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Article

Patterns in Ectomycorrhizal Diversity, Community Composition, and Exploration Types in European Beech, Pine, and Spruce Forests

by
Christoph Rosinger
1,
Hans Sandén
1,
Bradley Matthews
1,
Mathias Mayer
1 and
Douglas L. Godbold
1,2,*
1
Institute of Forest Ecology, Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences, 1190 Vienna, Austria
2
Global Change Research Institute, Academy of Sciences of the Czech Republic, Department of Landscape Carbon Deposition, 37001 Ceské Budejovice, Czech Republic
*
Author to whom correspondence should be addressed.
Forests 2018, 9(8), 445; https://doi.org/10.3390/f9080445
Submission received: 12 June 2018 / Revised: 19 July 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue The Ecology of Fine Roots and Mycorrhizas in Forests)
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Abstract

:
Ectomycorrhizal (EM) fungi are pivotal drivers of ecosystem functioning in temperate and boreal forests. They constitute an important pathway for plant-derived carbon into the soil and facilitate nitrogen and phosphorus acquisition. However, the mechanisms that drive ectomycorrhizal diversity and community composition are still subject to discussion. We investigated patterns in ectomycorrhizal diversity, community composition, and exploration types on root tips in Fagus sylvatica, Picea abies, and Pinus sylvestris stands across Europe. Host tree species is the most important factor shaping the ectomycorrhizal community as well as the distribution of exploration types. Moreover, abiotic factors such as soil properties, N deposition, temperature, and precipitation, were found to significantly influence EM diversity and community composition. A clear differentiation into functional traits by means of exploration types was shown for all ectomycorrhizal communities across the three analyzed tree species. Contact and short-distance exploration types were clearly significantly more abundant than cord- or rhizomorph-forming long-distance exploration types of EM fungi. Medium-distance exploration types were significantly lower in abundance than contact and short-distance types, however they were the most frequent EM taxa and constituted nearly half of the EM community. Furthermore, EM taxa exhibit distinct ecological ranges, and the type of soil exploration seemed to determine whether EM taxa have small or rather big environmental ranges.

1. Introduction

Ectomycorrhizal fungi play a fundamental role in the functioning of boreal and temperate forest ecosystems [1,2]. It has been suggested that up to 75% of phosphorus (P) and 80% of nitrogen (N) acquisition is facilitated by mycorrhizal fungi [3,4,5]. They constitute up to 40% of the total soil microbial biomass C [6,7] and contribute substantially to autotrophic CO2 respiration [8]. Several studies have shown that up to 68% of photosynthetically derived C was allocated to EM fungi for growth and respiration [5,9].
EM communities are highly diverse [10]. Diversity is highest in temperate and boreal latitudes [11,12], contrasting with the latitudinal gradient of taxa richness found for most groups of organisms [13]. In temperate and boreal forest ecosystems, the richness and diversity of ectomycorrhizal fungi strongly contrast with the relatively low number of tree species with which they form symbiosis. This relationship is exemplified in monoculture tree stands, where several studies showed dozens of different ectomycorrhizal taxa over less than 0.1 ha [14,15]. Even single root branches can be colonized by several different taxa [16].
The diversity of EM assemblages also manifests in functional diversity, i.e., EM taxa exhibit differences in functional traits. Those functional traits and their effect on ecosystem functioning have received more and more attention in EM community ecology [17]. For example, considerable differences among taxa of EM fungi have been shown in terms of nutrient acquisition [18,19] and other functions, such as C costs for maintenance respiration [20]. Moreover, functional differences of EM taxa that follow different strategies by means of development and differentiation of their external mycelium are evident [21,22], such as differences in which form of N is preferentially taken up [23,24], and the rate at which the hyphal mass decomposes [25]. Classification into different exploration types is a widely used parameter to assess functional diversity of EM taxa, as it is also relevant for ecosystem functioning [26,27,28]. However, knowledge of these functional traits is restricted to dominant EM taxa. Moreover, our understanding about functional traits and exploration type patterns on a community-level, as well as on larger geographic scales, is still poor and requires further attention.
Several studies showed that EM community composition is altered by environmental factors such as temperature [29,30,31], water availability [32,33], or N deposition [34]. Changes in community composition can also result in shifts in functional traits at the EM community level [29,35,36]. Although it is known that environmental factors affect EM taxa distribution [37,38], to date, little is known if the distribution of EM taxa is directly restricted by environmental parameters or whether the potential ecological range is mainly a reflection of the ecological range of the host tree species.
Due to the distinctive ecological niches of EM fungi [39,40], understanding biogeographic patterns of EM communities is particularly critical to evaluate the effect of future climate change on EM diversity and community composition. So far, there are only a few studies (see e.g., [11,41,42]) examining large-scale biogeographic patterns of EM fungi. Host specificity was found to be the strongest driver of EM community composition on a global scale [11], while mean annual precipitation and temperature were the strongest drivers of EM fungal richness [11]. On their global analyses on EM communities associated with alder (Alnus spp.), Põlme et al. [42] showed that phylogenetic relations among alder species was the best predictor of EM community composition, and soil calcium concentrations positively affected EM fungal richness. Across European oak forests, N deposition and edaphic variables, such as soil pH, C:N ratio, and root density, were found to be the strongest drivers of EM richness and evenness [41]. So far, it remains unclear whether these mechanisms also hold true for EM communities and the different exploration types of the most dominant tree species in Europe, namely Fagus sylvatica (L.), Picea abies (L.) Karst., and Pinus sylvestris (L.) [43].
Therefore, this study aims to explore patterns in EM diversity, community composition, and exploration types across these tree species, and to link particular patterns to environmental and host specific controls. For that, we compiled available literature on morphological and molecular based studies of EM fungal communities on root tips in Fagus sylvatica (L.), Picea abies (L.) Karst., and Pinus sylvestris (L.) stands across Europe.

2. Materials and Methods

2.1. Data Collection

We carried out an analysis based on studies investigating EM fungal community composition on root tips of Fagus sylvatica, Picea abies, and Pinus sylvestris stands in Europe. We searched for relevant literature in Web of Science and Google Scholar using the following search terms: European beech, Norway spruce, Scot’s pine, Fagus sylvatica, Picea abies, Pinus sylvestris, ectomycorrhizal diversity, and community structure. Relevant publications were then checked against expert knowledge for appropriateness. To ensure the data sets were comparable, the following prerequisites had to be fulfilled: (1) identification of EM taxa on root tips using morphotyping or morphotyping followed by molecular identification techniques; (2) EM taxa abundances in each dataset sum up to 100% and no taxa were combined into groups; (3) the area of the sampling sites was between 10−2 and 102 ha; (4) the stand was at least 25 years old. A total of 98 sites from 20 different publications satisfied these criteria (F. sylvatica, n = 31; P. abies, n = 31; P. sylvestris, n = 36), extending from Central Sweden to Central Italy (Table S1, Figure 1). As ectomycorrhizas may be composed of one or more fungal organisms we have used the term ‘taxa’ throughout the text [44].
All studies that identified root tips by molecular methods (18 out of 20 studies) used National Center for Biotechnology Information (NCBI) and UNITE databases to verify their sequence identity likelihood. Morphological identification only was carried out on two of the used studies (see Table S1). However, studies comparing morphotyping only and presorting, followed by molecular identification, have shown that these two techniques produce similar results [45,46]. Where possible, differently denoted taxonomic units from older studies were aligned with current taxonomic denominations. We did not align the sequence identity of unidentified taxa from older studies due to the paucity of metadata. Metadata on climate (mean annual air temperature, MAT; mean annual precipitation, MAP), site (host taxon, host age, soil pH, N deposition) and sampling (root tip counts) characteristics were compiled, and where missing, obtained directly from the authors. If MAT and/or MAP data could not be obtained, they were subsequently taken from Climate-Data.org based on the geographical coordinates of the site. Data on N deposition (NH4, NO3, total) were obtained from the Coordination Centre for Effects (CCE) and the European Monitoring and Evaluation Programme (EMEP) [47]. N deposition data from the year 2015 were taken, with the data distributed over a 50 × 50 km grid.

2.2. Data Analysis

In order to account for different sampling strategies between the studies (e.g., sampling design, number of replicates), EM abundance data as used for Figure 2 were standardized prior to analysis. For each site, the ectomycorrhizal taxa were set in relation to the most dominant taxa, yielding relative abundance values between 0 and 1. This standardization was not applied for the other analysis.
In order to evaluate community-level patterns by means of an exploration strategy for EM fungi, we used the Information System for Characterization and Determination of Ectomycorrhizae (DEEMY) database [48], as well as two publications [22,49], to categorize the EM taxa associated with F. sylvatica, P. abies, and P. sylvestris into contact (C), short-distance (SD), medium-distance (MD), and long-distance (LD) exploration types (see Table S2). Data were not used in cases of uncertainty or when two or more exploration types were ascribed at both the species or genus level. One-way ANOVA and Post-hoc Tukey tests were used to test for significant differences at the p < 0.05 level between the different exploration types on EM root tips.
Canonical correspondence analysis (CCA) was used to investigate the effects of environmental variables, including host taxon (tree species), stand age, MAT, MAP, nitrogen deposition (total, NH4, NO3), and pH, on both EM community composition and EM exploration types. The significances of the environmental variables were tested by means of Monte Carlo permutation tests (n = 999). A detailed explanation of this multivariate statistical technique can be found in Reference [50]. CCA’s on community composition were restricted to EM operational taxonomic units (OTUs), which were identified at the taxa level and that occurred at least at three sites. A total of 39 sites were used for the analyses, including 12 Fagus sylvatica sites, 12 Picea abies sites, and 15 Pinus sylvestris sites. Data were gathered from 11 studies, employing either morphotyping (11 sites) or morphotyping followed by molecular identification techniques (28 sites) to identify the ectomycorrhizal taxa on the sampled root tips (as indicated in Table S1). A potential bias between these techniques was tested by adding a variable “identification method” to the CCA. However, no significant effect of the identification method on EM community composition was determined (p = 0.175). Forms of N deposition were analyzed separately and only NO3 deposition showed a significant effect on the EM community composition. CCA’s were conducted in R [51] using the package “vegan” [52]. For reasons of clarity, only a selection of the most abundant EM taxa was written out in Figure 3 The rest of the EM taxa are indicated as “plus”.
Environmental ranges of EM taxa associated with F. sylvatica, P. abies, and P. sylvestris were identified using the data on host age, MAT, MAP, N deposition, and soil pH. In order to increase the informative value, we only considered EM taxa that occurred at a minimum of five plots and full data on all environmental parameters were available. The minimum and maximum values of the environmental parameters of an EM taxa associated with the three tree species in Europe were used to define an environmental range within the geographical occurrence of the sites within the data set.
Site-level taxa richness was evaluated by means of Simpson’s diversity (Simpson’s D) and evenness measures [53]. One-way ANOVA was used to test for tree species effects on Simpson’s diversity and evenness measures. Regression analysis was used to test for significant relationships between Simpson’s D and the following environmental variables: stand age, soil pH, MAT, MAP, and N deposition. The following functions were tested: linear, Gaussian, and logarithmic; and the best model was selected according to the highest R2 and the lowest p value.

3. Results

3.1. Patterns in Frequency and Abundance of EM Taxa

Across all 98 sites of F. sylvatica, P. sylvestris, and P. abies, a total of 664 different EM denominations were found, of which 269 (41%) and 177 (27%) were identified to the taxa and genus level, respectively, and 218 (33%) were unidentified (Table S3). The highest number of EM nominations (253) was found in P. abies. However, only 84 nominations were identified as taxon, leaving the majority unidentified. In contrast, EM fungal taxa associated with P. sylvestris exhibited the lowest taxa richness but the highest level of identification. Only 19 nominations were not identified as taxa. The EM fungal communities of F. sylvatica had 240 different EM nominations, of which the vast majority were identified to either taxon or genus level.
Across all sites, the majority of EM taxa (82% in Fagus, 51% in Pinus, and 66% in Picea) occurred solely on one or two sites (Figure 2). The relative abundance of taxa at these sites varied strongly, and over 60% of the EM taxa had a relative abundance less than 0.1, meaning that their abundance was less than a tenth of the taxa with the highest abundance on their respective site. The two most dominant taxa were Russula ochroleuca and Cenococcum geophilum, both of them considered multi-host taxa, and occurred on a large number of sites with a high relative abundance. There were EM taxa that had a low site frequency but a high relative abundance at the site on which they occurred. An example for such a taxon was Hebeloma sinapizans in F. sylvatica stands, which occurred only at one site. However, at that site they were the most abundant taxa colonizing ≈12% of the total root tips. Other examples were Russula pectinatoides and Piceirhiza sulfo-incrustata in P. sylvestris stands. Both occurred only at one site but had an abundance of 44% and 39%, respectively. A few EM taxa, such as Piloderma croceum or Tylospora fibrillosa, showed a highly variable abundance across sites (see Table S1 for detailed information).

3.2. Patterns in Soil Exploration of EM Fungi

In terms of abundance, contact (C) and short distance (SD) exploration types showed a higher mean abundance as compared to medium distance (MD) and cord- or rhizomorph-forming long distance (LD) exploration types of EM fungi across all three tree species (Figure 4a). In F. sylvatica and P. sylvestris stands, the mean abundance of C exploration types was 11.4 ± 1.7% and 12.1 ± 2.3%, respectively, and thereby significantly higher than any other exploration type. In P. abies stands, SD exploration types with an abundance of 11.5 ± 1.6% were the most abundant, and significantly different to the MD and LD exploration types.
MD exploration types exhibited a rather low mean abundance of 4.7 ± 0.6%, 5.9 ± 0.7% and 5.0 ± 0.4% in F. sylvatica, P. abies and P. sylvestris stands, respectively, yet the majority of all EM taxa that could be categorized into exploration types were MD exploration types, with 43.1%, 45.8%, and 50.8% in F. sylvatica, P. abies, and P. sylvestris stands, respectively (Figure 4b). Although C exploration types had the highest mean abundance of all exploration types in the F. sylvatica and P. sylvestris stands (Figure 4a), this resulted from only a small number of taxa (Figure 4b), being 17.6% of all taxa in F. sylvatica, and only 8.5% of the taxa of the EM community in P. sylvestris stands. In the P. abies stands, SD exploration types had the highest abundance, and constituted on average 30.2% of all EM taxa found in the community. As for the LD exploration types, low mean abundances of 4.8 ± 1.0%, 3.4 ± 1.0% and 4.0 ± 0.5% in F. sylvatica, P. abies, and P. sylvestris stands, respectively, were recorded as well as low taxa numbers within the EM community, with 11.3%, 6.9%, and 16.3%, respectively.

3.3. Environmental Drivers of EM Communities and Exploration Types of EM Fungi

Environmental variables explained 36% of the total variation (constrained variation) in the EM community composition as assessed by CCA (Figure 3a, Table S4). CCA axis 1 (CCA1) and CCA axis 2 (CCA2) explained 8.4% and 7.5% of the variation, respectively. Host tree species (p < 0.001), soil pH (p < 0.001), MAT (p < 0.001), and NO3 deposition (p = 0.012) were detected to be significant variables (Table S4). A total of 36% of the variation in EM exploration types was explained by environmental variables. CCA1 and CCA2 explained 26.2% and 9.9% of the variation, respectively. Soil pH (p = 0.002), MAT (p = 0.041), and host taxon (p = 0.016) were detected to be significant variables (Figure 3b, Table S4).

3.4. Environmental Ranges of EM Fungal Taxa

EM taxa and exploration types varied in their range within each environmental parameter (Figure 5b). By trend, abundant multi-host EM taxa, such as Cenococcum geophilum, Russula ochroleuca, Tylospora fibrillosa, or Lactarius rufus, showed broad ranges for all the environmental parameters examined. These four taxa are considered to be contact and short-distance exploration types. However, broad environmental ranges were also found for low abundant multi-host taxa such as Thelephora terrestris, which is considered to be a medium-distance exploration type. On the other hand, the frequently occurring taxon Xerocomus badius, a long-distance exploration EM type, exhibited a low mean abundance and was restricted to plots with low precipitation. Abundant EM taxa that were restricted to a single host were found to exhibit confined ecological ranges for at least one of our examined parameters. Two examples were the contact exploration type Piceirhiza gelatinosa, which seemed to be restricted by MAP, and Lactarius subdulcis, which was found in confined ranges for pH, MAT, and N deposition. Low abundant single-host EM taxa such as Scleroderma citrinum, which belongs to the long-distance exploration types, showed narrower environmental ranges for both MAT and MAP, as they were solely found on warm and dry sites (Figure 5b).

3.5. EM Taxa Richness and Evenness

The mean Simpson’s Diversity Index of the EM communities were 7.76 ± 0.95, 6.81 ± 0.51, and 7.57 ± 0.58 for Fagus sylvatica, Picea abies, and Pinus sylvestris forests, respectively, and no significant differences between the tree species were detected (F = 0.499, p = 0.609). The mean Simpson’s Evenness Index of the EM communities were 0.016 ± 0.002, 0.015 ± 0.003, and 0.017 ± 0.006 for the F. sylvatica, P. abies, and P. sylvestris stands, respectively, and no significant differences between the tree species were detected (F = 0.090, p = 0.914).
Linear regression analyses revealed no effect of MAT, MAP, or total N deposition on EM diversity by means of Simpson’s Diversity Index across all three tree species (Table S5), but diversity was found to be highest at an intermediate tree stand age (p = 0.014) and soil pH of 6.5 (p = 0.017) (Figure 6). Stand age was negatively correlated to Simpson’s evenness measure (F = 3.152, R2 = −0.249; p = 0.039) (Table S5), indicating that EM fungi become more evenly distributed with increasing stand age. Otherwise, no significant correlation was found for other environmental variables and taxa evenness by means of Simpson’s Evenness Index (Table S5).

4. Discussion

4.1. Environmental Drivers of EM Communities and Exploration Types

In accordance with global-scale studies [11,12], host tree species is the most important factor shaping EM communities in Fagus sylvatica, Picea abies, and Pinus sylvestris forests in Europe (Table S4). The circumstance that tree species appears as an explanatory variable might be an indirect effect of the site conditions where the tree species grow. Superimposed upon the filtering effect of the host tree are edaphic and climatic factors such as soil pH, N deposition, or mean annual temperature. In line with our study, large-scale meta-analyses and experimental manipulation studies provide ample evidence that temperature significantly influences EM diversity and community composition [11,29,30,36,54,55]. Soil pH exhibited a significant effect on both EM diversity and community structure. A similar pattern is evident for both bacterial and fungal communities, where community composition strongly varies with soil pH [12,56], and highest diversity levels were found at soils close to neutral pH [12,57]. The significant effect of pH on EM community composition of Fagus sylvatica, Picea abies, and Pinus sylvestris forests in Europe contrasts with findings from Tedersoo et al. [11], which highlighted that climatic factors, such as mean annual temperature and mean annual precipitation, were the main predictors of EM fungal richness. However, that study might have neglected soil chemical parameters, as pH was found to significantly shape EM community composition [42,58,59]. Moreover, the highest EM diversity was also found for an intermediate stand age, probably due to the fact that late- and early-stage EM fungi (sensu [60]) appear together at stands with an intermediate tree age. Increased N levels due to deposition is known to alter above- and below-ground EM diversity and community composition as well as mycelial growth [34,61,62,63,64]. While most studies investigated local and regional scales, the significant N deposition effects on EM communities is also valid on a European scale [41]. We found NO3 deposition, yet not total N deposition, to be a significant driver of EM diversity and community composition in our dataset. This could be due to the limited quality of our meta-data on N deposition, which had to be gathered from other sources for most publications. Mean annual precipitation showed no effect on EM diversity and community composition across all three analyzed tree species. This is in contrast to global-scale analyses of EM fungi, or rather fungi in general, where a negative effect of increased precipitation was found [11,12]. This negative effect was ascribed to stress from low oxygen availability in water-saturated soils, which is particularly problematic for tropical soils. However, as our analysis just covered temperate and boreal biomes, and because we had a quite small range in mean annual precipitation across our sites (Table S1), this could be the reason why we did not find an effect regarding precipitation. Furthermore, studies that experimentally induced drought showed shifts in the EM community composition [31,65,66,67], yet the effect on EM diversity was not so clear. For example, no significant effect of drought on EM diversity was found in two studies analyzing Quercus spp. [65,67], while drought significantly decreased the EM fungal richness of Fagus sylvatica, Picea abies [31], and Pinus edulis dominated forest systems [66].
Mean annual temperature, pH, and tree species were also found to significantly influence the distribution of the different exploration types across all tree species (Table S4). Within the confines of our dataset, the type of soil exploration seems, at least for the most frequent and dominant EM taxa, to determine whether EM taxa have small or rather big environmental ranges (see Figure 5). Contact or short-distance exploration types of EM fungi had, for the most part, broad environmental ranges. Cenococcum geophilum, a short distance exploration type, is reported to be the most frequently detected EM taxon worldwide [68], exhibiting an extremely wide host and habitat range. In our study it was found on 74% of all sites and was the most dominant taxon on every fifth site. Cenococcum geophilum is known for its high pioneer capability [69] and its potential to cope with extreme abiotic conditions [70,71]. In contrast, LD exploration types of EM fungi, such as Xerocomus badius, Scleroderma citrinum, or Paxillus involutus, were particularly restricted to sites with high mean annual temperatures and low precipitation. Higher abundance of rhizomorph-forming EM fungi due to warming has been found for terrestrial arctic ecosystems [29,54] and at the boreal-temperate ecotone [72], where compositional shifts were linked to host photosynthetic rates. Therefore, increased photosynthetic rates might allow the tree hosts a higher allocation of C to their fungal symbionts [73], which sustains rhizomorph-forming MD and LD exploration types that are thought to be more carbon demanding than C and SD exploration types [74,75]. Moreover, decreasing precipitation favors EM taxa with long rhizomorphs because of their ability to better explore the soil matrix and, subsequently, to transport water more efficient [76]. There is strong evidence of functional changes in EM communities by means of exploration strategy due to anthropogenic N deposition [23,41]. MD fringe exploration EM types declined drastically, while positive responses were noticed for C, SD, and MD smooth exploration types [63,77,78]. In contrast to those studies, N deposition was not found to be a significant driver of exploration type distribution in our dataset. This could be attributed to the lacking quality of meta-data on N deposition, which were not given in most studies and had to be gathered from other sources. There is evidence that N deposition is inversely correlated with soil pH [41,79]. Therefore, we could see the effect of N deposition indirectly through soil pH, as this variable was found to significantly alter the distribution of EM exploration types. In support of this, other parameters that are also linked to soil pH, such as K, Ca, or Mg concentrations, were also found to significantly affect EM community composition [42,58].

4.2. Patterns in Frequency and Abundance of EM Taxa and Exploration Types

Environmental parameters drive EM communities in which rarity is a pronounced phenomenon. There are several aspects that impede our understanding of rare EM taxa: (1) the temporal and spatial dynamics of EM communities [80,81], (2) the spatially patchy and clumped distribution of many smaller genets [82], (3) the fact that detection of rare taxa is strongly coupled to sampling effort [82,83], and (4) the circumstance that taxa with a similar appearance are either not recognized or misidentified [84,85]. Multiple dimensions of rarity must be considered. Not only can EM taxa be rare at the plot-level, the vast majority of EM taxa occurred solely on one or two out of 98 sites. Though, a taxon may exhibit a high on-site abundance on one or two sites, but a low across-site frequency. Such taxa dominate a niche and competitively exclude other taxa. Yet, the mechanisms behind local dominance but landscape rarity are yet to be discovered. In the case of low across- and on-site abundance, rare EM taxa may either have a high degree of functional complementarity [86], or they are restricted by the selective pressure of the environment, as our analysis suggests. Hence, rare EM taxa might be prone to extinction due to the stronger selective pressure that environmental factors exhibit on them. Those rare taxa might be particularly important to ensure functional stability under changing seasonal or environmental conditions [87,88].
Our community-level analysis of exploration types indicates a dominance of contact and short-distance exploration types over medium- and long-distance types by means of abundance (see Figure 4). Yet, nearly half of all EM taxa in these communities were medium-distance types. Long-distance exploration types, on the other hand, were both low in frequency and abundance. The variation in morphology (rhizomorphic in LD vs. diffuse in C and SD) and biochemistry (hyaline in LD vs. melanine in C and SD) of the different exploration types has implications for ecosystem functioning. For example, long-distance types are able to form common mycelial networks [3]. These types exhibit higher acquisition of organic N [24,26,77,89], increased water withdrawal and nutrient uptake efficiency [28], proportionally higher N and C cycling within the plant-fungus complex, and higher turnover of soil organic matter than C [90]. On the other hand, short-range exploration types (C and SD) preferentially take up N as nitrate and ammonium [24,26,78,89], and they have been found to facilitate humus build-up due to the biochemical nature of the mycelium [91], leading to a high C sequestration in soils. Thus a shift in exploration type composition driven by environmental change will likely affect the C and N cycling of the ecosystem.

4.3. Limitations of the Study

Although we assembled a data set using all available published data that fulfilled our criteria, there are clearly shortcomings. A number of the studies lacked information on sampling design and effort, and it is known that sample number and volume significantly affect estimates of EM diversity [11,82]. Only a small number of publications evaluated their sampling effort by calculating taxa accumulation curves. While abundant EM taxa might still be reliably estimated, the number of less frequent taxa might be disproportionately underestimated, owing to the high taxa richness in EM fungi, their clustered distribution and their temporal and spatial variation [92]. Although we have not detected any effect of sampling effort on EM taxa richness or Simpson’s D across all tree species, an earlier examination of Fagus sylvatica stands revealed a significant relationship between the number of counted root tips and EM taxa richness [84]. Furthermore, the study sites used in our analyses are spatially unequally distributed across Europe and might not cover the whole environmental range of the host trees, and as many EM taxa can also be associated with other tree species, the potential environmental range of EM taxa is probably larger than we could capture with our dataset. Despite these limitations, our results shed light on the structure of EM communities in European forests and the importance of large-scale environmental parameters that, among others, considerably affect the distribution of EM fungi.

5. Conclusions

In this study we show that EM communities in European forests comprise of a small number of abundant taxa and a large number of rare taxa. Contact and short-distance exploration types were highest in abundance, while medium-distance exploration types constituted around half of the EM communities. Long-distance exploration types were both low in frequency and abundance. We furthermore show that both host taxon and environmental parameters significantly shape EM communities and exploration types across European forests. The four most abundant and frequent taxa Cenococcum geophilum, Piceirhiza gelatinosa, Russula ochroleuca, and Tylospora fibrillosa, all of which are considered contact or short-distance exploring EM taxa, show broad ranges for all the environmental parameters examined. On the other hand, low abundant EM taxa show narrower environmental niche breadths for both MAT and MAP, which seem to limit their occurrence. EM taxa with narrow ecological ranges are particularly threatened by environmental changes. As climatic conditions in Europe are expected to change in the future, shifts in EM community composition are therefore very likely. This might have significant effects on biogeochemical processes and C sequestration in European forest ecosystems.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/9/8/445/s1, Table S1: Abundance of EM taxa in Fagus sylvatica, Picea abies, and Pinus sylvestris from 98 sites including site characteristics and further information; Table S2: Data sheet for EM exploration types on root tips in Fagus sylvatica, Picea abies, and Pinus sylvestris stands; Table S3: Levels of identification in ectomycorrhizal communities found on root tips of Fagus sylvatica, Picea abies, and Pinus sylvestris; Table S4: Results from Canonical Correspondence Analyses (CCA) on ectomycorrhizal (EM) community composition and exploration types of EM fungi. Shown are the total inertia of CCA’s and inertia explained by the constrained environmental variables pH, mean annual temperature (MAT), mean annual precipitation (MAP), host age, NO3 deposition, and host taxon (tree species). Further shown are the eigenvalues of the first and second CCA axes and the significances (p-values) of the individual environmental variables as assessed by Monte Carlo permutation tests; Table S5: Regression analysis between environmental parameters (pH; mean annual temperature, MAT; mean annual precipitation, MAP; stand age; N deposition) and Simpson’s Diversity Index (Simpson´s D), as well as Simpson’s Evenness Index (Simpson´s E) of the EM communities in Fagus sylvatica, Picea abies, and Pinus sylvestris stands across Europe.

Author Contributions

Conceptualization, C.R., H.S., D.L.G.; Methodology, C.R., B.M., M.M.; Formal Analysis, C.R., B.M., M.M.; Investigation, C.R.; Data Curation, C.R., B.M., M.M.; Writing—Original Draft Preparation, C.R., H.S., B.M., M.M., D.L.G.; Writing—Review & Editing, C.R., H.S., B.M., M.M., D.L.G.; Visualization, C.R., B.M., M.M.; Supervision, H.S., D.L.G.; Funding Acquisition, D.L.G.

Funding

This work was funded by a Marie Curie Actions Career Integration Grant GPF333996 LINKTOFUN and an Austrian Academy of Sciences grant C-Alp. This work was also funded by the Ministry of Education, Youth and Sports of CR within the National Sustainability Program NPU I, grant No. LO1415.

Acknowledgments

We thank many researchers for providing metadata and Blandine Bossy for assisting with data collection and processing. This work is supported by the BOKU Vienna Open Access Publishing Fund.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Finlay, R.D. Ecological aspects of mycorrhizal symbiosis: With special emphasis on the functional diversity of interactions involving the extraradical mycelium. J. Exp. Bot. 2008, 59, 1115–1126. [Google Scholar] [CrossRef] [PubMed]
  2. Smith, S.; Read, D. Mycorrhizal Symbiosis, 3rd ed.; Academic Press: San Diego, CA, USA, 2008. [Google Scholar]
  3. Simard, S.W.; Jones, M.D.; Durall, D.M. Carbon and nutrient fluxes within and between mycorrhizal plants. In Mycorrhizal Ecology; Springer: Berlin, Germany, 2003; pp. 33–74. [Google Scholar]
  4. Van der Heijden, M.G.; Bardgett, R.D.; Van Straalen, N.M. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 2008, 11, 296–310. [Google Scholar] [CrossRef] [PubMed]
  5. Hobbie, J.E.; Hobbie, E.A. 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in arctic tundra. Ecology 2006, 87, 816–822. [Google Scholar] [CrossRef]
  6. Högberg, M.N.; Briones, M.J.; Keel, S.G.; Metcalfe, D.B.; Campbell, C.; Midwood, A.J.; Thornton, B.; Hurry, V.; Linder, S.; Näsholm, T. Quantification of effects of season and nitrogen supp. ly on tree below-ground carbon transfer to ectomycorrhizal fungi and other soil organisms in a boreal pine forest. New Phytol. 2010, 187, 485–493. [Google Scholar] [CrossRef] [PubMed]
  7. Clemmensen, K.; Bahr, A.; Ovaskainen, O.; Dahlberg, A.; Ekblad, A.; Wallander, H.; Stenlid, J.; Finlay, R.; Wardle, D.; Lindahl, B. Roots and associated fungi drive long-term carbon sequestration in boreal forest. Science 2013, 339, 1615–1618. [Google Scholar] [CrossRef] [PubMed]
  8. Heinemeyer, A.; Hartley, I.P.; Evans, S.P.; Carreira de la Fuente, J.A.; Ineson, P. Forest soil CO2 flux: Uncovering the contribution and environmental responses of ectomycorrhizas. Glob. Chang. Biol. 2007, 13, 1786–1797. [Google Scholar] [CrossRef]
  9. Hobbie, E.A. Carbon allocation to ectomycorrhizal fungi correlates with belowground allocation in culture studies. Ecology 2006, 87, 563–569. [Google Scholar] [CrossRef] [PubMed]
  10. Rinaldi, A.; Comandini, O.; Kuyper, T.W. Ectomycorrhizal fungal diversity: Seperating the wheat from the chaff. Fungal Divers. 2008, 33, 1–45. [Google Scholar]
  11. Tedersoo, L.; Bahram, M.; Toots, M.; Diedhiou, A.G.; Henkel, T.W.; Kjøller, R.; Morris, M.H.; Nara, K.; Nouhra, E.; Peay, K.G. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol. 2012, 21, 4160–4170. [Google Scholar] [CrossRef] [PubMed]
  12. Tedersoo, L.; Bahram, M.; Põlme, S.; Kõljalg, U.; Yorou, N.S.; Wijesundera, R.; Ruiz, L.V.; Vasco-Palacios, A.M.; Thu, P.Q.; Suija, A. Global diversity and geography of soil fungi. Science 2014, 346, 1256688. [Google Scholar] [CrossRef] [PubMed]
  13. Tedersoo, L.; Nara, K. General latitudinal gradient of biodiversity is reversed in ectomycorrhizal fungi. New Phytol. 2010, 185, 351–354. [Google Scholar] [CrossRef] [PubMed]
  14. Kjøller, R. Disproportionate abundance between ectomycorrhizal root tips and their associated mycelia. FEMS Microbiol. Ecol. 2006, 58, 214–224. [Google Scholar] [CrossRef] [PubMed]
  15. Pena, R.; Offermann, C.; Simon, J.; Naumann, P.S.; Geßler, A.; Holst, J.; Dannenmann, M.; Mayer, H.; Kögel-Knabner, I.; Rennenberg, H.; et al. Girdling affects ectomycorrhizal fungal (EMF) diversity and reveals functional differences in emf community composition in a beech forest. App. Lied Environ. Microbiol. 2010, 76, 1831–1841. [Google Scholar] [CrossRef] [PubMed]
  16. Bruns, T.D. Thoughts on the processes that maintain local species diversity of ectomycorrhizal fungi. In The Significance and Regulation of Soil Biodiversity; Springer: Berlin, Germany, 1995; pp. 63–73. [Google Scholar]
  17. Courty, P.-E.; Munoz, F.; Selosse, M.A.; Duchemin, M.; Criquet, S.; Ziarelle, F.; Buée, M.; Plassard, C.; Taudière, A.; Garbaye, J. Into the functional ecology of ectomycorrhizal communities: Environmental filtering of enzymatic activities. J. Ecol. 2016, 104, 1585–1598. [Google Scholar]
  18. Courty, P.E.; Pritsch, K.; Schloter, M.; Hartmann, A.; Garbaye, J. Activity profiling of ectomycorrhiza communities in two forest soils using multiple enzymatic tests. New Phytol. 2005, 167, 309–319. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Courty, P.-E.; Pouysegur, R.; Buée, M.; Garbaye, J. Laccase and phosphatase activities of the dominant ectomycorrhizal types in a lowland oak forest. Soil Biol. Biochem. 2006, 38, 1219–1222. [Google Scholar] [CrossRef]
  20. Gorissen, A.; Kuyper, T.W. Fungal species-specific responses of ectomycorrhizal Scots pine (Pinus sylvestris) to elevated CO2. New Phytol. 2000, 146, 163–168. [Google Scholar] [CrossRef]
  21. Agerer, R.; Rambold, G. Deemy—An Information System for Characterization and Determination of Ectomycorrhizae. Available online: www.deemy.de (accessed on 15 November 2016).
  22. Agerer, R. Exploration types of ectomycorrhizae. Mycorrhiza 2001, 11, 107–114. [Google Scholar] [CrossRef]
  23. Pena, R.; Lang, C.; Lohaus, G.; Boch, S.; Schall, P.; Schöning, I.; Ammer, C.; Fischer, M.; Polle, A. Phylogenetic and functional traits of ectomycorrhizal assemblages in top soil from different biogeographic regions and forest types. Mycorrhiza 2016, 27, 1–13. [Google Scholar] [CrossRef] [PubMed]
  24. Tedersoo, L.; Smith, M.E. Lineages of ectomycorrhizal fungi revisited: Foraging strategies and novel lineages revealed by sequences from belowground. Fungal Biol. Rev. 2013, 27, 83–99. [Google Scholar] [CrossRef]
  25. Fernandez, C.W.; Langley, J.A.; Chapman, S.; McCormack, M.L.; Koide, R.T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 2016, 93, 38–49. [Google Scholar] [CrossRef] [Green Version]
  26. Hobbie, E.A.; Agerer, R. Nitrogen isotopes in ectomycorrhizal sporocarps correspond to belowground exploration types. Plant Soil 2010, 327, 71–83. [Google Scholar] [CrossRef]
  27. Tedersoo, L.; Naadel, T.; Bahram, M.; Pritsch, K.; Buegger, F.; Leal, M.; Kõljalg, U.; Põldmaa, K. Enzymatic activities and stable isotope patterns of ectomycorrhizal fungi in relation to phylogeny and exploration types in an afrotropical rain forest. New Phytol. 2012, 195, 832–843. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Koide, R.T.; Fernandez, C.; Malcolm, G. Determining place and process: Functional traits of ectomycorrhizal fungi that affect both community structure and ecosystem function. New Phytol. 2014, 201, 433–439. [Google Scholar] [CrossRef] [PubMed]
  29. Morgado, L.N.; Semenova, T.A.; Welker, J.M.; Walker, M.D.; Smets, E.; Geml, J. Summer temperature increase has distinct effects on the ectomycorrhizal fungal communities of moist tussock and dry tundra in arctic Alaska. Glob. Chang. Biol. 2015, 21, 959–972. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Deslipp, E.J.; Hartmann, M.; Mohn, W.; Simard, S. Long-term experimental manipulation of climate alters the ectomycorrhizal community of Betula nana in arctic tundra. Glob. Chang. Biol. 2011, 17, 1625–1636. [Google Scholar] [CrossRef]
  31. Nickel, U.T.; Weikl, F.; Kerner, R.; Schäfer, C.; Kallenbach, C.; Munch, J.C.; Pritsch, K. Quantitative losses vs. Qualitative stability of ectomycorrhizal community responses to 3 years of experimental summer drought in a beech–spruce forest. Glob. Chang. Biol. 2017, e560–e576. [Google Scholar] [CrossRef] [PubMed]
  32. Gehring, C.A.; Theimer, T.C.; Whitham, T.G.; Keim, P. Ectomycorrhizal fungal community structure of pinyon pines growing in two environmental extremes. Ecology 1998, 79, 1562–1572. [Google Scholar] [CrossRef]
  33. Gordon, G.J.; Gehring, C.A. Molecular characterization of pezizalean ectomycorrhizas associated with pinyon pine during drought. Mycorrhiza 2011, 21, 431–441. [Google Scholar] [CrossRef] [PubMed]
  34. Lilleskov, E.A.; Fahey, T.J.; Horton, T.R.; Lovett, G.M. Belowground ectomycorrhizal fungal community change over a nitrogen deposition gradient in Alaska. Ecology 2002, 83, 104–115. [Google Scholar] [CrossRef]
  35. Geml, J.; Morgado, L.N.; Semenova, T.A.; Welker, J.M.; Walker, M.D.; Smets, E. Long-term warming alters richness and composition of taxonomic and functional groups of arctic fungi. FEMS Microbiol. Ecol. 2015, 91, fiv095. [Google Scholar] [CrossRef] [PubMed]
  36. Treseder, K.K.; Marusenko, Y.; Romero-Olivares, A.L.; Maltz, M.R. Experimental warming alters potential function of the fungal community in boreal forest. Glob. Chang. Biol. 2016, 22, 3395–3404. [Google Scholar] [CrossRef] [PubMed]
  37. Miyamoto, Y.; Sakai, A.; Hattori, M.; Nara, K. Strong effect of climate on ectomycorrhizal fungal composition: Evidence from range overlap between two mountains. ISME J. 2015, 9, 1870–1879. [Google Scholar] [CrossRef] [PubMed]
  38. Bahram, M.; Põlme, S.; Kõljalg, U.; Zarre, S.; Tedersoo, L. Regional and local patterns of ectomycorrhizal fungal diversity and community structure along an altitudinal gradient in the hyrcanian forests of northern Iran. New Phytol. 2012, 193, 465–473. [Google Scholar] [CrossRef] [PubMed]
  39. Buée, M.; Courty, P.; Mignot, D.; Garbaye, J. Soil niche effect on species diversity and catabolic activities in an ectomycorrhizal fungal community. Soil Biol. Biochem. 2007, 39, 1947–1955. [Google Scholar] [CrossRef]
  40. Dickie, I.A.; Xu, B.; Koide, R.T. Vertical niche differentiation of ectomycorrhizal hyphae in soil as shown by T-RFLP analysis. New Phytol. 2002, 156, 527–535. [Google Scholar] [CrossRef] [Green Version]
  41. Suz, L.M.; Barsoum, N.; Benham, S.; Dietrich, H.P.; Fetzer, K.D.; Fischer, R.; García, P.; Gehrman, J.; Kristöfel, F.; Manninger, M. Environmental drivers of ectomycorrhizal communities in Europe’s temperate oak forests. Mol. Ecol. 2014, 23, 5628–5644. [Google Scholar] [CrossRef] [PubMed]
  42. Põlme, S.; Bahram, M.; Yamanaka, T.; Nara, K.; Dai, Y.C.; Grebenc, T.; Kraigher, H.; Toivonen, M.; Wang, P.H.; Matsuda, Y. Biogeography of ectomycorrhizal fungi associated with alders (Alnus spp.) in relation to biotic and abiotic variables at the global scale. New Phytol. 2013, 198, 1239–1249. [Google Scholar] [CrossRef] [PubMed]
  43. Köble, R.; Seufert, G. Novel Maps for Forest Tree Species in Europe. In Proceedings of the 8th European Symposium on the Physico-Chemical Behaviour of air Pollutants: A Changing Atmosphere, Torino, Italy, 17–20 September 2001; pp. 17–20. [Google Scholar]
  44. Read, D.J.; Leake, J.R.; Perez-Moreno, J. Mycorrhizal fungi as drivers of ecosystem processes in heathland and boreal forest biomes. Can. J. Bot. 2004, 82, 1243–1263. [Google Scholar] [CrossRef]
  45. Horton, T.; Bruns, T. Multiple-host fungi are the most frequent and abundant ectomycorrhizal types in a mixed stand of douglas fir (Pseudotsuga menziesii) and bishop pine (Pinus muricata). New Phytol. 1998, 139, 331–339. [Google Scholar] [CrossRef]
  46. Sakakibara, S.M.; Jones, M.D.; Gillespie, M.; Hagerman, S.M.; Forrest, M.E.; Simard, S.W.; Durall, D.M. A comparison of ectomycorrhiza identification based on morphotyping and PCR-RFLP analysis. Mycol. Res. 2002, 106, 868–878. [Google Scholar] [CrossRef]
  47. Schöpp, W.; Posch, M.; Mylona, S.; Johansson, M. Long-term development of acid deposition (1880–2030) in sensitive freshwater regions in europe. Hydrol. Earth Syst. Sci. Discuss. 2003, 7, 436–446. [Google Scholar] [CrossRef] [Green Version]
  48. Rambold, G.; Agerer, R. Deemy–the concept of a characterization and determination system for ectomycorrhizae. Mycorrhiza 1997, 7, 113–116. [Google Scholar] [CrossRef]
  49. Wallander, H.; Ekblad, A.; Godbold, D.; Johnson, D.; Bahr, A.; Baldrian, P.; Björk, R.; Kieliszewska-Rokicka, B.; Kjøller, R.; Kraigher, H. Evaluation of methods to estimate production, biomass and turnover of ectomycorrhizal mycelium in forests soils—A review. Soil Biol. Biochem. 2013, 57, 1034–1047. [Google Scholar] [CrossRef]
  50. Paliy, O.; Shankar, V. Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 2016, 25, 1032–1057. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. R Core Team, R. A language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2014. [Google Scholar]
  52. Oksanen, J.; Blanchet, F.G.; Kindt, R.; Legendre, P.; Minichin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; Stevens, M.H.H.; Wagner, H. Vegan: Community Ecology Package. Available online: http://CRAN.R-project.org/package=vegan (accessed on 3 April 2017).
  53. Simpson, E.H. Measurement of diversity. Nature 1949, 163, 688. [Google Scholar] [CrossRef]
  54. Clemmensen, K.E.; Michelsen, A.; Jonasson, S.; Shaver, G.R. Increased ectomycorrhizal fungal abundance after long-term fertilization and warming of two arctic tundra ecosystems. New Phytol. 2006, 171, 391–404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Penton, C.R.; St Louis, D.; Cole, J.R.; Luo, Y.; Wu, L.; Schuur, E.G.; Zhou, J.; Tiedje, J.M. Fungal diversity in permafrost and tallgrass prairie soils under experimental warming conditions. App. Lied Environ. Microbiol. 2013, 79, 7063–7072. [Google Scholar] [CrossRef] [PubMed]
  56. Lauber, C.L.; Hamady, M.; Knight, R.; Fierer, N. Soil ph as a predictor of soil bacterial community structure at the continental scale: A pyrosequencing-based assessment. App. Lied Environ. Microbiol. 2009, 75, 5111–5120. [Google Scholar] [CrossRef] [PubMed]
  57. Fierer, N.; Leff, J.W.; Adams, B.J.; Nielsen, U.N.; Bates, S.T.; Lauber, C.L.; Owens, S.; Gilbert, J.A.; Wall, D.H.; Caporaso, J.G. Cross-biome metagenomic analyses of soil microbial communities and their functional attributes. Proc. Natl. Acad. Sci. USA 2012, 109, 21390–21395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Tedersoo, L.; Suvi, T.; Jairus, T.; Ostonen, I.; Põlme, S. Revisiting ectomycorrhizal fungi of the genus Alnus: Differential host specificity, diversity and determinants of the fungal community. New Phytol. 2009, 182, 727–735. [Google Scholar] [CrossRef] [PubMed]
  59. Erland, S.; Söderström, B. Effects of lime and ash treatments on ectomycorrhizal infection of Pinus sylvestris L. seedlings planted in a pine forest. Scand. J. For. Res. 1991, 6, 519–525. [Google Scholar] [CrossRef]
  60. Deacon, J.; Fleming, L. Interactions of ectomycorrhizal fungi. In Mycorrhizal Functioning: An Integrative Plant-Fungal Process; Springer: Berlin, Germany, 1992; Volume 1249. [Google Scholar]
  61. Nilsson, L.O.; Bååth, E.; Falkengren-Grerup, U.; Wallander, H. Growth of ectomycorrhizal mycelia and composition of soil microbial communities in oak forest soils along a nitrogen deposition gradient. Oecologia 2007, 153, 375–384. [Google Scholar] [CrossRef] [PubMed]
  62. Lilleskov, E.; Fahey, T.; Lovett, G. Ectomycorrhizal fungal aboveground community change over an atmospheric nitrogen deposition gradient. Ecol. Appl. 2001, 11, 397–410. [Google Scholar] [CrossRef]
  63. Kjøller, R.; Nilsson, L.O.; Hansen, K.; Schmidt, I.K.; Vesterdal, L.; Gundersen, P. Dramatic changes in ectomycorrhizal community composition, root tip abundance and mycelial production along a stand-scale nitrogen deposition gradient. New Phytol. 2012, 194, 278–286. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Arnolds, E. Decline of ectomycorrhizal fungi in europe. Agric. Ecosyst. Environ. 1991, 35, 209–244. [Google Scholar] [CrossRef]
  65. Richard, F.; Roy, M.; Shahin, O.; Sthultz, C.; Duchemin, M.; Joffre, R.; Selosse, M.-A. Ectomycorrhizal communities in a mediterranean forest ecosystem dominated by Quercus ilex: Seasonal dynamics and response to drought in the surface organic horizon. Ann. For. Sci. 2011, 68, 57–68. [Google Scholar] [CrossRef]
  66. Swaty, R.L.; Deckert, R.J.; Whitham, T.G.; Gehring, C.A. Ectomycorrhizal abundance and community composition shifts with drought: Predictions from tree rings. Ecology 2004, 85, 1072–1084. [Google Scholar] [CrossRef]
  67. Cavender-Bares, J.; Izzo, A.; Robinson, R.; Lovelock, C. Changes in ectomycorrhizal community structure on two containerized oak hosts across an experimental hydrologic gradient. Mycorrhiza 2009, 19, 133–142. [Google Scholar] [CrossRef] [PubMed]
  68. LoBuglio, K.F.; Berbee, M.L.; Taylor, J.W. Phylogenetic origins of the asexual mycorrhizal symbiont Cenococcum geophilum Fr. and other mycorrhizal fungi among the ascomycetes. Mol. Phylogenet. Evol. 1996, 6, 287–294. [Google Scholar] [CrossRef] [PubMed]
  69. Trapp, E.J.M. Cenococcum Graniforme—Its Distribution, Ecology, Mycorrhiza Formation, And Inherent Variation. Ph.D. Thesis, University of Washington, Seattle, WA, USA, 1962. [Google Scholar]
  70. Haselwandter, K.; Read, D. Fungal associations of roots of dominant and sub-dominant plants in high-alpine vegetation systems with special reference to mycorrhiza. Oecologia 1980, 45, 57–62. [Google Scholar] [CrossRef] [PubMed]
  71. Koide, R.; Suomi, L.; Stevens, C.; McCormick, L. Interactions between needles of pinus resinosa and ectomycorrhizal fungi. New Phytol. 1998, 140, 539–547. [Google Scholar] [CrossRef]
  72. Fernandez, C.W.; Nguyen, N.H.; Stefanski, A.; Han, Y.; Hobbie, S.E.; Montgomery, R.A.; Reich, P.B.; Kennedy, P.G. Ectomycorrhizal fungal response to warming is linked to poor host performance at the boreal-temperate ecotone. Glob. Chang. Biol. 2016, 23, 1598–1609. [Google Scholar] [CrossRef] [PubMed]
  73. Ekblad, A.; Wallander, H.; Godbold, D.; Cruz, C.; Johnson, D.; Baldrian, P.; Björk, R.; Epron, D.; Kieliszewska-Rokicka, B.; Kjøller, R. The production and turnover of extramatrical mycelium of ectomycorrhizal fungi in forest soils: Role in carbon cycling. Plant Soil 2013, 366, 1–27. [Google Scholar] [CrossRef]
  74. Fransson, P. Elevated CO2 impacts ectomycorrhiza-mediated forest soil carbon flow: Fungal biomass production, respiration and exudation. Fungal Ecol. 2012, 5, 85–98. [Google Scholar] [CrossRef]
  75. Alberton, O.; Kuyper, T.W. Ectomycorrhizal fungi associated with pinus sylvestris seedlings respond differently to increased carbon and nitrogen availability: Implications for ecosystem responses to global change. Glob. Chang. Biol. 2009, 15, 166–175. [Google Scholar] [CrossRef]
  76. Lamhamedi, M.S.; Bernier, P.Y.; André-Fortin, J. Hydraulic conductance and soil water potential at the soil–root interface of pinus pinaster seedlings inoculated with different dikaryons of pisolithus sp. Tree Physiol. 1992, 10, 231–244. [Google Scholar] [CrossRef] [PubMed]
  77. Lilleskov, E.; Hobbie, E.; Horton, T. Conservation of ectomycorrhizal fungi: Exploring the linkages between functional and taxonomic responses to anthropogenic n deposition. Fungal Ecol. 2011, 4, 174–183. [Google Scholar] [CrossRef]
  78. Cox, F.; Barsoum, N.; Lilleskov, E.A.; Bidartondo, M.I. Nitrogen availability is a primary determinant of conifer mycorrhizas across complex environmental gradients. Ecol. Lett. 2010, 13, 1103–1113. [Google Scholar] [CrossRef] [PubMed]
  79. Van Breemen, N.; Driscoll, C.; Mulder, J. Acidic deposition and internal proton sources in acidification of soils and waters. Nature 1984, 307, 599. [Google Scholar] [CrossRef]
  80. Erland, S.; Taylor, A.F. Diversity of ecto-mycorrhizal fungal communities in relation to the abiotic environment. In Mycorrhizal Ecology; Springer: Berlin, Germany, 2002; pp. 163–200. [Google Scholar]
  81. Jonsson, L.M.; Nilsson, M.C.; Wardle, D.A.; Zackrisson, O. Context dependent effects of ectomycorrhizal species richness on tree seedling productivity. Oikos 2001, 93, 353–364. [Google Scholar] [CrossRef]
  82. Taylor, A.F. Fungal diversity in ectomycorrhizal communities: Sampling effort and species detection. In Diversity and Integration in Mycorrhizas; Springer: Berlin, Germany, 2002; pp. 19–28. [Google Scholar]
  83. Rosinger, C.; Sandén, H.; Godbold, D.L. Ectomycorrhizal diversity in beech dominated stands in Central Europe. In Soil Biological Communities and Ecosystem Resilience; Springer: Berlin, Germany, 2017; pp. 143–156. [Google Scholar]
  84. Lindahl, B.D.; Nilsson, R.H.; Tedersoo, L.; Abarenkov, K.; Carlsen, T.; Kjøller, R.; Kõljalg, U.; Pennanen, T.; Rosendahl, S.; Stenlid, J. Fungal community analysis by high-throughput sequencing of amplified markers—A user’s guide. New Phytol. 2013, 199, 288–299. [Google Scholar] [CrossRef] [PubMed]
  85. Nguyen, N.H.; Smith, D.; Peay, K.; Kennedy, P. Parsing ecological signal from noise in next generation amplicon sequencing. New Phytol. 2015, 205, 1389–1393. [Google Scholar] [CrossRef] [PubMed]
  86. Buée, M.; Vairelles, D.; Garbaye, J. Year-round monitoring of diversity and potential metabolic activity of the ectomycorrhizal community in a beech (Fagus silvatica) forest subjected to two thinning regimes. Mycorrhiza 2005, 15, 235–245. [Google Scholar] [CrossRef] [PubMed]
  87. Yachi, S.; Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. USA 1999, 96, 1463–1468. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  88. Sterk, M.; Gort, G.; Klimkowska, A.; Van Ruijven, J.; Van Teeffelen, A.; Wamelink, G. Assess ecosystem resilience: Linking response and effect traits to environmental variability. Ecol. Indic. 2013, 30, 21–27. [Google Scholar] [CrossRef]
  89. Pritsch, K.; Garbaye, J. Enzyme secretion by ECM fungi and exploitation of mineral nutrients from soil organic matter. Ann. For. Sci. 2011, 68, 25–32. [Google Scholar] [CrossRef] [Green Version]
  90. Phillips, L.A.; Ward, V.; Jones, M.D. Ectomycorrhizal fungi contribute to soil organic matter cycling in sub-boreal forests. ISME J. 2014, 8, 699–713. [Google Scholar] [CrossRef] [PubMed]
  91. Clemmensen, K.E.; Finlay, R.D.; Dahlberg, A.; Stenlid, J.; Wardle, D.A.; Lindahl, B.D. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. New Phytol. 2015, 205, 1525–1536. [Google Scholar] [CrossRef] [PubMed]
  92. Bahram, M.; Peay, K.G.; Tedersoo, L. Local-scale biogeography and spatiotemporal variability in communities of mycorrhizal fungi. New Phytol. 2015, 205, 1454–1463. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Map of all 98 study sites in Europe; red dots represent Pinus sylvestris stands; green dots represent Fagus sylvatica stands; yellow dots represent Picea abies stands. Map data © 2018 Google.
Figure 1. Map of all 98 study sites in Europe; red dots represent Pinus sylvestris stands; green dots represent Fagus sylvatica stands; yellow dots represent Picea abies stands. Map data © 2018 Google.
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Figure 2. Relative abundances of ectomycorrhizal taxa across 98 stands of Fagus sylvatica (n = 31), Picea abies (n = 31) and Pinus sylvestris (n = 36) in Europe. To account for possible effects of sampling size and design, all ectomycorrhizal taxa were set in relation to the most dominant taxon of each site, yielding relative abundance values between 0 and 1 for each ectomycorrhizal taxon; e.g., a relative abundance of 1 means that it is the most abundant taxon on all the sites where it is present; or an ectomycorrhizal taxon with a relative abundance of 0.2 is on average a fifth as abundant as the most abundant taxon on the respective plots. Error bars indicate standard error (SE) and are only shown for EM taxa present at more than 10 sites.
Figure 2. Relative abundances of ectomycorrhizal taxa across 98 stands of Fagus sylvatica (n = 31), Picea abies (n = 31) and Pinus sylvestris (n = 36) in Europe. To account for possible effects of sampling size and design, all ectomycorrhizal taxa were set in relation to the most dominant taxon of each site, yielding relative abundance values between 0 and 1 for each ectomycorrhizal taxon; e.g., a relative abundance of 1 means that it is the most abundant taxon on all the sites where it is present; or an ectomycorrhizal taxon with a relative abundance of 0.2 is on average a fifth as abundant as the most abundant taxon on the respective plots. Error bars indicate standard error (SE) and are only shown for EM taxa present at more than 10 sites.
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Figure 3. Patterns of variation in (a) EM community composition and (b) exploration type composition across F. sylvatica, P. abies, and P. sylvestris stands as explained by pH, mean annual temperature (MAT), mean annual precipitation (MAP), host age, and N deposition (NO3) using canonical correspondence analysis (CCA). Small dots represent different plots and large dots represent site averages. Colored dots represent the different sites (green, F. sylvatica; yellow, P. abies; red, P. sylvestris), while grey “plus”-symbols represent different EM taxa. A selection of the most abundant taxa is highlighted with their scientific names, while the rest of the taxa are indicated with a “plus”-symbol. Abbreviations of exploration types: unknown type (UK), contact type (C), short distance type (SD), medium distance type (MD), long distance type (LD). CCA axes show the explained variations in brackets.
Figure 3. Patterns of variation in (a) EM community composition and (b) exploration type composition across F. sylvatica, P. abies, and P. sylvestris stands as explained by pH, mean annual temperature (MAT), mean annual precipitation (MAP), host age, and N deposition (NO3) using canonical correspondence analysis (CCA). Small dots represent different plots and large dots represent site averages. Colored dots represent the different sites (green, F. sylvatica; yellow, P. abies; red, P. sylvestris), while grey “plus”-symbols represent different EM taxa. A selection of the most abundant taxa is highlighted with their scientific names, while the rest of the taxa are indicated with a “plus”-symbol. Abbreviations of exploration types: unknown type (UK), contact type (C), short distance type (SD), medium distance type (MD), long distance type (LD). CCA axes show the explained variations in brackets.
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Figure 4. (a) Mean abundance of EM exploration types associated with F. sylvatica, P. abies, and P. sylvestris. Bars represent mean abundance of each respective exploration type ± 1 SE. Different letters above the bars indicate significant differences between exploration types within each tree species. * p < 0.05; ** p < 0.01. (b) Number of EM taxa within each exploration type associated with F. sylvatica, P. abies, and P. sylvestris. EM taxa proportions of 14.5%, 37.0%, and 20.4% associated with F. sylvatica, P. abies, and P. sylvestris, respectively, could not be adequately categorized into one of the four EM exploration types.
Figure 4. (a) Mean abundance of EM exploration types associated with F. sylvatica, P. abies, and P. sylvestris. Bars represent mean abundance of each respective exploration type ± 1 SE. Different letters above the bars indicate significant differences between exploration types within each tree species. * p < 0.05; ** p < 0.01. (b) Number of EM taxa within each exploration type associated with F. sylvatica, P. abies, and P. sylvestris. EM taxa proportions of 14.5%, 37.0%, and 20.4% associated with F. sylvatica, P. abies, and P. sylvestris, respectively, could not be adequately categorized into one of the four EM exploration types.
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Figure 5. (a) Environmental ranges provided by each tree species; (b) environmental range of EM taxa associated with F. sylvatica, P. abies, and P. sylvestris found on at least five sites; numbers in parentheses indicates frequency on number of sites (from a total of 98 sites); letters in parentheses indicate the exploration type (C, contact type; SD, short-distance type; MD, medium-distance type; LD, long distance type) and the different host tree species (Fs, Fagus sylvatica; Ps, Pinus sylvestris; Pa, Picea abies). Triangles represent the median, and error bars represent minimum and maximum environmental range.
Figure 5. (a) Environmental ranges provided by each tree species; (b) environmental range of EM taxa associated with F. sylvatica, P. abies, and P. sylvestris found on at least five sites; numbers in parentheses indicates frequency on number of sites (from a total of 98 sites); letters in parentheses indicate the exploration type (C, contact type; SD, short-distance type; MD, medium-distance type; LD, long distance type) and the different host tree species (Fs, Fagus sylvatica; Ps, Pinus sylvestris; Pa, Picea abies). Triangles represent the median, and error bars represent minimum and maximum environmental range.
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Figure 6. Regression between Simpson’s diversity Index (Simpson’s D) and (a) stand age, (b) mean annual temperature, (c) N deposition, (d) pH, and (e) mean annual precipitation across F. sylvatica, P. abies, and P. sylvestris stands in Europe; solid lines denote Gaussian (a,d) and linear (b,c,e) regression lines.
Figure 6. Regression between Simpson’s diversity Index (Simpson’s D) and (a) stand age, (b) mean annual temperature, (c) N deposition, (d) pH, and (e) mean annual precipitation across F. sylvatica, P. abies, and P. sylvestris stands in Europe; solid lines denote Gaussian (a,d) and linear (b,c,e) regression lines.
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MDPI and ACS Style

Rosinger, C.; Sandén, H.; Matthews, B.; Mayer, M.; Godbold, D.L. Patterns in Ectomycorrhizal Diversity, Community Composition, and Exploration Types in European Beech, Pine, and Spruce Forests. Forests 2018, 9, 445. https://doi.org/10.3390/f9080445

AMA Style

Rosinger C, Sandén H, Matthews B, Mayer M, Godbold DL. Patterns in Ectomycorrhizal Diversity, Community Composition, and Exploration Types in European Beech, Pine, and Spruce Forests. Forests. 2018; 9(8):445. https://doi.org/10.3390/f9080445

Chicago/Turabian Style

Rosinger, Christoph, Hans Sandén, Bradley Matthews, Mathias Mayer, and Douglas L. Godbold. 2018. "Patterns in Ectomycorrhizal Diversity, Community Composition, and Exploration Types in European Beech, Pine, and Spruce Forests" Forests 9, no. 8: 445. https://doi.org/10.3390/f9080445

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