Increasing forest disturbances in Europe and their impact ... · Increasing forest disturbances in...

Nature Climate Change 4, 806–810 (2014)

Increasing forest disturbances in Europe and their impact on carbon storageRupert Seidl, Mart-Jan Schelhaas, Werner Rammer and Pieter Johannes Verkerk

In the version of this supplementary file previously published, the values given in Table 6 for net ecosystem productivity and net primary productivity were incorrect; this has no impact on the reported results. These errors have been corrected in this file 4 September 2014.

CORRECTION NOTICE

© 2014 Macmillan Publishers Limited. All rights reserved.

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2318

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 11

Supplementary Information 1

2

Increasing forest disturbances in Europe and their impact on carbon storage 3

4

Rupert Seidl, Mart-Jan Schelhaas, Werner Rammer, Pieter Johannes Verkerk 5

6

We here combined empirical models of disturbance damage with scenario simulations of 7

forest development under a range of future climate scenarios and management strategies in 8

order to quantify trajectories of forest disturbance damage in Europe. Subsequently, an 9

analytical model of disturbance effects on forest C storage capacity was used to quantify the 10

impact of future disturbance regimes on forest C stocks in Europe. The main components of 11

our assessment are summarized in Supplementary Figure 1. 12

This Supplementary Information first gives additional details on the tools and methods 13

applied, and provides further information on the scenarios studied (Supplementary Methods: 14

Tools and scenarios). Subsequently, an in-depth evaluation and uncertainty analysis of crucial 15

assessment steps is presented (Supplementary Methods: Evaluation and uncertainty analyses). 16

Finally, additional details on key results are given in order to aid the interpretation of the 17

findings reported in the main text (Supplementary Results). 18

19


http://www.nature.com/doifinder/10.1038/nclimate2318

2

20

Supplementary Figure 1. The materials and methods applied to estimate future disturbance 21

trajectories and their impact on ecosystem C in Europe's forests. GCM: global circulation 22

model, RCM: regional climate model, EFISCEN: the European Forest Information Scenario 23

Model 1,2

, SEM: structural equation models of disturbance damage 3, REGIME: an analytical 24

model of disturbance effects on forest C storage 4. 25

26

27

Supplementary Methods – Tools and scenarios 28

The EFISCEN model 29

The European Forest Information SCENario model EFISCEN is a large-scale forest scenario 30

model that projects forest resource development on regional to European scale 2,5–7

. In 31

socio-economic

scenarios

climate

scenarios

scenarios of forest

development

future

disturbance regimes

carbon impact

of disturbance regimes

GCMs / RCMs

EFISCEN

SEM

REGIME


3

EFISCEN, the forest is described as an area distribution over age- and volume-classes in 32

matrices, based on national forest inventory data. Transitions of area between matrix cells 33

during the simulation represent different natural processes such as growth and mortality, and 34

are influenced by external drivers such as management strategies and changes in forest area. 35

The aging of forests is simulated by moving area proportions to a higher age class, while 36

growth is simulated by an area transition to a higher volume class. The latter transition 37

probabilities are derived from forest inventory data or yield table information. The effects of 38

climate change are implemented in EFISCEN based on assessments with detailed process-39

based models 8. For this study, we in particular used the results of Reyer et al.

9, who 40

estimated productivity changes for several European tree species with the process-based 41

model 4C (see Schelhaas et al. 10

for details), as well as the approach and data of Veroustraete 42

et al. 11

. 43

Management strategies are specified at two levels in EFISCEN. First, a basic management 44

regime defines the period during which thinnings can take place and a minimum age for final 45

fellings. These management regime parameters can be regarded as constraints on the total 46

harvest level. Second, the demand for wood is specified separately for thinnings and final 47

fellings, and EFISCEN may harvest the demanded wood volume if available under the 48

previously defined constraints. Harvest residues from thinning and final felling are either 49

extracted or left on site in management operations. 50

EFISCEN calculates stem wood volume, increment, age-class distribution, removals, forest 51

area, natural mortality and deadwood at five year time-steps. With the help of biomass 52

expansion factors, stem wood volume is converted into whole-tree biomass and subsequently 53

whole tree carbon stocks. Information on litterfall rates, felling residues and natural mortality 54

is used as input into the soil module YASSO 12

, which is dynamically linked to EFISCEN and 55

delivers information on forest soil carbon stocks. EFISCEN has been validated previously, 56


4

using long-term forest inventory data for Finland and Switzerland 13,14

. These validation 57

studies showed that EFISCEN is capable to reliably project the development of forest 58

resources for periods up to 50–60 years. Differences between projected and observed forest 59

structure (e.g., growing stock, age-class distribution) are largest at the regional or species 60

level as a result of uncertainties in the distribution of harvest removals over regions and 61

species. Here, following the European Forest Sector Outlook Study II 15

, we thus relied on 62

robust national-level projections. Data of EFISCEN projections are available from the 63

UNECE website (http://www.unece.org/efsos2.html). For a detailed technical documentation 64

of EFISCEN we refer to Schelhaas et al. 1. 65

66

The empirical models of disturbance damage 67

The empirical models of wind, bark beetle, and forest fire damage were developed using 68

country-scale disturbance data for the period 1958-2001 16

. The observational basis for these 69

relationships is a compilation of >29,000 disturbance records across Europe, summarized in 70

the European Forest Disturbance Database DFDE 17

. The data were logarithmically 71

transformed (after adding a value of +1) to remedy the nonnormality inherent in disturbance 72

data. All explanatory and response variables were standardized by subtracting their time-73

series mean and dividing by the standard deviation. The list of potential explanatory variables 74

considered in disturbance modeling is given in Supplementary Table 1. 75

76

77


5

Supplementary Table 1. Potential explanatory variables considered in empirical disturbance 78

modeling (see Seidl et al 3). 79

factor

group description

disturbance agent

wind bark

beetles

forest

fire

forest forest area × × ×

growing stock × × ×

proportion of conifers on growing stock × × ×

median age × × ×

proportion of forest area >100 years × × ×

skewness of age class distribution × × ×

climate mean annual temperature × ×

seasonal temperature (MAM, JJA, SON, DJF) × × ×

lagged annual temperature × ×

lagged seasonal temperature (MAM, JJA, SON, DJF) × ×

annual precipitation × ×

seasonal precipitation (MAM, JJA, SON, DJF) × × ×

lagged annual precipitation × ×

lagged seasonal precipitation (MAM, JJA, SON, DJF) × ×

percentage of DJF precipitation as rain ×

daily peak wind, annual aggregation ×

daily peak wind, seasonal (MAM, JJA, SON, DJF) ×

monthly peak wind, annual aggregation ×

monthly peak wind, seasonal (MAM, JJA, SON, DJF) × ×

interaction wind damage ×

lagged wind damage ×

MAM= spring, JJA= summer, SON= fall, DJF= winter 80

81

From these potential explanatory variables those with a significant influence on the respective 82

disturbance agent were selected by means of unsupervised machine learning using the 83

Random Forest algorithm 18

. Variables were eliminated iteratively, starting from the full set of 84

potential predictors (see Supplementary Table 1), and only variables reducing the mean 85

square error over random permutations of the same variable were retained. Subsequently, the 86

selected country- and agent-specific explanatory variables were related to disturbance damage 87

by means of structural equation modeling (SEM, 19

). The a priori path model used modeling 88


6

is given in Supplementary Figure 2, and shows that we applied a structure where climate 89

change and changes in forest structure, composition, and extent were combined in two main 90

latent variables. For every country and disturbance agent the measurement model for these 91

latent variables consisted of the manifest variables (i.e., drivers) selected by means of the 92

above described Random Forest analysis. The disturbance agents wind, bark beetles, and 93

forest fire were represented by independent models at the country-scale. However, 94

interactions between wind and bark beetle damage were considered where such an interaction 95

term significantly improved the mean square error in the Random Forest analysis. More 96

details on the derivation of these SEMs as well as on their goodness of fit are given by Seidl 97

et al. 3. An in-depth evaluation in the context of the current study is presented in the section 98

Supplementary Methods: Evaluation and uncertainty analyses below. 99

Based on the fitted SEM parameters, the skewness of age-class distribution was found to be 100

the most influential forest change predictors of wind disturbance across all countries 101

considered. For bark beetle damage, this age-related variable was found to be equally 102

influential as growing stock and species composition (i.e., proportion of conifers), with the 103

latter being significant in 50% of all the individual SEMs. For wildfire, the most frequently 104

retained variable over all SEMs was growing stock, which had a significant influence in two 105

thirds of the SEMs. The most influential variable on forest fires was median age. With regard 106

to climate variables, wind damage was most strongly driven by peak wind speeds, with a 107

particular influence of the wind climate in the winter season. For bark beetle damage and 108

forest fires, the interplay between low precipitation and high temperatures was found to be the 109

main climatic driver. Spring and summer precipitation was found to be particularly influential 110

on bark beetle damage, while fire was in addition also related to the temperature and 111

precipitation regimes at annual timescales 3. Supplementary Table 2 lists the top five climate- 112

and forest-related drivers of continental-scale disturbance damage in Europe over all agents 113

and SEMs. 114


7

115

Supplementary Figure 2. The generic design of the structural equation models used for 116

disturbance modeling. Ellipses indicate latent variables, boxes represent manifest variables 117

(note that the three indicators shown for the latent variables climate and management 118

graphically represent the full set {1,2,…,n} of drivers selected from the potential list by 119

means of Random Forest, cf. Supplementary Table 1), single-headed arrows denote a direct 120

influence in the model, double-headed arrows represent interactions between variables, ε 121

denote error terms. C1-Cn= climate indicators, M1-Mn= management indicators, D= 122

disturbance indicator, A= auxiliary interaction indicators. Dashed lines: not included for all 123

disturbance agents. Source: Seidl et al. 3. 124

125

We here used the thus parameterized disturbance models to estimate future forest disturbance 126

levels in Europe, based on input from climate scenarios and their effect on forest resources as 127

estimated with EFISCEN (Supplementary Figure 1). It has to be noted, however, that these 128

disturbance SEMs are purely data-driven, empirical models. A downside in applying 129

empirical models in this context is their inherent limitation with regard to the prediction of 130

novel future conditions 20

. In order to acknowledge this limitation we applied a number of 131

restrictions to our disturbance modeling. We restricted our disturbance SEMs to a prediction 132

climate

management

disturbance

C1

C2

Cn

M1

M2

Mn

D

1

1

1

10

βC

βM

εC1

εC2

εCn

εM1

εM2

εMn

αC1

αC2

αCn

αM1

αM2

αMn

γCM

A2 εA2

λA1

λA2

A1 εA1

interaction


8

period of 20 years (2011-2030), i.e., a considerably shorter time period than the one used for 133

model parameterization (1958-2001). We furthermore chose to use the SEMs in a 134

distribution-based mode of prediction, i.e., future changes in the disturbance regime were 135

estimated in terms of changes in the quantiles of the empirical distributions underlying the 136

models. This means that extrapolation beyond the parameterization data was omitted, and 137

future predictions are constrained by the most severe observation of 1958–2001. The future 138

disturbance levels calculated here are thus conservative estimates, as climatic changes might 139

lead to novel conditions (e.g., novel host agent combinations) that have the potential to 140

transgress the observed system boundaries of the past 21

. Furthermore, we restricted our 141

modeling in space, focusing on the core area of the respective disturbance agent. If an agent 142

was not reported with sufficient frequency and quality in the calibration period in a certain 143

ecoregion, we did not include it in our assessment of that particular region (see also 144

Supplementary Table 5 below). The fact that climate change might also alter the distribution 145

of disturbance agents, both with regard to their trailing and leading edges 22,23

, is thus not 146

considered in our estimates. Furthermore, while we here used annual and seasonal aggregates 147

of climate as drivers, future developments towards the use of process-based disturbance 148

models could improve e.g., the representation of climatic extremes in modeling 20,24

. 149

150


9

Supplementary Table 2. The most influential forest- and climate-related drivers of 151

disturbance damage in Europe. Values denote multiplicative combinations of the standardized 152

path coefficients α and β (see Supplementary Figure 2), averaged over all SEMs in which the 153

variables were significant (percentage of models in parenthesis). Influence ranks were 154

determined by weighting path coefficients with number of SEMs, and averaging over all three 155

disturbance agents. 156

factor

group rank variable

disturbance agent

wind bark beetles forest fires

forest 1 skewness of age-class distribution 0.250

(53.8%)

0.373

(37.5%)

0.286

(47.6%)

2 proportion of conifers 0.206

(53.8%)

0.376

(50.0%)

0.179

(47.6%)

3 growing stock 0.081

(38.5%)

0.371

(37.5%)

0.231

(66.7%)

4 median age 0.208

(30.8%)

0.270

(37.5%)

0.357

(38.1%)

5 proportion of forest area >100 years 0.215

(23.1%)

0.161

(37.5%)

0.271

(47.6%)

climate 1 mean annual precipitation - 0.07

(50.0%)

0.282

(47.6%)

2 precipitation in spring (MAM) - 0.346

(25.0%)

0.166

(38.1%)

3 precipitation in summer (JJA) - 0.265

(25.%)

0.206

(28.6%)

4 daily peak wind speed in winter (DJF) 0.228

(30.8%) - -

5 mean annual temperature - - 0.165

(38.1%)

157

158

The REGIME model 159

Modeling approach 160

The REGIME model 4 provides a general theoretical framework for quantitatively assessing 161

the effects of disturbances on ecosystem carbon storage at large spatial scales. The main 162

constituents of the C cycle in REGIME are net primary production (NPP), the size of biomass 163

and soil carbon pools, and their residence times. The disturbance regime is characterized via 164


10

disturbance interval and disturbance severity, assuming Poisson-distributed disturbance 165

events. The disturbance effect on ecosystem C is expressed as Equation 1, 166

(1)

with X the expected total ecosystem carbon, U the net primary productivity, the carbon 167

residence time in the ecosystem, the mean disturbance interval (years), s the fraction of 168

biomass removed by a disturbance event (severity), and the residence time of carbon in the 169

biomass pool. Equation 1 illustrates that high C uptake U (i.e., a swift recovery from 170

disturbance) and long C residence times positively affect ecosystem C storage, while high 171

disturbance levels ( and s) reduce the C stored in the system. The disturbance effect is 172

furthermore modulated by the residence time of the C pool directly affected by disturbance 173

( ). The longer-lived the C that is lost through disturbance, the bigger is the impact on the 174

total ecosystem C budget (cf. the “slow in, rapid out” nature of forest C 25

). Weng et al. 4 175

showed that disturbance frequency and severity can be combined into a single disturbance 176

index (σ) for the purpose of large scale modeling, with σ defined as the fraction of live 177

biomass C removed by disturbance per unit of time (Equation 2), 178

(2)

Through transformation and substitution we obtain a four-parameter model of C stocks as 179

affected by disturbance (Equation 3): 180

(3)

Here we made two modifications to the original REGIME model formulation: First, while 181

forest management is not explicitly considered in the original REGIME model, we expanded 182

the model to include management, as our focus here is primarily on managed forest 183

ecosystems. Conceptually, management reduces the flux from living biomass to the litter and 184


11

detritus pools via the removal of biomass C, with denoting the biomass fraction entering 185

the litter and soil pools (and, inversely, 1- the harvest fraction). Second, since we did not 186

have separate information on European-scale soil and litter pools available for this study, we 187

lumped these two compartments into a single pool of dead organic matter, with its C 188

residence time. The modified version of Eq. 3 was thus rendered as Equation (4): 189

(4)

190

Model parameters and drivers 191

Residence time parameters for REGIME were estimated using country-level EFISCEN 192

predictions for biomass and soil carbon stocks in combination with NPP estimates. Residence 193

times for the live biomass pool and the combined detritus and soil organic matter pool 194

were derived via Equation 5: 195

(5)

with 196

Also the harvest fraction ( was estimated from EFISCEN simulations of removals 197

from harvest (xH) and litterfall (xL) (equation 6). 198

(6)


12

These REGIME parameters were determined separately at the country-level for each of the 199

studied scenarios. Exemplarily, parameterization data and parameter estimates for the 200

reference management strategy are given in Supplementary Table 3. It has to be noted that 201

dynamic feedbacks of disturbances on forest structure and composition were not explicitly 202

considered in this study, as we used scenarios of (undisturbed) forest development as external 203

drivers in our modeling (see also Supplementary Figure 1). 204

The REGIME disturbance index σ (i.e., the fraction of live biomass removed by disturbance 205

per year) corresponds well to the disturbance percentages p (i.e., damaged timber volume as a 206

fraction of growing stock) estimated using SEMs (see Figure 2). However, in order to convert 207

p into σ additional assumptions on salvage harvesting and the fate of foliage, branch, and root 208

biomass after disturbance had to be made. As salvage harvesting is the default operation after 209

wind and bark beetle disturbance in managed forest ecosystems in Europe 26,27

we assumed 210

stemwood C to be removed from the ecosystem after disturbance by these two agents. C in 211

foliage, branch and root compartments, on the other hand, were assumed to remain on site in 212

our calculations. For wildfire, area-based disturbance estimates were converted to volume-213

based values using the ecoregion-specific conversion factors estimated by Schelhaas et al. 16

. 214

Higher consumption rates of foliage and branches can be expected for fire compared to wind 215

and bark beetles 28,29

, while the salvage proportion is likely to be lower for this agent. As 216

parsimonious baseline we assumed full compensation between these two effects and 217

implemented the same ratio of p/σ for fire as for wind and bark beetles. In order to test the 218

effect of these assumptions regarding consumption and salvage after wildfire on our overall 219

results we conducted a local sensitivity analysis for these parameters (see below). 220

221


13

Supplementary Table 3. REGIME model parameters used to estimate the effects of 222

disturbance on ecosystem C storage. All values are for the period 2021-2030 and pertain to 223

the reference management strategy under stable climate. 224

Xa X1a Ua

b b

b

(Mg·C·ha-1) (Mg·C·ha-1) (Mg·C·ha-1) (years) (years) (dim.c)

Alpine 274 137 9.6 28 14 0.21

Pannonic 206 101 7.2 29 14 0.22

Sub-Atlantic 202 104 7.4 27 14 0.23

Northern 149 58 5.5 27 11 0.21

Atlantic 144 70 5.9 24 12 0.20

Central Mediterranean 135 76 4.2 33 18 0.19

Mediterranean East 116 42 3.7 38 13 0.16

Mediterranean West 89 36 3.3 28 12 0.17

Europe 162 75 5.8 29 13 0.21

a EFISCEN simulation results 225

b REGIME parameter estimates (Equations 5-6) 226

c dimensionless 227

228

229

Climatic forcing and tree growth response 230

As the main approach to address future uncertainty in projecting trajectories of disturbance 231

damage scenario analysis was used. We studied 14 scenarios of future climate change and tree 232

growth (Supplementary Table 4). These scenarios comprise three different IPCC storylines of 233

future development 30

. Storyline A1B and B1 both describe a world of low population growth, 234

but differ with regard to economic growth (higher in A1B) and technological development 235

(geared towards a service and information technology in B1). While A1B and B1 assume 236

global convergence, storyline B2 emphasizes local solutions and more diverse technological 237

change 30

. The corresponding changes in the climate system were derived from runs with 238

three different sets of global circulation models (GCM) and regional climate models (RCM). 239

Furthermore, in order to address uncertainties with regard to tree growth changes, two 240


14

alternative responses to climatic changes were included. These variants differ with regard to 241

the underlying assumptions of a CO2 fertilization effect on forest productivity, with one 242

assuming acclimation of photosynthesis to rising CO2 (at a level that is functionally 243

equivalent to a concentration of 350 ppm, i.e., no CO2 fertilization effect on tree growth), 244

while the other simulates a persistent stimulation of photosynthesis through increasing CO2 245

concentration 9. Furthermore, for one climate model combination (ECHAM5-CCLM) and two 246

storylines (A1B and B1) a second realization of the climate model runs was also available for 247

analysis. These are runs that assume an identical forcing as in the first realization, but differ 248

with regard to the assumed starting conditions in the climate simulations (e.g., concerning the 249

state of oscillating climate phenomena such as ocean circulation). In order to achieve a 250

balanced ensemble of scenarios, and to not overemphasize any particular storyline or climate 251

model, only the first realization of all climate model runs was included in the ensemble 252

analysis reported in the main text. We used the median and interquartile range (IQR) to 253

describe the ensemble’s central tendency and spread, respectively. A no climate change 254

scenario was also simulated, serving as a baseline for the assessment of climate change 255

effects. 256

For each climate scenario, relative signals were derived by standardizing the prediction period 257

to a past baseline period. We used annual and seasonal (March-May, June-August, 258

September-November, December-February) aggregates of temperature and precipitation 259

anomalies in the prediction of disturbance damage (see Supplementary Table 1). In addition 260

we also calculated maximum daily and monthly wind speed anomalies from the climate 261

model data, and derived an index of winter wetness (percentage of winter precipitation falling 262

as rain). These variables were subsequently used as input in the structural equation models 263

used to estimate the climate change impact on disturbance damage in Europe. Climatic effects 264

on forest growth were estimated by means of simulation analyses with process-based tree 265


15

growth models 9,11

, and were implemented into EFISCEN via relative growth change values 266

per decade 6,31

. 267

268

Supplementary Table 4. The 14 scenarios of future climate change and tree growth, and 269

their changes in 2021-2030 relative to the baseline period 1971-2001. Please note that only 270

the first realization scenarios where used in the ensemble analysis reported in the main text, 271

while results of the second realization were used to quantify within-scenario uncertainties. 272

story-

line 30

climate modeling growth response modeling changes relative to 1971-2001

source models

(GCM-RCM)

reali-

sation model

CO2

fertilization

effect

MATa

(°C)

MAPb

(%)

MWSc

(%)

A1B ECHAM5-CCLM 1 4C acclimated +0.84 +2.79 +2.78 9,32

A1B ECHAM5-CCLM 1 4C persistent +0.84 +2.79 +2.78 9,32

A1B HadCM3-HadRM3 1 4C acclimated +1.67 +3.60 +1.02 9,33

A1B HadCM3-HadRM3 1 4C persistent +1.67 +3.60 +1.02 9,33

A1B Arpège-HIRHAM3 1 4C acclimated +1.06 -2.19 +0.73 9,33

A1B Arpège-HIRHAM3 1 4C persistent +1.06 -2.19 +0.73 9,33

B1 ECHAM5-CCLM 1 4C acclimated +0.55 +4.66 +0.26 9,34

B1 ECHAM5-CCLM 1 4C persistent +0.55 +4.66 +0.26 9,34

B2 HadCM3-HadRM3 1 C-Fix acclimated +1.33 +0.71 ±0.00 11,15

no climate change ±0.00 ±0.00 ±0.00 -

A1B ECHAM5-CCLM 2 4C acclimated +0.66 +4.81 +0.93 9,35

A1B ECHAM5-CCLM 2 4C persistent +0.66 +4.81 +0.93 9,35

B1 ECHAM5-CCLM 2 4C acclimated +1.11 +1.69 -0.94 9,36

B1 ECHAM5-CCLM 2 4C persistent +1.11 +1.69 -0.94 9,36

a mean annual temperature 273

b mean annual precipitation sum 274

c mean annual maximum daily wind speed. 275

276

The spatial grain of our analysis was determined by the resolution of the Europe-wide 277

disturbance data compiled in the DFDE 17

, which is the country scale. This is also the spatial 278

resolution for which the structural equation models (SEMs) of forest disturbance damage have 279

been parameterized previously 3. All gridded and point-based climate information was 280


16

interpolated to and/ or aggregated to the country level. Disturbance SEMs were applied 281

annually in order to capture the interannual variability in climate. Since EFISCEN does not 282

operate on annual time step, however, the state of the forest was kept constant for disturbance 283

projections within a given period, and results were aggregated to decadal scale averages for 284

analysis. Following previous works 3,16

, the 29 countries investigated were grouped into eight 285

ecoregions for continental-scale analysis (see Supplementary Table 5). Data gaps for a limited 286

number of countries and time steps were filled via the disturbance percentage of the 287

respective ecoregion. For all ecoregions we focused on the forest area that is available for 288

wood supply as defined by UNECE and FAO 15

, i.e., forest area where any legal, economic, 289

or specific environmental restrictions do not have a significant impact on the supply of wood. 290

Our study thus focuses on forests that are (potentially) managed, and exclude reserves and 291

areas that are exempt from management due to difficult terrain or strict forest protection. 292

Disturbances and C storage in designated wilderness areas or in the macchia shrublands of the 293

Mediterranean ecoregions, for example, are thus not included in the figures reported by this 294

study. Overall, the forest area available for wood supply according to this definition was 295

estimated to be 82.5% of the total forest area in our study region in 2010. 296

297


17

Supplementary Table 5. The 29 countries of the study region were grouped into eight 298

ecoregions. Of the three disturbance agents wind, bark beetles, and forest fires only those 299

agents for which sufficient data were available in an ecoregion (depending on the relevance of 300

an agent in an ecoregion as well as on the historical data quality) were considered for analysis. 301

ecoregion countries disturbance agents

Atlantic United Kingdom, Republic of Ireland wind, forest fire

Northern Norway, Sweden, Finland wind, forest fire

Sub-Atlantic Denmark, Germany, France, the Netherlands,

Belgium, Luxemburg

wind, bark beetles, forest

fire

Alpine Austria, Switzerland wind, bark beetles, forest

fire

Pannonic Czech Republic, Slovak Republic, Poland,

Hungary, Romania

wind, bark beetles, forest

fire

Mediterranean West Spain, Portugal forest fire

Central

Mediterranean

Italy, Slovenia, Croatia, Serbia, Bosnia,

Macedonia, Albania forest fire

Mediterranean East Greece, Bulgaria forest fire

302

303

Management strategies 304

For each of the 14 scenarios of future climate and tree growth (Supplementary Table 4) four 305

different management strategies were simulated. The reference strategy describes a 306

continuation of business-as-usual forest management 15

. It assumes that total wood demand 307

increases by 1.51% per year on average in Europe from 2010 to 2030, with stemwood 308

removals increasing accordingly in order to meet the growing wood demand. Also forest area 309

increases moderately by 0.11% per year in the reference scenario. Forest area and growing 310

stock reach 138.8·106 ha and 25.3·10

9 m³ by the end of the study period under the reference 311

management strategy (+5.5% and +18.8% relative to 2005, ensemble median). The proportion 312


18

of conifers on growing stock increases slightly to 65.4%, while median age decreases by 5 313

years (Supplementary Table 6). Overall, Europe’s forest resources are slowly but steadily 314

expanding under the reference management strategy. A detailed description of the 315

development of the reference strategy is given in UNECE and FAO15

; Supplementary Table 4 316

shows the state of Europe’s forest ecosystems as projected for the period 2021-2030 under the 317

reference strategy. 318

319

Supplementary Table 6. The state of Europe's forest ecosystems in 2021-2030 under the 320

reference management strategy. Measures of central tendency and spread over the studied 321

ensemble of climate change scenarios are given. Note that the effect of natural disturbances is 322

not considered here. 323

criteria indicator median 25th – 75th percentile

structure and

composition

forest area (ha) 138.8·106 138.8·106 – 138.8·106

growing stock (m³) 25.3·109 25.1·109 – 25.5·109

median age (years) 45.2 45.2 – 45.2

conifer share (% of growing stock) 65.4 65.3 – 65.4

C stocks total ecosystem carbon (Mg) 22.4·109 22.3·109 – 22.5·109

live biomass C (Mg) 10.4·109 10.3·109 – 10.5·109

detritus and soil C (Mg) 12.0·109 12.0·109 – 12.0·109

C fluxes net primary productivity (Mg yr-1) 799·106 783·106 – 805·106

removal by harvests (Mg yr-1) 153·106 153·106 – 153·106

net ecosystem productivity (Mg yr-1) 92.2·106 79.1·106 – 95.4·106

324

The first alternative management strategy studied puts a focus on maximizing the C stored in 325

forest biomass (in short henceforward referred to as carbon strategy). This strategy assumes 326

that there is an incentive for the forest owner to maximize carbon in the forest, for example 327

through a subsidy or carbon market at a sufficient level to cover the extra costs of the 328

modified management regime. Rotation lengths were increased in 5-year steps to a maximum 329


19

increase of 25 years in the carbon strategy. The maximum age of thinning was increased 330

accordingly. The final rotation length and removal from thinnings were optimized to yield the 331

highest in situ C storage at the country level, balancing increased growth of remaining trees 332

with C loss from removals. On average over all countries, the rotation age was increased by 333

11.6 years in the carbon strategy until 2021-2030. Thinning removals were on average 53.1% 334

of the total harvested volume 15

. As a result, both the growing stock and median age increased 335

considerably under the carbon strategy, compared to reference management (Supplementary 336

Table 7). 337

As second alternative management strategy a scenario prioritizing the conservation of 338

biodiversity was implemented (henceforward in short referred to as biodiversity strategy). 339

This management strategy assumes that political decision makers give priority to the 340

protection of biological diversity, and shape the political framework for the forest sector 341

according to the goal of conserving and enhancing biodiversity. In particular, it is assumed 342

that an additional 5% of the forest area currently under management will be set aside by 2030. 343

Since we here study only managed forests (forests available for wood supply sensu UNECE 344

and FAO 15

) this means that the forest area studied decreases under the biodiversity strategy 345

(see also Supplementary Table 7). In the remaining managed forests, rotation ages are 346

increased by 10 and 20 years for broadleaved species and conifers, respectively. Overall, 347

these measures result in 12% lower timber removals in 2021-2030 under the biodiversity 348

management strategy compared to reference management. Furthermore, harvesting residues 349

remain on site after all harvesting operations. In order to promote a convergence of the tree 350

species composition with the potential natural vegetation, 50% of conifer-dominated forests 351

are converted to broadleaved forest types after clearfelling. Overall, the biodiversity strategy 352

results in older forests and – despite a decreasing forest area in focus – an increase in total 353

growing stock and C storage due to lower biomass removals 15

. 354


20

The third alternative management strategy investigated aims at promoting the use of forest 355

biomass for energy production 37

(henceforward referred to in short as wood energy strategy). 356

The assumptions underlying this strategy relate to the ambitious goals for green energy use in 357

the European Union until 2020, and extrapolate this trend until the end of the study period in 358

2030. While this strategy assumes a considerable shift in the utilization of the extracted forest 359

biomass towards energy production, changes in forest structure and composition are only 360

moderate compared to the carbon and biodiversity strategies. Since removals are capped with 361

the maximum sustainable harvest in the EFISCEN simulations for all scenarios, the rising 362

demand on biomass for bioenergy only moderately reduces growing stock in this scenario 363

(Supplementary Table 7). The strong implications of this scenario on trade and material use 364

are discussed elsewhere 15

, and are of no direct relevance here. 365

366


21

367

Supplementary Table 7. Percent changes in forest extent, structure, and composition in the alternative management strategies (period 2021-2030) 368

relative to reference management (cf. Supplementary Table 6). All values relate to median changes over the ensemble of studied climate change 369

scenarios. 370

carbon strategy biodiversity strategy wood energy strategy

forest

areaa

growing

stock

median

age

conifer

shareb

forest

areaa

growing

stock

median

age

conifer

shareb

forest

areaa

growing

stock

median

age

conifer

shareb

Alpine ±0.0 +10.4 +15.2 +1.6 -4.9 -2.7 +1.7 -1.5 ±0.0 -0.6 -1.3 -0.1

Pannonic +0.1 +3.7 +6.3 +0.4 -3.4 +15.1 +26.9 +1.6 ±0.0 -0.3 -0.4 ±0.0

Sub-Atlantic ±0.0 +11.2 +17.8 -0.2 -5.0 +6.2 +14.1 1.1 ±0.0 -0.7 -1.1 ±0.0

Northern ±0.0 +3.6 +6.4 -0.4 -3.0 +5.7 +10.8 -1.1 ±0.0 -1.4 -1.1 ±0.0

Atlantic ±0.0 +8.9 +5.5 +0.6 -4.2 +34.8 +24.4 +2.3 ±0.0 -1.2 -2.1 ±0.0

Central Mediterranean +0.2 +1.6 +5.7 -1.4 -3.7 -1.3 +9.2 -3.0 ±0.0 -0.4 -0.1 -0.2

Mediterranean East ±0.0 +3.3 +26.3 -3.5 -3.5 +2.9 +31.6 -3.6 ±0.0 ±0.0 ±0.0 ±0.0

Mediterranean West ±0.0 +2.9 +17.3 +2.2 -5.3 +12.2 +31.4 -5.0 ±0.0 -1.5 -4.5 +0.1

Europe ±0.0 +5.2 +10.2 -0.1 -3.9 +6.2 +15.3 -0.4 ±0.0 -0.7 -1.0 ±0.0

a relates to forest area available for wood supply as defined by UNECE and FAO

15. 371

b relative to growing stock 372

373

374


22

Supplementary Methods: Evaluation and uncertainty analyses 375

Evaluation of forest disturbance projections 376

We conducted an in-depth evaluation of our continental-scale disturbance modeling 377

framework to test its predictive ability against data from the calibration period 1958-2001 as 378

well as against independent data (i.e., data that have not been used in fitting the models). We 379

first compared observed and predicted disturbance damage at decadal scale for the period 380

1958-2001. Subsequently, we evaluated the predicted disturbance change between the periods 381

1958-2001 (calibration period) and 2002-2010 (independent evaluation period). For the latter, 382

the European Forest Disturbance Database DFDE 17

was updated with observations for the 383

period 2002-2010. This second exercise aims particularly at testing the ability of the models 384

to estimate disturbance levels under novel conditions. This performance is a crucial factor for 385

the predictive application of these models in the current study. Thirdly, we tested if the 386

empirical disturbance models are able to reproduce the observed decadal-scale variation in 387

damage from the three disturbance agents wind, bark beetles, and forest fires. To that end we 388

related the damage levels for each period to the mean damage level 1958-2010, and compared 389

the predicted relative differences to the observed data. 390

We used reanalysis climate for the years 1958-2010 38

for our model evaluation. In addition to 391

the forest data used in model fitting 3 information for the period 2002-2010 was compiled 392

from recent reports on the state of Europe’s forests 39

. Predictions were done for all years in 393

the calibration and evaluation periods, and for all countries for which disturbance data for 394

model calibration and evaluation were available from the updated DFDE 17

. These cover 395

81.8% (wind), 74.0% (bark beetle), and 87.0% (fire) of the total area considered for the 396

respective agent in the year 2005. All analyses were done at the level of ecoregions, however, 397

conversely to the predictions presented in the main manuscript, no gap-filling was done in the 398

evaluations, in order not to impair the assessment of model performance. 399


23

Our evaluation exercise revealed that the SEMs were generally well able to reproduce the 400

observed patterns in disturbance damage in the calibration period (Supplementary Figure 3). 401

The models explained between 40.3% and 69.5% of the variation in decadal-scale disturbance 402

damage at the level of ecoregions. In the independent evaluation period 2002-2010, predictive 403

success did not deteriorate significantly compared to the calibration period (mean R² over all 404

agents of 0.625). This underlines the utility of our empirical disturbance models also for 405

short- to mid-term predictions under novel conditions. Furthermore, we found the models to 406

be generally well able to reproduce the observed changes in disturbance levels between the 407

periods 1958-2001 and 2002-2010 (Supplementary Figure 4). 408

409

410

Supplementary Figure 3. Evaluation of decadal-scale mean annual disturbance damage by 411

(A) wind, (B) bark beetles, and (C) forest fire at the level of ecoregions. White symbols 412

indicate the calibration period 1958-2001 whereas grey symbols show results for the 413

independent evaluation period 2002-2010. Note that the axes are scaled logarithmically. 414


24

Supplementary Figure 4. Observed (white) and predicted (grey) disturbance damage by (A) wind, (B) bark beetles, and (C) forest fires in the 415

periods 1958-2001 (calibration period) and 2002-2010 (independent evaluation period). Predictions are made using SEMs 3, and error indicators 416

give the 5% to 95% confidence interval estimated via a Monte Carlo simulations over the parameter space of the empirical models (n=5000). Note 417

that horizontal axes are scaled logarithmically. 418


25

Observed annual bark beetle damage, for instance, increased in all ecoregions between these 419

two periods, a trend that was also captured by the disturbance models. Furthermore, for five 420

out of the six ecoregions for which increases in area burnt were reported for the most recent 421

observation period the model predictions agreed with this observed trend. Despite acceptable 422

overall performance the evaluation exercises also revealed that some of the SEMs were biased 423

in their predictions (e.g. Alpine ecoregion for wildfire, Supplementary Figure 4). The largest 424

divergence between observed and predicted disturbance damage, as well as the largest 425

uncertainty ranges, were found for wind disturbance. 426

Also with regard to decadal-scale variation the wind models performed poorest among the 427

three considered disturbance agents. The observed levels of wind damage were particularly 428

underestimated in four selected periods and ecoregions (Supplementary Figure 5). This 429

suggests that not all aspects contributing to highly damaging wind events might be captured in 430

the respective SEMs, an issue that seems to particularly concern the Northern and Alpine 431

ecoregions (see also Supplementary Figure 4). Overall, however, also the variation in damage 432

levels between periods was well captured by the disturbance SEMs (Supplementary Figure 5). 433

For bark beetles and forest fires (as well as for wind when the above mentioned outliers were 434

omitted), our analysis revealed unbiased predictions of decadal-scale damage levels (p-values 435

of 0.376 and 0.323 for bark beetles and forest fires, respectively). Over all agents and periods, 436

the correlation between predicted and observed variation was 0.263 (p=0.044), and improved 437

to 0.495 (p<0.001) when the above described outliers for wind were omitted. 438


26

439

Supplementary Figure 5. Comparison of predicted decadal-scale variation in disturbance 440

damage (relative to their 1958-2010 mean value) to observed values at the level of ecoregions. 441

A value of 1 (dotted gridlines) indicates that the observed and/ or predicted damage in the 442

period equals the long-term mean damage from that agent. Note that the axes are scaled 443

logarithmically. 444

445

446

Effects of uncertain starting points in climate scenarios 447

The ensemble analysis conducted to assess possible future disturbance trajectories and their 448

implications for forest C storage considered a range of different scenario storylines, climate 449

models, and growth responses to changing environmental conditions. To investigate the 450

uncertainties within a set of climate model runs we also analyzed a second realization of 451

ECHAM5-CCLM simulations for the storylines A1B and B1 (see Supplementary Table 4). 452


27

This analysis examined the uncertainties introduced by unknown initial conditions in climate 453

modeling (e.g., with regard to the state of oscillating ocean circulation patterns), and how they 454

translate into climate impact modeling. As shown in Supplementary Figure 6, the disturbance 455

levels estimated for the second realization are generally within the ensemble of values 456

obtained with the first realization of the same climate model. A pairwise test between 457

realizations indicated no significant differences for wind and forest fire (p=0.507 and 458

p=0.416, paired Wilcoxon signed rank test) for the period 2021-2030. With regard to bark 459

beetle damage, however, realization 2 differed significantly from realization 1 (p<0.001). This 460

indicates that not only different scenario storylines and climate models but also different 461

starting points in climate modeling can have a significant influence on climate change impact 462

assessments. In the particular case of our analysis these findings highlight that our ensemble 463

analysis could be underestimating ensemble spread due to within-scenario uncertainty (e.g., 464

with regard to different starting points in climate modeling). However, since different 465

realizations were not available for the other scenarios studied here, a broader inclusion of this 466

aspect in the ensemble analysis was not possible. 467


28

468

Supplementary Figure 6. Disturbance damage in scenario storylines A1B and B1 as 469

projected with ECHAM5-CCLM in four management strategies and two scenarios of growth 470

response to CO2 fertilization (see Supplementary Table 4). Solid lines are for the first 471

realization of climate model runs (used also in the ensemble analysis reported in the main 472

text); dashed lines indicate results for the same scenario assumptions and models but 473

considering alternative starting points in climate modeling (second realization). Note that the 474

y-axis is logarithmically scaled. 475

476

Sensitivity analyses of the REGIME model 477

To corroborate the plausibility of modeling the disturbance impacts on C cycling and gain 478

insights into the effects of parameter uncertainty we conducted a set of sensitivity analyses of 479

the REGIME model. While the general approach was analyzed in depth previously 4, we here 480

focused on the performance of the model in the specific context of this study. Supplementary 481


29

Figure 7 shows the sensitivity of total ecosystem C stocks to different disturbance levels 482

relative to undisturbed conditions. As expected, C stocks decrease with increasing level of 483

disturbance σ. However, the magnitude of the disturbance impact on C storage also depends 484

on NPP (as an indicator of recovery speed) and the level of living biomass stock (i.e., the 485

level of C affected by disturbance), decreasing with the former and increasing with the latter. 486

This model behavior is in line with theoretical considerations and previous observations of 487

disturbance impacts on forest ecosystem C 40

. 488

489

Supplementary Figure 7. Sensitivity of the disturbance impact on total ecosystem carbon 490

stocks (relative to undisturbed systems) derived with the REGIME model under different 491

levels of disturbance (i.e., σ of 0.1%, 0.5%, and 1.0%) over (A) NPP, and (B) live biomass 492

stocking levels. All other parameter values were kept constant at the level of their continental-493

scale averages for this sensitivity analysis (see Supplementary Table 3). 494

495

REGIME calculates the C effect of disturbances under the assumption of equilibrium 496

conditions 4. As managed forests rarely are in equilibrium we examined the effect of this 497


30

assumption in conjunction with the above described parameterization routine, conducting a 498

sensitivity analysis of the C lost from disturbance to different (fluctuating) levels of biomass 499

(X1) and soil (Xs) carbon. To test the sensitivity of the disturbance effect on forest ecosystem 500

C storage (estimated to 503.4 Tg C in 2021-2030 with the default model parameterization 501

under climate change, see Table 1) to the equilibrium assumption underlying REGIME we 502

varied the living biomass and soil C stocks by ±20% and recalculated the continental-scale 503

effect of disturbances. This local sensitivity analysis (i.e., with all other parameters remaining 504

at their default values) aims to gauge how sensitive our results are to disequilibria and 505

transient changes in C pools in Europe's forests within our ten-year assessment periods. The 506

continental-scale C effect of disturbance in 2021-2030 varied by ±10.1% for a change in XS of 507

±20%. Effects of similar changes in live biomass stocks were more distinct, with the C 508

reduction through disturbance ranging from 372 to 669 Tg C for an X1 of ±20%. The relative 509

differences between alternative management strategies (cf. Table 2), however, remained 510

unchanged in these sensitivity tests. 511

We furthermore tested the effect of our assumptions with regard to salvage and consumption 512

from wildfires. Since uncertainties remain with regard to the C lost in fires 28 and local 513

differences in fire consumption 29 had to be lumped into a single, country-specific factor in 514

our large-scale analysis, it was of interest to assess the sensitivity of our overall results to this 515

particular parameter. We thus set up a local sensitivity analysis varying the share of biomass 516

C lost in fires by ±20%. Using the total continental-scale C effect of disturbance in 2021-2030 517

as evaluation criterion (cf. Table 1) we found that a ±20% variation in the ratio p/σ for forest 518

fires affected the overall results only moderately (±2.8%). This finding adds confidence that 519

our large-scale estimates of disturbance effects are robust despite the remaining uncertainties 520

on salvage and consumption as well as the high local variability in fire regimes. 521


31

Supplementary Results 522

Projected future disturbance regimes in relation to past observations 523

The continental-scale wind damage of 44.5·106 m³ yr

-1 predicted for 2021-2030 (ensemble 524

median under reference management) corresponds to the 93nd

percentile of the observed 525

continental-scale wind damage in 1971-2001 (Supplementary Figure 8). According to this 526

analysis, the wind disturbance levels that were on average exceeded only every 15 years in the 527

last decades of the 20th

century can be expected to occur every other year by 2030. The 528

ensemble median of predicted bark beetle damage for 2021-2030 (17.9·106 m³ yr

-1) even 529

corresponded to the 97th

percentile of the historically observed damage levels, conforming to 530

a historic once-in-32-years event. The area of forest predicted to burn on average every other 531

year in 2021-2030 (406,700 ha yr-1

) was historically only exceeded once in 18 years. Values 532

for the individual scenarios in the ensemble varied, but were significantly greater than the 533

historical values (p<0.01, Wilcoxon signed rank test). 534

535

Regional trajectories of future disturbance damage 536

Forest fires were projected to increases most strongly in the western Mediterranean ecoregion 537

(Figure 1). The main driver behind this predicted increase was climate change, in particular a 538

combination of increasing temperatures and decreasing precipitation. This interaction between 539

changes in temperature and precipitation was particularly severe in the scenarios for the 540

period 2011-2020, but persisted throughout the projection period for the western 541

Mediterranean ecoregion. In contrast, such an amplifying interaction of changes in 542

temperature and precipitation was not projected for the central and eastern Mediterranean 543

region by the studied climate scenarios. In these parts of the Mediterranean the climate was 544

predicted to warm more slowly in most scenarios, and precipitation sums even increased 545


32

slightly compared to the reference period, resulting in a less pronounced increase in the 546

projected forest fire trajectories compared to the western Mediterranean ecoregion. 547

Bark beetle damage was projected to increase most strongly in the Alpine ecoregion (Figure 548

1). The most prominent driver behind this projected trend was a strong increase in growing 549

stock in the area, inter alia mediated by climate change-related improvements of growing 550

conditions in the mountain forests of this ecoregion. Concurrently, climate was projected to 551

warm disproportionally in the Alpine ecoregion, and summer precipitation declined slightly, 552

additionally contributing an increasing level of damage from bark beetles. In absolute terms, 553

the highest levels of bark beetle damage were predicted for the Sub-Atlantic ecoregion. Also 554

there, rising temperatures and higher growing stocks contributed to increasing bark beetle 555

damages in the projections. 556

Wind disturbance showed the highest temporal variation of all three disturbance agents, 557

underlining the fact that individual extreme events are strongly driving the overall damage 558

from wind in Europe. This is, for instance, evident for the observation period in the Atlantic 559

and Northern ecoregions, which show strong peaks in relation to “the Great Storm of 1987” in 560

the UK and the storm “Gudrun” (January 2005) in Scandinavia. While making predictions 561

about the future occurrence of such extreme events remains difficult, some studies indicate 562

the possibility of increasing cyclone activity in the northern Atlantic in the future 41

. Based on 563

daily wind speed data from the employed climate models we here found that wind disturbance 564

increased particularly in mid-latitude ecoregions (Figure 1). Growing stock increased 565

significantly in these regions, and was a main driver of the projected increase in wind damage. 566

In addition, more frequent wet winter conditions (i.e., and indicator for wet and unfrozen 567

soils) in combination with moderately increased maximum daily winter windspeeds 568

contributed to rising wind disturbance levels (please note that all trajectories and changes 569

discussed in this section refer to the reference management strategy). 570


33

571

Supplementary Figure 8. Distribution of the observed annual disturbance damage in Europe 572

in the period 1971-2001 for (a) wind, (b) bark beetles, and (c) forest fires, approximated by a 573

lognormal distribution. The mean damage 1971-2001 is indicated by a dashed vertical line. 574

Shaded areas indicate the ensemble spread of predictions for the period 2021-2030 (min-max 575

range: light grey; interquartile range: dark grey). The predicted ensemble median is indicated 576

as a white vertical line. Predictions relate to simulations under reference management. 577


34

Climate sensitivity of future disturbance regimes 578

In order to gain insight into the climate sensitivity of the forest disturbance regime, and to test 579

whether the predicted disturbance changes are attributable to changes in the climate system 580

(and not, e.g., to age-related changes or changes in forest structure 42

), we also made 581

projections for a scenario assuming stable climatic conditions (see Supplementary Table 4). 582

Simulations under this scenario resulted in significantly lower damage levels for all three 583

disturbance agents, compared to climate change runs (p<0.01, Wilcoxon signed rank test). 584

The no climate change simulation lay outside the ensemble of studied climate change 585

scenarios for all agents (Supplementary Figure 9). The predicted disturbance damage in 2021-586

2030 was between 13.5% and 49.3% lower when the effects of climate change were 587

disregarded, compared to the ensemble median under climate change. However, projected 588

damage levels for all agents did not return to the significantly lower values of the 1970s under 589

the no climate change scenarios, but remained at levels that were observed for the 1990s and 590

2000s. 591

592

The effect of alternative management strategies on disturbance regimes 593

The sensitivity of disturbance regimes to the management-induced changes in forest structure 594

and composition in the three alternative management strategies differed by agent 595

(Supplementary Table 8). Overall, wind damage increased most strongly under the 596

biodiversity strategy (+37.6%) relative to reference management. In contrast, under this 597

strategy the area burned by wildfires decreased by -14.2%. While the wood energy scenario 598

had the lowest wind disturbance level in 2021-2030, bark beetle damage was projected to be 599

lowest in the carbon strategy. This indicates that none of the four management strategies is the 600

single best strategy with regard to reducing disturbance damage. Consequently, the locally 601


35

and regionally changing importance of disturbance agents and their drivers 43

as well as the 602

heterogeneity in starting conditions needs to be accounted for when developing large scale 603

risk management strategies for Europe's forests 44

. Nonetheless, the considerable sensitivity of 604

disturbance regimes to management changes documented here underlines the potential of 605

management to mitigating intensifying disturbance regimes 45,46

. 606

607

608

Supplementary Figure 9. The effect of climate change on trajectories of future disturbance 609

damage in Europe. Solid lines and envelopes indicate the results of the climate change 610

ensemble analysis (Supplementary Table 4), while dashed lines are projections for a scenario 611

assuming stable climate. For all runs a continuation of business-as-usual forest management 612

was assumed (reference strategy). The ensemble envelopes indicate the ensemble median, 613

interquartile range (dark grey), and minimum – maximum range (light grey). Please note that 614

the y-axis is logarithmically scaled. 615


36

Forest carbon implications 616

The highest future C pools in Europe were simulated for the Northern ecoregion 617

(Supplementary Table 9). EFISCEN simulations (without disturbance) showed that alternative 618

forest policies could either strongly increase the C stored in Europe's forests (carbon strategy) 619

or reduce it as a result of an intensified utilization of forest resources (wood energy strategy 620

15). With regard to increasing the in situ C storage (carbon strategy), for instance, the Alpine 621

and Sub-Atlantic ecoregions were the most responsive regions 47

, with C storage increases 622

exceeding 5% compared to reference management by 2021-2030. Disturbance damage 623

considerably reduced forest C storage in all management strategies, with a disturbance effect 624

of between 494 and 584 Tg C (compared to the respective undisturbed runs, see also Table 2). 625

The most negatively affected areas in terms of carbon were the Sub-Atlantic and Eastern 626

Mediterranean ecoregions, in which disturbances reduced ecosystem C stocks by 5.4% and 627

3.8%, respectively, compared to undisturbed simulations (reference management). 628

629


37

Supplementary Table 8. Relative changes (%) in disturbance percentage (i.e., the mean annual percent damage relative to growing stock) in 630

alternative management strategies relative to reference management (period 2021-2030). 631

wind bark beetles wildfire

carbon

strategy

biodiversity

strategy

wood

energy

strategy

carbon

strategy

biodiversity

strategy

wood

energy

strategy

carbon

strategy

biodiversity

strategy

wood

energy

strategy

Alpine +49.9 -62.2 +0.5 -7.6 -5.0 +0.4 +6.5 -26.5 +0.4

Pannonic +56.7 +54.7 -3.7 +10.6 +23.8 -0.2 -57.4 -91.2 +2.1

Sub-Atlantic +15.2 +35.2 -0.9 -9.9 -4.2 +0.7 -9.3 -41.5 +0.7

Northern -2.3 +63.9 +7.6 ±0.0a ±0.0a ±0.0a -3.4 -37.0 +1.4

Atlantic +12.0 +132.5 -0.2 ±0.0a ±0.0a ±0.0a -3.4 +2.6 -0.9

Central Mediterranean ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a -2.6 +3.2 +0.4

Mediterranean East ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a -2.0 -2.0 ±0.0

Mediterranean West ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a ±0.0a -19.9 -19.9 +1.6

Europe +24.7 +37.6 -0.3 -3.9 +3.7 +0.5 -6.5 -14.2 +0.8

a disturbance agent not relevant/ not modeled in this ecoregion. See also Supplementary Table 3. 632

633

634

635


38

Supplementary Table 9. Total forest ecosystem C stocks (biomass and soil, in Tg C) under alternative management strategies in Europe. Shown 636

are results for simulations without disturbance (EFISCEN) and for runs including the effect of disturbance from wind, bark beetle, and wildfire for 637

the period 2021-2030. For both renderings, the median over the scenario ensemble is presented. 638

without disturbance with disturbance

reference

strategy

carbon

strategy

biodiversity

strategy

wood energy

strategy

reference

strategy

carbon

strategy

biodiversity

strategy

wood energy

strategy

Alpine 1,247 1,313 1,206 1,234 1,209 1,266 1,189 1,196

Pannonic 4,097 4,147 4,250 4,063 4,000 4,016 4,102 3,967

Sub-Atlantic 5,352 5,625 5,349 5,294 5,063 5,323 5,005 5,009

Northern 6,662 6,768 6,661 6,551 6,648 6,755 6,643 6,538

Atlantic 499 516 552 495 498 515 550 494

Central Mediterranean 2,404 2,433 2,348 2,374 2,393 2,423 2,338 2,364

Mediterranean East 879 890 868 872 846 856 834 839

Mediterranean West 1,270 1,279 1,324 1,256 1,251 1,262 1,308 1,237

Europe 22,421 22,983 22,574 22,152 21,917 22,428 21,990 21,658

639

640

641


Supplementary references 642

1. Schelhaas, M. J. et al. Model documentation for the European Forest Information 643

Scenario model (EFISCEN 3.1.3). 118 (Alterra report 1559, Alterra, and EFI technical 644

report 26, European Forest Institute, 2007). 645

2. Sallnäs, O. A matrix model of the Swedish forest. Stud. For. Suec. 183, 23 (Studia 646

Forestalia Suecica, Swedish University of Agricultural Science, 1990). 647

3. Seidl, R., Schelhaas, M.-J. & Lexer, M. J. Unraveling the drivers of intensifying forest 648

disturbance regimes in Europe. Glob. Chang. Biol. 17, 2842–2852 (2011). 649

4. Weng, E. et al. Ecosystem carbon storage capacity as affected by disturbance regimes: A 650

general theoretical model. J. Geophys. Res. 117, G03014 (2012). 651

5. Nabuurs, G. J., Pussinen, A., Brusselen, J. & Schelhaas, M. J. Future harvesting pressure 652

on European forests. Eur. J. For. Res. 126, 391–400 (2007). 653

6. Eggers, J., Lindner, M., Zudin, S., Zaehle, S. & Liski, J. Impact of changing wood 654

demand, climate and land use on European forest resources and carbon stocks during the 655

21st century. Glob. Chang. Biol. 14, 2288–2303 (2008). 656

7. Verkerk, P. J., Anttila, P., Eggers, J., Lindner, M. & Asikainen, A. The realisable potential 657

supply of woody biomass from forests in the European Union. For. Ecol. Manage. 261, 658

2007–2015 (2011). 659

8. Nabuurs, G.-J., Pussinen, A., Karjalainen, T., Erhard, M. & Kramer, K. Stemwood volume 660

increment changes in European forests due to climate change-a simulation study with the 661

EFISCEN model. Glob. Chang. Biol. 8, 304–316 (2002). 662

9. Reyer, C. et al. Projections of regional changes in forest net primary productivity for 663

different tree species in Europe driven by climate change and carbon dioxide. Ann. For. 664

Sci. 71, 211–225 (2014). 665

10. Schelhaas, M. J. et al. Adaptive forest management to account for climate change-induced 666

productivity and species suitability changes in Europe. Reg. Environ. Chang. submitted 667

(2014). 668

11. Veroustraete, F., Sabbe, H. & Eerens, H. Estimation of carbon mass fluxes over Europe 669

using the C-Fix model and Euroflux data. Remote Sens. Environ. 83, 376–399 (2002). 670

12. Liski, J., Palosuo, T., Peltoniemi, M. & Sievanen, R. Carbon and decomposition model 671

Yasso for forest soils. Ecol. Modell. 189, 168–182 (2005). 672


40

13. Nabuurs, G.-J., Schelhaas, M.-J. & Pussinen, A. Validation of the European forest in- 673

formation scenario model ( EFISCEN ) and a projection of finnish forests. Silva Fenn. 34, 674

167–179 (2000). 675

14. Thürig, E. & Schelhaas, M.-J. Evaluation of a large-scale forest scenario model in 676

heterogeneous forests : a case study for Switzerland. Can. J. For. Res. 36, 671–683 677

(2006). 678

15. UNECE and FAO. The European Forest Sector Outlook Study II. 2010-2030. 107 (United 679

Nations Economic Commission for Europe, 2011). 680

16. Schelhaas, M.-J., Nabuurs, G. & Schuck, A. Natural disturbances in the European forests 681

in the 19th and 20th centuries. Glob. Chang. Biol. 9, 1620–1633 (2003). 682

17. DFDE. Database on Forest Disturbances in Europe. Eur. For. Inst. (2013). at 683

<http://www.efi.int/databases/dfde/> 684

18. Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001). 685

19. Grace, J. B. Structural Equation Modeling and Natural System. 378 (Cambridge 686

University Press, 2006). 687

20. Seidl, R. et al. Modelling natural disturbances in forest ecosystems: a review. Ecol. 688

Modell. 222, 903–924 (2011). 689

21. Raffa, K. et al. Cross-scale drivers of natural disturbances prone to anthropogenic 690

amplification: the dynamics of bark beetle eruptions. Bioscience 58, 501–518 (2008). 691

22. Jönsson, A. M., Appelberg, G., Harding, S. & Bärring, L. Spatio-temporal impact of 692

climate change on the activity and voltinism of the spruce bark beetle, Ips typographus. 693

Glob. Chang. Biol. 15, 486–499 (2009). 694

23. Marini, L., Ayres, M. P., Battisti, A. & Faccoli, M. Climate affects severity and altitudinal 695

distribution of outbreaks in an eruptive bark beetle. Clim. Change 115, 327–341 (2012). 696

24. Jentsch, A. & Beierkuhnlein, C. Research frontiers in climate change: Effects of extreme 697

meteorological events on ecosystems. Comptes Rendus Geosci. 340, 621–628 (2008). 698

25. Körner, C. Slow in, rapid out--carbon flux studies and Kyoto targets. Science 300, 1242–699

1243 (2003). 700

26. Lindenmayer, D. et al. Salvage harvesting policies after natural disturbance. Science 303, 701

1303 (2004). 702

27. Lindenmayer, D. B., Burton, P. J. & Franklin, J. F. Salvage logging and its ecological 703

consequences. 246 (Island Press, 2008). 704


41

28. Vilén, T. & Fernandes, P. M. Forest fires in Mediterranean countries: CO2 emissions and 705

mitigation possibilities through prescribed burning. Environ. Manage. 48, 558–567 706

(2011). 707

29. Campbell, J., Donato, D., Azuma, D. & Law, B. Pyrogenic carbon emission from a large 708

wildfire in Oregon, United States. J. Geophys. Res. 112, G04014 (2007). 709

30. IPCC. Emissions Scenarios. 570 (Intergovernmental Panel on Climate Change, Cambridge 710

University Press, 2000). 711

31. Nabuurs, G., Pussinen, A. R. I., Karjalainen, T. & Erhard, M. Stemwood volume 712

increment changes in European forests due to climate change - a simulation study with the 713

EFISCEN model. 304–316 (2002). 714

32. Lautenschläger, M. et al. Climate Simulation with CLM, Scenario A1B run no.1, Data 715

Stream 3: European region MPI-M/MaD. (World Data Center for Climate, 2009). 716

doi:10.1594/ WDCC/CLM_A1B_1_D3 717

33. Van der Linden, P. & Mitchell, J. ENSEMBLES: climate change and its impacts: 718

summary of research and results from the ENSEMBLES project. (Met Office Hadley 719

Centre, 2009). 720

34. Lautenschläger, M. et al. Climate simulation with CLM, Scenario B1 run no.1, Data 721


doi:10.1594/ WDCC/CLM_B1_1_D3 723

35. Lautenschläger, M. et al. Climate Simulation with CLM, Scenario A1B run no.2, Data 724


doi:10.1594/ WDCC/CLM_A1B_2_D3 726

36. Lautenschläger, M. et al. Climate Simulation with CLM, Scenario B1 run no.2, Data 727


doi:10.1594/ WDCC/CLM_B1_2_D3 729

37. Böttcher, H., Verkerk, P. J., Gusti, M., HavlÍk, P. & Grassi, G. Projection of the future EU 730

forest CO2 sink as affected by recent bioenergy policies using two advanced forest 731

management models. GCB Bioenergy 4, 773–783 (2012). 732

38. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 733

77, 437–471 (1996). 734

39. MCPFE. State of Europe’s forests 2007. The MCPFE report on sustainable forest 735

management in Europe. Europe 247 (Ministerial Conference on the Protection of Forests 736

in Europe, 2007). 737


42

40. Turner, M. G. et al. A revised concept of landscape equilibrium : Disturbance and stability 738

on scaled landscapes. Landsc. Ecol. 8, 213–227 (1993). 739

41. Donat, M. G., Leckebusch, G. C., Pinto, J. G. & Ulbrich, U. European storminess and 740

associated circulation weather types: future changes deduced from a multi-model 741

ensemble of GCM simulations. Clim. Res. 42, 27–43 (2010). 742

42. Vilén, T. et al. Reconstructed forest age structure in Europe 1950–2010. For. Ecol. 743

Manage. 286, 203–218 (2012). 744

43. Thom, D., Seidl, R., Steyrer, G., Krehan, H. & Formayer, H. Slow and fast drivers of the 745

natural disturbance regime in Central European forest ecosystems. For. Ecol. Manage. 746

307, 293–302 (2013). 747

44. Jactel, H. et al. A multicriteria risk analysis to evaluate impacts of forest management 748

alternatives on forest health in Europe. Ecol. Soc. 17, 52 (2012). 749

45. Seidl, R., Rammer, W., Jäger, D. & Lexer, M. J. Impact of bark beetle (Ips typographus 750

L.) disturbance on timber production and carbon sequestration in different management 751

strategies under climate change. For. Ecol. Manage. 256, 209–220 (2008). 752

46. Jactel, H. et al. The influences of forest stand management on biotic and abiotic risks of 753

damage. Ann. For. Sci. 66, 701–701 (2009). 754

47. Nabuurs, G. J. et al. Hotspots of the European forests carbon cycle. For. Ecol. Manage. 755

256, 194–200 (2008). 756

757


Date post:	22-May-2020
Category:	Documents
Upload:	others
View:	12 times
Download:	0 times

Increasing forest disturbances in Europe and their impact ... · Increasing forest disturbances in...

Documents