ORIGINAL PAPER

Modeling the effects of climate change and managementon the dead wood dynamics in boreal forest plantations

Adriano Mazziotta • Mikko Monkkonen •

Harri Strandman • Johanna Routa •

Olli-Pekka Tikkanen • Seppo Kellomaki

Received: 13 June 2013 / Revised: 17 November 2013 / Accepted: 12 December 2013 / Published online: 23 December 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract The present research examines the joint effects

of climate change and management on the dead wood

dynamics of the main tree species of the Finnish boreal

forests via a forest ecosystem simulator. Tree processes are

analyzed in stands subject to multiple biotic and abiotic

environmental factors. A special focus is on the implica-

tions for biodiversity conservation thereof. Our results

predict that in boreal forests, climate change will speed up

tree growth and accumulation ending up in a higher stock

of dead wood available as habitat for forest-dwelling spe-

cies, but the accumulation processes will be much smaller

in the working landscape than in set-asides. Increased

decomposition rates driven by climate change for silver

birch and Norway spruce will likely reduce the time the

dead wood stock is available for dead wood-associated

species. While for silver birch, the decomposition rate will

be further increased in set-aside in relation to stands under

ordinary management, for Norway spruce, set-asides can

counterbalance the enhanced decomposition rate due to

climate change thereby permitting a longer persistence of

different decay stages of dead wood.

Keywords Adaptive management � Decomposition �Generalized estimating equations � Birch � Scots pine �Norway spruce

Introduction

Timber is the main and most economically important good

that forests provide for the society. Nevertheless, forests

provide many other goods and services like non-timber

products, recreation, maintenance of global and local cli-

matic conditions, as well as biodiversity. Multi-goal man-

agement is needed to balance different function of forests

and avoid conflicts in management for different purposes.

In this context, ecosystem models provide many opportu-

nities to evaluate the potential of forests in producing or

maintaining alternative goods and services (Coates and

Burton 1997; Landsberg 2003; Vanclay 2003; Nelson et al.

2009; Wolfslehner and Seidl 2010). In general, the ability

of a forest ecosystem to provide the alternative goods and

services in time ultimately depends on how trees regener-

ate, grow and die in the demographic process controlling

the dynamics populations and communities of trees.

In the forest ecosystems, trees grow in the populations

of single species and/or in the communities of populations

of several species in varying mixtures. Dynamics of pop-

ulations and communities is determined by the regenera-

tion (birth), growth and mortality of trees. These processes

are controlled by the climatic and edaphic factors as related

Communicated by Jorg Muller.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10342-013-0773-3) contains supplementarymaterial, which is available to authorized users.

A. Mazziotta (&) � M. Monkkonen

Department of Biological and Environmental Science,

University of Jyvaskyla, POB 35, 40014 Jyvaskyla, Finland

e-mail: adriano.mazziotta@jyu.fi

H. Strandman � S. Kellomaki

School of Forest Sciences, University of Eastern Finland,

P.O. Box 111, 80101 Joensuu, Finland

J. Routa

Finnish Forest Research Institute, P.O. Box 68,

80101 Joensuu, Finland

O.-P. Tikkanen

Department of Biology, University of Eastern Finland,

P.O. Box 111, 80101 Joensuu, Finland

123

Eur J Forest Res (2014) 133:405–421

DOI 10.1007/s10342-013-0773-3

to the properties of site occupied by trees or tree stands.

This implies that the growth and mortality are correlated as

found in growth and yield studies; i.e., high growth rate of

stem wood implies high productivity but early mortality of

trees and high amount of dead wood (Koivisto 1959;

Harmon 2009). Consequently, it can be hypothesized that

trees growing on fertile site die earlier than those growing

on poor site. This is because the life cycle of trees proceeds

faster on fertile site making trees to mature and die earlier

than on poor site (Kellomaki et al. 2008; Pretzsch 2010;

Pretzsch et al. 2013a, b). On the other hand, the fast growth

rate triggers the self-thinning due to crowding earlier on

fertile site than on poor site, if the initial density of the

population is the same. The correlation between growth

and mortality holds also for litter originating from foliage

and branches of living trees, where falling rate of litter is

linearly related to the growth rate of stem wood (Matala

et al. 2008).

In boreal forests, dead wood is an essential component

of the structure of forest ecosystem (Harmon 2009). Dead

wood refers to woody parts of litter, mainly stem wood,

representing dead trees standing and later falling down in

the different phases of forest succession. Dead wood

accumulated on soil represents several age cohorts from

young to old dead wood in varying degree of decay, pro-

viding a wide range of niches for species depending on this

resource. These species represent a diverse group of bio-

logical organisms (around 45 % of the species living in

boreal forest) and food webs (Stokland et al. 2012), which

are very important in cycling nutrients in the ecosystem

and carbon between atmosphere and ecosystem. Further-

more, dead wood contributes to the soil formation, pro-

viding sites for seedlings to establish and storing carbon in

the ecosystem (Harmon et al. 1986; Harmon 2009; Laiho

and Prescott 2004). Dynamics of dead wood is among the

core questions when managing the boreal forests for sus-

tainable provision of varying ecosystem goods and ser-

vices. We expect that management for timber extraction by

reducing the total standing yield limits tree mortality and

limits the input of dead wood to the ecosystem, ultimately

reducing the habitat for saproxylic species (Krankina and

Harmon 1995; Shanin et al. 2010; Hjalten et al. 2012;

Gossner et al. 2013).

The mortality of trees is driven by multiple mechanisms.

Exogenic mortality is induced by abiotic (frost, wind,

snow, fire) and biotic (pathogens) forces exceeding the

resistance of trees, whereas endogenic mortality is related

to biotic factors (pathogens, insect and fungal pests)

reducing growth and disturbing competition capacity of

trees (Franklin et al. 1987). Ultimate reasons behind the

endogenic mortality are not easy to identify, but many

factors, such as tree species, site fertility and density of tree

population/community, can reduce the ability of a tree to

compete for resources consequently altering endogenic

mortality (Pretzsch 2010). The scarcity of resources affects

the probability that a tree will die in self-thinning or natural

thinning (Yoda et al. 1963; White and Harper 1970;

Waring 1987; Peet and Christensen 1987), controlling the

density of tree populations Reineke (1933).

Climate change may enhance both exogenic and endo-

genic mortalities. This is because climate change is likely

to amplify the variability in environmental conditions and

increase the likelihood of extreme conditions (McDowell

et al. 2011). In this respect, the boreal forests in northern

Europe are among the most vulnerable regions; i.e., an

increase of up to 6 �C in the annual mean temperature may

occur by 2100 due to the doubling of atmospheric CO2

(Solomon 2007), with an increase in precipitation and

changes in seasonal patterns precipitation. Such changes

are likely to enhance regeneration, growth and mortality in

many places in northern Europe (Bergh et al. 2003; Ains-

worth and Long 2005; Solomon 2007; Kellomaki et al.

2008) if the availability of water is high enough (Allen

et al. 2010; Huang et al. 2010; Hartmann 2011). In fact, the

supply of water may be reduced locally due to the

enhanced evaporation and the reduced accumulation of

snow replenishing soil water. However, the climate

change-induced reduction in productivity can be also

determined either by an increase in cloudiness, reducing

incoming solar radiation, or by heat stress caused by

wildfire (actively suppressed in Fennoscandia) or by

increased probability and severity of pest attacks (Dudley

1998; Johnston et al. 2009). In Finland, for example, the

south–north gradient in temperature and evapotranspiration

affects the likelihood of short supply of water throughout

the country even under the current climate, but climate

change may modify these effects depending on the region.

Model calculations show that the drought episodes may

become more frequent in southern Finland, whereas in

northern Finland, short supply of water is not evident

(Kellomaki et al. 2008; Ge et al. 2013).

In the boreal conditions, the growth may be further

increased due to the elongation of growing season and the

enhanced mineralization of nitrogen bound in dead wood

and litter due to warming climate (Bergh et al. 2003).

Furthermore, the climatic warming is likely to increase the

share of deciduous trees in the tree species composition,

which will increase the leaf litter with the fast decay and

mineralization of nitrogen. For example, the laboratory

tests show that the decomposition rate increases under

elevated temperature but reduces rapidly if temperature

exceeds an optimum value defined by the properties of

ecosystem (Shorohova et al. 2008; Zell et al. 2009; Tuomi

et al. 2011). Studies on latitudinal gradients as a proxy of

potential rate of detrital decay show that relatively large

pieces of CWD that intermittently enter forest ecosystems

406 Eur J Forest Res (2014) 133:405–421

123

may be more rapidly decayed in warmer climates (Woodall

and Liknes 2008). Increase in annual rainfall reduces the

decomposition rate (Zell et al. 2009). The decomposition of

litter is the fastest in silver birch stands as a result of the

higher lignin/nitrogen ratio respect to coniferous species,

and equally low in Norway spruce and Scots pine stands,

both with highly decay-resistant heartwood (Hillis 1977;

Zell et al. 2009). For coniferous trees, decomposition rate

is also higher in the south than in north following the

prevailing temperature conditions (Yatskov et al. 2003;

Makinen et al. 2006; Shorohova et al. 2012). In this

respect, sufficient soil moisture content can increase the

decomposition rate if only the oxygen depletion does not

inhibit microbial activity (Yin1999; Laiho and Prescott

2004; Shorohova et al. 2008; Tuomi et al. 2011). On the

other hand, decomposer activity is limited below the fiber

saturation point (*30 % moisture content) (Griffin 1977).

An increase in the decomposition rate is likely to reduce

the time of persistence of dead wood with specific prop-

erties (certain decomposition classes), thus reducing

resource availability either for saproxylic species requiring

more time to complete their biological cycle or for species

associated with high trophic level in the food chain. In

general, the effects of climate change on the dynamics of

dead wood are still poorly known.

Ecosystem models have widely been used in determin-

ing how environmental factors (like temperature, light, soil

nitrogen and moisture) influence the demographic pro-

cesses (birth, growth and death) in tree populations. Fur-

thermore, ecosystem models provide tools to analyze how

management and climate change may effect on the

dynamics of tree populations and dead wood in the suc-

cession of forest ecosystem (LeMay and Marshall 2001;

Landsberg 2003). In temporal and spatial scales, the model

approaches vary substantially from each other depending

on applications. Dynamic global vegetation models

(DGVMs) (Cramer et al. 2001 for an overview of DGVMs)

are good examples, if a rather coarse spatial resolution is

used in identifying the climate change effects on the boreal

forests (McDowell et al. 2011) and decay of dead wood.

Regarding local scale, gap-type models, for example,

provide the scale which allows the detailed analysis of the

interaction between growth and mortality, and the conse-

quent dynamics of dead wood and its decomposition, and

the consequent availability of dead wood for the resource

availability of different saproxylic species.

The objective of this study is to investigate at the local

scale the interaction between growth and mortality in Scots

pine (Pinus sylvestris), Norway spruce (Picea abies) and

silver birch (Betula pendula) stands in order to analyze

how management regime and climate change effect on the

availability of dead woods in the boreal conditions for

saproxylic species. Two contrasting management regimes

were used in the simulations based on a gap-type model:

set aside to maximize habitat availability versus manage-

ment to maximize timber production. In this context, the

initial density of trees, forest type and region were varied to

account for their interacting effects on the demographic

processes in long term. The time span in the simulations

was 80 years, which is typically needed in the boreal for-

ests for trees to reach maturity and to evaluate the effects of

an altered forest ecosystem dynamics on the dead wood

availability for saproxylic species (Tikkanen et al. unpub-

lished data). Finally, the implications of a climate-driven

change in the dead wood dynamics for the conservation of

forest-dwelling species are addressed in order to identify

the role of the management regime in increasing or

reducing the habitats of saproxylic species.

Methods

Outlines of the model

The simulations were done by employing the ecosystem

model SIMA (Kellomaki et al. 1992a, b; Kolstrom 1998;

Kellomaki et al. 2008), which is a non-spatial gap-type

model based on the properties of individual trees and uti-

lizing a time step of 1 year. In the model, regeneration is

partly stochastic and partly controlled by the availability of

light, soil moisture and temperature (Kolstrom 1998). The

growth of a tree is based on the diameter growth, which is

the product of potential diameter growth and environ-

mental factors (Fig. 1). Simulations are based on the Monte

Carlo simulation technique, i.e., certain events, such as tree

recruitment and death, are partially stochastic events. Each

time such an event is possible (e.g., it is possible for a tree

to die every year), the algorithm selects whether or not the

event will take place by comparing a random number with

the probability of occurrence of the event. The probability

of an event is a function of the state of the forest ecosystem

at the time when the event is possible. Each run of the code

is one realization of all possible time courses of ecosystem

development. Therefore, the simulations are repeated 20

times for each of the 192 combinations of tree species,

climate, management regime, forest type, density and

region (overall 3,840 runs) in order to determine the central

tendency of variations (average values) in the time

behavior of the forest ecosystem. The model is run on an

annual basis for patches of 100 m2, which are the basic plot

size used in the simulations, but results are given per

hectare. The computations represent scenarios for growth

and mortality based on given initial stands excluding

regeneration in order to analyze the dead wood dynamics.

Eur J Forest Res (2014) 133:405–421 407

123

Growth

The growth of stem and other tree organs are based on the

rate of diameter growth (1.3 m above ground level):

DD i; jð Þ ¼ DDo i; jð Þ xM1x; . . .; xMn

where DD(i,j) is the diameter growth of the species i in the

year j, DD0(i,j) the diameter growth in optimal conditions,

and M1,…,Mn are multipliers representing the temperature

sum (TS; ?5 �C threshold), prevailing light conditions,

soil moisture and nitrogen supply. Optimal conditions refer

to growth under no shading and no limitation of soil

moisture and nitrogen supply. The values of DD(i,j) are

further related to maturity of the tree (diameter of tree, D(i,

j) cm) and the atmospheric CO2 (Kellomaki et al. 2008):

DD0ði; jÞ ¼ exp �1:307þ 1:643

0:01� CO2

� �� Dði; jÞ

� eDGRO�Dði;jÞ ð1Þ

The diameter was further used to calculate the mass

Mass(i,k) of foliage, branches, stem and roots by applying

allometric equations with species-specific parameter

values:

Massði; kÞ ¼ exp aði; kÞ þ bði; kÞ � Dði; jÞcði; kÞ þ Dði; jÞ

� �ð2Þ

where a(i,k), b(i,k) and c(i,k) are parameters specific for

tree species (i) and mass component (k). Furthermore, the

stem volume was calculated using method presented by

Laasasenaho (1982). In Eq. (1) DGRO and in (2), a, b and

c are parameters based on the material representing tree

species populations throughout the country. The values of

the parameters are estimated based on the material gener-

ated by the comparable models developed by Malkonen

(1974, 1977) for P. sylvestris and Betula spp., and for P.

abies by Hakkila (1971). DGRO (expressed in cm-1) was

estimated by the values of the mean diameter growth

obtained from the growth and yield tables for natural stands

(Koivisto 1959). All the parameter values are reported in

Kellomaki et al. (1992b). The diameter was also used in

calculating the tree height by applying the height model of

Naslund, modified by including the current temperature

sum (TS) for the selected sites (Kellomaki et al. 2008). The

current TS indicates the geographical location of the plot,

i.e., the ecotype differences (provenances) in the growth

responses of trees to the climate.

Mortality and decomposition of litter

The endogenic mortality of whole trees was analyzed

including intrinsic death due to random reasons and

growth-dependent death determined by crowding, with

consequent reduction in growth and increase in the prob-

ability of a particular tree to die at a given moment (Fig. 2).

Intrinsic death is calculated by comparing the values of

the factor: stand densityC(i)/agemax (i) (stand density is

expressed in stems/ha) with the value of a random number,

both ranging from 0 to 1. If the value of the random

number is lower than the value of the factor, the tree will

die. C(i) is a constant different for each i species, and

Fig. 1 Outlines of the SIMA

model used in the simulations.

Picture modified from

Kellomaki et al. (1992b)

408 Eur J Forest Res (2014) 133:405–421

123

agemax (i) is the maximum age of species to survive, i.e.,

350 years for Scots pine, 180 years for Norway spruce and

120 years for silver birch. In growth-dependent death, a

tree is assigned dead if it does not grow, i.e., the annual

diameter increment [DD(i,j)] remains under a defined

minimum value [DDmin(i)] for two consecutive years. The

minimum value is defined by the product of the potential

diameter growth of the species DDo(i,j) and the diameter of

tree. DDmin(i) is the fraction of the potential growth of tree

decreasing with latitude.

The mass of different tree organs (e.g., foliage and

branches) in living trees is dying and falling down forming

litter cohorts, which refers to the annual amount of dead

material originating from the tree (Fig. 3). Litter is divided

into foliage, twig, root and woody litter. Thus, the total

annual litterfall includes in separate cohorts dead foliage,

twigs, coarse roots and fine roots, branches and stem wood.

Woody litter (standing and laying branches/stems) is fur-

ther divided in the diameter classes (\10 cm, 10–20 cm,

….,80–90 cm, [90 cm) to indicate the size distribution of

dead stem wood.

Litter cohorts lose weight in the decomposition, whose

rate is determined by the quality of litter and prevailing

climatic conditions. Decomposition is initiated by calcu-

lating the ash-free weight of cohort, and the amount of

carbon (C) and nitrogen contents (N) in cohort. The weight

loss of a litter (WLoss, %) is a function of the current ratio

between lignin (L) and nitrogen (L/N) contents and the

evapotranspiration (Meentemeyer 1978; Pastor and Post

1986; Meentemeyer and Berg. 1986):

WLoss ¼ A� B� ðL=NÞ ð3Þ

where A and B are variables dependent on the evapo-

transpiration (AET, cm). The slopes and intercepts of these

regressions have been regressed against AET as follows:

A = 0.9804 ? 0.09352 9 (AET) and B = -0.4956 ?

0.00193 9 (AET). Whenever the nitrogen concentration of

decaying litter in a particular cohort exceeds the critical

concentration, the organic matter and nitrogen in the cohort

are transferred to organic matter and nitrogen in humus,

which refers to the organic matter with no clear origin any

more. Decomposition of humus is dependent on AET and

the ratio between carbon and nitrogen contents in humus

(C/N). Weight loss of litter and humus is converted to

carbon dioxide, which is emitted to the atmosphere. For the

detailed mechanisms behind the decomposition, see Fig. 3.

Fig. 2 Outlines to calculate the mortality of trees in the SIMA model

Eur J Forest Res (2014) 133:405–421 409

123

The decomposition of litter determines the weight loss,

nitrogen immobilization, lignin decay and carbon dioxide

loss from the decomposing litter cohort. The decomposi-

tion of humus determines nitrogen mineralization, weight

loss of humus and carbon dioxide loss from humus. In the

calculations, litter and humus have been treated as cohorts.

The litter cohort indicates the amount of dead material

originating from trees and ground vegetation annually.

Litter is divided into different components: foliage, twigs,

roots and wood (stems of standing and fallen dead trees).

Each part is further divided into ten size classes by diam-

eter (\10 cm, 10–20 cm,…., 80–90 cm, [90 cm).

Decayed woody litter is transferred into well-decayed

wood. The treatment of a new litter cohort starts with the

calculation of ash-free weight of litter, carbon content and

nitrogen content of leaf and twig litter. These values are

further used in the calculation of the nitrogen–carbon

(N/C) ratio of leaf and twig litter, this ratio being needed in

the calculation of nitrogen mineralization.

The percent (%) weight loss of a litter cohort is a

function of the current ratio between lignin and nitrogen

(L/N) as follows, weightloss = A-B 9 (Lcurr/Ncurr), where

A and B are parameters dependent on the actual

evapotranspiration.

The current fraction of nitrogen Ncurr depends on the

following ratio:

Ncurr ¼Ntot þ UPN

Wcurr �Wloss

where Ntot is the total content of nitrogen, UPN the total

amount of nitrogen immobilized, Wcurr the current weight

of the litter cohort, and Wloss the weight loss of litter cohort

(all the measures in t ha-1).Similarly, the current fraction

of lignin Lcurr depends on another ratio:

Lcurr ¼ Align � Blign

Wcurr

Wo

where Align and Blign are parameters dependent on the

cohort and for foliage, also on the tree species (values in

Kellomaki et al. 1992b), and Wo and Wcurr are the original

and current weight of the litter cohort (t ha-1),

respectively.

Annually, the percent weightloss is multiplied by the

decay multiplier DECMLT for the canopy disturbance

Fig. 3 Mechanisms in the SIMA model of decomposition of litter and humus, with mineralization of nitrogen for reuse in tree growth.

Explanations are provided in the methods section ‘‘Mortality and decomposition of litter’’

410 Eur J Forest Res (2014) 133:405–421

123

which is dependent on the available soil moisture and the

canopy closure:

DECMLT ¼ 1þ �0:50þ 0:75 � ASWð Þ � 1� TYLL

CCLL

� �

where TYLL is the amount of leaf litter in the current year

and CCLL the leaf production in the closed canopy in

relation to available soil water, i.e., CCLL = 1.54 ?

0.457 9 ASW, where ASW is the available soil water as a

function of soil texture, which is the difference between the

field capacity (FC in cm) and the wilting point (DRY, in

cm). The decay multiplier indicates canopy openings and

the subsequent increase in decay rates because of changes

in the microclimate. The weight loss is, however, limited to

\20 % year-1 for twigs, 10 % year-1 for wood from trees

with stem diameters (DBH) \10 cm and 3 % year-1 for

wood from trees with DBH [10 cm.

Whenever the nitrogen concentration of the decaying

litter of a particular cohort exceeds the critical nitrogen

concentration, the organic matter and nitrogen of the cohort

are transferred to organic matter and nitrogen in humus.

Woody litter is, however, first transferred in the cohort of

well-decayed wood. It will stay there until the nitrogen

concentration exceeds another critical value and woody

litter is transferred to humus.

The mineralization of nitrogen (TNMIN, t ha-1) is a

function of the nitrogen–carbon ratio (N/C) of the humus

and the prevailing conditions:

TNMIN ¼HWEIGHT �0:000379 N

C

� ��0:02984þ N

C

� �DECMLT � AETM

where HWEIGHT is the amount of humus, DECMLT the

decay multiplier for the canopy disturbance, and AETM the

multiplier representing the local climate conditions. The

multiplier AETM scales the mineralization rate to the value

on a site representing the annual precipitation of 600 mm

as follows

AETM ¼ �AET

�1200þ AET

where AET is the annual evapotranspiration from the given

site. The scaling factor AETM is 1.0, if AET C600 mm,

and 0.5, if AET = 400 mm. The final decomposition of

litter immobilizes nitrogen, whose amount UPN is equal to

the Wloss multiplied by the nitrogen equivalent that is the

amount of nitrogen immobilized per gram weight loss (g/

g). The total immobilization is calculated as a sum of

nitrogen needed during the decay of each cohort of litter.

The total available nitrogen (AVAILN, t ha-1) for the trees

and the ground vegetation (vascular plants) is obtained by

subtracting from the total mineralized nitrogen TNMIN the

immobilized nitrogen in decomposition (TNIMOB, t ha-1)

(i.e., AVAILN = TNMIN—TNIMOB) plus the nitrogen

deposited in the site and added via through fall and fertil-

ization. The addition via through fall is assumed to be

16 % of the nitrogen on the foliage. The above nitrogen

mineralization–immobilization procedure is repeated

annually. The amount of nitrogen bound annually in the

biomass is linearly related to the growth of the different

compartments of the trees and the ground cover and their

nitrogen concentration, respectively.

Performance of the model

The validation of the model with the parameter values for

the main Finnish tree species has been discussed previously

by Kellomaki et al. (2008) and Routa et al. (2011). A close

correlation was found between measured growth values

from the National Forest Inventory and those simulated

with the model. Furthermore, Routa et al. (2011) showed a

good agreement in the parallel growth simulations based on

the SIMA and MOTTI models. The latter model is a sta-

tistical growth and yield model, whose parameterization is

based on the Finnish National Forest Inventory data rep-

resenting forests throughout Finland (Hynynen 2002). For

further details of the growth validation, see the studies

referred above.

Table 1 shows that the share of dead wood from the total

production in northern Finland is very small in the MOTTI

model calculations and very high in the SIMA calculations.

In southern Finland, both models give fairly similar results

except with low densities in Norway spruce stands where

the MOTTI model values are very low compared with the

SIMA values. Both of the results are modeled ones, and

therefore, it is impossible to say which model is the more

realistic one. Nevertheless, the general trends obtained with

both models are in line with those provided by growth yield

studies (Table 1, Koivisto 1959), but the SIMA model is

likely to overestimate and the MOTTI model to underes-

timate mortality in northern Finland.

Simulations

The simulations were subjected to two locations, one in

southern (N 62�390, E 29�370) and one in northern Finland

(N 66�350, E 26�050). In both cases, the simulations were

done across a fertility gradient corresponding to the pre-

sence of certain ground vegetation in the sites, in accor-

dance with the Cajander (1949) classification. In the

Finnish boreal forest, from poor through medium to high

fertility, we have the following types of ground vegetation,

named ‘‘forest types’’: lichen type (Calluna, CT), cowberry

type (Vaccinium, VT), bilberry type (Myrtillus, MT) and

herb-rich type (Oxalis-Myrtillus, OMT). This gradient is

also representative for water holding capacity, which is the

Eur J Forest Res (2014) 133:405–421 411

123

highest for the OMT site and the lowest for the CT site

(Cajander 1949). In the simulations, Scots pine was

established on the sites of any type, whereas Norway

spruce and silver birch were established on the sites of MT

and OMT types, according to the current planting recom-

mendations in Finland (Yrjola 2002). In each case, the

initial densities of plantations were 1,200, 2,400 and 4,800

trees per hectare. The stands were of single species,

reflecting typical Finnish monoculture stands, with the

initial mean diameter of 2 cm at the height of 1.3 m above

the ground level. Totally, 48 initial stands were used in the

simulations, which included two management regimes. In

the first management regime, no thinning or clear cutting

was done excluding any timber/biomass harvest [set-aside

regime (SA)]. In the second management regime, the cur-

rent recommendations ((Yrjola 2002) were used in thin-

nings done one to two times per rotation [business-as-usual

(BAU)]. After thinnings, non-commercial residual biomass

was left above ground. In both cases, the rotation/moni-

toring period was 80 years. When BAU was used, a clear-

cut leaving five retention trees per hectare was done at the

end rotation. In applying SA and BAU regimes, any abiotic

and biotic disturbances were excluded.

The simulations were done using the current climate and

changing climate. The current climate refers to the sta-

tionary climate in the period 1971–2000 over the whole of

Finland at 10 km grid resolution, whereas the changing

climate extends over the period 2010–2099 at 49-km grid

resolution (Venalainen et al. 2005; Jylha 2009). In both

cases, the climate data represented the daily values over the

seasons introducing the inter-annual variability around the

trends in the climate variables. The data from the closest

grid cell (tri-decadal averages and standard deviations) to

each location were used by the forest simulator to calculate

the monthly mean temperature and the monthly mean

precipitation with the standard deviations for the rotation

time. Regarding the atmospheric CO2, the annual mean

values were used in the simulations. Under the current

climate, the atmospheric CO2 was a constant of 352 ppm,

whereas under the changing climate, the CO2 increased

from the current one to 841 ppm based on the IPCC SRES

A2 emission scenario (Jylha 2009), with concurrent chan-

ges in temperature and precipitation. In southern Finland,

the mean annual temperature in the period 2,070-2,099

was 4.6 �C, higher than the current 2.4 �C. Similarly, the

mean annual temperature increased in northern Finland up

Table 1 The share of dead wood (%), from the total production calculated with simulated results of SIMA model and MOTTI model and

comparison to the values from growth and yield studies (Koivisto 1959)

Tree

species

Site

type

Density,

trees ha-1Southern Finland Northern Finland

SIMA

model (%)

MOTTI

model (%)

Growth and

yield table (%)

SIMA

model (%)

MOTTI

model (%)

Growth and

yield table (%)

Norway spruce MT 1,200 21.9 5.5 45.4 5.5 –

2,400 28.1 21.6 22 48.3 12.8

4,800 33.6 34 54.3 23.3

OMT 1,200 23.9 10.5 50.4 4.6 –

2,400 30.5 24.9 16.6 55.9 13.5

4,800 36.2 34 58.5 26.4

Scots pine VT 1,200 18.9 15.6 24.9 13.7

2,400 26.8 26.8 31 31.3 23.8 35.7

4,800 35.8 36 41 32.1

MT 1,200 28.7 24.7 41.3 15

2,400 37.8 36.5 29.2 45.7 31.8 41.2

4,800 40.3 44.8 49.6 40.1

OMT 1,200 27.3 19.9 – – –

2,400 34.2 33.1 27.6

4,800 40.6 39.7

Silver birch MT 1,200 39.6 33.7 44.4 7.7 –

2,400 38.5 43.2 32.5 41.8 17.4

4,800 38.6 47.5 46.7 26.5

OMT 1,200 44.8 39.6 55 9.8 –

2,400 43 47.9 35.5 49.5 23.2

4,800 44 52.2 51.7 32

412 Eur J Forest Res (2014) 133:405–421

123

to 4.9 �C, from the current 0.5 �C (Table 2). This meant

52 % increase in the temperature sum (the threshold

?5 �C) in the south and 65 % in the north. In both loca-

tions, the annual precipitation increased about 20 % as did

the evaporation. In the period 2070–2099, the evaporation

was in the south 83 % and in the north 90 % of the pre-

cipitation. Seasonal precipitation changes were larger in

the winter (?10–40 %) than in the summer (?0–20 %) by

the end of the century, and they were more drastic in the

north than in the south. Even if climate warming was

predicted being less pronounced in the summer than in the

other seasons, nevertheless the increase in summer tem-

peratures is likely to be biologically very important.

Data analysis

Regarding all the simulation cases, the following variables

were used to indicate the dynamics of trees and dead wood:

(1) the annual tree growth (m3 ha-1 year-1); (2) the mor-

tality of trees (number of death events ha-1 year-1); (3) the

annual input of dead wood (m3 ha-1 year-1); (4) the

decomposition rate, i.e., the loss in volume of dead wood

from a year to the next (m3 ha-1 year-1) (this decompo-

sition rate is not either the decomposition constant (k) or

the percent mass loss (d) calculated in Shorohova et al.

2012); and (5) the amount of dead wood volume on the

site(m3 ha-1).

The dependence between the indicators (response vari-

ables) and the categorical predictor variables (i.e., climate

change, management regime, forest type, region, density)

was analyzed by using generalized estimating equations

(GEEs). GEE is a technique used to estimate the

parameters of a generalized linear model with a possible

unknown correlation between outcomes (Hardin and Hilbe

2003). The focus of GEE is on estimating the average

response over the population (‘‘population-averaged’’

effects) rather than the regression parameters that would

enable the predictions of the effects of changing one or

more covariates on a given individual. For each indicator,

we conducted the analyses separately for the three tree

species. The GEE method is based on the quasilikelihood

theory; i.e., the distribution of the dependent variables does

not need to be normal. The distribution of errors (random

part of the model) and the associated link function (sys-

tematic part) between the dependent variable and the

covariates in the model varied among the five response

indicators. All the models were performed with SPSS 20.0

IBM Corp. (2011).

The values of the time series of the tree mortality

response were analyzed with negative binomial distribu-

tion and log link function, after verifying the absence of

zero inflation (for the three species, the frequency of 0 s

was always \15 %). The time series of the other

responses were analyzed with gamma distribution and log

link function after adding a constant: i.e., annual growth,

annual input of dead wood, the accumulation of volume

of dead wood and decomposition rate of dead wood. The

independent working correlation matrix of the time series,

where correlations are assumed to be 0 for all pairwise

combinations of variables, resulted to be the best in terms

of the lowest corrected quasilikelihood under the inde-

pendence model criterion (QICc: Pan 2001; Shults et al.

2009). Model selection methods for GEE include choos-

ing the model minimizing the values of QICc. In this

case, models with distribution families for the response

variable other than negative binomial and gamma

increased strongly the values of these criteria, demon-

strating their inefficiency, and were not reported (for a

review of GEE models, see Hardin and Hilbe 2003). The

estimated values of the GEE models for the predictor

variables and, when biologically and significantly impor-

tant, their interaction terms, are reported in Table 3 while

the statistical details are reported in Supplementary

material in Table 1S.

Results

The effects of region, forest type, density and climate

change

The forest processes influenced by climate change were

dependent on other environmental factors, primarily on

region, tree density and forest type (Table 3). For all the

tree species, the production of dead wood was faster in the

Table 2 Values of some climate variables for the current reference

climate (1971–2000) and the changing climate (period 2070–2099) in

Finland estimated by Venalainen et al. (2005) and Jylha (2009)

Climate

variable

Current values

(1971–2000)

Predicted values

(2070–2099)

% Predicted

changes

Temperature (�C)

South 2.4 7

North 0.5 5.4

Temperature sum (d.d.)

South 1,129 1,713 52

North 826 1,365 65

Precipitation (mm)

South 534 639 20

North 447 531 19

Evaporation (mm)

South 444 532 20

North 395 479 21

Percentages of variation from the current values are also reported

Eur J Forest Res (2014) 133:405–421 413

123

Ta

ble

3A

ver

age

val

ues

for

each

ind

icat

or

esti

mat

edb

yG

EE

mo

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imat

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enar

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agem

ent

cate

go

ries

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rest

typ

es,

reg

ion

,d

ensi

tyan

din

tera

ctio

ns

of

clim

ate

wit

hm

anag

emen

t

and

reg

ion

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eM

ean

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mat

eS

ign

.M

anag

emen

tS

ign

.F

ore

stty

pe

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n.

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ion

Sig

n.

SC

CC

BA

US

AC

TV

TM

TO

MT

NS

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nu

alg

row

th(m

2h

a-1

yea

r-1)

Sil

ver

bir

ch6

.55

.18

.1*

**

**

4.5

9.1

**

**

**

*5

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**

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8.3

**

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**

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tsp

ine

3.5

34

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**

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.63

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rway

spru

ce5

4.8

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rtal

ity

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1)

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ver

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11

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6.9

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nu

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(m3

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1)

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ver

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20

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ce0

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(m3

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1)

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spru

ce2

7.3

24

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8.8

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sity

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n.

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spru

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9.3

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1.2

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4.4

*

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nu

alin

pu

tv

olu

me

of

dea

dw

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d(m

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r-1)

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414 Eur J Forest Res (2014) 133:405–421

123

south than in the north, and this between-region difference

was associated with higher annual growth, mortality (sig-

nificantly for silver birch and Norway spruce) and annual

input of dead wood, but also with increased decomposition

rate in the south. For all the tree species, annual growth,

annual input of dead wood, and consequently, the pro-

duction of dead wood volumes increased with the fertility

of the forest type. Likewise, decomposition rate increased

with the fertility of the forest type (significantly for Scots

pine and Norway spruce). The mortality of Scots pine was

comparably the highest in the less (CT) and the more

(OMT) fertile soil types. More generally while for Scots

pine and Norway spruce, mortality increased with the

fertility of forest type, for silver birch, an increase in fer-

tility determined a lower mortality. An increase in the

initial tree density had always a positive influence in rais-

ing the annual growth and the four mortality indicators

(i.e., mortality, annual and accumulated volume of dead

trees, decay rate), even if its importance varied with the

tree species. In northern Finland, a strong speedup of

annual growth induced by climate change was observed for

silver birch (?86.0 %), Scots pine (?63.8 %) and for

Norway spruce (?33.6 %). In southern Finland, climate

change caused an increase in growth only for silver birch

(?38.3 %) and Scots pine (?16.5 %), but a growth

reduction for Norway spruce (-9.8 %). For Norway

spruce, a significant increase in mortality induced by cli-

mate change was also observed in the South (?28.4 %;

Table 3). For silver birch and Scots pine, climate change

significantly increased the annual input volume of dead

wood more in the north (respectively, ?83.7 and ?74.1 %)

than in the south (respectively, ?42.5 and ?46.7 %) finally

significantly increasing the dead wood volume only for

silver birch more in the north (?84.2 %) than in the south

(?42.7 %).

Annual growth

Climate change enhanced tree growth but management

regime had a larger effect than climate on growth

(Table 3). The effects of climate and management on the

annual growth were consistently larger in silver birch

(relative increase ?59 and ?101 % for climate change and

management effects, respectively) than in Scots pine (?37

and ?70 %) and larger in Scots pine than in Norway

spruce (?10 and ?35 %). The overall interaction term

between climate and management was positively signifi-

cant only for silver birch, for which under climate change

annual growth increased much more under SA regime than

under BAU, while for the two coniferous trees, climate

change effects on growth were parallel under both man-

agement regimes.Ta

ble

3co

nti

nu

ed

Tre

eM

ean

Den

sity

Sig

n.

Cli

mat

eX

man

agem

ent

Sig

n.

Cli

mat

eX

reg

ion

Sig

n.

1,2

00

2,4

00

4,8

00

SC

XB

AU

SC

XS

AC

CX

BA

UC

CX

SA

SC

XN

SC

XS

CC

XN

CC

XS

Sco

tsp

ine

0.9

06

0.9

1.1

**

*0

.41

0.5

1.8

*0

.50

.90

.91

.3*

No

rway

spru

ce1

.41

.01

.31

.8*

**

**

0.7

1.7

0.9

2.5

*1

1.2

1.4

1.9

Dec

om

po

siti

on

rate

(m3

ha-

1y

ear-

1)

Sil

ver

bir

ch0

.06

0.0

43

00

.06

12

0.0

75

4*

*0

.02

95

0.0

56

60

.04

67

0.1

07

2*

0.0

27

30

.05

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0.0

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50

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00

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.00

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01

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01

6

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rway

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ce0

.00

30

.00

19

0.0

02

60

.00

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0.0

01

30

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04

0.0

06

90

.00

16

**

*0

0.0

01

70

.00

30

.00

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dw

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dv

olu

me

(m3

ha-

1)

Sil

ver

bir

ch7

9.5

65

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0.8

94

.8*

*5

1.5

75

.67

7.7

13

1.4

43

.19

0.1

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.41

28

.6*

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tsp

ine

15

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31

5.1

26

.1*

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**

8.6

16

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2.5

31

.4*

8.6

16

.91

4.5

27

.1

No

rway

spru

ce2

7.3

16

.42

6.8

46

**

**

*1

7.9

32

.92

1.3

43

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2.6

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9.6

31

.7

Th

ere

lati

ve

imp

ort

ance

of

each

sig

nifi

can

tp

red

icto

rin

the

mo

del

,d

efin

edac

cord

ing

toW

ald

chi-

squ

are

(rep

ort

edin

the

app

end

ix),

isd

efin

edb

yd

iffe

ren

tn

um

ber

of

aste

risk

s,w

ith

no

aste

risk

ind

icat

ing

no

tsi

gn

ifica

nt

val

ues

–=

val

ues

no

tes

tim

ated

,S

C=

stat

ion

ary

clim

ate,

CC

clim

ate

chan

ge,

BA

Ub

usi

nes

s-as

-usu

al,

SA

set-

asid

e,C

T,

VT

,M

T,

OM

Tfo

rest

typ

eso

fin

crea

sin

gfe

rtil

ity

lev

elac

cord

ing

toC

ajan

der

(19

49

),N

and

SN

ort

her

nan

dS

ou

ther

nF

inla

nd

Eur J Forest Res (2014) 133:405–421 415

123

Mortality

Climate change altered tree mortality, but management

regime had a larger effect than climate on mortality.

Mortality was predicted to be less frequent under climate

change for silver birch (-7 %), while it increased similarly

under climate change for Scots pine (?17) and Norway

spruce (?14 %). Mortality was predicted to be more fre-

quent under the SA regime than under the BAU regime

(Table 3). The effects of management on the annual mor-

tality were the highest for Scots pine (?108 %), interme-

diate for Norway spruce (?89 %) and the lowest for silver

birch (?45 %) (Table 3). The interaction term between

climate and management showed that under climate change

for silver birch, a lower mortality occurred only under

BAU regime, while for Scots pine, a much higher mortality

occurred under SA than under BAU regime.

Annual input volume of dead wood

There was a positive effect of climate change on the annual

input of dead wood volume, which was the highest one for

silver birch (?60 %), the intermediate one for Scots pine

(?57 %) and the lowest one for Norway spruce (?40 %)

(Table 3) Nevertheless, management had a larger effect

than climate on the annual input volume of dead wood, and

the SA management regime produced a larger annual input

of dead wood than BAU management regime for all tree

species, i.e., the input under SA was ?209 % for Scots

pine, ?154 % for Norway spruce and ?74 % for silver

birch larger than those under BAU. For all the tree species,

the positive effect of climate change on the annual input

was significantly higher under SA than under BAU, under

which climate change had a more limited effect on the

annual input of dead wood (Table 3).

Decomposition rate

Climate change increased the decomposition rate of dead

wood representing Norway spruce (?407 %) and silver

birch (?78 %) but not in the case of Scots pine (Table 3).

Management had a larger effect than climate change on

decomposition rate for silver birch and Scots pine while

climate was more important in Norway spruce. However,

the direction of this effect was not consistent among tree

species. The annual decomposition rate of dead woody

material was 115 % larger for silver birch in the SA regime

than in the BAU regime but 96 and 75 % lower for Scots

pine and Norway spruce, respectively. In Scots pine and

Norway spruce, these differences followed an opposite

pattern in respect of the annual input of dead wood, but this

did not hold for silver birch. In Scots pine, there was no

interaction between climate change and management

regime. In silver birch, the enhancing effect of climate

change on the decomposition rate was significantly larger

in the SA scenario than in the BAU scenario. In Norway

spruce, the increase in the decomposition rate due to cli-

mate change was larger under the BAU regime than under

the SA regime (Table 3). This implies that climate change

boosted the decomposition rate of dead wood of Norway

spruce under the BAU regime, whereas the same occurred

for silver birch under the SA regime.

Dead wood volume

Climate change tended to increase the total volume of dead

wood, with a similar magnitude for silver birch (?62 %) and

Scots pine (?65 %) and less for Norway spruce (?26 %).

While for silver birch, climate change had a larger effect than

management on the dead wood volumes, management

regime had a larger effect for Scots pine and Norway spruce.

The SA regime increased substantially the volume of dead

wood compared to the BAU regime. On the relative scale, the

volume of dead wood in the former case was 120, 94 and

58 % higher in Scots pine, Norway spruce and silver birch

than in the latter case, respectively (Table 3). The interaction

term between climate and management was not significant

for silver birch and Norway spruce suggesting that the effects

of climate change are consistent between the two manage-

ment regimes for these two tree species. For Scots pine under

climate change, there was a higher increase in dead wood

under the SA regime than under BAU.

Discussion

The effects of region, forest type, density and climate

change

We confirmed the positive effect of regions at lower lati-

tude, forest types of increasing fertility and higher initial

tree density in accelerating dead wood dynamics (Pretzsch

2010; Pretzsch et al. 2013a, b). As expected from previous

research, climate change enhanced the growth, increased

the annual input and volume of dead wood, finally accel-

erating the decomposition (Kellomaki et al. 2008;

Shorohova et al. 2008; Woodall and Liknes 2008; Zell

et al. 2009; Tuomi et al. 2011). On the other hand in our

study, climate change had direct effect on increasing the

mortality rate as such for the two coniferous trees,

according to Harmon (2009) and McDowell et al. (2011),

while climate change reduced mortality in silver birch. The

increase in tree growth is explained by the contribution of

climate change in enhancing the mineralization of nitrogen,

via an increased evapotranspiration, when soil moisture is

not a limiting factor. Therefore, growth increase in our

416 Eur J Forest Res (2014) 133:405–421

123

simulations confirmed the general high water availability in

the soils of boreal forest. However, this general trend was

not confirmed for Norway spruce, for which growth in

southern localities was proven to decrease under climate

change, probably as a response to drought (Kellomaki et al.

2008). In general, climate change provokes an earlier cul-

mination of diameter growth and enhanced maturation and

the reduction in growth in older and larger trees (Harmon

2009). This explains why the enhanced growth indirectly

increased the annual input of dead wood. At the same time,

the decomposition of dead wood was enhanced, but the

increase was smaller than that of the dead wood input.

Consequently, climate change increased the accumulation

of dead wood. This was especially the case for silver birch

stands, where a large enhancement of annual growth gen-

erating a considerably higher annual input of dead wood, in

spite of the lower mortality, resulted in a large increase in

dead wood volumes despite increased decomposition rate.

This held also for Scots pine stands where an enhancement

of annual growth generated higher annual input of dead

wood, boosted by the increased mortality and coupled with

a stable decomposition rate resulted in the increase in dead

wood volumes. Finally for Norway spruce stands, climate

change generated a modest increase in dead wood volumes

because of relatively small increase in annual growth

generating a smaller increase in annual input of dead wood,

boosted by the increased mortality, and much faster

decomposition. We confirmed the general higher increase

in annual growth expected for northern Finland with

respect to the south for all the tree species. Our simulations

showed that in Norway spruce, climate change with

increased frequency of droughts is likely reducing the

potential for growth and increasing mortality in the South,

whereas in the north, growing conditions will likely

improve, confirming the results of Kellomaki et al. (2008)

and Ge et al. (2013).

The effect of management

The management regime (no thinning/thinning) was a more

important driver than climate in altering the growth and

mortality and the consequent amount of dead wood in the

site regardless of location and tree species, confirming the

results of Shanin et al. (2010), Hjalten et al. (2012) and

Gossner et al. (2013). This was expected because more

space is created in thinning for remaining trees thus

avoiding too early reduction in growth and the consequent

death. On the other hand, thinning was done from below,

thus removing the suppressed trees, which are most sus-

ceptible for death due to reducing growth. Thinning

reduced substantially the dead wood input and increased

the decomposition rate of coniferous dead wood while

increasing the decomposition rate of silver birch. In these

respects, the exclusion of thinning increased dead wood

input substantially boosting the accumulation of dead wood

for all the tree species, but while for coniferous trees, the

retention time of dead wood was increased by a slower

decomposition rate, for silver birch accumulated dead

wood had a faster turnover (cf. Briceno-Elizondo et al.

2006; Garcia-Gonzalo et al. 2007). For all the tree species

under climate change, the BAU regime will provide lower

increase in annual input of dead wood respect to the SA

regime. This input of dead wood will be decomposed faster

under SA regime for silver birch, while for Norway spruce,

set-aside will reduce the increase in decomposition, guar-

antee the persistence of the vanishing Norway spruce dead

wood, especially in the south.

Consequences for forest biodiversity

In the boreal forest, climate change may result in an increase

in the availability of cumulated ‘‘productive’’ energy, i.e., of

the energy stored in the tree wood volume, causing a general

increase in species richness (Evans et al. 2005; Honkanen

et al. 2010; Reich et al. 2011). This, however, critically

depends on what happens to critical resources the species

require, and may thus vary among taxa. Our simulations

showed that climate change is likely to increase the total

volume of dead wood available for forest-dwelling species,

despite an increased decomposition rate. The increase in

dead wood will likely provide more resources and improve

habitat availability, especially for the rare red-listed species

dependent particularly on dead birch and Scots pine trees.

According to our study under climate change on average,

dead wood volumes will meet the thresholds of 20–40 m3

recommended to sustain populations of the majority of the

threatened species in boreal forest (Muller and Butler 2010;

Junninen and Komonen 2011). On the other hand, our

results show that forest management can reduce the amount

of dead wood, as already observed by Shanin et al. (2010),

Hjalten et al. (2012) and Gossner et al. (2013). This reduc-

tion can be stronger in regions at higher latitudes and on

forest types of low fertility. This is relevant when consid-

ering that, in general, poorly productive areas that are

marginally good habitats for species have been often chosen

in boreal forest for settlement of protected areas (Nilsson

and Gotmark 1992; Virkkala and Raijasarkka 2007). Tik-

kanen et al. (2006) estimated that out of the total of 457

boreal red-listed species, 60 % are dependent on dead wood.

Out of these 276 saproxylic species, 20 species occur on

birch and 48 species on Scots pine. But Norway spruce

harbors the largest number of saproxylic species (65; Tik-

kanen et al. 2006) for which our simulations predicted a

slight increase (?26 %) in the overall availability of

resources dead wood with climate change. Climate change

is likely to change the tree species composition with

Eur J Forest Res (2014) 133:405–421 417

123

decreasing growth and reduced success of Norway spruce in

southern Finland and increasing dominance of silver birch

and Scots pine; in the north, Norway spruce will still thrive

(e.g., Kellomaki et al. 2008; Ge et al. 2013). This may imply

further endangerment of Norway spruce-associated sapr-

oxylic species. Their persistence would critically be

dependent on their ability to disperse and colonize new sites

with advancing climate change. In the south, we may evi-

dence a drastic reduction in the relative abundance of the

characteristic Norway spruce-associated taiga species and

an increase in more southern species dependent on birch.

Planting conditions of Norway spruce stands can be adjusted

to adapt for climate change. An adaptive strategy in man-

agement of Norway spruce under the changing climate is to

assure the long-term persistence of Norway spruce, with its

associated saproxylic species, by choosing an ecotype of

more southern provenance for regeneration (Kellomaki

et al. 2008; Weslien et al. 2009) and planting Norway spruce

on sites that are edaphically most favorable for it (Ge et al.

2013). However, our results show that the persistence of

Norway spruce dead wood in the landscape can be guaran-

teed by setting aside stands, neutralizing in this way the

strong increase in decomposition rate induced by climate

change.

Climate change influences the processes regulating dead

wood dynamics but with different intensity for different

tree species. Also the tempo and mode of many of the

forest processes are dramatically changing. The retention

time of the dead wood stock on the soil will be reduced by

an increased decomposition rate for silver birch and Nor-

way spruce. As a consequence, dead wood in advanced

decay stages will disappear faster potentially causing

problems for species associated with well-decayed dead

wood. On the other hand, recently died woody material will

become more available favoring species associated with

fresh dead wood. Otherwise, many specialist saprotrophic

wood-fungi (e.g., Fomitopsis rosea, Edman et al. 2006),

cambium-living beetles (e.g., Callidium coriaceum, Bius

thoracicus, Cyphaea latiuscula, Dicerca moesta, Carp-

hoborus cholodkowskyi), certain noctuid moths (Xestia-

species) and other groups of invertebrates such as spiders

(Ehnstrom 2001) prefer to live in slow-growing wood.

Increased growth rate as a consequence of climate change

will likely have a negative impact on the habitat avail-

ability of these species. Anyway especially in non-thinned

stands, there can still be variability in growth rate: Those

trees that win in the intraspecific competition will grow

faster while the others will grow slowly and finally die.

Adaptation strategies

Current strategies to adapt to climate change from the point

of view of economic efficiency in commercial forests

include more frequent thinning and reducing forest rotation

lengths to utilize the increased productivity (Kellomaki

et al. 2008; Alam et al. 2008). Both will likely have a

negative effect on habitat availability of saproxylic

organisms. Thinning will effectively remove the trees that

would have otherwise entered the dead wood stock, and

more frequent thinning would result in further reduction in

dead wood in managed forests. Likewise, shorter rotation

coupled with shorter retention time of the dead wood stock

due to increased decomposition rate may make it more

difficult for many dead wood-associated species to colo-

nize rapidly enough the suitable dead wood resources

(Ehnstrom 2001; Schroeder et al. 2007). Thus, even though

the annual input of dead woody material may be higher in

future forests, from species perspective, this may still mean

reductions in habitat availability if economic efficiency is

emphasized. Our results suggest that the most effective

single strategy to provide more dead wood resources for

saproxylic species under climate change is to grow stands

unmanaged (unthinned). This would ensure larger amounts

of dead wood and reduce the decay (turnover) rate of

conifer trees, thus providing more stable resource base for

saproxylic organisms. This should be economically sus-

tainable first because Tikkanen et al. (2012) showed rela-

tively low costs (reductions in growth) from growing

stands unthinned, and in some cases, refraining from

thinnings was also economically a better option. Secondly,

the improved growth would make it economically sus-

tainable to leave at least a part of stands without man-

agement and still maintain the current timber flow. A

balance between the quest for capitalizing the increased

productivity and the maintenance of habitat availability for

dead wood-associated species should be found to guarantee

long-term persistence of forest-dwelling species. At the

landscape scale, in addition to intensive even-aged man-

agement, applying a combination of management regimes

such as growing stands unthinned or with extended rota-

tions (Tikkanen et al. 2007, 2012; Monkkonen et al. 2011)

and uneven-aged and cohort forest management systems

(Axelsson and Angelstam 2011) would likely provide both

economic and ecological benefits (Monkkonen et al (2014),

in press) and thus improve sustainability especially under

climate change.

Model limitations

For studies on the effects of the changing climate on the

growth and yield, the SIMA model is an appropriate

compromise between growth and yield tables and models

based on the physiology of trees (Kellomaki et al. 1992a, b).

Although the possible immigration driven by climate

change of tree species is not included in the computations,

418 Eur J Forest Res (2014) 133:405–421

123

this has no major effect on the model output, since the

change in temperature occurs within a period too short for

any species now outside the simulation area to invade it

and achieve dominance on the sites included in the study.

The tree species favored by the suggested climatic change

are estimated to advance into boreal forests at the rate of

about 100–200 m year-1. No major change in the tree

species composition results from the temperature increase

for the double carbon dioxide concentration applied in the

A2 scenario, as compared to the pattern for the current

climate. Finally, the present version of the SIMA model

does not simulate the occurrence of some phenomena

during forest rotation, whose incidence are predicted to be

higher under climate change, as wildfire, wind and insect

attacks. These phenomena have been excluded from the

simulations in order to consider just the pure effects of

climatic variability on the forest processes (Kellomaki

et al. 1992a, b).

Acknowledgments This research was funded by the Academy of

Finland (Project Number: 21000012421). We are grateful to Pasi Re-

unanen and Maria Trivino De la Cal, for improving the manuscript with

their comments. This paper was initially submitted, reviewed and

revised in Peerage of Science (http://www.peerageofscience.org/), and

we are grateful to an anonymous peer for constructive comments.

Conflict of interest The authors declare that they have no conflict

of interest.

References

Ainsworth EA, Long SP (2005) What have we learned from 15 years

of free-air CO2 enrichment (FACE)? A meta-analytic review of

the responses of photosynthesis, canopy properties and plant

production to rising CO2. New Phytol 165:351–372. doi:10.

1111/j.1469-8137.2004.01224.x

Alam A, Kilpelainen A, Kellomaki S (2008) Impacts of thinning on

growth, timber production and carbon stocks in Finland under

changing climate. Scand J Forest Res 23:501–512

Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N,

Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH,

Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J,

Allard G, Running SW, Semerci A, Cobb N (2010) A global

overview of drought and heat-induced tree mortality reveals

emerging climate change risks for forests. For Ecol Manage

259:660–684. doi:10.1016/j.foreco.2009.09.001

Axelsson R, Angelstam P (2011) Uneven-aged forest management in

boreal Sweden: local forestry stakeholders’ perceptions of

different sustainability dimensions. Forestry 84:567–579.

doi:10.1093/forestry/cpr034

Bergh J, Freeman M, Sigurdsson B, Kellomaki S, Laitinen K, Niinisto

S, Peltola H, Linder S (2003) Modelling the short-term effects of

climate change on the productivity of selected tree species in

Nordic countries. For Ecol Manage 183:327–340. doi:10.1016/

S0378-1127(03)00117-8

Briceno-Elizondo E, Garcia-Gonzalo J, Peltola H, Matala H,

Kellomaki S (2006) Sensitivity of growth of Scots pine, Norway

spruce and silver birch to climate change and forest management

in boreal condition. For Ecol Manage 232:152–167

Cajander AK (1949) Forest types and their significance. Suomen

metsA¤tieteellinen seura. Acta Forestalia Fennica 56(5):1–71

Coates KD, Burton PJ (1997) A gap-based approach for development

of silvicultural systems to address ecosystem management

objectives. For Ecol Manage 99:337–354. doi:10.1016/S0378-

1127(97)00113-8

Cramer W, Bondeau A, Woodward FI, Prentice IC, Betts RA,

Brovkin V, Cox PM, Fisher V, Foley JA, Friend AD, Kucharik

C, Lomas MR, Ramankutty N, Sitch S, Smith B, White A,

Young-Molling C (2001) Global response of terrestrial ecosys-

tem structure and function to CO2 and climate change: results

from six dynamic global vegetation models. Global Change Biol

7:357–373

Dudley N (1998) Forests and climate change. A Report for World

Wildlife Fund International, Gland, Switzerland. url: http://

www.equilibriumresearch.com/upload/document/climatechange

andforests. pdf

Edman M, Moller R, Ericson L (2006) Effects of enhanced tree

growth rate on the decay capacities of three saprotrophic wood-

fungi. For Ecol Manage 232:12–18. doi:10.1016/j.foreco.2006.

05.001

Ehnstrom B (2001) Leaving dead wood for insects in boreal forests:

suggestions for the future. Scand J For Res 16:91–98. doi:10.

1080/028275801300090681

Evans KL, Warren PH, Gaston KJ (2005) Species–energy relation-

ships at the macroecological scale: a review of the mechanisms.

Biol Rev 80:1–25. doi:10.1017/S1464793104006517

Franklin J, Shugart H, Harmon M (1987) Tree Death as an Ecological

Process. Bioscience 37:550–556. doi:10.2307/1310665

Garcia-Gonzalo J, Peltola H, Briceno-Elizondo E, Kellomaki S

(2007) Changed Thinning regimes may increase carbon stock

under climate change: a case study from a Finnish boreal forest.

Clim Change 81:431–454

Ge Z-M, Kellomaki S, Peltola H, Zhou X, Vaisanen H, Strandman H

(2013) Impact of climate change on primary production and

carbon sequestration of boreal Norway spruce forests: Finland as

a model. Climatic Change 118:259–273

Gossner MM, Lachat T, Brunet J, Isacsson G, Bouget C, Brustel H,

Brandl R, Weisser WW, Muller J (2013) Current near-to-nature

forest management effects on functional trait composition of

saproxylic beetles in beech forests. Conserv Biol. doi:10.1111/

cobi.12023

Griffin DM (1977) Water potential and wood-decay fungi. Annu Rev

Phytopathol 15:319–329

Hakkila (1971) Coniferous branches as a raw material source. A

subproject of the joint Nordic research programme for the

utilization of the logging residues. Seloste: Havupuun oksat

raaka-ainelahteena. Yhteispohjoismainen hakkuutahdetutkimuk-

sen alaprojecti. Comun Inst For Fenn 75(1):1–60

Hardin JW, Hilbe JM (2003) Generalized estimating equations.

Chapman and Hall/CRC, Boca Raton, FL

Harmon ME, Franklin JF, Swanson FJ, Sollins P, Gregory SV, Lattin

JD, Anderson NH, Cline SP, Aumen NG, Sedell JR, Lienkaem-

per GW, Cromack Jr. K, Cummins KW (1986) Ecology of

coarse woody debris in temperate ecosystems. In: Advances in

Ecological Research. Academic Press, pp 59–234

Harmon ME (2009) Woody detritus mass and its contribution to

carbon dynamics of old-growth forests. Wirth C, Gleixner G;

HM Old-growth forests: function, fate and value. Springer-

Verlag, Berlin, pp 159–190

Hartmann H (2011) Will a 385 million year-struggle for light become

a struggle for water and for carbon? How trees may cope with

more frequent climate change-type drought events. Glob Change

Biol 17(1):642–655

Hillis WE (1977) Secondary changes in wood. Rec Adv Phytochem

11:247–309

Eur J Forest Res (2014) 133:405–421 419

123

Hjalten J, Stenbacka F, Pettersson RB, Gibb H, Johansson T, Danell

K, Ball JP, Hilszczanski (2012) Micro and Macro-Habitat

Associations in Saproxylic Beetles: Implications for Biodiversity

Management. PLoS ONE 7(7):e41100. doi:10.1371/journal.

pone.0041100

Honkanen M, Roberge J, Rajasarkka A, Monkkonen M (2010)

Disentangling the effects of area, energy and habitat heteroge-

neity on boreal forest bird species richness in protected areas.

Global Ecol Biogeogr 19:61–71. doi:10.1111/j.1466-8238.2009.

00491.x

Huang J, Tardif JC, Bergeron Y, Denneler B, Berninger F, Girardin

MP (2010) Radial growth response of four dominant boreal tree

species to climate along a latitudinal gradient in the eastern

Canadian boreal forest. Glob Change Biol 16(2):711–731

Hynynen J (2002) Models for predicting stand development in MELA

system. Finnish Forest Research Institute, Vantaa Research

Centre, Vantaa

IBM Corp (2011) IBM SPSS Statistics for Windows, Version 20.0.

IBM Corp, Armonk, NY

Johnston M, Campagna M, Gray P, Kope H, Loo J, Ogden A, O’Neill

GA, Price D, Williamson T (2009) Vulnerability of Canada’s

tree species to climate change and management options for

adaptation: An overview for policy makers and practitioners.

Canadian Council of Forest Ministers, Ottawa 40 pp

Junninen K, Komonen A (2011) Conservation biology of boreal

polypores: a review. Biol Conserv 144:11–20

Jylha K (2009) Arvioita Suomen muuttuvasta ilmastosta sop-

eutumistutkimuksia varten. ACCLIM-hankkeen raportti 2009.

Rapoetteja _ Rapporter _ Reports. (The changing climate in

Finland: estimates for adaptation studies. ACCLIM project

report 2009.) Ilmatieteen laitos, Raportteja 2009: 4, 102 s. In

Finnish, abstract, extended abstract and captions for figures and

tables in English)

Kellomaki S, Vaisanen H, Hanninen H, Kolstrom T, Lauhanen R,

Mattila U, Pajari B (1992a) A simulation model for the

succession of the boreal forest ecosystem. Silva Fenn 26:1–18

Kellomaki S, Vaisanen H, Hanninen H, Kolstrom T, Lauhanen R,

Mattila U, Pajari B (1992b) SIMA: A model for forest

succession based on the carbon and nitrogen cycles with

application to silvicultural management of the forest ecosystem.

Joensuun yliopisto, [Joensuu]

Kellomaki S, Peltola H, Nuutinen T, Korhonen KT, Strandman H

(2008) Sensitivity of managed boreal forests in Finland to

climate change, with implications for adaptive management.

Philos Trans R Soc B: Biol Sci 363:2339–2349. doi:10.1098/

rstb.2007.2204

Koivisto P (1959) Growth and yield tables. Communicationes Instituti

Forestalis Fenniae 51: 47 pp

Kolstrom M (1998) Ecological simulation model for studying

diversity of stand structure in boreal forests. Ecol Model

111:17–36. doi:10.1016/S0304-3800(98)00102-1

Krankina ON, Harmon ME (1995) Dynamics of the Dead Wood

Carbon Pool in Northwestern Russian Boreal Forests. In: Apps

MJ, Price DT, Wisniewski J, Northwestern Russian Boreal

Forests, pp 227–238

Laasasenaho J (1982) Taper curve and volume functions for pine,

spruce and birch. Commun Inst For Fenn 108:1–74

Laiho R, Prescott CE (2004) Decay and nutrient dynamics of coarse

woody debris in northern coniferous forests: a synthesis. Can J

For Res 34:763–777. doi:10.1139/x03-241

Landsberg (2003) Modelling forest ecosystems: state of the art,

challenges, and future directions. Can. J. For. Res. 33:385–397

LeMay V, Marshall P (2001) Proceedings of forest modelling for

ecosystem management, forest certification and sustainable

management conference, 12–17 Aug 2001, Vancouver BC

Makinen H, Hynynen J, Siitonen J, Sievanen R (2006) Predicting the

decomposition of Scots pine, Norway spruce and birch stems in

Finland. Ecol Appl 16:1865–1879. doi:10.1890/1051-

0761(2006)016

Malkonen E (1974) Annual primary production and nutrient cycle in

some Scots pine stands. Seloste: Vuotuinen primaarituotos ja

ravinteiden kiertokulku mannikossa. Comun Inst For Fenn

84(5):1–87

Malkonen E (1977) Annual primary production and nutrient cycle in a

birch stand. Seloste: Vuotuinen primaarituotos ja ravinteiden

kiertokulku eraassa koivikossa. Comun Inst For Fenn 91(5):1–28

Matala J, Kellomaki S, Nuutinen T (2008) Litterfall in relation to

volume growth of trees: analysis based on literature. Scand J

Forest Res 23:194–202

McDowell NG, Beerling DJ, Breshears DD, Fisher RA, Raffa KF,

Stitt M (2011) The interdependence of mechanisms underlying

climate-driven vegetation mortality. Trends Ecol Evol

26:523–532. doi:10.1016/j.tree.2011.06.003

Meentemeyer V (1978) Macroclimate and lignin control of litter

decomposition rates. Ecology 59:465–472

Meentemeyer V, Berg B (1986) Regional variation in rate of mass

loss of Pinus sylvestris needle litter in Swedish pine forests as

influenced by climate and litter quality. Scan J Forest Res

1:167–180

Monkkonen M, Reunanen P, Kotiaho JS, Juutinen A, Tikkanen O-P,

Kouki J (2011) Cost-effective strategies to conserve boreal forest

biodiversity and long-term landscape-level maintenance of

habitats. Eur J Forest Res 130:717–727

Monkkonen M, Juutinen J, Mazziotta A, Miettinen K, Podkopaev D,

Reunanen P, Salminen H, Tikkanen O-P (2014) Spatially

dynamic forest management to sustain biodiversity and eco-

nomic returns. J Environ Manage (In press)

Muller J, Butler R (2010) A review of habitat thresholds for dead

wood: a baseline for management recommendations in European

forests. Eur J Forest Res 129:981–992

Nelson E, Mendoza G, Regetz J, Polasky S, Tallis H, Cameron DR,

Chan KMA, Daily GC, Goldstein J, Kareiva PM, Lonsdorf E,

Naidoo R, Ricketts TH, Shaw MR (2009) Modeling multiple

ecosystem services, biodiversity conservation, commodity pro-

duction, and tradeoffs at landscape scales. Front Ecol Environ

7:4–11. doi:10.1890/080023

Nilsson C, Gotmark F (1992) Protected areas in Sweden: is natural

variety adequately represented? Conserv Biol 6:232–242

Pan W (2001) Akaike’s information criterion in generalized estimat-

ing equations. Biometrics 57:120–125. doi:10.1111/j.0006-

341X.2001.00120.x

Pastor J, Post W (1986) Influence of climate, soil moisture, and

succession on forest carbon and nitrogen cycles. Biogeochem-

istry 2:3–27. doi:10.1007/BF02186962

Peet R, Christensen N (1987) Competition and tree death. Bioscience

37:586–595. doi:10.2307/1310669

Pretzsch H (2010) Forest Dynamics. From Measurement to Model,

Springer, Growth and Yield, p 664

Pretzsch H, Biber P, Schutze Bielak K (2013a) Changes of forest

stand dynamics in Europe facts from long-term observational

plots and their relevance for forest ecology and management. For

Ecol Manage. doi:10.1016/j.foreco.2013.07.050

Pretzsch H, Bielak K, Block J, Bruchwald A, Dieler J, Ehrhart H-P,

Kohnle U, Nagel J, Spellmann H, Zasada M, Zingg A (2013b)

Productivity of mixed versus pure stands of oak (Quercus

petraea (Matt.) Liebl. and Quercus robur L.) and European

beech (Fagus sylvatica L.) along an ecological gradient. Eur J

Forest Res 132(2):263–280

Reich PB, Frelich LE, Voldseth RA, Bakken P, Adair C (2011)

Understorey diversity in southern boreal forests is regulated by

420 Eur J Forest Res (2014) 133:405–421

123

productivity and its indirect impacts on resource availability and

heterogeneity. J Ecol 100(2):539–545

Reineke LH (1933) Perfecting a stand-density index for even-aged

forests. J Agric Res 46:627–638

Routa J, Kellomaki S, Peltola H, Asikainen A (2011) Impacts of

thinning and fertilization on timber and energy wood production

in Norway spruce and Scots pine: scenario analyses based on

ecosystem model simulations. Forestry 84:159–175. doi:10.

1093/forestry/cpr003

Schroeder LM, Ranius T, Ekbom B, Larsson S (2007) Spatial

occurrence of a habitat-tracking saproxylic beetle inhabiting a

managed forest landscape. Ecol Appl 17:900–909

Shanin VN, Mikhailov AV, Bykhovets SS, Komarov AS (2010)

Global climate change and carbon balance in forest ecosystems

of boreal zones: simulation modeling as a forecast tool. Biol Bull

37:619–629

Shorohova E, Ignatyeva O, Kapitsa E, Kauhanen H, Kuznetsov A,

Vanha-Majamaa I (2012) Stump decomposition rates after clear-

felling with and without prescribed burning in southern and

northern boreal forests in Finland. For Ecol Manage 263:74–84.

doi:10.1016/j.foreco.2011.09.006

Shorohova E, Kapitsa E, Vanha-Majamaa I (2008) Decomposition of

stumps in a chronosequence after clear-felling versus clear-

felling with prescribed burning in a southern boreal forest in

Finland. For Ecol Manage 255:3606–3612. doi:10.1016/j.foreco.

2008.02.042

Shults J, Sun W, Tu X, Kim H, Amsterdam J, Hilbe JM, Ten-Have T

(2009) A comparison of several approaches for choosing

between working correlation structures in generalized estimating

equation analysis of longitudinal binary data. Stat Med

28:2338–2355. doi:10.1002/sim.3622

Solomon S (2007) Intergovernmental panel on climate change.

working group I. Climate change 2007: contribution of working

group i to the fourth assessment report of the intergovernmental

panel on climate change. Cambridge University Press,

Cambridge

Stokland JN, Siitonen J, Jonsson BG (2012) Biodiversity in dead

wood. Cambridge University Press, Cambridge

Tikkanen O-P, Martikainen P, Hyvarinen E, Junninen K, Kouki J

(2006) Red-listed boreal forest species of Finland: associations

with forest structure, tree species and decaying wood. Annls.

Zool. Fennici 43:373–383

Tikkanen O-P, Heinonen T, Kouki J, Matero J (2007) Habitat

suitability models of saproxylic red-listed boreal forest species in

long-term matrix management: cost-effective measures for

multi-species conservation. Biol Conserv 140:359–372

Tikkanen O-P, Matero J, Monkkonen M, Juutinen A, Kouki J (2012)

To thin or not to thin: bio-economic analysis of two alternative

practices to increase amount of coarse woody debris in managed

forests. Eur J Forest Res 131:1411–1422

Tuomi M, Laiho R, Repo A, Liski J (2011) Wood decomposition

model for boreal forests. Ecol Model 222:709–718. doi:10.1016/

j.ecolmodel.2010.10.025

Vanclay J (2003) Realizing opportunities in forest growth modelling.

Can J For Res -Rev Can Rech For. 33:536–541. doi:10.1139/

X02-117

Venalainen A, Tuomenvirta H, Pirinen P, Drebs A (2005) A basic

Finnish climate data set 1961–1990—description and illustra-

tions. Reports 2005:5

Virkkala R, Rajasarkka A (2007) Uneven regional distribution of

protected areas in Finland: consequences for boreal forest bird

populations. Biol Conserv 134:361–371

Waring R (1987) Characteristics of trees predisposed to die.

Bioscience 37:569–574. doi:10.2307/1310667

Weslien J, Finer L, Jonsson JA, Koivusalo H, Lauren A, Ranius T,

Sigurdsson BD (2009) Effects of increased forest productivity

and warmer climates on carbon sequestration, run-off water

quality and accumulation of dead wood in a boreal landscape: a

modelling study. Scand J For Res 24:333–347. doi:10.1080/

02827580903085171

White J, Harper JL (1970) Correlated changes in plant size and

number in plant populations. J Ecol 58:467–485

Wolfslehner B, Seidl R (2010) Harnessing ecosystem models and

multi-criteria decision analysis for the support of forest man-

agement. Environ Manage 46:850–861. doi:10.1007/s00267-

009-9414-5

Woodall CW, Liknes GC (2008) Relationships between forest fine

and coarse woody debris carbon stocks across latitudinal

gradients in the United States as an indicator of climate change

effects. Ecol Indic 8:686–690

Yatskov M, Harmon ME, Krankina ON (2003) A chronosequence of

wood decomposition in the boreal forests of Russia. Can J For

Res 33:1211–1226

Yin X (1999) The decay of forest woody debris: numerical modeling

and implications based on some 300 data cases from North

America. Oecologia 121:81–98. doi:10.1007/s004420050909

Yoda K, Kira T, Ogawa H, Hozumi H (1963) Self-thinning in

overcrowded pure stands under cultivated and natural conditions.

J Biol Osaka Cy Univ 14:107–129

Yrjola T (2002) Forest management guidelines and practices in

Finland, Sweden and Norway European Forest Institute Internal

Report 11

Zell J, Kandler G, Hanewinkel M (2009) Predicting constant decay

rates of coarse woody debris—A meta-analysis approach with a

mixed model. Ecol Model 220:904–912

Eur J Forest Res (2014) 133:405–421 421

123