Globally, soil temperature to 1 m depth is predicted to be up to 4 ∘C warmer by the end of this century, with pronounced effects
expected in temperate forest regions. Increased soil temperatures will
potentially increase the release of carbon dioxide (CO2) from temperate forest soils,
resulting in important positive feedback on climate change. Dark CO2
fixation by microbes can recycle some of the released soil CO2, and
CO2 fixation rates are reported to increase under higher temperatures.
However, research on the influence of temperature on dark CO2 fixation
rates, particularly in comparison to the temperature sensitivity of
respiration in soils of temperate forest regions, is missing. To determine
the temperature sensitivity (Q10) of dark CO2 fixation and
respiration rates, we investigated soil profiles to 1 m depth from beech
(deciduous) and spruce (coniferous) forest plots of the Hummelshain forest,
Germany. We used 13C-CO2 labelling and incubations of soils at 4
and 14 ∘C to determine CO2 fixation and net soil respiration
rates and derived the Q10 values for both processes with depth. The
average Q10 for dark CO2 fixation rates normalized to soil dry
weight was 2.07 for beech and spruce profiles, and this was lower than the
measured average Q10 of net soil respiration rates with ∼2.98. Assuming these Q10 values, we extrapolated that net soil
respiration might increase 1.16 times more than CO2 fixation under a
projected 4 ∘C warming. In the beech soil, a proportionally
larger fraction of the label CO2 was fixed into soil organic carbon
than into microbial biomass compared to the spruce soil. This suggests
a primarily higher rate of microbial residue formation (i.e. turnover as
necromass or release of extracellular products). Despite a similar abundance
of the total bacterial community in the beech and spruce soils, the beech
soil also had a lower abundance of autotrophs, implying a higher proportion
of heterotrophs when compared to the spruce soil; hence this might partly explain
the higher rate of microbial residue formation in the beech soil.
Furthermore, higher temperatures in general lead to higher microbial
residues formed in both soils. Our findings suggest that in temperate forest
soils, CO2 fixation might be less responsive to future warming than net
soil respiration and could likely recycle less CO2 respired from
temperate forest soils in the future than it does now.
Introduction
Most of Earth's terrestrial carbon stock is found in soils, with
∼36 % occurring in the top 1 m depth of forest soils
(Jobbágy and Jackson, 2000)
based on the new carbon inventory of the global soil carbon pool
(Hugelius
et al., 2014; Schuur et al., 2015). Decomposition of soil organic carbon
(SOC) provides one of the largest sources of carbon dioxide (CO2) to
the atmosphere
(Rastogi et al.,
2002; Lal, 2004). Microbes can refix 3 %–6 % of CO2 in temperate
forest mineral soils before its release to the atmosphere
(Akinyede et al., 2020; Spohn
et al., 2019), through so-called dark CO2 fixation
(Miltner
et al., 2005; Šantrůčková et al., 2018). Dark CO2
fixation in soils is mediated by chemolithoautotrophic bacteria, largely via
the Calvin–Benson–Bassham (CBB) pathway
(Niederberger
et al., 2015; Wu et al., 2014), the Wood–Ljungdahl pathway (WLP), or the
reverse tricarboxylic acid (rTCA) pathway
(Beulig
et al., 2016; Liu et al., 2018). Heterotrophic bacteria can also contribute
to dark CO2 fixation via anaplerotic carboxylation reactions associated
with central microbial metabolism (Erb, 2011). The
genetic potential for both autotrophic and heterotrophic CO2 fixation
has been demonstrated in various soils
(Miltner et
al., 2005; Šantrůčková et al., 2018) including temperate
forest soils (Akinyede
et al., 2022a, 2020; Kaiser et al., 2016).
The biomass of microbial communities serves as the entry point of carbon
fixed from CO2 into SOC, which includes both the intact microbial
biomass carbon (MBC) pool and released microbial residues (Miltner et al.,
2004, 2005; Spohn et al., 2019). Microbial residues constitute any
non-living organic material of microbial origin including necromass and
extracellular metabolites (Geyer et al., 2020). Since the transformation of
CO2 and release of the fixed carbon via microbial residues vary for
different microbial groups (Berg et al., 2011; Miltner et al., 2005), the
composition and abundance of microbial communities play a vital role in
CO2 fixation rates in soils. High CO2 fixation rates in soils have
been reportedly associated with higher abundance of obligate autotrophs and
specific bacterial groups like Proteobacteria (Long et al., 2015; Xiao et al., 2018). As
the microbial communities fixing CO2 are sensitive to changes in
edaphic conditions (Berg, 2011; Hügler
and Sievert, 2011), various biotic and abiotic predictors of CO2
fixation rates have been identified. Factors like CO2 concentration
(Beulig
et al., 2016; Spohn et al., 2019; Akinyede et al., 2020), SOC content and
quality
(Miltner
et al., 2005; Šantrůčková et al., 2018; Xiao et al., 2018;
Akinyede et al., 2022a), and pH (Šantrůčková et al.,
2005; Long et al.,
2015) affect dark CO2 fixation
rates in many soils including those of temperate forests.
Here, we focus on temperature as a factor determining soil CO2 fixation
rates. Biological processes are generally faster under higher temperatures
due to accelerated rates of enzymatic reactions (Arrhenius, 1889; Van 't
Hoff, 1898; Davidson and Janssens, 2006). Hence, temperature presumably
affects dark CO2 fixation rates but also the rates of CO2
production through decomposition. If moisture is not limiting, warmer
temperatures increase CO2 emission from temperate forest soils
(Melillo
et al., 2017, 2011, 2002; Walker et al., 2018; Winkler et al., 1996), and
the degree of response is similar to depths of 1 m
(Hicks Pries et al., 2017;
Soong et al., 2021). Such responses coincide with a reduction in the total
SOC pool and have been mostly attributed to increased microbial respiration
(Melillo et al., 2011). The net change in total
CO2 efflux from soil (net soil respiration) includes the effects of
temperature on both CO2 production (decomposition) and CO2
fixation. These effects may change with soil depth from the relatively
organic carbon-rich surface soils to the more carbon-limited deeper soils.
A previous study describing the influence of temperature on CO2
fixation rates described ∼10 times higher fixation rates at
25 ∘C than at 4 ∘C in a range of mostly Alisol and Retisol soils in
afro-temperate forest and grassland ecosystems of the lower-latitude
regions (Nel and Cramer, 2019), suggesting potentially large temperature
effects on CO2 fixation rates. However, a systematic study comparing
the responses of dark CO2 fixation and net soil respiration from
temperate forest soils is currently lacking. The relative temperature
responses of these processes are important because globally, soil
temperatures are projected to warm by ∼4∘C until
2100 (based on simulations under the Representative Concentration Pathway
(RCP) 8.5 scenario; IPCC, 2013; Soong
et al., 2020).
The temperature sensitivity (Q10), the increase in reaction rates for a
10 ∘C rise in temperature, is a commonly reported value when
describing the response of soil microbial processes to higher temperatures
(Davidson and Janssens, 2006; Fang et al., 2005;
Leifeld and Fuhrer, 2005). Hicks Pries et al. (2017) reported a Q10 of
2.4 for net respiration rates in temperate coniferous forest soil. Similar
Q10 values of between 2 and 3 for net respiration rates have also been
described for other soil environments
(Conant
et al., 2008; Li et al., 2021). Considering that dark CO2 fixation
rates and soil respiration rates increase with temperature as described
above and that dark CO2 fixation rates have been shown to correlate
linearly with net soil respiration rates
(Miltner
et al., 2005; Šantrůčková et al., 2018), the Q10 values of
dark CO2 fixation rates with depth might correlate with those of net
soil respiration rates.
This study describes the temperature sensitivity (Q10) of dark CO2
fixation rates and compares it to that of net soil respiration rates across
soil profiles of deciduous and coniferous forests, the two temperate forests
based on vegetation (Dreiss and Volin, 2014; Adams et al., 2019). Soils of two
acidic forest plots from the Hummelshain forest, Germany, dominated by beech
(deciduous) and spruce (coniferous) tree species were incubated under two
temperature conditions (4 and 14 ∘C). We used a
13CO2-labelling approach to quantify dark CO2 fixation rates.
We also measured net soil respiration rates and determined the Q10 values of both processes across depth. We thus hypothesize that the
Q10 values of dark CO2 fixation rates with depth correlate with those of
net soil respiration rates. Using the derived Q10 values, we evaluated
the potential changes in dark CO2 fixation rates and net soil
respiration rates under projected increase in global soil temperature. We
further explored the microbial community composition in the beech and spruce
soil, with the aim to assess potential differences in the community that
might influence dark CO2 fixation rates and, thereby, its Q10
across temperate forest soil profiles.
Materials and methodsSite description and soil classification
The study sites (beech plot
50∘45′28.0′′ N, 11∘37′21.0′′ E; spruce plot
50∘45′30.0′′ N, 11∘37′23.0′′ E) are located within the forested areas of the Hummelshain municipality (∼362 m a.s.l.) in Thuringia, central Germany. The study site
was established on a former coniferous forest, and it involved the planting
of European beech trees within Norway spruce and Scot's pine stands. The
main purpose for this conversion was to counteract the low pH of the topsoil
under the coniferous stands and, thus, biologically activate the forest
floors (Graser, 1928). The mean annual rainfall in this area is about 630 mm,
and the mean annual air temperature is around 7.8 ∘C
(Achilles et al., 2020). The two study plots located
<1 km apart are dominated by European beech (Fagus sylvatica L.) and Norway
spruce (Picea abies (L.) H.Karst.) tree stands, respectively, and feature similar soil
geology
(Achilles et
al., 2020). The soils are mostly sandy (40 %–50 % sand and silt) with a
clay enrichment with depth (Table 1; Eckelmann et al., 2006; Bormann, 2007) due to the Triassic sandstone bedrock in the
Hummelshain area
(Achilles et
al., 2020). The soils in this region are predominantly quartz-rich (50 %–60 % quartz), consisting of sandstones and silt–mud stones, and are
classified as Luvisols with an F-mull-over-loess layer
(IUSS Working Group WRB, 2015; Achilles et al., 2020). Both soils feature a
low pH (<4) and a high C/N ratio (Table 1). The beech soil was
slightly lower in SOC, MBC, total nitrogen (TN), and moisture content than the spruce soil
across depth. The beech soil profile also featured a lower clay but higher
sandy texture when compared to the spruce soil profile. Further description
of the forest sites and the soil characteristics in the Hummelshain locality
can be found in Achilles et al. (2020).
Geochemical properties of soil cores obtained from beech and spruce soil plots at the Hummelshain forest. Soil pH, moisture content, soil organic carbon (SOC), carbon / nitrogen (C/N)
ratio, microbial biomass carbon (MBC), total nitrogen (TN), MBC / SOC ratio, natural abundance of 13C of SOC and MBC, bacterial abundance (16S rRNA gene copies), and soil texture class are reported for three depths definitions
for the beech and spruce soils of the Hummelshain forest. Each reported
value represents the mean of three replicate soil cores taken from bulk
soils during the sampling campaign. The abbreviation “dw” denotes dry weight.
The sampling was carried out in September 2020, towards the end of the
summer season. By driving in an
84 mm wide closed auger into the soil with the aid of a motor hammer (Cobra
Combi, Atlas Copco AB, Nacka, Sweden), 6 replicate soil cores, 1–2 m
apart, were obtained from each of the sampling plots, leading to a total of
12 soil cores for the beech and spruce plots. To avoid direct impact from
stem flow and to prevent larger roots from impeding the soil coring process,
all soil cores were taken ∼2 m away from the base of the
trees. Soil sampling began from the mineral horizon, and the organic layer
was ignored. Three segments were extracted from each soil core by depths
chosen according to the similarity of the horizon among all replicate cores
to obtain samples representing the AB horizon (0–20 cm), Bv horizon (20–55 cm), and BvT horizon (55–100 cm) for the beech plot and the AB horizon
(0–20 cm), Bv horizon (20–55 cm), and BvT horizon (55–92 cm) for the
spruce sample plot. Soil samples from the same depth intervals of each of
the six replicate cores were homogenized in pairs to yield three replicate
cores each for the beech and spruce forest plot. Afterwards, all soil
samples were sieved using a 2 mm sieve to remove stones and roots prior to
the incubation experiments. Fresh subsamples for later DNA extraction and
geochemical analysis were also taken and immediately stored by freezing in
liquid nitrogen.
Geochemical parameters and isotope measurements
The total and inorganic carbon and nitrogen concentration, pH, and
gravimetric water content as well as carbon isotope signatures of all soil
samples were determined as previously described by Akinyede et al. (2020)
with values reported in Tables 1 and S1 in the Supplement. The
13C signature of the bulk soil total organic carbon was analysed using
an elemental analyser isotope ratio mass spectrometer (EA-IRMS) (EA 1110, CE
Instruments, Milan, Italy) coupled to a Delta+ IRMS (Thermo Finnigan,
Bremen, Germany) through a ConFlo III interface. The extraction of microbial biomass carbon content
was performed by chloroform fumigation extraction (CFE)
(Nowak
et al., 2015; Vance et al., 1987) using 0.05 M K2SO4 following
methods described previously (Akinyede et al., 2020). The microbial biomass
carbon content (MBC) extracted as the chloroform soluble carbon content was
derived by taking the difference between the dissolved organic carbon (DOC)
content in the unfumigated (Cunfum) and the fumigated soil extract
fractions (Cfum) for all soil samples. Values from all samples were
divided by a correction factor KEC (of 0.45) that accounts for the
extraction efficiency. This factor corrects for the incomplete release of
carbon from the living microbial cells into the solution and is widely
applied to different soils (Joergensen and
Mueller, 1996; Joergensen et al., 2011; Wu et al., 1990) as CFE only
measures the fraction of microbial biomass rendered extractable in
K2SO4 solution after lysis with chloroform, which is likely the
very labile microbial fraction (e.g. the cytoplasm) (Ocio and Brookes,
1990; Wu et al., 1990). The MBC content was thus calculated as follows:
MBC(mg)=[Cfum-Cunfum]KEC.
Despite previous studies showing no strong variations in the KEC of 0.45
between soils or incubation temperatures (Martens, 1995; Joergensen et al.,
2011), we cannot exclude possible effects resulting from differences in CFE efficiency on our results, especially in comparisons of the rates
across the different soil depths or between the beech and spruce soils.
To determine the δ13C signature of the bulk soil MBC, the
13C signature of the DOC from the fumigated and unfumigated CFE
fractions was analysed using an isotope ratio mass spectrometer (DELTA V IRMS,
Thermo Fisher Scientific, Bremen, Germany) coupled to a high-performance liquid chromatography (HPLC) system (UltiMate 3000, Dionex Softron GmbH, Germering, Germany) via an LC IsoLink interface (Thermo Fisher Scientific, Bremen, Germany). All 13C isotope ratios were reported in
the delta notation (δ) expressed as δ13C values
(13C/12C ratios) in per mil (‰), relative to
the international reference material Vienna Pee Dee Belemnite (V-PDB)
(Coplen et al., 2006).
δ13C(‰)=13C12Csample13C12Creference-1×1000
Afterwards, the δ13C in per mil (‰) of
microbial biomass carbon (MBC) was derived by applying an isotope mass
balance to the measured 13C signals measured for all fumigated and
unfumigated DOC fractions from CFE as previously described (Akinyede et al.,
2020).
δ13CMB(‰)=δ13Cfum×Cfum-δ13Cunfum×CunfumCfum-Cunfum
13C-CO2-labelling incubation experiment
The CO2 fixation rates were determined using microcosm incubations.
Four replicates for each sieved soil sample (60 g wet weight) obtained from
all six soil cores in both the beech and the spruce sampling plots were placed
in sterilized 1000 mL serum bottles, closed with butyl rubber stoppers. The
large headspace-to-soil-volume ratio was chosen to ensure minimal changes in
the headspace CO2 concentration and the 13C isotope signatures as
no further additions to the headspace CO2 were performed throughout the
incubation period with labelled 13CO2. The four replicate jars
were split into two pairs of two replicates each prior to a 4 d
preincubation period. The first pair was preincubated at 4 ∘C
and the second at 14 ∘C. Before preincubation, all jars were
opened for several minutes to allow the CO2 concentration in the jar to
equilibrate with the ambient concentration. After the preincubation period,
gas samples were obtained with the aid of a gas syringe for CO2
measurement. Afterwards, the jars were opened, and homogenized soil samples
were subsampled for (1) the determination of total and organic carbon and
nitrogen content as well as 13C isotope signatures of the bulk soil,
(2) extraction to determine initial microbial biomass carbon content and its
13C isotope signature, and (3) storage for later DNA analysis.
The remainder of the soil (∼30 g) was placed in the
incubation jar, which was then flushed with synthetic air (75 % N2 and 25 % O2). One replicate of each temperature set was adjusted to 2 % (v/v) 13C-CO2 in the headspace, and the second replicate
was adjusted to 2 % (v/v) headspace 12C-CO2 concentration,
serving as treatments and controls, respectively. All soils exposed to the 2 % (v/v) 13C-CO2, and controls were then incubated statically in
the dark for 21 d under the same temperature as used in the preincubation
phase (4 and 14 ∘C). At the end of the incubation
period, microcosms were opened and soil samples from all incubations were
split into three parts, and geochemical parameters were analysed as after the
preincubation phase. Parameters like SOC, MBC, the C/N ratio, and water content
measured after incubation for the beech and spruce plots are described in
Table S2 in the Supplement and did not differ with temperature and
throughout the incubation period. In addition, the δ13C signals
of MBC and SOC from all incubated soil samples were measured as done for the
bulk soil prior to the start of the rate measurements.
Determination of CO2 fixation rates, respiration rates, and
temperature sensitivity (Q10)
To calculate the CO2 fixation rates for all soil incubations at both 4
and 14 ∘C, the actual 13C/12C ratio taken up into the
total soil pool and into microbial biomass carbon (MBC) pool was measured as
described in Sect. 2.3. This was derived from the measured and derived
13C values (for all treatments (13C labelled) and controls
(13C unlabelled/natural abundance)) of SOC and MBC, respectively.
The 13C/12C ratios were calculated based on the 13C/12C
ratio of the international V-PDB standard as done previously (Akinyede et
al., 2020, 2022a), where 0.0111802 is taken as the 13C/12C ratio of
the international V-PDB standard (Werner and Brand,
2001):
13C12C=δ13C1000+1×0.0111802.
Subsequently, the excess 13C ratio for the soil pool and the MBC pool
was derived from the increase in the 13C/12C ratio between the
13C-labelled treatment and the 12C-labelled controls (13C
natural abundance level) normalized to the respective carbon content of the
soil and of the microbial biomass (MBC) as follows:
excess13C(mg)=13Clabelled12Clabelled×MBC/SOC-13Cunlabelled12Cunlabelled×MBC/SOC.
These values were then divided by the incubation time and expressed per gram
of the bulk soil dry weight and per gram of microbial biomass carbon to
obtain the CO2 fixation rates per gram of soil dry weight
(g-1(dw)soild-1) and per gram of MBC (g-1MBCd-1),
respectively.
Following Spohn et al. (2019), the net respiration rates for all soil
preincubations were determined based on the difference in the CO2
concentrations of the glass jars measured at the beginning and at the end of
the incubation period using a gas chromatograph system for trace gas
analysis of air samples (Agilent 6890 GC FID ECD PDD, USA). Gas samples were
taken from the headspace of the jars using a gas syringe attached to 250 mL
evacuated vials. A period of 30 s was allowed for the gas vials to
equilibrate with the incubation jars, after which the gas vials were
disconnected from the vials and connected via a gas line to the gas
chromatograph system for CO2 measurement (in ppm). Using the ideal gas
equation, net soil respiration rates were calculated according to
Dossa et al. (2015), expressed as micrograms of carbon per gram of soil dry weight per day.
As net respiration rates
represent CO2 produced minus CO2 fixed, the total CO2
production or decomposition rates were subsequently derived by adding the
net respiration rates to the CO2 fixation rates measured for all beech
and spruce soil samples.
The temperature sensitivities of the CO2 fixation (per unit soil and
MBC) and net respiration rates (per gram of soil only), as well as the
decomposition rate (per gram of soil only), were determined by calculating
Q10 values according to Leifeld and Fuhrer (2005):
Q10=k2k110T2-T1,
where T2 and T1 denote the higher and lower temperatures (in
∘C) at which the soils were incubated, and k2 and k1 represent the corresponding derived CO2 fixation rates, net respiration rate and decomposition rates for temperatures T2 and T1, respectively.
DNA extraction and 16S rRNA gene sequencing
DNA was extracted from 0.25 g of all bulk soil and incubation samples using
the DNeasy PowerSoil DNA kit (Qiagen, Hilden, Germany) according to the
manufacturer's protocol. For Illumina MiSeq sequencing, libraries of
amplicon sequences of bacterial 16S rRNA genes were generated. All libraries
were prepared with the NEBNext Ultra DNA Library Prep Kit for Illumina (New
England Biolabs, Hitchin, UK) using a two-step barcoding approach. For the
first step, forward (Bact_341F) and reverse
(Bact_785R) primers targeting the V3 to V4 hypervariable
regions of the bacterial 16S rRNA gene were used
(Klindworth et al., 2013). For Illumina
sequencing, the primers were modified with an adaptor overhang which allowed
for barcoding in a second PCR step. During the first PCR step, all DNA
samples (>10ngµL-1) were amplified in a 20 µL reaction
volume containing 10 µM of each primer, 0.67 µgµL-1 of BSA
(bovine serum albumin), 5.67 µL nuclease-free water, and 10 µL
HotStarTaq Master Mix (Qiagen, Hilden, Germany). The PCR conditions used
consisted of an initial denaturation at 95 ∘C for 45 min,
followed by 26 to 30 cycles of denaturation (94 ∘C for 45 s),
annealing (55 ∘C for 45 s), and extension (72 ∘C for
45 s) and then a final extension step at 72 ∘C for 10 min. While
samples from the AB and Bv horizon were amplified using 26 to 27 cycles, a
few samples from the BvT depth with low DNA concentration were amplified
using 30 cycles with the same cycling conditions. All amplified sequences
from the first PCR step were barcoded in a second PCR step using 1 µL
of the initial PCR products, 0.5 µM of barcoded primer set from
Illumina (sequences provided in Table S4 in the Supplement), and Ruby Taq
Master Mix (Jena Bioscience, Germany) following the cycling conditions of 6
cycles at 95 ∘C for 45 s, 55 ∘C for 45 s, and 72 ∘C for 45 s for denaturing,
annealing, and extension steps, respectively. All samples were analysed by gel
electrophoresis using 1 % agarose gel to ensure all amplicons were
∼500 bp in length. Subsequently, prepared libraries were
sequenced on a MiSeq (Illumina, Inc, San Diego, CA, USA) using v3 chemistry (2×250 bp).
The raw sequences generated were analysed using mothur (Schloss et al., 2009; http://www.mothur.org, last access: 19 January 2021) and the mothur MiSeq SOP as of 19
January 2021. Paired reads were combined, and sequences were trimmed, saving
only sequences with the desired length of between 360 and 500 bp. Trimmed
sequences were aligned to the SILVA reference database v132 release
(Quast et al., 2013), and sequences with
differences of up to four bases were pre-clustered. Chimeras were removed
using UCHIME and the GOLD reference database implemented in mothur
(Edgar et al., 2011). Subsequently, the
taxonomic classification of the sequences against the SILVA database was
performed.
Determination of chemolithoautotrophic CO2 fixation potential in
the Hummelshain forest soils
To determine the potential for chemolithoautotrophic CO2 fixation among
all soil samples, the abundance of functional genes involved in autotrophic
CO2 fixation was first predicted for all bacteria communities. Here,
the representative sequences from operational taxonomic unit (OTUs) generated from mothur were analysed
using version 2 (v2.2.0 beta) of the PICRUSt (Phylogenetic Investigation of
Communities by Reconstruction of Unobserved States) software package
(Douglas et al., 2020). All OTU sequences were de-gapped and
placed in a reference taxonomic tree based on the Integrated Microbial
Genomes database. EPA-ng and GAPPA tools were used to determine the best
position of these placed OTUs in the reference phylogeny
(Barbera et
al., 2019; Czech and Stamatakis, 2019; http://www.hmmer.org, last access: 19 January 2021) after
which KEGG orthologues for key enzymes involved in dark CO2 fixation were
predicted for each OTU. Using the derived KEGG Orthology (KO) numbers for
different key genes for CO2 fixation, the six known autotrophic
pathways were deduced for all samples as previously done
(Akinyede et al., 2022a, 2020). These include the
Calvin–Benson–Bassham (CBB) pathway (or Calvin cycle), the reductive citric
acid (rTCA) pathway, the Wood–Ljungdahl pathway (WLP), the
3-hydroxypropionate–malyl-CoA (3HP) cycle, the
3-hydroxypropionate–4-hydroxybutyrate (HP/HB) cycle, and the
dicarboxylate–4-hydroxybutyrate (DC/HB) cycle.
Based on PICRUSt2 predictions, the abundance of functional genes belonging
to two CO2 fixation pathways, the Calvin cycle and the rTCA pathway,
were determined by quantitative PCR for the bulk soil as well in all soil
samples incubated at 4 and 14 ∘C. Gene abundance of bacterial 16S rRNA, RuBisCO marker genes (cbbL IA, cbbL IC, cbbM) for the Calvin–Benson–Bassham cycle and ATP
citrate lyase genes, and aclA belonging to the reductive citric acid cycle was
determined by quantitative PCR (qPCR) on the CFX96 Touch Real-Time PCR system (Bio-Rad, Singapore) using Maxima SYBR Green Master Mix (Agilent, CA, USA).
Primer pair Bac 8Fmod–Bac 338R was used to target the 16S rRNA genes (Loy et al., 2002; Daims et
al., 1999), while F-cbbL IA–R-cbbL IA, F-cbbL IC–R-cbbL IC, and F-cbbM–R-cbbM were used to target both the form-I (cbbL IA and cbbL IC) and
form-II (cbbM) RuBisCO marker genes
(Alfreider et al., 2012,
2003), which is specific to both obligate and facultative
chemolithoautotrophic bacteria groups like Proteobacteria (Selesi et al., 2005). Primer
pair F-g-acl-Nit–R-g-acl-Nit was used to target the alpha subunit of the ATP citrate lyase (aclA) gene,
which is specific to nitrite-oxidizing bacteria and complete
ammonia-oxidizing (comammox) bacteria, e.g. Nitrospira
(Alfreider et al., 2018). All cycling conditions
and standards used for quantification are found in Akinyede et al. (2020)
and Herrmann et
al. (2012, 2015). Due to the absence of a reliable standardized qPCR protocol
and primer sets to target genes for the WLP and the rest of the
other autotrophic pathways, the presence of these pathways was based only on
the predictions by PICRUSt2.
Statistical analysis
We compared the CO2 fixation rates per gram of soil and per gram of MBC
between all soil samples incubated at 4 and 14 ∘C using Student's
t test. To compare the respective Q10 values between the beech and
spruce profile and across individual horizons, analysis of covariance (ANCOVA) and one-way ANOVA with
Tukey's test were conducted, respectively. To compare other parameters
between the beech and spruce soil profiles, e.g. net soil respiration rates
between 4 and 14 ∘C and 13C signal of SOC and MBC, ANCOVA
was also conducted. When comparing parameters between the beech and spruce
soils using ANCOVA, soil depth also accounted for the variability in the
measured parameters and was used as the covariate in the analysis. When
deriving the CO2 fixation and net soil respiration rates under
projected future temperatures increase (from 8 to 12 ∘C), the mean Q10 values for the beech and spruce profiles
were used in the Q10 equation described in Eq. (6) of the method
in Sect. 2.5. As rates at 4 ∘C were low and, in some samples, below the
detection limit (e.g. net respiration rates at the beech soil BvT depth),
the rates per gram of soil measured at 14 ∘C were used (as
k2, T2) in the Q10 equation to derive rates at 8 ∘C (k1, T1). The derived rates at 8 ∘C
were then used (as k1, T1) for the subsequent derivation of
rates at 12 ∘C (k2, T2). Following Geyer et al. (2020), we quantified the proportion of excess 13C transferred into the
SOC pool from the MBC pool via microbial residues as the total amount of
excess 13C fixed in the SOC pool minus the excess 13C fixed in the
intact MBC pool.
The variations in the bacterial community between the beech and spruce soil
and with temperature were determined by measuring the beta diversity. Beta
diversity was measured by performing principal coordinate analysis (PCoA)
based on Bray–Curtis dissimilarity using the package vegan contained in R
(Oksanen et al., 2008). Here, the bacterial communities from all beech and
spruce soil samples were clustered based on their similarity and dissimilarity between the soils and with temperature. To determine the significance of the
factors accounting for OTU variances shown in the PCoA plot between the two
soils, across individual soil depth, and between temperatures, permutational
multivariant analysis of variance (PERMANOVA) was performed with 999
permutations using “adonis” functions. Differences in the abundance of all
predicted and quantified CO2 fixation genes between the beech and
spruce soil were analysed using ANOVA and Tukey's test. For all statistical
tests, differences with p<0.05 were considered statistically
significant. All statistical analyses were conducted with the R environment
(v.3.6.1) and RStudio (v1.1.463).
ResultsEffects of temperature on dark CO2 fixation rates in beech and spruce soils
All soil incubations exposed to 13CO2 were significantly enriched
in δ13C relative to the controls at both 4 and 14 ∘C, indicating dark CO2 fixation (Fig. S1 in the
Supplement). In the top depths of both the beech and the spruce soils,
significantly higher CO2 fixation rates were observed at 14 ∘C than at 4 ∘C. For the top AB horizon of the beech soil,
CO2 fixation rates expressed in relation to soil dry weight
(µgCg-1(dw)soild-1) (Fig. 1a) were almost 2 times higher with
0.033 ± 0.006 µgCg-1(dw)soild-1 at 14 ∘C compared to 0.018 ± 0.006 µgCg-1(dw)soild-1 at 4 ∘C (p=0.04; Student's t test). Similarly, the top AB depth of
the spruce soil also featured ∼2 times higher fixation rates
at 14 ∘C with 0.030 ± 0.003 µgCg-1(dw)soild-1 than at 4 ∘C with
0.014 ± 0.002 µgCg-1(dw)soild-1 (p=0.005). In the lower depths, however, no
significant differences in fixation rates expressed per gram of soil dry weight
were observed between soils incubated at 4 and 14 ∘C for either the beech soils (p=0.3 and p=0.6 at the Bv and BvT horizons,
respectively) or the spruce soils (p=0.2 and p=0.4 at the Bv and BvT
horizons, respectively). While we observed an expected decrease in fixation
rates per gram of soil with depth in both soils due to the decreasing SOC
content, there were no significant differences in rates between the beech
and spruce soil, neither at 4 nor at 14 ∘C. Across
the depth profiles, changes in rates with temperature did not differ between
the spruce (1.5–3.2-fold changes) and the beech soil (0.9–2.7-fold
changes) (p=0.08) as both soils showed a 70 %–90 % increase in the 13C
signal with temperature (Fig. S1).
Dark CO2 fixation rate measured from soil microcosms
supplemented with 2 % 13CO2 at 4 and 14 ∘C. Shown
are (a)13C uptake rates in soil expressed in µgCg-1(dw)soild-1 (micrograms of carbon per gram dry
weight (dw) of soil per day) and (b)13C uptake rates into
MBC expressed in µgCg-1MBCd-1 (micrograms of carbon per gram of microbial biomass carbon per
day) after 21 d of incubation with 2 % 13CO2 at 4 (blue
bars) and 14 ∘C (red bars) across three horizons of the beech and
spruce soils. Error bars indicate the standard deviation of incubations from
three replicate soil cores. * denotes p<0.05.
When expressed in relation to microbial biomass carbon (MBC), dark CO2
fixation rates in the top AB horizon of the spruce soil were 1.6 times
higher at 14 ∘C with 145.95 ± 27.13 µgCg-1MBCd-1 than at 4 ∘C with 88.29 ± 17.12 µgCg-1MBCd-1 (p=0.04) (Fig. 1b). For the beech soil, however,
values in the top AB depth were similar at 4 and 14 ∘C (p=0.3) with 108.18 ± 8.82 and 125.11 ± 23.76 µgCg-1MBCd-1,
respectively. In the lower horizons, no significant differences between
temperature treatments were observed for the two soils. Rates expressed per
gram of MBC were approximately constant with depth, excepting the BvT horizon of
the beech soil, which had lower rates. Taken together across depth profiles,
stronger differences with temperature were observed for the spruce (1.3–2.6-fold changes) than the beech soil (0.9–1.4-fold changes) (p=0.003).
These differences in rates reflect the observed difference in the 13C
signals of MBC with temperature between the soils. While up to a 124 %
increase in the 13C signal of MBC was found for the spruce soil, the beech
soil showed no more than a 23 % increase in the 13C signal of MBC with
temperature (Fig. S1). These differences between the beech and spruce soil
suggest that drivers of dark CO2 fixation may differ between soils.
Q10 of dark CO2 fixation rates for beech and spruce soil profiles
The Q10 values, the factor by which CO2 fixation rates differed
with the 10 ∘C rise in temperature, were 1.81 ± 0.17 across
depths (for rates per gram of soil) for the beech soil, and 2.34 ± 0.21 for the spruce soil (Fig. 2a) with a mean Q10 value of 2.07 ± 0.34 for all beech and spruce soils. Both the beech and the spruce soils
showed large variability in the Q10 values with depth with values
ranging from 1.97 ± 0.84 at the AB depth and 1.63 ± 0.86 at the
bottom BvT depth of the beech soil to 2.11 ± 0.07 and 2.53 ± 0.70 through the spruce soil profile. Thus, no significant
differences between corresponding depths of both soil profiles were
observed (p=0.81, p=0.32, and p=0.23 for the AB, Bv, and BvT
horizons, respectively). However, differing trends across individual depths
for the beech and spruce soil were observed, with Q10 values decreasing
with depth in beech soil but increasing with depth in spruce soil
(R2=0.92, p=3.2×10-8, ANCOVA).
Temperature sensitivity (Q10) of dark CO2 fixation rate
measured from soil microcosms supplemented with 2 % 13CO2 at
4 and 14 ∘C. Shown are the Q10 (temperature sensitivity)
values of dark CO2 fixation rates derived from fixation rates expressed
in (a)µgCg-1(dw)soild-1 (micrograms of carbon per gram dry
weight (dw) of soil per day) and in (b)µgCg-1MBCd-1 (micrograms of carbon per gram of microbial biomass carbon per day) after 21 d
of incubation with 2 % 13CO2 across three horizons in the beech
and spruce soils.
We also calculated the Q10 for rates per gram of MBC, as the microbial
cells are responsible for dark CO2 fixation and should be primarily
affected by temperature. Compared to the Q10 based on soil dry weight,
the Q10 based on MBC was lower in the beech soil (p=0.008), which is
linked to the smaller differences in the 13C signal of MBC and
calculated CO2 fixation rates with temperature. For the spruce soil, no
difference between the Q10 based on MBC and the Q10 based on soil
dry weight was observed (p=0.13). As a result, the spruce soil profile
featured a higher mean Q10 based on rates per gram of MBC with 1.9 ± 0.63 (Fig. 2b) than the beech soil profile with 1.1 ± 0.20 across depth (p=0.003; ANOVA and Tukey's test).
Allocation of 13C in the beech and spruce soils
As both the fixation rates and Q10 values differed between the beech
and spruce soil, we aimed to determine if this was reflected by differences
in the partitioning or transfer of the fixed 13C via microbial residues
between the MBC and SOC pools. On average, the 13C signals of the MBC
pool were significantly lower across the beech soil profile compared to the
spruce soil profile at 14 ∘C (R2=0.91, p=5.24×10-8, ANCOVA) with no clear difference observed between forest types at
4 ∘C (Fig. S2 in the Supplement). In contrast, the 13C
signatures measured in SOC were on average higher in the beech than in the
spruce soil across depth for soils incubated both at 4 and at
14 ∘C (R2=0.98 and p=3.90×10-12 for 4 ∘C and R2=0.96 and p=1.05×10-10 for 14 ∘C,
ANCOVA). As a result, a higher proportion of fixed 13C was found to be
allocated to the MBC pool in the spruce soil with up to 64 % compared to
the beech soil with up to 32 % (Fig. 3). Hence, in the beech soil, a
greater amount of 13C allocation into the SOC pool was observed
compared to the spruce soil. In general, higher temperatures were associated
with a larger increase in 13C allocation to SOC compared to MBC pools in
the beech and spruce soils (Fig. 3). For instance, at the AB depth of the
beech soil, 13C fixed into SOC increased from 67 % to 81 %, and in
the spruce soil, it went up from 36 % to 63 %.
Proportion of fixed carbon recovered in MBC and SOC pools of the
beech and spruce soils. The pie charts show the relative proportions of
microbially derived 13CO2 into MBC (orange fractions) and SOC
(brown fractions) pools expressed as a percentage of the total fixed carbon
after 21 d of incubation with 2 % 13CO2 for soils
incubated at (a) 4 and (b) 14 ∘C across three horizons (AB, Bv,
BvT) in beech and spruce soils. Values are the mean of three replicate
incubations.
Effects of temperature on net soil respiration rates
In addition to the CO2 fixation rates, we also determined net soil
respiration rates during the preincubation phase. As expected, net
respiration rates across all beech and spruce soil samples were 20–70 times
higher than CO2 fixation rates (p=0.005) with values as high as 2.89 ± 1.26 and 2.31 ± 0.9 µgCg-1(dw)soild-1 at 14 ∘C at the top AB horizon
of the beech and spruce soils, respectively. Net respiration rates were
higher in all soils incubated under 14 ∘C than at 4 ∘C
for both the beech (R2=0.73, p=0.03, ANCOVA) and spruce
(R2=0.58, p=0.02, ANCOVA) profiles (Fig. 4). As rates were highly
variable across replicates, no significant differences between the beech and the
spruce soil or with depth were observed.
Net respiration rates and Q10 values measured from soil
microcosms incubated at 4 and 14 ∘C. Shown are (a) net
respiration rates in beech and spruce soils expressed in µgCg-1(dw)soild-1 (micrograms of carbon per gram dry weight (dw) of soil per
day) at 4 (blue bars) and 14 ∘C (red bars) and (b)Q10 (temperature sensitivity) of net respiration rates measured after 4 d
of preincubation in beech soils (yellow bars) and spruce soils (green bars)
across depth. Error bars indicate the standard deviation of incubations from
three replicate soil cores. BDL and ND denote values “below the detection
limit” and values “not determined”, respectively.
In response to warming, the Q10 values of net respiration rates per gram of
soil for the beech and spruce soils were 2.87 ± 0.81 and 3.06 ± 0.78, respectively. Taken together, the mean Q10 for net respiration
rates across the beech and spruce soil profiles at 2.98 ± 0.69 was
significantly higher than the Q10 of fixation rates relative to soil
dry weight (R2=0.95, p=3.0×10-5, ANCOVA). Values ranged
between 2.29 ± 0.004 and 3.44 ± 1.43 across the two AB and Bv
depths in the beech soil and between 2.60 ± 0.12 and 3.96 ± 3.38 across all three depths of the spruce soil. Due to the high variations in
net respiration rates among the soil samples, the Q10 values did not
differ significantly between the beech and spruce soil and across the
individual depth profiles. As net respiration rates were much higher than
CO2 fixation rates, the derived decomposition rates were nearly the
same as the net respiration rates for the beech and spruce soils. Hence the
Q10 values were also similar, having a mean value of 2.95 ± 1.34 (Table S3 in the Supplement).
Bacterial communities of the Hummelshain forest soils
The lower 13C allocation in MBC but higher allocations to SOC for beech
than for spruce soils indicated a higher turnover of fixed 13C from MBC
to SOC in the beech soil compared to the spruce soil. We thus further
checked if this higher turnover was accompanied by differences in the
overall bacterial community composition and abundance. We investigated the
16S rRNA gene amplicons at OTU level and
determined 16S rRNA gene copies by qPCR (Table 1 and Fig. S3 in the
Supplement). Principal coordinate analysis (PCoA) revealed differences in
the composition between the beech and spruce soil (R2=0.23, p=0.001, PERMANOVA, Fig. 5a). This was most pronounced in the top AB horizon
(R2=0.67, p=0.003). Furthermore, the bacterial community
composition also differed with soil depth (R2=0.30, p=0.001).
As expected, the microbial abundance decreased with depth in both soils
(Table 1). However, no difference in the microbial abundance between soils
was observed at comparable depths. With respect to temperature, no shifts in
the community composition were found in either beech (R2=0.014, p=0.99) or spruce (R2=0.015, p=0.98) soils (Fig. 5b). Likewise, the
microbial abundances did not differ with temperature (Table S2).
Bacterial community composition and community structure from beech
and spruce bulk soil. Shown are the bacterial community structure of (a) the
beech (yellow symbols) and spruce (green symbols) bulk soils before
incubation and of (b) beech and spruce soils incubated with 2 %
13CO2 at 4 (blue symbols) and 14 ∘C (red symbols). PCoA
plots are based on OTU level analysis (Bray–Curtis dissimilarity) of 16S rRNA
gene amplicons generated by Illumina MiSeq sequencing with three independent
data points per depth obtained from beech and spruce soils.
Abundance of genes for CO2 fixation
Based on presumed differences in residue formation and in the community
composition between the soils, we speculate that the potential key players
in the beech soil were composed of a higher proportion of groups with faster
life cycles when compared to the spruce soil. As the rich SOC content in
forest soils generally promotes faster growth of heterotrophs over
chemolithoautotrophs, we further hypothesized that the beech bulk soil
contains lower fractions of autotrophs compared to the spruce soil. We used
PICRUSt2 to predict and quantify the genetic potential for CO2 fixation
in both soils to test this hypothesis. Predicted autotrophic OTUs made up
∼11 % of the total bacterial community in all samples.
Most of the autotrophic OTUs were predicted to possess genes affiliated with
RuBisCO of the CBB pathway for CO2 fixation, with ∼9 %, while genes of the WLP and the rTCA pathway were predicted in
∼2 % and 0.1 % of the OTUs, respectively. The spruce
bulk soil featured higher abundances of OTUs predicted to possess the
RuBisCO gene than the beech bulk soil (Fig. 6a), with significantly higher
proportions in the AB horizon (p=0.007).
Relative abundance of RuBisCO genes in the beech and spruce soil.
Shown are the relative abundances of (a) predicted and (b) quantified
RuBisCO genes coding for the CBB pathway in the beech and spruce soil. Data
in (a) are based on predictions by PICRUSt2 analysis of bacterial 16S rRNA
gene amplicon sequence data for the beech and spruce bulk soil, while data in
(b) are acquired by qPCR of cbbL IC genes for both soils incubated at 4 and 14 ∘C. *, **, and *** denote
p<0.05, p<0.01, and p<0.001, respectively.
Quantitative PCR of marker gene coding for the CBB (RuBisCO (cbbL IA, cbbL IC,
cbbM)) and the rTCA pathway (ATP citrate lyase alpha subunit (aclA)) was performed to
confirm the predicted potential for autotrophic CO2 fixation. Of the
detected gene variants, the cbbL IC gene was the most abundant with up to 5 %
of the bacterial 16S rRNA gene copies in both soils, whereas other RuBisCO
and aclA genes constituted less than 1 % (Fig. S4 in the Supplement). The
proportions of cbbL IC genes across the depths were significantly higher in the
spruce than in the beech soil at 4 and 14 ∘C (p=0.007, p=1.03×10-6, respectively) (Fig. 6b). In the bulk soil, however, this
was only observed in the BvT horizon (p=0.03).
Discussion
This study shows that the derived Q10 values of dark CO2 fixation rates
per gram of soil, with a mean value of 2.07 across the beech and spruce soil
depths, were significantly lower than the average Q10 of net soil
respiration rates per gram of soil with 2.98 for both soils, which suggests
that soil respiration is more sensitive to warming than CO2 fixation.
Our Q10 values of net soil respiration rates fall in the range of
those reported for agricultural soils (1.5) and temperate mixed forest soils
(3.1)
(Fang
et al., 2005; Conant et al., 2008; Hicks Pries et al., 2017; Li et al.,
2021). For Q10 values of dark CO2 fixation rates, a similar value
of ∼2.5 was extrapolated for afro-temperate forest soils
(Nel and Cramer, 2019).
Comparing the responses of dark CO2 fixation and net soil respiration
to temperature for the same soil is important in understanding the dynamics
of SOC fluxes within the context of global climate change. Since dark
CO2 fixation can recycle up to 4 % of CO2 respired from
temperate forest soils (Spohn et al., 2019),
higher CO2 fixation rates might as well be affecting the magnitude of
SOC losses from temperate forest soils under warming. By assuming a Q10 of 2.07 and 2.98 for dark CO2 fixation rate and net soil respiration
rates, respectively, we extrapolated the effect of future warming on the
forest SOC fluxes. With a 4 ∘C increase in mean annual temperate
forest soil temperature (∼8 now to 12 ∘C by 2100) to 1 m deep by the end of this century
(IPCC, 2013; Soong et al., 2020),
dark CO2 fixation rates to 1 m depth would increase by 33 % while
net soil respiration rates would increase by 55 %. This indicates that
future increase in net soil respiration might be 1.16 times higher than
CO2 fixation upon 4 ∘C warming. Hence, the potential for
dark CO2 fixation to recycle or modulate carbon respired from temperate
forest soils could decrease under future warming scenarios. However, the
temperature response of dark CO2 fixation and respiration in soils is
likely also affected by varying temperatures occurring in different
temperate forest biomes. Differences in carbon allocation between MBC and
SOC also show that not all components of the soil carbon cycle will have the
same response to soil temperature changes. Furthermore, higher temperature
might alter primary production and root exudation, resulting in changes in
soil carbon inputs and, consequently, soil pore space CO2 concentrations
and effluxes (Jakoby et al., 2020; Way and Oren, 2010; Yin et al., 2013).
Thus, the estimates presented here are associated with a range of
uncertainty.
Our measured soil CO2 production in all soil incubations does not
represent decomposition rates but the net soil respiration rates, as these
rates include the effects of temperature on both CO2 production
(decomposition) and CO2 fixation, with both processes occurring
simultaneously (Braun et al., 2021). Thus, to accurately derive the
decomposition rates, CO2 fixation rates have to be added to measured
net soil respiration rates. Although our measured CO2 fixation rates
were very small with only marginal effects on the Q10 of soil CO2
production, that is, a Q10 of 2.98 vs. 2.95 for net soil respiration and
decomposition rate, respectively, dark CO2 fixation may
result in an overestimation of Q10 of decomposition rates if only net
soil respiration is measured. This is especially the case in scenarios where
high CO2 fixation rates are expected.
Unexpectedly,
the two soils, beech and spruce, showed differences in their temperature
response. Although Q10 values for CO2 fixation rates per gram of
soil were similar between the beech and spruce topsoil, the Q10 values differed in their depth trends, with decreasing Q10 with depth in the
beech soil and increasing Q10 with depth in the spruce soil.
Furthermore, while both soils showed similar temperature responses in terms
of rates of CO2 production per gram of soil, the temperature effect of
CO2 fixation rates expressed per gram of MBC was smaller in the beech
than in the spruce soil. The lower Q10 in the beech soil was
accompanied by higher proportions of newly fixed 13C in the SOC pool
but lower proportions in the MBC pool when compared to the spruce soil,
especially for soils incubated at 14 ∘C. This suggested that
there was a higher transfer of microbially derived carbon from the MBC pool
into the SOC pool in the beech soil. Through microbial residue formation,
fixed 13C is transferred from the MBC into the SOC pool (Geyer et al.,
2020; Miltner et al., 2012). A higher rate of residue formation in the beech
soil will lead to a lower fraction of fixed 13C remaining in the MBC
pool and, thereby, an underestimation of the CO2 fixation rates per gram
of MBC when compared to the spruce soil. Hence, a higher rate of microbial
residue transfer in our 21 d incubations might explain the lower Q10 of fixation rates per gram of MBC in the beech than in the spruce soil. Such
rapid residue formation is not uncommon in soils as it was observed in as
little as 6 h in a temperate forest soil (Geyer et al., 2020).
Microbial biomass can also turn over as necromass within a few days to weeks
in soils (Kästner et al., 2021; Miltner et al., 2012) with turnover
times of 18–21 d reported in agricultural soils (Cheng, 2009) and 33 d in temperate forest soils (Spohn et al., 2016). Thus, the formation of
microbial residues can be observed within the timescale of our incubation
experiment.
Accelerated microbial residue formation in the beech compared to the spruce
soil might have been related to differences in soil abiotic parameters, in
particular, factors differentially affecting either the lifespan (turnover)
of microbial cells or the formation of extracellular metabolites from living
cells. The biggest difference between the soils was soil texture, with lower
clay and higher sand content in the beech compared to the spruce soil. Soil
texture is known to affect microbial biomass turnover in soils
(Van
Veen et al., 1984; Sakamoto and Hodono, 2000; Prévost-Bouré et al.,
2014), with high-clay-content soils often associated with slow biomass
turnover into necromass compared to low-clay-content soils
(Ali
et al., 2020; Gregorich et al., 1991; Van Veen et al., 1985) due to the
capacity of clay-rich soils to protect or preserve microbial cells, reducing
overall the death rate
(Van Veen et al.,
1985, 1984). Interaction of microbial biomass with the negatively charged
clay mineral particles was suggested as the mechanism causing biomass
stability
(Ali
et al., 2020; Six et al., 2006). Clay–microbe interactions may promote
microbial growth by maintaining an optimal pH range (Stotzky and Rem, 1966) and
helping to adsorb metabolites inhibitory to microbial growth
(Martin et al., 1976). The fine particles and small pore space
characteristic of clay-rich soils also lead to a higher water-holding
capacity
(Fan
et al., 2004; Jommi and Della Vecchia, 2016; Miltner et al., 2009; Tsubo et
al., 2007), resulting in the observed higher moisture content in the spruce
compared to the beech soil. This higher moisture typical of clay-rich soils
might partly be responsible for protecting microbes against moisture
limitations when compared to sandy soils
(Meisner et
al., 2018; Schnürer et al., 1986; Bitton et al., 1976). Furthermore,
small pores also restrict the access of higher organisms like protozoa,
providing protection against predation
(Elliott et al., 1980;
Rutherford and Juma, 1992). Microbial protection promotes recycling or
transfer of microbial extracellular products among the living communities,
thus preventing further release into the soil pool (Gregorich et al.,
1991). All of these mechanisms imply that the formation of microbial
residues in the more clay-rich spruce soils should be slower than in the
beech soil, as we observed. Due to the presence of larger mineral surface
areas of clay in the spruce soils, association with clay surfaces can also
lower residue formation and the amount of 13C label transferred would
be a smaller proportion of total SOC. However, as the mineral composition of
the soils was not measured, we cannot verify this assumption.
The higher microbial residue formation in the beech soil was accompanied by
a different community composition and a lower proportion of genes for
chemolithoautotrophic CO2 fixation. Considering that both soils
featured a similar abundance of the total bacterial community, this implied
a higher proportion of heterotrophs among the beech soil communities. Growth
of heterotrophs is favoured by high quantities of simple and complex carbon
substrates released as root exudates
(Huang
et al., 2022; Li et al., 2018; Lladó et al., 2017; Vijay et al., 2019), whereas soils are usually deficient in reduced inorganic compounds
(Jones et al., 2018) required as energy sources
for autotrophic growth (Brock et al., 2003; Berg, 2011).
The redox potential of half reactions utilized by chemolithoautotrophs for
energy often leads to lower energy yield than commonly observed for
heterotrophs, causing chemolithoautotrophs to grow more slowly
(Hooper
and DiSpirito, 2013; Madigan et al., 2015; In't Zandt et al., 2018). As
cell growth correlates with microbial residue formation (Geyer et al., 2020;
Hagerty et al., 2014; Kästner et al., 2021), it is likely that heterotrophs in
soils also form residues at faster rates than their chemolithoautotrophic
counterparts. Hence, the suggested higher proportion of heterotrophs in the
beech soil could also explain the higher rate of microbial residue formation
observed when compared to the spruce soil. In both the beech and the spruce
soil, the majority of the chemolithoautotrophic genes were affiliated to
facultative autotrophs or mixotrophs which can also utilize SOC as a carbon
source for growth (Yuan et al., 2012). This versatility allows them to be
more active or grow faster than obligate autotrophs (Madigan et al., 2015).
Hence, mixotrophs might contribute to microbial residue formation into the
SOC pool especially in the beech soils.
The proportion of labelled carbon transferred to the SOC pool increased with
temperature in both beech and spruce soils and across all depths,
indicating higher inputs of microbial residues under warming. Higher
temperatures have been often reported to increase inputs of microbial
residues into soil (Ding et al., 2019;
Hagerty et al., 2014; Li et al., 2019). Increasing soil temperature by 10 ∘C
(15 to 25 ∘C) has been reported to double the specific death rate of
microbial communities in soil due to increased protein turnover
(Joergensen et al., 1990).
Increased microbial residue formation of soil microbial biomass has been
suggested to result from higher rates of enzymatic activities or changes in
the abundance and composition of the soil microbial community
(Ding et al., 2019; Hagerty et al.,
2014). However, we did not find changes in the composition and abundance of
the microbial community with warming in both the beech and the spruce soils
during this short incubation time. Previous studies have shown that even
after 4 or 5 years of warming, no increase in bacterial and fungal
biomass is observed for a temperate forest soil
(Schindlbacher et al.,
2011) and it can take up to a decade to detect temperature-related changes
in the soil community composition
(Rinnan
et al., 2009, 2007). In agreement, DeAngelis et al. (2015) revealed that 5 ∘C soil warming had a significant impact on bacterial community
structure in mixed deciduous temperate forest soils only after 20 years of
warming. Some authors have suggested that soil communities are more likely driven by
gradual warming-induced changes in aboveground plant biomass and
composition and associated shifts in carbon substrate, moisture, and
nutrient conditions rather than just by elevated soil temperature effects
(Sarathchandra
et al., 1989; Rinnan et al., 2007; Frey et al., 2008; DeAngelis et al.,
2015). This indicates that increases in dark CO2 fixation in temperate
forest soils as a response to short-term warming may not be caused by an
increased microbial abundance or a shift in community composition but
likely by an increase in the formation and release of microbial residues.
Conclusion
In response to warming, we measured an average Q10 of 2.07 for CO2
fixation rates per gram of soil across 1 m depth profiles for soils
dominated by deciduous beech and coniferous spruce trees. As net soil
respiration rates across depth displayed a higher mean Q10 of 2.98, we
estimated that net soil respiration might increase 1.16-fold more than
CO2 fixation rates under projected warming scenarios of 4 ∘C. The observed higher 13C signatures in the SOC pool of the beech soil
suggested higher microbial residue formation, and this was reflected in the
lower Q10 values for CO2 fixation rates per gram of microbial
biomass for the beech than for the spruce soil. Also, the higher allocation
of CO2-derived carbon to the SOC pool at higher temperatures indicates
that warming primarily results in an increased residue formation of
microbial cells. Findings from this study indicate that dark CO2
fixation in temperate forest soils might be less responsive to future
warming than net respiration and, as a result, could recycle less CO2
respired from temperate forest soils in the future than it does now.
Code availability
Data analysis was performed using only standard tests and plotting commands in R.
These codes are available on request from the corresponding author.
Data availability
Raw data associated with this study can be accessed at https://doi.org/10.17617/3.EFHWIY (Akinyede et al., 2022b). Generated
sequences obtained for all soil samples in this study are deposited in the
NCBI Sequence Read Archive (SRA) database with accession numbers
SAMN26148471, SAMN26148472, SAMN26148473, SAMN26148474, SAMN26148475, and
SAMN26148476 under the BioProject accession number PRJNA607916 and
submission SUB11118201 (https://submit.ncbi.nlm.nih.gov/subs/biosample/SUB11118201/overview, last access: 7 March 2022).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-4011-2022-supplement.
Author contributions
RA and KK planned the sampling campaign; RA carried out the campaign and
performed the experiments and measurements; RA and MT analysed the data and
wrote the manuscript draft. MS, ST, and KK reviewed and edited the
manuscript.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We thank Beate Michalzik and Florian Achilles for providing us with useful
information on the soil properties and the history of the study site. We are
grateful to Marco Pöhlmann, Jens Wurlitzer, and Stefan Riedel for soil
sampling and incubation set-ups. We wish to acknowledge the contributions of
Iris Kuhlmann for her support with CFE, Armin Jordan for helping
with gas measurements, and members of the routine measurements and
analytical departments of the Max Planck Institute for Biogeochemistry,
Jena.
Financial support
This study was jointly supported by the Max Planck Institute for
Biogeochemistry, Jena (MPI BGC); the International Max Planck Research School
for Global Biogeochemical Cycles, Jena (IMPRS-gBGC); Deutscher Akademischer
Austauschdienst (DAAD); and the Collaborative Research Centre 1076 AquaDiva
(CRC AquaDiva), Germany. Funding by the DFG under Germany's Excellence
Strategy – EXC 2051 – Project ID 390713860 was also provided.
Review statement
This paper was edited by Akihiko Ito and reviewed by two anonymous referees.
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