FEMS Microbiology Ecology, 92, 2016, fiv171
doi: 10.1093/femsec/fiv171
Advance Access Publication Date: 28 December 2015
Research Article
RESEARCH ARTICLE
Abundance and potential metabolic activity of
methanogens in well-aerated forest and grassland
soils of an alpine region
Katrin Hofmann∗ , Nadine Praeg, Mira Mutschlechner, Andreas O. Wagner
and Paul Illmer
Institute of Microbiology, University of Innsbruck, Technikerstraße 25 d, 6020 Innsbruck, Austria
∗
Corresponding author: Institute of Microbiology, University of Innsbruck, Technikerstraße 25 d, 6020 Innsbruck, Austria. Tel: +43 (0)512 507 51345;
Fax: +43 (0)512 507 2928; E-mail: [email protected]
One sentence summary: Various methanogenic guilds were found to be present in aerated forest and grassland soils located at the montane and
subalpine belts of the European Alps.
Editor: Cindy Nakatsu
ABSTRACT
Although methanogens were recently discovered to occur in aerated soils, alpine regions have not been extensively studied
for their presence so far. Here, the abundance of archaea and the methanogenic guilds Methanosarcinales, Methanococcales,
Methanobacteriales, Methanomicrobiales and Methanocella spp. was studied at 16 coniferous forest and 14 grassland sites
located at the montane and subalpine belts of the Northern Limestone Alps (calcareous) and the Austrian Central Alps
(siliceous) using quantitative real-time PCR. Abundance of archaea, methanogens and the methanogenic potentials were
significantly higher in grasslands than in forests. Furthermore, methanogenic potentials of calcareous soils were higher due
to pH. Methanococcales, Methanomicrobiales and Methanocella spp. were detected in all collected samples, which indicates that
they are autochthonous, while Methanobacteriales were absent from 4 out of 16 forest soils. Methanosarcinales were absent
from 10 out of 16 forest soils and 2 out of 14 grassland soils. Nevertheless, together with Methanococcales they represented
the majority of the 16S rRNA gene copies quantified from the grassland soils. Contrarily, forest soils were clearly dominated
by Methanococcales. Our results indicate a higher diversity of methanogens in well-aerated soils than previously believed
and that pH mainly influences their abundances and activities.
Keywords: 16S rRNA gene abundance; methanogens; parent rock; methanogenic potential; vegetation; quantitative
real-time PCR
INTRODUCTION
Methane (CH4 ) is the second most important greenhouse gas
in the atmosphere after carbon dioxide (CO2 ) and substantially contributes to global change (Denman et al. 2007). Despite the fact that CH4 is less abundant in the atmosphere
than CO2 , its global warming potential is estimated to be
25 times higher due to its efficiency in trapping radiation
(Crutzen and Lelieveld 2001). The microbial key players responsible for the formation of CH4 (methanogenesis) in the
terminal step of the anaerobic degradation of organic matter are called methanogens and form a unique group of the
domain archaea. Methanogenic strains are exclusively affiliated with the phylum Euryarchaeota and distributed among the
seven orders Methanosarcinales, Methanobacteriales, Methanomicrobiales, Methanococcales, Methanopyrales, Methanocellales and
Received: 4 August 2015; Accepted: 23 December 2015
C FEMS 2015. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
Methanoplasmatales (Garcia, Patel and Ollivier 2000; Sakai et al.
2008; Dridi et al. 2012; Paul et al. 2012).
For decades, methanogens have been thought to be restricted to highly reduced environments only. This assumption
was mainly based on physiological aspects since oxygen causes
damage to the F420 -hydrogenase complex, an important enzyme
involved in methanogenesis (Schönheit, Keweloh and Thauer
1981). Pure culture studies further revealed that reactive oxygen species which are formed in the presence of oxygen damaged cell membranes and walls of methanogens (Storz et al.
1990). Nevertheless, during the last decades there has been increasing evidence that methanogens are not as sensitive to oxic
conditions as previously believed. It has been possible to activate methanogenesis in desert, forest, meadow and savannah
soils after incubation as slurries under anoxic conditions (Peters
and Conrad 1995). Following research efforts demonstrated that,
under favourable conditions, even alpine soils can be methane
sources (West and Schmidt 2002). Recent studies along a Central European alpine glacier foreland and subalpine meadow,
fallow and abandoned soil revealed that methane production
potential depends on soil age and moisture conditions as well
as on the degree of cattle grazing, respectively (Hofmann, Reitschuler and Illmer 2013; Prem, Reitschuler and Illmer 2014).
The occurrence of methanogenic sequences has been recently
demonstrated in arid biological soil crusts (Angel, Claus and
Conrad 2011) and in high-altitudinal cold deserts (Aschenbach
et al. 2013). The majority of these sequences could be assigned to
the genera Methanosarcina and Methanocella. Accordingly, it was
hypothesized that these methanogens could be autochthonous
members of aerated soils worldwide (Angel, Matthies and Conrad 2011; Angel, Claus and Conrad 2012; Aschenbach et al.
2013). Apart from this, sequences affiliated with the classes
Methanobacteria, Methanomicrobia and Methanococci were retrieved from high-altitudinal soils of the Tibetan plateau (Wang
et al. 2015). Methanobrevibacter was recently detected in a subalpine fallow soil from the Austrian Central Alps (Praeg, Wagner and Illmer 2014). Based on these findings, we suggested
that besides Methanosarcina spp. and Methanocella spp., other
methanogens might also be members of the microbial communities in non-wetland soils. In addition, knowledge about the
abundance of the methanogenic community in uplands is so
far limited to a few reports from soils of the arid and semiarid regions (Angel, Matthies and Conrad 2011; Angel, Claus and
Conrad 2012; Aschenbach et al. 2013).
Quantitative real-time PCR (qPCR) is a widely used method
to study the 16S rRNA or functional gene abundances of microbial communities (Bustin et al. 2009). The comparability of
the results depends, however, on both the nucleic acid extraction protocol and sample-specific features such as adsorption
of DNA to soil compounds and coextracted inhibitors, which
hinder isolation of DNA samples representative for the communities present (Miller et al. 1998). Subsequent PCR applications are also hampered by the above-mentioned obstacles,
mainly through PCR efficiency loss (Bustin 2004). In this study,
we spiked soil samples with known amounts of target microbes prior to extraction, to correct for soil- and assay-specific
biases.
In the region of Tyrol, coniferous forest is the dominant vegetation type and covers about 29% of the total area of this region. Another 20% of the area is covered by natural grasslands.
The area is generally mountainous and shaped by the Austrian
Central Alps as well as the Northern Limestone Alps. Therefore, the investigated forests and grasslands distributed among
the montane and the subalpine/alpine altitudinal belts. Addi-
tionally, the soils are derived from two different parent materials: calcareous (Northern Limestone Alps) and siliceous (Austrian Central Alps). To determine the biogeographical patterns
of methanogenic abundance (as measured by qPCR of 16S rRNA
gene sequences) in this region, we conducted a comprehensive
study of 30 sampling points located in the Austrian Northern
Limestone Alps and Central Alps at the montane (500, 1000 m
above sea level) and subalpine (1500, 2000 m above sea level)
altitudinal belts. We aimed to (I) test if, apart from the frequently detected Methanosarcinales (MSL) and Methanocella spp.
(Mcell), methanogens of the orders Methanobacteriales (MBT),
Methanomicrobiales (MMB) and Methanococcales (MCC) are present
and abundant in upland soils, (II) to study the spatial distribution of methanogenic abundance and the corresponding potential metabolic activities of typical subalpine and montane grasslands and forests in relation to land cover type and parent material of soil formation, and (III) to link abundance and potential
activity to possible environmental drivers such as physicochemical soil properties.
MATERIALS AND METHODS
Soil sampling and physicochemical properties
A total of 30 sites were sampled across North Tyrol, Austria,
during summer 2013. The sites included grassland soils (n =
14) and coniferous forest soils (n = 16), which differed according to the parent material of soil formation (calcareous vs.
siliceous) and according to altitudinal location (500 and 1000 m
on the montane belt and 1500 and 2000 m on the subalpine belt)
(Table 1). About 29% of the total area of North Tyrol (Austria)
is covered by coniferous forests. Since Norway spruce constitutes the most abundant tree species, and to ensure comparability, the forest soil samples were taken in the area close
to spruce stands. Soil samples were taken after plant cover
removal at depths of 10–15 cm from the litter surface, corresponding to the A horizon. At each site, we collected soil
from three replicate plots (2 m × 2 m). Five randomly distributed subsamples were collected on each of the three plots
and merged to three replicate samples. The soil was transported
to the laboratory immediately after sampling and sieved at 2
mm. Samples were stored at 4◦ C (physicochemical analyses) and
–20◦ C (DNA isolation, potential methanogenic activity) prior to
analysis.
Soil dry mass was assessed gravimetrically by drying 10 g of
sieved soil at 105◦ C overnight. Maximum water holding capacity was determined by weighing 10 g of sieved soil into glass
cylinders with pores at one side. The cylinders were placed in
deionized water for 1 h. After water saturation, the samples
were placed on 10 cm quartz sand for 3 h. The wet samples
were weighed and dried for 2 days at 105◦ C. Subsequent weighing allowed determination of maximum water holding capacity. Soil pH was determined in 10 mM CaCl2 at a mixing ratio of
1:2.5 (w/v) after 2 h of incubation on room temperature (Schinner
et al. 1996). Electrical conductivity was measured in 1:2.5 (w/v)
slurries of soil and deionized water. Determination of organic
matter was carried out using the loss on ignition method. Ovendried (105◦ C, overnight) soils were incinerated for 4 h at 430◦ C for
this analysis (Schinner et al. 1996). Total C (Ct ) and N (Nt ) contents of the soils were measured on a CHN analyser (Truspec
CHN, Leco, MI, USA). NH4 + -N and NO3 − -N were determined as
described in Schinner et al. (1996) after extraction in 2 M KCl.
Total phosphorus (Pt ) was extracted using 0.5 M NaHCO3 − , and
measured at 882 nm after dyeing (Schinner et al. 1996).
Hofmann et al.
3
Table 1. List of primers used for quantitative real-time PCR in this study. D, denaturation; A, primer annealing; E, elongation step.
Oligo namea
A787 F
A1059 R
MSL812 F
MSL1159 R
MCC495 F
MCC832 R
MBT857 F
MBT1196 R
Mmic F
Mmic R
Mcl282 F
Mcl832 R
Ta [◦ C]b
Amplicon
size
ATTAGATACCCSBGTAGTCC
61
273
Yu, Lee and Hwang
(2005)
D: 95◦ C, 20 s A: 61◦ C,
20 s E:72◦ C, 20 s
GCCATGCACCWCCTCT
GTAAACGATRYTCGCTAGGT
63
354
Yu, Lee and Hwang
(2005)
D: 95◦ C, 20 s A: 63◦ C,
15 s E:72◦ C, 15 s
GGTCCCCACAGWGTACC
TAAGGGCTGGGCAAGT
60
337
Yu, Lee and Hwang
(2005)
D: 95◦ C, 15 s A: 60◦ C,
20 s E:72◦ C, 15 s
CACCTAGTYCGCARAGTTTA
CGWAGGGAAGCTGTTAGT
61
343
Yu, Lee and Hwang
(2005)
D: 95◦ C, 20 s A: 61◦ C,
20 s E:72◦ C, 20 s
TACCGTCGTCCACTCCTT
GTGATAAGGGAACCYCGAG
61
126
Vigneron et al. (2013)
D: 95◦ C, 15 s A: 63◦ C,
15 s E:72◦ C, 15 s
GCTACGRACGCTTTAAGCC
ATCMGTACGGGTTGTGGG
60
510
Angel, Claus and
Conrad (2012)
D: 95◦ C, 20 s A: 60◦ C,
20 s E:72◦ C, 20 s
Sequence (5 -3 )
Target
Archaea, 16S rRNA
gene
Methanosarcinales,
16S rRNA gene
Methanococcales, 16S
rRNA gene
Methanobacteriales,
16S rRNA gene
Methanomicrobiales,
16S rRNA gene
Methanocella spp.,
16S rRNA gene
Reference
Modified qPCR
conditionsc
CACCTAGCGRGCATCGTTTAC
a
Forward primer, F; reverse primer, R.
Annealing temperatures of the qPCR assays conducted in this study.
c
The PCR templates were subjected to denaturation at 95◦ C for 10 min prior to amplification.
b
Potential methane production rates
To determine potential methane production of the grassland
and forest soils, 5 g of sieved soil was incubated in 50 ml serum
flasks. Subsequently the samples were flushed with N2 and
closed using butyl rubber stoppers and crimp seals in order to establish and maintain gastight conditions throughout the entire
incubation period. Incubation was performed at 25◦ C for 49 days.
Gas samples for determination of CH4 in the headspace were removed after 7, 14, 21, 28 and 49 days using a syringe and analysed immediately. For each soil sample, three replicates were set
up. To consider and correct for possible abiotic CH4 production
of the samples, we also measured controls containing doubleautoclaved soils, which were treated as mentioned above. CH4
was quantified on a GC-2010 Plus gas chromatograph (Shimadzu
Co., Japan) equipped with a Shin Carbon TS 100/120 mesh (2 m
x 1 mm) column (Restek, USA) and a flame ionization detector.
The instrument was operated using the following temperatures:
injector 160◦ C, column 140◦ C and detector 180◦ C. N2 served as a
carrier gas with a flow rate of 14.5 ml min−1 . Potential methane
production rates were calculated from a linear regression of the
measured CH4 concentrations against time points. The quality
of the fitting was assessed on the basis of the coefficient of determination R2 .
DNA extraction, spiking and qPCR
Genomic DNA was isolated from 0.25 g of sieved and untreated soil using a commercially available kit (NucleoSpin
Soil, Macherey-Nagel, Germany) as recommended by the manufacturer. Additionally, separate soil samples were spiked with
Methanosarcina thermophila at concentrations of 108 cells g−1
(archaea and MSL assays), with Methanothermobacter wolfei
(DSM2970), Methanococcus voltae (DSM4254), Methanoculleus bourgensis (DSM3045) and Methanocella conradii (DSM24694) at concentrations of 106 cells g−1 (MBT, MCC, MMB and Mcell assays) before DNA extraction. The percentage of recovered cells
was used to correct the gene abundances measured in the untreated soils. Strain cultivation was performed as recommended
by DSMZ.
qPCR was conducted on a Corbett Life Science (Qiagen, the
Netherlands) Rotor-Gene 6000 system using the SensiMix SYBR
no-ROX kit (Bioline, United Kingdom). The primer pairs targeting the entire archaeal community as well as the methanogenic
orders MSL, MBT, MCC, MMB and the genus Mcell we used
in this study are listed in Table 1. Each reaction (20 μl) consisted of 10 μl SensiMix SYBR no-Rox kit. Primer concentrations
were set to 200 nM (Arc, MSL and MMB), 250 nM (MBT, MCC)
and 150 nM (Mcell). Final concentrations of MgCl2 (50 mM) and
BSA (Sigma Aldrich, USA) were 5 mM and 0.04% (v/v), respectively. The qPCR assay targeting total archaeal 16S rRNA gene
was carried out as described previously (Hofmann, Reitschuler
and Illmer 2013). Cycling conditions for quantification of the
assays targeting methanogenic 16S rRNA gene copies were altered based on the assays described by Yu, Lee and Hwang
(2005), Angel, Claus and Conrad (2012), Reitschuler, Lins and
Illmer (2014) and Vigneron et al. (2013)(Table 1). Prior to amplification, the samples were subjected to an initial denaturation
step at 95◦ C for 10 min. Each run included negative controls containing non-template DNA (Escherichia coli) and non-template
controls (UltraPure DNase/RNase-Free Distilled Water, Invitrogen, USA). After quantification, PCR products were checked via
melting curve analysis. Run efficiencies ranged between 80%
and 89% (R2 = 0.9949 - 0.9999). For construction of all calibration curves, we used genomic DNA standards. These were derived from M. acetivorans (DSM2834) for the qPCR assays targeting both total archaea and MSL 16S rRNA genes and from M.
bourgensis (DSM3045) for the assay targeting the respective gene
of MMB. For the MBT, MCC and Mcell assays, we used DNA extracted from M. wolfei (DSM2970), M. voltae (DSM4254) and M.
conradii (DSM24694) pure cultures. Concentrations of the standard DNA were determined by using the Quant-iT PicoGreen dsDNA reagent (Invitrogen, USA) according to the manufacturer’s
instructions. 16S rRNA gene copy numbers were calculated as
stated by Yu, Lee and Hwang (2005).
4
FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
Figure 1. Characteristics of forest and grassland soils sampled on calcareous and siliceous parent materials. (A) pH, (B) electrical conductivity, (C) maximum water
holding capacity (MWHC) and (D) dry mass (DM). Data are given as means ± SE (forest/siliceous n = 8; forest/calcareous n = 8; grassland/calcareous n = 9; grassland/siliceous n = 5). Significant differences (P < 0.05) between groups are indicated by letters.
Statistical analyses
All statistical analyses were conducted using Statistica version
9.1 (StatSoft, USA). Gene abundance data was Log10 -transformed
before data analysis. The physicochemical variables were
checked for normal distributions and Log10 -transformed prior
to parametric statistics, if necessary. Effects of land cover type
and parent material on gene abundances of methanogens and
potential methanogenic activities were assessed by factorial
ANOVA. Because of the unequal numbers of observations per
land cover type and parent material (forest, calcareous, n = 8;
forest, siliceous, n = 8; grassland, calcareous, n = 5; grassland,
siliceous, n = 9), type III-ANOVA was chosen as suggested in
Quinn and Keough (2011). To discover differences between the
groups, we applied Fisher’s Least Significant Differences Test. Relationships of abundance and potential methanogenic activity
with the physicochemical soil properties were described by Pearson’s correlation coefficients. Differences between the groups
and correlations were regarded as significant when P values
were below 0.05.
RESULTS
Soil properties
Soil pH was significantly lower (P < 0.01, mean difference = 0.44)
in forest soils as compared to grassland soils. Moreover, forest
and grassland soils derived from calcareous parent material exhibited significantly higher (P < 0.001, mean difference = 1.34)
pH values than those derived from siliceous parent materials
(Fig. 1A). By contrast, electrical conductivity did not differ significantly among the different land cover types as well as parent
materials (Fig. 1B). Dry mass was significantly lower (P < 0.01,
mean difference = 0.11 g g−1 ) in forest soils, while maximum
water holding capacity was significantly higher (P < 0.001, mean
difference = 1.0 g H2 O g−1 ). For both soil properties, no differences according to parent material were detected (Fig. 1C and D).
Organic matter content was 0.40 ± 0.2 and 0.36 ± 0.2 g g−1 dry soil
in forests derived from calcareous and siliceous rocks, respectively. Organic matter contents of grasslands were significantly
lower (P < 0.001, mean difference = 0.20 g g−1 dry soil) compared
to the values measured for forests. Organic matter was 0.15 ±
0.1 and 0.19 ± 0.2 g g−1 dry soil in grasslands on calcareous and
siliceous rocks, respectively (Fig. 2A). The same situation was detected regarding NH4 + -N (Fig. 2B), whereas NO3 − -N was significantly higher (P < 0.01, mean difference = 6.1 μg NO3 − -N g−1 dry
soil) in the investigated grassland soils compared to forest soils
(Fig. 2C). Contrarily, no significant differences according to land
cover type or parent material were detected for total phosphorus
(Pt ) (Fig. 2D). Ct and nitrogen Nt were also significantly higher (P
< 0.001, mean difference = 2.8 mg N g−1 dry soil; P < 0.05, mean
difference = 95.8 mg C g−1 dry soil) in forest soils than grassland
soils (Fig. 2E and F).
Hofmann et al.
5
Figure 2. Characteristics of forest and grassland soils sampled on calcareous and siliceous parent materials. (A) soil organic matter, (B) ammonium-N, (C) nitrate-N,
(D) total phosphorus, (E) total carbon and (F) total nitrogen. Data are given as means ± SE (forest/siliceous n = 8; forest/calcareous n = 8; grassland/calcareous n = 9;
grassland/siliceous n = 5). Significant differences (P < 0.05) between groups are indicated by letters.
Potential methanogenic activities
We determined the potential methanogenic activities of montane and subalpine soils in relation to land cover type, parent
material (either calcareous or siliceous) and altitude above sea
level. Methanogenic potential could be detected in almost all of
the investigated soil samples except for forest soil 419 (siliceous)
and grassland soils 675 and 889 (both siliceous) (Table 2).
In detail, potential methanogenic activities were significantly
(P < 0.01) influenced by land cover type with higher rates
in grassland soils compared to forest soils (Fig. 3A). Soil derived from calcareous parent materials had significantly higher
(P < 0.01, mean difference = 5.7 μg g−1 dry soil d−1 ) potential methane production rates compared to those formed on
siliceous parent materials (Fig. 3B). Additionally, methanogenic
activities followed a distinct altitudinal pattern (Fig. 3C). Irrespective of the land cover type, we detected significantly higher
methane emissions in soils located at 500 and 1000 m above sea
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FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
Table 2. Potential methane production rates of montane and subalpine forest and grassland soils sampled on siliceous and calcareous basement
rocks. The values are given as means ± standard deviations (n = 3).
a
b
Soil
sample
Coordinates
405
875
374
422
423
434
421
424
427
404
418
414
419
420
431
426
47.3102989◦ N;
47.2410698◦ N;
47.2769508◦ N;
47.3857536◦ N;
47.3862228◦ N;
47.3137474◦ N;
47.4208450◦ N;
47.3848724◦ N;
47.3497772◦ N;
47.2786102◦ N;
47.2418747◦ N;
47.2050056◦ N;
47.2789650◦ N;
47.2394791◦ N;
47.0686111◦ N;
47.0590744◦ N;
11.6034336◦ E
11.3374872◦ E
11.3382978◦ E
11.2344131◦ E
11.2873974◦ E
11.4448042◦ E
11.3410339◦ E
11.3403473◦ E
11.2337999◦ E
11.1268549◦ E
11.2293282◦ E
11.3369389◦ E
11.0739908◦ E
11.4960794◦ E
11.4923582◦ E
11.5448027◦ E
845
895
907
880
679
886
888
889
902
881
891
672
675
682
47.2765656◦ N;
47.3148575◦ N;
47.3153610◦ N;
47.3506508◦ N;
47.3143005◦ N;
47.2968712◦ N;
47.2785149◦ N;
47.2575569◦ N;
47.2749939◦ N;
47.1686478◦ N;
47.2398376◦ N;
47.2430801◦ N;
47.2423477◦ N;
47.0602303◦ N;
11.3910255◦ E
11.1008120◦ E
11.0214615◦ E
11.1278009◦ E
11.1801605◦ E
11.1005516◦ E
11.1531553◦ E
11.4963436◦ E
11.5495911◦ E
11.3888807◦ E
11.4695396◦ E
11.0733585◦ E
11.1790133◦ E
11.4393921◦ E
Basement
rock
Elevation/m
(a. s. l.)
Lag phaseb
/days
Potential methane production
rate/ng CH4 g−1 d.w. d−1
Forest
Carbonate
Silicate
Carbonate
Carbonate
Carbonate
Carbonate
Carbonate
Carbonate
Carbonate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
500
500
1000
1000
1000
1000
1500
1500
1500
1000
1000
1500
1500
1500
1500
2000
7
7
7
7
7
9
7
9
9
14
7
7
9
21
14
0.8 (±0.24)
1.0 (±0.70)
1.3 (±0.97)
28900.3 (±3189.46)
24749.8 (±1798.39)
59.8 (±48.10)
4109.9 (±1497.06)
62.0 (±41.27)
1.7 (±1.00)
0.8 (±0.15)
5.8 (±2.16)
3950.05 (±1923.59)
0.00 (±0.00)
0.8 (±0.19)
0.8 (±0.13)
0.7 (±0.07)
Grassland
Carbonate
Carbonate
Carbonate
Carbonate
Carbonate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
Silicate
500
500
500
1000
1500
500
500
500
500
1000
1000
2000
2000
2000
14
7
7
7
7
14
7
7
23
9
9
7
259.2 (±207.04)
19812.3 (±7405.18)
4123.3 (±3723.52)
24913.0 (±6239.52)
17473.2 (±2478.92)
16023.0 (±1476.64)
10397.9 (±4949.01)
BLDa
27705.4 (±19825.33)
10.6 (±15.02)
714.1 (±894.28)
8.7 (±4.10)
BLDa
18.7 (±2.81)
Land cover
BLD, below the limit of detection.
Period of time before linear methane production started.
level (montane belt), whereas soils at 1500 and 2000 m above
sea level (subalpine belt) were characterized by a significantly
(P < 0.05, mean difference = 6.9 μg g−1 dry soil d−1 ) reduced potential to release CH4 . Potential methanogenic activity in both
forests and grasslands was positively related to pH (r = 0.82, P
< 0.001; r = 0.70, P < 0.001), NO3 − -N (r = 0.47, P < 0.05; r = 0.66,
P < 0.001) and abundance of Methanobacteriales (r = 0.62, P < 0.01;
r = 0.69, P < 0.001). Soil dry mass was negatively correlated (r =
0.52, P < 0.05) to potential methanogenic activity in forest soils.
Moreover, we observed a positive correlation between potential
methanogenic activity and the abundance of Methanosarcinales
(r = 0.73, P < 0.01) in forest soils and with Methanomicrobiales
(r = 0.50, P < 0.05) in grassland soils.
Quantification of archaea and methanogenic guilds
We used qPCR in order to determine the extent of the archaeal
and methanogenic microbial communities. Spiking of forest soil
sample 374 with M. thermophila led for instance to the following
result: only 39.6 ± 1.52% of the expected archaeal 16S rRNA gene
copies g−1 soil were recovered. Based on this, the archaeal 16S
rRNA gene copies g−1 soil measured in the untreated DNA extracts were corrected (Fig. 4).
Methanogens belonging to the order Methanosarcinales were
only present in 18 of the 30 investigated soils (corresponding to
60%), whereas 16S rRNA genes affiliated with Methanomicrobiales,
Methanococcales and Methanocella spp. were detected in all soil
samples. Sequences affiliated with the order Methanobacteriales
could be retrieved from all grassland soils and 12 forest soils except for the four sites 422, 423, 427 and 875 (Table S1, Supporting
Information). Total archaeal 16S rRNA gene abundance differed
relative to the land cover types (P < 0.001), whereas no significant influence of altitude and parent material was observed.
Archaeal 16S rRNA gene abundance was generally higher (P <
0.001, mean difference = 1.5 × 108 16S rRNA gene copies g−1 dry
soil) in the grassland soils compared to the forest soils (Fig. 5).
Similar patterns were also observed for the 16S rRNA genes affiliated with the methanogenic orders Methanosarcinales (P < 0.001,
mean difference = 6.3 × 106 16S rRNA gene copies g−1 dry soil),
Methanobacteriales (P < 0.001, mean difference = 2.7 × 104 16S
rRNA gene copies g−1 dry soil), Methanococcales (P < 0.001, mean
difference = 1.1 × 105 16S rRNA gene copies g−1 dry soil), Methanomicrobiales (P < 0.01, mean difference = 1.0 × 105 16S rRNA
gene copies g−1 dry soil), as well as Methanocella spp. (P < 0.01,
mean difference = 8.1 × 104 16S rRNA gene copies g−1 dry soil)
(Fig. 5).
Abundance of Methanobacteriales was positively correlated to
pH in both forests and grasslands (r = 0.74, P < 0.001; r = 0.47,
P < 0.05), while abundance of Methanomicrobiales was positively
correlated (r = 0.50, P < 0.05) to pH only in grassland soils. Contrarily, abundance of Methanocella spp. was negatively related to
pH (r = 0.50, P < 0.05) in forest soil. Additionally, we detected a
Hofmann et al.
7
Figure 4. Spiking of soil sampled at forest site 374 with known amounts of 16S
rRNA gene copies g−1 soil prior to DNA isolation and qPCR. The percentage difference of the amount of recovered 16S rRNA gene copies g−1 soil after DNA isolation procedure and qPCR from the expected amount was used to correct the
abundances of target genes quantified in separately isolated DNA samples of the
same soil. Values are given as means ± standard deviations (n = 3).
Figure 3. Potential methane production in the investigated soils according to (A)
land cover type (forest/grassland), (B) parent material (calcareous/ siliceous) and
(C) altitude above sea level. Data are given as means ± SE. Significant differences
(P < 0.05) between groups are indicated by letters.
weak but significant negative relationship (r = –0.35, P < 0.05)
between Methanomicrobiales and dry mass.
DISCUSSION
We measured the abundance and potential activities of
methanogens in temperate forest and grassland soils located at
the montane and the subalpine altitudinal belts of the Northern Limestone Alps (calcareous) and Central European Alps
Figure 5. 16S rRNA gene abundance of total archaea and methanogens in forest
(A) and grassland soils (B) derived from siliceous and calcareous parent materials. Bars represent means ± SE (forest/siliceous n = 8; forest/calcareous n =
8; grassland/calcareous n = 9; grassland/siliceous n = 5). Significant differences
(P < 0.05) between the abundances of the groups are indicated by letters.
8
FEMS Microbiology Ecology, 2016, Vol. 92, No. 2
(siliceous). Although the studied soils were well drained, potential methanogenesis could be detected under favourable (anoxic)
conditions in each soil, except for one forest soil (1500 m.a.s.l.)
and two grassland soils (500 and 2000 m.a.s.l.). Within similar studies concerning methanogenic potential of well-aerated
soils, slurries were set up to facilitate the establishment of
anoxic conditions (Angel, Claus and Conrad 2012; Aschenbach
et al. 2013). In contrast, with our samples it was neither necessary to prepare slurries nor to provide any precursors to initiate methane production as this always spontaneously started
within 7 days to ∼23 days of anoxic incubation. Thus, the soils
of the present investigation seem to harbour the microbial community capable of anaerobic decomposition of organic matter
which could be activated under favourable conditions.
Methanogenic potential of forest soils was significantly lower
(P < 0.01, mean difference = 6.3 μg g−1 dry soil d−1 ) compared
to grasslands, although most of the measured physicochemical soil properties known to facilitate CH4 production (e.g. organic matter, maximum water holding capacity, water content,
NH4 + -N) were higher in forest soils. Lower NO3 − -N concentrations detected in the studied forest soils could not either explain this difference. The observed activity pattern coincided
with the 16S rRNA gene copy numbers of total archaea and all
measured methanogenic groups. Because the average difference
of pH between forest and grassland soil was only 0.5 units, it
also seems unlikely that pH is responsible for the disparity of
abundances and functionality between the distinct land cover
types and leads to the supposition that vegetation may be influential. Although we have no information about the quantity
and quality, there may be a considerable impact of root exudates on the (methanogenic) archaea inhabiting our soils as evidenced by other studies (Bomberg and Timonen 2009; Bomberg,
Montonen and Timonen 2010; Karlsson, Johansson and Bengtson 2012). In a microcosm study, Karlsson, Johansson and Bengtson (2012) found that archaeal abundance near coniferous trees
was reduced by high rates of root exudation, but also varied depending on tree species. A similar mechanism might
cause the reduced (methanogenic) archaeal abundances and
methanogenic potentials in the coniferous forest soils studied
here.
Although pH alone could not explain the difference between the land cover types, this factor positively affected
methanogenic potential among forest soils and grassland soils
when both forests and grasslands were analysed separately.
Compared to a recent study that identified pH as the best
predictor of relative abundance of Euryarchaeota in Chinese
upland and paddy soils (Hu, Yuan and He 2013), differences
of methanogen abundance between forest and grassland soils
could not be sufficiently explained by pH in this study. However, within each land cover type the abundance of some
methanogenic groups was, albeit unequally, affected by soil
pH. Relative to pH, abundance of Methanomicrobiales was positively correlated in only grassland soils, whereas abundance of
Methanobacteriales was positively correlated in both forest and
grassland soils. This indicates that different methanogenic traits
might respond differently to pH changes, which could be a consequence of different community structures. As evidenced from
a negative impact of increasing pH in forest soils, Methanocella
spp. seems to be better adapted to low pH values compared to
the other strains present in these sites.
Nevertheless, the observed difference of potential
methanogenic activity between calcareous and siliceous
soils is likely to be pH mediated. Both rocks are the main parent
materials of soil formation in the Northern Limestone Alps (cal-
careous) and the Austrian Central Alps (siliceous) respectively,
and are characterized by different weathering mechanisms and
thus facilitate different pH regimes. Whereas the lower pH values of siliceous soils coincided with significantly lower potential
methanogenic activities and longer lag times, calcareous soils
characterized by relatively higher pH values had a higher potential to release CH4 . We suppose that this difference might be due
to physiological constraints or interactions with the vegetation
rather than the methanogenic abundances, which were not
significantly different regarding bedrock type. Although most
characterized methanogens are optimally growing at neutral
pH values (6.0–8.0) (Garcia, Patel and Ollivier 2000) and may thus
be exposed to physiological stress in acidic soils, adaptation is
possible (Le Mer and Roger 2001; Bräuer et al. 2006b). In acidic
soils, enrichment culture studies showed that acid-tolerant
methanogens are physiologically active at low pH (Sizova et al.
2003; Bräuer et al. 2006b). Additionally, several acid-tolerant and
acidophilic strains have been isolated in recent years (Bräuer
et al. 2006a, 2011; Kotsyurbenko et al. 2007; Cadillo-Quiroz, Yavitt
and Zinder 2009; Cadillo-Quiroz et al. 2014).
Increasing soil water contents may enhance the potential of
the studied soils to emit CH4 , possibly by increasing the number of anoxic microsites suitable as habitats for methanogens
(Zausig, Stepniewski and Horn 1991; Peters and Conrad 1995;
Bomberg and Timonen 2007). Indeed, we found a negative relationship between dry mass and methanogenic potential, though
only in forest soils (data not shown). Furthermore, in most cases
methanogenesis was activated after a relatively short incubation time of around 7 days. Consequently, some of the soils studied here might act as CH4 sources under field conditions at least
temporarily as soon as the environmental conditions become
favourable as it was shown in a previous study regarding CH4
emission in high-alpine soils along the foreland of a receding
glacier in the European Central Alps (Hofmann, Reitschuler and
Illmer 2013). Dry mass was also negatively related to gene abundance of Methanomicrobiales in forest soils, though the correlation was rather weak. Response to changing soil water regimes
seems to depend on both the methanogenic trait and the land
cover type. For instance, abundance of Methanobacteriales was
only affected in forest soil but not in grasslands, whereas abundance of Methanococcales was affected neither in grasslands nor
in forests. Several experiments on pure cultures have shown that
methanogens do survive exposure to oxygen and desiccation for
a limited time, although they do not produce any resting stages
(Kiener and Leisinger 1983; Fetzer and Conrad 1993; Fetzer, Bak
and Conrad 1993; Liu et al. 2008). This has been attributed to
the production of oxygen-protecting enzymes by some strains
(Takao, Yasui and Oikawa 1991; Brioukhanov et al. 2000; Shima
and Thauer 2005). Recently, catalase gene transcripts specific for
the genera Methanosarcina and Methanocella could even be recovered from environmental samples (Angel, Matthies and Conrad
2011).
Positive relationships between NO3 − and potential methane
emission from forests and grasslands are at first sight counterintuitive, because high NO3 − concentrations usually indicate a
high degree of aeration. However, it has to be kept in mind that
the methanogenic potentials were measured in anoxically incubated samples. Putting aside the negative impact of NO3 − as
an electron acceptor, NO3 − may have enhanced methanogenic
potential by providing surplus nitrogen that can be directly
used by the methanogenic community instead of N2 fixation
(Garcia, Patel and Ollivier 2000). Our data concerning uplands is in stark contrast to the response of methanogenic
potential found in wetlands. In these studies, increased NO3 −
Hofmann et al.
concentrations reduced methanogenesis, either because of
competition for H2 with denitrifying soil bacteria (Le Mer and
Roger 2001; Bodelier and Laanbroek 2004) or inhibition by toxic
intermediates of denitrification (Roy and Conrad 1999).
Methanogenic archaea were traditionally thought to be restricted to highly anoxic habitats such as marine sediments,
wetland soils or rice paddies (Liu and Whitman 2008). However, community fingerprinting results of soils sampled globally
have changed this point of view and led to the suggestion that
Methanosarcina spp. and Methanocella spp. are autochthonous
microorganisms in aerated uplands (Angel, Claus and Conrad
2012). Other studies detected the same genera for instance in
managed and unmanaged grasslands (Nicol, Glover and Prosser
2003), barley field soil (Poplawski et al. 2007), glacier moraine
soil (Aschenbach et al. 2013) and subalpine fallow (Praeg, Wagner
and Illmer 2014). Reliable quantification of both methanogenic
traits was unfortunately only possible in soil samples at the end
of the incubation period under anoxic conditions (Angel, Claus
and Conrad 2012). In our study, we detected Methanocella spp.
in all forest and grassland soils investigated and therefore further support the view of the global distribution of this genus.
By contrast, Methanosarcinales could only be reliably detected
and quantified in 18 of the 30 soils studied (corresponding to
60%). About 66.6% of the positive soils were grasslands, whereas
only 33.4% were forest soils. Thus, our finding is contradictory to
the proposed ubiquity of Methanosarcina spp. in uplands (Angel,
Claus and Conrad 2012). Nevertheless, the observed difference
between forest and grassland soils could also be caused by the
low pH of forest soil. For instance, a study conducted by Horn
et al. (2003) led to the suggestion that lowering of soil pH leads
to a shift from aceticlastic to hydrogenotrophic methanogens in
peat bog. Likewise, compared to the other methanogenic traits
measured in our study, Methanosarcinales were most frequently
present in grassland soils and, at least in the more neutral calcareous grasslands, represented the majority of the measured
methanogenic 16S rRNA genes. In siliceous grasslands, however, abundances of Methanosarcinales and Methanococcales were
at the same level. Contrarily, in forest soils 16S rRNA gene sequences of the Methanococcales were more abundant compared
with those of the other methanogenic groups. Wang et al. (2015)
even retrieved sequences affiliated with Methanococci from
high-altitudinal forest soils at the Tibetan plateau, but did not
report which genera they found. Presumably, the strains present
in these high-altitudinal, cold soils might be more closely related to the mesophilic genus Methanococcus than to the extremely thermophilic genera Methanothermococcus, Methanocaldococcus and Methanotorris (Nazaries et al. 2013). Methanococcaleslike sequences were also discovered in 59 upland soils covering
grasslands, forests and agricultural soils (Hu, Yuan and He 2013).
The same study reported the relative abundance of Methanobacteriales to be lowest among all detected methanogenic orders,
which is what we observed in grasslands. Methanomicrobiales was
the second most abundant methanogenic order in the soils we
studied, and is in agreement with previous findings concerning
natural and agricultural upland soils (Hu, Yuan and He 2013).
To our knowledge, this is the first comprehensive study evaluating methanogenic 16S rRNA gene abundance and potential
in temperate non-wetland forest and grassland soils across a
larger geographical scale in the Northern Limestone Alps and
the Austrian Central Alps. Moreover, we spiked the soil with target microbes to correct for DNA isolation loss, inhibition of PCR
reactions and, thus, biased qPCR data. The observed strong differences of total archaeal and methanogenic abundances and
potential activity between coniferous forests and grasslands
9
highlight the importance of vegetation in structuring microbial traits. Apart from the often detected methanogenic orders
Methanosarcinales and Methanocellales, our results indicated that
forests and grasslands of this region may also provide niches
for Methanococcales, Methanomicrobiales and to a limited extent
for Methanobacteriales. Methane formation started within a few
days in the field moist samples under anoxic conditions, likely
due to the existence of a well-adapted methanogenic community. Considering the high degree of spatial and temporal heterogeneity of soils, it seems very likely that these communities
are metabolically active under field conditions and thus essentially contribute to the global methane cycle. Because it was possible to link methanogenic potential and abundances to some
soil properties, our study might contribute to understanding
the ecological mechanisms which determine the distribution of
methanogens in aerated soils.
SUPPLEMENTARY DATA
Supplementary data are available at FEMSEC online.
ACKNOWLEDGEMENTS
We thank Sieglinde Farbmacher (University of Innsbruck) for
sampling and laboratory assistance and the government of Tyrol
for providing the database used for site selection.
FUNDING
This study was supported by the Tyrolean Research Fund (TWF
project no. UNI-0404/1452).
Conflict of interest. None declared.
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