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The European spruce bark beetle Ips typographus L. causes significant economic losses in managed coniferous forests in Central and Northern Europe. New infestations either occur in previously undisturbed forest stands (i.e., spot initiation) or depend on proximity to previous years' infestations (i.e., spot spreading). Early identification of newly infested trees over the forested landscape limits the effective control measures. Accurate forecasting of the spread of bark beetle infestation is crucial to plan efficient sanitation felling of infested trees and prevent further propagation of beetle-induced tree mortality. We created a predictive model of subsequent year spot initiation and spot spreading within the TANABBO decision support system. The algorithm combines open-access Landsat-based vegetation change time-series data, a digital terrain model, and forest stand characteristics. We validated predicted susceptibility to bark beetle attack (separately for spot initiation and spot spreading) against beetle infestations in managed forests in the Bohemian Forest in the Czech Republic (Central Europe) in yearly time steps from 2007 to 2010. The predictive models of susceptibility to bark beetle attack had a high degree of reliability (area under the ROC curve - AUC: 0.75-0.82). We conclude that spot initiation and spot spreading prediction modules included within the TANABBO model have the potential to help forest managers to plan sanitation felling in managed forests under pressure of bark beetle outbreak. [ABSTRACT FROM AUTHOR]

• The Czech Republic has become an epicentre of disturbance intensification in Europe. • A new framework for a country-wide prognosis of forest resources is presented. • Projecting recent disturbance intensity up to 2050 dramatically decreases growing stock. • The major disturbance wave was projected to last 16 years until resource depletion. • The prognosis suggests a need for major structural changes in the regional forestry sector. Forest management decisions increasingly rely on modelling tools, which help identify future risks, optimize management decisions, and provide a suite of indicators beyond timber production. Here we developed and tested a novel simulation and upscaling framework (SUF) and used it for prognosing forest resources of the Czech Republic (Central Europe), which is currently one of Europe's hotspots of disturbance intensification. The SUF is based on an empirical forest model that simulates the development of 8 240 forest stands representing forest conditions in 206 administrative districts of the Czech Republic. The effect of natural disturbances is considered via empirical species- and age-specific mortality probability (MP) functions parameterized based on the national forest damage reports and remote sensing data. An upscaling procedure was developed to obtain district- and country-wide estimates. We tested this framework for its ability to (i) reproduce the initial forest conditions from the year 2003, (ii) reproduce forest dynamics in 2003–2016 (i.e., before the recent disturbance wave), (iii) reproduce the recent mortality pulse in 2017–2020, and (iv) generate plausible and consistent outputs under several disturbance and management settings in 2003–2050. The SUF reliably reproduced forest dynamics in both testing periods. The country-wide growing stock (GS) simulated for 2004–2050 oscillated around the initial value of 661 mill. m3 if the reference MP and management were considered. Using the elevated MP (corresponding with the recent disturbance period) increased the mean annual mortality rate from 0.78 % to 1.19 % and caused GS to decrease by 21 % in 2050. The wave of elevated mortality lasted 16 years, ceasing in 2033 due to the depletion of vulnerable stands. Reducing the rotation length by 40 % increased the harvests temporarily and caused GS to decrease by 29 and 33 % in 2050 under reference and elevated MP, respectively. At the same time, mortality was reduced by up to 18 % due to the removal of potentially vulnerable stands. The presented SUF is able to accommodate diverse forestry data, reproduce real forest dynamics, and generate outputs that correspond with the national forestry statistics. Flexible adoption of different mortality and management regimes makes it a versatile tool for supporting management decisions and policies. The presented simulations highlighted the negative prospects of the regional forests and the need for a profound transformation of management practices and the regional forest-based sector. [ABSTRACT FROM AUTHOR]

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular. [ABSTRACT FROM AUTHOR]