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Running head: Climate change-bark beetle interactions 1

Cross-scale interactions among bark beetles, climate change and wind disturbances: a 2

landscape modeling approach 3

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Christian Temperli1*, Harald Bugmann1, Ché Elkin1 5

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1Swiss Federal Inst. of Technology, ETH Zurich, Department of Environmental Systems Sci-7

ence, Forest Ecology, Universitätstrasse 22, CH-8092 Zürich, Switzerland 8

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E-mail addresses: christian.temperli@env.ethz.ch, harald.bugmann@env.ethz.ch, 10

che.elkin@env.ethz.ch 11

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* Corresponding author; email: christian.temperli@env.ethz.ch 13

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Abstract 15

Bark beetles are a key forest disturbance agent worldwide, with their impact shaped by climate, 16

forest susceptibility, and interactions with other disturbances such as windthrow and fire. There 17

is ample evidence on the interactions among these factors at small spatial and temporal scales, 18

but projecting their long-term and landscape-scale impacts remains a challenge. 19

We developed a spatially explicit model of European spruce bark beetle (Ips typographus) 20

dynamics that incorporates beetle phenology and forest susceptibility, and integrated it in a cli-21

mate-sensitive landscape model (LandClim). We first corroborated model outputs at various spa-22

tial and temporal scales and then applied the model in three case studies (in the Black Forest, 23

Germany, and Davos, Switzerland) that cover an extended climatic gradient. We used this model 24

and case study framework to examine the mechanisms and feedbacks that are driving short-term 25

and long-term interactions among beetle disturbance, climate change and windthrow, and how 26

they may shift in the future. 27

At the current cold-wet end of the Norway spruce (Picea abies) distribution, climate 28

change is projected to increase temperature and drought, such that beetles become a more domi-29

nant disturbance agent. At the warm-dry end of the spruce distribution, where under current cli-30

mate beetle outbreaks were confined to the simultaneous occurrence of drought and windthrow, 31

the simulated level of drought alone sufficed for triggering beetle outbreaks, such that elevated 32

drought- and beetle-induced spruce mortality would negatively feed back on beetle disturbance 33

in the long term leading to receding beetle populations due to the local extinction of Norway 34

spruce. 35

These results suggest that depending on initial environmental conditions climate change 36

may shift the importance of direct and indirect drivers of disturbances. These shifts may affect 37

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the sign and strength of cross-scale disturbance interactions and may impact the cost-benefit 38

trade-off between beetle suppression and preventive management strategies. 39

Keywords: Bark beetles, Central Europe, climate change, disturbance interaction, landscape 40

model, regime shift 41

Introduction 42

Bark beetles are a key determinant of forest dynamics, with some species periodically erupting to 43

landscape-scale outbreaks that cause widespread tree mortality. In western North America, 44

Mountain pine beetles (Dendroctonus ponderosae Hopkins, Col. Scol.) have severely impacted 45

lodgepole pine (Pinus contorta Douglas) forests (Aukema et al. 2006), while in Europe the 46

spruce bark beetle (Ips typographus L., Col. Scol.) is one of the most important forest disturb-47

ance (Schelhaas et al. 2003, Wermelinger 2004). The intensity and extent of bark beetle disturb-48

ances are influenced by direct and indirect interactions among the beetles, climate (Jönsson et al. 49

2009, Bentz et al. 2010, Jactel et al. 2012), forest susceptibility (Netherer and Nopp-Mayr 2005), 50

and other forest disturbances such as windthrow (Wichmann and Ravn 2001, Schroeder and 51

Lindelöw 2002) and fire (Kulakowski et al. 2003). Due to the complexity of these interactions, 52

projecting how bark beetle disturbances will impact forest ecosystems under climate change re-53

mains a challenge (Jönsson et al. 2012) that is accentuated because the direction and importance 54

of these interactions may switch at different spatial and temporal scales (Bigler et al. 2005, 55

Simard et al. 2011). 56

Climate change may facilitate bark beetle disturbances at a regional level (Bentz et al. 2010), as a 57

rise in temperature decreases beetle’s winter mortality and increases the number of generations 58

per season (Wermelinger and Seifert 1998, Logan and Bentz 1999). Less obvious is the spatial 59

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and temporal scale at which drought and other forest disturbances influence forest susceptibility 60

to bark beetles. For example, while drought drives forest dynamics at a regional scale, and 61

windthrow influence forest dynamics at a patch scale (Allen et al. 2010, Panayotov et al. 2011), 62

both factors can directly influence bark beetle disturbance by increasing the susceptibility of in-63

dividual trees and forest patches to bark beetle attacks (Evangelista et al. 2011, McDowell et al. 64

2011). Drought can impair a tree’s defense against beetle attacks (Dale et al. 2001, Kane and 65

Kolb 2010, Ganey and Vojta 2011, Jactel et al. 2012) and windthrow events that are projected to 66

increase in frequency and intensity under climate change (Usbeck et al. 2010, Scaife et al. 2011) 67

cause stem breakage, root failure and other damage that decreases tree resistance to bark beetles. 68

Such disturbance-related reductions in tree vitality are thought to play a key role in triggering 69

bark beetle outbreaks and in enhancing their severity (Schroeder and Lindelöw 2002, 70

Wermelinger 2004, Bouget and Duelli 2004, Gandhi et al. 2007). 71

Empirical studies of these relationships have primarily focused on the scale at which bark beetles 72

interact with their host trees (Schroeder and Lindelöw 2002; Fig. 1a). However, there are few 73

studies that examine how these direct, fine grain, short-term and positive interactions will influ-74

ence forest dynamics and bark beetle impacts indirectly through changes in forest state at larger 75

spatial and temporal scales (Fig. 1d). Here we tested whether such indirect interactions among 76

climate (temperature and drought), windthrow and bark beetles will change or even switch in 77

effect direction with increasing spatial and temporal extents (landscape and centuries). 78

We hypothesized that when moving from the patch to the landscape scale, wind disturbance that 79

acts at the patch scale will decrease in importance compared to drought that acts at a regional 80

extent (Fig. 1c). While drought will increase a tree’s susceptibility to bark beetles in the short 81

term (Dobbertin et al. 2007, Faccoli 2009), in the long term increased drought can precipitate a 82

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change in forest composition thereby reducing the availability of beetle breeding habitat (Økland 83

and Berryman 2004, Allen et al. 2010, Bentz et al. 2010; Fig. 1b). Over longer time frames, wind 84

disturbances may also contribute to making forest stands less susceptible to bark beetle infesta-85

tions by altering the age and diameter distribution of trees and making forest stands less homo-86

geneous (Panayotov et al. 2011; Fig. 1b). 87

To gain insight in these dynamics, a landscape level tool is needed that can depict forest dynam-88

ics at large temporal scales, while still capturing small-scale interactions among disturbances. 89

We approached this by coupling a climate-sensitive bark beetle model with a forest landscape 90

succession model that explicitly accounts for drought and wind disturbance: LandClim (Schu-91

macher et al. 2004). We used previous empirical and modeling work at the patch scale to model 92

small-scale bark beetle-forest interactions (e.g., Seidl et al. 2007, Fahse and Heurich 2011). The 93

model aimed to capture the direct interactions between bark beetles and other landscape level 94

disturbances (specifically drought and windthrow), as well as the indirect interactions that are 95

transmitted through shifts in forest dynamics. 96

The broad objective was to increase the knowledge of disturbance interactions at the landscape 97

scale by testing how climate change drives the dynamics of disturbance interactions in land-98

scapes that differ in environmental conditions and management histories. We first present a nov-99

el approach to integrate bark beetle disturbance as a component (module) in the forest succession 100

model LandClim. Second, we corroborated the new bark beetle model by comparing model out-101

put with forest and disturbance data at the smaller spatial and temporal scales where data are 102

available (cf. Fig. 1a-c). Third, we applied the model in three climatically different case study 103

areas to address the following questions: What are the mechanisms that cause direct and indirect 104

interactions among bark beetle disturbance, windthrow, climate change and the forest state? At 105

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which spatial and temporal scale do these feedback loops among disturbances become manifest? 106

Will climate change shift the relative importance of direct vs. indirect interactions among bark 107

beetle, wind and drought disturbance? 108

Methods 109

Integration of a bark beetle disturbance module in LandClim 110

LandClim 111

The process-based stochastic landscape model LandClim incorporates both fine grain forest pro-112

cesses and larger extent disturbances to simulate a dynamic forest landscape. Forest dynamics 113

within the model are driven by temperature, drought (a function of temperature, precipitation and 114

soil water holding capacity), fire and wind disturbance as well as forest management (Schu-115

macher et al. 2004, Schumacher and Bugmann 2006). The model has been applied in a variety of 116

environments, where simulated forest states and processes were consistent with empirical data 117

(Schumacher et al. 2006, Colombaroli et al. 2010, Henne et al. 2011, Elkin et al. 2012). Land-118

scapes are simulated on a grid of 25 × 25 m. In each cell a simplified gap model (Bugmann 119

2001) represents forest dynamics by simulating several cohorts of trees of the same species and 120

age. LandClim thus captures forest responses to large-scale disturbance events while still simu-121

lating fine-grain processes, and allows forest dynamics to be simulated over both the short (year-122

ly/decadal) and the long (hundreds of years) term. 123

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Bark beetle module 124

Overview 125

Our simulation of bark beetle dynamics integrates forest susceptibility to bark beetle infestation 126

(a proxy for the quality of bark beetle habitat) and bark beetle pressure, a framework common to 127

previous studies that have simulated bark beetle disturbances at the stand scale (Shore and 128

Safranyik 1992, Seidl et al. 2007, Overbeck and Schmidt 2012). 129

We employed this approach at the landscape scale to first determine the spatial distribution of 130

bark beetle infestation and second to determine bark beetle-induced tree mortality, with dynamic 131

linkages to the underlying landscape forest dynamics model. The distribution of beetle infesta-132

tions is resolved at the spatial grain of the model (25 × 25 m cell), with forest susceptibility and 133

tree mortality evaluated at the tree cohort scale within each simulation cell. 134

In accordance with the wind and fire disturbance modules in LandClim, we determined bark bee-135

tle disturbance on a decadal time step. The forest susceptibility assessment was thereby conduct-136

ed every decade, whereas the bark beetle pressure assessment was based on yearly bioclimatic 137

data. In the following sections we describe the steps involved to calculate forest susceptibility, 138

bark beetle pressure, infestation distribution and beetle-induced mortality (labeled square boxes 139

in Fig. 2). 140

Assessment of tree and cell susceptibility to beetle infestation 141

At the cell scale the susceptibility of live trees to beetle attack was evaluated using three factors 142

commonly used in stand susceptibility assessments (Lorio et al. 1982, Shore and Safranyik 1992, 143

Netherer and Nopp-Mayr 2005, Overbeck and Schmidt 2012): 1) the stress status of Norway 144

spruce, reflecting resistance to bark beetles (Lieutier 2004, McDowell et al. 2011). As a stress 145

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proxy we used the drought status of Norway spruce, a key factor influencing tree growth and 146

mortality (Schumacher et al. 2004); 2) the dominant age of Norway spruce, which accounts for 147

the observed higher bark beetle damages in old stands (Bakke 1983, Becker and Schröter 2000); 148

and 3) the relative basal area of Norway spruce because Norway spruce resistance to bark beetles 149

was found to be reduced in highly spruce-dominated forests, where low understory light availa-150

bility may lead to the formation of short crowns and reduced primary resin flow, the first defense 151

mechanism against bark beetles trying to enter the bark (Christiansen et al. 1987, Baier et al. 152

2002). To translate these factors to drought-, spruce age- and spruce share-induced susceptibility 153

(drScell, ageScell, and sprScell), respectively, we adopted the sigmoidal relationships proposed by 154

Netherer and Nopp-Mayr (2005) and Seidl et al. (2007). These susceptibility indices are all 155

scaled between 0 and 1, with 0 indicating no and 1 maximum susceptibility, respectively. The 156

monotonically increasing sigmoidal relationship between drought and drought-induced suscepti-157

bility that we adopted from Seidl et al. (2007) does not account for a potential increase in the 158

resistance of spruce to bark beetle attacks under low to moderate drought conditions that a num-159

ber of authors have hypothesized (e.g., Lieutier 2004, Salle et al. 2008, Jactel et al. 2012). We 160

thus tested the sensitivity of our model to alternative functions for drS that account for this hy-161

pothesis. We found simulated bark beetle disturbance to be robust to these alternatives and thus 162

for the rest of our analysis retained Seidl et al.’s (2007) relationship (see Appendix A for details). 163

The second criterion for the susceptibility of live trees in a cell was the presence of windthrown 164

Norway spruce trees within the focal cell, or in adjacent cells. This criterion has often been ne-165

glected in previous work (e.g., Seidl et al. 2007). In newly (<2 years) windthrown spruce trees 166

the defense system is curtailed such that they constitute ideal bark beetle breeding habitat 167

(Lindelöw and Schroeder 2008, Komonen et al. 2011). The probability of trees in the vicinity of 168

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windthrown trees being attacked is increased due to two processes: First, beetles colonizing 169

windthrown trees release aggregation pheromones that attract other beetles to the tree and some 170

of them may colonize nearby susceptible trees (Byers 2004). Second, the generation of beetles 171

that emerges from the windthrown trees has a higher probability of killing nearby trees. This 172

increased beetle infestation probability around windthrown trees has been documented up to 500 173

m away from windthrown Norway spruce trees by Wichmann and Ravn (2001) and is probably a 174

result of a higher probability of colonization of living trees in locations with higher beetle densi-175

ties. A large amount of windthrown Norway spruce biomass at a specific location can thus define 176

the windthrow-induced susceptibility of a larger surrounding area. We captured this effect over a 177

distance Dwind as follows: First, we determined the windthrow-induced susceptibility of all cells 178

that are within Dwind around a focal cell i. We then converted the windthrown spruce biomass 179

(windB) in a cell to an index that is scaled between 0 and 1 and describes windthrow-induced 180

susceptibility (windScell) using a linear relationship (windScell windB; see Appendix A). Se-181

cond, we determined the maximum windthrow-induced susceptibility within Dwind around cell i 182

and assign it to cell i as follows: 183

windNScell,i = max({windScell,k : windScell,k d(i,k) < Dwind}) (1) 184

where windNScell,i is the susceptibility in focal cell i that is induced by the windthrown spruce 185

biomass in cell i plus the neighboring cells k located within the distance d(i,k) < Dwind around cell 186

i. We parameterized Dwind using the empirical outbreak data from the Bavarian forest as de-187

scribed below (section “Model parameterization”). 188

We additively combined the susceptibility induced by drought, spruce age, spruce share and 189

wind to create a cell-specific susceptibility index Scell. We assigned equal weights to the influ-190

ence of the live tree component and the windthrow component: 191

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Scell = (drS + ageS + sprS)/6 + windNScell/2 (2) 192

Assessment of bark beetle pressure 193

Bark beetle pressure (btlP) was calculated as the potential number of bark beetles that could in-194

fest a cell, hence the virulence of the bark beetle population in a cell. We assessed btlP based on 195

three factors: first, bark beetle activity of the previous time-step, as bark beetle infestations are 196

more frequent at places that have been infested in the previous year (Wichmann and Ravn 2001). 197

This was represented by the variable Dt-1, which we linearly related to beetle-killed biomass in 198

decade t-1, with Dt-1 = 0 representing no bark beetle damage and Dt-1 = 1 the maximum possible 199

beetle-killed spruce in t-1, i.e. 300 t/ha, which is the maximum possible biomass per cell in 200

LandClim (maxB, Schumacher et al. 2004). 201

The second factor reflects bark beetle phenology in response to temperature (Jönsson et al. 202

2009). We captured this relationship with the index G that is based on the bark beetle phenology 203

assessment tool PHENIPS (Baier et al. 2007). PHENIPS combines a bark beetle developmental 204

model that relates yearly temperature sums to the number of beetle generations realized per year 205

(Wermelinger and Seifert 1998) with a hazard-rating system based on the number of beetle gen-206

erations (Netherer and Nopp-Mayr 2005). Thereby, beetle hazard is related to the number of bee-207

tle generations with a sigmoidal relationship that accounts for a beetle density-related negative 208

feedback in beetle population growth (Appendix A: Eq. A10). G is scaled between 0 and 1 and is 209

a proxy for the potential number of bark beetles that are able to develop per season contingent on 210

the temperature sum. 211

The third factor is the availability of breeding habitat within the landscape (Slsc). Because the 212

model operates on a decadal time step, Slsc accounts for the intra-decadal expansion potential of 213

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the bark beetle population. To obtain Slsc we averaged Scell across the landscape. It needs to be 214

highlighted here that Scell and Slsc not only differ in the spatial scale at which they are assessed, 215

but also in how they are used. Whereas Scell determines where on the landscape infestations occur 216

(section “Assessment of tree and cell susceptibility to beetle infestation”), Slsc constrains the size 217

of the bark beetle population. 218

We combined Dt-1 and G additively, as these two factors contribute to beetle pressure inde-219

pendently. In contrast Slsc was incorporated multiplicatively based on the rationale that if there is 220

no breeding habitat there can be no intrinsic beetle pressure. Additionally, we included a scaling 221

coefficient (bpC) such that the combined effect of these three indices on the overall bark beetle 222

model behavior can be controlled. We used empirical infestation data from the Bavarian Forest 223

to parameterize bpC (section “Model parameterization”). 224

Finally, we accounted for the influence of the bark beetle populations outside the simulated land-225

scape by assuming that even under poor conditions for bark beetle development a low number of 226

bark beetles enters the simulation landscape and maintains a small but persistent population. We 227

thus set a lower bound of 0.01 for btlP. 228

btlP = max(1/2 · (Dt-1 + G) · Slsc · bpC, 0.01) (3) 229

Distribution of bark beetle infestations 230

We simulated the spatial distribution of bark beetle damage as determined by the two distinct 231

processes of beetle dispersal and beetle infestation (Esker 2007). We modeled infestation by first 232

determining the potential spatial extent of the infestation, and secondly by determining the actual 233

locations (cells) being infested. Thereby, both these sub-characteristics of infestation are con-234

trolled by cell-specific susceptibilities (Scell) and by the beetle pressure in the cell (btlP). 235

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Bark beetle dispersal is commonly modeled using diffusion kernels (e.g., Helland et al. 1989, 236

Kautz et al. 2011). However, because active flight distance of bark beetles is up to 500 m 237

(Wermelinger 2004), and wind aided dispersal can greatly exceed that, we assumed that bark 238

beetles have the potential to, within one decade, disperse across distances that cover the land-239

scape extent commonly simulated with LandClim (10-100 km2). We therefore assigned the cumu-240

lative bark beetle pressure at the landscape scale (mean btlP of all cells in the landscape) to each 241

cell. 242

Risk of bark beetle infestation: As a basis for the representation of infestation extent and location, 243

we createed an infestation risk surface by multiplying Scell and btlP in each cell to obtain a cell-244

specific infestation risk index (infRcell; Eq. 4). Therefore, a cell has a risk of infestation only 245

when the live tree community is susceptible and beetle pressure for the cell is greater than zero. 246

A maximal infRcell of 1 is given when both Scell and btlP are maximal, i.e. 1. 247

infRcell = Scell · btlP (4) 248

Extent of bark beetle infestations: In this step we determined the proportion of the landscape that 249

is infested by bark beetles. We did not calculate a proportion relative to landscape area, but a 250

proportion relative to the potential maximum availability of bark beetle habitat. Since bark bee-251

tles infest host trees rather than a location, we captured the size of the bark beetle habitat by us-252

ing Norway spruce biomass as the accounting unit. We calculated infested Norway spruce bio-253

mass (infB) by multiplying the potential maximum infested Norway spruce biomass (infBmax) 254

with the landscape mean of infRcell (infRlsc, Eq. 4). As a reference for infBmax we used a value that 255

equals 150 t/ha multiplied by the size of the simulated landscape in hectares (ha). This value is 256

the upper bound of infestation size and would be realized if a) the life tree community was max-257

imally susceptible (Scell = 0.5 in all cells, meaning that the life tree community is old, pure spruce 258

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and highly drought-stressed, whereby the effect of windthrown spruce trees is not accounted for) 259

and b) the live tree community reaches a maximum biomass of 300 t/ha or greater (maxB, cf. 260

Schumacher et al. 2004) and c) bark beetle pressure (Pr) was maximal (Eq. 5). 261

infB = infBmax · (infRlsc + rbeta) (5) 262

rbeta in Eq. 5 is a random term that accounts for processes our model does not capture with the 263

implemented relationships. rbeta is derived from a beta distribution, which is commonly used to 264

model stochastic processes (Moore and Conroy 2006) and was parameterized so that the standard 265

deviation of the term infRlsc + rbeta ranges between 0.28 and 0.32% of the range of 0 to1 that 266

infRlsc is bound to (see Appendix A for details). 267

Location of bark beetle infestations: We simulated the locations of beetle infestations on the 268

landscape based on the infRcell surface. Bark beetle infestations are added to the landscape by 269

ranking cells in decreasing order according to infRcell and continuing to add infestations to cells 270

until the sum of spruce biomass in the infested cells (sprB) equals infB. Thus, the calculated in-271

fested Norway spruce biomass (infB) defines the spatial extent of the infestation. 272

This approach of allocating beetle infestation to individual cells results in realistic infestation 273

patterns that reflect the aggregating behavior of the beetles (Fahse and Heurich 2011, Kautz et al. 274

2011). In situations where Norway spruce trees are under low drought stress these patterns are 275

strongly determined by the influence of windthrown spruce on cell susceptibility (Eq. 1), as well 276

as by forest composition and age. In contrast, under high drought conditions, drought-induced 277

susceptibility is increasingly important in determining the location of beetle infestations, and 278

functions to dampen the degree to which beetle aggregations are driven by windthrown spruce 279

(see infestation maps in Appendix B: Fig. B5). 280

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Bark beetle-induced tree mortality 281

Within each cell we assessed the susceptibility of each Norway spruce cohort (Scohort) to deter-282

mine bark beetle-induced mortality. We determined Scohort based on three factors: The first two, 283

the cohort’s drought-stress stratus and its age, account for the cohort’s ability to resist bark beetle 284

attacks. We calculated drought- and age-induced cohort susceptibility, drtScohort and ageScohort, 285

respectively, analogously to cell susceptibility using Netherer & Nopp-Mayr’s (2005) sigmoidal 286

relationships and combined them additively (Eq. 6). The third factor, windNScell, accounts for 287

increased susceptibility if the cohort is located nearby windthrown spruce trees. We used the 288

maximum function to combine windNScell with drScohort and ageScohort, in order to avoid penaliz-289

ing Scohort in the frequent cases where windNScell = 0, due to the localized nature of wind disturb-290

ance. 291

Scohort = max((drScohort + ageScohort)/2, windNScell) (6) 292

The mortality probability (mPbeetle) of a single tree following bark beetle infestation was calculat-293

ed for each tree cohort from the cohort susceptibility index (Scohort) and the bark beetle pressure 294

index btlP (Eq. 7). 295

mPbeetle = (Scohort + Pr)/2 (7) 296

Model parameterization 297

We used measurements of infested area from the recent outbreak in the Bavarian Forest, Germa-298

ny (Kautz et al. 2011), as an empirical basis to estimate the influence of wind disturbance and 299

temperature on bark beetle disturbance at the landscape scale. Using the available empirical data 300

it was impossible to independently parameterize Dwind and bpC; the two parameters that deter-301

mine the importance of the wind and temperature influence on beetle disturbance. We therefore 302

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tested 100 parameter combinations using 10 estimates for Dwind (50, 100, … , 500 m) and 10 es-303

timates for bpC (2, 4, … , 20), respectively. 304

To approximate conditions in the Bavarian Forest, we simulated a 1000-cell landscape (62.5 ha) 305

at an elevation of 900 m above sea level (a.s.l.) (Heurich et al. 2001) with mesic to dry soil con-306

ditions (Lausch et al. 2011). Monthly climate data from the Black Forest that covered the period 307

1950 to 2000 was corrected for elevation and used. Annual averages of these elevation-corrected 308

data (temperature: 6.7° C and precipitation sum: 1120 mm) corresponded well to published cli-309

mate data of the Bavarian Forest (Heurich et al. 2001). We used the LandClim management 310

module (Temperli et al. 2012) to generate a forest initialization dataset that represents the >100 311

year old “highly homogenous spruce stands” that originated from afforestations following bark 312

beetle gradations in the 1860s and 1870s (Kautz et al. 2011). Starting from this initialization da-313

taset we ran 100 replicate simulations for each Dwind-pbC parameter combination. The replicates 314

were run with unique climate time series that were constructed by randomly sampling years from 315

the 1950-2000 dataset. 316

We compared the yearly infestation rates (medians of 100 replicates) resulting from the Dwind-317

bpC parameter combinations to yearly infestation rates measured in the Bavarian forest. We 318

found an isocline in the Dwind-bpC parameter space for which simulated infestation rates matched 319

the measured data (Fig. 3). From this isocline we selected the Dwind-bpC parameter combination 320

of 200 m and 14, respectively, so as to avoid unrealistically high wind-induced susceptibilities 321

(windNScell) and overly high bark beetle pressures (btlP). 322

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Model corroboration using available process knowledge 323

We corroborated the bark beetle model by analyzing simulation results at the three spatio-324

temporal scales, for which interactions among climate, wind disturbance, forests state and bark 325

beetles are known (Fig. 1a-c). At each of these scales we evaluated whether the model captures 326

the expected interactions. For this model corroboration we used the test landscape described 327

above and climate data from our case study in the Black Forest region, Germany (Table 1). We, 328

however, varied soil conditions and management regimes between the different corroboration 329

tests as described below. 330

Short-term, patch scale 331

At this scale (cf. Fig. 1a) we first tested how the four susceptibility factors drought, spruce age, 332

spruce share and windthrown spruce biomass affect the susceptibility of the tree community, and 333

whether this corresponds to empirical relationships (Netherer and Nopp-Mayr 2005, Appendix 334

A). Second, we tested whether the model simulates increased local infestation probabilities in 335

response to increasing forest patch susceptibility. This allowed us to corroborate whether the 336

model captures observed higher infestation probabilities during large compared to small out-337

breaks (epidemic vs. endemic infestations; cf. Raffa et al. 2008). This second test is particularly 338

important since the relationships between patch susceptibility, outbreak size and infestation 339

probability are emergent properties of the model. In order to cover a wide range of drought states 340

across the test landscape, we used a soil map that featured soil water storage capacity of 5 to 20 341

cm randomly distributed over the landscape. To create heterogeneity in the Norway spruce share 342

and tree age, we implemented uneven-aged management regimes that promoted Norway spruce 343

to various degrees (thinning of 10, 20, ... , 100% of species other than Norway spruce, respec-344

tively), and were randomly assigned to individual cells in the landscape (cf. Temperli et al. 2012, 345

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in press). We analyzed the results once the simulations reached a pseudo-equilibrium, i.e. after 346

250 decades. 347

Short-term, landscape scale 348

At this scale (cf. Fig. 1b) we first evaluated whether simulated impacts of windthrow, tempera-349

ture and drought on bark beetle disturbance are evident at all. This test was necessary because 350

these driving factors act on forest patches (Netherer and Nopp-Mayr 2005, Baier et al. 2007) but 351

are expected to also have impacts at the landscape scale (Marini et al. 2012, in press). We used 352

Pearson’s correlation coefficient to evaluate decadal changes in bark beetle-killed biomass 353

against decadal changes in the four driving factors (drought index, windthrown Norway spruce 354

biomass, relative degree-day sum and infRlsc, respectively, cf. Appendix A). For this test we ap-355

plied an uneven-aged management regime (patch-scale harvest of trees >60 cm diameter at 356

breast height, dbh) to simulate a spruce-dominated forest (spruce biomass share of 43%). Soil 357

water holding capacity was set to the median value of the Black Forest case study landscape (12 358

cm), from which we again used the climate data (Table 1). 359

As a second test, we compared our bark beetle model outputs with infestation data from the Ba-360

varian Forest (Kautz et al. 2011). This comparison allowed us to evaluate whether the simulated 361

extent of beetle infestations corresponds to empirical data. To account for uncertainty regarding 362

the factors which triggered the recent outbreak in the Bavarian forest (Lausch et al. 2011, Svo-363

boda et al. 2012), we simulated two wind scenarios: a baseline wind disturbance regime derived 364

from historical storm data (Schumacher et al. 2004) and a wind scenario that produces 2.1 times 365

more windthrown timber on average. Finally, we compared the area that was infested in the Ba-366

varian forest during the 23-year observation period with the percentage of the landscape area that 367

was cumulatively infested over 2 and 3 simulated decades. 368

Temperli et al.

18

Long-term, patch scale 369

To corroborate long-term model behavior at the patch scale we assessed the development of for-370

est susceptibility through time in response to windthrow and drought. We expected that follow-371

ing these disturbances patch susceptibility to bark beetle disturbance would be temporarily re-372

duced due to changes in stand structure (Kulakowski et al. 2012, in press; Fig 1c). We evaluated 373

the wind influence by focusing on how forest susceptibility to beetle attack varies in cells that 374

have been damaged by windthrow. To examine the long-term influence of drought we used data 375

from a simulation run under the HCCPR climate change scenario (Table 1). 376

Long-term, landscape-scale model application 377

To examine the long-term, large-scale interactions among beetle disturbance, climate change, 378

wind disturbance and forest succession (Fig. 1d) we employed three case study landscapes. Two 379

of them are situated within a 2 × 10 km2 strip along an elevation gradient from 250 to 1000 m 380

a.s.l. in the Northern Black Forest region, Germany (cf. Temperli et al. 2012), and represent the 381

warm-dry end of the Norway spruce distribution (cf. Table 1). In this region, historic promotion 382

of Norway spruce for timber production has resulted in uneven-aged mixed Norway spruce 383

stands at lower (<500 m a.s.l.) elevations and almost entirely Norway spruce-dominated even-384

aged stands at higher elevations (map of forest stands provided by Forstliche Versuchsanstalt 385

Baden-Württemberg). Due to this distinction in management and ecological zonation we treated 386

the lower and the upper areas as two separate case studies, with the lower region representing 387

warm, dry conditions and the upper region representing mild, mesic conditions. 388

The third case study is the 1720 ha subalpine Dischma catchment near Davos, Switzerland, lo-389

cated between 1540 and 2810 m a.s.l. and represents the cold-wet (Table 1) end of the Norway 390

spruce distribution. In the Dischma valley Norway spruce naturally dominate the species compo-391

Temperli et al.

19

sition, with European Larch (Larix decidua Mill.), Swiss stone pine (Pinus cembra L.) and green 392

alder (Alnus viridis [Chaix] DC.) increasing in abundance near treeline. 393

For all three case studies we ran simulations under three climate and three windthrow scenarios. 394

We assumed a continuation of the current management regimes in each of the regions: uneven-395

aged mixed forest (patch-scale harvest of trees >48 cm dbh, natural regeneration) in the low-396

elevation Black Forest area, even-aged Norway spruce (clearcut at a dominant dbh of >45 cm, 397

replanting of spruce) in the high elevation Black Forest area, and management for protection 398

from rockfall and avalanches (promotion of natural regeneration through gap cuts) in Davos. In 399

order to provide a baseline comparison, we also simulated forest dynamics without bark beetle 400

disturbance under current climate and under climate change. We initialized the scenarios with 401

simulation data generated by simulating the past management under current climate until a pseu-402

do-equilibrium state was reached. 403

The range of climate scenarios that we used encompasses the current range of climate change 404

projections (IPCC 2007). Besides the baseline we used a weak (SMHI, Table 1) and a strong 405

climate change scenario (HCCPR). We started these simulations with climate data from the year 406

2000, and used the scenario data to simulate to 2100. We extended the 2100 climate scenarios 407

based on the zero-order approximation that climate would remain constant at the 2100 level, and 408

constructed a 100-year time series by randomly sampling climate years from the 2080-2100 pe-409

riod. 410

To account for projected increases in windstorm frequencies (Usbeck et al. 2010, Scaife et al. 411

2011) we simulated a baseline wind regime (Schumacher et al. 2004) and two wind scenarios 412

that resulted in a 1.5- and 2-fold increase in windthrown biomass, respectively. We replicated 413

each simulation 15 times. 414

Temperli et al.

20

Our simulation experiments allowed us to isolate how Norway spruce biomass was affected by 415

climate change (i.e. the combined effect of changes in temperature and drought), bark beetle dis-416

turbance and the interaction between climate change and bark beetles. We determined the cli-417

mate change (δc) and the bark beetle (δb) effect as the difference between the Norway spruce 418

biomass in the reference simulations (no-bark beetle, no-climate change; R), and the Norway 419

spruce biomass in the simulations, where only climate change (C) and only bark beetle disturb-420

ance (B) were included (Eq. 8a-b). We calculated the climate change-bark beetle interaction ef-421

fect (θ) by subtracting both the bark beetle effect (δb) and the climate change effect (δc) from the 422

combined bark beetle-climate change effect, i.e. the difference between the Norway spruce bio-423

mass under both climate change and bark beetle disturbance (CB) and the reference simulations 424

(R, Eq. 8c): 425

δc = R-C (8a) 426

δb = R-B (8b) 427

θ = R-CB-δb-δc (8c) 428

Results 429

We first focus on the model corroboration that tested whether simulation results match observed 430

empirical bark beetle infestation patterns at the patch and the decadal time scale (Fig. 1a), the 431

distribution of beetle infestations at the landscape scale (Fig. 1b), and the temporal dynamics that 432

are expected at the patch scale in the longer term (Fig. 1c). Second, we report results from the 433

three case study landscapes where we tested how bark beetle disturbance interacts with climate 434

change and windthrow at the landscape scale, and how these interactions develop through time. 435

Temperli et al.

21

Model corroboration 436

Short term, patch scale 437

The simulated impact of drought, Norway spruce age and share on bark beetle susceptibility 438

matched the sigmoidal relationships proposed by Netherer and Nopp-Mayr (2005; Fig. 4a-c). 439

Windthrow-induced susceptibility (windScell) increased with windthrown Norway spruce biomass 440

as expected based on the underlying linear relationship (Fig. 4d). Note that the considerable vari-441

ance in the responses to the individual factors was due to the mutually additive effect of the other 442

factors. 443

The combination of individual susceptibility factors resulted in an increase in the simulated in-444

festation probability of an individual cell (Fig. 4e). Furthermore, in cells with equal live tree sus-445

ceptibility, our simulations exhibited the expected higher infestation probabilities under epidemic 446

than under endemic conditions (Fig. 4e). Differences in endemic and epidemic infestation proba-447

bilities were not deterministically related to susceptibility, but rather were an emergent property 448

of ranking cells by infestation risk (infRcell) and determining the infestation extent by the availa-449

ble Norway spruce trees that are suitable for breeding. Increased infestation probabilities oc-450

curred in decades that feature large windthrow events, which matches the observed outbreak-451

triggering effect of windthrows (Schroeder and Lindelöw 2002). 452

Short term, landscape scale 453

The simulations also captured the expected short-term (decadal) impacts of windthrow, drought 454

and temperature on bark beetle disturbance at the landscape scale (Fig. 5). These landscape-scale 455

interactions emerged from processes implemented at the patch scale, such as the impact of 456

drought and windthrow on tree and patch susceptibility (Eq. 2), and the temperature-driven de-457

velopment of bark beetle populations (Eq. 3, PHENIPS in Appendix A). At the landscape scale 458

Temperli et al.

22

windthrow had a comparatively weak influence on bark beetle damage, because it affected only a 459

small fraction of the landscape (Fig. 5a), whereas changes in temperature and drought affected 460

trees throughout the landscape (Fig. 5b, c). The drought index employed here combines the ef-461

fects of temperature and precipitation (Schumacher et al. 2004); changes in drought thus incorpo-462

rated changes in temperature and hence correlated better with changes in bark beetle damage 463

than temperature alone (cf. Fig. 5e). 464

The influences of windthrow, drought and temperature combined to determine infestation risk 465

(Eq. 4, Fig. 5d). The remaining variation in bark beetle damage resulted from stochastic process-466

es built into LandClim (e.g., Eq. 5). The short-term sensitivity of bark beetle disturbance to 467

windthrow, temperature and drought is further evident when the temporal development of these 468

driving factors and bark beetle damage are compared (Fig. 5e): peaks of windthrow, temperature 469

or drought resulted in clear peaks of bark beetle damage. 470

Comparing simulation results with measured data from the Bavarian Forest National Park, from 471

which Dwind and pbC were estimated, corroborated another aspect of short-term, landscape-scale 472

model behavior: the infestation extent during an outbreak situation (Fig. 6). Simulated cumula-473

tive infested area approximated the infested area observed after 23 years of bark beetle outbreak, 474

depending on whether two or three simulated decades and whether the baseline or a stronger 475

windthrow regime was considered. Given the uncertainty with respect to the triggering factors 476

that caused the outbreak in the Bavarian Forest (Lausch et al. 2011, Svoboda et al. 2012), the 477

range of simulated infestation area corresponded well with measurements. 478

Long-term, patch scale 479

The analysis of long-term model behavior revealed the expected negative impact of windthrow 480

and drought on bark beetle susceptibility at the patch scale (Fig. 1b). However, the impacts of 481

Temperli et al.

23

wind and drought differed in the rate and duration over which negative interactions with beetle 482

susceptibility were observed. Windthrow is a sudden event that increased forest susceptibility in 483

the decade it occurs, but turned the forest patch into a less, or even non-susceptible state, in the 484

following decades (Fig. 7a). Climate change-induced drought, in contrast, increased susceptibil-485

ity slowly over a longer period. Only when it exceeded Norway spruce’s drought-tolerance (in 486

our simulations, in the year 2060; Fig. 7b) did it affect forest susceptibility negatively by reduc-487

ing the share of Norway spruce. 488

Long-term, landscape scale bark beetle dynamics and interactions with disturbances 489

Bark beetle disturbance dynamics 490

Bark beetle disturbance differed strongly between case studies and climate scenarios. In the low-491

elevation Black Forest area, which represents the hot-dry distribution limit of Norway spruce, the 492

impact of bark beetles on standing Norway spruce biomass was particularly high. Under baseline 493

simulations (no bark beetles, current climate), Norway spruce biomass amounted to an average 494

of 65 t/ha. When bark beetle disturbance was included in the simulations, Norway spruce bio-495

mass was 8 t/ha (-88%) with bark beetles killing 0.14 t/ha (range: 0.05-0.21) of Norway spruce 496

biomass per decade. Under climate change, both standing and bark beetle-killed Norway spruce 497

biomass decreased, approaching zero at the end of the 21st century (Fig. 8, Appendix B: Fig. B1). 498

In the high-elevation Black Forest area, bark beetle disturbance under current climate (decadal 499

average: 0.54 t/ha; range: 0.33-0.83 t/ha killed Norway spruce biomass) resulted in on average 500

18% lower Norway spruce biomass compared to the simulations without bark beetle disturbance 501

(103 and 126 t/ha, respectively; Fig. 8, Appendix B: Fig. B2). If climate change was additionally 502

considered, Norway spruce biomass decreased to values less than 2 t/ha by 2090. Beetle-killed 503

Norway spruce biomass strongly increased until it peaked in the middle of the 21st century (1.51 504

Temperli et al.

24

t/ha of beetle-killed Norway spruce in 2040 under the strong climate change and the baseline 505

wind scenario); thereafter it decreased to values below 0.03 t/ha by 2120. 506

In the subalpine case study Davos, the impact of bark beetles on Norway spruce biomass under 507

current climate was low. In the presence of bark beetles, spruce biomass was merely 4% lower 508

than with bark beetles (79 t/ha vs. 82 t/ha), and decadal beetle-killed Norway spruce biomass 509

was below 0.02 t/ha. However, under strong climate change, bark beetle damages increased to-510

wards the end of the 21st century up to a decadal maximum of 0.44 t/ha killed Norway spruce 511

biomass in 2150 (Fig. 8). Compared to simulations that included climate change but not beetle 512

disturbance, this increase in bark beetle activity resulted in a reduction of Norway spruce bio-513

mass by 25% in 2200 (Fig. 8). 514

Interactions between climate change and bark beetle disturbances 515

Our results suggest that the combined effects of climate change and bark beetle disturbance on 516

Norway spruce biomass are not simply additive and that climate change can either amplify or 517

dampen bark beetle disturbance depending on environmental conditions and projected climate 518

change. 519

In the low-elevation Black Forest area, climate change weakened bark beetle disturbance (Fig. 520

9). Within this region, climate change-induced drought was projected to cause a large fraction of 521

the Norway spruce trees to die back by 2060. Under these conditions, additional bark beetle dis-522

turbance did not result in an additional reduction in Norway spruce biomass simply because there 523

was very little Norway spruce biomass left. 524

In contrast, in the high-elevation Black Forest area climate change interacted positively with bark 525

beetle disturbance (Fig. 9). This effect peaked in 2090. Under climate change, but in the absence 526

Temperli et al.

25

of bark beetles, Norway spruce biomass was projected to be reduced by 37%, while a reduction 527

solely due to bark beetles would amount to 17%. When climate change and bark beetles acted 528

together, however, they removed 99% of spruce biomass by 2090, such that 45% of the loss of 529

spruce biomass was attributed to the beetle-amplifying effect of climate change. After 2090, the 530

climate change-bark beetle interaction effect decreased because most Norway spruce had already 531

been killed. 532

In simulations that included climate change and bark beetle disturbance for Davos, Norway 533

spruce biomass was lower than in simulations that included climate change but no bark beetle 534

disturbance (Fig. 8, 9). Given that the bark beetle impact on Norway spruce biomass under cur-535

rent climate was minute (Fig. 9), this difference in Norway spruce biomass was primarily due to 536

climate change-bark beetle interactions. Hence, in this area climate change was a prerequisite for 537

bark beetle disturbances to become prominent. While the interaction between climate change and 538

bark beetles was projected to be positive throughout the simulation period (Fig. 9), projected 539

shifts in species composition, driven by both climate change and bark beetles, were simulated to 540

decrease forest susceptibility to bark beetles in the long term (Fig. 8, Appendix B: Fig. B4). 541

Interactions with wind disturbance 542

Under current climate, a doubling of windthrown Norway spruce biomass at the high-elevation 543

Black Forest area was projected to increase bark beetle damage by 38% (Fig. 8). However, in the 544

low-elevation Black Forest area and in Davos, a more severe windstorm regime had virtually no 545

effect. At low-elevations in the Black Forest area drought and bark beetle were projected to in-546

dependently reduce Norway spruce abundance, while in Davos low temperatures, and vigorous 547

trees, constrain bark beetle activity even under increased windthrow conditions. 548

Temperli et al.

26

When climate change was included in the simulations of the high-elevation Black Forest area, 549

beetle damage under the medium and high wind scenarios was projected to increase sharply be-550

tween 2000 and 2040. Subsequently, however, beetle damages developed similarly irrespective 551

of the wind scenarios (Fig. 8). In Davos, bark beetle damages increased to higher amounts under 552

climate change when heavier wind disturbance regimes were assumed: a doubling of windthrown 553

Norway spruce biomass resulted in 46% more bark beetle damage between 2100 and 2200 under 554

the strong climate change scenario (HCCPR). 555

Discussion 556

Long-term interactions among bark beetles, climate change, other disturbances and forest dy-557

namics are critical for projecting how forests will change in the future (Seidl et al. 2007, Peltzer 558

et al. 2009, Jactel et al. 2012), and for developing robust adaptive management strategies. Previ-559

ous research has demonstrated that there are three principle pathways along which climate 560

change and other disturbances interact with beetles (Bentz et al. 2010). First, directly through 561

increasing temperatures that accelerate bark beetle development (Wermelinger and Seifert 1998). 562

Second, through a short-term increase of forest susceptibility (Netherer and Nopp-Mayr 2005, 563

Overbeck and Schmidt 2012). Third, indirectly through long-term interactions that modulate 564

forest structure and composition towards a less susceptible state, i.e. drought- and windthrow-565

induced host tree depletion (Allen et al. 2010). 566

We demonstrated that the long-term interplay of these interactions can result in either net posi-567

tive or negative feedbacks on future bark beetle disturbance, depending on the relative im-568

portance of the three pathways, and their environment-dependent sensitivity to climate change. 569

Importantly, our results demonstrate that this balance between positive and negative climate 570

Temperli et al.

27

change effects is (1) constrained by current climate and forest conditions and (2) controlled by 571

interactions with other disturbance agents and forest management. 572

Scale-dependence of direct and indirect disturbance interactions 573

Climate change and bark beetle disturbance 574

Increased temperature under climate change can accelerate beetle brood development 575

(Wermelinger and Seifert 1998, 1999) and consequentially increase the number of beetles on the 576

landscape (Netherer and Nopp-Mayr 2005, Baier et al. 2007). In our case studies, warmer tem-577

perature under the weak (SMHI) and strong (HCCPR) climate change scenarios resulted in an 578

increase of the number of beetle generations by one half and one, respectively, implying that 579

temperature under the HCCPR scenario would allow for the development of one complete gen-580

eration in Davos and up to three generations in the lower elevation Black forest area by the year 581

2100 (Appendix A: Table A1). This strong increase in the potential for bark beetle outbreaks 582

corroborates projections from other case studies and climate scenarios (Lange et al. 2006, 583

Jönsson et al. 2009, Bentz et al. 2010). 584

However, beetles can only propagate when a suitable thermal environment occurs jointly with 585

susceptible host trees (Chapman et al. 2012, Raffa et al. 2008, Bentz et al. 2010). In the short-586

term, drought can diminish the trees’ defense mechanisms and thereby produce positive feed-587

backs on bark beetle population development (McDowell et al. 2011, Jactel et al. 2012; Appen-588

dix B: Figs. B2-B4). In contrast, over a longer period and at the landscape scale, climate change 589

can indirectly and negatively impact beetle populations through drought (Aitken et al. 2008, Al-590

len et al. 2010) and beetle-induced (Økland and Berryman 2004) tree mortality that reduces 591

breeding habitat availability. These negative feedbacks were apparent in the two Black Forest 592

Temperli et al.

28

areas, where Norway spruce was projected to decline as a result of climate change (Fig. 8, cf. 593

also Temperli et al. 2012). 594

Our simulations demonstrate how the importance of the direct and indirect climate change-beetle 595

interactions may shift with the environment. If drought increases the susceptibility of Norway 596

spruce to beetles, but remains below the trees’ drought mortality threshold, the net climate 597

change effect on bark beetle disturbance is synergistic (positive). If, in contrast, climate change-598

induced drought directly causes tree mortality, climate change negatively affects (i.e. attenuates) 599

bark beetle disturbance. Similar synergistic mechanisms have also observed in bark beetle-fire 600

interactions (Wallin et al. 2003, Powell et al. 2011, but cf. Elkin and Reid 2004). The opposite 601

mechanism, i.e. that an indirect disturbance interaction is buffering the system when mediated by 602

large and/or long-term changes in stand structure and species composition, has also been docu-603

mented for heavily fire-disturbed stands that were less affected by beetle and subsequent wind 604

disturbance (Kulakowski and Veblen 2002, Kulakowski et al. 2003). 605

Windthrow and bark beetle disturbance 606

Wind and bark beetle disturbances interact indirectly through changes in forest state (Schroeder 607

and Lindelöw 2002, Lindelöw and Schroeder 2008). Our simulations (Fig. 5) showed that within 608

a decade the landscape-scale beetle-killed Norway spruce biomass is approximately 5 times 609

higher than the windthrown spruce biomass. Accounting for the fact that in our simulations 610

stands were not cleared from wind-felled trees, this 5:1 ratio is in broad agreement with empiri-611

cal data in two German case studies (Hochharz and Bannwald Napf) where windthrown trees 612

were left uncleared. There, the ratio between beetle-killed and windthrown trees within 4 years 613

following the windstorm was 3:1 and 5.4:1, respectively (Schroeder and Lindelöw 2002). In our 614

simulations for the high-elevation Black Forest area under current climate and in Davos under 615

Temperli et al.

29

climate change (Fig. 8), the ratio was somewhat higher (<10:1) under the baseline wind regime. 616

This is likely due to the management regimes that we applied, which resulted in lower tree sizes 617

compared to the corroboration simulations (Fig. 5) and the two unmanaged German study sites 618

reviewed above. This in turn resulted in fewer wind-susceptible forest stands and less 619

windthrown Norway spruce biomass. 620

Our simulations further suggest that projected positive interactions between wind disturbances 621

and beetles may be compounded by climate change. Under scenarios of increased wind disturb-622

ance, we projected increases in bark beetle disturbance, but only when increased drought moder-623

ately increased Norway spruce susceptibility (Fig. 8). If, in contrast, increased drought resulted 624

in Norway spruce becoming highly susceptible (high-elevation Black Forest under climate 625

change after ca. 2040, Fig. 8), bark beetle dynamics were influenced less by windthrow. Thus, 626

beetle populations were projected to be first driven by climate change directly, and later (>2070) 627

indirectly via the limited host tree availability, such that the positive windthrow-beetle interac-628

tion was masked. 629

Under such conditions, increased temperature and drought in Norway spruce are sufficient to 630

exceed the threshold for an outbreak to occur (Raffa et al. 2008), so that the additional triggering 631

effect of windthrow plays a minor role. In contrast, under low to moderate drought conditions, 632

windthrow can be a key determinant of when and where beetle outbreaks occur (cf. Wermelinger 633

2004, Bouget and Duelli 2004, Komonen et al. 2011). The projected shift in the relative im-634

portance of drought compared to windthrow as a primary trigger of European spruce bark beetle 635

outbreaks thus highlights the need to dynamically evaluate the long-term, joint effects of small-636

scale processes. 637

Temperli et al.

30

Relative importance of bark beetle disturbance under current and future conditions 638

Increasing drought under climate change will shift the distribution range of bark beetles towards 639

higher elevations and latitudes (Jönsson et al. 2009, Bentz et al. 2010). In Davos, i.e. towards the 640

cold-wet edge of the Norway spruce range, increased temperatures and drought are projected to 641

allow beetles to colonize forests that have so far been unaffected. Our simulations showed that 642

within this climate range, bark beetles have the potential to become a disturbance agent that is 643

similar in importance to windthrow under moderate climate change (SMHI), and considerably 644

more important under a strong climate change scenario (HCCPR; Fig. 8). In the Black Forest, at 645

the warm-dry edge of the Norway spruce range, increased temperature and drought are projected 646

to increase the importance of beetle disturbance in the short term, while suitable spruce breeding 647

material for the beetles remains available. In the long-term, however, host depletion due to 648

drought- and beetle-induced Norway spruce mortality will restrict spruce bark beetles to regions 649

with more abundant spruce habitat, i.e. at the core of the Norway spruce range. Hence, the im-650

portance of beetle disturbance at warm and dry sites is projected to decrease in the long term as 651

the direct impact of drought on tree growth and mortality becomes a more important driver of 652

forest dynamics. 653

These differences in the importance of beetle disturbance between the three case study areas 654

partly reflect the influence of past and present management practices. For example, at higher 655

elevations in the Black Forest case study region, even-aged spruce monocultures have been pro-656

moted strongly, whereas at lower elevations uneven-aged mixed forests are more common (Figs. 657

8 & 9). While the historic promotion of spruce monocultures increases the potential for bark bee-658

tle damage today, and will maintain the availability of beetle breeding habitat into the future, the 659

continuation of planting and thinning in these stands also promotes tree vigor, which under cur-660

Temperli et al.

31

rent climatic conditions may compensate for beetle-induced losses of spruce. In contrast, at low 661

elevations and under climate change losses of spruce due to beetle disturbance cannot be com-662

pensated by the promotion of spruce and suitable breeding habitat for the beetles will not be 663

maintained by forest management. 664

Adaptive management for short- and long-term interactions among disturbances 665

Bark beetle management can involve suppression, prevention, or a combined strategy 666

(Wermelinger 2004). Suppression aims at keeping bark beetle populations at low levels, but usu-667

ally does not take into account forest health. Interventions include sanitation logging of infested 668

trees before adult beetles emerged, the removal of windthrown logs, and bark beetle trapping 669

(Drumont et al. 1992, Göthlin et al. 2000, Wermelinger 2004). If effective, these sanitation 670

treatments suppress the natural bark beetle disturbance regime, thus keeping forests in a state of 671

high susceptibility, such that sanitation and salvage logging efforts are needed continuously 672

(Foster and Orwig 2006). Such a strategy is appropriate when the costs of sanitation management 673

are outweighed by the ecosystem services provided by Norway spruce, e.g. timber and/or ava-674

lanche protection (Brang et al. 2008). However, our results suggest that in regions where forests 675

are expected to be exposed to increased drought and bark beetle pressure in the future, the cost of 676

maintaining spruce monocultures through sanitation treatments is likely to exceed its benefit. 677

Hence, forward-looking adaptive management needs to consider prevention-oriented measures 678

that aim at reducing forest susceptibility in the long term. This includes reducing Norway spruce 679

dominance, keeping stands in a young development phase and promoting both vertical (stand 680

structure) and horizontal, landscape-scale heterogeneity. These prevention measures may fore-681

stall drought- and beetle-induced Norway spruce dieback in a controlled and monetarily exploit-682

able way (Knoke et al. 2008), by emulating the attenuating interactions between climate change 683

Temperli et al.

32

and bark beetles. In addition, increasing forest heterogeneity may also increase the attractiveness 684

for a range of bark beetle parasitoids and predators that promote natural beetle control (cf. 685

Muzika and Liebhold 2000, Fayt et al. 2005). 686

We have shown that the development of bark beetle disturbance under climate change depends 687

on forest state, current environmental conditions and the climate scenario. Thus, the trade-off 688

between short-term suppression and long-term prevention measures needs to be evaluated for 689

every situation anew. Where synergies between multiple factors (e.g., susceptible forest state, 690

drought plus windthrow) are a prerequisite for beetle disturbance, such as in Davos under climate 691

change, suppression strategies may be cost-effective. In contrast, in situations similar to the 692

Black Forest case study where the relative importance of the triggers of beetle outbreaks may 693

shift due to climate change, beetle management needs to be adapted. Thereby it is crucial to con-694

sider the spatial scale at which the triggers for outbreaks act: the projected increase in tempera-695

ture and drought will affect forests at the regional to the continental scale so that the direct tem-696

perature effect and the indirect drought effect may suffice for bark beetles to become epidemic at 697

large scales. Conversely, other triggering factors such as windthrow and forest age structure that 698

we projected to become less important under future climate act on bark beetle populations at the 699

local scale (Raffa et al. 2008). Management strategies that target these local-scale triggers thus 700

may not be effective under a future climate. Management must therefore adopt a prevention 701

strategy that includes the reduction of beetle-susceptible Norway spruce so as to deprive beetles 702

of their substrate. Thereby, local site characteristics such as drought exposure, current and future 703

demand for multiple ecosystem services and landscape features such as the susceptibility of ad-704

jacent forests need to be taken into account. 705

Temperli et al.

33

Conclusions 706

While building on previous assessments of patch-scale bark beetle susceptibility (Netherer and 707

Nopp-Mayr 2005) and phenology (Baier et al. 2007), we integrated a bark beetle disturbance 708

model into the forest landscape simulator LandClim to test the emergent disturbance and forest 709

dynamics that arise due to interactions among bark beetles, climate change, wind disturbance and 710

forest management. The resulting forest landscape simulation tool bears the important advantage 711

that interactions among forest succession, climate and disturbances can be assessed from the 712

patch to the landscape scale and from short (decade) to long (several centuries) timeframes. This 713

explicit consideration of scale-dependent interactions in one consistent modeling framework is a 714

major step beyond previous work that incompletely integrated landscape scale disturbance inter-715

actions (Sturtevant et al. 2004, Crookston and Dixon 2005, Aukema et al. 2008, Jönsson et al. 716

2009, Seidl et al. 2009, Bentz et al. 2010, Jönsson et al. 2012) and/or focused on short 717

timeframes and the stand scale (Seidl et al. 2007, Fahse and Heurich 2011). 718

Using this new model we examined the short- and long-term as well as direct and indirect inter-719

actions among bark beetle disturbance, drought and windthrow in three case studies situated 720

along a climatic gradient under climate change. We first showed that throughout the Norway 721

spruce range climate change will increase bark beetle pressure due to elevated temperatures that 722

accelerate beetle brood development. Second, climate change will shift the relative importance of 723

synergistic short-term drought-beetle and windthrow-beetle interactions. At the warm-dry end of 724

the Norway spruce range the increase in drought-induced Norway spruce susceptibility reaches a 725

level where drought alone suffices for triggering beetle outbreaks and beetle outbreaks are not 726

confined to the simultaneous occurrence of drought and windthrow anymore. Third, at these 727

warm-dry sites enhanced drought- and beetle-induced Norway spruce mortality will negatively 728

Temperli et al.

34

feed back on beetle population dynamics through the depletion of the beetle’s breeding substrate. 729

We project this long-term indirect effect to become increasingly important up to the point where 730

the local extinction of Norway spruce forces beetles to relocate to less drought-affected spruce 731

forests. These results showed that with climate change the sign and strength of disturbance inter-732

actions may shift in non-linear ways due to cross-scale shifts in the importance of individual 733

driving forces. 734

Site-specific adaptive management must account for these shifts. Under a future climate, bark 735

beetle suppression strategies may be effective at the cold-wet edge of the Norway spruce distri-736

bution, the prevention of bark beetle disturbance by converting spruce forests to less drought-737

sensitive deciduous forests is inevitable at the warm-dry end of the Norway spruce distribution. 738

Further research should focus on testing the effectiveness of such bark beetle suppression and 739

prevention strategies, and on revealing trade-offs between multiple ecosystem goods and services 740

that may emerge concomitantly. 741

Acknowledgements 742

We thank L. Fahse for inspiring discussions and M. P. Ayres and two anonymous reviewers for 743

their suggestions to improve the manuscript. This research was funded by the MOTIVE project 744

within the European Commission’s 7th Framework Program (grant agreement no. 226544). 745

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Appendix A 970

Additional information on relationships implemented in the LandClim beetle module. 971

Appendix B 972

Figures showing results of baseline (no bark beetle disturbance) simulations, the full set of bark 973

beetle disturbance simulations including the development of susceptibility factors and spatially 974

explicit patterns of beetle-induced spruce mortality. 975

976

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Tables 977

Table 1: Regional circulation model realizations for the IPPC AR4 A1B emission scenarios at 978

median elevations in the three case study landscapes in the Black Forest and Davos, respectively. 979

Black Forest lower area

(473 m a.s.l.

Black Forest upper

area (845 m a.s.l.)

Davos (2000 m a.s.l.)

Climate scenar-

ios

Temp. (°C) Precip.

(mm)

Temp.

(°C)

Precip.

(mm)

Temp.

(°C)

Precip.

(mm)

Current climate

(1950-2000)

8.7 891 7.0 1095 1.4 1122

SMHI (2080-

2100)1

10.9 846 9.2 1054 3.8 1071

HCCPR (2080-

2100)2

13.3 849 11.6 1050 6.0 1005

Temp.: Mean annual temperature 980

Precip: Mean annual precipitation sum 981

1SMHI: Climate scenario by the Swedish Meteorological and Hydrological Institute (Black For-982

ests: RCA30/CCSM3 climate model; Davos: RCA/BCM). 983

2HCCPR: Hadley Center for Climate Prediction and Research (Black Forest and Davos: 984

HadCM3Q0/HadRM3Q0) 985

986

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Figure legends 987

Fig. 1: Empirical (a, c) and predicted (b, d) interactions among forest disturbance agents at the 988

forest patch (represented by a cell in LandClim) and landscape scale, and over a short (decade) 989

and long (>100 years) time period. Solid lines represent the direct impacts of disturbances (e.g., 990

drought-induced stress, windthrow damages) on forest health (i.e. the resilience of the forest 991

against bark beetle disturbances). Dashed lines represent the indirect impact of disturbances on 992

beetles as mediated by changes in forest state and composition. Red lines indicate negative im-993

pacts while green lines indicate positive impacts, with the relative strength indicated by line 994

weight. Relationships between other disturbances and beetle disturbances in panel a) are based 995

on empirical data. Within an individual forest patch over the long term (b) wind and drought 996

disturbances that cause tree mortality are expected to have an indirect negative impact on beetle 997

disturbances as a result of the age and size distribution of spruce shifting towards less susceptible 998

stages. At the landscape scale over the short term (c) wind and harvest have a weaker impact on 999

beetles due to the spatially localized nature of these disturbances. In contrast drought is a land-1000

scape level disturbance, and its impact on beetles remains high. We varied the direct impact of 1001

wind and climate change (parameters λW and λCC) so as to shift the magnitude of these external 1002

drivers of beetle disturbances within a decade, and within a cell and examined the parameter 1003

range of these variables in order to assess positive or negative correlations between the disturb-1004

ances at the landscape scale over a longer time period (d). 1005

Fig. 2: Structure of the bark beetle model and its relation to other components of LandClim. 1006

Temperli et al.

48

Fig. 3: Simulated yearly infested area (median of 100 replicates) under different combinations of 1007

wind influence distance (Dwind) and beetle pressure coefficient (bpC). Solid arrows indicate the 1008

parameter combination that was selected (see text for selection criteria) to yield simulation out-1009

puts that match the observed 1.3% observed in the Bavarian Forest (dashed contour line). 1010

Fig. 4: Short-term, patch/cell scale model behavior. Open circles in panels a-d) show the bark 1011

beetle susceptibility in cells in response to the cell susceptibility factors dominant spruce age a), 1012

spruce basal-area share b), drought index c) and windthrown spruce d). Solid circles indicate the 1013

susceptibility of cells without live spruce (but with other tree species and possibly windthrown 1014

spruce). In these cells susceptibility was considered to be >0 in case of available windthrown 1015

spruce biomass (cf. two solid circles in panel d) in order to account for the effect of windthrown 1016

spruce on adjacent cells (Eq. 1). Black lines in panels a-d) indicate the contribution of each sus-1017

ceptibility factor to total susceptibility as a function of the forest state. Note: Realized cell-1018

susceptibility (grey circles) is higher than the implemented forest state-susceptibility relation-1019

ships (black lines), because individual susceptibility factors are combined additively to total cell 1020

susceptibility (Eq. 2). Panel e) shows the infestation probability for cells with different bark bee-1021

tle susceptibility and under endemic and epidemic outbreaks (above and below median infesta-1022

tion size, respectively). 1023

Temperli et al.

49

Fig. 5: Short-term, landscape scale model behavior illustrated by scatter-plots in panels a-d) 1024

showing the relationship between decade-to-decade changes in bark beetle-killed biomass and 1025

the landscape scale factors wind (change in windthrown biomass; a), drought (change in drought 1026

index; b), temperature (change in relative degree day sum; c) and the landscape risk index d), 1027

respectively, that integrates all these factors in addition to the forest’s susceptibility. Panel e) 1028

shows the development of landscape scale bark beetle-killed biomass, temperature, susceptibil-1029

ity, windthrown biomass and drought through time. Note the peaks in bark beetle-killed biomass 1030

in decades of extreme wind disturbance, drought and/or temperature, respectively. 1031

Fig. 6: Comparison between empirical (dashed line) and simulated (boxplots) cumulated infested 1032

area. Simulated infested area is shown for 100 realizations after 2 and 3 simulated decades and 1033

under current wind disturbance severity (baseline wind) and under a high severity wind scenario 1034

that represents a 2.1 fold increase in windthrown tree biomass. 1035

Fig. 7: Long-term, patch/cell scale model behavior depicted by the development of bark beetle 1036

susceptibility in one selected cell through time. Panel a) shows the impact of a windthrow event 1037

in 2050 and panel b) shows how susceptibility increases with increasing drought up to the point 1038

when susceptibility decreases due to drought-induced spruce mortality. 1039

Temperli et al.

50

Fig. 8: Development of tree species biomass and Norway spruce biomass damaged by bark bee-1040

tles and windthrow. Presented are results for the three case studies in rows (Black Forest, low 1041

elevation; Black Forest, high elevation; Davos) and four scenario simulations that include bark 1042

beetle disturbance in columns (baseline and high wind disturbance, current climate and climate 1043

change [HCCPR]). As a reference, the species biomass in simulations that do not include bark 1044

beetle disturbance is indicated as stacked bars at the left (mean of species biomass from 2000-1045

2200 from simulations under current climate) and right (species biomass in 2200 from simula-1046

tions under climate change) end of each row. 1047

Fig. 9. Effect of climate change, bark beetles and their interaction on the development of Norway 1048

spruce biomass. The top panels graphically represent a theoretical example of how climate 1049

change (the combined effect of increased temperature and drought) and beetle disturbances can 1050

influence spruce biomass individually and in combination, and what this means with respect to 1051

positive and negative disturbance interactions (see also Eq. 8a-c). Below, the left column shows 1052

the development of Norway spruce biomass in the three case studies under four scenarios: cur-1053

rent climate, no bark beetle disturbance (baseline); current climate plus bark beetle disturbance; 1054

climate change, no bark beetle disturbance; and climate change plus bark beetle disturbance. The 1055

right column shows the development of the climate change-beetle interactions effect. 1056

1057

Temperli et al.

51

Figures 1058

1059

Figure 1 1060

1061

a) c)

b) d)

λCC

λW

Forest patch/cell Landscape

Sho

rt te

rmLo

ng t

erm

Windthrow

Climate change

Beetles

Forest health

Harvest

Windthrow

Climate change

Beetles

Forest health

Harvest

Windthrow

Climate change

Beetles

Forest health

Harvest

?

?Windthrow

Climate change

Beetles

Forest health

Harvest

Temperli et al.

52

1062

Figure 2 1063

1064

Susceptibility assessment

Cohort susceptibiltiy

(Scohort)

Cell susceptibility 

(Scell)

Landscape susceptibility 

(Slsc)

Windthrown spruce 

(windScell)

Drought stress (drS)

Spruce age 

(ageS)

Spruce share (sprS)

Climate

Wind

Bark beetle‐induced mortality

mortality probability of trees in cohort 

(mPbeetle)

Tree mortality

Bark beetle pressure

Past bark beetle 

damage (Dt‐1)

Bark beetle pressure in cell (btlP)

Phenology, # generations 

(G)

Bark beetle infestation distribution

Infestation area & spatial distribution(Rkcell, infB)

Bark beetle dispersal

Infestation risk of cells (infRcell)

Spruce biomass (sprB)

Bark beetles

Fire

Management

Forest succession

pbCDwind

Temperli et al.

53

1065

Figure 3 1066

1067

0.0

0.5

1.0

1.5

2.0

2.5

5 10 15 20

100

200

300

400

500

1.3

Bavarian Forest 1988-2009(Kautz et al. 2011)

Median infested area (%/year)

bpC

Dw

ind (

m)

Temperli et al.

54

1068

Figure 4 1069

Sus

cept

ibili

ty (

live

tree

s)0 50 100 150 200

0.0

0.2

0.4

0.6

0.8

1.0

Spruce age (years)a)

Cells

with live sprucewithout live spruce

0 25 50 75 100

0.0

0.2

0.4

0.6

0.8

1.0

Sus

cept

ibili

ty (

live

tree

s)

Spruce share (%)b)

Sus

cept

ibili

ty (

live

tree

s)

0.00 0.05 0.10 0.15 0.20

0.0

0.2

0.4

0.6

0.8

1.0

Drought indexc)

0 50 100 150 200 250 300

0.0

0.2

0.4

0.6

0.8

1.0

Windthrown sprucebiomass (t/ha)

Sus

cept

ibili

ty (

live

+ w

indt

hrow

n tr

ees)

d)

Susceptibility (live trees)

P(in

fest

atio

n)

0.0

0.2

0.4

0.6

0.8

1.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EndemicEpidemic

e)

Temperli et al.

55

1070

Figure 5 1071

-20

010

2030

-10 -5 0 5

corr = 0.4

Winda)

Wind damage (t/ha)

-20

010

2030

-0.2 -0.1 0.0 0.1 0.2 0.3

corr = 0.51

Temperatureb)

Relative degree day sum

Drought index

-20

010

2030

-0.05 0.00 0.05

corr = 0.71

Droughtc)

i nf R ls c

-20

010

2030

-0.1 0.0 0.1 0.2

corr = 0.96

Infestation riskd)

B

eetle

dam

age

(t/h

a)

B

eetle

dam

age

(t/h

a)

Time (decades)

0 10 20 30 40 50

040

80

010

20

11.

52

00.

1Beetle damage (t/ha)Wind damage (t/ha)

Temperature (rel. degree days)Drought index

e)

Temperli et al.

56

1072

Figure 6 1073

1074

2 2 3 3

020

4060

8010

0

Number of decades

Cum

ulat

ed in

fest

ed a

rea

(%)

Baseline windHigh wind

Bavarian Forest 1988-2009(Kautz et al. 2011)

Temperli et al.

57

1075

Figure 7 1076

1077

Su

sce

ptib

ility

Windthrow

a)0

.00

.20

.40

.60

.81

.0

2000 2050 2100

Current climate

00

.10

.2

2000 2050 2100

SusceptibilityDrought

Drought-inducedspruce mortality

b)

Climate change (HCCPR)

Year

Temperli et al.

58

1078

Figure 8 1079

1080

01

50

30

0

01

2

00

.10

.2Black Forest, low elevation

Nobeetles

Baseline wind High wind Baseline wind High wind Nobeetles

Current climate Climate change0

15

03

00

01

2

00

.10

.2Black Forest, high elevation

Species biomass (t/ha)

Norway spruceOther species

01

50

30

0

01

2

00

.10

.2Davos

Disturbance damage (t/ha)

Bark beetlesWindthrow

2000 2100 2200 2000 2100 2200 2000 2100 2200 2000 2100 2200

2000-2200

2200

Year

Temperli et al.

59

1081

Figure 9 1082

1083

010

020

0 Theory

BaselineCurrent climate, beetlesClimate change (HCCPR), no beetlesClimate change (HCCPR), beetles

-100

010

0

Climate change-beetle interaction (t/ha)

010

020

0

Black Forest, low elevation

-100

010

0

010

020

0 Black Forest, high elevation

2000 2050 2100 2150 2200-1

000

100

010

020

0

Davos

-100

010

02000 2050 2100 2150 2200

Nor

way

spr

uce

biom

ass

(t/h

a)

Clim

ate

chan

ge-b

eetle

inte

ract

ion

(t/h

a)Year

θ

δcθ

δbδc