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Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non‐wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process‐based ecosystem modeling and robust multi‐criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi‐functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the last decades, the natural disturbance is increasingly putting pressure on European forests. Shifts in disturbance regimes may compromise forest functioning and the continuous provisioning of ecosystem services to society, including their climate change mitigation potential. Although forests are central to many European policies, we lack the long‐term empirical data needed for thoroughly understanding disturbance dynamics, modeling them, and developing adaptive management strategies. Here, we present a unique database of >170,000 records of ground‐based natural disturbance observations in European forests from 1950 to 2019. Reported data confirm a significant increase in forest disturbance in 34 European countries, causing on an average of 43.8 million m3 of disturbed timber volume per year over the 70‐year study period. This value is likely a conservative estimate due to under‐reporting, especially of small‐scale disturbances. We used machine learning techniques for assessing the magnitude of unreported disturbances, which are estimated to be between 8.6 and 18.3 million m3/year. In the last 20 years, disturbances on average accounted for 16% of the mean annual harvest in Europe. Wind was the most important disturbance agent over the study period (46% of total damage), followed by fire (24%) and bark beetles (17%). Bark beetle disturbance doubled its share of the total damage in the last 20 years. Forest disturbances can profoundly impact ecosystem services (e.g., climate change mitigation), affect regional forest resource provisioning and consequently disrupt long‐term management planning objectives and timber markets. We conclude that adaptation to changing disturbance regimes must be placed at the core of the European forest management and policy debate. Furthermore, a coherent and homogeneous monitoring system of natural disturbances is urgently needed in Europe, to better observe and respond to the ongoing changes in forest disturbance regimes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Robinia pseudoacacia is one of the most frequent non‐native species in Europe. It is a fast‐growing tree of high economic and cultural importance. On the other hand, it is an invasive species, causing changes in soil chemistry and light regime, and consequently altering the plant communities. Previously published models developed for the potential distribution of R. pseudoacacia concerned 2070, and were based mainly on data from Western and Central Europe; here we extended these findings and included additional data from Eastern Europe. To fill the gap in current knowledge of R. pseudoacacia distribution and improve the reliability of forecasts, we aimed to (i) determine the extent to which the outcome of range modeling will be affected by complementing R. pseudoacacia occurrence data with sites from Central, Southeastern, and Eastern Europe, (ii) identify and quantify the changes in the availability of climate niches for 2050 and 2070, and discuss their impacts on forest management and nature conservation. We showed that the majority of the range changes expected in 2070 will occur as early as 2050. In comparison to previous studies, we demonstrated a greater eastward shift of potential niches of this species and a greater decline of potential niches in Southern Europe. Consequently, future climatic conditions will likely favor the occurrence of R. pseudoacacia in Central and Northeastern Europe where this species is still absent or relatively rare. There, controlling the spread of R. pseudoacacia will require monitoring sources of invasion in the landscape and reducing the occurrence of this species. The expected effects of climate change will likely be observed 20 years earlier than previously forecasted. Hence we highlighted the urgent need for acceleration of policies aimed at climate change mitigation in Europe. Also, our results showed the need for using more complete distribution data to analyze potential niche models. [ABSTRACT FROM AUTHOR]

Current challenges to global food security require sustainable intensification of agriculture through initiatives that include more efficient use of nitrogen (N), increased protein self-sufficiency through homegrown crops, and reduced N losses to the environment. Such challenges were addressed in a continental-scale field experiment conducted over 3 years, in which the amount of total nitrogen yield (Ntot ) and the gain of N yield in mixtures as compared to grass monocultures (Ngainmix ) was quantified from four-species grass-legume stands with greatly varying legume proportions. Stands consisted of monocultures and mixtures of two N2 -fixing legumes and two nonfixing grasses. The amount of Ntot of mixtures was significantly greater (P ≤ 0.05) than that of grass monocultures at the majority of evaluated sites in all 3 years. Ntot and thus Ngainmix increased with increasing legume proportion up to one-third of legumes. With higher legume percentages, Ntot and Ngainmix did not continue to increase. Thus, across sites and years, mixtures with one-third proportion of legumes attained ~95% of the maximum Ntot acquired by any stand and had 57% higher Ntot than grass monocultures. Realized legume proportion in stands and the relative N gain in mixture (Ngainmix /Ntot in mixture) were most severely impaired by minimum site temperature (R = 0.70, P = 0.003 for legume proportion; R = 0.64, P = 0.010 for Ngainmix /Ntot in mixture). Nevertheless, the relative N gain in mixture was not correlated to site productivity (P = 0.500), suggesting that, within climatic restrictions, balanced grass-legume mixtures can benefit from comparable relative gains in N yield across largely differing productivity levels. We conclude that the use of grass-legume mixtures can substantially contribute to resource-efficient agricultural grassland systems over a wide range of productivity levels, implying important savings in N fertilizers and thus greenhouse gas emissions and a considerable potential for climate change mitigation., (© 2015 John Wiley & Sons Ltd.)