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Article

Host Phylogenetic Relatedness and Soil Nutrients Shape Ectomycorrhizal Community Composition in Native and Exotic Pine Plantations

by
Chen Ning
1,2,3,*,
Gregory M. Mueller
2,3,
Louise M. Egerton-Warburton
2,3,
Wenhua Xiang
1 and
Wende Yan
1
1
Faculty of Life Science and Technology, Central South University of Forestry and Technology, Changsha 410004, China
2
Program in Plant Biology and Conservation, Northwestern University, Evanston, IL 60208, USA
3
Chicago Botanic Garden, Glencoe, IL 60022, USA
*
Author to whom correspondence should be addressed.
Forests 2019, 10(3), 263; https://doi.org/10.3390/f10030263
Submission received: 1 March 2019 / Revised: 12 March 2019 / Accepted: 12 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue The Ecology of Fine Roots and Mycorrhizas in Forests)
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Abstract

:
Exotic non-native Pinus species have been widely planted or become naturalized in many parts of the world. Pines rely on ectomycorrhizal (ECM) fungi mutualisms to overcome barriers to establishment, yet the degree to which host specificity and edaphic preferences influence ECM community composition remains poorly understood. In this study, we used high-throughput sequencing coupled with soil analyses to investigate the effect of host plant identity, spatial distance and edaphic factors on ECM community composition in young (30-year-old) native (Pinus massoniana Lamb.) and exotic (Pinus elliottii Engelm.) pine plantations in China. The ECM fungal communities comprised 43 species with the majority belonging to the Thelephoraceae and Russulaceae. Most species were found associated with both host trees while certain native ECM taxa (Suillus) showed host specificity to the native P. massoniana. ECM fungi that are known to occur exclusively with Pinus (e.g., Rhizopogon) were uncommon. We found no significant effect of host identity on ECM communities, i.e., phylogenetically related pines shared similar ECM fungal communities. Instead, ECM fungal community composition was strongly influenced by site-specific abiotic factors and dispersal. These findings reinforce the idea that taxonomic relatedness might be a factor promoting ECM colonization in exotic pines but that shifts in ECM communities may also be context-dependent.

1. Introduction

Plantation forestry has been widely used to accelerate reforestation, offset greenhouse gas emissions, and relieve timber demands from native forests [1]. Fast-growing and high-yielding exotic tree species such as Pinus and Eucalyptus are intensively planted in monocultures and frequently introduced outside their natural range. However, a tree species planted outside its native range can have dramatic and long-lasting impacts on biogeochemical cycling (carbon, nitrogen), water and nutrient availability, and mycorrhizal communities [2,3]. Here, we focus on ectomycorrhizal (ECM) fungi, the ubiquitous symbiont of many forest trees, and Pinus (pine), the most widely planted plantation tree [4].
Pines are obligately dependent on symbioses with ECM fungi for nutrient uptake, growth and plant survival, and ECM are therefore important components of forest management. More than 200 species of ECM fungi representing 54 genera have been intentionally introduced in the roots of pine seedlings during the development of large-scale plantations [5]. This co-introduction has facilitated a massive range expansion of the ECM–Pinus mutualism, a situation that is widely compared to co-invasion (or ‘enemy escape’ [4,6,7,8,9]). Exotic pines may also form novel symbioses with native ECM fungi or cosmopolitan mutualists [10]. Nevertheless, plants may be limited by the availability of compatible ECM fungal inoculum. Exotic pine ECM communities are remarkable for their low species richness (<50 taxa) in comparison to native ECM forests [11], and an abundance of ECM taxa that are almost exclusively associated with Pinaceae (e.g., Suillus, Rhizopogon [12,13]).
Such low-diversity limitations may reflect both neutral (dispersal) and niche processes (abiotic factors; e.g., [14,15,16,17]). Studies show that longer distances (>10 km) may limit the stochastic dispersal of fungal propagules from one location (native forest) to another (plantation) whereas deterministic traits such as dispersal via spores [14,18] versus mycorrhizal root tips and hyphal networks may operate at finer scales [19,20,21,22]. In addition, biotic (e.g., host nutrient demands, seed dispersal) and abiotic factors (e.g., soil chemistry) may facilitate ECM fungal species with physiological and ecological adaptations depending on the environmental context [23].
Recent evidence has demonstrated that phylogenetic distance between exotic and native hosts might explain their (dis)similarities in ECM community composition and richness [24,25]. For example, studies have shown that co-occurring exotic pine and native trees host similar ECM fungal communities and share the same dominant ECM fungal species [8,10,26,27,28]. Most ECM fungal species were also host generalists and, as a result, species-specific ECM taxa were rare [29,30]. Conversely, other studies show that hosts in different subgenera (e.g., hard pines vs. soft pines) better explained the covariation between exotic and native pines and their ECM communities [10,25]. Thus, a major challenge is to disentangle the effects of host phylogeny from environmental covariation on ECM-host interactions.
Here, we examined the influence of phylogenetic relatedness, distance, and environmental factors on ECM communities in plantations of exotic slash pine (Pinus elliottii Engelm.) adjacent to native Pinus massoniana Lamb. (masson pine) at two sites. Our study sites were selected to represent forests with inherent differences in soil N and P fertility. Both plantations were located in southern China. Plantations comprise ~69 million hectares of forest in China, or ~36% of the total forest area [31]. Masson pine is a slow-growing, dominant native species in native forests in subtropical China. Slash pine was introduced from the southeastern U.S. during reforestation efforts in the 1920s [32], and is now one of the most popular plantation timber species due to its high-quality timber, fast growth, and resistance to insect damage [33]. The standardized forestry practices (e.g., thinning) used across both study sites makes this a model system in which to examine the effects of native versus exotic pines on ECM communities. We used high-throughput sequencing analyses of ECM communities coupled with analyses of soil physicochemical properties to address two questions:
(1)
Do closely related exotic and native host species support similar ECM communities?
(2)
To what extent do neutral (dispersal) versus niche (abiotic factors) shape ECM assemblages in exotic and native pines?

2. Materials and Methods

2.1. Study Sites

The study was carried out at two forest areas of south China, Longli Forest Farm, Guizhou Province (LFF, 26°45′ N, 106°45′ E), and Hunan Botanic Garden, Hunan Province (HBG, 28°60′ N, 113°20′ E) (Figure S1). LFF has a subtropical humid climate characterized by abundant precipitation and mild temperatures; mean annual precipitation is 1077 mm and average temperatures is 15 °C. The soil is a well-drained sandy-loam yellow soil developed from quartz sandstone parent rock, classified as Hapli-Udic Ferrosols according to the Chinese Soil Taxonomic Classification System (CSTC [34]). In HBG, the climate is typical mid-range subtropical monsoonal with mean annual precipitation of 1422 mm and annual air temperature of 17.2 °C. The soil is a well-drained clay-loam red soil developed from slate parent rock, classified as Alliti-Udic Ferrosols by the CSTC. Both masson pines and slash pine plantations were planted in 1988 as monocultures after clear-cutting of mixed evergreen broadleaved (Fagaceae)-conifer mixed forests (masson pines).
In each site and forest type, two (HBG) or three (MA) 20 × 20 m plots were established at least 50 m apart; the small stand size in HBG limited the number of plots possible (Figure S1). Within each plot, five randomly selected mature pine trees (DBH 15–20 cm) separated by at least 4 m were selected as ‘island’ trees.

2.2. Sampling Methods

We sampled the ECM community in each site and tree species using a combination of sporocarp surveys (collected monthly since August 2015) and hyphal in-growth bags. In-growth bags were made of anti-static polyester fabric with 50 μm diameter pores and filled with sand (0.6~1.8 mm) that had been washed with 5% (v/v) HCl and 10% (v/v) H2O2, thoroughly rinsed with deionized water, and dried. Such bags allow for efficient, well-replicated community sampling of active ECM fungi hyphae growing in soil [35,36]. In July 2016, we planted three 5 × 5 cm mesh bags under each island tree. Bags were buried 45 cm from the base of the tree along one of the four cardinal directions, and 5 cm below the soil surface (A horizon). Additional bags containing sand were stored in the laboratory as negative controls. All bags were harvested in October 2016 and transported to the laboratory over ice. At harvest, soil cores (5 cm diameter × 10 cm deep) were collected next to each buried bag. Soil cores were homogenized and pooled by each ‘island’ tree, sieved to 2-mm to remove gravel and coarse organic matter, and used for soil physical and chemical analyses.

2.3. Soil Physicochemical Analysis

Soil pH was determined in a soil suspension with soil: water ratio of 1:2.5 (w/v) using a Delta 320 pH-meter (Melter-Toledo Instruments Co., Shanghai, China). Soil texture was determined by the hydrometer method for estimating particle size. Soil ammonium (NH4-N), nitrate (NO3-N), and phosphate (PO4) were extracted with 2M KCl and measured using a continuous flow analyzer (SAN++, Skakar, Breda, Holland). Available potassium (K) was determined on ammonium acetate extraction by flame photometry (Model FP6410; Jingke Ltd. Co. Shanghai, China). Total N was measured using Semi-micro-Kjedahl digestion with a mixture of H2SO4, K2SO4, CuSO4 and Se. Additional soil samples were digested with HNO3: HCl (1:3), and analyzed for total P using a molybdenum colorimetric method and macro- or micronutrients (Ca, Mg, Fe, Cu, Pb, Zn, Mn) using atomic absorption spectrophotometry (Mode AA-7000; Shimadzu Corp. Nakagyo-ku, Kyoto, Japan). Soil samples for organic matter analysis were oven-dried at 105 °C, milled and sieved through 0.25 mm mesh and analyzed by wet combustion method (Walkley-Black procedure) with potassium dichromate.

2.4. Molecular Identification of Ectomycorrhizal Fungal Community

Fungal hyphae were obtained from the mesh bag following [36]. Briefly, each bag was emptied into a sterile 50 mL centrifuge tube, 10 mL of sterile deionized water was added and each tube was vortexed for 2 min. After a 5 min settling period, the top 2 mL of suspension was transferred to a new, sterile 2 mL centrifuge tube and the contents pelleted via centrifugation. Hyphal pellets were pooled by ‘island tree’, which yielded 55 samples from forest types across both the LFF and HBG sites. We also extracted DNA from 18 different ECM sporocarps that had been collected opportunistically from both sites. These extracts were only used to create a positive control (or mock ECM community) comprising equimolar aliquots of genomic DNA from each fungal species (Table S2). We did not include these fungi in analyses of masson and slash pine ECM communities because sporocarp sampling was infrequent, and local farmers collect edible ECM sporocarps (e.g., Lactarius) in pine plantations.
DNA from sample mesh bags and negative controls (bags stored in the laboratory) was extracted using Mobio PowerSoil kit (Hercules, CA, USA) following the manufacturer’s instructions for maximum DNA yield. The ITS1 region of the fungal rDNA subunit was amplified by polymerase chain reaction (PCR) (95 °C for 2 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s and a final extension at 72 °C for 5 min) using primers ITS1F and ITS2 [37]. PCR reactions were performed in triplicate—20 μL mixture containing 4 μL of 5 × FastPfu Buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of each primer (5 μM), 0.4 μL of FastPfu Polymerase, and 10 ng of template DNA. Amplicons were extracted from 2% agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using QuantiFluor™-ST (Promega, Fitchburg, WI, USA). Purified amplicons were pooled in equimolar concentrations and paired-end sequenced (2 × 250 bp) on an Illumina MiSeq platform according to the standard protocols at Majorbio Technic Group Laboratory, Shanghai, China.
Raw Illumina fastq files were demultiplexed and quality-filtered using QIIME v1.17 [38] with the following criteria: reads were truncated at any site receiving an average quality score of <20 over a 50-bp sliding window; truncated reads shorter than 50 bp were discarded; any read containing one or more ambiguous base calls was discarded; only sequences that overlapped by more than 10 bp were assembled according to their overlap sequence. Reads that could not be assembled were discarded. The ITS1 sub-region was then extracted from each sequence using FungalITSextractor (http://emeren-cia.org/FungalITSextractor.html) [39]. Operational units (OTUs) were initially clustered with 97% similarity cutoff by USEARCH v7 [40] and followed by de novo chimera checking using the UNITE-QIIME reference (http://unite.ut.ee/repository.php) [41]. All global singletons were removed to avoid data artifacts [42,43]. Taxonomy was assigned using NCBI BLAST+ v2.2.29 [44] and a custom database compiled from sequences in UNITE v7 and vouchered mushrooms collected from the field sites. The mycorrhizal status of OTUs was tentatively assigned using the ‘FUNGuild’ algorithm [45] and checked manually [46].
After bioinformatic processing, we first calibrated our clustering threshold [47] and cut-off for low abundance OTUs [48]. To do so, we examined and compared the number and abundance of OTUs recovered after sequencing versus the original composition of the mock ECM community (Table S2). Using 97% clustering threshold resulted in the recovery of 22 species out of the 18 species. Adjusting the clustering threshold to 96.8% (de novo UCLUST) resulted in the recovery of the correct number of species (Table S2). Analyses also showed that four sequence reads (OTU read abundance ≥4) was the lowest cut-off that was not significantly different from a 10-sequence threshold (p > 0.05; pairwise Wilcoxon rank-sum tests). We used this value as a conservative threshold for taxon inclusion in the species richness analysis.
DNA sequences in this study have been deposited in Sequence Read Archive of the NCBI database under accession number SRP113701.

2.5. Statistical Analyses

All statistical analyses were conducted in R v3.3.2 (R Foundation for Statistical Computing; available at http://www.R-project.org) with indicated packages.
Prior to all tests, read abundances were normalized using the ‘metagenomeSeq’ package [49], since rarefaction may reduce the sensitivity of analyses [50,51]. Soil properties were standardized using scale transformation (function ‘scale’ in R). Data sets were then checked for multi-collinearity among variables using the variation inflation factor (VIF) function of the ‘FMSB’ package, and highly correlated variables removed (variation inflation factor scores >5: C, K, Cu, Fe, % sand and clay).
We calculated ECM species richness (S), evenness (Shannon–Wiener index), estimated species richness (Chao1 richness), and species accumulation curves (Mao Tau) for each sample. Differences in species richness and evenness between host tree species and sites were analyzed using two-way analysis of variance (ANOVA) with post-hoc Tukey Honestly Significant Difference (HSD) tests for significant variables. Indicator species analysis was used to identify OTUs unique to each host species and site (‘indicspecies’ [52]). This analysis was based on presence-absence of each ECM fungal OTU in each ‘focal’ tree. The indicator values were group-equalized and their statistical significance was tested by a randomization procedure with 999 permutations. Heat maps and network-based analyses (Cytoscape 3.7.0 [53]) were used to visualize the number of ECM species that were cosmopolitan (present in all forest types), specific to a host species or site (i.e., only detected in one forest type) or broadly dispersed (found in two or three sites).
Next, an ECM community distance matrix (Bray–Curtis dissimilarity, OTU abundances) and environmental matrix (Euclidean dissimilarity, spatial distance and soil properties) were constructed. We tested for differences in ECM community and soil properties between sampling sites and host species using permutational multivariate analysis of variance (PERMANOVA; adonis function in vegan, 999 permutations), and multivariate homogeneity of group dispersions (betadisper function in vegan). Patterns of ECM community dissimilarity were visualized using non-metric multidimensional scaling (NMDS). Differences in community structure were compared visually using centroids and 95% confidence intervals associated with a χ2-distribution around the standard error of the centroid. To disentangle the relative importance of soil factors controlling ECM community composition, we identified and fitted significant environmental vectors to the NMDS (envfit function in vegan; 999 permutations). Mantel tests were used to directly compare geographic distances with community composition differences (mantel.rtest in ade4 package) and to determine differences in spatial scaling between sites (mgram in ecodist package). These analyses were run using both abundance-based and presence–absence ECM community data. Both analyses showed similar results (data not shown), and only the abundance-based data are presented.

3. Results

Our initial dataset included a total of 2,921,761 raw sequence reads. After quality filtering, there were 1,992,789 sequences for downstream analysis, with an average of 37,060 (±12,664 standard error (SE)) reads per sample. These sequences clustered to 403 OTUs. Eighty-percent of the sequences (1,593,614) were identified as putative ECM fungal species.
The ECM community comprised a broad range of phylogenetic lineages. A total of 43 ECM fungal species were assigned to 16 genera, many of which were native to China. The most abundant taxa were Russula and Tomentella with 10 OTUs each (Table 1; Figure 1). The majority of ECM species were either cosmopolitan (14%) and found in all sites with both tree species (e.g., Cenococcum) or broadly dispersed across sites and/or species (63%; e.g., Tylospora, Clavulina; Table 1; Figure 1 and Figure 2). In contrast, Suillus OTU_963 and Lactarius OTU_984 were specific to masson pine in both LFF and HBG while ECM specific to slash pine were site dependent, e.g., Lactifluus in LFF and Chloridium OTU_349 in HBG. Further, fewer fungal taxa were indicator species in masson pine than slash pine (IndVal; Table 1). Analyses showed that ECM fungi of other native pines (e.g., P. yunnanensis) were indicator species of masson pine. In contrast, a variety of ECM were indicator species in slash pine, including taxa that were best matched to pines in North America (Clavulina) and Eastern Europe (Pseudotomentella; Table 1). We also found significant differences in ECM community between sites (Figure 1 and Figure 2), whereby 44% of ECM fungal species were specific to HBG or LFF. Of the 43 ECM species identified in the study, 16 were recovered only in LFF, most of which were species of Pseudotomentella (2 species), Tomentella (6 species), or Russula (5 species). Conversely, only three ECM were restricted to HBG: Laccaria OTU_1219, Chloridium OTU_349, and Russula OTU_1000.
Rarefaction curves showed that the number of ECM fungal species was higher in LFF than HBG. However, species richness did not reach saturation, suggesting that additional ECM species could be added with further sampling (Figure 3). Host × Site interactions influenced both observed and estimated ECM fungal richness (Table 2). Species richness was significantly higher in slash than masson pine at LFF whereas there was no significant difference in richness between pine species at HBG. Species evenness was significantly higher in slash than masson pines (Table 2).
NMDS ordination coupled with PERMANOVA analyses (Table 3) showed that ECM communities differed significantly between Site and Host (Figure 4). This distribution was nonrandom (PERMDISP; Table 3), and best explained by site (R2 = 0.151, p = 0.001) rather than host plant (R2 = 0.057, p < 0.02; Table 3). NMDS also showed strong influence of local soil factors on ECM community. Although soil factors varied significantly between Site and Host (Table 3; see supplementary Table S1), the largest effects were detected between sites (R2 = 0.929, p = 0.001). In particular, levels of soil N (including N as NO3), P, and K were higher in HBG than LFF whereas macronutrients (Ca, Mg, Mn) and silt levels were higher in LFF than HBG (Table S1). Within each site cluster, HBG ECM communities were more homogeneous than those in LFF (PERMDISP; Table 3).
ECM communities scaled very differently between sites. In LFF, ECM communities were more similar than expected by chance within ~10 m (Figure 5). In HBG, ECM communities were also more similar than expected at ~10 m and 350 m but more dissimilar than expected at spatial scales ranging from ~80 m to 400 m (Figure 5). Within each site, however, host effects on ECM community and soil property scaling were not correlated (LFF Mantel test: r = −0.0675, p = 0.63; HBG Mantel test: r = 0.0196, p = 0.73, respectively).

4. Discussion

In this study, we systematically investigated the effect of host plant identity, distance and edaphic factors on ECM community composition in native and exotic pine plantations using hyphal ingrowth bags and high throughput sequencing. Although the hyphal ingrowth bag approach has its limitations (see [54]), we found no significant effect of plant identity on ECM communities, i.e., phylogenetically related exotic and native pines shared similar ECM communities (Q1). In the absence of a phylogenetic effect, we found that ECM community composition was influenced by site-specific abiotic factors and dispersal (Q2).
The few differences in ECM community between slash and masson pine (both subgenus Pinus) reinforces the idea that taxonomic relatedness might be a factor influencing ECM fungal colonization in exotic pines. Plant–ECM interactions are also believed to be conserved at the family level [55]. Analogous results have been noted in previous studies of ECM in native and exotic pines [8,10,24,26]. However, other studies have found that ECM host specialization contributes significantly to the composition of ECM communities [25,56]. Although the reason(s) for these opposing responses are not clear, they may reflect differences in the evolutionary co-adaptation between ECM fungi and their host [25,30], or differences in host range [57].
We found that exotic slash pine readily developed ECM with native fungi. Overall, the ECM community in exotic pine plantations was composed of a subset of the ECM fungi usually associated with native pines in their native ranges. Other studies have similarly shown a restricted suite of ECM species in exotic pine species (e.g., [12]). However, masson and slash pines exhibited very little systematic differences in ECM community composition and relative abundance of ECM species. Most of the recorded ECM fungi are considered cosmopolitan with the result that out of 43 ECM fungal taxa identified, 40 were associated with non-native slash pine (vs 35 in masson). Compositionally, members of the Thelephoraceae (e.g., Tomentella) emerged as the most species-rich, frequent and abundant genera on both pine species and sites. These taxa typically dominate ECM communities in many systems [58], form ECM with a wide range of plant host species, and exhibit broad ecological profiles [59,60]. Along with Inocybe and Laccaria, these taxa also form relatively long-lived spore banks that have the capacity to rapidly colonize roots of new plants [16]. This is important because it indicates that in plantations there is an ECM community that can immediately assist with reforestation.
Only a small number of ECM species displayed host specificity. Inspection of ECM communities shows that certain ECM fungi were specific to masson or slash pine. Clavulina corralloides (OTU_1120) and Pseudotomentella (OTU_1025) were only found with slash pine. Sequence similarity shows that they are best matched with species of known North American (Clavulina) and Eastern Europe origin (Pseudotomentella). These species were likely co-introduced with slash pine. Suillus OTU_963- masson pine was an example of strict host specificity in both sites. Based on sequence similarity, this species is part of a distinct Asian Suillus clade [61], and its high masson pine- ECM specificity is consistent with patterns reported in other Suillus- pine associations [62]. The low abundance of Rhizopogon and Suillus in both pine species, however, was unexpected. These ECM genera are considered pine-specific [63] and abundant in pine roots in other ECM surveys (e.g., [20]). Yet, neither taxa were indicator species in masson or slash pine. These conflicting results may be due to the fact that Rhizopogon and Suillus are early successional taxa, and ECM fungal communities in 30 year-old plantation trees are not reflective of seedling ECM communities [64]. That Rhizopogon was equally abundant in both pine species and sites, however, supports the wide ecological amplitude of this taxon [65] and suggests that Rhizopogon has dispersed to these sites in the past and formed soil reservoirs of resistant spores that remain dormant in the soil for long periods [66].
We found that edaphic factors best explained the differences in ECM community composition between sites, not host specialization. Initially, we had expected ECM communities primarily to be structured by host plant identity. However, they were structured by differences in soil pH and nutrient levels between sites. Specifically, ECM communities in HBG were shaped by soil pH and macronutrient (N, P, K) levels whereas those in LFF were influenced by silt, Mn and base cations (Mg, Ca). These differences suggest that ECM fungi are limited by niche requirements (or abiotic tolerances) and that different ECM fungi may be available to plants depending on soil chemistry. This strong edaphic segregation is similar to previous large-scale ECM studies [14,67,68,69]. In addition, the correlation between soil N and P (and pH) and ECM community composition in HBG is consistent with previous work in forest systems showing a strong role for soil fertility (primarily N) in driving ECM community composition and richness (e.g., [70,71,72,73,74]).
More interestingly, the ECM community composition in LFF was correlated with soil Ca, Mg and Mn, and silt. In addition, many ECM species were detected in LFF but not HBG (e.g., Tomentella, Russula), which suggests that there are underlying physiological differences in ECM species that influence ECM community composition. Soil Mn levels are known to influence ECM fungi differentially [75]. Calcium and Mg are important plant macronutrients but have not been found to influence ECM community composition or favor certain ECM species in other studies [76]. The specific mechanism(s) driving this response is beyond the scope of this study. However, there are several possible processes that could generate this pattern: Soil Mn, Ca, and Mg availability is linked to soil pH, so that the response of ECM communities to these cations signals the effects of soil pH [77]. Plant morphology also varies with soil fertility meaning that major differences in root traits (root length, fine root density) could indirectly alter the ECM community [78]. Alternatively, the abiotic correlates of LFF and HBG ECM communities may simply reflect the idiosyncrasies in soil types that developed from two different parent materials.
We found consistent and strong spatial structuring of ECM populations in both study sites. Within each site, the highest spatial autocorrelation occurred at small scales (<5 m). This result lends support to dispersal limitation in our sites [79], and is in general agreement with the degree of spatial patterning found in temperate forests [21,30,66,80]. As a result, these ECM communities likely comprise individuals that are closely related or have similar environmental requirements. Against this backdrop, we found that ECM fungal taxa co-occurred less often than expected by chance (segregation) at ~80 and 400 m in HBG; a similar result was noted by Yamamoto et al. [81].
One possibility is that microhabitat differentiation inside HBG may have created a mosaic of environments that provided conditions for ECM fungi with varying environmental requirements [82]. Alternatively, there may be competition between ECM species [83] or between ECM and endophytic fungi for resources, such as host C [80]. These patterns may also be attributed to differences in ECM phenology or hyphal exploration networks. For example, ECM fungi with medium or long-distance exploratory hyphae (e.g., Tomentella, Tylospora) might compete over larger distances so that there is open space for colonization by second and subsequent ECM species with contact- or short-range hyphae (Russula). Distinguishing between these possibilities is beyond the scope of this study but merits further investigation.

5. Conclusions

Our data sets provide important insights for separating the impact of host phylogenetic relatedness from abiotic and spatial factors on ECM community composition. Our results revealed that phylogenetic relatedness between pine trees resulted in a similar ECM community composition in both sites, and slash and masson pine were not limited by the availability of compatible ECM fungal inoculum. Instead, both dispersal and soil factors were stronger drivers of differences in ECM community composition between sites, meaning that ECM fungal communities in exotic pines are variable and context-dependent. In managed forests, improved productivity is reliant on belowground resource availability, uptake, and use efficiency, all of which are driven by ECM. Given the importance of soil and spatial factors in our study, there is now a need to better understand and align soil conditions with ECM fungi across a range of spatial scales, and identify how differences in ECM community functioning might feed back to influence host tree growth and productivity.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/10/3/263/s1, Figure S1: Map of sampling regime and sampling locations across the continental China. Circles represent individual sampling locations mapped on Google Map images. (A) Individual sampling plots are shown on a map of south China. (B) Individual plots 20 × 20 m within a sampling site are shown in Longli Forest Farm, Guizhou. (C) Individual plots 20 × 20 m within a sampling site are shown in Hunan Botanic Garden, Hunan. lat, latitude; lon, longitude, Table S1: Soil physico-chemical characteristics among different habitats, Table S2: Comparison of sequence abundances and operational taxomomic unit (OTU) counts in a mock community of 18 Basidiomycota species collected in local area.

Author Contributions

Conceptualization, C.N., G.M.M. and L.M.E.-W.; methodology, C.N.; software, C.N.; validation, C.N.; formal analysis, C.N.; investigation, C.N.; resources, C.N.; data curation, C.N.; writing—original draft preparation, C.N., L.M.E.-W., and G.M.M.; writing—review and editing, L.M.E.-W., G.M.M., W.X., and W.Y.; visualization, C.N.; supervision, G.M.M.; funding acquisition, C.N., W.X., and W.Y.

Funding

This research was funded by National Science Foundation of China (31570447), program of National Key (cultivating) Disciplines in Central South University of Forestry and Technology, combination program of Plant Biology and Conservation in Northwestern University and Chicago Botanic Garden, and China Scholarship Council (CSC201408430072, awarded to C.N.).

Acknowledgments

Specially thank Peter Avis for his comments on an earlier version of the paper. We thank the assistance of Xiaowei Ni, Xinghao Huang, Zhizhou Liu, Yi Chen, and Qian Wu in the field and laboratory. We also thank Forest Administration of Hunan and Forest Station of Longli for the permission to use sites.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 10 00263 g001 550
Figure 1. Ectomycorrhizal fungal operational units (OTUs) showing sequence read abundance across sites for each pine host. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
Figure 1. Ectomycorrhizal fungal operational units (OTUs) showing sequence read abundance across sites for each pine host. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
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Figure 2. A network map showing the interactions of the OTUs among all the samples from different hosts at each site. Each point represents one independent fungal OTU (43 OTUs in total). OTUs in the left column were unique to one forest type, while those in the right belonged to multiple forest types. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
Figure 2. A network map showing the interactions of the OTUs among all the samples from different hosts at each site. Each point represents one independent fungal OTU (43 OTUs in total). OTUs in the left column were unique to one forest type, while those in the right belonged to multiple forest types. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
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Figure 3. Sample-size-based species accumulation rarefaction (solid line segment) and extrapolation (dotted line segments) sampling curves with 95% confidence intervals (shaded areas) for estimated total ectomycorrhizal OTU richness across different host/sites. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
Figure 3. Sample-size-based species accumulation rarefaction (solid line segment) and extrapolation (dotted line segments) sampling curves with 95% confidence intervals (shaded areas) for estimated total ectomycorrhizal OTU richness across different host/sites. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
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Figure 4. Non-metric multidimensional scaling (NMDS) of ectomycorrhizal fungal community composition. Point size reflects the richness of each sample. Significant abiotic variables (p < 0.05) were fitted as vectors onto the NMDS graph. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
Figure 4. Non-metric multidimensional scaling (NMDS) of ectomycorrhizal fungal community composition. Point size reflects the richness of each sample. Significant abiotic variables (p < 0.05) were fitted as vectors onto the NMDS graph. LFF—Longli Forest Farm; HBG—Hunan Botanic Garden.
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Figure 5. Mantel correlogram showing the strength of the correlation between spatial proximity and ectomycorrhizae for samples within different distance species composition classes in (a) Longli Forest Farm and (b) Hunan Botanic Garden. Points are plotted at the midpoint of each distance class. Solid circles represent significant correlations (p < 0.05), and open circles show correlations that are not significantly different from zero (p > 0.05).
Figure 5. Mantel correlogram showing the strength of the correlation between spatial proximity and ectomycorrhizae for samples within different distance species composition classes in (a) Longli Forest Farm and (b) Hunan Botanic Garden. Points are plotted at the midpoint of each distance class. Solid circles represent significant correlations (p < 0.05), and open circles show correlations that are not significantly different from zero (p > 0.05).
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Table
Table 1. Indicator species analyses. The table shows the number of ectomycorrhizal (ECM) fungal molecular taxa (OTU) significantly associated with each habitat type, their indicator species value [52] and associated probability. Based on species hypotheses, the distribution area and possible host species was assigned by closest similar representative sequence data in UNITE. MA-masson pine, SL-slash pine.
Table 1. Indicator species analyses. The table shows the number of ectomycorrhizal (ECM) fungal molecular taxa (OTU) significantly associated with each habitat type, their indicator species value [52] and associated probability. Based on species hypotheses, the distribution area and possible host species was assigned by closest similar representative sequence data in UNITE. MA-masson pine, SL-slash pine.
SiteSpecies NameOTU No.Indicate Valuep-ValueClosest BLAST Match in GenbankClosest UNITE Species Hypothesis MatchDistribution 1Host of Pinus
Longli Forest Farm, LFF
MARussula virescensOTU_9880.6830.003KU552087 (100)SH179772.07FUChinanot indicated
Russula violeipesOTU_10070.6190.012LT201954 (100)SH191296.07FUYunnan (China)P. yunnanensis
Russula.sp1OTU_9920.4470.05LT602950 (97)SH218430.07FUYunnan (China)P. yunnanensis
SLAmphiema.spOTU_12640.9610.001LC176645 (100)SH193510.07FUChinaP. massoniana
Tomentella stuposaOTU_11540.8560.001UDB024437 (100)SH529807.07FULaosnot indicated
Clavulina corralloidesOTU_11200.8160.001KF359593 (94)SH220215.07FUNorth AmericaP. banksiana
Russula sp.2OTU_9900.7740.001AB636419 (93)SH218466.07FUKoreaP. koraiensis
Russula sp.3OTU_9930.6820.001AB211253 (95)SH186553.07FUJapanP. densiflora
Tomentella sp.1OTU_11570.6320.001UDB018462 (97)SH010050.07FUChinanot indicated
Tomentella sp.2OTU_11290.6320.002UDB018462 (99)SH010050.07FUChinanot indicated
Pseudotomentella sp1OTU_10270.6280.011AB839386 (100)SH223400.07FUEast EuropeP. sylvestris
Pseudotomentella sp2OTU_10250.5770.011AB587791 (100)SH189639.07FUChinaP. massoniana
Tomentella.sp.3OTU_11330.5160.011AB587791 (100)SH189639.07FUJapan, KoreaP. thunbergii
SiteRussula sp.4OTU_9890.9410.001AB211253 (99)SH186553.07FUJapannot indicated
Pseudotomentella sp.3OTU_10260.9130.001AB839386 (100)SH223400.07FUEast EuropeP. sylvestris
Hunan Botanic Garden, HBG
MARussula sp.5OTU_9830.7850.001AB839393 (100)SH201481.07FUSouth ChinaP. massoniana
Laccaria amethystinaOTU_12190.5480.015KF692988 (100)SH220964.07FUChina, JapanP. densiflora
Laccaria aurantiaOTU_11910.5470.03KU685645 (99)SH179274.07FUYunnan (China)P. yunnanensis
SLRussula sp.6OTU_10000.5770.001UDB032527 (100)SH189355.07FULaosnot indicated
Tomentella.sp.4OTU_11580.5770.044AB587783 (100)SH189355.07FUa.w.multiple
Russula sp.7OTU_10010.5760.006KP866130 (99)SH199912.07FUHunan (China)P. elliottii
SiteTomentella sp.5OTU_11550.8910.003JX556209 (100)SH189353.07FUSouth Chinanot indicated
Scleroderma sp.1OTU_11640.7720.006KP866131 (100)SH189277.07FUHunan (China)P. elliottii
1 a.w.-reprehensive sequences have wild distribution all over the world.
Table
Table 2. Single-sample based observed operational taxonomic unit (OTU) richness, Chao1, and Shannon-Wiener indices for ECM fungal communities from all forest types across the sites. Different letters indicate significant differences among forest types based on Tukey Honestly Significant Difference (HSD) post-hoc test (p < 0.05).
Table 2. Single-sample based observed operational taxonomic unit (OTU) richness, Chao1, and Shannon-Wiener indices for ECM fungal communities from all forest types across the sites. Different letters indicate significant differences among forest types based on Tukey Honestly Significant Difference (HSD) post-hoc test (p < 0.05).
Analysis of Variance (ANOVA) TestMean (Standard Error) of Index
Longli Forest Farm (LFF)Hunan Botanic Garden (HBG)
Masson Pine
(n = 15)		Slash Pine
(n = 15)		Masson Pine
(n = 10)		Slash Pine
(n = 15)
Observed Richness FP
Host2.110.1527.07
(0.62) b		10.33
(0.8) a		7.50
(1) ab		6.27
(0.53) b
Site7.950.007
Interaction9.310.004
Chao1 FP
Host0.430.5138.13
(0.88) ab		11.32
(0.8) a		8.85
(1.6) ab		6.77
(0.71) b
Site5.360.025
Interaction7.510.008
Shannon FP0.47
(0.09) b		0.72
(0.1) a		0.30
(0.09) b		0.58
(0.12) a
Host5.490.023
Site2.930.174
Interaction0.020.899
Table
Table 3. PERMANOVA analyses performed to test the differences in ECM fungal community and soil properties among forest types. Post hoc PERMDISP pair-wise analysis tests were applied to data from all forest types across the sites. Values represent the site mean with standard error in parentheses. Different letters indicate significant differences among forest types based on Tukey HSD post-hoc test (p < 0.05).
Table 3. PERMANOVA analyses performed to test the differences in ECM fungal community and soil properties among forest types. Post hoc PERMDISP pair-wise analysis tests were applied to data from all forest types across the sites. Values represent the site mean with standard error in parentheses. Different letters indicate significant differences among forest types based on Tukey HSD post-hoc test (p < 0.05).
PERMANOVA TestPERMDISP Pair-Wise Test
Longli Forest Farm (LFF)Hunan Botanic Garden (HBG)
Masson PineSlash PineMasson PineSlash Pine
FPR2
ECM fungal communityHost3.890.002 0.0570.42
(0.05) c		0.57
(0.03) b		0.51
(0.03) ab		0.64
(0.01) a
Site10.260.001 0.151
Interaction2.940.004 0.043
Soil nutrient status FPR2
Host9.660.007 0.0120.04
(0.01)		0.05
(0.01)		0.08
(0.02)		0.04
(0.01)
Site828.020.001 0.929
Interaction2.50.1010.003

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Ning, C.; Mueller, G.M.; Egerton-Warburton, L.M.; Xiang, W.; Yan, W. Host Phylogenetic Relatedness and Soil Nutrients Shape Ectomycorrhizal Community Composition in Native and Exotic Pine Plantations. Forests 2019, 10, 263. https://doi.org/10.3390/f10030263

AMA Style

Ning C, Mueller GM, Egerton-Warburton LM, Xiang W, Yan W. Host Phylogenetic Relatedness and Soil Nutrients Shape Ectomycorrhizal Community Composition in Native and Exotic Pine Plantations. Forests. 2019; 10(3):263. https://doi.org/10.3390/f10030263

Chicago/Turabian Style

Ning, Chen, Gregory M. Mueller, Louise M. Egerton-Warburton, Wenhua Xiang, and Wende Yan. 2019. "Host Phylogenetic Relatedness and Soil Nutrients Shape Ectomycorrhizal Community Composition in Native and Exotic Pine Plantations" Forests 10, no. 3: 263. https://doi.org/10.3390/f10030263

APA Style

Ning, C., Mueller, G. M., Egerton-Warburton, L. M., Xiang, W., & Yan, W. (2019). Host Phylogenetic Relatedness and Soil Nutrients Shape Ectomycorrhizal Community Composition in Native and Exotic Pine Plantations. Forests, 10(3), 263. https://doi.org/10.3390/f10030263

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