AOB community structure and richness under European beech, sessile oak, Norway spruce and Douglas-fir at three temperate forest sites

Title: AOB community structure and richness under European beech, sessile oak, Norway spruce and Douglas-fir at three temperate forest sites.

Authors: Sandrine MALCHAIR* _{and Monique CARNOL}

Affiliation: Laboratory of Plant and Microbial Ecology, Department of Biology, Ecology, Evolution. Botany B22, University of Liège, Boulevard du Rectorat 27, B-4000 Liege, Belgium

Corresponding author:

E-mail address: S.Malchair@ulg.ac.be, Tel: +32-4-366-38-66; fax: +32-4-366-38-17 Abstract 1

2

5

Background and aims The relations between tree species, microbial diversity and activity can alter ecosystem

functioning. We investigated ammonia oxidizing bacteria (AOB) community structure and richness, microbial/environmental factors related to AOB diversity and the relationship between AOB diversity and the nitrification process under several tree species.

Methods Forest floor (Of, Oh) was sampled under European beech, sessile oak, Norway spruce and Douglas-fir

at three sites. AOB community structure was assessed by PCR-DGGE and sequencing. Samples were analyzed for net N mineralization, potential nitrification, basal respiration, microbial biomass, microbial or metabolic quotient, pH, total nitrogen, extractable ammonium, organic matter content and exchangeable cations.

Results AOB community structure and tree species effect on AOB diversity were site-specific. AOB richness

was not related to nitrification. Factors regulating ammonium availability, i.e. net N mineralization or microbial biomass, were related to AOB community structure.

Conclusion Our research shows that, at larger spatial scales, site specific characteristics may be more important

than the nature of tree species in determining AOB diversity (richness and community structure). Within sites, tree species influence AOB diversity. The absence of a relation between AOB richness and nitrification points to a possibly role of AOB abundance, phenotypic plasticity or the implication of ammonia oxidizing archaea.

Keywords: forest soil/tree species/ammonia oxidizing bacteria/ PCR-DGGE /soil chemical properties/microbial activities Introduction 32 33

34

35

36

38

39

40

42

43

45

46

47

60

The relationship between biodiversity and ecosystem function remains a controversial subject with numerous open questions , while the Agenda 21 action plan (United Nations Conference on Environment and Development) emphasized its importance already in 1992. Although microorganisms play an essential role in organic matter decomposition and nutrient cycling, their importance in ecosystem functioning is poorly understood. This is mainly due to previous methodological limitations and the assumption of microbial redundancy. Indeed, the rapid growth of microorganisms, their high degree of diversity and their capacity of genetic exchange led to the belief that a change in diversity would not restrict ecosystem functioning . However, recent studies revealed that microbial communities can be essential drivers in ecosystem functioning . For example, in a transplant experiment between grassland and forest soils, changes in microbial processes were related to soil microbial community composition, independently of the abiotic environment .

Since the 19th _{century, conifers, especially Norway spruce (Picea abies (L.) KARST), have been planted in the}

temperate and boreal zones of Europe, due to its ease of establishment and high annual production . At the end of the 20th _{century, increased concerns about ecological and economical risks posed by coniferous monocultures}

have amplified interest in sustainable forest management, maintaining ecosystem functioning . In Europe, the conversion of coniferous monocultures into broadleaved or mixed stands, reflecting the potential natural vegetation at the site has been suggested . Such a change in the dominant tree species could affect soil microbial communities and /or nutrient cycling processes through their influence on the quality and quantity of litter and root exudates , ground vegetation , soil physical characteristics , or their specific nutrition .

We studied the relation between belowground microbial diversity and function, using forest stands with different tree species as a framework. We focused on ammonia oxidizing bacteria (AOB), responsible for the first rate-limiting step of the nitrification process, i.e. the oxidation of ammonia to nitrite. Nitrification is a key process influencing primary productivity through preferential ammonium or nitrate uptake; it also acts as a gatekeeper of nitrogen losses through nitrate leaching or denitrification. Nitrate leaching causes, in addition to the soil nitrogen losses, base cation leaching and net soil acidification .

Although the nitrification process was discovered a century ago, important knowledge gaps, such as the role and distribution of cultured and uncultured AOB within and across ecosystems persist. All AOB isolated or enriched from soils so far belong to the β-Proteobacteria and share overall physiology (aerobic autotrophy). At least ten clusters can be reproducibly recognised within the phylogenetic tree based on 16S rDNA sequences amplified from environmental samples, five belonging to the genus Nitrosospira and five belonging to the genus

Nitrosomonas . More recent studies suggest a role of archaea belonging to the Thaumarchaea (AOA) in soil

ammonia oxidation . Even if AOA outnumber AOB in many environments, uncertainties exist about their

63

64

65

66

67

68

69

70

71

72

74

75

76

77

78

79

80

82

83

84

85

86

88

89

90

91

92

93

relative contribution to ammonia oxidation in terrestrial ecosystems. Control of nitrification by AOB (e.g. Long et al. 2012), AOA or both AOB and AOA has been reported in the literature.

As major uncertainties remain about the relationship between microbial diversity and function and on the influence of trees species on soil microbial communities and processes, we investigated the relationship between tree species, diversity and activity of AOB. The specific aims of this study were to investigate (i) AOB community structure and richness under several tree species (European beech, sessile oak, Norway spruce and Douglas-fir) at three study sites, (ii) microbial/environmental factors related to AOB community structure and the presence of specific AOB sequences, (iii) the relationship between AOB diversity and the nitrification process.

Materials and methods

Study sites

This study was carried out at three sites: Chimay (50°01’N, 04°24’E, Belgium), Vielsalm (50°18’N, 05°58’E, Belgium) and Rambrouch (49°49’N, 5°47’E, Grand-Duchy of Luxembourg). Elevation of these sites ranges from 340 to 460 m, with slopes below 3%. Climate conditions are temperate with a mean annual temperature of 7°C to 8°C and a mean annual rainfall between 1000 to 1300 mm. At each site, soil sampling was performed in neighbouring stands located on the same geological parent material and covered with different tree species. At Chimay, 3 stands covered with European beech, sessile oak or a mixture of these two species were studied. At Vielsalm and Rambrouch, 4 stands covered with European beech, sessile oak, Norway spruce or Douglas-Fir stands were selected. All stands were situated on acid brown soils (Cambisol (Dystric); IUSS Working Group WRB, 2006). The spruce stand at Rambrouch was characterized by an important cover of ground vegetation (80-90%), principally composed of whortleberry (Vaccinium myrtillus L. (75% coverage)) and wavy hairgrass (Deschampsia flexuosa (L.) Trin. (5% coverage)).

Forest floor sampling and chemical analysis

In each stand of the 3 sites, soil sampling was conducted in autumn under four randomly selected mature trees. Under each tree, a composite sample of ten cores (diameter 4 cm) from the upper organic horizon (F, H), taken at 1 m from the base of the trunk was collected. In the mixed beech-oak stand at Chimay, the 10 sub-samples

94

95

97

98

99

100

101

102

109

110

111

112

113

114

115

116

117

118

123

124

were taken along transect (8-10 m) between one oak and one beech. The composite samples were sieved (4 mm mesh) on flame-sterilized sieves to remove stones, roots and coarse woody debris and homogenised. A sub-sample of the sieved soil was immediately freeze-dried for molecular analysis and stored frozen. Remaining soil was stored field moist at 4°C for up to 7 days prior to characterising microbial and chemical soil properties. The organic matter content was determined as loss on ignition (LOI) at 550°C. Soil pH was measured in distilled water with a soil:solution ratio of 1:1, according to . Total C and N were measured using a C-N-S elemental analyser (Carlo Erba, Italy). NH4+-N was analysed colorimetrically with a continuous flow analyser

(AutoAnalyser3, BranLuebbe, Germany), after extraction with 1M KCl (1:5, w:v). The content of exchangeable cations (Al3+_{, Ca}2+_{, Mg}2+_{, K}+_{) were determined in 0.1M BaCl}

2 extract (1:5, w:v; ), by ICP–AESS (Varian,

Australia).

Microbial activities

Potential nitrification (nitpot) was determined by the shaken soil slurry method . This method consists of shaking 10g fresh soil in 100ml buffered medium containing 1.5mM NH4+ over 30h at 25°C. Inorganic N content of

slurries was analysed colorimetrically with a continuous flow analyser (AutoAnalyser3, BranLuebbe, Germany). Potential nitrification rates were calculated by linear regression of nitrate concentrations over time (mg NO3--N

g-1_{DW soil d}-1_{). Net N mineralisation (Nmin) was studied in aerobic laboratory incubation lasting 31 days at}

constant temperature (25°C) in the dark at field moisture . Inorganic N was extracted with 1M KCl (1:5, w:v) and analysed colorimetrically, as described above. The net N mineralisation rate (Nmin) was calculated by dividing the net increase in inorganic N (NO3--N + NH4+-N) during the incubation period by the number of

incubation days. Basal respiration rate (BR) was determined by measuring CO2 evolution from field moist soil

(20 g) incubated in an amber bottle (Supelco, USA) at 15°C for 3h. Evolved CO2 was measured with an infrared

absorption gas analyser (EGM-4, PPSystem, UK). BR was estimated by linear regression of CO2-C

accumulation against time (mg g-1 _{DW soil h}-1_).

Microbial biomass C and N (MB-C, MB-N) were determined using the chloroform fumigation extraction method , followed by 0.5M K2SO4 (1:5, w:v) extraction of both fumigated and non-fumigated samples. Extracts

were analysed for organic C using a Total Organic Carbon analyzer (LabToc, Pollution and Process Monitoring limited, UK) and for organic N using a continuous flow analyzer equipped with an UV digester (Autoanalyser3, Bran Luebbe, Germany). MB-C and MB-N were estimated by the difference of total extract before and after fumigation using a conversion factor of 0.35 for biomass C and 0.54 for biomass N . The metabolic quotient

125

126

127

128

130

131

132

133

134

139

140

141

142

143

144

145

146

147

148

149

151

152

153

154

155

(qCO2) was calculated by dividing basal respiration with soil microbial biomass C . The microbial quotient

(qmic) represents the availability of soil C and was calculated by dividing microbial biomass C by Corg with Corg = LOI/ 1.72 .

DNA extraction and denaturing gradient gel electrophoresis (DGGE) profiles

To improve extraction efficiency, a pre-lysis buffer (Sodium phosphate buffer 0.1M) washing step was introduced before extraction procedure . Total genomic DNA was extracted from 0.2 g freeze-dried soil using the PowerClean Soil DNA kit (MoBio, CA, USA), according to manufacturer’s instructions with a minor modification: 200 μl AlNH4(SO4)2 100mM were added before the first step of lyses to remove soil-based

inhibitors . DNA extraction and integrity were checked by electrophoresis on agarose gel (1% w:v agarose). The composition of AOB communities was assessed by PCR-DGGE. 16S rRNA genes were amplified using a nested PCR approach. The first-round PCR was carried out with the universal primer pair pA and pH, which amplify an approximately 1.5-kb fragment of the 16S rRNA gene . The reaction mixture (25 µl) contained 1µl of genomic DNA as template, 0.5 µl of each primer (30µM, Invitrogen, Belgium), 0.2 µl dNTPs (25 mM each, Bioline Ltd, UK), 2.5 µl of PCR buffer 10X and 0.2 µl of Taq DNA polymerase (5U µl-1_{, Sigma, USA). The}

amplification regime consisted of an initial cycle of denaturation for 94°C for 2 min followed by 35 cycles of 30 s at 92°C, 1 min at 55°C and 1min at 72°C (+1sec/cycle), and a final extension at 72°C for 5 min.

First-stage PCR products were diluted ten-fold and used as template (1 µl) in second-round PCR using GC-clamped CTO 189f-ABC/ CTO 654r primer set amplifying a 465 bp fragment of the 16S rRNA gene specific for the AOB. The PCR reaction mixture (25 µl) consisted of 0.5 µl of each primer (30 µM, Invitrogen, Belgium), 0.15 µl dNTPs (25 mM each, Bioline Ltd, UK), 2.5 µl bovine serum albumine (10g ml-1_{, Fluka Analytical,}

Switzerland), 2.5 µl of Accubuffer buffer 10X and 0.5 µl of Accuzyme polymerase (2.5U µl-1_{, Sigma, USA).}

The amplification conditions were 2 min at 94°C followed by cycles of 30 s at 92°C, 1 min at 59°C, 45 s (+1s cycle-1_{) at 72°C, and a final extension of 5 min at 72°C.}

DGGE was performed with the DCode gel system (BioRad, USA) using a 6% acrylamide gel with a 30-60% denaturant gradient, where 100% denaturant was defined as 7 mM urea plus 40% formamide. The fragments were separated by electrophoresis at a constant temperature of 60 °C for 10 min at 200 V followed by 16 h at 80 V. The gels were stained for 30 min with SybrGold (Molecular Probes, The Netherlands) before visualization by a CCD camera under a blue-light transillumination table (Dark Reader, Clare Chemical, UK). To aid analysis of the DGGE gels, a marker using cluster controls derived from clones of known sequences was added to the outer

156

157

158

163

164

165

166

168

169

170

171

172

173

175

176

177

178

179

180

182

183

184

185

186

lanes of the gels. The reference markers were used as a standard during gel normalization and analysis, to ensure gel-to-gel comparability.

Bands were excised from the gel, re-amplified under the conditions described for the second-round PCR and sequenced (Genomex, France). The DNA sequences obtained were compared to sequences available in GenBank database using BLAST software (http://www.ncbi.nlm.nih/gov/blast/). Our sequences have been deposited in GenBank under the accession numbers GU001867-GU001878 and JF418997- JF419016.

Statistical analysis

DGGE patterns were analysed and compared using GelCompar II (Applied Maths, Belgium). For each profile, the number of AOB bands, recognized as bands affiliated to AOB after sequencing ('AOB richness'), was evaluated. We used two-way analysis of variance for unbalanced design (general linear procedure, GLM) to test the effect of site and tree species on AOB richness. As interactions between independent variables were significant, one-way ANOVA for unbalanced designs was applied . Post-hoc Duncan tests were performed to separate multiple means (P<0.05). Analyses were performed using SAS 9.1 (SAS, SAS Institute Inc, Cary, USA).

Differences in AOB community structure between samples across all sites and within each site were assessed using hierarchical clustering analysis, joining similar profiles into groups . Similarity matrices for this clustering analysis were generated from pairwise comparison of banding patterns (presence/absence of bands) of all samples, using the Dice Coefficient (CD) as follows: CD= 2j/(a+b), where j = number of bands in common

between lanes A and B, a = the total number of bands in lane A, b = the total number of bands in lane B. Samples generating similar banding patterns were clustered by means of the unweighted pair group method with arithmetic averages (UPGMA), resulting in the construction of dendrograms.

Differences in AOB community structure between samples were also assessed using Principal Component Analysis (Statistica 8, StatSoft, France). DGGE bands were scored as present (score=1) or absent (score=0). The data matrix for this analysis used site/species as variables, band scores as values within each variable and the correlation coefficient to calculate the similarity matrix. Pearson’s correlations were used to assess the relationship between changes of the AOB community structure (using principal components calculated from DGGE profiles as variables) with environmental (pH(H2O), LOI, NH4+-N, C/N, Ntot-N, Al3+, Ca2+, Mg2+, K+) and

microbial activities (MB-C, MB-N, BR, qmic, qCO2, Nmin, nitpot). Pearson’s correlation coefficients were also

calculated for AOB richness and potential nitrification (SAS 9.1, Sas Institute Inc., Cary, USA).

187

188

190

191

192

197

198

199

200

201

202

204

205

206

207

208

209

211

212

213

214

215

216

217

Binomial logistic regression with forwardstepwise (F-statistic was set to 0.05 for variable addition and 0.10 for variable removal) and a logit link function was used for predicting the presence orabsence of a specific AOB band in relation with a set of predictorvariables (Statistica 8, StatSoft, France). We used the variance inflation factor (VIF) to detect multicollinearity between variables and removed redundant variables which interfered with the regression model (VIF<10; ).

Results

Environmental and microbial properties of study sites

Range, across species present at each site, of variables used in correlation and regression are presented in Table 1. All stands were located on highly acidic soils with pHH2O ranging from 3.7 to 4.3. Organic matter ranged from

16.6 to 54.3%. Exchangeable Ca2+_{, Mg}2+ _{and K}+ _{were low across all stands (respectively <3.9, 0.4 and 0.9 meq}

(100g)-1 _{DW). Exchangeable ammonium was up to 16.4 mg N g}-1_{DW. Across sites, the potential nitrification was}

low and the net N mineralisation observed was in the range 0.6-5.3 µg N g-1_{DW d}-1 _{(Table 1). The microbial and}

the metabolic quotient ranged from 0.7 to 2.7% and from 0.3-1.5 µg CO2-C mg-1 MBC h-1, respectively. Effects

of tree species at each site on these parameters are analysed in . Table 1

AOB richness and community structure under tree species

BLASTn analysis revealed that sixteen of the thirty-two bands (Fig.1) excised from all DGGE profiles, reamplified and sequenced were affiliated with AOB. Non-target sequences were mainly present in the upper part of the gel (Fig. 1) and were located in the mobility range of Nitrosomonas cluster control (clusters V and VI). These non-targeted sequences are affiliated with γ-Proteobacteria or with other β-Proteobacteria. AOB bands were located in a mobility range of approximatively 45 to 57% of denaturant and co-migrating with

Nitrosospira-like and Nitrosomonas-like clusters controls (Fig. 1). AOB sequences showed best matches in the

NCBI Genbank database with different uncultured strains from soils, related to Nitrosospira-like sequences (minimum similarity level: 94%) and to Nitrosomonas-like sequences (minimum similarity level: 90%). Within the Nitrosospira lineage, we found sequences grouping into cluster 0 (MN4 and MN6), cluster 2 (MN10, MN11), cluster 3 (MN3, MN7, MN8, MN9, MN12) and cluster 4 (MN5) (Koops et al. 2003). We also found a

218

219

220

221

222

229

230

231

232

233

234

240

241

242

243

244

245

246

247

248

sequence related to Nitrosomonas cluster 6 (MN2). No bands/sequences related to AOB clusters 1, 5, 7 and 8 were detected.

Fig.1

Mean soil AOB richness (number of DGGE bands recognised as AOB) ranged from 1 (Rambrouch, spruce) to 6 (Chimay, beech) (Table 2). Soil AOB richness was significantly affected by tree species at Chimay and Vielsalm but not at Rambrouch. At Chimay, AOB richness was significantly higher (P=0.017) under beech-oak mixture and beech than under oak. At Vielsalm, AOB richness was significantly higher (P=0.015) under beech and oak than under Douglas-fir and spruce.

Table 2

Clustering analysis across sites indicated that AOB banding patterns were distributed in two main clusters (Fig. 2a). All AOB profiles from Rambrouch formed a separated cluster at 12 % of similarity from the cluster grouping AOB profiles from Vielsalm and Chimay together. Within this second cluster, profiles under coniferous species at Vielsalm separated from AOB profiles under deciduous species at Chimay and Vielsalm (grouped at 55% similarity). The dissimilarity of AOB patterns between sites could be illustrated by the distribution of bands (Fig. 1). Only band MN10, affiliated to cluster 2, was observed in all sites. At Rambrouch, only bands related to cluster 2 (MN10) and 3 (MN7, 9, 12) were observed. At Vielsalm, bands related to cluster 2 (MN10, 11) and 3 (MN12) appeared but additional bands affiliated to cluster 0 (MN6), 4 (MN5) were also detected. At Chimay, we identified bands affiliated to the same clusters ie cluster 2 (MN10, 11), 3 (MN3, 8), 0 (MN4, 6), 4 (MN5) and an additional cluster: the cluster 6 (MN2) (see Fig.1 for their phylogenetic affiliations). Fig. 2

Dendrograms, constructed for each site, were used to investigate the difference in AOB community structure under tree species. For Chimay (Fig. 3a), AOB patterns under oak grouped together and separated from AOB patterns under beech and mixed oak-beech stands, at 35% similarity. Under oak, AOB sequences related to clusters 0, 3, 6 were detected. Under beech and mixture of beech-oak, additional sequences related to clusters 2, 4 were observed. The dendrogram obtained for soils sampled at Rambrouch showed two distinct clusters (30% similarity), the first grouping patterns under Douglas-fir, and the second one, separating samples under oak and one sample under beech from samples under spruce and beech (Fig. 3b). At Rambrouch, we observed sequences related to cluster 3 in all profiles. The band MN9 (cluster 3) was observed in all patterns. The patterns under Douglas-fir were distinct due to the presence of bands affiliated to cluster 2 (MN10) and cluster 3 (MN7) (Fig.1). An additional band (MN12) was detected under oak. The dendrogram generated from samples collected at Vielsalm revealed two main nodes (Fig. 3c). AOB community structure under Douglas-fir and beneath two

249

250

253

254

255

256

259

260

261

262

263

264

265

266

267

270

271

272

273

274

275

276

277

278

279

spruce trees was clearly distinct from all other samples (30% similarity). These other patterns formed a joint cluster with two separate sub-clusters: profiles beneath two spruce trees were separated at 48% similarity from AOB profiles under beech and under oak. Under spruce and Douglas-fir, only sequences affiliated to cluster 2 and band MN1 were detected. Additional sequences related to clusters 0, 3 and 4 were detected under beech and oak.

Fig. 3

Relationship between AOB community structure/ richness, environmental and microbial parameters

The first three components of principal components analysis explained 63.3% of the variability of all DGGE profiles (Fig. 2b). Overall, the first principal component expressed the largest portion (31.9%) of the total variance when compared to the subsequent two components (20.2% and 11.2%). The correspondence between results obtained by clustering (Fig. 2a) and ordination (Fig. 2b) methods revealed the robustness of the analysis. The ordination method using PCA was chosen for further work because the principal components (PC) could then be analysed by correlation. PC1 were significantly correlated with microbial quotient, net N mineralisation, organic matter content (LOI), active acidity (pHH2O), total nitrogen, exchangeable aluminium and magnesium

(Table 3). PC2 were significantly correlated with microbial biomass (cmic), metabolic quotient, net N mineralisation and potential nitrification. PC3 was correlated significantly with metabolic quotient. AOB richness was significantly correlated with organic matter content (LOI), active acidity (pHH2O), microbial

quotient, total nitrogen, exchangeable aluminium and magnesium. Table 3

Relationship between the presence of specific sequences, environmental and microbial parameters

The logistic regression revealed that N mineralisation and microbial quotient were explanatory variables in 4 or 5 of the 12 models created, respectively (Table 4). ROC scores ranged from 0.68 to 0.978 indicating a good to excellent fit for each of the bands. Nmin showed a negative coefficient which signifies that an increase in Nmin would result in an increase in the probability of presence of MN1, MN5, MN6, MN12. Similarly, an increase in qmic would result in an increase in the occurrence of bands MN2, MN3, MN5, MN6 and MN10. The probability of presence of MN4 and MN8 increased if the quantity of exchangeable cations increased (Al3+ _{and Ca}2+ _{or K}+_,

respectively). On contrary, the presence of MN11 was linked to the decrease in content of exchangeable

280

281

282

283

284

290

291

292

293

294

295

296

297

298

299

305

306

307

308

309

310

aluminium. The probability of presence of MN1 and MN9 were, respectively, linked to the increase or decrease of metabolic quotient. The probability of presence of MN12 increased with the increase in extractable ammonium content.

Table 4

Relation between AOB community structure/ richness and function

As shown in Table 3, there was no significant correlation between AOB richness and potential nitrification (P=0.1654). A very weak correlation was observed between PC2 and potential nitrification (r=-0.406; P< 0.01). As PC2 reflected 20.19% of the variability in DGGE patterns, this indicated a very weak relation between AOB community structure and potential nitrification.

Discussion

The results of this study clearly indicate a difference in AOB community structure between the 3 sites studied and, within sites, an effect of tree species on AOB diversity (community structure and richness). Thus, AOB community structure was first determined by site characteristics and, at a lower spatial scale, by tree species. Factors affecting AOB diversity were mainly related to NH4 availability, such as N mineralization. We found no relation between potential nitrification and AOB richness, but a weak link with AOB community structure.

AOB community structure and richness under tree species at three forest sites

In the acid soils investigated, we found sequences related to both Nitrosomonas and Nitrosospira, with a higher number of sequences related to Nitrosospira. Numerous studies showed the preponderance of Nitrosospira-like AOB in terrestrial environments , and a prevalence of cluster 2 under acidic conditions . In contrast, found a dominance of Nitrosomonas-like AOB in acid forest soils under oak and spruce. We found only two sequence related to cluster 2, one of which was ubiquitous across sites. The majority of sequences retrieved in our study were affiliated to cluster 3; often found in neutral arable soils . This corroborates the idea that cluster 2 would not be the most representative and abundant cluster in acids soils and that cluster 3 can be found across many soil types (Fierer et al. 2009). We also detected sequences affiliated to cluster 4, which has been reported to be prevalent in unmanaged forests and characteristic of temperate soils .

311

312

313

319

320

321

326

327

328

329

334

335

336

337

338

339

340

341

Our results demonstrated that tree species influenced AOB community structure at local scale (within sites). At one site (Vielsalm), the difference in AOB community structure under Douglas fir and Norway spruce compared to deciduous species was linked to lower AOB richness. This change in AOB community structure might be explained by lower quality of coniferous litter , leading to the lower mineralisation rates measured at the site and, subsequently, lower substrate availability for the AOB. In contrast, a similar AOB community structure under spruce and deciduous species was observed at another site (Rambrouch). At this site, the important understory vegetation under spruce could have had a predominant effect on microbial community through root exudates , supplanting a potential tree species effect. At Chimay, AOB community structure was different between the deciduous stands investigated (beech, oak), with a lower AOB richness under oak. Oak litter has a high allelochemical content and a potential effect of these allelochemicals on AOB community composition cannot be excluded.

The observed effect of tree species on AOB community structure at individual sites could not be generalised at higher spatial scale, i.e. across sites. The understanding of the biogeographic distribution of soil microbial communities is still in its infancy due to the relatively recent development of molecular tools allowing the characterisation of microbial communities without cultivation . The major influences of the study sites on AOB community structure and richness observed in this study are in accordance with the “distance-decay” pattern reported for communities from all domains of life . This pattern, stating a decrease in the similarity of species composition with distance, could be governed by environmental conditions and/or by historical events, namely dispersal limitation and past environmental conditions . For example, in the study of , the genetic similarities of soil inhabiting bacteria were influenced by both environmental heterogeneity and historical events. Across a wide array of ecosystem types in North and South America, soil acidity has been recognised as a major environmental factor influencing microbial community structure . Soil moisture has also been determined as the main parameter influencing microbial community structure in forest soils across several biogeoclimatic zones . Also, across a broad range of ecosystem types and climatic region, AOB community structure was strongly correlated with mean annual temperature . In our study, site influence on AOB community structure could however not be explained by temperature, soil moisture or acidity, as these factors were similar between sites. Here, site-specificity of AOB community might be related to other factors than temperature, soil moisture or acidity (see below), such as dispersal limitation, past management, past environmental conditions or current environmental conditions.

AOB community structure and richness related to environmental and microbial properties

342

343

344

345

346

347

348

349

350

351

352

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

Little is known on the controlling factors of AOB community structure and AOB richness, although the regulation of nitrification process by abiotic factors has been largely documented . The abundance, distribution and community structure of ammonia oxidizing organisms may be affected by temperature , moisture , and pH . In contrast to these studies on neutral grassland or agricultural soils, investigating one factor at one site only, we were able to analyse multiple factors across sites. In our study, factors affecting AOB community structure and richness were thus mainly related to ammonium availability, as previously shown in grassland ecosystems . AOB community structure/richness was related to net N mineralisation rate, microbial quotient, acidity, organic matter content, total N and microbial biomass. Furthermore, occurrences of several AOB sequences increased with mineralisation rate, extractable ammonium content or microbial quotient (indicator for organic carbon availability). A link between net N mineralisation and AOB community structure has been demonstrated by . Net N mineralisation is the main process providing ammonium for AOB. Ammonium availability can be a selective factor through its selective influence on AOB species with different substrate affinity , as shown in agricultural soils . Furthermore, type and availability of organic matter can impact AOB community structure through its influence on the competition for inorganic nitrogen between AOB and heterotrophs. The influence of total nitrogen content and microbial biomass could be explained by the role of microbial biomass in the depolymerisation of organic nitrogen to dissolved organic nitrogen, via the excretion of extracellular enzymes. This process could be the initial rate-limiting step of N mineralisation , again affecting ammonium availability. This hypothesis is not supported by the absence of correlation between exchangeable ammonium content and AOB community structure. However, extractable ammonium of bulk soil does not reflect ammonium availability to microorganisms, as demonstrated by .

Relationships between AOB diversity and the nitrification process

We observed no relation between potential nitrification and AOB richness, as well as a very weak link with AOB community structure. It has been advanced that AOB density, rather than AOB community structure would control nitrate productions and in acid forest and grassland soils. Alternatively, according to the theory on the relation between biodiversity and ecosystem functioning , idiosyncratic or no-response relationship might be due to horizontal gene transfer and changes in gene expression (phenotypic plasticity) . However, both redundancy and rivet-popping relationship could occur if AOB richness in our study is above a potential threshold for observing a decrease in function. Studies under controlled conditions, manipulating AOB diversity, are

373 374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

398

399

400

401

402

403

necessary for elucidating the relationship between AOB diversity and function. Another explanation for the absence of a relationship between AOB community structure and richness and potential nitrification could be the role of other organisms in this process. Indeed, showed that ammonia-oxidizing Archaea (AOA) possess ammonia monooxygenase, potentially enabling them to perform ammonia oxidation and AOA have been found in high numbers in many habitats, including forest soils . Nitrification in acid soils has nurtured discussions for nearly 100 years . Whilst it was initially attributed to heterotrophs , to AOB at microsites with higher pH and/or to specific AOB clusters , recent research provided evidence for a more important role of AOA than AOB in strongly acid soils . Currently, none of these explanations can be discarded, as their validity has been demonstrated at some sites. We need to test these different possibilities across biogeographical gradients and environmental conditions. In particular data on the relative contribution of AOA and AOB to the nitrification process are required for elucidating which organisms are the main drivers and whether there is a diversity-function link for nitrification in acid forest soils.

Conclusion

The effects of tree species on AOB diversity and on the presence of particular AOB clusters were site specific and could not be generalized. Thus, at larger spatial scales, site characteristics may be more important than the nature of tree species in determining AOB diversity. Differences in AOB diversity were related to processes influencing ammonium availability, such as the total soil N content, net N mineralization and soil microbial biomass. The absence of a clear relation between AOB richness and nitrification indicates a possibly role of AOB abundance, phenotypic plasticity or the implication of ammonia oxidizing archaea in acid forest soils.

Acknowledgments

Funding for S.M. was provided by the ‘Ministère de la Culture, de l’Enseignement supérieur et de la Recherche du Grand-Duché de Luxembourg’ through a ‘bourse formation-recherche’. This research was supported by the ‘Fonds de la Recherche Scientifique’ (FNRS), Liège University (Special Research Fund-FSR) and the Belgian Science Policy within the project ‘Feasibility of forest conversion: ecological, social and economic aspects’ (SPDII, Mixed actions MA/04). We would like to thank staff of the ‘Service Public de Wallonie -Département de la Nature et des Forêts’ for their interest in our work and access to study sites. Assistance in site selection in

404

405

406

407

408

409

410

411

412

413

414

415

420

421

422

423

424

430

431

432

433

434

the Grand-Duchy of Luxembourg by F. Theisen and S. Hermes (Ministère de l’Environnement, Administration des Eaux et Forêts) was greatly appreciated. We thank M. Jonard and F. Plume for providing general site information and help during. We are grateful to Professor P. Gerard for his precious help with statistics. Constructive comments on the manuscript by Professor Q.Ponette were greatly appreciated. We are grateful to Dr. A. Wilmotte for providing access to database and software analytical tools. We are thankful to M.-C. Requier and A. Piret for assistance during sampling. This work was performed within the framework of the EFI-Project Centre CONFOREST.

References

Allen SE (1989) Chemical Analysis of Ecological Materials. Blackwell Scientific

Publications, London

Anderson T-H, Domsch K (1990) Application of eco-physiological quotients (qCO2 and qD)

on microbial biomasses from soils of different histories. Soil Biol Biochem

22:251-255

Augusto L, Ranger J, Binkley D, Rothe A (2002) Impact of several tree species of European

temperate forests on soil fertility. Ann For Sci 59:233-253

Avrahami S, Conrad R (2005) Cold-temperate climate: a factor for selection of ammonia

oxidizers in upland soil? Can J Microbiol 51:709-714

Avrahami S, Liesack W, Conrad R (2003) Effects of temperature and fertilizer on activity and

community structure of soil ammonia oxidizer. Environ Microbiol 5:691-705

Baar F (2001) PRO SILVA Europe, 11 années de réflexion et de projets concrets. Forêt

Wallonne 49-50 :12-16

Bäckman JSK, Hermansson A, Tebbe CC, Lindgren P-E (2003) Liming induces growth of a

diverse flora of ammonia oxidising bacteria in acid spruce forest soil as determined by

SSCP and DGGE. Soil Biol Biochem 35:1337-1347

Baldrian P, Kolaiřík M, Štursová M, Kopecký J, Valášková V, Větrovský T, Žifčáková L,

Šnajdr J, Rídl J, Vlček Č, Voříšková J (2012) Active and total microbial communities

in forest soil are largely different and highly stratified during decomposition. ISME J

6:248-258

Balser TC, Firestone MK (2005) Linking microbial community composition and soil

processes in two California ecosystems Biogeochemistry 73:395-415

Beck T, Joergensen R, Kandeler E, Makeschin F, Nuss E, Oberholzer H, Scheu S (1997) An

inter-laboratory comparison of ten different ways of measuring soil microbial biomass

C. Soil Biol Biochem 29:1023-1032

Belsley D, Kuh E, Welsch R (1980) Regression diagnosis: Identifying influential data and

sources of collinearity. New York

Boyle-Yarwood S, Bottomley P, Myrold D (2008) Community composition of

ammonia-oxidizing bacteria and archaea in soils under stands of red alder and Douglas fir in

Oregon. Environ Microbiol 10:2956-2965

Braid MD, Daniels LM, Kitts CL (2003) Removal of PCR inhibitors from soil DNA by

chemical flocculation. J Microbiol Meth 52:389-393

Briones AM, Okabe S, Umemiya Y, Ramsing N-B, Reichardt W, Okuyama H (2002)

Influence of different cultivars on populations of ammonia-oxidizing bacteria in the

root environment of rice. Appl Environ Microbiol 68:3067-3075

435

436

437

438

439

440

441

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

Brockett BFT, Prescott CE, Grayston SJ (2012) Soil moisture is the major factor influencing

microbial community structure and enzyme activities across seven biogeoclimatic

zones in western Canada. Soil Biol Biochem 44: 9-20

Brookes PC, Landmann A, Prugen G, Jenkinson DS (1985) Chloroform fumigation and the

release of nitrogen: a rapid direct extraction method to measure microbial biomass

nitrogen in soil. Soil Biol Biochem 17:837-842

Bruns MA, Stephen JR, Kowalchuk GA, Prosser JI, Paul EA (1999) Comparative diversity of

ammonia oxidiser, 16S rRNA gene sequence in native, tilled, and successional soil.

Appl Environ Microbiol 65:2994-3000

Carney KC, Matson PA, Bohannan BJM (2004) Diversity and composition of tropical soil

nitrifiers across a plant diversity gradient and among land-use types. Ecol Lett

7:684-694

Carnol M (1999) Abiotic factors controlling nitrification in acid forest soils. Going

Underground-Ecological Studies in forest soils. Research Signpost, Trivandrum, India

Carnol M, Ineson P, Anderson JM, Beese F, Berg MP, Bolger T, Couteaux MM, Cudlin P,

Dolan S, Raubuch M, Verhoef HA (1997) The effects of ammonium sulphate

deposition and root sinks on soil solution chemistry in coniferous forest soils

Biogeochemistry 38:255-280

Carnol M, Kowalchuk A, De Boer W (2002) Nitrosomonas europaea-like bacteria detected as

the dominant β-subclass Proteobacteria ammonia oxidisers in reference and limed

forest soils. Soil Biol Biochem 34:1047-1050

Chapin FSI, Matson PA, Mooney HA (2002) Principles of Terrestrial Ecosystem Ecology.

Springer Sciences, New York

Cody RR, Smith JK (1991) Applied Statistics and the SAS Programming Language, Third

edition. Englewood Cliffs, NJ. USA

Davidson EA, Hackler JL (1994) Soil heterogeneity can mask the effects of ammonium

availability on nitrification. Soil Biol Biochem 26:1449-1453

deLaplante K, Picasso V (2011) The Biodiversity-Ecosystem Function Debate in Ecology. In:

Dov MG, Bryson B, Paul T et al. (eds) Philosophy of Ecology. North-Holland,

Amsterdam, pp 169-200

Delgado-Baquerizo M, Gallardo A (2011) Depolymerization and mineralization rates at 12

Mediterranean sites with varying soil N availability. A test for the Schimel and Bennet

model. Soil Biology and Biochemistry 43:693-696

Edwards RA, Rogall T, Blöcker H, Emde M, Böttger EC (1989) Isolation and direct complete

nucleotide determination of entire genes. Characterisation of a gene coding for 16S

ribosomal RNA Nucleic Acids Res 17:7843-7853

Fierer N, Carney KM, Horner-Devine MC, Megonigal JP (2009) The biogeography of

ammonia-oxidizing bacterial communities in soil. Microb Ecol 58 (2):435-445

Fierer N, Jackson RB (2006) The diversity and biogeography of soil bacterial communities.

Proc Natl Acad Sci U S A 103:626-631

Focht DD, Verstraete W (1977) Biochemical ecology of nitrification and denitrification. Adv

Microb Ecol 1:135-214

Fromin N, Hamelin J, Tarnawski S, Roesti D, Jourdain-Miserez K, Forestier N,

Teyssier-Cuvelle S, Gillet F, Aragno M, Ross IP (2002) Statistical analysis of denaturing gel

electrophoresis (DGE) fingerprinting patterns. Environ Microbiol 4:634-643

Glaser K, Hackl E, Inselsbacher E, Strauss J, Wanek W, Zechmeister-Boltenstern S, Sessitsch

A (2010) Dynamics of ammonia-oxidizing communities in barley planted bulk soil

and rhizosphere following nitrate and ammonium fertilizer amendment. FEMS

Microbiol Ecol 74:575-591

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

Grayston S, Vaughan D, Jones D (1996) Rhizosphere carbon flow in trees, in comparison

with annual plants: the importance of root exudation and its impact on microbial

activity and nutrient availability. Appl Soil Ecol 5:29-56

Green JL, Holmes AJ, Westoby M, Oliver I, Briscoe D, Dangerfield M, Gillings M, Beattie A

(2004) Spatial scaling of microbial eukaryote diversity. Nature 432:747-750

Gubry-Rangin C, Nicol GW, Prosser JI (2010) Archaea rather than bacteria control

nitrification in two agricultural acidic soils. FEMS Microbiol Ecol 74:566-574.

doi:10.1111/j.1574-6941.2010.00971.x

Hannes P, Beier S, Bertilsson S, Lindström ES, Langenheder S, Tranvik LJ (2011)

Function-specific response to depletion of microbial diversity. ISME J 5:351-361

Hart SC, Stark JM, Davisdson EA, Firestone MK (1994) Nitrogen mineralization,

immobilization and nitrification. In: Spaks DL (ed) Methods of Soil Science Analysis,

part II. Soil Science Society of America, Madison, pp 985-1018

Hawkes CV, Wren IF, Herman DJ, Firestone MK (2005) Plant invasion alters nitrogen

cycling by modifying the soil nitrifying community. Ecol Lett 8:976-985

He J-Z, Hu H-W, Zhang L-M (2012) Curent insights into the autotrophic thaumarchaeal

ammonia oxidation in acidic soils. Soil Biol Biochem:1-9

He J, Xua Z, Hughes J (2005) Pre-lysis washing improves DNA extraction from a forest soil.

Soil Biol Biochem 37:2337-2341

Hendershot W, Duquette M (1986) A simple barium chloride for determining cation exchange

capacity and exchangeable cations. Soil Sci Soc Am J 50:605-608

Hobbie SE (1992) Effects of plant species on nutrient cycling. Trends Ecol Evol 7:336-339

Horner-Devine MC, Lage M, Hughes JB, Bohannan BJ (2004) A taxa-area relationship for

bacteria. Nature 432 (7018):750-753

Horz H-P, Barbrook A, Field CB, Bohannan BJM (2004) Ammonia-oxidizing bacteria

respond to multifactorial climate change. P Natl Acad Sci U S A 101:15136-15141

Howard PJA, Howard DM (1991) Inhibition of nitrification by aqueous extracts of tree leaves

litter. Revue d'écologie et de biologie du sol 28:255-264

Jackson RB, Linder CR, Lynch M, Purugganan M, Somerville S, Thayer SS ( 2002) Linking

molecular insight and ecological research. Trends Ecol Evol 17:409-414

Janowiak MK, Webster JCR (2010) Promoting Ecological Sustainability in Woody Biomass

Harvesting. J Forest 108:16-23

Kinzig AP, Pacala SW, Tilman D (2002) The functional Consequences of Biodiversity:

Empirical Progress and Theorical Extensions, vol 33. Monographs in Ecology.

Princeton University Press, Princeton

Koops H-P, Pommerening-Röser A (2001) Distribution and ecophysiology of the nitrifying

bacteria emphasizing cultured species. FEMS Microbiol Ecol 37:1-9

Koops HP, Purkhold U, Pommerening-Röser A, Timmermann G, Wagner M (2003) The

litoautotrophic ammonia-oxidizing bacteria. In: Dworkin M, et al. (eds) The

Prokaryotes : An Evolving Electronics Resource for the Microbiological community.

Springer-Verlag, New-York, pp 778-811.

Kowalchuk GA, Stephen LR, De Boer W, Prosser JI, Embley TM, Woldendorp JW (1997)

Analysis of ammonia-oxidizing bacteria of the beta subdivision of the class

Proteobacteria in coastal sand dunes by denaturing gradient gel electrophoresis and

sequencing of PCR-amplified 16S ribosomal DNA fragments. Appl Environ

Microbiol 63:1489-1497

Kowalchuk GA, Stienstra AW, Heilig GHJ, Stephen JR, Woldendorp JW (2000) Changes in

the community structure of ammonia-oxidizing bacteria during secondary succession

in calcareous grasslands. Environ Microbiol 2:99-110

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

Lavelle P, Spain AV (2001) Soil Ecology. Kluwer Academic Publishers, Dordrecht,The

Netherlands

Laverman AM, Speksnijder A, Braster M, Kowalchuk GA, Verhoef HA, van Verseveld HW

(2001) Spatiotemporal stability of an ammonia-oxidizing community in a

nitrogen-saturated forest soil. Microbial Ecol 42:35-45

Loreau M, Naeem S, Inchausti P (2002) Biodiversity and Ecosystem Functioning: Synthesis

and Perspectives. Oxford University Press, Oxford, UK.

Malchair S, Carnol M (2009) Microbial biomass and C and N transformations in forest floors

under European beech, sessile oak, Norway spruce and Douglas-fir at four temperate

forest sites. Soil Biol Biochem 41:831-839

Malchair S, De Boeck H, Lemmens C, Ceulemans R, Merckx R, Nijs I, Carnol M (2010)

Diversity-function relationship of ammonia-oxidizing bacteria in soils among

functional groups of grassland species under climate warming. Appl Soil Ecol

44:15-23

Martens-Habbena W, Berube PM, Urakawa H, de la Torre JR, Stahl DA (2009) Ammonia

oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria.

Nature 461:976-979

Martigny JBH, Eisen JA, Penn K, Allison SD, Horner-Devine MC (2011) Drivers of bacterial

β-diversity depends on spatial scale. Proc Natl Acad Sci USA 108:7850-7854

Meyer O (1993) Functionnal groups of microorganisms. In: Schulze E-D, Mooney HA (eds)

Biodiversity and Ecosystem Function. Springer Verlag, New York, pp 67-96

Nekola JC, White PS (1999) The distance decay of similarity in biogeography and ecology. J

Biogeogr 26:867-878

Nicol GW, Leininger S, Schleper C, Prosser JI (2008) The influence of soil pH on the

diversity, abundance and transcriptional activity of ammonia oxidizing archaea and

bacteria. Environ Microbiol 10:2966-2978.

Nicolaisen MH, Risgaard-Petersen N, Revsbech NP, W. Reichardt W, Ramsing NB (2004)

Nitrification-denitrification dynamics and community structure of ammonia oxidizing

bacteria in a high yield irrigated Philippine rice field. FEMS Microbiol Ecol

49:359-369

Okano Y, Hristova KR, Leutenegger CM, Jackson LE, Denison RF, Gebreyesus B, LeBauer

D, Scow KM (2004) Application of real-time PCR to study effects of ammonium on

population size of ammonia-oxidizing bacteria in soil. Appl Environ Microbiol

20:1008-1016

Olsson MO, Falkengren-Grerup U (2003) Partitioning of nitrate uptake between trees and

understory in oak forests Forest Ecol Manag 179 :311-320

Prosser JI (1989) Autotrophic nitrification in bacteria. Adv Microb Physiol 30:125-181

Prosser JI (2011) Soil nitrifiers and nitrification. In: Ward BB, Arp DJ, Klotz MG (eds)

Nitrification. ASM Press, Washington, D.C., pp 347-383

Purkhold U, Pommerening-Roser A, Juretschko S, Schmid MC, Koops HP, Wagner M (2000)

Phylogeny of all recognized species of ammonia oxidizers based on comparative 16S

rRNA and amoA sequence analysis: implications for molecular diversity surveys.

Appl Environ Microbiol 66:5368-5382

Ramette A, Tiedje JM (2007) Multiscale responses of microbial life to spatial distance and

environmental heterogeneity in a patchy ecosystem. Proc Natl Acad Sci U S A 104

(8):2761-2766

Remacle J (1977) The role of heterotrophic nitrification in acid forest soils-preliminary

results. Ecol Bull 25:560-561

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

Rothe A, Huber C, Kreutzer K, Weis W ( 2002) Deposition and soil leaching in stands of

Norway spruce and European beech: results from the Höglwald research in

comparison with other European case studies. Plant Soil 240:33-45

Schmidt CS, Hultman KA, Robinson D, Killham K, Prosser JI (2007) PCR profiling of

ammonia-oxidizer communities in acidic soils subjected to nitrogen and sulphur

deposition. FEMS Microbiol Ecol 61:305-316

Scott N, Binkley D (1997) Foliage litter quality and annual net N mineralization: comparison

across North American forest sites. Oecologia 111:151-159

Sparling GP, West AW (1990) A Comparison of Gas-Chromatography and Differential

Respirometer Methods to Measure Soil Respiration and to Estimate the Soil Microbial

Biomass. Pedobiologia 34 (2):103-112

Spiecker H (2003) Silvicultural management in maintaining biodiversity and resistance of

forests in Europe-temperate zone. J Environ Manag 67:55-65

Stephen JR, Mc Caig AE, Smith Z, Prosser, J.I., , Embley TM (1996) Molecular diversity of

soil and marine 16S rRNA gene sequences related to β-subgroup ammonia-oxidizing

bacteria. Appl Environ Microbiol 62:4147-4154

Taylor AE, Zeglin LH, Dooley S, Myrold DD, Bottomley PJ (2010) Evidence for different

contributions of archaea and bacteria to the ammonia-oxidizing potential of diverse

Oregon soils. Appl Environ Microbiol 76:7691-7698

Tietema A, Beier C, de Visser PHB, Emmett BA, Gundersen P, Kjønaas OJ, Koopmans CJ

(1997) Nitrate leaching in coniferous forest ecosystems: The European Field-Scale

Manipulation Experiments NITREX (Nitrogen Saturation Experiments) and EXMAN

(Experimental Manipulation of Forest Ecosystems). Global Biogeochem Cy

11:617-626

Tourna M, Freitag TE, Nicol GW, Prosser JI (2008) Growth, activity and temperature

responses of ammonia-oxidizing Archaea and Bacteria in soil microcosms. Environ

Microbiol 10 :1357-1364

Treusch AH, Leininger S, Kletzin A, Schuster SC, Klenk HP, Schleper C ( 2005) Novel genes

for nitrite reductase and Amo-related proteins indicate a role of uncultivated

mesophilic crenarchaeota in nitrogen cycling. Environ Microbiol 7:1985-1995

Vance ED, Brookes PC, Jenkinson DS (1987) An extraction method for measuring soil

microbial biomass C. Soil Biology & Biochemistry 19:703-707

von Lüpke B, et al. (2004) Silvicultural strategies for conversion. In: Spiecker H, Hansen J,

Klimo E (eds) Norway spruce conversion-options and consequences. Brill Leiden,

Boston, pp 121-164

Wessén E, Nyberg K, Jansson JK, Hallin S (2010) Responses of bacterial and archaeal

ammonia oxidizers to soil organic and fertilizer amendments under long-term

management. Appl Soil Ecol 45 (3):193-200

Wu J, Liu Z, Wang X, Sun Y, Zhou L, Lin Y, Fu S (2011) Effects of understory removal and

tree girdling on soil microbial community composition and litter decomposition in two

Eucalyptus plantations in South China. Funct Ecol 25: 921-931

Yao H, Gao Y, Nicol GW, Campbell CD, Prosser JI, Zhang L, Han W, Singh BK (2011)

Links between ammonia oxidizer community structure, abundance, and nitrification

potential in acidic soils. Appl Environ Microbiol 77 (13):4618-4625

Zerbe S (2002) Restoration of natural broad-leaved woodland in Central Europe on sites with

coniferous forest plantations. Forest Ecol Manag 167:27-42

Zhang LM, Hu HW, Shen JP, He JZ (2012) Ammonia-oxidizing archaea have more important

role than ammonia-oxidizing bacteria in ammonia oxidation of strongly acidic soils.

The ISME Journal 6:1032-1045

626

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

674

675