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.

Modelling the effects of climate and management on the distribution of deadwood in European forests.

Deadwood is a key old-growth element in European forests and a cornerstone of biodiversity conservation practices in the region, recognized as an important indicator of sustainable forest management. Despite its importance as a legacy element for biodiversity, uncertainties remain on the drivers of deadwood potentials, its spatial distribution in European forests and how it may change in the future due to management and climate change. To fill this gap, we combined a comprehensive deadwood dataset to fit a machine learning and a Bayesian hurdle-lognormal model against multiple environmental and socio-economic predictors. We deployed the models on the gridded predictors to forecast changes in deadwood volumes in Europe under alternative climate (RCP4.5 and RCP8.5) and management scenarios (biodiversity-oriented and production-oriented strategies). Our results show deadwood hotspots in montane forests of central Europe and unmanaged forests in Scandinavia. Future climate conditions may reduce deadwood potentials up to 13% under a mid-century climate, with regional losses amounting to up to 22% in Southern Europe. Nevertheless, changes in management towards more biodiversity-oriented strategies, including an increase in the share of mixed forests and extended rotation lengths, may mitigate this loss to a 4% reduction in deadwood potentials. We conclude that adaptive management can promote deadwood under changing environmental conditions and thereby support habitat maintenance and forest multifunctionality.

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.

Time matters: Resilience of a post-disturbance forest landscape.

Present-day disturbances are transforming European forest landscapes, and their legacies determine the vulnerability and resilience of the emergent forest generation. To understand these legacy effects, we investigated the resilience of the aboveground forest biomass (Babg) to a sequence of disturbances affecting the forest in different recovery phases from the initial large-scale impact. We used the model iLand to simulate windthrows that affected 13-24% of the Babg in a Central European forest landscape. An additional wind event was simulated 20, 40, 60, or 80 years after the initial impact (i.e., sequences of two windthrows were defined). Each windthrow triggered an outbreak of bark beetles that interacted with the recovery processes. We evaluated the resistance of the Babg to and recovery after the impact. Random Forest models were used to identify factors influencing resilience. We found that Babg resistance was the lowest 20 years after the initial impact when the increased proportion of emergent wind-exposed forest edges prevailed the disturbance-dampening effect of reduced biomass levels and increased landscape heterogeneity. This forest had a remarkably high recovery rate and reached the pre-disturbance Babg within 28 years. The forest exhibited a higher resistance and a slower recovery rate in the more advanced recovery phases, reaching the pre-disturbance Babg within 60-80 years. The recovery was enhanced by higher levels of alpha and beta diversity. Under elevated air temperature, the bark beetle outbreak triggered by windthrow delayed the recovery. However, the positive effect of increased temperature on forest productivity caused the recovery rate to be higher under the warming scenario than under the reference climate. We conclude that resilience is not a static property, but its magnitude and drivers vary in time, depending on vegetation feedbacks, interactions between disturbances, and climate. Understanding these mechanisms is an essential step towards the operationalization of resilience-oriented stewardship.

Biodiversity response to forest management intensity, carbon stocks and net primary production in temperate montane forests.

Managed forests are a key component of strategies aimed at tackling the climate and biodiversity crises. Tapping this potential requires a better understanding of the complex, simultaneous effects of forest management on biodiversity, carbon stocks and productivity. Here, we used data of 135 one-hectare plots from southwestern Germany to disentangle the relative influence of gradients of management intensity, carbon stocks and forest productivity on different components of forest biodiversity (birds, bats, insects, plants) and tree-related microhabitats. We tested whether the composition of taxonomic groups varies gradually or abruptly along these gradients. The richness of taxonomic groups was rather insensitive to management intensity, carbon stocks and forest productivity. Despite the low explanatory power of the main predictor variables, forest management had the greatest relative influence on richness of insects and tree-related microhabitats, while carbon stocks influenced richness of bats, birds, vascular plants and pooled taxa. Species composition changed relatively abruptly along the management intensity gradient, while changes along carbon and productivity gradients were more gradual. We conclude that moderate increases in forest management intensity and carbon stocks, within the range of variation observed in our study system, might be compatible with biodiversity and climate mitigation objectives in managed forests.

Forest carbon allocation modelling under climate change.

Carbon allocation plays a key role in ecosystem dynamics and plant adaptation to changing environmental conditions. Hence, proper description of this process in vegetation models is crucial for the simulations of the impact of climate change on carbon cycling in forests. Here we review how carbon allocation modelling is currently implemented in 31 contrasting models to identify the main gaps compared with our theoretical and empirical understanding of carbon allocation. A hybrid approach based on combining several principles and/or types of carbon allocation modelling prevailed in the examined models, while physiologically more sophisticated approaches were used less often than empirical ones. The analysis revealed that, although the number of carbon allocation studies over the past 10 years has substantially increased, some background processes are still insufficiently understood and some issues in models are frequently poorly represented, oversimplified or even omitted. Hence, current challenges for carbon allocation modelling in forest ecosystems are (i) to overcome remaining limits in process understanding, particularly regarding the impact of disturbances on carbon allocation, accumulation and utilization of nonstructural carbohydrates, and carbon use by symbionts, and (ii) to implement existing knowledge of carbon allocation into defence, regeneration and improved resource uptake in order to better account for changing environmental conditions.

Multiple uncertainties require a change of conservation practices for saproxylic beetles in managed temperate forests.

In Europe, intensive forest management has severely compromised the habitat of forest insects, especially saproxylic beetles, due to the removal of deadwood and veteran trees. The loss of insect diversity may disrupt ecosystem functioning and affect the provision of important ecosystem goods and services in the future. Here we propose a novel approach for the implementation of conservation policies, by optimally allocating forest reserves and deadwood islands under multiple sources of uncertainty and minimizing economic risk. We use the saproxylic beetle Lucanus cervus as umbrella species, requiring that deadwood islands were spaced within its dispersal capacity. We show that current management and conservation practices are increasingly inefficient under changing environmental conditions and that the consideration of uncertainty requires a major expansion of conservation areas. Moreover, our results indicate that a strong diversification of management regimes, with a focus on selection forest systems, is required to reduce economic risk of forest management. We conclude that the integration of uncertainty into conservation planning may reduce the trade-off between production and conservation objectives in forest landscapes and is key to increase the efficiency of forest management in the future.