Process-oriented models of leaf senescence are biased towards the mean: Impacts on model performance and future projections.

The timing of leaf senescence in deciduous trees influences carbon uptake and the resources available for tree growth, defense, and reproduction. Therefore, simulated biosphere-atmosphere interactions and, eventually, estimates of the biospheric climate change mitigation potential are affected by the accuracy of process-oriented leaf senescence models. However, current leaf senescence models are likely to suffer from a bias towards the mean (BTM). This may lead to overly flat trends, whereby errors would increase with increasing difference from the average timing of leaf senescence, ultimately distorting model performance and projected future shifts. However, such effects of the BTM on model performance and future shifts have rarely been investigated. We analyzed >17 × 106 past dates and >49 × 106 future shifts of leaf senescence simulated by 21 process-oriented models that had been calibrated with >45,000 observations from Central Europe for three major European tree species. The surmised effects on model performance and future shifts occurred in all 21 models, revealing strong model-specific BTM. In general, the models performed only slightly better than a null model that just simulates the average timing of leaf senescence. While standard comparisons of model performance favored models with stronger BTM, future shifts of leaf senescence were smaller when projected by models with weaker BTM. Overall, the future shifts for 2090-2099 relative to 1990-1999 increased by an average of 13-14 days after correcting for the BTM. In conclusion, the BTM substantially affects simulations by state-of-the-art leaf senescence models, which compromises model comparisons and distorts projections of future shifts. Smaller shifts result from flatter trends associated with stronger BTM. Therefore, smaller shifts according to models with weaker BTM illustrate the considerable uncertainty in current leaf senescence projections. It is likely that state-of-the-art projections of future biosphere behavior under global change are distorted by erroneous leaf senescence models.

Accuracy, realism and general applicability of European forest models.

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.

Growth resistance and resilience of mixed silver fir and Norway spruce forests in central Europe: Contrasting responses to mild and severe droughts.

Extreme droughts are expected to increase in frequency and severity in many regions of the world, threatening multiple ecosystem services provided by forests. Effective strategies to adapt forests to such droughts require comprehensive information on the effects and importance of the factors influencing forest resistance and resilience. We used a unique combination of inventory and dendrochronological data from a long-term (>30 years) silvicultural experiment in mixed silver fir and Norway spruce mountain forests along a temperature and precipitation gradient in southwestern Germany. We aimed at examining the mechanisms and forest stand characteristics underpinning the resistance and resilience to past mild and severe droughts. We found that (i) fir benefited from mild droughts and showed higher resistance (i.e., lower growth loss during drought) and resilience (i.e., faster return to pre-drought growth levels) than spruce to all droughts; (ii) species identity determined mild drought responses while species interactions and management-related factors strongly influenced the responses to severe droughts; (iii) intraspecific and interspecific interactions had contrasting effects on the two species, with spruce being less resistant to severe droughts when exposed to interaction with fir and beech; (iv) higher values of residual stand basal area following thinning were associated with lower resistance and resilience to severe droughts; and (v) larger trees were resilient to mild drought events but highly vulnerable to severe droughts. Our study provides an analytical approach for examining the effects of different factors on individual tree- and stand-level drought response. The forests investigated here were to a certain extent resilient to mild droughts, and even benefited from such conditions, but were strongly affected by severe droughts. Lastly, negative effects of severe droughts can be reduced through modifying species composition, tree size distribution and stand density in mixed silver fir-Norway spruce forests.

Stand-scale climate change impacts on forests over large areas: transient responses and projection uncertainties.

The increasing impacts of climate change on forest ecosystems have triggered multiple model-based impact assessments for the future, which typically focused either on a small number of stand-scale case studies or on large scale analyses (i.e., continental to global). Therefore, substantial uncertainty remains regarding the local impacts over large areas (i.e., regions to countries), which is particularly problematic for forest management. We provide a comprehensive, high-resolution assessment of the climate change sensitivity of managed Swiss forests (~10,000 km2 ), which cover a wide range of environmental conditions. We used a dynamic vegetation model to project the development of typical forest stands derived from a stratification of the Third National Forest Inventory until the end of the 22nd century. Two types of simulations were conducted: one limited to using the extant local species, the other enabling immigration of potentially more climate-adapted species. Moreover, to assess the robustness of our projections, we quantified and decomposed the uncertainty in model projections resulting from the following sources: (1) climate change scenarios, (2) local site conditions, and (3) the dynamic vegetation model itself (i.e., represented by a set of model versions), an aspect hitherto rarely taken into account. The simulations showed substantial changes in basal area and species composition, with dissimilar sensitivity to climate change across and within elevation zones. Higher-elevation stands generally profited from increased temperature, but soil conditions strongly modulated this response. Low-elevation stands were increasingly subject to drought, with strong negative impacts on forest growth. Furthermore, current stand structure had a strong effect on the simulated response. The admixture of drought-tolerant species was found advisable across all elevations to mitigate future adverse climate-induced effects. The largest uncertainty in model projections was associated with climate change scenarios. Uncertainty induced by the model version was generally largest where overall simulated climate change impacts were small, thus corroborating the utility of the model for making projections into the future. Yet, the large influence of both site conditions and the model version on some of the projections indicates that uncertainty sources other than climate change scenarios need to be considered in climate change impact assessments.

Assessing the response of forest productivity to climate extremes in Switzerland using model-data fusion.

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long-term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3-PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960-2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha-1  year-1  km-1 for P. abies and 0.93 ± 0.010 Mg C ha-1  year-1  km-1 for F. sylvatica). During warm-dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm-dry extremes. Importantly, cold-dry extremes had negative impacts on regional forest NPP comparable to warm-dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.

Bayesian calibration of a growth-dependent tree mortality model to simulate the dynamics of European temperate forests.

Dynamic vegetation models (DVMs) are important tools to understand and predict the functioning and dynamics of terrestrial ecosystems under changing environmental conditions. In these models, uncertainty in the description of demographic processes, in particular tree mortality, is a persistent problem. Current mortality formulations lack realism and are insufficiently constrained by empirical evidence. It has been suggested that empirically estimated mortality submodels would enhance DVM performance, but due to the many processes and interactions within a DVM, the claim has rarely been tested. Here, we compare the performance of three alternative growth-dependent tree mortality submodels in the DVM ForClim: (1) a mortality function with theoretical foundation (ForClim v3.3); (2) a mortality function with parameters directly estimated based on forest inventory data; and (3) the same function, but with parameters estimated using an inverse approach through Bayesian calibration (BC). Time series of inventory data from 30 ecologically distinct Swiss natural forest reserves collected over 35+ yr, including the main tree species of Central Europe, were used for the calibration and subsequent validation of the mortality functions and the DVM. The recalibration resulted in mortality parameters that differed from the direct empirical estimates, particularly for the relationship between tree size and mortality. The calibrated parameters outperformed the direct estimates, and to a lesser extent the original mortality function, for predicting decadal-scale forest dynamics at both calibration and validation sites. The same pattern was observed regarding the plausibility of their long-term projections under contrasting environmental conditions. Our results demonstrate that inverse calibration may be useful even when direct empirical estimates of DVM parameters are available, as structural model deficiencies or data problems can result in discrepancies between direct and inverse estimates. Thus, we interpret the good performance of the inversely calibrated model for long-term projections (which were not a calibration target) as evidence that the calibration did not compensate for model errors. Rather, we surmise that the discrepancy was mainly caused by a lack of representativeness of the mortality data. Our results underline the potential for learning more about elusive processes, such as tree mortality or recruitment, through data integration in DVMs.

How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model.

Dynamic Vegetation Models (DVMs) are designed to be suitable for simulating forest succession and species range dynamics under current and future conditions based on mathematical representations of the three key processes regeneration, growth, and mortality. However, mortality formulations in DVMs are typically coarse and often lack an empirical basis, which increases the uncertainty of projections of future forest dynamics and hinders their use for developing adaptation strategies to climate change. Thus, sound tree mortality models are highly needed. We developed parsimonious, species-specific mortality models for 18 European tree species using >90,000 records from inventories in Swiss and German strict forest reserves along a considerable environmental gradient. We comprehensively evaluated model performance and incorporated the new mortality functions in the dynamic forest model ForClim. Tree mortality was successfully predicted by tree size and growth. Only a few species required additional covariates in their final model to consider aspects of stand structure or climate. The relationships between mortality and its predictors reflect the indirect influences of resource availability and tree vitality, which are further shaped by species-specific attributes such as maximum longevity and shade tolerance. Considering that the behavior of the models was biologically meaningful, and that their performance was reasonably high and not impacted by changes in the sampling design, we suggest that the mortality algorithms developed here are suitable for implementation and evaluation in DVMs. In the DVM ForClim, the new mortality functions resulted in simulations of stand basal area and species composition that were generally close to historical observations. However, ForClim performance was poorer than when using the original, coarse mortality formulation. The difficulties of simulating stand structure and species composition, which were most evident for Fagus sylvatica L. and in long-term simulations, resulted from feedbacks between simulated growth and mortality as well as from extrapolation to very small and very large trees. Growth and mortality processes and their species-specific differences should thus be revisited jointly, with a particular focus on small and very large trees in relation to their shade tolerance.

A synthesis of radial growth patterns preceding tree mortality.

Tree mortality is a key factor influencing forest functions and dynamics, but our understanding of the mechanisms leading to mortality and the associated changes in tree growth rates are still limited. We compiled a new pan-continental tree-ring width database from sites where both dead and living trees were sampled (2970 dead and 4224 living trees from 190 sites, including 36 species), and compared early and recent growth rates between trees that died and those that survived a given mortality event. We observed a decrease in radial growth before death in ca. 84% of the mortality events. The extent and duration of these reductions were highly variable (1-100 years in 96% of events) due to the complex interactions among study species and the source(s) of mortality. Strong and long-lasting declines were found for gymnosperms, shade- and drought-tolerant species, and trees that died from competition. Angiosperms and trees that died due to biotic attacks (especially bark-beetles) typically showed relatively small and short-term growth reductions. Our analysis did not highlight any universal trade-off between early growth and tree longevity within a species, although this result may also reflect high variability in sampling design among sites. The intersite and interspecific variability in growth patterns before mortality provides valuable information on the nature of the mortality process, which is consistent with our understanding of the physiological mechanisms leading to mortality. Abrupt changes in growth immediately before death can be associated with generalized hydraulic failure and/or bark-beetle attack, while long-term decrease in growth may be associated with a gradual decline in hydraulic performance coupled with depletion in carbon reserves. Our results imply that growth-based mortality algorithms may be a powerful tool for predicting gymnosperm mortality induced by chronic stress, but not necessarily so for angiosperms and in case of intense drought or bark-beetle outbreaks.

Improvement of water and light availability after thinning at a xeric site: which matters more? A dual isotope approach.

Thinning fosters individual tree growth by increasing the availability of water, light and nutrients. At sites where water rather than light is limiting, thinning also enhances soil evaporation and might not be beneficial. Detailed knowledge of the short- to long-term physiological response underlying the growth responses to thinning is crucial for the management of forests already suffering from recurrent drought-induced dieback. We applied a dual isotope approach together with mechanistic isotope models to study the physiological processes underlying long-term growth enhancement of heavily thinned Pinus sylvestris in a xeric forest in Switzerland. This approach allowed us to identify and disentangle thinning-induced changes in stomatal conductance and assimilation rate. At our xeric study site, the increase in stomatal conductance far outweighed the increase in assimilation, implying that growth release in heavily thinned trees is primarily driven by enhanced water availability rather than increased light availability. We conclude that in forests with relatively isohydric species (drought avoiders) that are growing close to their physiological limits, thinning is recommended to maintain a less negative water balance and thus foster tree growth, and ultimately the survival of forest trees under drought.

Does one model fit all? Patterns of beech mortality in natural forests of three European regions.

Large uncertainties characterize forest development under global climate change. Although recent studies have found widespread increased tree mortality, the patterns and processes associated with tree death remain poorly understood, thus restricting accurate mortality predictions. Yet, projections of future forest dynamics depend critically on robust mortality models, preferably based on empirical data rather than theoretical, not well-constrained assumptions. We developed parsimonious mortality models for individual beech (Fagus sylvatica L.) trees and evaluated their potential for incorporation in dynamic vegetation models (DVMs). We used inventory data from nearly 19,000 trees from unmanaged forests in Switzerland, Germany, and Ukraine, representing the largest dataset used to date for calibrating such models. Tree death was modelled as a function of size and growth, i.e., stem diameter (dbh) and relative basal area increment (relBAI), using generalized logistic regression accounting for unequal re-measurement intervals. To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics. Validation with independent datasets was performed to assess model generality. Our results demonstrate strong variability in beech mortality that was independent of environmental or stand characteristics. Mortality patterns in Swiss and German strict forest reserves were dominated by competition processes as indicated by J-shaped mortality over tree size and growth. The Ukrainian primeval beech forest was additionally characterized by windthrow and a U-shaped size-mortality function. Unlike the mortality model based on Ukrainian data, the Swiss and German models achieved good discrimination and acceptable transferability when validated against each other. We thus recommend these two models to be incorporated and examined in DVMs. Their mortality predictions respond to climate change via tree growth, which is sufficient to capture the adverse effects of water availability and competition on the mortality probability of beech under current conditions.

Towards a common methodology for developing logistic tree mortality models based on ring-width data.

Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth-mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth-mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth-mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth-mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth-mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth-mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.

The agony of choice: different empirical mortality models lead to sharply different future forest dynamics.

Dynamic models are pivotal for projecting forest dynamics in a changing climate, from the local to the global scale. They encapsulate the processes of tree population dynamics with varying resolution. Yet, almost invariably, tree mortality is modeled based on simple, theoretical assumptions that lack a physiological and/or empirical basis. Although this has been widely criticized and a growing number of empirically derived alternatives are available, they have not been tested systematically in models of forest dynamics. We implemented an inventory-based and a tree-ring-based mortality routine in the forest gap model ForClim v3.0. We combined these routines with a stochastic and a deterministic approach for the determination of tree status (alive vs. dead). We tested the four new model versions for two Norway spruce forests in the Swiss Alps, one of which was managed (inventory time series spanning 72 years) and the other was unmanaged (41 years). Furthermore, we ran long-term simulations (-400 years) into the future under three climate scenarios to test model behavior under changing environmental conditions. The tests against inventory data showed an excellent match of simulated basal area and stem numbers at the managed site and a fair agreement at the unmanaged site for three of the four empirical mortality models, thus rendering the choice of one particular model difficult. However, long-term simulations under current climate revealed very different behavior of the mortality models in terms of simulated changes of basal area and stem numbers, both in timing and magnitude, thus indicating high sensitivity of simulated forest dynamics to assumptions on tree mortality. Our results underpin the potential of using empirical mortality routines in forest gap models. However, further tests are needed that span other climatic conditions and mixed forests. Short-term simulations to benchmark model behavior against empirical data are insufficient; long-term tests are needed that include both nonequilibrium and equilibrium conditions. Thus, there is the potential to greatly improve the robustness of future projections of forest dynamics via more reliable tree mortality submodels.

Short- and long-term efficacy of forest thinning to mitigate drought impacts in mountain forests in the European Alps.

In many regions of the world, drought is projected to increase under climate change, with potential negative consequences for forests and their ecosystem services (ES). Forest thinning has been proposed as a method for at least temporarily mitigating drought impacts, but its general applicability and longer-term impacts are unclear. We use a process-based forest model to upscale experimental data for evaluating the impacts of forest thinning in a drought-susceptible valley in the interior of the European Alps, with the specific aim of assessing (1) when and where thinning may be most effective and (2) the longer-term implications for forest dynamics. Simulations indicate that forests will be impacted by climate-induced increases in drought across a broad elevation range. At lower elevations, where drought is currently prevalent, thinning is projected to temporarily reduce tree mortality, but to have minor impacts on forest dynamics in the longer term. Thinning may be particularly useful at intermediate and higher elevations as a means of temporarily reducing mortality in drought-sensitive species such as Norway spruce and larch, which currently dominate these elevations. However, in the longer term, even intense thinning will likely not be sufficient to prevent a climate change induced dieback of these species, which is projected to occur under even moderate climate change. Thinning is also projected to have the largest impact on long-term forest dynamics at intermediate elevations, with the magnitude of the impact depending on the timing and intensity of thinning. More intense thinning that is done later is projected to more strongly promote a transition to more drought-tolerant species. We conclude that thinning is a viable option for temporarily reducing the negative drought impacts on forests, but that efficient implementation of thinning should be contingent on a site-specific evaluation of the near term risk of significant drought, and how thinning will impact the rate and direction of climate driven forest conversion.

Temporal stability in forest productivity increases with tree diversity due to asynchrony in species dynamics.

Theory predicts a positive relationship between biodiversity and stability in ecosystem properties, while diversity is expected to have a negative impact on stability at the species level. We used virtual experiments based on a dynamic simulation model to test for the diversity-stability relationship and its underlying mechanisms in Central European forests. First our results show that variability in productivity between stands differing in species composition decreases as species richness and functional diversity increase. Second we show temporal stability increases with increasing diversity due to compensatory dynamics across species, supporting the biodiversity insurance hypothesis. We demonstrate that this pattern is mainly driven by the asynchrony of species responses to small disturbances rather than to environmental fluctuations, and is only weakly affected by the net biodiversity effect on productivity. Furthermore, our results suggest that compensatory dynamics between species may enhance ecosystem stability through an optimisation of canopy occupancy by coexisting species.

Mother trees, altruistic fungi, and the perils of plant personification.

There are growing doubts about the true role of the common mycorrhizal networks (CMN or wood wide web) connecting the roots of trees in forests. We question the claims of a substantial carbon transfer from 'mother trees' to their offspring and nearby seedlings through the CMN. Recent reviews show that evidence for the 'mother tree concept' is inconclusive or absent. The origin of this concept seems to stem from a desire to humanize plant life but can lead to misunderstandings and false interpretations and may eventually harm rather than help the commendable cause of preserving forests. Two recent books serve as examples: The Hidden Life of Trees and Finding the Mother Tree.

A harmonized database of European forest simulations under climate change.

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch.

The ability of tree species to cope with anticipated decrease in water availability is still poorly understood. We evaluated the potential of Norway spruce, Scots pine, European larch, black pine, and Douglas-fir to withstand drought in a drier future climate by analyzing their past growth and physiological responses at a xeric and a mesic site in Central Europe using dendroecological methods. Earlywood, latewood, and total ring width, as well as the δ(13) C and δ(18) O in early- and latewood were measured and statistically related to a multiscalar soil water deficit index from 1961 to 2009. At the xeric site, δ(13) C values of all species were strongly linked to water deficits that lasted longer than 11 months, indicating a long-term cumulative effect on the carbon pool. Trees at the xeric site were particularly sensitive to soil water recharge in the preceding autumn and early spring. The native species European larch and Norway spruce, growing close to their dry distribution limit at the xeric site, were found to be the most vulnerable species to soil water deficits. At the mesic site, summer water availability was critical for all species, whereas water availability prior to the growing season was less important. Trees at the mesic were more vulnerable to water deficits of shorter duration than the xeric site. We conclude that if summers become drier, trees growing on mesic sites will undergo significant growth reductions, whereas at their dry distribution limit in the Alps, tree growth of the highly sensitive spruce and larch may collapse, likely inducing dieback and compromising the provision of ecosystem services. However, the magnitude of these changes will be mediated strongly by soil water recharge in winter and thus water availability at the beginning of the growing season.

Updating beliefs and combining evidence in adaptive forest management under climate change: a case study of Norway spruce (Picea abies L. Karst) in the Black Forest, Germany.

We study climate uncertainty and how managers' beliefs about climate change develop and influence their decisions. We develop an approach for updating knowledge and beliefs based on the observation of forest and climate variables and illustrate its application for the adaptive management of an even-aged Norway spruce (Picea abies L. Karst) forest in the Black Forest, Germany. We simulated forest development under a range of climate change scenarios and forest management alternatives. Our analysis used Bayesian updating and Dempster's rule of combination to simulate how observations of climate and forest variables may influence a decision maker's beliefs about climate development and thereby management decisions. While forest managers may be inclined to rely on observed forest variables to infer climate change and impacts, we found that observation of climate state, e.g. temperature or precipitation is superior for updating beliefs and supporting decision-making. However, with little conflict among information sources, the strongest evidence would be offered by a combination of at least two informative variables, e.g., temperature and precipitation. The success of adaptive forest management depends on when managers switch to forward-looking management schemes. Thus, robust climate adaptation policies may depend crucially on a better understanding of what factors influence managers' belief in climate change.

Adaptive management for competing forest goods and services under climate change.

Developing adaptive forest management strategies is essential to maintain the provisioning of forest goods and services (FGS) under future climate change. We assessed how climate change and forest management affect forest development and FGS for a diverse case-study landscape in Central Europe. Using a process-based forest model (LandClim) we simulated forest dynamics and FGS under a range of climate change and management scenarios in the Black Forest, Germany, which is shaped by various management practices. We focused on the interdependencies between timber production and forest diversity, the most valued FGS in this region. We found that the conversion to more drought-adapted forest types is required to prevent climate change-induced forest dieback and that this conversion must be the target of any adaptive management, especially in areas where monocultures of drought-sensitive Norway spruce (Picea abies) were promoted in the past. Forest conversion takes up to 120 years, however, with past and future adaptive management being the key drivers of timber and forest diversity provision. The conversion of drought-sensitive conifer monocultures maintains timber production in the short-term and enhances a range of forest diversity indices. Using uneven-aged forest management that targets a drought-adapted, diverse, and resilient species mixture, high species diversity can be combined with timber production in the long term. Yet, the promotion of mature-stand attributes requires management restrictions. Selecting future adaptive management options thus implies the consideration of trade-offs between forest resource use and environmental objectives, but also the exploitation of synergies between FGS that occur during forest conversion. Lastly, the large impact of past management practices on the spatial heterogeneity of forest dynamics underpins the need to assess FGS provisioning at the landscape scale.

The evolution, complexity and diversity of models of long-term forest dynamics.

To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity-with the exception of global models-can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.