m yr−1 (mean 280 m yr−1) in the case of F. sylvatica. Feurdean et al. (2013) highlighted the importance of outlier populations for speeding up the migration rate. They also concluded that the cultivation of tree species beyond their natural range has a similar eﬀect as microrefuges had in post-glacial times. At least for anthropogenically favored tree species we can assume that the migration rate is independent of natural dispersal constraints (cf. also Kullman, 2008). We found diﬀerent migration rates for our investigated species. The reason for this, however, is not the diﬀerent dispersal strategies (Meier et al., 2012) or the evolutionary adaptation of the dispersal strategy to the existing ﬂora and fauna (Myczko et al., 2014), but the variables and their shape (S-shaped or bell-shaped). Nevertheless, it could be that pioneer species, which are often anemochores, may select less strict climate and soil variables or tend to S-shaped response curves which allow a faster expansion under climate change conditions. In our data we did not ﬁnd such a pattern. Fig. 6. Potential area gain of the investigated species in relationship to the mean site index of the species within the respective species distribution under a temperature warming of +2.9 °C (RCP 4.5; ﬁgures for +4.5 °C, RCP 8.5 and respective data can be found in Supplementary material 7). The site index is climate-sensitive and determined for a reference tree age of 80 years. Abbreviation of species as in Fig. 4. monspessulanum had the lowest suitability among the 12 considered species. P. abies and F. sylvatica also showed an unfavorable relationship between growth potential and potential area loss. 4. Discussion The question of which tree species we should promote in the face of climate change is an ongoing debate in forest ecology and management (e.g. Albert et al., 2017). From a total of 12 thermophilic and rare tree species we distinguished tree species like U. laevis, Q. rubra, Q. cerris and R. pseudoacacia that – from the viewpoint of their climatic-edaphic requirements and growth potential – are able to satisfy the expectations of the Central Europe forest sector and those that are not (A. monspessulanum, L. kaempferi, A. grandis). 4.1. Northward shift In our ﬁrst hypothesis we claimed that the northward shift of the investigated species is comparable. According to our results, however, this is clearly not the case: some species distributions exhibit a shift of approx. 300 km (C. sativa, A. monspessulanum), while other species like A. grandis or Q. rubra shift up to 600 km (distances for +2.9 °C scenario). On average, the species move approx. 461 km northwards under climatic warming of 2.9 °C and 697 km under 4.5 °C. Among studies that can be compared to ours, McKenney et al. (2007) investigated the change in the latitude of climate envelopes of 130 North American trees species and found an average shift of 700 km. The results summarized the emission scenarios A2 and B2 (Nakicenovic et al., 2000), which assume drastic and moderate climate warming, respectively. Hamann and Wang (2006) also focused on North American species distributions. For some tree species they came up with a potential habitat shift of at least 100 km per decade northwards. However, it is generally diﬃcult to compare such study results. The magnitude of the shift is always related to the applied scenarios. Besides, the used explanatory variables have a great impact because some are more stable under climate change projection than others, which could also explain the diﬀerent shifts. Also for Europe several studies modeled potential northward shifts of tree species (e.g. Hanewinkel et al., 2013; Meier et al., 2012; Thuiller et al., 2006) but the presentation of precise migration distances is rare. Meier et al. (2012) calculated the realistic migration rate for 14 European tree species with the tree species model TreeMig. They found an 4.1.2. Migration constraints Apart from the dispersal speed, our results showed – in accordance with other studies – that the migration rate is not a mere function of average temperature and precipitation. Among others, unsuitable soil conditions and late frost are mentioned in literature as important migration constraints (e.g. Aitken et al., 2008; Coudun et al., 2006; Falk and Hempelmann, 2013). Land-use eﬀects (Meier et al., 2012) and geographic barriers like the decreasing land area towards Northern Europe also aﬀect the migration rate. Forest density increased from south to north, in the same direction as the areal shift of the species. In Mediterranean regions, the land-use eﬀect can be considerable and endangers these sparsely wooded areas (García-Valdés et al., 2015). However, forest distribution is a variable factor (the forest area in Europe has expanded by 17.5 million ha over the last 25 years, Forest Europe, 2015), and changes in climate and society will certainly modify this development in the future (e.g. Gehrig-Fasel et al., 2007). In our study we integrated soil conditions on a coarse scale by a residual analysis. A joint model together with climate failed because some soil parameters were too strongly correlated with climate on the continental scale. For example, PARMADO1 class 6 ‘unconsolidated glacial deposits and glacial drift’ can be found predominantly in Northern Europe and therefore correlates with lower temperatures. The generated distribution maps based on joint models performed well with training data but transferability into the future was not given. 493 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. decisive factor for a species to win or lose distribution area under warming is closely related to the latitudinal center of the current distribution and the associated temperature adaption. In a rough generalization, the third hypothesis is correct in the sense that the area gain is stronger for the southern tree species than for the northern ones. This is in line with the ﬁndings of Dyderski et al. (2018). The land area in Europe increases up to 48°N and decreases with higher latitudes, which also inﬂuences the area gain or loss of species (e.g. P. abies has no area to escape to when temperatures rise and distribution shifts northward). Other studies which focused on mountain areas showed that climate change led to an upward shift of most of the investigated plant species (e.g. Lenoir et al., 2008; Penuelas and Boada, 2003; Rigling et al., 2013; Walther et al., 2005). In terms of available land area the situation is comparable with the northward shift in Europe: the potential distribution area towards the mountaintop decreases just like towards Northern Europe. U. laevis exhibited the greatest area gain under climatic warming. Compared to the distribution map of Collin (2003), it can be seen that our distribution range is smaller, especially at the western border. In Western Europe, the rareness of the species led to a grouping of all three Ulmus species to one Ulmus spp. class. Because of the diﬀerent site requirements of the three species we decided to ignore these plots. This makes it more diﬃcult to model the western border correctly. How much the species really spreads is diﬃcult to predict. U. laevis was determined to be a species with very limited competitive strength which can only dominate in riparian forest. However, in Southern Asia it is also well established in steppic areas, like in Finland. Currently, very little is known about this species (Collin, 2003) and the results show that further investigations are necessary. Nevertheless, with the residual approach we showed that the integration of soil led to a signiﬁcant improvement of the models. Other studies also found a signiﬁcant model improvement due to the integration of soil (Coudun et al., 2006). In Piedallu et al. (2016), for instance, the prediction of tree species distribution in French national forest inventory data improved signiﬁcantly when soil parameters were included (nutrient availability, the pH value and the C/N proportion). In our study, for example, the probability of occurrence of Q. rubra decreased in calcareous soils and shallow soils (< 40 cm). This is in accordance with Meredieu et al. (1996), who found in ﬁeld experiments that the survival of young Q. rubra improved in non-calcareous, deep soils. In our variable selection, categorical parameters like dominant parent material (PARMADO1) and nutrient availability (NUTRI1) where more often selected than the numerical pH units. Of course, the parameters are correlated to a certain degree, especially pH and nutrient availability (NUTRI1). Mellert et al. (2018) also performed models based on the NUTRI1 variable. They assumed that the categorical variable NUTRI1 involves a conglomeration of soil parameters important for plant physiologically, which may explain why PARMADO1 ﬁts better to the soil preferences of the species than pH. We already pointed out the importance of late frost events in determining the cold distribution limits of tree species. The late frost sensitivity of trees depends on several parameters such as the species’ leaf ﬂushing, the sensitivity of the buds and the threshold of freezing (for additional information see Vitasse et al., 2014). A study by Muﬄer et al. (2016) showed that the late frost resistance is highly correlated with the continentality of the species’ distribution. Especially for the thermophilic species late frost has to be considered in distribution models, so we tried to capture late frost resistance by the inclusion of a continentality index, which was chosen within the automated variable selection for eight of the 14 species. Late frost risk is expected to change more slowly than annual temperature because late frost events are inﬂuenced by atmospheric circulations (Becker et al., 2014; Meehl et al., 2000; Strong and McCabe, 2017). Conrad’s continentality index is nearly constant under RCP scenarios 4.5 and 8.5 because it includes the latitude, which doesn’t change, and also the annual temperature range, which is relatively stable (see Eq. (1)). Thus, models using this index are conservative with regard to late frost events. 4.2.1. Non-native species A special section is dedicated to non-native species among the selected tree species since extensive silvicultural experience is available from the regions of their origin. A species which is expected by forest management in Central Europe to be an attractive alternative under a drier climate situation is Abies grandis (Fletcher, 1986; Liesebach and Weissenbacher, 2007). However, our results showed a drastic decrease of its potential range. The reason is that it is bound to a relatively small and northern distribution in Europe with moist, oceanic climate conditions. In North America, A. grandis has a much greater distribution range from the dry regions of the Blue Mountains in Oregon to the moist coastal regions of British Columbia. The question is: How good is a SDM which was supplied with species data from plantations far from its native distribution range? Our model, which is only based on European data, may be too restrictive on the non-native species. This problem has already been described by other authors (Boiﬃn et al., 2017; Booth, 2017; Broennimann et al., 2007). Boiﬃn et al. (2017) tested the transferability of a North American species distribution model for Pseudotsuga menziesii (Mirb.) Franco to Europe with moderate success. Chakraborty et al. (2016) followed, ﬁnding that the low level of introduced genetic diversity matched only a fraction of the European climate that would potentially be suitable for P. menziesii. Similarly, the broad climatic range of A. grandis in North America is known to include diﬀerent adapted varieties or provenances. In European forests mainly the Coast grand ﬁr (A. grandis ssp. grandis) was introduced and not the Interior grand ﬁr (Abies grandis var. idahoensis). The problem is further compounded by the selective and unknown importation of seed 200 years ago. Depending on biogeographic region and taking into consideration provenances, the assessment of A. grandis might be different from our European perspective. Larix kaempferi also showed a northern center of distribution with a clear focus on oceanic regions. In the case of L. kaempferi, the problem is not as critical as A. grandis because its home range in Japan is relatively small with not such a great climatic gradient. Summer drought sensitivity is known (Pâques, 1992) and the resulting area loss under climatic warming seems to be plausible (Huang et al., 2017). 4.2. Areal winners and losers Under this provocative title we address our second and third hypotheses, that (2) the potential distribution area of the thermophilic tree species increases under climate change while it decreases for the more temperate and boreal species F. sylvatica and P. abies and (3) the potential area gain increases the further south a species is distributed today. Indeed, the present study showed a drastic decrease of the potential distribution area for P. abies and F. sylvatica. The 63% loss of area for P. abies lies in the same order as in several other studies such as Duputié et al. (2014), Schüler et al. (2014) and Dyderski et al. (2018). The 56% loss of F. sylvatica supports previous studies that also assumed a great loss of habitat suitability for F. sylvatica (Maiorano et al., 2013; Meier et al., 2012), contradicting others that supposed an increase in its distribution (Dyderski et al., 2018; Falk and Hempelmann, 2013). Diﬀerences to the study of Falk and Hempelmann (2013), who also used GAMs as statistical models, are probably due more to the choice of response variables (minimum winter temperature, precipitation and temperature sum of vegetation period) and their shape than to presences, environmental data or climate change scenarios. The temperature sum of the vegetation period has a very smooth bell-shaped form, whereas Bio5 (max. temperature of warmest month), which was often chosen in our models, has a rather sharp bell shape. As for the thermophilic and rare tree species, nine of the 12 were modeled to experience a gain in their potential distribution. The 494 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. the area loss of P. abies and F. sylvatica. Dyderski et al. (2018) showed that Q. petraea is able to preserve its distribution in Central Europe and spread into the boreal zone under future climate conditions. According to distribution models for Q. petraea by Schueler et al. (2014), Walentowski et al. (2017) and Dyderski et al. (2018), Q. petraea would be comparable to C. sativa or P. avium in our species ranking in Fig. 6. For the other two non-European species considered in this study, Q. rubra and R. pseudoacacia, the situation is diﬀerent. They were planted especially in drier zones of Europe, so climatic warming will support their distribution in Europe. The found area gain of R. pseudoacacia is in agreement with Li et al. (2014) and Dyderski et al. (2018). 4.3. Area gain and growth performance Forestry promotion of a species in times of climate change depends not only on species’ potential area gain but also on the ability of a species to produce a good quality of wood in suﬃcient quantities. The quality aspect was taken into account when selecting the tree species to be investigated. As for the potential quantity, we developed a suitability index which merges growth potential and the expected area gain or loss. Fig. 6 is a simpliﬁed, descriptive overview and ignores to a certain degree the diﬀerent growth courses, soil conditions and potential stand densities (see Pretzsch, 2009). Nevertheless, the trend is obvious: at the species level better adaptation to a warm and dry climate comes at the cost of (stem height) growth, e.g. due to lower leaf area index and duration, higher leaf thickness and a higher root-to-shoot ratio (Bréda et al., 2006). Those species which might lose distribution area under future climate scenarios, like P. abies, F. sylvatica, L. kaempferi or A. grandis, show the greatest site indices, while those which might gain area typically achieve lower site indices. As shown in Fig. 6, the species U. laevis, Q. rubra, Q. cerris and R. pseudoacacia seem to provide a reasonable trade-oﬀ between potential area expansion and growth performance. In the case of Central Europe, P. nigra and C. sativa are also attractive alternatives, especially under unfavorable soil conditions. The good results of Q. rubra can be explained by its high drought tolerance typical of oaks (Abrams, 1990) and its better growth performance in comparison to native European oaks (Major et al., 2013; Seidel and Kenk, 2003). Despite the lower growth performance of Q. cerris, it reached a comparable suitability index. The reason for this is its great climatic tolerance. It is known that Q. cerris is drought resistant (Nardini et al., 1999) as well as relatively frost hardy (Bussotti, 1994). This combination allows her to achieve the greatest expansion into the continental region under climatic warming, compared to the other investigated species (see Fig. 4). 5. Conclusion Twelve thermophilic and rare tree species were investigated with respect to their future potential distribution and growth performance in Europe under a moderate and strong climate change scenario (+2.9 °C and +4.5 °C). Some tree species like U. laevis, Q. rubra, Q. cerris and R. pseudoacacia showed a great potential to contribute to future wood production in Central Europe while others are less suitable (A. monspessulanum, L. kaempferi, A. grandis). U. laevis as the most suitable alternative needs more attention in distinguishing the diﬀerent elm species in ﬁeld surveys in Western Europe. Based on our ﬁndings experimental trials are needed to test species requirements at its predicted margins and to increase our knowledge about the species. Among the investigated species, Q. rubra ranked as a very suitable alternative for P. abies and F. sylvatica. But at the same time Q. rubra is a good example of a potential invasive species, and therefore the subject of much discussion between forestry and nature conservation bodies (Krumm and Vítková, 2016; Vor, 2015). Clearly, SDM studies only cover selected aspects to evaluate the possible promotion of tree species that are currently still rare, but the climate-edaphic suitability is fundamental before new investigations deal with other aspects. Now, one task is to investigate the potential eﬀects of an early promotion of tree species on existing communities (e.g. Kuehne et al., 2014) within the detected time frame. Additionally, further investigations are needed to extend the suitability index to a higher complexity of information, e.g. considering the biomass growth performance. Mortality models for instance can specify the risk inside the species’ climatic distribution range (Neumann et al., 2017). Approaches like in Biber et al. (2015) can integrate ecosystem services on a quantitative basis. Regardless of whether introduced or native species, the anthropogenic promotion of the species establishes microrefuges (Feurdean et al., 2013). These refuges will promote an increased ‘natural’ migration rate of the species (with possible invasive behavior). On the one hand, these microrefuges might help forestry to adapt to the speed of climate change; on the other hand they bear the risk of producing unstable ecosystems which collapse under unexpected abiotic and biotic diseases. For forestry practice, it is of supreme importance to ﬁnd successful species combinations which minimize such risks. Mixtures are not only the sum of two species’ trades but a process of facilitation/complementarity and competition (e.g. del Río et al., 2017; Thurm and Pretzsch, 2016). The species ranking in this study is a good starting point for initializing trials with diﬀerent combinations of tree species with high future potential for the forest sector. 4.3.1. Non-native species with high suitability We are aware that the recommendation of a species, especially a non-native one, must be part of a broader context than just the site requirements and growth performance (Hasenauer et al., 2016). Besides the economic impact of a forced forest conversion, it has to be kept in mind that the introduction of non-native species comes with an invasion potential (Richardson, 1998). Q. rubra is only one example of how intensive planting of a non-native species is responsible for an invasion in some European forest communities (Woziwoda et al., 2014). Assuming that silvicultural options to eﬃciently control invasive species exist (Vítková et al., 2017), these species should not be excluded a priori (regarding their great growth performance). Due to the necessity to maintain them under silvicultural control, they should be introduced in semi-natural managed forests rather than in more natural forest communities. Also, species like C. sativa (as well as other expanding species) will migrate into existing forest communities where they are not native today. Millar et al. (2007) described the result of this development as neo-native forests. It results from the changing climate and has to be observed carefully, too. The establishment of neo-native forest communities, as well as the promotion of non-native species, always comes with ecological and economic uncertainties – more so with non-native species than with neo-native species (Bolte et al., 2010). At the same time native tree species that were not considered in this study, like Abies alba, Pinus sylvestris and Quercus petraea, are widespread in Europe and may buﬀer Acknowledgments The study was funded by the Bavarian State Ministry of Food, Agriculture and Forestry (project number B76). The evaluation was based on data that were collected by partners of the oﬃcial UNECE ICP Forest Network (http://icp-forests.net/contributors). Part of the data was co-ﬁnanced by the European Commission (data achieved on 2 June 2016). We are grateful for the helpful comments of the two anonymous referees. We would like to thank Tobias Mette, Birgit Reger and Susanne Brandl for supporting the analysis. Additionally, we thank Rudolf May, Stefan Tretter and Patrizia Gasparini, who helped collect the data. 495 Forest Ecology and Management 430 (2018) 485–497 E.A. Thurm et al. Author contributions Dolos, K., Bauer, A., Albrecht, S., 2015. 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