Ectomycorrhizal fungal communities associated with Masson pine (Pinus massoniana) and white oak (Quercus fabri) in a manganese mining region in Hunan Province, China

f u n g a l e c o l o g y 9 ( 2 0 1 4 ) 1 e1 0

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Ectomycorrhizal fungal communities associated with Masson pine (Pinus massoniana) and white oak (Quercus fabri) in a manganese mining region in Hunan Province, China Jian HUANGa,c, Kazuhide NARAb, Kun ZONGc,1, Jun WANGd, Shengguo XUEd, Kejian PENGe, Zhenguo SHENf, Chunlan LIANc,* a

College of Forestry, Northwest A&F University, Yangling 712100, China Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8563, Japan c Asian Natural Environmental Science Center, The University of Tokyo, 1-1-8 Midoricho, Nishitokyo, Tokyo 188-0002, Japan d School of Metallurgical Science and Engineering, Central South University, Changsha 410083, China e Hunan Research Academy of Environmental Sciences, Changsha 410004, China f College of Life Sciences, Nanjing Agricultural University, Nanjing 210095, China b

article info

abstract

Article history:

The ecological responses of ectomycorrhizal fungi to heavy-metal contamination could

Received 26 April 2013

affect the establishment and survival of trees under stress conditions due to industrial

Revision received 28 November 2013

mining operations. In this study, we investigated species composition and diversity of

Accepted 1 December 2013

ectomycorrhizal fungi associated with Masson pine (Pinus massoniana) and white oak

Available online

(Quercus fabri) growing in Xiangtan manganese mining area in China. Canonical corre-

Corresponding editor:

spondence analysis revealed that soil Mn, Cu, Cd and several other soil cofactors related to

Kabir G. Peay

manganese mining activities simultaneously structured the ectomycorrhizal community; in addition, a strong host effect was observed. The pine ectomycorrhizal community was

Keywords:

dominated by four taxa (Atheliaceae, Thelephoraceae, Russulaceae, and Cenococcum), while

Ectomycorrhizal fungal community

the oak community was dominated by Thelephoraceae and Cenococcum. The relative

Manganese

abundance of Atheliaceae was positively correlated with soil manganese concentrations

Mine wasteland

while that of Russulaceae decreased with increasing soil manganese. These ecological

Pinus massoniana

responses of ectomycorrhizal fungi may reflect the different physiological sensitivity of

Quercus fabri

ectomycorrhizal fungal species to manganese. ª 2014 Elsevier Ltd and The British Mycological Society. All rights reserved.

Introduction Ectomycorrhizal (ECM) fungi are symbiotic rhizosphere microorganisms that form mutually beneficial associations

with many dominant tree species in cool-temperate to tropical forest ecosystems (Smith and Read, 2008; Morris et al., 2009). ECM associations improve their host’s survival mainly by enhancing nutrient and water uptake from the soil. In

* Corresponding author. Tel.: þ81 42 465 5601; fax: þ81 42 465 5616. E-mail address: [email protected] (C. Lian). 1 http://park.itc.u-tokyo.ac.jp/symbio/. 1754-5048/$ e see front matter ª 2014 Elsevier Ltd and The British Mycological Society. All rights reserved. http://dx.doi.org/10.1016/j.funeco.2014.01.001

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addition, ECM associations can alleviate heavy-metal toxicity in trees and enable trees to survive in heavy-metal contaminated soils (Wilkinson and Dickinson, 1995; Meharg and Cairney, 2000). Heavy-metal tolerance varies among ECM fungal species and isolates, as revealed by mycelial culture experiments (Jones and Hutchinson, 1988; Hartley et al., 1997; Blaudez et al., 2000; Colpaert et al., 2000; Goncalves et al., 2007), which suggests that heavy metals could be a significant determinant of the structure of ECM fungal communities in the field. ECM fungal communities have been investigated at several sites affected by heavy metal contamination; e.g., Lead (Pb) (Hui et al., 2011; Huang et al., 2012), Copper (Cu) (Cripps, 2003), PbeZinc (Zn)eCu (Hrynkiewicz et al., 2008; Huang et al., 2012), PbeZneCadmium (Cd) (Krpata et al., 2008) and nickel (Ni)eCu (Ruotsalainen et al., 2009). In these studies, the effect of heavy metals on ECM fungal diversity was inconsistent. For example, significantly reduced diversity was reported in a polluted area adjacent to NieCu smelters (Ruotsalainen et al., 2009) and a uranium mining heap (Staudenrausch et al., 2005), while highly diverse ECM fungal communities have been reported at various heavy metal contaminated sites (Blaudez et al., 2000; Cripps, 2003; Krpata et al., 2008; Hui et al., 2011; Huang et al., 2012). Confounding factors associated with mining activities; e.g., the removal of organic soil and low concentrations of nutrients associated with immature soils may partly account for the observed inconsistency in previous studies (Cripps, 2001; Staudenrausch et al., 2005; Huang et al., 2012). In addition, because different heavy metals affect the physiological activities of ECM fungi differently (Blaudez et al., 2000), the type of heavy metal exposure could also affect ECM fungal communities. For Manganese (Mn), some studies have examined the physiological effects of exposure on selected ECM fungi in vitro (Thompson and Medve, 1984; Li et al., 2012), but none have addressed the effect of Mn on ECM fungal communities. Mn toxicity is a serious issue in Mn mining areas, where waste rocks and black tailing residues are a source of large amounts of Mn that can be released into the soil. According to a 2009 Toxics Release Inventory, a total of 6 185 metric tons of Mn from 1 929 large processing facilities, and 73 644 tons of Mn compounds from 1 656 facilities were released to the environment in the USA. In China, there are over 270 Mn mine facilities in Guangxi Province and Hunan Province in southern China, which are responsible for a severe deterioration of local ecological systems (Luo et al., 2008). Plants growing in mine wastelands often accumulate high concentrations of Mn, and find it particularly difficult to colonize tailing ponds due to Mn toxicity and poor aeration. Mn tailings and other Mn ore wastes often contain toxic levels of Cu, Pb, or Cd, but are not significantly deficient in N, P and organic matter (Li et al., 2007; Wang et al., 2008). This unique property of Mn tailings and mining wastes provides an ideal opportunity to study the effects of heavy metals on ECM fungal communities, while eliminating some confounding factors (i.e., nutrient and organic matter deficiency). In this study, we attempted to determine: (1) whether the diversity of ECM fungi decreases with increasing soil Mn levels, (2) whether the ECM fungal composition changes with increasing soil Mn levels, and if so (3) how individual ECM

J. Huang et al.

fungal lineages in the community respond to the increased soil Mn levels.

Materials and methods Site description Xiangtan Mn mine (112 370 E, 27 400 N) is located in northern Xiangtan City, Hunan Province, China (Fig S1), which is often referred to as “the manganese capital of China” due to its substantial Mn deposits and large-scale electrolytic Mn production since 1914. Xiangtan has a humid subtropical and monsoon climate with an annual average rainfall of 1 431 mm and an annual average temperature of 17.4  C. The mining area is located in a hilly region with elevations of less than 150 m. The mining activities have destroyed most of the initial forest coverage in the area, but some small forest patches remain. These are composed of mainly Masson pine (Pinus massoniana) and white oak (Quercus fabri). Because Mn ores contain a high organic fraction and P content, and a large amount of ammonium sulfate is generated in the tailing mud during separation processes (Wang et al., 2008), the tailings contain high concentrations of N, P and organic matter (81 580 mg g1 total Mn, 800 mg g1 total N, 3 600 mg g1 total P and >7 % organic matter). Approximately 42 000 t of mud waste per year is produced from the electro-refining plants, and is discharged into the Xiaohu tailing ponds and the surrounding area. Moreover, in the mining area, unauthorized mining activities have resulted in the unauthorized deposition of further mine wastes (Xue et al., 2004; Wang et al., 2008). Thus, during the long mining history in the region, blackish Mn mine wastes have accumulated, resulting in contamination of soil throughout the area (Liu, 2006). Severe symptoms of Mn toxicity (i.e., chlorosis, brown needles, and defoliation) have been observed in the needles of most Masson pine trees in this area.

Sampling strategy Sampling was performed in an area of approximately 2  2 km around the Xiaohu tailing ponds in Apr., 2009. In total, 80 Masson pine trees (w30 yr old) and 32 white oak trees (5e8 yr old) were selected for sampling (Fig S1). The distance between the selected trees was always >5 m in an attempt to secure the independence of the samples. From each selected tree, a main root (w15e30 cm in length) with fine root tips was collected from a soil depth of 5e30 cm, and a sample of rhizosphere soil (w50 ml) was also collected for chemical analyses. We also sampled leaves of selected trees for chemical analyses.

ECM morphotyping and molecular identification Root samples were cleaned carefully with tap water and then observed under a dissecting microscope to examine ECM colonization. ECM root tips in each root sample were assigned a morphotype according to Agerer (1987e1993). One to eight ECM root tips for each morphotype, from each root system, were randomly selected for further molecular identification. In total, we morphotyped 3 108 pine ECM tips and 1 020 oak

Ectomycorrhizal fungal communities

ECM tips, from which 807 and 291 tips, respectively, were used for molecular identification. The crude genomic DNA was extracted using a modified cetyl-trimethylammonium bromide (CTAB) method (Lian et al., 2003). The fungal internal transcribed spacer (ITS) regions were amplified by fungal-specific primer pairs as described by Huang et al. (2012). Restriction fragment length polymorphism (RFLP) analysis was conducted by digesting PCR products using AluI and HinfI (1.5 U; Takara Otsu, Shiga, Japan). One representative sample with a unique RFLP pattern for each root sample was selected for direct sequencing with primers ITS1-F, ITS1 or ITS4 (Gardes and Bruns, 1993; White et al., 1990) using a BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA). Sequencing electrophoresis was performed using an Applied Biosystems 3130xl Genetic Analyzer. The obtained sequences were edited and corrected manually in BioEdit 7.0.8. These sequences were clustered into operational taxonomic units (OTUs) by BLASTclust with 97 % sequence similarities for species delimitation. Taxonomic assignments of the OTUs were performed by querying the GenBank and UNITE (Koljalg et al., 2005) online databases using Megablast according to the following criterion: sequences with similarity 97 % were identified at the species level, while sequences with a similarity between 90 % and 97 % were identified at the genus level.

Chemical analysis of soils and leaves Large pieces of plant debris and rocks were removed from soil samples after being air-dried for 1 week. The soil samples were then passed through a 2 cm mesh screen and ground for pH, EC and elemental determination. Pine needles and oak leaves were washed, oven-dried, and finally milled for metal elemental determination. The soil pH and EC values (solid: deionized water ¼ 1:5) were measured using a HM-30 pH meter (TOA Electronics Ltd., Kobe, Japan) and a B-173 EC meter (Horiba, Ltd., Tokyo, Japan), respectively. For analyses of soil N and P contents, soil samples were digested in sulfuric acid and perchloric acid on a hotplate and then the digests were filtered before analysis. Total N in the filtrates was determined by the indophenol blue method (Dora, 1976) and total P was determined by the molybdenum blue method (Olsen and Sommers, 1982). The total amounts of Mn, Pb, Zn, Cu, Cd and K in soil samples and plant leaves (excluding K and Cd) were determined using a Z-6100 Polarized Zeeman atomic absorption spectrometer (Hitachi, Co., Tokyo, Japan) after digestion with a HNO3 and HClO4 mixture (4:1, v/v for soil; 7:1, v/v for leaves).

Data analysis Correlations between the concentrations of metals (Mn, Pb, Zn and Cu) in soil and leaf samples were analyzed statistically. The relative abundance of an ECM fungal species in a tree was defined as the ratio of ECM tips colonized by that species within the root sample. The frequency of each ECM fungal species was the percentage of trees colonized by that species. Observed species richness was the total number of detected ECM fungal species. Species-sample accumulation curves were generated using EstimateS, version 8.2.0 (Colwell, 2009). In addition, we evaluated ECM fungal diversity by potential species richness (Jackknife1, Jackknife2 and Chao2

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estimators), Simpson’s Index (1/D) and the ShannoneWiener Index (H0 ) using EstimateS. Sørensen similarity indices were used to compare the ECM fungal communities within and between the pine and oak. To determine the effects of soil parameters and host species on ECM fungal community, canonical correspondence analysis (CCA) was performed on the relative abundance of ECM OTUs using R 3.0.1 software (R Development Core Team, 2013) with the Vegan package (Dixon, 2003). Rare species (species found only on one tree in the whole dataset) were excluded from the analysis. Envfit function in Vegan package was used to assess significant environmental factors structuring ECM fungal species distribution in the ordination space. To further determine the effect of soil Mn on the ECM fungal richness and diversity, linear relationship or General Linear Model could not be obtained between soil Mn concentrations and ECM fungal richness/diversity against individual samples because the species richness per sample was generally restricted to oneethree species, therefore, we performed the correlation analyses after pooling pine root samples into eight subgroups (10 samples per subgroup) according to the rank of soil Mn concentrations. This correlation analysis could not be applied to the oak ECM fungal community due to an insufficient sample size. To evaluate the effect of soil Mn concentration on the abundance of major ECM fungal taxa, we used one-way ANOVA after pooling all pine and oak samples (112) into 10 Mn concentration ranks (11 samples per subgroup, two samples with the highest soil Mn concentrations were excluded). Relationships between the abundance of major ECM fungal taxa and soil Mn concentrations in the rank-pooled data were also studied by regression analyses after logarithmic transformation of the Mn concentrations. All the above statistical analyses were conducted using SPSS ver. 11.5 (SPSS Inc., Chicago, IL, USA).

Results Rhizosphere soil characteristics and heavy-metal concentrations in the leaves of Masson pine and oak All rhizosphere soils were acidic with an average pH value of 5.3  0.5, which was within the range of a non-saline environment (122.3  39.8 mS cm1). As shown in Fig S2, soil Mn concentrations varied considerably among samples, ranging from 66.6 to 22 598.4 mg kg1 with a median value of 3 182.1 mg kg1. The Mn concentrations in 79 % of the soil samples (88/112) exceeded the benchmark (538 mg kg1) for Xiangtan city (Liu, 2006). Cd was detected in 66 soil samples (0.2e15.3 mg kg1), 45 of which contained over 1.0 mg kg1 (Grade III toxic threshold value of the China Environmental Quality Standard for Soil). The mean soil N and P concentrations were 589.1  411.6 mg kg1 and 784.2  298.9 mg kg1, respectively. The levels of Cu, Pb and Zn in all soil samples were under the Grade III toxic threshold (Cu: 400 mg kg1; Pb: 500 mg kg1 and Zn: 500 mg kg1). Most of the soil elements concentrations were intercorrelated with each other as in Fig S3. In particular, the Mn concentrations were positively correlated with all other heavy metals (Cu, Pb, Zn and Cd) and P, while negatively correlated with N.

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Table 1 e Ectomycorrhizal fungi identified on Pinus massoniana and Quercus fabri growing in the Xiangtan Mn mining area of Hunan Province, China OTUs

Accession number

Query/aligned portion length (bp) (similarity, %)a

AB769882 AB769883 AB769884 AB769885 AB769886 AB769888 AB769889 AB769890 AB769891 AB769892

427/425 426/424 495/480 597/597 573/573 535/535 1 021/998 1 046/1 000 611/607 735/735

(100 %) (100 %) (100 %) (99 %) (99 %) (97 %) (99 %) (95 %) (99 %) (92 %)

Coltricia sp.2

AB769893

590/567 (99 %)

Helotiales sp.1 Helotiales sp.2 Inocybaceae sp. Laccaria amethystina Phialocephala fortinii Phialocephala sp. Pisolithus tinctorius Rhizopogon sp. Lactarius quieticolor Lactarius sp. Russula amoenolens Russula citrina Russula sp.1 Russula sp.2 Russula sp.3 Russula sp.4 Russula sp.5 Russula sp.6 Scleroderma sp. Sebacinaceae Suillus luteus Pseudotomentella griseopergamacea Thelephora sp.1 Thelephora sp.2 Thelephora terrestris Thelephoraceae sp. Tomentella ellisii Tomentella sp.1 Tomentella sp.2 Tomentella sp.3

AB769894 AB769895 AB769896 AB769897 AB769901 AB769887 AB769902 AB769904 AB769898 AB769899 AB769905 AB769906 AB769907 AB769908 AB769909 AB769910 AB769911 AB769912 AB769913 AB769914 AB769915 AB769903

551/551 499/465 782/683 435/435 529/526 543/520 682/647 728/595 682/682 690/690 656/614 645/645 659/603 605/512 653/653 651/579 684/499 647/647 569/569 631/601 662/662 506/502

(93 %) (97 %) (99 %) (99 %) (99 %) (94 %) (98 %) (94 %) (98 %) (93 %) (97 %) (97 %) (92 %) (92 %) (90 %) (94 %) (90 %) (94 %) (94 %) (92 %) (99 %) (99 %)

AB769916 AB769917 AB769918 AB769919 AB769920 AB769921 AB769922 AB769923

639/639 608/582 747/712 663/630 662/580 766/653 604/597 635/628

(92 %) (94 %) (98 %) (91 %) (98 %) (94 %) (95 %) (94 %)

In Genbank/EMBJ/DDBJ/Uniteb AB015694 Amanita ceciliae EU046087 Uncultured ascomycete HM208732 Ascomycota sp. FN565250 Uncultured Atheliaceae AB456674 Uncultured Tylospora AM084698 Cenococcum geophilum JN129390 Cenococcum geophilum DQ179119 Cenococcum geophilum JF273551 Clavulina sp. FR731276 Uncultured ectomycorrhizal fungus AB253525 Uncultured ectomycorrhizal fungus AB598090 Helotiales sp. HE814101 Helotiales sp. JF273524 Inocybaceae sp. AB211270 Laccaria amethystina EU888625 Phialocephala fortinii HM595528 Phialocephala sp. AF374631 Pisolithus tinctorius AJ297263 Rhizopogon buenoi UDB000880 Lactarius quieticolor UDB000379 Lactarius fulvissimus UDB000343 Russula amoenolens JF908661 Russula citrina UDB011327 Russula medullata UDB000327 Russula heterophylla UDB000893 Russula mustelina EU598194 Russula crustosa UDB000894 Russula livescens UDB011318 Russula sanguinea HM189957 Scleroderma citrinum GQ240919 Uncultured Sebacinaceae GU222326 Suillus luteus UDB001617 Pseudotomentella griseopergamacea UDB000119 Thelephora caryophyllea UDB000119 Thelephora caryophyllea UDB003346 Thelephora terrestris UDB003309 Tomentella cinerascens UDB000219 Tomentella ellisii UDB003347 Tomentella UDB003302 Tomentella stuposa UDB002428 Tomentella stuposa

Relative abundance/relative frequency (%)c Masson pine (n ¼ 80)

Oak (n ¼ 32) 0.6/3.1

0.3/2.5 30.6/48.8 7.9/11.3 4.9/7.5 4.7/3.8 1.1/1.3 0.1/1.3 1.5/3.8

0.1/3.1 1.5/3.1 2.2/3.1 2.2/3.1 17.3/37.5

0.9/1.3 0.8/3.8 0.1/1.3 0.4/1.3 0.1/1.3 0.4/1.3 2.8/7.5 0.4/1.3 1.9/3.8 1.9/6.3 0.2/1.3 1.1/2.5 1.4/1.3 2.3/2.5 0.1/1.3 0.1/1.3 1.5/1.3 5.4/10.0 1.2/1.3 0.4/1.3

0.6/3.1 2.5/3.1

3.1/3.1 1.3/9.4

3.8/9.4 0.7/1.3 0.2/1.3 0.2/1.3 7.9/10.0 1.9/2.5 0.7/2.5 0.2/1.3 1.7/5.0

11.0/6.3 3.1/9.4 1.6/6.3

J. Huang et al.

Amanita ceciliae Ascomycota sp.1 Ascomycota sp.2 Atheliaceae sp.1 Atheliaceae sp.2 Cenococcum geophilum1 Cenococcum geophilum2 Cenococcum sp. Clavulina sp. Coltricia sp.1

Closest BLAST match accession

5

Leaf Mn concentration was generally high but varied considerably, ranging from 39.8 to 2 735.2 mg kg1. No significant difference was found in the leaf Mn concentration of pine (39.8e2 311.2 mg kg1, median: 866.3 mg kg1) and oak (195.4e2 735.2 mg kg1, median: 860.2 mg kg1). The maximum concentrations of Cu, Pb and Zn in leaf samples were 16.4, 452.3 and 237.6 mg kg1, respectively. Oak leaves accumulated significantly higher levels of Pb and Zn than pine needles (Fig S4). We found no statistically significant correlations between heavy metal concentrations in leaves and those in soil.

17.5/3.1 0.9/3.1 0.6/3.1 3.1/6.3

ECM fungal identification and communities associated with Masson pine and white oak We identified a total of 51 ECM OTUs, of which 43 and 20 were found in Masson pine and white oak, respectively (Table 1). Twelve OTUs (20.8 %) were shared between the two species. Forty and 11 OTUs belonged to basidiomycetes and ascomycetes, respectively. The most OTU-rich family was Thelephoraceae with 17 OTUs, followed by Russulaceae with 10 OTUs, while the other families did not have more than four OTUs. The rarefaction curves did not level off for both tree species, indicating more OTUs would be found with additional sampling efforts (Fig 1). The Chao2, Jackknife1 and Jackknife2 estimators for ECM fungi were 95.9, 65.7 and 83.3 in pine, and 40.2, 30.7 and 38.3 in oak, respectively. The Shannon H0 and Simpson’ 1/D indices were 2.8 and 8.4 in pine, and 2.3 and 7.3 in oak, respectively. The mean ECM fungal richness per root sample was 1.8 and 1.6 in pine and oak, respectively. The similarity (Sørensen index) of OTU compositions between Masson pine and white oak (0.035) was significantly lower than that within either pine (0.15) or oak (0.13) samples 60

White oak Sobs Oak Sobs 95% CI Masson pine Sobs Masson pine Sobs 95% CI

50 Ectomycorrhizal OTU richness

a Similarity values are shown as percentage matches between the query and its reference sequences. b Closest BLAST matches with informative taxonomic identity were used. c Relative frequency refers to the percentage of trees colonized by that OTU.

0.6/1.3 0.7/1.3

4.1/7.5 0.4/3.8 0.2/1.3 1.8/2.5

Tomentella Tomentella Tomentella Tomentella Tomentella Tomentella Tomentella Tomentella Tuber sp. Unknown

sp.4 sp.5 sp.6 sp.7 sp.8 sp.9 sp.10 sp.11

AB769924 AB769925 AB769926 AB769927 AB769928 AB769929 AB769930 AB769931 AB769932 AB769933

667/666 664/640 664/654 665/665 759/759 666/643 639/639 643/639 563/532 538/538

(96 %) (92 %) (96 %) (92 %) (92 %) (93 %) (92 %) (94 %) (98 %) (90 %)

UDB011637 Tomentella stuposa UDB002646 Tomentella tedersooi UDB003335 Tomentella UDB011637 Tomentella stuposa DQ822833 Tomentella sublilacina UDB003315 Tomentella terrestris UDB003326 Tomentella ellisii UDB002428 Tomentella stuposa AB553457 Tuber sp. DQ493568 Uncultured ectomycorrhizal fungus

4.5/6.3

3.1/12.5 24.0/21.9

Ectomycorrhizal fungal communities

40 30 20 10 0

0

10

20

30 40 50 Number of Samples

60

70

80

Fig 1 e Rarefaction curves with 95 % confidence intervals (CI) of ectomycorrhizal fungal OTUs associated with Masson pine and white oak in the Xiangtan Mn mining area. The observed numbers of OTU are expressed as Mao Tau estimates (Sobs) calculated using EstimateS.

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J. Huang et al.

Table 2 e Effect of soil parameters and host species on ectomycorrhizal fungal species distribution in the whole ectomycorrhizal fungal community by canonical correspondence analysis (CCA) Factors Host Cu P Cd Mn pH Ec Pb N Zn

R2

P-value

CCA1

CCA2

0.699 0.471 0.341 0.337 0.255 0.255 0.233 0.088 0.070 0.066

*** *** *** *** *** *** *** 0.106 0.130 0.197

0.997 0.999 0.742 0.166 0.317 0.031 0.035 0.986 0.541 0.671

0.068 0.035 0.671 0.986 0.949 0.999 0.999 0.164 0.841 0.741

Significance: ***P < 0.001. P-values based on 999 permutations.

(P < 0.01). CCA analysis also revealed a strong effect of plant host species structuring the species distribution of ECM fungi (Table 2). The most dominant ECM fungal family on pine trees was Atheliaceae (2 OTUs, 38.5 % relative abundance), followed by Thelephoraceae (13 OTUs, 24.5 %), Russulaceae (10 OTUs, 15.9 %) and Cenococcum (three OTUs, 10.7 %). At the species level, Atheliaceae sp.1 was the most dominant and occupied 30.6 % of ECM tips, occurring on 39 of 80 trees. It was followed by Atheliaceae sp.2 (7.9 % relative abundance, nine trees) and Thelephora terrestris (7.9 %, 8 trees) (Table 1). The most dominant ECM fungal lineage on oak trees was Thelephoraceae (eight OTUs, 61.9 % relative abundance), followed by Cenococcum (two OTUs, 19.5 %). Although Atheliaceae was the most dominant family on pine trees, it occurred on only 3.7 % of oak ECM tips. Russulaceae was not detected in oak root samples (Table 1). A chi-square test indicated that Atheliaceae sp.1, Atheliaceae sp.2 and Russula sp.6 had a significant preference for pine

Fig 2 e Canonical correspondence analysis (CCA) on ectomycorrhizal fungal OTUs occurring on more than one sample, and correlation with host and soil parameters.

Fig 3 e Soil Mn concentrations and the occurrence of four major ectomycorrhizal fungal taxa. The plot shows medians (lines), 25th and 75th percentiles (boxes), and minima and maxima (1.5 3 25th (or 75th) percentile) (whiskers). Data outside of this range are plotted individually (circles). Letters above the bars of the boxplot indicate significant differences in Mn concentrations among the four taxa based on one-way ANOVA analysis (P < 0.05).

trees, whereas Tomentella sp.6, Cenococcum geophilum 2, Tomentella sp.9, Inocybaceae sp. and Tuber sp. were biased toward oak trees.

Effect of soil parameters and host on ECM fungal communities Canonical correspondence analysis (CCA) revealed that nine soil factors and host species explained 19.8 % (P ¼ 0.003) of variance in ECM fungal community data. Environmental fitting test (permutations ¼ 999) revealed that plant host species and six soil parameters (Cu, P, Cd, Mn, pH and Ec) had significant effects on the ECM fungal community (Table 2). Among those factors, host species explained largest variance in the ECM fungal community data in CCA. Among the soil parameters, Cu, P and Cd explained larger variance than Mn (Table 2). As displayed in the CCA plot (Fig 2), Cenococcum and Atheliaceae sp. tended to increase their relative abundance along the gradient of Mn, Cd and Cu. In contrast, the Russula-Lactarius lineage was restricted to habitat with lower concentrations of heavy metals and high N. The Tomentella-Thelephora lineage was distributed across a wide range of soil conditions. One-way ANOVA analysis indicated that the occurrence of Russulaceae in pine and oak samples was significantly biased towards lower soil Mn concentrations compared to the other three taxa (P < 0.001) (Fig 3). We examined the relative abundance of the four major groups (Atheliaceae, Thelephoraceae, Cenococcum, and Russulaceae) along the soil Mn ranks in both pine and oak

Ectomycorrhizal fungal communities

samples. The relative abundance of Atheliaceae increased linearly with increasing soil Mn levels (Fig 4A). The abundance of Russulaceae was negatively correlated with soil Mn concentrations and it was absent in the highest Mn level (Fig 4B). No clear trend in the relative abundance of Cenococcum and Thelephoraceae was observed in relation to soil Mn levels. However, Thelephoraceae was most dominant at lowest and highest Mn ranks (Fig S5). Significant negative correlations were found between soil Mn concentrations and observed species richness (R2 ¼ 0.57, P ¼ 0.03), or species diversity index (H‘) (R2 ¼ 0.56, P ¼ 0.03) in pine ECM fungal community (Fig 5).

Discussion We investigated ECM fungal communities associated with Masson pine and white oak in a Mn mine wasteland. The similarity in the ECM community composition was lower between Masson pine and white oak than within Masson pine or white oak, and obvious host effects were also revealed by

A

B

Fig 4 e Relative abundance of major fungal taxa on both Masson pine and white oak according to soil Mn levels. (A) Atheliaceae ( y [ 25.3x e 48.7). (B) Russulaceae ( y [ L24.1x D 90.5) after removing the one obviously deviated point and last two point posited lateral axis.

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CCA analysis (Table 2). Ishida et al. (2007) revealed a positive correlation between host taxonomic distance and the dissimilarity of ECM communities in a mixed coniferebroadleaf forest. Tedersoo et al. (2013) also demonstrated that ECM fungal community was significantly structured by host phylogenetic distance. Thus, the contrasting ECM fungal communities between Masson pine and white oak may be explained by the host identity. In this study, however, the host age was quite different between the host groups, i.e. Masson pine was in adult stages while the white oak was in young stages (5e8 yr). As ECM communities could change with the stand age (Twieg et al., 2007), host developmental stages could also account for the observed difference in ECM fungal communities between the host groups. The ECM fungal communities in the Mn mine site were composed of diverse and rich species (43 OTUs), as indicated by diversity indices (Shannon H0 and Simpson’ 1/D were 2.8 and 8.4 for pine, 2.3 and 7.3 for oak, respectively). These values were similar to those (23 OTUs in 23 trees, H0 : 2.6 and 1/D: 10.2) for ECM fungal communities associated with

R2=0.57, P=0.03

R2=0.56, P=0.03

Fig 5 e Correlation analysis of soil Mn concentration vs. observed pine ECM fungal OTUs number, and the diversity index (H0 ) in subgroups. The subgroups were obtained after pooling the 80 samples into 10 subgroups (10 samples per subgroups) according to its rank of its soil Mn concentrations.

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Masson pine growing in a non-polluted forest about 170 km away (Huang et al., 2012). Thus, Mn mining wastes may not necessarily decrease the total diversity of ECM fungi within a mining site. The existence of diverse ECM species was also reported at other mining areas. Krpata et al. (2008) found a broad taxonomic range of ECM fungi (observed richness: 54 OTUs, H0 ¼ 2.0, 1eD ¼ 0.19) associated with Populus tremula on a heavy metal (Pb, Zn and Cd)-contaminated site. Hui et al. (2011) also documented a rich ECM fungal community in a heavily Pb-contaminated forest soil. Some authors have suggested that heavy-metal-tolerant ECM fungi are numerous and contribute to the observed diversity of ECM fungal communities in contaminated soils (Blaudez et al., 2000; Meharg and Cairney, 2000). In addition, the heterogeneity in soil heavy metal concentrations within a site may also enable diverse fungal species to colonize the site by providing various microhabitats for different ECM fungi (Hui et al., 2011). In the present study, soil Mn concentrations varied from 66.6 to 22 598.4 mg kg1, as well as other heavy metals, and ECM compositions varied considerably with the heavy metal levels (Fig 2). Although soil heterogeneity in mining sites has rarely been addressed in previous ECM fungal community studies, it may in part account for the diversity of ECM fungi within the mining sites. To our knowledge, the present study is the first to assess the effect of Mn mining operations on ECM fungal communities. Mn has often been considered as a low toxic metal element, especially compared with Cd and Cu. Some studies have assayed Mn tolerance of some easily culturable ECM species by in vitro culture (Thompson and Medve, 1984; Li et al., 2012) and in symbiosis (Ducic et al., 2008). In the present study, we found ECM richness and diversity decreased significantly with the increase of soil Mn concentrations (Fig 5). We also found an apparent shift in ECM fungal compositions according to soil Mn level. For example, the relative abundance of Atheliaceae linearly increased with increasing soil Mn, while Russulaceae decreased (Fig 4). These results indicate that elevated Mn in soils could have negative effects on some ECM fungal lineages and eventually on total ECM fungal diversity. However, we should be cautious about the effect of Mn, because soil Mn concentration was significantly correlated with other heavy metal concentrations in this mine site. Furthermore, some other heavy metals explained more variance in ECM fungal community data than Mn (Table 2). Thus, the observed effect of Mn on ECM fungal diversity and compositions could be confounded by other soil factors, especially Cu with reference to CCA and envfit analyses (Fig 2 and Table 2). Because mine wastelands are often contaminated with several heavy-metal elements simultaneously, it may be difficult to completely isolate the effect of a single heavy metal on ECM fungal communities in the field. ECM fungal communities of Masson pine and white oak were mainly composed of four major groups, i.e. Atheliaceae, Thelephoraceae, Russulaceae and Cenococcum. Russulaceae was deficient in soil samples with high Mn levels (Fig 4B), and its occurrence was restricted to Mn concentrations below the phytotoxic level (5 000 mg kg1, Alloway, 1995). This pattern was also displayed in the CCA plot, in which Russula-Lactarius lineage was restricted to low metal and high N conditions

J. Huang et al.

(Figs 2 and 3). In our previous study, we also found Russulaceae species to be sensitive to Pb and Zn in PbeZn tailings (Huang et al., 2012). As they are often defined as a late-stage group with a limited ability to adapt to harsh environments, Russulaceae species could be sensitive to various heavy metals. Thelephoraceae was the most species-rich ECM fungal lineage in the ECM communities. Thelephoraceae is one of the most frequent and abundant ECM partners of many deciduous trees and conifers in uncontaminated forests (Horton and Bruns, 2001). It is also abundant in mine sites contaminated by other heavy metals (Cripps, 2003; Krpata et al., 2008; Hui et al., 2011; Huang et al., 2012). In the present study, species of TomentellaThelephora were distributed in a wide range of habitat conditions (Fig 2). In particular, Thelephoraceae was dominant at lowest or highest Mn conditions (Fig S5). Some species, such as Thelephora terrestris, as shown in CCA plot (Fig 2), preferred high Mn and Cd conditions. T. terrestris has a strong tolerance to metals both in vitro and in symbiosis (Katia et al., 2000). Therefore, our results suggest that Thelephoraceae species could have wide variation in metal-tolerance in the field. The Atheliaceae group is commonly associated with Masson pine in central south China (Huang et al., 2012) and other conifer forests (Wang and Guo, 2010; Hui et al., 2011). In the Xiangtan Mn mine site, the relative abundance of Atheliaceae was positively correlated with the soil Mn concentration, and particularly abundant at higher soil Mn concentrations (relative abundance >40 %, Fig 4A), indicating that Atheliaceae is a Mn-tolerant group. In conclusion, ECM fungal communities in a Mn mine wasteland were investigated for the first time. Soil Mn and several co-factors simultaneously influenced the ECM community structure. The ECM fungal species richness and diversity decreased with increasing Mn concentrations in rhizosphere soils of Masson pine. The species composition of ECM fungi also varied along the gradients of soil Mn concentration and several soil cofactors. Atheliaceae tended to increase their relative abundance along the gradient of Mn, Cd and Cu. In contrast, Russula-Lactarius lineage was restricted to habitat with lower concentrations of heavy metals and high N, and its abundance decreased with increasing Mn. These results improve our understanding of the ecological responses of ECM fungi to heavy metals, which are a serious problem in mining areas worldwide.

Acknowledgments This research was supported by grants-in-aid from the Japan Society of the Promotion of Sciences (20380087). We are grateful to Dr. Kabir G. Peay, and two anonymous reviewers for their critical comments on this manuscript.

Appendix A. Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.funeco.2014.01.001.

Ectomycorrhizal fungal communities

references

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