Ecology, floristic-vegetational features and future perspectives of spruce forests affected by Ips typographus: insights from the Southern Alps

Ecology, floristic-vegetational features and future perspectives of spruce forests affected by Ips typographus: insights from the Southern Alps | 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 Ecology, floristic-vegetational features and future perspectives of spruce forests affected by Ips typographus: insights from the Southern Alps Luca Giupponi, Riccardo Panza, Davide Pedrali, Stefano Sala, Annamaria Giorgi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5878691/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 In recent years, many spruce ( Picea abies ) forests have been severely affected by bark beetle ( Ips typographus ) outbreaks in the Southern Alps, but their ecological impacts remain poorly studied. This research analyzed the distribution, ecological, and floristic-vegetational characteristics of forests recently affected by the bark beetle in the upper basin of the Oglio River (Northern Italy) and developed a MaxEnt model to predict severe insect attacks in the coming decades. The results showed that the spruce forests affected by the bark beetle are located exclusively in the sub-mountain and mountain belts (below 1,600 m a.s.l.) and that 85% of them are found in areas with high annual solar radiation (> 3,500 MJ m − ²). The predictive model for areas susceptible to severe bark beetle attacks proved highly accurate (AUC = 0.91) and was primarily defined by the mean temperature of the dry winter quarter (contribution: 80.1%), with values between − 2.5 and 2.5°C being particularly suitable for the pest. According to the model, more than 58% of the current spruce forests in the study area will exhibit high susceptibility (probability > 0.7) to severe bark beetle attacks by 2080. The floristic-vegetational and ecological analysis of plant communities of 11 bark beetle-affected areas indicated that more thermophilic and significantly different forest communities (in both floristic and physiognomic terms) are expected to develop compared to those of pre-disturbance. Furthermore, the high coverage/density of spruce snags appears to accelerate plant succession, enabling the establishment of mature forest communities in a shorter time frame. Picea abies Snag Species distribution models Bark beetle Plant succession Plant ecology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Natural disturbance regimes (e.g., windstorms, fires, insect outbreaks) are intensifying because of climate change, often with negative consequences on the ecosystem services provided by boreal forests (Thom and Seidl 2016 ). Biodiversity, timber production, tourism, clean water supply, hydrogeological stability, and landscapes are all impacted by bark beetle outbreaks. Although these outbreaks are a natural component of forest dynamics, we are compelled to manage them due to the increasing threat to our economy, health, and cultural heritage (Hlásny et al. 2021 ). The European spruce bark beetle ( Ips typographus , Coleoptera: Scolytinae), is the most significant pest of the Norway spruce ( Picea abies ), which, in turn, is one of the most important timber tree species in Europe. For centuries, the spruce has been prioritized above other species for its economic value, in the Alps and elsewhere (Marini et al. 2017 ). The spruce has been artificially spread to latitudes and elevations beyond its ecological optimum, where trees face continuous stress and are consequently less able to defend themselves from pests (Washaya et al. 2024 ). In spruce forests, I. typographus typically persists at low population densities (endemic phase), breeding only in dying or severely weakened trees. However, in the presence of large numbers of stressed trees – such as those affected by windstorms or drought – this bark beetle can undergo population explosions lasting up to 10 years (epidemic phase), during which it also attacks healthy trees (Arthur et al. 2024 ; Hlásny et al. 2021 ). In normal conditions healthy trees repel bark beetles with resin, but this defense is not sufficient during epidemic phases, and it is even weaker in stressed trees. Furthermore, pure spruce stands facilitate I. typographus expansion, providing a continuous source of breeding substrate for the beetles (Faccoli and Bernardinelli 2014 ). I. typographus is highly adaptable, adjusting its voltinism (number of generations per year) according to temperature and photoperiod. It can produce one generation annually at higher latitudes and elevations, but up to three generations in milder conditions (Washaya et al. 2024 ; Ogris et al. 2019 ). Rising temperatures and more frequent droughts promote I. typographus multivoltinism while simultaneously stressing spruce trees, weakening their natural defenses against this pest (Hofmann et al. 2024 ). Consequently, over recent decades, large-scale spruce diebacks have been increasingly observed across Europe, transforming a long-standing forestry issue into a matter of public concern, with significant economic, social, and political implications (Hlásny et al. 2021 ). In the Southern Alps, an unprecedented outbreak of I. typographus began in 2015, dramatically exacerbated by the 2018 extreme windstorm “Vaia” and subsequent droughts. Storm Vaia (known also as Storm Adrian) compromised 8.5 million m³ of timber over six Alpine regions in Italy, including Lombardy (Chirici et al. 2019 ; Giupponi et al. 2023 ). Possibly spreading from Central and Northern Europe, the spruce completely occupied the Alps during the end of the Pleistocene, around 10,000 years ago (Ravazzi 2002 ). Currently, in the Southern Alps, spruce forests are primarily found in the mountain, high-mountain, and subalpine belts, forming diverse plant associations of Piceion excelsae phytosociological alliance ( Piceetalia excelsae order, Vaccinio-Piceetea class) (Mucina et al. 2016 ). Due to human intervention, driven by the economic value of the Norway spruce, this species is often found in the hilly and submountain belts (or even at lower elevations), forming secondary vegetation that replaces broadleaf forests such as beech ( Fagus sylvatica ) or sessile oak ( Quercus petraea ) woods. Despite its ecological plasticity (Del Favero 2004 ), the climatic optimum of the Norway spruce in the Southern Alps is found in the high-mountain and subalpine belts, primarily because this species is of the Eurosiberian chorological type (Pignatti et al. 2017), adapted to long, cold winters and short, warm summers. Several biotic and abiotic factors are closely linked to the spruce and I. typographus dynamics, including breeding substrate availability, natural enemies (predators and parasitoids), forest composition (mixed or pure stands), intraspecific competition, weather (temperature and rainfall), and solar radiation (Kozhoridze et al. 2024 ; Pirtskhalava-Karpova et al. 2024 ; Huo et al. 2024 ). Faccoli and Bernardelli (2014) indicate that forest composition and elevation are major drivers of I. typographus dynamics in the Southern Alps. Indeed, mixed forests (composed of multiple tree species) and those at higher elevations are less susceptible to bark beetle damage compared to pure spruce stands at lower elevations. These patterns align with observations from Central and Northern Europe (Kozhoridze et al. 2024 ; Sommerfeld et al. 2021 ). Given the rapid pace at which the bark beetle is destroying the Norway spruce forests of the Southern Alps (and the resulting ecological, economic, and hydrogeological problems), there is growing interest among researchers in understanding how I. typographus outbreaks will respond to the fast pace of climate change in mountainous regions (Marini et al. 2017 ). However, in the Southern Alps and Europe in general, the processes of vegetation succession following bark beetle outbreaks remain poorly explored/understood. Researchers have primarily focused on forestry implications and tree regeneration (Svoboda et al. 2010 ; Kupferschmid et al. 2006 ) largely leaving the floristic and ecological aspects of plant communities (pre- and post-bark beetle attack) unexplored (Matuszkiewicz et al. 2024 ). This gap should be addressed, if for no other reason than that vegetation can be used as a "super-indicator", useful for understanding the mechanisms that regulate forest ecosystems (and others) and thus supporting the proper management of these ecosystems. This research aims to provide further information regarding the environmental characteristics of the spruce forests in the upper basin of the Oglio River (Southern Alps) affected by recent bark beetle infestations, analyzing their geographical, bioclimatic, and vegetational features. Furthermore, through the application of species distribution models and the interpretation of floristic-vegetational data, it seeks to provide an overview of the forests in the study area that are highly likely to be infested/destroyed by the bark beetle in the coming decades, and how vegetation succession may proceed, in order to provide tools for improving the management of these forest ecosystems. Material and methods Study area and sampling sites The upper basin of the Oglio River (UBOR) is in the Southern Alps within the Lombardy region of Italy (Latitude: 46° 00' N, Longitude: 10° 20' E) (Fig. 1 ). It covers a large area of 1,444 km² and includes 49 municipalities spread across the Valle Camonica in the province of Brescia and Val di Scalve in the province of Bergamo. From an orographic perspective, the UBOR is situated between the Central and Eastern Lombard Prealps and the Southern Rhaetian Alps (Marazzi 2005 ). The Central and Eastern Lombard Prealps are primarily composed of sedimentary limestone rocks, while the Southern Rhaetian Alps feature sedimentary, metamorphic, and intrusive rocks with neutral and acidic reaction (Bona 2019 , Previtali 1992). The elevation range within the study area is considerable, spanning 3,300 m: the southern valley areas (near Lake Iseo) are situated at around 200 m a.s.l., while the highest peak (Mount Adamello) exceeds 3,500 m a.s.l.. The climate in the UBOR is quite diverse: in the southern mountainous areas, there is a sub-oceanic climate (with rainfall concentrated around the equinoxes), while in the northern areas there is a sub-continental climate (with precipitation peaks in summer) (Cerabolini et al. 2012 ). Across the entire UBOR, the driest period occurs during the cold winter months, from December to March (Fig. 1 ). The vascular flora (tracheophytes) of the UBOR includes 2,732 taxa (species and subspecies) (Bona 2019 ; Martini et al. 2012 ). The vegetation is largely composed of forests, with types that vary mainly according to altitude, substrate, and land management practices (Del Favero 2004 ; Verde et al 2010 ). Spruce forests cover about 30% of the forested area in the study region and are primarily located within the mountain and high-mountain vegetational belts, ranging from 700 to 1,800 m a.s.l. ( https://www.geoportale.regione.lombardia.it ). These forests mainly consist of plant communities dominated by spruce, with larch ( Larix decidua ) and Swiss pine ( Pinus cembra ) at higher elevations and more thermophilic trees (such as silver fir, beech and chestnut) at lower elevations. From a phytosociological perspective, the spruce forests of the mountain and high-mountain belts belong to the Calamagrostio arundinaceae-Piceetum association ( Vaccinio-Abietenion sub-alliance, Piceion excelsae alliance, Piceetalia excelsae order, Vaccinio-Piceetea class), which is the most widespread spruce forest association in this part of Lombardy (Verde 2010, Andreis 2009). The Calamagrostio arundinaceae-Piceetum association is characterized by abundant moss cover and the presence of tracheophytes, including Picea abies (dominant tree), Larix decidua , Betula pendula , Sorbus aucuparia , Lonicera nigra , Calamagrostis arundinacea , Vaccinium myrtillus , Oxalis acetosella , Luzula nivea , Saxifraga cuneifolia and Phegopteris connectilis . At higher elevations, in the subalpine belt, forest communities contain less spruce and more larch, with Rhododendron ferrugineum and Luzula nivea in the understory ( Luzulo niveae-Piceetum rhododendretosum ferruginei ) (Andreis 2009). In the highest subalpine areas, spruce becomes sporadic in larch-dominated forests ( Astrantio minoris-Laricetum deciduae ), where Pinus cembra is also present (Andreis 2005, 2009). For the study of the vegetation of the spruce forests affected by the bark beetle (and control/unaffected forests), 11 sampling sites were identified across the entire study area, at altitudes ranging from 700 to 1,300 m a.sl. (Fig. 1 ). These sites were selected based on the presence of at least one hectare of forest with over 90% dead spruce trees, following a bark beetle infestation that occurred in the previous 3–7 years. Analysis of the current and future distribution of the bark beetle The analysis of the current forests affected by bark beetles in the UBOR utilized cartographic data (polygon shapefile) sourced from the Geoportal of the Lombardy Region ( https://www.geoportale.regione.lombardia.it ). The shapefile of bark beetle-affected areas in Lombardy (updated to 2021 with 760 polygons corresponding to approximately 2,070 ha of forests affected by bark beetles) was downloaded from the geoportal and analyzed using ArcGis Pro software. Specifically, 241 polygons within the UBOR were considered. For each 50-meter altitude interval (from 300 to 2,150 meters above sea level), the affected area of spruce forest impacted by bark beetle was calculated, as well as the percentage of affected forest relative to the total area of spruce forests (including both pure spruce forests and mixed forests with spruce). For each polygon, the average slope, elevation, and aspect were calculated. These data, along with the geographic coordinates of each polygon, were then used to calculate the theoretical annual global radiation for each area using software developed by ENEA ( http://www.solaritaly.enea.it/indexEn.php ), to assess whether a relationship exists between bark beetle-affected areas and incident solar energy. Species distribution models (SDMs) were adopted to predict the spatial distribution (current and future) of I. typographus in UBOR based on bioclimatic data. 19 bioclimatic variables (Table 1 ) and 14 georeferenced points (occurrence points) where the bark beetle destroyed more than 20 ha of spruce forest in the Lombardy Alps, were considered as severely infested areas. The coordinates of the occurrence points were extracted from the Geoportal of the Lombardy Region shapefile, considering the areas where the bark beetle has been particularly destructive. The bioclimatic layers were obtained from the WorldClim 2.1 data website ( http://worldclim.org ) at a spatial resolution of 0.5 arc-minutes (~ 0.60 km 2 ). All the bioclimatic variables were used to establish the distribution model of I. typographys (with high destructive capacity) in UBOR under current climatic conditions (2016–2020) and for three future periods: 2021–2040, 2041–2060 and 2061–2080. Table 1 Bioclimatic variables used to model the distribution of I. typographus epidemics in the UBOR Code/Unit Bioclimatic variable BIO1 (°C) Annual Mean Temperature BIO2 (°C) Mean Diurnal Range (Mean of monthly (max temp - min temp)) BIO3 (-) Isothermality (BIO2/BIO7 × 100) BIO4 (°C) Temperature Seasonality (standard deviation × 100) BIO5 (°C) Max Temperature of Warmest Month BIO6 (°C) Min Temperature of Coldest Month BIO7 (°C) Temperature Annual Range (BIO5-BIO6) BIO8 (°C) Mean Temperature of Wettest Quarter BIO9 (°C) Mean Temperature of Driest Quarter BIO10 (°C) Mean Temperature of Warmest Quarter BIO11 (°C) Mean Temperature of Coldest Quarter BIO12 (mm) Annual Precipitation BIO13 (mm) Precipitation of Wettest Month BIO14 (mm) Precipitation of Driest Month BIO15 (-) Precipitation Seasonality (Coefficient of Variation) BIO16 (mm) Precipitation of Wettest Quarter BIO17 (mm) Precipitation of Driest Quarter BIO18 (mm) Precipitation of Warmest Quarter BIO19 (mm) Precipitation of Coldest Quarter The Shared Socio-economic Pathway (SSP) 2-4.5 future scenario ("Middle of the road scenario") was considered in this analysis because it represents a plausible future pathway characterized by medium challenges to mitigation and adaptation efforts (IPCC 2021 ). The CNRM-CM6-1 global climate model (Voldoire et al. 2019 ), used for evaluating and forecasting the impact of the SSPs scenarios on future climate, was acquired from the WorldClim data website. This model, representing the latest fully coupled atmosphere-ocean general circulation model of the sixth generation, was utilized to project the effects of the SSP2-4.5 scenario onto the climate of the periods 2021–2040, 2041–2060 and 2061–2080. The distribution model of I. typographus was generated with MaxEnt (Phillips et al. 2006 ) using the “dismo” package (Hijmans et al. 2024 ) of R software. MaxEnt (Maximum Entropy) is an algorithm widely used for making predictions of species distribution, particularly well-suited for applications involving presence-only data (occurrence data). The relative contribution of each bioclimatic variable to the model was extracted, and response curves were generated for each one. The accuracy of the model was evaluated by computing the Area Under the Curve (AUC) of the Receiver Operating characteristic Curve (ROC), a widely used and robust approach of model evaluation. The AUC values range from 0 to 1 and the higher the value of AUC, the better the performance of the model. The final output of the distribution model is a map indicating areas with a high/low probability (0 for low probability; 1 for high probability) of the bark beetle having particularly destructive effects. In this research, the probability of occurrence was transformed into a binary score (presence or absence) considering the threshold of 0.70, and then four presence/absence maps of I. typographus in UBOR were created: one considering the current climatic conditions (2016–2020) and three considering the future periods 2021–2040, 2041–2060 and 2061–2080. Vegetation data collection and analysis For each sampling site (Fig. 1 ), 6 phytosociological relevés were carried out on 100 m 2 (10x10 m) using the methods of Braun-Blanquet ( 1964 ): 3 relevés were conducted in areas affected by the bark beetle (i.e., those with more than 90% of spruce snags), and the other 3 were conducted in adjacent areas with no spruce snags (control). Given the difficulty in diagnosing whether live spruce trees were affected by the bark beetle (in the early stages of infestation, spruce trees show no symptoms but only little bark holes, which may be located several meters up the trunk), this study conventionally considered "affected forests" those with high coverage of standing dead spruce trees (snags), and "unaffected forests" those without snags. For each relevé, tracheophytes of the plant communities were identified using the “Flora d’Italia” dichotomous keys of Pignatti ( 2017 ) and their coverage was estimated using the conventional abundance/dominance scale of Braun-Blanquet ( 1964 ): r, rare species in the relevé; +, coverage 5–25%; 3, coverage > 25–50%; 4, coverage > 50–75%; 5, coverage > 75–100%. In each relevé, two coverage values were assigned for Picea abies : one for snag trees and one for live trees. Additionally, the total moss coverage percentage was estimated. The relevés were performed in June-July 2023 and 2024. The data of the relevés were arranged in a matrix (relevés x species) where Braun-Blanquet abundance/dominance indexes were converted into percentage of plant coverage as proposed by Canullo et al. ( 2012 ) (r, 0.01%; +, 0.5%; 1, 3.0%; 2, 15.0%; 3, 37.5%; 4, 62.5%; 5, 87.5%); subsequently, a power transformation with the exponent 0.5 on these values was carried out according to Tichý et al. ( 2020 ). Cluster analysis and Detrended Correspondence Analysis (DCA) were performed to identify floristic-physiognomic similarities/differences among the relevés using the “vegan” package of R (Dixon 2003 ). Cluster analysis was performed using the Unweighted Pair Group Method with Arithmetic mean method (UPGMA) and the chord distance coefficient (Legendre and Gallagher 2001 ). Furthermore, Pearson's phi coefficient ( Φ ) was used to identify the diagnostic species (plants significantly associated with the different types of vegetation) as proposed by Chytry et al. (2002) and Tichý and Chytry (2006). The coefficient Φ was calculated as the following formula: $$\:\varPhi\:=\:\frac{N\bullet\:\:{n}_{p}-n\bullet\:{N}_{p}}{\sqrt{n\bullet\:{N}_{p}\bullet\:(N-n)\bullet\:(N-{N}_{p})}}$$ 1 where N is the number of relevés in the data set, N p is the number of the relevés in a target group of relevés, n is the number of occurrences of the species in the data set and n p is the number of occurrences of the species in the target group of relevés. The coefficient Φ can assume values from 1 (the species is concentrated in the target relevés group) to -1 (the species is under-represented in the target relevés group). The identification of the diagnostic species was carried out using the “indicspecies” R package (De Cáceres 2013 ). Phytosociological analysis of vegetation was performed using the hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities of the European vegetation (Mucina et al. 2016 ) and “Prodromo della vegetazione d’Italia” (Biondi and Blasi 2015 ). Therefore, the phytosociological classes of the species of the relevés were assigned (considering only the vegetation classes of the Southern Alps) to determine the ecological features of the plant communities. Additionally, the indices of Landolt et al. ( 2010 ) were applied for synecological analysis. Specifically, for each relevé (and groups of relevés), the mean values of T - temperature, K - continentality, L - light intensity, F - soil moisture, R - substrate reaction, N - nutrients, H - humus, and D - soil aeration, were calculated. Ecological differences between bark beetle-affected and unaffected forests were determined using Student’s t -test. A p -value of less than 0.05 was considered statistically significant. The scientific names of the plant species are in accordance with Pignatti ( 2017 ), while the names and the codes of syntaxa follow Mucina et al. ( 2016 ). The statistical analyses were performed using R 3.6.1 software (R Development Core Team 2023). Results Current and future distribution of the bark beetle and susceptible spruce forests Based on data from the Geoportal of the Lombardy Region, the study area contains spruce forests (26,800 ha) ranging in altitude from 300 m to 2,150 m (Fig. 2 a). Of these, 88% are in the mountain and high-mountain belt between 800 m and 1,700 m a.s.l. The area of forest affected by the bark beetle amounts to 725 ha, equivalent to 2.7% of the total spruce forest area in the study. These affected forests are confined to altitudes between 600 m and 1,600 m a.s.l. The highest rates of spruce forest damage (> 8.2%) occur at lower elevations (600–700 m a.s.l.) (Fig. 2 b). Significant damage is also observed in the elevation band between 1,150 m and 1,300 m a.s.l., where the percentage of affected forest exceeds 5%. In the upper high-mountain belt, above 1,600 m a.s.l., and in the subalpine zone, no spruce forests currently show signs of bark beetle infestation. Figure 3 shows that in the study area, the extent of spruce forest affected by bark beetle gradually increases with higher levels of annual solar radiation. In fact, 85% of the infested spruce stands are in areas where the theoretical annual solar energy exceeds 3,500 MJ m − 2 . Figure 4 shows maps of current and future areas with bioclimatic conditions suitable for the bark beetle to cause significant damage to existing spruce forests and the percentages of forests lightly/highly susceptible to mass infestations. The maps reveal that areas with suitable climatic conditions for the bark beetle shift in extent and elevation over time (from the current situation to projections for 2080). Specifically, the suitable areas for bark beetle attacks in future periods (2021–2040, 2041–2060, and 2061–2080) are projected to be smaller than those under current conditions but are generally located at higher elevations. As a result, the percentage of forests highly susceptible to mass bark beetle infestations is expected to increase from 45.1–58.2% over the next 60 years (Fig. 4 b). Based on habitat suitability maps (Fig. 4 a), in the future, spruce forests may find optimal conditions (low likelihood of severe bark beetle attacks) above 1,800 m a.s.l. in southern areas of the UBOR (lower Camonica Valley) and above 1,500 m a.s.l. in northern areas (upper Camonica Valley). The distribution model generated by MaxEnt, used to produce habitat suitability maps, has high predictive accuracy (AUC = 0.91). Among the 19 bioclimatic variables considered, BIO9 (mean temperature of the driest quarter) contributes the most to the model's formulation (percent contribution: 80.1%; permutation importance: 77.6%) (Table S1 ). Figure 5 shows the relationship (response curve) between the probability of massive bark beetle infestations and the BIO9 variable. The response curve indicates that the most suitable habitat for severe bark beetle outbreaks occurs where the mean temperature of the driest quarter (which in the study area corresponds to winter) ranges between 4°C and − 4°C. Areas with BIO9 values below − 4°C, typically at higher elevations, are less suitable for the bark beetle (probability of presence < 0.2). Similarly, areas with BIO9 values above 4°C are also unsuitable for massive bark beetle attack. Vegetation features 198 species of herbs, shrubs and trees were identified (Table S2 ). Figure 6 a shows the dendrogram generated by cluster analysis, highlighting two main vegetation types: cluster A and cluster B. Cluster A includes all relevés conducted in forests affected by the bark beetle, while cluster B comprises those conducted in forests unaffected by the pest. These two clusters are characterized by distinct plant communities (Fig. 6 b) and are mainly differentiated by the presence of live/dead spruces and diagnostic species listed in Table 2 . The diagnostic species of cluster B ( Picea abies , Vaccinium myrtillus and Saxifraga cuneifolia ) are characteristic of holarctic coniferous forests ( Vaccinio-Piceetea phytosociological class), with an understory featuring substantial moss coverage. In contrast, the diagnostic species of cluster A are primarily associated with shrublands of the Robinietea class (seral forest-clearing and anthropogenic successional scrub and thickets) and forest edges or clearings of the Epilobietea angustifolii class (tall-herb vegetation of forest edges and clearings), as well as spruce snags. Table 2 Diagnostic species of cluster A (forest affected by bark beetle) and cluster B (forest not affected by bark beetle). Phytosociological class code, Pearson's phi coefficient ( Φ ) and p -value of each diagnostic species are reported. The codes of phytosociological class are the same used by Mucina et al. ( 2016 ): PIC, Vaccinio-Piceetea ; ROB, Robinietea ; FAG, Carpino-Fagetea sylvaticae ; EPI, Epilobietea angustifolii ; POP, Alno glutinosae-Populetea albae ; RHA, Crataego-Prunetea ; PAR, Papaveretea rhoeadis ; QUE, Quercetea robori-petraeae ; ULI, Calluno-Ulicetea ; NAR, Nardetea strictae . Key: *, significant ( p < 0.05); **, significant ( p < 0.01) Cluster Diagnostic species Phytosociological class code Φ P- value A Picea abies (died) PIC 0.951 0.005 ** Rubus idaeus ROB 0.470 0.005 ** Mycelis muralis FAG, EPI 0.406 0.005 ** Fragaria vesca EPI 0.295 0.005 ** Solanum dulcamara POP 0.286 0.040 * Sambucus racemosa ROB 0.285 0.015 * Rubus ulmifolius RHA 0.270 0.005 ** Betula pendula ROB 0.266 0.005 ** Geranium robertianum FAG, EPI 0.260 0.015 * Galium aparine POP, EPI 0.234 0.015 * Taraxacum officinale (group) - 0.226 0.010 ** Sambucus nigra ROB, POP 0.205 0.005 ** Galeopsis tetrahit PAR, EPI 0.186 0.005 ** Buddleja davidii ROB 0.167 0.005 ** Salix caprea ROB 0.144 0.005 ** Urtica dioica ROB, POP, EPI 0.142 0.005 ** B Picea abies (alive) PIC 0.990 0.005 ** Mosses - 0.309 0.030 * Vaccinium myrtillus PIC, QUE, ULI, NAR 0.280 0.030 * Saxifraga cuneifolia PIC 0.228 0.040 * Each main vegetation cluster is further subdivided into subclusters (Fig. 6 ), which reveal floristic/physiognomic differences in plant communities. These differences are mainly attributable to the environmental conditions of the specific survey sites, such as soil type, microclimate, and anthropogenic management or disturbance. For cluster A, four subclusters/plant communities have been identified: A1 – plant community dominated by heliophilous shrubs such as Rubus idaeus (the dominant species), typical of clearings, disturbed forests, or areas affected by treefall or logging ( Rubetum idaei association; Robinietea class). In this study, it occurs in areas where the density or coverage of dead spruce trees is low. A2 – plant community characterized by a high density and coverage of dead spruce trees, with an understory featuring limited Rubus idaeus and abundant Vaccinium myrtillus , Luzula nivea , ferns, and mosses. It also includes young broadleaf trees ( Fagus sylvatica , Sorbus aucuparia , Castanea sativa ) typical of mature forest communities with occasional silver fir ( Abies alba ) and spruce. A3 – shrubland with Rubus idaeus and a high density of exotic species such as Buddleja davidii , Senecio inaequidens and Erigeron canadensis . These species are associated with anthropogenic disturbance in areas near roads or settlements where spruce trees have been cut and removed. A4 – shrubland composed of thermophilic species, including Rubus ulmifolius (the dominant species), Corylus avellana , Populus tremula , Fagus sylvatica and Salix caprea (Fig. 6 , Table S2 ). Cluster B can also be divided into four subclusters, although these are less dissimilar compared to those in Cluster A (Fig. 6 ): B1 – dense, thermophilic spruce forest with low coverage/presence of herbaceous plants and bryophytes, and with young Castanea sativa trees. B2 – spruce forest with bryophytes, Vaccinium myrtillus , and young broadleaf trees ( Fagus sylvatica , Betula pendula , Castanea sativa , Quercus petraea , Acer pseudoplatanus ). B3 – spruce forests with well-established spruce regeneration and high coverage of Oxalis acetosella and bryophytes in the understory. B4 – spruce forests on neutral to basic soils, characterized by basiphilous herbaceous plants ( Sesleria caerulea , Helleborus niger , Carex alba , Hepatica nobilis ) and Fagus sylvatica (Fig. 6 , Table S2 ). In general, the forest communities affected by the spruce bark beetle (cluster A) are predominantly composed of species of Carpino-Fagetea sylvaticae class (mesic deciduous and mixed forests of temperate Europe) and Robinietea class, with a smaller proportion of species of Vaccinio-Piceetea and Quercetea robori-petraeae (acidophilous oak and oak-birch forests) (Fig. 7 ). Conversely, in spruce forests unaffected by the bark beetle, the percentage of Vaccinio-Piceetea species is the highest, followed by those of Carpino-Fagetea sylvaticae and Quercetea robori-petraeae classes. In cluster B, Robinietea species are almost absent (Fig. 7 ). Table S3 shows the results of the ecological indices of Landolt et al. ( 2010 ), while Fig. 8 shows the values of key ecological variables ( P < 0.05 in more than 60% of the sampling sites) that distinguished the vegetation communities of beetle-affected and unaffected forests across the study sites. The graphs reveal that, at all sampling sites, communities established after a bark beetle attack were more thermophilic compared to those in unaffected forests. Additionally, the communities in beetle-affected forests consisted of more light-demanding (heliophilous) species that require less organic matter (or litter) in the soil. Discussion The results of the analyses regarding the characteristics of the areas currently affected by the spruce bark beetle provided valuable insights into the ecology of spruce forests and bark beetle outbreaks in the Southern Alps, considering both current climatic conditions and future scenarios. It was evident that in the study area elevation, solar radiation, and the temperature of the driest quarter of the year are the most relevant variables for the development of severe bark beetle outbreaks. In particular, spruce forests located in areas warmer during the winter months (generally at lower elevations and/or in areas with good exposure to sunlight) were found to be the most susceptible to intense bark beetle outbreaks. These conditions are currently observed below 1,600 m a.s.l. in the submountain and mountain vegetation belts, where spruce trees are likely to experience greater stress due to high temperatures, which, in turn, allow the bark beetle to increase the number of generations per year. These results are consistent with Kozhoridze et al. ( 2024 ), who found a negative relationship between infestation and elevation, with higher infestation rates below 900 m a.s.l.. Also, Jakoby et al. ( 2019 ) observed that, in the Swiss Alps, the number of I. typographus generations per year is 2 at 500 m of elevation, 1.5 at 1,000 m and 1 at 1,700 m. Although there are no studies on the annual number of bark beetle generations in the UBOR, it is reasonable to assume that, at the same elevations as the Swiss Alps, the number of I. typographus generations is similar, if not higher, given that the Swiss Alps are located further north (in the inner Alps) where temperatures are (and will be) generally lower compared to those in the Southern Alps (Kotlarski et al. 2023 ). In addition to elevation/temperature, solar radiation – which primarily depends on slope and aspect – also increases the flight activity and voltinism of I. typographus (Singh et al. 2024 ; Jacoby et al. 2019). This confirms the findings of this research, which revealed that the affected forest area also increases in line with annual theoretical radiation (Fig. 3 ). Monitoring solar radiation, combined with temperature data for a specific area, can enable precise assessment of I. typographus development within a spruce forest. In fact, Baier et al. ( 2007 ) developed a model (PHENIPS), based on topoclimatic data, for the spatial and temporal simulation of the seasonal development of I. typographus at the Kalkalpen National Park (Austria), which yielded good results. This model could also be applied in the UBOR region to monitor the expansion of the bark beetle over time, assess the accuracy of the distribution maps produced by MaxEnt (Fig. 4 ), and determine where and when to implement bark beetle containment measures. In the literature, summer temperature appears to be the most relevant factor in the outbreak dynamics of I. typographus (Singh et al. 2024 ; Kozhoridze et al. 2023 ; Fischer et al., 2013 ). However, this research suggests that the mean temperature of the driest winter months (BIO9) could be equally important and should be considered when understanding and predicting the distribution (current and future) of bark beetle infestations, at least in the Southern Alps. The results of this study indicate that a high probability (> 0.5) of intense bark beetle outbreaks occurs where BIO9 values range between − 2.5 and 2.5°C. This is explained by the fact that winter temperatures within this range result in higher survival rates for overwintering individuals (particularly eggs, larvae, and pupae), leading to more severe infestations in spring and summer (Jönsson et al. 2011 ). Areas with BIO9 values that are too high (> 5°C) are entirely unsuitable for the bark beetle (Fig. 5 ), primarily because these warmer zones lack spruce forests and are instead dominated by broadleaf forests, which are not susceptible to I. typographus . Similarly, areas with excessively low winter temperatures (BIO9 < -5°C), typically located at higher elevation (subalpine and alpine belts), are also unsuitable for the bark beetle. This is due to several factors, including extreme minimum temperature peaks capable of killing overwintering individuals, the absence of spruce forests at the highest elevations in the study area (the alpine belt in the UBOR is dominated by grasslands, rocks, scree, and glaciers), and/or the presence of mixed forests of spruce, larch, and Swiss pine in the subalpine belt. In fact, in forests composed of various tree species (mixed forests), spruce trees are less susceptible to bark beetle attacks because they are harder for the pest to locate due to the lower presence/concentration of host volatiles emitted by spruce wood (Lindelöw et al. 1992 ) in the forest. Additionally, within the context of plant-insect interactions, it is well known that volatile compounds emitted by non-host plant species can interfere with the insect's response to aggregation pheromones (Byers et al. 1998 ). Such knowledge supports the strategies of converting pure spruce forests into mixed forests, which can significantly reduce bark beetle damage (Seidl et al. 2008 ). To date, no damage caused by I. typographus has been recorded above 1,600 m a.s.l. in the study area, and there is a low percentage (< 2%) of damaged spruce forests in the altitude range between 1,300 and 1,600 m (Fig. 2 ), where attacks are not severe. This suggests that, currently, the spruce forests of the UBOR located in the high mountain and subalpine belts are free from massive bark beetle outbreaks, confirming the findings of Faccoli and Bernardinelli ( 2014 ). It also indicates that current efforts to mitigate damage should focus on submountain and mountain areas. However, based on the future climate models considered in this research, it is likely that in the coming decades, the bark beetle will find optimal climatic conditions for intense outbreaks even in the high mountain vegetation belt, above 1,800 m a.s.l. in lower Camonica Valley and above 1,500 m a.sl. in upper Camonica Valley. If this occurs, it will result, within the next 60 years, in the near-total destruction/change of the current pure spruce forests in lower Camonica Valley (where there are also few areas above 1,800 m for the spruce forest to expand). Meanwhile, in upper Camonica Valley, spruce forests will persist above 1,800 m and in areas that may develop at higher altitudes, such as abandoned alpine and subalpine pastures (Cislaghi et al. 2019 ). The absence of current bark beetle infestations in the (few) spruce forests found at the lowest elevations of the UBOR (between 300 m and 600 m a.s.l.) (Fig. 2 ) is probably due to the fact that these secondary forests, mostly small in size, are fragmented and scattered within other forest formations of the hilly belt (broadleaf forests), making them difficult for the bark beetle to locate. The results of the floristic-vegetational and ecological analysis of the phytosociological relevés provided a series of insights into the current characteristics of forests affected by the bark beetle, suggesting the composition/type of future forest communities of the mountain belt. Except in rare cases, these are likely to be very different from those of Calamagrostio arundinaceae-Piceetum . The plant communities of the forests attacked by the bark beetle were all found to be very different from those not attacked (Fig. 6 ), because the death of the spruce trees (which constituted the dominant component of the plant community) altered a series of environmental variables in the ecosystem, favoring the growth of plant species different from those of Vaccinio-Piceetea and initiating a progressive secondary succession (Loidi 2017 ). One of the ecological variables that changed most due to bark beetle attack is the availability of light in the understory, which is greater in areas affected by the pest due to the loss of leaves from dead spruces. This was confirmed by the application of the L index of Landolt et al. ( 2010 ), whose values, in all sampling sites, showed post-disturbance communities to be more heliophilous than pre-disturbance ones, sometimes with very marked differences (Fig. 8 ). The different availability of light in the understory, mainly due to the coverage/density of spruce snags, appears to be one of the main factors determining the establishment of Rubetum idaei (cluster A1) and its variants (A3 and A4), rather than the A2 community (Fig. 6 b). The plant communities of clusters A1, A3, and A4 are composed of various heliophilous pioneer species of forest-clearing ( Robinietea class), which distinguish them from A2, characterized by less heliophilous species and which already in this early stage of secondary succession has young trees, herbs, and shrubs of mature forest communities. A good degree of shade due to the high coverage/density of spruce snags seems to accelerate ecological succession, bypassing the Rubetum idaei stage and achieving the final stage more quickly (Fig. 9 ). The ecological role of “succession accelerator” played by communities with spruce snags deserves further study, considering it is likely that due to the bark beetle, these vegetation types will become widespread in the Southern Alps in the coming decades. Moreover, studying these ecosystems could provide important information for managing areas affected by the bark beetle and defining new nature-based solutions (NBSs) – defined as “actions inspired by, supported by or copied from nature” (Bauduceau et al. 2015 ) – for forest restoration. Based on the data collected in this research and field observations, it seems that the plant community with spruce snags could perform an ecological function similar to that of Piceo-Sorbetum aucupariae ( Robinietea class), which is the stage of the acidophilus dynamic series of silver fir and spruce ( Calamagrostio arundinaceae-Piceo excelsae sigmetum ) that, in the study area, precedes the potential natural vegetation of Piceion excelsae (Verde et al. 2010 ). Although the bark beetle-affected forests of the UBOR fall within the Calamagrostio arundinaceae-Piceo excelsae sigmetum – which includes the Calamagrostion arundinaceae (fringe) stage, followed by a shrub stage of Sambuco-Salicion capreae ( Rubetum idaei , Piceo-Sorbetum aucupariae ), and the spruce forest (potential natural vegetation) (Verde et al. 2010 ) – it is likely that, given the current environmental/climatic conditions, the final stage (spruce forest of Piceion excelsae ) cannot be reached. Indeed, the plant communities of all areas attacked by the bark beetle were found to be more thermophilous (some markedly so) than those of undisturbed spruce forests (Fig. 8 ) and often composed of young trees of mature forest communities other than spruce, such as chestnut, beech, and silver fir (clusters A2 and A4). The presence of these young trees and a complex of other species (trees, shrubs and herbs), typical of mature forests of broadleaf trees of the Carpino-Fagetea sylvaticae and Quercetea robori-petraeae phytosociological classes (Fig. 7 ), suggests new types of “current potential vegetation” (Biondi 2011 ) outlined in Fig. 9 . Specifically, it is likely that under current climatic conditions and those predicted for the coming decades, the submountain and mountain spruce forests attacked by the bark beetle in the study area could gradually (and spontaneously) be replaced by chestnut and/or oak forests in the submountain belt, beech and silver fir forests in the mountain belt, and silver fir forests with spruce in the high-mountain belt (Fig. 9 ). Spruce-dominated forests are likely to be primarily located in the subalpine belt, which will expand due to the upward shift of the tree line. This deduction is supported not only by the results of this research but also by the fact that in Europe (and other parts of the world), a significant upward shift in forest plant species and vegetational belts is being observed and modeled (Lenoir et al. 2008 ; Giupponi et al. 2023 ; Zhang et al. 2021 ; Zischg et al. 2021 ). In particular, Lenoir et al. ( 2008 ) detected an upward shift in species optimum elevation averaging 29 m per decade in western Europe, and Zischg et al. ( 2021 ) modeled an upward shift of vegetation belts in Swiss forests, more or less pronounced depending on the different climate change prediction models and the different topoclimatic characteristics of the territory considered. Furthermore, in the UBOR, Giupponi et al. ( 2023 ), following the destruction of spruce forests by Storm Vaia, observed Quercus petraea (which has an ecology similar to chestnut) growing at 1,300 m a.s.l., 300 m higher than the altitude at which oak/chestnut forests are mapped in the Italian vegetational series map of Blasi (2010). In the coming years, it would be advisable to monitor changes in the forest communities of the UBOR to confirm/integrate the scheme in Fig. 9 so that it can become a useful tool for technicians and forest managers in the study area (and the Southern Alps in general) to understand vegetation potentials and adopt correct measures and actions for sustainable management of the forest cover. Specifically, it would be beneficial to collect floristic data and better define the mature forest communities (and the stages of dynamic series) that will be present where spruce forests currently attacked by the bark beetle and/or particularly susceptible to the insect are located (Fig. 4 ). Based on the floristic data collected in this research, it is likely that the beech forest communities that will replace spruce forests on basic soils (cluster B4) will be significantly different from those that will grow on acidic soils (e.g., replacing cluster B2). Indeed, the former are likely to be forests of Aremonio-Fagion , and the latter of Luzulo-Fagion sylvaticae (Del Favero 2002 ; Mucina et al. 2016 ). More data and studies aimed at defining the current (and future) vegetational potentials of spruce forests attacked by the bark beetle in the Southern Alps will undoubtedly be useful, if only to update the forest type maps of Lombardy (Del Favero 2002 ) and other vegetational maps of the Italian Alps, which are very useful tools for territorial management provided they are not too outdated given the rapidity of climate change. Knowing the current and future vegetation potentials of areas that are (or will be) attacked by the bark beetle could suggest which species to use for the restoration of forest communities. This action should be carried out quickly (preferably using native species) at least in bark beetle-affected forests located on steep slopes. Indeed, the rapid and simultaneous death of all (or most) trees in a pure spruce forest leads, within a few years, to the decomposition of their roots, which could cause significant hydrogeological instability problems since it is well known that tree roots contribute significantly to stabilizing the soil on mountain slopes (Bischetti et al. 2009 ; Vergani et al. 2012 ; Chiaradia et al. 2016 ). More information should be gathered on the biotechnical characteristics of the roots of herbs and shrubs that could be used for environmental restoration and soil bioengineering interventions (Giupponi et al. 2019 ). It would also be important to conduct studies to identify bioindicators that can help more accurately determine which forests might be more susceptible to the bark beetle than species distribution models currently allow. In this sense, this research identified Saxifraga cuneifolia as a diagnostic species of spruce forests not attacked by the bark beetle (Table 2 ). This species, together with a high coverage of mosses, appears particularly associated with microthermal forests, and its absence (or low coverage) in forests not yet attacked by the pest seems to indicate their high susceptibility to the insect. S. cuneifolia is indeed a species of Vaccinio-Piceetea (Mucina et al. 2016 ) widespread throughout the Alps (Aeschimann 2002), microthermal, shade-loving, and requiring soil with good availability of humus/litter (Landolt et al. 2010 ; Tichý et al. 2022; Dengler et al. 2023 ). In the study area, it is absent (or with very low coverage values) in spruce forests not yet attacked by the bark beetle where thermophilic broadleaf trees (chestnut and/or beech) are present (Table S2 ). The actual effectiveness of S. cuneifolia as an indicator of bark beetle-susceptible forest should be further investigated in studies that also consider other study areas in the Southern Alps, as well as analyzing the presence/absence of individual moss species in more or less stressed spruce forests to understand if some of them could also behave similarly to S. cuneifolia . Vaccinium myrtillus also proved to be a diagnostic species of forests not attacked by the bark beetle, but the latter is not an exclusive species of Vaccinio-Piceetea as it also contributes to the formation of forest communities of Quercetea robori-petraeae (Table 2 ) and is therefore indicative of acidophilic forest communities in general. If S. cuneifolia appears to be an indicator of less stressed and less bark beetle-susceptible spruce forests, the presence of exotic species is certainly an indicator of a degraded environment, often caused by anthropogenic interventions/disturbances (Giupponi et al. 2015 ). An example is the plant community of cluster A3, which is the result of recent cutting and removal actions of spruce snags that were dangerous for roads and houses near the forest affected by the bark beetle (sampling site 4). This community is indeed to be considered a degraded variant of Rubetum idaei (which developed due to the low density/coverage of spruce snags) in which various exotic species with high coverage are present, including Senecio inaequidens (Table S2 ), the only African species present in the UBOR and rapidly spreading (Martini et al. 2012 ; Bona 2019 ). Since it is very likely that the spread of exotic species present in cluster A3 is due to the use of tools/machinery from the forestry site that carried out the cutting and removal work of the spruce snags, contaminated with seeds of exotic plants, it would be advisable, if similar operations are to be carried out in other bark beetle-affected forests, to require forestry workers to clean their tools/machinery before moving to intervention areas, especially if these are located in protected areas or areas with few exotic species, as is the case in most mountain areas of the Alps (Dainese et al. 2014 ). This action, in addition to preserving the integrity of Alpine ecosystems, would also enable better utilization of their benefits/services. In fact, shrublands dominated by Rubus idaeus and other species of Robinietea class (such as those that develop in areas affected by bark beetles with low density/coverage of spruce snags) are valuable for the production of edible fruits and, even more so, for the foraging of the domestic bee ( Apis mellifera ) and wild pollinators, whose survival is currently threatened, among other factors, by climate change (Hristov et al. 2020 ; Rahimi and Jung 2024 ). In the Alps, bees not only feed on the nectar of R. idaeus (and other nectar-producing species) but also facilitate the production of raspberry honey, a prized agri-food product with interesting phytochemical and nutritional characteristics (Leoni et al. 2024 ). However, the presence of S. inaequidens (alongside R. idaeus ) in cluster A3 prevents the production of high-quality honey because this species contains toxic compounds (pyrrolizidine alkaloids) that contaminate honey through pollen. These toxins can enter the food chain and pose a risk to human health (Sadgrove 2022 ). This is one of the reasons why S. inaequidens is included on the "Black List" of invasive alien species subject to monitoring and containment in the Lombardy region (Regional Law 10/2008). From the perspective of vegetation dynamics, it is likely that the A3 community plays a role similar to that of the typical Rubetum idaei , but it would be prudent to monitor it over time to confirm the dynamic described in the vegetation scheme of Fig. 9 . Communities with S. inaequidens , Buddleja davidii and other exotic pioneer/invasive species are relatively new to the Alps, and their role in succession is still unclear, especially considering the current and future effects of climate change. Conclusion This research has clarified the main environmental characteristics of the spruce forests in the UBOR region that are affected by and/or susceptible to bark beetle attacks. The most impacted forests were found to be those located below 1,600 m a.s.l. (in the sub-mountain and mountain belts) and in areas with high solar radiation. Additionally, through the development of a MaxEnt model with high predictive accuracy, primarily defined by the mean temperature of the winter driest quarter, it was found that over 58% of the current spruce forests in UBOR will have a high susceptibility to intense bark beetle attacks in the next 60 years. The analysis of floristic-vegetational and synecological characteristics suggested that in areas attacked by the bark beetle, mature forest communities which are more thermophilic and significantly different (both floristically and physiognomically) from the pre-disturbance spruce forests will develop. Furthermore, based on the interpretation of the results obtained, a model of plant succession was developed that, along with the following suggestions/precautions, could be useful for land managers in order to limit damage from bark beetles, promote rapid forest regeneration, and prevent the degradation of forest ecosystems in the Southern Alps: Focus on measures/interventions that mitigate the spread of the bark beetle, especially in spruce forests of the sub-mountain and mountain vegetational belts (particularly in areas with higher winter temperatures and high solar radiation), as these are the areas with the most favorable climatic conditions for the development of intense pest outbreaks. Promote the conversion of pure spruce forests into mixed forests, prioritizing native species and considering both their ecology and economic value. Avoid the spread/planting of spruce outside its natural/ecological range, where it could experience more stress and be more vulnerable to bark beetle attacks and/or other pests/diseases. Encourage, where possible, the spread of spruce in the subalpine belt, where it is unlikely that favorable climatic conditions for intense bark beetle attacks will occur in the coming decades. Promote rapid actions/interventions to facilitate tree growth in forests affected by the bark beetle on steeper slopes to prevent hydrogeological instability. Avoid the removal of spruce snags (in forests affected by the bark beetle) if the goal is to accelerate vegetation succession and achieve mature forest communities more quickly (which may differ significantly from pre-disturbance communities). In cases of spruce snag removal (for protective, productive, and/or ecological-conservation purposes) or other anthropogenic interventions, ensure that measures are adopted to contain exotic species, such as cleaning tools/machinery before their transport/use in the intervention area. These tools/suggestions represent an initial contribution to supporting a more informed and sustainable management of areas affected by and/or susceptible to the bark beetle in UBOR and areas with similar environmental conditions. It is to be hoped they will soon be integrated with the results of other research, given the speed at which the bark beetle is spreading in the Southern Alps and the magnitude of the effects it is causing to their forest ecosystems and landscapes. Declarations Funding: This research was supported by the Agritech National Research Centre and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4—D.D. 1032 17/06/2022, CN00000022), and from the Horizon Project “Accelerating transformative climate adaptation for higher resilience in European Mountain regions” (MountResilience) project n°: 101112876 Conflicts of interest The authors declare that they have no conflicts of interest. Author Contribution LG and AG conceived and designed the study and interpreted the results. LG collected the data and carried out the floristic, ecological and statistical analyses. LG, RP, DP and SS analyzed the data and wrote the manuscript. LG created figures and tables. LG and AG are the project administrators. All authors have read and agreed to this version of the manuscript. Acknowledgement We wish to thank Matteo Ottoveggio for his help in collecting and analyzing floristic-vegetational data, and Fabio Maffezzoni for supporting GIS analysis. Availability of data and material The raw data supporting the conclusions of this manuscript will be made available by the corresponding author, without undue reservation, to any qualified researcher. Code availability Not applicable. References Aeschimann D, Lauber K, Moser DM, Theurillat J-P (2004) Flora alpina. Haupt, Bern-Stuttgart-Wien Andreis C, Armiraglio S, Caccianiga M, Bortolas D, Broglia A (2005) Pinus cembra L. nel settore sud-alpino lombardo (Italia settentrionale) «NATURA BRESCIANA» Ann Mus Civ Sc Nat Brescia 34:19-39 Andreis C, Armiraglio S, Caccianiga M, Cerabolini BEL (2009) Forest vegetation of the order Piceetalia excelsae Pawl., in Pawl. et al. 1928, in the Lombardy Alps. 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Science of The Total Environment 780:146615. https://doi.org/10.1016/j.scitotenv.2021.146615 Zischg AP, Frehner M, Gubelmann P, Augustin S, Brang P, Huber B (2021) Participatory modelling of upward shifts of altitudinal vegetation belts for assessing site type transformation in Swiss forests due to climate change. Applied Vegetation Science 24:e12621. https://doi.org/10.1111/avsc.12621 Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Table S1 Percentage contribution and permutation importance of the predictor variables to the MaxEnt model. TableS2.xlsx Table S2Phytosociological table of relevés. Cover indices refer to the Braun-Blanquet (1964) abundance/dominance scale: r, rare; +, <1%; 1, 1–5%; 2, 6–25%; 3, 26–50%; 4, 51–75%; 5, 76–100%. TableS3.xlsx Table S3 Average values of ecological indices for vegetation types (forests affected and unaffected by bark beetles) across each sampling area. Key: T - temperature, K - continentality, L - light intensity, F - soil moisture, R - substrate reaction, N - nutrients, H - humus, D - soil aeration; *, significant (p < 0.05); **, significant (p < 0.01). 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-5878691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406118888,"identity":"8ede3d10-588c-4127-897b-68a4ca7934c0","order_by":0,"name":"Luca Giupponi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACPgglAaESKhgY2BgYGxgS8GhhQ9VyBqYFjx42FB5jG4yFT4tE8sMPDL8sErdLNx978HDe4Tw+scMNDA9/4NOSZizB2CeRuHPOsXSDxG2Hi9mkEwk4TCKHQYKxRyJxw40cMwmglsQ2IrQw/0BomUOcFjYJhh8wLQ3EaOF5ZmaR2CBhDPRLmkTCsXSwlgMJabi18LMnP77x4U+dLCjEJH/UWCfOn53+8OEPG9xawCARGB0GEkgCBwhoAII/aFpGwSgYBaNgFCADALG7T++nQ0ReAAAAAElFTkSuQmCC","orcid":"","institution":"University of Milan","correspondingAuthor":true,"prefix":"","firstName":"Luca","middleName":"","lastName":"Giupponi","suffix":""},{"id":406118889,"identity":"040e792a-7e2d-4460-814d-113234932c54","order_by":1,"name":"Riccardo Panza","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Riccardo","middleName":"","lastName":"Panza","suffix":""},{"id":406118890,"identity":"e9933fad-0423-485a-aa7f-e1d0f40aae41","order_by":2,"name":"Davide Pedrali","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Pedrali","suffix":""},{"id":406118891,"identity":"37b454d0-ace5-4867-972a-ab88cb77da8d","order_by":3,"name":"Stefano Sala","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Stefano","middleName":"","lastName":"Sala","suffix":""},{"id":406118892,"identity":"d321927f-66f3-4f0b-9c53-a370ecb36fc4","order_by":4,"name":"Annamaria Giorgi","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Annamaria","middleName":"","lastName":"Giorgi","suffix":""}],"badges":[],"createdAt":"2025-01-22 07:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5878691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5878691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74898995,"identity":"9fc5d230-1452-4b2c-b71c-b2d3ed33bcf1","added_by":"auto","created_at":"2025-01-28 07:00:57","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":654362,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area (UBOR) with sampling sites (the identification code for each sampling site is indicated within circles) (a), and Walter-Lieth charts developed using Zepner et al. (2020) (b)\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/42f9346600be2aa10251a880.jpeg"},{"id":74898998,"identity":"50ce0c27-a554-4174-8db1-8db1cbe80f0f","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276597,"visible":true,"origin":"","legend":"\u003cp\u003eExtent of spruce forests (affected and unaffected by bark beetle) across the different altitudinal ranges of the study area (a) and percentage of spruce forest affected by bark beetle (b). The numbers above the columns refer to affected forests. The red box highlights the current altitudinal distribution of bark beetle damage\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/9331b65d79a19f0eab5c70e3.jpeg"},{"id":74900129,"identity":"483143d7-61d4-4f56-a21b-5c1fd4a91496","added_by":"auto","created_at":"2025-01-28 07:08:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60856,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of spruce forests attacked by bark beetle for each potential annual solar radiation range in the UBOR\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/50a0caeee3dca29c22e83475.jpeg"},{"id":74900133,"identity":"0d065baf-569d-4164-bb79-1344620214ae","added_by":"auto","created_at":"2025-01-28 07:08:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":792878,"visible":true,"origin":"","legend":"\u003cp\u003eCurrent and future areas of UBOR with particularly suitable climatic conditions for intense bark beetle attacks (probability of infestation \u0026gt;70%) returned by MaxEnt model (a), and percentage of spruce forests with low (probability of infestation \u0026lt;70%) and high susceptibility (probability of infestation \u0026gt;70%) to \u003cem\u003eI. typographus\u003c/em\u003e from 2020 to 2080 (b)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/d2738c99e622d51c70ec06ac.jpeg"},{"id":74899012,"identity":"5bb28346-c53b-49f5-8097-93430005b4d9","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90652,"visible":true,"origin":"","legend":"\u003cp\u003eMaxEnt response curve of severe bark beetle attack to the mean temperature of driest quarter variable (BIO9)\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/00668a05c05a1e4f16885ca5.jpeg"},{"id":74899022,"identity":"f59b9204-79b6-467f-9eb4-7b39b93d7021","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":340326,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram (a) and DCA biplot of relevés (b). The two main clusters (A - spruce forests affected by bark beetle; B - forests unaffected by bark beetle) and subclusters are highlighted. The DCA biplot is overlaid with blue contour lines indicating the percentage of live spruce cover (blue line) and spruce snags cover (green line). In the DCA biplot, species are marked with crosses, with some species highlighted: \u003cem\u003ePicea_a\u003c/em\u003e, \u003cem\u003ePicea abies\u003c/em\u003e (alive); \u003cem\u003eHedera_h\u003c/em\u003e, \u003cem\u003eHedera helix\u003c/em\u003e; \u003cem\u003eVaccinium_m\u003c/em\u003e, \u003cem\u003eVaccinium myrtillus\u003c/em\u003e; \u003cem\u003eLaburnum_a\u003c/em\u003e, \u003cem\u003eLaburnum alpinum\u003c/em\u003e; \u003cem\u003eHelleborus_n\u003c/em\u003e, \u003cem\u003eHelleborus niger\u003c/em\u003e; \u003cem\u003ePteridium_a\u003c/em\u003e, \u003cem\u003ePteridium aquilinum\u003c/em\u003e; \u003cem\u003eAbies_a\u003c/em\u003e, \u003cem\u003eAbies alba\u003c/em\u003e; \u003cem\u003eOxalis_a\u003c/em\u003e, \u003cem\u003eOxalis acetosella\u003c/em\u003e; \u003cem\u003eVeronica_o\u003c/em\u003e, \u003cem\u003eVeronica officinalis\u003c/em\u003e; \u003cem\u003eLarix_d\u003c/em\u003e, \u003cem\u003eLarix decidua\u003c/em\u003e; \u003cem\u003eSorbus_a\u003c/em\u003e, \u003cem\u003eSorbus aucuparia\u003c/em\u003e; \u003cem\u003eFagus_s\u003c/em\u003e, \u003cem\u003eFagus sylvatica\u003c/em\u003e; \u003cem\u003eCorylus_a\u003c/em\u003e, \u003cem\u003eCorylus avellana\u003c/em\u003e; \u003cem\u003eAcer_p\u003c/em\u003e, \u003cem\u003eAcer pseudoplatanus\u003c/em\u003e; \u003cem\u003eFraxinus_e\u003c/em\u003e, \u003cem\u003eFraxinus excelsior\u003c/em\u003e; \u003cem\u003eMoehringia_t\u003c/em\u003e, \u003cem\u003eMoehringia trinervia\u003c/em\u003e; \u003cem\u003eJuglans_r\u003c/em\u003e, \u003cem\u003eJuglans regia\u003c/em\u003e; \u003cem\u003eCastanea_s\u003c/em\u003e, \u003cem\u003eCastanea sativa\u003c/em\u003e; \u003cem\u003eGalium_a\u003c/em\u003e, \u003cem\u003eGalium aparine\u003c/em\u003e; \u003cem\u003eSambucus_r\u003c/em\u003e, \u003cem\u003eSambucus racemosa\u003c/em\u003e; \u003cem\u003eSambucus_n\u003c/em\u003e, \u003cem\u003eSambucus nigra; Saxifraga_c, Saxifraga cuneifolia;\u003c/em\u003e \u003cem\u003eRubus_i\u003c/em\u003e, \u003cem\u003eRubus idaeus\u003c/em\u003e; \u003cem\u003eEpilobium_m\u003c/em\u003e, \u003cem\u003eEpilobium montanum\u003c/em\u003e; \u003cem\u003eLathyrus_p\u003c/em\u003e; \u003cem\u003eLathyrus pratensis\u003c/em\u003e; \u003cem\u003eSolanum_d\u003c/em\u003e, \u003cem\u003eSolanum dulcamara\u003c/em\u003e; \u003cem\u003eRubus_u\u003c/em\u003e, \u003cem\u003eRubus ulmifolius\u003c/em\u003e; \u003cem\u003eUrtica_d\u003c/em\u003e, \u003cem\u003eUrtica dioica\u003c/em\u003e; \u003cem\u003eSalix_c\u003c/em\u003e, \u003cem\u003eSalix caprea\u003c/em\u003e; \u003cem\u003eClematis_v\u003c/em\u003e, \u003cem\u003eClematis vitalba\u003c/em\u003e; \u003cem\u003eSenecio_i\u003c/em\u003e, \u003cem\u003eSenecio inaequidens\u003c/em\u003e; \u003cem\u003eBuddleja_d\u003c/em\u003e, \u003cem\u003eBuddleja davidii\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/7f7bcebfabc8913422ff8553.jpeg"},{"id":74899002,"identity":"b2612540-c4a1-43ec-82c8-c0b36d6e50a7","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":85163,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots of the main phytosociological classes of species identified in forests affected by bark beetles and in those unaffected, including average values (indicated with a “x”). The symbols above the boxes indicate significant differences as determined by the t-test: **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ns, not significant. Key: PIC, \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e; FAG, \u003cem\u003eCarpino-Fagetea sylvaticae\u003c/em\u003e; ROB, \u003cem\u003eRobinietea\u003c/em\u003e; QUE, \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/5d42bda6afde5d825a7051cb.jpeg"},{"id":74899009,"identity":"df0d8d4e-eb2b-4623-9ad5-9a5bc7b5edd8","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":283614,"visible":true,"origin":"","legend":"\u003cp\u003eAverage values of the ecological indices T (temperature), L (light), and H (humus) for plant communities of forests affected and unaffected by bark beetles for each sampling area (SA). The numerical codes for the SAs are the same used in Figure 1. The symbols above the bars indicate significant differences in the ecological features of plant communities as determined by the t-test: *, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05; **, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01; ns, not significant\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/7d8f793ce8e30cfa0d8a4123.jpeg"},{"id":74899014,"identity":"9efc3167-1b4f-4505-b7cb-a7136114f448","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":481221,"visible":true,"origin":"","legend":"\u003cp\u003eScheme of the vegetation dynamics of sub-mountain and mountain spruce forests affected by bark beetle in the UBOR. The black arrows with solid lines indicate the presumed dynamic relationships between the plant communities, while the black arrows with dotted lines indicate hypothetical paths that vegetation succession could follow. The grey arrows indicate the main ecological differences between the plant communities\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/6a273ab0de861da822a9c6dc.jpeg"},{"id":80202675,"identity":"1748b605-b37c-4354-9a25-621fc1af7bb5","added_by":"auto","created_at":"2025-04-09 07:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4049322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/39b74919-21d9-4b48-ae96-90219d9e5d49.pdf"},{"id":74900131,"identity":"ef790024-02de-4713-9ea9-fee4049db618","added_by":"auto","created_at":"2025-01-28 07:08:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9571,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e Percentage contribution and permutation importance of the predictor variables to the MaxEnt model.\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/affc8f0e515aa71aa88af32e.xlsx"},{"id":74899000,"identity":"e59d946f-5d69-48c9-be15-7b14d643c879","added_by":"auto","created_at":"2025-01-28 07:00:58","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":64222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003ePhytosociological table of relevés. Cover indices refer to the Braun-Blanquet (1964) abundance/dominance scale: r, rare; +, \u0026lt;1%; 1, 1–5%; 2, 6–25%; 3, 26–50%; 4, 51–75%; 5, 76–100%.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/8523024101754e72e5368b58.xlsx"},{"id":74900135,"identity":"418ab776-4af7-4e3d-86ec-b65c5e1e77af","added_by":"auto","created_at":"2025-01-28 07:08:58","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20011,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e Average values of ecological indices for vegetation types (forests affected and unaffected by bark beetles) across each sampling area. Key: T - temperature, K - continentality, L - light intensity, F - soil moisture, R - substrate reaction, N - nutrients, H - humus, D - soil aeration; *, significant (p \u0026lt; 0.05); **, significant (p \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5878691/v1/7b2a72bf7640c4ee31531249.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ecology, floristic-vegetational features and future perspectives of spruce forests affected by Ips typographus: insights from the Southern Alps","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNatural disturbance regimes (e.g., windstorms, fires, insect outbreaks) are intensifying because of climate change, often with negative consequences on the ecosystem services provided by boreal forests (Thom and Seidl \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Biodiversity, timber production, tourism, clean water supply, hydrogeological stability, and landscapes are all impacted by bark beetle outbreaks. Although these outbreaks are a natural component of forest dynamics, we are compelled to manage them due to the increasing threat to our economy, health, and cultural heritage (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe European spruce bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e, Coleoptera: Scolytinae), is the most significant pest of the Norway spruce (\u003cem\u003ePicea abies\u003c/em\u003e), which, in turn, is one of the most important timber tree species in Europe. For centuries, the spruce has been prioritized above other species for its economic value, in the Alps and elsewhere (Marini et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The spruce has been artificially spread to latitudes and elevations beyond its ecological optimum, where trees face continuous stress and are consequently less able to defend themselves from pests (Washaya et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In spruce forests, \u003cem\u003eI. typographus\u003c/em\u003e typically persists at low population densities (endemic phase), breeding only in dying or severely weakened trees. However, in the presence of large numbers of stressed trees \u0026ndash; such as those affected by windstorms or drought \u0026ndash; this bark beetle can undergo population explosions lasting up to 10 years (epidemic phase), during which it also attacks healthy trees (Arthur et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In normal conditions healthy trees repel bark beetles with resin, but this defense is not sufficient during epidemic phases, and it is even weaker in stressed trees. Furthermore, pure spruce stands facilitate \u003cem\u003eI. typographus\u003c/em\u003e expansion, providing a continuous source of breeding substrate for the beetles (Faccoli and Bernardinelli \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eI. typographus\u003c/em\u003e is highly adaptable, adjusting its voltinism (number of generations per year) according to temperature and photoperiod. It can produce one generation annually at higher latitudes and elevations, but up to three generations in milder conditions (Washaya et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ogris et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rising temperatures and more frequent droughts promote \u003cem\u003eI. typographus\u003c/em\u003e multivoltinism while simultaneously stressing spruce trees, weakening their natural defenses against this pest (Hofmann et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, over recent decades, large-scale spruce diebacks have been increasingly observed across Europe, transforming a long-standing forestry issue into a matter of public concern, with significant economic, social, and political implications (Hl\u0026aacute;sny et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the Southern Alps, an unprecedented outbreak of \u003cem\u003eI. typographus\u003c/em\u003e began in 2015, dramatically exacerbated by the 2018 extreme windstorm \u0026ldquo;Vaia\u0026rdquo; and subsequent droughts. Storm Vaia (known also as Storm Adrian) compromised 8.5\u0026nbsp;million m\u0026sup3; of timber over six Alpine regions in Italy, including Lombardy (Chirici et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Giupponi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePossibly spreading from Central and Northern Europe, the spruce completely occupied the Alps during the end of the Pleistocene, around 10,000 years ago (Ravazzi \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Currently, in the Southern Alps, spruce forests are primarily found in the mountain, high-mountain, and subalpine belts, forming diverse plant associations of \u003cem\u003ePiceion excelsae\u003c/em\u003e phytosociological alliance (\u003cem\u003ePiceetalia excelsae\u003c/em\u003e order, \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e class) (Mucina et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to human intervention, driven by the economic value of the Norway spruce, this species is often found in the hilly and submountain belts (or even at lower elevations), forming secondary vegetation that replaces broadleaf forests such as beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e) or sessile oak (\u003cem\u003eQuercus petraea\u003c/em\u003e) woods. Despite its ecological plasticity (Del Favero \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), the climatic optimum of the Norway spruce in the Southern Alps is found in the high-mountain and subalpine belts, primarily because this species is of the Eurosiberian chorological type (Pignatti et al. 2017), adapted to long, cold winters and short, warm summers.\u003c/p\u003e \u003cp\u003eSeveral biotic and abiotic factors are closely linked to the spruce and \u003cem\u003eI. typographus\u003c/em\u003e dynamics, including breeding substrate availability, natural enemies (predators and parasitoids), forest composition (mixed or pure stands), intraspecific competition, weather (temperature and rainfall), and solar radiation (Kozhoridze et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Pirtskhalava-Karpova et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Faccoli and Bernardelli (2014) indicate that forest composition and elevation are major drivers of \u003cem\u003eI. typographus\u003c/em\u003e dynamics in the Southern Alps. Indeed, mixed forests (composed of multiple tree species) and those at higher elevations are less susceptible to bark beetle damage compared to pure spruce stands at lower elevations. These patterns align with observations from Central and Northern Europe (Kozhoridze et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sommerfeld et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven the rapid pace at which the bark beetle is destroying the Norway spruce forests of the Southern Alps (and the resulting ecological, economic, and hydrogeological problems), there is growing interest among researchers in understanding how \u003cem\u003eI. typographus\u003c/em\u003e outbreaks will respond to the fast pace of climate change in mountainous regions (Marini et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, in the Southern Alps and Europe in general, the processes of vegetation succession following bark beetle outbreaks remain poorly explored/understood. Researchers have primarily focused on forestry implications and tree regeneration (Svoboda et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kupferschmid et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) largely leaving the floristic and ecological aspects of plant communities (pre- and post-bark beetle attack) unexplored (Matuszkiewicz et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This gap should be addressed, if for no other reason than that vegetation can be used as a \"super-indicator\", useful for understanding the mechanisms that regulate forest ecosystems (and others) and thus supporting the proper management of these ecosystems.\u003c/p\u003e \u003cp\u003eThis research aims to provide further information regarding the environmental characteristics of the spruce forests in the upper basin of the Oglio River (Southern Alps) affected by recent bark beetle infestations, analyzing their geographical, bioclimatic, and vegetational features. Furthermore, through the application of species distribution models and the interpretation of floristic-vegetational data, it seeks to provide an overview of the forests in the study area that are highly likely to be infested/destroyed by the bark beetle in the coming decades, and how vegetation succession may proceed, in order to provide tools for improving the management of these forest ecosystems.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and sampling sites\u003c/h2\u003e \u003cp\u003eThe upper basin of the Oglio River (UBOR) is in the Southern Alps within the Lombardy region of Italy (Latitude: 46\u0026deg; 00' N, Longitude: 10\u0026deg; 20' E) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It covers a large area of 1,444 km\u0026sup2; and includes 49 municipalities spread across the Valle Camonica in the province of Brescia and Val di Scalve in the province of Bergamo.\u003c/p\u003e \u003cp\u003eFrom an orographic perspective, the UBOR is situated between the Central and Eastern Lombard Prealps and the Southern Rhaetian Alps (Marazzi \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The Central and Eastern Lombard Prealps are primarily composed of sedimentary limestone rocks, while the Southern Rhaetian Alps feature sedimentary, metamorphic, and intrusive rocks with neutral and acidic reaction (Bona \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Previtali 1992). The elevation range within the study area is considerable, spanning 3,300 m: the southern valley areas (near Lake Iseo) are situated at around 200 m a.s.l., while the highest peak (Mount Adamello) exceeds 3,500 m a.s.l..\u003c/p\u003e \u003cp\u003eThe climate in the UBOR is quite diverse: in the southern mountainous areas, there is a sub-oceanic climate (with rainfall concentrated around the equinoxes), while in the northern areas there is a sub-continental climate (with precipitation peaks in summer) (Cerabolini et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Across the entire UBOR, the driest period occurs during the cold winter months, from December to March (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe vascular flora (tracheophytes) of the UBOR includes 2,732 taxa (species and subspecies) (Bona \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Martini et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The vegetation is largely composed of forests, with types that vary mainly according to altitude, substrate, and land management practices (Del Favero \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Verde et al \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpruce forests cover about 30% of the forested area in the study region and are primarily located within the mountain and high-mountain vegetational belts, ranging from 700 to 1,800 m a.s.l. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geoportale.regione.lombardia.it\u003c/span\u003e\u003cspan address=\"https://www.geoportale.regione.lombardia.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These forests mainly consist of plant communities dominated by spruce, with larch (\u003cem\u003eLarix decidua\u003c/em\u003e) and Swiss pine (\u003cem\u003ePinus cembra\u003c/em\u003e) at higher elevations and more thermophilic trees (such as silver fir, beech and chestnut) at lower elevations. From a phytosociological perspective, the spruce forests of the mountain and high-mountain belts belong to the \u003cem\u003eCalamagrostio arundinaceae-Piceetum\u003c/em\u003e association (\u003cem\u003eVaccinio-Abietenion\u003c/em\u003e sub-alliance, \u003cem\u003ePiceion excelsae\u003c/em\u003e alliance, \u003cem\u003ePiceetalia excelsae\u003c/em\u003e order, \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e class), which is the most widespread spruce forest association in this part of Lombardy (Verde 2010, Andreis 2009). The \u003cem\u003eCalamagrostio arundinaceae-Piceetum\u003c/em\u003e association is characterized by abundant moss cover and the presence of tracheophytes, including \u003cem\u003ePicea abies\u003c/em\u003e (dominant tree), \u003cem\u003eLarix decidua\u003c/em\u003e, \u003cem\u003eBetula pendula\u003c/em\u003e, \u003cem\u003eSorbus aucuparia\u003c/em\u003e, \u003cem\u003eLonicera nigra\u003c/em\u003e, \u003cem\u003eCalamagrostis arundinacea\u003c/em\u003e, \u003cem\u003eVaccinium myrtillus\u003c/em\u003e, \u003cem\u003eOxalis acetosella\u003c/em\u003e, \u003cem\u003eLuzula nivea\u003c/em\u003e, \u003cem\u003eSaxifraga cuneifolia\u003c/em\u003e and \u003cem\u003ePhegopteris connectilis\u003c/em\u003e. At higher elevations, in the subalpine belt, forest communities contain less spruce and more larch, with \u003cem\u003eRhododendron ferrugineum\u003c/em\u003e and \u003cem\u003eLuzula nivea\u003c/em\u003e in the understory (\u003cem\u003eLuzulo niveae-Piceetum rhododendretosum ferruginei\u003c/em\u003e) (Andreis 2009). In the highest subalpine areas, spruce becomes sporadic in larch-dominated forests (\u003cem\u003eAstrantio minoris-Laricetum deciduae\u003c/em\u003e), where \u003cem\u003ePinus cembra\u003c/em\u003e is also present (Andreis 2005, 2009).\u003c/p\u003e \u003cp\u003eFor the study of the vegetation of the spruce forests affected by the bark beetle (and control/unaffected forests), 11 sampling sites were identified across the entire study area, at altitudes ranging from 700 to 1,300 m a.sl. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These sites were selected based on the presence of at least one hectare of forest with over 90% dead spruce trees, following a bark beetle infestation that occurred in the previous 3\u0026ndash;7 years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of the current and future distribution of the bark beetle\u003c/h3\u003e\n\u003cp\u003eThe analysis of the current forests affected by bark beetles in the UBOR utilized cartographic data (polygon shapefile) sourced from the Geoportal of the Lombardy Region (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geoportale.regione.lombardia.it\u003c/span\u003e\u003cspan address=\"https://www.geoportale.regione.lombardia.it\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The shapefile of bark beetle-affected areas in Lombardy (updated to 2021 with 760 polygons corresponding to approximately 2,070 ha of forests affected by bark beetles) was downloaded from the geoportal and analyzed using ArcGis Pro software. Specifically, 241 polygons within the UBOR were considered. For each 50-meter altitude interval (from 300 to 2,150 meters above sea level), the affected area of spruce forest impacted by bark beetle was calculated, as well as the percentage of affected forest relative to the total area of spruce forests (including both pure spruce forests and mixed forests with spruce). For each polygon, the average slope, elevation, and aspect were calculated. These data, along with the geographic coordinates of each polygon, were then used to calculate the theoretical annual global radiation for each area using software developed by ENEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.solaritaly.enea.it/indexEn.php\u003c/span\u003e\u003cspan address=\"http://www.solaritaly.enea.it/indexEn.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), to assess whether a relationship exists between bark beetle-affected areas and incident solar energy.\u003c/p\u003e \u003cp\u003eSpecies distribution models (SDMs) were adopted to predict the spatial distribution (current and future) of \u003cem\u003eI. typographus\u003c/em\u003e in UBOR based on bioclimatic data. 19 bioclimatic variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and 14 georeferenced points (occurrence points) where the bark beetle destroyed more than 20 ha of spruce forest in the Lombardy Alps, were considered as severely infested areas. The coordinates of the occurrence points were extracted from the Geoportal of the Lombardy Region shapefile, considering the areas where the bark beetle has been particularly destructive. The bioclimatic layers were obtained from the WorldClim 2.1 data website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://worldclim.org\u003c/span\u003e\u003cspan address=\"http://worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) at a spatial resolution of 0.5 arc-minutes (~\u0026thinsp;0.60 km\u003csup\u003e2\u003c/sup\u003e). All the bioclimatic variables were used to establish the distribution model of \u003cem\u003eI. typographys\u003c/em\u003e (with high destructive capacity) in UBOR under current climatic conditions (2016\u0026ndash;2020) and for three future periods: 2021\u0026ndash;2040, 2041\u0026ndash;2060 and 2061\u0026ndash;2080.\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\u003eBioclimatic variables used to model the distribution of \u003cem\u003eI. typographus\u003c/em\u003e epidemics in the UBOR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode/Unit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBioclimatic variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO1 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Mean Temperature\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO2 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Diurnal Range (Mean of monthly (max temp - min temp))\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO3 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsothermality (BIO2/BIO7 \u0026times; 100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO4 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature Seasonality (standard deviation \u0026times; 100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO5 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax Temperature of Warmest Month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO6 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin Temperature of Coldest Month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO7 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature Annual Range (BIO5-BIO6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO8 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of Wettest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO9 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of Driest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO10 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of Warmest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO11 (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean Temperature of Coldest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO12 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual Precipitation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO13 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Wettest Month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO14 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Driest Month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO15 (-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation Seasonality (Coefficient of Variation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO16 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Wettest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO17 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Driest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO18 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Warmest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIO19 (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecipitation of Coldest Quarter\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 Shared Socio-economic Pathway (SSP) 2-4.5 future scenario (\"Middle of the road scenario\") was considered in this analysis because it represents a plausible future pathway characterized by medium challenges to mitigation and adaptation efforts (IPCC \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The CNRM-CM6-1 global climate model (Voldoire et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), used for evaluating and forecasting the impact of the SSPs scenarios on future climate, was acquired from the WorldClim data website. This model, representing the latest fully coupled atmosphere-ocean general circulation model of the sixth generation, was utilized to project the effects of the SSP2-4.5 scenario onto the climate of the periods 2021\u0026ndash;2040, 2041\u0026ndash;2060 and 2061\u0026ndash;2080.\u003c/p\u003e \u003cp\u003eThe distribution model of \u003cem\u003eI. typographus\u003c/em\u003e was generated with MaxEnt (Phillips et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) using the \u0026ldquo;dismo\u0026rdquo; package (Hijmans et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) of R software. MaxEnt (Maximum Entropy) is an algorithm widely used for making predictions of species distribution, particularly well-suited for applications involving presence-only data (occurrence data). The relative contribution of each bioclimatic variable to the model was extracted, and response curves were generated for each one. The accuracy of the model was evaluated by computing the Area Under the Curve (AUC) of the Receiver Operating characteristic Curve (ROC), a widely used and robust approach of model evaluation. The AUC values range from 0 to 1 and the higher the value of AUC, the better the performance of the model.\u003c/p\u003e \u003cp\u003eThe final output of the distribution model is a map indicating areas with a high/low probability (0 for low probability; 1 for high probability) of the bark beetle having particularly destructive effects. In this research, the probability of occurrence was transformed into a binary score (presence or absence) considering the threshold of 0.70, and then four presence/absence maps of \u003cem\u003eI. typographus\u003c/em\u003e in UBOR were created: one considering the current climatic conditions (2016\u0026ndash;2020) and three considering the future periods 2021\u0026ndash;2040, 2041\u0026ndash;2060 and 2061\u0026ndash;2080.\u003c/p\u003e\n\u003ch3\u003eVegetation data collection and analysis\u003c/h3\u003e\n\u003cp\u003eFor each sampling site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 6 phytosociological relev\u0026eacute;s were carried out on 100 m\u003csup\u003e2\u003c/sup\u003e (10x10 m) using the methods of Braun-Blanquet (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1964\u003c/span\u003e): 3 relev\u0026eacute;s were conducted in areas affected by the bark beetle (i.e., those with more than 90% of spruce snags), and the other 3 were conducted in adjacent areas with no spruce snags (control). Given the difficulty in diagnosing whether live spruce trees were affected by the bark beetle (in the early stages of infestation, spruce trees show no symptoms but only little bark holes, which may be located several meters up the trunk), this study conventionally considered \"affected forests\" those with high coverage of standing dead spruce trees (snags), and \"unaffected forests\" those without snags.\u003c/p\u003e \u003cp\u003eFor each relev\u0026eacute;, tracheophytes of the plant communities were identified using the \u0026ldquo;Flora d\u0026rsquo;Italia\u0026rdquo; dichotomous keys of Pignatti (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and their coverage was estimated using the conventional abundance/dominance scale of Braun-Blanquet (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1964\u003c/span\u003e): r, rare species in the relev\u0026eacute;; +, coverage\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;1%; 1, coverage 1\u0026ndash;5%; 2, coverage\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;5\u0026ndash;25%; 3, coverage\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;25\u0026ndash;50%; 4, coverage\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;50\u0026ndash;75%; 5, coverage\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;75\u0026ndash;100%. In each relev\u0026eacute;, two coverage values were assigned for \u003cem\u003ePicea abies\u003c/em\u003e: one for snag trees and one for live trees. Additionally, the total moss coverage percentage was estimated. The relev\u0026eacute;s were performed in June-July 2023 and 2024.\u003c/p\u003e \u003cp\u003eThe data of the relev\u0026eacute;s were arranged in a matrix (relev\u0026eacute;s x species) where Braun-Blanquet abundance/dominance indexes were converted into percentage of plant coverage as proposed by Canullo et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (r, 0.01%; +, 0.5%; 1, 3.0%; 2, 15.0%; 3, 37.5%; 4, 62.5%; 5, 87.5%); subsequently, a power transformation with the exponent 0.5 on these values was carried out according to Tich\u0026yacute; et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCluster analysis and Detrended Correspondence Analysis (DCA) were performed to identify floristic-physiognomic similarities/differences among the relev\u0026eacute;s using the \u0026ldquo;vegan\u0026rdquo; package of R (Dixon \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Cluster analysis was performed using the Unweighted Pair Group Method with Arithmetic mean method (UPGMA) and the chord distance coefficient (Legendre and Gallagher \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Furthermore, Pearson's phi coefficient (\u003cem\u003eΦ\u003c/em\u003e) was used to identify the diagnostic species (plants significantly associated with the different types of vegetation) as proposed by Chytry et al. (2002) and Tich\u0026yacute; and Chytry (2006). The coefficient \u003cem\u003eΦ\u003c/em\u003e was calculated as the following formula:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\varPhi\\:=\\:\\frac{N\\bullet\\:\\:{n}_{p}-n\\bullet\\:{N}_{p}}{\\sqrt{n\\bullet\\:{N}_{p}\\bullet\\:(N-n)\\bullet\\:(N-{N}_{p})}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eN\u003c/em\u003e is the number of relev\u0026eacute;s in the data set, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the number of the relev\u0026eacute;s in a target group of relev\u0026eacute;s, \u003cem\u003en\u003c/em\u003e is the number of occurrences of the species in the data set and \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the number of occurrences of the species in the target group of relev\u0026eacute;s. The coefficient \u003cem\u003eΦ\u003c/em\u003e can assume values from 1 (the species is concentrated in the target relev\u0026eacute;s group) to -1 (the species is under-represented in the target relev\u0026eacute;s group). The identification of the diagnostic species was carried out using the \u0026ldquo;indicspecies\u0026rdquo; R package (De C\u0026aacute;ceres \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhytosociological analysis of vegetation was performed using the hierarchical floristic classification system of vascular plant, bryophyte, lichen, and algal communities of the European vegetation (Mucina et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and \u0026ldquo;Prodromo della vegetazione d\u0026rsquo;Italia\u0026rdquo; (Biondi and Blasi \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, the phytosociological classes of the species of the relev\u0026eacute;s were assigned (considering only the vegetation classes of the Southern Alps) to determine the ecological features of the plant communities. Additionally, the indices of Landolt et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) were applied for synecological analysis. Specifically, for each relev\u0026eacute; (and groups of relev\u0026eacute;s), the mean values of T - temperature, K - continentality, L - light intensity, F - soil moisture, R - substrate reaction, N - nutrients, H - humus, and D - soil aeration, were calculated. Ecological differences between bark beetle-affected and unaffected forests were determined using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. A \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eThe scientific names of the plant species are in accordance with Pignatti (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), while the names and the codes of syntaxa follow Mucina et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The statistical analyses were performed using R 3.6.1 software (R Development Core Team 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCurrent and future distribution of the bark beetle and susceptible spruce forests\u003c/h2\u003e \u003cp\u003eBased on data from the Geoportal of the Lombardy Region, the study area contains spruce forests (26,800 ha) ranging in altitude from 300 m to 2,150 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Of these, 88% are in the mountain and high-mountain belt between 800 m and 1,700 m a.s.l. The area of forest affected by the bark beetle amounts to 725 ha, equivalent to 2.7% of the total spruce forest area in the study. These affected forests are confined to altitudes between 600 m and 1,600 m a.s.l.\u003c/p\u003e \u003cp\u003eThe highest rates of spruce forest damage (\u0026gt;\u0026thinsp;8.2%) occur at lower elevations (600\u0026ndash;700 m a.s.l.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Significant damage is also observed in the elevation band between 1,150 m and 1,300 m a.s.l., where the percentage of affected forest exceeds 5%. In the upper high-mountain belt, above 1,600 m a.s.l., and in the subalpine zone, no spruce forests currently show signs of bark beetle infestation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that in the study area, the extent of spruce forest affected by bark beetle gradually increases with higher levels of annual solar radiation. In fact, 85% of the infested spruce stands are in areas where the theoretical annual solar energy exceeds 3,500 MJ m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows maps of current and future areas with bioclimatic conditions suitable for the bark beetle to cause significant damage to existing spruce forests and the percentages of forests lightly/highly susceptible to mass infestations. The maps reveal that areas with suitable climatic conditions for the bark beetle shift in extent and elevation over time (from the current situation to projections for 2080). Specifically, the suitable areas for bark beetle attacks in future periods (2021\u0026ndash;2040, 2041\u0026ndash;2060, and 2061\u0026ndash;2080) are projected to be smaller than those under current conditions but are generally located at higher elevations. As a result, the percentage of forests highly susceptible to mass bark beetle infestations is expected to increase from 45.1\u0026ndash;58.2% over the next 60 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Based on habitat suitability maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), in the future, spruce forests may find optimal conditions (low likelihood of severe bark beetle attacks) above 1,800 m a.s.l. in southern areas of the UBOR (lower Camonica Valley) and above 1,500 m a.s.l. in northern areas (upper Camonica Valley).\u003c/p\u003e \u003cp\u003eThe distribution model generated by MaxEnt, used to produce habitat suitability maps, has high predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.91). Among the 19 bioclimatic variables considered, BIO9 (mean temperature of the driest quarter) contributes the most to the model's formulation (percent contribution: 80.1%; permutation importance: 77.6%) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the relationship (response curve) between the probability of massive bark beetle infestations and the BIO9 variable. The response curve indicates that the most suitable habitat for severe bark beetle outbreaks occurs where the mean temperature of the driest quarter (which in the study area corresponds to winter) ranges between 4\u0026deg;C and \u0026minus;\u0026thinsp;4\u0026deg;C. Areas with BIO9 values below \u0026minus;\u0026thinsp;4\u0026deg;C, typically at higher elevations, are less suitable for the bark beetle (probability of presence\u0026thinsp;\u0026lt;\u0026thinsp;0.2). Similarly, areas with BIO9 values above 4\u0026deg;C are also unsuitable for massive bark beetle attack.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eVegetation features\u003c/h2\u003e \u003cp\u003e198 species of herbs, shrubs and trees were identified (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea shows the dendrogram generated by cluster analysis, highlighting two main vegetation types: cluster A and cluster B.\u003c/p\u003e \u003cp\u003eCluster A includes all relev\u0026eacute;s conducted in forests affected by the bark beetle, while cluster B comprises those conducted in forests unaffected by the pest. These two clusters are characterized by distinct plant communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) and are mainly differentiated by the presence of live/dead spruces and diagnostic species listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The diagnostic species of cluster B (\u003cem\u003ePicea abies\u003c/em\u003e, \u003cem\u003eVaccinium myrtillus\u003c/em\u003e and \u003cem\u003eSaxifraga cuneifolia\u003c/em\u003e) are characteristic of holarctic coniferous forests (\u003cem\u003eVaccinio-Piceetea\u003c/em\u003e phytosociological class), with an understory featuring substantial moss coverage. In contrast, the diagnostic species of cluster A are primarily associated with shrublands of the \u003cem\u003eRobinietea\u003c/em\u003e class (seral forest-clearing and anthropogenic successional scrub and thickets) and forest edges or clearings of the \u003cem\u003eEpilobietea angustifolii\u003c/em\u003e class (tall-herb vegetation of forest edges and clearings), as well as spruce snags.\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\u003eDiagnostic species of cluster A (forest affected by bark beetle) and cluster B (forest not affected by bark beetle). Phytosociological class code, Pearson's phi coefficient (\u003cem\u003eΦ\u003c/em\u003e) and \u003cem\u003ep\u003c/em\u003e-value of each diagnostic species are reported. The codes of phytosociological class are the same used by Mucina et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e): PIC, \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e; ROB, \u003cem\u003eRobinietea\u003c/em\u003e; FAG, \u003cem\u003eCarpino-Fagetea sylvaticae\u003c/em\u003e; EPI, \u003cem\u003eEpilobietea angustifolii\u003c/em\u003e; POP, \u003cem\u003eAlno glutinosae-Populetea albae\u003c/em\u003e; RHA, \u003cem\u003eCrataego-Prunetea\u003c/em\u003e; PAR, \u003cem\u003ePapaveretea rhoeadis\u003c/em\u003e; QUE, \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e; ULI, \u003cem\u003eCalluno-Ulicetea\u003c/em\u003e; NAR, \u003cem\u003eNardetea strictae\u003c/em\u003e. Key: *, significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05); **, significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnostic species\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhytosociological class code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eΦ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePicea abies\u003c/em\u003e (died)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRubus idaeus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMycelis muralis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAG, EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFragaria vesca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSolanum dulcamara\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSambucus racemosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRubus ulmifolius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBetula pendula\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGeranium robertianum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAG, EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGalium aparine\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOP, EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTaraxacum officinale\u003c/em\u003e (group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSambucus nigra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB, POP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGaleopsis tetrahit\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePAR, EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBuddleja davidii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalix caprea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eUrtica dioica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROB, POP, EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePicea abies\u003c/em\u003e (alive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMosses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eVaccinium myrtillus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC, QUE, ULI, NAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSaxifraga cuneifolia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e*\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\u003eEach main vegetation cluster is further subdivided into subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which reveal floristic/physiognomic differences in plant communities. These differences are mainly attributable to the environmental conditions of the specific survey sites, such as soil type, microclimate, and anthropogenic management or disturbance. For cluster A, four subclusters/plant communities have been identified:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA1 \u0026ndash; plant community dominated by heliophilous shrubs such as \u003cem\u003eRubus idaeus\u003c/em\u003e (the dominant species), typical of clearings, disturbed forests, or areas affected by treefall or logging (\u003cem\u003eRubetum idaei\u003c/em\u003e association; \u003cem\u003eRobinietea\u003c/em\u003e class). In this study, it occurs in areas where the density or coverage of dead spruce trees is low.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA2 \u0026ndash; plant community characterized by a high density and coverage of dead spruce trees, with an understory featuring limited \u003cem\u003eRubus idaeus\u003c/em\u003e and abundant \u003cem\u003eVaccinium myrtillus\u003c/em\u003e, \u003cem\u003eLuzula nivea\u003c/em\u003e, ferns, and mosses. It also includes young broadleaf trees (\u003cem\u003eFagus sylvatica\u003c/em\u003e, \u003cem\u003eSorbus aucuparia\u003c/em\u003e, \u003cem\u003eCastanea sativa\u003c/em\u003e) typical of mature forest communities with occasional silver fir (\u003cem\u003eAbies alba\u003c/em\u003e) and spruce.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA3 \u0026ndash; shrubland with \u003cem\u003eRubus idaeus\u003c/em\u003e and a high density of exotic species such as \u003cem\u003eBuddleja davidii\u003c/em\u003e, \u003cem\u003eSenecio inaequidens\u003c/em\u003e and \u003cem\u003eErigeron canadensis\u003c/em\u003e. These species are associated with anthropogenic disturbance in areas near roads or settlements where spruce trees have been cut and removed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA4 \u0026ndash; shrubland composed of thermophilic species, including \u003cem\u003eRubus ulmifolius\u003c/em\u003e (the dominant species), \u003cem\u003eCorylus avellana\u003c/em\u003e, \u003cem\u003ePopulus tremula\u003c/em\u003e, \u003cem\u003eFagus sylvatica\u003c/em\u003e and \u003cem\u003eSalix caprea\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCluster B can also be divided into four subclusters, although these are less dissimilar compared to those in Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eB1 \u0026ndash; dense, thermophilic spruce forest with low coverage/presence of herbaceous plants and bryophytes, and with young \u003cem\u003eCastanea sativa\u003c/em\u003e trees.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eB2 \u0026ndash; spruce forest with bryophytes, \u003cem\u003eVaccinium myrtillus\u003c/em\u003e, and young broadleaf trees (\u003cem\u003eFagus sylvatica\u003c/em\u003e, \u003cem\u003eBetula pendula\u003c/em\u003e, \u003cem\u003eCastanea sativa\u003c/em\u003e, \u003cem\u003eQuercus petraea\u003c/em\u003e, \u003cem\u003eAcer pseudoplatanus\u003c/em\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eB3 \u0026ndash; spruce forests with well-established spruce regeneration and high coverage of \u003cem\u003eOxalis acetosella\u003c/em\u003e and bryophytes in the understory.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eB4 \u0026ndash; spruce forests on neutral to basic soils, characterized by basiphilous herbaceous plants (\u003cem\u003eSesleria caerulea\u003c/em\u003e, \u003cem\u003eHelleborus niger\u003c/em\u003e, \u003cem\u003eCarex alba\u003c/em\u003e, \u003cem\u003eHepatica nobilis\u003c/em\u003e) and \u003cem\u003eFagus sylvatica\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, the forest communities affected by the spruce bark beetle (cluster A) are predominantly composed of species of \u003cem\u003eCarpino-Fagetea sylvaticae\u003c/em\u003e class (mesic deciduous and mixed forests of temperate Europe) and \u003cem\u003eRobinietea\u003c/em\u003e class, with a smaller proportion of species of \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e and \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e (acidophilous oak and oak-birch forests) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Conversely, in spruce forests unaffected by the bark beetle, the percentage of \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e species is the highest, followed by those of \u003cem\u003eCarpino-Fagetea sylvaticae\u003c/em\u003e and \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e classes. In cluster B, \u003cem\u003eRobinietea\u003c/em\u003e species are almost absent (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e shows the results of the ecological indices of Landolt et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), while Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the values of key ecological variables (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in more than 60% of the sampling sites) that distinguished the vegetation communities of beetle-affected and unaffected forests across the study sites. The graphs reveal that, at all sampling sites, communities established after a bark beetle attack were more thermophilic compared to those in unaffected forests. Additionally, the communities in beetle-affected forests consisted of more light-demanding (heliophilous) species that require less organic matter (or litter) in the soil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the analyses regarding the characteristics of the areas currently affected by the spruce bark beetle provided valuable insights into the ecology of spruce forests and bark beetle outbreaks in the Southern Alps, considering both current climatic conditions and future scenarios. It was evident that in the study area elevation, solar radiation, and the temperature of the driest quarter of the year are the most relevant variables for the development of severe bark beetle outbreaks. In particular, spruce forests located in areas warmer during the winter months (generally at lower elevations and/or in areas with good exposure to sunlight) were found to be the most susceptible to intense bark beetle outbreaks. These conditions are currently observed below 1,600 m a.s.l. in the submountain and mountain vegetation belts, where spruce trees are likely to experience greater stress due to high temperatures, which, in turn, allow the bark beetle to increase the number of generations per year. These results are consistent with Kozhoridze et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who found a negative relationship between infestation and elevation, with higher infestation rates below 900 m a.s.l.. Also, Jakoby et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed that, in the Swiss Alps, the number of \u003cem\u003eI. typographus\u003c/em\u003e generations per year is 2 at 500 m of elevation, 1.5 at 1,000 m and 1 at 1,700 m. Although there are no studies on the annual number of bark beetle generations in the UBOR, it is reasonable to assume that, at the same elevations as the Swiss Alps, the number of \u003cem\u003eI. typographus\u003c/em\u003e generations is similar, if not higher, given that the Swiss Alps are located further north (in the inner Alps) where temperatures are (and will be) generally lower compared to those in the Southern Alps (Kotlarski et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to elevation/temperature, solar radiation \u0026ndash; which primarily depends on slope and aspect \u0026ndash; also increases the flight activity and voltinism of \u003cem\u003eI. typographus\u003c/em\u003e (Singh et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jacoby et al. 2019). This confirms the findings of this research, which revealed that the affected forest area also increases in line with annual theoretical radiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Monitoring solar radiation, combined with temperature data for a specific area, can enable precise assessment of \u003cem\u003eI. typographus\u003c/em\u003e development within a spruce forest. In fact, Baier et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) developed a model (PHENIPS), based on topoclimatic data, for the spatial and temporal simulation of the seasonal development of \u003cem\u003eI. typographus\u003c/em\u003e at the Kalkalpen National Park (Austria), which yielded good results. This model could also be applied in the UBOR region to monitor the expansion of the bark beetle over time, assess the accuracy of the distribution maps produced by MaxEnt (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and determine where and when to implement bark beetle containment measures.\u003c/p\u003e \u003cp\u003eIn the literature, summer temperature appears to be the most relevant factor in the outbreak dynamics of \u003cem\u003eI. typographus\u003c/em\u003e (Singh et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kozhoridze et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fischer et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, this research suggests that the mean temperature of the driest winter months (BIO9) could be equally important and should be considered when understanding and predicting the distribution (current and future) of bark beetle infestations, at least in the Southern Alps. The results of this study indicate that a high probability (\u0026gt;\u0026thinsp;0.5) of intense bark beetle outbreaks occurs where BIO9 values range between \u0026minus;\u0026thinsp;2.5 and 2.5\u0026deg;C. This is explained by the fact that winter temperatures within this range result in higher survival rates for overwintering individuals (particularly eggs, larvae, and pupae), leading to more severe infestations in spring and summer (J\u0026ouml;nsson et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Areas with BIO9 values that are too high (\u0026gt;\u0026thinsp;5\u0026deg;C) are entirely unsuitable for the bark beetle (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), primarily because these warmer zones lack spruce forests and are instead dominated by broadleaf forests, which are not susceptible to \u003cem\u003eI. typographus\u003c/em\u003e. Similarly, areas with excessively low winter temperatures (BIO9 \u0026lt; -5\u0026deg;C), typically located at higher elevation (subalpine and alpine belts), are also unsuitable for the bark beetle. This is due to several factors, including extreme minimum temperature peaks capable of killing overwintering individuals, the absence of spruce forests at the highest elevations in the study area (the alpine belt in the UBOR is dominated by grasslands, rocks, scree, and glaciers), and/or the presence of mixed forests of spruce, larch, and Swiss pine in the subalpine belt. In fact, in forests composed of various tree species (mixed forests), spruce trees are less susceptible to bark beetle attacks because they are harder for the pest to locate due to the lower presence/concentration of host volatiles emitted by spruce wood (Lindel\u0026ouml;w et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) in the forest. Additionally, within the context of plant-insect interactions, it is well known that volatile compounds emitted by non-host plant species can interfere with the insect's response to aggregation pheromones (Byers et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Such knowledge supports the strategies of converting pure spruce forests into mixed forests, which can significantly reduce bark beetle damage (Seidl et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo date, no damage caused by \u003cem\u003eI. typographus\u003c/em\u003e has been recorded above 1,600 m a.s.l. in the study area, and there is a low percentage (\u0026lt;\u0026thinsp;2%) of damaged spruce forests in the altitude range between 1,300 and 1,600 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), where attacks are not severe. This suggests that, currently, the spruce forests of the UBOR located in the high mountain and subalpine belts are free from massive bark beetle outbreaks, confirming the findings of Faccoli and Bernardinelli (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). It also indicates that current efforts to mitigate damage should focus on submountain and mountain areas. However, based on the future climate models considered in this research, it is likely that in the coming decades, the bark beetle will find optimal climatic conditions for intense outbreaks even in the high mountain vegetation belt, above 1,800 m a.s.l. in lower Camonica Valley and above 1,500 m a.sl. in upper Camonica Valley. If this occurs, it will result, within the next 60 years, in the near-total destruction/change of the current pure spruce forests in lower Camonica Valley (where there are also few areas above 1,800 m for the spruce forest to expand). Meanwhile, in upper Camonica Valley, spruce forests will persist above 1,800 m and in areas that may develop at higher altitudes, such as abandoned alpine and subalpine pastures (Cislaghi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe absence of current bark beetle infestations in the (few) spruce forests found at the lowest elevations of the UBOR (between 300 m and 600 m a.s.l.) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) is probably due to the fact that these secondary forests, mostly small in size, are fragmented and scattered within other forest formations of the hilly belt (broadleaf forests), making them difficult for the bark beetle to locate.\u003c/p\u003e \u003cp\u003eThe results of the floristic-vegetational and ecological analysis of the phytosociological relev\u0026eacute;s provided a series of insights into the current characteristics of forests affected by the bark beetle, suggesting the composition/type of future forest communities of the mountain belt. Except in rare cases, these are likely to be very different from those of \u003cem\u003eCalamagrostio arundinaceae-Piceetum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe plant communities of the forests attacked by the bark beetle were all found to be very different from those not attacked (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), because the death of the spruce trees (which constituted the dominant component of the plant community) altered a series of environmental variables in the ecosystem, favoring the growth of plant species different from those of \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e and initiating a progressive secondary succession (Loidi \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). One of the ecological variables that changed most due to bark beetle attack is the availability of light in the understory, which is greater in areas affected by the pest due to the loss of leaves from dead spruces. This was confirmed by the application of the L index of Landolt et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), whose values, in all sampling sites, showed post-disturbance communities to be more heliophilous than pre-disturbance ones, sometimes with very marked differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe different availability of light in the understory, mainly due to the coverage/density of spruce snags, appears to be one of the main factors determining the establishment of \u003cem\u003eRubetum idaei\u003c/em\u003e (cluster A1) and its variants (A3 and A4), rather than the A2 community (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The plant communities of clusters A1, A3, and A4 are composed of various heliophilous pioneer species of forest-clearing (\u003cem\u003eRobinietea\u003c/em\u003e class), which distinguish them from A2, characterized by less heliophilous species and which already in this early stage of secondary succession has young trees, herbs, and shrubs of mature forest communities. A good degree of shade due to the high coverage/density of spruce snags seems to accelerate ecological succession, bypassing the \u003cem\u003eRubetum idaei\u003c/em\u003e stage and achieving the final stage more quickly (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The ecological role of \u0026ldquo;succession accelerator\u0026rdquo; played by communities with spruce snags deserves further study, considering it is likely that due to the bark beetle, these vegetation types will become widespread in the Southern Alps in the coming decades. Moreover, studying these ecosystems could provide important information for managing areas affected by the bark beetle and defining new nature-based solutions (NBSs) \u0026ndash; defined as \u0026ldquo;actions inspired by, supported by or copied from nature\u0026rdquo; (Bauduceau et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) \u0026ndash; for forest restoration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the data collected in this research and field observations, it seems that the plant community with spruce snags could perform an ecological function similar to that of \u003cem\u003ePiceo-Sorbetum aucupariae\u003c/em\u003e (\u003cem\u003eRobinietea\u003c/em\u003e class), which is the stage of the acidophilus dynamic series of silver fir and spruce (\u003cem\u003eCalamagrostio arundinaceae-Piceo excelsae sigmetum\u003c/em\u003e) that, in the study area, precedes the potential natural vegetation of \u003cem\u003ePiceion excelsae\u003c/em\u003e (Verde et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the bark beetle-affected forests of the UBOR fall within the \u003cem\u003eCalamagrostio arundinaceae-Piceo excelsae sigmetum\u003c/em\u003e \u0026ndash; which includes the \u003cem\u003eCalamagrostion arundinaceae\u003c/em\u003e (fringe) stage, followed by a shrub stage of \u003cem\u003eSambuco-Salicion capreae\u003c/em\u003e (\u003cem\u003eRubetum idaei\u003c/em\u003e, \u003cem\u003ePiceo-Sorbetum aucupariae\u003c/em\u003e), and the spruce forest (potential natural vegetation) (Verde et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) \u0026ndash; it is likely that, given the current environmental/climatic conditions, the final stage (spruce forest of \u003cem\u003ePiceion excelsae\u003c/em\u003e) cannot be reached. Indeed, the plant communities of all areas attacked by the bark beetle were found to be more thermophilous (some markedly so) than those of undisturbed spruce forests (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) and often composed of young trees of mature forest communities other than spruce, such as chestnut, beech, and silver fir (clusters A2 and A4). The presence of these young trees and a complex of other species (trees, shrubs and herbs), typical of mature forests of broadleaf trees of the \u003cem\u003eCarpino-Fagetea sylvaticae\u003c/em\u003e and \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e phytosociological classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), suggests new types of \u0026ldquo;current potential vegetation\u0026rdquo; (Biondi \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Specifically, it is likely that under current climatic conditions and those predicted for the coming decades, the submountain and mountain spruce forests attacked by the bark beetle in the study area could gradually (and spontaneously) be replaced by chestnut and/or oak forests in the submountain belt, beech and silver fir forests in the mountain belt, and silver fir forests with spruce in the high-mountain belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Spruce-dominated forests are likely to be primarily located in the subalpine belt, which will expand due to the upward shift of the tree line.\u003c/p\u003e \u003cp\u003eThis deduction is supported not only by the results of this research but also by the fact that in Europe (and other parts of the world), a significant upward shift in forest plant species and vegetational belts is being observed and modeled (Lenoir et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Giupponi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zischg et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In particular, Lenoir et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) detected an upward shift in species optimum elevation averaging 29 m per decade in western Europe, and Zischg et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) modeled an upward shift of vegetation belts in Swiss forests, more or less pronounced depending on the different climate change prediction models and the different topoclimatic characteristics of the territory considered. Furthermore, in the UBOR, Giupponi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), following the destruction of spruce forests by Storm Vaia, observed \u003cem\u003eQuercus petraea\u003c/em\u003e (which has an ecology similar to chestnut) growing at 1,300 m a.s.l., 300 m higher than the altitude at which oak/chestnut forests are mapped in the Italian vegetational series map of Blasi (2010).\u003c/p\u003e \u003cp\u003eIn the coming years, it would be advisable to monitor changes in the forest communities of the UBOR to confirm/integrate the scheme in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e so that it can become a useful tool for technicians and forest managers in the study area (and the Southern Alps in general) to understand vegetation potentials and adopt correct measures and actions for sustainable management of the forest cover. Specifically, it would be beneficial to collect floristic data and better define the mature forest communities (and the stages of dynamic series) that will be present where spruce forests currently attacked by the bark beetle and/or particularly susceptible to the insect are located (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on the floristic data collected in this research, it is likely that the beech forest communities that will replace spruce forests on basic soils (cluster B4) will be significantly different from those that will grow on acidic soils (e.g., replacing cluster B2). Indeed, the former are likely to be forests of \u003cem\u003eAremonio-Fagion\u003c/em\u003e, and the latter of \u003cem\u003eLuzulo-Fagion sylvaticae\u003c/em\u003e (Del Favero \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mucina et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). More data and studies aimed at defining the current (and future) vegetational potentials of spruce forests attacked by the bark beetle in the Southern Alps will undoubtedly be useful, if only to update the forest type maps of Lombardy (Del Favero \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and other vegetational maps of the Italian Alps, which are very useful tools for territorial management provided they are not too outdated given the rapidity of climate change.\u003c/p\u003e \u003cp\u003eKnowing the current and future vegetation potentials of areas that are (or will be) attacked by the bark beetle could suggest which species to use for the restoration of forest communities. This action should be carried out quickly (preferably using native species) at least in bark beetle-affected forests located on steep slopes. Indeed, the rapid and simultaneous death of all (or most) trees in a pure spruce forest leads, within a few years, to the decomposition of their roots, which could cause significant hydrogeological instability problems since it is well known that tree roots contribute significantly to stabilizing the soil on mountain slopes (Bischetti et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Vergani et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Chiaradia et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). More information should be gathered on the biotechnical characteristics of the roots of herbs and shrubs that could be used for environmental restoration and soil bioengineering interventions (Giupponi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt would also be important to conduct studies to identify bioindicators that can help more accurately determine which forests might be more susceptible to the bark beetle than species distribution models currently allow. In this sense, this research identified \u003cem\u003eSaxifraga cuneifolia\u003c/em\u003e as a diagnostic species of spruce forests not attacked by the bark beetle (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This species, together with a high coverage of mosses, appears particularly associated with microthermal forests, and its absence (or low coverage) in forests not yet attacked by the pest seems to indicate their high susceptibility to the insect. \u003cem\u003eS. cuneifolia\u003c/em\u003e is indeed a species of \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e (Mucina et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) widespread throughout the Alps (Aeschimann 2002), microthermal, shade-loving, and requiring soil with good availability of humus/litter (Landolt et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tich\u0026yacute; et al. 2022; Dengler et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the study area, it is absent (or with very low coverage values) in spruce forests not yet attacked by the bark beetle where thermophilic broadleaf trees (chestnut and/or beech) are present (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). The actual effectiveness of \u003cem\u003eS. cuneifolia\u003c/em\u003e as an indicator of bark beetle-susceptible forest should be further investigated in studies that also consider other study areas in the Southern Alps, as well as analyzing the presence/absence of individual moss species in more or less stressed spruce forests to understand if some of them could also behave similarly to \u003cem\u003eS. cuneifolia\u003c/em\u003e. \u003cem\u003eVaccinium myrtillus\u003c/em\u003e also proved to be a diagnostic species of forests not attacked by the bark beetle, but the latter is not an exclusive species of \u003cem\u003eVaccinio-Piceetea\u003c/em\u003e as it also contributes to the formation of forest communities of \u003cem\u003eQuercetea robori-petraeae\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and is therefore indicative of acidophilic forest communities in general.\u003c/p\u003e \u003cp\u003eIf \u003cem\u003eS. cuneifolia\u003c/em\u003e appears to be an indicator of less stressed and less bark beetle-susceptible spruce forests, the presence of exotic species is certainly an indicator of a degraded environment, often caused by anthropogenic interventions/disturbances (Giupponi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An example is the plant community of cluster A3, which is the result of recent cutting and removal actions of spruce snags that were dangerous for roads and houses near the forest affected by the bark beetle (sampling site 4). This community is indeed to be considered a degraded variant of \u003cem\u003eRubetum idaei\u003c/em\u003e (which developed due to the low density/coverage of spruce snags) in which various exotic species with high coverage are present, including \u003cem\u003eSenecio inaequidens\u003c/em\u003e (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), the only African species present in the UBOR and rapidly spreading (Martini et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bona \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Since it is very likely that the spread of exotic species present in cluster A3 is due to the use of tools/machinery from the forestry site that carried out the cutting and removal work of the spruce snags, contaminated with seeds of exotic plants, it would be advisable, if similar operations are to be carried out in other bark beetle-affected forests, to require forestry workers to clean their tools/machinery before moving to intervention areas, especially if these are located in protected areas or areas with few exotic species, as is the case in most mountain areas of the Alps (Dainese et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis action, in addition to preserving the integrity of Alpine ecosystems, would also enable better utilization of their benefits/services. In fact, shrublands dominated by \u003cem\u003eRubus idaeus\u003c/em\u003e and other species of \u003cem\u003eRobinietea\u003c/em\u003e class (such as those that develop in areas affected by bark beetles with low density/coverage of spruce snags) are valuable for the production of edible fruits and, even more so, for the foraging of the domestic bee (\u003cem\u003eApis mellifera\u003c/em\u003e) and wild pollinators, whose survival is currently threatened, among other factors, by climate change (Hristov et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rahimi and Jung \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the Alps, bees not only feed on the nectar of \u003cem\u003eR. idaeus\u003c/em\u003e (and other nectar-producing species) but also facilitate the production of raspberry honey, a prized agri-food product with interesting phytochemical and nutritional characteristics (Leoni et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the presence of \u003cem\u003eS. inaequidens\u003c/em\u003e (alongside \u003cem\u003eR. idaeus\u003c/em\u003e) in cluster A3 prevents the production of high-quality honey because this species contains toxic compounds (pyrrolizidine alkaloids) that contaminate honey through pollen. These toxins can enter the food chain and pose a risk to human health (Sadgrove \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is one of the reasons why \u003cem\u003eS. inaequidens\u003c/em\u003e is included on the \"Black List\" of invasive alien species subject to monitoring and containment in the Lombardy region (Regional Law 10/2008).\u003c/p\u003e \u003cp\u003eFrom the perspective of vegetation dynamics, it is likely that the A3 community plays a role similar to that of the typical \u003cem\u003eRubetum idaei\u003c/em\u003e, but it would be prudent to monitor it over time to confirm the dynamic described in the vegetation scheme of Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. Communities with \u003cem\u003eS. inaequidens\u003c/em\u003e, \u003cem\u003eBuddleja davidii\u003c/em\u003e and other exotic pioneer/invasive species are relatively new to the Alps, and their role in succession is still unclear, especially considering the current and future effects of climate change.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research has clarified the main environmental characteristics of the spruce forests in the UBOR region that are affected by and/or susceptible to bark beetle attacks. The most impacted forests were found to be those located below 1,600 m a.s.l. (in the sub-mountain and mountain belts) and in areas with high solar radiation. Additionally, through the development of a MaxEnt model with high predictive accuracy, primarily defined by the mean temperature of the winter driest quarter, it was found that over 58% of the current spruce forests in UBOR will have a high susceptibility to intense bark beetle attacks in the next 60 years. The analysis of floristic-vegetational and synecological characteristics suggested that in areas attacked by the bark beetle, mature forest communities which are more thermophilic and significantly different (both floristically and physiognomically) from the pre-disturbance spruce forests will develop. Furthermore, based on the interpretation of the results obtained, a model of plant succession was developed that, along with the following suggestions/precautions, could be useful for land managers in order to limit damage from bark beetles, promote rapid forest regeneration, and prevent the degradation of forest ecosystems in the Southern Alps:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFocus on measures/interventions that mitigate the spread of the bark beetle, especially in spruce forests of the sub-mountain and mountain vegetational belts (particularly in areas with higher winter temperatures and high solar radiation), as these are the areas with the most favorable climatic conditions for the development of intense pest outbreaks.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote the conversion of pure spruce forests into mixed forests, prioritizing native species and considering both their ecology and economic value.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAvoid the spread/planting of spruce outside its natural/ecological range, where it could experience more stress and be more vulnerable to bark beetle attacks and/or other pests/diseases.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEncourage, where possible, the spread of spruce in the subalpine belt, where it is unlikely that favorable climatic conditions for intense bark beetle attacks will occur in the coming decades.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote rapid actions/interventions to facilitate tree growth in forests affected by the bark beetle on steeper slopes to prevent hydrogeological instability.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAvoid the removal of spruce snags (in forests affected by the bark beetle) if the goal is to accelerate vegetation succession and achieve mature forest communities more quickly (which may differ significantly from pre-disturbance communities).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn cases of spruce snag removal (for protective, productive, and/or ecological-conservation purposes) or other anthropogenic interventions, ensure that measures are adopted to contain exotic species, such as cleaning tools/machinery before their transport/use in the intervention area.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese tools/suggestions represent an initial contribution to supporting a more informed and sustainable management of areas affected by and/or susceptible to the bark beetle in UBOR and areas with similar environmental conditions. It is to be hoped they will soon be integrated with the results of other research, given the speed at which the bark beetle is spreading in the Southern Alps and the magnitude of the effects it is causing to their forest ecosystems and landscapes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was supported by the Agritech National Research Centre and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) - MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4\u0026mdash;D.D. 1032 17/06/2022, CN00000022), and from the Horizon Project \u0026ldquo;Accelerating transformative climate adaptation for higher resilience in European Mountain regions\u0026rdquo; (MountResilience) project n\u0026deg;: 101112876\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConflicts of interest\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLG and AG conceived and designed the study and interpreted the results. LG collected the data and carried out the floristic, ecological and statistical analyses. LG, RP, DP and SS analyzed the data and wrote the manuscript. LG created figures and tables. LG and AG are the project administrators. All authors have read and agreed to this version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe wish to thank Matteo Ottoveggio for his help in collecting and analyzing floristic-vegetational data, and Fabio Maffezzoni for supporting GIS analysis.\u003c/p\u003e\u003ch2\u003eAvailability of data and material\u003c/h2\u003e \u003cp\u003eThe raw data supporting the conclusions of this manuscript will be made available by the corresponding author, without undue reservation, to any qualified researcher.\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAeschimann D, Lauber K, Moser DM, Theurillat J-P (2004) Flora alpina. 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Applied Vegetation Science 24:e12621. https://doi.org/10.1111/avsc.12621\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":"Picea abies, Snag, Species distribution models, Bark beetle, Plant succession, Plant ecology","lastPublishedDoi":"10.21203/rs.3.rs-5878691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5878691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, many spruce (\u003cem\u003ePicea abies\u003c/em\u003e) forests have been severely affected by bark beetle (\u003cem\u003eIps typographus\u003c/em\u003e) outbreaks in the Southern Alps, but their ecological impacts remain poorly studied. This research analyzed the distribution, ecological, and floristic-vegetational characteristics of forests recently affected by the bark beetle in the upper basin of the Oglio River (Northern Italy) and developed a MaxEnt model to predict severe insect attacks in the coming decades. The results showed that the spruce forests affected by the bark beetle are located exclusively in the sub-mountain and mountain belts (below 1,600 m a.s.l.) and that 85% of them are found in areas with high annual solar radiation (\u0026gt;\u0026thinsp;3,500 MJ m\u003csup\u003e\u0026minus;\u003c/sup\u003e\u0026sup2;). The predictive model for areas susceptible to severe bark beetle attacks proved highly accurate (AUC\u0026thinsp;=\u0026thinsp;0.91) and was primarily defined by the mean temperature of the dry winter quarter (contribution: 80.1%), with values between \u0026minus;\u0026thinsp;2.5 and 2.5\u0026deg;C being particularly suitable for the pest. According to the model, more than 58% of the current spruce forests in the study area will exhibit high susceptibility (probability\u0026thinsp;\u0026gt;\u0026thinsp;0.7) to severe bark beetle attacks by 2080. The floristic-vegetational and ecological analysis of plant communities of 11 bark beetle-affected areas indicated that more thermophilic and significantly different forest communities (in both floristic and physiognomic terms) are expected to develop compared to those of pre-disturbance. Furthermore, the high coverage/density of spruce snags appears to accelerate plant succession, enabling the establishment of mature forest communities in a shorter time frame.\u003c/p\u003e","manuscriptTitle":"Ecology, floristic-vegetational features and future perspectives of spruce forests affected by Ips typographus: insights from the Southern Alps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 07:00:53","doi":"10.21203/rs.3.rs-5878691/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":"79b9a249-7ac4-47e4-a3c7-9d6e80cc0e0d","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-09T06:53:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-28 07:00:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5878691","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5878691","identity":"rs-5878691","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}