Infestation symptoms as indicators of a sustained bark beetle outbreak in conserved and managed Norway spruce forests in south-eastern Finland

Infestation symptoms as indicators of a sustained bark beetle outbreak in conserved and managed Norway spruce forests in south-eastern Finland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Infestation symptoms as indicators of a sustained bark beetle outbreak in conserved and managed Norway spruce forests in south-eastern Finland Diana-Cristina Simon, Päivi Lyytikäinen-Saarenmaa, Mikko Pelto-Arvo, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5355177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract European spruce bark beetle ( Ips typographus L., SBB) infestations are extending in northern Europe due to increases in temperature and drought, which increase the risk of outbreaks in Norway spruce ( Picea abies L.) forests. The severity of SBB damage may be decreased by timely detection and management measures. In this study, we analysed the SBB infestation levels of trees, the overall SBB damage at the stand level, the relationship between SBB damage and stand characteristics, and the effect of an outbreak over time on the volume and basal area in managed and conserved areas. We visually observed SBB symptoms at the stem level (entrance-exit holes, resinous flows, bark damage) and crown level (defoliation, discoloration) in 60 sampling plots in south-eastern Finland. These plots were established in an SBB outbreak area triggered by a severe wind disturbance in August 2010. Data were collected in 2014–2017 in conserved areas and in 2019–2021 in both conserved and managed areas. The results showed that in conserved areas, 70% of the trees were already highly infested in 2015, reaching 90% in 2017. During 2019–2021, the conserved areas were significantly more damaged than the managed ones. The volume of the stands decreased over time on average by 80% in conserved areas and 40% in managed areas, with the highest decrease occurring six to seven years after the initial SBB colonization. The damage estimated based on resinous flows and entrance-exit holes was similar regardless of the year or treatment. Our detection method may be used to support timely risk assessment and management of SBB outbreaks and decrease damage at the landscape level. forest disturbance forest management Ips typographus tree symptoms ground-based visual observations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Climate change induces multiple abiotic and biotic risks to forests and forestry in northern Europe (Venäläinen et al. 2020 ). Simultaneously, the risk of detrimental cascading events of combined abiotic and biotic disturbances increases. This is the case for severe bark beetle outbreaks, which can be triggered by large-scale wind damage or prolonged drought events (Marini et al. 2017 ; Venäläinen et al. 2020 ). The European spruce bark beetle ( Ips typographus L., SBB; Coleoptera: Curculionidae, Scolytinae) (e.g., Seidl et al. 2014 ; Hlásny et al. 2021 ) is the most damaging biotic agent in Europe. It has caused extensive damage in Norway spruce forests in recent decades, especially in central and eastern Europe (Hlásny et al. 2019 , 2021 ; Patacca et al. 2022 ). The increasing trend in annual thermal sums due to climate warming (IPCC 2021 ; Ruosteenoja and Jylhä 2021 ) is escalating the risk of severe SBB outbreaks in the southern regions of the European boreal zone (Jönsson et al. 2012 ; Hof and Svahlin 2016 ; Venäläinen et al. 2020 ; Tikkanen and Lehtonen 2023 ). This is because climate warming is creating favourable conditions for SBB reproduction (Hlásny et al. 2019 ; Venäläinen et al. 2020 ). Moreover, increasing wind damage due to decreasing soil frost durations from late autumn to early spring (Peltola et al. 1999 ; Venäläinen et al. 2020 ), coupled with the presence of wind-damaged wood left on site, may augment the availability of breeding material for SBB, promoting a higher risk of SBB outbreaks (e.g., Hlásny et al. 2019 ). SBB is a hazardous pest especially in older, mature Norway spruce ( Picea abies (L.) H. Karst.)-dominated forests (Schlyter et al. 2006 ; Eriksson et al. 2007 ; Hlásny et al. 2019 , 2021 ). Various factors have made Norway spruce forests susceptible to bark beetle outbreaks, such as the establishment of forests outside their natural range and on sites with lower soil water holding capacity; increases in growing stocks; and changes in forest age structure and composition (Hlásny et al. 2019 ; Jandl 2020 ). Initially, SBB attack Norway spruce trees or fresh wind-felled trees, but during an outbreak, SBB also attack nearby healthy standing trees (Eriksson et al. 2007 ; Kärvemo et al. 2016 ; Økland et al. 2016 ). Following SBB colonization of standing trees, the trees show infestation symptoms that are visible to the human eye at the stem (entrance-exit holes, resinous flow, bark damage) and crown (defoliation, discoloration) level (Blomqvist et al. 2018 ). These SBB attack symptoms appear in different stages of the attack (Kautz et al. 2023 ), with the entrance holes and resinous flows emerging in the earlier stage, while defoliation and discoloration occur in the later stage. Depending on the vitality of a tree, a very thin resinous flow may be exuded beneath the entrance holes due to the activated host defence mechanism (Baier et al. 2002 ). The bark of the spruce trees is gradually peeled away due to maternal galleries and feeding larvae in the phloem and inner bark later in the season (Grodzki and Fronek 2017 ; Schroeder and Cocoş 2018 ) and woodpeckers feeding on the larvae and pupae (Kautz et al. 2023 ). Crown symptoms, defoliation, and discoloration appear weeks later (Kautz et al. 2023 ; Huo et al. 2023 ) due to a loss of phloem tissue and penetrating hyphae of a blue-stain fungi blocking moisture transmission from roots to the crown (Netherer et al. 2021 ). Considering that Norway spruce is one of the most economically valuable tree species in central and northern Europe (Caudullo et al. 2016 ), the SBB damage causes large economic losses for the European forestry sector (Hlásny et al. 2019 ). In Finland, Norway spruce accounts for 30% of the total volume of growing stock (Vaahtera et al. 2023 ). In managed forests, sanitation and salvage logging can be used to prevent the spread of SBB infestations (Schroeder and Lindelöw 2002 ). On the other hand, conserved forests are protected from any forest management activities (Korhonen et al. 2021 ; Vaahtera et al. 2023 ). Therefore, SBB infestations may also spread from conserved forests to neighbouring managed forests, causing large economic losses for forest owners (Hlásny et al. 2021 ). Due to the increasing risk of SBB outbreaks and damaged areas in the past decades (e.g., Marini et al. 2017 ), there is an urgent need to expand our knowledge of time- and cost-efficient detection and monitoring methods. Recently in Finland, progress has been made in detection and monitoring using remote sensing technologies (e.g., Näsi et al. 2018 ; Junttila et al. 2022 ; Kanerva et al. 2022 ; Östersund et al. 2024 ). These technologies offer advantages for early detection by using multispectral or hyperspectral images (e.g., Junttila et al. 2022 ; Huo et al. 2023 ) and further detection and monitoring on larger scales (e.g., Fernandez-Carrillo et al. 2020 ). However, methods that require simple equipment and basic training, such as ground-based visual observations of individual trees attacked by SBB, could be an effective tool for forest owners, managers, non-professionals, and the scientific community. Promoting the use of ground-based visual observation to detect and monitor SBB-damaged areas based on evaluating SBB infestations at the stem and crown level could lead to more timely action and reduce the harmful effects on forests (Kautz et al. 2023 ). In this study, we assessed SBB damage using ground-based visual observation of individual trees for SBB infestations at the stem (i.e., resinous flow, entrance and exit holes, bark damage) and crown (i.e., defoliation, and discoloration) level in managed and conserved Norway spruce-dominated forests. Based on this, we analysed the impact of SBB damage on stands during an SBB outbreak in south-eastern Finland. We focused specifically on comparing the conserved and managed forests regarding (i) the overall SBB damage of the stand, (ii) the relationship between SBB damage and stand characteristics, and (iii) changes in volume and basal area of the stands during an SBB outbreak. Our results demonstrate that detection of SBB damage based on tree symptoms is beneficial to diminish the harmful effects of an SBB outbreak. Our study is particularly relevant for the development of SBB damage assessments under boreal conditions. Our method could be used to support the development and validation of remote sensing-based detection methods and the detection and monitoring of SBB infestations by forest owners, managers, non-professionals, and the scientific community. Materials and Methods Study area The study area covered four Norway spruce-dominated forests, Viitalampi (0.74 km 2 ), Paajasensalo (0.56 km 2 ), Ryhmälehdonmäki (2.35 km 2 ) and Murtomäki (1.25 km 2 ), in the Ruokolahti municipality (61.17030 N 28.49010 S) in south-eastern Finland (Fig. 1 ). All experimental forests were situated within 10 km from each other to ensure similar climatic conditions. The forest site types in the area were mostly mesic (MT, Myrtillus type) and herb-rich (OMT, Oxalis-Myrtillus type) according to Cajander’s site type classification (Cajander 1926 ; Mikola 1982 ). A detailed description of the ground floor vegetation and soil types can be found in Kosunen et al. ( 2019 , 2020 ). The mature spruce stands were mixed with a lower proportion of Scots pine ( Pinus sylvestris L.), downy birch ( Betula pubescens L.), silver birch ( B. pendula Roth), and a minor share of other deciduous tree species. The terrain in all experimental forests was generally flat with a mean altitude of 120 m above sea level (a.s.l.), except for the Paajasensalo site, which was situated on steeper ridges and had an altitude range from 115 to 160 m (a.s.l.). The mean air temperature and precipitation sum of the summer months (May–August) for the period 2014–2021 were 14.0°C and 252 mm, respectively (Finnish Meteorological Institute). More detailed annual climate data can be found in Table A1 in the supplementary information (SI). A major summer thunderstorm hit the region in late July 2010, followed by another one in early August due to exceptionally warm weather (Viiri et al. 2019 ). Following the storms, the area was a mosaic of completely windthrown stands, narrow gaps caused by downbursts and scattered windthrown trees. The large amount of freshly felled mature Norway spruces caused an SBB population explosion from 2011 onwards. The management measures applied in the managed area included the removal of wind-felled trees if the volume of damaged spruces exceeded 10 m 3 per hectare and sanitation cuttings of highly infested forest compartments. These were responsive measures as a part of the compulsory SBB management in Finland according to the Forest damage prevention act (Finlex 2013 ). However, due to the high amount of fallen trees, the sanitation operation was not carried out on time, which increased the attacks of SBB. As a result, the colonisation gradually shifted from windthrown trees to standing and living trees in 2013 (Lyytikäinen-Saarenmaa et al., unpublished data). Based on our observations in the study area, SBB produced a second annual generation in several years since 2011 due to warm summer months (Table A1, SI). Shortly after the large-scale wind damage, the Viitalampi (VL) and Paajasensalo (PS) sites were conserved as METSO (Forest Biodiversity Program for Southern Finland) sites, and no logging or transportation of dead wood were allowed there. The conservation decision made these over-mature stands highly susceptible to bark beetle reproduction (cf. Schroeder 2010 ; Kärvemo et al. 2014 ). The Murtomäki (MM) and Ryhmälehdonmäki (RM) sites were under normal forest management practice. These two areas were partially spared from the highest impact of the storms, and sanitation logging was conducted when required, particularly at susceptible forest edges. The SBB population became epidemic in RM after a mild drought and warm summer months in 2018. Sampling plots and tree measurements Sampling plots were established in the vicinity of windthrown areas in stands affected by SBB where living trees remained. The plots were established in the portions of stands experiencing visual signs of SBB damage. Each plot was expected to have a minimum of two dead or dying spruces surrounded by declining spruces with symptoms of SBB attack. In total, 60 circular plots were established between 2014 and 2019 (21 plots in 2014, 24 in 2015, 2 in 2016, and 13 in 2019), except in 2018 (‘New’ in Table 1 ). The plots were reassessed for SBB attack symptoms once a year in August in 2015–2017 and 2019–2021 (‘ReM’ in Table 1 ). Between 2014 and 2017, the stand characteristics and SBB attack symptoms for individual trees were assessed only in conserved areas, while between 2019 and 2021 they were assessed in both conserved and managed areas. Table 1 Sampling plots in conserved (VL = Viitalampi and PS = Paajasensalo) and managed areas (RM = Ryhmälehdonmäki and MM = Murtomäki) in the Ruokolahti study area. New = newly established plots, ReM = remeasured plots. Study area Variable Plot size = 5 m radius Plot size = 10 m radius 2014 2015 2015 2016 2016 2017 2019–2021* 2019 ** 2020–2021 New New ReM New ReM ReM ReM New ReM ReM PS Plots 13 5 13 - 18 18 18 - 18 18 Trees 105 45 105 - 150 150 106 210 150 360 Trees/plot 8 9 8 - 8 6 6 - - 20 VL Plots 8 19 8 2 27 29 29 - 29 29 Trees 70 120 70 15 182 197 139 302 139 441 Trees/plot 9 6 9 8 7 7 5 - - 15 MM Plots - - - - - - - 6 - 6 Trees - - - - - - - 127 - 127 Trees/plot - - - - - - - 21 - 21 RM Plots - - - - - - - 7 - 7 Trees - - - - - - - 109 - 109 Trees/plot - - - - - - - 16 - 16 Total Plots 21 24 21 2 45 47 47 13 47 60 Trees 175 165 175 15 332 347 245 748 289 1037 * In 2019, the fallen trees that were killed by SBB were excluded from the inventory ** In VL and PS, there were no newly established plots, but there were new trees included in plots with a 5- and 10-m radius When each sampling plot was established, the location of the plot centre was recorded with a Trimble GPS device (Trimble Navigation Ltd., Sunnyvale, CA, USA) with an accuracy of up to 0.5 m after post-processing. In 2014–2017, circular plots with a radius of 5 m were established in conserved areas (VL and PS). In 2019, new damage spots were found in managed areas (MM and RM), where 13 circular plots with a radius of 10 m were established. The radius of field surveys for previous plots was also increased to 10 m due to a high proportion of dead and fallen trees within a radius of 5 m. Moreover, the trees that fell due to SBB-induced mortality, boosted by windy events in winters between 2017–2019, were excluded from the 2019 inventories. The data collected from the sampling plots consisted of tree-wise measurements regarding tree characteristics and symptoms for SBB infestations. Tree-wise measurements consisted of diameter at breast height (dbh ≥ 10 cm, at 1.3 m above stem base), the height of the plot-wise median tree, the height of every seventh Norway spruce across the plots (Vuokila 1987 ), and the vitality status of each tree (e.g., dead, alive, fallen, broken, etc.). These characteristics were measured in the year when the plots were established and remeasured in 2019 when the size of the plot was increased. In addition, individual trees in the plots were located by measuring the distance and azimuth angle from the plot centre to the centre of each tree. The symptoms were evaluated annually using a scoring system based on the severity of the attack shown at the stem (entrance-exit holes, resinous flow, bark damage) and crown (defoliation, discoloration) level (cf. Blomqvist et al. 2018 ). Infestation symptom assessment and scoring The symptoms for SBB infestation were assessed in the years when the plots were established and the following years until 2021, except 2018 (see Table 1 ). The assessment was done from mid to late August when all the stem and crown symptoms were visible and the impact of SBB on tree vitality was relevant. All living and dead standing spruces (dbh ≥ 10 cm) in the plots were assessed and ranked according to the severity of the symptoms. The entrance and exit holes were assessed up to a height of 2 m, resinous flow and bark damage were assessed up to the lowermost branch, while defoliation and discoloration were assessed from a distance corresponding to approximately a tree height as was done by Blomqvist et al. ( 2018 ). Each visual symptom provided a value from one to three (symptoms on tree stem) or one to four (symptoms on crown area) (Table 2 ). Examples of SBB tree symptoms can be seen in Fig. B1, SI. Table 2 Ranking of infestation symptom scores and description of the visual symptoms indicating the severity of SBB attack. Symptom Score Description Hole (entrance and exit) 1 2 3 0 holes ≤ 10 holes > 10 holes Resinous flow 1 2 3 0 spots 1–30 spots > 30 spots Bark damage 1 2 3 No damage Moderate damage High damage Needle discoloration 1 2 3 4 Green Yellowish–yellow Reddish–red Grey (dead tree) Defoliation 1 2 3 4 0–24% 25–49% 50–74% 75–100% The scoring classifies each tree into three categories of infestation: not visually observable ( NotVO ; score 1), moderate infestation (score 2 for stem symptoms and 2 or 3 for crown symptoms), and high infestation (score 3 for stem symptoms and 4 for crown symptoms). The SBB damage (damage index of the plot, DIplot) was calculated using the scoring values of each symptom for all the spruces in the plot. Firstly, a damage index (DI) was calculated for each symptom of the tree by dividing the score recorded during field assessment by the maximum score it can take (Eq. 1). Secondly, the damage index of the tree was calculated using Eq. 2. Lastly, the SBB damage of the plot (DIplot) was calculated as the average DI of all the trees from the plot (Eq. 3). DIplot took values between 0 and 1, providing an estimate about the degree of damage (0: not damaged; 1: completely damaged). $$\:{DIs}_{holes/\:resin/\:bark}=\:\frac{Field\:score}{3}\:\:\:\:\:\:\:\:\:\:\text{o}\text{r}\:\:\:\:\:\:\:\:\:{DIs}_{defoliation/discoloration}=\:\frac{Field\:score}{4}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:{DI}_{tree}=\frac{{DI}_{holes}+\:{DI}_{resin}+\:{DI}_{bark}+\:{DI}_{defoliation}+\:{DI}_{discoloration}}{5}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ $$\:{DI}_{plot}=\:\frac{\sum\:{DI}_{tree}}{N},\:\text{w}\text{h}\text{e}\text{r}\text{e}\:\text{N}\:\text{i}\text{s}\:\text{t}\text{h}\text{e}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{t}\text{r}\text{e}\text{e}\text{s}\:\text{i}\text{n}\:\text{t}\text{h}\text{e}\:\text{p}\text{l}\text{o}\text{t}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ Data analysis The data analysis was carried out using R (version 4.2.2 and 4.2.3) and R Studio (version 2022.12.0-353, R Core Team 2017 ), mainly using the tidyverse package (v2.0.0: Wickham et al. 2019 ). The statistical analysis involved several steps. First, the Shapiro–Wilk test was used to test the normal distribution of the data (Wilcox 2009 ). Second, a parametric mixed-design ANOVA was used to investigate differences in the damage index of the plots among different treatments (conserved and managed) and years (2019–2021) (Dudek 2023 ). In this analysis, the year was used as the within-group variable, and the treatment as the between-group variable. Pairwise comparisons within each year and treatment were conducted using the Tukey method. Third, the unpaired T -test with Bonferroni correction was used to examine differences in the SBB damage of the plots, as indicated by each symptom separately (i.e., resinous flow, entrance and exit holes, bark damage, defoliation, and discoloration). This was assessed separately for each year and treatment. Fourth, the unpaired T -test was used to determine whether the change in DIplot from 2019 to 2021 differed between the treatments (Wilcox 2009 ). Fifth, the Pearson’s correlation coefficient was used to assess the relationship between DIplot in 2019 and different stand variables (Wilcox 2009 ). In this analysis, highly correlated variables (r > 0.7) were excluded. Lastly, a Boosted Regression Tree model (BRT: Elith et al. 2008 ) estimated the extent to which the non-correlated variables explained the variability in DIplot, using the gbm package (version 2.1.8.1; Ridgeway and Ridgeway 2004 ). The model utilized a Gaussian distribution and fitted 5,000 decision trees to determine the best predictors. Results Observed SBB infestation levels of trees The overall severity of SBB infestation differed according to the size of the plots in conserved areas (see figures for conserved areas in Fig. 2 A and B). The trees in the 5-m-radius plots had high levels of infestation in 2014 regardless of the symptom type (> 50% of the trees). Between 2015 and 2021 the infestations increased so that over 80% of the trees were highly infested at the end of 2021 (Fig. 2 A). For the trees in 10-m-radius plots (established in 2019), the overall infestation levels were lower (Fig. 2 B) due to the larger size of the plots, showing the advance of the infestations from the infestation spot (the centre of the plot). In addition, the levels of infestation were higher in conserved areas compared to managed areas. In the conserved areas, around 50% of the trees were highly infested in 2019 and around 55% by 2021 (5% increase). In the managed areas, only around 4% of the trees were highly infested in 2019 and around 12% (8% increase) by 2021. The proportion of trees showing high infestation levels based on resinous flow, attack holes, bark damage, and defoliation was greater than that based on discoloration. For example, in 2021 in the managed areas (Fig. 2 B, Managed), 14% of the trees presented high infestation levels according to resinous flow, entrance-exit holes, bark damage, and defoliation, while only 4% did so based on needle discoloration. In the conserved areas, all the studied symptoms gave evidence for high levels of infestation for over 50% of the trees. However, based on discoloration, 43% of the trees had a green crown (score 1 in Table 2 ) in 2021. SBB damage in the plots Large numbers of highly infested trees in the conserved areas resulted in a high damage index at the plot level. This was observed especially for plots in the conserved areas where the trees were situated within a 5-m radius from the centre of the damage spot. The SBB damage (DIplot) was already 0.9 (max 1.0) in 2014, with an increase of 6% by 2021 (Fig. C1, SI). Considering the number of trees with not visually observable (NotVO) or moderate infestation levels in the 10-m-radius plots (Fig. 2 , B), the SBB damage was generally lower. However, in this case, conserved areas had higher damage index values compared to managed areas (DIplot, Fig. 3 A). The overall DIplot in the conserved areas was 0.75 in 2019, with an increase of 5% by 2021. While in managed areas the initial DIplot was 0.46, with an increase of 15% by 2021. Thus, the SBB damage in conserved areas was significantly higher than that in managed areas annually for the period 2019–2021 ( P -value < 0.0001) (Table D2 abc, SI). However, in conserved areas, the change in DIplot between 2019 and 2021 was significantly lower than that in the managed areas ( P -value < 0.05), showing the retrogradation phase in the conserved areas. When the damage was analysed separately by each symptom (i.e., resinous flow, attack holes, bark damage, defoliation, and discoloration), in general, the symptoms had unequal contributions (DIs_plot, Fig. 3 B). An exception was for the damage indices given by resin flows and entrance-exit holes, which were statistically similar regardless of the year or treatment ( T -test; P -value > 0.05 at a confidence level of 95%; Table D1, SI). Additionally, the damage index given by defoliation in the conserved areas was statistically similar with the ones given by bark damage and entrance-exit holes. Resinous flows and entrance-exit holes showed on average a higher damage index (i.e., 0.83 in conserved areas and 0.57 in managed areas in 2019), while the lowest damage index was shown on average by discoloration (i.e., 0.65 in conserved areas and 0.28 in managed areas in 2019). The relationship between SBB damage index and stand variables The SBB damage index values significantly correlated with the stand basal area of the living trees and the age of spruce, which together explained over 50% of the variation of DIplot (Table 3 ). There were strong negative correlations between the basal area of the living trees and the volume of the living trees. On the contrary, there were strong positive correlations among the variables related to dead trees (Correlated variables, i.e., proportion, basal area, and volume of dead trees; Table 3 ). There was a weak correlation between SBB damage and the proportion of spruce, explaining only 11% of the variation of damage index values. This may be because 85% of the plots had more than 80% spruce. In conserved areas, more than half of the plots with over 60% spruce were highly damaged in 2019 (Fig. C2, SI). The SBB damage showed a weak correlation with the proportion of both large and small spruces, likely due to the high infestation rate (> 50%) across all diameter classes in conserved areas (Fig. C3, SI). Table 3 The correlation coefficients (r) showing the relation of DIplot with the stand variables and the variation in damage score explained by each variable (BRT). The analysis was done based on the information for 2019. Stand variable r BRT (%) Correlated variables (r > 0.7) Basal area of the living trees (m 2 /ha) −0.79* 38.76 + Volume of the living trees (m 3 /ha) − Proportion of the dead trees (%) − Basal area of dead trees (m 2 /ha) − Volume of the dead trees (m 3 /ha) Age of spruce (years) + 0.49* 14.95 Volume of spruce (m 3 /ha) −0.12 14.47 + Volume of the stand (m 3 /ha) + Basal area (m 2 /ha) Proportion of basal area occupied by spruce (%) −0.23 10.92 − Basal area of other species (m 2 /ha) − Volume of other species (m 3 /ha) Proportion of spruce with big diameters (DBH > 30 cm) + 0.20 10.55 − Basal area weighted diameter (cm) Proportion of spruce with small diameters (DBH < 20 cm) −0.05 10.35 − The proportion of spruce with diameters 20–30 cm * P -value < 0.05 +, positive relationship and −, negative relationship Changes in characteristics and the dynamics of tree mortality during an SBB outbreak For the plots with a 5-m radius in conserved areas, there were significant differences in the stand basal area, volume, and density between the two periods (2014–2017 and 2019–2021; Fig. 4 A; subfigures a. b. c.). For the plots with a 10-m radius (Fig. 4 B), the conserved areas generally had larger variations in stand characteristics than the managed areas. Of the stand characteristics measured, only the proportion and the average age of Norway spruce were significantly different (Fig. 4 B; subfigures e., f.). Hence, conserved areas had a significantly lower proportion of Norway spruce and older trees on average. The median of the basal area weighted mean diameter (Dba) was similar in both conserved and managed areas, but a higher variation was observed in conserved areas (Fig. 4 AB; subfigure d.). The standing volume and basal area decreased as a result of increasing tree mortality and number of fallen trees due to SBB damage over time (Fig. 5 ). In plots with a 5-m radius in conserved areas, both volume and basal area decreased annually on average by 10%. In plots with a 10-m radius, the decrease was smaller: on average 6% per year in conserved areas and 3% per year in managed areas. However, the largest decrease (25%) was observed between 2017 and 2019. Consequently, the trees started falling 6–7 years after the initial colonisation. The dead trees remained standing for a while before falling, which explains a high proportion of dead trees in conserved areas, being over 80% throughout the study period (Fig. 5 A). In the conserved areas with 10-m-radius plots, the trees killed by SBB represented 40% of the standing trees, while in the managed areas, the corresponding proportion was negligible (Fig. 5 B). Discussion Evaluation of the methodology In this study, we evaluated symptoms once a year in August, when both early symptoms (resin flows and entrance-exit holes) and late symptoms (bark damage, crown defoliation, and needle discoloration) were visible (Kautz et al. 2023 , 2024 ). This was done to meet the needs of practical forestry to identify the damaged stands where sanitation felling should be done. Typically, in boreal conditions in Finland, SBB completes only one generation per year and begins hibernation in August (Annila 1969 ), but this situation may change under a warming climate. Currently in Finland, sanitation interventions are mainly carried out during winter. Thus, a detection of SBB attack based on tree symptoms and evaluation of the stand damage at the end of the SBB annual cycle would contribute to decision making regarding SBB management (i.e., sanitation felling) and reduce the spreading beetles in the nearby stands, as was also noted by Kautz et al. ( 2024 ). A similar methodology, based on ground-based visual observations of SBB attack symptoms at tree level, was also used earlier by Blomqvist et al. ( 2018 ) and Kautz et al. ( 2023 ). In addition to the symptoms we assessed, Kautz et al. ( 2023 ) considered boring dust as the assessment was done every second week. They found that boring dust is the most accurate symptom for timely detection of infestation in spring or early summer, but not later. When the symptoms are evaluated later in the summer, as was our case in August, the dust is hardly visible as it is spread out by wind and rain. With the methodology we used, we could not detect the infestations at the tree level in early stages of the SBB attack (i.e., ‘pre-emergence detection’ defined by Kautz et al. 2024 ) or identify the susceptible trees before the attack. The potential of remote sensing methods combined with machine learning techniques have been studied for the early detection of SBB infestations (e.g., Junttila et al. 2019 ; Huo et al. 2021 ) or detection of susceptible trees (e.g., based on water stress Huo et al. 2023 ; Mandl and Lang 2023 ). While the remote sensing detection methods have advantages, they currently face practical challenges such as low accuracy and robustness, as well as high costs (Marvasti-Zadeh et al. 2023 ; Kautz et al. 2024 ). An early detection in our study was not possible as our study started in 2014 when the trees in the conserved areas were in a more advanced stage of SBB infestation. Despite the limitations of this study, this work is valuable as it detected for the first time the dynamics of SBB infestations and mortality over several years under Finnish conditions. The SBB outbreak in conserved and managed areas In our study, the SBB outbreak proceeded after a massive wind disturbance caused by two summer thunderstorms in 2010. The attack on standing trees started in 2013, i.e., in the third year after the storm. In 2014, a high percentage of the standing trees were already highly infested. Similarly, Økland et al. ( 2016 ) found that the transition of SBB from the fallen trees to the standing trees happened during the third and fourth years after the windfall. The available resources in the nearby area of the windfall and the temperature conditions (e.g., Mezei et al. 2017 ) further affect the reproductive rate of SBB, followed by the attack and damage of the living trees the following (e.g., Kärvemo et al. 2014 ) or second summer after the storm (Komonen et al. 2011 ). At the time of our assessment (2019–2021), the stands in the conserved areas had significantly higher SBB damage compared to those in managed areas. Likewise, Schroeder and Lindelöw ( 2002 ) and Mezei et al. ( 2017 ) found a lower tree mortality during an SBB outbreak in managed areas. However, in our case, the initial infestation levels of the stands were not assessed, so the possibility of the conserved and managed stands having a different initial infestation level at the time of our assessment cannot be fully excluded. Regular management may increase the vitality of trees, promoting better adaptation to the site conditions and a lower susceptibility to disturbances (de Groot et al. 2019 ). Although we found symptoms of highly infested trees in managed areas, the tree mortality rate was lower than that in conserved areas. This may be at least partially explained by a better vitality and a faster recovery capacity of the trees, which was observed by Blomqvist et al. ( 2018 ). However, more research is needed to confirm this. Tree mortality in conserved areas started in the 2nd year after the initial colonization, with the highest increase after the 3rd year. Consequently, a high decrease in standing volume and basal area of living trees was observed 6–7 years after the initial colonisation. Other studies found that the mortality was the highest at the beginning of the outbreak (e.g., Schroeder and Lindelöw 2002 ; Kärvemo et al. 2014 , 2016 ; Mezei et al. 2017 ) and then decreased frequently due to host tree depletion (e.g., Mezei et al. 2017 ). According to Hekkala et al. ( 2021 )d rvemo et al. (2017), forest conservation measures, such as the gap-cut method, create a high risk of new SBB attacks after only 5 years. The results of the gap-cut method are similar to those of storm gaps, thus supporting our results. Potterf et al. ( 2022 ) and Sommerfeld et al. ( 2018 ) found that managed forests are less resistant to SBB attacks than conserved forests (i.e., natural reserves). This can be explained by the fact that conserved forests may have a higher functional and structural diversity, which increases their resistance and resilience against disturbance agents (Hlásny et al. 2019 ). Nonetheless, recently established, smaller conservation areas, such as in our study, may also have a lower resistance against SBB compared to larger (and older) nature reserve areas (see e.g., Potterf et al. 2022 ). Still, differences in forest structure (e.g., age and species composition, stocking), and environmental (climate, site) conditions may explain differences between studies. The relationship of SBB damage with stand variables In our study, SBB damage correlated significantly with the basal area of living trees and the age of spruce trees. Similarly, Kärvemo et al. ( 2014 ) and Sproull et al. ( 2015 ) found that the basal area of living spruces predicted tree mortality by SBB. However, Sproull et al. ( 2015 ) reported that the age of spruce trees did not influence tree mortality caused by SBB. In contrast, other issues related to SBB infestations (i.e., infestation risk in Trubin et al. 2022 ; individual predisposition in Seidl et al. 2007 ; and cumulative disturbance in Potterf et al. 2022 ) were shown to increase significantly with tree age. Our results showed that SBB damage was independent of tree size (diameter of the host tree), unlike in some previous studies (e.g., Kärvemo et al. 2014 ; Mezei et al. 2014 ; Sproull et al. 2015 ; Korolyova et al. 2022 ). This may be because the outbreak was in an advanced stage (culmination and retrogradation phase) during most of the study period. Further, year 2015 may represent a culmination of the outbreak in the conserved areas. Yet, the outbreak pattern was patchy over the study area. According to Mezei et al. ( 2014 ), for the pre-outbreak to progradation phase and the culmination phase, diameter is an important predictor for tree mortality induced by SBB, but not in the retrogradation phase. Likewise, Sproull et al. ( 2015 ) found that the diameter had a low contribution from the progradation to the culmination phase. In European temperate forests, SBB has damaged large Norway spruce-dominated areas (e.g., Kamińska et al. 2021 ; Potterf et al. 2022 ). The increasing amount of susceptible mature Norway spruce in temperate forests has provided favourable conditions for SBB to infest in the past decades (Marini et al. 2017 ; Hlásny et al. 2021 ). In our study, the proportion of spruce failed to explain the SBB damage, likely due to the low variation of the spruce proportion in the study area. Typically, in Norway spruce stands, broadleaves are harvested during thinning operations according to ‘Best practices for sustainable forest management in Finland’ (see https://metsanhoidonsuositukset.fi/en ). Conclusions and future perspectives Using data from ground-based visual observations, we assessed symptoms for SBB infestation, which provided results about the infestation levels of the trees and the damage of the plots. In conserved areas, 70% of the trees within the 5-m-radius plots were highly infested in the 2nd year (in 2014) after the initial colonization of the standing trees, reaching 90% in the 5th year (2017). Stand-level analysis showed that the conserved areas had significantly higher damage than managed areas during the study period (2019–2021). The variation in SBB damage at the stand level was best explained by the basal area of living trees and the age of spruces. When the SBB damage was analysed separately by each symptom, resinous flows and entrance-exit holes showed similar levels of damage regardless of the year or treatment. Over the course of the outbreak (until 2021), the volume of the stands decreased on average by 80% in conserved areas and 40% in managed areas. The results of this study demonstrate the potential use of tree-level symptom assessment for the detection of SBB attack and the role of forest management in damaged areas on decreasing the harmful effects of an SBB outbreak at the stand level in managed boreal forests. The ground-based visual assessment of tree symptoms offered a reliable means to evaluate the damage of the stands and could support forest owners and managers in decision making regarding sanitation felling. Such methodology may also support advancement and validation of remote sensing-based detection methods. Additionally, ground-based visual observations of SBB infestation symptoms are inexpensive and only require basic training. Therefore, they could be used to develop applications used for monitoring infestations by forest owners and non-professionals. Such application could help detect SBB infestations to support timely measures and decrease damage in Norway spruce forests. In addition to response management, more attention should be given to proactive measures that contribute to risk prevention and provide better preparedness for SBB outbreaks in managed forests (EFI 2020 ; Hlásny et al. 2021 ). For example, avoiding the cultivation of pure spruce stands, which is still a practice in boreal forests, can help reduce the risk. This is particularly important on forest sites with a relatively low water holding capacity where trees are more susceptible to drought and further SBB outbreaks. Growing mixed forests (admixtures with deciduous species) on suitable sites may also increase forest resilience against SBB outbreaks due to fewer host trees and higher populations of natural enemies (e.g., Kausrud et al. 2012 ; de Groot and Ogris 2019 ; Hlásny et al. 2021 ; Müller et al. 2022 ). Timely and regular thinning operations to improve tree vigour (outbreak prevention), harvesting of infested trees (sanitation felling and salvage logging), and removal of harvested and wind-damaged trees before swarming of the first annual SBB generation in early summer are necessary risk prevention measures. The use of shorter rotation periods (or lower target diameters) may also enhance forest resilience against SBB outbreaks (e.g., de Groot et al. 2019 ; Hlásny et al. 2019 ; Venäläinen et al. 2022 ). Ultimately, in risk assessment and management, the areas with higher risk of SBB outbreaks and damage should be prioritised. Declarations Acknowledgments We wish to thank Minna Blomqvist, Pentti Henttonen, Micke Malm, Jaana Turunen, and Juho Äyräs for their help with the field work, and Tuula Kantola and Hannu Saarenmaa for valuable advice on the study design and assessment of SBB symptoms. Tornator Ldt., particularly the former heads of forest resources Antero Pasanen and Maarit Sallinen, and the current head of forest resources Kimmo Kortelainen, are thanked for enabling this study in Ruokolahti and providing compartment data. We also thank Stora Enso Ltd., especially Jarmo Hakalisto, for facilities in Viitalampi during our annual field work. Diana-Cristina Simon wishes to also thank the LUMETO Doctoral Program (formerly FORES) at the Faculty of Science, Forestry and Technology, University of Eastern Finland. Finally, we wish to thank to the anonymous reviewers for their contribution to improving this research paper and to PhD J. Mackay from Cambridge Proofreading for proofreading the manuscript. Funding: This study was funded by the Maj and Tor Nessling Foundation, the Marjatta and Eino Kolli Foundation, the Niemi Foundation, the Ministry of Agriculture and Forestry of Finland (project MONITUHO, decision number 647/03.02.06.00/2018; project SPRUCERISK, decision number VN/5292/2021), and the Academy of Finland (project OPTIMAM, grant number 317741; project UNITE flagship, grant numbers 337127 and 357906; and project MULTIRISK, grant numbers 353263 and 353264). Conflict of interest: The authors declare that they have no conflict of interest. Availability of data and material: The data of this study are available upon request from the corresponding author Diana-Cristina Simon or Docent, Dr. Päivi Lyytikäinen-Saarenmaa. Code availability: Not applicable Authors’ contribution: Design of field experiment (PLS, MK, EH, RN, MPA), field data collection (PLS, MK, MPA), data curation (DCS, PLS, MPA, JT), the design of the study (DCS, PLS, HP, OPT, AK), data visualization and statistics (DCS), writing – original draft preparation (DCS, PLS, HP), writing – review and editing (DCS, PLS, HP, MPA, MK, EH, RN, OPT, AK, JT), funding responsibilities (PLS, HP, EH). Compliance with ethical standards References Annila E (1969) Influence of temperature upon the development and voltinism of Ips typographus L. (Coleoptera, Scolytidae). In: Annales Zoologici Fennici. Societas Biologica Fennica Vanamo, pp 161–208 Baier P, Führer E, Kirisits T, Rosner S (2002) Defence reactions of Norway spruce against bark beetles and the associated fungus Ceratocystis polonica in secondary pure and mixed species stands. Forest Ecology and Management 159(1–2):73–86. https://doi.org/10.1016/S0378-1127(01)00711-3 Blomqvist M, Kosunen M, Starr M, Kantola T, Holopainen M, Lyytikäinen-Saarenmaa P (2018) Modelling the predisposition of Norway spruce to Ips typographus L. infestation by means of environmental factors in southern Finland. European Journal of Forest Research 137(5):675–691. https://doi.org/10.1007/s10342-018-1133-0 Cajander A (1926) The theory of forest types. Acta Forestalia Fennica 29(3):1–108. https://doi.org/10.14214/aff.7193 Caudullo G, Tinner W, de Rigo D (2016) Picea abies . European Atlas of Forest Tree Species 114–116. https://forest.jrc.ec.europa.eu/en/european-atlas/atlas-data-and-metadata/ de Groot M, Diaci J, Ogris N (2019) Forest management history is an important factor in bark beetle outbreaks: Lessons for the future. Forest Ecology and Management 433:467–474. https://doi.org/10.1016/j.foreco.2018.11.025 de Groot M, Ogris N (2019) Short-term forecasting of bark beetle outbreaks on two economically important conifer tree species. Forest Ecology and Management 450:117495. https://doi.org/10.1016/j.foreco.2019.117495 Dudek B (2023) Mixed ANOVA Design in R. https://www.datanovia.com/en/lessons/mixed-anova-in-r/ EFI (2020) Towards holistic forest crisis management - adapting to changing disturbance regimes in Europe. SURE, European Forest Risk Facility. https://sure.efi.int/sites/default/files/2020 08/Policy Brief_Towards holistic forest crisis management.pdf Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. Journal of Animal Ecology 77(4):802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x Eriksson M, Neuvonen S, Roininen H (2007) Retention of wind-felled trees and the risk of consequential tree mortality by the European spruce bark beetle Ips typographus in Finland. Scandinavian Journal of Forest Research 22(6):516–523. https://doi.org/10.1080/02827580701800466 Fernandez-Carrillo A, Patočka Z, Dobrovolný L, Franco-Nieto A, Revilla-Romero B (2020) Monitoring bark beetle forest damage in central Europe. A remote sensing approach validated with field data. Remote Sensing 12(21):1–19. https://doi.org/10.3390/rs12213634 Finlex (2013) Laki metsätuhojen torjunnasta [Forest Damage Prevention Act]. 1087/2013. Ministry of Justice. Helsinki. https://www.finlex.fi/fi/laki/alkup/2013/20131087 [In Finnish] Finnish Meteorological Institute. Annual Weather Statistics. https://ilmatieteenlaitos.fi/ vuositilastot Accessed 20 December 2022 Grodzki W, Fronek W (2017) Reproduction abilities of Ips typographus (L.) (Coleoptera: Curculionidae, Scolytinae) in the stands damaged by the wind in 2013 in the Koscieliska Valley (Tatra National Park). Folia Forestalia Polonica, Series A – Forestry 59(4):258–264 Hekkala AM, Kärvemo S, Versluijs M, Weslien J, Björkman C, Löfroth T, Hjältén J (2021) Ecological restoration for biodiversity conservation triggers response of bark beetle pests and their natural predators. Forestry 94(1):115–126. https://doi.org/10.1093/forestry/cpaa016 Hlásny T, König L, Krokene P, Lindner M, Montagné-Huck C, Müller J, Qin H, Raffa KF, Schelhaas MJ, Svoboda M, Viiri H, Seidl R (2021) Bark beetle outbreaks in Europe: State of knowledge and ways forward for management. Current Forestry Reports 7(3):138–165. https://doi.org/10.1007/s40725-021-00142-x Hlásny T, Krokene P, Liebhold A, Montagné-Huck C, Müller J, Qin H, Raffa K, Schelhaas M-J, Seidl R, Svoboda M, Viiri H (2019) Living with bark beetles: impacts, outlook and management options. In: From Science to Policy 8. European Forest Institute Hof AR, Svahlin A (2016) The potential effect of climate change on the geographical distribution of insect pest species in the Swedish boreal forest. Scandinavian Journal of Forest Research 31(1):29–39. https://doi.org/10.1080/02827581.2015.1052751 Huo L, Lindberg E, Bohlin J, Persson HJ (2023) Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions. Remote Sensing of Environment 287:113484. https://doi.org/10.1016/j.rse.2023.113484 Huo L, Persson HJ, Lindberg E (2021) Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS). Remote Sensing of Environment 255. https://doi.org/10.1016/j.rse.2020.112240 IPCC (2021) Climate Change 2021: The Physical Science Basis. In: Masson-Delmotte V, P Zhai, A Pirani, SL Connors, C Péan, S Berger, N Caud, Y Chen (eds) Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/doi:10.1017/9781009157896 Jandl R (2020) Climate-induced challenges of Norway spruce in Northern Austria. Trees, Forests and People 1:100008. https://doi.org/10.1016/j.tfp.2020.100008 Jönsson AM, Schroeder LM, Lagergren F, Anderbrant O, Smith B (2012) Guess the impact of Ips typographus - An ecosystem modelling approach for simulating spruce bark beetle outbreaks. Agricultural and Forest Meteorology 166–167:188–200. https://doi.org/10.1016/j.agrformet.2012.07.012 Junttila S, Holopainen M, Vastaranta M, Lyytikäinen-Saarenmaa P, Kaartinen H, Hyyppä J, Hyyppä H (2019) The potential of dual-wavelength terrestrial lidar in early detection of Ips typographus (L.) infestation – Leaf water content as a proxy. Remote Sensing of Environment 231:111264. https://doi.org/10.1016/j.rse.2019.111264 Junttila S, Näsi R, Koivumäki N, Imangholiloo M, Saarinen N, Raisio J, Holopainen M, Hyyppä H, Hyyppä J, Lyytikäinen-Saarenmaa P, Vastaranta M, Honkavaara E (2022) Multispectral imagery provides benefits for mapping spruce tree decline due to bark beetle infestation when acquired late in the season. Remote Sens 14:909. https://doi.org/https://doi.org/10.3390/ rs14040909 Kamińska A, Lisiewicz M, Kraszewski B, Stereńczak K (2021) Mass outbreaks and factors related to the spatial dynamics of spruce bark beetle ( Ips typographus ) dieback considering diverse management regimes in the Białowieża forest. Forest Ecology and Management 498. https://doi.org/10.1016/j.foreco.2021.119530 Kanerva H, Honkavaara E, Näsi R, Hakala T, Junttila S, Karila K, Koivumäki N, Alves Oliveira R, Pelto-Arvo M, Pölönen I, Tuviala J, Östersund M, Lyytikäinen-Saarenmaa P (2022) Estimating tree health decline caused by Ips typographus L. from UAS RGB images using a deep one-stage object detection neural network. Remote Sensing 14(24):6257. https://doi.org/10.3390/rs14246257 Kärvemo S, Björkman C, Johansson T, Weslien J, Hjältén J (2017) Forest restoration as a double-edged sword: the conflict between biodiversity conservation and pest control. Journal of Applied Ecology 54(6):1658–1668. https://doi.org/10.1111/1365-2664.12905 Kärvemo S, Johansson V, Schroeder M, Ranius T (2016) Local colonization - extinction dynamics of a tree - killing bark beetle during a large-scale outbreak. Ecosphere 7(3):1–14, e01257 Kärvemo S, Rogell B, Schroeder M (2014) Dynamics of spruce bark beetle infestation spots: Importance of local population size and landscape characteristics after a storm disturbance. Forest Ecology and Management 334:232–240. https://doi.org/10.1016/j.foreco.2014.09.011 Kausrud K, Økland B, Skarpaas O, Grégoire JC, Erbilgin N, Stenseth NC (2012) Population dynamics in changing environments: The case of an eruptive forest pest species. Biological Reviews 87(1):34–51. https://doi.org/10.1111/j.1469-185X.2011.00183.x Kautz M, Feurer J, Adler P (2024) Early detection of bark beetle ( Ips typographus ) infestations by remote sensing – A critical review of recent research. Forest Ecology and Management, 556:121595. https://doi.org/10.1016/j.foreco.2023.121595 Kautz M, Peter FJ, Harms L, Kammen S, Delb H (2023) Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle. Journal of Pest Science 96(1):403–414. https://doi.org/10.1007/s10340-022-01490-8 Komonen A, Schroeder LM, Weslien J (2011) Ips typographus population development after a severe storm in a nature reserve in southern Sweden. Journal of Applied Entomology 135(1–2):132–141. https://doi.org/10.1111/j.1439-0418.2010.01520.x Korhonen KT, Ahola A, Heikkinen J, Henttonen HM, Hotanen JP, Ihalainen A, Melin M, Pitkänen J, Räty M, Sirviö M, Strandström M (2021) Forests of Finland 2014–2018 and their development 1921–2018. Silva Fennica 55(5):1–49. https://doi.org/10.14214/sf.10662 Korolyova N, Buechling A, Ďuračiová R, Zabihi K, Turčáni M, Svoboda M, Bláha J, Swarts K, Poláček M, Hradecký J, Červenka J, Němčák P, Schlyter F, Jakuš R (2022) The last trees standing: Climate modulates tree survival factors during a prolonged bark beetle outbreak in Europe. Agricultural and Forest Meteorology 322. https://doi.org/10.1016/j.agrformet.2022.109025 Kosunen M, Lyytikäinen-Saarenmaa P, Ojanen P, Blomqvist M, Starr M (2019) Response of soil surface respiration to storm and Ips typographus (L.) disturbance in boreal Norway spruce stands. Forests 10(4). https://doi.org/10.3390/f10040307 Kosunen M, Peltoniemi K, Pennanen T, Lyytikäinen-Saarenmaa P, Adamczyk B, Fritze H, Zhou X, Starr M (2020) Storm and Ips typographus disturbance effects on carbon stocks, humus layer carbon fractions and microbial community composition in boreal Picea abies stands. Soil Biology and Biochemistry 148. https://doi.org/10.1016/j.soilbio.2020.107853 Mandl L, Lang S (2023) Uncovering early traces of bark beetle induced forest stress via semantically enriched sentinel-2 data and spectral indices. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 91(3):211–231. https://doi.org/10.1007/s41064-023-00240-4 Marini L, Økland B, Jönsson AM, Bentz B, Carroll A, Forster B, Grégoire JC, Hurling R, Nageleisen LM, Netherer S, Ravn HP, Weed A, Schroeder M (2017) Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40(12):1426–1435. https://doi.org/10.1111/ecog.02769 Marvasti-Zadeh SM, Goodsman D, Ray N, Erbilgin N (2023) Early detection of bark beetle attack using remote sensing and machine learning: A review. ACM Computing Surveys 56(4). https://doi.org/10.1145/3625387 Metsänhoidon Suositukset. Best practices for sustainable forest management in Finland. https://metsanhoidonsuositukset.fi/en Accessed 24 October 2024 Mezei P, Blaženec M, Grodzki W, Škvarenina J, Jakuš R (2017) Influence of different forest protection strategies on spruce tree mortality during a bark beetle outbreak. Annals of Forest Science 74(4). https://doi.org/10.1007/s13595-017-0663-9 Mezei P, Grodzki W, Blaženec M, Škvarenina J, Brandysova V, Jakuš R (2014) Host and site factors affecting tree mortality caused by the spruce bark beetle ( Ips typographus ) in mountainous conditions. Forest Ecology and Management 331:196–207. https://doi.org/10.1016/j.foreco.2014.07.031 Mikola P (1982) Application of vegetation science to forestry in Finland. In: Jahn G (ed) Handbook of vegetation science, part 12. Dr W. Junk Publishers, The Hague, pp 199–224 Müller M, Olsson PO, Eklundh L, Jamali S, Ardö J (2022) Features predisposing forest to bark beetle outbreaks and their dynamics during drought. Forest Ecology and Management 523. https://doi.org/10.1016/j.foreco.2022.120480 National Land Survey of Finland (2023) Topographic map (raster). Licensed with the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/deed.en). [Dataset]. Available at https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/topographic-map-series-raster. [Accessed at 27 June 2023] Näsi R, Honkavaara E, Blomqvist M, Lyytikäinen-Saarenmaa P, Hakala T, Viljanen N, Kantola T, Holopainen M (2018) Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening 30:72–83. https://doi.org/10.1016/j.ufug.2018.01.010 Netherer S, Kandasamy D, Jirosová A, Kalinová B, Schebeck M, Schlyter F (2021) Interactions among Norway spruce, the bark beetle Ips typographus and its fungal symbionts in times of drought . Journal of Pest Science 94(3):591–614. https://doi.org/10.1007/s10340-021-01341-y Økland B, Nikolov C, Krokene P, Vakula J (2016) Transition from windfall- to patch-driven outbreak dynamics of the spruce bark beetle Ips typographus . Forest Ecology and Management 363:63–73. https://doi.org/10.1016/j.foreco.2015.12.007 Östersund M, Honkavaara E, Oliveira RA, Näsi R, Hakala T, Koivumäki N, Pelto-Arvo M, Tuviala J, Nevalainen O, Lyytikäinen-Saarenmaa P (2024) Exploring forest changes in an Ips typographus L. outbreak area: insights from multi-temporal multispectral UAS remote sensing. European Journal of Forest Research https://doi.org/10.1007/s10342-024-01734-5 Patacca M, Lindner M, Lucas-Borja ME, Cordonnier T, Fidej G, Gardiner B, Hauf Y, Jasinevičius G, Labonne S, Linkevičius E, Mahnken M, Milanovic S, Nabuurs GJ, Nagel TA, Nikinmaa L, Panyatov M, Bercak R, Seidl R, Ostrogović Sever MZ, Socha J, Thom D, Vuletic D, Zudin S, Schelhaas MJ (2022) Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology 1359–1376. https://doi.org/10.1111/gcb.16531 Peltola H, Kellomäki S, Väisänen H (1999) Model computations of the impact of climatic change on the windthrow risk of trees. Climatic Change 41(1):17–36. https://doi.org/10.1023/A:1005399822319 Potterf M, Svitok M, Mezei P, Jarcuska B, Jakus R, Blazenec M, Hlásny T (2022) Contrasting Norway spruce disturbance dynamics in managed forests and strict forest reserves in Slovakia. Forestry 1–12. https://doi.org/https://doi.org/10.1093/forestry/cpac045 R Core Team (2017) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria http://www.R-project.org/ Ridgeway G, Ridgeway MG (2004) The GBM Package. R Foundation for Statistical Computing, Vienna, Austria, 5(3) Ruosteenoja K, Jylhä K (2021) Projected climate change in Finland during the 21st century calculated from CMIP6 model simulations. Geophysica 56(1–2):39–69 Schlyter P, Stjernquist I, Bärring L, Jönsson AM, Nilsson C (2006) Assessment of the impacts of climate change and weather extremes on boreal forests in northern Europe, focusing on Norway spruce. Climate Research 31(1):75–84. https://doi.org/10.3354/cr031075 Schroeder L (2010) Colonization of storm gaps by the spruce bark beetle: influence of gap and landscape characteristics. Agricultural and Forest Entomology 12(1):29-39 Schroeder M, Cocoş D (2018) Performance of the tree-killing bark beetles Ips typographus and Pityogenes chalcographus in non-indigenous lodgepole pine and their historical host Norway spruce. Agricultural and Forest Entomology 20:347-357. https://doi.org/10.1111/afe.12267 Schroeder LM, Lindelöw Å (2002) Attacks on living spruce trees by the bark beetle Ips typographus (Col. Scolytidae) following a storm-felling: A comparison between stands with and without removal of wind-felled trees. Agricultural and Forest Entomology 4(1):47–56. https://doi.org/10.1046/j.1461-9563.2002.00122.x Seidl R, Baier P, Rammer W, Schopf A, Lexer MJ (2007) Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecological Modelling 206(3–4):383–399. https://doi.org/10.1016/j.ecolmodel.2007.04.002 Seidl R, Schelhaas M, Rammer W, Verkerk PJ (2014) Increasing forest disturbances in Europe and their impact on carbon storage. Nature Climate Change 4. https://doi.org/10.1038/NCLIMATE2318 Sommerfeld A, Senf C, Buma B, D’Amato AW, Després T, Díaz-Hormazábal I, Fraver S, Frelich LE, Gutiérrez ÁG, Hart SJ, Harvey BJ, He HS, Hlásny T, Holz A, Kitzberger T, Kulakowski D, Lindenmayer D, Mori AS, Müller J, Paritsis J, Perry GLW, Stephens SL, Svoboda M, Turner MG, Veblen TT, Seidl R (2018) Patterns and drivers of recent disturbances across the temperate forest biome. Nature Communications 9(1). https://doi.org/10.1038/s41467-018-06788-9 Sproull GJ, Adamus M, Bukowski M, Krzyzanowski T, Szewczyk J, Statwick J, Szwagrzyk J (2015) Tree and stand-level patterns and predictors of Norway spruce mortality caused by bark beetle infestation in the Tatra Mountains. Forest Ecology and Management 354:261–271. https://doi.org/10.1016/j.foreco.2015.06.006 Tikkanen O, Lehtonen I (2023) Changing climatic drivers of European spruce bark beetle outbreaks: a comparison of locations around the Northern Baltic Sea . Silva Fennica 57(3):1–21. https://doi.org/10.14214/sf.23003 Trubin A, Mezei P, Zabihi K, Surový P, Jakuš R (2022) Northernmost European spruce bark beetle Ips typographus outbreak: Modelling tree mortality using remote sensing and climate data. Forest Ecology and Management 505:119829. https://doi.org/10.1016/j.foreco.2021.119829 Vaahtera E, Peltola A, Torvelainen J, Uotila E (2023) Finnish Statistical Yearbook of Forestry. Luonnonvarakeskus Venäläinen A, Lehtonen I, Laapas M, Ruosteenoja K, Tikkanen OP, Viiri H, Ikonen VP, Peltola H (2020) Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global Change Biology 26(8):4178–4196. https://doi.org/10.1111/gcb.15183 Venäläinen A, Ruosteenoja K, Lehtonen I, Laapas M, Tikkanen OP, Peltola H (2022) Managing Forest Ecosystems Forest Bioeconomy and Climate Change. In: Hetemäki L, Kangas J, Peltola H (eds) Forest Bioeconomy and Climate Change. Springer Nature Switzerland AG, Switzerland. https://doi.org/10.1007/978-3-030-99206-4 Viiri H, Viitanen J, Mutanen A, Leppänen J (2019) Metsätuhot vaikuttavat Euroopan puumarkkinoihin – Suomessa vaikutukset toistaiseksi vähäisiä. Metsätieteen Aikakauskirja , (In Finnish). https://doi.org/https://doi.org/10.14214/ma.10200 Vuokila Y (1987) Metsikkökokeiden maastotyöohjeet. Metsäntutkimuslaitoksen tiedonantoja 257. Hakapaino Oy, Helsinki. (In Finnish). ISBN 951-40-0853-7. 237 p Wermelinger B (2004) Ecology and management of the spruce bark beetle Ips typographus - A review of recent research. Forest Ecology and Management 202(1–3):67–82. https://doi.org/10.1016/j.foreco.2004.07.018 Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, Müller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019) Welcome to the Tidyverse. Journal of Open Source Software 4(43):1686. https://doi.org/10.21105/joss.01686 Wilcox RR (2009) Basic statistics: understanding conventional methods and modern insights. Oxford University Press Additional Declarations No competing interests reported. Supplementary Files Supplementaryinformation.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5355177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372326638,"identity":"24991382-433d-4459-ab96-04aab47e2b7f","order_by":0,"name":"Diana-Cristina Simon","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDCCwxCKB4gZHzAwyDGwN5OghdmAgcGYgecwIS0HEEw2CbCWA7iUQgHfceZnD34w1Mnotvceq+apMGDgYSegRfIwm7lhD8NhHrMz59Ju85wBamEmoMXgMIOZBA/DAR6zGzlmt3nb/jDYE9bC/k3yD0MdWEsx7z+ibOExk+ZhYAZrYeZtIEKL5GGeMmkZkMYzZ4wl5xwz4CGohe/88W2Sbyrq7M2O9xh+eFNjIMfDf4CAHojzEEweYtSPglEwCkbBKCAAADbTNkGpECNoAAAAAElFTkSuQmCC","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":true,"prefix":"","firstName":"Diana-Cristina","middleName":"","lastName":"Simon","suffix":""},{"id":372326639,"identity":"69578de8-4f75-4bed-be35-1198edb52c2c","order_by":1,"name":"Päivi Lyytikäinen-Saarenmaa","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Päivi","middleName":"","lastName":"Lyytikäinen-Saarenmaa","suffix":""},{"id":372326640,"identity":"2c61da5e-6882-407b-8565-edea6334c064","order_by":2,"name":"Mikko Pelto-Arvo","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Mikko","middleName":"","lastName":"Pelto-Arvo","suffix":""},{"id":372326641,"identity":"f3d15ae3-5a2d-40b6-a367-c3a3296927ea","order_by":3,"name":"Johanna Tuviala","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Tuviala","suffix":""},{"id":372326642,"identity":"e077e1ec-c5c3-4099-b3c8-cf14db9d67bd","order_by":4,"name":"Maiju Kosunen","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Maiju","middleName":"","lastName":"Kosunen","suffix":""},{"id":372326643,"identity":"0d1dc26b-4d32-4215-b7a4-f539491a97d8","order_by":5,"name":"Eija Honkavaara","email":"","orcid":"","institution":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland","correspondingAuthor":false,"prefix":"","firstName":"Eija","middleName":"","lastName":"Honkavaara","suffix":""},{"id":372326644,"identity":"6cac9877-13be-43db-bc58-f50206b33eac","order_by":6,"name":"Roope Näsi","email":"","orcid":"","institution":"Finnish Geospatial Research Institute (FGI), National Land Survey of Finland","correspondingAuthor":false,"prefix":"","firstName":"Roope","middleName":"","lastName":"Näsi","suffix":""},{"id":372326645,"identity":"67623e32-6120-4e6d-8786-a3bfc1c05a71","order_by":7,"name":"Olli-Pekka Tikkanen","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Olli-Pekka","middleName":"","lastName":"Tikkanen","suffix":""},{"id":372326646,"identity":"54ef4255-a881-450d-be27-2eee7bf95d93","order_by":8,"name":"Antti Kilpeläinen","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Antti","middleName":"","lastName":"Kilpeläinen","suffix":""},{"id":372326647,"identity":"2991a289-1a3f-45ab-afa3-e56195675af9","order_by":9,"name":"Heli Peltola","email":"","orcid":"","institution":"University of Eastern Finland (UEF)","correspondingAuthor":false,"prefix":"","firstName":"Heli","middleName":"","lastName":"Peltola","suffix":""}],"badges":[],"createdAt":"2024-10-29 14:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5355177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5355177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68722363,"identity":"e6834e3a-ce90-437f-9f31-229efdf973ad","added_by":"auto","created_at":"2024-11-11 10:56:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2151150,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the Ruokolahti study area in southeastern Finland (left), and the sample plots in Murtomäki, Paajasensalo, Viitalampi and Ryhmälehdonmäki study sites (right). The sample plots were established during 2014–2019 (Table 1). The background map (right) contains data from the National Land Survey of Finland Topographic (background rasters covering M5411D and M5411F rectangulars, National Land Survey of Finland 2023). The map was created using ArcGIS Desktop 10.5 by Esri (Redlands, CA, USA) (ArcGIS is the intellectual property of Esri and is used herein under license. All rights reserved.)\u003c/p\u003e","description":"","filename":"Fig1Ruokolahtistudyarea.png","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/9efeb0598e14b282e4b5e9e6.png"},{"id":68722364,"identity":"8dc213f9-953f-4a34-8c9d-b32a0a491e6c","added_by":"auto","created_at":"2024-11-11 10:56:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":645030,"visible":true,"origin":"","legend":"\u003cp\u003eThe SBB infestation levels of the trees in plots with a 5-m (A) and 10-m (B) radius, in conserved and managed areas. The light colour represents a not visually observable (NotVO) infestation level and the dark colour represents high infestation levels.\u003c/p\u003e","description":"","filename":"Fig2Treesinfestation.png","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/c1035621ab804ad820ae9d22.png"},{"id":68722631,"identity":"67aa18f7-a090-4e73-aa1f-37ed29fe5962","added_by":"auto","created_at":"2024-11-11 11:04:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":674547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e The average SBB damage (DIplot) of the 10-m-radius plots in conserved and managed areas. The different patterns in the bars represent the share of each symptom contributing to DIplot. The error bars represent standard error. \u003cstrong\u003eB.\u003c/strong\u003e The boxplots show the SBB damage according to each symptom separately at the plot level (DIs_plot).\u003c/p\u003e","description":"","filename":"Fig3Damageindex10m.png","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/cdc70ce6e98bae5ed33e79c7.png"},{"id":68722633,"identity":"785f7d3b-d48b-42b2-8d57-1b812b0ef240","added_by":"auto","created_at":"2024-11-11 11:04:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":849756,"visible":true,"origin":"","legend":"\u003cp\u003eStand characteristics of the conserved and managed areas. Subfigures: a. Basal area (m\u003csup\u003e2\u003c/sup\u003e/ha), b. Volume (m\u003csup\u003e3\u003c/sup\u003e/ha), c. Stand density, d. Basal area weighted mean diameter (cm), e. The proportion of Norway spruce (%), f. The age of Norway spruce (years). The horizontal line inside the boxplot indicates the median value. Error bars represent standard error. In figure A, the \u003cem\u003eT\u003c/em\u003e-test is for paired data, showing the differences within periods. In figure B, the \u003cem\u003eT\u003c/em\u003e-test is for unpair data, showing the differences between conserved and managed areas. Figure A. f. does not have a value for \u003cem\u003eT\u003c/em\u003e-test because of the constant change within periods. The \u003cem\u003eT\u003c/em\u003e-test values in bold indicate significant results.\u003c/p\u003e","description":"","filename":"Fig4Boxplotsvariables.png","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/6647c2b6308342a16f7e08ea.png"},{"id":68724422,"identity":"40e4ff07-88ea-4c05-9dfe-ab8a59f4b7e5","added_by":"auto","created_at":"2024-11-11 11:12:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":349325,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of volume and basal area (±SE) of the standing trees during the study period in plots with a 5-m radius (A) in conserved areas and with a 10-m radius (B) in conserved and managed areas. The bars of the stand variables show the total basal area and volume, divided between living trees (grey coloured bars), SBB-killed trees (black coloured bars, Dead_SBB), and trees that experienced unidentified mortality (dashed black coloured bars, Dead_Other). The error bars represent standard error.\u003c/p\u003e","description":"","filename":"Fig5Standdynamics.png","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/eef133d0600e5d86b759149f.png"},{"id":69616192,"identity":"3c238513-140e-4ad2-8bb2-de422e05e786","added_by":"auto","created_at":"2024-11-22 09:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5520655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/a10cfd6a-f5fb-48e4-a837-50326e3deaa8.pdf"},{"id":68722368,"identity":"8335dee4-88f4-4dc3-a4fb-747eacf73ce0","added_by":"auto","created_at":"2024-11-11 10:56:26","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":820817,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5355177/v1/352f0e0d7ccbbaf328f7e49d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Infestation symptoms as indicators of a sustained bark beetle outbreak in conserved and managed Norway spruce forests in south-eastern Finland","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change induces multiple abiotic and biotic risks to forests and forestry in northern Europe (Ven\u0026auml;l\u0026auml;inen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Simultaneously, the risk of detrimental cascading events of combined abiotic and biotic disturbances increases. This is the case for severe bark beetle outbreaks, which can be triggered by large-scale wind damage or prolonged drought events (Marini et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ven\u0026auml;l\u0026auml;inen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The European spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e L., SBB; Coleoptera: Curculionidae, Scolytinae) (e.g., Seidl et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is the most damaging biotic agent in Europe. It has caused extensive damage in Norway spruce forests in recent decades, especially in central and eastern Europe (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Patacca et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increasing trend in annual thermal sums due to climate warming (IPCC \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ruosteenoja and Jylh\u0026auml; \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is escalating the risk of severe SBB outbreaks in the southern regions of the European boreal zone (J\u0026ouml;nsson et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hof and Svahlin \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ven\u0026auml;l\u0026auml;inen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tikkanen and Lehtonen \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is because climate warming is creating favourable conditions for SBB reproduction (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ven\u0026auml;l\u0026auml;inen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, increasing wind damage due to decreasing soil frost durations from late autumn to early spring (Peltola et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ven\u0026auml;l\u0026auml;inen et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), coupled with the presence of wind-damaged wood left on site, may augment the availability of breeding material for SBB, promoting a higher risk of SBB outbreaks (e.g., Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSBB is a hazardous pest especially in older, mature Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e (L.) H. Karst.)-dominated forests (Schlyter et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Eriksson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Various factors have made Norway spruce forests susceptible to bark beetle outbreaks, such as the establishment of forests outside their natural range and on sites with lower soil water holding capacity; increases in growing stocks; and changes in forest age structure and composition (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jandl \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Initially, SBB attack Norway spruce trees or fresh wind-felled trees, but during an outbreak, SBB also attack nearby healthy standing trees (Eriksson et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; K\u0026auml;rvemo et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; \u0026Oslash;kland et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Following SBB colonization of standing trees, the trees show infestation symptoms that are visible to the human eye at the stem (entrance-exit holes, resinous flow, bark damage) and crown (defoliation, discoloration) level (Blomqvist et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These SBB attack symptoms appear in different stages of the attack (Kautz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with the entrance holes and resinous flows emerging in the earlier stage, while defoliation and discoloration occur in the later stage. Depending on the vitality of a tree, a very thin resinous flow may be exuded beneath the entrance holes due to the activated host defence mechanism (Baier et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The bark of the spruce trees is gradually peeled away due to maternal galleries and feeding larvae in the phloem and inner bark later in the season (Grodzki and Fronek \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schroeder and Cocoş \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and woodpeckers feeding on the larvae and pupae (Kautz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Crown symptoms, defoliation, and discoloration appear weeks later (Kautz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) due to a loss of phloem tissue and penetrating hyphae of a blue-stain fungi blocking moisture transmission from roots to the crown (Netherer et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering that Norway spruce is one of the most economically valuable tree species in central and northern Europe (Caudullo et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the SBB damage causes large economic losses for the European forestry sector (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Finland, Norway spruce accounts for 30% of the total volume of growing stock (Vaahtera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In managed forests, sanitation and salvage logging can be used to prevent the spread of SBB infestations (Schroeder and Lindel\u0026ouml;w \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). On the other hand, conserved forests are protected from any forest management activities (Korhonen et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vaahtera et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, SBB infestations may also spread from conserved forests to neighbouring managed forests, causing large economic losses for forest owners (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the increasing risk of SBB outbreaks and damaged areas in the past decades (e.g., Marini et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), there is an urgent need to expand our knowledge of time- and cost-efficient detection and monitoring methods. Recently in Finland, progress has been made in detection and monitoring using remote sensing technologies (e.g., N\u0026auml;si et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Junttila et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kanerva et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; \u0026Ouml;stersund et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These technologies offer advantages for early detection by using multispectral or hyperspectral images (e.g., Junttila et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and further detection and monitoring on larger scales (e.g., Fernandez-Carrillo et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, methods that require simple equipment and basic training, such as ground-based visual observations of individual trees attacked by SBB, could be an effective tool for forest owners, managers, non-professionals, and the scientific community. Promoting the use of ground-based visual observation to detect and monitor SBB-damaged areas based on evaluating SBB infestations at the stem and crown level could lead to more timely action and reduce the harmful effects on forests (Kautz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we assessed SBB damage using ground-based visual observation of individual trees for SBB infestations at the stem (i.e., resinous flow, entrance and exit holes, bark damage) and crown (i.e., defoliation, and discoloration) level in managed and conserved Norway spruce-dominated forests. Based on this, we analysed the impact of SBB damage on stands during an SBB outbreak in south-eastern Finland. We focused specifically on comparing the conserved and managed forests regarding (i) the overall SBB damage of the stand, (ii) the relationship between SBB damage and stand characteristics, and (iii) changes in volume and basal area of the stands during an SBB outbreak. Our results demonstrate that detection of SBB damage based on tree symptoms is beneficial to diminish the harmful effects of an SBB outbreak. Our study is particularly relevant for the development of SBB damage assessments under boreal conditions. Our method could be used to support the development and validation of remote sensing-based detection methods and the detection and monitoring of SBB infestations by forest owners, managers, non-professionals, and the scientific community.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study area covered four Norway spruce-dominated forests, Viitalampi (0.74 km\u003csup\u003e2\u003c/sup\u003e), Paajasensalo (0.56 km\u003csup\u003e2\u003c/sup\u003e), Ryhm\u0026auml;lehdonm\u0026auml;ki (2.35 km\u003csup\u003e2\u003c/sup\u003e) and Murtom\u0026auml;ki (1.25 km\u003csup\u003e2\u003c/sup\u003e), in the Ruokolahti municipality (61.17030 N 28.49010 S) in south-eastern Finland (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All experimental forests were situated within 10 km from each other to ensure similar climatic conditions. The forest site types in the area were mostly mesic (MT, \u003cem\u003eMyrtillus\u003c/em\u003e type) and herb-rich (OMT, \u003cem\u003eOxalis-Myrtillus\u003c/em\u003e type) according to Cajander\u0026rsquo;s site type classification (Cajander \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1926\u003c/span\u003e; Mikola \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). A detailed description of the ground floor vegetation and soil types can be found in Kosunen et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The mature spruce stands were mixed with a lower proportion of Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e L.), downy birch (\u003cem\u003eBetula pubescens\u003c/em\u003e L.), silver birch (\u003cem\u003eB. pendula\u003c/em\u003e Roth), and a minor share of other deciduous tree species. The terrain in all experimental forests was generally flat with a mean altitude of 120 m above sea level (a.s.l.), except for the Paajasensalo site, which was situated on steeper ridges and had an altitude range from 115 to 160 m (a.s.l.). The mean air temperature and precipitation sum of the summer months (May\u0026ndash;August) for the period 2014\u0026ndash;2021 were 14.0\u0026deg;C and 252 mm, respectively (Finnish Meteorological Institute). More detailed annual climate data can be found in Table A1 in the supplementary information (SI).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA major summer thunderstorm hit the region in late July 2010, followed by another one in early August due to exceptionally warm weather (Viiri et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Following the storms, the area was a mosaic of completely windthrown stands, narrow gaps caused by downbursts and scattered windthrown trees. The large amount of freshly felled mature Norway spruces caused an SBB population explosion from 2011 onwards.\u003c/p\u003e \u003cp\u003eThe management measures applied in the managed area included the removal of wind-felled trees if the volume of damaged spruces exceeded 10 m\u003csup\u003e3\u003c/sup\u003e per hectare and sanitation cuttings of highly infested forest compartments. These were responsive measures as a part of the compulsory SBB management in Finland according to the Forest damage prevention act (Finlex \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, due to the high amount of fallen trees, the sanitation operation was not carried out on time, which increased the attacks of SBB. As a result, the colonisation gradually shifted from windthrown trees to standing and living trees in 2013 (Lyytik\u0026auml;inen-Saarenmaa et al., unpublished data). Based on our observations in the study area, SBB produced a second annual generation in several years since 2011 due to warm summer months (Table A1, SI).\u003c/p\u003e \u003cp\u003eShortly after the large-scale wind damage, the Viitalampi (VL) and Paajasensalo (PS) sites were conserved as METSO (Forest Biodiversity Program for Southern Finland) sites, and no logging or transportation of dead wood were allowed there. The conservation decision made these over-mature stands highly susceptible to bark beetle reproduction (cf. Schroeder \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; K\u0026auml;rvemo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The Murtom\u0026auml;ki (MM) and Ryhm\u0026auml;lehdonm\u0026auml;ki (RM) sites were under normal forest management practice. These two areas were partially spared from the highest impact of the storms, and sanitation logging was conducted when required, particularly at susceptible forest edges. The SBB population became epidemic in RM after a mild drought and warm summer months in 2018.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling plots and tree measurements\u003c/h3\u003e\n\u003cp\u003eSampling plots were established in the vicinity of windthrown areas in stands affected by SBB where living trees remained. The plots were established in the portions of stands experiencing visual signs of SBB damage. Each plot was expected to have a minimum of two dead or dying spruces surrounded by declining spruces with symptoms of SBB attack. In total, 60 circular plots were established between 2014 and 2019 (21 plots in 2014, 24 in 2015, 2 in 2016, and 13 in 2019), except in 2018 (\u0026lsquo;New\u0026rsquo; in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The plots were reassessed for SBB attack symptoms once a year in August in 2015\u0026ndash;2017 and 2019\u0026ndash;2021 (\u0026lsquo;ReM\u0026rsquo; in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between 2014 and 2017, the stand characteristics and SBB attack symptoms for individual trees were assessed only in conserved areas, while between 2019 and 2021 they were assessed in both conserved and managed areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling plots in conserved (VL\u0026thinsp;=\u0026thinsp;Viitalampi and PS\u0026thinsp;=\u0026thinsp;Paajasensalo) and managed areas (RM\u0026thinsp;=\u0026thinsp;Ryhm\u0026auml;lehdonm\u0026auml;ki and MM\u0026thinsp;=\u0026thinsp;Murtom\u0026auml;ki) in the Ruokolahti study area. New\u0026thinsp;=\u0026thinsp;newly established plots, ReM\u0026thinsp;=\u0026thinsp;remeasured plots.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStudy area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003ePlot size\u0026thinsp;=\u0026thinsp;5 m radius\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003ePlot size\u0026thinsp;=\u0026thinsp;10 m radius\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2019\u0026ndash;2021*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2019\u003c/b\u003e**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2020\u0026ndash;2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNew\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNew\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eNew\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003eNew\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eReM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e 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colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e 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\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e109\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees/plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlots\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrees\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e175\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e748\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003e* In 2019, the fallen trees that were killed by SBB were excluded from the inventory\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003e** In VL and PS, there were no newly established plots, but there were new trees included in plots with a 5- and 10-m radius\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen each sampling plot was established, the location of the plot centre was recorded with a Trimble GPS device (Trimble Navigation Ltd., Sunnyvale, CA, USA) with an accuracy of up to 0.5 m after post-processing. In 2014\u0026ndash;2017, circular plots with a radius of 5 m were established in conserved areas (VL and PS). In 2019, new damage spots were found in managed areas (MM and RM), where 13 circular plots with a radius of 10 m were established. The radius of field surveys for previous plots was also increased to 10 m due to a high proportion of dead and fallen trees within a radius of 5 m. Moreover, the trees that fell due to SBB-induced mortality, boosted by windy events in winters between 2017\u0026ndash;2019, were excluded from the 2019 inventories.\u003c/p\u003e \u003cp\u003eThe data collected from the sampling plots consisted of tree-wise measurements regarding tree characteristics and symptoms for SBB infestations. Tree-wise measurements consisted of diameter at breast height (dbh\u0026thinsp;\u0026ge;\u0026thinsp;10 cm, at 1.3 m above stem base), the height of the plot-wise median tree, the height of every seventh Norway spruce across the plots (Vuokila \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), and the vitality status of each tree (e.g., dead, alive, fallen, broken, etc.). These characteristics were measured in the year when the plots were established and remeasured in 2019 when the size of the plot was increased. In addition, individual trees in the plots were located by measuring the distance and azimuth angle from the plot centre to the centre of each tree. The symptoms were evaluated annually using a scoring system based on the severity of the attack shown at the stem (entrance-exit holes, resinous flow, bark damage) and crown (defoliation, discoloration) level (cf. Blomqvist et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eInfestation symptom assessment and scoring\u003c/h3\u003e\n\u003cp\u003eThe symptoms for SBB infestation were assessed in the years when the plots were established and the following years until 2021, except 2018 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The assessment was done from mid to late August when all the stem and crown symptoms were visible and the impact of SBB on tree vitality was relevant. All living and dead standing spruces (dbh\u0026thinsp;\u0026ge;\u0026thinsp;10 cm) in the plots were assessed and ranked according to the severity of the symptoms. The entrance and exit holes were assessed up to a height of 2 m, resinous flow and bark damage were assessed up to the lowermost branch, while defoliation and discoloration were assessed from a distance corresponding to approximately a tree height as was done by Blomqvist et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Each visual symptom provided a value from one to three (symptoms on tree stem) or one to four (symptoms on crown area) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Examples of SBB tree symptoms can be seen in Fig. B1, SI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRanking of infestation symptom scores and description of the visual symptoms indicating the severity of SBB attack.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHole (entrance and exit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 holes\u003c/p\u003e \u003cp\u003e\u0026le;\u0026thinsp;10 holes\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 holes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResinous flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 spots\u003c/p\u003e \u003cp\u003e1\u0026ndash;30 spots\u003c/p\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 spots\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBark damage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo damage\u003c/p\u003e \u003cp\u003eModerate damage\u003c/p\u003e \u003cp\u003eHigh damage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeedle discoloration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGreen\u003c/p\u003e \u003cp\u003eYellowish\u0026ndash;yellow\u003c/p\u003e \u003cp\u003eReddish\u0026ndash;red\u003c/p\u003e \u003cp\u003eGrey (dead tree)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefoliation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003cp\u003e2\u003c/p\u003e \u003cp\u003e3\u003c/p\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;24%\u003c/p\u003e \u003cp\u003e25\u0026ndash;49%\u003c/p\u003e \u003cp\u003e50\u0026ndash;74%\u003c/p\u003e \u003cp\u003e75\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe scoring classifies each tree into three categories of infestation: \u003cem\u003enot visually observable\u003c/em\u003e (\u003cem\u003eNotVO\u003c/em\u003e; score 1), \u003cem\u003emoderate infestation\u003c/em\u003e (score 2 for stem symptoms and 2 or 3 for crown symptoms), and \u003cem\u003ehigh infestation\u003c/em\u003e (score 3 for stem symptoms and 4 for crown symptoms).\u003c/p\u003e \u003cp\u003eThe SBB damage (damage index of the plot, DIplot) was calculated using the scoring values of each symptom for all the spruces in the plot. Firstly, a damage index (DI) was calculated for each symptom of the tree by dividing the score recorded during field assessment by the maximum score it can take (Eq.\u0026nbsp;1). Secondly, the damage index of the tree was calculated using Eq.\u0026nbsp;2. Lastly, the SBB damage of the plot (DIplot) was calculated as the average DI of all the trees from the plot (Eq.\u0026nbsp;3). DIplot took values between 0 and 1, providing an estimate about the degree of damage (0: not damaged; 1: completely damaged).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{DIs}_{holes/\\:resin/\\:bark}=\\:\\frac{Field\\:score}{3}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\text{o}\\text{r}\\:\\:\\:\\:\\:\\:\\:\\:\\:{DIs}_{defoliation/discoloration}=\\:\\frac{Field\\:score}{4}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{DI}_{tree}=\\frac{{DI}_{holes}+\\:{DI}_{resin}+\\:{DI}_{bark}+\\:{DI}_{defoliation}+\\:{DI}_{discoloration}}{5}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{DI}_{plot}=\\:\\frac{\\sum\\:{DI}_{tree}}{N},\\:\\text{w}\\text{h}\\text{e}\\text{r}\\text{e}\\:\\text{N}\\:\\text{i}\\text{s}\\:\\text{t}\\text{h}\\text{e}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{t}\\text{r}\\text{e}\\text{e}\\text{s}\\:\\text{i}\\text{n}\\:\\text{t}\\text{h}\\text{e}\\:\\text{p}\\text{l}\\text{o}\\text{t}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe data analysis was carried out using R (version 4.2.2 and 4.2.3) and R Studio (version 2022.12.0-353, R Core Team \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), mainly using the tidyverse package (v2.0.0: Wickham et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The statistical analysis involved several steps. First, the Shapiro\u0026ndash;Wilk test was used to test the normal distribution of the data (Wilcox \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Second, a parametric mixed-design ANOVA was used to investigate differences in the damage index of the plots among different treatments (conserved and managed) and years (2019\u0026ndash;2021) (Dudek \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this analysis, the year was used as the within-group variable, and the treatment as the between-group variable. Pairwise comparisons within each year and treatment were conducted using the Tukey method. Third, the unpaired \u003cem\u003eT\u003c/em\u003e-test with Bonferroni correction was used to examine differences in the SBB damage of the plots, as indicated by each symptom separately (i.e., resinous flow, entrance and exit holes, bark damage, defoliation, and discoloration). This was assessed separately for each year and treatment. Fourth, the unpaired \u003cem\u003eT\u003c/em\u003e-test was used to determine whether the change in DIplot from 2019 to 2021 differed between the treatments (Wilcox \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Fifth, the Pearson\u0026rsquo;s correlation coefficient was used to assess the relationship between DIplot in 2019 and different stand variables (Wilcox \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In this analysis, highly correlated variables (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) were excluded. Lastly, a Boosted Regression Tree model (BRT: Elith et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) estimated the extent to which the non-correlated variables explained the variability in DIplot, using the gbm package (version 2.1.8.1; Ridgeway and Ridgeway \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The model utilized a Gaussian distribution and fitted 5,000 decision trees to determine the best predictors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eObserved SBB infestation levels of trees\u003c/h2\u003e \u003cp\u003eThe overall severity of SBB infestation differed according to the size of the plots in conserved areas (see figures for conserved areas in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). The trees in the 5-m-radius plots had high levels of infestation in 2014 regardless of the symptom type (\u0026gt;\u0026thinsp;50% of the trees). Between 2015 and 2021 the infestations increased so that over 80% of the trees were highly infested at the end of 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For the trees in 10-m-radius plots (established in 2019), the overall infestation levels were lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) due to the larger size of the plots, showing the advance of the infestations from the infestation spot (the centre of the plot). In addition, the levels of infestation were higher in conserved areas compared to managed areas. In the conserved areas, around 50% of the trees were highly infested in 2019 and around 55% by 2021 (5% increase). In the managed areas, only around 4% of the trees were highly infested in 2019 and around 12% (8% increase) by 2021.\u003c/p\u003e \u003cp\u003eThe proportion of trees showing high infestation levels based on resinous flow, attack holes, bark damage, and defoliation was greater than that based on discoloration. For example, in 2021 in the managed areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Managed), 14% of the trees presented high infestation levels according to resinous flow, entrance-exit holes, bark damage, and defoliation, while only 4% did so based on needle discoloration. In the conserved areas, all the studied symptoms gave evidence for high levels of infestation for over 50% of the trees. However, based on discoloration, 43% of the trees had a green crown (score 1 in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSBB damage in the plots\u003c/h3\u003e\n\u003cp\u003eLarge numbers of highly infested trees in the conserved areas resulted in a high damage index at the plot level. This was observed especially for plots in the conserved areas where the trees were situated within a 5-m radius from the centre of the damage spot. The SBB damage (DIplot) was already 0.9 (max 1.0) in 2014, with an increase of 6% by 2021 (Fig. C1, SI).\u003c/p\u003e \u003cp\u003eConsidering the number of trees with not visually observable (NotVO) or moderate infestation levels in the 10-m-radius plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, B), the SBB damage was generally lower. However, in this case, conserved areas had higher damage index values compared to managed areas (DIplot, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The overall DIplot in the conserved areas was 0.75 in 2019, with an increase of 5% by 2021. While in managed areas the initial DIplot was 0.46, with an increase of 15% by 2021. Thus, the SBB damage in conserved areas was significantly higher than that in managed areas annually for the period 2019\u0026ndash;2021 (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table D2 abc, SI). However, in conserved areas, the change in DIplot between 2019 and 2021 was significantly lower than that in the managed areas (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), showing the retrogradation phase in the conserved areas.\u003c/p\u003e \u003cp\u003eWhen the damage was analysed separately by each symptom (i.e., resinous flow, attack holes, bark damage, defoliation, and discoloration), in general, the symptoms had unequal contributions (DIs_plot, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). An exception was for the damage indices given by resin flows and entrance-exit holes, which were statistically similar regardless of the year or treatment (\u003cem\u003eT\u003c/em\u003e-test; \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 at a confidence level of 95%; Table D1, SI). Additionally, the damage index given by defoliation in the conserved areas was statistically similar with the ones given by bark damage and entrance-exit holes. Resinous flows and entrance-exit holes showed on average a higher damage index (i.e., 0.83 in conserved areas and 0.57 in managed areas in 2019), while the lowest damage index was shown on average by discoloration (i.e., 0.65 in conserved areas and 0.28 in managed areas in 2019).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThe relationship between SBB damage index and stand variables\u003c/h3\u003e\n\u003cp\u003eThe SBB damage index values significantly correlated with the stand basal area of the living trees and the age of spruce, which together explained over 50% of the variation of DIplot (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There were strong negative correlations between the basal area of the living trees and the volume of the living trees. On the contrary, there were strong positive correlations among the variables related to dead trees (Correlated variables, i.e., proportion, basal area, and volume of dead trees; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere was a weak correlation between SBB damage and the proportion of spruce, explaining only 11% of the variation of damage index values. This may be because 85% of the plots had more than 80% spruce. In conserved areas, more than half of the plots with over 60% spruce were highly damaged in 2019 (Fig. C2, SI). The SBB damage showed a weak correlation with the proportion of both large and small spruces, likely due to the high infestation rate (\u0026gt;\u0026thinsp;50%) across all diameter classes in conserved areas (Fig. C3, SI).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe correlation coefficients (r) showing the relation of DIplot with the stand variables and the variation in damage score explained by each variable (BRT). The analysis was done based on the information for 2019.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStand variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelated variables (r\u0026thinsp;\u0026gt;\u0026thinsp;0.7)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBasal area of the living trees (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;0.79*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e38.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+ Volume of the living trees (m\u003csup\u003e3\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Proportion of the dead trees (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Basal area of dead trees (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Volume of the dead trees (m\u003csup\u003e3\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge of spruce (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;0.49*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e14.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVolume of spruce (m\u003csup\u003e3\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e14.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+ Volume of the stand (m\u003csup\u003e3\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+ Basal area (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProportion of basal area occupied by spruce (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Basal area of other species (m\u003csup\u003e2\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Volume of other species (m\u003csup\u003e3\u003c/sup\u003e/ha)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of spruce with big diameters (DBH\u0026thinsp;\u0026gt;\u0026thinsp;30 cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; Basal area weighted diameter (cm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of spruce with small diameters (DBH\u0026thinsp;\u0026lt;\u0026thinsp;20 cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus; The proportion of spruce with diameters 20\u0026ndash;30 cm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e+, positive relationship and \u0026minus;, negative relationship\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eChanges in characteristics and the dynamics of tree mortality during an SBB outbreak\u003c/h2\u003e \u003cp\u003eFor the plots with a 5-m radius in conserved areas, there were significant differences in the stand basal area, volume, and density between the two periods (2014\u0026ndash;2017 and 2019\u0026ndash;2021; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; subfigures a. b. c.). For the plots with a 10-m radius (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), the conserved areas generally had larger variations in stand characteristics than the managed areas. Of the stand characteristics measured, only the proportion and the average age of Norway spruce were significantly different (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB; subfigures e., f.). Hence, conserved areas had a significantly lower proportion of Norway spruce and older trees on average. The median of the basal area weighted mean diameter (Dba) was similar in both conserved and managed areas, but a higher variation was observed in conserved areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eAB; subfigure d.).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe standing volume and basal area decreased as a result of increasing tree mortality and number of fallen trees due to SBB damage over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In plots with a 5-m radius in conserved areas, both volume and basal area decreased annually on average by 10%. In plots with a 10-m radius, the decrease was smaller: on average 6% per year in conserved areas and 3% per year in managed areas. However, the largest decrease (25%) was observed between 2017 and 2019. Consequently, the trees started falling 6\u0026ndash;7 years after the initial colonisation. The dead trees remained standing for a while before falling, which explains a high proportion of dead trees in conserved areas, being over 80% throughout the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the conserved areas with 10-m-radius plots, the trees killed by SBB represented 40% of the standing trees, while in the managed areas, the corresponding proportion was negligible (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the methodology\u003c/h2\u003e \u003cp\u003eIn this study, we evaluated symptoms once a year in August, when both early symptoms (resin flows and entrance-exit holes) and late symptoms (bark damage, crown defoliation, and needle discoloration) were visible (Kautz et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This was done to meet the needs of practical forestry to identify the damaged stands where sanitation felling should be done. Typically, in boreal conditions in Finland, SBB completes only one generation per year and begins hibernation in August (Annila \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1969\u003c/span\u003e), but this situation may change under a warming climate. Currently in Finland, sanitation interventions are mainly carried out during winter. Thus, a detection of SBB attack based on tree symptoms and evaluation of the stand damage at the end of the SBB annual cycle would contribute to decision making regarding SBB management (i.e., sanitation felling) and reduce the spreading beetles in the nearby stands, as was also noted by Kautz et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA similar methodology, based on ground-based visual observations of SBB attack symptoms at tree level, was also used earlier by Blomqvist et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Kautz et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to the symptoms we assessed, Kautz et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) considered boring dust as the assessment was done every second week. They found that boring dust is the most accurate symptom for timely detection of infestation in spring or early summer, but not later. When the symptoms are evaluated later in the summer, as was our case in August, the dust is hardly visible as it is spread out by wind and rain.\u003c/p\u003e \u003cp\u003eWith the methodology we used, we could not detect the infestations at the tree level in early stages of the SBB attack (i.e., ‘pre-emergence detection’ defined by Kautz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or identify the susceptible trees before the attack. The potential of remote sensing methods combined with machine learning techniques have been studied for the early detection of SBB infestations (e.g., Junttila et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Huo et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or detection of susceptible trees (e.g., based on water stress Huo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mandl and Lang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While the remote sensing detection methods have advantages, they currently face practical challenges such as low accuracy and robustness, as well as high costs (Marvasti-Zadeh et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kautz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). An early detection in our study was not possible as our study started in 2014 when the trees in the conserved areas were in a more advanced stage of SBB infestation. Despite the limitations of this study, this work is valuable as it detected for the first time the dynamics of SBB infestations and mortality over several years under Finnish conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eThe SBB outbreak in conserved and managed areas\u003c/h2\u003e \u003cp\u003eIn our study, the SBB outbreak proceeded after a massive wind disturbance caused by two summer thunderstorms in 2010. The attack on standing trees started in 2013, i.e., in the third year after the storm. In 2014, a high percentage of the standing trees were already highly infested. Similarly, Økland et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that the transition of SBB from the fallen trees to the standing trees happened during the third and fourth years after the windfall. The available resources in the nearby area of the windfall and the temperature conditions (e.g., Mezei et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) further affect the reproductive rate of SBB, followed by the attack and damage of the living trees the following (e.g., Kärvemo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or second summer after the storm (Komonen et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the time of our assessment (2019–2021), the stands in the conserved areas had significantly higher SBB damage compared to those in managed areas. Likewise, Schroeder and Lindelöw (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Mezei et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found a lower tree mortality during an SBB outbreak in managed areas. However, in our case, the initial infestation levels of the stands were not assessed, so the possibility of the conserved and managed stands having a different initial infestation level at the time of our assessment cannot be fully excluded. Regular management may increase the vitality of trees, promoting better adaptation to the site conditions and a lower susceptibility to disturbances (de Groot et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although we found symptoms of highly infested trees in managed areas, the tree mortality rate was lower than that in conserved areas. This may be at least partially explained by a better vitality and a faster recovery capacity of the trees, which was observed by Blomqvist et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, more research is needed to confirm this.\u003c/p\u003e \u003cp\u003eTree mortality in conserved areas started in the 2nd year after the initial colonization, with the highest increase after the 3rd year. Consequently, a high decrease in standing volume and basal area of living trees was observed 6–7 years after the initial colonisation. Other studies found that the mortality was the highest at the beginning of the outbreak (e.g., Schroeder and Lindelöw \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kärvemo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mezei et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and then decreased frequently due to host tree depletion (e.g., Mezei et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). According to Hekkala et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)d rvemo et al. (2017), forest conservation measures, such as the gap-cut method, create a high risk of new SBB attacks after only 5 years. The results of the gap-cut method are similar to those of storm gaps, thus supporting our results.\u003c/p\u003e \u003cp\u003ePotterf et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Sommerfeld et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that managed forests are less resistant to SBB attacks than conserved forests (i.e., natural reserves). This can be explained by the fact that conserved forests may have a higher functional and structural diversity, which increases their resistance and resilience against disturbance agents (Hlásny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Nonetheless, recently established, smaller conservation areas, such as in our study, may also have a lower resistance against SBB compared to larger (and older) nature reserve areas (see e.g., Potterf et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Still, differences in forest structure (e.g., age and species composition, stocking), and environmental (climate, site) conditions may explain differences between studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship of SBB damage with stand variables\u003c/h2\u003e \u003cp\u003eIn our study, SBB damage correlated significantly with the basal area of living trees and the age of spruce trees. Similarly, Kärvemo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Sproull et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that the basal area of living spruces predicted tree mortality by SBB. However, Sproull et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported that the age of spruce trees did not influence tree mortality caused by SBB. In contrast, other issues related to SBB infestations (i.e., infestation risk in Trubin et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; individual predisposition in Seidl et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; and cumulative disturbance in Potterf et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) were shown to increase significantly with tree age.\u003c/p\u003e \u003cp\u003eOur results showed that SBB damage was independent of tree size (diameter of the host tree), unlike in some previous studies (e.g., Kärvemo et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mezei et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sproull et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Korolyova et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This may be because the outbreak was in an advanced stage (culmination and retrogradation phase) during most of the study period. Further, year 2015 may represent a culmination of the outbreak in the conserved areas. Yet, the outbreak pattern was patchy over the study area. According to Mezei et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), for the pre-outbreak to progradation phase and the culmination phase, diameter is an important predictor for tree mortality induced by SBB, but not in the retrogradation phase. Likewise, Sproull et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that the diameter had a low contribution from the progradation to the culmination phase.\u003c/p\u003e \u003cp\u003eIn European temperate forests, SBB has damaged large Norway spruce-dominated areas (e.g., Kamińska et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Potterf et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The increasing amount of susceptible mature Norway spruce in temperate forests has provided favourable conditions for SBB to infest in the past decades (Marini et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hlásny et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, the proportion of spruce failed to explain the SBB damage, likely due to the low variation of the spruce proportion in the study area. Typically, in Norway spruce stands, broadleaves are harvested during thinning operations according to ‘Best practices for sustainable forest management in Finland’ (see \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metsanhoidonsuositukset.fi/en\u003c/span\u003e\u003cspan address=\"https://metsanhoidonsuositukset.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003c/div\u003e "},{"header":"Conclusions and future perspectives","content":"\u003cp\u003eUsing data from ground-based visual observations, we assessed symptoms for SBB infestation, which provided results about the infestation levels of the trees and the damage of the plots. In conserved areas, 70% of the trees within the 5-m-radius plots were highly infested in the 2nd year (in 2014) after the initial colonization of the standing trees, reaching 90% in the 5th year (2017). Stand-level analysis showed that the conserved areas had significantly higher damage than managed areas during the study period (2019–2021). The variation in SBB damage at the stand level was best explained by the basal area of living trees and the age of spruces. When the SBB damage was analysed separately by each symptom, resinous flows and entrance-exit holes showed similar levels of damage regardless of the year or treatment. Over the course of the outbreak (until 2021), the volume of the stands decreased on average by 80% in conserved areas and 40% in managed areas.\u003c/p\u003e\u003cp\u003eThe results of this study demonstrate the potential use of tree-level symptom assessment for the detection of SBB attack and the role of forest management in damaged areas on decreasing the harmful effects of an SBB outbreak at the stand level in managed boreal forests. The ground-based visual assessment of tree symptoms offered a reliable means to evaluate the damage of the stands and could support forest owners and managers in decision making regarding sanitation felling. Such methodology may also support advancement and validation of remote sensing-based detection methods. Additionally, ground-based visual observations of SBB infestation symptoms are inexpensive and only require basic training. Therefore, they could be used to develop applications used for monitoring infestations by forest owners and non-professionals. Such application could help detect SBB infestations to support timely measures and decrease damage in Norway spruce forests.\u003c/p\u003e\u003cp\u003eIn addition to response management, more attention should be given to proactive measures that contribute to risk prevention and provide better preparedness for SBB outbreaks in managed forests (EFI \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hlásny et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, avoiding the cultivation of pure spruce stands, which is still a practice in boreal forests, can help reduce the risk. This is particularly important on forest sites with a relatively low water holding capacity where trees are more susceptible to drought and further SBB outbreaks. Growing mixed forests (admixtures with deciduous species) on suitable sites may also increase forest resilience against SBB outbreaks due to fewer host trees and higher populations of natural enemies (e.g., Kausrud et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; de Groot and Ogris \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hlásny et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Müller et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Timely and regular thinning operations to improve tree vigour (outbreak prevention), harvesting of infested trees (sanitation felling and salvage logging), and removal of harvested and wind-damaged trees before swarming of the first annual SBB generation in early summer are necessary risk prevention measures. The use of shorter rotation periods (or lower target diameters) may also enhance forest resilience against SBB outbreaks (e.g., de Groot et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hlásny et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Venäläinen et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ultimately, in risk assessment and management, the areas with higher risk of SBB outbreaks and damage should be prioritised.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe wish to thank Minna Blomqvist, Pentti Henttonen, Micke Malm, Jaana Turunen, and Juho \u0026Auml;yr\u0026auml;s for their help with the field work, and Tuula Kantola and Hannu Saarenmaa for valuable advice on the study design and assessment of SBB symptoms. Tornator Ldt., particularly the former heads of forest resources Antero Pasanen and Maarit Sallinen, and the current head of forest resources Kimmo Kortelainen, are thanked for enabling this study in Ruokolahti and providing compartment data. We also thank Stora Enso Ltd., especially Jarmo Hakalisto, for facilities in Viitalampi during our annual field work. Diana-Cristina Simon wishes to also thank the LUMETO Doctoral Program (formerly FORES) at the Faculty of Science, Forestry and Technology, University of Eastern Finland. Finally, we wish to thank to the anonymous reviewers for their contribution to improving this research paper and to PhD J. Mackay from Cambridge Proofreading for proofreading the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eThis study was funded by the Maj and Tor Nessling Foundation, the Marjatta and Eino Kolli Foundation, the Niemi Foundation, the Ministry of Agriculture and Forestry of Finland (project MONITUHO, decision number 647/03.02.06.00/2018; project SPRUCERISK, decision number VN/5292/2021), and the Academy of Finland (project OPTIMAM, grant number 317741; project UNITE flagship, grant numbers 337127 and 357906; and project MULTIRISK, grant numbers 353263 and 353264).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e The data of this study are available upon request from the corresponding author Diana-Cristina Simon or Docent, Dr. P\u0026auml;ivi Lyytik\u0026auml;inen-Saarenmaa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u003c/strong\u003e Design of field experiment (PLS, MK, EH, RN, MPA), field data collection (PLS, MK, MPA), data curation (DCS, PLS, MPA, JT), the design of the study (DCS, PLS, HP, OPT, AK), data visualization and statistics (DCS), writing \u0026ndash; original draft preparation (DCS, PLS, HP), writing \u0026ndash; review and editing (DCS, PLS, HP, MPA, MK, EH, RN, OPT, AK, JT), funding responsibilities (PLS, HP, EH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical standards\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnnila E (1969) Influence of temperature upon the development and voltinism of \u003cem\u003eIps typographus\u003c/em\u003e L. (Coleoptera, Scolytidae). In: Annales Zoologici Fennici. Societas Biologica Fennica Vanamo, pp 161\u0026ndash;208\u003c/li\u003e\n\u003cli\u003eBaier P, F\u0026uuml;hrer E, Kirisits T, Rosner S (2002) Defence reactions of Norway spruce against bark beetles and the associated fungus \u003cem\u003eCeratocystis polonica\u003c/em\u003e in secondary pure and mixed species stands. Forest Ecology and Management 159(1\u0026ndash;2):73\u0026ndash;86. https://doi.org/10.1016/S0378-1127(01)00711-3\u003c/li\u003e\n\u003cli\u003eBlomqvist M, Kosunen M, Starr M, Kantola T, Holopainen M, Lyytik\u0026auml;inen-Saarenmaa P (2018) Modelling the predisposition of Norway spruce to \u003cem\u003eIps typographus\u003c/em\u003e L. infestation by means of environmental factors in southern Finland. European Journal of Forest Research 137(5):675\u0026ndash;691. https://doi.org/10.1007/s10342-018-1133-0\u003c/li\u003e\n\u003cli\u003eCajander A (1926) The theory of forest types. Acta Forestalia Fennica 29(3):1\u0026ndash;108. https://doi.org/10.14214/aff.7193\u003c/li\u003e\n\u003cli\u003eCaudullo G, Tinner W, de Rigo D (2016) \u003cem\u003ePicea abies\u003c/em\u003e. European Atlas of Forest Tree Species 114\u0026ndash;116. https://forest.jrc.ec.europa.eu/en/european-atlas/atlas-data-and-metadata/\u003c/li\u003e\n\u003cli\u003ede Groot M, Diaci J, Ogris N (2019) Forest management history is an important factor in bark beetle outbreaks: Lessons for the future. Forest Ecology and Management 433:467\u0026ndash;474. https://doi.org/10.1016/j.foreco.2018.11.025\u003c/li\u003e\n\u003cli\u003ede Groot M, Ogris N (2019) Short-term forecasting of bark beetle outbreaks on two economically important conifer tree species. Forest Ecology and Management 450:117495. https://doi.org/10.1016/j.foreco.2019.117495\u003c/li\u003e\n\u003cli\u003eDudek B (2023) Mixed ANOVA Design in R. https://www.datanovia.com/en/lessons/mixed-anova-in-r/\u003c/li\u003e\n\u003cli\u003eEFI (2020) Towards holistic forest crisis management - adapting to changing disturbance regimes in Europe. SURE, European Forest Risk Facility. https://sure.efi.int/sites/default/files/2020 08/Policy Brief_Towards holistic forest crisis management.pdf\u003c/li\u003e\n\u003cli\u003eElith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. Journal of Animal Ecology 77(4):802\u0026ndash;813. https://doi.org/10.1111/j.1365-2656.2008.01390.x\u003c/li\u003e\n\u003cli\u003eEriksson M, Neuvonen S, Roininen H (2007) Retention of wind-felled trees and the risk of consequential tree mortality by the European spruce bark beetle \u003cem\u003eIps typographus\u003c/em\u003e in Finland. Scandinavian Journal of Forest Research 22(6):516\u0026ndash;523. https://doi.org/10.1080/02827580701800466\u003c/li\u003e\n\u003cli\u003eFernandez-Carrillo A, Patočka Z, Dobrovoln\u0026yacute; L, Franco-Nieto A, Revilla-Romero B (2020) Monitoring bark beetle forest damage in central Europe. A remote sensing approach validated with field data. Remote Sensing 12(21):1\u0026ndash;19. https://doi.org/10.3390/rs12213634\u003c/li\u003e\n\u003cli\u003eFinlex (2013) Laki mets\u0026auml;tuhojen torjunnasta [Forest Damage Prevention Act]. 1087/2013. Ministry of Justice. Helsinki. https://www.finlex.fi/fi/laki/alkup/2013/20131087 [In Finnish]\u003c/li\u003e\n\u003cli\u003eFinnish Meteorological Institute. Annual Weather Statistics. https://ilmatieteenlaitos.fi/ vuositilastot Accessed 20 December 2022\u003c/li\u003e\n\u003cli\u003eGrodzki W, Fronek W (2017) Reproduction abilities of \u003cem\u003eIps typographus\u003c/em\u003e (L.) (Coleoptera: Curculionidae, Scolytinae) in the stands damaged by the wind in 2013 in the Koscieliska Valley (Tatra National Park). Folia Forestalia Polonica, Series A \u0026ndash; Forestry 59(4):258\u0026ndash;264\u003c/li\u003e\n\u003cli\u003eHekkala AM, K\u0026auml;rvemo S, Versluijs M, Weslien J, Bj\u0026ouml;rkman C, L\u0026ouml;froth T, Hj\u0026auml;lt\u0026eacute;n J (2021) Ecological restoration for biodiversity conservation triggers response of bark beetle pests and their natural predators. Forestry 94(1):115\u0026ndash;126. https://doi.org/10.1093/forestry/cpaa016\u003c/li\u003e\n\u003cli\u003eHl\u0026aacute;sny T, K\u0026ouml;nig L, Krokene P, Lindner M, Montagn\u0026eacute;-Huck C, M\u0026uuml;ller J, Qin H, Raffa KF, Schelhaas MJ, Svoboda M, Viiri H, Seidl R (2021) Bark beetle outbreaks in Europe: State of knowledge and ways forward for management. Current Forestry Reports 7(3):138\u0026ndash;165. https://doi.org/10.1007/s40725-021-00142-x\u003c/li\u003e\n\u003cli\u003eHl\u0026aacute;sny T, Krokene P, Liebhold A, Montagn\u0026eacute;-Huck C, Müller J, Qin H, Raffa K, Schelhaas M-J, Seidl R, Svoboda M, Viiri H (2019) Living with bark beetles: impacts, outlook and management options. In: From Science to Policy 8. European Forest Institute\u003c/li\u003e\n\u003cli\u003eHof AR, Svahlin A (2016) The potential effect of climate change on the geographical distribution of insect pest species in the Swedish boreal forest. Scandinavian Journal of Forest Research 31(1):29\u0026ndash;39. https://doi.org/10.1080/02827581.2015.1052751\u003c/li\u003e\n\u003cli\u003eHuo L, Lindberg E, Bohlin J, Persson HJ (2023) Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions. Remote Sensing of Environment 287:113484. https://doi.org/10.1016/j.rse.2023.113484\u003c/li\u003e\n\u003cli\u003eHuo L, Persson HJ, Lindberg E (2021) Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red \u0026amp; SWIR (NDRS). Remote Sensing of Environment 255. https://doi.org/10.1016/j.rse.2020.112240\u003c/li\u003e\n\u003cli\u003eIPCC (2021) Climate Change 2021: The Physical Science Basis. In: Masson-Delmotte V, P Zhai, A Pirani, SL Connors, C P\u0026eacute;an, S Berger, N Caud, Y Chen (eds) Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. https://doi.org/doi:10.1017/9781009157896\u003c/li\u003e\n\u003cli\u003eJandl R (2020) Climate-induced challenges of Norway spruce in Northern Austria. Trees, Forests and People 1:100008. https://doi.org/10.1016/j.tfp.2020.100008\u003c/li\u003e\n\u003cli\u003eJ\u0026ouml;nsson AM, Schroeder LM, Lagergren F, Anderbrant O, Smith B (2012) Guess the impact of \u003cem\u003eIps typographus\u003c/em\u003e - An ecosystem modelling approach for simulating spruce bark beetle outbreaks. Agricultural and Forest Meteorology 166\u0026ndash;167:188\u0026ndash;200. https://doi.org/10.1016/j.agrformet.2012.07.012\u003c/li\u003e\n\u003cli\u003eJunttila S, Holopainen M, Vastaranta M, Lyytik\u0026auml;inen-Saarenmaa P, Kaartinen H, Hyypp\u0026auml; J, Hyypp\u0026auml; H (2019) The potential of dual-wavelength terrestrial lidar in early detection of \u003cem\u003eIps typographus\u003c/em\u003e (L.) infestation \u0026ndash; Leaf water content as a proxy. Remote Sensing of Environment 231:111264. https://doi.org/10.1016/j.rse.2019.111264\u003c/li\u003e\n\u003cli\u003eJunttila S, N\u0026auml;si R, Koivum\u0026auml;ki N, Imangholiloo M, Saarinen N, Raisio J, Holopainen M, Hyypp\u0026auml; H, Hyypp\u0026auml; J, Lyytik\u0026auml;inen-Saarenmaa P, Vastaranta M, Honkavaara E (2022) Multispectral imagery provides benefits for mapping spruce tree decline due to bark beetle infestation when acquired late in the season. Remote Sens 14:909. https://doi.org/https://doi.org/10.3390/ rs14040909\u003c/li\u003e\n\u003cli\u003eKamińska A, Lisiewicz M, Kraszewski B, Stereńczak K (2021) Mass outbreaks and factors related to the spatial dynamics of spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e) dieback considering diverse management regimes in the Białowieża forest. Forest Ecology and Management 498. https://doi.org/10.1016/j.foreco.2021.119530\u003c/li\u003e\n\u003cli\u003eKanerva H, Honkavaara E, N\u0026auml;si R, Hakala T, Junttila S, Karila K, Koivum\u0026auml;ki N, Alves Oliveira R, Pelto-Arvo M, P\u0026ouml;l\u0026ouml;nen I, Tuviala J, \u0026Ouml;stersund M, Lyytik\u0026auml;inen-Saarenmaa P (2022) Estimating tree health decline caused by \u003cem\u003eIps typographus\u003c/em\u003e L. from UAS RGB images using a deep one-stage object detection neural network. Remote Sensing 14(24):6257. https://doi.org/10.3390/rs14246257\u003c/li\u003e\n\u003cli\u003eK\u0026auml;rvemo S, Bj\u0026ouml;rkman C, Johansson T, Weslien J, Hj\u0026auml;lt\u0026eacute;n J (2017) Forest restoration as a double-edged sword: the conflict between biodiversity conservation and pest control. Journal of Applied Ecology 54(6):1658\u0026ndash;1668. https://doi.org/10.1111/1365-2664.12905\u003c/li\u003e\n\u003cli\u003eK\u0026auml;rvemo S, Johansson V, Schroeder M, Ranius T (2016) Local colonization - extinction dynamics of a tree - killing bark beetle during a large-scale outbreak. Ecosphere 7(3):1\u0026ndash;14, e01257\u003c/li\u003e\n\u003cli\u003eK\u0026auml;rvemo S, Rogell B, Schroeder M (2014) Dynamics of spruce bark beetle infestation spots: Importance of local population size and landscape characteristics after a storm disturbance. Forest Ecology and Management 334:232\u0026ndash;240. https://doi.org/10.1016/j.foreco.2014.09.011\u003c/li\u003e\n\u003cli\u003eKausrud K, \u0026Oslash;kland B, Skarpaas O, Gr\u0026eacute;goire JC, Erbilgin N, Stenseth NC (2012) Population dynamics in changing environments: The case of an eruptive forest pest species. Biological Reviews 87(1):34\u0026ndash;51. https://doi.org/10.1111/j.1469-185X.2011.00183.x\u003c/li\u003e\n\u003cli\u003eKautz M, Feurer J, Adler P (2024) Early detection of bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e) infestations by remote sensing \u0026ndash; A critical review of recent research. Forest Ecology and Management, 556:121595. https://doi.org/10.1016/j.foreco.2023.121595\u003c/li\u003e\n\u003cli\u003eKautz M, Peter FJ, Harms L, Kammen S, Delb H (2023) Patterns, drivers and detectability of infestation symptoms following attacks by the European spruce bark beetle. Journal of Pest Science 96(1):403\u0026ndash;414. https://doi.org/10.1007/s10340-022-01490-8\u003c/li\u003e\n\u003cli\u003eKomonen A, Schroeder LM, Weslien J (2011) \u003cem\u003eIps typographus\u003c/em\u003e population development after a severe storm in a nature reserve in southern Sweden. Journal of Applied Entomology 135(1\u0026ndash;2):132\u0026ndash;141. https://doi.org/10.1111/j.1439-0418.2010.01520.x\u003c/li\u003e\n\u003cli\u003eKorhonen KT, Ahola A, Heikkinen J, Henttonen HM, Hotanen JP, Ihalainen A, Melin M, Pitk\u0026auml;nen J, R\u0026auml;ty M, Sirvi\u0026ouml; M, Strandstr\u0026ouml;m M (2021) Forests of Finland 2014\u0026ndash;2018 and their development 1921\u0026ndash;2018. Silva Fennica 55(5):1\u0026ndash;49. https://doi.org/10.14214/sf.10662\u003c/li\u003e\n\u003cli\u003eKorolyova N, Buechling A, Ďuračiov\u0026aacute; R, Zabihi K, Turč\u0026aacute;ni M, Svoboda M, Bl\u0026aacute;ha J, Swarts K, Pol\u0026aacute;ček M, Hradeck\u0026yacute; J, Červenka J, Němč\u0026aacute;k P, Schlyter F, Jaku\u0026scaron; R (2022) The last trees standing: Climate modulates tree survival factors during a prolonged bark beetle outbreak in Europe. Agricultural and Forest Meteorology 322. https://doi.org/10.1016/j.agrformet.2022.109025\u003c/li\u003e\n\u003cli\u003eKosunen M, Lyytik\u0026auml;inen-Saarenmaa P, Ojanen P, Blomqvist M, Starr M (2019) Response of soil surface respiration to storm and \u003cem\u003eIps typographus\u003c/em\u003e (L.) disturbance in boreal Norway spruce stands. Forests 10(4). https://doi.org/10.3390/f10040307\u003c/li\u003e\n\u003cli\u003eKosunen M, Peltoniemi K, Pennanen T, Lyytik\u0026auml;inen-Saarenmaa P, Adamczyk B, Fritze H, Zhou X, Starr M (2020) Storm and \u003cem\u003eIps typographus\u003c/em\u003e disturbance effects on carbon stocks, humus layer carbon fractions and microbial community composition in boreal \u003cem\u003ePicea abies\u003c/em\u003e stands. Soil Biology and Biochemistry 148. https://doi.org/10.1016/j.soilbio.2020.107853\u003c/li\u003e\n\u003cli\u003eMandl L, Lang S (2023) Uncovering early traces of bark beetle induced forest stress via semantically enriched sentinel-2 data and spectral indices. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science 91(3):211\u0026ndash;231. https://doi.org/10.1007/s41064-023-00240-4\u003c/li\u003e\n\u003cli\u003eMarini L, \u0026Oslash;kland B, J\u0026ouml;nsson AM, Bentz B, Carroll A, Forster B, Gr\u0026eacute;goire JC, Hurling R, Nageleisen LM, Netherer S, Ravn HP, Weed A, Schroeder M (2017) Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40(12):1426\u0026ndash;1435. https://doi.org/10.1111/ecog.02769\u003c/li\u003e\n\u003cli\u003eMarvasti-Zadeh SM, Goodsman D, Ray N, Erbilgin N (2023) Early detection of bark beetle attack using remote sensing and machine learning: A review. ACM Computing Surveys 56(4). https://doi.org/10.1145/3625387\u003c/li\u003e\n\u003cli\u003eMets\u0026auml;nhoidon Suositukset. Best practices for sustainable forest management in Finland. https://metsanhoidonsuositukset.fi/en Accessed 24 October 2024\u003c/li\u003e\n\u003cli\u003eMezei P, Blaženec M, Grodzki W, \u0026Scaron;kvarenina J, Jaku\u0026scaron; R (2017) Influence of different forest protection strategies on spruce tree mortality during a bark beetle outbreak. Annals of Forest Science 74(4). https://doi.org/10.1007/s13595-017-0663-9\u003c/li\u003e\n\u003cli\u003eMezei P, Grodzki W, Blaženec M, \u0026Scaron;kvarenina J, Brandysova V, Jaku\u0026scaron; R (2014) Host and site factors affecting tree mortality caused by the spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e) in mountainous conditions. Forest Ecology and Management 331:196\u0026ndash;207. https://doi.org/10.1016/j.foreco.2014.07.031\u003c/li\u003e\n\u003cli\u003eMikola P (1982) Application of vegetation science to forestry in Finland. In: Jahn G (ed) Handbook of vegetation science, part 12. Dr W. Junk Publishers, The Hague, pp 199\u0026ndash;224\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller M, Olsson PO, Eklundh L, Jamali S, Ard\u0026ouml; J (2022) Features predisposing forest to bark beetle outbreaks and their dynamics during drought. Forest Ecology and Management 523. https://doi.org/10.1016/j.foreco.2022.120480\u003c/li\u003e\n\u003cli\u003eNational Land Survey of Finland (2023) Topographic map (raster). Licensed with the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/deed.en). [Dataset]. Available at https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/datasets-and-interfaces/product-descriptions/topographic-map-series-raster. [Accessed at 27 June 2023]\u003c/li\u003e\n\u003cli\u003eN\u0026auml;si R, Honkavaara E, Blomqvist M, Lyytik\u0026auml;inen-Saarenmaa P, Hakala T, Viljanen N, Kantola T, Holopainen M (2018) Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry \u0026amp; Urban Greening 30:72\u0026ndash;83. https://doi.org/10.1016/j.ufug.2018.01.010\u003c/li\u003e\n\u003cli\u003eNetherer S, Kandasamy D, Jirosov\u0026aacute; A, Kalinov\u0026aacute; B, Schebeck M, Schlyter F (2021) Interactions among Norway spruce, the bark beetle \u003cem\u003eIps typographus\u003c/em\u003e and its fungal symbionts in times of drought\u003cem\u003e. \u003c/em\u003eJournal of Pest Science 94(3):591\u0026ndash;614. https://doi.org/10.1007/s10340-021-01341-y\u003c/li\u003e\n\u003cli\u003e\u0026Oslash;kland B, Nikolov C, Krokene P, Vakula J (2016) Transition from windfall- to patch-driven outbreak dynamics of the spruce bark beetle \u003cem\u003eIps typographus\u003c/em\u003e. Forest Ecology and Management 363:63\u0026ndash;73. https://doi.org/10.1016/j.foreco.2015.12.007\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;stersund M, Honkavaara E, Oliveira RA, N\u0026auml;si R, Hakala T, Koivum\u0026auml;ki N, Pelto-Arvo M, Tuviala J, Nevalainen O, Lyytik\u0026auml;inen-Saarenmaa P (2024) Exploring forest changes in an \u003cem\u003eIps typographus\u003c/em\u003e L. outbreak area: insights from multi-temporal multispectral UAS remote sensing. European Journal of Forest Research https://doi.org/10.1007/s10342-024-01734-5\u003c/li\u003e\n\u003cli\u003ePatacca M, Lindner M, Lucas-Borja ME, Cordonnier T, Fidej G, Gardiner B, Hauf Y, Jasinevičius G, Labonne S, Linkevičius E, Mahnken M, Milanovic S, Nabuurs GJ, Nagel TA, Nikinmaa L, Panyatov M, Bercak R, Seidl R, Ostrogović Sever MZ, Socha J, Thom D, Vuletic D, Zudin S, Schelhaas MJ (2022) Significant increase in natural disturbance impacts on European forests since 1950. Global Change Biology 1359\u0026ndash;1376. https://doi.org/10.1111/gcb.16531\u003c/li\u003e\n\u003cli\u003ePeltola H, Kellom\u0026auml;ki S, V\u0026auml;is\u0026auml;nen H (1999) Model computations of the impact of climatic change on the windthrow risk of trees. Climatic Change 41(1):17\u0026ndash;36. https://doi.org/10.1023/A:1005399822319\u003c/li\u003e\n\u003cli\u003ePotterf M, Svitok M, Mezei P, Jarcuska B, Jakus R, Blazenec M, Hl\u0026aacute;sny T (2022) Contrasting Norway spruce disturbance dynamics in managed forests and strict forest reserves in Slovakia. Forestry 1\u0026ndash;12. https://doi.org/https://doi.org/10.1093/forestry/cpac045\u003c/li\u003e\n\u003cli\u003eR Core Team (2017) R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria http://www.R-project.org/\u003c/li\u003e\n\u003cli\u003eRidgeway G, Ridgeway MG (2004) The GBM Package. R Foundation for Statistical Computing, Vienna, Austria, 5(3)\u003c/li\u003e\n\u003cli\u003eRuosteenoja K, Jylh\u0026auml; K (2021) Projected climate change in Finland during the 21st century calculated from CMIP6 model simulations. Geophysica 56(1\u0026ndash;2):39\u0026ndash;69\u003c/li\u003e\n\u003cli\u003eSchlyter P, Stjernquist I, B\u0026auml;rring L, J\u0026ouml;nsson AM, Nilsson C (2006) Assessment of the impacts of climate change and weather extremes on boreal forests in northern Europe, focusing on Norway spruce. Climate Research 31(1):75\u0026ndash;84. https://doi.org/10.3354/cr031075\u003c/li\u003e\n\u003cli\u003eSchroeder L (2010) Colonization of storm gaps by the spruce bark beetle: influence of gap and landscape characteristics. Agricultural and Forest Entomology 12(1):29-39\u003c/li\u003e\n\u003cli\u003eSchroeder M, Cocoş D (2018) Performance of the tree-killing bark beetles \u003cem\u003eIps typographus\u003c/em\u003e and \u003cem\u003ePityogenes chalcographus\u003c/em\u003e in non-indigenous lodgepole pine and their historical host Norway spruce. Agricultural and Forest Entomology 20:347-357. https://doi.org/10.1111/afe.12267\u003c/li\u003e\n\u003cli\u003eSchroeder LM, Lindel\u0026ouml;w \u0026Aring; (2002) Attacks on living spruce trees by the bark beetle \u003cem\u003eIps typographus\u003c/em\u003e (Col. Scolytidae) following a storm-felling: A comparison between stands with and without removal of wind-felled trees. Agricultural and Forest Entomology 4(1):47\u0026ndash;56. https://doi.org/10.1046/j.1461-9563.2002.00122.x\u003c/li\u003e\n\u003cli\u003eSeidl R, Baier P, Rammer W, Schopf A, Lexer MJ (2007) Modelling tree mortality by bark beetle infestation in Norway spruce forests. Ecological Modelling 206(3\u0026ndash;4):383\u0026ndash;399. https://doi.org/10.1016/j.ecolmodel.2007.04.002\u003c/li\u003e\n\u003cli\u003eSeidl R, Schelhaas M, Rammer W, Verkerk PJ (2014) Increasing forest disturbances in Europe and their impact on carbon storage. Nature Climate Change 4. https://doi.org/10.1038/NCLIMATE2318\u003c/li\u003e\n\u003cli\u003eSommerfeld A, Senf C, Buma B, D\u0026rsquo;Amato AW, Despr\u0026eacute;s T, D\u0026iacute;az-Hormaz\u0026aacute;bal I, Fraver S, Frelich LE, Guti\u0026eacute;rrez \u0026Aacute;G, Hart SJ, Harvey BJ, He HS, Hl\u0026aacute;sny T, Holz A, Kitzberger T, Kulakowski D, Lindenmayer D, Mori AS, M\u0026uuml;ller J, Paritsis J, Perry GLW, Stephens SL, Svoboda M, Turner MG, Veblen TT, Seidl R (2018) Patterns and drivers of recent disturbances across the temperate forest biome. Nature Communications 9(1). https://doi.org/10.1038/s41467-018-06788-9\u003c/li\u003e\n\u003cli\u003eSproull GJ, Adamus M, Bukowski M, Krzyzanowski T, Szewczyk J, Statwick J, Szwagrzyk J (2015) Tree and stand-level patterns and predictors of Norway spruce mortality caused by bark beetle infestation in the Tatra Mountains. Forest Ecology and Management 354:261\u0026ndash;271. https://doi.org/10.1016/j.foreco.2015.06.006\u003c/li\u003e\n\u003cli\u003eTikkanen O, Lehtonen I (2023) Changing climatic drivers of European spruce bark beetle outbreaks: a comparison of locations around the Northern Baltic Sea\u003cem\u003e. \u003c/em\u003eSilva Fennica 57(3):1\u0026ndash;21. https://doi.org/10.14214/sf.23003\u003c/li\u003e\n\u003cli\u003eTrubin A, Mezei P, Zabihi K, Surov\u0026yacute; P, Jaku\u0026scaron; R (2022) Northernmost European spruce bark beetle \u003cem\u003eIps typographus\u003c/em\u003e outbreak: Modelling tree mortality using remote sensing and climate data. Forest Ecology and Management 505:119829. https://doi.org/10.1016/j.foreco.2021.119829\u003c/li\u003e\n\u003cli\u003eVaahtera E, Peltola A, Torvelainen J, Uotila E (2023) Finnish Statistical Yearbook of Forestry. Luonnonvarakeskus\u003c/li\u003e\n\u003cli\u003eVen\u0026auml;l\u0026auml;inen A, Lehtonen I, Laapas M, Ruosteenoja K, Tikkanen OP, Viiri H, Ikonen VP, Peltola H (2020) Climate change induces multiple risks to boreal forests and forestry in Finland: A literature review. Global Change Biology 26(8):4178\u0026ndash;4196. https://doi.org/10.1111/gcb.15183\u003c/li\u003e\n\u003cli\u003eVen\u0026auml;l\u0026auml;inen A, Ruosteenoja K, Lehtonen I, Laapas M, Tikkanen OP, Peltola H (2022) Managing Forest Ecosystems Forest Bioeconomy and Climate Change. In: Hetem\u0026auml;ki L, Kangas J, Peltola H (eds) Forest Bioeconomy and Climate Change. Springer Nature Switzerland AG, Switzerland. https://doi.org/10.1007/978-3-030-99206-4\u003c/li\u003e\n\u003cli\u003eViiri H, Viitanen J, Mutanen A, Lepp\u0026auml;nen J (2019) Mets\u0026auml;tuhot vaikuttavat Euroopan puumarkkinoihin \u0026ndash; Suomessa vaikutukset toistaiseksi v\u0026auml;h\u0026auml;isi\u0026auml;. \u003cem\u003eMets\u0026auml;tieteen Aikakauskirja\u003c/em\u003e, (In Finnish). https://doi.org/https://doi.org/10.14214/ma.10200\u003c/li\u003e\n\u003cli\u003eVuokila Y (1987) Metsikk\u0026ouml;kokeiden maastoty\u0026ouml;ohjeet. Mets\u0026auml;ntutkimuslaitoksen tiedonantoja 257. Hakapaino Oy, Helsinki. (In Finnish). ISBN 951-40-0853-7. 237 p\u003c/li\u003e\n\u003cli\u003eWermelinger B (2004) Ecology and management of the spruce bark beetle \u003cem\u003eIps typographus\u003c/em\u003e - A review of recent research. Forest Ecology and Management 202(1\u0026ndash;3):67\u0026ndash;82. https://doi.org/10.1016/j.foreco.2004.07.018\u003c/li\u003e\n\u003cli\u003eWickham H, Averick M, Bryan J, Chang W, McGowan L, Fran\u0026ccedil;ois R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen T, Miller E, Bache S, M\u0026uuml;ller K, Ooms J, Robinson D, Seidel D, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019) Welcome to the Tidyverse. Journal of Open Source Software 4(43):1686. https://doi.org/10.21105/joss.01686\u003c/li\u003e\n\u003cli\u003eWilcox RR (2009) Basic statistics: understanding conventional methods and modern insights. Oxford University Press\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"forest disturbance, forest management, Ips typographus, tree symptoms, ground-based visual observations","lastPublishedDoi":"10.21203/rs.3.rs-5355177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5355177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEuropean spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e L., SBB) infestations are extending in northern Europe due to increases in temperature and drought, which increase the risk of outbreaks in Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e L.) forests. The severity of SBB damage may be decreased by timely detection and management measures. In this study, we analysed the SBB infestation levels of trees, the overall SBB damage at the stand level, the relationship between SBB damage and stand characteristics, and the effect of an outbreak over time on the volume and basal area in managed and conserved areas. We visually observed SBB symptoms at the stem level (entrance-exit holes, resinous flows, bark damage) and crown level (defoliation, discoloration) in 60 sampling plots in south-eastern Finland. These plots were established in an SBB outbreak area triggered by a severe wind disturbance in August 2010. Data were collected in 2014\u0026ndash;2017 in conserved areas and in 2019\u0026ndash;2021 in both conserved and managed areas. The results showed that in conserved areas, 70% of the trees were already highly infested in 2015, reaching 90% in 2017. During 2019\u0026ndash;2021, the conserved areas were significantly more damaged than the managed ones. The volume of the stands decreased over time on average by 80% in conserved areas and 40% in managed areas, with the highest decrease occurring six to seven years after the initial SBB colonization. The damage estimated based on resinous flows and entrance-exit holes was similar regardless of the year or treatment. Our detection method may be used to support timely risk assessment and management of SBB outbreaks and decrease damage at the landscape level.\u003c/p\u003e","manuscriptTitle":"Infestation symptoms as indicators of a sustained bark beetle outbreak in conserved and managed Norway spruce forests in south-eastern Finland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 10:56:21","doi":"10.21203/rs.3.rs-5355177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fbb83ca6-ba25-4748-8f5b-86c3897da435","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-22T09:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 10:56:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5355177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5355177","identity":"rs-5355177","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}