Factors influencing the wind–bark beetles’ disturbance system in the course of an Ips typographus outbreak in the Tatra Mountains

Forest Ecology and Management 312 (2014) 67–77

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Factors influencing the wind–bark beetles’ disturbance system in the course of an Ips typographus outbreak in the Tatra Mountains Pavel Mezei a,⇑, Wojciech Grodzki b, Miroslav Blazˇenec a,c, Rastislav Jakuš a,c a

Institute of Forest Ecology of the Slovak Academy of Sciences, Štúrova 2, 960 53 Zvolen, Slovak Republic Department of Forest Management in Mountain Regions, Forest Research Institute, ul. Fredry 39, 30-605 Kraków, Poland c ´cká 1176, 165 21 Praha 6, Suchdol, Department of Forest Protection and Game Management, Faculty of Forestry and Wood Sciences, Czech University of Life Sciences, Kamy Czech Republic b

a r t i c l e

i n f o

Article history: Received 1 March 2013 Received in revised form 9 October 2013 Accepted 15 October 2013 Available online 7 November 2013 Keywords: Disturbance Spruce bark beetle Wind Outbreak Ips typographus

a b s t r a c t An outbreak of spruce bark beetle (Ips typographus [L.]) in the Tatra Mountains in the Slovak Republic and Poland, Central Europe, was analysed. The study area was nearly 3000 ha. The 11 year outbreak lasted from 1990 to 2000. Three outbreak phases were identified: 1990–1994 (incipient epidemic), 1995–1996 (epidemic) and 1997–2000 (post-epidemic). More than 118,000 m3 of trees were damaged by wind and bark beetles. The analysis considered the relationship and succession of these two types of disturbances. Discrimination analysis, a multiple linear regression and boosted regression trees were used to determine the influence of 11 variables on tree mortality initiation and severity. The wind–bark beetles disturbance system was primarily influenced by stand related factors. Tree mortality initiation primarily depends on stand age and related changes in Norway spruce size and vitality. Wind caused tree mortality severity was primarily related to the tree or stand characteristics as well. The roles of host and environmental factors in the initiation and severity of tree mortality were influenced by the I. typographus outbreak phase. Stand, site and solar radiation variables were the most important factors impacting tree mortality severity caused by this disturbance system, especially in the epidemic phase. However, the severity of tree mortality caused by wind was primarily correlated with the stand characteristics. With the exception of elevation, the roles of the studied factors were similar in all gradation phases. Ó 2013 Elsevier B.V. All rights reserved.

1. Introduction Wind and bark beetles are the most important disturbances in spruce (Picea abies [L.] Karst.) ecosystems in Central Europe (Schelhaas et al., 2003; Svoboda et al., 2010, 2012). Damage from bark beetles, mainly Ips typographus (L.), is usually highly correlated with storm damage (Schelhaas et al., 2003). Many studies have usually analysed only a single disturbance agent, while only a few studies have examined the interactions between different disturbance agents (Klopcˇicˇ et al., 2009). Hanewinkel et al. (2008) showed that insect outbreaks clearly tend to follow large wind storm damage events, with a delay of approximately 2 years. Their research indicated that snow and storm damage events displayed certain periodicities. The shorter 1–2 year intervals could be explained by forest stand destabilisation and likely storm and snow damage, after which only stable trees remain. At a regional or supra-regional scale, the influence of other disturbance agents ⇑ Corresponding author. Tel.: +421 45 52411 302. E-mail address: [email protected] (P. Mezei). 0378-1127/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.foreco.2013.10.020

on wind events is mainly limited to indirect effects and mediated by changes in the age-class structure due to mortality from interacting disturbances (Seidl et al., 2011). A study of wind damage and bark beetle damage and their interaction is necessary to understand the disturbance dynamics in mountain spruce forests. The susceptibility of forest ecosystems to wind damage is determined by tree and stand characteristics as well as site characteristics. The occurrence and impact of wind disturbances are strongly driven by variables extrinsic to the forest ecosystem (such as weather and topographical position). The resulting disturbance regime largely reflects these drivers; that is, in contrast to other disturbances (such as insect pests and fires) (Seidl et al., 2011). Spruce stands are susceptible to wind damage at a certain age. Storm damage is a dynamic process. Initially (i.e. after the first heavy gusts), individual trees fall down. The first trees to be eliminated in the case of dispersed damage are probably the unstable ones (because of their poor anchorage, i.e. after fungal infections or because of their extreme slenderness). Other individuals are damaged by subsequent gusts. Only after some dissipation of the main networking framework, from so-called

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‘‘skeleton-trees’’ (the most stable ones), were gaps created, followed by total dissolution if wind gusts continued. The presence of gaps or edges, not small canopy openings, likely induces destabilisation (Schütz et al., 2006). Newly created windward-facing boundaries are particularly prone to endemic wind damage. Damage caused by frequently recurring peak winds are concentrated in areas of low stand stability and/or high wind exposure where recent harvesting or disturbances have increased wind loading (Gardiner et al., 2008; Scott and Mitchell, 2005). Wind damage is usually concentrated at forest edges (Olofsson and Blennow, 2005) because the wind load is the highest at the stand edges (Blennow and Sallnäs, 2004). However, edge trees continuously exposed to wind are more resistant (Foster, 1988). The forest edge is the key area related to wind–bark beetle interactions in mountain or boreal spruce forests. Windblown or wind-broken trees are a suitable breeding substrate for bark beetles. The spruce bark beetle is native to spruce forests and can influence spruce forest dynamics (Jonášová and Prach, 2004). When the bark beetle population grows, individual beetles attack trees on the fresh forest edges created by wind. I. typographus prefers trees adjacent to storm gaps. The ‘‘edge’’ effect cannot be fully explained by either a change in attacking mode to newly colonised wind-felled trees in the gaps or the high number of beetles that subsequently emerge from the wind-felled trees in the storm gaps. Another possible explanation is that I. typographus prefers a breeding substrate exposed to the sun. The newly exposed trees at forest edges may be weakened and more susceptible to attacks by I. typographus than trees in the interior of a stand (Schroeder and Lindelöw, 2002; Kautz et al., 2013). Favourable forest stand characteristics and weather conditions may allow bark beetles to spread further and affect entire stands. Without intervention, the dead trees that remain after a bark beetle attack may create a buffer zone that protects the remaining live trees from damage by wind and direct sunlight (Kiener, 1997; Kautz et al., 2013). The resulting conditions may be less suitable for bark beetles, temporarily limiting their further spread. Bark beetle damage continues to spread following the collapse of the buffer zone due to wood decay and subsequent wind action. Old, weakened trees or entire stands are killed by the interaction of wind and bark beetles (Jakuš et al., 2011). No protective buffer zone exists when the trees attacked by bark beetles are felled. Properly timed cutting and sanitation of the attacked trees stop the bark beetle spread (Jakuš, 1998a). However, recurring wind damage to forest edges will repeatedly trigger bark beetle activities. If the attacked trees are cut after the bark beetles leave, the damage may be accelerated. A specific issue arises in the area surrounding a no intervention zone or a nature reserve. During bark beetle gradation, a no intervention zone is a source of spruce bark beetle migration into fresh forest edges in the surrounding areas. In this case, large clearings are created by sanitary cutting (Grodzki et al., 2006). Panayotov et al. (2011) found cases when wind storms did not lead to bark beetle outbreaks (but several studies assumed that the interaction events of windstorms and bark beetles previously occurred (Svoboda et al., 2012). In the Tatra Mountains, the disturbance regime is driven by large-scale occasional disturbances (>100 ha) rather than by a gap-phase pattern (Zielonka et al., 2010). Hanewinkel et al. (2008, 2010) observed that damaged forest stands were more vulnerable to further damage compared to undamaged stands. Klopcˇicˇ et al. (2009) showed that previous disturbances strongly increased the susceptibility of spruce stands to windthrow and bark beetle attacks in the Julian Alps. Coulson et al. (1985) first utilised the damage caused by Dendroctonus frontalis Zimmerman to investigate the initiation and spreading/expansion processes. Worrall et al. (2005) described the processes of gap initiation and gap expansion. This literature

forms the basis for understanding the concept of separate analyses of damage initiation and spreading/expansion/severity. This study aimed to analyse the factors that influence spruce mortality caused by wind storm damage and the related bark beetle outbreaks (system wind–bark beetles) during a Tatra Mountain bark beetle outbreak from 1990 to 2000. This time period represents the last completed bark beetle gradation within this area. Our study is limited by the available forestry data (forest inventories, forest management plans, and forestry evidence). In accordance with Coulson et al. (1985), Jakuš et al. (2003) and Worrall et al. (2005), we described tree mortality as 2 connected attributes: the initiation and the severity of tree mortality. Our analysis is focused on processes at the stand level. Thus damage initiation means the first presence of damage in a particular mature stand. Since we worked with database data without any spatial considerations, we were able to analyze severity of wind or spruce bark beetle-caused tree mortality as variables related to damage or gap expansion. A more detailed analysis of damage (infestation) expansion would require different types of data (detailed infestation maps) and thus analysis than we have conducted here. The advantage of our dataset used is that it represents a long time series and gives precise quantification of tree morality. Our work focused on the initiation and the severity of tree mortality caused by wind alone and that caused by the wind–bark beetles system. The initiation and the severity of bark beetle caused tree mortality will be analysed in future papers. Another limitation of our study is that we did not directly analyse the influence of management on tree mortality. This limitation was caused by methodological problems due to the relatively frequent changes in management type on the Slovak side of the study area, the need for a different statistical approach than we used and difficulties with determination of the timing of salvage cutting in relation to timing of bark beetle attack. Different types of management were used in both parts of the study area. However, since Grodzki et al. (2006) did not find statistically significant differences in the general course of bark beetle caused tree mortality between the Slovak and Polish sides of the study area over the course of the whole outbreak, we feel that our approach is justified. The detailed analyses of the influence of management on the disturbances will be the subject of a future paper. 2. Methods 2.1. Study area The study was conducted in the Javorová, Široká and Bielovodská/Białki valleys and surrounding areas of the High Tatra Mountains. The Tatra Mountains are located in the Western Carpathian region on the border between Slovakia and Poland. The study area encompassed both the Slovak and Polish sides of the mountains. Our analysis included 2824 ha of the forest compartments. A more detailed description of the study area was published by Grodzki et al. (2006). 2.2. Data preparation The analysis of outbreak dynamics utilised data from forest inventories (forest management plans kindly supplied by the two national parks: the Polish Tatrzan´ski Park Narodowy – TPN and the Slovak Tatransky´ národny´ park – TANAP administration) and a detailed database of the yearly (1990–2000) volume of trees broken/felled by wind and killed by bark beetles. Forest inventories are updated every 10 years as part of the process of forest management planning. Data from the forest inventories were referenced in

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P. Mezei et al. / Forest Ecology and Management 312 (2014) 67–77 Table 1 List of the variables used in the analyses. Variable

Short description

Source

Stand age Percentage of spruce Stand height Stand DBH Slenderness ratio Site quality Slope Elevation pH Vegetation period Solar radiation

Mean age of the stand (years) Percentage of spruce in the stand (%) Mean height of the stand (m) Mean stand diameter at breast height (cm) Ratio of the mean stand height and stand DBH Potential mean height of the stand at 100 years (m) Relief slope (°) Stand elevation (m a.s.l.) pH of soil Maximum duration of vegetation period (days) Potential global solar radiation (kW h/m2)

Forest management Forest management Forest management Forest management Forest management Forest management Forest management Forest management Kukla (1993) Hancˇinsky´ (1972) Derived from DEM

the forest subcompartments as basic area units. We utilised 11 environmental and stand variables (Table 1) in each subcompartment, with a total of 313 forest subcompartments included in the analysis. Average subcompartment size was 8.9 ha and ranged from 0.28 to 68.3 ha. Tree mortality records in cubic meters (m3) were updated each year in the study period. The amount of tree mortality was recorded according to damage (by either wind or bark beetles). Mean indices of yearly tree mortality per hectare (ha) were calculated for each forest compartment. Two different events were then calculated: tree mortality caused by wind alone and that caused by both wind and spruce bark beetles together. These data were divided into three outbreak phases that corresponded to the course of the outbreak (Grodzki et al., 2006): (1) incipient epidemic (1990– 1994), (2) epidemic (1995–1996) and (3) post-epidemic (1997– 2000). We divided our study into two main sections to analyse tree mortality initiation and its severity. 2.3. Statistical analysis This data set presented a specific challenge in that the volume of tree mortality has a strong temporal autocorrelation. One way to address this problem is to utilise the rate of timber loss change as a dependent variable (Marini et al., 2012). We were unable to do this due to our limited dataset and our study frame (the initiation and severity of tree mortality, outbreak phases). Another possibility is to not consider this problem (Ogris and Jurc, 2010), but we did not consider this to be an appropriate approach. Therefore, we addressed this problem by dividing the tree mortality data into the processes of initiation and severity in the different phases of the outbreak. The problem of temporal autocorrelation is naturally excluded from the statistical analysis of the mortality initiation process since our data consisted of the presence or absence of tree mortality (first occurrence of tree mortality). We utilised a relatively robust method of boosted regression trees (Elith et al., 2008) to minimise this autocorrelation effect in the infestation severity analysis, however, we were unable to completely eliminate it. Thus, this must be taken into account when evaluating our results. The effect of disturbance history is partially included in our analytical approach. 2.3.1. Initiation of tree mortality A factor analysis was utilised to reduce the number of variables and to detect the structure of the relationships between the variables. The factors necessary for representing the data were differentiated by a principal components analysis. Varimax normalisation was used to make the factors more interpretable, and only the factors with eigenvalues greater than one were extracted. The scores for each factor were computed for each subcompartment in STATISTICA 8.0 (Statsoft 2007). These scores were then used

Abbreviation plan plan plan plan plan plan plan plan

Age Spruce H D H/D SiteQ Slope Elev pH VegPer Radiation

in discriminant analyses and multiple linear regressions. We combined variables into factors to minimise the influence of correlated variables. Mortality initiation was indicated as either first mortality initiation presence (1) or absence (0). A discriminant analysis was conducted in STATISTICA 8.0 to identify the main factors that influence the initiation of tree mortality. The relative importance of each factor was determined, and the factor scores were used as discriminating variables. To identify the important factors, we used a stepwise selection algorithm with Wilk’s lambda. In this method, the standardised coefficients for canonical variables are shown. To identify the values/rates at which these changes occurred, we used boosted regression trees (BRT, Elith et al., 2008). In the BRT, however, we used the 11 variables (Table 1) directly as independent variables (not factors from the factor analyses as in the discriminant analysis and multiple linear regression) because they can automatically account for interactions among predictors (Elith et al., 2008). The models were fitted in R version 2.14.1 (R Development Core Team, 2011) with the gbm package (Ridgeway, 2007) and an extension developed by Elith et al. (2008) and Elith and Leathwick (2011). BRT does not produce a single best model, but instead combines large numbers of simple tree models. With this method, unlike in conventional statistical techniques, there are no p values to indicate the relative significance of the model coefficients. The relative significance of individual variables is estimated based on how often the variable is selected and its ability to improve the model. The relative influence of the variables is scaled so that the sum adds up to 100%. Models are provided also by the characteristics how good each model is at explaining the observed data (training data correlation) and how good the model is at predicting left out data (CV correlation). We used the default 10fold-cross validation procedures. The models were fitted with the gbm.step function with a Bernoulli response type for the analysis of mortality initiation (0 and 1 for absence and presence, respectfully). The models were reduced to the most important variables with the gbm.simplify function. The relative influence of a predictor variable is shown as a percentage; a higher number indicates a greater influence of the predictor variable on the response variable. Visualising the fitted functions in a BRT model is achieved using partial dependence functions that show the effect of a variable on the response after accounting for the average effect of all other variables in the model (Elith et al., 2008). 2.3.2. Severity of tree mortality After the factor analysis, multiple linear regression analyses were performed to find the most important factors that influence the severity of tree mortality. The amount of trees felled by wind and attacked by bark beetles was used as the dependent variable. We considered the amount of fallen/attacked wood per ha as the dependent variable and the factors gathered through factor analysis as independent variables. We used backward stepwise

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regressions, and the intercept was set to zero. Only factors with a statistical significance at the 0.01 significance level are reported in the results. As with the tree mortality initiation analysis, we produced boosted regression trees to identify the values/rates at which these changes occurred (see previous chapter). The models were fitted with the gbm.step function with a Gaussian response type for the severity of tree mortality (m3 ha 1). 3. Results During an eleven year period, tree mortality reached approximately 118,000 m3. Of that amount 28,000 m3 was due to severe wind and 90,000 m3 due to bark beetle outbreak. A volume of 25,000 m3 of trees died in the incipient epidemic phase from 1990 to 1994 (15,000 m3 by wind and 10,000 m3 by bark beetles). This increased in the epidemic phase (1995 and 1996) to 71,000 m3 (500 m3 by wind and 70,500 by bark beetles). The volume of trees that died in the post-epidemic phase (1997–2000) decreased to 21,000 m3 (11,000 m3 by wind and 10,000 m3 by bark beetles). Tree mortality by wind was recorded in 128 of the 313 subcompartments while tree mortality caused by the system wind–bark beetles was recorded in 175. The number of subcompartments with recorded tree mortality by wind according to outbreak phases was as follows: incipient epidemic phase n = 75, epidemic phase n = 27 and post-epidemic phase n = 103. The number of subcompartments with tree mortality by the wind–bark beetles system according to the phase of the outbreak was slightly different: incipient epidemic phase n = 105, epidemic phase n = 119 and post-epidemic phase n = 138. Factor analysis reduced all of the variables to four main factors: Site, Stand, Soil and Sun (Table 2). These accounted for 75.43% of the total variation. The correlated variables Slenderness, Site Quality and Elevation were combined into the Site factor. Three other correlated variables (Mean stand age, Mean stand height and Mean DBH) were combined into the Stand factor. The variables pH and Solar radiation represented the Soil and Sun factors, respectively. The scores for each factor were computed in each case. These scores were used in subsequent analyses. 3.1. Tree mortality caused by wind The discriminant analysis results for the relationship between the main factors and mortality initiation caused by wind in the three outbreak phases are given in Table 3. The Stand factor was the main factor that influenced tree mortality initiation by wind in all three outbreak phases (i.e., parameters relevant to tree size).

Table 2 Results of the factor analysis between 11 variables (numbers in bold highlight the variables that were combined into factors). Factors Variables Radiation Age Spruce H D H/D SiteQ pH VegPer Slope Elev Eigenvalue % of total variance

Site

Host

Soil

Sun

0.02 0.47 0.35 0.21 0.20 0.75 0.81 0.02 0.61 0.55 0.83

0.05 0.82 0.30 0.94 0.96 0.22 0.06 0.06 0.04 0.02 0.05

0.11 0.07 0.06 0.02 0.04 0.01 0.05 0.90 0.39 0.44 0.32

0.85 0.07 0.53 0.12 0.02 0.14 0.26 0.11 0.24 0.46 0.00

3.55 32.30

2.43 22.05

1.23 11.16

1.10 9.92

The Site factor contributed to tree mortality initiation in the postepidemic phase while the Soil factor was related to tree mortality initiation during the entire outbreak period. Table 4 indicates the relationships between the volume of tree mortality caused by wind per hectare and the main factors. The Stand factor was the only factor that influenced tree mortality severity in all three outbreak phases and during the entire outbreak (i.e., parameters relevant to tree size). The BRT analysis of tree mortality initiation using the Bernoulli response type showed that the main factors influencing mortality initiation were from the ‘‘Stand’’ (the variables Mean tree height, Mean DBH and Age) and ‘‘Site’’ (Slenderness and Elevation) factor groups (Table 5). Meanwhile, mean tree height was the only variable influencing the severity of tree mortality in all three outbreak phases (Table 5). The variables Slenderness, Elevation, and Age were important in some phases. Elevation was the variable with the largest bias from one phase to another (Figs. 1 and 2). The remaining variables showed a distinct trend, e.g., larger tree height or tree DBH resulted in greater damage. However, Elevation did not show a clear trend. The results of the BRT analysis performed on other variables are presented in the Supplementary material. Only variables that showed influence after the gbm.simplify function in BRT analysis are shown. 3.2. Tree mortality caused by wind and bark beetles together (system wind–bark beetles) The discriminant analysis results for the relationship between the main factors and initiation of tree mortality caused by the wind–bark beetles system are given in Table 6. The factors that did not show statistically significant influence were not included in the discriminant functions. The Stand factor was the main factor influencing tree mortality initiation in all three outbreak phases. In the epidemic and post-epidemic phase, the Site factor contributed to tree mortality initiation, while the Soil factor also contributed to mortality initiation in the post-epidemic phase. Table 7 shows the relationships between the volume of trees per hectare damaged by the combination of wind and bark beetles and the main factors. The Stand factor was the main factor that influenced the spread of tree mortality in all three outbreak phases. The Sun (i.e., solar radiation) and Site factors also contributed to the spread of tree mortality caused by the wind–bark beetles system in the epidemic phase. The BRT analysis of tree mortality initiation showed that the most important variables influencing tree mortality initiation in all of the outbreak phases were mean stand height, mean DBH and elevation (Table 8). For the analysis of tree mortality severity (Table 8), we found that mean stand height, age of the stand and solar radiation were the most important variables in all of the outbreak phases. As for wind damage alone, elevation was the most influential characteristic affecting the initiation and severity of tree mortality caused by the wind–bark beetle system (Figs. 3 and 4). 3.3. Tree mortality according to outbreak phases The stand related factors were the most important for the initiation and severity of tree mortality caused by both processes (wind and by the wind–bark beetles system) in all outbreak phases. The BRT analyses showed a relatively strong correlation of elevation with the processes studied, especially for initiation of tree mortality in the incipient epidemic phase. However, the importance of this factor became less clear in the epidemic phase. There was a slightly positive correlation with higher elevation for tree mortality initiation (both processes) and tree mortality severity in the

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Epidemic

1.5 -0.5

0.5

fitted function

0.5 0.0 -0.5

fitted function

1.0

2.5

Incipient epidemic

1200

1400

1000

1600

1200

1400

Elev (14.7%)

Elev (4.9%)

Post-epidemic

Whole outbreak

1600

0.5 -0.5

0.0

fitted function

0.4 0.0 -0.4

fitted function

0.8

1000

1000

1200

1400

1000

1600

1200

1400

1600

Elev (11.7%)

Elev (11.1%)

Fig. 1. Influence of Elevation on tree mortality initiation caused by wind analysed with BRT in particular phases of the outbreak and over the whole outbreak.

1200

1400

1600

0.00

fitted function

0.5 0.0

fitted function

1000

0.05 0.10 0.15

Epidemic

1.0

Incipient epidemic

1000

Elev (3.5%)

1200

1400

1600

Elev (30.2%)

1.0 0.5 0.0

fitted function

Whole outbreak

1000

1200

1400

1600

Elev (5.9%) Fig. 2. Influence of elevation on tree mortality severity caused by wind analysed with BRT in particular phases of the outbreak and over the whole outbreak (elevation had no effect in the phases not shown).

wind–bark beetles system, while lower elevations were positively associated with the spread of tree mortality caused by wind. Meanwhile, mortality initiation was associated with higher elevations and stands with higher percentage of spruce in the post-epidemic phase.

The role of other factors was relatively small. In the case of mortality caused by wind, the BRT analysis showed a positive correlation with radiation in the epidemic phase only. Site factors were only important for the initiation of tree mortality caused by both disturbance types in the post-epidemic phase, while soil factors

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Epidemic 1.0 0.5 -1.0

1200

1400

1600

1000

1200

1400

Elev (35.9%)

Elev (9.4%)

Post-epidemic

Whole outbreak

1600

0.5 0.0

-0.6

-0.5

-0.2

0.2

fitted function

1.0

0.6

1000

fitted function

0.0

fitted function

0.5 0.0 -1.0

fitted function

1.0

Incipient epidemic

1000

1200

1400

1600

1000

1200

Elev (13.4%)

1400

1600

Elev (18.4%)

Fig. 3. Influence of elevation on wind and spruce bark beetle caused (disturbance system wind–bark beetles) tree mortality initiation analysed with BRT in particular phases of the outbreak and over the whole outbreak.

Epidemic

3 2 -1

0

1

fitted function

1.0 0.5 0.0

fitted function

4

1.5

Incipient epidemic

1000

1200

1400

1600

1000

Elev (3.4%)

1200

1400

1600

Elev (9.7%)

3 2 1 0 -1

fitted function

4

5

Whole outbreak

1000

1200

1400

1600

Elev (15.4%) Fig. 4. Influence of elevation on wind and spruce bark beetle caused (disturbance system wind–bark beetles) tree mortality severity analysed with BRT in particular phases of the outbreak and over the whole outbreak (elevation had no effect in the phases not shown).

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influenced mortality initiation for the wind–bark beetle system in the same phase.

spruce stands, with minimal presence of wind or bark beetle damage.

4. Discussion

4.2. Initiation of tree mortality

4.1. General aspects

4.2.1. Wind Although most of the damage in our study area was caused by spruce bark beetles and wind, drought and warm weather in the incipient epidemic phase triggered a 11-year bark beetle outbreak in the Tatra Mountains (Grodzki et al., 2006). Our results suggest that the most important group of factors related to the initiation of tree mortality caused by wind was the ‘‘Stand factors’’, represented by mean stand height, DBH and stand age. In the BRT analysis, slenderness, elevation and slope played an important role. These variables were important in other studies concerning the occurrence or extent of wind disturbances (e.g. Dobbertin, 2002; Klopcˇicˇ et al., 2009; Olofsson and Blennow, 2005; Scott and Mitchell, 2005). Hanewinkel et al. (2010) observed that some of these characteristics are correlated, yet the tree and stand characteristics were seemingly more important than the site characteristics. Either tree or stand appearance would be a good predictor of possible future damage. Some studies identified soil properties or slope steepness as more significant risk factors for wind damage occurrence in forests (Mayer et al., 2005; Schütz et al., 2006).

A total of 10 of the 11 analysed variables had some influence on the initiation and the severity of tree mortality caused by wind and the wind–bark beetles system. Only the ‘‘Vegetation period’’ variable was excluded from each of the models. Percentage of spruce only served a minor role. However, the study area was not completely appropriate to test the relationship between tree species composition and disturbances due to the small number of compartments with a larger proportion of broadleaves. The variables related to tree size (stand characteristics) were important for the initiation and severity of tree mortality in all phases of the outbreak. The most important variable was stand height. The findings are in agreement with several studies that dealt with tree mortality caused by wind (Dobbertin, 2002; Klopcˇicˇ et al., 2009; Olofsson and Blennow, 2005; Scott and Mitchell, 2005; Mayer et al., 2005; Schütz et al., 2006; Hanewinkel et al., 2010) or bark beetles (Jakuš, 1998b; Eriksson et al., 2005; Akkuzu et al., 2009; Schroeder, 2010). The variables related to local geography (site characteristics) were important in some cases. The importance of these variables varied with the phase of the outbreak and according to the process which caused the initiation and the severity of tree mortality. We considered all variables and chose Elevation as an example to demonstrate the results of the BRT analysis because elevation was the most ‘‘drifting’’ variable. For example, Fig. 3 shows the influence of elevation on wind and spruce bark beetle caused tree mortality initiation in relation to outbreak phase. In the factor analysis, the slenderness ratio was included in the site factors, because this variable was strongly correlated with site quality and elevation. Wang et al. (1998) and Holeksa et al. (2007) showed similar results. This explains why this variable was included into site factors, although it is most likely a stand characteristic. While this variable should theoretically be a very good predictor of wind damage, the best predictor of tree mortality caused by wind was average stand height. These results are in agreement with Schütz et al. (2006) and Mayer et al. (2005). Another potentially important variable, ‘‘slope’’, was shown to have only minor importance having a slightly positive association with tree mortality in several cases. This differs from the results of Schütz et al. (2006) and Mayer et al. (2005). This difference in our results was probably caused by specific local conditions, because mature stands were mostly on steep slopes. The relatively flat bottoms of valleys were covered by relatively young

4.2.2. Wind–bark beetles The factors affecting the wind–bark beetles tree mortality initiation were nearly the same as those affecting the initiation of tree mortality caused by wind (Tables 3 and 6). Again, the stand factors were the most important, followed by the site and soil factors. The sun did not play any significant role in the initiation. Tree mortality by wind and bark beetle are the two integral parts of the wind–bark beetles disturbance system in spruce forests. The initiation of this disturbance system is primarily related to the ‘‘stand’’ group of factors. The key factor is either tree growth or aging. This result indicates that the disturbance process may begin at a certain biological age. This conclusion agrees with that of Jakuš et al. (2011) for bark beetle damage and Foster (1988) for wind damage. The general trend of increasing wind susceptibility with age is explained by the progressive changes in tree structure and architecture. Increasing canopy height likewise increases the turning moment length. Tree wind resistance is undermined by this change in tandem with canopy extension with age, faster wind speeds at taller heights, and frequent incidences of disease and weakened structures (Foster, 1988; Rich et al., 2007). Rich et al. (2007) speculated that there may be a closer linkage between canopy mortality and forest development phase compared to the relationship between canopy mortality and stand age.

Table 3 Discriminant analysis results of the relationship between the main factors and presence (1) or absence (0) of tree mortality initiation caused by wind in the particular outbreak phases. Factors

Standardised coefficients and factor structure coefficients Incipient epidemic (n = 75)

Site Stand Soil Sun

–

–

0.97** 0.26 –

0.96** 0.25 –

DCC % Significance

75 0.0001

Epidemic (n = 27) –

Post-epidemic (n = 103) *

– 0.98**

– 0.21

0.98** – 0.19

91 0.0000

DCC – discriminant classification correctness. Standardised coefficients – discriminant function coefficients. Factor structure coefficients – correlations between the variables and the discriminant functions. * Significant at p = 0.05. ** Significant at p = 0.01.

0.34 0.91** 0.3 – 69 0.0000

*

0.30 0.89** 0.27 –

Whole outbreak (n = 128) 0.3 0.91** 0.38* – 71 0.0000

0.26 0.88** 0.33* –

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Table 4 The results of the multiple linear regressions between the volumes of trees mortality caused by wind per hectare and the main factors. Factors

r and p values Incipient epidemic (n = 75)

Epidemic (n = 27)

Post-epidemic (n = 103)

Whole outbreak (n = 128)

Site Stand Soil Sun

– 0.15 – –

–

–

–

r p

0.18 0.0447

– 0.0069 – –

– 0.0009 – –

0.19 – –

– 0.0048 – –

0.16 – –

0.19 0.0009

0.17 0.0111

0.2 – –

– 0.0012 – –

0.21 0.0036

p – Significant at p = 0.05.

4.3. Severity of tree mortality 4.3.1. Wind The intensity of further propagation of tree mortality caused by wind was primarily related to the group of Stand factors. However, separate analysis of each variable in the BRT analysis indicated that slenderness and elevation were variables correlated with the severity of wind damage (Table 5). Worrall et al. (2005) demonstrated that the expansion and coalescence of smaller canopy gaps appear to create extensive forest disturbances. The causes of gap expansion play a major role in forest dynamics. Wind was a major cause of gap expansion, particularly in the middle and upper elevations. 4.3.2. Wind–bark beetles Wind damage increased the propensity for bark beetle attacks and further windstorm damage in the trees on the edge of the stand. Tree stress compromised the insect resistance mechanisms, and trees of low vigour are more susceptible to bark beetle attacks. Tree stands in localities susceptible to wind damage with ideal living conditions for spruce bark beetles may be repeatedly damaged by wind and spruce bark beetle. The factors that affected tree mortality initiation did not show significant differences between the wind damage and wind–bark beetles damage. However, the severity of tree mortality was driven by different factors for wind damage and wind–bark beetles damage (compare Tables 4 and 7, and Tables 3 and 6). The stand factors were related to severity of tree mortality in the case of wind alone

(Table 4). On the other hand, the site and sun factors were also important for the wind–bark beetles system (Table 7) while elevation played only a minor role (Fig. 4). These results support previous research on the importance of solar radiation for bark beetle attacks (Jakuš et al., 2011) or the northwards spread of an outbreak (Jakuš et al., 2003). This process is driven by the abrupt increase in solar radiation at the newly opened forest edges or at gaps inside the forests (Schopf and Köhler, 1995; Kautz et al., 2013). When suitable hosts in the windstorm area or previously attacked spots are depleted, bark beetles move to the trees in the surroundings, primarily at the recently opened stand edges (Mezei et al., 2011; Schroeder and Lindelöw, 2002). New windthrow events, bark beetle attacks or human activities (e.g., logging and sanitary cutting) create gaps and edges. All of these phenomena were present in the outbreak analysed in this paper. These gaps and edges can increase the amount of solar radiation that enters the forest stand (Hardy et al., 2004), alter the dispersion patterns of pheromones (Fares et al., 1980) and change the evapotranspiration regime in the stand. Finally, bark beetle outbreaks can impact the water balance in the stand’s surroundings (Kaiser et al., 2013). This process as a whole may lead to decreased stand resistance to disturbances. 4.4. The initiation and severity of spruce mortality caused by bark beetles in different stages of the outbreak Worrall et al. (2005) suggested that the disturbance agents involved in gap initiation differ from the agents involved in gap

Table 5 Relative contribution (%) of variables to initiation of tree mortality caused by wind (Wi) and severity of tree mortality caused by wind (Ws) according to the BRT analysis in the particular outbreak phases and over the whole outbreak. Factor

Variable

Incipient epidemic (n = 75)

Epidemic (n = 27)

Post-epidemic (n = 103)

Whole outbreak (n = 128)

Wi

Ws

Wi

Ws

Wi

Ws

Wi

Ws

Site

H/D SiteQ Elev

21.62 – 14.65

36.62 2.36 3.53

9.49 – 4.86

– – 30.2

17.61 – 11.14

25.63 – –

20.12 7.32 11.69

12.97 1.75 5.94

Stand

Age H D

10.15 9.29 22.63

19.37 10.54 5.58

17.56 52.75 3.71

– 48.44 –

8.35 25.63 12.1

– 74.37 –

8.99 15.05 12.69

6.57 25.8 6.29

Soil Sun

pH Radiation

– 9.96

2.49 19.52

– 11.62

– 21.36

– 6.56

– –

2.89 5.8

4.04 16.71

Other

Spruce VegPer Slope

– – 11.68

– – –

– – –

– – –

10.68 – 7.93

– – –

9.14 – 6.3

– – 19.34

Training data correlation

0.75

0.54

0.67

0.60

0.59

0.39

0.70

0.63

CV correlation Se

0.53 0.03

0.19 0.08

0.4 0.06

0.32 0.12

0.35 0.05

0.2 0.08

0.47 0.04

0.32 0.08

CV deviance Se

0.817 0.034

6.86 3.93

0.48 0.03

0.02 0.01

1.14 0.04

0.72 0.26

1.11 0.04

7.61 3.49

CV (cross-validation procedure).

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Table 6 Discriminant analysis results of the relationship between the main factors and presence (1) or absence (0) of tree mortality initiation caused by bark beetles and wind in the particular outbreak phases. Factors

Standardised coefficients and factor structure coefficients Incipient epidemic (n = 105)

Site Stand Soil Sun

– – 0.27

DCC % Significance

65 0.0000

Epidemic (n = 119) 0.51** 0.91** 0.16

– 0.97**

0.96**

Post-epidemic (n = 138)

0.40** 0.85** 0.12

0.54** 0.84** 0.29*

0.24

– 74 0.0000

Whole outbreak (n = 175)

0.48** 0.79** 0.25* –

0.26 0.95** 0.26

0.21 0.93** 0.22

–

71 0.0000

67 0.0000

DCC – discriminant classification correctness. Standardised coefficients – discriminant function coefficients. Factor structure coefficients – correlations between the variables and the discriminant functions. * Significant at p = 0.05. ** Significant at p = 0.01.

Table 7 The results of the multiple linear regressions between the volume of tree mortality (attacked by bark beetles or felled by wind) per hectare and the main factors. Factors

r and p values Incipient epidemic (n = 105)

Site Stand Soil Sun

–

r p

0.18 0.0045

Epidemic (n = 119)

– 0.0028 – –

0.16 – –

0.22 0.25

Post-epidemic (n = 138)

0.0000 0.0000 – 0.0469

– 0.11

–

– 0.0000 – –

0.28 – –

0.35 0.0000

Whole outbreak (n = 175)

0.28 0.0000

0.18 0.3 – –

0.0009 0.0000 – –

0.36 0.0000

p – significant at p = 0.05.

Table 8 Relative contribution (%) of variables for tree mortality initiation (Mi) and mortality severity (Ms) for trees attacked by bark beetles or felled by wind according to the BRT analysis in the particular outbreak phases and over the whole outbreak. Factor

Variable

Incipient epidemic (n = 105)

Epidemic (n = 119)

Post-epidemic (n = 138)

Mi

Ms

Mi

Ms

Mi

Ms

Whole outbreak (n = 175) Mi

Ms

Site

H/D SiteQ Elev

– – 35.86

9.17 0.83 3.38

– – 9.43

– – 9.67

12.28 – 13.72

13.84 – –

– – 18.4

2.17 7.99 15.46

Stand

Age H D

– 30.47 33.67

10.13 34.79 19.34

8.47 41.49 20.86

10.48 9.33 55.03

10.87 12.34 32.71

20.35 54.26 –

– 17.54 24.92

5.86 18.92 34.24

Soil Sun

pH Radiation

– –

0.68 19.38

– 5.82

– 15.5

– –

– 11.55

– –

0.97 10.31

Other

Spruce VegPer Slope

– – –

– – 2.3

– – 13.93

– – –

10.6 – 7.84

– – –

21.92 – 17.22

0.61 – 3.48

Training data correlation

0.66

0.38

0.71

0.58

0.61

0.58

0.65

0.63

CV correlation Se

0.47 0.04

0.23 0.05

0.57 0.04

0.47 0.05

0.43 0.04

0.34 0.08

0.5 0.03

0.41 0.07

CV deviance Se

1.05 0.05

25.31 16.78

0.97 0.05

23.48 7.6

1.18 0.04

2.36 0.64

1.1 0.3

56.94 17.31

expansion. These authors observed post-epidemic conditions and concluded that the spruce bark beetle (Dendroctonus rufipennis) and plant diseases predominated as agents of gap initiation, while windthrow/windsnap, chronic wind stress and Armillaria spp. root disease were important agents of gap expansion. In their study, gap expansion was more frequent than gap initiation. Our study found that wind damage was more important in the process of initiation during the incipient epidemic phase, and bark beetles were more important in the process of expansion, especially in the outbreak epidemic phase. The difference in the results found in our study and those of Worrall et al. (2005) may be explained by the

dissimilar forest structure. Worrall et al. (2005) describe disturbance initiation in a forest with a closed canopy, while in our study, damage by the wind–bark beetles disturbance system began at the forest edges created by past tree cutting in low situated valley bottoms. Klopcˇicˇ et al. (2009) demonstrated that previous cuttings increased the risk of disturbance occurrence. This factor was not tested in our study. In the case of spruce bark beetle, the roles of host and environmental factors in the initiation and severity of tree mortality are influenced by the outbreak stage or phase (Jakuš et al., 2003; Kautz et al., 2011; Lausch et al., 2011).

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4.4.1. Incipient epidemic phase Wind was the prevailing cause of tree mortality in the incipient epidemic phase. Of the factors tested, the stand related factors were the most important. We also observed a positive association between the initiation of tree mortality and decreasing elevation. Klopcˇicˇ et al. (2009), Hanewinkel et al. (2008) and Worrall et al. (2005) detected opposite trends. However, Mayer et al. (2005) obtained results similar to ours. We hypothesise that the influence of climatic factors in mountain localities may affect our results, i.e., the elevation gradient may somehow influence tree size and stand structure (Holeksa et al., 2007), which may explain the importance of the stand related factors. Hanewinkel et al. (2008) and Svoboda et al. (2012) suggested that the elevation gradient has a greater relation to changes in disturbance severity (i.e., wind damage increases with elevation). In our case, wind damage decreased with elevation, most likely due to the forest structure. Tree mortality started probably on the forest edges created by previous tree cutting or wind damages in the lower situated valley bottoms, especially in the Slovakian part of the study area. 4.4.2. Epidemic phase In the epidemic phase, more than 99% of tree mortality was caused by bark beetles, while the role of wind was marginal. The stand related factors were also the most important in this phase. In the epidemic phase, beetle pressure should become the most important determinant of tree mortality caused by bark beetles, because bark beetle populations are sufficiently large to overcome the defenses of healthy trees via mass attack (Logan et al., 1998; Walter and Platt, 2013). In the case of the wind–bark beetles disturbance system, site factors also played an important role for both of the processes studied while radiation was important for tree mortality severity. Solar radiation is a variable logically connected with tree mortality caused by spruce bark beetle (Jakuš et al., 2003, 2011; Kautz et al., 2013). 4.4.3. Post-epidemic phase The proportion of tree mortality caused by wind in the post-epidemic phase was slightly higher than that caused by bark beetles. As in the other phases, the stand related factors were the most important for both processes studied. In this phase of outbreak, bark beetles have exploited a large part of the available resources and are attacking marginal host trees in less suitable habitats (Nelson et al., 2007; Raffa et al., 2008). The higher level of tree mortality caused by bark beetles in higher elevations could have been caused by exhaustion of available resources at lower elevations in the previous phases of the outbreak (Jakuš et al., 2003). The association of mortality initiation with stands having a higher percentage of spruce could be also described by this process. According to Walter and Platt (2013), species composition may be important for bark beetle caused tree mortality late in the outbreak primarily when it crashes due to a lack of suitable host trees. Species composition is also an important variable related to stand susceptibility to wind damage (Schütz et al., 2006). 4.5. Forest management consideration Although we did not directly analyze the influence of forest management on the disturbances, our results could have important implications for practical forest management and nature conservancy. Wind and bark beetle caused disturbance works as one disturbance system, thus, it is very important to consider this in practical forest management. Mature spruce stands are relatively stable until the initiation of mortality by wind or bark beetle. After that moment, the break-up of stands starts and is impossible to stop. All silvicultural and forest protection measures should be focused to avoiding this moment or to postponing it to a higher age.

In the case of tree mortality caused by wind or bark beetle, the best prevention is planting of irregularly spaced spruce or mixed stands (if possible) with long crowns (Schütz et al., 2006; Jakuš et al., 2011). The stands should have high vitality and any trees attacked by I. typographus or other aggressive bark beetle species should be removed or sanitized at the right time. It is very important to keep the forest canopy below the wind damage destabilization threshold (Schütz et al., 2006). In the case when creation of fresh forest edges is necessary, wind and sun exposed edges should be avoided or minimised (Schütz et al., 2006; Jakuš et al., 2011; Kautz et al., 2013). Once the disturbance system begins, we are able only to control tree mortality caused by bark beetles. Precise forest protection measures can temporarily stop or control the bark beetle population (Jakuš, 1998a; Grodzki et al., 2006). However, the process of wind damage still continues and can trigger a new bark beetle outbreak. Usually, the intensity of wind damage is much lower than the tree mortality caused by bark beetle in the culmination phase of an outbreak. Thus, precise bark beetle control can significantly slow down the speed of stand break-up. For nature conservation and when dealing with core zones of national parks, it is important to remember that, after initiation of a disturbance in a mature spruce stand, it is impossible to stop stand break up only by pest control.

5. Conclusion The wind–bark beetles disturbance system was primarily influenced by stand related factors. Tree mortality initiation was primarily influenced by stand age and related changes in spruce size and vitality. The severity of tree mortality caused by wind was primarily related to the stand factors as well. The roles of stand and environmental factors in the initiation and severity of tree mortality were influenced by the I. typographus outbreak phase. In the incipient epidemic phase, tree mortality was mostly caused by wind, with stand related factors being the most important. In the epidemic phase, almost all mortality was caused by bark beetles. Except for the most important stand related factors, radiation was an important variable which correlated with the severity of tree mortality. In the post-epidemic phase the proportion of tree mortality caused by wind was slightly higher than that of bark beetle. The proportion of infestation initiation of bark beetle caused tree mortality was minimal. The stand related factors were the most important for both studied processes. Other factors played relatively small roles in the case of tree mortality severity. In the case of tree mortality initiation, the role of site factors for both processes and soil for the wind–bark beetles disturbance system were also important.

Acknowledgments The authors wish to thank the staff of the state forests of Tatra National Park and the staff of the Polish Tatra National Park for their cooperation in the data collection. We are indebted to Dr. Benjamín Jarcˇuška. The study was funded by the European INCO Copernicus project: ‘‘Integrated risk assessment and new pest management technology in ecosystems affected by forest decline and bark beetle outbreaks’’ and by the APVV project ‘‘The analysis of natural risks concerning the evolution of landscape ecosystems under the conditions of climate change in Slovakia’’ (APVV-042310). This publication is also the result of the project Prognosticinformation systems for improving the efficiency of management, ITMS:26220220109, supported by the Research & Development

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