Models are pivotal for assessing future forest dynamics under the impacts of changing climate and management practices, incorporating representations of tree growth, mortality, and regeneration. Quantitative studies on the importance of mortality submodels are scarce. We evaluated 15 dynamic vegetation models (DVMs) regarding their sensitivity to different formulations of tree mortality under different degrees of climate change. The set of models comprised eight DVMs at the stand scale, three at the landscape scale, and four typically applied at the continental to global scale. Some incorporate empirically derived mortality models, and others are based on experimental data, whereas still others are based on theoretical reasoning. Each DVM was run with at least two alternative mortality submodels. Model behavior was evaluated against empirical time series data, and then, the models were subjected to different scenarios of climate change. Most DVMs matched empirical data quite well, irrespective of the mortality submodel that was used. However, mortality submodels that performed in a very similar manner against past data often led to sharply different trajectories of forest dynamics under future climate change. Most DVMs featured high sensitivity to the mortality submodel, with deviations of basal area and stem numbers on the order of 10–40% per century under current climate and 20–170% under climate change. The sensitivity of a given DVM to scenarios of climate change, however, was typically lower by a factor of two to three. We conclude that (1) mortality is one of the most uncertain processes when it comes to assessing forest response to climate change, and (2) more data and a better process understanding of tree mortality are needed to improve the robustness of simulated future forest dynamics. Our study highlights that comparing several alternative mortality formulations in DVMs provides valuable insights into the effects of process uncertainties on simulated future forest dynamics.

Abstract. The growth behavior of coexisting tree species under climate change is important from an ecological, silvicultural and economic perspective. While many previous studies are concerned with climatic limits for species occurrence, we focus on climate related shifts in interspecific competition. A landmark for these changes in competition is the 'climatic turning point' (CTP): those climate conditions under which a rank reversal between key tree species occurs. Here, we used a common type of temperate mixed forest in Central Europe with European beech (Fagus sylvatica L.) and sessile oak (Quercus petraea (Matt.) Liebl.) to explore the CTP under a future climate projection of increasing temperature and aridity. We selected a dry region where the prerequisite of differential climate sensitivity in mixed beech-oak forests was fulfilled: In-situ dendrochronological analyses demonstrated that the currently more competitive beech was more drought sensitive than sessile oak. We then used two complementary forest growth models, namely SILVA and LandClim, to investigate the climate induced rank-reversal in species dominance and to quantify it as the CTP from beech to oak by simulating future forest development from the WETTREG 2010 A1B climate projection. Utilizing two models allowed us to draw conclusions robust against the assumptions of a particular model. Both models projected a CTP at a mean annual temperature of 11-128C (July temperature .188C) and a precipitation sum of 500-530 mm. However, the change in tree species composition can exhibit a time-lag of several decades depending on past stand development and current stand structure. We conclude that the climatic turning point is a simple yet effective reference measure to study climate related changes in interspecific competition, and confirm the importance of competition sensitivity in climate change modeling.

We used six models, ranging from simple parameter-sparse models to complex process-based 7 models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial 8 degree of uncertainty about parameter values was expressed in a prior probability distribution. 9Inventory data for Scots pine on tree height and diameter, with estimates of measurement 10 uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and 11Finland. From each country, we used data from two sites of the National Forest Inventories (NFI), 12 and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested 13 against the PSP-data. Calibration was done both per country and for all countries simultaneously, 14 thus yielding country-specific and generic parameter distributions. We assessed model 15 performance by sampling from prior and posterior distributions and comparing the growth 16 predictions of these samples to the observations at the PSP"s. 17We found that BC reduced uncertainties strongly in all but the most complex model. 18 Surprisingly, country-specific BC did not lead to clearly better within-country predictions than 19 generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the 20 most plausible model before calibration, with 4C taking its place after calibration. In this BMC, 21 model plausibility was quantified as the relative probability of a model being correct given the 22 information in the PSP-data. We discuss how the method of model initialisation affects model 23 performance. Finally, we show how BMA affords a robust way of predicting forest growth that 24 accounts for both parametric and model structural uncertainty. 25 26 27

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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