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A transnational network of genetic conservation units for forest trees was recently documented in Europe aiming at the conservation of evolutionary processes and the adaptive potential of natural or man-made tree populations. In this study, we quantified the vulnerability of individual conservation units and the whole network to climate change using climate favourability models and the estimated velocity of climate change. Compared to the overall climate niche of the analysed target species populations at the warm and dry end of the species niche are underrepresented in the network. However, by 2100, target species in 33-65 % of conservation units, mostly located in southern Europe, will be at the limit or outside the species' current climatic niche as demonstrated by favourabilities below required model sensitivities of 95%. The highest average decrease in favourabilities throughout the network can be expected for coniferous trees although they are mainly occurring within units in mountainous landscapes for which we estimated lower velocities of change. Generally, the species-specific estimates of favourabilities showed only low correlations to the velocity of climate change in individual units, indicating that both vulnerability measures should be considered for climate risk analysis. The variation in favourabilities among target species within the same conservation units is expected to increase with climate change and will likely require a prioritization among co-occurring species. The present results suggest that there is a strong need to intensify monitoring efforts and to develop additional conservation measures for populations in the most vulnerable units. Also, our results call for continued transnational actions for genetic conservation of European forest trees, including the establishment of dynamic conservation populations outside the current species distribution ranges within European assisted migration schemes.

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 Â 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.

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Ch., Sutcliffe L., Leuschner Ch., 2017. Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Ann. For. Res. 60(1): 101-126. Abstract.We compared results derived using three different approaches to assess the suitability of common tree species on the Franconian Plateau in southern Germany under projected warmer and drier climate conditions in the period 2061-2080. The study area is currently a relatively warm and dry region of Germany. We calculated species distribution models (SDMs) using information on species' climate envelopes to predict regional species spectra under 63 different climate change scenarios. We complemented this with fine-scale ecological niche analysis using data from 51 vegetation surveys in seven forest reserves in the study area, and tree-ring analysis (TRA) from local populations of five tree species to quantify their sensitivity to climatic extreme years. The SDMs showed that predicted future climate change in the region remains within the climate envelope of certain species (e.g. Quercus petraea), whilst for e.g. Fagus sylvatica, future climate conditions in one third of the scenarios are too warm and dry. This was confirmed by the TRA: sensitivity to drought periods is lower for Q. petraea than for F. sylvatica. The niche analysis shows that the local ecological niches of Quercus robur and Fraxinus excelsior are mainly characterized by soils providing favorable water supply than by climate, and Pinus sylvestris (planted) is strongly influenced by light availability. The best adapted species for a warmer and potentially drier climate in the study region are Acer campestre, Sorbus torminalis, S. aria, Ulmus minor, and Tilia platyphyllos, which should therefore play a more prominent role in future climate-resilient mixed forest ecosystems.

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Climate is the main environmental driver determining the spatial distribution of most tree species at the continental scale. We investigated the distribution change of European beech and Norway spruce due to climate change. We applied a species distribution model (SDM), driven by an ensemble of 21 regional climate models in order to study the shift of the favourability distribution of these species. SDMs were parameterized for 1971–2000, as well as 2021–2050 and 2071–2100 using the SRES scenario A1B and three physiological meaningful climate variables. Growing degree sum and precipitation sum were calculated for the growing season on a basis of daily data. Results show a general north-eastern and altitudinal shift in climatological favourability for both species, although the shift is more marked for spruce. The gain of new favourable sites in the north or in the Alps is stronger for beech compared to spruce. Uncertainty is expressed as the variance of the averaged maps and with a density function. Uncertainty in species distribution increases over time. This study demonstrates the importance of data ensembles and shows how to deal with different outcomes in order to improve impact studies by showing uncertainty of the resulting maps.

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.