FEMS Microbiology Ecology Advance Access published June 10, 2015

1 1

Microbial ecology in a future climate: effects of temperature and moisture on microbial communities

2

of two boreal fens

3 4

Krista Peltoniemi1, Raija Laiho2, Heli Juottonen1, Oili Kiikkilä1, Päivi Mäkiranta1, Kari Minkkinen3, Taina

5

Pennanen1,Timo Penttilä1, Tytti Sarjala2, Eeva-Stiina Tuittila4, Tero Tuomivirta1 & Hannu Fritze1

6 7

1

Natural Resources Institute Finland (Luke), Vantaa, Finland; 2 Natural Resources Institute Finland (Luke),

8

Parkano, Finland; 3Department of Forest Sciences, University of Helsinki, Helsinki, Finland; and 4School of

9

Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland, Joensuu, Finland

10 11

*Correspondence: Krista Peltoniemi, Natural Resources Institute Finland, Jokiniemenkuja 1, FI-01370

12

Vantaa, Finland

13

Tel.: +358 29 532 5585; fax: +358 29532 2103; e-mail: [email protected]

14

Running title: Environmental effects on peatland microbes

15 16 17

Keywords: peatland, climate warming; water-level drawdown; microbial communities; fungi; mycorrhizae

2 17 18

Abstract

19 20

Impacts of warming with open-top chambers on microbial communities in wet conditions and in conditions

21

resulting from moderate water-level drawdown (WLD) were studied across 0–50 cm depth in northern and

22

southern boreal sedge fens. Warming alone decreased microbial biomass especially in the northern fen.

23

Impact of warming on microbial PLFA and fungal ITS composition was more obvious in the northern fen

24

and linked to moisture regime and sample depth. Fungal specific PLFA increased in the surface peat in the

25

drier regime and decreased in layers below 10 cm in the wet regime after warming. OTUs representing

26

Tomentella and Lactarius were observed in drier regime and Mortierella in wet regime after warming in the

27

northern fen. The ectomycorrhizal fungi responded only to WLD. Interestingly, warming together with

28

WLD decreased archaeal 16S rRNA copy numbers in general, and fungal ITS copy numbers in the northern

29

fen. Expectedly, many results indicated that microbial response on warming may be linked to the moisture

30

regime. Results indicated that microbial community in the northern fen representing Arctic soils would be

31

more sensitive to environmental changes. The response to future climate change clearly may vary even

32

within a habitat type, exemplified here by boreal sedge fen.

3 33 34

Introduction

35 36

Peatlands such as bogs and fens store at least one third of terrestrial carbon (C) as partially decomposed

37

vegetation (peat) and play an important role in global C cycling (Page et al., 2011). Sequestration of C as

38

peat depends on several factors but is ultimately facilitated by a sufficiently high and persistent water table

39

that can maintain anoxic conditions and thus slow down microbial decomposition. Recent and predicted

40

climate warming may accelerate the decomposition rate in northern peatlands and create a large and positive

41

feedback to atmospheric carbon and global climate (Dorrepaal et al., 2009; Vicca et al., 2009).

42

Palaeoecological studies suggest, however, that the response of peatlands to an increase in mean annual

43

temperature is dependent on the local moisture regime; warm and wet periods in the past were associated

44

with rapid expansion of northern peatlands, unlike in warm but dry conditions (Weckström et al., 2010).

45

Approximately half of all peatlands occur in northern Eurasia (Joosten, 2010) where the climate is expected

46

to become warmer and gradually wetter (Arzhanov et al., 2012; Monier et al., 2013), with a broader range of

47

summer rainfall among regions (ROSHYDROMET, 2008; Screen, 2013) and some peatlands expect to

48

suffer drought more frequently (Gorham, 1991; Roulet et al., 1992). As such, projections for the response of

49

northern peatlands to climate warming should be developed under wetter as well as drier scenarios.

50

Ecosystem respiration and gross primary production are influenced by both temperature and moisture

51

but their responses to warming and drying of peatlands may vary, such that the net exchange of C may

52

increase, decrease or remain the same (Sullivan et al., 2008; Chivers et al., 2009). This variation seems to be

53

related to plant community composition and microtopography, factors that are in turn created by the local

54

hydrologic regime (Laine et al., 1995; Strack et al., 2006; Riutta et al., 2007). Major changes in vegetation

55

due to climate change may greatly affect the C sequestration potential of northern peatlands (Tahvanainen,

56

2011; Loisel & Yu, 2013). The role of microbial decomposers within this dynamic and variable system has

57

yet to be established, although earlier studies indicate that changes in plant community composition drive

58

successional changes in aerobic microbial communities via shifts in litter quality (Jaatinen et al., 2007;

59

Peltoniemi et al., 2009).

4 60

Although microbes perform the bulk of organic decomposition and play a major role in ecosystem

61

respiration, little is known of the likely effects of climate warming on the diverse soil microflora.

62

Temperature regulates the rate of microbial metabolism and thereby all biogeochemical cycles driven by

63

microbes (Bradford, 2013). An increase in temperature accelerates microbial CO2 production in mineral

64

soils (Vanhala et al., 2008), and can reduce soil quality because labile substrates are depleted faster than

65

more recalcitrant ones (Davidson and Janssens, 2006). Furthermore, the availability of labile C to microbes

66

is lower in experimentally warmed soils (Hartley et al., 2007; Bradford et al., 2008; Curiel Yuste et al.,

67

2010), and this has been suggested as one of the means by which microbes and microbial communities

68

become locally adapted (Bradford et al., 2013). In contrast to the stimulating effect of a warmer temperature,

69

bacterial activity in a sub-Arctic heath decreased after long-term warming (Rinnan et al., 2011), and the

70

density of fungi producing phenol-oxidase was also reduced in a fen after warming (Jassey et al., 2011).

71

Thormann et al. (2004) observed how microbial decomposition became dominated by fungi rather than

72

bacteria as the temperature increased in boreal peatlands. Although it is a common knowledge that moisture

73

is one of the important factors affecting the temperature sensitivity of the microbial processes (Davidson and

74

Janssens, 2006) temperature driven effects on microbial communities in peatlands under different moisture

75

regimes have not been investigated.

76

Our earlier research has shown that even a moderate water-level drawdown (WLD) can induce changes

77

in the microbial communities of boreal peatlands (Jaatinen et al., 2007, 2008; Peltoniemi et al., 2009). In

78

natural mesotrophic fens, fungal decomposers showed a positive response to WLD (Jaatinen et al., 2007)

79

and ectomycorrhizal (ECM) fungi appeared in community profiles (Jaatinen et al., 2008). While WLD

80

appears to be the main factor regulating the succession of microbial communities, increasing temperature

81

can promote species turnover and affect microbial respiration (Conant et al., 2011). Interactions between

82

abiotic and biotic factors do not appear to be simple, and some evidence suggests that warming and drying

83

may have interactive, non-additive effects on microbial processes (A’Bear et al., 2014). For example, while

84

warming had a slight effect on PLFA composition in a dry heath soil, the same increase had little or no

85

effect in a wet heath soil (Rinnan et al., 2007, 2008). Different microbial groups may also vary in the extent

86

to which they are affected by climate, e.g., when field soil was warmed experimentally, the abundance of

5 87

fungi increased while bacterial biomass decreased regardless of moisture level (Castro et al., 2010). Even

88

though microbial communities are key elements of the peatland C cycle, the interactive effects of

89

temperature and moisture on their activity, abundance and structure are poorly understood.

90

Fens are minerotrophic peatlands dominated by graminoids that receive nutrient-rich water from

91

surrounding mineral soils. They are considered to be more vulnerable to disturbance than bogs, especially

92

with respect to WLD (Laine et al., 1995; Komulainen et al., 1999; Jaatinen et al., 2007). Our experiment

93

considered two alternative future climate scenarios and their effects on fen microbes, i.e., a higher mean

94

temperature in a wetter or drier moisture regime. We studied the soil microbes in pristine (i.e., wetter) sites

95

and sites after moderate WLD (i.e., drier) of two boreal fens that had received an increased temperature

96

treatment for three years prior to sampling. We hypothesized that temperature-driven changes in the

97

microbial community are dependent on moisture regime. In particular, we studied changes in microbial (i)

98

activity, (ii) abundance, (iii) community composition, and iv) occurrence of fungi.

99 100

Materials and methods

101 102

Study site, experimental design and sampling

103 104

Experimental sites intended to simulate future climate scenarios were established in 2008 in two locations of

105

the boreal zone to test the impacts of warming on plant-soil processes in a variable moisture regime. Sites

106

were located in Orivesi (the southern fen, Lakkasuo, 61°48' N 24°19' E) and Kittilä (the northern fen,

107

Lompolojänkkä, 67°60' N 24°12' E). In the southern fen from 1981 to 2011, the mean annual temperature

108

and precipitation were ca. +3.5 °C and 700 mm, respectively, and the accumulative temperature sum (+5 °C

109

threshold) was ca. 1050 degree days (d.d.). Correspondingly, values for the northern fen were -1.4 °C, 511

110

mm and 700 d.d. The sites included pristine plots as well as plots slightly drained by shallow ditches dug in

111

2008. Ditching lowered water levels approximately 6 and 3 cm on an average in the southern and the

112

northern fen, respectively (Table 1); the differences were greater during the growing season.

6 113

Even though both sites were classified as sedge fens, there were some different patterns in their

114

vegetation. In both locations, the field layer was characterized by sedges such as Carex lasiocarpa

115

(especially the southern fen), C. rostrata (especially the northern fen), C. chordorrhiza and C. limosa along

116

with dwarf shrubs Andromeda polifolia and Vaccinium oxycoccos (more abundant in the northern fen) and

117

the herb Menyanthes trifoliata, and in the northern fen additionally Equisetum fluviatile and Comarum

118

palustre. In the moss layer Sphagnum papillosum and S. flexuosum were abundant in the southern fen with

119

small patches of S. fallax, S. subfulvum and S. subnitens, while in the northern fen the moss layer was more

120

patchy and consisted mainly of S. fallax, S. riparium and S. flexuosum, along with some S. jensenii.

121

Chemical analyses were performed prior to the start of the experiment from a separate set of peat core

122

samples from 5–15 and 15–25 cm below the moss layer. They showed that the northern fen was higher in

123

calcium, potassium, magnesium and phosphorus and the southern site had more total nitrogen (Table S1 in

124

Supplementary material). Carbon and nitrogen were determined from an air-dried peat sample with a LECO

125

CHN-1000 analyzer, and the concentrations of other elements with an ICP-emission spectrometer (ARL

126

3580) using dry ash dissolved in hydrochloric acid.

127

Experimental sites were established in a split-plot design, i.e., each plot with either ambient or lowered

128

water level was divided into six subplots; three of which received no temperature manipulation (i.e.,

129

controls) and the other three received seasonal artificial warming. Warming the surface air and soil of the

130

subplots was realized passively with small open-top chambers (OTC), widely used to simulate climate

131

warming (Marion et al., 1997; Hollister & Webber 2000). We used hexagonal OTCs made of clear plastic

132

sheet, 60 cm tall and each side was 76 cm long (max. diagonal distance of 131 cm). OTC side panels were

133

inclined 60% to improve the transmittance of solar radiation and help trap heat, and were placed on 10 cm

134

tall supports that allow ventilation of the air space and reduce disturbance to the soil surface. Because our

135

experiment took place on groundwater-fed fens, OTCs had little or no effect on the water level of each

136

subplot (i.e., they did not induce WLD as such; Aerts, 2006). During the growing season, OTCs increased

137

average daily air temperature within the subplot by ca. 1.5 °C at 15 cm above the peat surface. With respect

138

to controls, the effective temperature sum in the OTCs increased by ca. 320 and 200 degree days in the

139

southern fen and the northern fen, respectively. In 2011, the average daily temperature immediately below

7 140

the moss layer was approximately 0.8 °C higher and at 5cm depth 0.3 °C higher under the OTCs compared

141

to control subplots. Notable differences were not observed in soil layers 5 cm below the surface.

142

Sample cores were taken with a 6×8 cm box-corer from each subplot in September 2011. Sampling was

143

conducted to the depth of 20–50 cm; some sample cores were so wet that only first 20 cm of peat could be

144

obtained. Cores were divided into sub-samples at 10 cm intervals from the peat surface (L1: 0–10, L2: 10–

145

20, L3: 20–30, L4: 30–40 and L5: 40–50 cm) (Table 1). In total, 100 sub-samples from two experimental fen

146

sites were obtained from 3–5 depths from pristine and WLD plots of three OTC treatments and their

147

controls. 71 of the sub-samples were from layers L1–L3.

148 149

In-growth mesh bag experiment for harvesting ECM fungi

150 151

We used in-growth mesh bags (Wallander et al., 2001) to study the ECM fungal community. Bags were

152

filled with 150 g acid-washed quartz sand (particle size 0.5–1.5 mm, SP Minerals OY AB, Finland) and

153

buried horizontally just below the living moss layer in each subplot during June–October of 2010 and June–

154

October of 2011. Bags were harvested after three months and transported to the laboratory where they were

155

opened, immersed in water, and mycelia were removed with the aid of a stereo-microscope and stored at -20

156

°C prior to molecular analysis. The ergosterol concentration (µg g-1 dry weight) in mesh bags was

157

determined using a modification of a method developed by Nylund and Wallander (1992), consisting of

158

ethanol extraction, saponification with KOH (Merck) and further extraction in pentane (Merck) with two

159

replicate samples from each mesh bag. Analysis of ergosterol was performed with High-Powered Liquid

160

Chromatography (HPLC) with a Merck Hitachi UV–VIS detector (280 nm wavelength) and a LiCrospher®

161

100 RP-18 column with methanol as eluent.

162 163

Microbial basal respiration and growth rate measurements

164 165

Within 24 h of collection, peat samples were transported to the laboratory and sub-samples were transferred

166

to +14 °C for two days prior to the respiration measurements. The basal respiration rate (BR) was measured

8 167

as the amount of CO2 evolved after 24 h and 48 h from 6 ml of fresh peat soil placed into a cut-end syringe

168

and inserted into a 120 ml incubation bottle as described by Pietikäinen and Fritze (1995). Peat soil pH was

169

determined in distilled water (1:3, vol/vol) after BR measurements from the same bottles.

170

Bacterial and fungal growth rates and fungal ergosterol were determined for L1 and L2 (0–20 cm) which

171

showed the largest microbial biomass according to PLFA. For the measurement of bacterial growth rate, a

172

small sub-sample of fresh peat (equivalent of 0.7 g of loss on ignition) was mixed with 100 ml of ultrapure

173

water and shaken at 200 rpm for 1 hour. After centrifugation (750×g, 10 min) and filtration through quartz

174

wool, 1.4 ml of the supernatant was transferred to microcentrifuge tubes. An aliquot (3.5 µl, 0.1 MBq) of

175

methyl-3 H-thymidine (740 GBq mmol-1, Moravek Biochemicals) was added and samples were incubated for

176

2 hours at 22oC. Samples were then washed to remove excess tracer and the measurement of radioactivity

177

was described in detail by Bååth et al. (2001). Relative bacterial growth is expressed as radioactivity (DPM,

178

disintegrations per minute) in a sample.

179

For the measurement of fungal growth rate, 0.5 g of fresh peat was placed in a test tube with 1.5 ml of

180

ultrapure water, and 0.13 µmol (0.3 MBq) 14C-acetate solution (1-14C-acetic acid, sodium salt, 2.2 GBq

181

mmol-1, Moravek Biochemicals) and 0.35 µmol of 1 mM non-radioactive acetate were then added. After

182

incubating the mixture for 20 hours at 20 °C, formalin was added and samples were centrifuged, and the

183

supernatant was discarded. The ergosterol was then extracted as in Bååth (2001) and measured with HPLC

184

and 14C-ergosterol with a radioactivity monitor (Berthold, LB 506 C-1). Fungal growth is expressed as DPM

185

14

C-acetate per ergosterol per g dry soil.

186 187

Phospholipid fatty acid (PLFA) analyses of peat soil

188 189

Peat samples were kept at 4 °C between sampling and PLFA analyses. Dry weights were determined after

190

drying sub-samples overnight at 105 °C. Moisture content of the sub-samples ranged between 87−95 % after

191

drying. The phospholipid extraction and analysis of PLFAs were carried out as described by Frostegård et

192

al. (1993). The green vegetation part (mostly mosses and fresh litter) was excluded from the analysis since

193

the plant-derived PLFAs would interfere with the interpretation of microbial signatures. Briefly, fresh peat

9 194

soil corresponding to 1.5 g of dry peat was extracted with chlorophorm:methanol:citrate (1:2:0.8) buffer and

195

lipids were separated into neutral lipids, glycolipids and phospholipids in a silic acid column. Phospholipids

196

were subjected to a mild alkaline methanolysis and the fatty acid methyl esters were detected by gas

197

chromatography using a flame ionization detector and 50-m HP-5 capillary column. Thirty-nine different

198

PLFAs were identified from each sample and they were expressed as mole percentage (mol % =area % of a

199

single PLFA from the area sum of all identified PLFAs).

200

The following PLFAs were considered to be predominantly of bacterial origin (i15:0, a15:0, 15:0, i16:0,

201

16:1ω9, 16:1ω7t, i17:0, a17:0, 17:0, cy17:0, 18:1ω7 and cy19:0) and chosen as an index of bacterial

202

biomass (Frostegård & Bååth, 1996). The amount of 18:2ω6 was used as an indicator of fungal biomass

203

because it is suggested to mainly be of fungal origin in soil (Federle, 1986) and is known to correlate

204

strongly with the amount of ergosterol (Frostegård & Bååth, 1996). PLFAs 10Me16, 10Me17, 10Me18 are

205

considered to be of actinobacterial origin (Kroppenstedt, 1985). The two deepest sample layers (L4 and L5:

206

30–50 cm) were discarded from the final analysis since their PLFA content was constantly at or below the

207

detection limit.

208 209

PCR-DGGE community fingerprinting and fungal sequence analyses

210 211

A small amount of each sample was kept frozen at -20 °C for DNA analysis. DNA was extracted from L1

212

(0–10 cm) and L2 (10–20 cm), which contained most of the fungal biomass according to PLFA analysis.

213

Extractions were conducted according to the slightly modified protocol of the PowerSoil DNA extraction kit

214

(MO BIO Laboratories Inc., CA, USA). Briefly, cell disruption and homogenization were conducted with a

215

FastPrep instrument (3 × 20 s at 5.5 m s-1). After homogenization, extraction tubes were incubated at 65 °C

216

for 30 mins. Mycelia recovered from in-growth mesh bags were subjected to DNA extraction as described

217

by Vainio et al. (1998) and Jaatinen et al. (2008) with slight modifications in that some DNA extracts

218

needed an extra purification step with PEG-solution (20 % PEG in 2.5 M NaCl) where DNA:PEG ratio was

219

10:6 and some extracts were precipitated with 3 M sodium acetate (DNA:sodium acetate ratio 10:1) and

220

99,5 % EtOH (DNA:EtOH ratio 1:2) to obtain sufficient DNA for further analyses.

10 221

Fungal ITS regions were PCR amplified using GC-clamped ITS1F (Gardes & Bruns, 1993, Muyzer et

222

al., 1993) and ITS2 primers (White et al., 1990). PCR reactions were conducted in Bio-Rad Thermal cycler

223

(Bio-Rad Laboratories, Hercules, USA) using 50 μl reaction mixtures containing 10× reaction buffer (100

224

mM Tris/HCl, pH 8.8; 15 mM MgCl2; 500 mM KCl; 1 % Triton X-00), 0.25 U or 0.5 U of DNA polymerase

225

(Biotools B&M Labs S.A, Madrid, Spain), 200 μM of each dNTP (Thermo Fisher Scientific Biosciences,

226

Germany), 0.5 mM of both primers (MWG Biotech AG, Edersberg, Germany) and 1 μl of DNA extract.

227

Thermal cycling parameters were for ITS as described in Korkama et al. (2007).

228

Fungal communities from peat soil and in-growth mesh bags were analyzed by denaturing gradient gel

229

electrophoresis (DGGE) using the INGENYphorU-system (Ingeny, Netherlands) with denaturing gradient of

230

25–60 % made of 7.5 % weight per volume (w/v) acrylamide/bisacrylamide (37:5:1), urea and formamide.

231

Gels were run at 70 V and 60 °C for 16 h, stained with SYBR Gold II (Molecular Probes, Eugene, OR,

232

USA) and visualized with blue light on a Dark Reader transilluminator (Clare Chemical Research Inc.,

233

Dolores, CO, USA). DGGE bands from each operational taxonomic unit (OTU) representing a distinct

234

mobility pattern were selected for sequencing, excised, re-amplified with 20 or 25 cycles of PCR and

235

purified with the GeneJET PCR purification kit (Thermo Fisher Scientific Biosciences, Germany). Excised

236

ITS bands that derived from peat soil samples were coded with a unique number-letter combination. Excised

237

ITS bands were sent to a commercial sequencing service (Macrogen Europe, Amsterdam, The Netherlands)

238

and resulting sequences were compared to reference sequences on the public databases of

239

GenBank/EMBL/DDBJ (MEGABLAST algorithm) and the International Nucleotide Sequence Databases

240

(INSD) in UNITE (Kõljalg et al., 2013) with BLASTN and FASTA3 algorithms for ITS sequences. Similar

241

sequences were aligned with Geneious 6.1.2 (Biomatters, New Zealand) and novel high-quality sequences

242

were deposited to GenBank databases (KJ588522 to KJ588613).

243 244

Quantitative PCR

245 246

PCR products obtained with fungal ITS1F and ITS2 (White et al., 1990; Gardes and Bruns, 1993), bacterial

247

16S rRNA 1055F and 1392R (Olsen et al., 1986; Woese, 1987; Stahl et al., 1988; Lee et al., 1993) and

11 248

archaeal 16S rRNA Arch967f and Arch1060R primer pairs (Amann et al., 1990; Reysenbach 6 Pace, 1995;

249

Riley-Buckley, 2001) were cloned. Standard curves were constructed with plasmids containing

250

corresponding inserts, taking into account the molecular mass of the plasmid, including the insert, and the

251

plasmid concentration. We ran qPCR (Rotor-Gene 6000, QIAGEN, Netherlands) with Maxima TM SYBR

252

Green qPCR Master Mix (2×) (Thermo Fisher Scientific Biosciences, Germany) in a 20 µl final reaction

253

volume containing 1µl template, 0.3 µM of each primer and 1× qPCR master mix. Fluoresence was

254

measured at the end of each extension step. Each qPCR run was carried out under the following conditions:

255

initial denaturation at 95°C for 10 min; 40 cycles denaturation at 95°C for 15 sec, annealing at 55°C (ITS

256

and archaeal 16S rRNA) or 68°C (bacterial 16S rRNA) for 15 (ITS) or 30 (16S rRNA) sec, extension at

257

72°C for 15 (ITS), 20 (archaeal 16S rRNA) or 30 sec (bacterial 16S rRNA); and final extension at 72°C for

258

10 min. All samples were replicated and lack of PCR inhibition was verified through 1:10 dilution. The copy

259

numbers in samples were calculated based on comparison to threshold cycle values of the standard curve

260

and are given per gram of soil (dry weight).

261 262

Statistical analyses

263 264

We explored the effects of warming, moisture regime (wetter pristine or drier after WLD), fen site, sampling

265

depth in the peat profile, pH and their interactions on microbial community composition and activity with

266

multilevel (mixed) models from peat layers L1–L3 (0–30 cm) (n=71), acknowledging the hierarchical

267

structure in the data. Our observations were derived from different depths at each sampling plot, and the

268

sampling plots were clustered within treatment plots, following the split-plot experimental design within

269

each fen. These four hierarchical levels (i.e., depth, sampling plot, treatment plot, site) were included in the

270

basic structure of the models constructed using MLwiN 2.26 (Rasbash et al., 2012). They were effectively

271

applied as random variables except for cases where one or a few were used as a fixed effect (e.g., when

272

specifically analyzing the effect of sampling depth or fen). Statistical significance of differences was

273

evaluated based on a coefficient:standard error ratio ≥2. The -2loglikelihood value was used to compare

274

models of increasing complexity. The restricted iterative generalized least square (RIGLS) algorithm was

12 275

applied, a decision motivated by the size of the data set. RIGLS is formally equivalent to residual maximum

276

likelihood (REML) under normality.

277

Preliminary analyses indicated that treatment effects were complex, often site and/or depth dependent,

278

and did not always show interactive effects. Consequently, we tested treatment effects both separately and in

279

combination, i.e., they were added to the models as one-factorial variables indicating three situations:

280

warming, WLD, and warming+WLD, or as two-factorial variables indicating warming (irrespective of

281

moisture regime) and WLD (irrespective of warming). Model construction began with site and depth as

282

explanatory variables, and complexity was gradually increased by applying treatment effects, first as pure

283

effects, and next in interaction with site, depth, and site+depth.

284

We conducted microbial community analyses from PLFA- and two ITS-derived data sets (from peat soil

285

and in-growth bags) with multivariate analyses using CANOCO 5.0 (ter Braak 6 Šmilauer, 2012). DGGE-

286

derived ITS data were converted to a binary matrix (absence=0, presence=1). We first examined the extent

287

of variation (heterogeneity) in the PLFA and two ITS data sets with detrended correspondence analysis

288

(DCA). According to the length of the main gradient, we applied methods with a linear species response

289

model for the PLFA data and unimodal species response model for the ITS data. PLFA data were then log-

290

transformed and the overall variation in PLFA composition was analysed using principal components

291

analysis (PCA). The PLFA data were also analyzed with a redundancy analysis (RDA) to investigate the

292

relationships between community composition and environmental variables (fen site, sampling depth,

293

warming treatment, moisture regime and their interactions). We used partial canonical correspondence

294

analysis (CCA) for the ITS data to investigate whether the relationships between fungal community

295

composition and environmental variables (sampling year or depth, fen site, warming treatment, moisture

296

regime and their interactions) were significant. The individual or combined effects of each environmental

297

variable in RDA or CCA were tested by extracting the effects of other variables by using them as covariates.

298

The significance of the axes (P

Microbial ecology in a future climate: effects of temperature and moisture on microbial communities of two boreal fens.

Impacts of warming with open-top chambers on microbial communities in wet conditions and in conditions resulting from moderate water-level drawdown (W...
463KB Sizes 0 Downloads 10 Views