Katharina Albrich

European spruce bark beetle (Ips typographus L., SBB) outbreaks are expected to intensify under climate change and increasingly affect the boreal forests of Northern Europe, where they have historically caused only minor damage. Integrating SBB disturbances into forest dynamics models can support risk assessment and integrative forest management. Reliable simulations of SBB outbreaks require adaptation of model parameters to regional phenological conditions. This study aimed to (i) evaluate the transferability of the bark beetle module implemented in iLand from temperate Central European to boreal Northern European conditions, (ii) develop and test boreal-specific parameter values where necessary, and (iii) examine the internal processes controlling annual beetle development and generation timing. Key parameters differing between temperate and boreal conditions were identified and evaluated through sensitivity analyses. Model simulations were compared with observations from an SBB outbreak in Finland. Parameters requiring modification under boreal conditions were related to infestation onset, beetle development, and overwintering. Simulations using the original parameterisation did not sustain the SBB outbreak following a large-scale wind disturbance. In contrast, the boreal parameterisation more closely reproduced the observed outbreak. Overwintering parameters had the strongest influence on model transferability. Day length and the proportion of beetles overwintering in the soil were identified as the most sensitive parameters. Under both parameterisations, filial and sister-brood generations developed every year, while the second parental generation was limited. The proposed boreal parameterisation improves the applicability of the iLand bark beetle module under boreal forest conditions and supports more robust simulations across climatic regions.

Climate change is expected to alter species assemblages by affecting the outcome of competition between species. Investigating processes of competition remains challenging in tree communities, as they unfold over extensive spatio-temporal scales. Here, we used a deep learning-based meta-model trained on 135 million simulated tree responses to climate across Europe to investigate changes in the competitiveness of nine major tree species under future climate. We harnessed projections from local process models to train a Deep Neural Network of forest state transitions to investigate climate-induced changes in competition at continental scale. We found decreasing competitive strength for evergreen conifers across their distribution, while deciduous broadleaved species increased in competitiveness. Most investigated species lost competitive strength at their warm range edges. Consequently, up to 25% of Europe’s forests could experience a change in the dominant tree species until the end of the 21 ^st century, suggesting a profound climate-induced reassembly of Europe’s forests.

Wildfires, insect outbreaks, and storms cause large pulses of tree mortality. Climate change amplifies these forest disturbances, yet their future magnitude and extent remain uncertain. Here, we simulated future forest disturbance regimes at 100-meter resolution across Europe using a deep learning–based simulation framework. Our results show that forest disturbances will continue to increase throughout the 21st century, with disturbed areas more than doubling relative to the recent past under an unabated continuation of climate change. Wildfires are the main agent driving future disturbance change. Changing disturbances result in an increase in young forests, substantially altering Europe’s forest demography. Because of their profound implications for forest carbon storage and the habitat value of forest ecosystems, disturbances should be a priority of forest policy and management.

European mountain forests have been strongly shaped by past human activities, which have influenced their structure and composition. Assessing the natural tree-species composition of current forest landscapes is essential for evaluating their biodiversity potential and for informing management prioritization. High levels of compositional naturalness are often associated with greater ecosystem functioning, but it remains unclear whether forest landscapes that are closer to their potential forest composition are also less vulnerable to future climate change and natural disturbances. Using a process-based forest landscape model, we quantified the naturalness and the vulnerability to disturbances across a large forested area in the Italian Alps. We developed a spatially-explicit index to evaluate how closely current tree species composition matches potential forest composition. We then simulated future forest dynamics under multiple climate change and disturbance scenarios, using two different initial vegetation conditions on the same landscape – potential vs. current forest – and compared their vulnerability based on changes in species dominance, vegetation structure, and height heterogeneity. Results indicate that current forests exhibit generally low naturalness compared with their potential forest composition, reflecting historical management and agro-silvopastoral practices. The naturalness score changed depending on elevation across the landscape: forests at low (2100 m a.s.l.) elevations had low naturalness, while those in the mid-elevation range (1500–2100 m) exhibited medium to high levels of naturalness. Vulnerability to disturbances under climate change differed markedly between the two initial vegetation conditions. Current forest was more susceptible to bark beetle outbreaks, driven by past promotion of Norway spruce and further amplified by warming. In contrast, the potential forest was more vulnerable to wind disturbance, likely due to old-growth characteristics, such as greater height heterogeneity and canopy roughness, that increase blowdown susceptibility. This study provides the first assessment of forest naturalness using spatially explicit dynamic landscape modelling. Given the projected intensification of natural disturbances under future climates, our findings suggest that promoting more natural forest conditions alone may not guarantee higher resilience to climate-induced disturbances. Instead, management approaches should aim at increasing landscape-level structural and compositional heterogeneity in a balanced manner to minimizing future disturbance vulnerability.

Context: Across Alpine landscapes, a combination of land-use abandonment and climate change is driving forest expansion and promoting the upward migration of trees on grasslands. Yet, it remains unclear how rapidly the upper forest-grassland ecotone will shift and how tree species composition will change, both in terms of species proportions and along elevational gradients. Objectives: Our aim is to investigate the future forest expansion in a landscape in the Eastern Alps under potential grassland abandonment, climate change, natural disturbances (wind and bark beetle), and forest management. Methods: We used the iLand forest landscape model to simulate long-term dynamics (2020–2200) under different scenarios. We coupled model outputs with the concave hull algorithm to identify potential changes in the forest-grassland ecotone, tracking tree species expansion and quantifying elevation and compositional shifts. Results: Under a potential abandonment of alpine grasslands, forest will likely expand rapidly within the twenty-first century regardless climate warming. This because the current upper forest-grassland ecotone is mainly constrained by land use rather than climate. Our simulations also showed that ecotone shifts will be more pronounced on S-facing slopes, while climate change will affect more future tree species composition and forest stocking at higher elevations. Conclusions: Our outcomes provide useful insights on future dynamics of the upper forest ecotone by using a forest landscape model and by integrating not only species migration and climate but also other factors such as disturbances and management. Our results could provide useful information for designing landscape management strategies in rapidly changing Alpine mountain valleys.

Simulation models are important tools to study the impacts of climate change and natural disturbances on forest ecosystems. Being able to track tree demographic processes in a spatially explicit manner, process-based forest landscape models are considered the most suitable to provide robust projections that can aid decision-making in forest management. However, landscape models are challenging to parameterize and setting up new study areas for application studies largely depends on data availability. The aim of this study is to demonstrate the parameterization process, including model testing and evaluation, for setting up a study area in the Italian Alps in a process-based forest landscape model using available data. We processed soil, climate, carbon pools, vegetation, disturbances and forest management data, and ran iterative spin-up simulations to generate a virtual landscape best resembling current conditions. Our results demonstrated the feasibility of initializing forest landscape models with data that are typically available from forest management plans and national forest inventories, as well as openly available mapping products. Evaluation tests proved the ability of the model to capture the environmental constraints driving regeneration dynamics and inter-specific competition in forests of the Italian Alps, as well as to simulate natural disturbances and carbon dynamics. The model can subsequently be applied to investigate forest landscape development under a suite of future scenarios and provide recommendations for adapting forest management decisions.

Inter-annual climate variability (hereafter climate variability) is increasing in many forested regions due to climate change. This variability could have larger near-term impacts on forests than decadal shifts in mean climate, but how forests will respond remains poorly resolved, particularly at broad scales. Individual trees, and even forest communities, often have traits and ecological strategies—the legacies of exposure to past variable conditions—that confer tolerance to subsequent climate variability. However, whether local legacies also shape global forest responses is unknown. Our objective was to assess how past and current climate variability influences global forest productivity. We hypothesized that forests exposed to large climate variability in the past would better tolerate current climate variability than forests for which past climate was relatively stable. We used historical (1950–1969) and contemporary (2000–2019) temperature, precipitation, and vapor pressure deficit (VPD) and the remotely sensed enhanced vegetation index (EVI) to quantify how historical and contemporary climate variability relate to patterns of contemporary forest productivity. Consistent with our hypothesis, forests exposed to large temperature variability in the past were more tolerant of contemporary temperature variability than forests where past temperatures were less variable. Forests were 19-fold times less sensitive to contemporary temperature variability where historical inter-annual temperature variability was 0.66 °C (two standard deviations) greater than the global average historical temperature variability. We also found that larger increases in temperature variability between the two study periods often eroded the tolerance conferred by the legacy effects of historical temperature variability. However, the hypothesis was not supported in the case of precipitation and VPD variability, potentially due to physiological tradeoffs inherent in how trees cope with dry conditions. We conclude that the sensitivity of forest productivity to imminent increases in temperature variability may be partially predictable based on the legacies of past conditions.