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Article

Managed and Unmanaged Pinus sylvestris Forest Stands Harbour Similar Diversity and Composition of the Phyllosphere and Soil Fungi

by
Jūratė Lynikienė
1,
Diana Marčiulynienė
1,
Adas Marčiulynas
1,
Artūras Gedminas
1,
Miglė Vaičiukynė
1 and
Audrius Menkis
2,*
1
Institute of Forestry, Lithuanian Research Centre for Agriculture and Forestry, Liepų str. 1, Girionys, LT-53101 Kaunas, Lithuania
2
Department of Forest Mycology and Plant Pathology, Uppsala BioCenter, Swedish University of Agricultural Sciences, P.O. Box 7026, SE-75007 Uppsala, Sweden
*
Author to whom correspondence should be addressed.
Microorganisms 2020, 8(2), 259; https://doi.org/10.3390/microorganisms8020259
Submission received: 7 January 2020 / Revised: 4 February 2020 / Accepted: 11 February 2020 / Published: 15 February 2020
(This article belongs to the Section Environmental Microbiology)
Browse Figures
Versions Notes

Abstract

:
The aim was to assess fungal communities associated with living needles and soil of Pinus sylvestris in managed and unmanaged forest stands to get a better understanding of whether and how different intensities of forest management affects fungal diversity and community composition under the north temperate forest zone conditions. The study was carried out in three national parks in Lithuania. Each included five study sites in managed stands and five in unmanaged stands. At each site, three random soil cores and five random last-year needle samples were collected. Following DNA isolation, a DNA fragment of the ITS2 rRNA gene region of each sample was individually amplified and subjected to high-throughput sequencing. Analysis of 195,808 high-quality reads showed the presence of 1909 fungal taxa. Richness and composition of fungal taxa were similar in each substrate (needles and soil) in managed vs. unmanaged sites. The most common fungi in needles were Coleosporium campanulae (12.4% of all fungal sequences), Unidentified sp. 3980_1 (12.4%), Unidentified sp. 3980_4 (4.1%) and Sydowia polyspora (3.1%). In soil: Unidentified sp. 3980_21 (8.6%), Umbelopsis nana (8.2%), Archaeorhizomyces sp. 3980_5 (8.1%) and Penicillium spinulosum (6.3%). The results demonstrated that managed and unmanaged P. sylvestris stands support similar diversity and composition of fungal communities associated with living needles and soil.

1. Introduction

European forests are used for multiple purposes, which often include the potentially conflicting goals of timber production and biodiversity conservation [1]. To protect natural habitats and biodiversity, in many countries, the establishment of protected areas was initiated, where management activities are prohibited [2,3]. As a result, ca. 10% of the forest area in Europe is left unmanaged [4]. In unmanaged forests, natural disturbances such as wind throws or forest fires lead to heterogeneous structure and different successional stages of forest development [5,6,7,8]. Such forests usually contain old-growth trees and accumulate larger volumes of dead wood that provide valuable habitats and support biodiversity [5]. Unmanaged forests have been shown to have, in general, higher biological diversity than managed forests [5].
Intensive forest management may often result in drastic changes leading to loss of naturalness and biodiversity in forest ecosystems [9], causing remarkable changes to forest structure and function [2]. This may eventually lead to lower buffering capacity and adaptability of such forests to climate change. Clear-cuttings especially can lead to changes in tree age structure and species composition, and may also modify the microclimate and/or soil conditions, and ultimately affect the functioning of forest ecosystems [3]. These changes can reduce or even threaten different organisms that depend on natural forest habitats [5]. However, many of these forests may still have relatively high biodiversity [6]. For example, Horák et al. [4] have shown that some beetles thrive in managed forests. Indeed, the richness of several ground beetle species was found to be either higher or did not differ significantly when compared between managed and unmanaged stands [10,11,12,13]. Many of these beetles are considered as indicator species, reflecting habitat conditions [14].
In Europe, coniferous forests, including Scots pine (Pinus sylvestris), are widely distributed and are of enormous ecological and economic importance [15,16,17,18]. Although these forests generally have lower biodiversity than deciduous forests [4], P. sylvestris provides habitats to a large number of different organisms [19,20]. The phyllosphere, which in conifers is dominated by needles and is the key component responsible for photosynthesis and transpiration, represents one of the largest terrestrial habitats for microorganisms [21]. Fungi in this habitat represent the largest microbial component that may influence different physiological processes, such as tree growth and adaptation to different abiotic and biotic stress factors [22]. Soil fungi are equally important as many of these are responsible for decomposition of organic matter and nutrient recycling, while mutualistic mycorrhizal fungi may provide nutritional benefits to host trees [23,24,25]. However, fungal communities can be sensitive to different forest transformations, including changes in plant diversity, composition and structure [23,26]. Indeed, forest management may negatively affect fungal communities, resulting in lower diversity [3]. For example, regular timber harvesting results in soil that is more compact, which may cause changes in soil fungal community structure [23]. On the other hand, under certain conditions (depending on tree species, age and type of forest management), recurrent disturbances that occur in managed stands could lead to higher biodiversity [23,27]. The above observations suggest that the impact of forest management on fungal diversity and community composition may depend on different factors.
High-throughput sequencing methods provide powerful tools to explore fungal diversity directly from environmental samples [28]. While providing detailed and semi-quantitative information, they enable us to study the effects of different factors on fungal diversity and community composition [29,30]. However, we need to be aware of potential risks, including methodological biases, limitations of markers and bioinformatics challenges, and to learn possible solutions [31]. While different sequencing platforms have advantages and limitations, the Pacific Biosciences (PacBio) platform offers a low error rate by re-sequencing of circular molecules multiple times, especially for shorter reads [32], and thus, was used in the present study.
The aim of the present study was to assess fungal communities associated with living needles and soil of P. sylvestris in managed and unmanaged forest stands in Lithuania in order to get a better understanding on whether and how different intensities of forest management affect fungal diversity and community composition under the north temperate forest zone conditions. In Lithuania, forests occupy ca. 2.2 million ha, constituting 33.2% of the total land area. P. sylvestris is among the most important tree species as its stands occupy ca. 35% of the total forest area. Protected areas cover 16.6% of the country’s territory, where P. sylvestris stands occupy ca. 45% of this area [33].

2. Materials and Methods

2.1. Study Sites and Sampling

The study sites were in P. sylvestris stands at Aukštaitija national park (ANP), Dzūkija national park (DNP) and Žemaitija national park (ZNP) in Lithuania (Figure 1). In each national park, five study sites were established in managed stands and five in unmanaged stands. Information on stand characteristics is in Table 1. Managed stands were stands used for commercial timber production. Unmanaged stands were stands left for natural development. Unmanaged stands were in the territory of three respective strict forest reserves, namely Girutiškis at ANP (established. in 1992 with a total area of 1394 ha), Skroblus at DNP (established in 1983 with a total area of 810 ha) and Plokštinė at ZNP (established in 1991 with a total area of 845 ha). After the establishment, the territory of each forest reserve was restricted to public access. In each national park, managed and unmanaged territories are separated by a buffer zone. Before the establishment of forest reserves, these areas were used for commercial timber production.
In each national park, both managed and unmanaged study sites were selected based on the forest inventory data. The criteria used for selection were: (a) close proximity of managed and unmanaged sites, (b) P. sylvestris being prevailing tree species, (c) similar soil type [34], and (d) similar vegetation type [35]. At ANP, study sites were on nutrient-poor, sandy soils with normal humidity of vaccinium- myrtillosum vegetation type; at DNP, these were on very poor sandy soil with normal humidity of cladoniosum forest type; and at ZNP, these were on moderately rich soil of light texture and normal humidity of oxalidosum forest type (Table 1). In each national park, managed and unmanaged sites were within a radius of 500 m, i.e., within the same geographical area and with similar climatic conditions.
At each site, sampling was carried out in October 2017 by taking three random soil samples and five random last-year living needle samples (Table 2). At the time of sampling, the mean monthly temperature and the total monthly precipitation were: 6.9 °C and 87 mm at ANP, 7.3 °C and 111 mm at DNP, and 6.9 °C and 161 mm at ZNP. For a sampling of soil, the upper litter layer was removed, and samples were taken down to 20 cm depth by using a 2 cm diameter soil core, which was carefully cleaned between individual samples. Each individual sample constituted ca. 100 g of soil. Each needle sample was collected from branches of an individual tree by randomly taking up to 25 healthy-looking needles. A telescopic secateurs was used to cut branches with needles from the middle part of crowns. Needles were sampled using forceps, which were cleaned between samples. Individual soil and needle samples were placed separately into plastic bags and labelled. The same day of sampling, samples were transported to the laboratory and placed in −20 °C for storage. A total of 150 needle samples and 90 soil samples were collected (Table 2).

2.2. DNA Isolation and Sequencing

Principles of DNA work followed a study by Menkis et al. [36]. Prior to isolation of DNA, each sample (soil or needles) was freeze-dried at −60 °C for 2 days. For needles, no surface sterilization was carried out. Lyophilised needles were cut into smaller fractions and ca. 60 mg dry weight of each sample was placed into a 2-mL screw-cap centrifugation tube together with glass beads and homogenized using a Precellys 24 tissue homogenizer (Montigny-le-Bretonneux, France). DNA was isolated using CTAB extraction buffer (0.5 M EDTA pH 8.0, 1 M Tris-HCL pH 8.0, 5 M NaCl and 3% CTAB) followed by incubation at 65 °C for 1 h. After centrifugation, the supernatant was transferred to a new 1.5 mL Eppendorf tube and mixed with an equal volume of chloroform. Aqueous solution was transferred to a new tube, and an equal volume of 2-propanol was added to precipitate the DNA, which was pelleted by centrifugation. The pellet was washed in 500 μL 70% ethanol, dried, and dissolved in 50 μL of sterile milli-Q water. Within the same study site, samples were pooled together resulting in 30 DNA samples from the needles altogether (Table 2). Differing from needle samples, three lyophilised soil samples representing the same study site were mixed together prior to DNA extraction, resulting in 30 soil samples altogether (Table 2). For each sample, DNA extraction was done from 1 g dry-weight of soil using the NucleoSpin®Soil kit (Düren, Germany) according to the manufacturer’s instructions.
The DNA concentration in individual samples (needles and soil) was determined using a NanoDrop™ One spectrophotometer (Thermo Scientific, Rodchester, NY, USA) and adjusted to 1–10 ng/µL. Amplification by PCR of the ITS2 rRNA region was done using barcoded fungal specific primer gITS7 [37] and barcoded universal primer ITS4 [38]. PCR was performed in 50 μL reactions containing 2.5 µL of DNA template. Each reaction included 1% of DreamTaq Green Polymerase (5 μ/μL) (Thermo Scientific, Waltham, MA, USA); 11% of 10× Buffer; 11% of dNTPs (10 mM); 1% of MgCl2 (25 mM); 2% of each primer (200 nM) and 72% of milli-Q water. Amplifications were performed using the Applied Biosystems 2720 thermal cycler (Foster City, CA, USA). The PCR started with an initial denaturation at 95 °C for 5 min, followed by 30 cycles of 95 °C for 30 s, annealing at 56 °C for 30 s and 72 °C for 30 s, followed by a final extension step at 72 °C for 7 min. The PCR products were analyzed using gel electrophoresis on 1% agarose gels stained with Nancy-520 (Sigma-Aldrich, Stockholm, Sweden). PCR products were purified by centrifugation in 1:20 volume of 3 M sodium acetate (pH 5.2) (Applichem Gmbh, Darmstadt, Germany) and 96% ethanol mixture. Purified PCR products were quantified using a Qubit fluorometer 4.0 (Thermo Fisher Scientific, Waltham, MA, USA), and an equimolar mix of all PCR products was used for high-throughput sequencing using Pacific Biosciences platform (Menlo Park, CA, USA). Construction of the sequencing library and sequencing using Sequel II one SMRT cell was done at the SciLifeLab (Uppsala, Sweden).

2.3. Bioinformatics

The sequences generated were subjected to quality control and clustering in the SCATA NGS sequencing pipeline (http://scata.mykopat.slu.se). Quality filtering of the sequences included the removal of short sequences (<200 bp), sequences with low read quality, primer dimers and homopolymers, which were collapsed to 3 base pairs (bp) before clustering. Sequences that were missing a tag or primer were excluded. The primer and sample tags were then removed from the sequence, but information on the sequence association with the sample was stored as meta-data. The sequences were then clustered into different taxa using single-linkage clustering based on 98% similarity. The most common genotype (real read) for each cluster was used to represent each taxon. For clusters containing only two sequences, a consensus sequence was produced. Fungal taxa were taxonomically identified using both the RDP classifier available at https://pyro.cme.msu.edu/index.jsp (Centre for Microbial Ecology, Michigan State University, Michigan, USA) and GenBank (NCBI) database using the Blastn algorithm. The criteria used for identification were: sequence coverage >80%; similarity to taxon level 98–100%, similarity to genus level 94–97%. Sequences not matching these criteria were considered unidentified and were given unique names, as shown in Table 3 and Table 4 and Table S1. Representative sequences of all fungal non-singletons are available from GenBank under accession numbers MN902354 - MN904183.

2.4. Statistical Analyses

Rarefaction analysis was performed using Analytical Rarefaction v.1.3 available at http://www.uga.edu/strata/software/index.html. Differences in the richness of fungal taxa in managed and unmanaged forest sites (data pooled from all sites) were compared by nonparametric chi-square testing [39]. The Shannon diversity index, qualitative Sorensen similarity index and principal component analysis (PCA) in Canoco 5 [39,40,41] were used to characterize the diversity and composition of fungal communities. MANOVA in Minitab v. 18.1 (PA, USA) was used to evaluate the degree of separation between the fungal communities in needles and soil in managed and unmanaged sites, respectively, and between different types of substrates (needles and soil), when managed and unmanaged sites were combined together. The nonparametric Mann-Whitney test in Minitab was used to test if the Shannon diversity indexes between managed and unmanaged sites were statistically similar or not.

3. Results

High-throughput sequencing of fungal ITS2 rRNA from pooled 30 needle and 30 soil amplicon samples resulted in 330,964 reads. Quality filtering showed that 195,808 (59.2%) were of high quality, and 135,156 (40.8%) were of low quality, which were excluded from further analyses. Clustering of high-quality reads showed the presence of 2429 non-singleton contigs at 98% similarity representing different taxa. In addition, there were 3298 singletons, which were excluded. Taxonomic identification showed that among all non-singleton taxa, 1909 (78.6%) were fungal (all non-singleton fungal taxa are in Table S1) and 520 (21.4%) were non-fungal, which were excluded. Rarefaction analysis showed that fungal taxa detected in both needles and soil from respective managed and unmanaged stands vs. the number of sequences did not reach the asymptote (Figure 2). The detected fungi were 59.3% Ascomycota, 37.7% Basidiomycota, 2.6% Mucoromycota, 0.3% Chytridiomycota and 0.1% Glomeromycota.
Analysis of pooled needle data showed that the absolute richness of fungal taxa was marginally higher in managed (1036 taxa out of 54,584 sequences) than in unmanaged stands (1029 out of 60,270) and that 804 taxa were shared between both types of stands. Similarly, soil data showed that the absolute richness of fungal taxa was marginally higher in managed (872 taxa out of 24,873 sequences) than in unmanaged stands (855 out of 25,143) and that 541 taxa were shared between both types of stands. If the same number of sequences had been taken from each type of samples, the difference in chi-squared test was still not significant (p > 0.05). Information on the 20 most common fungal taxa in needles and soil representing 60.0% and 58.1% of all fungal sequences in each dataset is in Table 3 and Table 4, all respectively. The most common fungi in needles were Coleosporium campanulae (12.4% of all fungal sequences), Unidentified sp. 3980_1 (12.4%), Unidentified sp. 3980_4 (4.1%) and Sydowia polyspora (3.1%) (Table 3). The most common fungi in soil were Unidentified sp. 3980_21 (8.6%), Umbelopsis nana (8.2%), Archaeorhizomyces sp. 3980_5 (8.1%) and Penicillium spinulosum (6.3%) (Table 4). Consequently, fungi from the class Dothideomycetes dominated the fungal community in needles and Archaeorhizomycetes in soil (Figure 3).
Conoco analysis has shown that the response data were compositional and had a gradient 3.9 SD units long, indicating that a linear method, i.e., principal component analysis (PCA), is appropriate. PCA of fungal communities explained 30.0% variation on Axis 1, 8.3% on Axis 2 and 0.05% on Axis 3. PCA showed that within each respective substrate (needles or soil), fungal communities from managed and unmanaged stands were largely intermingled (Figure 4). MANOVA showed that fungal communities from managed vs. unmanaged stands did not differ significantly both in needles (p > 0.22) and in the soil (p > 0.84). By contrast, fungal communities in needles vs. soil (managed and unmanaged stands combined) were separated on Axis 1 (Figure 4), and this separation was statistically significant (p < 0.0001). In needles, the Shannon diversity index was 3.2–4.3 in managed stands and 1.5–4.4 in unmanaged stands (Table 2). In soil, it was 0.7–4.3 in managed stands and 2.5–4.2 in unmanaged stands (Table 2). Comparison by Mann-Whitney test showed that the Shannon diversity index was significantly higher in needles of managed stands than in unmanaged stands (p < 0.036), but there was no significant difference when the corresponding comparison was done for the soil (p > 0.51). The Sorensen similarity index of fungal communities between managed and unmanaged stands was 0.77 in needles and 0.63 in the soil.

4. Discussion

The results demonstrated that managed and unmanaged P. sylvestris stands support similar diversity and composition of fungal communities associated with living needles and soil under the north temperate forest zone conditions (Figure 2, Figure 3 and Figure 4). In support, Parlade et al. [42] have shown that, in P. sylvestris stands, a different intensity of forest management has little effect on the overall diversity of soil fungi. The diversity and composition of fungal communities may also depend on the forest structure, i.e., the age and composition of the tree species [23,26], which together with other factors may determine the habitat heterogeneity [4]. As both managed and unmanaged stands within each national park were in the same geographical area and with similar environmental conditions, it appears that the management abandonment had little effect on the stand structure, which remained largely unchanged and resembled managed forests (Table 1). The possibility should not be excluded that this has also contributed to the observed similarities in fungal communities. In comparison, near-natural forests possess higher structural complexity compared to managed stands [43]. The results indicate that changes in fungal communities and the biodiversity recovery can be slow and generally undetectable after 25–34 years since forest management has ceased. Meta-analysis of species richness in Europe have shown that in the first 20 years, species richness can be higher in managed than in unmanaged forests, while the older management abandonment may lead to higher species richness in unmanaged forests [5]. Nevertheless, the overall richness of fungal taxa was high in both managed and unmanaged forest stands of each national park (Table 2), showing that managed forests can also be an important habitat for fungi associated with living needles and soil. As managed forests constitute the majority of forest habitats in Europe, their role in supporting certain groups of fungi should not be underestimated.
In the present study, the detected diversity of fungal taxa was high and comparable to similar studies on the phyllosphere and soil fungi [17,30,42]. Despite that, the rarefaction curves did not reach the asymptote, indicating that a higher richness of fungal taxa could be detected by deeper sequencing. The fungal community structure was similar in needles from managed and unmanaged stands as compared between corresponding sites of each national park, and when these sites were combined together (Figure 3). An exception was the class of Pucciniomycetes, which was less common in managed than in unmanaged stands (4.1% vs. 20.1%, p < 0.0001, all sites combined). The Pucciniomycetes class is species-rich and includes many important plant pathogens known as rust fungi [44]. Their higher relative abundance in unmanaged stands is unclear, but may have a negative impact on the health of these forest stands. Although the fungal community structure in soil was also similar between managed and unmanaged stands of each national park (Figure 3), forest management had a positive effect on the relative abundance of the class Archaeorhizomycetes (22.8% vs. 13.7%, p < 0.0001, all sites combined), which includes a recently described ubiquitous soil fungi with a largely unknown function [45,46]. In support, a high similarity of fungal communities within needle and soil samples was also demonstrated by high values of the Sorensen similarity index. Furthermore, PCA and MANOVA provided evidence that fungal communities were largely specific to each particular substrate (needles or soil) and generally unaffected by the type of forest management (Figure 4).
Coleosporium campanulae dominated fungal communities in needles (Table 3) with a significantly lower relative abundance in managed than in unmanaged stands (4.1% vs. 20.0%, p < 0.0001). Fungi from the genus Coleosporium include pathogens on two-needle pines (Pinus spp.) that are mainly distributed in the northern hemisphere [47]. Although this group of fungi has been shown to cause moderate damage in P. sylvestris plantations, their response to changes in forest management is generally unknown [48]. Sydowia polyspora was also among the most common fungi in needles, but with a similar relative abundance in managed and unmanaged stands (Table 3). Sydowia polyspora can be latent in plant tissues [49], but it was also reported in association with current-season needle necrosis [30,50]. Different bark beetles were shown to be vectors for S. polyspora, which can rapidly spread and colonise trees following beetle attacks [51,52]. In addition, S. polyspora might also benefit from the forest damage caused by other pathogens [53]. As in the present study, the collected needles were healthy-looking, the establishment of S. polyspora was probably latent. Interestingly, a number of dominant fungal taxa remained unidentified (Table 3), thereby limiting our understanding about their importance and functional roles.
Similarly, the most common fungus detected in soil samples (Unidentified sp. 3980_21) remained unidentified (Table 4). This fungus appears to be distantly related to Archaeorhizomycetes, i.e., a class of fungi that taxonomy, ecology and function yet to be resolved. Unidentified sp. 3980_21 and another dominant fungus Archaeorhizomyces sp. 3980_5 have shown a higher relative abundance in managed than in unmanaged stands (12.1% vs. 5.2%, p < 0.0001 and 10.1% vs. 6.1%, p < 0.0001, respectively), suggesting that forest management can favour some taxa within Archaeorhizomycetes. It appears that these fungi are relatively common and diverse in this geographical area, including Archaeorhizomyces borealis [45]. Fungi in this class are strongly associated with soil environments containing plant roots. However, experimental analyses suggest that interactions with roots are neither mycorrhizal nor pathogenic. Instead, species in the Archaeorhizomycetes may exist along a continuum from root endophytic to free-living saprophytic life strategies. It is possible that Archaeorhizomycetes are mycoparasitic, but these life strategies have not yet been studied [54]. Among the other dominant soil fungi, the ectomycorrhizal Ascomycete Wilcoxina rehmii showed a higher relative abundance in managed than in unmanaged stands (2.5% vs. 0.03%, p < 0.0001), while the entomopathogenic fungus Beauveria pseudobasiana showed a higher relative abundance in unmanaged stands (0.2% vs. 1.4%, p < 0.0001), showing certain specificity of these fungi and the potential response to forest management, including site disturbance. Indeed, Wilcoxina spp. was shown to prevail on sites following soil disturbance [55,56,57]. Beauveria fungi can be important in controlling insect pests [58]. However, their natural occurrence and abundance in soils may depend on site disturbance as much higher relative abundances were reported in natural forest soils than in recent reforestations or agricultural soils [59].

5. Conclusions

Managed and unmanaged P. sylvestris stands support similar diversity and composition of fungal communities associated with living needles and soil. Some fungal taxa have shown a strong association with either managed or unmanaged stands, thereby providing valuable insights into their ecology and adaptation mechanisms.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2607/8/2/259/s1, Table S1: Relative abundance of fungal taxa sequenced from needles and soil of Pinus sylvestris from managed and unmanaged forest stands in Lithuania.

Author Contributions

Conceptualization, D.M. and A.M. (Audrius Menkis); methodology, J.L., D.M., A.M. (Artūras Gedminas), and A.M. (Audrius Menkis); validation, J.L., D.M., A.M. (Artūras Gedminas), A.G. and M.V.; formal analysis, J.L. and A.M. (Audrius Menkis); investigation, J.L., D.M., A.M. (Artūras Gedminas), A.G and M.V.; resources, D.M. and A.G.; data curation, J.L. and A.M. (Audrius Menkis); writing-original draft preparation, J.L.; writing-review and editing, J.L., D.M., A.M. (Artūras Gedminas), A.G., M.V. and A.M. (Audrius Menkis); visualization, J.L. and A.M. (Audrius Menkis); supervision, A.M. (Audrius Menkis); project administration, D.M. and A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Research Council of Lithuania, grant no. S-MIP-17-6.

Conflicts of Interest

The authors declare no conflict of interest.

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Microorganisms 08 00259 g001 550
Figure 1. Map of Lithuania (position shown by shading on the north European map in the lower left corner) showing principal locations of the Aukštaitija national park (ANP), Dzūkija national park (DNP) and Žemaitija national park (ZNP), where sampling of living needles and soil was carried out in managed and unmanaged Pinus sylvestris forest stands.
Figure 1. Map of Lithuania (position shown by shading on the north European map in the lower left corner) showing principal locations of the Aukštaitija national park (ANP), Dzūkija national park (DNP) and Žemaitija national park (ZNP), where sampling of living needles and soil was carried out in managed and unmanaged Pinus sylvestris forest stands.
Microorganisms 08 00259 g001
Microorganisms 08 00259 g002 550
Figure 2. Rarefaction curves showing the relationship between the cumulative number of fungal taxa and the number of ITS2 rRNA sequences from living needles and soil from managed and unmanaged Pinus sylvestris forest stands.
Figure 2. Rarefaction curves showing the relationship between the cumulative number of fungal taxa and the number of ITS2 rRNA sequences from living needles and soil from managed and unmanaged Pinus sylvestris forest stands.
Microorganisms 08 00259 g002
Microorganisms 08 00259 g003 550
Figure 3. The relative abundance of different fungal classes in living needles and soil from managed and unmanaged Pinus sylvestris forest stands at the Aukštaitija National Park (ANP), Dzūkija National Park (DNP) and Žemaitija National Park (ZNP). In ALL, data from different sites is combined.
Figure 3. The relative abundance of different fungal classes in living needles and soil from managed and unmanaged Pinus sylvestris forest stands at the Aukštaitija National Park (ANP), Dzūkija National Park (DNP) and Žemaitija National Park (ZNP). In ALL, data from different sites is combined.
Microorganisms 08 00259 g003
Microorganisms 08 00259 g004 550
Figure 4. Ordination diagram based on principal component analysis (PCA) of fungal communities in living needles and soil from managed and unmanaged Pinus sylvestris forest stands from three national parks in Lithuania. Each point in the diagram represents a single site, and the size of the point reflects the richness of fungal taxa.
Figure 4. Ordination diagram based on principal component analysis (PCA) of fungal communities in living needles and soil from managed and unmanaged Pinus sylvestris forest stands from three national parks in Lithuania. Each point in the diagram represents a single site, and the size of the point reflects the richness of fungal taxa.
Microorganisms 08 00259 g004
Table
Table 1. Characteristics of investigated Pinus sylvestris stands. Information is based on the forest inventory data.
Table 1. Characteristics of investigated Pinus sylvestris stands. Information is based on the forest inventory data.
StandPositionAge (y)Mean Height
(m)		Mean Diameter
(cm)		Stocking LevelForest Site Type *Forest Vegetation Type **Tree Species Composition ***
Aukštaitija National Park (ANP)
Managed55°13′26″ N, 25°50′49″ E3714.8180.7Nblvm60% P, 30% S, 10% B
55°13′17″ N, 25°50′51″ E3714.8180.7Nblvm60% P, 30% S, 10% B
55°13′12″ N, 25°51′26″ E3716180.8Nblvm70% P, 40% S, 20% B
55°12′51″ N, 25°52′14″ E3417.6180.8Nblvm60% P, 20% S, 20% B
55°12′44″ N, 25°52′26″ E10727.6370.6Nblvm100% P
Unmanaged55°13′12″ N, 25°51′08″ E2718190.6Nblvm70% P, 40% B, 20%S
55°13′11″ N, 25°51′41″ E6725.6280.8Nblvm70% P, 30% S
55°12′55″ N, 25°51′59″ E7725.2280.7Nblvm80% P, 20% B
55°12′51″ N, 25°52′07″ E6725.2280.7Nblvm90% P, 10% B
55°12′50″ N, 25°52′22″ E3415.2160.8Nblvm50% P, 30% B, 20% S
Dzūkija National Park (DNP)
Managed54°04′36″ N, 24°19′16″ E10624.5370.7Nalcl100% P
54°04′11″ N, 24°19′34″ E5619.5200.9Nalcl80% P, 20% B
54°04′10″ N, 24°20′30″ E9625.6350.7Nblvm100% P
54°04′08″ N, 24°20′56″ E11125.4350.7Nalcl100% P
54°03′53″ N, 24°21′45″ E11624.4370.6Nalcl100% P
Unmanaged54°05′29″ N, 24°18′51″ E6123.5260.7Nalcl100% P
54°05′23″ N, 24°18′53″ E10126.6330.6Nalcl100% P
54°05′11″ N, 24°18′58″ E3611.8140.9Nalcl80% P, 20% B
54°04′58″ N, 24°18′58″ E5119.7200.8Nalcl80% P, 20% B
54°04′47″ N, 24°19′04″ E8124.9320.8Nalcl100% P
Žemaitija National Park (ZNP)
Managed56°02′09″ N, 21°54′45″ E238.890.7Nclox50% P, 30% S, 20% B
56°02′01″ N, 21°55′04″ E2914130.6Nblvm80% P, 20% S
56°02′11″ N, 21°55′23″ E5723.5250.9Nclox100% P
56°02′35″ N, 21°56′29″ E6727.3290.7Nclox40% P, 40% S, 20% B
56°02′42″ N, 21°57′00″ E3218.9190.8Nclox50% P, 30% S, 20% B
Unmanaged56°01′22″ N, 21°54′44″ E6727.3290.6Nclox60% P, 30% S, 10% B
56°01′35″ N, 21°54′20″ E5220.6250.6Nclox40% P, 40% S, 20% B
56°01′24″ N, 21°54′07″ E5219.6210.7Nclox60% P, 40% S
56°00′58″ N, 21°52′54″ E7226.3310.8Nclox90% P, 10% B
56°00′48″ N, 21°52′18″ E13227.1361.0Pbncsps40% P, 40%, 20% B
* N: Normal humidity, a: very poor fertility, b: poor fertility, c: moderate fertility, l: light soil texture; Pbn: oligotrophic soils of drained peatland [34]. ** vm: vaccinio-myrtilliosum, cl: cladoniosum, ox: oxalidosum, csps: carico-sphagnosum [35]. *** P: Pinus sylvestris, S: Picea abies, B: Betula pendula. In each stand, tree species composition is based on the volume.
Table
Table 2. Sampled needles and soil of Pinus sylvestris in managed and unmanaged forest stands, generated high-quality ITS2 rRNA fungal sequences, and detected diversity of fungal taxa.
Table 2. Sampled needles and soil of Pinus sylvestris in managed and unmanaged forest stands, generated high-quality ITS2 rRNA fungal sequences, and detected diversity of fungal taxa.
StandNeedles Soil
No. of Needle Samples/Amplicon PoolsNo. of SequencesNo. of Fungal TaxaShannon Diversity IndexNo. of Soil Samples/PoolsNo. of SequencesNo. of Fungal TaxaShannon Diversity Index
Aukštaitija National Park (ANP)
Managed5/117082024.23/1486773.2
5/132892843.93/118751553.5
5/116681833.83/112691604.0
5/143853184.33/1220.7
5/134962863.83/126371443.5
All Managed25/514,546598 15/56269312
Unmanaged5/158193784.43/128662003.7
5/134341963.03/14301143.9
5/116971703.53/17181403.9
5/1209713.43/1921602.6
5/141062924.13/119191333.4
All Unmanaged25/515,265595 15/56854399
All ANP50/1029,811799 30/1013,123525
Dzūkija National Park (DNP)
Managed5/158112403.33/111381994.1
5/147982763.63/126361583.7
5/188023413.63/141041992.9
5/124601743.33/114642164.3
5/115361343.23/16811654.3
All Managed25/523,407553 15/510,023519
Unmanaged5/113881353.63/1306903.7
5/129631942.93/121761192.9
5/130432073.43/116721183.1
5/116381413.33/118411313.4
5/153012473.53/12048992.9
All Unmanaged25/514,333434 15/58043308
All DNP50/1037,740662 30/1018,066616
Žemaitija National Park (ZNP)
Managed5/149772593.63/117831743.8
5/19881834.23/143661512.3
5/16661063.43/112561202.3
5/162693393.73/1210984.2
5/137312673.73/19661614.2
All Managed25/516,631638 15/58581450
Unmanaged5/140582782.83/124251512.5
5/152143134.13/110501734.2
5/157511351.53/18871594.2
5/174092082.93/12,9522123.9
5/182402292.93/12,9341512.9
All Unmanaged25/530,672648 15/510,248517
All ZNP50/1047,303870 30/1018,829741
All150/30114,8541261 90/3050,0181186
Table
Table 3. Relative abundance of the 20 most common fungal taxa sequenced from needles of Pinus sylvestris from managed and unmanaged forest stands. Data from different sites is combined.
Table 3. Relative abundance of the 20 most common fungal taxa sequenced from needles of Pinus sylvestris from managed and unmanaged forest stands. Data from different sites is combined.
TaxonPhylumClassGenBank ReferenceSimilarity, % *Managed, %Unmanaged, %All, %
Coleosporium campanulaeBasidiomycotaPucciniomycetesKY810468322/322 (100%)4.120.012.4
Unidentified sp. 3980_1AscomycotaDothideomycetesKP897304244/244 (100%)14.210.812.4
Unidentified sp. 3980_4AscomycotaDothideomycetesKP891553259/259 (100%)3.84.54.1
Sydowia polysporaAscomycotaDothideomycetesMG888613256/256 (100%)3.13.03.1
Unidentified sp. 3980_13AscomycotaEurotiomycetesMG827663262/262 (100%)3.82.12.9
Unidentified sp. 3980_3BasidiomycotaTremellomycetesKU687386302/307 (98%)3.12.52.8
Phaeococcomyces eucalyptiAscomycotaEurotiomycetesNR_120226246/248 (99%)2.62.02.3
Unidentified sp. 3980_10AscomycotaDothideomycetesMG827778258/258 (100%)2.81.82.3
Microsphaeropsis olivaceaAscomycotaDothideomycetesMH871969249/249 (100%)2.11.92.0
Epithamnolia xanthoriaeAscomycotaLeotiomycetesKY814539234/238 (98%)2.41.62.0
Cladosporium cladosporioidesAscomycotaDothideomycetesMH042811243/243 (100%)2.11.51.8
Unidentified sp. 3980_33AscomycotaLeotiomycetesKP897394223/258 (86%)2.01.11.6
Unidentified sp. 3980_37AscomycotaDothideomycetesKP897394236/259 (91%)2.50.61.5
Curvibasidium cygneicollumBasidiomycotaMicrobotryomycetesKY102972310/310 (100%)1.11.71.4
Unidentified sp. 3980_30AscomycotaLeotiomycetesKY742593242/242 (100%)1.90.91.4
Vishniacozyma victoriaeBasidiomycotaTremellomycetesLC085209234/234 (100%)0.62.01.3
Unidentified sp. 3980_25AscomycotaEurotiomycetesKP891398255/255 (100%)1.61.01.3
Seimatosporium lichenicolaAscomycotaSordariomycetesJF320818247/248 (99%)2.40.21.2
Heterotruncatella spartiiAscomycotaSordariomycetesMK012418245/245 (100%)1.40.91.1
Lophodermium pinastriAscomycotaLeotiomycetesMH856647239/239 (100%)1.60.61.1
Total of 20 taxa 59.360.660.0
* Similarity column shows base pairs compared between the query sequence and the reference sequence at NCBI databases, and the percentage of sequence similarity in the parenthesis.
Table
Table 4. Relative abundance of the 20 most common fungal taxa sequenced from soil from managed and unmanaged stands of Pinus sylvestris. Data from different sites is combined.
Table 4. Relative abundance of the 20 most common fungal taxa sequenced from soil from managed and unmanaged stands of Pinus sylvestris. Data from different sites is combined.
TaxonPhylumClassGenBank ReferenceSimilarity, % *Managed, %Unmanaged, %All, %
Unidentified sp. 3980_21AscomycotaArchaeorhizomycetesKC965182219/219 (100%)12.15.28.6
Umbelopsis nanaMucoromycotaMucoromycotinaMH857049293/293 (100%)7.48.98.2
Archaeorhizomyces sp. 3980_5AscomycotaArchaeorhizomycetesMH248043207/207 (100%)10.16.18.1
Penicillium spinulosumAscomycotaEurotiomycetesMK131675251/251 (100%)3.88.76.3
Oidiodendron chlamydosporicumAscomycotaDothideomycetesMG597466235/235 (100%)4.46.05.2
Oidiodendron echinulatumAscomycotaDothideomycetesMG597467236/236 (100%)2.02.82.4
Sagenomella verticillataAscomycotaEurotiomycetesMH860215263/263 (100%)2.81.72.3
Unidentified sp. 3980_28MucoromycotaMucoromycotinaHQ022209299/299 (100%)0.93.62.2
Tolypocladium geodesAscomycotaSordariomycetesMH864065248/248 (100%)0.53.62.0
Pseudogymnoascus roseusAscomycotaDothideomycetesMH865208241/241 (100%)1.81.51.7
Meliniomyces bicolorAscomycotaLeotiomycetesMG597461237/238 (99%)1.71.51.6
Cladosporium cladosporioidesAscomycotaDothideomycetesMH042811243/243 (100%)1.51.51.5
Wilcoxina rehmiiAscomycotaPezizomycetesMF926519253/254 (99%)2.50.031.2
Unidentified sp. 3980_60BasidiomycotaMicrobotryomycetesHQ021811320/320 (100%)1.40.71.1
Unidentified sp. 3980_64AscomycotaSordariomycetesKJ826970317/322 (98%)1.20.91.0
Malassezia restrictaBasidiomycotaUstilaginomycotinaCP030254368/369 (99%)0.91.11.0
Pseudeurotium sp. 3980_68AscomycotaDothideomycetesMF692976229/241 (95%)1.80.21.0
Hyphodiscus sp. 3980_71AscomycotaLeotiomycetesNR_155151235/243 (97%)0.21.70.9
Aspergillus cervinusAscomycotaEurotiomycetesMH865525262/262 (100%)0.81.00.9
Beauveria pseudobassianaAscomycotaSordariomycetesMF872419255/255 (100%)0.21.40.8
Total of 20 taxa 58.258.058.1
* Similarity column shows base pairs compared between the query sequence and the reference sequence at NCBI databases, and the percentage of sequence similarity in the parenthesis.

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Lynikienė, J.; Marčiulynienė, D.; Marčiulynas, A.; Gedminas, A.; Vaičiukynė, M.; Menkis, A. Managed and Unmanaged Pinus sylvestris Forest Stands Harbour Similar Diversity and Composition of the Phyllosphere and Soil Fungi. Microorganisms 2020, 8, 259. https://doi.org/10.3390/microorganisms8020259

AMA Style

Lynikienė J, Marčiulynienė D, Marčiulynas A, Gedminas A, Vaičiukynė M, Menkis A. Managed and Unmanaged Pinus sylvestris Forest Stands Harbour Similar Diversity and Composition of the Phyllosphere and Soil Fungi. Microorganisms. 2020; 8(2):259. https://doi.org/10.3390/microorganisms8020259

Chicago/Turabian Style

Lynikienė, Jūratė, Diana Marčiulynienė, Adas Marčiulynas, Artūras Gedminas, Miglė Vaičiukynė, and Audrius Menkis. 2020. "Managed and Unmanaged Pinus sylvestris Forest Stands Harbour Similar Diversity and Composition of the Phyllosphere and Soil Fungi" Microorganisms 8, no. 2: 259. https://doi.org/10.3390/microorganisms8020259

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