Experimental evidence of reduced Ips typographus damage in mixed spruce plantations

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As the forests most often damaged are pure spruce plantations, the question has arisen as to whether increasing tree species diversity could improve the resistance of spruce forests to the damage caused by these bark beetles. We took advantage of a spruce bark beetle infestation in a tree diversity experiment in Germany, where spruce trees were planted in pure or mixed plots, with one to three other tree species in a substitutive design, to test this hypothesis of associational resistance. Using aerial images, we retrospectively counted the number of spruces killed in all pure and mixed spruce plots from 2019 to 2023, and monitored new colonization by I. typographus in 2024 using pheromone traps. Bark beetle damage decreased significantly when the proportion of spruce trees in mixed plots was lower, a consequence of greater tree species richness. The damage and colonisation by bark beetles decreased even more when taller heterospecific neighbours, in particular Douglas firs, overshadowed the spruces, which probably reduced their visual and chemical apparency. This associational resistance likely stems from a combination of reduced host tree availability and release of non-host volatiles, causing disruption to the localization of the hosts by the beetles. These results confirm that mixing tree species can help prevent forest insect damage, and give an insight into the species composition of more resistant mixed spruce plantations, particularly with the association of other fast-growing species. biodiversity bark beetle associational resistance tree diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Insect pests are increasingly damaging forest ecosystems as a result of climate change (Patacca et al. 2023; Forzieri et al. 2024). They take advantage of warmer temperatures that reduce their winter mortality and accelerate their life cycle (Jactel et al. 2019). In addition under drier conditions, water-stressed trees are more vulnerable to infestation (Jactel et al. 2012; Gely et al. 2020). A striking example is provided by bark beetles, which have developed large-scale epidemics across North America and Europe since the beginning of the 20th century (Hlasny et al. 2021a), causing unprecedented mortality in coniferous forests (Kurz et al. 2008; Hlasny et al. 2021c). Ips typographus (Linnaeus, 1758), the spruce bark beetle, is solely responsible for several hundred cubic meters of mortality in large Central European Norway spruce plantations, following recurrent events of windstorms, drought and heat waves (Mezei et al. 2017; Hlasny et al. 2021c). Once triggered, outbreaks of bark beetles are very difficult to manage. The most common curative method is sanitary felling (Leverkus et al. 2018), i.e. the removal of attacked trees to reduce local insect populations. However, its effectiveness remains limited (Stadelmann et al. 2013; Havasova et al., 2017), unless a considerable number of attacked trees are removed at early stages of infection (Dobor et al. 2020 ab; Augustynczik et al. 2021), which comes with very high intervention costs. Preventive methods are therefore of great interest. Reducing tree density through thinning operations has been shown to reduce the success of bark beetle attacks as it can improve the vigour of individual trees (Fettig et al. 2007) and therefore their ability to produce more constitutive (e.g. resin) and induced (e.g. tannins) chemical defences (Krokene 2015). However, thinning is costly and needs to be spaced over time, particularly to avoid increasing the vulnerability of stands to wind damage (Jactel et al. 2009). Another method of prevention is to increase the specific and functional diversity of forests in order to improve their resistance to insect pests. Numerous works have shown that, overall, mixed-species forests are more resistant to pest damage than tree monocultures (Jactel et al. 2021). This ‘associational resistance’ (Barbosa et al. 2009) operates for many guilds of herbivorous insects, including bark and wood borers (Jactel et al. 2021). Several empirical studies have focused on the effect of forest composition on vulnerability to I. typographus . In many cases they reported on lower damage in mixed than in pure spruce stands (Klopcic et al. 2009; Kausrud et al. 2012; Hlasny et al. 2013; Stadelmann et al. 2013; Faccoli et al. 2014; Grodzki et al. 2014; Pasztor et al. 2014; Muller et al. 2022; Nardi et al. 2023; de Groot et al. 2023; Khozoridze et al. 2024; Table 1). A few observational studies revealed no difference in the level of spruce bark beetle damage between pure spruce and mixed forests, but these were carried out under epidemic conditions with very high bark beetle population densities (Hilszczanski et al. 2006; Kamińska et al. 2020; Stereńczak et al. 2020; Table 1). Three main mechanisms have been suggested to explain the associational resistance of mixed forests to insect herbivores (Jactel et al. 2021). In the temporal order of forest habitat colonisation and exploitation of tree resources by forest pests, species mixing may first reduce the ability of insects to find suitable host trees. In mixed forests, heterospecific neighbouring trees can physically, if they are taller for example, or chemically, if they release repellent odours, hide focal host trees from their pest, i.e. the reduced plant apparency hypothesis (Castagneyrol et al. 2013). In the case of I. typographus , it has been shown that deciduous trees can emit non-host volatile substances that disrupt the beetles' ability to recognise and then attack spruce trees (Zhang and Schlyter 2004; Table 1). The second step is the exploitation of host tree resources by insects to feed and lay eggs and thus build up their population. In this case, the main driver is the quantity of suitable host trees, which is proportionally reduced in mixed stands, i.e. the resource dilution hypothesis (Hambäck et al. 2014). The reduction in proportion of spruce trees in mixed forests is the explanation most often cited in the literature to explain the decrease in damage by I. typographus (e.g. Netherer & Nopp-Mayr, 2005; Grodzki et al. 2014; Pasztor et al. 2014; Müller et al. 2022; Nardi et al. 2022; Table 1). Once established in their new habitat, insect herbivores may be preyed upon by insectivorous birds, bats or arthropods, or killed by insect parasitoids. It has been shown that increasing the diversity of trees in forests leads to higher abundance, diversity and activity of insect’ natural enemies (Stemmelen et al. 2022; Vázquez‐González et al. 2024). Very few studies have addressed this “natural enemies hypothesis” in the case of I. typographus infestations (Table 1) but Warzee et al. (2006) mention that the predatory clerid beetle Thanasimus formicarius (Linnaeus 1758) is able to reproduce better in mixed stands of pine and spruce than in pure spruce stands, as the pines provide a better substrate for pupation. The vast majority of empirical studies dealing with the effect of species mixing on damage caused by I. typographus were based on field observations in managed spruce forests, which often confound the effect of tree species composition with other variables, including abiotic conditions or management interventions. They therefore do not allow for specific testing of the effect of the species composition of mixed stands, nor for unraveling the mechanisms of associative resistance at work, as is the case in tree diversity experiments (e.g. Berthelot et al. 2021). Table 1 . List of published studies on the effect of mixing other species with spruce on susceptibility to attacks by the Ips typographus , the spruce bark beetle. The main effect indicates if I. typographus damage were lower (↘) or not changed (→) in mixed spruce stands compared to pure spruce stands. The three last columns indicate the mechanisms involved, as proposed in the discussion part of the cited papers. Host dilution relates to the negative correlation between the proportion of spruce (host resource) in the mixture and the amount of bark beetle damage. Non-host disruption refers to the reduced probability of I. typographus finding host spruce trees in the presence of non-host tree species (broadleaved, other conifers) due to lower apparency or release of repellent non-host volatiles. “Natural enemies” corresponds to the biological control of I. typographus by its natural enemies (e.g. predators) that are supposed to be more diverse, abundant and active in mixed forests. Date Reference Method Country Type of mixture with spruce Beetle population density Main effect Host dilution Non-host disruption Natural enemies 2002 Baier et al. Experimental Sweden Broadleaved endemic ↘ x 2004 Zhang et al. Review Sweden Broadleaved endemic ↘ x 2005 Netherer et al. Modelling Slovakia, Poland low epidemic ↘ x 2006 Hilszczahski et al. Empirical Poland Broadleaved high epidemic → x 2006 Warzee et al. Empirical Belgium Broadleaved, Conifers endemic ↘ x 2011 Schiebe et al. Experimental Germany endemic ↘ x 2012 Kausrud et al. Modelling International Broadleaved (review) ↘ x 2012 Overbeck et al. Modelling Germany Broadleaved low epidemic ↘ x 2013 Hlásny et al. Empirical Slovakia Broadleaved high epidemic ↘ x 2013 Stadelmann et al. Empirical Switzerland Broadleaved, Conifers low epidemic ↘ x 2014 Faccoli et al. Empirical Italy Broadleaved, Conifers low epidemic ↘ x 2014 Grodzki et al. Empirical Poland high epidemic ↘ x 2014 Kärvemo et al. Empirical Sweden Broadleaved high epidemic ↘ x 2014 Pasztor et al. Empirical Austria Broadleaved, Conifers high epidemic ↘ x 2018 Akinci et al. Empirical Turkey Broadleaved, Conifers low epidemic ↘ x 2020 Kaminska et al. Empirical Poland Broadleaved high epidemic → x 2020 Sterenczak et al. Empirical Poland Broadleaved, Conifers high epidemic → x 2022 Müller et al. Empirical Germany Broadleaved, Conifers high epidemic ↘ x 2022 Nardi et al. Empirical France Conifers high epidemic ↘ x 2023 De Groot et al. Empirical Slovenia Broadleaved, Conifers high epidemic ↘ x x 2024 Kozhoridze et al. Empirical Czechia Broadleaved, Conifers high epidemic ↘ x 2024 Vodde et al. Empirical Estonia Broadleaved high epidemic ↘ x The BIOTREE tree diversity experiment in Thuringia (Germany) is the oldest tree diversity experiment in temperate Europe, explicitly testing the relationship between tree diversity and ecosystem processes (Scherer-Lorenzen et al. 2007). It was subject to severe spruce bark beetle epidemics that developed in Germany due to hot and dry weather conditions in 2018. This provided a unique opportunity to experimentally test the effect of tree species richness and composition on the level of tree mortality caused by attacks from I. typographus , by comparing different types of spruce stands mixed with pure spruce stands of the same age, density and management. Specifically we tested the following hypotheses: H1) Mixed spruce plots are less damaged by the spruce bark beetle than pure spruce plots: the associational resistance hypothesis H2) The damage caused by I. typographus decreases as the proportion of spruce trees declines in species-rich mixed stands: the resource dilution hypothesis H3) The presence of taller non-host species in mixed spruce stands leads to a lower probability of spruce being attacked by I. typographus : the plant apparency hypothesis H4) The higher density of the predator Thanasimus formicarius contributes to better control of I. typographus populations in mixed stands: the natural enemies hypothesis . Material and methods Experimental site The study was carried out in the BIOTREE-SPECIES experiment near the town of Kaltenborn, Thuringia, Germany, which is part of the Tree Diversity Network (https://treedivnet.ugent.be/experiments/BIOTREE.html). Trees were planted in 2004 on a former meadow with homogeneous soil conditions in order to establish a diversity gradient ranging from 1- (monoculture) to 2-, 3- and 4-species mixes of four dominant species: spruce ( Picea abies ), Douglas fir ( Pseudotsuga menziezii ), oak ( Quercus petraea ) and beech ( Fagus sylvatica ). All possible combinations were realized, and the 4-species mixture was replicated, resulting in a total of 16 plots (see Figure 1). Each plot of 72 m x 48 m with a given tree composition was subdivided into three equally sized sub-plots to test three management alternatives: no management (U), thinning using close-to-nature silviculture (M), and thinning in plots in which four additional species were interspersed at a lower density to mimic enrichment ( Abies alba, Acer pseudoplatanus, Fraxinus excelsior and Sorbus aucuparia ) (M+). A thinning took place during the winter of 2020-2021 in the M and M+ subplots where 30% of the basal area of all component species were felled, with felled trees remaining in the plots. The trees were planted in 8 m x 8 m monospecific cells, so that the mixed subplots were mosaics of 30 pure cells of different species. The cells were randomly distributed within a sub-plot. Within a cell, trees were planted in rows 2 m apart, 2 m apart for spruce and Douglas-fir (i.e. 16 trees/cell) and 1 m apart for oak and beech (i.e. 27 trees/cell) (Fig.1) . More details on the experimental design can be found in Scherer-Lorenzen et al. (2017). Counting trees attacked by Ips typographus In 2018, an extreme drought event resulted in record-breaking average growing season temperatures and vapor pressure deficits for the region, with increases of 3.3°C and 3.2 hPa above the long-term averages from 1961 to 1990 respectively (Schuldt et al., 2020). This led to unprecedented drought-induced mortality in adult trees across multiple species and triggered outbreaks of the spruce bark beetle I. typographus . The first signs of its infestation were detected at BIOTREE-Kaltenborn in summer 2018 and the first dead trees were observed in 2019. In 2022, another drought year occurred in the region. To estimate the number of spruce trees attacked and killed by the spruce beetle, we visually checked annual aerial images of the site, taken each year in late spring and downloaded from the Thuringian Geoportal (https://geoportal.thueringen.de/gdi-th). Tree positions from the experimental design were converted into geographic coordinates using GeoDispo software and imported as a vector layer in QGIS to locate each planted tree precisely. Each spruce was visually assessed for crown discoloration, as an indicator of attack and mortality caused by I. typographus . In 2024, we conducted a ground survey to validate these assessments. 19 trees whose crowns showed dead branches in the aerial photographs (out of 507; 3.7%) were in fact still alive, which corresponded to attacks by Pityogenes chalcographus (Linnaeus 1761). The other dead trees were attributed to successful infestation by I. typographus . Only trees newly attacked each year were taken into account to estimate annual mortality levels. We were thus able to calculate the percentage of trees attacked by the beetle per (pure) spruce cell in the 27 pure and mixed sub-plots with spruce of the experiment, from 2019 to 2023, for a total of 330 spruce cells. For counts from 2021 to 2023, we took into account the thinning operation to use the new standing density of spruce and to calculate the percentage of newly attacked trees. The total height of the trees was measured during the winter of 2018-2019. 920 trees were measured, i.e. 200 for each of the main four species and 30 for each of the additional four species. In spring 2024, we again measured the total height of a sample of 160 trees, i.e. 20 trees of each of the eight species. We ensured that the trees were always measured in two adjacent rows, one at the edge of a spruce focal cell and the other in the neighbouring cell of other tree species, in order to take account of any facilitation or competition effects on height growth. However, we did not find any significant difference in the height of the spruce trees between the different compositions of the neighbouring cells. We used these measurements to calculate a spruce cell apparency index ( SCA ), as the difference in total height between focal spruce cells and their nearest neighbouring cells, using the following formula (Damien et al. 2016): With Hs the average height of the spruce trees in the experiment, Hi the average height of the trees in the neighbouring cells of other tree species, and n varying from 8 (when a pure spruce cell was surrounded by eight other cells, in the centre of the sub-plot) to 3 (e.g. when the pure spruce cell was in the corner of a sub-plot and was therefore only bordered by three cells of other tree species). The SCA could not be properly estimated in the pure spruce subplots because the spruce cells were not surrounded by cells of other tree species. Setting Hi to zero in this case would have resulted in an overestimation of the apparency of the pure spruce subplots. We noted that the first attacks of I. typographus occurred in pure spruce plots in 2018-2019 (Fig.2). To take into account the possibility that subsequent infestations were due to beetles originating from the initial infestation spot, we calculated with QGis the Euclidean distance ( DIS , in m) from the barycentre of the pure spruce plot to each pure spruce cell in the experiment. Pheromone trapping of Ips typographus To study the colonisation behaviour of I. typographus across the tree diversity experiment, we set up a pheromone trap in the centre of 18 of the 27 pure and mixed spruce subplots (we excluded subplots M+ with other tree species). We used black interception barrier traps baited with the commercial lure Ipsowit ®, composed of ipsdienol and cis-verbenol (Heber et al. 2021). The traps were suspended 1.5 m above the ground from a rope stretched between two adjacent spruce trees. The traps were activated from the end of April to the end of June 2024, covering the spring generation of spruce beetles. They were assessed twice without permutation between positions. The insects caught were sorted by species. Due to the high number of I. typographus in the pheromone traps, the total captures per assessment period were estimated using a volumetric method. Spruce bark beetles were transferred to a graduated vial and the volume was converted to a number by following an abacus (Öhrn et al. 2014). In addition, we counted the number of predatory insects of I. typographus . Statistical analyses We tested how tree species diversity influenced spruce bark beetle damage. To measure this, we counted the number of newly killed trees in each spruce cell (NKT) across 27 subplots where spruce was present. This included 2 pure spruce plots (M and U) and 25 mixed plots, 24 of these had spruce combined with three other main tree species, while one "pure" spruce subplot (M+) also had four extra species. Based on a visual assessment of the damage from aerial photos, we focused on two years, 2019 and 2023, as the number of newly attacked trees was too low in the other years in between. As the BIOTREE-Kaltenborn mixing scheme followed a substitutive design (same number of planting cells per sub-plot, independent of species richness), tree species richness, Shannon diversity index and proportion of spruce per sub-plot were highly correlated. We decided to retain the proportion of spruce as an explanatory variable in order to better test the host concentration hypothesis and to compare our results with the many studies that have also used this dependent variable. As the spruce trees were planted as pure cells, neighbouring trees were not independent of each other. In addition, a sample of sub-plots (M and M+) was thinned during the study. We therefore decided to consider a spruce cell as the spatial statistical unit, independently of spruce tree density, in order to be able to better compare the results of the two assessment years. Finally, we calculated the proportion of spruce in a sub-plot as the number of spruce cells divided by the total number of cells ( PSP ). We tested the effect of the composition of sub-plots with spruce on the number of killed spruces by I. typographus with a generalised linear mixed model (GLMM) with a binomial distribution and a logit link. The response variable was the number of killed trees in each cell of spruce ( NKT ) compared to the number of alive spruce trees . We proceeded in two stages. In the first stage, we used the dataset with all the sampled subplots and used the proportion of spruce trees in the subplot ( PSP ), the distance ( DIS ) from the initial infestation spot in plot number 13 (spruce monoculture, Fig.1) and the year ( YEAR , 2019 and 2023) as fixed factors. The year was also tested in interaction with the other two explanatory variables. In a second step, we focused on mixed spruce subplots only and used as fixed factors the proportion of spruce in the subplot ( PSP ), the distance from the initial infestation plot ( DIS ), the apparency of spruce cells (SCA) and the year (2019 and 2023), again in interaction with the other three explanatory variables. In all models, the random factor was the subplot nested within the plot. We tested the effect of the composition of the subplots with spruces on the number of I. typographus captured in the pheromone traps using a GLMM with a Poisson distribution and a log link. We used the same two-step procedure, first testing the effect of the proportion of spruces in the subplot ( PSP ) and the distance to the initial infestation spot ( DIS ) for all subplots and associated traps, then we tested PSP , DIS and the apparency of spruce cells ( SCA ) for only mixed spruce plots and associated traps. The random factor was the plot. We applied the model to the sum of the catches from the two trap assessments. We followed the same approach with the trap captures of Thanasimus formicarius and also tested their correlations with the captures of I. typographus in the same traps. All statistical analyses were conducted in R (R Core Team, 2016, version 4.4.0) using the lmerTest library. Results Ips typographus infestations The BIOTREE-Kaltenborn site experienced two waves of spruce bark beetle infestation from 2019 to 2023. The number of trees attacked peaked in 2019, with 120 spruces killed (2.3% of the total number of spruce trees in the experiment), and in 2023, with 317 trees killed (6.3%), while the number of newly killed spruces remained below 1% on average from 2020 to 2022 (Fig. 1). The percentage of spruces killed by I. typographus was consistently much higher in pure than in mixed stands. In 2019, most of the spruce infestations were concentrated in the pure plot (Fig. 2), whereas the epidemic spread to the whole experiment in the following years. After five years of the epidemic, 29.2% of the spruces were killed by I. typographus in the pure plot (#13 in Fig.1), compared with 2.6% in the mixed spruce plots. More specifically, the cumulative mortality rate for spruce was 29.2% in the pure stands, 5.4% in the two-species mixed spruce plots, 0.8% in the three-species spruce plots and 1.1% in the four-species spruce plots, which is much lower than the expected mortality in mixed plots if it were simply proportional to the proportion of spruce, i.e. 14.6%, 9.6% and 7.3% respectively (in mixed plots with 2, 3 and 4 species, i.e. with 50%, 33%, 25% spruce). Plots of spruce mixed with oak and beech (#4), beech and Douglas fir (#9) and Douglas fir (#10) had less than 1% spruce mortality. Effect of forest characteristics on spruce mortality due to bark beetle attacks With the GLMM model on the entire dataset, we found that the proportion of spruce and the distance from the pure spruce stand had a significant effect, alone and in interaction with the year (Table 2), on the number of spruces killed (NKT). NKT increased with the proportion of spruces in the subplot (PSP) and decreased with the distance from the initially infested pure plot (DIS), with steeper slopes in 2023 than in 2019 (Table 2, Fig. 3AB). Focusing solely on the mixed spruce subplots, we also found that the number of spruces killed (NKT) increased significantly with the proportion of spruces (PSP), decreased with the distance from the initially infested pure plot (DIS), and increased with the apparency of spruce cells, with steeper slopes in 2023 than in 2019 (Table 2, Figure 4ABC). The percentage of spruces killed by I. typographus was very low in the mixed spruce subplots in 2019. Table 2 . Summary of GLMM models outcomes testing the effect of sub-plot characteristics (proportion of spruce, PSP ; spruce cell apparency, SCA ; distance from the initially infested pure spruce plot #13 DIS ; and the year of assessment, YEAR ) on the number of spruce trees killed by Ips typographus . Coefficient ± S.E Z-value Pr(>|z|) R²m (R²c) n Full data set with all pure and mixed spruce sub-plots 0.72 (0.78) 18 intercept -8.1884 ± 0.8511 -9.622 2.00.10 -16 *** % spruce ( PSP ) 0.4320 ± 0.1794 2.408 0.01603 * distance ( DIS ) -3.3784 ± 0.7783 -4.341 1.42.10 -5 *** year ( YEAR ) 3.5711 ± 0.7506 4.758 1.96.10 -6 *** year x % spruce 0.8429 ± 0.2141 3.936 8.27.10 -5 *** year x distance 3.0417 ± 0.7842 3.879 0.00010 *** Data set with only mixed spruce sub-plots 0.48 (0.48) 16 intercept -3.9864 ± 0.5043 -7.904 2.70.10 -15 *** % spruce ( PSP ) 1.2085 ± 0.5630 2.147 0.03183 * distance ( DIS ) -1.0650 ± 0.3502 -3.041 0.00236 ** Spruce cell apparency ( SCA ) 6.6783 ± 2.1868 3.054 0.00226 ** year ( YEAR ) 0.3081 ± 0.6018 0.512 0.60864 year x % spruce 1.0290 ± 0.6425 1.602 0.10924 year x distance -0.3854 ± 0.4406 -0.875 0.38176 year x apparency -5.0194 ± 2.2578 -2.223 0.02621 * The spruce cell apparency was lower in the spruce mixture with Douglas fir (Fig.5) and greater in the mixture with only broadleaved species (oak and beech). Douglas fir (11.1 ± 0.1m in 2019, 13.2 ± 0.4m in 2023 on average) was the only tree species taller than Norway spruce (10.8 ± 0.1m in 2019, 13.0 ± 0.2m in 2023 on average) in the BIOTREE experiment. Effect of forest characteristics on bark beetle trap captures Taking the data from all the subplots with spruce, we found no significant effect of the explanatory variables (% spruce and distance from the initially colonized pure plot # 13) on the number of trapped I. typographus beetles (Table 3). Focusing solely on the mixed spruce subplots, we only found a significant and positive effect of the apparency of spruce cells, indicating that we captured more spruce bark beetles when the spruces were taller than the trees of the associated species. (Table 3, fig.6). Table 3 . Summary of GLMM models outcomes testing the effect of sub-plot characteristics (proportion of spruce, PSP ; spruce cell apparency, SCA ; distance from the initially infested pure spruce plot #13 DIS ; and the year of assessment, YEAR ) on the number of Ips typographus caught per trap. Coefficient ± S.E Z-value Pr(>|z|) R²m (R²c) n Full data set with all pure and mixed spruce sub-plots 0.01 (0.57) 18 intercept 7.4433 ± 0.1400 53.161 2.00.10 -16 *** % spruce ( PSP ) -0.0629 ± 0.1816 -0.336 0.729 distance ( DIS ) -0.0266 ± 0.1784 -0.149 0.881 Data set with only mixed spruce sub-plots 0.48 (0.48) 16 intercept 7.3322 ± 0.1098 66.737 2.00.10 -16 *** % spruce ( PSP ) -0.3468 ± 0.2080 -1.667 0.0954 distance ( DIS ) -0.0193 ± 0.1106 -0.174 0.8615 Spruce cell apparency ( SCA ) 0.2826 ± 0.0920 3.073 0.0021 ** Effect of forest characteristics on Thanasimus formicarius , predatory insect of Ips typographus Four species of insect predators of I. typographus were captured in pheromone traps, namely Rhizophagus dispar (Paykull, 1800), Rhizophagus bipustulatus (Fabricius, 1792), Thanasimus femoralis (Zetterstedt, 1828) and Thanasimus formicarius . The statistical analyses could only be carried out with T. formicarius , because the number of individuals of the other species varied between 2 and 3 individuals compared with 26 individuals for T. formicarius , i.e. 79% of the trapped predators. None of the characteristics of the subplots could significantly explain the level of capture of T. formicarius . The quantity of T . formicarius trapped was only marginally and positively correlated with the abundance of the prey, I. typographus , in the traps. ( P = 0.085; Z = 1.719). Discussion By following the spatiotemporal dynamics of Ips typographus infestations for five years in a tree diversity experiment where spruce was planted in pure and mixed plots, we were able to show that the spruce bark beetle exhibited a strong and persistent preference for spruce monoculture. The bark beetle epidemic began and remained concentrated in the pure spruce plots, despite the close proximity of the other plots with mixed spruce. In contrast, the mixed spruce plots were less frequently colonized and damaged, with fewer trees killed in the more species-diverse spruce mixtures. This diversity effect was not only due to a reduced proportion of spruce in the mixed plots, but also to the species composition of the mixed plots, with taller non-host tree species having a greater effect on reducing bark beetle attacks. The lower infestation in mixed spruce plots compared to pure spruce plots, which corresponds to an associational resistance pattern (Jactel et al. 2021), was clearly driven by the reduced amount of spruce trees (Table 2, Fig.3A), consistent with the resource concentration hypothesis (Underwood et al. 2014). This hypothesis predicts that specialist herbivores are more likely to immigrate into forest stands dominated by their host tree and less likely to emigrate from them. I. typographus is an oligophagous insect that feeds on a few host species with a clear preference for Picea species (Wermelinger 2004). As expected, it first immigrated into the pure spruce subplot (#13) of the BIOTREE experiment during the first year of the outbreak in 2019 and rarely emigrated from this subplot over the next 4 years, as shown by the few attacks in other neighbouring spruce subplots. This process is probably exacerbated by the infestation behaviour of bark beetles, which use aggregation pheromones to develop a cooperative mass attack to overwhelm the defences of the host tree (Berryman et al. 1989). The result is a concentrated infestation, in the form of clusters of killed trees, which are more likely to occur and persist in spruce monocultures. This may also explain why we observed that the mortality rate of the spruces decreased in the BIOTREE experiment with the increase in distance from the initial infestation spot by I. typographus (Fig3B). It is therefore not surprising that in the vast majority of cases where the mixture of spruces with other tree species has been studied as a method of management or prevention of bark beetle epidemics, the main factor observed to reduce attacks was the decrease in the quantity or proportion of host spruces (Table 1). The effect of tree species diversity on I. typographus attacks in mixed spruce stands, independently of the reduced proportion of spruce in the mixture, has been less well studied. This is due to the difficulty of disentangling the two effects. One approach has been to analyse a large dataset of forest inventory plots and use complex statistical models to disentangle the effect of spruce proportion and the diversity of associated trees. Following this approach, De Groot et al. (2023) found that the probability or amount of sanitary felling following attacks by I. typographus decreased when the percentage of spruces was lower and the diversity of tree species in the local forest increased, with a significant interaction suggesting that the effect of diversity decreased when the proportion of spruces increased. Similarly, Hlasny et al. (2013), Kärvemo et al. (2014) and Kozhoridze et al. (2024) have found, regardless of the proportion of spruce, that the amount of deciduous, birch or mixed forests, respectively, in the stand or surrounding landscape, had a negative effect on the extent of the damage caused by I. typographus . In this study, we used a different approach, taking advantage of an outbreak of spruce bark beetles in an experiment on tree diversity where several mixtures of tree species are compared for each level of tree species richness, to independently test the effect of the proportion of spruces and the composition of tree species on the level of infestation. By estimating spruce apparency in the mixtures, i.e. the difference in height with the companion species, we were able to show that it was not the diversity of the associated species but their identity that was at the origin of the reduction of the damage caused by I. typographus , namely the presence of Douglas fir, the tallest tree in the experiment (Fig.4, 5). The reduction of the host tree's apparency has emerged as an important factor of associational resistance in mixed-species forests (Castagneyrol et al. 2013), because the disruption of host-finding cues by bigger heterospecific neighbours represents a significant obstacle to the colonization of new forest habitats by patrolling forest insects (Jactel et al. 2021). Taller heterospecific neighbours can hide host trees from the sight of attacking insects (Dulaurent et al. 2012), but the most common underlying mechanism for reducing the accessibility of trees is the release of non-host volatiles (NHVs) by heterospecific neighbours, which mask the attractive signals of host trees or directly repel their herbivorous enemies. By enhancing tree species diversity, one increases the chances of incorporating into the mix one or more species capable of releasing repellent NHVs, i.e. the “semiochemical diversity hypothesis” (Zhang and Schlyter 2004). This was confirmed experimentally on I. typographus with synthetic NHV lures (Schiebe et al. 2011). This is also confirmed by the results we obtained with pheromone traps, where fewer beetles were captured in the mixed spruce plots with lower apparency, the emission of repellent NHVs interfering with the attraction of the aggregation pheromone (Fig. 6). With regards to our calculation of spruce apparency, it is obvious that taller non-host trees release more NHVs due to the greater volume of foliage. However, here again, the identity of the associated species must be taken into account, as tree species differ in the chemical composition of the NHVs. Zhang and Schlyter (2004) stated that mixing deciduous species (e.g. birch) with conifers is more likely to provide associational resistance to bark beetles, as angiosperm trees have a more contrasting semiochemical profile. It is therefore surprising that in the BIOTREE experiment, beech or oak did not provide better protection against attacks from I. typographus than Douglas fir. This can be explained by the fact that the deciduous trees have always been smaller than the neighbouring spruces in our experiment and therefore have not been able to reduce the spruce's apparency. Another explanation is the particular terpene profile of the Douglas fir resin. Compared to Picea abies (Hakola et al. 2023), Pseudotsuga menziesii (Copeland et al. 2014) has a higher proportion of alpha-pinene in the monoterpene fraction of emitted volatile organic compounds, ca. 73% vs. 23%, respectively. And yet, it has been shown that a high concentration of alpha-pinene can reduce the attraction of I. typographus to its aggregation pheromone (Olenici et al. 2007), which could explain why we caught fewer of them in traps set in spruce and Douglas fir mixtures, and also the lower level of killed trees in the corresponding plots. The diversity of tree species is also known to favour the diversity and abundance of natural enemies of insects (Stemmelen et al. 2022), which can lead to better biological control of forest pests in more diverse forests, the so-called natural enemies hypothesis (Staab and Schuldt 2020). In this study, we were unable to observe any effect of the composition or diversity of the spruce plots on the abundance of I. typographus predator insects. This may be due to numerous factors, such as the pheromone trap sampling method, the lack of appropriate complementary food resources (e.g. other prey associated with companion tree species) or breeding substrate ( T. formicarius is known to prefer pine trees for oviposition; Warzee et al. 2006), or intraguild predation (for example by insectivorous birds, which are more active in mixed forests; Vasquez-Gonzalez et al. 2024). Finally, it has been suggested that tree diversity can alter the suitability of the tree as a host resource through changes in the content of the tree's tissue defences (Jactel et al. 2021), because heterospecific neighbours can alter the accessibility and use of light, water or nutrient resources and therefore the allocation of energy to growth (primary metabolites) or defence (secondary metabolites) (Felix et al. 2023). Baier et al. (2002) have in particular demonstrated experimentally that spruce trees of mixed stands produced more reactive resin against a simulated attack by bark beetles than trees in pure spruce stands. However, we were unable to verify this mechanism in our study. Conclusion Our study adds to previous observations that monospecific spruce forests are very susceptible to bark beetle outbreaks. Increasing the diversity of tree species in spruce plantations can significantly reduce the number of trees killed by I. typographus . One of the main factors in reducing infestation in mixed spruce forests is the lower number of host trees, which limits the beetles' ability to find a suitable habitat and build up their population on a large amount of breeding substrate. We were able to confirm this process, but by focusing on an experiment involving the manipulation of tree species richness together with composition, we were able to demonstrate that the identity of companion species in the mixed spruce stand is also crucial. In particular, the presence of taller non-host trees resulted in a lower apparency of the spruce, i.e. it was less easily found by spruce bark beetles. Under the conditions of the experiment, the best protection was provided by Douglas fir, which grew taller than spruce. This discovery paves the way for the design of new spruce plantations for reforestation in European regions where the huge bark beetle epidemics have led to large-scale salvage logging. Depending on the local soil and climate conditions and the forest sectors, companion species with equal or faster growth and equivalent commercial value could be identified to serve as companion species to the spruce, with other potentially beneficial effects on total productivity, carbon sequestration, stability against other disturbances, associated biodiversity or cultural services (Messier et al. 2021). Further studies, focusing on the productivity of these mixed plantations and their relevance to the bioeconomy, would enable to better support the development of more multifunctional and sustainable spruce-based forests. Declarations Author Contribution Statement HJ, EP, MSL and JF conceived and designed research. EP, HJ, JBR and PH conducted experiments. EP, LS, NP and HJ analyzed data. HJ wrote the manuscript. All authors read and approved the manuscript. Acknowledgement The study was supported by the AXA Foundation, as part of the Forests For Good project. 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Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Journal of Pest Science → Version 1 posted Editorial decision: Revision requested 16 Jul, 2025 Reviews received at journal 20 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviews received at journal 19 Jun, 2025 Reviews received at journal 09 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 06 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor assigned by journal 05 Apr, 2025 Submission checks completed at journal 05 Apr, 2025 First submitted to journal 03 Apr, 2025 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. 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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-6370382","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":450391921,"identity":"17d3c5af-050d-4399-be9b-5e89408c5faf","order_by":0,"name":"Hervé Jactel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIie3PsYrCQBCA4RmEsQmXVht9hRXBU1B8FeUglaClxRWGFDY5bLfzFXyEhIFNkwdIIWg4SJ3SwsKVWFitKQX3Z9kwsB+7AbDZ3jBRfei+YVQ+xrIe0SuW1YiyBqkUO1CDfDf/ilYJX113H0Q8Xh2X7naDwdpARmEyaEugnlQ044UoRjKN0E9ND8u8Rt8BwgM5QhMWkM2a+cZETkWjfwWaHsgteahJ93RG30gywn/973N9CzBoIjJ4QVIP81DQj1SeiENNeuncN5NEQXRZq8ku4Px8ubLoJBwbyQOq5wlfA91vnUM2m832qd0AoIFPQWohrt4AAAAASUVORK5CYII=","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":true,"prefix":"","firstName":"Hervé","middleName":"","lastName":"Jactel","suffix":""},{"id":450391922,"identity":"a419ece5-53c1-40ae-be45-9b3434b85efc","order_by":1,"name":"Emma Pluchard","email":"","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"","lastName":"Pluchard","suffix":""},{"id":450391923,"identity":"c9bf6615-31ad-42d2-b1a7-7c7fac7eee62","order_by":2,"name":"Laura Schillé","email":"","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Schillé","suffix":""},{"id":450391924,"identity":"8a885517-f17c-4eb3-bb36-45769fcba645","order_by":3,"name":"Nattan Plat","email":"","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":false,"prefix":"","firstName":"Nattan","middleName":"","lastName":"Plat","suffix":""},{"id":450391925,"identity":"96cb8467-2a4a-4103-990e-4250ec5722da","order_by":4,"name":"Séverin Jouveau","email":"","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":false,"prefix":"","firstName":"Séverin","middleName":"","lastName":"Jouveau","suffix":""},{"id":450391926,"identity":"1d7d909f-8725-4fc7-8838-76121f0b99cc","order_by":5,"name":"Jean-Baptiste Rivoal","email":"","orcid":"","institution":"University of Bordeaux, INRAE, umr Biogeco","correspondingAuthor":false,"prefix":"","firstName":"Jean-Baptiste","middleName":"","lastName":"Rivoal","suffix":""},{"id":450391927,"identity":"74bbb929-8575-4475-9c33-c6c6dc0d8b03","order_by":6,"name":"Peter Hajek","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Hajek","suffix":""},{"id":450391928,"identity":"cea36b77-9a62-4053-8d8b-53c2ae1d862e","order_by":7,"name":"Jochen Fründ","email":"","orcid":"","institution":"University of Hamburg","correspondingAuthor":false,"prefix":"","firstName":"Jochen","middleName":"","lastName":"Fründ","suffix":""},{"id":450391929,"identity":"1fe3c08f-3962-44bc-afa6-1478ea9d7f38","order_by":8,"name":"Michael Scherer-Lorenzen","email":"","orcid":"","institution":"University of Freiburg","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Scherer-Lorenzen","suffix":""}],"badges":[],"createdAt":"2025-04-03 14:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6370382/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6370382/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10340-025-01995-y","type":"published","date":"2025-12-19T15:57:13+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82178846,"identity":"6e208118-ab0c-4d42-bf3b-d6df632a0347","added_by":"auto","created_at":"2025-05-07 11:29:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":210709,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the experimental design of the BIOTREE-SPECIES experiment Kaltenborn, Germany. (Background photography: GoogleTM).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/11330593897a43fa97e25eba.jpg"},{"id":82176396,"identity":"192e3d4d-53e8-4df0-a0f1-b6dd42e7afd8","added_by":"auto","created_at":"2025-05-07 11:13:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2661128,"visible":true,"origin":"","legend":"\u003cp\u003eMap of alive and attacked spruce trees (killed by \u003cem\u003eIps typographus\u003c/em\u003e) in BIOTREE Kaltenborn, from 2019 to 2023. (Background photography: Google Earth)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/c2be22453130d4b86f7de79f.png"},{"id":82176378,"identity":"44694f7b-84ef-493e-836d-2d65cbf0c3fa","added_by":"auto","created_at":"2025-05-07 11:13:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28849,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of spruce trees killed by \u003cem\u003eIps typographus\u003c/em\u003ein BIOTREE – Kaltenborn from 2019 to 2023, in pure and mixed plots of Norway spruce.\u003c/p\u003e","description":"","filename":"3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/756a1ee5a438484382022f62.jpeg"},{"id":82176380,"identity":"cbbc2198-5e0d-4144-9601-70dd38161f5b","added_by":"auto","created_at":"2025-05-07 11:13:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eFigure 3\u003c/u\u003e. Relationship between the percentage of spruces killed by \u003cem\u003eIps typographus\u003c/em\u003e per subplot and (A) the proportion of spruces, and (B) the distance from the pure plot of spruce (#13, Fig.1) initially infested.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/e9fa14249d1dba139f9799ed.png"},{"id":82177527,"identity":"c6c1f7bf-e00c-4794-8892-0ad24d5b726d","added_by":"auto","created_at":"2025-05-07 11:21:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":55623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eFigure 4\u003c/u\u003e. Relationship between the percentage of spruces killed by \u003cem\u003eIps typographus\u003c/em\u003e per mixed spruce subplot and (A) the proportion of spruce, (B) the distance from the initially infested pure spruce plot (# 13, Fig.1) and (C) the spruce cells apparency.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/3e18f28df66de5fb895b4da7.png"},{"id":82176384,"identity":"20cde103-c41d-4f0f-a604-51090be3719e","added_by":"auto","created_at":"2025-05-07 11:13:06","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eFigure 5\u003c/u\u003e. Mean (± standard error) spruce cell apparency (m) in 2019 and 2023\u003c/p\u003e","description":"","filename":"6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/7201bc43bb1326ee32b51fa3.jpeg"},{"id":82177529,"identity":"72571dc2-04b0-45df-a9db-01191a1d9776","added_by":"auto","created_at":"2025-05-07 11:21:06","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":120282,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cu\u003eFigure 6\u003c/u\u003e. Relationship between the number of \u003cem\u003eIps typographus\u003c/em\u003e beetles caught in pheromone trap during the spring generation and the spruce cell apparency in the mixed sub-plots. The tree species composition of the subplot is mentioned with S = spruce, D = Douglas fir, O = oak and B = beech.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/c1fdcf502a429ba61176fc90.jpg"},{"id":98814142,"identity":"348b9cec-c16d-4568-8c29-4f183794fa72","added_by":"auto","created_at":"2025-12-22 16:11:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3694038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6370382/v1/01cb66cd-c8dd-47a6-8037-6fcd555e8cea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Experimental evidence of reduced Ips typographus damage in mixed spruce plantations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInsect pests are increasingly damaging forest ecosystems as a result of climate change (Patacca et al. 2023; Forzieri et al. 2024). They take advantage of warmer temperatures that reduce their winter mortality and accelerate their life cycle (Jactel et al. 2019). In addition under drier conditions, water-stressed trees are more vulnerable to infestation (Jactel et al. 2012; Gely et al. 2020). A striking example is provided by bark beetles, which have developed large-scale epidemics across North America and Europe since the beginning of the 20th century (Hlasny et al. 2021a), causing unprecedented mortality in coniferous forests (Kurz et al. 2008; Hlasny et al. 2021c). \u003cem\u003eIps typographus\u0026nbsp;\u003c/em\u003e(Linnaeus, 1758), the spruce bark beetle, is solely responsible for several hundred cubic meters of mortality in large Central European Norway spruce plantations, following recurrent events of windstorms, drought and heat waves (Mezei et al. 2017; Hlasny et al. 2021c).\u003c/p\u003e\n\u003cp\u003eOnce triggered, outbreaks of bark beetles are very difficult to manage. The most common curative method is sanitary felling (Leverkus et al. 2018), i.e. the removal of attacked trees to reduce local insect populations. However, its effectiveness remains limited (Stadelmann et al. 2013; Havasova et al., 2017), unless a considerable number of attacked trees are removed at early stages of infection (Dobor et al. 2020 ab; Augustynczik et al. 2021), which comes with very high intervention costs. Preventive methods are therefore of great interest. Reducing tree density through thinning operations has been shown to reduce the success of bark beetle attacks as it can improve the vigour of individual trees (Fettig et al. 2007) and therefore their ability to produce more constitutive (e.g. resin) and induced (e.g. tannins) chemical defences (Krokene 2015). However, thinning is costly and needs to be spaced over time, particularly to avoid increasing the vulnerability of stands to wind damage (Jactel et al. 2009). Another method of prevention is to increase the specific and functional diversity of forests in order to improve their resistance to insect pests. Numerous works have shown that, overall, mixed-species forests are more resistant to pest damage than tree monocultures (Jactel et al. 2021). This \u0026lsquo;associational resistance\u0026rsquo; (Barbosa et al. 2009) operates for many guilds of herbivorous insects, including bark and wood borers (Jactel et al. 2021). Several empirical studies have focused on the effect of forest composition on vulnerability to \u003cem\u003eI. typographus\u003c/em\u003e. In many cases they reported on lower damage in mixed than in pure spruce stands (Klopcic et al. 2009; Kausrud et al. 2012; Hlasny et al. 2013; Stadelmann et al. 2013; Faccoli et al. 2014; Grodzki et al. 2014; Pasztor et al. 2014; Muller et al. 2022; Nardi et al. 2023; de Groot et al. 2023; Khozoridze et al. 2024; Table 1). A few observational studies revealed no difference in the level of spruce bark beetle damage between pure spruce and mixed forests, but these were carried out under epidemic conditions with very high bark beetle population densities (Hilszczanski et al. 2006; Kamińska et al. 2020; Stereńczak et al. 2020; Table 1).\u003c/p\u003e\n\u003cp\u003eThree main mechanisms have been suggested to explain the associational resistance of mixed forests to insect herbivores (Jactel et al. 2021). In the temporal order of forest habitat colonisation and exploitation of tree resources by forest pests, species mixing may first reduce the ability of insects to find suitable host trees. In mixed forests, heterospecific neighbouring trees can physically, if they are taller for example, or chemically, if they release repellent odours, hide focal host trees from their pest, i.e. the reduced plant apparency hypothesis (Castagneyrol et al. 2013). In the case of \u003cem\u003eI. typographus\u003c/em\u003e, it has been shown that deciduous trees can emit non-host volatile substances that disrupt the beetles\u0026apos; ability to recognise and then attack spruce trees (Zhang and Schlyter 2004; Table 1). The second step is the exploitation of host tree resources by insects to feed and lay eggs and thus build up their population. In this case, the main driver is the quantity of suitable host trees, which is proportionally reduced in mixed stands, i.e. the resource dilution hypothesis (Hamb\u0026auml;ck et al. 2014). The reduction in proportion of spruce trees in mixed forests is the explanation most often cited in the literature to explain the decrease in damage by \u003cem\u003eI. typographus\u003c/em\u003e (e.g. Netherer \u0026amp; Nopp-Mayr, 2005; Grodzki et al. 2014; Pasztor et al. 2014; M\u0026uuml;ller et al. 2022; Nardi et al. 2022; Table 1). Once established in their new habitat, insect herbivores may be preyed upon by insectivorous birds, bats or arthropods, or killed by insect parasitoids. It has been shown that increasing the diversity of trees in forests leads to higher abundance, diversity and activity of insect\u0026rsquo; natural enemies (Stemmelen et al. 2022; V\u0026aacute;zquez‐Gonz\u0026aacute;lez et al. 2024). Very few studies have addressed this \u0026ldquo;natural enemies hypothesis\u0026rdquo; in the case of \u003cem\u003eI. typographus\u003c/em\u003e infestations (Table 1) but Warzee et al. (2006) mention that the predatory clerid beetle \u003cem\u003eThanasimus formicarius\u003c/em\u003e (Linnaeus 1758) is able to reproduce better in mixed stands of pine and spruce than in pure spruce stands, as the pines provide a better substrate for pupation.\u003c/p\u003e\n\u003cp\u003eThe vast majority of empirical studies dealing with the effect of species mixing on damage caused by \u003cem\u003eI. typographus\u003c/em\u003e were based on field observations in managed spruce forests, which often confound the effect of tree species composition with other variables, including abiotic conditions or management interventions. They therefore do not allow for specific testing of the effect of the species composition of mixed stands, nor for unraveling the mechanisms of associative resistance at work, as is the case in tree diversity experiments (e.g. Berthelot et al. 2021).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 1\u003c/u\u003e. List of published studies on the effect of mixing other species with spruce on susceptibility to attacks by the \u003cem\u003eIps typographus\u003c/em\u003e, the spruce bark beetle.\u003c/p\u003e\n\u003cp\u003eThe main effect indicates if \u003cem\u003eI. typographus\u003c/em\u003e damage were lower (↘) or not changed (\u0026rarr;) in mixed spruce stands compared to pure spruce stands. The three last columns indicate the mechanisms involved, as proposed in the discussion part of the cited papers. Host dilution relates to the negative correlation between the proportion of spruce (host resource) in the mixture and the amount of bark beetle damage. Non-host disruption refers to the reduced probability of \u003cem\u003eI. typographus\u003c/em\u003e finding host spruce trees in the presence of non-host tree species (broadleaved, other conifers) due to lower apparency or release of repellent non-host volatiles. \u0026ldquo;Natural enemies\u0026rdquo; corresponds to the biological control of \u003cem\u003eI. typographus\u003c/em\u003e by its natural enemies (e.g. predators) that are supposed to be more diverse, abundant and active in mixed forests.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of mixture with spruce\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeetle population density\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMain effect\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHost dilution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-host disruption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNatural enemies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eBaier et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eendemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eZhang et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eReview\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eendemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNetherer et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSlovakia, Poland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003elow epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHilszczahski et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eWarzee et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eBelgium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eendemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSchiebe et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eExperimental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eendemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eKausrud et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eInternational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e(review)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOverbeck et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eModelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003elow epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHl\u0026aacute;sny et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSlovakia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eStadelmann et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSwitzerland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003elow epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eFaccoli et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003elow epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eGrodzki et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eK\u0026auml;rvemo et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSweden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003ePasztor et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eAustria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eAkinci et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003elow epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eKaminska et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eSterenczak et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003ePoland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026rarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eM\u0026uuml;ller et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eNardi et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eConifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eDe Groot et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eSlovenia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eKozhoridze et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eCzechia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved, Conifers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVodde et al.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eEmpirical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eEstonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003eBroadleaved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8px;\"\u003e\n \u003cp\u003ehigh epidemic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e↘\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe BIOTREE tree diversity experiment in Thuringia (Germany) is the oldest tree diversity experiment in temperate Europe, explicitly testing the relationship between tree diversity and ecosystem processes (Scherer-Lorenzen et al. 2007). It was subject to severe spruce bark beetle epidemics that developed in Germany due to hot and dry weather conditions in 2018. This provided a unique opportunity to experimentally test the effect of tree species richness and composition on the level of tree mortality caused by attacks from \u003cem\u003eI. typographus\u003c/em\u003e, by comparing different types of spruce stands mixed with pure spruce stands of the same age, density and management. Specifically we tested the following hypotheses:\u003c/p\u003e\n\u003cp\u003eH1) Mixed spruce plots are less damaged by the spruce bark beetle than pure spruce plots: \u003cem\u003ethe associational resistance hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH2) The damage caused by \u003cem\u003eI. typographus\u003c/em\u003e decreases as the proportion of spruce trees declines in species-rich mixed stands: \u003cem\u003ethe resource dilution hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH3) The presence of taller non-host species in mixed spruce stands leads to a lower probability of spruce being attacked by \u003cem\u003eI. typographus\u003c/em\u003e: \u003cem\u003ethe plant apparency hypothesis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eH4) The higher density of the predator \u003cem\u003eThanasimus formicarius\u003c/em\u003e contributes to better control of \u003cem\u003eI. typographus\u003c/em\u003e populations in mixed stands: \u003cem\u003ethe natural enemies hypothesis\u003c/em\u003e.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cu\u003eExperimental site\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe study was carried out in the BIOTREE-SPECIES experiment near the town of Kaltenborn, Thuringia, Germany, which is part of the Tree Diversity Network (https://treedivnet.ugent.be/experiments/BIOTREE.html). Trees were planted in 2004 on a former meadow with homogeneous soil conditions in order to establish a diversity gradient ranging from 1- (monoculture) to 2-, 3- and 4-species mixes of four dominant species: spruce (\u003cem\u003ePicea abies\u003c/em\u003e), Douglas fir (\u003cem\u003ePseudotsuga menziezii\u003c/em\u003e), oak (\u003cem\u003eQuercus petraea\u003c/em\u003e) and beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e). All possible combinations were realized, and the 4-species mixture was replicated, resulting in a total of 16 plots (see Figure 1). Each plot of 72 m x 48 m with a given tree composition was subdivided into three equally sized sub-plots to test three management alternatives: no management (U), thinning using close-to-nature silviculture (M), and thinning in plots in which four additional species were interspersed at a lower density to mimic enrichment (\u003cem\u003eAbies alba, Acer pseudoplatanus, Fraxinus excelsior\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Sorbus aucuparia\u003c/em\u003e) (M+). A thinning took place during the winter of 2020-2021 in the M and M+ subplots where 30% of the basal area of all component species were felled, with felled trees remaining in the plots. The trees were planted in 8 m x 8 m monospecific cells, so that the mixed subplots were mosaics of 30 pure cells of different species. The cells were randomly distributed within a sub-plot. Within a cell, trees were planted in rows 2 m apart, 2 m apart for spruce and Douglas-fir (i.e. 16 trees/cell) and 1 m apart for oak and beech (i.e. 27 trees/cell) (Fig.1) .\u003c/p\u003e\n\u003cp\u003eMore details on the experimental design can be found in Scherer-Lorenzen et al. (2017).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCounting trees attacked by \u003cem\u003eIps typographus\u003c/em\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eIn 2018, an extreme drought event resulted in record-breaking average growing season temperatures and vapor pressure deficits for the region, with increases of 3.3\u0026deg;C and 3.2 hPa above the long-term averages from 1961 to 1990 respectively (Schuldt et al., 2020). This led to unprecedented drought-induced mortality in adult trees across multiple species and triggered outbreaks of the spruce bark beetle \u003cem\u003eI. typographus\u003c/em\u003e. The first signs of its infestation were detected at BIOTREE-Kaltenborn in summer 2018 and the first dead trees were observed in 2019. In 2022, another drought year occurred in the region.\u003c/p\u003e\n\u003cp\u003eTo estimate the number of spruce trees attacked and killed by the spruce beetle, we visually checked annual aerial images of the site, taken each year in late spring and downloaded from the Thuringian Geoportal (https://geoportal.thueringen.de/gdi-th). Tree positions from the experimental design were converted into geographic coordinates using GeoDispo software and imported as a vector layer in QGIS to locate each planted tree precisely. Each spruce was visually assessed for crown discoloration, as an indicator of attack and mortality caused by \u003cem\u003eI. typographus\u003c/em\u003e. In 2024, we conducted a ground survey to validate these assessments. 19 trees whose crowns showed dead branches in the aerial photographs (out of 507; 3.7%) were in fact still alive, which corresponded to attacks by \u003cem\u003ePityogenes chalcographus\u0026nbsp;\u003c/em\u003e(Linnaeus 1761). The other dead trees were attributed to successful infestation by \u003cem\u003eI. typographus\u003c/em\u003e. Only trees newly attacked each year were taken into account to estimate annual mortality levels. We were thus able to calculate the percentage of trees attacked by the beetle per (pure) spruce cell in the 27 pure and mixed sub-plots with spruce of the experiment, from 2019 to 2023, for a total of 330 spruce cells. For counts from 2021 to 2023, we took into account the thinning operation to use the new standing density of spruce and to calculate the percentage of newly attacked trees.\u003c/p\u003e\n\u003cp\u003eThe total height of the trees was measured during the winter of 2018-2019. 920 trees were measured, i.e. 200 for each of the main four species and 30 for each of the additional four species. In spring 2024, we again measured the total height of a sample of 160 trees, i.e. 20 trees of each of the eight species. We ensured that the trees were always measured in two adjacent rows, one at the edge of a spruce focal cell and the other in the neighbouring cell of other tree species, in order to take account of any facilitation or competition effects on height growth. However, we did not find any significant difference in the height of the spruce trees between the different compositions of the neighbouring cells.\u003c/p\u003e\n\u003cp\u003eWe used these measurements to calculate a spruce cell apparency index (\u003cem\u003eSCA\u003c/em\u003e), as the difference in total height between focal spruce cells and their nearest neighbouring cells, using the following formula (Damien et al. 2016):\u003c/p\u003e\n\u003cp\u003e\u003cimg 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n6UBcB45DX0tiuCzLABt+4+AlZ4qYkIO43qOGj6fPULxuMR5OLwzGN0mz4O7VaMB9xcQF93Cyi3n8dm3Prj7+wEIrPtimHsbZF49hENnwhF2JhB5PBPEXTmHwqJcHNi+B3yHAbAs/xP7T4Sx7TkwQsJVpr0kH79sDwBp2xvd25kyPxiV2LzgM5wvNMZnXpOglHMRcxbtRufRU2FSdAl7gi4g7LfDyGzQQsylP5B44xYSSpthysc9mR+POvy6ciGOxYoxadZ4VFzbirWnq5i2zji2aRVuJt5G0Nmr0NLg43x4DJugg8MrMWZCP+50DBH2LvfGsWQZpk3/BCXX/BFebQFbpRQcD72CwAOHoeM0GG7WBtBRF8Pv+9UoMvDAyO627K0bE5XcwdYf1uFU6CVcZH4An72EhoaiiBjCue2/I6FSf887/5Ohbt4V29auwmSvxejRNxF7D+xGr3ZGkMiUMWz+WjgEf495/uFQnrALvvMcca6lABN/vYKCqqEYIm8/9QPmbIyA2vQ98PvSHmKxBNKqB9CevQo2p9ch9zqPrSbkVceNxGTYOC9EM/YvA6e2r8KFcnNcnP8xe77Z+l/CoKKqAVlpAlZtCcTwb/diqJsZ4oNqUdugDB3VF/+eXb50EVrt3NHZ+tlZB5zKNKzy3QfPL3dieFcLpJyuRb1EFbpqirfvZvR16NjPfvy4BBmxiK7Xw3SmMv1rBL9s3QrjPlPQ0cgAms2UkVH2kG3hG3fA5yuGoPb2DYT9eRP7/Degg6kEysN6Ijw8Fp/O6Ii5KzxRn3wTl+XtO31hbyYDP78nIq4lYPGHjog+vg2ByXycurgArZQJpPqG0DE0goOVCcRlFszt14GkR+O3S7HYd3AzOplqQFQvZj+Uib9tQ0CCFKdOfwFDIsCZfCk0tTVApLVwGj4fXXvHInz0EkTmq8Bvoy+u7KzDgiMCdnxULjVkF3ZdqcCvoV/AmikeLb8PxhQmnpOWgO+3+EFyuxMiouLh5dkGslohlE3aY86Ev3cqx/9y8OBBxMXFsYvKUm+fr68vd+3VvMlj6/8Yx+EzcCXqArqQ25gwbj7uCwnUTdrChukSR8VEwcChH8b1d2T35REldrVeg+ZWsGmui1vRkTDrMhTT+tqz7apM10LDuA3atmqG8FsxMLbrAfkyc/IublGRFD37erD7oSodJ/6IxpjZsx6fgMtX05CfToYrx/cjLCkP2eFnsdl3BQ5F12L7Pj84Gr3gyyArQnxkJqxtnJ86kbex66d+wYX4HBQyz3GL70rsiyjHpt1+6NycqQtJMXIzJejV88lae2kJCVDXbIk2rf77XFn54bWSmCDsj67B6MGdUVFaiMoaIWoqqtn21h1coF+bhcSiBnw4/lMmIcpfBQn4SspQ0dKEgWUHGNZnI6GwHoM/mcQkRPnfkoLPU4a6jnz4oBZHDp6A28efopU8yzEvTGpCFDRbd4GNPtCqrTOMpbmIz6/BxzM+YxOinKaavFKuwuG9gRBKxTjltxurvvsONbbjcHDTHKgoN4OdrQXuxV5GjbErFs8dyX6I01PSYe7uAgP2XmoQuOcQ7IaOgy3Xm1biq0GVudg4dWGqaQ24eNggJfkO2xYXfgmGrqPQtRX7Tj9Hk0mYPmt+Yr9kL7ps3rwZUwe/5uKv1L8PN7b4XiiNP0bcOtiS4KRSRUCUTyb1siEbzqYrtomMbJjZj3hO91WculKbQ0a7WJBvg+LZ1qfU3CPT+/QkB24VsZv3zm8h7ex7k6tMxpWrSDxOXJ0HkSs5z07UkpI103oTz5mbSbmgklRX/9VELkZtOhnl0p547brCBRTqRDWPT8fZPG8g+WDKBlIqrCQCQeOJUMzNM84Tt66DSfj9J3G/hUPJgM93c1v/RV0BWTplOJm+6Huyfbsf2bH9ZzK8qw3pN8OP24GQ7Cu7iXuvIeTuo6dQlUw+7NyZbDuveD1zwveRLj0HktRHZ4BUp5KP3FzJ5vOZhFQmkF4OzsQ/LEPRVp9HJve2IZN8FdPN5KeoPGBu7+Tck0QXPnMkoSSO9HHrSvz+iGOfb5VIMc3sCQlZMcKRDF5ygNsuItM9+5L1J7mDUg+TSb9OrszjvKPYfpb89Jijq4lrr9lEIKsgy+bPI5G5/31iWE1xGvFdtpD4+Pi88OLt7U32h9zi9qbede90pZhw9XdE3VV09+Q01FWhZdYJ5kaKAx2CohQ8EFrAydaU3ZYUxyD2nhJmzfuUPbGhpigNpTw7eHazZtsbe3g3FrdrTeBoyZQ1pXexdddxVBu3RHs9GVKS7kNMpJDPplJmqg+5muIMprK8x1xTgrSB6S4z3XAD7WbQ0dFBdmI4bt55ssryU1S0YdqMD1GdfHIXpywR0+b54IGiaFPcH9NuqNUM2trayEu+husp+WxbUtR11CppoZ0xDwlpGUykAum3c9CjTxcU56Uhv0LE7ves68d2Il69E3Zt/BZz586D19zPMaSLJarKS8Cue8qUkomJiYBWW5hxJWzU6aOoMHTHGM92bHtSUgJkmu3QQk/RHnv2OMr13TBugDVk1dXsgqhESf4Rk+LcsR0IT1VG/242KMlIhKCBh3vpt6Hdwg3tzZ7+GBKZFNLaajSo6rLPV1dDiuthF1Eg4GZqNOQhrVQbA3u7sJvVd+KRVsdUy65WuBObgjqZDGJpPZRUFe8NERUj7EosO32OxVStbR1c0YyXhICte2DU5ROmSvw7E8Oo98k7nRRDD63D2u2HkFVQhKz7ydjsHwz3UWPgbqY4WJEdF42s/Lu4l5OF3JwErFq+Aa0+nImxLookmRp+EWXaBrAxViTRxng8VZDKFPy4fCm+/ikEvYYOhpkwCd+tXoW4hyKY2HZHR50szJsxE0uXfQnvr35ALrdeUR/PYSiJPoapC7+Cz8Lp+PHAOfA1Gx8VbUSlBQYP/wAZl4Nx7U4+cjNSsHTOlyivN4MxN0jYq89wVCacwOQvlmDxoplYu+cslDUU98cjPEjz0rFuxVqklMi/9hLIaiS4vN8XW34Ng5La813ogqST+NF3L2xc+jweVJYJCnEnMRcFhenIka9jyiNIvXkTJdW5uJNfhrjrgfA7eQc+338DM/nQKNOecuMG056DO3mliL8RhG1Hk7Fw9VKYMc1KxjZwsVWF//fM6+e9BHlKduhmrYvAbeuwMfAK+MxbFB12GfoOzo3muyrwmO5qTztj/PzdIvgsXYLPvWYiJL4Y2pqKj2tZSjRy6wkcbBUruYgb6iAty8HPPyzF7+l5UDe0w2AXfWxatgCLl36Fz2YvwM0c4VP/sZFBy7bQqC/CiVsPMXXc0ytMP4t2n/+/vNNHn/VamqNZfTESk1KQnlsEj0HTMH1498entx7bswNlep3R0bwW8VE5cBg4GV4T+jw+fF9SXA5z+y7o4Wzz3CF9DVMbtDfUQoVUFRNmz2GXL+JL6tC+xwiM7+3MJDNd9OjtAV5ZCcRK+pjhvRD9nBXLGVl07AYXUw0UlpbD2LYL5n0xH+2N//v4nq1HP9iaVON6xE2kM19qY9uumD97Ikz1FdVLS4cucG+pg4KSMhi0ccGcBQvgYKY4KGPaygJqTA1k2X0UJvWTj5tqwcJcH3XKhpg6ZzZa6z57LE2E6yERkDa3hZ62GuztHdgpXvnpcUgXasDBxgx6hq3RprkA2346h66fDEHNvXjcL1TH+AU+6GOvOD0JDdn4efM5uI0dgrrMRGTm8fHJ/EXwdJSnRIaKDlw62kNY/hCWXUdj1tjBsGqljjrVFpg7fy4MVGuQX9SA3kM+RBuzR4eIOMwPUk/PATCoK0OFqAFuQz/F3ElDweVEppotA9GxwMC+7tBk3jhNw+bQVeLBuEN3eI0fzLyXSnDt3QuG9QKUCwlGz/gck55NWqJChEXmYc7KtXD4i/eG+j/EdaObxMCBA8mRI0e4rSe8vLzI+vXruS0F+UPLzMzktl6DpJJM82xL5gdc4wLUq3iYcIK4ePQhkfnPL88kV5n0G3H1+ICE5zw73vcvJl8Gix2nrSS+PjPJ+uAoNkxRjTVZ93nDhg2wtrbGuHHjuIjC3Llz4e/vz209ERERgQEDXv8UifqSFKYSUYKJHh0reh2pMTdQXs3UnUpPVlxpLC2Waa/iQVv53ZklKi6Iw8QRgzFu/CyUNB+ELz9251oo6okm6z736NEDR48eZVfVfUQ+BzQmJoa93r9/f3Tv3p29Lte6dWuEhIRARUUFDg7/e1bGs6SiGhSLZVBT0YC9Y3uov8y0AOoxoUgEJVVVqOqaooOlYgy2sZpaEQjz3qjJ262eb/83qq8ToiivBG4jZmPhRM/343y0t4HU4NKxXTgfWwwXZ1vkJVzCtp/3olijJezN38Dc4n+5JlkQIigoCAcOHMC5c+e4iKJyzM7Oxo4dO2BjY4PZs2djyZIlXKvCi25HUdTbJMHlfT/iQvxdXIxIwZyvVyErPgLl9yIRkW+JUxd/hd3zxyXfK03yYylPbH379uW2FOQJUJ4Q/4q7uzvOnz+PrKwsLkJR1FvVUIsWbqOweLE3zGofIiTqAXzWr8d33hOg0lAD6duvof5xTZIU5YlN3h1+VfIxSLlbt26x/1IU9ZbxdWDnaIfKe9eQp2wJ7y+mQN5hvpeUAr5VG7Rt9v4PQzXZsEqrVq24a6+mTZt3a01Einr38RBz+SqIrRu6W8rPIq3HpYh0uDh3UUx5fc/RsWaKop4hwI2kAnTz8GBPeCcVD5BeXIbunu7ITUxEWePZV++hJkuKeXl53LVXk5mZyV2jKOrtIxAXZ+J+yUM4dVYsMiKR1YEnqsaJXesQEJEAVbX3e5WfJkmKAwcORE5ODrf18h4dYJEfcKEoqgkQQCzjo9+YORjsqlh6jm9oi8+8F6OPR3d4z50C3fd8WLFJkuLUqVOxe/dubuvlBQcHswn10QEXiqLeMh4P2s3tscBnIaz1HqUHNfQbPwc+Xp/C4P9gwK3J/uMq+Yna8q7wqyS4QYMGsQn12VkwFEVRb0uT5f3169dj48aN3Nb/du3aNWRkZNCESFFUk2rS/+JUPnNlzZo1L5Xo5JWlfP6zfHogRVFUU3kv/t9niqKoN+X/YNiUoijq5dGkSFEU1QhNihRFUY3QpEhRFNUITYoURVGPAf8Btse+viqDcjEAAAAASUVORK5CYII=\" height=\"55\" width=\"325\"\u003e\u003c/p\u003e\n\u003cp\u003eWith \u003cem\u003eHs\u003c/em\u003e the average height of the spruce trees in the experiment, \u003cem\u003eHi\u003c/em\u003e the average height of the trees in the neighbouring cells of other tree species, and \u003cem\u003en\u003c/em\u003e varying from 8 (when a pure spruce cell was surrounded by eight other cells, in the centre of the sub-plot) to 3 (e.g. when the pure spruce cell was in the corner of a sub-plot and was therefore only bordered by three cells of other tree species). The SCA could not be properly estimated in the pure spruce subplots because the spruce cells were not surrounded by cells of other tree species. Setting \u003cem\u003eHi\u003c/em\u003e to zero in this case would have resulted in an overestimation of the apparency of the pure spruce subplots.\u003c/p\u003e\n\u003cp\u003eWe noted that the first attacks of \u003cem\u003eI. typographus\u003c/em\u003e occurred in pure spruce plots in 2018-2019 (Fig.2). To take into account the possibility that subsequent infestations were due to beetles originating from the initial infestation spot, we calculated with QGis the Euclidean distance (\u003cem\u003eDIS\u003c/em\u003e, in m) from the barycentre of the pure spruce plot to each pure spruce cell in the experiment.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePheromone trapping of \u003cem\u003eIps typographus\u003c/em\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo study the colonisation behaviour of \u003cem\u003eI. typographus\u003c/em\u003e across the tree diversity experiment, we set up a pheromone trap in the centre of 18 of the 27 pure and mixed spruce subplots (we excluded subplots M+ with other tree species). We used black interception barrier traps baited with the commercial lure Ipsowit \u0026reg;, composed of ipsdienol and cis-verbenol (Heber et al. 2021). The traps were suspended 1.5 m above the ground from a rope stretched between two adjacent spruce trees. The traps were activated from the end of April to the end of June 2024, covering the spring generation of spruce beetles. They were assessed twice without permutation between positions.\u003c/p\u003e\n\u003cp\u003eThe insects caught were sorted by species. Due to the high number of \u003cem\u003eI. typographus\u003c/em\u003e in the pheromone traps, the total captures per assessment period were estimated using a volumetric method. Spruce bark beetles were transferred to a graduated vial and the volume was converted to a number by following an abacus (\u0026Ouml;hrn et al. 2014). In addition, we counted the number of predatory insects of \u003cem\u003eI. typographus\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical analyses\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe tested how tree species diversity influenced spruce bark beetle damage. To measure this, we counted the number of newly killed trees in each spruce cell (NKT) across 27 subplots where spruce was present. This included 2 pure spruce plots (M and U) and 25 mixed plots, 24 of these had spruce combined with three other main tree species, while one \u0026quot;pure\u0026quot; spruce subplot (M+) also had four extra species. Based on a visual assessment of the damage from aerial photos, we focused on two years, 2019 and 2023, as the number of newly attacked trees was too low in the other years in between.\u003c/p\u003e\n\u003cp\u003eAs the BIOTREE-Kaltenborn mixing scheme followed a substitutive design (same number of planting cells per sub-plot, independent of species richness), tree species richness, Shannon diversity index and proportion of spruce per sub-plot were highly correlated. We decided to retain the proportion of spruce as an explanatory variable in order to better test the host concentration hypothesis and to compare our results with the many studies that have also used this dependent variable. As the spruce trees were planted as pure cells, neighbouring trees were not independent of each other. In addition, a sample of sub-plots (M and M+) was thinned during the study. We therefore decided to consider a spruce cell as the spatial statistical unit, independently of spruce tree density, in order to be able to better compare the results of the two assessment years. Finally, we calculated the proportion of spruce in a sub-plot as the number of spruce cells divided by the total number of cells (\u003cem\u003ePSP\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eWe tested the effect of the composition of sub-plots with spruce on the number of killed spruces by \u003cem\u003eI. typographus\u0026nbsp;\u003c/em\u003ewith a generalised linear mixed model (GLMM) with a binomial distribution and a logit link. The response variable was the number of killed trees in each cell of spruce (\u003cem\u003eNKT\u003c/em\u003e) compared to the number of alive spruce trees . We proceeded in two stages. In the first stage, we used the dataset with all the sampled subplots and used the proportion of spruce trees in the subplot (\u003cem\u003ePSP\u003c/em\u003e), the distance (\u003cem\u003eDIS\u003c/em\u003e) from the initial infestation spot in plot number 13 (spruce monoculture, Fig.1) and the year (\u003cem\u003eYEAR\u003c/em\u003e, 2019 and 2023) as fixed factors. The year was also tested in interaction with the other two explanatory variables. In a second step, we focused on mixed spruce subplots only and used as fixed factors the proportion of spruce in the subplot (\u003cem\u003ePSP\u003c/em\u003e), the distance from the initial infestation plot (\u003cem\u003eDIS\u003c/em\u003e), the apparency of spruce cells (SCA) and the year (2019 and 2023), again in interaction with the other three explanatory variables. In all models, the random factor was the subplot nested within the plot.\u003c/p\u003e\n\u003cp\u003eWe tested the effect of the composition of the subplots with spruces on the number of \u003cem\u003eI. typographus\u003c/em\u003e captured in the pheromone traps using a GLMM with a Poisson distribution and a log link. We used the same two-step procedure, first testing the effect of the proportion of spruces in the subplot (\u003cem\u003ePSP\u003c/em\u003e) and the distance to the initial infestation spot (\u003cem\u003eDIS\u003c/em\u003e) for all subplots and associated traps, then we tested \u003cem\u003ePSP\u003c/em\u003e, \u003cem\u003eDIS\u003c/em\u003e and the apparency of spruce cells (\u003cem\u003eSCA\u003c/em\u003e) for only mixed spruce plots and associated traps. The random factor was the plot. We applied the model to the sum of the catches from the two trap assessments. We followed the same approach with the trap captures of \u003cem\u003eThanasimus formicarius\u003c/em\u003e and also tested their correlations with the captures of \u003cem\u003eI. typographus\u003c/em\u003e in the same traps.\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted in R (R Core Team, 2016, version 4.4.0) using the lmerTest library.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003e\u003cu\u003eIps typographus\u003c/u\u003e\u003c/em\u003e\u003cu\u003e\u0026nbsp;infestations\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe BIOTREE-Kaltenborn site experienced two waves of spruce bark beetle infestation from 2019 to 2023. The number of trees attacked peaked in 2019, with 120 spruces killed (2.3% of the total number of spruce trees in the experiment), and in 2023, with 317 trees killed (6.3%), while the number of newly killed spruces remained below 1% on average from 2020 to 2022 (Fig. 1). The percentage of spruces killed by \u003cem\u003eI. typographus\u003c/em\u003e was consistently much higher in pure than in mixed stands.\u003c/p\u003e\n\u003cp\u003eIn 2019, most of the spruce infestations were concentrated in the pure plot (Fig. 2), whereas the epidemic spread to the whole experiment in the following years. After five years of the epidemic, 29.2% of the spruces were killed by \u003cem\u003eI. typographus\u003c/em\u003e in the pure plot (#13 in Fig.1), compared with 2.6% in the mixed spruce plots. More specifically, the cumulative mortality rate for spruce was 29.2% in the pure stands, 5.4% in the two-species mixed spruce plots, 0.8% in the three-species spruce plots and 1.1% in the four-species spruce plots, which is much lower than the expected mortality in mixed plots if it were simply proportional to the proportion of spruce, i.e. 14.6%, 9.6% and 7.3% respectively (in mixed plots with 2, 3 and 4 species, i.e. with 50%, 33%, 25% spruce). Plots of spruce mixed with oak and beech (#4), beech and Douglas fir (#9) and Douglas fir (#10) had less than 1% spruce mortality.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEffect of forest characteristics on spruce mortality due to bark beetle attacks\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWith the GLMM model on the entire dataset, we found that the proportion of spruce and the distance from the pure spruce stand had a significant effect, alone and in interaction with the year (Table 2), on the number of spruces killed (NKT). NKT increased with the proportion of spruces in the subplot (PSP) and decreased with the distance from the initially infested pure plot (DIS), with steeper slopes in 2023 than in 2019 (Table 2, Fig. 3AB).\u003c/p\u003e\n\u003cp\u003eFocusing solely on the mixed spruce subplots, we also found that the number of spruces killed (NKT) increased significantly with the proportion of spruces (PSP), decreased with the distance from the initially infested pure plot (DIS), and increased with the apparency of spruce cells, with steeper slopes in 2023 than in 2019 (Table 2, Figure 4ABC). The percentage of spruces killed by \u003cem\u003eI. typographus\u003c/em\u003e was very low in the mixed spruce subplots in 2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 2\u003c/u\u003e. Summary of GLMM models outcomes testing the effect of sub-plot characteristics (proportion of spruce, \u003cem\u003ePSP\u003c/em\u003e; spruce cell apparency, \u003cem\u003eSCA\u003c/em\u003e; distance from the initially infested pure spruce plot #13 \u003cem\u003eDIS\u003c/em\u003e; and the year of assessment, \u003cem\u003eYEAR\u003c/em\u003e) on the number of spruce trees killed by \u003cem\u003eIps typographus\u003c/em\u003e.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZ-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u0026sup2;m (R\u0026sup2;c)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFull data set with all pure and mixed spruce sub-plots\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.72\u003cem\u003e\u0026nbsp;(0.78)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-8.1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-9.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00.10\u003csup\u003e-16\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% spruce (\u003cem\u003ePSP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01603 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edistance (\u003cem\u003eDIS\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.3784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-4.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.42.10\u003csup\u003e-5\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear (\u003cem\u003eYEAR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.5711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.96.10\u003csup\u003e-6\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear x % spruce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.27.10\u003csup\u003e-5\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear x distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.0417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.7842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00010 ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eData set with only mixed spruce sub-plots\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e0.48 (0.48)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.9864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-7.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.70.10\u003csup\u003e-15\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% spruce (\u003cem\u003ePSP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.2085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.03183 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edistance (\u003cem\u003eDIS\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.0650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-3.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00236 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpruce cell apparency (\u003cem\u003eSCA\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.6783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.1868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00226 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear (\u003cem\u003eYEAR\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.3081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.60864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear x % spruce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0290\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.6425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.10924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear x distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.4406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.38176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eyear x apparency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-5.0194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-2.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.02621 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The spruce cell apparency was lower in the spruce mixture with Douglas fir (Fig.5) and greater in the mixture with only broadleaved species (oak and beech). Douglas fir (11.1 \u0026plusmn; 0.1m in 2019, 13.2 \u0026plusmn; 0.4m in 2023 on average) was the only tree species taller than Norway spruce (10.8 \u0026plusmn; 0.1m in 2019, 13.0 \u0026plusmn; 0.2m in 2023 on average) in the BIOTREE experiment.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eEffect of forest characteristics on bark beetle trap captures\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTaking the data from all the subplots with spruce, we found no significant effect of the explanatory variables (% spruce and distance from the initially colonized pure plot # 13) on the number of trapped \u003cem\u003eI. typographus\u003c/em\u003e beetles (Table 3). Focusing solely on the mixed spruce subplots, we only found a significant and positive effect of the apparency of spruce cells, indicating that we captured more spruce bark beetles when the spruces were taller than the trees of the associated species. (Table 3, fig.6).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable 3\u003c/u\u003e. Summary of GLMM models outcomes testing the effect of sub-plot characteristics (proportion of spruce, \u003cem\u003ePSP\u003c/em\u003e; spruce cell apparency, \u003cem\u003eSCA\u003c/em\u003e; distance from the initially infested pure spruce plot #13 \u003cem\u003eDIS\u003c/em\u003e; and the year of assessment, \u003cem\u003eYEAR\u003c/em\u003e) on the number of \u003cem\u003eIps typographus\u003c/em\u003e caught per trap.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eZ-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u0026sup2;m (R\u0026sup2;c)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eFull data set with all pure and mixed spruce sub-plots\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e0.01 (0.57)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;7.4433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00.10\u003csup\u003e-16\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% spruce (\u003cem\u003ePSP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edistance (\u003cem\u003eDIS\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eData set with only mixed spruce sub-plots\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e0.48 (0.48)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.3322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e66.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00.10\u003csup\u003e-16\u003c/sup\u003e ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e% spruce (\u003cem\u003ePSP\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.3468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-1.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edistance (\u003cem\u003eDIS\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.0193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpruce cell apparency (\u003cem\u003eSCA\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.2826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026plusmn;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0021 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cu\u003eEffect of forest characteristics on \u003cem\u003eThanasimus formicarius\u003c/em\u003e, predatory insect of \u003cem\u003eIps typographus\u003c/em\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eFour species of insect predators of \u003cem\u003eI. typographus\u003c/em\u003e were captured in pheromone traps, namely \u003cem\u003eRhizophagus dispar\u0026nbsp;\u003c/em\u003e(Paykull, 1800),\u003cem\u003e\u0026nbsp;Rhizophagus bipustulatus\u0026nbsp;\u003c/em\u003e(Fabricius, 1792),\u003cem\u003e\u0026nbsp;Thanasimus femoralis\u0026nbsp;\u003c/em\u003e(Zetterstedt, 1828)\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Thanasimus formicarius\u003c/em\u003e. The statistical analyses could only be carried out with \u003cem\u003eT. formicarius\u003c/em\u003e, because the number of individuals of the other species varied between 2 and 3 individuals compared with 26 individuals for \u003cem\u003eT. formicarius\u003c/em\u003e, i.e. 79% of the trapped predators. None of the characteristics of the subplots could significantly explain the level of capture of \u003cem\u003eT. formicarius\u003c/em\u003e. The quantity of T\u003cem\u003e. formicarius\u003c/em\u003e trapped was only marginally and positively correlated with the abundance of the prey, \u003cem\u003eI. typographus\u003c/em\u003e, in the traps. (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.085; Z = 1.719).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy following the spatiotemporal dynamics of \u003cem\u003eIps typographus\u003c/em\u003e infestations for five years in a tree diversity experiment where spruce was planted in pure and mixed plots, we were able to show that the spruce bark beetle exhibited a strong and persistent preference for spruce monoculture. The bark beetle epidemic began and remained concentrated in the pure spruce plots, despite the close proximity of the other plots with mixed spruce. In contrast, the mixed spruce plots were less frequently colonized and damaged, with fewer trees killed in the more species-diverse spruce mixtures. This diversity effect was not only due to a reduced proportion of spruce in the mixed plots, but also to the species composition of the mixed plots, with taller non-host tree species having a greater effect on reducing bark beetle attacks.\u003c/p\u003e\n\u003cp\u003eThe lower infestation in mixed spruce plots compared to pure spruce plots, which corresponds to an associational resistance pattern (Jactel et al. 2021), was clearly driven by the reduced amount of spruce trees (Table 2, Fig.3A), consistent with the resource concentration hypothesis (Underwood et al. 2014). This hypothesis predicts that specialist herbivores are more likely to immigrate into forest stands dominated by their host tree and less likely to emigrate from them. \u003cem\u003eI. typographus\u0026nbsp;\u003c/em\u003eis an oligophagous insect that feeds on a few host species with a clear preference for \u003cem\u003ePicea\u0026nbsp;\u003c/em\u003especies (Wermelinger 2004). As expected, it first immigrated into the pure spruce subplot (#13) of the BIOTREE experiment during the first year of the outbreak in 2019 and rarely emigrated from this subplot over the next 4 years, as shown by the few attacks in other neighbouring spruce subplots. This process is probably exacerbated by the infestation behaviour of bark beetles, which use aggregation pheromones to develop a cooperative mass attack to overwhelm the defences of the host tree (Berryman et al. 1989). The result is a concentrated infestation, in the form of clusters of killed trees, which are more likely to occur and persist in spruce monocultures. This may also explain why we observed that the mortality rate of the spruces decreased in the BIOTREE experiment with the increase in distance from the initial infestation spot by \u003cem\u003eI. typographus\u003c/em\u003e (Fig3B). It is therefore not surprising that in the vast majority of cases where the mixture of spruces with other tree species has been studied as a method of management or prevention of bark beetle epidemics, the main factor observed to reduce attacks was the decrease in the quantity or proportion of host spruces (Table 1).\u003c/p\u003e\n\u003cp\u003eThe effect of tree species diversity on \u003cem\u003eI. typographus\u003c/em\u003e attacks in mixed spruce stands, independently of the reduced proportion of spruce in the mixture, has been less well studied. This is due to the difficulty of disentangling the two effects. One approach has been to analyse a large dataset of forest inventory plots and use complex statistical models to disentangle the effect of spruce proportion and the diversity of associated trees. Following this approach, De Groot et al. (2023) found that the probability or amount of sanitary felling following attacks by \u003cem\u003eI. typographus\u003c/em\u003e decreased when the percentage of spruces was lower and the diversity of tree species in the local forest increased, with a significant interaction suggesting that the effect of diversity decreased when the proportion of spruces increased. Similarly, Hlasny et al. (2013), K\u0026auml;rvemo et al. (2014) and Kozhoridze et al. (2024) have found, regardless of the proportion of spruce, that the amount of deciduous, birch or mixed forests, respectively, in the stand or surrounding landscape, had a negative effect on the extent of the damage caused by \u003cem\u003eI. typographus\u003c/em\u003e. In this study, we used a different approach, taking advantage of an outbreak of spruce bark beetles in an experiment on tree diversity where several mixtures of tree species are compared for each level of tree species richness, to independently test the effect of the proportion of spruces and the composition of tree species on the level of infestation. By estimating spruce apparency in the mixtures, i.e. the difference in height with the companion species, we were able to show that it was not the diversity of the associated species but their identity that was at the origin of the reduction of the damage caused by \u003cem\u003eI. typographus\u003c/em\u003e, namely the presence of Douglas fir, the tallest tree in the experiment (Fig.4, 5). The reduction of the host tree\u0026apos;s apparency has emerged as an important factor of associational resistance in mixed-species forests (Castagneyrol et al. 2013), because the disruption of host-finding cues by bigger heterospecific neighbours represents a significant obstacle to the colonization of new forest habitats by patrolling forest insects (Jactel et al. 2021). Taller heterospecific neighbours can hide host trees from the sight of attacking insects (Dulaurent et al. 2012), but the most common underlying mechanism for reducing the accessibility of trees is the release of non-host volatiles (NHVs) by heterospecific neighbours, which mask the attractive signals of host trees or directly repel their herbivorous enemies. By enhancing tree species diversity, one increases the chances of incorporating into the mix one or more species capable of releasing repellent NHVs, i.e. the \u0026ldquo;semiochemical diversity hypothesis\u0026rdquo; (Zhang and Schlyter 2004). This was confirmed experimentally on \u003cem\u003eI. typographus\u003c/em\u003e with synthetic NHV lures (Schiebe et al. 2011). This is also confirmed by the results we obtained with pheromone traps, where fewer beetles were captured in the mixed spruce plots with lower apparency, the emission of repellent NHVs interfering with the attraction of the aggregation pheromone (Fig. 6). With regards to our calculation of spruce apparency, it is obvious that taller non-host trees release more NHVs due to the greater volume of foliage. However, here again, the identity of the associated species must be taken into account, as tree species differ in the chemical composition of the NHVs. Zhang and Schlyter (2004) stated that mixing deciduous species (e.g. birch) with conifers is more likely to provide associational resistance to bark beetles, as angiosperm trees have a more contrasting semiochemical profile. It is therefore surprising that in the BIOTREE experiment, beech or oak did not provide better protection against attacks from \u003cem\u003eI. typographus\u003c/em\u003e than Douglas fir. This can be explained by the fact that the deciduous trees have always been smaller than the neighbouring spruces in our experiment and therefore have not been able to reduce the spruce\u0026apos;s apparency. Another explanation is the particular terpene profile of the Douglas fir resin. Compared to \u003cem\u003ePicea abies\u003c/em\u003e (Hakola et al. 2023), \u003cem\u003ePseudotsuga menziesii\u003c/em\u003e (Copeland et al. 2014) has a higher proportion of alpha-pinene in the monoterpene fraction of emitted volatile organic compounds, ca. 73% vs. 23%, respectively. And yet, it has been shown that a high concentration of alpha-pinene can reduce the attraction of \u003cem\u003eI. typographus\u003c/em\u003e to its aggregation pheromone (Olenici et al. 2007), which could explain why we caught fewer of them in traps set in spruce and Douglas fir mixtures, and also the lower level of killed trees in the corresponding plots.\u003c/p\u003e\n\u003cp\u003eThe diversity of tree species is also known to favour the diversity and abundance of natural enemies of insects (Stemmelen et al. 2022), which can lead to better biological control of forest pests in more diverse forests, the so-called natural enemies hypothesis (Staab and Schuldt 2020). In this study, we were unable to observe any effect of the composition or diversity of the spruce plots on the abundance of \u003cem\u003eI. typographus\u003c/em\u003e predator insects. This may be due to numerous factors, such as the pheromone trap sampling method, the lack of appropriate complementary food resources (e.g. other prey associated with companion tree species) or breeding substrate (\u003cem\u003eT. formicarius\u003c/em\u003e is known to prefer pine trees for oviposition; Warzee et al. 2006), or intraguild predation (for example by insectivorous birds, which are more active in mixed forests; Vasquez-Gonzalez et al. 2024).\u003c/p\u003e\n\u003cp\u003eFinally, it has been suggested that tree diversity can alter the suitability of the tree as a host resource through changes in the content of the tree\u0026apos;s tissue defences (Jactel et al. 2021), because heterospecific neighbours can alter the accessibility and use of light, water or nutrient resources and therefore the allocation of energy to growth (primary metabolites) or defence (secondary metabolites) (Felix et al. 2023). Baier et al. (2002) have in particular demonstrated experimentally that spruce trees of mixed stands produced more reactive resin against a simulated attack by bark beetles than trees in pure spruce stands. However, we were unable to verify this mechanism in our study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study adds to previous observations that monospecific spruce forests are very susceptible to bark beetle outbreaks. Increasing the diversity of tree species in spruce plantations can significantly reduce the number of trees killed by \u003cem\u003eI. typographus\u003c/em\u003e. One of the main factors in reducing infestation in mixed spruce forests is the lower number of host trees, which limits the beetles\u0026apos; ability to find a suitable habitat and build up their population on a large amount of breeding substrate. We were able to confirm this process, but by focusing on an experiment involving the manipulation of tree species richness together with composition, we were able to demonstrate that the identity of companion species in the mixed spruce stand is also crucial. In particular, the presence of taller non-host trees resulted in a lower apparency of the spruce, \u003cem\u003ei.e.\u003c/em\u003e it was less easily found by spruce bark beetles. Under the conditions of the experiment, the best protection was provided by Douglas fir, which grew taller than spruce. This discovery paves the way for the design of new spruce plantations for reforestation in European regions where the huge bark beetle epidemics have led to large-scale salvage logging. Depending on the local soil and climate conditions and the forest sectors, companion species with equal or faster growth and equivalent commercial value could be identified to serve as companion species to the spruce, with other potentially beneficial effects on total productivity, carbon sequestration, stability against other disturbances, associated biodiversity or cultural services (Messier et al. 2021). Further studies, focusing on the productivity of these mixed plantations and their relevance to the bioeconomy, would enable to better support the development of more multifunctional and sustainable spruce-based forests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHJ, EP, MSL and JF conceived and designed research. EP, HJ, JBR and PH conducted experiments. EP, LS, NP and HJ analyzed data. HJ wrote the manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the AXA Foundation, as part of the Forests For Good project.\u003c/p\u003e\n\u003cp\u003eThe BIOTREE experiment has been established by the Max-Planck-Institute for Biogeochemistry Jena, Germany, and we are very grateful to Prof. Dr. Ernst-Detlef Schulze for making this project possible. The BIOTREE site in Kaltenborn is maintained by the Federal Forestry Office Th\u0026uuml;ringer Wald (Bundesforstamt Th\u0026uuml;ringer Wald) and we acknowledge the support by Dietrich Mackensen and Klaus Hahner.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkinci, H. A., Aksu, Y. (2018). Analyzing the Local Spread of Ips typographus (L.)(Coleoptera: Curculionidae, Scolytinae) by Pheromone Catches in Turkey\u0026apos;s Hatila Valley National Park. 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Ecology. 95(5). 1370-1383.\u003c/li\u003e\n\u003cli\u003eHakola, H., Taipale, D., Praplan, A., Schallhart, S., Thomas, S., Tykk\u0026auml;, T., .,. Hell\u0026eacute;n, H. (2023). Emissions of volatile organic compounds from Norway spruce and potential atmospheric impacts. Frontiers in Forests and Global Change, 6, 1116414.\u003c/li\u003e\n\u003cli\u003eHava\u0026scaron;ov\u0026aacute;. M ., Ferenč\u0026iacute;k. J ., Jaku\u0026scaron;. R . (2017) . Interactions between windthrow. bark beetles and forest management in the Tatra national parks . Forest Ecology and Management. 391. 349-361.\u003c/li\u003e\n\u003cli\u003eHilszczanski. J., Janiszewski. W,. Negron. J, Munson. A. S. (2006). Stand characteristics and Ips typographus (L.)(Col., Curculionidae. Scolytinae) infestation during outbreak in northeastern Poland. Folia Forestalia Polonica. Series A-Forestry. 48: 53-64., 53-64.\u003c/li\u003e\n\u003cli\u003eHl\u0026aacute;sny. T., Turč\u0026aacute;ni. M. (2013). 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Agricultural and Forest Entomology. 6(1). 1-20.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-pest-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pest","sideBox":"Learn more about [Journal of Pest Science](https://www.springer.com/journal/10340)","snPcode":"10340","submissionUrl":"https://submission.nature.com/new-submission/10340/3","title":"Journal of Pest Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"biodiversity, bark beetle, associational resistance, tree diversity","lastPublishedDoi":"10.21203/rs.3.rs-6370382/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6370382/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to ongoing climate change, outbreaks of the spruce bark beetle \u003cem\u003eIps typographus\u003c/em\u003ehave caused massive tree mortality across Europe. As the forests most often damaged are pure spruce plantations, the question has arisen as to whether increasing tree species diversity could improve the resistance of spruce forests to the damage caused by these bark beetles.\u003c/p\u003e\n\u003cp\u003eWe took advantage of a spruce bark beetle infestation in a tree diversity experiment in Germany, where spruce trees were planted in pure or mixed plots, with one to three other tree species in a substitutive design, to test this hypothesis of associational resistance. Using aerial images, we retrospectively counted the number of spruces killed in all pure and mixed spruce plots from 2019 to 2023, and monitored new colonization by \u003cem\u003eI. typographus\u003c/em\u003e in 2024 using pheromone traps.\u003c/p\u003e\n\u003cp\u003eBark beetle damage decreased significantly when the proportion of spruce trees in mixed plots was lower, a consequence of greater tree species richness. The damage and colonisation by bark beetles decreased even more when taller heterospecific neighbours, in particular Douglas firs, overshadowed the spruces, which probably reduced their visual and chemical apparency. This associational resistance likely stems from a combination of reduced host tree availability and release of non-host volatiles, causing disruption to the localization of the hosts by the beetles.\u003c/p\u003e\n\u003cp\u003eThese results confirm that mixing tree species can help prevent forest insect damage, and give an insight into the species composition of more resistant mixed spruce plantations, particularly with the association of other fast-growing species.\u003c/p\u003e","manuscriptTitle":"Experimental evidence of reduced Ips typographus damage in mixed spruce plantations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:13:01","doi":"10.21203/rs.3.rs-6370382/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-16T15:44:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T15:45:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67987854636994325680959336616858803685","date":"2025-06-20T15:42:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-19T09:38:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-09T10:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73487150488780413238143060948663584839","date":"2025-06-02T06:31:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157195265207610254748266897577427048731","date":"2025-05-06T14:40:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T20:10:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-05T07:48:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-05T07:47:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Pest Science","date":"2025-04-03T14:44:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-pest-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pest","sideBox":"Learn more about [Journal of Pest Science](https://www.springer.com/journal/10340)","snPcode":"10340","submissionUrl":"https://submission.nature.com/new-submission/10340/3","title":"Journal of Pest Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7613d5ad-dd66-4bd4-a524-6e9b461395cb","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:05:00+00:00","versionOfRecord":{"articleIdentity":"rs-6370382","link":"https://doi.org/10.1007/s10340-025-01995-y","journal":{"identity":"journal-of-pest-science","isVorOnly":false,"title":"Journal of Pest Science"},"publishedOn":"2025-12-19 15:57:13","publishedOnDateReadable":"December 19th, 2025"},"versionCreatedAt":"2025-05-07 11:13:01","video":"","vorDoi":"10.1007/s10340-025-01995-y","vorDoiUrl":"https://doi.org/10.1007/s10340-025-01995-y","workflowStages":[]},"version":"v1","identity":"rs-6370382","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6370382","identity":"rs-6370382","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}