Phosphorus availabilities in beech (Fagus sylvatica L.) forests impose habitat filtering on ectomycorrhizal communities and impact tree nutrition

Soil Biology & Biochemistry 98 (2016) 127e137

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Phosphorus availabilities in beech (Fagus sylvatica L.) forests impose habitat filtering on ectomycorrhizal communities and impact tree nutrition Aljo
sa Zavi
si c a, Pascal Nassal b, Nan Yang a, Christine Heuck c, Marie Spohn c, Sven Marhan b, Rodica Pena a, Ellen Kandeler b, Andrea Polle a, * a b c

€ttingen, Go €ttingen, Germany Department of Forest Botany and Tree Physiology, Georg-August University of Go Institute of Soil Science and Land Evaluation, Soil Biology Department, University of Hohenheim, Stuttgart, Germany Department of Soil Science, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 December 2015 Received in revised form 19 March 2016 Accepted 3 April 2016

Phosphorus (P) is an important nutrient, whose concentrations are declining in many European forest ecosystems. Here, we selected five old-aged temperate beech (Fagus sylvatica) forests that represented a sequence of decreasing soil P resources. We addressed the following hypotheses: (i) root P concentrations correspond to soil P concentrations, when P availability is suboptimal for tree nutrition, (ii) decreasing soil P concentrations, and increasing host P demand foster increasing ectomycorrhizal fungal (EMF) species richness and lead to a shift in the EMF community structure towards increasing soil exploration. We found that the decrease in P concentrations along the geosequence was less steep in the organic layer than that in the mineral topsoil. P concentrations in roots showed a positive relationship with P concentrations in soil, with a stronger correlation in coarse than in fine roots. This finding indicates that low P availability mainly affected P storage of the host. The root tips were completely colonized with EMF. In the organic layer EMF biomass was higher than that of saprophytic fungi, and correlated with inorganic P (Pi). In the mineral topsoil EMF biomass was about 10-fold lower than in the organic layer and biomass of saprophytes and microbial P, but not that of EMF, was correlated with Pi and phosphatase activities. Based on these results, we propose that beech P nutrition was mainly achieved by EMF in the organic layer. Variation in EMF species richness was unrelated to P in soil and decreased with increasing N in the organic layer. The EMF community structures were taxonomically divergent and filtered by habitat soil chemistry in the mineral layer and Pi in the organic layer between the P-rich forest and the P-poor forest. Changes in the taxonomic structures of the EMF did not result in corresponding changes in soil exploration. In conclusion, our results support a relationship between soil P concentrations and P storage in roots, but do not support mono-causal relationships between soil P and EMF species richness or hyphal soil exploration. Our results suggest that the taxonomic dissimilarities of the EMF along the P gradient were mainly driven by Pi concentrations in the organic layer and by the nutrient resources in the mineral layer. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Organic P Inorganic P Exploration types Mycorrhizal diversity Functional traits P recycling

1. Introduction Phosphorus (P) is an important macronutrient required for many essential metabolic processes and cellular structures in plants. The availability of P for plant nutrition varies strongly in different soil types and is particularly constrained in acid and old,

* Corresponding author. E-mail address: [email protected] (A. Polle). http://dx.doi.org/10.1016/j.soilbio.2016.04.006 0038-0717/© 2016 Elsevier Ltd. All rights reserved.

weathered soils (Plassard and Dell, 2010; Richardson et al., 2004). Often, inorganic P is sequestered in forms that are unavailable to plants, for example, in precipitates with aluminum, calcium or iron (Jones and Oburger, 2011; Plassard et al., 2011). Furthermore, a considerable fraction of P in soil is present in organic forms, where it occurs mainly bound (Pb) in P-esters (Cairney, 2011; Plassard et al., 2011). Pb becomes plant-available after degradation of the macromolecules and solubilization of P (Becquer et al., 2014). This process involves bacteria, saprotrophic and ectomycorrhizal fungi

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(EMF) by secretion of P-targeting enzymes such as various phosphatases, phytases, etc (Kluber et al., 2010; Uroz et al., 2007). In acid soils, the release of acid monophosphoesterases by microbes is instrumental to render organic P plant available in the form of HPO2
(Plassard et al., 2011). HPO2
(here denominated as Pi) 4 4 generally occurs only in very low concentrations in soil solutions (<10 mM, Hinsinger, 2001). To overcome Pi limitations, most plant species form associations with mycorrhizal fungi. In temperate forests, the roots of most tree species are colonized by EMF, which ensheath the root tips and explore the soil with emanating hyphae (Smith and Read, 2008). According to the characteristics of the emanating hyphae, EMF have been functionally classified as contact, short distance, intermediate distance and long distance exploration types (Agerer, 2001). Thereby, EMF can reach nutrients in different distances from the host root. Because the hyphal diameter is much smaller than that of roots, they can access soil pores inaccessible to the plant and improve host plant nutrition (Smith and Read, 2008). The beneficial effect of EMF on P nutrition of forest trees has often been documented (Cairney, 2011; Plassard and Dell, 2010). In recent years decreasing P concentrations in foliage of deciduous forests have been observed (Braun et al., 2010; Crowley et al., 2012; Duquesnay et al., 2000; Flückiger and Braun, 1998; Ilg et al., 2009; Jonard et al., 2015; Leuschner et al., 2006; Manzoni et al., 2010; Prietzel and Stetter, 2010; Talkner et al., 2015; Trichet et al., 2009). In a European survey of beech (Fagus sylvatica) forests, Talkner et al. (2015) found that on 22% of the study sites foliar P concentrations were below the threshold for sufficient nutrition of 1.2 mg P g
1. Decreasing P concentrations in forest trees are a matter of great concern because P limitations lead to decreases in photosynthesis, growth reduction and vitality loss (Talkner et al., 2015; Yang et al., 2016). Talkner et al. (2015) speculated that nitrogen (N) deposition and climate change may have disturbed the nutrient balance among P, N and other major cations. Because the reasons for the declining P trends are not clear a better understanding of P cycles in forest ecosystems is urgently needed. In severely weathered soils in Australia, where P availability is extremely low P and N, were inversely related to EMF richness (Horton et al., 2013), while a positive relationship of soil P was found with EMF hyphal biomass (Teste et al., 2016). Talkner et al. (2015) found a tight correlation between foliar and soil P concentrations in the organic layer that faded away with decreasing soil depth (Talkner et al., 2015). Therefore, the stratification of saprophytic and mycorrhizal fungi between the organic layer and the mineral topsoil may be crucial for tree P nutrition in temperate ecosystems, but these aspects have received only little attention. In Central Europe, beech forests are wide-spread in mesic to moderately dry climate conditions, and occur on a range of different soil types (Eyre, 2013; Leuschner et al., 2006). The roots are colonized by complex assemblages of taxonomically highly e et al., 2009, 2005; Lang and Polle, 2011; Lang diverse EMF (Bue et al., 2013; Pena et al., 2010), whose responses to different P availabilities are not yet known. Therefore, the main question of this study addresses the relationship between P availability in soil and EMF biomass, species richness and community composition. A further important question is as to whether any of those parameters was related to P nutrition of beech roots. To investigate these relationships we selected five beech forests along a geo-sequence, which was defined as a geographic gradient representing decreasing P concentrations in the top mineral soil from 2.8 to 0.7 mg P g
1 soil dry mass due to different parent material (Haußmann and Lux, 1997). Ectomycorrhizal fungal activities and tree P nutrition are unlikely to function independently from the availability of other soil nutrients and the activities of soil microbes contributing to Pi solubilization (saprophytic fungi, bacteria).

Therefore, measurements of those parameters in different soil strata were also included. To minimize potential effects of forest structure, which has a strong impact on the EMF community composition (Twieg et al., 2009), our study was conducted in evenaged, mono-specific beech plots. We hypothesized that (i) decreasing P concentrations in soil are mirrored by decreasing P concentrations in beech roots, if P availability is not in the optimum range. We further hypothesized that (ii) P limitations are associated with high EMF species richness and EMF biomass to enable enhanced soil exploration and to increase the access to P resources by higher diversity of exploration types. To address these hypotheses, we determined Pi and Pb as well as nutrient and non-essential elements (C, N, Ca, K, P, S, Mg, Mn, Fe, Al, and Na), and pH in the organic soil layer and in the mineral topsoil as indicators for P availability and soil chemistry. The elemental compositions of fine and coarse roots of beech were determined as a measure for the nutritional status. EMF species richness and community structure, microbial biomass, ectomycorrhizal and saprophytic hyphal biomass were analyzed in relation to soil P and nutrient chemistry. 2. Materials and methods 2.1. Site characteristics and sampling scheme The areas selected for this study were stocked mixed beechconifer forests of about 120 years (Table 1). Pure beech sections were chosen for this study. The climatic conditions varied with regard to the mean annual temperatures in range from 4.9 to 8  C and the annual precipitation from 779 to 1749 mm (Table 1). The forest Bad Brückenau (BBR) is located in the biosphere reservation € n” with an Atlantic climate. The soil type is of “Bayerische Rho volcanic origin (basalt). Mitterfels (MIT) is situated in the Central German Uplands with montane climate. The soil-type is brown podzol. The research forest Vessertal (VT) is located in the Thuringian Forest with a montane climate. The soil type is brown earth with a moderate coarse stone fraction. The forest Conventwald (CON) is located in a sub-mountainous and mountainous transitional area of the Middle Black Forest. The soil type is brownearth with a coarse stone fraction of 30e60% v/v. The forest Unterlüss (LUE) is situated in Lower Saxony with Atlantic climate. The soil was sandy loam. Further details on the sites, soil profiles and soil properties can be found under http://www.ecosystemnutrition.uni-freiburg.de/standorte and Table 1. In each forest five study plots were selected in areas with pure beech stands. The central plot was located adjacent to the measuring station of the respective forest authority. Four further plots were used, one in each cardinal direction at a distance of >200 m from the central plot. In each study plot five soil cores (diameter 8 cm, depth 20 cm corresponding to a volume of 1 L) were collected at a distance of about 20 m from each other in October 2013. The organic layer could clearly be discerned by a dark border separating the humus from the mineral topsoil. The five samples per plot of the organic layer were pooled, resulting in one sample per plot and, thus, in a total of n ¼ 5 samples per forest. The mineral soil samples of each plot were also pooled in the same way, resulting in n ¼ 5 samples per forest. Larger stones (>2 cm) and branches were removed. The mineral soil was sieved (2 mm mesh). Aliquots of both soil layers were stored frozen at
20  C. Further aliquots were dried and stored or were used immediately for further analyses. The roots of each plot were pooled resulting in five samples per forest. Roots from the organic layer and mineral topsoil were not separated because the mineral layer often contained too few roots resulting in an insufficient number of EMF root tips for subsequent diversity analyses. Thereby, the EMF colonization and community

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Table 1 Stand parameters at five research sites in Germany. Data taken from relevant German forest authority (Haußmann and Lux, 1997); data collection occurred in 1995. Stand parameters

BBR

MIT

VT

CON

LUE

Geographical coordinates Elevation (m) Precipitation (mm)  Mean annual temperature ( C) Substrate Soil type (KAS/WRB)

50 350 N, 9 920 E 809 1031 5.8 Basalt Dystric Skeletic Cambisol Mull-like Moder 7

48 970 N, 12 870 E 1023 1229 4.9 Paragneiss Hyperdystric, Chromic Folic Cambisol Moder 11

50 600 N, 10 770 E 810 1200 5.5 Trachyandesite Hyperdystric, Skeletic Chromic Cambisol Moder 9

48 020 N, 7 960 E 884 1749 6.6 Paragneiss Hyperdystric, Skeletic Folic Cambisol Mor-like Moder 11

52 830 N, 10 360 E 115 779 8.0 Glacial sandy material Hyperdystric, Folic Cambisol Mor-like Moder 12

Hordelymo Fagetum Fagus sylvatica, Picea abis 120 25.3 595

Dryopteris Fagetum

Luzulo Fagetum

Galio Fagetum

Luzulo Fagetum

Fagus sylvatica, Picea abis, Abies alba 110 20.5 407

Fagus sylvatica

Fagus sylvatica, Picea abis, Fagus sylvatica, Picea abis, Abies alba Abies alba 100e150 120 30.4 30.6 528 520

Organic Layer type Depth of upper border of the organic layer (cm)a Vegetation (potential natural) Tree species Stand age (years) Tree height (m) Stem number n/ha a

120 29.3 665

From http://www.ecosystem-nutrition.uni-freiburg.de/standorte.

structures were analyzed to a uniform depth of 0.2 m for all forests. The roots were separated into fine (<2 mm) and coarse roots (>2 mm diameter). Aliquots were dried at 40  C for one week. Fresh fine root aliquots were stored at 4  C for mycorrhizal analyses. 2.2. Ectomycorrhizal analyses by morphotyping and ITS sequencing Fine roots (<2 mm diameter) were cut into small fragments of about 20 mm length, mixed, and randomly chosen for mycorrhizal analyses. In total 1000 root tips were counted per sample under a dissecting microscope (Leica M205 FA; Leica, Wetzlar, Germany). Each distinct morphotype was photographed at 10e40 magnification (Leica DFC 420 C). Different morphotypes on vital root tips were classified according to a simplified procedure and identification key by Agerer (2006) using color, shape, branching pattern, mantle surface texture, hyphal morphology, rhizomorph connection/shape and abundance. Based on microscopic observations and literature data the EMF were classified according to exploration type as contact, short-distance, medium-distance, and longdistance types (Agerer, 2001) (Supplemental Table S1). The percentage of EMF colonization was calculated as: EMF (%) ¼ (n mycorrhizal root tips/n vital root tips)100. Vital root tips were distinguished from dead and dry root tips according to the method used by Winkler et al. (2010). Root tip vitality (%) was calculated (n vital root tips/n total root tips)100. Each morphotype was collected (n ¼ 5e20 root tips) and stored at
20  C. Total genomic DNA was isolated from the morphotypes using the innuPREP Plant DNA Kit (Analytik Jena AG, Jena, Germany) as described by the manufacturer. The rRNA ITS-region was amplified by the polymerase chain reaction (PCR) with the primer pair ITS1F and ITS4 (Eurofins MWG Operon, Ebersberg, Germany) (White et al., 1990) using either the original genomic DNA sample or a dilution to remove inhibitors of the PCR as described previously (Lang and Polle, 2011). All PCR products were purified by addition of 35 mL 2-propanol (Roth, Karlsruhe, Germany). DNA was precipitated for 1 h at room temperature and centrifuged for 30 min at 20,000  g (Centrifuge 5417 R, Eppendorf, Hamburg, Germany). The obtained pellet was fully dehydrated for 10 min at 50  C (SPD 111V, SpeedVac, Thermo Savant, Holbrook, NY) and dissolved in 30 mL ultrapure H2O. The purified DNA was used for Sanger sequencing (Applied Biosystems 3730XL DNA Analyzer, Seqlab GmbH, € ttingen, Germany). Go Sequences were aligned with the Staden Package software (4.10, http://staden.sourceforge.net) and blasted in NCBI GenBank and

~ljalg et al., 2005). Due to higher accuracy for UNITE database (Ko fungal sequences and lower misidentification rates, the curated ribosomal DNA sequence database UNITE (https://unite.ut.ee/ index.php) was taken as the primary source of EMF identification. The public GenBank database (http://www.ncbi.nlm.nih.gov/) was used when there was no entry in the UNITE database system. With a sequence homology of >97% and a score >900 bits, the name suggested by the database (UNITE, NCBI) was accepted. Sequences were deposited in the NCBI Genbank with the accession numbers KT735265-KT735285. Details were compiled in Table S1. 2.3. Determination of ergosterol as a proxy for fungal biomass Saprophytic and EMF biomass were assessed by a method by Bååth et al. (2004) for measuring ergosterol in fresh soil (total fungal biomass), and after 6 months of incubation (saprophytic biomass). EMF biomass was calculated as: total fungal biomass e saprophytic biomass (Bååth et al., 2004). To determine EMF biomass, aliquots of fresh soil samples were separated into two parts: one part (130 g) was immediately frozen in liquid nitrogen and further stored at
20  C, the other part (130 g) was incubated for six months in darkness at 25  C, at field moisture (23% ± 5% soil humidity). Samples taken before and after incubation were freezedried. Dry soil samples (2 g) were incubated in 15 ml reaction vessels in 10 ml extraction solution (10% KOH in methanol, 200 mg L
1 4-methylphenol; 5 mg ml
1 cholesterol in methanol was used as internal standard). The solutions were incubated for 3 h at 60  C, centrifuged at 2000 rpm at 4  C for 15 min, and 4 ml of the supernatant was transferred to a fresh 15 ml reaction vessel to which 2 ml n-hexane, and 4 ml deionized water were added. The mixture was shaken vigorously on a horizontal shaker (SA-31, Yamato) for 15 min and subsequently centrifuged at 2000 rpm at 4  C for 15 min. One ml of the upper hexane phase was transferred to a 2 ml reaction vessel. The hexane extract was dried in a Speedvac (SPD 111V, SpeedVac, Thermo Savant, Holbrook, NY) and stored at
20  C. The samples were analyzed by gas chromatographyemass spectroscopy (GCQ, 2010; Finnigan Corporation, Austin, TX, USA) using an integrated GC-MS/MS system fitted with a high temperature ion source and an autosampler (SEG, Finnigan A 200S, Ringwood, Australia). The separation was carried out using a ZB-35 fused-silica column (30 m  0.25 mm diameter, 0.25 mm, Phenomenex, Torrance, CA, USA) and the following temperature program: 80  C for 1 min, then 40  C min
1 to 260  C, then 2  C min
1 to 320  C and holding for 2 min. Pressure programming

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gave a constant linear gas velocity of 35 cm s
1. Transfer line temperature was 300  C. Mass spectrometry parameters were set as follows: electron impact ionization (EI) at 70 eV, ion source temperature at 200  C. GC-MS solution software (Xcalibur, Fininigan Corp.) was used for data handling and GC-MS control (Nielsen and Madsen, 2000).

Ten g of fresh soil were fumigated with chloroform for 24 h. Pi was extracted in Bray-1 solution and measured by the malachite green assay. The conversion factor of 2.5 was used to calculate Pmic (Brookes et al., 1982).

2.4. Elemental analyses and soil pH

Data are means (n ¼ 5, ±SE, if not indicated otherwise). ANOVA was used to detect significant differences followed by comparisons of means with Tukey HSD (package: “stats”) using R version 2.9.1 (R Development Core Team, 2012). Means were considered to be significantly different from each other when p  0.05. To test for normal distribution and homogeneity of variances, residuals of the models were analyzed by performing a ShapiroeWilk test. When the data violated the assumption of normal distribution, they were log or square root-transformed, before ANOVA was performed. Generalized linear models were calculated in Statgraphics Centurion XV (StatPoint, Inc., St Louis, MO, USA) using Poisson regression to model EMF species richness. Principle component analyses (PCA), canonical correspondence analyses (CCA) and Analysis of similarity (ANOSIM) were conducted with PAST software package 3.08 (http://folk.uio.no/ohammer/past/, Hammer et al., 2001). Data for PCA were z-transformed and broken stick analysis was applied to determine the number of significant PCs. For similarity analysis, ANOSIM was used with 9999 iterations, and p values were sequentially corrected by the Bonferroni method. The Bray Curtis similarity index was used in the multivariate analyses.

Dry soil aliquots (organic layer, mineral soil), fine and coarse root samples were milled (Retsch MN 400, Haan, Germany). Aliquots were weighed into tin capsules and used for carbon (C) and nitrogen (N) analysis in an Elemental Analyzer (EA1108, Carbo Erba Strumentazione, Rodano, Italy). Further aliquots were extracted in 65% HNO3 at 160  C for 12 h according to (Heinrichs et al., 1986), filtered and used for elemental analysis of P, S, K, Mn, Mg, Ca, Fe, Na, and Al by Inductively Coupled Plasma Optical Emission Spectrometry (ICP OES, iCAP 6300 Series, Thermo Fischer Scientific, Dreieich, Germany). Twenty g of soil were shaken in 50 ml deionized water for 2 h at 200 rpm. After sedimentation of particles, the pH was measured (pH meter, WTW, Weilheim, Germany). 2.5. Determination of phosphorus (Pi, Presin and Pb) About 2 g of milled plant and soil materials were used for determination of Pi. The samples were extracted in 10 ml of Bray-1 solution (1 M NH4F, 0.5 M HCl) (Bray and Kurtz, 1945), shaken for 5 min at 180 rpm, and then filtered using phosphate free filter paper (MN 280 ¼, MachereyeNagel, Düren, Germany). Pi was determined colorimetrically at 645 nm (Specord 205, Analytik Jena, Germany) using malachite green oxalate (Sigma, St. Louis, MO, USA) as reagent according to the procedure described by (Lanzetta et al., 1979). Pb was calculated as: Pb ¼ Total P (determined by ICP OES) e Pi (determined after Bray-Kurtz extraction). Bioavailable Pi (Presin) was extracted by anion exchange according to Hedley et al. (1982). For this purpose 0.5 g of air-dried soil was extracted for 16 h in 25 ml demineralized H2O and incubated on a horizontal shaker at room temperature in the presence of a 2  3 cm anion exchange membrane (551642S, VWRInternational, Germany, Darmstadt). Afterward, the membrane was transferred to a fresh vessel with 25 ml H2O and was shaken gently for 15 min to remove any soil particles. Subsequently, the membrane was gently shaken in 25 ml of 0.5 M HCl for 2 h to remove the P from the anion exchange membrane. Presin in the solution was analyzed by the malachite green assay. 2.6. Determination of acid phosphatase activity and microbial P Phosphomonoesterases represent a class of enzymes with hydrolytic dephosphorylation activities that are released by roots as well as by microorganisms into the soil. Because of the acidic pH of our soils (pH 3.4 to 5.7) acid phosphomonoesterase was determined. The activity was assayed using a modified disodium phenylphosphate method: 5 g of soil was incubated in 10 ml of acetate buffer (pH 5) and 5 ml of 20 mM buffered disodium phenylphosphate solution at 37  C for 3 h; the release of phenol was determined colorimetrically at 614 nm, using 2,6-dibromchinonechlorimide as coloring reagent (Hoffmann, 1968; modified by € Ohlinger, 1996). Calibration blanks contained no soil, and controls had substrate solution replaced by water. Microbial P was determined by the chloroform fumigation extraction method. The method is based on the extraction and quantification of microbial constituents immediately following CHCl3 fumigation of the soil (Brookes et al., 1985; Vance et al., 1987).

2.7. Statistical analyses

3. Results 3.1. Ectomycorrhizal species richness and biomass along a P geosequence Total P concentrations in the mineral topsoil decreased in the order BBR > MIT > VT > CON > LUE along the selected P gradient (Table 2), in agreement with earlier data (Haußmann and Lux, 1997, http://www.ecosystem-nutrition.uni-freiburg.de/standorte). Total P concentrations in the organic layer also showed a decrease along this geo-sequence and were correlated with decreasing total P in the mineral layer (R ¼ 0.780, p < 0.001). The total P concentrations in the mineral layer were further correlated with the concentrations of Pb (p < 0.001) and Pi (p < 0.009) in the mineral layer and those of the organic layer with Pb (p < 0.001), Pi (p < 0.001) and Presin (p < 0.044) in the organic layer. Other soil nutrients such as N, K, S, Mg, Ca, Mn and Fe also showed significant differences among the sites, but in most cases not in the same order as P (Supplement Table S2). No significant differences in root tip vitality were observed among the beech forests (20.7 ± 1.9%, F4,20 ¼ 1.62, p ¼ 0.208). Nonmycorrhizal tips were rarely observed; differences in mycorrhizal colonization were not detected among the forests (98.7 ± 0.3%, F4.20 ¼ 1.41, p ¼ 0.266). Cumulative EMF species richness (Hmax) showed that EMF species were sampled to saturation in the beech plots of each forest (Fig. 1). Hmax differed significantly among the different forests (permutation test, p < 0.05 for pairwise comparisons), except between VT and LUES (p ¼ 0.135). CON exhibited the highest and MIT the lowest EM species richness. Hmax was significantly correlated with the mean species richness of the plots (Hplot) (Hplot ¼ 0.37  Hmax þ 0.48, R ¼ 0.998, p ¼ 0.002). To assess the stratification of soil fungi, we determined the amount of EMF and saprotrophic fungal biomass in the organic layer and in the mineral top soil. As a proxy for ectomycorrhizal fungal biomass, the difference between total and saprophytic ergosterol concentrations (measured after long-term incubation of

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sic et al. / Soil Biology & Biochemistry 98 (2016) 127e137 Table 2 Concentrations of different P forms in soil (mg g¡1 dw) along a geo-sequence. MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau. Data are means (SE, n ¼ 5). Different letters indicate significant differences at p < 0.05. Data for Presin were log-2 transformed prior to ANOVA and are given in mg g
1 dw. Location

BBR CON LUE MIT VT

Organic layer

Mineral Layer

P form

Mean ± SE

Total Total Total Total Total F p

1.994 ± 1.207 ± 1.111 ± 1.261 ± 1.666 ± 20.480 <0.001

0.120 0.059 0.040 0.057 0.105

P P P P P

Mean ± SE c a a a b

3.047 ± 0.882 ± 0.170 ± 1.423 ± 1.386 ± 19.780 <0.001

0.185 0.100 0.011 0.360 0.332

c b a b b

Pi Pi Pi Pi Pi F p

0.188 0.175 0.263 0.190 0.295 5.250 0.005

± ± ± ± ±

0.017 0.011 0.030 0.019 0.033

a a b a b

0.031 0.033 0.010 0.039 0.080 3.910 0.017

± ± ± ± ±

0.006 0.006 0.002 0.011 0.025

a a a a b

BBR CON LUE MIT VT

Pb Pb Pb Pb Pb F p

1.806 ± 1.032 ± 0.848 ± 1.071 ± 1.371 ± 23.930 <0.001

0.121 0.054 0.016 0.064 0.086

c a a a b

3.016 ± 0.848 ± 0.160 ± 1.384 ± 1.306 ± 21.260 <0.001

0.182 0.095 0.012 0.352 0.309

c b a b b

BBR CON LUE MIT VT

Presin Presin Presin Presin Presin F P

96.63 47.84 89.65 39.28 75.68 4.7 0.008

6.49 c 8.98 ab 9.43 c 15.83 a 14.81 bc

differences among the forests were found (Fig. 2A). In the mineral soil no significant differences between the overall biomass of saprophyte and EMF hyphae were detected (Fig. 2B), but the biomass of each fungal group varied among the forests. The extent of these variations was low because the overall biomass of the fungi was about 10-fold lower in the mineral soil than in the organic layer (Fig. 2A,B). In mineral soil, saprophytic biomass was lowest in LUE and highest in CON, while EMF biomass was lowest in MIT and in BBR (Fig. 2B). The variations of EMF biomass were unrelated to EMF species richness (pmineral soil ¼ 0.810, Chi2 ¼ 0.057, porganic 2 layer ¼ 0.559, Chi ¼ 0.340). 3.2. Relationships among P in soil and roots with fungi and microbes

BBR CON LUE MIT VT

± ± ± ± ±

131

5.14 ± 1.99 a 3.39 ± 0.71 a 2.50 ± 0.25 a 3.35 ± 0.50 a 15.58 ± 4.59 b 6.08 0.002

soil cores without plants) was used. In the organic layer EMF biomass was about 30% higher than that of the saprophytes and no

We tested whether EMF species richness was correlated with any of the P forms in soil such as Pi, Pb, Presin, or total P in the organic layer or in the mineral soil, but found no significant mono-causal relationships between EMF species richness and any of the determined P forms (p > 0.05, Supplement Table S3). EMF species richness was neither correlated with the total P concentrations in fine or coarse roots (p > 0.05, Supplement Table S3). We further tested whether fungal biomass of EMF or saprophytes in soil was related to any P fraction. EMF, but not saprophytic biomass in the organic layer was correlated with Pi in the organic layer (y ¼ 1.15x þ 113; R ¼ 0.464, p ¼ 0.019). In contrast to the organic layer, the saprophytic and not the EMF biomass in the mineral layer was significantly positively correlated with Pi in the mineral layer (Table 3). Saprophytic biomass was also correlated with phosphatase activities and Pmic, while EMF biomass showed no relationship with those variables (Table 3). The fine root P concentrations exhibited significant, positive relationships with various P forms in soil (mineral soil: Presin, Pi, Pb, and total P, organic layer: total P, Pb, Table S4, Fig. 3). The coarse root P concentrations were also significantly correlated with different soil P forms (mineral soil: Presin, Pi, Pb, organic layer: total P, Pi, Pb, Table S4). A comparison of these relationships revealed that the P concentration of coarse roots showed a pronounced increase with increasing P in the organic layer, whereas the increase of P in fine roots was moderate (Fig. 3). The slopes of these relationships were significantly different (p ¼ 0.017), but not the intercepts. 3.3. Ectomycorrhizal species richness in relation to soil chemistry

Fig. 1. Rarefaction curves for ectomycorrhizal species in five forests along a phosphorus gradient. In each forest five plots were analyzed. In each plot five 1 L samples were collected, pooled and used for the inspection of 1000 root tips. The number of vital root tips and the number different morphotypes were counted. The species identities of ectomycorrhizal morphotypes were determined by ITS sequencing. MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau.

Since EMF species richness showed no simple relationship with the different P forms, we employed GLM to find out whether combinations of P forms and other components of soil or root chemistry were potential drivers of EMF species richness. We did not include coarse roots in this analysis because they have no direct interaction with EMF species. For each of the three compartments, mineral soil, organic layer and fine roots, the concentrations of 11 elements including total P and for the analyses of the soil compartments also pH, Pi, and Pb were used as factors in the GLMs (Supplement Table S2). We calculated separate models using EMF species richness as the response variable to the chemistry of each compartment, but only nitrogen (N) in the organic layer exhibited a significant negative relationship with EMF species richness (Chi2 ¼ 4.43, p ¼ 0.035, coefficient for N ¼
0.05 ± 0.02). The N concentrations explained about 30% of the variation of EM fungal species richness (percent deviance explained by the model ¼ 29%). 3.4. Ectomycorrhizal community structure and functional traits in relation to soil chemistry In general, we found typical EMF communities of beech forests

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Fig. 3. Phosphorus concentrations in fine roots (open circles) and coarse roots (filled circles) in relation to the total P concentration in the organic layer. The equations and statistical results for the linear regression lines are indicated.

Fig. 2. Ergosterol concentrations in fungal biomass (mg g¡1 dry soil) in the organic layer (A) and in the mineral soil (B) in five forests along a phosphorus gradient. Saprophytic ergosterol (black bars) was determined after 6 months incubation of soil cores and ectomycorrhizal ergosterol (open bars) was determined as ergosterol of fresh soil-saprophytic ergosterol (see materials and methods for details). Data indicate means (n ¼ 5 ± SE). Pguild indicates the P value for the comparison of the saprophytic and ectomycorrhizal biomass (Mann Whitney test), PKS is the p value for the comparison of the distribution of the data by the KolmogoroveSmirnov test. Differences in the ergosterol concentrations were tested for each fungal guild separately and are indicated by Roman letters for saprophytes and by Greek letters for the ectomycorrhizal fungal biomass. Different letters indicate differences at P < 0.05. MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau.

Table 3 Regression analyses for Pi, saprotrophic fungal biomass (SAP), acid phosphatase activity (Phos) and microbial biomass P (Pmic) in the mineral topsoil. Pi data were log-2 transformed. Data above the diagonal indicate R and below P values of the regression (n ¼ 25). Bold letters indicate significant values.

Pb Pi SAP Phos Pmic EMF

Pb

Pi

SAP

Phos

Pmic

EMF

e 0.013 0.667 0.259 0.263 0.203

0.486 e 0.037 0.146 0.001 0.396

0.090 0.418 e 0.001 0.001 0.580

0.234 0.299 0.705 e 0.000 0.183

0.232 0.596 0.695 0.745 e 0.668

0.263
0.177 0.116 0.275 0.090 e

as indicated by numerous Russula species (Russula rosea, Russula mairei, Russula ochroleuca, Russula olivaceae). In the low-P forest LUE Lactarius blennius was the most abundant species, whereas in the high-P forest BBR Lactarius subdulcis, Sebacina epigeae, and Tetracladium setigerum were the dominant ones (Supplement

Table S1). The overall EMF community structures in the five forests were significantly different from each other, except those of CON and BBR (Table 4). We investigated whether soil and root chemistry on the one hand or distinct P forms on the other hand could explain the differentiation of the EM communities. PCAs were conducted to condense the variables for the nutrient elements of each compartment (roots, soil organic layer, and mineral top soil) into significant PCs. The PCs were used as a representative measure for “chemistry”. Based on the same set of eleven mineral elements for each compartment, we obtained two significant PCs for roots, explaining 48% and 21% of the variation, one for mineral soil (66%) and two for the organic layer (63% and 19%, Supplement Table S5). We used the data of these PCs together with Pi and Pb in the mineral and organic layer as environmental factors in a CCA for fungal species composition (Fig. 4). The EM communities were separated along the first axis, which explained 27% of the variation in the order BBR > CON ¼ MIT > VT > LUE (Fig. 4). The most important factors that determined this separation were soil chemistry in the mineral layer (
0.80), and Pi in the organic layer (0.70). The second axis of the CCA, which was related to root chemistry (
0.25) and soil chemistry of the organic layer (0.59), indicated only little structuring effects of these variables on the EMF community composition. To find out whether the EM assemblages reflected separation according to functional traits, an ANOSIM was conducted based on the exploration types of the EMF species. The EMF community of LUE was significantly different from those of CON, MIT, and VT (Table 4, lower diagonal). BBR was not separated from any other assemblage (Table 4, lower diagonal). CCA showed that BBR encompassed the largest functional diversity with regard to exploration types and therefore did not differ from any other of the studied forests (Fig. 5). CCA1 separated medium distance exploration types (md:
0.91), which occurred at MIT from contact types (co: 0.72) in LUE along the first axis (Fig. 5). CCA2 was determined by short distance exploration types (sd: 0.93), which were relatively predominant in the CON communities (Fig. 5). However, the distinction of traits between the assemblages was not explained by root or soil chemistry (Supplement Fig. S1).

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sic et al. / Soil Biology & Biochemistry 98 (2016) 127e137 Table 4 Similarity analysis of the ectomycorrhizal species composition in five forests along a phosphorus gradient. The species composition (p values above the diagonal) and the exploration types (p values below the diagonal) were analyzed by ANOSIM using Bray Curtis as the similarity measure. P-values after sequential Bonferroni correction are indicated. Significant p values (<0.05) are indicated in bold letters. MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau.

BBR CON LUE MIT VT

BBR

CON

LUE

MIT

VT

e 0.327 0.051 0.168 0.387

0.399 e 0.030 0.281 0.794

0.009 0.006 e 0.022 0.013

0.016 0.025 0.015 e 0.430

0.010 0.285 0.033 0.016 e

4. Discussion 4.1. P nutrition of beech roots in relation to soil P concentrations The beech forests exhibited a strong P gradient in the mineral soil in agreement with previous investigations (Haußmann and Lux, 1997). Still, the difference in leaf P concentrations between the most contrasting sites was not large (1.4 mg P g
1 dw in BBR versus 1.2 mg P g
1 dw in LUE, Haußmann and Lux, 1997). Similarly, we found only a moderate decline in P levels in fine roots along the soil P gradient, whereas P concentrations of coarse roots decreased strongly with decreasing P in soil. Since coarse roots of beech are storage organs (Druebert et al., 2009; Pena et al., 2010), the

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enrichment of P concentrations in the coarse roots in the high-P forest above, and the depletion in the low-P forest below fine root P concentrations suggest that P storage of the trees was suboptimal in low-P forests. For roots, critical thresholds for P nutrition have not been defined. For leaves, P concentrations below 1.07 mg P g
1 indicate acute and concentrations between 1.1 and 1.2 mg g
1 dwt latent P €ttlein, 2012). Together with the deficiency in beech (Mellert and Go foliar P levels, our data support that the trees in the low-P forest exist at the lower limit of P nutrition. Our hypothesis that root P levels decline with declining soil P, when trees exist below the optimum range of P supply, was underpinned by the P concentrations in coarse roots, but not by those in fine roots. The moderate changes in P concentrations in fine roots may indicate that they are preferred P sinks, even under P impoverishment, probably to maintain high physiological activities and growth. 4.2. Beech P nutrition, ectomycorrhizal biomass and functional traits A central question relating to beech P nutrition was whether differences in the concentrations of the soil P sources affected mycorrhizal associations and, thereby, the chief organs for beech P uptake. Beech trees have a shallow root system with the majority of fine roots (about 75%) residing in the topsoil, especially in the organic layer (Meier and Leuschner, 2008; Meinen et al., 2009a, 2009b). In agreement with other studies (Meier and Leuschner, 2008; Meinen et al., 2009a, 2009b; Rosling et al., 2003; Twieg

Fig. 4. Canonical correspondence analysis (CCA) of ectomycorrhizal species community composition and soil and root chemical variables from five beech forests. The following abbreviations were used for the environmental factors: Pi_ol ¼ Pi in the organic layer, Pi_min ¼ Pi in the mineral Layer, Pb_ol ¼ Pbound in the organic layer, Pb_min ¼ Pbound in the mineral layer, MinL_1 ¼ PC1 for the soil chemistry of the mineral layer, Olchem_1 and OLchem_2 ¼ PC1 and PC2 of the organic layer, FRchem_1 and FRchem_2 for PC1 and PC2 of fine root chemistry. Fungi were abbreviated as follows: Atne: Athelia neuhoffii, Cege: Cenococcum geophilum, Clcr: Clavulina cristata, Cosu: Cortinarius subsertipes, Hesp: Helotiales sp 3 BB_2010, Hysp: Hydnum sp., Laam: Laccaria amethystine, Labl: Lactarius blennius, Laci: Lactarius cinereus, Lasu: Lactarius subdulcis, undetermined morphotypes: MT13, MT16, MT25, MT27, Pisp: Piloderma sp., Ruma: Russula mairei, Ruoc: Russula ochroleuca, Ruol: Russula olivacea, Ruro: Russula rosea, Seep: Sebacina epigaea, Tese: Tetracladium setigerum, Toca: Tomentella castanea, Tosp: Tomentella sp. uncultured, Xepo: Xerocomus porosporus, Xepr: Xerocomus pruinatus. The forests are shown by MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau.

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Fig. 5. Canonical correspondence analysis (CCA) of ectomycorrhizal species community composition and exploration types in five beech forests. The following abbreviations were used for the factors: ld ¼ long distance, md ¼ medium distance, sd ¼ short distance and co ¼ contact. The ectomycorrhizal species were assigned to one of the four categories based on literature data and inspection of microscopic photos from this study (Supplementary Table 1). The relative abundance of each exploration types per plot was calculated and data were used as variables in the CCA. Fungi were abbreviated as follows: Atne: Athelia neuhoffii, Cege: Cenococcum geophilum, Clcr: Clavulina cristata, Cosu: Cortinarius subsertipes, Hesp: Helotiales sp 3 BB_2010, Hysp: Hydnum sp., Laam: Laccaria amethystine, Labl: Lactarius blennius, Laci: Lactarius cinereus, Lasu: Lactarius subdulcis, undetermined morphotypes: MT13, MT16, MT25, MT27, Pisp: Piloderma sp., Ruma: Russula mairei, Ruoc: Russula ochroleuca, Ruol: Russula olivacea, Ruro: Russula rosea, Seep: Sebacina epigaea, Tese: Tetracladium setigerum, Toca: Tomentella castanea, Tosp: Tomentella sp. uncultured, Xepo: Xerocomus porosporus, Xepr: Xerocomus pruinatus. The forests are shown by MIT ¼ Mitterfels, VT ¼ Vessertal, LUE ¼ Luess, CON ¼ Conventwald, BBR ¼ Bad Brückenau.

et al., 2009), we found the vast majority of mycorrhizal root tips in the organic layer. We did not count root tips, but our visual assessment was supported by a 10-fold higher EMF biomass in the organic layer than in the mineral soil. It is known that EMF species show stratification according to different soil layers (Rosling et al., 2003). This differentiation was not addressed in the current study. However, the low EMF biomass and the lack of correlation of EMF biomass with any P fraction or indicator of P mobilization in the mineral layer, render a strong contribution of EMF for P nutrition in that layer doubtful. Our data support that free soil microbes play a major role in P mobilization in the mineral layer because Pmic and saprophytic biomass were tightly correlated among each other and with Pi and phosphatase activities. However, a final conclusion on the relevance of these processes for plant nutrition is not possible until P fluxes have been determined. Therefore, in future studies the relative contributions of EMF in different soil depth to beech P uptake rates should be addressed. In contrast to the mineral layer, the organic layer was massively colonized by fungal hyphae and contained a higher fraction of EMF than of saprophytic fungi. ANOSIM of the exploration types did not indicate pronounced differentiation among the forests with regard to EMF soil exploration, particularly not between the high- and the low-P forest. The abundance of species with long distance exploration types (Xerocomus sp), which might have been expected to be promoted under low P conditions, was generally low. Similar observations were reported along a P gradient in a soil chronosequence in Australia (Teste et al., 2016). Teste et al. (2016) found decreasing EMF hyphae with decreasing soil P. A similar relationship was detected here for EMF biomass and Pi in the organic layer along the geo-sequence. Apparently, across different ecosystems P

impoverishment in soil is not compensated by enhanced EMF hyphal soil exploration. Thus, our initial working hypothesis that low P availability would increase the diversity of exploration types and result in higher soil exploration must be refuted. One explanation for the unresponsiveness of apparent EMF soil exploration could be that the P concentration in the organic layer was higher, and the gradient between the high- and low-P forest much flatter than in the mineral soil. High EMF biomass and the complete colonization of the root tips may imply that beech nutrition strongly relied on P recycling from degrading organic sources in the organic layer. This assumption is also supported by the observation that the P concentrations in leaves were correlated with the P concentration in the humus layer (Talkner et al., 2015). In our study, Pi was even higher in the organic layer of the low-P forest than in that of the high-P forest suggesting higher recycling of organic P under P-limiting than under P-sufficient conditions. This notion is also supported by a recent study showing that the resorption of P from senescent leaves was higher in the P-poor (LUE) than in the P-rich forest (BBR) (Hofmann et al., 2016). Taken together, our results support the concept of divergent nutrient acquisition strategies in P-rich and P-poor forests (Lang et al., 2016). It is well-known that EMF can mobilize P from inorganic and organic sources, for example from iron or calcium chelates and from litter, pollen or necromass (Plassard and Dell, 2010; Read and Perez-Moreno, 2003). In the present study the physiological activities of EMF to P recycling from organic matter or release of P from inorganic compounds were not addressed, and we can only speculate on the possible roles of EMF in this regard. Several EMF species detected here (Cortinarius, Lactarius, Piloderma) can mobilize inorganic P by the production and release of organic acids

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(Courty et al., 2010). This function is associated with the Russulaceae, particularly with L. blennius, an abundant fungus in acidic soils (Rühling and Tyler, 1990). L. blennius is a typical beech associate € ransson et al., 2006) and was clearly domi(Genet et al., 2000; Go nant in the EMF community of the P-poor forest. Fungal species of the russula lineage have lower phosphatase activities than those of other linages (e.g. Atheliales and tomentella-thelephora linages, Tedersoo et al., 2012). Therefore, the present observations might suggest that the acquisition of Pi, but not organic P degradation is an important function of EMF in the organic layer. Saprophytic fungi might be more relevant to access organic materials because of their greater arsenal of decomposition enzymes (Kohler et al., 2015). Divergent roles for EMF and saprophytes in litter degradation have also been suggested by Talbot et al. (2013) studying the organic layer in pine forests. They found that enzymes correlated with the degradation of recalcitrant N-bearing compounds were of EMF-origin, while enzymes correlated with the decomposition of carbohydrate- and P-containing molecules were of saprophytic origin. Whether the independent roles of EMF and saprophytic communities in organic matter decomposition in coniferous forests (Talbot et al., 2013) also hold true in temperate beech forests need to be investigated in the future.

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of distinct P species in the organic layer is an important factor for the composition of EMF assemblages. This finding is important because only few studies addressed the relationship between EMF assemblages and P resources. For instance, in coniferous temperate forests organic P in the forest floor was also a significant predictor for EMF diversity (Twieg et al., 2009). Laboratory experiments showed that host P concentrations increased when organic P was provided in the presence of a more diverse EMF community compared to mono-EMF species colonization, whereas this effect was not found when only inorganic P was supplied (Baxter and Dighton, 2005). Altogether, these results suggest that different forms of P resources impact on EMF species composition and may have implications for tree nutrition and ecosystem P cycling. In the future, experimental studies are needed to disentangle these relationships, and to clarify the relative importance of EMF, and saprophytes in different soil layers for the degradation of organic P and P uptake. Declaration The authors declare no conflict of interest. Acknowledgments

4.3. Resource composition as driver of ectomycorrhizal community structures Besides soil nutrients, many environmental factors such as drought (Pena and Polle, 2014; Pena et al., 2013; Shi et al., 2002), soil pollution (Rudawska et al., 2011), stand age (Twieg et al., 2009) or stand health (Horton et al., 2013) can affect the EMF assemblages. Overall, a strong influence of the latter factors was excluded by the selection criteria of the study sites (moist sites, similar tree age, similar stand density, the same host, similar root vitality). Nevertheless, EMF species richness and their community structure differed significantly among our study forests. Our initial hypothesis that low P availability fosters increased EMF species richness, similar to the effects of N (Cox et al., 2010; Kranabetter et al., 2009; Lilleskov et al., 2002), was not supported. Given the steep gradient of P in the mineral topsoil this result was unexpected, but other studies conducted along P gradients also revealed complex rather than simple relationships between soil P and EMF species richness (Horton et al., 2013; Twieg et al., 2009). For instance, Twieg et al. (2009) found a negative relationship between EMF species richness and both available P and stand age. Horton et al. (2013) reported that the variation in EMF species richness in temperate Eucalypt forests was negatively related with soil P and nitrate and further affected by soil pH and tree health. Our results highlight the importance of N in the organic layer for EMF species richness because we found a negative relationship between EMF species richness and N, which is in line with other studies (Cox et al., 2010; Horton et al., 2013; Kranabetter et al., 2009; Lilleskov et al., 2002). This is an important result because it suggests that a high concentration of N, but not of P, in the resource composition leads to an impoverishment of EMF species. In contrast to EMF species richness, our study pinpoints complex relationships among EMF community structures, different forms of P, and soil chemistry. The taxonomic EMF composition was filtered according to the chemical characteristics of the soil resources on the one hand, and by root chemistry on the other hand. The factors contributing most to the ordination and determining the greatest differences between the EMF assemblages of P-poor and P-richest forests, respectively, were Pi in organic layer and root chemistry (LUE) and mineral soil chemistry and Pb in the organic layer (BBR). These results suggest that in addition to the nutrient element composition of roots and the mineral layer, the abundance

We thank M. Franke-Klein for excellent technical assistance and €ttingen) for help with the T. Klein and M. Reichel (University of Go sample collection. We thank D. Janz (PhD) for help with the statistical data analysis, Dr. M. Corre (Department for Tropical Soil €ttingen) for the determination of mineral elements, and Science, Go M. Zomorrodi (Department for Molecular Wood Biotechnology and €ttingen) for technical support in measuring Technical Mycology, Go the ergorsterol concentrations. We are grateful to the DFG for financial support to the Programme SPP 1685 by funding the projects Ka1590/12-1, Pe2256/1-1, Po362/22-1, and Sp1389/4-1. N. Yang is grateful to the People's Republic of China for granting a CSC doctoral scholarship. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.soilbio.2016.04.006. References Agerer, R., 2006. Colour Atlas of Ectomycorrhizae. Einhorn-Verlag., Schwaebisch Gmuend, Germany. Agerer, R., 2001. Exploration types of ectomycorrhizae: a proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza 11, 107e114. €ransson, H., Wallander, H., 2004. Can the extent of Bååth, E., Nilsson, L.O., Go degradation of soil fungal mycelium during soil incubation be used to estimate ectomycorrhizal biomass in soil? Soil Biol. Biochem. 36, 2105e2109. http:// dx.doi.org/10.1016/j.soilbio.2004.06.004. Baxter, J.W., Dighton, J., 2005. Phosphorus source alters host plant response to ectomycorrhizal diversity. Mycorrhiza 15, 513e523. http://dx.doi.org/10.1007/ s00572-005-0359-0. Becquer, A., Trap, J., Irshad, U., Ali, M.A., Claude, P., 2014. From soil to plant, the journey of P through trophic relationships and ectomycorrhizal association. Front. Plant Sci. 5, 548. http://dx.doi.org/10.3389/fpls.2014.00548. Braun, S., Thomas, V.F.D., Quiring, R., Flückiger, W., 2010. Does nitrogen deposition increase forest production? the role of phosphorus. Environ. Pollut. 158, 2043e2052. http://dx.doi.org/10.1016/j.envpol.2009.11.030. Bray, R., Kurtz, L., 1945. Determination of total, organic, and available forms of phosphorus in soils. Soil Sci. 59, 39e46. Brookes, P.C., Landman, A., Pruden, G., Jenkinson, D.S., 1985. Chloroform fumigation and the release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen in soil. Soil Biol. Biochem. 17, 837e842. http:// dx.doi.org/10.1016/0038-0717(85)90144-0. Brookes, P.C., Powlson, D.S., Station, R.E., 1982. Measurement of microbial biomass phosphorus in soil. Soil Biol. Biochem. 14, 319e329. e, M., Reich, M., Murat, C., Morin, E., Nilsson, R.H., Uroz, S., Martin, F., 2009. 454 Bue

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