Context: Historically, large-scale outbreaks of the European spruce bark beetle were initiated mainly by windthrows. However, after 2018, a severe drought triggered the hitherto largest bark beetle outbreak observed in Europe, signalling a major shift in the disturbance regime. Objectives: Develop and test an approach that allows simulating this novel disturbance dynamics and evaluate landscape-scale compound impacts of wind- and drought-initiated outbreaks throughout the twenty-first century. Methods: We incorporated drought-initiated outbreaks into the forest landscape simulation model iLand, using critical values of vapour pressure deficit as the outbreak trigger. Forest management records and remote sensing-based disturbance maps were used to derive model parameters and evaluate simulated dynamics in a Central European forest landscape (41,000 hectares). The period 1961–2021 was used for model evaluation, and the years until 2100 for scenario analysis. Results: Incorporating drought as outbreak trigger led to a notable decoupling of wind and bark beetle disturbances, which have historically formed a typical disturbance cascade in European forests. While forest growing stock and species composition were resilient to a wind-dominated disturbance regime, this resilience diminished under the compounded impact of wind- and drought-triggered disturbances. The new disturbance regime caused a persistent decline in Norway spruce and resulted in an overall decrease in landscape-level growing stock. Conclusions: Our findings underscore the urgent need for new approaches to evaluate increasingly complex disturbance dynamics and suggest that the future impacts of bark beetles on forest landscapes may be greater than previously anticipated.

In the last decade, thousands of hectares of forests have been lost in the Czech Republic, primarily related to European spruce bark beetle (Ips typographus L.), while more than 50% of the remaining Czech forests are in great danger, thus posing severe threats to the resilience, stability, and functionality of those forests. The role of remote sensing in monitoring dynamic structural changes caused by pests is essential to understand and sustainably manage these forests. This study hypothesized a possible correlation between tree health status and multisource time series remote sensing data using different processed layers to predict the potential spread of attack by European spruce bark beetle in healthy trees. For this purpose, we used WorldView-2, Pléiades 1B, and SPOT-6 images for the period of April to September from 2018 to 2020; unmanned aerial vehicle (UAV) imagery data were also collected for use as a reference data source. Our results revealed that spectral resolution is crucial for the early detection of infestation. We observed a significant difference in the reflectance of different health statuses, which can lead to the early detection of infestation as much as two years in advance. More specifically, several bands from two different satellites in 2018 perfectly predicted the health status classes from 2020. This method could be used to evaluate health status classes in the early stage of infestation over large forested areas, which would provide a better understanding of the current situation and information for decision making and planning for the future.

The aim of this study was to develop methods for constructing a simple model describing tree mortality caused by Ips typographus L. using a minimum number of variables. We developed a model for areas spanning natural mountain forests in the Tatra National Park (Slovakia) and the Šumava National Park (Czech Republic), and in managed Czech forests located in four areas varying in environmental conditions. The model describes the time series of tree mortality dynamics caused by I.typographus using two submodels: a long-term dynamics submodel, and a short-term dynamics autoregressive distributed lag(ADL) model incorporating a two year delay and temperature variable averaged over the April-May period. The quality of fit for our models (R² value) ranged from 0.87 to 0.91. The model was formulated to capture the average monthly temperature effect, a key weather factor. We found that for high-elevation stands located at least 1000 ma.s.l., forest damage was predominantly influenced by May temperatures. For lower-elevation managed forests with warmer climates, the weather effect was insignificant.