Compartmentalized and contrasted response of ectomycorrhizal and soil fungal communities of Scots pine forests along elevation gradients in France and Spain.

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Environmental Microbiology (2015) 17(8), 3009–3024

doi:10.1111/1462-2920.12894

Compartmentalized and contrasted response of ectomycorrhizal and soil fungal communities of Scots pine forests along elevation gradients in France and Spain

Ana Rincón,1* Blanca Santamaría-Pérez,1 Sonia G. Rabasa,2 Aurore Coince,3 Benoit Marçais3 and Marc Buée3 1 Instituto de Ciencias Agrarias, ICA and 2 Museo Nacional de Ciencias Naturales, MNCN, CSIC, Serrano 115bis, 28006 Madrid, Spain. 3 UMR1136 INRA Nancy – Université de Lorraine, Interactions Arbres-Microorganismes, Lab of Excellence ARBRE, INRA, 54280 Champenoux, France. Summary Fungi are principal actors of forest soils implied in many ecosystem services and the mediation of tree’s responses. Forecasting fungal responses to environmental changes is necessary for maintaining forest productivity, although our partial understanding of how abiotic and biotic factors affect fungal communities is restricting the predictions. We examined fungal communities of Pinus sylvestris along elevation gradients to check potential responses to climate change-associated factors. Fungi of roots and soils were analysed at a regional scale, by using a highthroughput sequencing approach. Overall soil fungal richness increased with pH, whereas it did not vary with climate. However, when representative subassemblages, i.e. Ascomycetes/Basidiomycetes, and families were analysed, they differentially answered to climatic and edaphic variables. This response was dependent on where they settled, i.e. soil versus roots, and/or on their lifestyle, i.e. mycorrhizal or not, suggesting different potential functional weights within the community. Our results revealed a highly compartmentalized and contrasted response of fungal communities in forest soils. The different response of fungal sub-assemblages indicated a range of possible selective direct and indirect (i.e. via host) impacts of climatic variations on these commu-

Received 13 January, 2015; revised 29 April, 2015; accepted 30 April, 2015. *For correspondence. E-mail [email protected]; Tel. (+34) 917452500; Fax (+34) 915640800.

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd

nities, of unknown functional consequences, that helps in understanding potential fungal responses under future global change scenarios. Introduction Microorganisms are principal actors of soil food webs implied in many forest ecosystem services (i.e. carbon sequestration or nutrient cycling) and are mediators of the host–plant response to different global change drivers (Lau and Lennon, 2012; Clemmensen et al., 2013; Philippot et al., 2013). Climate change is a growing worldwide concern, with climatic models forecasting large-scale warming and rainfall alterations over the next century, leading to increased drought in many vulnerable areas of the world IPCC (2013). Changes in climate-associated parameters directly impact biodiversity and global carbon and nutrient cycles, affecting the functioning of forest ecosystems (Nadrowski et al., 2010). Pinus sylvestris L. is a representative tree species in European forests, with its southernmost edge of distribution located in Spain. Predicted shifts in tree species distribution ranges to higher elevations in response to warmer and drier climate scenarios (Hickler et al., 2012) have already been observed for P. sylvestris in Swedish forests (Kullman and Öberg, 2009). However, recent studies point to demographic and ontogenetic processes, and to biotic effects such as competition with neighbouring plants and interaction with soil microorganisms, to be even more relevant than environmental gradients explaining forest dynamics (Armas et al., 2013; Rousk et al., 2013). Among microorganisms, fungi are a key component of forest soils involved in carbon and nutrient turnover, their mycelium being an important carbon reservoir, and interacting with trees in a number of possible ways according to their varied lifestyle ranges (e.g. saprotrophic, pathogenic, lichenic, endophytic or mycorrhizal) (Tedersoo and Smith, 2013). The fungal kingdom is highly rich, with approximately 100 000 described species and 10–50 times more estimated un-described ones (Hawksworth, 2012). The rapid development of high-throughput sequence-based techniques is currently drawing this vast

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richness, allowing the in-depth analysis of the distribution patterns of fungal diversity in a variety of habitats and scales (Buée et al., 2009; Schmidt et al., 2013; Tedersoo et al., 2014). Particularly, the ectomycorrhizal (EM) fungal guild comprises approximately 25 000 species, belonging to more than 60 independently evolved lineages, although much EM fungal diversity remains still cryptic (Tedersoo et al., 2012; Tedersoo and Smith, 2013). EM fungi form symbiosis with the roots of trees improving their nutrient and water uptake in exchange for carbohydrates (Smith and Read, 2008). Host benefits can vary depending on the balance in the relative carbon cost for maintaining the EM symbiosis and on the fungal efficiency in enzymatic access to organically bound nutrients, nutrient and water uptakes, and their transference to the tree host (Smith and Read, 2008). In future climatic scenarios, this mutualistic association may be critical for ensuring forest productivity and resilience by affecting demographic processes through the improvement of nutrient and water supply to trees and the facilitation of seedling establishment (Pickles et al., 2012). Other major fungal guild involved in the carbon cycle are saprobes, which are free-living fungi able to degrade complex organic molecules such as lignin, cellulose and hemi-cellulose by the production of powerful enzymes, being major decomposers in forest ecosystems (Eastwood et al., 2011), a function directly dependent on temperature and soil moisture, among other factors. Climate can thus directly affect forest fungal communities by changing the edaphic properties, and/or indirectly via plant-mediated resource dynamics such as root carbon exudates, litter production or organic matter (OM) accumulation (Clemmensen et al., 2013; Díez et al., 2013; Averill et al., 2014; Hobbie et al., 2014). Although patterns of diversity have been deeply reported for animals and plant communities, microorganisms have been less studied although the rapid development of molecular technologies is letting the topic rapidly progress (Bik et al., 2012; Zimmerman et al., 2014). Species diversity usually decreases with latitude and elevation (Legendre, 2008; Kraft et al., 2011), but it seems not to be a clear pattern in the case of fungi, particularly of EM ones (Gao et al., 2013; Coince et al., 2014; Tedersoo et al., 2014). Soil fungal richness and species composition dynamics have been examined along edaphic and climatic gradients in a number of recent studies with varied results (Cox et al., 2010; Bahram et al., 2012; Coince et al., 2013; 2014; Davey et al., 2013; Jarvis et al., 2013; Geml et al., 2014; Miyamoto et al., 2014). At present, much research effort in fungal ecology is focused on identifying the environmental driver(s) governing diversity patterns and possible mechanisms underlying these patterns, although these last are often difficult to differentiate being dependent on multiple variables and pro-

cesses operating at different scales (Kraft et al., 2011; Tedersoo et al., 2012; 2014; Suz et al., 2014). In this study, we examined the fungal communities associated with P. sylvestris along elevation gradients, as a proxy of climatic gradients (Körner, 2007), to check potential responses of these communities to climate change. We used a 454-pyrosequencing approach to analyse the diversity and structure of fungal communities in ectomycorrhizas and soils of Scots pine forests located in Spain and France. We hypothesized that at a regional scale, the abiotic component (i.e. climatic and edaphic properties) would be the main driver structuring fungal communities, inducing changes in their richness and specific composition. In addition, a divergent small-scale response of fungal communities, i.e. root tips and soil, was expected particularly for EM communities. Indeed, EM fungi tightly rely on both the tree host (biotic component) and the surrounding environment (abiotic component) for resources acquisition through well-differentiated symbiotic compartments (i.e. mantle and Hartig net in roots, and the external mycelium spread in soil). Gaining knowledge about how forest fungal communities respond to climatic and edaphic variations will aid in better understanding the meaning of the complex spatiotemporal feedbacks between environment–fungus–plant interactions, which is of prime importance for the functioning and management of forest ecosystems. Results Sequencing yields and total estimated fungal richness A total of 164 023 good quality-filtered sequences were retained. The mean number of sequences per treatment according to the type of sample was 2681 ± 712 for roots and 3394 ± 563 for soil (Table S1). The combined clustering analysis of roots and soil sequences determined a total of 2524 molecular operational taxonomic units (MOTUs), and 1622 after removal of singletons (35.7%) (Table S1). The average of sequences per sample was 2991 (ranging from 1373 to 4223), and the mean number of MOTUs per sample was 174 (ranging from 82 to 363). Each MOTU had on average 99.6 sequences (ranging from 1 to 11 713 sequences per MOTU) and occurred on average in 5.8 samples (ranging from 1 to 52 out of a total of 54 samples). Among all MOTUs, 58% had more than five sequences and 42% were found in more than three samples. Concerning the type of sample, the number of MOTUs was 845 in roots and 1487 in soil, with 773 of these MOTUs shared (47.7% of the total), and 6.5% and 45.8% of them found exclusively in roots and soil respectively (Fig. 1A; Table S1). Regarding the region, 27.4% of the MOTUs were exclusively found in the southern location (Gu), 14% only in the Pyrenees (Py) and 15.7% only in the

© 2015 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology, 17, 3009–3024

Fungi of Scots pine along elevation gradients

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Fig. 1. (A) Number of sequences (without singletons; in cursive) and percentages of fungal molecular operational taxonomic units (MOTUs) by type of sample: root tips (RT) and bulk soil (BS), region: Gu = Guadarrama, Py = Pyrenees, Vo = Vosges; and elevation site: L = low, M = mid, H = high, after removal of singletons (yields detailed in Table S1); the percentages of MOTUs shared by all or each two treatments are within dark and light grey squares respectively. (B) Location of regions and sites (altitudinal gradients) of Pinus sylvestris L. forests.

northern location (Vo), and all regions shared 15% of the total MOTUs (Fig. 1A; Table S1). The percentages of MOTUs found exclusively at each elevation site were quite similar (14.8–16.8%), with 27.9% of the total MOTUs found in all sites (Fig. 1A; Table S1). When analysed in a per plot basis, the number of observed MOTUs varied from 82 to 162, and from 164 to 363, respectively for roots and soil samples, averaging of 115 and 234 fungal MOTUs per plot for roots and soil respectively. By region, for roots, the southern location showed low total estimated richness per plot (Gu = 101 MOTUs) compared with the intermediate and northern sites (Py = 134 and Vo = 109 MOTUs), whereas the opposite happened in the case of soil with 295, 211 and 195 fungal MOTUs found in Gu, Py and Vo respectively. Regarding the site, the number of observed MOTUs was of 109, 122 and 114 in roots and of 223, 246 and 231 in soil, respectively at low, mid and high elevation. Rarefaction curves were unsaturated for both root and soil samples (Fig. S1).

Lifestyle and taxonomic identification of fungi The lifestyle of 57.8% of the total MOTUs (∼91% of the taxonomically assigned) could be inferred (Fig. 2A), and mycorrhizal fungi were the most representative in the overall communities, particularly in root samples, as was expected (Fig. 2A; Table 1). The saprotrophic group represented less than half of the mycorrhizal one in both kinds of samples, especially in roots (Fig. 2A). Other fungi that could be classed as endophytic, pathogenic, yeasts and lichenic maintained similar percentages in roots and soil, representing in all cases less than 3% of the total MOTUs (Fig. 2A). Taxonomic identification was given to approximately 63% of the retained MOTUs, among which 22.7% were Ascomycetes, 34.2% Basidiomycetes, 5.1% Zygomycetes, 0.6% Chytridiomycetes and 37.4% uniden-

tified (many without BLAST hit) (Fig. 2B). These proportions were maintained within each type of sample, except for the most representative group, which in roots were Basidiomycetes and in soil ‘unidentified fungi’ (Fig. 2B; Table S2). Approximately 52% of the MOTUs were ascribed to the order level, with a total of 54 orders found among which Agaricales, Russulales, Thelephorales and Helotiales were predominant both in roots and soil (Fig. 2B; Table S2). Of the MOTUs, 54.1% could be ascribed to the genus level making a total of 268 genera, of which Cortinarius, Russula, Mortierella, Inocybe, Tomentella, Lactarius and Amanita were among the most species-rich ones (Fig. 2B; Table S2). Close to 43% of the MOTUs were identified at the species level, with a total of 583 species found (Table S2). Among the 20 most abundant MOTUs, Russula and Tomentella were well represented in roots; Mortierella, Lactarius and Cryptococcus in soil (Table 1); and remarkably, in both cases, one third of the top 20 were unknown fungi (Table 1). Only 5 out of the top 20 MOTUs were shared between roots and soil databases (Table 1), among them the unknown fungi MOTU-2 and MOTU-4 with sequences closely similar to the recently described Archaeorhizomycetes class (Rosling et al., 2011). The top 10 most representative fungal MOTUs per region and site in each root and soil samples are listed in Table S3.

Richness and assemblage of fungal communities, and relationships with abiotic variables Significant spatial autocorrelation was detected by Mantel test for fungi in roots (rM = 0.6621; P < 0.001) and soil (rM = 0.7488; P < 0.001), indicating that both communities were spatially structured. This was drawn by a first ordination analysis, which showed some similarity between roots and soil fungal communities with a strong recognizable region effect (Fig. S2).


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Fig. 2. (A) Total fungal molecular operational taxonomic units (MOTUs) grouped by lifestyle, and their respective distribution in root tips and bulk soil samples in Pinus sylvestris L. Forests; percentages of MOTUs found in root tips and bulk soil respect to the total are indicated. (B) Relative percentages of total fungal MOTUs, and their respective distribution in root tips and bulk soil samples, grouped by phylum, order and genus.

Regarding the climatic variables, fungal estimated richness did not respond to temperature or precipitation in any case (Table 2), although the assemblage of fungi significantly did (Table 3). When EM fungi were exclusively considered, their richness did not respond to cli-

matic variables (Table 2), but their grouping differed depending on the compartment in which they settled, i.e. root tips or mycelium (soil), being significantly affected by precipitation only in roots but not in soil (Table 3). Similarly, the richness of Ascomycetes and Basidiomycetes



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Table 1. Identification of the 20 most abundant MOTUs in each type of sample root tips and bulk soil, with shared MOTUs indicated in bold. Identification Root tips 3 MOTU-3 2 MOTU-2 7 Suillus variegatus 5 Russula xerampelina 6 Russula integra 9 Penicilium spinulosum 11 Piloderma sp. 20 Tomentella sp. 8 Lactarius hepaticus 18 Russula sp. 27 Tomentella badia 22 Cortinarius biformis 26 MOTU-26 4 MOTU-4 25 MOTU-25 13 Russula ochroleuca 32 Phialocephala sp. 19 MOTU-19 35 MOTU-40 34 Russula laricina Others Bulk Soil 1 Mortierella humilis 2 MOTU-2 4 MOTU-4 10 MOTU-10 12 Tricholoma portentosum 16 Asterophora sp. 15 Pseudagerita viridis 17 Mortierella minutissima 23 Cryptococcus terrícola 14 Craterellus tubaeformis 28 Mortierella sp. 29 Cryptococcus humicolus 21 MOTU-21 31 MOTU-32 8 Lactarius hepaticus 33 MOTU-36 13 Russula ochroleuca 30 Lactarius rufus 24 Cortinarius diasemospermus 19 MOTU-19 Others

Lifestyle

AB EM

AB Soil

FQ EM

FQ Soil

gi|#

BLAST ID

Sim‡

E-value

Myc Myc Myc Myc Myc Sap Myc Myc Myc Myc Myc Myc Myc Myc Myc Myc Myc Myc Myc Myc

4.7 4.1 3.8 3.8 3.3 2.7 2.3 2.2 1.7 1.7 1.6 1.6 1.4 1.4 1.3 1.2 1.2 1.2 1.2 1.2 56.3

0.3 4.2 0.1 0.4 0.7 0.4 0.5 0.1 1.2 0.5 0.2 0.4 0.5 2.5 0.6 1.1 0.2 0.9 0.1 0.2 85.1

26 25 20 12 15 21 17 11 5 9 18 7 7 13 18 10 27 15 15 13

25 27 13 10 13 24 17 10 15 9 15 9 10 23 22 14 24 18 13 12

545 716 080 351 511 000 10 638 130 23 393 829 22 024 462 83 627 157 18 700 676 1 930 068 12 002 101 311 334 297 391 358 006 253 993 612 126 360 707 353 529 323 507 418 785 344 333 604 545 716 087 291 045 783 388 847 151 371 572 527

Uncultured Cortinarius Uncultured fungus Suillus variegatus Russula xerampelina Russula integra Penicilium spinulosum Uncultured Piloderma Tomentella sp. Lactarius hepaticus Russula sp. Tomentella badia Cortinarius biformis Uncultured fungus Uncultured fungus Uncultured Helotiales Russula ochroleuca Phialocephala fortinii Helotiales sp. Uncultured Thelephora Russula laricina

99 100 96 93 96 93 94 90 96 94 93 93 96 97 96 93 98 99 95 96

4e-77 3e-50 9e-89 2e-69 2e-87 4e-43 5e-77 5e-49 9e-93 2e-85 1e-77 1e-61 3e-84 1e-49 4e-82 2e-80 2e-79 1e-66 4e-83 1e-82

Sap Myc Myc – Myc Pa Sap Sap Sap Myc Sap Sap Myc Myc Myc – Myc Myc Myc Myc

0.3 4.1 1.4 0.0 0.0 0.0 0.1 0.0 0.1 0.8 0.0 0.0 0.6 0.1 1.7 0.1 1.2 0.4 0.8 1.2 86.9

12.6 4.2 2.5 2.5 2.1 1.8 1.8 1.8 1.6 1.4 1.4 1.3 1.3 1.2 1.2 1.1 1.1 1.0 1.0 0.9 56.4

21 25 13 2 7 10 14 9 13 7 7 14 9 9 5 4 10 11 9 15

27 27 23 23 15 27 27 25 27 16 19 27 19 19 15 9 14 12 13 15

109 390 554 351 511 000 353 529 323 6 706 192 164 416 093 294 861 925 116 804 996 171 467 218 357 435 497 14 579 577 11 757 1267 8 698 558 388 846 347 226 349 083 12 002 101 306 846 784 344 333 604 82 491 461 61 657 590 291 045 783

Mortierella humilis Uncultured fungus Uncultured fungus Uncultured Ascomycete Tricholoma portentosum Asterophora sp. Pseudagerita viridis Mortierella minutissima Cryptococcus terrícola Craterellus tubaeformis Mortierella sp. Cryptococcus humicolus Uncultured fungus Uncultured fungus Lactarius hepaticus Uncultured fungus Russula ochroleuca Lactarius rufus Cortinarius diasemospermus Helotiales sp.

100 100 97 96 96 95 87 95 98 96 97 97 96 98 96 93 93 99 99 99

5e-75 3e-50 1e-49 2e-64 7e-96 4e-72 6e-38 2e-59 2e-75 2e-71 9e-63 2e-42 2e-80 3e-88 9e-93 1e-40 2e-80 3e-104 2e-65 1e-66

AB = relative abundance; FQ = frequency (out of 27 trees). Upper-indexed numbers (first column) = position in the general rank of abundance, independently of the type of sample. Myc = mycorrhizal; Sap = saprotrophic; Pa = parasitic; – = unknown. Sim‡ = Similarity (%).

was unaffected by climatic variables (Table 2). Fungal communities, the EM ones included, were highly structured by pH, affecting their richness (Table 2) and assemblage (Table 3). Ascomycetes richness did not vary with pH, whereas that of Basidiomycetes usually increased (Table 2). EM Basidiomycetes richness did also respond to the carbon : nitrogen (CN) ratio (Table 2), but differently depending on the type of sample, being unaffected in roots and significantly decreasing in soil as CN increased (Table 2). Ascomycetes and Basidiomycetes assemblages were significantly affected by temperature and pH, and also by precipitation in almost all cases (Table 3), with a strong region effect interacting with site (Table 3).

Richness response of representative fungal families to climatic and edaphic conditions When analysed at deeper taxonomic levels, the estimated richness of representative fungal families was usually higher in soil compared with roots. This was the case of Inocybaceae and, as expected, of saprotrophic families such as Herpotrichiellaceae, Mortierellaceae, Tremellaceae, Trichocomaceae or Umbelopsidaceae (Fig. S3). However, typical EM families such as Boletaceae, Cortinariaceae, Suillaceae or Thelephoraceae were similarly represented in roots and soil samples, and Russulaceae were even richer in roots (Fig. S3).


0.2 13.4 1.4 – 0.5 9.6 2.3 – 0.0 1.4 1.8 – 6.0 2.3 2.4 – 0.7 35.3 7.1 – 4.7 31.3 6.9 –

−0.06 ± 0.02 0.23 ± 0.06 −0.01 ± 0.00 – 0.001 ± 0.00 0.19 ± 0.05 −0.01 ± 0.00 –

−0.01 ± 0.04 0.08 ± 0.07 −0.01 ± 0.00 – −0.01 ± 0.00 0.06 ± 0.04 −0.05 ± 0.00 –

−0.02 ± 0.04 0.29 ± 0.05 −0.01 ± 0.00 – −0.001 ± 0.00 0.28 ± 0.05 −0.001 ± 0.00 –

F

ns *** ns – ns *** ns –

ns ns ns – ns ns ns –

ns ** ns – ns ** ns –

P-value

−0.07 ± 0.04 0.21 ± 0.09 −0.01 ± 0.00 0.71 ± 0.74 0.001 ± 0.00 0.18 ± 0.08 −0.01 ± 0.01 1.04 ± 0.68

0.02 ± 0.05 −0.01 ± 0.10 0.01 ± 0.00 0.15 ± 0.70 −0.001 ± 0.00 0.02 ± 0.09 0.01 ± 0.01 0.08 ± 0.68

−0.05 ± 0.03 0.13 ± 0.06 0.01 ± 0.00 0.24 ± 0.52 0.01 ± 0.00 0.11 ± 0.05 −0.01 ± 0.00 0.46 ± 0.44

Estimate ± SE

0.3 29.0 0.3 0.9 0.2 30.3 1.8 2.3

0.6 0.1 0.3 0.1 0.3 0.2 0.4 0.1

0.5 11.4 0.4 0.2 3.9 24.2 0.1 1.1

F

in EM

ns *** ns ns ns *** ns ns

ns ns ns ns ns ns ns ns

0.486 ** 0.552 0.639 0.068 *** 0.838 0.319

P-value

−0.04 ± 0.41 0.35 ± 0.06 −0.001 ± 0.00 – 0.001 ± 0.00 0.34 ± 0.05 −0.01 ± 0.00 –

0.08 ± 0.03 −0.005 ± 0.05 −0.01 ± 0.00 – −0.01 ± 0.00 0.002 ± 0.07 0.02 ± 0.00 –

−0.03 ± 0.04 0.23 ± 0.05 −0.01 ± 0.00 – 0.01 ± 0.00 0.22 ± 0.05 −0.01 ± 0.00 –

Estimate ± SE

Mycorrhizal fungib

0.4 41.3 11.7 – 4.0 34.4 11.7 –

4.9 0.1 0.1 – 6.4 0.1 0.2 –

0.0 23.4 12.0 – 1.6 20.5 12.1 –

F

in soil

ns **** ** – ns **** ** –


0.995 *** ** – 0.218 *** ** –

P-value

0.2 0.5 0.6 – 0.1 0.2 0.8 – 4.6 6.9 0.2 – 3.0 8.2 0.1

0.05 ± 0.04 0.12 ± 0.05 −0.002 ± 0.05 – −0.01 ± 0.00 0.13 ± 0.05 −0.00 ± 0.00 –

2.7 10.8 1.1 – 0.1 13.3 0.7 –

F

−0.03 ± 0.05 0.07 ± 0.10 −0.003 ± 0.00 – −0.000 ± 0.00 0.045 ± 0.09 −0.004 ± 0.00 –

−0.04 ± 0.02 0.24 ± 0.07 0.003 ± 0.00 – 0.000 ± 0.00 0.202 ± 0.06 0.002 ± 0.00 –

Estimate ± SE

in soil

Othersc



0.119 ** 0.309 – 0.794 ** 0.410 –

P-value

The ‘region’ and ‘site nested within region’ were introduced as random factors within models (data not shown). Degrees of freedom in all models: numerator numDF = 1 and denominator denDF = 15; Models: < lme (log (Richness) ∼ climatic variable + ph + CN + roots (only in RT), method = ‘REML’, random = ∼ |region/site, data). Bonferroni corrected P-value * < .05; ** < .01; *** < .001;**** < .0001 are indicated in bold; ns = not significant; C/N = carbon/nitrogen ratio. Region: Guadarrama (Spain), Pyrenees (Spain), Vosges (France); Site: Low, Middle and High elevations. Two types of samples: root tips (rt) and bulk soil (bs). a. Complete soil database. b. Only mycorrhizal fungi in rt or bs databases. c. No mycorrhizal fungi.

All MOTUs Temperature pH C/N roots Precipitation pH C/N roots Ascomycetes Temperature pH C/N roots Precipitation pH C/N roots Basidiomycetes Temperature pH C/N roots Precipitation pH C/N roots

Estimate ± SE

in soil

All fungia

Table 2. GLMMs testing the response of soil and mycorrhizal fungal communities to climatic (independent models for temperature and precipitation) and edaphic variables, considering estimated richness as fixed effect.

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Table 3. Non-parametric multivariate analysis of variance (ADONIS) testing the response of fungal assemblage to climatic (independent models for temperature and precipitation), edaphic and location variables. All fungia

Mycorrhizal fungib

BS

All MOTUs Region Temperature pH C/N Region : site Residuals Region Precipitation pH C/N Region : site Residuals Ascomycetes Region Temperature pH C/N Region : site Residuals Region Precipitation pH C/N Region : site Residuals Basidiomycetes Region Temperature pH C/N Region : site Residuals Region Precipitation pH C/N Region : site Residuals

2

in RT

F

R

P-value

F

R

11.7 4.5 2.6 1.0 2.3

0.39 0.07 0.04 0.02 0.23

*** *** * ns ***

11.4 4.4 2.7 1.1 2.5

11.7 2.7 4.3 1.1 2.3

0.39 0.05 0.07 0.02 0.23 0.24

*** ** *** ns ***

11.4 2.7 4.1 1.3 2.5

13.5 4.1 2.4 0.7 1.2

0.44 0.07 0.04 0.01 0.20 0.24 0.44 0.03 0.07 0.01 0.20 0.24

*** ** * ns **

17.8 3.3 3.1 0.6 2.8

*** ns *** ns **

17.8 2.2 4.3 0.7 2.7

0.35 0.06 0.04 0.02 0.24 0.27 0.35 0.04 0.06 0.02 0.25 0.27

*** *** * ns ***

5.11 2.7 1.7 1.4 1.8

*** * *** ns ***

5.4 1.8 2.3 1.7 1.8

13.5 2.0 4.3 0.8 2.0

9.7 3.6 2.2 0.9 2.3 9.7 2.2 3.5 1.0 2.3

2

Othersc in BS

in BS P-value

F

R2

P-value

0.36 0.06 0.04 0.02 0.25 0.26 0.36 0.03 0.06 0.25 0.26

*** ** * ns ***

13.5 5.6 2.9 0.9 2.3

*** *** ** ns ***

*** ns ** ns ***

13.5 3.7 4.8 1.0 2.3

0.41 0.09 0.04 0.01 0.21 0.23 0.41 0.06 0.07 0.01 0.21 0.23

0.45 0.07 0.06 0.02 0.20 0.18 0.45 0.03 0.10 0.20 0.18

*** ** ** ns ***

13.4 3.9 2.5 0.7 2.0

*** ** * ns **

*** * *** ns ***

13.4 2.2 4.0 0.8 2.1

0.43 0.06 0.04 0.01 0.20 0.24 0.44 0.03 0.06 0.01 0.20 0.24

0.28 0.06 0.04 0.02 0.28 0.29 0.29 0.05 0.06 0.03 0.28 0.29

*** *** ** ns ***

6.0 3.2 2.1 0.8 1.4

*** *** * ns *

*** ** *** ns ***

6.0 1.7 3.5 0.9 1.4

0.28 0.07 0.05 0.02 0.21 0.36 0.29 0.04 0.08 0.02 0.20 0.35

P-value

F

R

0.37 0.07 0.04 0.02 0.24 0.24 0.37 0.04 0.07 0.02 0.24 0.24

*** *** ** ns ***

10.5 3.3 2.4 1.0 2.4

*** ** *** ns ***

10.5 1.9 3.6 1.1 2.4

0.48 0.04 0.04 0.01 0.22 0.20 0.48 0.03 0.06 0.01 0.22 0.20

*** * * ns ***

18.4 5.7 5.0 1.3 2.8

*** ns ** ns ***

18.4 2.5 8.2 1.8 2.7

0.25 0.06 0.03 0.03 0.25 0.35 0.25 0.04 0.05 0.04 0.25 0.35

*** *** * ns ***

7.3 3.0 3.3 1.2 2.4

*** * ** ns ***

7.3 2.4 2.9 1.4 2.4

2

*** ** *** ns ***

*** * ** ns ***

*** * *** ns *

Degrees of freedom in all models: 2 Region, 1 Temperature, 1 pH, 1 C/N, 6 Region:Site, 15 Residuals. Models: ADONIS (data) ∼ climatic variable + ph + CN + region:site, permutations = 99 999). Significant values are indicated in bold. C/N = carbon/ nitrogen ratio. Region: Guadarrama (Spain), Pyrenees (Spain), Vosges (France); Site: Low, Middle and High elevations. P-value = * < 0.05; ** < 0.01; *** < 0.001; **** < 0.0001; ns = no significant. Two samples: root tips (RT) and bulk soil (BS). a. Complete soil database. b. Only mycorrhizal fungi. c. No mycorrhizal fungi.

When the estimated richness of representative fungal groups was modelled, some of them differentially responded to climatic variables depending on the compartment in which they settled, i.e. mycorrhizal tips (roots) or mycelium (soil), e.g. Cortinariaceae and Suillaceae significantly decreased with temperature and the former increased with precipitation, but only in roots, not in soil (Table 4). But, the richness of most of the fungal groups did not respond to climatic variables (Table 4). In both roots and soil, the richness of almost all fungal groups analysed

was highly influenced by pH, positively (e.g. Cortinariaceae, Inocybaceae, Rhizopogonaceae, Russulaceae, Thelephoraceae, Tricholomataceae, Umbelopsidaceae) or negatively [e.g. Archaeorhizomycetes, Boletaceae (Table 4)]. The richness of Herpotrichiellaceae, Mortierellaceae and Suillaceae did not vary with pH in any case (Table 4). In some cases, a differential response to pH between roots and soil was observed, e.g. Russulaceae increasing in soil but being unaffected in roots, or Inocybaceae, which was unaffected in soil but


3016 A. Rincón et al. Table 4. GLMMs testing the response of representative fungal groups to climatic (independent analyses for temperature Ta and precipitation Pp) and edaphic variables, considering estimated richness as fixed effect. Root tips Estimate ± SE

Bulk soil

Root tips

F

P

Estimate ± SE

5.0 32.8 0.1

ns **** ns

0.05 ± 0.07 −0.36 ± 0.08 −0.01 ± 0.00

0.4 18.1 2.0

ns **** ns

2.4 6.4 6.1

ns ns ns

−0.06 ± 0.11 −0.10 ± 0.21 −0.02 ± 0.01

0.7 0.3 5.3

11.0 0.0 2.1

** ns ns

−0.15 ± 0.06 0.04 ± 0.07 −0.01 ± 0.01

0.0 0.8 2.2

ns ns ns

1.4 9.1 1.0

ns ** ns

3.7 1.6 2.9

Bulk soil

Estimate ± SE

F

P

Estimate ± SE

Pp pH C/N

0.02 ± 0.00 −0.37 ± 0.06 −0.01 ± 0.00

4.8 34.1 0.3

ns **** ns

0.01 ± 0.00 −0.24 ± 0.12 −0.01 ± 0.01

0.4 4.2 1.9

ns ns ns

ns ns ns

Pp pH C/N

−0.01 ± 0.00 −0.32 ± 0.10 −0.02 ± 0.01

0.0 9.6 5.7

ns ** ns

−0.01 ± 0.00 0.04 ± 0.19 −0.02 ± 0.01

0.0 0.0 5.9

ns ns ns

1.2 31.3 0.5

ns **** ns

Pp pH C/N

0.01 ± 0.00 −0.17 ± 0.10 0.01 ± 0.00

13.4 1.8 1.3

** ns ns

0.01 ± 0.01 0.23 ± 0.14 −0.01 ± 0.01

0.3 2.3 1.3

ns ns ns

6.6 0.1 0.1

ns ns ns

Pp pH C/N

0.01 ± 0.00 0.11 ± 0.10 0.01 ± 0.01

0.1 1.0 2.0

ns ns ns

−0.01 ± 0.00 0.18 ± 0.128 0.01 ± 0.00

4.8 2.3 0.1

ns ns ns

−0.12 ± 0.16 0.65 ± 0.28 0.01 ± 0.01

0.1 5.0 0.3

ns ns ns

Pp pH C/N

0.01 ± 0.01 0.68 ± 0.25 0.01 ± 0.01

0.2 8.3 0.6

ns * ns

−0.01 ± 0.01 0.55 ± 0.26 0.01 ± 0.01

0.5 4.6 0.2

ns ns ns

ns ns ns

−0.03 ± 0.04 0.12 ± 0.09 −0.01 ± 0.01

0.2 1.7 0.8

ns ns ns

Pp pH C/N

0.01 ± 0.00 0.17 ± 0.19 −0.02 ± 0.01

4.2 0.1 4.1

ns ns ns

0.00 ± 0.00 0.09 ± 0.07 −0.01 ± 0.01

0.5 1.2 1.3

ns ns ns

0.1 11.5 1.4

ns ** ns

0.04 ± 0.08 0.30 ± 0.09 −0.02 ± 0.01

1.7 11.3 2.7

ns ** ns

Pp pH C/N

0.00 ± 0.00 0.03 ± 0.19 0.01 ± 0.01

0.1 4.2 1.7

ns ns ns

−0.01 ± 0.00 0.30 ± 0.09 −0.01 ± 0.01

2.8 10.2 1.5

ns ** ns

0.1 5.2 0.3

ns ns ns

0.01 ± 0.06 0.24 ± 0.08 −0.01 ± 0.01

0.7 10.7 3.2

ns ** ns

Pp pH C/N

0.01 ± 0.00 0.18 ± 0.07 0.01 ± 0.01

0.1 6.1 0.1

ns ns ns

−0.01 ± 0.00 0.23 ± 0.08 −0.01 ± 0.01

2.8 9.6 2.7

ns ** ns

10.5 2.7 0.3

* ns ns

0.01 ± 0.01 0.29 ± 0.11 0.01 ± 0.01

1.0 6.6 1.5

ns ns ns

Pp pH C/N

0.01 ± 0.00 −0.16 ± 0.19 −0.02 ± 0.00

7.7 0.3 0.5

ns ns ns

−0.01 ± 0.0 0.28 ± 0.10 0.01 ± 0.01

0.3 7.4 1.5

ns ns ns

0.1 23.5 0.6

ns *** ns

0.02 ± 0.07 0.35 ± 0.13 −0.01 ± 0.01

1.9 6.8 2.3

ns ns ns

Pp pH C/N

0.01 ± 0.00 0.29 ± 0.11 −0.01 ± 0.01

0.9 6.6 0.9

ns ns ns

−0.01 ± 0.00 0.37 ± 0.13 −0.01 ± 0.01

1.2 7.0 2.5

ns ns ns

0.4 62.4 0.1

ns **** ns

−0.12 ± 0.05 0.71 ± 0.07 −0.02 ± 0.01

0.5 121.8 6.4

ns **** ns

Pp pH C/N

0.01 ± 0.00 0.61 ± 0.08 −0.01 ± 0.01

0.8 57.7 0.9

ns **** ns

0.01 ± 0.00 0.49 ± 0.14 −0.02 ± 0.01

0.1 9.5 5.7

ns ** ns

1.4 0.1 5.1

ns ns ns

−0.03 ± 0.05 0.47 ± 0.09 −0.01 ± 0.01

1.8 26.2 1.0

ns **** ns

Pp pH C/N

0.01 ± 0.00 0.04 ± 0.12 0.02 ± 0.01

4.0 0.1 2.2

ns ns ns

0.01 ± 0.00 0.49 ± 0.14 −0.02 ± 0.01

0.1 9.5 5.7

ns *** ns

F

P

F

P

§

Archaeorhizomycetes Ta −0.02 ± 0.05 pH −0.37 ± 0.06 C/N −0.01 ± 0.01 ‡ Boletaceae −0.04 ± 0.08 Ta pH −0.30 ± 0.10 C/N −0.02 ± 0.01 ‡ Cortinariaceae −0.14 ± 0.05 Ta pH −0.01 ± 0.12 C/N 0.01 ± 0.00 § Herpotrichiellaceae −0.01 ± 0.1 Ta pH 0.11 ± 0.10 C/N 0.01 ± 0.01 ‡ Inocybaceae Ta −0.13 ± 0.12 pH 0.84 ± 0.28 C/N 0.01 ± 0.01 § Mortierellaceae −0.17 ± 0.09 Ta pH 0.35 ± 0.24 C/N −0.01 ± 0.01 ‡ Rhizopogonaceae Ta −0.12 ± 0.08 pH 0.37 ± 0.07 C/N 0.01 ± 0.01 ‡ Russulaceae −0.02 ± 0.06 Ta pH 0.17 ± 0.07 C/N 0.01 ± 0.01 ‡ Suillaceae Ta −0.28 ± 0.07 pH 0.23 ± 0.09 C/N 0.01 ± 0.01 ‡ Thelephoraceae −0.07 ± 0.05 Ta pH 0.29 ± 0.06 C/N −0.01 ± 0.01 ‡ Tricholomataceae Ta −0.11 ± 0.06 pH 0.63 ± 0.1 C/N −0.01 ± 0.01 § Umbelopsidaceae Ta −0.14 ± 0.09 pH 0.06 ± 0.12 C/N 0.03 ± 0.01

0.12 ± 0.052 0.01 ± 0.07 0.00 ± 0.01

‘Region’ and ‘site nested within region’ were random factors within models (results not shown). Degrees of freedom in all models: numerator numDF = 1 and denominator denDF = 15; Models: < lme [log (Richness) ∼ climatic variable + ph + CN, method = ‘REML’, random = ∼ |region/site, data]. Bonferroni corrected P-value = * < .05; ** < .01; *** < .001;**** < .0001 are indicated in bold; ns = not significant; Region: Guadarrama (Spain), Pyrenees (Spain), Vosges (France); Site: Low, Middle and High elevations. § Mostly saprotrophic, fungal groups. ‡ Mostly mycorrhizal fungal groups.

increased with pH in roots (Table 4). The richness of Tricholomataceae was highly responsive to pH in roots and soil, similar to that of Umbelopsidaceae in soil (Table 4). Similar to that previously observed when the overall soil fungal community was analysed (Fig. S2), the ordination analysis revealed a clear region effect on the grouping

of representative fungal families and, accordingly, geographic variables were significantly correlated, both in roots and soil (Fig. 3). Precipitation was clearly correlated probably influencing the assemblage of fungal families when associated with roots and soil, whereas temperature was only significantly correlated in roots (Fig. 3A).



3017

Fig. 3. Non-metric multidimensional scaling (NMDS) analysis drawing the assemblage of representative fungal groups in (A) root tips (k = 3; stress = 0.08; R2 = 0.992) and (B) bulk soil (k = 3; stress = 0.07; R2 = 0.995) in Pinus sylvestris L. Forests by region (squares = Guadarrama, Spain; triangles = Pyrenees, Spain; circles = Vosges, France) and site (white = low; grey = middle; black = high). The two first dimensions are drawn. Vectors represent the strength/direction of the weight of environmental variables (Ta = annual mean temperature; Pre = annual mean precipitation; CN = carbon : nitrogen ratio; P = phosphorus; K = potassium; Mg = magnesium; Mn = manganese; Lat = latitude; Lon = longitude) on the distribution of fungal groups (*P < .05; **P < .01; ***P < .001). Arch = Archaeorhizomycetes; Bol = Boletaceae; Cor = Cortinariaceae; Her = Herpotrichiellaceae; Ino = Inocybaceae; Mor = Mortierellaceae; Rhiz = Rhizopogonaceae; Rus = Russulaceae; Sui = Suillaceae; The = Thelephoraceae; Trich = Tricholomataceae; Umb = Umbelopsidaceae.

The pH, the content of phosphorus, as well as cations such as K, Mg and Mg, were highly correlated suggesting them as contributing factors influencing the grouping of families in roots and soil (Fig. 3). Similarly, the CN ratio was highly correlated only in soil, but not in roots, probably influencing the assemblage of fungal families (Fig. 3B).

Discussion General overview of fungal communities in Scots pine forests The high-throughput sequencing yields obtained gave an accurate picture of the vast fungal diversity of Scots pine forests, as previously reported in similar studies on other host trees (Buée et al., 2009; Coince et al., 2013; Geml et al., 2014; Meiser et al., 2014), and compared with traditional sequencing on the same tree species (Cox et al., 2010; Pickles et al., 2010; Jarvis et al., 2013). It should be still considered that, unlike our study, most of the root-tip works on Scots pine have been conducted in rather low diversity habitats in terms of plant species. On the other hand, potential sources of inflated diversity estimation could be the short length of sequences obtained by highthroughput sequencing (Nguyen et al., 2015), and the fact

that fungi present as spores but not necessarily as mycelium on root tips can be also picked up (Anderson et al., 2014). Our results supported the recognized feature of fungi as a taxonomically challenging group with more than one quarter of their total diversity not fitting with any described sequence at any taxonomical rank, as previously stated (Buée et al., 2009; Toju et al., 2013; Pickles and Pither, 2014), and with almost half of fungi not identified at the level of species. Fungal communities showed a recognizable pattern of many low-abundant fungi, and few highly dominant ones mainly within the genera Cortinarius, Lactarius-Russula and Inocybe, as well as TomentellaThelephora particularly representative in root tips, or Mortierella highly representative in the soil. Previous morphotyping-based surveys likely underestimated the fungal diversity in Scots pine forests, with e.g. approximately 8–10 times fewer taxa on EM tips reported in similar regional scale studies (Cox et al., 2010; Jarvis et al., 2013). In these and other fine-scale studies performed in P. sylvestris forests (Pickles et al., 2010; Anderson et al., 2014), as well as in other high-throughput sequence-based ones in temperate forests (Buée et al., 2009; Jumpponen et al., 2010; Coince et al., 2013; Toju et al., 2013), similar dominating fungal groups as those found in our study have been reported. Remarkably,


3018 A. Rincón et al. unknown fungi were highly abundant in fungal communities of root tips and soil, two of them close to the lately described class of Archaeorhizomycetes (Rosling et al., 2011), as also recently reported in Picea mariana forests (Taylor et al., 2014). Molecular surveys in many different habitats are currently yielding up fungal diversity much higher than previously reported (Hawksworth, 2012) with many unidentified and cryptic species, some of them very representative like in our study suggesting they may have a functional weight within the community. Understanding the biodiversity effects on ecosystem functions is a highly topical area in fungal community ecology (Koide et al., 2014), especially when persisting fungal taxonomic instability is revealed even at higher ranks, with major new classes and phyla that continue to be described (Jones et al., 2011; Rosling et al., 2011; Hibbett and Taylor, 2013; Glass et al., 2014). Is soil a good proxy for EM fungal communities? Total fungal richness in soil largely doubled that of root tips, and more than 93% of MOTUs in root tips were also found in bulk soil, pointing out to soil as a good proxy for estimating EM fungal richness at a regional scale, as previously suggested by different approaches (Landeweert et al., 2005; Coince et al., 2013). Mycorrhizal fungi were highly representative in both root tips and soil, as indeed unknown fungi with no lifestyle ascription were, particularly in the case of soil, and caution should be paid to possible biases when analysing soil databases on the basis of fungal ecology. In parallel, relative abundances of some fungi highly differed between both environmental samples, indicating that using soil as a proxy of EM fungal communities could be misleading if only presence/absence and not also taxa abundance is considered. Differences in taxa abundance can be related with the small-scale spatial and functional compartments in which the EM community settles (i.e. root tips and mycelium spreading in soil). Individual fungi can be differentially distributed in these compartments depending for example on their exploration strategy, and fungi producing abundant hyphal strands could be more probably represented in soil. Additionally, in soil-based surveys a possible tarnishing effect of spores DNA has been suggested (Anderson et al., 2014; Peay and Bruns, 2014) with a risk of overestimating the real richness of EM fungi associated with forest stand. On the contrary, in our study, a not-negligible portion of non-mycorrhizal fungi was also found on root tips, although they could be also attributable to remnant soil particles. Using spatially finescale sampling designs, Anderson and colleagues (2014) have shown that the numbers of individual EM species as mycelium were not the same as on root tips, highlighting the spatial distribution disparity between both compart-

ments, although at large geographic scales where negative spatial correlations are revealed with distance (Bahram et al., 2013), this small-scale spatial mismatches could become secondary. Impact of the abiotic environment structuring the overall fungal community The small number of shared MOTUs (∼15%) in all regions, together with the fact that barely one third were found at all elevation sites, indicated a prevalent local distribution of taxa emphasizing the importance of local environment and processes. In a recent meta-analysis of varied fungal high-throughput databases, Meiser and colleagues (2014) found little sharing of species, suggesting that globally distributed taxa and habitat generalists could be rather exceptional, as previously suggested by Smith and colleagues (2009) for EM fungi. Primary drivers structuring all fungal communities are hardly consensual, mainly considering that their relative importance can be scale dependent and reliant on the idiosyncrasy of each fungal guild or lifestyle (Bahram et al., 2013; Koide et al., 2014; Taylor et al., 2014). As was predicted, climatic and edaphic factors were highly correlated with strong structuring the overall fungal community of Scots pine forest soils, shaping the fungal assemblage, but having less impact on richness. The positive temperature–richness relationship commonly observed for many organisms, EM fungi included (Kernaghan and Harper, 2001; Bahram et al., 2012; Tedersoo et al., 2014), was not found neither in soil nor in root tips, although temperature significantly impacted their community composition. Our results are consistent with those recently reported by Coince and colleagues (2014) in European beech forests, and for other fungal guilds such as saprobes (Meier et al., 2010) and mossassociated fungi (Davey et al., 2013). The temperature impact on richness could be somehow attenuated by variables more relevant at a local scale, or by the buffering replacement of fungi for others better adapted to the given conditions guaranteeing the ecosystem resilience. When analysing fungal sub-assemblages, neither the richness of Ascomycetes or Basidiomycetes responded to temperature or precipitation. Contrarily, Geml and colleagues (2014) found higher Ascomycetes richness in warm and dry forests in Andes, and Coince and colleagues (2014) have recently reported a positive response to temperature in beach forests suggesting a mid-domain effect model of distribution for this fungal guild. The grouping of the EM community was driven by precipitation, whereas its richness was unaffected in contrast with that reported in a recent Scots pine study (Jarvis et al., 2013), probably indicating underlying scale-dependent mechanisms (Tedersoo et al., 2012; Bahram et al., 2013). However, a


Fungi of Scots pine along elevation gradients more complex set of responses to climate of fungal communities is likely to occur, as it has been recently evidenced by the strong negative temperature–precipitation interactions affecting fungal fruiting patterns in tight relation with host tree phenology and differentially for mycorrhizal or saprotrophic fungi (Díez et al., 2013). Furthermore, as reported by Körner (2007), numerous confounding factors, specific to each altitudinal gradient, have introduced confusion in the scientific literature on altitude phenomena. The assemblage of overall fungal communities was highly affected by the climatic variables and pH, with a strong region effect interacting with elevation. The pH significantly impacted overall soil fungal richness, and that of EM fungal communities both in soil and roots. Ascomycetes richness did not vary with pH in any case, whereas a uniform positive response of Basidomycetes was observed. This last relationship was opposite to previous studies that only focused on sporocarp richness (Tyler, 1985; Ayer et al., 2003). Fungi differ in their preferences for pH and CN, usually significant predictors of their richness and assemblage (Rousk et al., 2010). In our study, the CN ratio exclusively affected the richness of mycorrhizal Basidiomycetes decreasing uniquely in soil but not in root tips. All these results reveal the compartmentalized complex set of responses to environmental and tree host influences of fungal communities in forest soils. Differential response of representative fungal groups in root tips and soil Climatic variables significantly affected the assemblage of representative fungal groups, especially in root tips (e.g. temperature) and, in particular, precipitation was revealed by the ordination as highly correlated with strong structuring the fungal communities in both soil and roots. In parallel, a contrasted response to climatic variables was observed, e.g. Cortinariaceae and Suillaceae richness decreasing with temperature and the former increasing with precipitation but only on root tips. In a long-term warming assay with Betula nana in Alaska, Deslippe and colleagues (2011) showed the opposite effect of temperature on Cortinarius spp., a group of fungi with high proteolytic capacity, suggesting that global warming could cause profound alterations on nutrient cycling. Soil pH highly influenced the assemblage of fungal groups, and a consistent soil versus root tips response of richness was observed, positive (e.g. Rhizopogonaceae and Thicholomataceae) or negative (e.g. Archaeorhizomycetes), whereas other families (e.g. Inocybaceae and Russulaceae) differentially responded depending if they were forming mycorhizas or in soil. The negative impact of pH on Archaeorhizomycetes could be expected because this group seems to be preferentially

3019

associated to coniferous forests and acidic soils (Rosling et al., 2011). On the other hand, the compartmentalized differential response of some families could be related with pH variations in the rhizospheric zone of influence of the tree host. The pH is crucial for mycelial growth and physiology (i.e. transport and enzymatic processes), being an important agent structuring fungal communities (Leprince and Quiquampoix, 1996; Rousk et al., 2009; Coince et al., 2014). Richness did not vary with CN, whereas the ordination showed this ratio to be highly correlated with strong structuring of the fungal communities but in soil, not of root tips. Fungi are highly conditioned by nitrogen availability (i.e. sporocarp production, myceliar growth, root colonization), being an important agent shifting these communities, and particularly the EM ones, across environmental gradients (Cox et al., 2010; Lilleskov et al., 2011; Bahram et al., 2012). In any case, all these results have to be cautiously interpreted given that most edaphic-climatic variables are usually interconnected, making it difficult to establish direct variable causal effects, as it has been previously indicated (Coince et al., 2014; Geml et al., 2014). The structural changes in fungal communities and subassemblages observed in our study could be determined by the high inter- and intra-specific variability among fungi and/or fungal groups in their optimum conditions (i.e. pH, soil moisture, temperature) for growth and resource acquisition (Olsson et al., 2002; Johnson et al., 2012). Besides, the discrepancies observed between root and soil fungal communities could reflect that fungi on root tips may rely more on the host, being less dependent on the edaphic environment compared with fungi in the bulk soil. Nevertheless, at present, the consequences of structural alterations of fungal communities in terms of ecosystem functionality remain largely unknown (Johnson et al., 2012), with consistent response patterns failing to emerge, and it has been recently proposed that taxonbased approach to community ecology may not be the best option (i.e. functional traits may be preferable) (Dickie and Koide, 2014; Koide et al., 2014). A better knowledge of the forces structuring fungal communities and accurate estimates of fungal richness are crucial under current and future global change scenarios of biodiversity losses, for understanding and predicting fungal– host–environmental feedbacks and their consequences for the functioning of forest ecosystems (Koide et al., 2014; Taylor et al., 2014). Experimental procedures Description of locations The study was conducted in P. sylvestris forests sited at different locations in a latitudinal gradient across Spain and France: (i) Guadarrama (Gu) in Segovia, Spain (40°85′N,


3020 A. Rincón et al. 4°02′W); (ii) Pyrenees (Py) in Lleida, Spain (42°44′N, 1°17′E); and (iii) Vosges (Vo) in Lorraine, France (48°35′N, 7°15′E) (Fig. 1B). Mean monthly temperature ranges between −3.5°C and 27.5°C in Gu, −6.1°C and 25.3°C in Py and −3.5°C and 23.3°C in Vo, and average annual precipitation is of 880 mm, 750 mm and 1130 mm, respectively, in each site, with most of this falling between April and June [(Climatic Atlas Database, Spain (Ninyerola et al., 2005); AURELHY model, France (Benichou and Le Breton, 1987)]. In all locations, soils are brown acidic on siliceous mother rock. The plant community was dominated by P. sylvestris in all forests. The mid-storey was primarily composed of scattered Quercus pyrenaica Willd. and Ilex aquifolium L. in Gu region; Sorbus aucuparia L., Pinus uncinata L. and Prunus avium L. in Py; and rare S. aucuparia L., Quercus robur L. and Fagus sylvatica L. in Vo region. The under-storey was composed of dispersed evergreen shrubs (Gu: Juniperus communis L., Cytisus purgans, Vaccinium myrtillus L., Crataegus monogyna Jacq.; Py: Rhododendron ferrugineum L., J. communis, C. purgans, Vaccinium sp., Arctostaphylos uva-ursi (L.) Spreng.; Vo: V. myrtillus), lichens and mosses.

internal transcribed spacer region ITS-1 of the nuclear ribosomal DNA was chosen for amplification with tagged primers. Genomic DNA of the 270 samples was amplified by using the fungal primer pair ITS1F-ITS2 (∼ 300 bp amplicon size), respectively preceded by the pyrosequencing adaptors A and B plus, and in the case of the ITS1F, a barcoding key A-xxx (Buée et al., 2009) for sample identification, 1 per each of the 27 treatments and type of sample. The polymerase chain reaction (PCR) conditions used were the same described by Buée and colleagues (2009) and negative controls without DNA were included in all cases. Each PCR product was purified using the Illustra™ GFX™ DNA and Gel Band Purification Kit (GE Healthcare, UK). Amplicons were successively quantified and pooled into two equimolar libraries, corresponding to roots and soil, each containing 27 uniquely tagged samples. Pyrosequencing was carried out on two independent runs (roots tips and bulk soil) in a GsFLX-454 system (Roche Applied Biosystems, USA) at GenoScreen (Lille, France), individual tags from combined FASTA files were extracted for each library, and a total of 232 298 reads (108 521 for roots and 123 777 for soil) finally passed the quality filters (Droege and Hill, 2008).

Experimental design and collection of samples Three transects: low (L), middle (M) and high (H) within a similar elevation interval of 300 m were delimited in each region (Fig. 1B). Three plots per transect separated of at least 30 m were selected, and five trees per plot (one central and the others in the north, south, east and west directions respectively) distant of more than 5 m were chosen for sampling. The experimental design consisted of three replicated regions (Gu, Py and Vo), three sites (elevation transects L, M and H), three plots per site, and a total of 27 treatments and 135 trees sampled. Two kinds of samples per tree were collected: (i) EM root tips and (ii) bulk soil, ‘roots’ and ‘soil’, respectively, from now on. Samples were obtained by excavating soil holes of 10 × 10 × 15 cm at 1 m from the trunk of each tree in the north, southeast and southwest directions. Most fungi are located in topsoil layers, but it should be kept in mind that deeper communities might show different responses (Pickles and Pither, 2014). Sub-samples from the same tree were pooled, and a total of five samples per plot were collected. Roots were removed from soil by hand and cleaned thoroughly with water over 2 mm sieves to remove any remaining soil particles and debris. Collected roots were weighted for determining root fresh biomass and observed under a dissecting microscope for carefully selecting all the EM root tips attending to the presence of fungal mantle and mycelium, and to the lack of root hairs. The EM root tips were frozen in liquid nitrogen, lyophilized, pulverized and an aliquot of 50 mg used for DNA extraction. Pooled soil corresponding to each sample was frozen at −80°C, and an aliquot of 500 mg was used for DNA extraction.

Molecular analyses Genomic DNA was extracted from roots and soil samples using respectively the kits DNeasy Plant MiniKit (Quiagen, Germany) and PowerSoil DNA Isolation kit (MoBio, CA, USA), according to manufacturer instructions. The variable

Bioinformatics: sequence data and taxonomic assignment The 454 .sff files and the raw data were deposited in the Sequence Read Archive (RSA-NCBI, http://www.ncbi.nlm .nih.gov/sra) as SRP045166. The sequences were sorted out into individual files attending to their multiplex identifier and trimmed and cleaned by using the command trim.flows of MOTHUR v.1.20.1 software with default parameters (Schloss et al., 2009). The internal transcribed spacer ITS-1 of the nuclear ITS region was extracted with the FUNGAL ITS EXTRACTOR v.2 (Nilsson et al., 2010), and sequences shorter than 100 bp were discarded. The 70.6% (164 023) of the initial set of sequences (232 298) was retained. MOTUs were obtained by clustering at 97% similarity with UCLUST v.3.0 (Edgar, 2010), using the most abundant sequence types as cluster seeds. The most frequent sequence type in each cluster was taken for the BLAST searches, using all fully identified entries in the GenBank or UNITE databases as reference sequences. Further taxonomic assignation against the curated GenBank database with the algorithm Blastn v 2.2.23 (Altschul et al., 1997) was done. In addition, an R script was used to assign MOTUs by the criteria: e-value ≤ e−50 identity > 90% for genus and ≥ 97% for species level. Once the tentative taxonomic identification was achieved, MOTUs were classed by ecological preference as putative mycorrhizal, saprobe, endophytic, pathogen, yeast, lichenic or unknown.

Environmental variables Remaining soil samples were air dried, sieved to 2 mm and measured for different variables: pH (1:5 w/v in H2O), electrical conductivity (1:5 w/v in H2O), OM, total C, total N (determined by the Kjeldahl method), and contents of P, K, Mg, Fe and Mn were measured by inductively coupled plasma spectrometry (Optima 4300DV, Perkin-Elmer). Climatic data (annual mean temperature and precipitation) for each eleva-


Fungi of Scots pine along elevation gradients tion site were extracted from the WorldClim database (http:// www.worldclim.org) (1 km2 resolution grid), using the geographic coordinates and the raster function of the Raster package of the R software v.3.0.1 (Hijmans et al., 2014). Geographic, climatic, and edaphic characteristics of the studied locations are detailed in Table S4.

Analysis of the richness and structure of fungal communities The number of sequences was considered as a proxy for the abundance of the corresponding MOTU (Amend et al., 2010). Previous to statistical analyses, one sequence per MOTU was eliminated at random from the abundance data set for discarding singletons (Unterseher et al., 2011). The sample size was unequal across plots with the number of sequences ranging from 1373 to 3871 for roots, and from 2053 to 4223 for soil. Fungal richness was estimated by rarefaction analysis using the rarefy function in the VEGAN R package (R Core Development Team, 2011; Oksanen et al., 2013), and variance stabilization on the raw data was performed by using the DESEQ R package (Anders and Huber, 2010) according to McMurdie and Holmes (2014), prior to community structure analyses. The spatial structure of fungal communities was examined by Mantel tests using a distance matrix of similarity in species composition built by the Bray–Curtis index and a Euclidean geographic distance matrix obtained from geographic coordinates. The analyses were carried out using the function mantel in the ECODIST R package. The assemblage of fungal communities was explored by non-parametric multivariate analysis of variance (ADONIS), and non-metric multidimensional scaling (NMDS) analyses. The Bray distance matrix of fungal species was analysed with the function metaMDS in the VEGAN R package (Oksanen et al., 2013), using the matrix of fungal species previously normalized by DESEQ. The envfit function in the VEGAN R package was used to fit the environmental variables into the NMDS space checking for significant correlations structuring the fungal communities. Rare fungi (only found in one sample) were discarded for performing these analyses.

Relationships of fungal communities with climate and edaphic conditions The fungal estimated richness was modelled as function of climatic and edaphic variables using generalized linear mixed models. Models were separately fitted for roots and soil and for climatic variables by using the NLME package in R (Pinheiro et al., 2014). We used as response variables total estimated richness and that of representative fungal sub-assemblages (i.e. Ascomycetes, Basidiomycetes, Archaeorhizomycetes class, and representative fungal families), assuming a Gaussian distribution function. The ‘region’ and ‘site nested within region’ were considered as random factors in all the models, and in the case of roots, root biomass was introduced as fixed effect to account for potential covariance effects. All statistical analyses were carried out using the R software v.3.0.1 (R Core Development Team, 2011; Oksanen et al., 2013).

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Acknowledgements We gratefully acknowledge C Bach, R Benavides and E Granda for fieldwork assistance, and J Lengellé and M Zabal for bioinformatics support. We thank the reviewers of the manuscript for their constructive comments. This study was supported by the European project, Biodiversity and Climate Change: A Risk Analysis (BACCARA, no 22000325). The ICA-CSIC group is supported by a grant of the Spanish Ministry of Innovation and Science (CGL2011-29585). The UMR IaM is supported by a grant overseen by the French National Research Agency (ANR) as part of the ‘Investissements d’Avenir’ program (ANR-11-LABX-0002-01. Lab of Excellence ARBRE). The authors have no conflict of interest to declare.

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: File S1. This EPS file contains 3 supplementary figures, each with its legend, showing: rarefaction curves (Fig. S1), general NMDS ordination of fungal assemblages (Fig. S2), and the estimated richness of the most representative fungal groups (Fig. S3). File S2. This file contains 4 supplementary tables describing sequencing and clustering yields (Table S1), the list of fungi by taxonomical ranking (Table S2), the most representative fungi per sample, region and elevation (Table S3), and the abiotic variables of the studied locations (Table S4).


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