1852  |    Glob Change Biol. 2019;25:1852–1867.wileyonlinelibrary.com/journal/gcb
 
Received: 1 October 2018  |  Accepted: 21 January 2019
DOI: 10.1111/gcb.14594  
P R I M A R Y  R E S E A R C H  A R T I C L E
Carbon flux from decomposing wood and its dependency 
on temperature, wood N2 fixation rate, moisture and fungal 
composition in a Norway spruce forest
Katja T. Rinne‐Garmston  |   Krista Peltoniemi |   Janet Chen  |   Mikko Peltoniemi |   
Hannu Fritze |   Raisa Mäkipää
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in 
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© 2019 The Authors. Global Change Biology Published by John Wiley & Sons Ltd
Natural Resources Institute Finland (Luke), 
Helsinki, Finland
Correspondence
Katja T. Rinne‐Garmston, Natural Resources 
Institute Finland (Luke), Helsinki, Finland.
Email: katja.rinne‐garmston@luke.fi
Funding information
Research Council for Biosciences, Health 
and the Environment, Academy of Finland, 
Grant/Award Number: 292899
Abstract
Globally 40–70 Pg of carbon (C) are stored in coarse woody debris on the forest floor. 
Climate change may reduce the function of this stock as a C sink in the future due to 
increasing temperature. However, current knowledge on the drivers of wood decom‐
position is inadequate for detailed predictions. To define the factors that control 
wood respiration rate of Norway spruce and to produce a model that adequately 
describes the decomposition process of this species as a function of time, we used an 
unprecedentedly diverse analytical approach, which included measurements of res‐
piration, fungal community sequencing, N2 fixation rate, nifH copy number, 
14C‐dat‐
ing as well as N%, δ13C and C% values of wood. Our results suggest that climate 
change will accelerate C flux from deadwood in boreal conditions, due to the ob‐
served strong temperature dependency of deadwood respiration. At the research 
site, the annual C flux from deadwood would increase by 27% from the current 117 g 
C/kg wood with the projected climate warming (RCP4.5). The second most important 
control on respiration rate was the stage of wood decomposition; at early stages of 
decomposition low nitrogen content and low wood moisture limited fungal activity 
while reduced wood resource quality decreased the respiration rate at the final 
stages of decomposition. Wood decomposition process was best described by a 
Sigmoidal model, where after 116 years of wood decomposition mass loss of 95% 
was reached. Our results on deadwood decomposition are important for C budget 
calculations in ecosystem and climate change models. We observed for the first time 
that the temperature dependency of N2 fixation, which has a major role at providing 
N for wood‐inhabiting fungi, was not constant but varied between wood density 
classes due to source supply and wood quality. This has significant consequences on 
projecting N2 fixation rates for deadwood in changing climate.
K E Y W O R D S
activation energy, boreal forest, carbon flux, coarse woody debris, N2 fixation, nifH, 
respiration rate, wood‐inhabiting fungi
     |  1853RINNE‐GARMSTON ET Al.
1  | INTRODUC TION
Decomposition of deadwood releases globally billions of tons of 
carbon (C) per year to the atmosphere (Harmon, Krankina, Yatskov, 
& Matthews, 2001), a magnitude that is similar to the emissions 
from fossil fuel combustion (Le Quéré et al., 2013). However, the 
current estimate is based on an incomplete knowledge of drivers 
of wood decomposition and their relation to ambient conditions, 
such as temperature and soil moisture, which are likely to change 
in the future. Consequently, it is important to quantify the factors 
that control respiration rates and to determine the pattern and 
magnitude of C flux rates at different stages of decomposition, so 
that the contribution of this C stock to total ecosystem respiration 
can be adequately accounted for in ecosystem, C accounting and 
climate change models.
The dynamics of wood respiration are still relatively poorly 
understood in comparison to other fluxes of forest respiration 
(Liu, Schaefer, Qiao, & Liu, 2013). Firstly, the published models de‐
scribing wood decomposition have substantial differences: they 
predict different temporal patterns of decomposition and highly 
variable residence times for deadwood of the same tree species 
on forest floor (Harmon et al., 1986). The negative exponential 
model, which is one of the most commonly used to estimate wood 
decomposition, predicts fastest decomposition rate for Norway 
spruce at the beginning of decomposition and a long total decom‐
position time (>100 years; Melin, Petersson, & Nordfjell, 2009 
and references therein). In contrast, for the same tree species, 
the multiple‐exponential model of Mäkinen, Hynynen, Siitonen, 
and Sievänen (2006) predicts a slow decomposition rate for the 
first couple of decades followed by a fast rate of decomposition, 
which leads to 100% mass loss 60 years after death. Secondly, it 
has been proven difficult to disentangle the relative importance 
of the different factors driving decomposition. For example, ac‐
cording to some studies temperature and wood moisture content 
are the dominant factors influencing decomposition rates via their 
impact on microbial activity (Olajuyigbe, Tobin, & Nieuwenhuis, 
2012; Wang, Bond‐Lamberty, & Gower, 2002), while other stud‐
ies have reported that these variables explain small portions of 
total variance in decomposition rate (Bradford et al., 2014; Yang 
et al., 2016). Other factors reported to affect the decomposition 
process include substrate quality (e.g. concentration of slowly 
decomposing lignin) (Mackensen & Bauhus, 2003; Tuomi, Laiho, 
Repo, & Liski, 2011), wood nitrogen (N) content (Weedon et al., 
2009), soil macrofauna (Jacobs & Work, 2012) and fungal diver‐
sity (Valentin et al., 2014) and community structure (Rayner & 
Boddy, 1988).
Although community structure of wood‐inhabiting fungi has 
been linked in several studies to decomposition rate of dead‐
wood, the actual affiliated mechanisms are still poorly under‐
stood (Rajala, Peltoniemi, Pennanen, & Makipaa, 2012). One 
of the controlling factors is likely the succession of the fungal 
communities along which also physical and chemical properties 
of wood change. The successional change in fungal community 
of boreal forests generally follows a temporal pattern, initiated 
by the relatively inefficient soft‐rot fungi, followed by brown‐rot 
fungi and then by white‐rot fungi, which have the unique capa‐
bility to efficiently decompose wood lignin (Hyde & Jones, 2002; 
Rajala, Peltoniemi, Hantula, Mäkipää, & Pennanen, 2011). Finally, 
in boreal forests, wood becomes dominated by ectomycorrhi‐
zal (ECM) fungi, which transport N and C between soil and host 
plants, at the last stages of decomposition (Mäkipää et al., 2017). 
In addition, bacteria are considered to play a minor role in wood 
decomposition (Clausen, 1996), but recent studies have proven 
early hypothesis (Cowling & Merrill, 1966) that e.g. N2 fixing bac‐
teria may have a remarkable contribution on N availability in the 
decomposing wood (Rinne et al., 2017).
Little is still known about how different fungal species present in 
deadwood influence wood decomposition rate (Hoppe et al., 2016; 
Valmaseda, Almendros, & Martínez, 1990). Another factor that has 
been linked to decomposition rate, however with significant con‐
troversy, is fungal richness, measured with DNA based methods as 
a number of operational taxonomic unit (OTU). Some studies have 
reported a positive relationship between the two variables at the 
early to mid‐stages of decomposition (Valentin et al., 2014), which 
has been explained by the positive impact the higher diversity of 
produced fungal enzymes with increasing number of OTUs has on 
communities’ wood decomposition efficiency (Gessner et al., 2010). 
However, other studies have found a negative relationship between 
decomposition rate and OTU richness (Lindner et al., 2011; Yang et 
al., 2016) or no relationship at all (Hoppe et al., 2016). Other sig‐
nificant factors that have been reported to affect decomposition 
rates are competition scenarios between fungi (Renvall, 1995) and 
the mutualistic relationship between fungi and N2 fixing diazotrophs 
(Hoppe et al., 2014).
Apart from being a substantial stock of terrestrial C, deadwood is 
an important component of nutrient cycling (Laiho & Prescott, 2004). 
This includes N, which accumulates in deadwood during the decom‐
position process mainly via asymbiotic N fixation (Rinne et al., 2017). 
N may also be used during the decomposition process via sporocarp 
and spore production or lost via wood fragmentation and leaching. 
Since saprotrophic fungi grow in wood with a very high C:N ratio 
and since they are at the same time highly dependent on the avail‐
ability of N for production of fungal material (Moore, Gange, Gange, 
& Boddy, 2008), it has been suggested that there may exist a signif‐
icant mutualistic relationship between N2‐fixing bacteria and wood 
decay fungi (Cowling & Merrill, 1966; Mäkipää et al., 2018). The first 
evidence of this was seen in the study of Hoppe et al. (2014), who re‐
ported a positive correlation between the number of fungal fruiting 
bodies and a nitrogenase‐related gene, nifH, diversity. How well nifH 
distribution in decomposing wood relates to actual N2‐fixation and 
respiration rates are yet to be determined. Furthermore, fungal fruit 
bodies, as used in the study of Hoppe et al. (2014), may not be rep‐
resentative of the fungal community present in deadwood (Hoppe 
et al., 2016), and hence fungal community sequencing could provide 
additional information on the associations between diazotrophs and 
wood‐habiting fungi.
1854  |     RINNE‐GARMSTON ET Al.
Both respiration and N2 fixation rates of fallen deadwood may 
increase as ambient temperature increases due to ongoing climate 
change, if wood moisture does not become limiting (too low or 
too high). Hence, climate warming may have significant conse‐
quences for the role of deadwood as a long‐term C reservoir and 
on the availability of N in acutely nutrient‐limited high latitude for‐
est ecosystems. The metabolic theory of ecology (MTE) (Brown, 
Gillooly, Allen, Savage, & West, 2004) argues that the impact of 
climate warming on the decomposition and N2 fixation rates of 
deadwood can be predicted from the temperature dependencies 
of the corresponding biochemical reactions, respiration and N2 
fixation, respectively. The temperature sensitivity of these re‐
actions are quantified by the activation energy (AE) (Arrhenius, 
1915), with a canonical value of 2.18 eV for the nitrogenase en‐
zyme (Ceuterick, Peeters, Heremans, Smedt, & Olbrechts, 1978) 
and of 0.60–0.70 eV for respiration (Allen, Gilloolz, & Brown, 
2005), for the temperature ranges 0°C–22°C and 0°C–30°C, re‐
spectively. However, the MTE does not take into account limita‐
tions and temperature induced changes in resource supply, which 
may lead to AEs that diverge from the canonical value. For exam‐
ple, Welter et al. (2015) reported that stream biofilm development 
in Iceland, amplified temperature response of respiration owing 
to increased N supply, which was the result of increased tempera‐
ture. Conversely, Follstad Shah et al. (2017), who used data from 
169 published studies, concluded that litter decomposition rates 
had AEs that were approximately half the value predicted by met‐
abolic theory due to limitations for example in litter supply regime, 
litter quality and detritivore density. For deadwood a similar study 
on the AE of respiration and N2 fixation has not been conducted to 
the best of our knowledge.
This study was conducted on deadwood of Norway spruce 
(Picea abies (L.) Karst.) to define the factors that control wood de‐
composition rates at different stages of decomposition and to re‐
solve the debated question on the type of model that best describes 
the decomposition process of this species as a function of time. 
Furthermore, we used this knowledge to obtain estimates of annual 
CO2 production rates from deadwood in current conditions and as 
a result of projected climate warming. To achieve our goals we used 
an unprecedentedly diverse dataset, which included measurements 
of respiration, fungal community sequencing, N2 fixation rate, nifH 
copy number, 14C‐dating as well as N content, δ13C and C% values 
of wood.
2  | MATERIAL S AND METHODS
2.1 | Site description and fieldwork
Fieldwork was carried out at the Lapinjärvi forest located in south‐
ern Finland (60°66N, 26°12E). It is an unmanaged site, with a living 
Norway spruce trees biomass of 119,000 kg/ha (296 m3/ha) that 
constitutes 72% of all tree species (Rajala et al., 2012). The biomass 
of spruce deadwood, which accounts for 89.4% of all deadwood, is 
shown in Table 1 for each decay class, as classified by the system of 
Harmon and Sexton (1996). Two 5 cm thick disks were obtained from 
49 decomposing logs. The collected disks were evenly distributed 
between the five decay classes (Mäkipää et al., 2018). The disks were 
packed into plastic bags and stored within the same day in a freezer 
at −20°C until processed. Under natural forest floor conditions logs 
experience freeze‐thaw ‐cycles annually, so the storage method 
was not considered to have an unnatural impact on the analytical 
results. Wood density was determined for each log as described in 
Rajala et al. (2012) using one of the two obtained discs. Details of 
the density classification (Table 1) are given in Rinne et al. (2017) 
and in Supporting Information S1. In addition, seven living Norway 
spruce trees were sampled to the pith using an increment borer for 
determining the initial wood C%.
2.2 | Meteorological data
A 2‐year record of average daily temperature measured with data 
loggers at 1 m above ground level in Ruotsinpyhtää forest was used. 
The forest, which is located 18 km from the Lapinjärvi forest, has 
a similar tree composition (spruce dominated, tree density), mean 
annual temperature (4.6°C) and annual precipitation (618 mm) as 
Lapinjärvi.
2.3 | Incubation and gas chromatography 
(GC) analysis
Each of the thawed disks was chopped into wood chips and divided 
into eight equal subsamples that were placed into 118 ml bottles 
with an air tight cap and the fresh weight was recorded. Seven of 
the bottles were used for CO2 measurements and one was reserved 
for other analyses (molecular and isotope analyses). First, all eight 
bottles were preincubated for 1 week at 14.5°C. Subsequently, the 
seven bottles reserved for gas chromatography were incubated 
5 days at different temperatures (8.7°C, 14.5°C, 20.5°C, 26°C, 
30°C, 34°C and 40.1°C) and the additional eighth bottle reserved 
for other analyses was incubated at 14.5°C. Samples were incu‐
bated under stable conditions by controlling the moisture content 
(replacement of evaporated water) and aerating the bottles (30 s 
per aeration) during the preincubation and incubation periods. The 
samples that were reserved for analyses other than GC (molecular 
and isotope analyses) were frozen and freeze‐dried following the 
incubation period.
For the incubated samples respiration rates were determined 
by sampling the headspace of a bottle and analysing the amount 
of CO2 using a gas chromatogram (Smolander, Kitunen, Tamminen, 
& Kukkola, 2010). The bottles were aerated 24 hr before sampling. 
Finally, samples were dried in an oven at 60°C for 48 hr and the dry 
mass was calculated for each sample.
2.4 | Molecular and bioinformatics analyses
The 49 freeze‐dried samples were milled to fine particle size to en‐
sure homogeneity for molecular and isotope analysis. Details about 
     |  1855RINNE‐GARMSTON ET Al.
DNA extraction, fungal internal transcribed spacer (ITS) sequencing 
procedure with Illumina platforms as well bioinformatics for the ob‐
tained fungal ITS data are described in Supporting Information S2. 
Shannon diversity indices from fungal ITS data were obtained by 
using the summary.single command in the MOTHUR software pack‐
age v.1.3.6 (Schloss et al., 2009). Venn diagrams were constructed 
to view unique and shared fungal OTUs in wood density classes by 
using the venn() command implemented in library gplots in R pack‐
age version 3.2.2 .
To detect the copy numbers of N2‐fixing bacteria, we con‐
ducted quantitative PCR (qPCR) targeting the nifH gene with PolF 
forward and PolR reverse primers (Poly, Monrozier, & Bally, 2001). 
First standard curves were constructed with plasmids containing 
corresponding inserts, taking into account the molecular mass 
of the plasmid, including the insert, and the plasmid concentra‐
tion. We ran qPCR (Rotor‐Gene 6000; QIAGEN, The Netherlands) 
with Maxima™SYBR Green qPCR Master Mix (2×) (Thermo Fisher 
Scientific, Germany) in a 20 ml final reaction volume containing 
1 µl template, 0.3 mM of each primer and 1× qPCR master mix. 
Fluorescence was measured at the end of each extension step. 
Each qPCR run was carried out under the following conditions: 
initial denaturation at 95°C for 10 min, 40 cycles denaturation at 
95°C for 15 s, annealing at 55°C for 30 s and extension at 72°C 
for 30 s, and final extension at 72°C for 10 min and from 60°C to 
99°C (ramp: 1°C per 5 s). All samples were replicated and lack of 
PCR inhibition was verified through 1:10 dilution. The copy num‐
bers in samples were calculated based on comparison to threshold 
cycle values of the standard curve and are given per gram of soil 
(dry weight).
2.5 | Stable isotope analysis
The 49 freeze‐dried and milled deadwood samples were weighed 
into tin capsules for C% and δ13C measurements in an elemen‐
tal analyser (EA; Costech 4010; Costech Analytical Technologies 
Inc., Valencia, CA, USA) connected in continuous‐flow mode to an 
TA B L E  1   Deadwood characteristics of Norway spruce from the Lapinjärvi forest
Density class
I (n = 10) II (n = 7) III (n = 8) IV (n = 13) V (n = 11)
Activation 
energy of 
N2 fixation 
(eV)
0.69 0.60 1.27 1.04 0.40
Activation 
energy of 
respiration 
(eV)
0.68 0.63 0.63 0.59 0.61
Biomass of 
deadwood 
(kg/ha)
8,641 9,214 3,011 3,982 427
C in 
deadwood 
(kg C/ha)
4,051 4,394 1,412 1726 231
Fungal 
Shannon 
index
2.3 ± 1.0 2.5 ± 1.5 2.9 ± 0.9 3.5 ± 0.5 3.6 ± 0.4
Moisture (%)a  29 ± 6 39 ± 11 61 ± 14 78 ± 5 79 ± 3
nifH copies/g 1.17 × 107 ± 1.35 × 107 3.89 × 107 ± 4.17 × 107 4.16 × 108 ± 6.71 × 108 1.22 × 109 ± 8.39 × 108 6.54 × 108 ± 3.93 × 108
Wood 
absolute N 
content 
(kg N/m3)
0.251 ± 0.025 0.249 ± 0.053 0.283 ± 0.134 0.355 ± 0.143 0.516 ± 0.112
Wood δ13C 
(‰)
−24.48 ± 0.60 −25.20 ± 0.92 −25.63 ± 0.50 −25.35 ± 0.75 −26.24 ± 0.85
Wood 
density (kg/
m3)
380 ± 30 310 ± 20 240 ± 30 180 ± 20 140 ± 10
Wood C 
content (%)
50.8 ± 1.4 52.4 ± 1.3 53.5 ± 1.4 52.7 ± 3.7 56.0 ± 2.5
aRinne et al. (2017). 
1856  |     RINNE‐GARMSTON ET Al.
isotope ratio mass spectrometer (IRMS; Finnigan Delta Plus XP; 
Thermo Fisher Scientific, Waltham, MA, USA). The standard de‐
viations for repeated analyses of reference materials were better 
than 0.2‰. The isotope signature is expressed in the delta no‐
tation δ13C = (Rsample/Rstandard − 1) × 1,000 (‰) relative to the in‐
ternational standard VPDB, where R = 13C/12C of the sample or 
standard.
2.6 | N2 fixation rate
The N2 fixation rate of Lapinjärvi deadwood and its dependency 
on density class and ambient temperature has been published in 
Rinne et al. (2017), where the methodology is described in de‐
tail. In short, an acetylene reduction assay (ARA) was performed 
following Leppänen, Salemaa, Smolander, Mäkipää, and Tiirola 
(2013). After 24 hr of incubation the concentration of ethylene in 
the headspace was analysed by a gas chromatograph. A conver‐
sion factor from acetylene reduction to N fixation of 4 was used 
(Brunner & Kimmins, 2003). In the present study the N2 fixation 
data (Rinne et al., 2017) is for the first time compared with respira‐
tion rate, nifH copy number and fungal community of Lapinjärvi 
deadwood, and used to calculate the AE of the N2 fixation rate for 
the different decay classes. The density, N content and moisture 
of deadwood used in the present study (Table 1) are from Rinne 
et al. (2017).
2.7 | Activation energies
Activation energies were estimated individually for respiration 
and N2 fixation using the Van’t Hoff‐Arrhenius relationship e
−E/(kT), 
where E is the AR (in eV), k is the Boltzmann constant (8.61 × 10−5 
eV/K, 1 eV = 96.485 kJ/mol) and T is temperature (K, here the incu‐
bation temperature) (Arrhenius, 1889). For each density class the 
data were plotted separately in a xy‐diagram, where x was 1/(kT) 
and y was the natural logarithm of each respiration or N2 fixation 
measurement. Linear least‐squares regression was used to describe 
the relationship between the variables. The AE is the absolute 
value of each slope.
2.8 | Statistical analysis
CurveExpert 1.4 software package was used to determine the 
best‐fitting trendlines for the respiration series. When obtaining 
best‐fit trendlines for the part of the respiration dataset that cov‐
ered only the temperature range typical for the study area (<20°C, 
Figure 1b), it was necessary to add data points that approximated 
zero values for both CO2 production rate and temperature (0.001 g 
CO2 kg
−1 day−1 and 0.001°C, respectively) for the trendline fitting 
process. With this addition the resulting best‐fit trendlines did 
not predict a significant production of CO2 at 0°C. The number 
of these added data points equalled to the number of samples 
F I G U R E  1   CO2 production and C flux of Lapinjärvi deadwood. (a) The measured respiration rates (dotted lines) and their standard 
deviation for each incubation temperature are shown for each density class. For each series the best‐fit trendline (Gaussian, p < 0.001) 
is given (bold lines) (r‐values: I = 0.45, II = 0.63, III = 0.55, IV = 0.62 and V = 0.72. (b) The best‐fit trendlines for the data that cover the 
temperature range 8.7°C–19.9°C only (I: saturation growth rate, II and III: power, IV: Farazdaghi‐Harris‐YD, V: polynomial regression). (c) 
The estimated annual levels of C flux of fallen spruce logs in Lapinjärvi. The results are shown separately for each of five density classes 
and for the years 2007 (left bar of a pair of bars) and 2008 (right bar of a pair of bars). The total CO2 production is also indicated. The white 
bars indicate the estimated increase in respiration rate for the time period 2040–2069 under the climate warming scenario RCP 4.5, when 
assuming that the store of downed deadwood does not change in time (Ruosteenoja et al., 2013)
     |  1857RINNE‐GARMSTON ET Al.
incubated at 8.8°C for each density class (nI = 10, nII = 7, nIII = 8, 
nIV = 11 and nV = 11).
IBM SPSS Statistics 22 software package (IBM Corp, Armonk, 
NY, USA) was used for other statistical analysis: ANOVA and 
ANCOVA with the Tukey post‐hoc test were used to determine any 
significant differences between the means of groups, and simple lin‐
ear and multiple regression analyses were used to study the relation‐
ship between two or more variables.
2.9 | The total amount of C stored in Lapinjärvi 
deadwood and its annual C flux
Annual CO2 production per hectare (kg C ha
−1 year−1, Figure 1c) was 
estimated for Lapinjärvi deadwood using the trendlines of Figure 1b, 
the weight of wood per hectare calculated for each decay class (“log 
density” in Table 1) and the meteorological data. The total annual 
level of C flux per gram of Lapinjärvi deadwood (g C kg−1 year−1) was 
calculated by dividing the density class specific results of Figure 1c 
by the density of fallen spruce logs in each density class (Table 1) 
and by summing the results. The total amount of C stored in Norway 
spruce deadwood in the study site was calculated for each density 
class (Table 1) using the C content (Figure 2), the density of each 
deadwood sample (Rinne et al., 2017) and the quantity of downed 
dead trees (Table 1).
2.10 | Climate warming scenario RCP 4.5
The potential impact of climate warming on CO2 production rates 
was calculated using the RCP4.5 temperature scenario, which as‐
sumes that a further increase in anthropogenic CO2 production rate 
is followed by a decrease starting around year 2040, for the time 
period 2040–2069 for southern Finland (Ruosteenoja, Räisänen, & 
Jylhä, 2013). The projected temperature increase is approximately 
1.4°C for the summer months and increasingly higher towards the 
winter months, where up to 3.8°C increase is estimated.
3  | RESULTS
3.1 | Respiration
For each density class the statistically significant temperature de‐
pendency of CO2 production followed the Gaussian model, where 
maxima was at 29.6°C (for wood in density class I–IV) or at 34.0°C 
(density class V) incubation temperature (Figure 1a). According to 
the Gaussian model, ambient temperature explained 20% (class I), 
40% (II), 34% (III), 38% (IV) and 52% (V) of the measured respiration 
rate. Decomposition rate was highest for the intermediate classes 
(II–IV) reaching respiration rates of up to 2.4 g CO2 kg
−1 day−1. When 
temperature was included as covariate, there was a statistically sig‐
nificant difference in mean decomposition rates for density classes I 
and III, I and IV and IV and V (see Supporting Information S3).
Increased wood moisture significantly increased respiration at 
early stages of decomposition, whereas N content had the strongest 
impact on respiration rate for class II–IV wood (Table 2). The rate of 
N2 fixation, on the other hand, did not correlate with respiration rate. 
No significant correlations were obtained for the respiration rate in 
comparison to δ13C, C% and nifH copy number of deadwood.
Density class II wood, which contains the highest amount of C 
(“C in deadwood” in Table 1), produced annually the largest amount 
of CO2 whereas the contribution of class V was minor (Figure 1c). 
Despite the low amount of class IV wood in the study site relative 
to wood at early stages of decomposition (I–II) (Table 1), this decay 
class is a source of a significant annual release of CO2 to the atmo‐
sphere due to its higher rate of respiration (Figure 1c). The total 
annual C flux from Lapinjärvi deadwood was calculated to be on 
an average 566 kg C ha−1 year−1 for the 2 years (Figure 1c), which 
equals to 117 g C kg−1 year−1. The potential impact of climate warm‐
ing on CO2 production rates was calculated using the RCP4.5 tem‐
perature scenario (Ruosteenoja et al., 2013). The calculated increase 
in the annual respiration rate for Norway spruce deadwood was 28% 
(Figure 1c) assuming that the deadwood quantity and distribution 
to density classes is similar to current stock. The total biomass and 
C stock of Norway spruce deadwood in the study site is shown in 
Table 1. It indicates that in our study site the majority of C (71%) is 
F I G U R E  2   C% and δ13C measurements of Lapinjärvi deadwood 
with respect to density. The distribution of the samples into five 
density classes is shown. Large symbols show means of each 
density classes. The linear trendlines, based on density classes I, II, 
III and V, are given for C% (R2 = 0.58) and δ13C (R2 = 0.49) [Colour 
figure can be viewed at wileyonlinelibrary.com]
1858  |     RINNE‐GARMSTON ET Al.
stored in wood that is at its early stages of decomposition (I–II) of 
which density class I (represents 34% of deadwood C stock) is least 
sensitive to temperature.
To produce the best possible estimates of annual respiration rates 
for deadwood at the study site, best‐fit trendlines were separately 
defined by decay class for the CO2 data covering the temperature 
range typically encountered in Finland (average day temperature 
<20°C) (Figure 1b). The best‐fit trendlines (Figure 1b, CurveExpert 
1.4) were a significantly better fit than the Gaussian trendlines of 
Figure 1a for the measured data at the temperature range 0–10°C. 
The annual respiration rate of deadwood was calculated for each den‐
sity class using the trendlines of Figure 1b and the temperature data 
representing the Lapinjärvi forest (Figure 3a). The rate of respiration 
with advancing stage of decomposition is best explained by a qua‐
dratic fit trendline (Figure 3a, CurveExpert 1.4). Using the temporal 
14C‐dating based decomposition model established for our study site 
(figure 6a in Rinne et al., 2017), it was possible to define the depen‐
dence of respiration rate (g C kg−1 year−1) on the time of decompo‐
sition since soil contact (years) as shown in Supporting Information 
S4. This dependency (equation in Supporting Information S4) was 
then used to calculate the decomposition of the current biomass of 
deadwood (Table 1) as a function of time (Figure 3b). According to 
Figure 3b, after 60 years of decomposition (the time it takes for class 
I wood to decompose to class V wood), 24% of the current biomass 
of deadwood still remains on the forest floor of the study site. For 
class I wood, with the approximate age of 8 years (Rinne et al., 2017), 
wood content was calculated also for the preceding 7 years using 
the established decomposition model. Consequently, it was possi‐
ble to determine the trendline that best describes wood decompo‐
sition during the first 69 years of decomposition (Sigmoidal model: 
y = (ab + cxd)/(b + xd), r = 1.00) (“Wood decomposition model” in 
Figure 3b). When the first 7 years of decomposition are excluded, 
the decomposition is well‐described by negative exponential trend‐
line (r = 1.00). Based on the Sigmoidal model, mass loss of 95% is 
reached after 116 years. The decomposition of Norway spruce as 
modelled in Mäkinen et al. (2006) is shown in Figure 3b for com‐
parison (secondary y‐axis: “remaining fraction”). It clearly differs 
from the decomposition pattern observed in the present study: (a) 
the decomposition rate in Mäkinen et al. (2006) is relatively slow for 
the first 20–30 years of decomposition (classes I–III/IV in this study), 
whereas in the present study decomposition rate was at maximum 
for class II–IV wood and (b) according to Mäkinen et al. (2006) logs 
disappear in much shorter time than can be expected based on the 
negative exponential decomposition of wood in the present study.
3.2 | Diazotrophic activity
The number of nifH copies was low in early stages of wood decom‐
position (I–II) but then significantly increased with advancing decom‐
position culminating at class IV (Figure 4, Table 1). The number of 
nifH copies peaked in communities associated with density class IV 
(Supporting Information S5). Wood moisture (Table 1) explained 52% 
(p < 0.01) of the nifH copy number for class I–IV wood. The inclusion 
of class V wood decreased the correlation, because of the opposite 
trends in nifH and moisture contents of wood (Table 1, Figure 4).
For the samples incubated at 14.5°C, which represents a typical 
summer temperature in the study region, (i.e. the temperature that 
was used for all analysis in the present study), N2 fixation rate in 
classes I–IV was best explained (Table 2, F3,34 = 9.850, p < 0.001) by 
the combined impact of wood nifH gene number, moisture and N 
TA B L E  2   Correlation coefficients between CO2 production, N2 fixation rate and measured wood properties of deadwood. Only these 
wood property variables are shown that significantly correlated with CO2 production or N2 fixation rate are shown. “Class combination” 
shows the combination of density classes with the highest correlation coefficient. The sample number in correlation analysis is given. The 
significance levels are p < 0.05 (*) and p < 0.001 (**)
Variables
Density class
I (n = 10) II (n = 7) III (n = 8) IV (n = 13) V (n = 11) Class combination
CO2 versus moisture
a  0.28 0.52 −0.51 −0.16 0.08 0.50* (I‐II)
CO2 versus N content
a  −0.09 0.67 0.83* 0.58* 0.21 0.61** (II‐IV)
CO2 versus Shannon index −0.33 −0.58 0.24 −0.39 −0.62* −0.49* (IV‐V)
N2 fixation versus moisture 0.74* 0.72 0.74* 0.38 0.21 0.76** (I‐III)
N2 fixation versus nifH 
copies
0.37 0.35 0.55 0.19 0.57* 0.52** (I‐IV)
N2 fixation versus M2
b  0.68** (I‐IV)
nifH copies versus moisture −0.06 0.64 0.64 0.52 0.53 0.72** (I‐IV)
nifH copies versus N 
content
0.27 0.01 0.18 −0.10 −0.65*
nifH copies versus Shannon 
index
−0.03 0.29 0.51 0.22 0.39 0.43** (I‐V)
aMoisture, N content and N2 fixation rate data were obtained from Rinne et al. (2017). 
bMultivariate analysis 2; independent variables: moisture, N 
content and nifH copy number. 
     |  1859RINNE‐GARMSTON ET Al.
content. The inclusion of class V into the analysis reduced the com‐
mon signal, because moisture and N content increased in wood from 
class IV to class V unlike N2 fixation rate and nifH gene number. The 
N2 fixation rate of the 49 samples were significantly dependent on 
the incubation temperature (Rinne et al., 2017). The density class 
dependency of N2 fixation (Figure 4) resembled the density class 
dependency of respiration (Figure 1a) with the exception of class II, 
where respiration—unlike N2 fixation—was already significantly ele‐
vated compared to the level encountered at class I.
However, the respiration and N2 fixation rates were significantly 
correlated only for class IV and V wood (for 8.7°C–25.1°C: rIV = 0.41 
and rV = 0.42, p < 0.01). For every incubation temperature, the N2 
fixation rates followed a similar trend in absolute values with ad‐
vancing decomposition as the number of nifH copies (Figure 4). The 
exception was the incubation temperature 40.3°C, which had a 
smaller replication.
3.3 | Activation energies
The highest incubation temperatures were excluded from AE cal‐
culations (Arrhenius, 1889) (Table 3), because at temperatures 
above 30°C and 26°C the rate of respiration and N2 fixation, re‐
spectively, did not increase with temperature (Figures 1 and 2). 
There was a strong relationship between ln‐transformed respira‐
tion and inverse temperature for every density class (Supporting 
Information S6). For respiration, AE was similar at all classes and 
agreed with the canonical value of 0.60–0.70 (Figure 5, Table 1) 
(Allen et al., 2005). AEs of N2 fixation, on the other hand, were 
highest for class III (1.27 eV) and IV (1.04 eV) wood but the calcu‐
lated AEs were always substantially less than the AE of the nitro‐
genase enzyme (2.18 eV) (Figure 5, Table 1) (Ceuterick et al., 1978).
3.4 | Relationship between fungi, respiration 
rate and diazotrophic activity
A detailed description of the obtained fungal OTUs, their phylo‐
types and functional groupings from this study can be found in 
Rinne et al. (2017) and in Supporting Information S2. A total of 
393,071 sequence reads scattered in 69 fungal phylotypes, and of 
these 25% could be placed into functional fungal groups accord‐
ing to a literature review (see Rinne et al., 2017). Of the identi‐
fied phylotypes 5% were white‐rot, 3% were brown‐rot and 12% 
were mycorrhizal fungi. Over 70% of the identified white‐rot fungi 
sequences belonged to four species: Resinicium bicolor, Phellopilus 
nigrolimitatus, Exidia sp. and Phellinus viticola. Almost 90% of 
the brown‐rot fungal sequences belonged to Antrodia serialis, 
Amyloporia sinuosa and Coniophora olivacea, and over 50% of myc‐
orrhizal fungal sequences to Pseudotomentella tristis and Piloderma 
byssinum. The number of fungal OTUs specific to density classes 
I, II, II, IV and V were 312, 104, 121, 381 and 396, respectively, 
and there were 482 OTUs common to all classes (Supporting 
Information S7). The most specific fungal phylotypes in a density 
class were not always the most dominant ones in a density class. 
For example, several typical mycorrhizal species (species of genera 
Cortinarius, Russula, Piloderma) specific to density class V were not 
among the most dominant ones (Supporting Information S8 and 
S9).
The fungal Shannon diversity index increased from class I to 
class V (Table 1). For all data, the index correlated with nifH copy 
number (r = 0.43, p < 0.01), moisture (r = 0.57, p < 0.01), density 
(r = −0.53, p < 0.01) and C% (r = 0.37, p < 0.05), but not with CO2 
production. The exception was a significant (negative) correlation 
between fungal diversity and respiration rate for class V wood 
(Table 2).
F I G U R E  3   Decomposition of Lapinjärvi deadwood. (a) 
Annual decomposition rate of deadwood with advancing stage 
of decomposition. The bars indicate the standard deviation of 
measurement for each density class. The amount of carbon 
released in the decomposition process per kilograms of deadwood 
per year is best explained by a quadratic fit trendline (continuous 
line, CurveExpert 1.4). The shaded area indicates the 95% 
confidence level. (b) The decomposition of the current deadwood 
stock in the Lapinjärvi forest as a function of time until 62 years 
from present, which is the time it takes for class I wood (age: 
8 years) to decompose to class V (age: 69 years) wood (Rinne et 
al., 2017). For class II–V wood it was thus necessary to forecast 
the decomposition model (a) beyond the age of class V wood 
(dashed lines). The secondary y‐axis shows the remaining fraction 
of deadwood for the “Wood decomposition model” of the present 
study and for the decomposition model of Mäkinen et al. (2006) 
[Colour figure can be viewed at wileyonlinelibrary.com]
1860  |     RINNE‐GARMSTON ET Al.
The relative abundance, as defined by the number of OTU reads, 
of white‐rot, brown‐rot and mycorrhizal fungi varied by density 
class (Figure 6). For white‐rot fungi relative abundance decreased 
from class I to III and then peaked at class IV. The relative abun‐
dance of brown‐rot fungi increased from class I (36%) to III (92%) and 
was relatively small in the samples of classes IV (15%) and V (13%). 
Mycorrhizal fungi were nearly absent in classes I–III (I: 0.0 ± 0.1%, 
II: 0.0 ± 0.2%, III: 0.1 ± 6%). Their relative abundance increased to 
2.3 ± 26% in class IV and reached 27 ± 42% in class V.
The relative abundance of brown‐rot fungi correlated nega‐
tively with nifH copy number (r = −0.32, p < 0.05) and positively 
with CO2 production rate (r = 0.33, p < 0.05) (Table 4). The only 
abundance for individual species of brown‐rot fungi that cor‐
related with wood properties (such as nifH copy No. and δ13C) was 
Amyloporia sinuosa, which significantly increased with respiration 
(Table 4). The total abundance of white‐rot fungi correlated neg‐
atively with respiration rate. However, at the species level the 
F I G U R E  4   Comparison of measured 
N2 fixation rates at different incubation 
temperatures and nifH gene copies per 
gram dry weight in Lapinjärvi deadwood 
at different stages of decomposition. 
NifH analysis were carried out on samples 
incubated at 14.5°C for Lapinjärvi 
deadwood at different stages of 
decomposition. Data points were joined 
with a continuous line for each series to 
enhance visual comparison
TA B L E  3   Linear least‐squares regression between ln‐
transformed respiration or N2 fixation and inverse temperature of 
Norway spruce deadwood by density class. The absolute value of 
each slope indicates the activation energy
Measurement Density class R2 P Slope
Respiration I 0.28 <0.001 −0.68
II 0.49 <0.001 −0.63
III 0.62 <0.001 −0.63
IV 0.59 <0.001 −0.59
V 0.64 <0.001 −0.61
N2 fixation I 0.30 0.001 −0.69
II 0.27 0.021 −0.60
III 0.50 <0.001 −1.27
IV 0.43 <0.001 −1.04
V 0.21 0.006 −0.40
F I G U R E  5   Calculated and theoretical activation energies of 
respiration and N2 fixation for each density class of Lapinjärvi 
deadwood [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E  6   The relative abundance of white‐rot, brown‐rot and 
ectomycorrhizal fungi in different density classes of Lapinjärvi 
deadwood
     |  1861RINNE‐GARMSTON ET Al.
abundance of the white‐rot fungi Resinicium bicolor correlated 
positively with respiration rate. Although the total abundance of 
white‐rot fungi and nifH copy number did not correlate signifi‐
cantly in general, there was a positive correlation for class IV wood 
(rwhite‐rotters vs. nifH = 0.58, p < 0.05). At the species level the abun‐
dance of white‐rot fungi Hyphodontia alutacea correlated posi‐
tively with nifH copy number. It should be noted, however, that 
these correlations have to be accepted with caution due to the 
DNA based approach taken in the analyses which might include 
fungi that are not active.
The abundance of mycorrhizal fungi did not correlate with res‐
piration rate or diazotrophic activity but was correlated with wood 
C% and δ13C values (positive and negative correlation, respectively, 
Table 4). The only relative abundance of mycorrhizal species that 
correlated with wood properties was that of Pseudotomentella tristis. 
Its increasing abundance was positively correlated with wood C%.
3.5 | Carbon content and δ13C of deadwood
The C content (C%) and the δ13C value of wood linearly increased and 
declined, respectively, with advancing wood decomposition with the 
exception of density class IV wood (Table 1, Supporting Information 
S3). For samples of class IV the C% values were lower (2.4%) and 
the δ13C values more 13C‐enriched (0.65‰) than expected from the 
general trend (Figure 2).
4  | DISCUSSION
4.1 | Temperature and wood property dependency 
of respiration
The significant temperature and wood density class dependency of 
respiration (Figure 1a) clearly shows that deadwood mass loss cannot 
be modelled assuming constant decomposition rates throughout the 
decomposition process. As with many other physiological processes, 
rates of fungal respiration increase with an increase in temperature 
until the heat denatures enzymes required for growth. The generally 
reported general temperature limits for fungal growth (0°C–45°C) 
as well as the more species specific optimum temperatures for 
growth (20°C–35°C) (Lacasse & Vanier, 1999) agree well with the 
temperature dependence of CO2 production observed for all den‐
sity classes in this study. Since the temperature optimum for fungi 
driven deadwood respiration is ~30°C (Figure 1a), climate warm‐
ing will accelerate C loss from downed deadwood in boreal condi‐
tions in the future, unless moisture becomes limiting (too low or 
too high) (Figure 1c). The current mean annual C flux of 117 g C/
kg measured for Norway spruce deadwood is similar to the flux re‐
ported in Forrester, Mladenoff, Gower, and Stoffel (2012) (109 and 
128 g C kg−1 year−1), who studied coarse woody debris across decay 
classes (CWD) in a hardwood forest in Wisconsin, USA, using instan‐
taneous CO2 flux measurements, and it is 30% of the C flux reported 
for CWD of tropical forests (Chambers, Schimel, & Bnobre, 2001). 
With projected climate warming the annual C flux in respiration of 
deadwood from the study site was calculated to increase to 149 g C/
kg. However, it should be noted that this calculation does not take 
into account changes in total store of deadwood or in the distribu‐
tion of density classes in the function of time. The current annual C 
flux (117 g C/kg) of deadwood accounts for 14% and 7% of the soil 
respiration and total ecosystem respiration (Korhonen, Pumpanen, 
Kolari, Juurola, & Nikinmaa, 2009), respectively. However, unlike our 
site in Lapinjärvi, the Scots pine dominated forest in Korhonen et al. 
(2009) is a managed forest, where deadwood content on forest floor 
is typically only <4 m3/ha. The forest in Korhonen et al. (2009) is 
located 170 km northwest from our study site.
Our results do not support the conclusion of Bradford et al. 
(2014); that climate is not a major driver of wood decomposition 
rates at local scales. In our study, ambient temperature explained 
~40% of the variation in respiration rate of wood that had been de‐
composing longer than 8 years (classes II–V, figure 1a in Bradford 
et al., 2014) the conclusion of the relatively low impact of tem‐
perature on respiration rate (vs. % of fungal colonization of wood) 
at the local scale may have been hampered by the experimental 
setting, where temperature was relatively constant (range of ~2°C) 
so that the degree for the level of fungal colonization (~2%–52%) 
emerged as a controlling factor at each research site. However, 
even Bradford et al. (2014) clearly stated that since local‐scale fac‐
tors–such as the availability of nitrogen and soil moisture–have 
a significant impact of fungal activity (Table 2), care should be 
TA B L E  4   Correlation coefficients between fungal abundance and deadwood properties. The r‐values with 95% (*) and 99% (**) 
significance levels are shown for three functional groups (brown‐rot [B], white‐rot [W] and ectomycorrhizal [EC] fungi) and for those fungal 
species (total of ≥5,000 OTUs) that significantly correlated with the wood properties measured
Wood variable
Fungi
B W EC
Resinicium 
bicolor (W)
Amyloporia 
sinuosa (B)
Hyphodontia 
alutacea (W)
Phellopilus 
nigrolimitatus (W)
Pseudotomentella 
tristis (M)
Respiration 0.33* −0.31* −0.14 0.28* 0.55** 0.04 −0.26 −0.10
N2 fixation −0.21 −0.19 −0.14 −0.08 −0.04 0.07 −0.17 −0.11
nifH copy no. −0.32* −0.21 0.03 −0.12 −0.11 0.54** −0.24 −0.01
C% −0.04 −0.32* 0.38** −0.16 −0.01 −0.02 −0.19 0.32*
δ13C −0.01 0.37* −0.43** 0.28 −0.10 −0.17 0.34* −0.32*
1862  |     RINNE‐GARMSTON ET Al.
excersised when taking local results for the response of deadwood 
respiration rates to changes in temperature, and scaling up for use 
in Earth‐system models.
Nevertheless, our results on temperature sensitivity of CO2 pro‐
duction (and N2 fixation) by wood density class can be implemented 
into models describing respiration of deadwood logs. With least ef‐
fort it can be done for models describing respiration class dynamics 
of individual logs, which are based on the similar deadwood classi‐
fication system as we used here (Peltoniemi, Penttilä, & Mäkipää, 
2013). However, density class representation is not strictly required 
as decay class can be associated with wood density with fair accu‐
racy (e.g. Rajala et al., 2012; this study). It is noteworthy, however, 
that dynamic respiration models require tying the temperature to 
the state variable describing the present density of decomposing 
matter. For one compartment model this yields a rate of mass loss 
dy/dt = f(T, y) y, where f(T, y) is some continuous function represent‐
ing mass loss as a function of temperature T and density y, which 
could be formulated based on our study and studies linking remain‐
ing mass (or density) to decay class. For more complex dynamic mod‐
els, possibly representing several biochemically/physically identified 
compartments, implementation of aggregate temperature function 
requires further consideration.
Other wood properties driving respiration were increasing mois‐
ture content of wood in the early stages of decomposition (explain‐
ing 25% of respiration rate, Table 2), increasing wood N content 
(37%, Table 2) and the total abundance of brown‐rot fungi (11%, 
Table 4), particularly abundance of the species Amyloporia sinuosa 
(30%, Table 4). The low level of moisture at early stages of decom‐
position probably explains why the temperature signal for res‐
piration was lower for class I wood (20%, Figure 1a) than for the 
other decay classes (on an average 41%). Wood moisture was 35% 
or lower in 90% of wood samples in class I (Table 1). Wood mois‐
ture content this low has been reported to inhibit growth of fungi 
within wood cells (Lopez‐Real & Swift, 1975). The lack of a direct 
dependency of respiration on wood moisture content for medium 
or well decomposed wood samples is in accordance with field stud‐
ies, where moisture content was not limiting or excessive (Forrester 
et al., 2012; Herrmann & Bauhus, 2013). In contrast, in the study 
of Hagemann, Moroni, Gleißner, and Makeshin (2010), respiration 
was reduced at times by both too low and too high wood moisture 
content, therefore wood moisture was reported to be the dominant 
environmental control over CWD respiration. Similarly, for soil res‐
piration, Reichstein et al. (2002) observed declining temperature 
sensitivity with increasing soil water deficit. In boreal forests with 
a climate similar to that of the Lapinjärvi forest, moisture may be a 
limiting factor only at the early stages of decomposition or for the 
standing dead trees (Table 2).
Our study does not support the results of Yang et al. (2016) 
who found that increasing fungal diversity reduced wood respira‐
tion rate. In contrast, both CO2 production and Shannon index of 
fungal community increased from class I to class IV wood (Table 1). 
Although the respiration rate and fungal diversity both increased 
from class I to class IV, the two were not significantly correlated 
(Table 2) suggesting that high fungal diversity is not essential for en‐
hancing respiration rate. The result is consistent with the findings 
of Valentin et al. (2014) who concluded that reduced biodiversity in 
the highly diverse fungal community of the late decay class wood did 
not influence rate of respiration. The divergent trend between CO2 
production rate (decrease) and fungal diversity (increase) from class 
IV to class V wood (Figure 1a) probably reflects the reduced wood 
resource quality affecting respiration rate, as opposed to any direct 
relationship between the two variables (Table 2). This conclusion 
is supported by the significant reduction in the relative abundance 
of saproxylic fungi in favour of mycorrhizal species from class IV to 
class V wood (Figure 6). Via deceleration in respiration rate, the re‐
duced wood quality also had a negative indirect effect on N2 fixation 
rate (Figure 4) by reducing the respiration rate and therefore reduc‐
ing ATP production from the decomposition process that is needed 
as an energy source for N2 fixation (Weißhaupt, Pritzkow, & Noll, 
2011).
Our results on the density class dependency of respiration 
rate, where an initial slow respiration rate (density class I) was 
followed by rapid respiration (II–IV) and finally by a clear decline 
(V) at all incubation temperatures with high sample replication 
(8.7°C–34.0°C) (Figure 1), have clear differences compared to 
previous studies on conifers. Wang et al. (2002) and Wu, Zhang, 
Wang, Sun, and Guan (2010) studied black spruce (Picea mariana) 
and Korean pine (Pinus koraiensis), respectively, and reported 
an increase in respiration rate from the least to moderately de‐
composed wood but no decline for the most decomposed wood. 
However, their classification system for deadwood consisted of 
only three classes (Lambert, Lang, & Reiners, 1980). The class spe‐
cific average wood densities of 0.41 g/cm3, 0.34 g/cm3 and 0.28 g/
cm3 reported for black spruce (Wang et al., 2002) are similar to the 
densities measured for classes I, II and III of the five classes used 
in our study (Table 1), which suggests that well‐decomposed wood 
was not used in the studies by Wang et al. (2002) and Wu et al. 
(2010). Such sampling would lead to different conclusions, when 
calculating models predicting mass loss due to respiration over 
time (Figure 3). Similarly, the study of Mäkinen et al. (2006), who 
modelled decomposition of Norway spruce in Finland, is based on 
samples that were on an average only 14 and at most 38 years 
old, which is significantly younger than the 62–77 years average 
age determined for well decomposed Norway spruce logs using 
14C dating (Krüger, Muhr, Hartl‐Meier, Schulz, & Borken, 2014; 
Petrillo et al., 2015; Rinne et al., 2017). These factors may explain 
why the decomposition model of Mäkinen et al. (2006) is very dif‐
ferent from the decomposition pattern observed in the present 
study (Figure 3b). The negative exponential decomposition model, 
which has been used in many studies to estimate decomposition 
of Norway spruce (Melin et al. (2009), is in a much better agree‐
ment with the results of the present study. However, our study 
indicates a clearly slower overall rate of decomposition. For exam‐
ple, after 60 years of decomposition the fraction of the deadwood 
stock remaining on forest floor is 24% in the present study and on 
an average ~10% according to the compilation of models in Melin 
     |  1863RINNE‐GARMSTON ET Al.
et al. (2009). This is at least partly explained by the failure of the 
exponential models to take into account the slow decomposition 
rate at the early stages of decomposition (Figures 1a and 3b). In 
addition, organic matter stabilization by fungi or other soil biota 
during the decomposition process (Chertov et al., 2017; Komarov 
et al., 2017) may explain slow decomposition rate of the remaining 
fraction in the late decay phases.
For density class II wood, respiration increased to levels compa‐
rable to those measured for class III and IV wood (Figure 1) despite 
the low accumulation rate of fixed atmospheric N (Figure 4), which 
is needed to facilitate fungal‐driven decomposition. The high rate of 
respiration at this stage may have been enabled by soil foraging fungi 
that have the capability to transport soil N to wood for the produc‐
tion of extracellular enzymes (Allison & Vitousek, 2005; Sterner & 
Elser, 2002) and fungal material (Merrill & Cowling, 1966; Moore et 
al., 2008). 80% of the Lapinjärvi deadwood samples contained at least 
one species that has been reported to form mycelial cords or rhizo‐
morphs for soil nutrient foraging (Rinne et al., 2017). The generally low 
N content measured for class II wood (Table 1) suggests that at this 
stage of decomposition there was a substantial need for N to sustain 
high fungal vegetative and generative growth (Hoppe et al., 2014).
4.2 | Activation energy
Due to the relatively low calculated AE of N2 fixation of deadwood, 
the impact of a temperature increase on N2 fixation rate of dead‐
wood was smaller than expected from the temperature response 
of the nitrogenase enzyme (Figure 5). This is typical for high lati‐
tudes, because as temperatures decline more enzyme is needed to 
achieve a given rate of N2 fixation, hence constraining maximal N2 
fixation rates (Houlton, Wang, Vitousek, & Field, 2008). However, 
the temperature dependency of this metabolic process was not 
constant for deadwood but varied according to wood supply and 
quality (Figure 5). For density classes I and II, low wood moisture 
(Table 1) reduced the temperature sensitivity of N2 fixation (Table 2). 
Additionally, for class I and V wood, the low amount of ATP produced 
via the slow decomposition process (Figure 1) significantly limited N2 
fixation rate (Weißhaupt et al., 2011). As a consequence, the AE of 
N2 fixation for class I, II and V wood (0.56 ± 0.15 eV) is significantly 
below the average AE of 1.1 eV reported in Houlton et al. (2008) in 
a unifying framework for terrestrial N2 fixation. N2 fixation of class 
III and IV wood, on the other hand, had a stronger response to in‐
creases in temperature (AE = 1.14 ± 0.18 eV). This is because wood 
at these stages of decomposition was not limited by wood quality 
and they benefited (Figure 4) from the increased production of ATP 
from the accelerated decomposition process (Figure 1).
The increases in N2 fixation rates with temperature did not af‐
fect the temperature dependency of respiration at any stage of de‐
composition for Lapinjärvi deadwood (Figure 5). This is in contrast 
to the study of Welter et al. (2015), who reported 2.7‐fold higher 
AEs of respiration than predicted by metabolic theory for biofilms 
in an Icelandic streamside channel due to increases in N2 fixation 
with temperature. However, a major difference between our study 
(Figure 5) and that of Welter et al. (2015) is that AEs calculated for 
biofilm N2 fixation rates approximated the AE of the nitrogenase re‐
action, but that was not the case for our study (Figure 5).
4.3 | Effect of fungal community on 
decomposition of cellulose and lignin
The general linear relationship between wood density and wood 
C% as well as between wood density and δ13C values mainly reflect 
the increasing relative abundance of lignin to cellulose (Figure 2). 
Cellulose, which has a lower C content (44% vs. 67%) and is from 
4‰ to 6‰ more 13C‐enriched than lignin (Benner, Fogel, Sprague, 
& Hodson, 1987), is degraded at a faster rate because it is a much 
more energy rich resource for wood decay fungi (Moorhead & 
Sinsabaugh, 2006). However, eventually the remaining cellulose 
becomes shielded by a lignin coating, which requires enhanced ac‐
tivity of white‐rot fungi to enable decomposition of more cellulose 
(Moorhead & Sinsabaugh, 2006; Rayner & Boddy, 1988). In our 
study, this increase in lignin breakdown seems to have occurred at 
density class IV, where the wood C% and δ13C values are smaller and 
more 13C‐enriched, respectively, than expected from the general lin‐
ear trend that they follow relative to wood density (Figure 2). This is 
supported by the observation that the relative abundance of white‐
rot fungi substantially increases from density class III to class IV, 
where white‐rot fungi dominates the three main functional groups 
(Figure 6). The significant role of white‐rot fungi in lignin breakdown 
is also indicated by the negative correlation between the abundance 
of this functional group in deadwood and wood C% and by the posi‐
tive correlation between the abundance and wood δ13C (Table 4). 
Since the white‐rot species H. alutacea was specific and clearly more 
common to class IV (Supporting Information S8 and S9) and since its 
abundance correlated positively with nifH copy number (Table 4), it 
is concluded that this species had a major role in the intensified lignin 
decomposition at stage IV (Figure 2) that was enabled by diazo‐
trophic N additions (Jurgensen, Larsen, Graham, & Harvey, 1987). 
Also, Hoppe et al. (2016) reported the dominance of H. alutacea in 
decay class IV spruce wood as well as a positive correlation between 
the abundance of this species and the respiration rate.
The significant breakdown of lignin at density class IV may have 
been enabled by the invasion of ECM fungi (Figure 6), which trans‐
fer simple sugars from trees to deadwood within their mycelia. The 
transfer of recent photosynthates with depleted δ13C value (Rinne 
et al., 2015) into deadwood is supported by the significant negative 
correlation between the relative abundance of ECM fungi and dead‐
wood δ13C and by the positive correlation between ECM abundance 
and C% values (Table 4). Pseudotomentella tristis was a particularly 
important species importing labile C compounds into wood (Table 4). 
The invasion of wood by ECM was key factor in enabling lignin break‐
down, because the consequent availability of labile C compounds in 
wood can function as co‐metabolites in this process (Melillo et al., 
1989). Furthermore, the increased availability of labile C compounds 
as well as the ATP generated during the faster lignin breakdown 
and the breakdown of the released cellulose would have benefited 
1864  |     RINNE‐GARMSTON ET Al.
diazotrophic activity leading to increased availability of N for white‐
rot fungi (Burgess & Lowe, 1996; Jurgensen, Larsen, Wolosiewicz, & 
Harvey, 1989). This mutualistic relationship between white‐rotters 
and diazotrophs is supported by the significant correlation between 
the abundance of the two in class IV wood (rwhite‐rotters vs. nifH = 0.58, 
p < 0.05), where both also reached their peak activity (Figures 4 and 
5). The invasion of wood by ECM fungi, which increased the content 
of labile C compounds in wood (Mäkipää et al., 2017), at late stages 
of decomposition could also explain why white‐rotted (further de‐
composed) wood has been reported to have a higher soluble sugar 
content than brown‐rotted (less decomposed) wood in Jurgensen et 
al. (1989), as opposed to the hypothesised higher metabolic activity 
of white‐rot fungi per se (Jurgensen et al., 1989). However, no data 
on the fungal composition of wood is available in Jurgensen et al. 
(1989) to evaluate this hypothesis.
In general, however, wood respiration rate was more dependent 
on brown‐rot than on white‐rot activity (Table 4). The most import‐
ant species in this respect seems to be Amyloporia sinuosa (Table 4), 
which was the fifth most abundant fungi identified at species level 
in the studied wood samples (Supporting Information S9). The sec‐
ond most abundant species, the white‐rot fungi Resinicium bicolor, 
was the only other identified species whose abundance correlated 
with the measured respiration rates. No significant correlations with 
respiration rate or any other measured variable were observed for 
Antrodia serialis, which was clearly the most abundant brown‐rot 
species. As discussed by Hoppe et al. (2016), high species richness 
may not be associated with wood respiration rate, if a species invests 
more energy into competing with other species than on producing 
wood‐degrading enzymes.
4.4 | Projecting respiration and N2 fixation rates 
with climate warming
According to our results, temperature was the main, overall control 
on the respiration rate of deadwood in the studied Norway spruce 
dominated forest in Finland. This highlights the importance of ade‐
quately accounting for the effect of decomposition of coarse woody 
debris, which globally contains 36–72 Pg of C (Cornwell et al., 2009), 
on C budget calculations in forest ecosystem and climate change 
models. Another factor that should be accounted for in such models 
is the quantity of wood at different stages of decomposition on the 
forest floor, since respiration rate is significantly dependent on the 
density class of deadwood. In Finland such data on quantity of dead‐
wood according to decomposition stages is available via the National 
Forest Inventories (Ihalainen & Mäkelä, 2009). Consequently, it is 
possible to upscale CO2 release from coarse woody debris to re‐
gional and national levels, when density class and temperature 
dependency of the respiration is known. However, since decomposi‐
tion rate of deadwood is also significantly dependent on local‐scale 
factors, such as N availability and soil moisture status (Table 2), the 
results of the present study should not be directly applied to other 
sites with significantly different environmental conditions. Another 
major outcome of our study was that the effect of temperature 
increase on N2 fixation rate of deadwood was smaller than expected 
from the temperature response of nitrogenase enzyme and that 
the temperature dependency of this metabolic process was density 
class dependent, which was caused by the class specific differences 
in wood quality and in supply of limiting resources. The density class 
dependency of AE has significant consequences on projecting N2 
fixation rates for deadwood with climate warming. According to the 
RCP4.5 temperature scenario of Ruosteenoja et al. (2013) and the 
AEs observed in the present study for deadwood, by year 2040 N2 
fixation will increase ~30% for class I, II and V wood and ~60% for 
class III and IV wood, whereas the AE reported for terrestrial N2 fixa‐
tion by Houlton et al. (2008) would predict 158% and 50% increases, 
respectively.
ACKNOWLEDG EMENTS
The work was supported by Academy of Finland (grant no. 292899). 
We thank Sirpa Tiikkainen, Anneli Rautiainen, Erno Michelsson, 
Saara‐Mariia Jokinen and Tahmineh Momeni for their valuable as‐
sistance. Further thanks go to Dr. Paavo Ojanen for providing mete‐
orological data and MSc Velma Aho for the help on sequence data 
analysis.
ORCID
Katja T. Rinne‐Garmston  https://orcid.
org/0000‐0001‐9793‐2549 
Janet Chen  https://orcid.org/0000‐0001‐6477‐4193 
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How to cite this article: Rinne‐Garmston KT, Peltoniemi K, 
Chen J, et al. Carbon flux from decomposing wood and its 
dependency on temperature, wood N2 fixation rate, moisture 
and fungal composition in a Norway spruce forest. Glob Change 
Biol. 2019;25:1852–1867. https://doi.org/10.1111/gcb.14594