Multi-scale approach to brown bear (Ursus arctos) foraging on trees: characteristics of damage to trees and stands in the north-eastern Carpathians

Multi-scale approach to brown bear (Ursus arctos) foraging on trees: characteristics of damage to... Abstract Bark stripping behaviour of bears, which may significantly reduce the value of timber, has been reported mostly from North America and Japan, but in recent decades also from Europe. We surveyed forest plots in the Bieszczady Mountains of southeast Poland and recorded bark stripping by brown bears. We distinguished two types of tree damage by brown bears – damage to single (since no other damaged trees were found in their vicinity) and groups of trees (when five or more damaged trees were found in a small area). We found that all wound parameters, but especially the wound area and the proportion of the trunk with missing bark, were greater on single damaged trees. Comparison of damaged and control tree plots (1000 m2) revealed that bears foraged in stands where silver firs (Abies alba Mill.) with larger circumferences were present. Moreover, in stands where damage occurred, bears tended to strip large trees. The preference ratio index clearly showed that bears mostly preferred to forage on firs with circumferences in the 120–180 cm range, even though a wide spectrum of tree circumferences were available. Our results suggest that the brown bear should not be viewed as a pest species in commercial tree stands in Poland. The trees preferred by bears for foraging are large enough for timber production. To minimize economical losses, forestry personnel should regularly monitor tree stands that consist of trees of dimensions preferred by bears. Introduction Mammals, from small rodents through large ungulates to omnivores like bears (Ursidae), tend to damage trees when foraging on the bark and/or vascular tissues. Foraging on trees normally takes place in winter or early spring, when access to other food resources is limited. Animals often display a preference for particular tree species, tree sizes or developmental stages (Gill, 1992; Vasiliauskas, 2001; Manning and Baltzer, 2011; Gerhardt et al., 2013). Moreover, the characteristics of stands, such as tree density, species composition, site productivity or management practices, can also determine the foraging patterns of animals (Gill and Beardall, 2001; Kowalczyk et al., 2011; Gerhardt et al., 2013). In all European forests, large herbivores, though mostly roe deer (Capreolus capreolus L.), red deer (Cervus elaphus L.) and elk (Alces alces L.), are mainly responsible for tree damage (Bobek et al., 1984; Putman, 1996; Borkowski and Ukalski, 2012). Bark stripping, browsing and grazing can impact forest ecosystems, by changing, for instance, the composition and structure of the vegetation. Also, there are direct economic costs as damaged trees can grow more slowly or become subject to pathogens and greater mortality (Putman et al., 2011). From the standpoint of forest management, an investigation of animals’ preferences for specific trees or forest stands may be important for effectively predicting and thus preventing or at least minimizing the damage associated with foraging. Many studies address damage caused by ungulates, in particular their impact on forest ecosystems and the associated economic damage (Gerhardt et al., 2013). In contrast, the economic and ecological importance of damage caused by brown bears (Ursus arctos L.) is poorly understood. Meanwhile, increases in damage caused by bears have been reported in some European countries in recent decades (Krapinec et al., 2011a, 2011b; Zyśk-Gorczyńska et al., 2016). Bears damage trees – mainly conifers – by stripping large pieces of bark off the trunk and then feeding on the newly forming vascular tissues, which transport the products of photosynthesis, mainly sugars (sucrose and glucose) (Radwan, 1969; Kimball et al., 1998a, 1998b, 1998c). Vascular tissues have high energy value for bears and help to maintain the normal functioning of the intestines (Seryodkin et al., 2017). It is worth stressing that in forest ecosystems this type of damage can be regarded as unique, because of its extent (up to on-third of a tree’s circumference), location (lower parts of the trunk), time of appearance (spring) and usually multiple occurrence (many trees damaged in the same stand). Teeth marks are usually visible in the wounds (usually four deep rows left by the two pairs of incisors), as are claw marks, produced when the bear strikes the tree trunk with its paw and when the bark is being stripped (Sullivan, 1993; Stewart et al., 2002; Ziegltrum, 2005). Bark stripping can pose a major problem to forest management (Schmidt and Gourley, 1992; Ziegltrum, 2005). In North America, damage caused by American black bears (Ursus americanus Pallas) can have serious economic consequences in intensively harvested, even-aged forest stands (Ziegltrum, 2005; Flowers et al., 2012). Likewise in Japan, Japanese black bears (Ursus thibetanus japonicas Schlegel), foraging in a similar way as American black bears, damage forest plantations with significant financial costs (Watanabe, 1980; Yamada and Fujioka, 2010; Kobashikawa and Koike, 2016). In most European countries, by contrast, such foraging behaviour is not treated in terms of damage (economic loss), probably because it tends to be on a small regional scale. In Poland, for instance, trees damaged by bears are treated in the same way as wind-thrown trees or those that have collapsed under the weight of heavy snow (Krapinec et al., 2011a, 2011b; Zyśk-Gorczyńska et al., 2015, 2016). The species and sizes of trees preferred by bears differ depending on the geographical region. In the case of North America and Asia, the preferences of bears for tree species and sizes are relatively well known. For instance, American black bears forage mostly on fast-growing conifers like Douglas fir (Pseudotsuga menziesii Mirb.), lodgepole pine (Pinus contorta Dougl. ex Loud), and western larch (Larix occidentalis Nutt.). In western Washington, USA, where clear-cutting has become the dominant harvesting method, the bears’ primary targets are 15–25-year-old stands with trees of ~20–40 cm DBH (Stewart et al., 1999). In the forests of central British Columbia, Canada, western red cedars with 12.5–22.4 cm DBH are the most severely damaged (Sullivan, 1993). In Asia (Japan), Japanese black bears prefer Japanese cedar (Cryptomeria japonica D. Don) and Japanese cypress (Chamaecyparis obtusa Endl.), with damage occurring on trees having trunk diameters from 9.8 to 29.8 cm (Furubayashi et al., 1980). In Europe brown bears mostly damage silver fir, larch (Larix decidua Mill.) and Norway spruce (Picea abies L. Karst) with DBHs from 22.5 to 77.5 cm (Krapinec et al., 2011a, 2011b). The objectives of this study were (1) to determine the multi-scale characteristics of tree stands in which damage by bears occurred and (2) to characterize and compare selected parameters of trees and wounds resulting from bear’s foraging on single trees and groups of trees. We assumed that the composition of tree species would be different in damaged and undamaged stands, with a distinct dominance of tree species preferred by bears in the stands where damage occurred. We examined whether the probability of damage was related to tree circumference. Thus, we predicted that bears would select trees with a relatively large circumference. In the case of single damaged trees, we expected that the wound parameters and circumference(s) of single damaged trees would be greater than in groups of damaged trees because single damaged trees would be exploited by bears to a greater extent. Material and methods Study area The study was carried out in the Bieszczady Mountains (north-eastern Carpathians, southeast Poland, N 49.23°E 22.58°) in an area extending over seven forest districts (Baligród, Cisna, Komańcza, Lesko, Lutowiska, Stuposiany, Ustrzyki Dolne) (~2000 km2; Figure 1). These forest districts are divided into 7–18 smaller subordinate forestry units of 15–34 km2 each. Figure 1 View largeDownload slide Study area showing the locations of the Forest Districts: Lesko (1), Ustrzyki Dolne (2), Komańcza (3), Baligród (4), Lutowiska (5), Cisna (6), Stuposiany (7) and the Bieszczady National Park in the Bieszczady Mountains (southeastern Poland, north-eastern Carpathians). Figure 1 View largeDownload slide Study area showing the locations of the Forest Districts: Lesko (1), Ustrzyki Dolne (2), Komańcza (3), Baligród (4), Lutowiska (5), Cisna (6), Stuposiany (7) and the Bieszczady National Park in the Bieszczady Mountains (southeastern Poland, north-eastern Carpathians). The mountain forest is dominated by common beech (Fagus sylvatica L.) and the higher locations are dominated by silver fir, with admixtures of Norway spruce. In the zone above the tree line (known as ‘połonina’), alpine meadows and subalpine grass and shrub communities are typical (Winnicki and Zemanek, 2009; Marszałek, 2011). There are 80–147 brown bears in Poland (Chapron et al., 2014; Śmietana et al., 2014). The Bieszczady Mountains, the most important refuge of the brown bear in Poland, hold the majority (55–83 individuals) with the density of bears estimated at 1–3 individuals/100 km2 (Śmietana et al., 2014). The second most important refuge is in the Tatra Mts. (12–15 individuals) and the third in the Beskid Żywiecki range, where bears are present and occasionally reproduce (Chapron et al., 2014). Fieldwork The fieldwork was carried out from 2004 to 2011. Sites with damaged trees were found during planned field monitoring and during routine (all-year-round) field inspections by forestry personnel. Each forestry unit has at least two qualified foresters, responsible for monitoring on a daily basis; they were asked to record any bear-damaged trees. For the purposes of this study we distinguished two categories of damage: groups of trees and single trees. The distinction was based on our previous long-term (over 23 years) fieldwork and observations. During this period, we recorded 6973 trees damaged by bears and measured ~1000 damaged trees of five species (Zyśk-Gorczyńska and Jakubiec, 2010, 2014; Zyśk-Gorczyńska et al., 2015, 2016, Zyśk-Gorczyńska, unpubl. data). Inspection of these trees indicated that bears tended to damage trees in two ways: groups of trees, where a few or more damaged trees were located in a relatively small forest area, and single trees, when only one or rarely two adjacent trees were damaged (Zyśk-Gorczyńska and Jakubiec, 2010). Sites where several (>5) damaged trees were found were referred to as ‘group damage’. This is where we established damaged and control (undamaged) plots. Firstly, plots measuring 50 × 20 m (1000 m2) were marked out in areas where damaged trees occurred (referred as ‘group damaged plots’). Then, all the trees with circumferences ≥10 cm were identified to species level and measured (trunk circumference). Where a species occurred infrequently in a plot (<10 per cent of species composition) it was placed in an ‘other’ category. In the case of damaged trees, additional measurements were made, including wound height (the distance between the two most distant points of the wound, measured vertically) and wound width (the distance between the two most distant points of the wound, measured horizontally) (Figure 2). From these measurements, we calculated the wound area (wound height × wound width) and the proportion of the circumference damaged (wound width/tree circumference). Figure 2 View largeDownload slide Wound measurements: (a) wound height (the vertical distance between the two most distant points of the wound) and (b) wound width (the horizontal distance between the two most distant points of the wound). Figure 2 View largeDownload slide Wound measurements: (a) wound height (the vertical distance between the two most distant points of the wound) and (b) wound width (the horizontal distance between the two most distant points of the wound). The control (undamaged) plots were always established 250 m to the north of the damaged plots. The numbers and sizes of the control plots were the same as the group damaged plots. As before, all the trees in the control plots were identified to species and their trunk circumferences were measured. Care was taken to ensure that no damaged trees were present in the control plots. If a damaged tree was found, a new control plot was identified, 250 m to the north of the unsatisfactory control plots in which a damaged tree(s) had been found. All of the group plots (damaged and control) were located in the larger forestry units (N = 6). For the purposes of this study, the sampling unit was the forestry unit instead of the total number of damaged and control plots. During the fieldwork single trees damaged by bears were also found. These trees were treated as single foraging sites/damaged plots, since no other damaged trees were found in their vicinity (i.e. either within the field of view or from searching an area within a radius of 200 m from the damaged tree). As with group damage, the instances of individual damage were identified by forestry personnel. For each single damaged tree we established a 20 × 20-m (400 m2) plot. Trees growing in these plots were identified to species level and measured, as before. As in the case of the group damaged plots, the single-tree plots were located in the larger forestry units (N = 4). Statistical analyses We used the χ2 test to compare the tree species composition between damaged and control plots. We used a nested ANOVA to test for differences between mean fir circumferences in damaged and control plots at the forestry unit level. For this analysis, we examined the effects of (1) plot type (damaged or control) as a nested factor and (2) forestry units as a fixed factor. Bears damaged almost exclusively fir, so we included only this tree species in the analysis. We performed generalized linear models with a logit-link function and binomial error distribution to describe the factors related to the probability of a fir being damaged or not by bears. Analyses were performed only for damaged plots with group damage and we used each tree (damaged and undamaged) as a separate datum. The following explanatory variables were tested: (1) fir circumference and (2) forestry unit. For the categorical variable forestry unit, the site with the lowest number of damaged trees was treated as the reference group. We used a nested ANOVA to test for differences between mean fir circumferences in groups and in single damage plots at the forestry unit level. For this analysis, we examined the effects of (1) ‘type of foraging’/plot type (groups or single) as a nested factor and (2) forestry unit as a fixed factor. We tested the differences between wound parameters (width, height, area and proportion of circumference damaged) for single and group damaged firs. The assumptions for parametric tests were not met by the data, so for this analysis we used the non-parametric Mann–Whitney U test. We identified 17 fir circumference classes (from 10 to 340 cm) for measured trees; firs damaged by bears were found in 10 of the classes. We used the Risenhoover (1987) preference ratio index to represent the relationship between brown bear damage and fir circumference for all damaged plots (single and group):   Pi=Ni∑NjEi∑Ej,where Pi– the preference ratio index; Ni– the number of damaged trees of circumference i in a circumference class; ∑Nj– the number of all damaged trees in a circumference class; Ei– the number of all trees (damaged and undamaged) of circumference i in a circumference class; and ∑Ej– the total number of all trees (damaged and undamaged) in a circumference class. We used the Kolmogorov–Smirnov test to test for normality and Levene’s test to test equality of variances. The significance level in all tests was set at P ≤ 0.05. STATISTICA (V.12.5; StatSoft Inc., 2015) was used for all the statistical analyses. Results We established 48 plots (24 damaged and 24 undamaged plots) in six forestry units, in which a total of 2483 trees of different species were measured (1154 trees in the damaged plots and 1329 trees in the control plots; Tables 1 and 2). Although fir and beech were the dominant species in both plot types, there was a significant difference in tree species composition owing to the admixture of other tree species (χ2 = 41.74, P < 0.001). There were significant differences between the mean circumferences of fir trees growing in damaged and those in the control plots (F1, 1279 = 11.84, P < 0.001) across forestry units (F10,1279 = 27.25, P < 0.001). Table 1 Stand characteristics for pooled plots with trees damaged by brown bears singly and as a group of trees. Tree species  Group damage on damaged plots (N = 24)  Group damage on control plots (N = 24)  Number of singly damaged trees (N = 23)  Trunk circumference average ± SD (min–max)  Total  N  Trunk circumference average ± SD (min–max)  N  Trunk circumference average ± SD (min–max)  Fir  607  100.7 ± 54.8 (12–324)  684  87.1 ± 56.5 (9–361)  227  96.0 ± 52.1 (28–324)  1518  Spruce  103  73.1 ± 32.0 (28–146)  86  93.9 ± 38.3 (26–184)  36  93.6 ± 41.8 (38–190)  225  Pine  8  131.8 ± 28.6 (73–167)  0  –  0  –  8  Larch  0  –  0  –  4  145 ± 13.0 (38–190)  4  Beech  409  60.9 ± 40.5 (9–257)  532  62.2 ± 49.1 (9–368)  61  66.0 ± 35.0 (20–211)  1002  Ash  0  –  0  –  9  81.3 ± 26.0 (38–112)  9  Maple  0  –  11  83.5 ± 27.3 (48–152)  21  91.3 ± 32.0 (34–180)  32  Hornbeam  6  46.2 ± 13.0 (31–67)  5  66.0 ± 26.5 (32–95)  9  91.0 ± 23.4 (70–130)  20  Cherry  3  76.7 ± 15.0 (61–91)  1  71  1  68  5  Birch  2  67.0 ± 4.2 (64–70)  2  92 ± 73.5 (40–144)  4  68.2 ± 12.6 (50–78)  8  Alder  13  54.8 ± 24.7 (25–99)  3  46 ± 32.1 (25–83)  4  63.5 ± 7.2 (55–70)  20  Bird Cherry  0  –  0  –  1  40  1  Willow  0  –  3  69.3 ± 18.6 (54–90)  0  –  3  Rowan  1  46  2  49.0 ± 29.7 (28–70)  1  65  4  Hazel  2  56.5 ± 16.3 (45–68)  0  –  0  –  2  Total  1154  –  1329  –  378  –  2861  Tree species  Group damage on damaged plots (N = 24)  Group damage on control plots (N = 24)  Number of singly damaged trees (N = 23)  Trunk circumference average ± SD (min–max)  Total  N  Trunk circumference average ± SD (min–max)  N  Trunk circumference average ± SD (min–max)  Fir  607  100.7 ± 54.8 (12–324)  684  87.1 ± 56.5 (9–361)  227  96.0 ± 52.1 (28–324)  1518  Spruce  103  73.1 ± 32.0 (28–146)  86  93.9 ± 38.3 (26–184)  36  93.6 ± 41.8 (38–190)  225  Pine  8  131.8 ± 28.6 (73–167)  0  –  0  –  8  Larch  0  –  0  –  4  145 ± 13.0 (38–190)  4  Beech  409  60.9 ± 40.5 (9–257)  532  62.2 ± 49.1 (9–368)  61  66.0 ± 35.0 (20–211)  1002  Ash  0  –  0  –  9  81.3 ± 26.0 (38–112)  9  Maple  0  –  11  83.5 ± 27.3 (48–152)  21  91.3 ± 32.0 (34–180)  32  Hornbeam  6  46.2 ± 13.0 (31–67)  5  66.0 ± 26.5 (32–95)  9  91.0 ± 23.4 (70–130)  20  Cherry  3  76.7 ± 15.0 (61–91)  1  71  1  68  5  Birch  2  67.0 ± 4.2 (64–70)  2  92 ± 73.5 (40–144)  4  68.2 ± 12.6 (50–78)  8  Alder  13  54.8 ± 24.7 (25–99)  3  46 ± 32.1 (25–83)  4  63.5 ± 7.2 (55–70)  20  Bird Cherry  0  –  0  –  1  40  1  Willow  0  –  3  69.3 ± 18.6 (54–90)  0  –  3  Rowan  1  46  2  49.0 ± 29.7 (28–70)  1  65  4  Hazel  2  56.5 ± 16.3 (45–68)  0  –  0  –  2  Total  1154  –  1329  –  378  –  2861  Table 2 Tree stand characteristics for plots with trees damaged by brown bears singly and as a group of trees (the category ‘others’ includes all the tree species where their presence was <10 per cent of the total). Group damage on damaged plots  Group damage on control plots  Singly damaged plots  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  I (4)  Fir  125  71.02  I (4)  Fir  225  77.59  I (2)  Fir  31  72.09  Beech  23  13.07  Spruce  42  14.48  Others  12  27.91  Others  28  15.91  Others  23  7.93  Total    176  100      290  100      43  100  II (4)  Fir  198  69.72  II (4)  Fir  171  60.00  II (3)  Fir  35  87.5  Beech  75  26.41  Beech  98  34.39  Beech  4  10.00  Others  11  3.87  Others  16  5.61  Others  1  2.5  Total    284  100      285  100      40  100  III (4)  Fir  100  53.76  III (4)  Fir  89  40.27  III (3)  Fir  36  61.02  Spruce  27  14.52  Beech  131  59.28  Beech  13  22.03  Beech  59  31.72  Others  1  0.45  Others  10  16.95  Total    186  100      221  100      59  100  IV (4)  Fir  52  34.67  IV (4)  Fir  84  52.17  IV (15)  Fir  125  52.97  Spruce  61  40.67  Spruce  26  16.15  Spruce  30  12.71  Beech  33  22.00  Beech  38  23.60  Beech  43  18.22  Others  4  2.67  Others  13  8.07  Others  38  16.10  Total    150  100      161  100      236  100  V (6)  Fir  71  25.63  V (6)  Fir  63  20.39          Beech  206  74.37  Beech  245  79.29          Others  1  0.32          Total    277  100      309  100          VI (2)  Fir  61  75.31  VI (2)  Fir  52  72.22          Beech  13  16.05  Beech  10  13.89          Others  7  8.64  Others  10  13.89          Total    81  100      72  100          Group damage on damaged plots  Group damage on control plots  Singly damaged plots  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  I (4)  Fir  125  71.02  I (4)  Fir  225  77.59  I (2)  Fir  31  72.09  Beech  23  13.07  Spruce  42  14.48  Others  12  27.91  Others  28  15.91  Others  23  7.93  Total    176  100      290  100      43  100  II (4)  Fir  198  69.72  II (4)  Fir  171  60.00  II (3)  Fir  35  87.5  Beech  75  26.41  Beech  98  34.39  Beech  4  10.00  Others  11  3.87  Others  16  5.61  Others  1  2.5  Total    284  100      285  100      40  100  III (4)  Fir  100  53.76  III (4)  Fir  89  40.27  III (3)  Fir  36  61.02  Spruce  27  14.52  Beech  131  59.28  Beech  13  22.03  Beech  59  31.72  Others  1  0.45  Others  10  16.95  Total    186  100      221  100      59  100  IV (4)  Fir  52  34.67  IV (4)  Fir  84  52.17  IV (15)  Fir  125  52.97  Spruce  61  40.67  Spruce  26  16.15  Spruce  30  12.71  Beech  33  22.00  Beech  38  23.60  Beech  43  18.22  Others  4  2.67  Others  13  8.07  Others  38  16.10  Total    150  100      161  100      236  100  V (6)  Fir  71  25.63  V (6)  Fir  63  20.39          Beech  206  74.37  Beech  245  79.29          Others  1  0.32          Total    277  100      309  100          VI (2)  Fir  61  75.31  VI (2)  Fir  52  72.22          Beech  13  16.05  Beech  10  13.89          Others  7  8.64  Others  10  13.89          Total    81  100      72  100          Note: The plot site numbers (Roman numerals) are the same as the numbers in Figure 3. We found 115 trees damaged by bears on the group damaged plots: 114 firs and 1 spruce. Twelve of these firs were girdled, and the average percentage of the tree circumference with missing bark was 41.2 per cent (Table 4). The mean circumference of damaged firs growing in the group damaged plots was significantly larger than that of undamaged firs (153.4 ± 34.3, 88.5 ± 53.2 SD, P < 0.001, respectively; Figure 3). Generalized linear models revealed that both fir circumference and forestry unit had a positive effect on the probability of bear damage (Table 3). Table 3 The results of logistic regression models testing the effect of fir circumference and plot site on the probability of bear damage (model tests for single and group damaged plots). Variables  Coefficient  SE  Wald test  P  Group damaged plots   Intercept  −4.14  0.38  119.49  <0.001   Fir circumference  0.02  0.00  70.28  <0.001   Plot site numbers:      26.04  <0.001    I  0.05  0.25  0.05  0.823    II  −0.58  0.27  4.71  0.030    III  −0.51  0.29  3.16  0.075    IV  1.28  0.29  20.13  <0.001    V  0.25  0.28  0.84  0.360  Single damaged plots   Intercept  3.69  0.50  54.58  <0.001   Fir circumference  −0.01  0.00  18.39  <0.001  Variables  Coefficient  SE  Wald test  P  Group damaged plots   Intercept  −4.14  0.38  119.49  <0.001   Fir circumference  0.02  0.00  70.28  <0.001   Plot site numbers:      26.04  <0.001    I  0.05  0.25  0.05  0.823    II  −0.58  0.27  4.71  0.030    III  −0.51  0.29  3.16  0.075    IV  1.28  0.29  20.13  <0.001    V  0.25  0.28  0.84  0.360  Single damaged plots   Intercept  3.69  0.50  54.58  <0.001   Fir circumference  −0.01  0.00  18.39  <0.001  Note: The plot site numbers are the same as the numbers in Table 2 and in Figure 3. Figure 3 View largeDownload slide Differences in mean circumferences of damaged (N = 114) and undamaged firs (N = 493) growing in group damaged plots with respect to different plot sites (forestry units). Whiskers are 95 per cent confidence intervals. Figure 3 View largeDownload slide Differences in mean circumferences of damaged (N = 114) and undamaged firs (N = 493) growing in group damaged plots with respect to different plot sites (forestry units). Whiskers are 95 per cent confidence intervals. Three hundred and seventy-eight trees were measured in 23 plots with single damaged trees (Table 1 and 2), including 28 bear-damaged firs, two of which were girdled. The mean circumference of firs damaged by bears in the single damaged plots was significantly larger than that of undamaged firs (140.3 ± 32.6; 89.6 ± 51.3, P < 0.001, respectively). Similarly, as in the group damaged plots, fir circumference had a positive effect on the probability of bear damage (Table 3). Comparison of the two bear foraging types showed that there were no differences between the mean circumferences of damaged firs (F1,132 = 1.85, P = 0.18). Instead, forestry unit and ‘type of foraging’ as nested variables had an effect on the mean circumference of damaged firs (F8,132 = 3.79, P < 0.001). Mean wound parameters (height, width, area, percentage of circumference with missing bark) were greater for single damaged trees than group damaged trees (Table 4), although the differences were not statistically significant (P > 0.05). Table 4 Comparison of measures of brown bear foraging. Variables (cm)  Single damaged trees (N = 28)  Groups of damaged trees (N = 114)  P-value  Average (±SD)  Min–max  Average (±SD)  Min–max  Wound height  107.3 (60.2)  30–270  96.9 (43.4)  20–245  0.53  Wound width  72.1 (48.8)  15–177  63.1 (46.8)  7–119  0.46  Wound area (cm2)  8953.9 (8136.3)  450–25680  7230.9 (7186.5)  140–28458  0.36  Percentage of circumference with missing bark  50.3 (30.1)  11–100  41.2 (29.7)  4–100  0.18  Variables (cm)  Single damaged trees (N = 28)  Groups of damaged trees (N = 114)  P-value  Average (±SD)  Min–max  Average (±SD)  Min–max  Wound height  107.3 (60.2)  30–270  96.9 (43.4)  20–245  0.53  Wound width  72.1 (48.8)  15–177  63.1 (46.8)  7–119  0.46  Wound area (cm2)  8953.9 (8136.3)  450–25680  7230.9 (7186.5)  140–28458  0.36  Percentage of circumference with missing bark  50.3 (30.1)  11–100  41.2 (29.7)  4–100  0.18  Bears could choose firs from a wide spectrum of circumferences (from 12 to 324 cm). The preference ratio, however, showed that the preferred fir circumferences ranged from 120 to 180 cm (R2 = 0.6, P < 0.001; Figure 4). Figure 4 View largeDownload slide The preference ratio index shown as the relationship of brown bear damage to circumferences of firs. This index was obtained for all damaged plots (single and group plots where 142 damaged and 692 undamaged firs were found). Figure 4 View largeDownload slide The preference ratio index shown as the relationship of brown bear damage to circumferences of firs. This index was obtained for all damaged plots (single and group plots where 142 damaged and 692 undamaged firs were found). Discussion Our findings showed that bears in the north-eastern Carpathians damage mostly fir trees and tend to choose stands containing firs with large circumferences. The results correspond with a number of other studies. Yamada and Fujioka (2010) found that large diameter trees were selected by Japanese black bears and that bears chose larger trees because they produced more vascular tissues than smaller trees. Similarly, Watanabe (1980) reported that bears preferably damaged larger trees rather than smaller ones. In habitats where we found evidence of bear foraging, the firs had a wide range of circumferences from 12 to 324 cm. However, our results indicated that bears preferably foraged on trees with circumferences from 120 to 180 cm. Interestingly, the foraging habits of bears in Croatia, where they also damage mostly firs, were found to be quite similar, the bears preferred circumferences of ~180 cm (Krapinec et al., 2011a, 2011b). Similar results were reported in Bosnia and Herzegovina where spruces with circumferences from 97 to 157 cm were preferred by bears (Kunovac et al., 2008). In general, the trees damaged by bears in Europe have larger circumferences than similarly affected trees in North America and Japan. Differences in tree sizes may well be the result of different forest management practices. Bears in America and Japan damage trees in intensively harvested, even-aged forest stands, whereas in Europe (in the Dinaric Alps and in the Bieszczady Mountains), the stands are selectively harvested so that trees reach larger sizes. Our results show that there are two basic types of brown bear foraging in the Carpathians: on single trees and on groups of trees. To the best of our knowledge, this is the first distinction of such types of damage. Many previous studies reported that bears were prolific when foraging on trees; a single bear, in a relatively small forest area, could damage several dozen trees per day (Ziegltrum, 1994). Schmidt and Gourley (1992) reported that bears tended to damage many trees (one bear could damage ~70 trees per day) and damaged trees were usually concentrated in a small area. Some studies indicated that more than ten bear-damaged trees per acre was considered to be ‘severe damage’ (Stewart et al., 1999; Ziegltrum and Nolte, 2001). In contrast, we could find no reports of damage to single trees. Single damaged trees may not have been described since they are overlooked in stands and the resulting economic loss is minimal. Moreover, such trees may be mistaken for trees marked by bears to indicate a territorial boundary or rubbed for hygiene (Green and Mattson, 2003; Puchkovskiy, 2009; Sato et al., 2013). Nolte et al. (2003) suspected that bears tested the phloem in early spring, which is what we suppose could be associated with single damaged trees. Previously, we had hypothesized that single damaged trees would be exploited by bears to a greater extent and assumed that they would have larger circumferences and wound parameters than trees damaged as a group. However the statistical analysis did not confirm our prognosis. Interestingly, however, the circumference of trees in fir stands where bears damaged single trees were similar to those of group damaged trees. We found that 227 firs were growing in such single-tree plots and that the circumferences of 21 per cent of these trees lay within the range preferred by bears. Similar conditions prevailed in the group damaged plots. Of the 607 firs growing in these plots, 26 per cent had circumferences preferred by bears. Undoubtedly, when foraging over large areas, bears may be a major problem for forest management, as generally bear-damaged logs have a lower cubic volume recovery than undamaged logs having the same diameters (Lowell et al., 2010). In Japan losses in Japanese cedar and Japanese cypress plantations mean that Japanese black bears are the second greatest cause of conflict between forestry and wildlife after sika deer (Cervus nippon Temminck) (Kobashikawa and Koike, 2016). In Europe, however, bear damage is not reported as a problem for forest management. Nevertheless, the phenomenon is dynamic, and an increase in the intensity of tree damage by brown bears has been recently recorded in some regions (Krapinec et al., 2011b; Zyśk-Gorczyńska et al., 2016). The recent expansion of fir in Poland, expressed by growing number of fir renewals and higher annual increments, coupled with the an increase in the brown bear population, have resulted in greater bear-induced damage to trees (Bošela et al., 2014; Zyśk-Gorczyńska et al., 2016). However, it is doubtful whether such damage will be economically significant, especially as the extent of bear damage in the Bieszczady Mountains – the only region where damage caused by bears has been recorded in Poland – was marginal (0.1 per cent) compared to that caused by ungulates and small mammals such as hares and beavers (Zyśk-Gorczyńska et al. unpubl. data). Moreover, our previous findings showed that bear-made wounds provide breeding and feeding sites especially for woodpeckers and for saproxylic insects (Zyśk-Gorczyńska et al., 2015). Therefore, bear-induced damage to trees could be important for the biological processes of a forest, including supporting natural levels of biodiversity. Conflict of interest statement None declared. Acknowledgements The comments of Editor, Professor Chris Johnson and two anonymous reviewers were very helpful in preparing this paper. We are very grateful to Katarzyna Bojarska, Grzegorz Neubauer, and Andrzej Wuczyński for their constructive suggestions. Krešimir Krapinec gave us the instructions on how to calculate preference ratio indices. Peter Senn kindly revised the English language. We are also grateful to Teresa Berezowska-Cnota for drawing the map of the study area. For long-term support during fieldwork we are very grateful to Grzegorz Gorczyński, Wiktor Chojnacki and all the foresters in the various forestry districts. This work was supported by the Institute of Nature Conservation, Polish Academy of Sciences (Kraków, Poland) through the Institute’s statutory funds. References Bobek, B., Boyce, M.S. and Kosobucka, M. 1984 Factors affecting red deer (Cervus elaphus) population density in Southeastern Poland. J. Appl. Ecol.  21, 881– 890. Google Scholar CrossRef Search ADS   Borkowski, J. and Ukalski, K. 2012 Bark stripping by red deer in a post-disturbance area: the importance of security cover. For. Ecol. Manage.  263, 17– 23. Google Scholar CrossRef Search ADS   Bošela, M., Petráš, R., Sitková, Z., Priwitzer, T., Pajtík, J., Hlavatá, H., et al.   2014 Possible causes of the recent rapid increase in the radial increment of silver fir in the Western Carpathians. Environ. Pollut.  184, 211– 221. Google Scholar CrossRef Search ADS PubMed  Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., et al.   2014 Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science  346, 1517– 1519. doi:10.1126/science.1257553. Google Scholar CrossRef Search ADS PubMed  Flowers, R., Kanaskie, A. and McWilliams, M. 2012 Aerial Survey of tree mortality in Northwest OR, Oregon Department of Forestry. Memorandum. Furubayashi, K., Hirai, K., Ikeda, K. and Mizuguchi, T. 1980 Relationships between occurrence of bear damage and clearcutting in central Honshu. Jpn. Int. Conf. Bear Res. Manage.  4, 81– 84. doi:10.2307/3872847. Gerhardt, P., Arnold, J.M., Hackländer, K. and Hochbichler, E. 2013 Determinants of deer impact in European forests – a systematic literature analysis. For. Ecol. Manage.  310, 173– 186. doi:10.1016/j.foreco.2013.08.030. Google Scholar CrossRef Search ADS   Gill, R.M.A. 1992 A review of damage by mammals in North Temperate Forests: 1. Deer. Forestry  65, 145– 169. doi:10.1093/forestry/65.2.145. Google Scholar CrossRef Search ADS   Gill, R.M.A. and Beardall, V. 2001 The impact of deer on woodlands: the effect of browsing and seed dispersal on vegetation structure and composition. Forestry  4, 209– 218. Google Scholar CrossRef Search ADS   Green, G.I. and Mattson, D.J. 2003 Tree rubbing by Yellowstone grizzly bears (Ursus arctos). Wildl. Biol.  9, 1– 9. Kimball, B.A., Nolte, D.L., Engeman, R.M., Johnston, J.J. and Stermitz, F.R. 1998a Chemically mediated foraging preference of black bears (Ursus americanus). J. Mammal.  79, 448– 456. Google Scholar CrossRef Search ADS   Kimball, B.A., Nolte, D.L., Griffin, D.L., Dutton, S.M. and Ferguson, S. 1998b Impacts of live canopy pruning on the chemical constituents of Douglas-fir vascular tissues: implications for black bear tree selection. For. Ecol. Manage.  109, 51– 56. Google Scholar CrossRef Search ADS   Kimball, B.A., Turnblom, E.C., Nolte, D.L., Griffin, D.L. and Engeman, R.M. 1998c Effects of thinning and nitrogen fertilization on sugar and terpenes in Douglas-fir vascular tissues: implications for black bear foraging. For. Sci.  44, 599– 602. Kobashikawa, S. and Koike, S. 2016 Spatiotemporal factors affecting bark stripping of conifer trees by Asiatic black bears (Ursus thibetanus) in Japan. For. Ecol. Manage.  380, 100– 106. doi:10.1016/j.foreco.2016.08.042. Google Scholar CrossRef Search ADS   Kowalczyk, R., Taberlet, P., Coissac, E., Valentini, A., Miquel, C., Kamiński, T., et al.   2011 Influence of management practices on large herbivore diet-case of European bison in Bialowieza Primeval Forest (Poland). For. Ecol. Manage.  261, 821– 828. doi:10.1016/j.foreco.2010.11.026. Google Scholar CrossRef Search ADS   Krapinec, K., Majnarič, D., Jovanovič, D., Kovač, I. and Medarič, I. 2011a Prvi rezultati istraživanja šumskih šteta koje čini smeđi medvjed (Ursus arctos) u šumama obične jele (Abies alba) u Hrvatskoj. Croat. J. For. Eng.  32, 259– 269. Krapinec, K., Majnarič, D., Tomac, A. and Kalčič, D. 2011b Braunbärschäden in Kroatiens Waldbeständen. Beitr. Jagd. Wild  36, 63– 73. Kunovac, S., Baši, M., Skrobo, N. and Ličanin, S. 2008 Brown bear (Ursus arctos L.) damages at forest stands in Central Bosnia Canton. Works Facul. For. Univ. Sarajevo  1, 79– 90. Lowell, E.C., Dykstra, D. and McFadden, G. 2010 Effects of bear damage on Douglas-fir lumber recovery. West. J. Appl. For.  25, 73– 80. Manning, J.L. and Baltzer, J.L. 2011 Impacts of black bear baiting on Acadian forest dynamics – an indirect edge effect? For. Ecol. Manage.  262, 838– 844. doi:10.1016/j.foreco.2011.05.017. Google Scholar CrossRef Search ADS   Marszałek, E. 2011 Gospodarka leśna w karpackiej części Regionalnej Dyrekcji Lasów Państwowych w Krośnie i jej wpływ na ochronę przyrody. Rocz. Bieszcz.  19, 59– 75. Nolte, D.L., Wagner, K.K. and Trent, A. 2003 Timber damage by black bears: approaches to control the problem. USDA Forest Service. Missoula, MT, 1– 14. Puchkovskiy, S.V. 2009 Selectivity of tree species as activity target of brown bear in Taiga. Contemp. Probl. Ecol.  2 ( 3), 260– 268. doi:10.1134/S1995425509030163. Google Scholar CrossRef Search ADS   Putman, R.J. 1996 Ungulates in temperate forest ecosystems: perspectives and for future research. For. Ecol. Manage.  88, 205– 214. Google Scholar CrossRef Search ADS   Putman, R., Apollonio, M. and Andersen, R. 2011 Ungulate Management in Europe: Problems and Practises . Cambridge University Press. Google Scholar CrossRef Search ADS   Radwan, M.A. 1969 Chemical composition of the sapwood of four tree species in relation to feeding by the black bear. For. Sci.  15, 11– 16. Risenhoover, K.L. 1987 Intraspecific variation in moose preference for willows. In Proceedings of a Symposium on Plant–Herbivore Interactions. F.D. Provenza, J.T. Flinders, and E.D. McArthur (eds). 7–9 August 1985; Snowbird, UT. Ogden, UT: US Department of Agriculture, Forest Service Intermountain Research Station Publication INT-222. pp. 58–63. Sato, Y., Kamiishi, C., Tokaji, T., Mori, M., Koizumi, S., Kobayashi, K., et al.   2013 Selection of rub trees by brown bears (Ursus arctos) in Hokkaido, Japan. Acta Theriol.  59, 129– 137. Google Scholar CrossRef Search ADS   Schmidt, W.C. and Gourley, M. 1992 Black bear. In Silvicultural approaches to animal damage management in pacific northwest forests. H.C. Black (ed.). USDA Tech. Rep. PNW-GTR-287:1–422. pp. 309–333. Seryodkin, I.V., Zakahernko, A.M., Dmitrenok, P.S. and Golokhvast, K.S. 2017 Biochemical Content of Cambium of Abies nephrolepis Eaten by Bears on the Far East of Russia. Biochem. Res. Int.  3, 1– 6. Google Scholar CrossRef Search ADS   Śmietana, W., Matosiuk, M., Czajkowska, M., Ratkiewicz, M., Rutkowski, R., Buś-Kicman, M., et al.   2014 Ocena rozmieszczenia i liczebności niedźwiedzia brunatnego Ursus arctos (L.) we wschodniej części polskich Karpat. Rocz. Bieszcz.  22, 289– 301. StatSoft, Inc. 2015 STATISTICA (Data Analysis Software System). Version 12.5 [computer program]. http://www.statsoft.com/. Stewart, W.B., Witmer, G.W. and Koehler, G.M. 1999 Black bear damage to forest stands in western Washington. West. J. Appl. For.  14, 128– 131. Stewart, W.B., Witmer, G.W., Koehler, G.M. and Norton, M. 2002 Incisor analysis technique to predict the gender of black bears damaging trees. Int. Biodeterior. Biodegrad.  49, 209– 212. doi:10.1016/S0964-8305(01)00106-8. Google Scholar CrossRef Search ADS   Sullivan, T.P. 1993 Feeding damage by bears in managed forests of western hemlock-western red cedar in mid coastal British Columbia. Can. J. For. Res.  23, 49– 54. Google Scholar CrossRef Search ADS   Vasiliauskas, R. 2001 Damage to trees due to forestry operations and its pathological significance in temperate forests: a literature review. Forestry  74, 319– 336. Google Scholar CrossRef Search ADS   Watanabe, H. 1980 Damage to conifers by the Japanese black bear. Int. Conf. Bear Res. Manage.  4, 67– 70. Winnicki, T. and Zemanek, B. 2009 Przyroda Bieszczadzkiego Parku Narodowego . Bieszczadzki Park Narodowy, p. 176. Yamada, A. and Fujioka, M. 2010 Features of planted cypress trees vulnerable to damage by Japanese black bears. Ursus  21, 72– 80. Google Scholar CrossRef Search ADS   Ziegltrum, G.J. 1994 Supplemental bear feeding program in western Washington. In Proceedings of the Sixteenth Vertebrate Pest Conference, Santa Clara, Calif., 28 February to 3 March 1994. W.S. Halverson and A.C. Crabb (eds.). University of California – Davis, Davis. pp. 36–40. Ziegltrum, G.J. 2005 Annual Report, Animal Damage Control Program. Washington Forest Protection Association. Olympia, Washington, USA. Ziegltrum, G.J. and Nolte, D.L. 2001 Black bear forest damage in Washington state, USA: economic, ecological, social aspects. Ursus.  12, 169– 172. Zyśk-Gorczyńska, E. and Jakubiec, Z. 2010 Żerowanie niedźwiedzia brunatnego (Ursus arctos) na jodłach w polskiej części Karpat. Chrońmy Przyr. Ojcz.  66, 71– 75. (in Polish). Zyśk-Gorczyńska, E. and Jakubiec, Z. 2014 Ranienie drzew przez niedźwiedzia brunatnego (Ursus arctos) w Bieszczadach. Sylwan  158, 377– 382. (in Polish). Zyśk-Gorczyńska, E., Jakubiec, Z., Wertz, B. and Wuczyński, A. 2016 Long-term study of damage to trees by brown bears Ursus arctos in Poland: increasing trends with insignificant effects on forest management. For. Ecol. Manage.  366, 53– 64. doi:10.1016/j.foreco.2016.02.007. Google Scholar CrossRef Search ADS   Zyśk-Gorczyńska, E., Jakubiec, Z. and Wuczyński, A. 2015 Brown bears (Ursus arctos) as ecological engineers: the prospective role of trees damaged by bears in forest ecosystems. Can. J. Zool.  93, 133– 141. doi:10.1139/cjz-2014-0139. Google Scholar CrossRef Search ADS   © Institute of Chartered Foresters, 2018. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Forestry: An International Journal Of Forest Research Oxford University Press

Multi-scale approach to brown bear (Ursus arctos) foraging on trees: characteristics of damage to trees and stands in the north-eastern Carpathians

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Abstract

Abstract Bark stripping behaviour of bears, which may significantly reduce the value of timber, has been reported mostly from North America and Japan, but in recent decades also from Europe. We surveyed forest plots in the Bieszczady Mountains of southeast Poland and recorded bark stripping by brown bears. We distinguished two types of tree damage by brown bears – damage to single (since no other damaged trees were found in their vicinity) and groups of trees (when five or more damaged trees were found in a small area). We found that all wound parameters, but especially the wound area and the proportion of the trunk with missing bark, were greater on single damaged trees. Comparison of damaged and control tree plots (1000 m2) revealed that bears foraged in stands where silver firs (Abies alba Mill.) with larger circumferences were present. Moreover, in stands where damage occurred, bears tended to strip large trees. The preference ratio index clearly showed that bears mostly preferred to forage on firs with circumferences in the 120–180 cm range, even though a wide spectrum of tree circumferences were available. Our results suggest that the brown bear should not be viewed as a pest species in commercial tree stands in Poland. The trees preferred by bears for foraging are large enough for timber production. To minimize economical losses, forestry personnel should regularly monitor tree stands that consist of trees of dimensions preferred by bears. Introduction Mammals, from small rodents through large ungulates to omnivores like bears (Ursidae), tend to damage trees when foraging on the bark and/or vascular tissues. Foraging on trees normally takes place in winter or early spring, when access to other food resources is limited. Animals often display a preference for particular tree species, tree sizes or developmental stages (Gill, 1992; Vasiliauskas, 2001; Manning and Baltzer, 2011; Gerhardt et al., 2013). Moreover, the characteristics of stands, such as tree density, species composition, site productivity or management practices, can also determine the foraging patterns of animals (Gill and Beardall, 2001; Kowalczyk et al., 2011; Gerhardt et al., 2013). In all European forests, large herbivores, though mostly roe deer (Capreolus capreolus L.), red deer (Cervus elaphus L.) and elk (Alces alces L.), are mainly responsible for tree damage (Bobek et al., 1984; Putman, 1996; Borkowski and Ukalski, 2012). Bark stripping, browsing and grazing can impact forest ecosystems, by changing, for instance, the composition and structure of the vegetation. Also, there are direct economic costs as damaged trees can grow more slowly or become subject to pathogens and greater mortality (Putman et al., 2011). From the standpoint of forest management, an investigation of animals’ preferences for specific trees or forest stands may be important for effectively predicting and thus preventing or at least minimizing the damage associated with foraging. Many studies address damage caused by ungulates, in particular their impact on forest ecosystems and the associated economic damage (Gerhardt et al., 2013). In contrast, the economic and ecological importance of damage caused by brown bears (Ursus arctos L.) is poorly understood. Meanwhile, increases in damage caused by bears have been reported in some European countries in recent decades (Krapinec et al., 2011a, 2011b; Zyśk-Gorczyńska et al., 2016). Bears damage trees – mainly conifers – by stripping large pieces of bark off the trunk and then feeding on the newly forming vascular tissues, which transport the products of photosynthesis, mainly sugars (sucrose and glucose) (Radwan, 1969; Kimball et al., 1998a, 1998b, 1998c). Vascular tissues have high energy value for bears and help to maintain the normal functioning of the intestines (Seryodkin et al., 2017). It is worth stressing that in forest ecosystems this type of damage can be regarded as unique, because of its extent (up to on-third of a tree’s circumference), location (lower parts of the trunk), time of appearance (spring) and usually multiple occurrence (many trees damaged in the same stand). Teeth marks are usually visible in the wounds (usually four deep rows left by the two pairs of incisors), as are claw marks, produced when the bear strikes the tree trunk with its paw and when the bark is being stripped (Sullivan, 1993; Stewart et al., 2002; Ziegltrum, 2005). Bark stripping can pose a major problem to forest management (Schmidt and Gourley, 1992; Ziegltrum, 2005). In North America, damage caused by American black bears (Ursus americanus Pallas) can have serious economic consequences in intensively harvested, even-aged forest stands (Ziegltrum, 2005; Flowers et al., 2012). Likewise in Japan, Japanese black bears (Ursus thibetanus japonicas Schlegel), foraging in a similar way as American black bears, damage forest plantations with significant financial costs (Watanabe, 1980; Yamada and Fujioka, 2010; Kobashikawa and Koike, 2016). In most European countries, by contrast, such foraging behaviour is not treated in terms of damage (economic loss), probably because it tends to be on a small regional scale. In Poland, for instance, trees damaged by bears are treated in the same way as wind-thrown trees or those that have collapsed under the weight of heavy snow (Krapinec et al., 2011a, 2011b; Zyśk-Gorczyńska et al., 2015, 2016). The species and sizes of trees preferred by bears differ depending on the geographical region. In the case of North America and Asia, the preferences of bears for tree species and sizes are relatively well known. For instance, American black bears forage mostly on fast-growing conifers like Douglas fir (Pseudotsuga menziesii Mirb.), lodgepole pine (Pinus contorta Dougl. ex Loud), and western larch (Larix occidentalis Nutt.). In western Washington, USA, where clear-cutting has become the dominant harvesting method, the bears’ primary targets are 15–25-year-old stands with trees of ~20–40 cm DBH (Stewart et al., 1999). In the forests of central British Columbia, Canada, western red cedars with 12.5–22.4 cm DBH are the most severely damaged (Sullivan, 1993). In Asia (Japan), Japanese black bears prefer Japanese cedar (Cryptomeria japonica D. Don) and Japanese cypress (Chamaecyparis obtusa Endl.), with damage occurring on trees having trunk diameters from 9.8 to 29.8 cm (Furubayashi et al., 1980). In Europe brown bears mostly damage silver fir, larch (Larix decidua Mill.) and Norway spruce (Picea abies L. Karst) with DBHs from 22.5 to 77.5 cm (Krapinec et al., 2011a, 2011b). The objectives of this study were (1) to determine the multi-scale characteristics of tree stands in which damage by bears occurred and (2) to characterize and compare selected parameters of trees and wounds resulting from bear’s foraging on single trees and groups of trees. We assumed that the composition of tree species would be different in damaged and undamaged stands, with a distinct dominance of tree species preferred by bears in the stands where damage occurred. We examined whether the probability of damage was related to tree circumference. Thus, we predicted that bears would select trees with a relatively large circumference. In the case of single damaged trees, we expected that the wound parameters and circumference(s) of single damaged trees would be greater than in groups of damaged trees because single damaged trees would be exploited by bears to a greater extent. Material and methods Study area The study was carried out in the Bieszczady Mountains (north-eastern Carpathians, southeast Poland, N 49.23°E 22.58°) in an area extending over seven forest districts (Baligród, Cisna, Komańcza, Lesko, Lutowiska, Stuposiany, Ustrzyki Dolne) (~2000 km2; Figure 1). These forest districts are divided into 7–18 smaller subordinate forestry units of 15–34 km2 each. Figure 1 View largeDownload slide Study area showing the locations of the Forest Districts: Lesko (1), Ustrzyki Dolne (2), Komańcza (3), Baligród (4), Lutowiska (5), Cisna (6), Stuposiany (7) and the Bieszczady National Park in the Bieszczady Mountains (southeastern Poland, north-eastern Carpathians). Figure 1 View largeDownload slide Study area showing the locations of the Forest Districts: Lesko (1), Ustrzyki Dolne (2), Komańcza (3), Baligród (4), Lutowiska (5), Cisna (6), Stuposiany (7) and the Bieszczady National Park in the Bieszczady Mountains (southeastern Poland, north-eastern Carpathians). The mountain forest is dominated by common beech (Fagus sylvatica L.) and the higher locations are dominated by silver fir, with admixtures of Norway spruce. In the zone above the tree line (known as ‘połonina’), alpine meadows and subalpine grass and shrub communities are typical (Winnicki and Zemanek, 2009; Marszałek, 2011). There are 80–147 brown bears in Poland (Chapron et al., 2014; Śmietana et al., 2014). The Bieszczady Mountains, the most important refuge of the brown bear in Poland, hold the majority (55–83 individuals) with the density of bears estimated at 1–3 individuals/100 km2 (Śmietana et al., 2014). The second most important refuge is in the Tatra Mts. (12–15 individuals) and the third in the Beskid Żywiecki range, where bears are present and occasionally reproduce (Chapron et al., 2014). Fieldwork The fieldwork was carried out from 2004 to 2011. Sites with damaged trees were found during planned field monitoring and during routine (all-year-round) field inspections by forestry personnel. Each forestry unit has at least two qualified foresters, responsible for monitoring on a daily basis; they were asked to record any bear-damaged trees. For the purposes of this study we distinguished two categories of damage: groups of trees and single trees. The distinction was based on our previous long-term (over 23 years) fieldwork and observations. During this period, we recorded 6973 trees damaged by bears and measured ~1000 damaged trees of five species (Zyśk-Gorczyńska and Jakubiec, 2010, 2014; Zyśk-Gorczyńska et al., 2015, 2016, Zyśk-Gorczyńska, unpubl. data). Inspection of these trees indicated that bears tended to damage trees in two ways: groups of trees, where a few or more damaged trees were located in a relatively small forest area, and single trees, when only one or rarely two adjacent trees were damaged (Zyśk-Gorczyńska and Jakubiec, 2010). Sites where several (>5) damaged trees were found were referred to as ‘group damage’. This is where we established damaged and control (undamaged) plots. Firstly, plots measuring 50 × 20 m (1000 m2) were marked out in areas where damaged trees occurred (referred as ‘group damaged plots’). Then, all the trees with circumferences ≥10 cm were identified to species level and measured (trunk circumference). Where a species occurred infrequently in a plot (<10 per cent of species composition) it was placed in an ‘other’ category. In the case of damaged trees, additional measurements were made, including wound height (the distance between the two most distant points of the wound, measured vertically) and wound width (the distance between the two most distant points of the wound, measured horizontally) (Figure 2). From these measurements, we calculated the wound area (wound height × wound width) and the proportion of the circumference damaged (wound width/tree circumference). Figure 2 View largeDownload slide Wound measurements: (a) wound height (the vertical distance between the two most distant points of the wound) and (b) wound width (the horizontal distance between the two most distant points of the wound). Figure 2 View largeDownload slide Wound measurements: (a) wound height (the vertical distance between the two most distant points of the wound) and (b) wound width (the horizontal distance between the two most distant points of the wound). The control (undamaged) plots were always established 250 m to the north of the damaged plots. The numbers and sizes of the control plots were the same as the group damaged plots. As before, all the trees in the control plots were identified to species and their trunk circumferences were measured. Care was taken to ensure that no damaged trees were present in the control plots. If a damaged tree was found, a new control plot was identified, 250 m to the north of the unsatisfactory control plots in which a damaged tree(s) had been found. All of the group plots (damaged and control) were located in the larger forestry units (N = 6). For the purposes of this study, the sampling unit was the forestry unit instead of the total number of damaged and control plots. During the fieldwork single trees damaged by bears were also found. These trees were treated as single foraging sites/damaged plots, since no other damaged trees were found in their vicinity (i.e. either within the field of view or from searching an area within a radius of 200 m from the damaged tree). As with group damage, the instances of individual damage were identified by forestry personnel. For each single damaged tree we established a 20 × 20-m (400 m2) plot. Trees growing in these plots were identified to species level and measured, as before. As in the case of the group damaged plots, the single-tree plots were located in the larger forestry units (N = 4). Statistical analyses We used the χ2 test to compare the tree species composition between damaged and control plots. We used a nested ANOVA to test for differences between mean fir circumferences in damaged and control plots at the forestry unit level. For this analysis, we examined the effects of (1) plot type (damaged or control) as a nested factor and (2) forestry units as a fixed factor. Bears damaged almost exclusively fir, so we included only this tree species in the analysis. We performed generalized linear models with a logit-link function and binomial error distribution to describe the factors related to the probability of a fir being damaged or not by bears. Analyses were performed only for damaged plots with group damage and we used each tree (damaged and undamaged) as a separate datum. The following explanatory variables were tested: (1) fir circumference and (2) forestry unit. For the categorical variable forestry unit, the site with the lowest number of damaged trees was treated as the reference group. We used a nested ANOVA to test for differences between mean fir circumferences in groups and in single damage plots at the forestry unit level. For this analysis, we examined the effects of (1) ‘type of foraging’/plot type (groups or single) as a nested factor and (2) forestry unit as a fixed factor. We tested the differences between wound parameters (width, height, area and proportion of circumference damaged) for single and group damaged firs. The assumptions for parametric tests were not met by the data, so for this analysis we used the non-parametric Mann–Whitney U test. We identified 17 fir circumference classes (from 10 to 340 cm) for measured trees; firs damaged by bears were found in 10 of the classes. We used the Risenhoover (1987) preference ratio index to represent the relationship between brown bear damage and fir circumference for all damaged plots (single and group):   Pi=Ni∑NjEi∑Ej,where Pi– the preference ratio index; Ni– the number of damaged trees of circumference i in a circumference class; ∑Nj– the number of all damaged trees in a circumference class; Ei– the number of all trees (damaged and undamaged) of circumference i in a circumference class; and ∑Ej– the total number of all trees (damaged and undamaged) in a circumference class. We used the Kolmogorov–Smirnov test to test for normality and Levene’s test to test equality of variances. The significance level in all tests was set at P ≤ 0.05. STATISTICA (V.12.5; StatSoft Inc., 2015) was used for all the statistical analyses. Results We established 48 plots (24 damaged and 24 undamaged plots) in six forestry units, in which a total of 2483 trees of different species were measured (1154 trees in the damaged plots and 1329 trees in the control plots; Tables 1 and 2). Although fir and beech were the dominant species in both plot types, there was a significant difference in tree species composition owing to the admixture of other tree species (χ2 = 41.74, P < 0.001). There were significant differences between the mean circumferences of fir trees growing in damaged and those in the control plots (F1, 1279 = 11.84, P < 0.001) across forestry units (F10,1279 = 27.25, P < 0.001). Table 1 Stand characteristics for pooled plots with trees damaged by brown bears singly and as a group of trees. Tree species  Group damage on damaged plots (N = 24)  Group damage on control plots (N = 24)  Number of singly damaged trees (N = 23)  Trunk circumference average ± SD (min–max)  Total  N  Trunk circumference average ± SD (min–max)  N  Trunk circumference average ± SD (min–max)  Fir  607  100.7 ± 54.8 (12–324)  684  87.1 ± 56.5 (9–361)  227  96.0 ± 52.1 (28–324)  1518  Spruce  103  73.1 ± 32.0 (28–146)  86  93.9 ± 38.3 (26–184)  36  93.6 ± 41.8 (38–190)  225  Pine  8  131.8 ± 28.6 (73–167)  0  –  0  –  8  Larch  0  –  0  –  4  145 ± 13.0 (38–190)  4  Beech  409  60.9 ± 40.5 (9–257)  532  62.2 ± 49.1 (9–368)  61  66.0 ± 35.0 (20–211)  1002  Ash  0  –  0  –  9  81.3 ± 26.0 (38–112)  9  Maple  0  –  11  83.5 ± 27.3 (48–152)  21  91.3 ± 32.0 (34–180)  32  Hornbeam  6  46.2 ± 13.0 (31–67)  5  66.0 ± 26.5 (32–95)  9  91.0 ± 23.4 (70–130)  20  Cherry  3  76.7 ± 15.0 (61–91)  1  71  1  68  5  Birch  2  67.0 ± 4.2 (64–70)  2  92 ± 73.5 (40–144)  4  68.2 ± 12.6 (50–78)  8  Alder  13  54.8 ± 24.7 (25–99)  3  46 ± 32.1 (25–83)  4  63.5 ± 7.2 (55–70)  20  Bird Cherry  0  –  0  –  1  40  1  Willow  0  –  3  69.3 ± 18.6 (54–90)  0  –  3  Rowan  1  46  2  49.0 ± 29.7 (28–70)  1  65  4  Hazel  2  56.5 ± 16.3 (45–68)  0  –  0  –  2  Total  1154  –  1329  –  378  –  2861  Tree species  Group damage on damaged plots (N = 24)  Group damage on control plots (N = 24)  Number of singly damaged trees (N = 23)  Trunk circumference average ± SD (min–max)  Total  N  Trunk circumference average ± SD (min–max)  N  Trunk circumference average ± SD (min–max)  Fir  607  100.7 ± 54.8 (12–324)  684  87.1 ± 56.5 (9–361)  227  96.0 ± 52.1 (28–324)  1518  Spruce  103  73.1 ± 32.0 (28–146)  86  93.9 ± 38.3 (26–184)  36  93.6 ± 41.8 (38–190)  225  Pine  8  131.8 ± 28.6 (73–167)  0  –  0  –  8  Larch  0  –  0  –  4  145 ± 13.0 (38–190)  4  Beech  409  60.9 ± 40.5 (9–257)  532  62.2 ± 49.1 (9–368)  61  66.0 ± 35.0 (20–211)  1002  Ash  0  –  0  –  9  81.3 ± 26.0 (38–112)  9  Maple  0  –  11  83.5 ± 27.3 (48–152)  21  91.3 ± 32.0 (34–180)  32  Hornbeam  6  46.2 ± 13.0 (31–67)  5  66.0 ± 26.5 (32–95)  9  91.0 ± 23.4 (70–130)  20  Cherry  3  76.7 ± 15.0 (61–91)  1  71  1  68  5  Birch  2  67.0 ± 4.2 (64–70)  2  92 ± 73.5 (40–144)  4  68.2 ± 12.6 (50–78)  8  Alder  13  54.8 ± 24.7 (25–99)  3  46 ± 32.1 (25–83)  4  63.5 ± 7.2 (55–70)  20  Bird Cherry  0  –  0  –  1  40  1  Willow  0  –  3  69.3 ± 18.6 (54–90)  0  –  3  Rowan  1  46  2  49.0 ± 29.7 (28–70)  1  65  4  Hazel  2  56.5 ± 16.3 (45–68)  0  –  0  –  2  Total  1154  –  1329  –  378  –  2861  Table 2 Tree stand characteristics for plots with trees damaged by brown bears singly and as a group of trees (the category ‘others’ includes all the tree species where their presence was <10 per cent of the total). Group damage on damaged plots  Group damage on control plots  Singly damaged plots  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  I (4)  Fir  125  71.02  I (4)  Fir  225  77.59  I (2)  Fir  31  72.09  Beech  23  13.07  Spruce  42  14.48  Others  12  27.91  Others  28  15.91  Others  23  7.93  Total    176  100      290  100      43  100  II (4)  Fir  198  69.72  II (4)  Fir  171  60.00  II (3)  Fir  35  87.5  Beech  75  26.41  Beech  98  34.39  Beech  4  10.00  Others  11  3.87  Others  16  5.61  Others  1  2.5  Total    284  100      285  100      40  100  III (4)  Fir  100  53.76  III (4)  Fir  89  40.27  III (3)  Fir  36  61.02  Spruce  27  14.52  Beech  131  59.28  Beech  13  22.03  Beech  59  31.72  Others  1  0.45  Others  10  16.95  Total    186  100      221  100      59  100  IV (4)  Fir  52  34.67  IV (4)  Fir  84  52.17  IV (15)  Fir  125  52.97  Spruce  61  40.67  Spruce  26  16.15  Spruce  30  12.71  Beech  33  22.00  Beech  38  23.60  Beech  43  18.22  Others  4  2.67  Others  13  8.07  Others  38  16.10  Total    150  100      161  100      236  100  V (6)  Fir  71  25.63  V (6)  Fir  63  20.39          Beech  206  74.37  Beech  245  79.29          Others  1  0.32          Total    277  100      309  100          VI (2)  Fir  61  75.31  VI (2)  Fir  52  72.22          Beech  13  16.05  Beech  10  13.89          Others  7  8.64  Others  10  13.89          Total    81  100      72  100          Group damage on damaged plots  Group damage on control plots  Singly damaged plots  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  Number of plot sites (number of marked plots)  Tree species  N  %  I (4)  Fir  125  71.02  I (4)  Fir  225  77.59  I (2)  Fir  31  72.09  Beech  23  13.07  Spruce  42  14.48  Others  12  27.91  Others  28  15.91  Others  23  7.93  Total    176  100      290  100      43  100  II (4)  Fir  198  69.72  II (4)  Fir  171  60.00  II (3)  Fir  35  87.5  Beech  75  26.41  Beech  98  34.39  Beech  4  10.00  Others  11  3.87  Others  16  5.61  Others  1  2.5  Total    284  100      285  100      40  100  III (4)  Fir  100  53.76  III (4)  Fir  89  40.27  III (3)  Fir  36  61.02  Spruce  27  14.52  Beech  131  59.28  Beech  13  22.03  Beech  59  31.72  Others  1  0.45  Others  10  16.95  Total    186  100      221  100      59  100  IV (4)  Fir  52  34.67  IV (4)  Fir  84  52.17  IV (15)  Fir  125  52.97  Spruce  61  40.67  Spruce  26  16.15  Spruce  30  12.71  Beech  33  22.00  Beech  38  23.60  Beech  43  18.22  Others  4  2.67  Others  13  8.07  Others  38  16.10  Total    150  100      161  100      236  100  V (6)  Fir  71  25.63  V (6)  Fir  63  20.39          Beech  206  74.37  Beech  245  79.29          Others  1  0.32          Total    277  100      309  100          VI (2)  Fir  61  75.31  VI (2)  Fir  52  72.22          Beech  13  16.05  Beech  10  13.89          Others  7  8.64  Others  10  13.89          Total    81  100      72  100          Note: The plot site numbers (Roman numerals) are the same as the numbers in Figure 3. We found 115 trees damaged by bears on the group damaged plots: 114 firs and 1 spruce. Twelve of these firs were girdled, and the average percentage of the tree circumference with missing bark was 41.2 per cent (Table 4). The mean circumference of damaged firs growing in the group damaged plots was significantly larger than that of undamaged firs (153.4 ± 34.3, 88.5 ± 53.2 SD, P < 0.001, respectively; Figure 3). Generalized linear models revealed that both fir circumference and forestry unit had a positive effect on the probability of bear damage (Table 3). Table 3 The results of logistic regression models testing the effect of fir circumference and plot site on the probability of bear damage (model tests for single and group damaged plots). Variables  Coefficient  SE  Wald test  P  Group damaged plots   Intercept  −4.14  0.38  119.49  <0.001   Fir circumference  0.02  0.00  70.28  <0.001   Plot site numbers:      26.04  <0.001    I  0.05  0.25  0.05  0.823    II  −0.58  0.27  4.71  0.030    III  −0.51  0.29  3.16  0.075    IV  1.28  0.29  20.13  <0.001    V  0.25  0.28  0.84  0.360  Single damaged plots   Intercept  3.69  0.50  54.58  <0.001   Fir circumference  −0.01  0.00  18.39  <0.001  Variables  Coefficient  SE  Wald test  P  Group damaged plots   Intercept  −4.14  0.38  119.49  <0.001   Fir circumference  0.02  0.00  70.28  <0.001   Plot site numbers:      26.04  <0.001    I  0.05  0.25  0.05  0.823    II  −0.58  0.27  4.71  0.030    III  −0.51  0.29  3.16  0.075    IV  1.28  0.29  20.13  <0.001    V  0.25  0.28  0.84  0.360  Single damaged plots   Intercept  3.69  0.50  54.58  <0.001   Fir circumference  −0.01  0.00  18.39  <0.001  Note: The plot site numbers are the same as the numbers in Table 2 and in Figure 3. Figure 3 View largeDownload slide Differences in mean circumferences of damaged (N = 114) and undamaged firs (N = 493) growing in group damaged plots with respect to different plot sites (forestry units). Whiskers are 95 per cent confidence intervals. Figure 3 View largeDownload slide Differences in mean circumferences of damaged (N = 114) and undamaged firs (N = 493) growing in group damaged plots with respect to different plot sites (forestry units). Whiskers are 95 per cent confidence intervals. Three hundred and seventy-eight trees were measured in 23 plots with single damaged trees (Table 1 and 2), including 28 bear-damaged firs, two of which were girdled. The mean circumference of firs damaged by bears in the single damaged plots was significantly larger than that of undamaged firs (140.3 ± 32.6; 89.6 ± 51.3, P < 0.001, respectively). Similarly, as in the group damaged plots, fir circumference had a positive effect on the probability of bear damage (Table 3). Comparison of the two bear foraging types showed that there were no differences between the mean circumferences of damaged firs (F1,132 = 1.85, P = 0.18). Instead, forestry unit and ‘type of foraging’ as nested variables had an effect on the mean circumference of damaged firs (F8,132 = 3.79, P < 0.001). Mean wound parameters (height, width, area, percentage of circumference with missing bark) were greater for single damaged trees than group damaged trees (Table 4), although the differences were not statistically significant (P > 0.05). Table 4 Comparison of measures of brown bear foraging. Variables (cm)  Single damaged trees (N = 28)  Groups of damaged trees (N = 114)  P-value  Average (±SD)  Min–max  Average (±SD)  Min–max  Wound height  107.3 (60.2)  30–270  96.9 (43.4)  20–245  0.53  Wound width  72.1 (48.8)  15–177  63.1 (46.8)  7–119  0.46  Wound area (cm2)  8953.9 (8136.3)  450–25680  7230.9 (7186.5)  140–28458  0.36  Percentage of circumference with missing bark  50.3 (30.1)  11–100  41.2 (29.7)  4–100  0.18  Variables (cm)  Single damaged trees (N = 28)  Groups of damaged trees (N = 114)  P-value  Average (±SD)  Min–max  Average (±SD)  Min–max  Wound height  107.3 (60.2)  30–270  96.9 (43.4)  20–245  0.53  Wound width  72.1 (48.8)  15–177  63.1 (46.8)  7–119  0.46  Wound area (cm2)  8953.9 (8136.3)  450–25680  7230.9 (7186.5)  140–28458  0.36  Percentage of circumference with missing bark  50.3 (30.1)  11–100  41.2 (29.7)  4–100  0.18  Bears could choose firs from a wide spectrum of circumferences (from 12 to 324 cm). The preference ratio, however, showed that the preferred fir circumferences ranged from 120 to 180 cm (R2 = 0.6, P < 0.001; Figure 4). Figure 4 View largeDownload slide The preference ratio index shown as the relationship of brown bear damage to circumferences of firs. This index was obtained for all damaged plots (single and group plots where 142 damaged and 692 undamaged firs were found). Figure 4 View largeDownload slide The preference ratio index shown as the relationship of brown bear damage to circumferences of firs. This index was obtained for all damaged plots (single and group plots where 142 damaged and 692 undamaged firs were found). Discussion Our findings showed that bears in the north-eastern Carpathians damage mostly fir trees and tend to choose stands containing firs with large circumferences. The results correspond with a number of other studies. Yamada and Fujioka (2010) found that large diameter trees were selected by Japanese black bears and that bears chose larger trees because they produced more vascular tissues than smaller trees. Similarly, Watanabe (1980) reported that bears preferably damaged larger trees rather than smaller ones. In habitats where we found evidence of bear foraging, the firs had a wide range of circumferences from 12 to 324 cm. However, our results indicated that bears preferably foraged on trees with circumferences from 120 to 180 cm. Interestingly, the foraging habits of bears in Croatia, where they also damage mostly firs, were found to be quite similar, the bears preferred circumferences of ~180 cm (Krapinec et al., 2011a, 2011b). Similar results were reported in Bosnia and Herzegovina where spruces with circumferences from 97 to 157 cm were preferred by bears (Kunovac et al., 2008). In general, the trees damaged by bears in Europe have larger circumferences than similarly affected trees in North America and Japan. Differences in tree sizes may well be the result of different forest management practices. Bears in America and Japan damage trees in intensively harvested, even-aged forest stands, whereas in Europe (in the Dinaric Alps and in the Bieszczady Mountains), the stands are selectively harvested so that trees reach larger sizes. Our results show that there are two basic types of brown bear foraging in the Carpathians: on single trees and on groups of trees. To the best of our knowledge, this is the first distinction of such types of damage. Many previous studies reported that bears were prolific when foraging on trees; a single bear, in a relatively small forest area, could damage several dozen trees per day (Ziegltrum, 1994). Schmidt and Gourley (1992) reported that bears tended to damage many trees (one bear could damage ~70 trees per day) and damaged trees were usually concentrated in a small area. Some studies indicated that more than ten bear-damaged trees per acre was considered to be ‘severe damage’ (Stewart et al., 1999; Ziegltrum and Nolte, 2001). In contrast, we could find no reports of damage to single trees. Single damaged trees may not have been described since they are overlooked in stands and the resulting economic loss is minimal. Moreover, such trees may be mistaken for trees marked by bears to indicate a territorial boundary or rubbed for hygiene (Green and Mattson, 2003; Puchkovskiy, 2009; Sato et al., 2013). Nolte et al. (2003) suspected that bears tested the phloem in early spring, which is what we suppose could be associated with single damaged trees. Previously, we had hypothesized that single damaged trees would be exploited by bears to a greater extent and assumed that they would have larger circumferences and wound parameters than trees damaged as a group. However the statistical analysis did not confirm our prognosis. Interestingly, however, the circumference of trees in fir stands where bears damaged single trees were similar to those of group damaged trees. We found that 227 firs were growing in such single-tree plots and that the circumferences of 21 per cent of these trees lay within the range preferred by bears. Similar conditions prevailed in the group damaged plots. Of the 607 firs growing in these plots, 26 per cent had circumferences preferred by bears. Undoubtedly, when foraging over large areas, bears may be a major problem for forest management, as generally bear-damaged logs have a lower cubic volume recovery than undamaged logs having the same diameters (Lowell et al., 2010). In Japan losses in Japanese cedar and Japanese cypress plantations mean that Japanese black bears are the second greatest cause of conflict between forestry and wildlife after sika deer (Cervus nippon Temminck) (Kobashikawa and Koike, 2016). In Europe, however, bear damage is not reported as a problem for forest management. Nevertheless, the phenomenon is dynamic, and an increase in the intensity of tree damage by brown bears has been recently recorded in some regions (Krapinec et al., 2011b; Zyśk-Gorczyńska et al., 2016). The recent expansion of fir in Poland, expressed by growing number of fir renewals and higher annual increments, coupled with the an increase in the brown bear population, have resulted in greater bear-induced damage to trees (Bošela et al., 2014; Zyśk-Gorczyńska et al., 2016). However, it is doubtful whether such damage will be economically significant, especially as the extent of bear damage in the Bieszczady Mountains – the only region where damage caused by bears has been recorded in Poland – was marginal (0.1 per cent) compared to that caused by ungulates and small mammals such as hares and beavers (Zyśk-Gorczyńska et al. unpubl. data). Moreover, our previous findings showed that bear-made wounds provide breeding and feeding sites especially for woodpeckers and for saproxylic insects (Zyśk-Gorczyńska et al., 2015). Therefore, bear-induced damage to trees could be important for the biological processes of a forest, including supporting natural levels of biodiversity. Conflict of interest statement None declared. Acknowledgements The comments of Editor, Professor Chris Johnson and two anonymous reviewers were very helpful in preparing this paper. We are very grateful to Katarzyna Bojarska, Grzegorz Neubauer, and Andrzej Wuczyński for their constructive suggestions. Krešimir Krapinec gave us the instructions on how to calculate preference ratio indices. Peter Senn kindly revised the English language. We are also grateful to Teresa Berezowska-Cnota for drawing the map of the study area. For long-term support during fieldwork we are very grateful to Grzegorz Gorczyński, Wiktor Chojnacki and all the foresters in the various forestry districts. This work was supported by the Institute of Nature Conservation, Polish Academy of Sciences (Kraków, Poland) through the Institute’s statutory funds. References Bobek, B., Boyce, M.S. and Kosobucka, M. 1984 Factors affecting red deer (Cervus elaphus) population density in Southeastern Poland. J. Appl. Ecol.  21, 881– 890. Google Scholar CrossRef Search ADS   Borkowski, J. and Ukalski, K. 2012 Bark stripping by red deer in a post-disturbance area: the importance of security cover. For. Ecol. Manage.  263, 17– 23. Google Scholar CrossRef Search ADS   Bošela, M., Petráš, R., Sitková, Z., Priwitzer, T., Pajtík, J., Hlavatá, H., et al.   2014 Possible causes of the recent rapid increase in the radial increment of silver fir in the Western Carpathians. Environ. Pollut.  184, 211– 221. Google Scholar CrossRef Search ADS PubMed  Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., et al.   2014 Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science  346, 1517– 1519. doi:10.1126/science.1257553. Google Scholar CrossRef Search ADS PubMed  Flowers, R., Kanaskie, A. and McWilliams, M. 2012 Aerial Survey of tree mortality in Northwest OR, Oregon Department of Forestry. Memorandum. Furubayashi, K., Hirai, K., Ikeda, K. and Mizuguchi, T. 1980 Relationships between occurrence of bear damage and clearcutting in central Honshu. Jpn. Int. Conf. Bear Res. Manage.  4, 81– 84. doi:10.2307/3872847. Gerhardt, P., Arnold, J.M., Hackländer, K. and Hochbichler, E. 2013 Determinants of deer impact in European forests – a systematic literature analysis. For. Ecol. Manage.  310, 173– 186. doi:10.1016/j.foreco.2013.08.030. Google Scholar CrossRef Search ADS   Gill, R.M.A. 1992 A review of damage by mammals in North Temperate Forests: 1. Deer. Forestry  65, 145– 169. doi:10.1093/forestry/65.2.145. Google Scholar CrossRef Search ADS   Gill, R.M.A. and Beardall, V. 2001 The impact of deer on woodlands: the effect of browsing and seed dispersal on vegetation structure and composition. Forestry  4, 209– 218. Google Scholar CrossRef Search ADS   Green, G.I. and Mattson, D.J. 2003 Tree rubbing by Yellowstone grizzly bears (Ursus arctos). Wildl. Biol.  9, 1– 9. Kimball, B.A., Nolte, D.L., Engeman, R.M., Johnston, J.J. and Stermitz, F.R. 1998a Chemically mediated foraging preference of black bears (Ursus americanus). J. Mammal.  79, 448– 456. Google Scholar CrossRef Search ADS   Kimball, B.A., Nolte, D.L., Griffin, D.L., Dutton, S.M. and Ferguson, S. 1998b Impacts of live canopy pruning on the chemical constituents of Douglas-fir vascular tissues: implications for black bear tree selection. For. Ecol. Manage.  109, 51– 56. Google Scholar CrossRef Search ADS   Kimball, B.A., Turnblom, E.C., Nolte, D.L., Griffin, D.L. and Engeman, R.M. 1998c Effects of thinning and nitrogen fertilization on sugar and terpenes in Douglas-fir vascular tissues: implications for black bear foraging. For. Sci.  44, 599– 602. Kobashikawa, S. and Koike, S. 2016 Spatiotemporal factors affecting bark stripping of conifer trees by Asiatic black bears (Ursus thibetanus) in Japan. For. Ecol. Manage.  380, 100– 106. doi:10.1016/j.foreco.2016.08.042. Google Scholar CrossRef Search ADS   Kowalczyk, R., Taberlet, P., Coissac, E., Valentini, A., Miquel, C., Kamiński, T., et al.   2011 Influence of management practices on large herbivore diet-case of European bison in Bialowieza Primeval Forest (Poland). For. Ecol. Manage.  261, 821– 828. doi:10.1016/j.foreco.2010.11.026. Google Scholar CrossRef Search ADS   Krapinec, K., Majnarič, D., Jovanovič, D., Kovač, I. and Medarič, I. 2011a Prvi rezultati istraživanja šumskih šteta koje čini smeđi medvjed (Ursus arctos) u šumama obične jele (Abies alba) u Hrvatskoj. Croat. J. For. Eng.  32, 259– 269. Krapinec, K., Majnarič, D., Tomac, A. and Kalčič, D. 2011b Braunbärschäden in Kroatiens Waldbeständen. Beitr. Jagd. Wild  36, 63– 73. Kunovac, S., Baši, M., Skrobo, N. and Ličanin, S. 2008 Brown bear (Ursus arctos L.) damages at forest stands in Central Bosnia Canton. Works Facul. For. Univ. Sarajevo  1, 79– 90. Lowell, E.C., Dykstra, D. and McFadden, G. 2010 Effects of bear damage on Douglas-fir lumber recovery. West. J. Appl. For.  25, 73– 80. Manning, J.L. and Baltzer, J.L. 2011 Impacts of black bear baiting on Acadian forest dynamics – an indirect edge effect? For. Ecol. Manage.  262, 838– 844. doi:10.1016/j.foreco.2011.05.017. Google Scholar CrossRef Search ADS   Marszałek, E. 2011 Gospodarka leśna w karpackiej części Regionalnej Dyrekcji Lasów Państwowych w Krośnie i jej wpływ na ochronę przyrody. Rocz. Bieszcz.  19, 59– 75. Nolte, D.L., Wagner, K.K. and Trent, A. 2003 Timber damage by black bears: approaches to control the problem. USDA Forest Service. Missoula, MT, 1– 14. Puchkovskiy, S.V. 2009 Selectivity of tree species as activity target of brown bear in Taiga. Contemp. Probl. Ecol.  2 ( 3), 260– 268. doi:10.1134/S1995425509030163. Google Scholar CrossRef Search ADS   Putman, R.J. 1996 Ungulates in temperate forest ecosystems: perspectives and for future research. For. Ecol. Manage.  88, 205– 214. Google Scholar CrossRef Search ADS   Putman, R., Apollonio, M. and Andersen, R. 2011 Ungulate Management in Europe: Problems and Practises . Cambridge University Press. Google Scholar CrossRef Search ADS   Radwan, M.A. 1969 Chemical composition of the sapwood of four tree species in relation to feeding by the black bear. For. Sci.  15, 11– 16. Risenhoover, K.L. 1987 Intraspecific variation in moose preference for willows. In Proceedings of a Symposium on Plant–Herbivore Interactions. F.D. Provenza, J.T. Flinders, and E.D. McArthur (eds). 7–9 August 1985; Snowbird, UT. Ogden, UT: US Department of Agriculture, Forest Service Intermountain Research Station Publication INT-222. pp. 58–63. Sato, Y., Kamiishi, C., Tokaji, T., Mori, M., Koizumi, S., Kobayashi, K., et al.   2013 Selection of rub trees by brown bears (Ursus arctos) in Hokkaido, Japan. Acta Theriol.  59, 129– 137. Google Scholar CrossRef Search ADS   Schmidt, W.C. and Gourley, M. 1992 Black bear. In Silvicultural approaches to animal damage management in pacific northwest forests. H.C. Black (ed.). USDA Tech. Rep. PNW-GTR-287:1–422. pp. 309–333. Seryodkin, I.V., Zakahernko, A.M., Dmitrenok, P.S. and Golokhvast, K.S. 2017 Biochemical Content of Cambium of Abies nephrolepis Eaten by Bears on the Far East of Russia. Biochem. Res. Int.  3, 1– 6. Google Scholar CrossRef Search ADS   Śmietana, W., Matosiuk, M., Czajkowska, M., Ratkiewicz, M., Rutkowski, R., Buś-Kicman, M., et al.   2014 Ocena rozmieszczenia i liczebności niedźwiedzia brunatnego Ursus arctos (L.) we wschodniej części polskich Karpat. Rocz. Bieszcz.  22, 289– 301. StatSoft, Inc. 2015 STATISTICA (Data Analysis Software System). Version 12.5 [computer program]. http://www.statsoft.com/. Stewart, W.B., Witmer, G.W. and Koehler, G.M. 1999 Black bear damage to forest stands in western Washington. West. J. Appl. For.  14, 128– 131. Stewart, W.B., Witmer, G.W., Koehler, G.M. and Norton, M. 2002 Incisor analysis technique to predict the gender of black bears damaging trees. Int. Biodeterior. Biodegrad.  49, 209– 212. doi:10.1016/S0964-8305(01)00106-8. Google Scholar CrossRef Search ADS   Sullivan, T.P. 1993 Feeding damage by bears in managed forests of western hemlock-western red cedar in mid coastal British Columbia. Can. J. For. Res.  23, 49– 54. Google Scholar CrossRef Search ADS   Vasiliauskas, R. 2001 Damage to trees due to forestry operations and its pathological significance in temperate forests: a literature review. Forestry  74, 319– 336. Google Scholar CrossRef Search ADS   Watanabe, H. 1980 Damage to conifers by the Japanese black bear. Int. Conf. Bear Res. Manage.  4, 67– 70. Winnicki, T. and Zemanek, B. 2009 Przyroda Bieszczadzkiego Parku Narodowego . Bieszczadzki Park Narodowy, p. 176. Yamada, A. and Fujioka, M. 2010 Features of planted cypress trees vulnerable to damage by Japanese black bears. Ursus  21, 72– 80. Google Scholar CrossRef Search ADS   Ziegltrum, G.J. 1994 Supplemental bear feeding program in western Washington. In Proceedings of the Sixteenth Vertebrate Pest Conference, Santa Clara, Calif., 28 February to 3 March 1994. W.S. Halverson and A.C. Crabb (eds.). University of California – Davis, Davis. pp. 36–40. Ziegltrum, G.J. 2005 Annual Report, Animal Damage Control Program. Washington Forest Protection Association. Olympia, Washington, USA. Ziegltrum, G.J. and Nolte, D.L. 2001 Black bear forest damage in Washington state, USA: economic, ecological, social aspects. Ursus.  12, 169– 172. Zyśk-Gorczyńska, E. and Jakubiec, Z. 2010 Żerowanie niedźwiedzia brunatnego (Ursus arctos) na jodłach w polskiej części Karpat. Chrońmy Przyr. Ojcz.  66, 71– 75. (in Polish). Zyśk-Gorczyńska, E. and Jakubiec, Z. 2014 Ranienie drzew przez niedźwiedzia brunatnego (Ursus arctos) w Bieszczadach. Sylwan  158, 377– 382. (in Polish). Zyśk-Gorczyńska, E., Jakubiec, Z., Wertz, B. and Wuczyński, A. 2016 Long-term study of damage to trees by brown bears Ursus arctos in Poland: increasing trends with insignificant effects on forest management. For. Ecol. Manage.  366, 53– 64. doi:10.1016/j.foreco.2016.02.007. Google Scholar CrossRef Search ADS   Zyśk-Gorczyńska, E., Jakubiec, Z. and Wuczyński, A. 2015 Brown bears (Ursus arctos) as ecological engineers: the prospective role of trees damaged by bears in forest ecosystems. Can. J. Zool.  93, 133– 141. doi:10.1139/cjz-2014-0139. Google Scholar CrossRef Search ADS   © Institute of Chartered Foresters, 2018. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Forestry: An International Journal Of Forest ResearchOxford University Press

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