Guess the impact of Ips typographus—An ecosystem modelling approach for simulating spruce bark beetle outbreaks

Agricultural and Forest Meteorology 166–167 (2012) 188–200

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Guess the impact of Ips typographus—An ecosystem modelling approach for simulating spruce bark beetle outbreaks Anna Maria Jönsson a,∗ , Leif Martin Schroeder b , Fredrik Lagergren a , Olle Anderbrant c , Benjamin Smith a a b c

Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden Department of Ecology, Swedish University of Agricultural Sciences, Box 7044, 750 07 Uppsala, Sweden Department of Biology, Lund University, Sölvegatan 37, SE-223 62 Lund, Sweden

a r t i c l e

i n f o

Article history: Received 21 December 2011 Received in revised form 17 July 2012 Accepted 18 July 2012 Keywords: Climate change Forest management LPJ-GUESS Risk analysis

a b s t r a c t Spruce bark beetle outbreaks are common in Norway spruce forests following windstorm damage, due to ample availability of brood material. The realization of an outbreak depends on factors regulating the Ips typographus population dynamics, such as weather conditions and salvage cutting. In this study, we take an ecosystem modelling approach to analyse the influence of multiple environmental factors on the risk for I. typographus outbreaks. Model calculations of I. typographus phenology and population dynamics as a function of weather and brood tree availability were developed and implemented in the LPJ-GUESS ecosystem modelling framework. The model simulations were driven by gridded climate data covering Sweden with a spatial resolution of 0.5◦ and a daily temporal resolution. Records on storm damage and I. typographus outbreak periods in Sweden for the period of 1960–2009 were used for model evaluation, and a sensitivity analysis was performed to examine the model behaviour. The model simulations replicated the observed pattern in outbreak frequency, being more common in southern and central Sweden than in northern Sweden. A warmer climate allowing for more than one generation per year can increase the risk for attacks on living trees. The effect of countermeasures, aiming at either reduce the availability of brood material or the I. typographus population size, is dependent on a non-linear relation between I. typographus attack density and reproductive success. The sensitivity analysis indicated a major reduction in the risk of attacks on living trees by timely salvage cutting and cutting of infested trees. Knowledge uncertainties associated with attacks on standing trees, i.e. factors influencing tree defence capacity and I. typographus reproductive success, should be further addressed. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Disturbances caused by drought, wind, forest fire, insect pests and pathogens play a key role in ecosystem dynamics, and should be considered when planning for sustainable forest management (Seidl et al., 2011). This has created a need for process-oriented impact modelling that can frame these concerns and transform the state-of-the art climate scenarios into information about ecosystem dynamics. Norway spruce (Picea abies) is one of the economically most important tree species in northern and central Europe. Its sensitivity to climate extremes is mainly manifested as high vulnerability to wind throw (Nilsson et al., 2004) and relatively low resistance to drought stress (Rouault et al., 2006). The spruce bark beetle, Ips typographus L. (Coleoptera, Curculionidae), is one of the main forest pests attacking Norway spruce, and millions of trees have been killed during large outbreaks (Schelhaas et al., 2003). The

∗ Corresponding author. Tel.: +46 46 222 94 10. E-mail address: Anna [email protected] (A.M. Jönsson). 0168-1923/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agrformet.2012.07.012

insect population may increase dramatically after storm events, as newly wind thrown trees are suitable for breeding (Komonen et al., 2011; Økland and Berryman, 2004). At high population densities, the ability of I. typographus to overcome the defence system of living spruce trees increases. The realization of an I. typographus outbreak depends on factors regulating the insect population dynamics. Information on brood tree availability (as influenced by weather conditions and active forest management), initial population size, and estimates of reproductive success at different attack densities (Anderbrant, 1990; Anderbrant et al., 1985) has to be combined in order to be able to foresee the changes in population size and tree-killing potential. Weather and climate can be used as predictors for the stochastic conversion of vital trees to available brood material serving as fuel for I. typographus outbreaks (Christiansen et al., 1987; Økland and Bjørnstad, 2006; Schelhaas et al., 2003). Tree mortality generally peaks two to three years after a storm disturbance, though the defence capacity of the living trees can influence the length of an outbreak period (Schroeder, 2003). Severe summer drought can result in lowered tree defence capacity, thereby increasing the risk

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for subsequent infestations of I. typographus (Rouault et al., 2006). Temperature determines the voltinism of I. typographus via direct effects on flight activity and developmental rate. Consequently, two generations (Scandinavia) or three generations (central Europe) may be more frequent within a single active season under a warmer future climate (Jönsson and Bärring, 2011; Jönsson et al., 2011). Due to the damage potential of outbreaks, risk-rating models have been developed for several bark beetle species to support forest management at a local to regional scale (Netherer and NoppMayr, 2005; Robertson et al., 2008; Yemshanov et al., 2009). The models are based on classification of forest site and stand characteristics associated with infestations, such as a high density of older coniferous trees, stand exposure and local risk for drought stress. By this approach it is possible to identify the location of ‘hot spots’ that may serve as starting points for insect outbreaks. Established rating systems are based on local correlations and expert knowledge and cannot automatically be applied to other sites, as a local rating system may in fact be highly influenced by the larger-scale conditions set by the landscape characteristics (Raffa et al., 2008). The objective of this study is to take an ecosystem modelling approach, independent of local correlations and expert judgements, to analyse the importance of environmental factors that might influence the risk for I. typographus outbreaks. This implies that we will try to capture the mechanisms behind an outbreak, focusing on I. typographus phenology and reproductive success, along with the brood tree availability and defence capacity (Fig. 1).

2. Materials and methods 2.1. Description of study area In Sweden, our study area, about 28 million ha are covered by forest, out of which 5.7 million ha is non-productive due to climatic or edaphic conditions (Loman, 2008). Norway spruce accounts for 42% of the standing volume and Scots pine 38%. The remaining part

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is dominated by deciduous forest. The general south to north temperature gradient across the country ranges from an average annual temperature of 7 ◦ C during the period 1961–1990 in the south to −3 ◦ C in the mountainous northeast. South Sweden (Götaland, latitude 55◦ N–58.7◦ N) is characterized by boreo-nemoral conditions (Ahti et al., 1968), and the forest contains both broadleaved and coniferous components, with Norway spruce being the dominating tree species at 49% of the standing volume (Loman, 2008). North Sweden (Norrland, latitude 63.5◦ N–68.3◦ N) and central Sweden (Svealand, latitude 58.7◦ N–63.5◦ N) are characterized by boreal and southern-boreal conditions, respectively (Ahti et al., 1968), and the forest is dominated by Norway spruce and Scots pine in roughly equal proportions.

2.2. Overview of the modelling approach and driving data-sets The spruce bark beetle model consists of two parts, one to simulate the temperature dependent annual cycle (Section 2.3) and one to simulate the population dynamics including attacks on wind thrown and living trees (Section 2.4). In this study, the model was implemented as a module in the LPJ-GUESS ecosystem modelling framework (program language C++) (Smith et al., 2001; Fig. 1). LPJGUESS simulates the growth of trees and the structural dynamics of the forest vegetation in response to climate trends and interannual and seasonal variation (see Section 2.5). LPJ-GUESS requires mean temperature, precipitation, incoming shortwave radiation, and atmospheric CO2 concentration as input data to drive the model simulations. In this study, daily maximum temperature was additionally used to simulate spruce bark beetle flight activity. Data from the regional climate model RCA3 with a daily temporal resolution and on a grid covering Sweden at a spatial resolution (grid cell size) of 0.44◦ (Samuelsson et al., 2011) were provided by the Rossby Centre of the Swedish Meteorological and Hydrological Institute from simulations performed as part of the ENSEMBLES project (Kjellström et al., 2011). At the lateral

Fig. 1. (a) In this study, the Ips typographus phenological model and the Ips typographus population model were incorporated in the LPJ-GUESS ecosystem modelling framework as one module. The three bold letters (A, B, and I) can be found in the concept map below. (b) Concept map of the components included in the LPJ-GUESS spruce bark beetle model. The letters A–I correspond to the different parts of the model (presented in Section 2.4) and the sensitivity analysis (Section 2.7, Table 4). Solid lines show the hierarchical linkage between components, dashed lines indicate the use of information stored in a component.

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boundaries, RCA3 was forced by data from ERA 40 for the period of 1961–2008. Data from ERA 40 are related to observed weather conditions and can therefore be used in chronological comparisons (Uppala et al., 2005). The simulation of one LPJ-GUESS stand, consisting of 150 patches, is driven by data from one climate gridcell. LPJ-GUESS simulations start at year 1701, as a spin-up period is required to simulate the development of vegetation, litter and soil carbon pools from bare ground to a state of dynamic equilibrium with the climate at the target period for study (here from 1961 onwards). As climate data are not available for this entire period, data for 1961–1990 were repeated continuously throughout the 260-year spin-up period. In the standard version of LPJ-GUESS, the soil classification (Sitch et al., 2003) is based on texture classes from the FAO global soil dataset (FAO, 1991). By interpolation we converted the CRU gridded soil data to the ENSEMBLES grid, applicable to the climate model data. To evaluate the performance of the spruce bark beetle model we compared the simulated incidence of timber damage through insect-induced tree mortality to recorded data on I. typographus attacks. The gridcell-specific results from the model simulations were added to represent the 24 counties of Sweden, including a subdivision of the extensive northernmost counties into climatically distinct inland, mountainous and coastal regions. This corresponds to the spatial resolution of data from records on I. typographus activity in Sweden for the period of 1960–2005, used for model evaluation. The storm damage records were used to prescribe the severity of storm events during the period of 1961–2008 (Nilsson, 2008; Nilsson et al., 2004). That is, we did not use wind-load data from the climate model for generic simulation of storm damage to avoid influence from uncertainties associated with the wind and storm damage modelling (Lagergren and Jönsson, 2010). 2.3. Modelling I. typographus annual cycle We use a phenological model for simulating the temperature dependent activity and development of I. typographus (Jönsson et al., 2007), including a model description of reproductive diapause triggered by cues from day length and temperature (Jönsson et al., 2011). The model has been developed to use gridded climate data with a spatial resolution of 0.25–0.5◦ and a daily temporal resolution as input. It is therefore by necessity more generalized in some aspects than modelling concepts developed to simulate local topoclimatic conditions (Baier et al., 2007; Netherer and Nopp-Mayr, 2005). The I. typographus activity is calculated using thermal sums, expressed as degree-days (dd) above a developmental threshold of +5 ◦ C (Annila, 1969). Emergence from winter hibernation is set to 120 dd. Flight in search of suitable breeding material is related to a temperature threshold. In an earlier study, the flight temperature threshold was calibrated to +16 ◦ C by using data on pheromone trap catches in combination with the gridded temperature data provided by the RCA3-ERA 40 climate data set (Jönsson et al., 2011). The average time to produce half of the eggs in a maternal gallery is estimated at 7 days, including the preoviposition period. The developmental time from egg to mature bark beetle is calculated as accumulation of daily mean temperature. Since the gridded temperature data does not reflect differences in microclimatological conditions, creating a range in breeding conditions, the model has two thermal thresholds for completed development. The lower limit for reaching maturity (625 dd) corresponds to development in sun-exposed trees and the upper limit (750 dd) to development in shaded trees (Harding and Ravn, 1985; Jönsson et al., 2007). The reproductive diapause of the first generation is modelled to occur after fulfilment of a day length requirement and a subsequent temperature requirement, T2mean < 15 ◦ C (Jönsson et al., 2011). The day length setting uses a local threshold value that varies

geographically, accounting for natural selection during years with cold autumn temperatures. The gridcell-specific threshold corresponds to the day length of the earliest date during the climate reference period (1961–1990) when the bark beetles were not able to reach the maturity state required for winter survival. This parameterization, evaluated against independent field monitoring data (Jönsson et al., 2011), follows approximately a latitudinal gradient from 15–16 h in central Europe to 20–22 h in the northern part of Scandinavia. The period of reproductive activity during spring and early summer, including the production of sister broods, is extended over several weeks. The developmental time is in turn influenced by the variation in sun exposure of brood material, which creates an even larger spread in timing for maturity of the first generation. The length of the flight period with initiation of a second generation was calculated as the interval between the first flight event after completion of the first generation and modelled onset of diapause (Jönsson et al., 2011). The occurrence of bivoltinism, and its impact on population dynamics, was quantified in relation to the length of the potential flight period. On average it will take one month with favourable flight conditions for the entire first generation to be able to initiate a second generation, as estimated from the spread in timing for maturity of the first generation including both the main brood and sister brood, sun-exposed and shaded conditions. 2.4. Modelling I. typographus population dynamics The model for calculating the population dynamics consists of three parts, brood tree availability, reproductive success and winter mortality, and migration of I. typographus during flight allowing for redistribution among simulated forest patches (Fig. 1). In the model description below, letters within brackets ([A]–[H]) refer to the default settings of the different model components illustrated in Fig. 1b. 2.4.1. Brood tree availability The LPJ-GUESS model (Section 2.5) keeps track of the number and size of living spruce trees, the number and size of storm-felled trees, as well as the rate of removal by storm-felled trees (salvage cutting). Only Norway spruce trees with a diameter at breast height above 15 cm were considered suitable brood trees, as I. typographus generally do not attack smaller trees. Salvage cutting was modelled to occur when the amount of storm-felled trees exceeded 4 forest cubic metre per ha, as the limit in formal regulations set by the National Forest Agency has varied between 3 and 5 forest cubic metre storm-felling per ha depending on the perceived risk for outbreaks (Svensson, 2007). The modelled rate of salvage cutting, forest cubic metre per month, was related to the harvesting capacity. The default setting [C] assumes that the stand level capacity of lumber machines correspond to a running average of the last 5 years’ thinning and clear cutting activity. The patch-specific month of cutting was randomly distributed among the patches containing storm-felled trees. The efficiency of the salvage cutting was set to 90%, to make the model simulation resemble a heterogeneous landscape where a few scattered storm-felled trees will not be harvested. The default threshold value for required attack density for killing of living trees [D] was set to 5 females per dm2 bark area (Hedgren and Schroeder, 2004; Weslien and Regnander, 1990), corresponding to 5000 females per “average brood tree”. The threshold for killing of living trees is commonly recognized to be influenced by tree drought stress (Aakala et al., 2011; Christiansen et al., 1987). The LPJ-GUESS model parameters wscal and wscal mean indicates the tree drought stress on a daily basis and as a cumulative effect throughout the season, respectively. The 95 percentile value of

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wscal mean (0.95) and wscal (0.70) was used to lower the threshold for killing of living trees to 2500 females. The parameter settings were analysed in a sensitivity test (see Section 2.7). The calculated number of trees killed by I. typographus was used as input to the vegetation dynamics—module to update the patch status. 2.4.2. Reproductive success and mortality of I. typographus Model description of reproductive success and mortality (Jönsson and Schroeder, 2006) was used to simulate the bark beetle population dynamics over several years. I. typographus adults reproduce only during one season (Austarå and Midtgaard, 1986). A proportion of all females successfully producing a first brood also produce a second brood (sister brood) in a different tree. The proportion depends on attack density and weather conditions (Anderbrant, 1989). For model parameterization we used an average of 30% occurring when 240 dd (+5 ◦ C) has accumulated after spring flight, recalculated from the published average value of 168 dd (+7.5 ◦ C) as the LPJ-GUESS standard setting for calculating degree days uses a threshold of 5 ◦ C. The initial population is by default set to 10 I. typographus per ha in year 1960 [B]. The reproductive success is calculated as a negative exponential function of attack density (parent females per bark area) according to laboratory experiments conducted by Anderbrant et al. (1985). Separate calculations are carried out for the main brood and the sister brood, as the attack density at the timing of the sister brood is influenced by the previous main brood and removal of storm-felled trees. In the default settings, stormfelled trees can be used as brood material, given the restrictions set by the patch-specific timing of salvage cutting. Field observations (Komonen et al., 2011, Eq. (1)) were used to evaluate the modelling of reproductive success (Anderbrant et al., 1985, Eq. (2)). A sensitivity analysis of the equation of reproductive success was carried out to assess the impact of countermeasures at different attack densities. A default correction for lower reproductive success in standing trees was implemented based on an evaluation of model performance [E]: log10 (Y ) = 1.44 − 0.31 log10 (X) Y=

38.8 exp(−0.87X 0.45 ) 2

,

(1) (2)

where X is the egg gallery density per m2 and Y is the daughters per female. The modelled maximum attack density on storm-felled trees was set to 8.44 females per dm2 , corresponding to a reproductive success of 2 daughters per female (Anderbrant et al., 1985). The population size required to reach the maximum attack density was calculated for each flight event, taking the population size, amount and age of available storm-felled trees, and earlier attacks into account. The number of standing trees killed by aggregated attacks was calculated as the part of the I. typographus population exceeding the maximum attack density on storm-felled trees, divided by the attack density required to overcoming the defence of living trees (Section 2.4.1). The winter mortality of the mature generation was estimated at 40% (Austarå and Midtgaard, 1986; Faccoli, 2002; Lekander, 1972; Weslien and Lindelöw, pers. comm.) and the winter mortality of an immature generation was set to 100% (Faccoli, 2002). The default model run assumes a flight mortality of 20% [G], further tested in a sensitivity analysis (Section 2.7) as very little is known about the significance of mortality during migration. Mortality of bark beetles caused by cutting of infested trees and removal of wind-thrown trees after initiation of the new generation was included in model calculations. The default cutting of infested trees [F] was set to 25% based on an estimate of average intensity, taking into account that cutting

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during autumn and winter is less efficient than cutting during summer before the beetles have started to disperse from the brood trees. 2.4.3. Migration of I. typographus The migration feature simulates an annual redistribution of I. typographus among patches, within stand. The redistribution is based on the attraction strength of the individual patches, i.e. the relative differences among patches in providing suitable breeding conditions. This was calculated according to three rules: (i) first, it is checked if any of the patches had attacks involving at least ten trees in the previous year, as this will increase the probability of a following attack in those patches (Hedgren et al., 2003). The attraction strength for a patch with aggregated attacks is set to a maximum of 5000 I. typographus per killed tree (i.e. the threshold to overcome the defence capacity of one non-stressed standing tree). The stand population size divided by the total number of trees in aggregated attacks is compared with the default value of 5000, and the lower of these two values is used in subsequent calculations to assure that this allocation of I. typographus does not exceed the total number of individuals. (ii) Secondly, the potential attraction strength of wind thrown trees is by default calculated as the number of I. typographus required for a maximum attack density of 8.44 females per dm2 assuming that 70% of the available bark is suitable for colonization by I. typographus [H]. Bark area less suitable for I. typographus may be colonized by other bark beetle species (Göthlin et al., 2000; Hedgren and Schroeder, 2004; Komonen et al., 2011; Schlyter and Anderbrant, 1993). The actual attraction strength of the wind thrown trees is then assessed by comparing the potential attraction strength with the number of I. typographus at stand level that are not already allocated to specific patches via the routine for estimating following attacks. The lower of these two values is used to allocate I. typographus among patches, based on the patch specific storm damage. (iii) Thirdly, the remaining I. typographus (if any) is distributed equally among all patches. This is done regardless of tree species, to account for mortality caused by dispersal. To avoid termination of the model simulations, a reintroduction of ten I. typographus was modelled to occur in each patch if the population became extinct in a gridcell (mainly of importance for the simulations of the north part of the country). 2.5. Ecosystem model framework The ecosystem model LPJ-GUESS (Smith et al., 2001) is an assessment model for impacts of climate change and management decisions on vegetation, ecosystem, biogeochemistry and biodiversity at regional to global scales (Koca et al., 2006; Miller et al., 2008; Schröter et al., 2005; Smith et al., 2008; Ahlström et al., in press). It consists of a process-based modelling framework of vegetation dynamics and biogeochemistry (Smith et al., 2001; Sykes et al., 2001), with a detailed scheme for simulating the forest stand structure and dynamics, based on neighbourhood-scale resource competition and demography (Hickler et al., 2004; Miller et al., 2008). Individual tree species can be distinguished, accounting for differences in phenology, allometry, bioclimatic tolerances and life history strategy (Koca et al., 2006). LPJ-GUESS has been validated inter alia with respect to metrics of vegetation structure and composition (Smith et al., 2001), ecosystem carbon balance (Morales et al., 2005) and forest productivity (Smith et al., 2008). LPJ-GUESS was set up to simulate one forest stand for each of the 186 grid cells covering Sweden in the grid of the RCA3 climate data. Each simulated stand thus represented the average structure and composition of forest in that grid cell, as predicted by the model based on climate conditions there. For each stand (grid cell), we simulated 150 patches (default number of replicates [A]) to account for variations across the landscape associated with management

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and stochastic vegetation processes (simulated natural regeneration, survival of seedlings and self-thinning). Each patch represents a simulated area of 1 ha. The standard version of LPJ-GUESS simulates the potential natural vegetation, in dynamic equilibrium with climate and soil conditions. However, as virtually all forest land in Sweden is more or less intensively managed, with resultant consequences for tree species composition and forest age structure, we implemented a scheme for constraining the simulated forests to actual forest conditions as reported in National Forest Inventory statistics (Loman, 2004) at county level in 2004 (the year before a severe storm event). The county-level data were used in the beginning of each model run to assign a target tree-species and age class of each simulated patch (Lagergren and Jönsson, 2010). The patchspecific timing of clear-cutting, planting and thinning required to obtain the prescribed state was calculated and implemented. The rotation period varies between 65 and 125 years, linearly related to the county specific site quality class. The average site quality class is in general higher in south Sweden than in north Sweden, due to climate conditions and nitrogen deposition. The generic disturbance scheme of LPJ-GUESS (Hickler et al., 2004) was turned off, and replaced by the prescribed storm damage and modelled mortality caused by I. typographus. The annually prescribed storm damage was distributed among patches using a patch specific sensitivity index (Lagergren and Jönsson, 2010) that accounts for the dependency of storm damage risk on tree species storm sensitivity and patch properties such as average canopy height, exposure among patches and among individual trees within a patch. 2.6. Data for evaluating the LPJ-GUESS spruce bark beetle model Three kinds of data sources (Table 1) were used for compiling a county based annual volume estimate of spruce trees killed by I. typographus in Sweden during 1960–2010 (Fig. 2). Data were collected from (a) scientific publications on insect pests in forests of the Nordic countries, (b) records on I. typographus activity described in published reports from the National Forest Agency, and (c) records on I. typographus activity described in memorandums from

Table 1 List of references used for mapping the occurrence of spruce bark beetle attacks on living trees in Sweden during the period of 1961–2010. Three different data sources were used: (S) scientific publications, (R) published reports from the National Forest Agency, and (M) memorandums from National Forest Agency meetings. Period

Kind of source

Reference

1961–1966 1967–1971 1971–1976 1972–1976 1971–1982 1971–1982 1977–1978 1977–1981 1982–1986 1987–1990 1992 1993 1994 1995

S S M S R R R S S S M M M M

1996 1997 1998 1994–2001 2005–2010

M R M M R

Christiansen (1970) Ehnström et al. (1974) Skogsstyrelsen (1976) Löyttyniemi et al. (1979) Risberg (1985) Eidmann (1983) Skogsstyrelsen (1979) Austarå et al. (1984) Ehnström et al. (1998) Harding et al. (1998) Lindelöw and Ehnström (1993) Samuelson (1994) Samuelson (1995) Lindelöw (1996), Samuelsson (1996) Samuelsson (1997) Skogsstyrelsen (2001) Lindelöw (1999) Lindelöw and Karlsson (2002) Enander and Svensson (2007), Svensson (2007), Enander and Eriksson (2008a,b), Wirtén and Eriksson (2008), Stridsman and Eriksson (2009), Svensson (2010)

National Forest Agency meetings (including entomologists from the Swedish University of Agriculture Sciences). Since the documentation of damage caused by I. typographus has not been strictly standardized, we had to make a few assumptions about the quality of the data, as follows. In the absence of records for any given year and region, a low damage level was assumed. Records without any volume estimates were regarded as minor activities. Records with volume estimates were considered to indicate an outbreak with more substantial tree killing. 2.7. Model evaluation, including sensitivity analysis The Swedish monitoring of trees killed by I. typographus contains both quantitative volume estimates and qualitative data. In addition, there are no records of the rate of salvage cutting, intensity of sanitation cutting or estimates of the initial I. typographus population size before the severe storm in 2005. To tackle the partial lack of information, we performed a multitude of LPJ-GUESS model runs to examine the model behaviour with respect to the model factors and parameters expected to influence the simulated incidence of insect-induced damages (Fig. 1, Table 4). The default model run represents a qualified guess on average conditions, and the sensitivity analysis was carried out as follows: (a) the number of simulated patches within a stand (150 ± 50 patches); (b) initial population size (10 or 100 I. typographus per ha in 1961); (c) rate of salvage cutting (five year average harvesting capacity ± 20%); (d) threshold for attack on living trees (fixed threshold of 5000 females versus a threshold of 5000 for unstressed trees and a lower threshold of 2500 for trees stressed by water deficit); (e) reproductive success in standing trees (default setting versus no model correction for lower reproductive success in standing trees); (f) cutting of infested trees (12%, 25%, 50%); (g) mortality of I. typographus during migration (20%, 40%, 60%); (h) attraction strength of storm felled trees during migration (5000–50,000 I. typographus per storm-felled tree); (i) climate change (temperature increase by 0.5 ◦ C and 1.0 ◦ C). County specific model results were summarized at the regional level to provide averages for the southern (Götaland), central (Svealand) and northern (Norrland) part of Sweden. The ecosystem model performance was only visually evaluated, as the data set on the spatial and temporal availability of brood material and the records of trees killed by spruce bark beetles were associated with large uncertainties and therefore not suitable for statistical evaluation. In addition, the I. typographus population model (Fig. 1) was implemented in MATLAB (R2010b, The MathWorks Inc.), for evaluation of model performance using prescribed parameter settings. The model simulation covering the period after the sever storm event, 2005–2009, was based on estimated regional averages on initial population size, available brood material, and countermeasures (Table 2) instead of default settings. Supplement 1 contains a sensitivity analysis of the population model using these settings, assessing the relative importance of uncertainties associated with different parameter estimates. 3. Results 3.1. The I. typographus population model The calculated volume of trees killed by I. typographus, using the prescribed parameter settings, amount to 0 m3 in 2005, 1.0 million m3 in 2006, 0.5 million m3 in 2007, 1.3 million m3 in 2008, and 0.5 million m3 in 2009. This adds up to a total volume of 3.3 million m3 (Supplement 1). The estimated potential attack density on storm-felled trees (parent females per square-metre bark) during spring flight was less than 1 in 2005, approximately 100 in

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Fig. 2. Available records on bark beetle attacks in Sweden during the period of 1961–2008 plotted for each county, based on the literature listed in Table 1. Blue dots indicate records with an estimate of the volume of killed spruce trees (left y-axes), red ‘+’ indicates the uncertainty associated with volume estimates for the Värmland-outbreak, and blue ticks on the x-axes indicate records of attacks without any volume estimate. The storm damage (forest cubic metre, m3 sk) is indicated by a black line (right y-axes) (data from Nilsson et al., 2004; Nilsson, 2008). The two upper rows indicate north Swedish conditions and the two last rows south Swedish conditions (N: Norrland, S: Svealand and G: Götaland). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

Table 2 Information on I. typographus, availability of brood material and countermeasures in southern Sweden after the storm in 2005, extracted from the compilations of monitoring activities reported by Enander and Svensson (2007), Svensson (2007), Enander and Eriksson (2008a,b), Wirtén and Eriksson (2008), Stridsman and Eriksson (2009), and Svensson (2010). The reproductive success in standing trees was provided by M. Schroeder (unpublished data). The number of generation was calculated by the model using temperature data.

Initial population in 2005 (females per hectare) Storm-felled trees available at the timing of spring flight, 80% Norway spruce (million m3 sk) Removal of storm-felled trees (%) after initiation and before completed maturation of the new generation Pheromone trap catches (no. of females per hectare) Cutting and removal of standing killed trees before emergence of new generation (%) Reproductive success in standing trees Number of generations

2005

2006

2007

50 60 20 0 0 0 1.0

– 2.5 55 0 25 5.2 1.3

– 9 57 127 37.5 2.9 1.0

2008 – 0.9 50 113 25 0.8 1.1

2009 – 0.5 95 35 25 0.9 1.0

Table 3 Comparison between observed reproductive success in standing trees during outbreaks, and calculated reproductive success (daughters per female), using the equation of Anderbrant et al. (1985). Reference

Number of trees

Egg galleries per m2 bark

Observed reproductive success

Butovitsch (1941) Lekander (1972) Weslien and Regnander (1990) Kärvemo and Schroeder (2010) Komonen et al. (2011)

9 40–50 per year (1947–1952) 61 86 82

682 535 481 401 446

1.5 1.5 1.5 NA 3.6

2.5 3.0 3.3 3.8 3.5

509

2.0

3.2 +60%

Mean value Average difference

Calculated reproductive success

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number of bark beetles or the availability of brood material, the density dependent reproductive success will be affected in different directions. The relative effect of a reduction in population size is somewhat lower at high population densities than at low population densities, as the reduced intraspecific competition can lead to an increased reproductive success of the remaining bark beetles. The opposite effect is observed at a reduction of available brood material, with less impact on the reproductive success at low population densities than at high population densities.

3.2. The LPJ-GUESS spruce bark beetle model Fig. 3. A comparison between calculated reproductive success in storm felled trees using the equations of Komonen et al. (2011) and Anderbrant et al. (1985). The two equations produce rather similar results, but the curve fitting methods used to explain observed relations between gallery density and daughters per female creates some discrepancies.

2006, 2007 and 2009, and roughly 500 in 2008. Field observations were used to evaluate the modelling of reproductive success, and the model equation agreed well with the relation found for stormfelled trees (Fig. 3). The discrepancies can be attributed largely to the selection of curve fitting methods. However, the model equation overestimated the reproductive success in standing trees by an average of 60% (Table 3). A model analysis of the reproductive success in storm-felled trees was carried out to further explore how countermeasures influence the population dynamics (Fig. 4). At densities below 200 females per m2 bark area (i.e. estimated for all years except 2008), the size of the parental generation limits the population growth more than the brood material. At densities above 200 females per m2 (i.e. in year 2008), the brood material is more limiting than the population size. If the number of bark beetles and the amount of available brood material are reduced in equal proportions, then the population growth will be reduced correspondingly. However, if the countermeasures are directed to reduce either the

The default model parameterization (Section 2.7) was used to establish the baseline conditions for the sensitivity analysis of the LPJ-GUESS spruce bark beetle model. The default values and tested parameter settings are presented in Table 4. Varying the number of patches within a stand between 100 and 200 had only a minor impact on the modelled volume of attacked living trees, indicating that the default use of 150 patches is satisfactory (Table 4A). The initial population size in year 1960 did not have any major influence on the simulated damages after five years (Table 4B), as the population size quickly reached equilibrium with the availability of brood material. Model simulations using the default value of 10 individuals at introduction resembled the observed level of bark beetle damage during the first five years, whereas the damage was overestimated using 100 individuals. A 20% reduction of the modelled rate of salvage cutting had on average no effect on the simulated I. typographus tree killing (Table 4C), but the outcome for a specific region and year was highly dependent on the timing of salvage cutting in relation to the temperature dependent initiation and development of a new generation (Fig. 5). The model run without any cutting of storm felled trees and cutting of infested standing trees indicated a 100–400% higher volume of trees killed than the default model run, based on the five year average harvesting capacity.

Fig. 4. The density dependent reproductive success of I. typographus (Anderbrant et al., 1985) influences the effect of countermeasures aiming to limit population growth. The population growth (upper row) can be restricted by a reduction in the initial population size (left column), or by a reduction in the amount of available brood material (right column). The non-linear effect is dependent on changes is the individual reproductive success (lower row). No countermeasure corresponds to 0% reduction. Blue line: 1 female per m2 , red line: 500 females per m2 , black solid line: 250 females per m2 , dashed lines indicating intervals of 50 females per m2 . (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

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195

Table 4 Sensitivity analysis of the LPJ-GUESS spruce bark beetle model, carried out to examine the impact of the main model components on the calculated volume of Norway spruce killed by I. typographus (Fig. 1). The analysis was carried out for three parts of the country, Götaland, Svealand and Norrland, representing boreo-nemoral, southern boreal, and boreal bioclimatic conditions, respectively. The differences between the default parameter settings and the test runs are expressed as the median percentage for the simulated period 1961–2008. The differences in total volume throughout the simulation period are given in brackets. Test

Model component

Parameter (default value)

Parameter setting

Götaland

Svealand

Norrland

A1 A2

Vegetation dynamics

Number of patches (150 patches)

100 patches 200 patches

0 (0) 0 (+1)

0 (0) 0 (+2)

0 (0) 0 (−1)

B1

I. typographus

100 indiv. per ha

+6 (+12)

+28 (+68)

−31 (+21)

C1

Brood tree availability

Initial population size (10 indiv. per ha) Salvage cutting of storm-felled trees (five year average harvesting capacity)

Reduction by 20%

0 (+2)

+2 (+3)

0 (+1)

Increase by 20% No salvage cutting, or cutting of infested trees Always 5000

+1 (+3) +340 (+413)

+20 (+18) +373 (+249)

−43 (−28) +136 (+97)

−27 (−18)

+4 (−2)

−51 (−38)

Cumulative water stress

−8 (+17)

+5 (+9)

−51 (−37)

Reproduction in standing trees according to Eq. (1) 50%

+2866 (+1138)

+6438 (+4108)

+187 (+219)

−90 (−85)

−90 (−70)

−100 (−81)

C2 C3 D1

Brood tree availability

Threshold for attack on living trees (daily water stress)

E1

I. typographus reproduction

F1

I. typographus mortality

Reduction according to empirical findings (Table 3) Cutting and removal of standing infested trees (25%)

D2

Difference (%)

12%

+157 (+177)

+174 (+127)

+32 (+28)

G1 G2

I. typographus

Flight mortality (20%)

40% 60%

−80 (−75) −100 (−96)

−74 (−59) −100 (−94)

−94 (−76) −100 (−97)

H1

I. typographus migration

Attraction strength (5000 I. typographus)

50,000 I. typographus per storm-felled tree

0 (+3)

+17 (+19)

−43 (−28)

F2

Fig. 5. Difference in LPJ-GUESS simulated volume of trees killed by I. typographus between two model runs, comparing the default rate of salvage cutting with a 20% reduction in harvesting capacity. The default range of cutting is set to zero, and the y-axis show the % difference. The harvesting capacity determines the timing of salvage cutting in relation to the timing of spruce bark beetle activity. The risk for attacks on standing trees will increase if a larger proportion of the storm felled trees are cut after the completed development of the first generation. Provided that a larger proportion serves as trap trees, the risk may however decrease. The figure shows the results for the three regions; Götaland (solid line), Svealand (dotted line), and Norrland (dashed line).

With a fixed threshold for lethal attack on a spruce tree of 5000 females, the simulated volume of killed trees was 0–40% lower than the default model run, having a threshold of 5000 for unstressed trees and 2500 for trees stressed by water deficit and 25% cutting of infested trees (Table 4D, Fig. 6). Using a cumulative water stress index (Fig. 7) instead of daily values modified the simulated impact of drought stress. In general, the drought stress became more pronounced in Götaland and Svealand, and less pronounced in Norrland (Table 4D). Model simulations dynamically restricting the number of trees susceptible to spruce bark beetle attacks proportionally to the tree drought stress had no impact on the model outcome, as only a very small fraction of the spruce trees were killed each year. Without taking the reduced reproductive success in standing trees into account, the simulated volume of killed trees was overestimated (Table 4E). Simulated cutting of infested trees, both storm-felled and standing, had a major impact on the model outcome (Table 4F, Fig. 6).

The default value of 25% cutting gives a model estimate that is within the range of observations for most years and regions, but it does not capture the outbreak in central Sweden during the 1970s. For this period, the lower level of 12% cutting of infested trees gives a better estimate. The tree killing was virtually zero when increasing the cutting of infested trees to 50%. This is in line with the findings that a changed parameterization of I. typographus flight mortality from 20% to 40% and 60% reduced the tree killing potential by 70–100% (Table 4G). The parameter settings for the attraction strength of storm-felled trees, of importance for I. typographus migration between patches, had a minor impact on the model results (Table 4H). A warmer climate will increase the risk for attacks on living trees, according to both model simulations with and without salvage and sanitary cutting (Fig. 8). The simulations indicated more tree killing during the years following a storm event, but not any longer outbreak periods.

196

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600 400 200 0 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1990

1995

2000

2005

2010

1990

1995

2000

2005

2010

Svealand x 1000 m3 sk

600 400 200 0 1960

1965

1970

1975

1980

1985 Götaland

x 1000 m3 sk

10000

5000

0 1960

1965

1970

1975

1980

1985 3

Fig. 6. Simulated spruce bark beetle killing of standing trees (forest cubic metre, m sk), using the LPJ-GUESS ecosystem model. The default model run (black solid line) has a dynamic threshold for killing of standings trees (5000 I. typographus females for unstressed trees and 2500 for trees stressed by water deficit) in combination with 25% cutting of infested trees. The blue solid line (above the black solid line) represents a model run with dynamic threshold where the cutting of infested trees was set to 12%. The black and blue dotted lines indicates model runs with a fixed threshold of 5000 in combination with 25% and 12% cutting of infested trees, respectively. Blue dots indicate recorded estimates of the volume of killed spruce trees. Blue dots on the x-axes indicate years without any volume estimates (note the different scales on the y-axes). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

4. Discussion 4.1. Empirical data

water scalar

I. typographus outbreaks have been more common in southern and central Sweden than in northern Sweden. The time-series of I. typographus attacks shows that the three main tree-killing episodes, involving several counties, were preceded by a storm event. However, not all storm disturbances were followed by major outbreaks. We recognize that the LPJ-GUESS default model run roughly replicates the geographical pattern in outbreak frequency, and that the outbreak periods are of a rather short duration which is in accordance with empirical data. Using estimated regional averages from 2005 and onwards as prescribed parameter settings indicated that knowledge on initial population size, rate of salvage Norrland 1 0.95 0.9

water scalar

1960

1980

1990

2000

2010

2000

2010

2000

2010

Svealand 1 0.95 0.9 1960

water scalar

1970

1970

1980

1990

Götaland 1 0.95 0.9 1960

1970

1980

1990

Fig. 7. Water scalar, a modelled measure used in LPJ-GUESS for assessing the average annual tree water availability (0-1 scale where 1 corresponds to no water limitation, and values less than 1 indicate periods of drought stress).

cutting and magnitude of sanitary cutting can improve the performance of the I. typographus population model. The outcome of the model simulations is discussed in relation to the prevailing conditions of the different outbreak episodes, and we focus on the three main agents involved: the forest owner, the spruce bark beetle and the Norway spruce. Finally, we discuss potential impacts of climate change. 4.2. The forest owner The competition for host substrate is regarded as the main population-regulating factor (Økland and Berryman, 2004). The availability of fuel for insect outbreaks is the only variable that can be controlled by foresters and thereby used as a basis for preventive actions (Netherer and Nopp-Mayr, 2005). Measures aiming to reduce the availability of brood material and/or the population size are vital for reducing the acute risk of a bark beetle outbreak. Experience shows, however, that the effect is highly dependent on the timing of various actions and how thoroughly they are carried out (Wermelinger, 2004), which was reflected by the relative performance of the LPJ-GUESS simulations with different rates of salvage cutting (Fig. 5). Continuous and timely removal of windthrown trees can be an important factor in limiting I. typographus population growth, as salvage cutting before reproduction restricts the potential reproductive success and salvage cutting after reproduction but before emergence of the new generation reduces the population by removal of infested trees. In the ecosystem model, the simulated removal of infested trees implies extra resources for salvage cutting adding to the stand level capacity of lumber machines during the three years with extreme storm felling (36 million m3 standing volume in 1969, 70–75 million m3 in 2005, and 16 million m3 in 2007). During 2005 (Fridh, 2006) and 2007, extra resources were in reality brought in from other parts of the country, as well as from abroad, to increase the rate of salvage cutting. In 1969, however, the effort to take care of all the storm-felled trees was less thorough. In 2005 the cutting of wind-thrown trees before reproduction did not limit the

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With salvage & sanitary cutting

Without salvage & sanitary cutting Norrland 800

300

600

x 1000 m3 sk

x 1000 m3 sk

Norrland 400

200 100 0 1960

1970

1980

1990

2000

400 200 0 1960

2010

1970

300

600

200 100

1970 4

1990

2000

0 1960

2010

2 1

1970

1980

1990

1970 4

8

3

0 1960

2000

2010

2000

2010

2000

2010

200

Götaland

x 10

1990

400

x 1000 m3 sk

x 1000 m3 sk

4

1980

1980

Svealand 800

x 1000 m3 sk

x 1000 m3 sk

Svealand 400

0 1960

197

2000

2010

1980

1990

Götaland

x 10

6 4 2 0 1960

1970

1980

1990

Fig. 8. Simulated spruce bark beetle killing of standing trees (forest cubic metre, m3 sk). Climate change signal: default model run (black line), adding 0.5 ◦ C (blue line, generally above the black solid line), adding 1 ◦ C (dashed red line). The left columns represents model runs with default settings of salvage cutting and cutting of infested trees, whereas right columns represents model runs with no management intervention (note the y-scale differences). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

population growth due to the extremely large amounts of wind-thrown trees, in combination with a relatively low initial population size. Subsequent storm feeling events in 2006–2009 were potential threats for further increase in population size, but by timely salvage cutting the storm-felled trees served as trap trees during the density-independent growth phase, facilitating the removal of spruce bark beetles before they began to attack standing trees. Recommended methods for limiting bark beetle outbreaks include sanitary cutting of infested trees and pheromone traps (Grodzki et al., 2003, 2006). It is however difficult to estimate the impact on population growth as the effect, ultimately linked to the availability of storm felled brood material and the defence capacity of standing trees via the non-linear relation between attack density and potential reproductive success, cannot be measured as the number of caught bark beetles (Fig. 4). 4.3. The spruce bark beetle The LPJ-GUESS model simulation without any salvage cutting or cutting of infested trees indicated that the spruce bark beetle had the potential to kill 1–18% of the standing forest during a 40year period, depending on regional climate conditions (i.e. more in the south of Sweden than in the north of Sweden). These kinds of “worst case” scenarios are difficult to evaluate, but the upper level of the simulated damages is within the range reported from the Arkhangelsk region (latitude 63◦ N) where about 21% of the spruce trees (dbh > 10 cm) were killed during a five-year long massive outbreak (Aakala et al., 2011). The sensitivity analysis indicated that

at a level of 40% winter mortality and 60% mortality during flight (Table 4, G2), i.e. a total mortality of 76%, there was no risk for attacks on living trees. This is in accordance with the threshold of 80% mortality reported by Fahse and Heurich (2011). The build-up of a large bark beetle population size, in combination with reduced tree defence capacity due to drought stress provides a plausible explanation for the long-lasting outbreak in Svealand during the 1970s. This is reflected in the simulations (Fig. 6), but the length of the outbreak period is not completely captured. During the 1990s there was a minor outbreak in Götaland, initiated and prolonged by several years with minor storm damage. In Kronoberg County, the epicentre of this outbreak, storm-fellings occurred during 8 out of 10 years. The outbreak was most likely aggravated in 1994, one of the driest years during the simulated 50-year period, indicated by the fact that this was the first year with a volume estimate of the tree killing. During 2006–2009, about 3.2 million m3 were killed by I. typographus (Enander and Svensson, 2007; Stridsman and Eriksson, 2009; Svensson, 2007; Wirtén and Eriksson, 2008). The LPJGUESS default model simulation overestimated the severity of this outbreak by approximately 3 times (e.g. ∼10 million m3 sk). The main reason for this is that the simulated initial population in 2005 was about twice as high as the estimated regional average (100 versus 50 females per ha), in combination with the use of predefined values for salvage and sanitary cutting. The population model run based on prescribed parameter settings gave a volume estimate closer to observed damage (Supplement 1).

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4.4. The Norway spruce The potential for a massive synchronous insect outbreak is related to the forest landscape characteristics and triggering weather events, such as windstorms and drought (Økland et al., 2005; Powers et al., 1999). Tree mortality commonly arises independently in separated sub-areas, and the large-scale outcome depends on local autocorrelations (Powers et al., 1999). Temporal variations in weather condition influence the wood volume at stake, via impact on host tree defence capacity and insect performance (Mulock and Christiansen, 1986; Økland and Bjørnstad, 2006; Wermelinger, 2004). The LPJ-GUESS sensitivity analysis indicated that knowledge uncertainty associated with the parameterization of reproductive success in standing trees has a large influence on model performance. This factor regulates the outbreak potential, e.g. the model parameterization had an impact on both the simulated length of the outbreak during the 1970s and the initial population in 2005. It also has implications for the simulated background mortality of spruce trees caused by I. typographus during endemic conditions. Both the reproductive success in standing trees and the background tree mortality are difficult to assess due to the lack of monitoring in-between outbreak periods. Rough estimates on the quantitative effect of tree drought stress on the I. typographus population dynamics was obtained by LPJ-GUESS model runs with different attack thresholds, though an explicit model representation of tree defence capacity is lacking. In addition, the reproductive success of I. typographus is influenced by predators and parasitoids (Feicht, 2006; Hedgren and Schroeder, 2004; Wermelinger, 2002; Weslien and Schroeder, 1999). This effect is, however, controversial (Wermelinger, 2004), and thereby difficult to model. The importance may differ between endemic and outbreak conditions (Fayt et al., 2005; Feicht, 2006; Wermelinger, 2002), and could potentially be linked to forest management practice (Weslien and Schroeder, 1999).

preventive measures against the expected gains in terms of reduced timber losses, and take into account alternative and complementary ecosystem services to commercial timber production, such as soil carbon sequestration (Seidl et al., 2008) and biodiversity (Hedgren et al., 2003; Muller et al., 2008; Schroeder, 2007). 5. Conclusion Ecosystem modelling can be used to assess the risk for spruce bark beetle outbreaks, influenced by climate impact on the availability of brood trees and I. typographus voltinism. The relation between I. typographus attack density on storm-felled trees and reproductive outcome is relatively well known, making it possible to estimate the effect of acute management operations. Model performance would, however, be improved by reducing knowledge uncertainties associated with attacks on standing trees, including tree defence capacity and I. typographus reproductive success. Acknowledgements This study has been financially supported by the Swedish Research Council FORMAS (project 2008-205 and 2010-822) and by the Foundation for Strategic Environmental Research (MISTRA) through the research programmes Mistra-SWECIA and Future Forests. We are grateful to Carin Nilsson for providing storm damage data. The study is a contribution to the Lund University Strategic Research Area Biodiversity and Ecosystem Services in a Changing Climate (BECC). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.agrformet.2012.07.012.

4.5. Potential implications of climate change

References

This study indicated that a warmer climate will increase the risk for I. typographus killing of spruce trees in terms of more severe attacks during the years following a storm event, but not in terms of longer outbreak periods. This pattern in population dynamics is caused by a high reproductive success in storm-felled trees and a low reproductive success in standing trees. A major challenge for climate change impact studies is, however, to quantify how a series of stressful weather events influence forest sensitivity to bark beetle attacks. The risk for weather conditions triggering an outbreak may also increase, as increased precipitation during autumn and less frozen ground during winter will increase the risk of stormfelling (Lagergren and Jönsson, 2010). Weather extremes, such as drought and flooding, can affect the defensive system of spruce trees, making them susceptible to bark beetle attacks (Christiansen and Bakke, 1988; Faccoli, 2009; Schroeder and Lindelöw, 2003; Wermelinger, 2004). The extensive, continuous boreal forests of northern Europe are vulnerable to outbreaks; monocultures of Norway spruce are known to be sensitive to bark beetle-mediated damages (Kalkstein, 1976), whereas the ratio between Norway spruce and other tree species may affect the probability of an attack as I. typographus tends to avoid non-host volatiles (Zhang and Schlyter, 2004). The availability and storm-sensitivity of trees at landscape level are influenced by long-term management strategies, and simulations with a climate-driven model like LPJ-GUESS are amenable to assessing the efficiency of alternative management strategies to modify the risks associated with a warmer climate. A comprehensive assessment of this kind would weigh the potential costs of

Aakala, T., Kuuluvainen, T., Wallenius, T., Kauhanen, H., 2011. Tree mortality episodes in the intact Picea abies-dominated taiga in the Arkhangelsk region of northern European Russia. J. Veg. Sci. 22 (2), 322–333. Ahti, T., Hämet-Ahti, L., Jalas, J., 1968. Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fenn. 5 (3), 169–211. Ahlström, A., Miller, P.A., Smith, B. Too early to infer a global NPP decline since 2000. Geophys. Res. Lett., in press. Anderbrant, O., 1989. Reemergence and 2nd brood in the bark beetle Ips typographus. Holarctic Ecol. 12 (4), 494–500. Anderbrant, O., 1990. Gallery construction and oviposition of the bark beetle Ips typographus (Coleoptera, Scolytidae) at different breeding densities. Ecol. Entomol. 15 (1), 1–8. Anderbrant, O., Schlyter, F., Birgersson, G., 1985. Intraspecific competition affecting parents and offspring in the bark beetle Ips typographus. Oikos 45 (1), 89–98. Annila, E., 1969. Influence of temperature upon the development and voltinism of Ips typographus L. (Coleoptera, Scolytiade). Ann. Zool. Fenn. 6, 161–208. Austarå, Ø., Midtgaard, F., 1986. On the longevity of Ips typographus L. adults. J. Appl. Entomol. 102 (1), 106–111. Austarå, Ø., Annila, E., Bejer, B., Ehnström, B., 1984. Insect pests in forests of the Nordic countries 1977–1981. Fauna Norv. Ser. B 31, 8–15. Baier, P., Pennerstorfer, J., Schopf, A., 2007. PHENIPS—a comprehensive phenology model of Ips typographus (L.) (Col., Scolytinae) as a tool for hazard rating of bark beetle infestation. Forest Ecol. Manage. 249 (3), 171–186. Butovitsch, V., 1941. Studier över granbarkborrens förökningsframgång i de av decemberstormen 1931 härjade skogarna i norraUppland. Meddelande från Statens Skogsförsöksanstalt 32, 297–360. Christiansen, E., 1970. Insect pests in forests of the Nordic countries 1961–1966. Norsk Entomol. Tidsskr., 153–158. Christiansen, E., Bakke, A., 1988. The spruce bark beetle of Eurasia. In: Dynamics of Forest Insect Populations. Plenum Publishing Corporation, pp. 479–503. Christiansen, E., Waring, R.H., Berryman, A.A., 1987. Resistance of conifers to bark beetle attack: searching for general relationships. Forest Ecol. Manage. 22 (1–2), 89–106. Ehnström, B., Annila, E., Austarå, Ø., Harding, S., Ottosson, J.G., 1998. Insect pests in forests of the Nordic countries 1982–1986. Rapport fra skogforskningen—Supplement 2. Norsk institutt for skogforskning, Ås, pp. 1–12.

A.M. Jönsson et al. / Agricultural and Forest Meteorology 166–167 (2012) 188–200 Ehnström, B., Bejer-Petersen, B., Löyttyniemi, K., Tvermyr, S., 1974. Insect pests in forests of the Nordic countries 1967–1971. Ann. Entomol. Fenn. 40 (1), 37–47. Eidmann, H., 1983. Hur gick det med granbarkborren? Härjningen 1971–82, bekämpningen och feromonerna. SLU Skogsfakta, Biologi och Skogsskötsel, vol. 11, pp. 1–4. Enander, G., Svensson, L., 2007. Skogsstyrelsens 3:e rapport om utvecklingen och bekämpningen av skadeinsekter år 2007. Swedish Forest Agency, Jönköping. Enander, G., Eriksson, L.-G., 2008a. Skogsstyrelsens 1:a rapport om utvecklingen och bekämpningen av skadeinsekter år 2008. Swedish Forest Agency, Jönköping, p. 14. Enander, G., Eriksson, L.-G., 2008b. Skogsstyrelsens 2:a rapport om utvecklingen och bekämpningen av skadeinsekter 2008. Swedish Forest Agency, Jönköping, p. 16. Faccoli, M., 2002. Winter mortality in sub-corticolous populations of Ips typographus (Coleoptera, Scolytidae) and its parasitoids in the south-eastern Alps. Anz. Schadl. 75 (3), 62–68. Faccoli, M., 2009. Effect of weather on Ips typographus (Coleoptera Curculionidae) phenology, voltinism, and associated spruce mortality in the Southeastern Alps. Environ. Entomol. 38 (2), 307–316. Fahse, L., Heurich, M., 2011. Simulation and analysis of outbreaks of bark beetle infestations and their management at the stand level. Ecol. Model. 222 (11), 1833–1846. FAO, 1991. The Digitized Soil Map of the World (Release 1.0), vol. 67/1. Food and Agriculture Organization of the United Nations. Fayt, P., Machmer, M.M., Steeger, C., 2005. Regulation of spruce bark beetles by woodpeckers—a literature review. Forest Ecol. Manage. 206 (1–3), 1–14. Feicht, E., 2006. Frequency, species composition and efficiency of Ips typographus (Col., Scolytidae) parasitoids in infested spruce forests in the National Park Bavarian Forest over three consecutive years. J. Pest Sci. 79 (1), 35–39. Fridh, M., 2006. Stormen 2005—en skoglig analys, Jönköping. Göthlin, E., Schroeder, L.M., Lindelow, Å., 2000. Attacks by Ips typographus and Pityogenes chalcographus an windthrown spruces (Picea abies) during the two years following a storm felling. Scand. J. Forest Res. 15 (5), 542–549. Grodzki, W., Jakus, R., Gazda, M., 2003. Patterns of bark beetle occurrence in Norway spruce stands of national parks in Tatra Mts. in Poland and Slovakia. Anz. Schadl. 76 (3), 78–82. Grodzki, W., Jakus, R., Lajzova, E., Sitkova, Z., Maczka, T., Skvarenina, J., 2006. Effects of intensive versus no management strategies during an outbreak of the bark beetle Ips typographus (L.) (Col.: Curculionidae, Scolytinae) in the Tatra Mts. in Poland and Slovakia. Ann. Forest Sci. 63 (1), 55–61. Harding, S., Annila, E., Ehnström, B., Halldórsson, G., Kvamme, T., 1998. Insect pests in forests of the Nordic countries 1987–1990. Rapport fra skogforskningen—Supplement 3. Norsk institutt for skogforskning, Ås, pp. 1–22. Harding, S., Ravn, H.P., 1985. Seasonal activity of Ips typographus L (Col, Scolytidae) in Denmark. Z. Angew. Entomol. 99 (2), 123–131. Hedgren, P.O., Schroeder, L.M., 2004. Reproductive success of the spruce bark beetle Ips typographus (L.) and occurrence of associated species: a comparison between standing beetle-killed trees and cut trees. Forest Ecol. Manage. 203 (1–3), 241–250. Hedgren, P.O., Schroeder, L.M., Weslien, J., 2003. Tree killing by Ips typographus (Coleoptera: Scolytidae) at stand edges with and without colonized felled spruce trees. Agric. Forest Entomol. 5 (1), 67–74. Hickler, T., Smith, B., Sykes, M.T., Davis, M.B., Sugita, S., Walker, K., 2004. Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA. Ecology 85 (2), 519–530. Jönsson, A.M., Harding, S., Krokene, P., Lange, H., Lindelöw, Å., Økland, B., Ravn, H.P., Schroeder, L.M., 2011. Modelling the potential impact of global warming on Ips typographus voltinism and reproductive diapause. Clim. Change 109, 695–718. Jönsson, A.M., Bärring, L., 2011. Future climate impact on spruce bark beetle life cycle in relation to uncertainties in regional climate model data ensembles. Tellus Ser. A—Dyn. Meteorol. Oceanogr. 63 (1), 158–173. Jönsson, A.M., Harding, S., Bärring, L., Ravn, H.P., 2007. Impact of climate change on the population dynamics of Ips typographus in southern Sweden. Agric. Forest Meteorol. 146 (1–2), 70–81. Jönsson, A.M., Schroeder, L.M., 2006. Granbarkborren—en scenarioanalys för 2006–2009. Skogsstyrelsen, Rapport 4:2006. Jönköping. Kalkstein, L.S., 1976. Effects of climatic stress upon outbreaks of the Southern Pine Beetle. Environ. Entomol. 5, 653–658. Kärvemo, S., Schroeder, L., 2010. A comparison of outbreak dynamics of the spruce bark beetle in Sweden and the mountain pine beetle in Canada (Curculionidae: Scolytinae). Entomol. Tidskr. 131 (3), 215–224. Kjellström, E., Nikulin, G., Hansson, U., Strandberg, G., Ullerstig, A., 2011. 21st century changes in the European climate: uncertainties derived from an ensemble of regional climate model simulations. Tellus Ser. A—Dyn. Meteorol. Oceanogr. 63 (1), 24–40. Koca, D., Smith, B., Sykes, M.T., 2006. Modelling regional climate change effects on potential natural ecosystems in Sweden. Clim. Change 78 (2–4), 381–406. Komonen, A., Schroeder, L.M., Weslien, J., 2011. Ips typographus population development after a severe storm in a nature reserve in southern Sweden. J. Appl. Entomol. 135 (1–2), 132–141. Lagergren, F., Jönsson A.M., 2010. Climate change and forests’ sensitivity to storm and spruce bark beetle damage. Mistra-Swecia Newsletter 1:10, p. 11–17. Lekander, B., 1972. A Mass Outbreak of Ips typographus in Gästrikland, Central Sweden in 1945–1952, 10. Institutionen for Skogszoologi Rapporter och Uppsatser, pp. 1–21. Lindelöw, Å., 1996. Rapporterade insektsskador i svenska skogar under 1995. Unpublished Report, 1996-01-20. SLU, Uppsala, Sweden.

199

Lindelöw, Å., 1999. Rapporterade insektsskador i svenska skogar under 1998. Unpublished Report, 1999-01-11. SLU, Uppsala, Sweden. Lindelöw, Å., Ehnström, B., 1993. Rapporterade insektsskador i svenska skogar 1992. Unpublished Report from SLU, 1993-01-15. SLU, Uppsala, Sweden. Lindelöw, Å., Karlsson, L., 2002-05-15. Ips typographus in Kronoberg County, pers. commun. SLU, Uppsala, Sweden. Loman, J.-O. (Ed.), 2008. Statistical Yearbook of Forestry 2008, Official Statistics of Sweden. Swedish Forest Agency, Jönköping, 337 pp. Loman, J.O., 2004. Swedish Statistical Yearbook of Forestry 2004. The National Board of Forestry, Jönköping, Sweden, 329 pp. Löyttyniemi, K., Austarå, Ø., Bejer, B., Ehnström, B., 1979. Insect pests in forests of the Nordic countries 1972–1976. Folia For. 395, 1–13. Miller, P.A., Giesecke, T., Hickler, T., Bradshaw, R.H.W., Smith, B., Seppa, H., Valdes, P.J., Sykes, M.T., 2008. Exploring climatic and biotic controls on Holocene vegetation change in Fennoscandia. J. Ecol. 96 (2), 247–259. Morales, P., Sykes, M.T., Prentice, I.C., Smith, P., Smith, B., Bugmann, H., Zierl, B., Friedlingstein, P., Viovy, N., Sabate, S., Sanchez, A., Pla, E., Gracia, C.A., Sitch, S., Arneth, A., Ogee, J., 2005. Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes. Global Change Biol. 11 (12), 2211–2233. Muller, J., Bussler, H., Gossner, M., Rettelbach, T., Duelli, P., 2008. The European spruce bark beetle Ips typographus in a national park: from pest to keystone species. Biodivers. Conserv. 17 (12), 2979–3001. Mulock, P., Christiansen, E., 1986. The threshold of successful attacks by Ips typographus on Picea abies—a field experiment. Forest Ecol. Manage. 14 (2), 125–132. Netherer, S., Nopp-Mayr, U., 2005. Predisposition assessment systems (PAS) as supportive tools in forest management—rating of site and stand-related hazards of bark beetle infestation in the High Tatra Mountains as an example for system application and verification. Forest Ecol. Manage. 207 (1–2), 99–107. Nilsson, C., 2008. Windstorms in Sweden—variations and impacts. Doctoral Thesis. Lund University, Lund, 180 pp. Nilsson, C., Stjernquist, I., Bärring, L., Schlyter, P., Jönsson, A.M., Samuelsson, H., 2004. Recorded storm damage in Swedish forests 1901–2000. Forest Ecol. Manage. 199 (1), 165–173. Økland, B., Berryman, A., 2004. Resource dynamic plays a key role in regional fluctuations of the spruce bark beetles Ips typographus. Agric. Forest Entomol. 6 (2), 141–146. Økland, B., Bjørnstad, O.N., 2006. A resource-depletion model of forest insect outbreaks. Ecology 87 (2), 283–290. Økland, B., Liebhold, A.M., Bjørnstad, O.N., Erbilgin, N., Krokene, P., 2005. Are bark beetle outbreaks less synchronous than forest Lepidoptera outbreaks? Oecologia 146 (3), 365–372. Powers, J.S., Sollins, P., Harmon, M.E., Jones, J.A., 1999. Plant–pest interactions in time and space: a Douglas-fir bark beetle outbreak as a case study. Landscape Ecol. 14 (2), 105–120. Raffa, K.F., Aukema, B.H., Bentz, B.J., Carroll, A.L., Hicke, J.A., Turner, M.G., Romme, W.H., 2008. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. Bioscience 58 (6), 501–517. Risberg, B., 1985. Estimation of Ips typographus damage in Värmland 1971–1982 by correcting and supplementing earlier results from aerial sketch mapping. Sver. Skogsvårdsförbunds Tidskr. 2, 21–31. Robertson, C., Wulder, M.A., Nelson, T.A., White, J.C., 2008. Risk rating for mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal regression modelling. Forest Ecol. Manage. 256 (5), 900–912. Rouault, G., Candau, J.N., Lieutier, F., Nageleisen, L.M., Martin, J.C., Warzee, N., 2006. Effects of drought and heat on forest insect populations in relation to the 2003 drought in Western Europe. Ann. Forest Sci. 63 (6), 613–624. Samuelson, H., 1994. Skador på skog under 1993. Unpublished Report, 1994-03-22. Protokoll från Nordiskt Skogsentomologmöte på Skalholt, Island. Swedish Forest Agency, Jönköping. Samuelson, H., 1995. Skador på skog under 1994. Unpublished Report, 1995-03-15. Swedish Forest Agency, Jönköping. Samuelsson, H., 1996. Skador på skog under 1996. Unpublished Report, 1996-03-13. Swedish Forest Agency, Jönköping, Sweden. Samuelsson, H., 1997. Skador på skog 1996. Unpublished Report, 1997-05-30. Swedish Forest Agency, Jönköping, Sweden. Samuelsson, P., Jones, C.G., Willen, U., Ullerstig, A., Gollvik, S., Hansson, U., Jansson, C., Kjellström, E., Nikulin, G., Wyser, K., 2011. The Rossby Centre Regional Climate model RCA3: model description and performance. Tellus Ser. A—Dyn. Meteorol. Oceanogr. 63 (1), 4–23. Schelhaas, M.J., Nabuurs, G.J., Schuck, A., 2003. Natural disturbances in the European forests in the 19th and 20th centuries. Global Change Biol. 9 (11), 1620–1633. Schlyter, F., Anderbrant, O., 1993. Competition and niche separation between two bark beetles—existence and mechanisms. Oikos 68 (3), 437–447. Schroeder, L.M., 2003. Tree mortality by the bark beetle Ips typographus (L.) in stormdisturbed stands. Integr. Pest Manage. Rev. 6, 169–175. Schroeder, L.M., 2007. Retention or salvage logging of standing trees killed by the spruce bark beetle Ips typographus: consequences for dead wood dynamics and biodiversity. Scand. J. Forest Res. 22, 524–530. Schroeder, L.M., Lindelow, A., 2003. Response of Ips typographus (Scolytidae: Coleoptera) and other bark- and wood-boring beetles to a flash-flood event. Scand. J. Forest Res. 18 (3), 218–224. Schröter, D., Cramer, W., Leemans, R., Prentice, I.C., Araujo, M.B., Arnell, N.W., Bondeau, A., Bugmann, H., Carter, T.R., Gracia, C.A., de la Vega-Leinert, A.C., Erhard, M., Ewert, F., Glendining, M., House, J.I., Kankaanpaa, S., Klein, R.J.T., Lavorel, S., Lindner, M., Metzger, M.J., Meyer, J., Mitchell, T.D., Reginster, I.,

200

A.M. Jönsson et al. / Agricultural and Forest Meteorology 166–167 (2012) 188–200

Rounsevell, M., Sabate, S., Sitch, S., Smith, B., Smith, J., Smith, P., Sykes, M.T., Thonicke, K., Thuiller, W., Tuck, G., Zaehle, S., Zierl, B., 2005. Ecosystem service supply and vulnerability to global change in Europe. Science 310 (5752), 1333–1337. Seidl, R., Fernandes, P.A.M., Fonseca, T., Gillet, F., Jönsson, A.M., Merganicova, K., Netherer, S., Arpaci, A., Bontemps, J.D., Bugmann, H., Gonzalez-Olabarria, J.R., Lasch, P., Meredieu, C., Moreira, F., Schelhaas, M.J., Mohren, G.M.J., 2011. Modelling natural disturbances in forest ecosystems: a review. Ecol. Model. 222 (4), 903–924. Seidl, R., Rammer, W., Jäger, D., Lexer, M.J., 2008. Impact of bark beetle (Ips typographus L.) disturbance on timber production and carbon sequestration in different management strategies under climate change. Forest Ecol. Manage. 256 (3), 209–220. Sitch, S., Smith, B., Prentice, I.C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J.O., Levis, S., Lucht, W., Sykes, M.T., Thonicke, K., Venevsky, S., 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biol. 9 (2), 161–185. Skogsstyrelsen, 1976. PM Bilaga 4, 1 (1). 1976-12-22. Swedish Forest Agency, Jönköping, Sweden. Skogsstyrelsen, 1979. Skogsskyddsutredningen 1977—virkeslagringen. Swedish Forest Agency, Jönköping, Sweden. Skogsstyrelsen, 2001. Skador på Skog, Rapport 80. Swedish Forest Agency, Jönköping, Sweden. Smith, B., Knorr, W., Widlowski, J.L., Pinty, B., Gobron, N., 2008. Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. Forest Ecol. Manage. 255 (12), 3985–3994. Smith, B., Prentice, I.C., Sykes, M.T., 2001. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecol. Biogeogr. 10 (6), 621–637. Stridsman, M., Eriksson, L.-G., 2009. Skogsstyrelsens 3:e rapport om utvecklingen och bekämpningen av skadeinsekter 2009. Swedish Forest Agency, Jönköping. Svensson, L., 2007. Övervakning av insektsangrepp—Slutrapport från Skogsstyrelsens regeringsuppdrag. Skogsstyrelsen Medd. 1, 153.

Svensson, L., 2010. Slutsatser och resultat från forskningsrapporter inom delprojekt Övervakning och bedömning ingående i projektet insektsbekämpning 2009. Swedish Forest Agenc, Jönköping, p. 4. Sykes, M.T., Prentice, I.C., Smith, B., Cramer, W., Venevsky, S., 2001. An introduction to the European Terrestrial Ecosystem Modelling Activity. Global Ecol. Biogeogr. 10 (6), 581–593. Uppala, S.M., Kållberg, P.W., Simmons, A.J., Andrae, U., Bechtold, V.D., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Holm, E., Hoskins, B.J., Isaksen, L., Janssen, P., Jenne, R., McNally, A.P., Mahfouf, J.F., Morcrette, J.J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P., Woollen, J., 2005. The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc. 131 (612), 2961–3012. Wermelinger, B., 2002. Development and distribution of predators and parasitoids during two consecutive years of an Ips typographus (Col., Scolytidae) infestation. Z. Angew. Entomol. 126 (10), 521–527. Wermelinger, B., 2004. Ecology and management of the spruce bark beetle Ips typographus—a review of recent research. Forest Ecol. Manage. 202 (1–3), 67–82. Weslien, J., Regnander, J., 1990. Colonization densities and offspring production in the bark beetle Ips typographus (L) in standing spruce trees. Z. Angew. Entomol. 109 (4), 358–366. Weslien, J., Schroeder, L.M., 1999. Population levels of bark beetles and associated insects in managed and unmanaged spruce stands. Forest Ecol. Manage. 115 (2–3), 267–275. Wirtén, H., Eriksson, L.-G., 2008. Skogsstyrelsens 3:e rapport om utvecklingen och bekämpningen av skadeinsekter 2008. Swedish Forest Agency, Jönköping. Yemshanov, D., Koch, F.H., McKenney, D.W., Downing, M.C., Sapio, F., 2009. Mapping invasive species risks with stochastic models: a cross-border United States-Canada application for Sirex noctilio Fabricius. Risk Anal. 29 (6), 868– 884. Zhang, Q.H., Schlyter, F., 2004. Olfactory recognition and behavioural avoidance of angiosperm nonhost volatiles by conifer-inhabiting bark beetles. Agric. Forest Entomol. 6 (1), 1–19.