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: [email protected]
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
del
sfo
rcl
imat
esc
enar
ios,
man
agem
ent
cate
go
ries
,fo
rest
typ
es,
reg
ion
,d
ensi
tyan
din
tera
ctio
ns
of
clim
ate
wit
hm
anag
emen
t
and
reg
ion
Tre
eM
ean
Cli
mat
eS
ign
.M
anag
emen
tS
ign
.F
ore
stty
pe
Sig
n.
Reg
ion
Sig
n.
SC
CC
BA
US
AC
TV
TM
TO
MT
NS
An
nu
alg
row
th(m
2h
a-1
yea
r-1)
Sil
ver
bir
ch6
.55
.18
.1*
**
**
4.5
9.1
**
**
**
*5
.77
.3*
**
5.0
8.3
**
**
**
Sco
tsp
ine
3.5
34
.1*
**
2.7
4.6
**
**
*1
.63
.35
5.4
**
**
**
3.0
4.2
**
**
No
rway
spru
ce5
4.8
5.2
**
4.3
5.8
**
**
**
4.7
5.4
**
*4
.95
.2*
Mo
rtal
ity
(tre
esy
ear-
1)
Sil
ver
bir
ch2
0.4
21
.21
9.7
**
16
.92
4.6
**
**
*2
1.6
19
.3*
**
*2
1.3
19
.6*
**
Sco
tsp
ine
10
.59
.71
1.3
**
*7
.21
5*
**
**
12
.48
.21
0.1
11
.7*
**
*9
.81
1.2
**
No
rway
spru
ce1
2.2
11
.61
3.2
**
91
6.9
**
**
11
.21
3.6
**
*1
2.0
12
.7
An
nu
alin
pu
tv
olu
me
of
dea
dw
oo
d(m
3h
a-1
yea
r-1)
Sil
ver
bir
ch3
.22
.54
.1*
**
**
2.4
4.2
**
**
**
*2
.83
.8*
**
*2
.54
.1*
**
**
*
Sco
tsp
ine
0.9
0.7
1.1
**
0.5
1.4
**
**
*0
.30
.71
.21
.5*
**
**
*0
.71
.1*
**
*
No
rway
spru
ce1
.41
.11
.6*
**
0.8
2.1
**
**
**
1.1
1.6
**
**
1.2
1.5
**
Dec
om
po
siti
on
rate
(m3
ha-
1y
ear-
1)
Sil
ver
bir
ch0
.06
0.0
43
0.0
76
7*
**
*0
.03
80
.08
2*
**
**
0.0
55
20
.06
44
0.0
46
30
.07
34
**
*
Sco
tsp
ine
0.0
01
0.0
01
20
.00
15
0.0
03
0*
**
*0
.00
06
0.0
00
80
.00
16
0.0
02
3*
**
0.0
01
00
.00
16
*
No
rway
spru
ce0
.00
30
.00
08
0.0
04
3*
**
**
0.0
04
0.0
01
**
**
0.0
01
60
.00
35
*0
.00
15
0.0
03
6*
*
Dea
dw
oo
dv
olu
me
(m3
ha-
1)
Sil
ver
bir
ch7
9.5
62
.41
01
.1*
**
*6
3.3
99
.7*
**
61
.41
02
.8*
**
**
58
.61
07
.6*
**
**
*
Sco
tsp
ine
15
.51
2.1
19
.9*
*1
0.4
22
.9*
**
*4
.31
1.6
28
37
.3*
**
**
*1
1.2
21
.4*
**
No
rway
spru
ce2
7.3
24
.33
0.7
**
19
.63
8*
**
*2
1.6
34
.4*
**
25
.92
8.8
*
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
An
nu
alg
row
th(m
2h
a-1
yea
r-1)
Sil
ver
bir
ch6
.55
.26
.57
.9*
**
*3
.86
.75
.31
2.2
**
3.6
76
.89
.7*
Sco
tsp
ine
3.5
33
3.6
3.8
*2
.33
.93
.15
.42
.33
.93
.84
.5*
*
No
rway
spru
ce5
4.3
5.1
5.7
**
**
*4
.25
.54
.46
.24
.25
.45
.64
.9*
**
*
Mo
rtal
ity
(tre
esy
ear-
1)
Sil
ver
bir
ch2
0.4
11
.12
03
7.8
**
**
**
18
.22
4.6
15
.82
4.6
*2
2.1
20
.32
0.6
18
.9
Sco
tsp
ine
10
.53
.69
.82
9.7
**
**
**
7.1
13
.17
.31
7.2
*9
.31
0.1
10
.31
2.4
No
rway
spru
ce1
2.2
4.6
11
.43
3.4
**
**
*8
.61
5.4
9.3
18
.51
21
1.2
12
.11
4.4
*
An
nu
alin
pu
tv
olu
me
of
dea
dw
oo
d(m
3h
a-1
yea
r-1)
Sil
ver
bir
ch3
.22
.73
.33
.8*
**
22
.22
.95
.6*
*1
.83
.43
.44
.9*
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
89
0.0
65
50
.08
8
Sco
tsp
ine
0.0
01
0.0
00
90
.00
13
0.0
01
8*
*0
.00
22
0.0
00
10
.00
29
0.0
00
10
.00
07
0.0
01
60
.00
13
0.0
01
6
No
rway
spru
ce0
.00
30
.00
19
0.0
02
60
.00
31
0.0
01
30
.00
04
0.0
06
90
.00
16
**
*0
0.0
01
70
.00
30
.00
55
Dea
dw
oo
dv
olu
me
(m3
ha-
1)
Sil
ver
bir
ch7
9.5
65
.58
0.8
94
.8*
*5
1.5
75
.67
7.7
13
1.4
43
.19
0.1
79
.41
28
.6*
Sco
tsp
ine
15
.59
31
5.1
26
.1*
**
**
8.6
16
.71
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
.82
2.6
26
.22
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.
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