Genotype, Tannin Capacity, and Seasonality Influence the Structure and Function of Symptomless Fungal Communities in Aspen Leaves, Regardless of Historical Nitrogen Addition

Genotype, Tannin Capacity, and Seasonality Influence the Structure and Function of Symptomless Fungal Communities in Aspen Leaves, Regardless of Historical Nitrogen Addition | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genotype, Tannin Capacity, and Seasonality Influence the Structure and Function of Symptomless Fungal Communities in Aspen Leaves, Regardless of Historical Nitrogen Addition Abu Bakar Siddique, Abu Bakar Siddique, Lovely Mahawar, Benedicte Albrectsen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4206868/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Plant mycobiomes play a crucial role in plant health, growth, and adaptation to changing environments, making their diversity and dynamics essential for agricultural and environmental contexts, including conservation efforts, climate change mitigation, and potential biotechnological applications. Horizontally transferred mycobiomes are established in plant organs, yet the dynamics of their colonization and establishment remain unknown. New molecular technologies offer a deeper insight into the establishment and dynamics of plant-associated mycobiomes. In this study, we investigated leaf-associated mycobiomes in cloned replicates of aspen ( Populus tremula ) with naturally varying phenolic profiles and a history of nitrogen fertilization. Main findings Using ITS2 metabarcoding of 344 samples collected from a ca ten-year-old common garden with small aspen trees at various time points over two consecutive years, we identified 30,080,430 reads in our database, corresponding to an average of 87,448 reads per sample clustered into 581 amplicon sequence variants (ASVs). Analysis of ASV patterns revealed changes in both richness and abundance among genotypes and across the seasons, with no discernible effect of fertilization history. Additionally, the number of reads was negatively correlated with the ability of the genotypes to synthesize and store condensed tannins. eDNA Illumina sequencing ITS2 mycobiome microorganisms bioinformatics aspen. condensed tannins Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Microorganisms play an important role in higher life, as increasingly demonstrated in metagenomic DNA profiles from microbiomes associated with animals, plants, soils, and water bodies, collectively known as environmental DNA (eDNA). Healthy microbiomes have become a concept, yet the precise meaning of a healthy microbiome remains complex [ 1 , 2 ]. In plants, some microorganisms are vertically transferred between generations and organs, such as systemic fungal endophytes in cold-season grasses, which provide their host with both tolerance and resistance benefits [ 3 , 4 , 5 , 6 ]. This has paved the way for commercial use of endophyte-infected grass seeds [ 7 ]. However, most plant-associated fungi appear to be nonsystemic and horizontally transferred with functions ranging from beneficial to antagonistic for the plant [ 8 , 9 , 10 , 11 ]. The most well-known and studied symbiotic association between plant roots and fungi is probably mycorrhizae, which provides both plants and fungi with advantages, primarily in terms of energy and nutrient supplies, respectively [ 12 , 13 , 14 , 15 , 16 , 17 ]. Fungal endophytes, which inhabit the inter- and intracellular spaces of various plant organs (such as stems, roots, petioles, leaves, bark, seeds, and latex) without causing visible symptoms [ 18 ] are also increasingly recognized for their direct and indirect positive potential for the host and as a source of commercial products. For example, they may produce chemicals such as phytohormones and substances that support photosynthesis [ 19 , 20 , 21 ], enhance nitrogen assimilation mechanisms [ 22 ], solubilize minerals [ 23 , 24 ], produce siderophores with iron-retaining properties [ 25 , 26 ], and generate substances that enhance defense and tolerance properties [ 27 , 28 , 29 , 30 ]. In return, the endophytes benefit from the sheltered life inside the plant, which is surrounded by nutrients, and they gain both competitive and dispersal advantages inside their host. There may be millions of endophytic fungi Worldwide, yet only a fraction of which are known [ 31 , 32 ] and an even smaller fraction has been isolated and studied for their biology and potential [ 33 , 34 , 35 ]. Fungal endophytes were previously discovered in plants in the 19th century [ 36 , 37 ], and they were f long isolated from sterilized plant parts; consequently, only culturable fungal endophytes could be studied and described based on their morphology [ 38 , 39 , 40 ]. Sanger sequencing subsequently enabled the molecular determination of culturable colonies [ 41 , 42 ]. Today, metabarcoding and shotgun sequencing of eDNA has widely replaced culturing as a way to assess endophyte community composition independently of culturing; and high-throughput metagenomic sequencing techniques are now standardly used to characterize the presence of fungal DNA strands in environmental samples [ 43 ]. The amplification and sequencing of conserved DNA regions with high interspecific and low intraspecific variation, are followed by downstream bioinformatic analyses, to reveal the diversity, composition, and function of leaf-associated fungal communities [ 42 ]. The conserved regions to be targeted vary among organism groups, and the nuclear ribosomal internal transcribed spacer (ITS) region (500 base-pairs) is the formal fungal barcode and the most sequenced genetic marker in mycology [ 44 , 45 ]. Based on its degree of conservation, the ITS region is divided into subunits, that with adequate primers may be targeted singly or in combination (i.e. ITS1 to ITS4); providing varied opportunities to estimate fungal richness and diversity in a sample [ 46 , 47 ]. While these techniques are highly competent generating diverse DNA sequence reads in an environmental sample, the ability to recognize the true diversity is limited by the lack of reference databases, the inability to distinguish endophyte DNA from that of epiphytes [ 48 ], and the inability to culture reads back to colonies that would allow for morphology description and chemical screening to characterize the potential function of the endophyte. Analytical tools and models are being developed to suggest ecological function and interrelated guild structures (FUNGuild: [ 49 ]; FungalTraits: [ 50 ]; FunFun: [ 51 ]), and while these models provide insights into an endophytic community, they are also limited by the depth of taxonomic determination d sequencing efforts and the interpretation of ecological function thus builds on known relationships among related taxonomic groups. Trees are long-lived organisms that are exposed to biological and abiotic stresses for many years. The ability of trees to grow and stay healthy has been associated with their ability to directly fight back by own means[ 52 , 53 ], and indirectly through symbiotic associations with endophytic fungi, for example. These associations may rely on chemical and physiological mechanisms, as well as on ecological processes [ 27 , 28 , 29 , 30 ]. The biosynthesis of chemical specialized products may for example be shared between the endophyte and its host (i.e. the production of camptothecin by the tree host Campotheca acuminata and its endophyte Fusarium solani [ 54 ]). The host tree appears to select or filter the fungal community that established in its organs, as reported for the leaves of beech [ 11 ], cacao [ 55 ], oak [ 56 ], elm [ 57 ], aspen [ 58 , 59 , 60 ], Populus trichocarpa [ 61 ], temple trees [ 62 ], and eucalyptus [ 63 ]. Specific associations with organs and growing sites are further demonstrated for e.g. beech [ 11 ], oak [ 56 ], and tropical forest trees [ 64 ]. However, our understanding of what makes an endophyte community “healthy” as well as processes underlying the inclusion and exclusion of fungal taxa by trees, is limited, although host substrate conditions have been suggested to play a role [ 57 , 60 ]. With European aspen ( Populus tremula ) as an example we explored the effect of plant genotype and its inherent ability to produce, and store condensed tannin phenolic polymers as a factor in determining the community of fungal endophytes across the short growth season in northern Sweden. In addition, this study allows for an assessment of the long-term effects of historic nutrient additions to a population of ca ten-year-old trees on the fungal endophyte community hidden in their leaves. Methods Study system To delineate the fungal communities associated with aspen foliage, we obtained aspen leaves from the TanAsp garden, and selected six replicated aspen genotypes with either a high or low capacity to generate and retain condensed tannins, as highlighted in the work of Bandau et al. [ 65 , 66 ]. The trees were planted in 2010, and our study aimed to assess the seasonal composition and dynamics of mycobiomes linked to the leaves of young trees within and between the years 2020 and 2021. To monitor the seasonal composition and dynamics of the mycobiomes associated with the leaves and to compare patterns over time, we revisited the same trees repeatedly. This included twelve replicates of each genotype in 2020, resulting in 72 trees. The number of trees was reduced to four genotype replicates in 2021, totaling 24 trees per sampling event. Plant materials Nondestructive phenotyping Visual inspections included : plant height measured in centimeters (from the plant-soil intersection to the tip of the canopy); counting of Harmandiola galls; assessment of symptoms of Venturia sp. as wilted branches relative to the total number of branches per canopy; other biotic stress symptoms, such as leaf hypersensitive response (HR), abundance of eriophyid mite colonies, damage from chewing insects, and rust pustules ( Melampsora pinitorqua [ 47 , 67 ]. Leaf damage symptoms were ranked on a 0–3 scale: 0 indicated no symptoms, 1 indicated low damage corresponding to symptoms on some leaves, 2 indicated moderate damage with symptoms on approximately half of the leaves, and 3 indicated a high level of damage with symptoms on more than half of the leaves. Pigment assessments included nitrogen balance, chlorophyll, anthocyanin, and flavanol indeces, which were measured with an optical meter ((DUALEX® Optical LeafClip meter, Force-A, Orsay, France). Three readings were performed on each of ten randomly collected leaves per canopy, generating a mean value per tree from 30 measurements. Destructive phenotyping Leaf harvest and sample preparation : In 2020, leaf sampling was conducted on the following dates: June 22, July 14, and August 6 and 23. In 2021, sampling occurred on June 23, July 14, and August 24. The selected trees were chosen in accordance with the original garden setup [ 65 , 66 ], including low-tannin genotypes (GT50, GT60, GT115) and high-tannin genotypes (GT5, GT65, GT72). Additionally, trees were selected based on a history of NH 4 NO 3 addition [ 66 ]. Therefore, during the 2020 sampling, three nitrogen treatment levels were considered, while in 2021, the sampling was limited to control trees that had not received fertilizer. Each sample, representing one tree and one sampling event, consisted of ten undamaged mature leaves collected from one canopy with gloves to avoid contamination, at breast height. Each leaf was cut at the base with a clean pair of scissors, leaving the petiole on the twig, and then added to a labelled plastic bag. The sample bags were flash-frozen on dry ice in the field, transported to the laboratory for approximately one hour in coolers, and then stored at -80°C until lyophilization for 24 hours in a LABOGENE freeze dryer (3450 Lillerød, Denmark). The dry leaves were subsequently ground to a fine powder and kept in labelled Falcon tubes (15 mL) at -20°C until DNA extraction. DNA extraction and library preparation DNA extraction and amplicon library preparation were conducted as described in Siddique et al. (2022) [ 46 ]. In short, the E.Z.N.A. Plant DNA Kit (OMEGA-BIO-TEK Inc, PW, GA, USA) was used for DNA extraction, and the fungal-specific ITS2 region was amplified using extended primers. After confirming the successful attachment of sample-specific index primers, the library was subjected to Illumina Miseq (300 bp) paired-end (PE) sequencing at NGI SciLifeLab (Stockholm). Bioinformatic analyses Amplicon sequencing analyses followed FAIR principles suggested by Ewels et al. (2020) [ 68 ] and Straub et al., (2020) [ 69 ]; the ampliseq pipeline ( https://github.com/nf-core/ampliseq ) was assessed on 14 April 2022). In brief, Raw Illumina, MiSeq v3 2-300 bp, paired-end reads were demultiplexed by SciLifeLab and delivered as sample specific fastq files, which were then individually quality checked with FastQC. Fungal primers were removed with the CutAdapt tool. DADA2 was used to generate a curated dataset of amplicon sequence variants (ASVs). The ITS region was truncated (settings: trunclenf = 223 and trunclenr = 162); chimera and “noisy” reads were removed using the DADA2 algorithm, which distinguishes between biological variation and sequencing errors; mitochondria, chloroplast, and archaea reads were identified and removed with the Barrnap tool; and only sequences with fungal ITS2 regions were retained. Based on the ITS region, fungal taxonomy was then assigned with DADA2 and QIIME2 against the UNITE-fungal reference database v.8.3. ASVs represented by fewer than five reads in a sample were removed to avoid rare sequences and singleton bias [ 70 ]. The fungal data are available as available as 06.1_rel-table-ASV_with-DADA2-tax.tsv scaled after total fungal DNA in a sample. The QC results and sample summary files were aggregated and visualized with MultiQC into a single report which is available at 04_multiqc_report.html. The reproducible pipeline, tools, codes, and parameters can be found in GitHub repository: https://github.com/abu85/ampliseq_ITS and the bioinformatic summary, output and tools wise detail reference lists can be found in the Zenodo repository: [ 10.5281/zenodo.10839669 ] -as referred to in the data availability section. The entire bioinformatic pipeline and analysis were run on the UPPMAX HPC server ( https://www.uppmax.uu.se/ ). The community analyses were built on the curated dataset. Community analyses The number of sequence reads (or ASVs) was used as a measurement of fungal abundance in a sample. The abundance of different ASVs was used as a metric of species richness. The identification depended on the taxonomic resolution that the ASVs allowed, and it was expected to vary among samples, genotypes, and sampling events. As the amplification threshold was set to 1000 reads per sequence in a sample, and to avoid including potential sequencing errors as unique reads [ 71 , 72 ], we removed samples with low coverage (< 1000 reads) from the database. Rarefaction curves Randomized species accumulation curves (rarefaction curves) were compared after season, genotype, and treatment to assess fungal richness. The specaccum function with two segments, coleman and rarefaction, was employed ('coleman' finds the expected richness, and 'rarefaction' finds the mean when accumulating ASVs [ 73 , 74 ]. Species diversity indices : To assess biodiversity in foliar mycobiome communities, we employed a suite of indices: Fisher’s alpha, a measure of species richness; the Shannon index, considering both richness and abundance; and the three Hill numbers that evaluate diversity indices for relative community data [ 75 ] (Hill, 1973). The Hill numbers primarily scrutinize communities based on species abundances: N1, the exponent of the Shannon index, emphasizes abundant species; N2, the inverse Simpson index, focuses on highly abundant species; and N3 emphasizes diversity measurements by comparing species of similar abundance within the community. Community structure Community composition was analysed and visualized using nonmetric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) of square root transformed ASV counts [ 76 ] to handle nonlinear relationships between samples in dissimilarity space. The distinctiveness of fungal assemblages was statistically tested with permutational multivariate analyses of variance using Bray–Curtis and AltGower distance matrices. Dissimilarities among mycobiome communities were tested across seasons, between years, among treatments, and among genotypes with permutational multivariate analysis of variance, using AltGower dissimilarity indexes on standardized count data [PERMANOVA/adonis2 [ 77 , 78 ]; number of permutations = 9999). Taxonomic composition Species abundances were visualized using normalized relative abundance data, and guild analyses were used to reveal the ecological function of annotated fungal species in the samples. The FUNGuild [ 49 ] package was used for those analyses. Results Sequencing output and quality The Amplicon Sequence Variant (ASV) dataset obtained from 344 aspen leaf samples comprised 30,081,923 reads, which were clustered into 581 ASV variants as detailed in the 06_asv_table.tsv. The average library size was 87,448 reads per sample, with a range from seven to 957,090 reads. Based on our criterion of having more than 1000 reads in a sample, we excluded seven samples. The final reads resulted in a refined dataset of 337 samples with 30,080,430 reads, equivalent to 99.99% of the initial total reads. Fungal richness and abundance across seasons The refined dataset allowed for the generation of rarefaction curves, indicating an increase in the number of fungal ASVs throughout the season in both 2020 (Fig. 1 a) and 2021 (Fig. 1 b). The pace at which the fungal community developed in the leaves exhibited similar trends in both years although the trajectory differed somewhat between the years (Fig. 1 c). However, a notable distinction among genotypes consistently emerged (Fig. 1 d), and fungal richness, expressed as the number of reads, also appeared to be greater in aspen leaves categorized as low-tannin genotypes than in those categorized as high- tannin genotypes (Fig. 1 d), corroborating the findings of Bandau et al. (2021) [ 66 ]. Fungal richness and abundance among Aspen genotypes Diversity indices characterize community structures based on aspects of species richness and abundance. Using five indices to calculate the diversity based on ASV reads, we obtained insights into these structures. We observed a significant effect of aspen genotype on each index, underscoring the importance of genotype for both species’ richness and abundance. The pattern of relative index differences was remarkably consistent among sampling events and years (Fig. 2 ). Pairwise comparisons further indicated a stronger differentiation of genotypes when assessing diversity with the Shannon index than with the Fisher's alpha index (Fig. 3 ), which suggests that effects of both richness and abundance separate fungal communities in aspen leaves. Table 1 ANOVA summaries of genotype and seasonal effects from two consecutive years on five measures of fungal biodiversity (five diversity indices, also see text) in aspen leaves from the TanAsp collection [ 65 , 66 ]. The results are provided by year with sample sizes N 2020 = 72 and N 2021 = 24. Significant results (P < 0.05) are shown in bold. Also see Fig. 2 . Diversity indices Fishers’ alpha Shannon Hill N1 Hill N2 Hill N3 Year Effect SS Dev P SS Dev P SS Dev P SS Dev P SS Dev P 2020 Genotype 172.24 3.1 0.001 7.47 0.3 0.004 40.32 0.73 0.001 25.66 0.39 0.001 23.55 0.3 0.001 Season 188.46 3.1 0.001 0.39 0.3 0.44 4.04 0.82 0.061 0.10 0.50 0.58 0.01 0.4 0.84 2021 Genotype 15.5 1.4 0.013 1.95 0.2 0.15 9.93 0.51 0.001 7.59 0.31 0.001 7.27 0.3 0.001 Season 48.69 1.2 0.001 0.08 0.3 0.75 0.65 0.64 0.40 0.03 0.46 0.77 0.01 0.4 0.87 Table 2 Summary of permutational multivariate analysis of variance (PERMANOVA) tests for 2020 and 2021, partitioning effects of season, historic nitrogen treatment, aspen genotype, and ability to synthesize and store condensed tannins (CTs) on the mycobiome (ASV abundances) in aspen leaves. The samples from 2021 included aspen trees that had not received any nitrogen fertilization. Significant responses are shown in bold ( p < 0.05). See also Fig. 4 . Year Effect df SS R 2 F P 2020 Season 3 -0.39 -0.05 -3.97 1.00 Treatment 2 0.08 0.01 1.24 0.20 Genotype 5 2.35 0.28 20.74 0.0001 CTs 1 0.34 0.04 11.47 0.0001 2021 Season 2 0.24 0.03 1.01 0.20 Genotype 5 3.35 0.42 9.11 0.0001 CTs 1 0.64 0.08 5.81 0.0001 Fisher’s alpha confirmed a seasonal effect, as did Hill N1, suggesting that there was both a change in species richness across the season and a change in the organization of abundant species. However, neither the Shannon index, nor Hill's N2 or N3 suggested a significant seasonal impact on the leaf-associated fungal community composition (Table 2 ). Association stability between aspen genotype and mycobiome To visualize the stability of the fungal association with genotype, two kinds of multivariate projections were performed. Both methods (the iterative hypothesis generating NMDS and the analytical correlative projection PCoA) reached similar conclusions from the data that included all samples from the refined dataset. In both cases the ASV reads strongly clustered after genotype, with two high tannin genotypes (72 and 5) being more distant from the other genotypes. The projection of tree phenotypic traits, collected nondestructively during sampling events, indicated that height and susceptibility to infection with Venturia (a necrotrophic fungal pathogen) were opposite vectors with significance for the projection of the samples in the multivariate space (Fig. 4 ), corroborating suggestions by Robinson et al (2012) [ 67 ]. Apart from the genotypic effect, neither the impact of seasonal variation nor the historical nitrogen addition treatment appeared to significantly influence the projection, as evidenced by permanova tests (Table 2 , also see Fig. S1 ). Fungi taxonomic resolution The taxonomic resolution for this study at the phylum level was 99.98%. However, only 1.44% of the ASVs could be classified at the class level, and 0.98% could be classified to the genus level (summing up to 49 genera, Fig. 5 a). The mycobiome of the aspen leaves was dominated by four orders within the Ascomycota phylum (Dothideales, Helotiales, Capnodiales and Saccharomycetales) with three genera dominating: Aureobasidium ( Dothideales), Candida ( Saccharomycetales) and Cladosporium ( Capnodiales) (Fig. 5 b). Among 24 identified fungal orders, 13 belonged to Ascomycota, 10 to Basidiomycota and one to Dothideomycetes (data to be found in the Zenodo repository: 10.5281/zenodo.10839669 ). Interestingly Helotiales was more abundant in the database and included more genera compared to Dothideales, Capnodiales and Saccharomycetales (Fig. 5 c). Representation of trophic modes in the mycobiome Out of 581 ASVs only 183 were determined to genus level and could be assigned to functional guilds. Despite this limited ability to identify fungi at lower taxonomic levels, we detected representatives of seven ecological guilds: pathotrophs, patho-saprotrophs, patho-symbiotrophs, patho-sapro-symbiotrophs, saprotrophs, sapro-symbiotrophs, and symbiotrophs. Patho-sapro-symbiotrophs clearly dominated the total dataset that included all sampling events (Fig. 5 d). Discussion Recent molecular advances have enabled us to study hidden microbial communities in plant organs. However, the worldwide number of tree-associated microorganisms is vast, their diversity is underexplored, and their functions are complex and largely unknown, warranting basic insight into their composition and dynamics [ 79 , 80 ]. Using Illumina sequencing, we revealed a seasonal increase in the abundance of fungal eDNA in aspen leaves for two consecutive years. We also demonstrated genotype differences in the abundance and composition of the foliar fungal microflora, which were negatively related to the presence of polyphenolic condensed tannin complexes in the leaves. Additionally, our study strongly indicated that fertilization with ammonium nitrate did not have any long-term effects on the mycobiome (Fig. S1 ), suggesting that genotype differences in host trees may be a strong factor in the maintenance of forest biodiversity. Genotype and environmental effects on aspen leaf-associated mycobiomes We have previously proposed a significant impact of host genotype on leaf-associated fungal communities in aspen trees cultivated within a greenhouse environment [ 58 ], as well as when comparing clones repeatedly grown indoors and outdoors [ 81 ]. This study, which was conducted on six cloned young aspen trees in a common garden setting, further reinforces the influence that hosts may exert on shaping the mycobiome within their leaves. Similar genotypic effects have also been observed in foliage from several studies of poplar trees including Populus angustifolia [ 82 ], and P. balsamifera [ 83 ] and in the roots of a selection of mainly hybrid poplar clones [ 84 ] as well as in willows [ 85 ]. It is also becoming evident that different organs may filter fungal-associated communities differently, as observed in studies on alder and hazel [ 86 ], beech [ 11 ], and oak [ 87 ]. The composition of the mycobiome may further depend on the general health condition of a tree [ 87 ] and, in some cases, may determine the severity of damage from leaf pathogens [ 88 ]. The growth environment may also affect the richness and abundance of the mycobiome [ 89 , 90 ]; however, this influence is not consistently corroborated by empirical data, as highlighted by Siddique et al. (2021) [ 11 ]. This assertion is further supported by the absence of long-term effects resulting from nitrogen fertilization, as reported in Fig. S1 . While tree species are often assumed to play a crucial role in biodiversity within ecosystems, our study thus highlights the diversity shaped by individual variation in a key player species such as aspen [ 91 ] thereby supporting the fundamental implication that genetic variation in primary producers may have for understanding terrestrial ecosystems [ 82 ]. The mycobiome reported here primarily identified ASV reads belonging to the Ascomycota phylum, predominantly representing the orders Dothideales, Helotiales, Capnodiales, and Saccharomycetales. Among these genera, three dominated: Aureobasidium (Dothideales), Candida (Saccharomycetales), and Cladosporium (Capnodiales). This division aligns with similar screenings reported in previous studies (e.g. [ 11 ]). Helotiales comprises a diverse array of fungi, encompassing numerous species that are yet to be formally described. These genera however have various lifestyles, many of which involve leaf and wood decay processes [ 92 ], and some species associated mainly with roots as endophytes act as facilitators of plant growth through promotion effects, while others exhibit pathogenic behavior [ 93 ]. Notably, within this order, Fraxinus excelsior stands out as a well-known pathogen responsible for inducing Ash dieback. However, Helotiales is also recognized for its potential for pharmaceutical discoveries [ 93 ]. Diversity indices could be calculated based on reads, supporting the genotype-specific mycobiome diversity findings, as both species richness and abundance significantly varied among genotypes in similar ways during both study years. Various diversity indices yielded similar suggestions, and projections in multivariate space repeatedly indicated that some genotypes might be closer to each other than others in terms of their impact on associated mycobiomes. What shapes mycobiomes and their functions? It is suggested that leaves may serve as a chemical landscape, and the metabolic dynamics of the leaf may determine the endophytes that colonize its interior [ 60 ]. Additionally, physical structures, such as bark surfaces, have been reported to create environments more conducive to the attachment of certain cryptogams and fungi than other structures e.g. [ 94 ]. The TanAsp common garden, utilized for the present study, was established to investigate the potential effects of aspen tannins on their association with surrounding organisms [ 66 ]. Here we observed differences in the abundance of fungal ASV reads corresponding to relative variations in the expected tannin profiles of the trees. Additionally, qualitative distinctions in the genet-related mycobiome were apparent, as demonstrated by multivariate projections. While two of the high tannin genotypes clustered separately, the third high-tannin genotype clustered together with the low-tannin genotypes. Hence, while our study suggested a negative impact of condensed tannins on the abundance of foliar-associated mycobiomes, it is premature to infer a simple relationship. Condensed tannins, for example, were reported to have a negative correlation with twig endophyte infection in poplars within a hybrid zone [ 95 ] as well as with the abundance of molecular markers of the biotrophic pathogen Melampsora pinitorqua [ 47 ], and the symptoms of necrotrophic pathogen Venturia infection in Populus tremula [ 66 ]. Moreover, in a review by Bhat et al (1998) [ 96 ], degradation primarily by filamentous fungi such as Aspergillus and Penicillium , along with yeasts, was found to degrade condensed tannins, potentially using them as substrate. Additionally, the ectomycorrhizal fungus Laccaria bicolor was reported to colocalize with condensed tannins in the roots of Populus tremula [ 97 ]. The function of mycobiomes evidently varies depending on the substrate composition of a plant organ, such as a leaf. Niche overlap may result in competition for substrates among the endophytic community [ 98 ], occurring in both the inter- and intracellular spaces of various plant organs where endophytes reside [ 18 ], which could be advantageous for some fungal taxa and disadvantageous for others. The increase in fungal species abundance observed throughout the season was a notable finding of this study. Speculations regarding mycobiome function in plant parts suggest that the establishment of degrading fungi could benefit litter decomposition of plant material and contribute to nutrient recycling in forest habitats potentially affected by tannins [ 65 ]), their resistance to degradation by soil microbes, and their ability to bind and stabilize microbial enzymes [ 99 , 100 ]. Despite the negligible species-level determination coverage of the mycobiome in this study, valuable insights were gained into the diverse functions that were represented in the leaves across two seasons. Different combinations of patho, -sapro, and -symbiotroph- related lifestyles were suggested based on the data. The dominating Aureobasidium , a sooty mold, is a taxonomic complex usually assigned to a saprotroph lifestyle similar to that of Candida [ 50 ]. In our study, symbiotrophic fungi were mainly lichenized and ectomycorrhizal endophytes, while pathotropic members were primarily associated with Curvularia . Methodological challenges and way to go The selection of appropriate taxonomic markers is crucial for successful DNA metabarcoding [ 101 ]. Primers should ideally encompass all members of an organism group in a sample while efficiently distinguishing nucleotide variability within that group to estimate taxonomic diversity. For fungi, the internal transcribed spacer region (ITS) of ribosomal RNA, including the ITS1, 5.8S, and ITS2 loci, is commonly utilized as the primary marker sequence [ 102 ]. ITS2, known for its low taxonomic bias, is frequently targeted for metabarcoding through Illumina MiSeq, the widely used high-throughput sequencing platform [ 45 ]. In our study, we also targeted the ITS2 region using the ITS3F and ITS4R primers[ 46 ]. However, we encountered coverage issues, with less than 1% of reads identifiable to the genus level, necessitating methodological improvements in subsequent studies, particularly during the experimental design phase of molecular work. The initial step of primer design may introduce biases in estimating major fungal taxa [ 102 ]. ITS primers, mostly adapted from Sanger's sequencing, may favour mono- or oligo-specific sequences, potentially mismatching or suboptimally amplifying the entire fungal community in an environmental DNA (eDNA) sample [ 103 , 104 ]. Moreover, the primers used in our study might suffer from mismatches with the target site, favouring certain fungal taxa over others in the amplicon. The use of degenerate primers specifically targeting ITS2 regions can mitigate primer bias and enhance taxonomic coverage [ 104 ]. However, some ITS2-specific degenerate primers, such as fITS7/ITS4 and gITS7/ITS4, may also amplify nontargeted host DNA, reducing sequencing depth and read length [ 48 ]. Although ITS1 and ITS2 information was considered, ITS1 was ultimately discarded due to the occurrence of undesired PCR bands and primer-dimers [ 46 ]. Another source of error is the potential coamplification of plant-fungal DNA in aspen leaves, as highlighted in recent literature. Utilizing peptide nucleic acid (PNA) clamps alongside primer pairs can effectively increase fungal reads and associated diversity by blocking the amplification of nontargeted organisms, such as the plant host [ 48 ]. Furthermore, achieving genus or species-level taxonomic resolution is challenging when targeting only the ITS2 region, which may be too short to distinguish closely related fungal species, potentially resulting in unassigned fungal taxonomy at the class-to-genus level (Fig. 5 a,[ 44 ]). Long-read high-throughput amplicon sequencing methods, such as Pacific Biosciences (PacBio), which map the entire ITS region and highly conserved flanking sites (small subunit-SSU and large subunit- LSU), offer an alternative for comprehensive fungal community analysis with improved taxonomic resolution [ 105 ]. However, taxon-specific PCR amplification biases can obscure community diversity in amplicon sequencing. PCR-free metagenomic sequencing presents a promising solution for overcoming this issue [ 106 , 102 ]. Despite these advancements, our knowledge of microbial life remains limited. While the UNITE databases contain entries for 3,846,536 ITS sequences, only 195,696 fungal species are hypothesized to have DOIs at the 1.5% threshold ( https://unite.ut.ee/ ). This is due to the unavailability of reference genomes for every organism. Thus, the scientific community faces the significant challenge of exploring potential beneficial organisms among the diverse unknown fungal communities associated with plants. In the realm of plant-associated microorganisms, inquiries into the internal, external, or combined lifestyle persist. Various microorganisms can access plant tissues through openings like stomata, residing within the apoplast or penetrating host cells as genuine endophytes. Conversely, some organisms primarily exist as external partners or epiphytes. This differentiation is fundamental, yet fully comprehending the physical association becomes challenging with eDNA applications, as both living and deceased DNA are amplified. In our investigation, we sterilized a subset of samples to explore potential sequencing discrepancies, and we were unable to differentiate between surface-sterilized and unsterilized leaves (Fig. S2). Consequently, we omitted this additional handling step from our study samples. Localization studies, such as immunoblotting or fluorescence in situ hybridization (FISHtechniques, which label target organisms with molecular markers, may be integrated into metagenome studies [ 106 ] to gain insights into the physical connection between the plant host and its associated mycobiome. However, this approach is currently meaningful only for fungal associations of particular interest. Conclusion In summary, our study emphasized that the impact of fungal microbes associated with aspen trees varies with genetic background, suggesting that within-population genetic variability of aspen trees may hold considerable importance for hidden forest biodiversity. Our study also exemplifies the need not only to study these hidden communities but also to optimize metagenome sequencing to refine the understanding of tree-associated mycobiome composition and function. Abbreviations ASV Amplicon sequence variant CTs Condensed tannins DNA Deoxyribonucleic Acid eDNA environmental DNA FASTQ refers to text-based biological sequence (usually nucleotide sequence) files. These files have the following extension:fastq ITS Ribosomal internal transcribed spacer LSU rDNA large subunit NMDS Nonmetric multidimensional scaling PCoA Principal coordinate analysis PCR polymerase chain reaction PNA Peptide nucleic acid RNA Ribo nucleic acid SRA Sequence read archive SSU rDNA small subunit Declarations Ethics approval : Not applicable Consent for publication : Not applicable. Availability of data and material: The authors declare that links to the data supporting the findings of this study are available within the paper and its Supplementary Information files. The sequencing reads (FASTQ) were registered in the Sequence Read Archive (SRA) atSRP496938, as BioProject:PRJNA1090416 with accession numbers SRX24011776 to SRX24012154 . The dataset information can be found as part of the Zenodo repository: [ 10.5281/zenodo.10839669 ]. The short ’ readme’ file contains the data and material content. Bioinformatic codes and R scripts are stored at https://github.com/abu85/ampliseq_ITS. Should any raw data files be needed in another format they are available from the corresponding author upon request. Acknowledgements We thank Igor Matyas and Khan Mohammad Salehin for their assistance in the field and lab work. This research was made possible due to support from the Knut and Alice Wallenberg Foundation in two projects linked to the Umeå Plant Science Centre (KAW 2018.0272) and SciLifeLab (KAW 2018.0273), which supported BRA and ABSI. We thank the Kempe Foundation for financial support for LM (JCSMK23-0066) and BRA, and the Erasmus Mundus Master Program in Plant Breeding enabled the involvement of ABSII. The sequencing data were generated from the NGI (Scilifelab) facility(Stockholm). The data handling was enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC, now NAISS) at UPPMAX (Uppsala Multidisciplinary Center for Advanced Computational Science) and was supported by SLUBI (SLU bioinformatics infrastructure). Authors information Abu Bakar Siddique Department of Plant Biology, Swedish University of Agricultural Sciences, 75007, Uppsala, Sweden. Email: [email protected] Abu Bakar Siddique Umeå Plant Science Centre (UPSC), Department of Plant Physiology, Umeå University, 90187 Umeå, Sweden. Tasmanian Institute of Agriculture (TIA), University of Tasmania, Prospect 7250, Tasmania, Australia. Email: [email protected] Lovely Mahawar Umeå Plant Science Centre (UPSC), Department of Plant Physiology, Umeå University, 90187 Umeå, Sweden. Email: [email protected] Benedicte Riber Albrectsen Umeå Plant Science Centre (UPSC), Department of Plant Physiology, Umeå University, 90187 Umeå, Sweden. Email: [email protected] References Trivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant-microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2021;19(1):72. https://doi.org/10.1038/s41579-020-0412-1 . Shanahan F, Ghosh TS, O'Toole PW. the healthy microbiome—what is the definition of a healthy gut microbiome? Gastroenterology. 2021;160(2):483–94. https://doi.org/10.1053/j.gastro.2020.09.057 . Carroll G. Fungal endophytes in stems and leaves - from latent pathogen to mutualistic symbiont. Ecology. 1988;69(1):2–9. https://doi.org/10.2307/1943154 . Clay K. comparative demography of three graminoids infected by systemic, clavicipitaceous fungi. Ecology. 1990;71(2):558–70. https://doi.org/10.2307/1940309 . Clay K, Schardl C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am Na. 2002;160:S99–127. https://doi.org/10.1086/342161 . Schardl CL, Leuchtmann A, Spiering MJ. Symbioses of grasses with seedborne fungal endophytes. Annu Rev Plant Biol. 2004;55:315–40. https://doi.org/10.1146/annurev.arplant.55.031903.141735 . Kauppinen M, Saikkonen K, Helander M, Pirttilä AM, Wäli PR. Epichloë grass endophytes in sustainable agriculture. Nat Plants. 2016;2(2). https://doi.org/10.1038/nplants.2015.224 . Schulz B, Boyle C. The endophytic continuum. Mycol Res. 2005;109:661–86. https://doi.org/10.1017/s095375620500273x . Raghavendra AKH, Newcombe G. The contribution of foliar endophytes to quantitative resistance to Melampsora rust . New Phytol. 2013;197(3):909–18. https://doi.org/10.1111/nph.12066 . Suryanarayanan TS. Endophyte research: going beyond isolation and metabolite documentation. Fungal Ecol. 2013;6(6):561–8. https://doi.org/10.1016/j.funeco.2013.09.007 . Siddique A, Biella P, Unterseher M, Albrectsen BR. Mycobiomes of young beech trees are distinguished by organ rather than by habitat, and community analyses suggest competitive interactions among twig fungi. Front Microbiol. 2021;12. https://doi.org/10.3389/fmicb.2021.646302 . Brundrett MC. Coevolution of roots and mycorrhizas in land plants. New Phytol. 2022;154:275–304. Lindahl BD, Finlay RD, Cairney JWG. Enzymatic activities of mycelia in mycorrhizal fungal communities. in the fungal community: its organization and role in the ecosystem. Taylor Francis. 2005. doi.org/10.1201/9781420027891.ch16 . http://ezproxy.uws.edu.au/login?url=http://dx. . Luginbuehl LH, Menard GN, Kurup S, Van Erp H, Radhakrishnan GV, Breakspear A, Oldroyd GED, Eastmond PJ. Fatty acids in arbuscular mycorrhizal fungi are synthesized by the host plant. Science. 2017;356(6343):1175–8. https://doi.org/10.1126/science.aan0081 . Huey CJ, Gopinath SCB, Uda MNA, Zulhaimi HI, Jaafar MN, Kasim FH, Yaakub ARW. Mycorrhiza: a natural resource assists plant growth under varied soil conditions. 3 Biotech. 2020;10(5). https://doi.org/10.1007/s13205-020-02188-3 . Shi JC, Wang XL, Wang E. Mycorrhizal symbiosis in plant growth and stress adaptation: from genes to ecosystems. Annu Rev Plant Biol. 2023;74:569–607. https://doi.org/10.1146/annurev-arplant-061722-090342 . Perotto S, Balestrini R. At the core of the endomycorrhizal symbioses: intracellular fungal structures in orchid and arbuscular mycorrhiza. New Phytol. 2023. https://doi.org/10.1111/nph.19338 . Digra S, Nonzom S. An insight into endophytic antimicrobial compounds: an updated analysis. Plant Biotechnol Rep. 2023;14:1–31. https://doi.org/10.1007/s11816-023-00824-x . Waqas M, Khan AL, Kamran M, Hamayun M, Kang SM, Kim YH, Lee IJ. Endophytic fungi produce gibberellins and indoleacetic acid and promotes host-plant growth during stress. Molecules. 2012;17(9):10754–73. https://doi.org/10.3390/molecules170910754 . Khan AL, Waqas M, Kang SM, Al-Harrasi A, Hussain J, Al-Rawahi A, Al-Khiziri S, Ullah I, Ali L, Jung HY, et al. Bacterial endophyte Sphingomonas sp LK11 produces gibberellins and IAA and promotes tomato plant growth. J Microbiol. 2014;52(8):689–95. https://doi.org/10.1007/s12275-014-4002-7 . Defez R, Esposito R, Angelini C, Bianco C. Overproduction of indole-3-acetic acid in free-living rhizobia induces transcriptional changes resembling those occurring in nodule bacteroids. Mol Plant Microbe Interact. 2016;29(6):484–95. https://doi.org/10.1094/mpmi-01-16-0010-r . Di Martino C, Torino V, Minotti P, Pietrantonio L, Del Grosso C, Palmieri D, Palumbo G, Crawford T, Carfagna S. Mycorrhized wheat plants and nitrogen assimilation in coexistence and antagonism with spontaneous colonization of pathogenic and saprophytic fungi in a soil of low fertility. Plants. 2022;11(7):924. https://doi.org/10.3390/plants11070924 . Kang SM, Radhakrishnan R, You YH, Joo GJ, Lee IJ, Lee KE, Kim JH. Phosphate solubilizing bacillus megaterium mj1212 regulates endogenous plant carbohydrates and amino acids contents to promote mustard plant growth. Indian J Microbiol. 2014;54(4):427–33. https://doi.org/10.1007/s12088-014-0476-6 . Oteino N, Lally RD, Kiwanuka S, Lloyd A, Ryan D, Germaine KJ, Dowling DN. Plant growth promotion induced by phosphate solubilizing endophytic Pseudomonas isolates. Front Microbiol. 2015;6:745. https://doi.org/10.3389/fmicb.2015.00745 . Singh D, Geat N, Rajawat MVS, Prasanna R, Kar A, Singh AM, Saxena AK. Prospecting endophytes from different Fe or Zn accumulating wheat genotypes for their influence as inoculants on plant growth, yield, and micronutrient content. Ann Microbiol. 2018;68(12):815–33. https://doi.org/10.1007/s13213-018-1388-1 . Cui KP, Xu T, Chen JW, Yang HY, Liu XM, Zhuo R, Peng YH, Tang W, Wang R, Chen LS, et al. Siderophores, a potential phosphate solubilizer from the endophyte Streptomyces sp. CoT10, improved phosphorus mobilization for host plant growth and rhizosphere modulation. J Clean Prod. 2022;367:133110. https://doi.org/10.1016/j.jclepro.2022.133110 . Cao RH, Liu XG, Gao KX, Mendgen K, Kang ZS, Gao JF, Dai Y, Wang X. Mycoparasitism of endophytic fungi isolated from reed on soilborne phytopathogenic fungi and production of cell wall-degrading enzymes in vitro. Curr Microbiol. 2009;59(6):584–92. https://doi.org/10.1007/s00284-009-9477-9 . Gajera HP, Vakharia DN. Production of lytic enzymes by Trichoderma isolates during in vitro antagonism with Aspergillus niger , the causal agent of collar rot of peanut. Braz J Microbiol. 2012;43(1):43–52. https://doi.org/10.1590/s1517-83822012000100005 . Verma A, Shameem N, Jatav HS, Sathyanarayana E, Parray JA, Poczai P, Sayyed RZ. Fungal endophytes to combat biotic and abiotic stresses for climate-smart and sustainable agriculture. Front Plant Sci. 2022;13. https://doi.org/10.3389/fpls.2022.953836 . García-Latorre C, Rodrigo S, Marin-Felix Y, Stadler M, Santamaria O. Plant-growth promoting activity of three fungal endophytes isolated from plants living in dehesas and their effect on Lolium multiflorum . Sci Rep. 2023;13(1). https://doi.org/10.1038/s41598-023-34036-8 . Petrini O. Fungal endophytes of tree leaves. In: Andrews JH, Hirano SS, editors Microbial ecology of leaves. Brock/Springer Series in Contemporary Bioscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3168-4_984 . Baldrian P, Vetrovsky T, Lepinay C, Kohout P. High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers. 2022;114(1):539–47. https://doi.org/10.1007/s13225-021-00472-y . Hardoim PR, van Overbeek LS, Berg G, Pirttilä AM, Compant S, Campisano A, Döring M, Sessitsch A. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol R. 2015;79(3):293–320. https://doi.org/10.1128/mmbr.00050-14 . Wu B, Hussain M, Zhang WW, Stadler M, Liu XZ, Xiang MC. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology. 2019;10(3):127–40. https://doi.org/10.1080/21501203.2019.1614106 . Phukhamsakda C, Nilsson RH, Bhunjun CS, de Farias ARG, Sun YR, Wijesinghe SN, Raza M, Bao DF, Lu L, Tibpromma S, et al. The numbers of fungi: contributions from traditional taxonomic studies and challenges of metabarcoding. Fungal Divers. 2022;114(1):327–86. https://doi.org/10.1007/s13225-022-00502-3 . Rayner MC. Obligate Symbiosis in Calluna vulgaris . Ann Botany. 1915;29(1):97–133. https://doi.org/10.1093/oxfordjournals.aob.a089540 . Rodriguez RJ Jr, Arnold JFW, Redman AE. Fungal endophytes: diversity and functional roles. New Phytol. 2009;182(2):314–30. https://doi.org/10.1111/j.1469-8137.2009.02773.x . Hamzah TNT, Lee SY, Hidayat A, Terhem R, Faridah-Hanum I, Mohamed R. diversity and characterization of endophytic fungi isolated from the tropical mangrove species, Rhizophora mucronata , and identification of potential antagonists against the soil-borne fungus, Fusarium solani . Front Microbiol. 2018;25(9):1707. https://doi.org/10.3389/fmicb.2018.01707 . Gong A, Xiao C, Jiang W, Zhou Y, Zhang J, Liang Q, Yang C, Zheng W, Zhang C. Association between dipsacus saponin VI level and diversity of endophytic fungi in roots of Dipsacus asperoides . World J Microb Biotechnol. 2019;35(42). https://doi.org/10.1007/s11274-019-2616-y . dos Reis JBA, Lorenzi AS, do Vale HMM. Methods used for the study of endophytic fungi: a review on methodologies and challenges, and associated tips. Arch Microbiol. 2022;204(11):675. https://doi.org/10.1007/s00203-022-03283-0 . Sun X, Guo L-D. Endophytic fungal diversity: review of traditional and molecular techniques. Mycology. 2012;3(1):65–76. Lindahl BD, Nilsson RH, Tedersoo L, Abarenkov K, Carlsen T, Kjoller R, Koljalg U, Pennanen T, Rosendahl S, Stenlid J, et al. Fungal community analysis by high-throughput sequencing of amplified markers - a user's guide. New Phytol. 2013;199(1):288–99. https://doi.org/10.1111/nph.12243 . Wang H, Liu ZY, Duan FF, Chen Y, Qiu KD, Xiong Q, Lin HT, Zhang J, Tan HB. Isolation, identification, and antibacterial evaluation of endophytic fungi from Gannan navel orange. Front Microbiol. 2023;14. https://doi.org/10.3389/fmicb.2023.1172629 . Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous PW, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for. Proc Natl Acad Sci USA. 2012;109(16):6241–6. https://doi.org/10.1073/pnas.1117018109 . Nilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Reviews Microbiol. 2019;17(2):95–109. https://doi.org/10.1038/s41579-018-0116-y . Siddique AB, Albrectsen BR, Ilbi H, Siddique AB. Optimization of protocol for construction of fungal ITS amplicon library for high-throughput illumina sequencing to study the mycobiome of aspen leaves. Appl Sci. 2022;12(3):1136. https://doi.org/10.3390/app12031136 . Siddique A, Menke L, Dinedurga M, Albrectsen BR. Molecular studies of rust on European aspen suggest an autochthonous relationship shaped by genotype. Front Plant Sci. 2023;14. https://doi.org/10.3389/fpls.2023.1111001 . Viotti C, Chalot M, Kennedy PG, Maillard F, Santoni S, Blaudez D, Bertheau C. Primer pairs, PCR conditions, and peptide nucleic acid clamps affect fungal diversity assessment from plant root tissues. Mycology. 2024. https://doi.org/10.1080/21501203.2023.2301003 . Nguyen NH, Williams LJ, Vincent JB, Stefanski A, Cavender-Bares J, Messier C, Paquette A, Gravel D, Reich PB, Kennedy PC. Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field-based tree experiment. Mol Ecol. 2016;25(16):4032–46. https://doi.org/10.1111/mec.13719 . Pölme S, Abarenkov K, Nilsson RH, Lindahl BD, Clemmensen KE, Kauserud H, Nguyen N, Kjoller R, Bates ST, Baldrian P, et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 2021;105:1–6. https://doi.org/10.1007/s13225-020-00466-2 . Krivonos DV, Konanov DN, Ilina EN, FunFun. ITS-based functional annotator of fungal communities. Ecol Evol. 2023;13(3). https://doi.org/10.1002/ece3.9874 . Ghanbary E, Kouchaksaraei MT, Mirabolfathy M, Sanavi SAMM, Rahaie M. Growth and physiological responses of Quercus brantii seedlings inoculated with Biscogniauxia mediterranea and Obolarina persica under drought stress. For Pathol. 2017;47(5). https://doi.org/10.1111/efp.12353 . Naidoo S, Slippers B, Plett JM, Coles D, Oates CN. The road to resistance in forest trees. Front Plant Sci. 2019;10. https://doi.org/10.3389/fpls.2019.00273 . Srikantaramas S, Asano T, Sudo H, Yamazaki M, Saito K. Camptothecin: therapeutic potential and Biotechnology. Curr Pharm Biotechnol. 2007;8:196–202. https://doi.org/10.2174/138920107781387447 . Arnold AE, Mejía LC, Kyllo D, Rojas EI, Maynard Z, Robbins N, Herre EA. Fungal endophytes limit pathogen damage in a tropical tree. Proc Natl Acad Sci USA. 2003;100(26):15649–54. https://doi.org/10.1073/pnas.2533483100 . Faticov M, Abdelfattah A, Roslin T, Vacher C, Hambäck P, Blanchet FG, Lindahl BD, Tack AJM. Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak. New Phytol. 2021;231(5):1770–83. https://doi.org/10.1111/nph.17434 . Blumenstein K, Albrectsen BR, Martín JA, Hultberg M, Sieber TN, Helander M, Witzell J. Nutritional niche overlap potentiates the use of endophytes in biocontrol of a tree disease. Biocontrol. 2015;60(5):655–67. https://doi.org/10.1007/s10526-015-9668-1 . Albrectsen BR, Siddique AB, Decker VHG, Unterseher M, Robinson KM. Both plant genotype and herbivory shape aspen endophyte communities. Oecologia. 2018;187(2):535–45. https://doi.org/10.1007/s00442-018-4097-3 . Randriamanana TR, Nissinen K, Ovaskainen A, Lavola A, Peltola H, Albrectsen BR, Julkunen-Tiitto R. Does fungal endophyte inoculation affect the responses of aspen seedlings to carbon dioxide enrichment? Fungal Ecol. 2018;33:24–31. https://doi.org/10.1016/j.funeco.2017.12.002 . Witzell J, Decker VHG, Agostinelli M, Romeralo C, Cleary M, Albrectsen BR. Aspen leaves as a chemical landscape for fungal endophyte diversity-effects of nitrogen addition. Front Microbiol. 2022;13. https://doi.org/10.3389/fmicb.2022.846208 . Busby PE, Peay KG, Newcombe G. Common foliar fungi of Populus trichocarpa modify Melampsora rust disease severity. New Phytol. 2016;209(4):1681–92. https://doi.org/10.1111/nph.13742 . Yu ZD, Ding HR, Shen KC, Bu FF, Newcombe G, Liu HX. Foliar endophytes in trees varying greatly in age. Eur J Plant Pathol. 2021;160(2):375–84. https://doi.org/10.1007/s10658-021-02250-7 . Messal M, Vivas M, Kemler M, Begerow D, Brachmann A, Witfeld F, Naidoo S, Slippers B. Fungal communities of Eucalyptus grandis leaves are influenced by the insect pest. Front Microbiol. 2022;13. https://doi.org/10.3389/fmicb.2022.841621 . Peay KGBM, Arnold AE. Not every fungus is everywhere: scaling to the biogeography of fungal–plant interactions across roots, shoots and ecosystems. New Phytol. 2010;185(4):878–82. https://doi.org/10.1111/j.1469-8137.2009.03158.x . Bandau F, Albrectsen BR, Julkunen-Tiitto R, Gundale MJ. Genotypic variability in Populus tremula L. affects how anthropogenic nitrogen enrichment influences litter decomposition. Plant Soil. 2017;410(1–2):467–81. https://doi.org/10.1007/s11104-016-3033-8 . Bandau F, Albrectsen BR, Robinson KM, Gundale MJ. European aspen with high compared to low constitutive tannin defenses grow taller in response to anthropogenic nitrogen enrichment. For Ecol Manag. 2021;487. https://doi.org/10.1016/j.foreco.2021.118985 . Robinson KM, Ingvarsson PK, Jansson S, Albrectsen BR. Genetic variation in functional traits influences arthropod community composition in aspen ( Populus tremula L). PLoS ONE. 2012;7(5):e37679. https://doi.org/10.1371/journal.pone.0037679 . Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38(3):276–8. https://doi.org/10.1038/s41587-020-0439-x . Straub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of environmental microbial community studies are biased by the selected 16S rRNA (Gene) amplicon sequencing pipeline. Front Microbiol. 2020;11. https://doi.org/10.3389/fmicb.2020.550420 . Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ, Singh A, Wilkins MJ, Wrighton KC, Williams KH, Banfield JF. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature. 2015;523(7559):208–11. https://doi.org/10.1038/nature14486 . Krehenwinkel H, Wolf M, Lim JY, Rominger AJ, Simison WB, Gillespie RG. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci Rep. 2017;7. https://doi.org/10.1038/s41598-017-17333-x . Dully V, Wilding TA, Mühlhaus T, Stoeck T. Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning. Comput Struct Biotec. 2021;19:2256–68. https://doi.org/10.1016/j.csbj.2021.04.005 . Gotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4(4):379–91. https://doi.org/10.1046/j.1461-0248.2001.00230.x . Unterseher M, Schnittler M, Dormann C, Sickert A. Application of species richness estimators for the assessment of fungal diversity. Fems Microbiol Lett. 2008;282(2):205–13. https://doi.org/10.1111/j.1574-6968.2008.01128.x . Hill MO. Diversity and Evenness: a unifying notation and its consequences. Ecology. 1973;54(2):427–32. https://doi.org/10.2307/1934352 . Bray JR, Curtis JT. An ordination of the upland forest communities of Southern Wisconsin. Ecol Monogr. 1957;27(4):326–49. https://doi.org/10.2307/1942268 . Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927–30. https://doi.org/10.1111/j.1654-1103.2003.tb02228.x . Anderson MJ, Ellingsen KE, McArdle BH. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9(6):683–93. https://doi.org/10.1111/j.1461-0248.2006.00926.x . Saikkonen S. Forest structure and fungal endophytes. Fungal Biology Reviews. 2007;21(2–3):67–74. https://doi.org/10.1016/j.fbr.2007.05.001 . Albrectsen BR, Witzell J. Disentangling functions of fungal endophytes in forest trees. In: Silva AP, Sol M, editors. Fungi: Types, Environmental Impact and Role in Disease. New York: Nova Science Publishers, Inc.; 2012. pp. 235–46. Albrectsen BR, Björkén L, Varad A, Hagner Å, Wedin M, Karlsson J, Jansson S. Endophytic fungi in European aspen (Populus tremula ) leaves-diversity, detection, and a suggested correlation with herbivory resistance. Fungal Divers. 2010;41(1):17–28. https://doi.org/10.1007/s13225-009-0011-y . Lamit LJ, Lau MK, Sthultz CM, Wooley SC, Whitham TG, Gehring CA. Tree genotype and genetically based growth traits structure twig endophyte communities. Am J Bot. 2014;101(3):467–78. https://doi.org/10.3732/ajb.1400034 . Bálint M, Tiffin P, Hallström B, O'Hara RB, Olson MS, Fankhauser JD, Piepenbring M, Schmitt I. Host Genotype Shapes the Foliar Fungal Microbiome of Balsam Poplar (Popular balsamifera). PLoS ONE. 2013;8(1). https://doi.org/10.1371/journal.pone.0053987 . Karlinski L, Rudawska M, Kieliszewska-Rokicka B, Leski T. Relationship between genotype and soil environment during colonization of poplar roots by mycorrhizal and endophytic fungi. Mycorrhiza. 2010;20(5):315–24. https://doi.org/10.1007/s00572-009-0284-8 . Hoeber S, Baum C, Weih M, Manzoni S, Fransson P. Site-dependent relationships between fungal community composition, plant genotypic diversity and environmental drivers in a salix biomass system. Front Fungal Biol. 2021;2. https://doi.org/10.3389/ffunb.2021.671270 . Küngas K, Bahram M, Poldmaa K. Host tree organ is the primary driver of endophytic fungal community structure in a hemiboreal forest. Fems Microbiol Ecol. 2020;96(2). https://doi.org/10.1093/femsec/fiz199 . Agostinelli M, Cleary M, Martín JA, Albrectsen BR, Witzell J. Pedunculate Oaks ( Quercus robur L.) differing in vitality as reservoirs for fungal biodiversity. Front Microbiol. 2018;9. https://doi.org/10.3389/fmicb.2018.01758 . Busby PE, Zimmerman N, Weston DJ, Jawdy SS, Houbraken J, Newcombe G. Leaf endophytes and genotype affect severity of damage from the necrotrophic leaf pathogen, Drepanopeziza populi . Ecosphere. 2013;4(10). https://doi.org/10.1890/ES13-00127.1 . Unterseher M, Siddique A, Brachmann A, Persoh D. diversity and composition of the leaf mycobiome of beech ( Fagus sylvatica ) are affected by local habitat conditions and leaf biochemistry. PLoS ONE. 2016;11(4). https://doi.org/10.1371/journal.pone.0152878 . Helander M, Ahlholm J, Sieber TN, Hinneri S, Saikkonen K. Fragmented environment affects birch leaf endophytes. New Phytol. 2007;175(3):547–53. https://doi.org/10.1111/j.1469-8137.2007.02110.x . Rogers PC, Pinno BD, Sebesta J, Albrectsen BR, Li GQ, Ivanova N, Kusbach A, Kuuluvainen T, Landhäusser SM, Liu HY, et al. A global view of aspen: conservation science for widespread keystone systems. Glob Ecol Conserv. 2020;21:e00828. https://doi.org/10.1016/j.gecco.2019.e00828 . Bahram M, Polme S, Koljalg U, Tedersoo L. A single European aspen ( Populus tremula ) tree individual may potentially harbour dozens of ITS genotypes and hundreds of species of ectomycorrhizal fungi. Fems Microbiol Ecol. 2011;75(2):313–20. https://doi.org/10.1111/j.1574-6941.2010.01000.x . Hosoya T. Systematics, ecology, and application of Helotiales: Recent progress and future perspectives for research with special emphasis on activities within JapanHelotiales: Recent progress and future perspectives for research with special emphasis on activities within Japan. Mycoscience. 2021;62(1):1–9. https://doi.org/10.47371/mycosci.2020.05.002 . Copot O, Tanase C. Substrate properties, forest structure and climate influences wood-inhabiting fungal diversity in broadleaved and mixed forests from Northeastern Romania. For Syst. 2020;29(3). https://doi.org/10.5424/fs/2020293-16728 . Bailey JK, Deckert R, Schweitzer JA, Rehill BJ, Lindroth RL, Gehring C, Whitham TG. Host plant genetics affect hidden ecological players: links among Populus , condensed tannins, and fungal endophyte infection. Can J Bot. 2005;83(4):356–61. https://doi.org/10.1139/b05-008 . Bhat TK, Singh B, Sharma OP. Microbial degradation of tannins - A current perspective. Biodegradation. 1998;9(5):343–57. https://doi.org/10.1023/a:1008397506963 . Chowdhury J, Ferdous J, Lihavainen J, Albrectsen BR, Lundberg-Felten J. Fluorogenic properties of 4-dimethylaminocinnamaldehyde (DMACA) enable high resolution imaging of cell-wall-bound proanthocyanidins in plant root tissues. Front Plant Sci. 2023;13. https://doi.org/10.3389/fpls.2022.1060804 . Blumenstein K, Macaya-Sanz D, Martín JA, Albrectsen BR, Witzell J. Phenotype MicroArrays as a complementary tool to next generation sequencing for characterization of tree endophytes. Front Microbiol. 2015;6:1033. https://doi.org/10.3389/fmicb.2015.01033 . Kraus TEC, Dahlgren RA, Zasoski RJ. Tannins in nutrient dynamics of forest ecosystems - a review. Plant Soil. 2003;256(1):41–66. https://doi.org/10.1023/A:1026206511084 . Joanisse GD, Bradley RL, Preston CM, Munson AD. Soil enzyme inhibition by condensed litter tannins may drive ecosystem structure and processes: the case of Kalmia angustifolia. New Phytol. 2007;175(3):535–46. https://doi.org/10.1111/j.1469-8137.2007.02113.x . Liu MX, Clarke LJ, Baker SC, Jordan GJ, Burridge CP. A practical guide to DNA metabarcoding for entomological ecologists. Ecol Entomol. 2020;45(3):373–85. https://doi.org/10.1111/een.12831 . Tedersoo L, Bahram M, Zinger L, Nilsson RH, Kennedy PG, Yang T, Anslan S, Mikryukov V. Best practices in metabarcoding of fungi: from experimental design to results. Mol Ecol. 2022;31(10):2769–95. https://doi.org/10.1111/mec.16460 . Bellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an approach reveals potential PCR biases. BMC Microbiol. 2010;10(189). https://doi.org/10.1186/1471-2180-10-189 . Ihrmark K, Bödeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, Strid Y, Stenlid J, Brandström-Durling M, Clemmensen KE, et al. New primers to amplify the fungal ITS2 region - evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82(3):666–77. https://doi.org/10.1111/j.1574-6941.2012.01437.x . Furneaux B, Bahram M, Rosling A, Yorou NS, Ryberg M. Long- and short-read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities. Mol Ecol Resour. 2021;21(6):1833–49. https://doi.org/10.1111/1755-0998.13387 . Garg D, Patel N, Rawat A, Rosado AS. Cutting edge tools in the field of soil microbiology. Curr Res Microb Sci. 2024;100226. https://doi.org/10.1016/j.crmicr.2024.100226 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4206868","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":287393381,"identity":"eb7d1104-172a-4c2d-a55e-7f5f689b60ce","order_by":0,"name":"Abu Bakar Siddique","email":"","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abu","middleName":"Bakar","lastName":"Siddique","suffix":""},{"id":287393382,"identity":"058d3ac8-1e93-456c-a682-d043c7109147","order_by":1,"name":"Abu Bakar Siddique","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Abu","middleName":"Bakar","lastName":"Siddique","suffix":""},{"id":287393384,"identity":"f6ba48a2-69ba-469b-9aa4-69043f54b8c5","order_by":2,"name":"Lovely Mahawar","email":"","orcid":"","institution":"Umeå University","correspondingAuthor":false,"prefix":"","firstName":"Lovely","middleName":"","lastName":"Mahawar","suffix":""},{"id":287393386,"identity":"23d6b7f0-2dbc-4cbb-a0d4-a9d2bf464434","order_by":3,"name":"Benedicte Albrectsen","email":"data:image/png;base64,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","orcid":"","institution":"Umeå University","correspondingAuthor":true,"prefix":"","firstName":"Benedicte","middleName":"","lastName":"Albrectsen","suffix":""}],"badges":[],"createdAt":"2024-04-02 12:59:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4206868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4206868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54366471,"identity":"d506a36b-93f2-43d0-82ba-d29de307b04d","added_by":"auto","created_at":"2024-04-09 12:36:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183473,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curves were generated based on amplicon sequence variant (ASV) counts to project seasonal patterns of fungal species richness in aspen leaves for \u003cstrong\u003ea)\u003c/strong\u003e2020, \u003cstrong\u003eb)\u003c/strong\u003e 2021, \u0026nbsp;and \u003cstrong\u003ec) \u003c/strong\u003eacross both years. The numbers 06, 07, and 08 refer to June, July, and August, respectively. w1 and w4 refer to sampling weeks in August. Years are indicated by the last digits 20 and 21. Additionally, rarefaction curves were generated for all samples, highlighting accumulation \u003cstrong\u003ed)\u003c/strong\u003e related to genotype, which were further divided according to Bandau et al. 2017 [65] into low tannin genotypes (LGT, blue colours) and high tannin genotypes (HGT, red colours). GT numbers (5, 50, 60, 65, 72, and 115) correspond to the identifiers defined in the SwAsp collection [67].\u003c/p\u003e","description":"","filename":"fig1.ab.png","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/1c361a0913cab6efd632c298.png"},{"id":54366473,"identity":"2ac73929-7e05-42fc-8e5c-61abc34b349c","added_by":"auto","created_at":"2024-04-09 12:36:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107435,"visible":true,"origin":"","legend":"\u003cp\u003eDiversity estimates, represented by \u003cstrong\u003ea) \u003c/strong\u003eFisher's alpha diversity index and \u003cstrong\u003eb)\u003c/strong\u003e Shannon's diversity index, showcase the mycobiomes in aspen leaves, categorized by year, genotype, and sampling event. Genotypes are color-coded to reflect their extreme abilities to produce and store condensed tannins—blue for low tannin contents and red for high tannin contents. The impacts of genotype, seasonality, and historic nitrogen addition are elucidated in Table 2, while Figure 3 visualizes pairwise genotypic differences in diversity measurements. Sample sizes: N\u003csub\u003e2020\u003c/sub\u003e = 72; N\u003csub\u003e2021\u003c/sub\u003e = 24.\u003c/p\u003e","description":"","filename":"fig2.ab.png","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/26eb30c1610a11bb04c3a670.png"},{"id":54366466,"identity":"fd62ae5a-af82-4f6d-a023-16f77fbddc83","added_by":"auto","created_at":"2024-04-09 12:36:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57987,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of genotype-specific diversity indices, including a) Fisher's alpha and b) Shannon's, based on data from seven (all) sampling events. Mean differences between genotypes are depicted with 95% confidence intervals. Significant differences (p adj \u0026lt; 0.05) are highlighted in brown.\u003c/p\u003e","description":"","filename":"fig3.ab.png","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/e5bffa96321bf424899b6784.png"},{"id":54366464,"identity":"dd7d9bf3-dcea-47c2-8fb8-fd5adf370eec","added_by":"auto","created_at":"2024-04-09 12:36:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159334,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate projections integrating metagenomic and phenotypic data from aspen leaves. Analyses were performed on all data (including nondetermined ASVs, see text) and are represented through ›\u003cstrong\u003ea)\u003c/strong\u003e Nonmetric multidimensional scaling (NMDS) and \u003cstrong\u003eb)\u003c/strong\u003e principal coordinate analysis (PCoA), both displayed for the first two dimensions. Assemblages are clustered according to the aspen host genotype (indicated by Swasp/Tanasp numbers). Growth traits are illustrated with labelled vectors (height=plant height; chl=leaf chlorophyll content; nbi= nitrogen balance index; anth=anthocyanidins), while damage traits include mites (\u003cem\u003eEriophyid\u003c/em\u003e mite gall symptoms), \u003cem\u003eVenturia \u003c/em\u003e(symptoms of the necrotrophic fungal pathogen on aspen, tentatively \u003cem\u003eVenturia radiosa\u003c/em\u003e), Rust (tentatively \u003cem\u003eMelampsora pinitorqua\u003c/em\u003e; Siddique et al. 2022, HR=hypersensitivity responses), and galls by \u003cem\u003eHarmandiola\u003c/em\u003e wasps.\u003c/p\u003e","description":"","filename":"fig4ab.png","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/e97dd8bf685eb21985af4368.png"},{"id":54366472,"identity":"c458a7a1-bd7d-405e-8801-b86093d1ac11","added_by":"auto","created_at":"2024-04-09 12:36:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101109,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic classification and trophic mode of the mycobiome in aspen leaves.\u003cstrong\u003e a) \u003c/strong\u003eThe determination of success as the number of ASVs that could be determined to taxonomic level as indicated on the x-axis. \u003cstrong\u003eb)\u003c/strong\u003e Each bar represents one of the four most abundant orders S = Saccharomycetales, H = Helotiales, D = Dothideales, and C = Capnodiales in the database with bar height relative to the abundance of the order in the database. The segments indicate the genera richness of sizes relative to their abundance. \u003cstrong\u003ec)\u003c/strong\u003e The relative abundance of the ten most abundant genera was determined after their relative abundance in the database. \u003cstrong\u003ed)\u003c/strong\u003e The representation of trophic modes was assessed for determined ASVs using FUNGuild software [49]. P = pathotrophs; PaSa = patho-saprotrophs; PaSaSy =patho-sapro-symbiotrophs; PaSy= Patho-symbiotrops; Sa = saprotrophs; Sy = Symbiotrophs.\u003c/p\u003e","description":"","filename":"fig5.ad.png","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/2419b401d01d9e295ea11b8f.png"},{"id":57760457,"identity":"9237fbe5-ba3f-4e0a-8721-a8195af8a2c4","added_by":"auto","created_at":"2024-06-05 09:22:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1372824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/ade4917e-35e7-4a0f-a5e0-93c2bf13a38d.pdf"},{"id":54366470,"identity":"0390cd1e-6468-4e23-836a-a41a80027c8d","added_by":"auto","created_at":"2024-04-09 12:36:51","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":227573,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4206868/v1/fe068322b0ebfea955098d13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genotype, Tannin Capacity, and Seasonality Influence the Structure and Function of Symptomless Fungal Communities in Aspen Leaves, Regardless of Historical Nitrogen Addition","fulltext":[{"header":"Background","content":"\u003cp\u003eMicroorganisms play an important role in higher life, as increasingly demonstrated in metagenomic DNA profiles from microbiomes associated with animals, plants, soils, and water bodies, collectively known as environmental DNA (eDNA). Healthy microbiomes have become a concept, yet the precise meaning of a healthy microbiome remains complex [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In plants, some microorganisms are vertically transferred between generations and organs, such as systemic fungal endophytes in cold-season grasses, which provide their host with both tolerance and resistance benefits [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This has paved the way for commercial use of endophyte-infected grass seeds [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, most plant-associated fungi appear to be nonsystemic and horizontally transferred with functions ranging from beneficial to antagonistic for the plant [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most well-known and studied symbiotic association between plant roots and fungi is probably mycorrhizae, which provides both plants and fungi with advantages, primarily in terms of energy and nutrient supplies, respectively [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Fungal endophytes, which inhabit the inter- and intracellular spaces of various plant organs (such as stems, roots, petioles, leaves, bark, seeds, and latex) without causing visible symptoms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] are also increasingly recognized for their direct and indirect positive potential for the host and as a source of commercial products. For example, they may produce chemicals such as phytohormones and substances that support photosynthesis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], enhance nitrogen assimilation mechanisms [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], solubilize minerals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], produce siderophores with iron-retaining properties [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and generate substances that enhance defense and tolerance properties [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In return, the endophytes benefit from the sheltered life inside the plant, which is surrounded by nutrients, and they gain both competitive and dispersal advantages inside their host.\u003c/p\u003e \u003cp\u003eThere may be millions of endophytic fungi Worldwide, yet only a fraction of which are known [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and an even smaller fraction has been isolated and studied for their biology and potential [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Fungal endophytes were previously discovered in plants in the 19th century [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and they were f long isolated from sterilized plant parts; consequently, only culturable fungal endophytes could be studied and described based on their morphology [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Sanger sequencing subsequently enabled the molecular determination of culturable colonies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Today, metabarcoding and shotgun sequencing of eDNA has widely replaced culturing as a way to assess endophyte community composition independently of culturing; and high-throughput metagenomic sequencing techniques are now standardly used to characterize the presence of fungal DNA strands in environmental samples [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The amplification and sequencing of conserved DNA regions with high interspecific and low intraspecific variation, are followed by downstream bioinformatic analyses, to reveal the diversity, composition, and function of leaf-associated fungal communities [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The conserved regions to be targeted vary among organism groups, and the nuclear ribosomal internal transcribed spacer (ITS) region (500 base-pairs) is the formal fungal barcode and the most sequenced genetic marker in mycology [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Based on its degree of conservation, the ITS region is divided into subunits, that with adequate primers may be targeted singly or in combination (i.e. ITS1 to ITS4); providing varied opportunities to estimate fungal richness and diversity in a sample [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile these techniques are highly competent generating diverse DNA sequence reads in an environmental sample, the ability to recognize the true diversity is limited by the lack of reference databases, the inability to distinguish endophyte DNA from that of epiphytes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and the inability to culture reads back to colonies that would allow for morphology description and chemical screening to characterize the potential function of the endophyte. Analytical tools and models are being developed to suggest ecological function and interrelated guild structures (FUNGuild: [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; FungalTraits: [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]; FunFun: [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]), and while these models provide insights into an endophytic community, they are also limited by the depth of taxonomic determination d sequencing efforts and the interpretation of ecological function thus builds on known relationships among related taxonomic groups.\u003c/p\u003e \u003cp\u003eTrees are long-lived organisms that are exposed to biological and abiotic stresses for many years. The ability of trees to grow and stay healthy has been associated with their ability to directly fight back by own means[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and indirectly through symbiotic associations with endophytic fungi, for example. These associations may rely on chemical and physiological mechanisms, as well as on ecological processes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The biosynthesis of chemical specialized products may for example be shared between the endophyte and its host (i.e. the production of camptothecin by the tree host \u003cem\u003eCampotheca acuminata\u003c/em\u003e and its endophyte \u003cem\u003eFusarium solani\u003c/em\u003e [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]). The host tree appears to select or filter the fungal community that established in its organs, as reported for the leaves of beech [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], cacao [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], oak [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], elm [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], aspen [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], \u003cem\u003ePopulus trichocarpa\u003c/em\u003e [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], temple trees [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], and eucalyptus [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Specific associations with organs and growing sites are further demonstrated for e.g. beech [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], oak [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and tropical forest trees [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, our understanding of what makes an endophyte community \u0026ldquo;healthy\u0026rdquo; as well as processes underlying the inclusion and exclusion of fungal taxa by trees, is limited, although host substrate conditions have been suggested to play a role [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWith European aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e) as an example we explored the effect of plant genotype and its inherent ability to produce, and store condensed tannin phenolic polymers as a factor in determining the community of fungal endophytes across the short growth season in northern Sweden. In addition, this study allows for an assessment of the long-term effects of historic nutrient additions to a population of ca ten-year-old trees on the fungal endophyte community hidden in their leaves.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy system\u003c/h2\u003e \u003cp\u003eTo delineate the fungal communities associated with aspen foliage, we obtained aspen leaves from the TanAsp garden, and selected six replicated aspen genotypes with either a high or low capacity to generate and retain condensed tannins, as highlighted in the work of Bandau et al. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The trees were planted in 2010, and our study aimed to assess the seasonal composition and dynamics of mycobiomes linked to the leaves of young trees within and between the years 2020 and 2021. To monitor the seasonal composition and dynamics of the mycobiomes associated with the leaves and to compare patterns over time, we revisited the same trees repeatedly. This included twelve replicates of each genotype in 2020, resulting in 72 trees. The number of trees was reduced to four genotype replicates in 2021, totaling 24 trees per sampling event.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eNondestructive phenotyping\u003c/h2\u003e \u003cp\u003e \u003cb\u003eVisual inspections included\u003c/b\u003e: plant height measured in centimeters (from the plant-soil intersection to the tip of the canopy); counting of \u003cem\u003eHarmandiola\u003c/em\u003e galls; assessment of symptoms of \u003cem\u003eVenturia\u003c/em\u003e sp. as wilted branches relative to the total number of branches per canopy; other biotic stress symptoms, such as leaf hypersensitive response (HR), abundance of \u003cem\u003eeriophyid\u003c/em\u003e mite colonies, damage from chewing insects, and rust pustules (\u003cem\u003eMelampsora pinitorqua\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Leaf damage symptoms were ranked on a 0\u0026ndash;3 scale: 0 indicated no symptoms, 1 indicated low damage corresponding to symptoms on some leaves, 2 indicated moderate damage with symptoms on approximately half of the leaves, and 3 indicated a high level of damage with symptoms on more than half of the leaves.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePigment assessments included\u003c/strong\u003e \u003cp\u003enitrogen balance, chlorophyll, anthocyanin, and flavanol indeces, which were measured with an optical meter ((DUALEX\u0026reg; Optical LeafClip meter, Force-A, Orsay, France). Three readings were performed on each of ten randomly collected leaves per canopy, generating a mean value per tree from 30 measurements.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDestructive phenotyping\u003c/h2\u003e \u003cp\u003e \u003cb\u003eLeaf harvest and sample preparation\u003c/b\u003e: In 2020, leaf sampling was conducted on the following dates: June 22, July 14, and August 6 and 23. In 2021, sampling occurred on June 23, July 14, and August 24. The selected trees were chosen in accordance with the original garden setup [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e], including low-tannin genotypes (GT50, GT60, GT115) and high-tannin genotypes (GT5, GT65, GT72). Additionally, trees were selected based on a history of NH\u003csub\u003e4\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e addition [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Therefore, during the 2020 sampling, three nitrogen treatment levels were considered, while in 2021, the sampling was limited to control trees that had not received fertilizer.\u003c/p\u003e \u003cp\u003eEach sample, representing one tree and one sampling event, consisted of ten undamaged mature leaves collected from one canopy with gloves to avoid contamination, at breast height. Each leaf was cut at the base with a clean pair of scissors, leaving the petiole on the twig, and then added to a labelled plastic bag. The sample bags were flash-frozen on dry ice in the field, transported to the laboratory for approximately one hour in coolers, and then stored at -80\u0026deg;C until lyophilization for 24 hours in a LABOGENE freeze dryer (3450 Liller\u0026oslash;d, Denmark). The dry leaves were subsequently ground to a fine powder and kept in labelled Falcon tubes (15 mL) at -20\u0026deg;C until DNA extraction.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDNA extraction and library preparation\u003c/strong\u003e \u003cp\u003eDNA extraction and amplicon library preparation were conducted as described in Siddique et al. (2022) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In short, the E.Z.N.A. Plant DNA Kit (OMEGA-BIO-TEK Inc, PW, GA, USA) was used for DNA extraction, and the fungal-specific ITS2 region was amplified using extended primers. After confirming the successful attachment of sample-specific index primers, the library was subjected to Illumina Miseq (300 bp) paired-end (PE) sequencing at NGI SciLifeLab (Stockholm).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatic analyses\u003c/h2\u003e \u003cp\u003eAmplicon sequencing analyses followed FAIR principles suggested by Ewels et al. (2020) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] and Straub et al., (2020) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]; the ampliseq pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/nf-core/ampliseq\u003c/span\u003e\u003cspan address=\"https://github.com/nf-core/ampliseq\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was assessed on 14 April 2022).\u003c/p\u003e \u003cp\u003eIn brief, Raw Illumina, MiSeq v3 2-300 bp, paired-end reads were demultiplexed by SciLifeLab and delivered as sample specific fastq files, which were then individually quality checked with FastQC. Fungal primers were removed with the CutAdapt tool. DADA2 was used to generate a curated dataset of amplicon sequence variants (ASVs). The ITS region was truncated (settings: trunclenf\u0026thinsp;=\u0026thinsp;223 and trunclenr\u0026thinsp;=\u0026thinsp;162); chimera and \u0026ldquo;noisy\u0026rdquo; reads were removed using the DADA2 algorithm, which distinguishes between biological variation and sequencing errors; mitochondria, chloroplast, and archaea reads were identified and removed with the Barrnap tool; and only sequences with fungal ITS2 regions were retained. Based on the ITS region, fungal taxonomy was then assigned with DADA2 and QIIME2 against the UNITE-fungal reference database v.8.3.\u003c/p\u003e \u003cp\u003eASVs represented by fewer than five reads in a sample were removed to avoid rare sequences and singleton bias [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The fungal data are available as available as 06.1_rel-table-ASV_with-DADA2-tax.tsv scaled after total fungal DNA in a sample. The QC results and sample summary files were aggregated and visualized with MultiQC into a single report which is available at 04_multiqc_report.html.\u003c/p\u003e \u003cp\u003eThe reproducible pipeline, tools, codes, and parameters can be found in GitHub repository: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/abu85/ampliseq_ITS\u003c/span\u003e\u003cspan address=\"https://github.com/abu85/ampliseq_ITS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and the bioinformatic summary, output and tools wise detail reference lists can be found in the Zenodo repository: [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.10839669\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.10839669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] -as referred to in the data availability section. The entire bioinformatic pipeline and analysis were run on the UPPMAX HPC server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uppmax.uu.se/\u003c/span\u003e\u003cspan address=\"https://www.uppmax.uu.se/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The community analyses were built on the curated dataset.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eCommunity analyses\u003c/h2\u003e \u003cp\u003eThe number of sequence reads (or ASVs) was used as a measurement of fungal abundance in a sample. The abundance of different ASVs was used as a metric of species richness. The identification depended on the taxonomic resolution that the ASVs allowed, and it was expected to vary among samples, genotypes, and sampling events. As the amplification threshold was set to 1000 reads per sequence in a sample, and to avoid including potential sequencing errors as unique reads [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], we removed samples with low coverage (\u0026lt;\u0026thinsp;1000 reads) from the database.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eRarefaction curves\u003c/strong\u003e \u003cp\u003eRandomized species accumulation curves (rarefaction curves) were compared after season, genotype, and treatment to assess fungal richness. The specaccum function with two segments, coleman and rarefaction, was employed ('coleman' finds the expected richness, and 'rarefaction' finds the mean when accumulating ASVs [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpecies diversity indices\u003c/b\u003e: To assess biodiversity in foliar mycobiome communities, we employed a suite of indices: Fisher\u0026rsquo;s alpha, a measure of species richness; the Shannon index, considering both richness and abundance; and the three Hill numbers that evaluate diversity indices for relative community data [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] (Hill, 1973). The Hill numbers primarily scrutinize communities based on species abundances: N1, the exponent of the Shannon index, emphasizes abundant species; N2, the inverse Simpson index, focuses on highly abundant species; and N3 emphasizes diversity measurements by comparing species of similar abundance within the community.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCommunity structure\u003c/strong\u003e \u003cp\u003eCommunity composition was analysed and visualized using nonmetric multidimensional scaling (NMDS) and principal coordinate analysis (PCoA) of square root transformed ASV counts [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e] to handle nonlinear relationships between samples in dissimilarity space. The distinctiveness of fungal assemblages was statistically tested with permutational multivariate analyses of variance using Bray\u0026ndash;Curtis and AltGower distance matrices. Dissimilarities among mycobiome communities were tested across seasons, between years, among treatments, and among genotypes with permutational multivariate analysis of variance, using AltGower dissimilarity indexes on standardized count data [PERMANOVA/adonis2 [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]; number of permutations\u0026thinsp;=\u0026thinsp;9999).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTaxonomic composition\u003c/strong\u003e \u003cp\u003eSpecies abundances were visualized using normalized relative abundance data, and guild analyses were used to reveal the ecological function of annotated fungal species in the samples. The FUNGuild [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] package was used for those analyses.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSequencing output and quality\u003c/h2\u003e \u003cp\u003eThe Amplicon Sequence Variant (ASV) dataset obtained from 344 aspen leaf samples comprised 30,081,923 reads, which were clustered into 581 ASV variants as detailed in the 06_asv_table.tsv. The average library size was 87,448 reads per sample, with a range from seven to 957,090 reads. Based on our criterion of having more than 1000 reads in a sample, we excluded seven samples. The final reads resulted in a refined dataset of 337 samples with 30,080,430 reads, equivalent to 99.99% of the initial total reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFungal richness and abundance across seasons\u003c/h2\u003e \u003cp\u003eThe refined dataset allowed for the generation of rarefaction curves, indicating an increase in the number of fungal ASVs throughout the season in both 2020 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The pace at which the fungal community developed in the leaves exhibited similar trends in both years although the trajectory differed somewhat between the years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, a notable distinction among genotypes consistently emerged (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), and fungal richness, expressed as the number of reads, also appeared to be greater in aspen leaves categorized as low-tannin genotypes than in those categorized as high- tannin genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), corroborating the findings of Bandau et al. (2021) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFungal richness and abundance among Aspen genotypes\u003c/h2\u003e \u003cp\u003eDiversity indices characterize community structures based on aspects of species richness and abundance. Using five indices to calculate the diversity based on ASV reads, we obtained insights into these structures. We observed a significant effect of aspen genotype on each index, underscoring the importance of genotype for both species\u0026rsquo; richness and abundance. The pattern of relative index differences was remarkably consistent among sampling events and years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pairwise comparisons further indicated a stronger differentiation of genotypes when assessing diversity with the Shannon index than with the Fisher's alpha index (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which suggests that effects of both richness and abundance separate fungal communities in aspen leaves.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANOVA summaries of genotype and seasonal effects from two consecutive years on five measures of fungal biodiversity (five diversity indices, also see text) in aspen leaves from the TanAsp collection [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The results are provided by year with sample sizes N\u003csub\u003e2020\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;72 and N\u003csub\u003e2021\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;24. Significant results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) are shown in bold. Also see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"17\" nameend=\"c17\" namest=\"c1\"\u003e \u003cp\u003eDiversity indices\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eFishers\u0026rsquo; alpha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eShannon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003eHill N1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c14\" namest=\"c12\"\u003e \u003cp\u003eHill N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c17\" namest=\"c15\"\u003e \u003cp\u003eHill N3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eDev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eDev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eDev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eDev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cem\u003eDev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e172.24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7.47\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e40.32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e25.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e23.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e188.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e15.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e9.93\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e7.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e7.27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e48.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of permutational multivariate analysis of variance (PERMANOVA) tests for 2020 and 2021, partitioning effects of season, historic nitrogen treatment, aspen genotype, and ability to synthesize and store condensed tannins (CTs) on the mycobiome (ASV abundances) in aspen leaves. The samples from 2021 included aspen trees that had not received any nitrogen fertilization. Significant responses are shown in \u003cb\u003ebold (\u003c/b\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05). See also Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYear\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eEffect\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSS\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFisher\u0026rsquo;s alpha confirmed a seasonal effect, as did Hill N1, suggesting that there was both a change in species richness across the season and a change in the organization of abundant species. However, neither the Shannon index, nor Hill's N2 or N3 suggested a significant seasonal impact on the leaf-associated fungal community composition (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation stability between aspen genotype and mycobiome\u003c/h2\u003e \u003cp\u003eTo visualize the stability of the fungal association with genotype, two kinds of multivariate projections were performed. Both methods (the iterative hypothesis generating NMDS and the analytical correlative projection PCoA) reached similar conclusions from the data that included all samples from the refined dataset. In both cases the ASV reads strongly clustered after genotype, with two high tannin genotypes (72 and 5) being more distant from the other genotypes. The projection of tree phenotypic traits, collected nondestructively during sampling events, indicated that height and susceptibility to infection with Venturia (a necrotrophic fungal pathogen) were opposite vectors with significance for the projection of the samples in the multivariate space (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), corroborating suggestions by Robinson et al (2012) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Apart from the genotypic effect, neither the impact of seasonal variation nor the historical nitrogen addition treatment appeared to significantly influence the projection, as evidenced by permanova tests (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, also see Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFungi taxonomic resolution\u003c/h2\u003e \u003cp\u003eThe taxonomic resolution for this study at the phylum level was 99.98%. However, only 1.44% of the ASVs could be classified at the class level, and 0.98% could be classified to the genus level (summing up to 49 genera, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The mycobiome of the aspen leaves was dominated by four orders within the Ascomycota phylum (Dothideales, Helotiales, Capnodiales and Saccharomycetales) with three genera dominating: \u003cem\u003eAureobasidium (\u003c/em\u003eDothideales), \u003cem\u003eCandida (\u003c/em\u003eSaccharomycetales) and \u003cem\u003eCladosporium (\u003c/em\u003eCapnodiales) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Among 24 identified fungal orders, 13 belonged to Ascomycota, 10 to Basidiomycota and one to Dothideomycetes (data to be found in the Zenodo repository: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.10839669\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.10839669\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Interestingly Helotiales was more abundant in the database and included more genera compared to Dothideales, Capnodiales and Saccharomycetales (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eRepresentation of trophic modes in the mycobiome\u003c/h2\u003e \u003cp\u003eOut of 581 ASVs only 183 were determined to genus level and could be assigned to functional guilds. Despite this limited ability to identify fungi at lower taxonomic levels, we detected representatives of seven ecological guilds: pathotrophs, patho-saprotrophs, patho-symbiotrophs, patho-sapro-symbiotrophs, saprotrophs, sapro-symbiotrophs, and symbiotrophs. Patho-sapro-symbiotrophs clearly dominated the total dataset that included all sampling events (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRecent molecular advances have enabled us to study hidden microbial communities in plant organs. However, the worldwide number of tree-associated microorganisms is vast, their diversity is underexplored, and their functions are complex and largely unknown, warranting basic insight into their composition and dynamics [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Using Illumina sequencing, we revealed a seasonal increase in the abundance of fungal eDNA in aspen leaves for two consecutive years. We also demonstrated genotype differences in the abundance and composition of the foliar fungal microflora, which were negatively related to the presence of polyphenolic condensed tannin complexes in the leaves. Additionally, our study strongly indicated that fertilization with ammonium nitrate did not have any long-term effects on the mycobiome (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), suggesting that genotype differences in host trees may be a strong factor in the maintenance of forest biodiversity.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenotype and environmental effects on aspen leaf-associated mycobiomes\u003c/h2\u003e \u003cp\u003eWe have previously proposed a significant impact of host genotype on leaf-associated fungal communities in aspen trees cultivated within a greenhouse environment [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], as well as when comparing clones repeatedly grown indoors and outdoors [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. This study, which was conducted on six cloned young aspen trees in a common garden setting, further reinforces the influence that hosts may exert on shaping the mycobiome within their leaves. Similar genotypic effects have also been observed in foliage from several studies of poplar trees including \u003cem\u003ePopulus angustifolia\u003c/em\u003e [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], and \u003cem\u003eP. balsamifera\u003c/em\u003e [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] and in the roots of a selection of mainly hybrid poplar clones [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e] as well as in willows [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is also becoming evident that different organs may filter fungal-associated communities differently, as observed in studies on alder and hazel [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], beech [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and oak [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The composition of the mycobiome may further depend on the general health condition of a tree [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e] and, in some cases, may determine the severity of damage from leaf pathogens [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. The growth environment may also affect the richness and abundance of the mycobiome [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]; however, this influence is not consistently corroborated by empirical data, as highlighted by Siddique et al. (2021) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This assertion is further supported by the absence of long-term effects resulting from nitrogen fertilization, as reported in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. While tree species are often assumed to play a crucial role in biodiversity within ecosystems, our study thus highlights the diversity shaped by individual variation in a key player species such as aspen [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] thereby supporting the fundamental implication that genetic variation in primary producers may have for understanding terrestrial ecosystems [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe mycobiome reported here primarily identified ASV reads belonging to the Ascomycota phylum, predominantly representing the orders Dothideales, Helotiales, Capnodiales, and Saccharomycetales. Among these genera, three dominated: \u003cem\u003eAureobasidium\u003c/em\u003e (Dothideales), \u003cem\u003eCandida\u003c/em\u003e (Saccharomycetales), and \u003cem\u003eCladosporium\u003c/em\u003e (Capnodiales). This division aligns with similar screenings reported in previous studies (e.g. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]). Helotiales comprises a diverse array of fungi, encompassing numerous species that are yet to be formally described. These genera however have various lifestyles, many of which involve leaf and wood decay processes [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], and some species associated mainly with roots as endophytes act as facilitators of plant growth through promotion effects, while others exhibit pathogenic behavior [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Notably, within this order, \u003cem\u003eFraxinus excelsior\u003c/em\u003e stands out as a well-known pathogen responsible for inducing Ash dieback. However, Helotiales is also recognized for its potential for pharmaceutical discoveries [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiversity indices could be calculated based on reads, supporting the genotype-specific mycobiome diversity findings, as both species richness and abundance significantly varied among genotypes in similar ways during both study years. Various diversity indices yielded similar suggestions, and projections in multivariate space repeatedly indicated that some genotypes might be closer to each other than others in terms of their impact on associated mycobiomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWhat shapes mycobiomes and their functions?\u003c/h2\u003e \u003cp\u003eIt is suggested that leaves may serve as a chemical landscape, and the metabolic dynamics of the leaf may determine the endophytes that colonize its interior [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, physical structures, such as bark surfaces, have been reported to create environments more conducive to the attachment of certain cryptogams and fungi than other structures e.g. [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. The TanAsp common garden, utilized for the present study, was established to investigate the potential effects of aspen tannins on their association with surrounding organisms [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Here we observed differences in the abundance of fungal ASV reads corresponding to relative variations in the expected tannin profiles of the trees. Additionally, qualitative distinctions in the genet-related mycobiome were apparent, as demonstrated by multivariate projections. While two of the high tannin genotypes clustered separately, the third high-tannin genotype clustered together with the low-tannin genotypes. Hence, while our study suggested a negative impact of condensed tannins on the abundance of foliar-associated mycobiomes, it is premature to infer a simple relationship.\u003c/p\u003e \u003cp\u003eCondensed tannins, for example, were reported to have a negative correlation with twig endophyte infection in poplars within a hybrid zone [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] as well as with the abundance of molecular markers of the biotrophic pathogen \u003cem\u003eMelampsora pinitorqua\u003c/em\u003e [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and the symptoms of necrotrophic pathogen \u003cem\u003eVenturia\u003c/em\u003e infection in \u003cem\u003ePopulus tremula\u003c/em\u003e [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Moreover, in a review by Bhat et al (1998) [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], degradation primarily by filamentous fungi such as \u003cem\u003eAspergillus\u003c/em\u003e and \u003cem\u003ePenicillium\u003c/em\u003e, along with yeasts, was found to degrade condensed tannins, potentially using them as substrate. Additionally, the ectomycorrhizal fungus \u003cem\u003eLaccaria bicolor\u003c/em\u003e was reported to colocalize with condensed tannins in the roots of \u003cem\u003ePopulus tremula\u003c/em\u003e [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. The function of mycobiomes evidently varies depending on the substrate composition of a plant organ, such as a leaf. Niche overlap may result in competition for substrates among the endophytic community [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], occurring in both the inter- and intracellular spaces of various plant organs where endophytes reside [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which could be advantageous for some fungal taxa and disadvantageous for others.\u003c/p\u003e \u003cp\u003eThe increase in fungal species abundance observed throughout the season was a notable finding of this study. Speculations regarding mycobiome function in plant parts suggest that the establishment of degrading fungi could benefit litter decomposition of plant material and contribute to nutrient recycling in forest habitats potentially affected by tannins [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]), their resistance to degradation by soil microbes, and their ability to bind and stabilize microbial enzymes [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Despite the negligible species-level determination coverage of the mycobiome in this study, valuable insights were gained into the diverse functions that were represented in the leaves across two seasons. Different combinations of patho, -sapro, and -symbiotroph- related lifestyles were suggested based on the data. The dominating \u003cem\u003eAureobasidium\u003c/em\u003e, a sooty mold, is a taxonomic complex usually assigned to a saprotroph lifestyle similar to that of \u003cem\u003eCandida\u003c/em\u003e [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In our study, symbiotrophic fungi were mainly lichenized and ectomycorrhizal endophytes, while pathotropic members were primarily associated with \u003cem\u003eCurvularia\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMethodological challenges and way to go\u003c/h2\u003e \u003cp\u003eThe selection of appropriate taxonomic markers is crucial for successful DNA metabarcoding [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. Primers should ideally encompass all members of an organism group in a sample while efficiently distinguishing nucleotide variability within that group to estimate taxonomic diversity. For fungi, the internal transcribed spacer region (ITS) of ribosomal RNA, including the ITS1, 5.8S, and ITS2 loci, is commonly utilized as the primary marker sequence [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. ITS2, known for its low taxonomic bias, is frequently targeted for metabarcoding through Illumina MiSeq, the widely used high-throughput sequencing platform [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our study, we also targeted the ITS2 region using the ITS3F and ITS4R primers[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, we encountered coverage issues, with less than 1% of reads identifiable to the genus level, necessitating methodological improvements in subsequent studies, particularly during the experimental design phase of molecular work. The initial step of primer design may introduce biases in estimating major fungal taxa [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. ITS primers, mostly adapted from Sanger's sequencing, may favour mono- or oligo-specific sequences, potentially mismatching or suboptimally amplifying the entire fungal community in an environmental DNA (eDNA) sample [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Moreover, the primers used in our study might suffer from mismatches with the target site, favouring certain fungal taxa over others in the amplicon. The use of degenerate primers specifically targeting ITS2 regions can mitigate primer bias and enhance taxonomic coverage [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. However, some ITS2-specific degenerate primers, such as fITS7/ITS4 and gITS7/ITS4, may also amplify nontargeted host DNA, reducing sequencing depth and read length [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Although ITS1 and ITS2 information was considered, ITS1 was ultimately discarded due to the occurrence of undesired PCR bands and primer-dimers [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnother source of error is the potential coamplification of plant-fungal DNA in aspen leaves, as highlighted in recent literature. Utilizing peptide nucleic acid (PNA) clamps alongside primer pairs can effectively increase fungal reads and associated diversity by blocking the amplification of nontargeted organisms, such as the plant host [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, achieving genus or species-level taxonomic resolution is challenging when targeting only the ITS2 region, which may be too short to distinguish closely related fungal species, potentially resulting in unassigned fungal taxonomy at the class-to-genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]). Long-read high-throughput amplicon sequencing methods, such as Pacific Biosciences (PacBio), which map the entire ITS region and highly conserved flanking sites (small subunit-SSU and large subunit- LSU), offer an alternative for comprehensive fungal community analysis with improved taxonomic resolution [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. However, taxon-specific PCR amplification biases can obscure community diversity in amplicon sequencing. PCR-free metagenomic sequencing presents a promising solution for overcoming this issue [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Despite these advancements, our knowledge of microbial life remains limited. While the UNITE databases contain entries for 3,846,536 ITS sequences, only 195,696 fungal species are hypothesized to have DOIs at the 1.5% threshold (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://unite.ut.ee/\u003c/span\u003e\u003cspan address=\"https://unite.ut.ee/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This is due to the unavailability of reference genomes for every organism.\u003c/p\u003e \u003cp\u003eThus, the scientific community faces the significant challenge of exploring potential beneficial organisms among the diverse unknown fungal communities associated with plants.\u003c/p\u003e \u003cp\u003eIn the realm of plant-associated microorganisms, inquiries into the internal, external, or combined lifestyle persist. Various microorganisms can access plant tissues through openings like stomata, residing within the apoplast or penetrating host cells as genuine endophytes. Conversely, some organisms primarily exist as external partners or epiphytes. This differentiation is fundamental, yet fully comprehending the physical association becomes challenging with eDNA applications, as both living and deceased DNA are amplified. In our investigation, we sterilized a subset of samples to explore potential sequencing discrepancies, and we were unable to differentiate between surface-sterilized and unsterilized leaves (Fig. S2). Consequently, we omitted this additional handling step from our study samples.\u003c/p\u003e \u003cp\u003eLocalization studies, such as immunoblotting or fluorescence in situ hybridization (FISHtechniques, which label target organisms with molecular markers, may be integrated into metagenome studies [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e] to gain insights into the physical connection between the plant host and its associated mycobiome. However, this approach is currently meaningful only for fungal associations of particular interest.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study emphasized that the impact of fungal microbes associated with aspen trees varies with genetic background, suggesting that within-population genetic variability of aspen trees may hold considerable importance for hidden forest biodiversity. Our study also exemplifies the need not only to study these hidden communities but also to optimize metagenome sequencing to refine the understanding of tree-associated mycobiome composition and function.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmplicon sequence variant\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCondensed tannins\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDeoxyribonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeDNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eenvironmental DNA\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFASTQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erefers to text-based biological sequence (usually nucleotide sequence) files. These files have the following extension:fastq\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibosomal internal transcribed spacer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLSU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erDNA large subunit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNonmetric multidimensional scaling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCoA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal coordinate analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epolymerase chain reaction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeptide nucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibo nucleic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSequence read archive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003erDNA small subunit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that links to the data supporting the findings of this study are available within the paper and its Supplementary Information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sequencing reads (FASTQ) were registered in the Sequence Read Archive (SRA) atSRP496938, as BioProject:PRJNA1090416 with accession numbers SRX24011776 to SRX24012154 .\u003c/p\u003e\n\u003cp\u003eThe dataset information can be found as part of the Zenodo repository: [\u003cstrong\u003e10.5281/zenodo.10839669\u003c/strong\u003e]. The short \u0026rsquo;\u003cstrong\u003ereadme\u0026rsquo;\u003c/strong\u003e file contains the data and material content.\u003c/p\u003e\n\u003cp\u003eBioinformatic codes and R scripts are stored at https://github.com/abu85/ampliseq_ITS. Should any raw data files be needed in another format they are available from the corresponding author upon request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Igor Matyas and Khan Mohammad Salehin for their assistance in the field and lab work. This research was made possible due to support from the Knut and Alice Wallenberg Foundation in two projects linked to the Ume\u0026aring; Plant Science Centre\u0026nbsp;(KAW 2018.0272) and SciLifeLab (KAW 2018.0273), which supported BRA and ABSI. We thank the Kempe Foundation for financial support for LM (JCSMK23-0066) and BRA, and the Erasmus Mundus Master Program in Plant Breeding enabled the involvement of ABSII. The sequencing data were generated from the NGI (Scilifelab) facility(Stockholm). The data handling was enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC, now NAISS) at UPPMAX (Uppsala Multidisciplinary Center for Advanced Computational Science) and was supported by SLUBI (SLU bioinformatics infrastructure).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbu Bakar Siddique\u003c/p\u003e\n\u003cp\u003eDepartment of Plant Biology, Swedish University of Agricultural Sciences, 75007, Uppsala, Sweden. Email: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbu Bakar Siddique\u003c/p\u003e\n\u003cp\u003eUme\u0026aring; Plant Science Centre (UPSC), Department of Plant Physiology, Ume\u0026aring; University, 90187 Ume\u0026aring;, Sweden.\u003c/p\u003e\n\u003cp\u003eTasmanian Institute of Agriculture (TIA), University of Tasmania, Prospect 7250, Tasmania, Australia. Email: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLovely Mahawar\u003c/p\u003e\n\u003cp\u003eUme\u0026aring; Plant Science Centre (UPSC), Department of Plant Physiology, Ume\u0026aring; University, 90187 Ume\u0026aring;, Sweden. Email: [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBenedicte Riber Albrectsen\u003c/p\u003e\n\u003cp\u003eUme\u0026aring; Plant Science Centre (UPSC), Department of Plant Physiology, Ume\u0026aring; University, 90187 Ume\u0026aring;, Sweden. Email: [email protected]\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrivedi P, Leach JE, Tringe SG, Sa T, Singh BK. Plant-microbiome interactions: from community assembly to plant health. Nat Rev Microbiol. 2021;19(1):72. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41579-020-0412-1\u003c/span\u003e\u003cspan address=\"10.1038/s41579-020-0412-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShanahan F, Ghosh TS, O'Toole PW. the healthy microbiome\u0026mdash;what is the definition of a healthy gut microbiome? Gastroenterology. 2021;160(2):483\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1053/j.gastro.2020.09.057\u003c/span\u003e\u003cspan address=\"10.1053/j.gastro.2020.09.057\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarroll G. Fungal endophytes in stems and leaves - from latent pathogen to mutualistic symbiont. Ecology. 1988;69(1):2\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1943154\u003c/span\u003e\u003cspan address=\"10.2307/1943154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClay K. comparative demography of three graminoids infected by systemic, \u003cem\u003eclavicipitaceous\u003c/em\u003e fungi. Ecology. 1990;71(2):558\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1940309\u003c/span\u003e\u003cspan address=\"10.2307/1940309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClay K, Schardl C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am Na. 2002;160:S99\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1086/342161\u003c/span\u003e\u003cspan address=\"10.1086/342161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchardl CL, Leuchtmann A, Spiering MJ. Symbioses of grasses with seedborne fungal endophytes. Annu Rev Plant Biol. 2004;55:315\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.arplant.55.031903.141735\u003c/span\u003e\u003cspan address=\"10.1146/annurev.arplant.55.031903.141735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKauppinen M, Saikkonen K, Helander M, Pirttil\u0026auml; AM, W\u0026auml;li PR. \u003cem\u003eEpichlo\u0026euml;\u003c/em\u003e grass endophytes in sustainable agriculture. Nat Plants. 2016;2(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nplants.2015.224\u003c/span\u003e\u003cspan address=\"10.1038/nplants.2015.224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulz B, Boyle C. The endophytic continuum. Mycol Res. 2005;109:661\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s095375620500273x\u003c/span\u003e\u003cspan address=\"10.1017/s095375620500273x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghavendra AKH, Newcombe G. The contribution of foliar endophytes to quantitative resistance to \u003cem\u003eMelampsora rust\u003c/em\u003e. New Phytol. 2013;197(3):909\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.12066\u003c/span\u003e\u003cspan address=\"10.1111/nph.12066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuryanarayanan TS. Endophyte research: going beyond isolation and metabolite documentation. Fungal Ecol. 2013;6(6):561\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funeco.2013.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.funeco.2013.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddique A, Biella P, Unterseher M, Albrectsen BR. Mycobiomes of young beech trees are distinguished by organ rather than by habitat, and community analyses suggest competitive interactions among twig fungi. Front Microbiol. 2021;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2021.646302\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2021.646302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrundrett MC. Coevolution of roots and mycorrhizas in land plants. New Phytol. 2022;154:275\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindahl BD, Finlay RD, Cairney JWG. Enzymatic activities of mycelia in mycorrhizal fungal communities. in the fungal community: its organization and role in the ecosystem. Taylor Francis. 2005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1201/9781420027891.ch16\u003c/span\u003e\u003cspan address=\"10.1201/9781420027891.ch16\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ezproxy.uws.edu.au/login?url=http://dx.\u003c/span\u003e\u003cspan address=\"http://ezproxy.uws.edu.au/login?url=http://dx.\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuginbuehl LH, Menard GN, Kurup S, Van Erp H, Radhakrishnan GV, Breakspear A, Oldroyd GED, Eastmond PJ. Fatty acids in arbuscular mycorrhizal fungi are synthesized by the host plant. Science. 2017;356(6343):1175\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aan0081\u003c/span\u003e\u003cspan address=\"10.1126/science.aan0081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuey CJ, Gopinath SCB, Uda MNA, Zulhaimi HI, Jaafar MN, Kasim FH, Yaakub ARW. Mycorrhiza: a natural resource assists plant growth under varied soil conditions. 3 Biotech. 2020;10(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13205-020-02188-3\u003c/span\u003e\u003cspan address=\"10.1007/s13205-020-02188-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi JC, Wang XL, Wang E. Mycorrhizal symbiosis in plant growth and stress adaptation: from genes to ecosystems. Annu Rev Plant Biol. 2023;74:569\u0026ndash;607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-arplant-061722-090342\u003c/span\u003e\u003cspan address=\"10.1146/annurev-arplant-061722-090342\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerotto S, Balestrini R. At the core of the endomycorrhizal symbioses: intracellular fungal structures in orchid and arbuscular mycorrhiza. New Phytol. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.19338\u003c/span\u003e\u003cspan address=\"10.1111/nph.19338\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDigra S, Nonzom S. An insight into endophytic antimicrobial compounds: an updated analysis. Plant Biotechnol Rep. 2023;14:1\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11816-023-00824-x\u003c/span\u003e\u003cspan address=\"10.1007/s11816-023-00824-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaqas M, Khan AL, Kamran M, Hamayun M, Kang SM, Kim YH, Lee IJ. Endophytic fungi produce gibberellins and indoleacetic acid and promotes host-plant growth during stress. Molecules. 2012;17(9):10754\u0026ndash;73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules170910754\u003c/span\u003e\u003cspan address=\"10.3390/molecules170910754\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan AL, Waqas M, Kang SM, Al-Harrasi A, Hussain J, Al-Rawahi A, Al-Khiziri S, Ullah I, Ali L, Jung HY, et al. Bacterial endophyte \u003cem\u003eSphingomonas sp\u003c/em\u003e LK11 produces gibberellins and IAA and promotes tomato plant growth. J Microbiol. 2014;52(8):689\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12275-014-4002-7\u003c/span\u003e\u003cspan address=\"10.1007/s12275-014-4002-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDefez R, Esposito R, Angelini C, Bianco C. Overproduction of indole-3-acetic acid in free-living rhizobia induces transcriptional changes resembling those occurring in nodule bacteroids. Mol Plant Microbe Interact. 2016;29(6):484\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/mpmi-01-16-0010-r\u003c/span\u003e\u003cspan address=\"10.1094/mpmi-01-16-0010-r\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Martino C, Torino V, Minotti P, Pietrantonio L, Del Grosso C, Palmieri D, Palumbo G, Crawford T, Carfagna S. Mycorrhized wheat plants and nitrogen assimilation in coexistence and antagonism with spontaneous colonization of pathogenic and saprophytic fungi in a soil of low fertility. Plants. 2022;11(7):924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants11070924\u003c/span\u003e\u003cspan address=\"10.3390/plants11070924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SM, Radhakrishnan R, You YH, Joo GJ, Lee IJ, Lee KE, Kim JH. Phosphate solubilizing \u003cem\u003ebacillus megaterium\u003c/em\u003e mj1212 regulates endogenous plant carbohydrates and amino acids contents to promote mustard plant growth. Indian J Microbiol. 2014;54(4):427\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12088-014-0476-6\u003c/span\u003e\u003cspan address=\"10.1007/s12088-014-0476-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOteino N, Lally RD, Kiwanuka S, Lloyd A, Ryan D, Germaine KJ, Dowling DN. Plant growth promotion induced by phosphate solubilizing endophytic Pseudomonas isolates. Front Microbiol. 2015;6:745. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2015.00745\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2015.00745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh D, Geat N, Rajawat MVS, Prasanna R, Kar A, Singh AM, Saxena AK. Prospecting endophytes from different Fe or Zn accumulating wheat genotypes for their influence as inoculants on plant growth, yield, and micronutrient content. Ann Microbiol. 2018;68(12):815\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13213-018-1388-1\u003c/span\u003e\u003cspan address=\"10.1007/s13213-018-1388-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCui KP, Xu T, Chen JW, Yang HY, Liu XM, Zhuo R, Peng YH, Tang W, Wang R, Chen LS, et al. Siderophores, a potential phosphate solubilizer from the endophyte \u003cem\u003eStreptomyces\u003c/em\u003e sp. CoT10, improved phosphorus mobilization for host plant growth and rhizosphere modulation. J Clean Prod. 2022;367:133110. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jclepro.2022.133110\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2022.133110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao RH, Liu XG, Gao KX, Mendgen K, Kang ZS, Gao JF, Dai Y, Wang X. Mycoparasitism of endophytic fungi isolated from reed on soilborne phytopathogenic fungi and production of cell wall-degrading enzymes in vitro. Curr Microbiol. 2009;59(6):584\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00284-009-9477-9\u003c/span\u003e\u003cspan address=\"10.1007/s00284-009-9477-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGajera HP, Vakharia DN. Production of lytic enzymes by \u003cem\u003eTrichoderma\u003c/em\u003e isolates during in vitro antagonism with \u003cem\u003eAspergillus niger\u003c/em\u003e, the causal agent of collar rot of peanut. Braz J Microbiol. 2012;43(1):43\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/s1517-83822012000100005\u003c/span\u003e\u003cspan address=\"10.1590/s1517-83822012000100005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerma A, Shameem N, Jatav HS, Sathyanarayana E, Parray JA, Poczai P, Sayyed RZ. Fungal endophytes to combat biotic and abiotic stresses for climate-smart and sustainable agriculture. Front Plant Sci. 2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.953836\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.953836\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Latorre C, Rodrigo S, Marin-Felix Y, Stadler M, Santamaria O. Plant-growth promoting activity of three fungal endophytes isolated from plants living in dehesas and their effect on \u003cem\u003eLolium multiflorum\u003c/em\u003e. Sci Rep. 2023;13(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-34036-8\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-34036-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrini O. Fungal endophytes of tree leaves. In: Andrews JH, Hirano SS, editors Microbial ecology of leaves. Brock/Springer Series in Contemporary Bioscience. Springer, New York, NY. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-1-4612-3168-4_984\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4612-3168-4_984\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldrian P, Vetrovsky T, Lepinay C, Kohout P. High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers. 2022;114(1):539\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13225-021-00472-y\u003c/span\u003e\u003cspan address=\"10.1007/s13225-021-00472-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardoim PR, van Overbeek LS, Berg G, Pirttil\u0026auml; AM, Compant S, Campisano A, D\u0026ouml;ring M, Sessitsch A. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol R. 2015;79(3):293\u0026ndash;320. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1128/mmbr.00050-14\u003c/span\u003e\u003cspan address=\"10.1128/mmbr.00050-14\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu B, Hussain M, Zhang WW, Stadler M, Liu XZ, Xiang MC. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology. 2019;10(3):127\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21501203.2019.1614106\u003c/span\u003e\u003cspan address=\"10.1080/21501203.2019.1614106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePhukhamsakda C, Nilsson RH, Bhunjun CS, de Farias ARG, Sun YR, Wijesinghe SN, Raza M, Bao DF, Lu L, Tibpromma S, et al. The numbers of fungi: contributions from traditional taxonomic studies and challenges of metabarcoding. Fungal Divers. 2022;114(1):327\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13225-022-00502-3\u003c/span\u003e\u003cspan address=\"10.1007/s13225-022-00502-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRayner MC. Obligate Symbiosis in \u003cem\u003eCalluna vulgaris\u003c/em\u003e. Ann Botany. 1915;29(1):97\u0026ndash;133. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/oxfordjournals.aob.a089540\u003c/span\u003e\u003cspan address=\"10.1093/oxfordjournals.aob.a089540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez RJ Jr, Arnold JFW, Redman AE. Fungal endophytes: diversity and functional roles. New Phytol. 2009;182(2):314\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2009.02773.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2009.02773.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamzah TNT, Lee SY, Hidayat A, Terhem R, Faridah-Hanum I, Mohamed R. diversity and characterization of endophytic fungi isolated from the tropical mangrove species, \u003cem\u003eRhizophora mucronata\u003c/em\u003e, and identification of potential antagonists against the soil-borne fungus, \u003cem\u003eFusarium solani\u003c/em\u003e. Front Microbiol. 2018;25(9):1707. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2018.01707\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2018.01707\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong A, Xiao C, Jiang W, Zhou Y, Zhang J, Liang Q, Yang C, Zheng W, Zhang C. Association between dipsacus saponin VI level and diversity of endophytic fungi in roots of \u003cem\u003eDipsacus asperoides\u003c/em\u003e. World J Microb Biotechnol. 2019;35(42). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11274-019-2616-y\u003c/span\u003e\u003cspan address=\"10.1007/s11274-019-2616-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edos Reis JBA, Lorenzi AS, do Vale HMM. Methods used for the study of endophytic fungi: a review on methodologies and challenges, and associated tips. Arch Microbiol. 2022;204(11):675. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00203-022-03283-0\u003c/span\u003e\u003cspan address=\"10.1007/s00203-022-03283-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Guo L-D. Endophytic fungal diversity: review of traditional and molecular techniques. Mycology. 2012;3(1):65\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindahl BD, Nilsson RH, Tedersoo L, Abarenkov K, Carlsen T, Kjoller R, Koljalg U, Pennanen T, Rosendahl S, Stenlid J, et al. Fungal community analysis by high-throughput sequencing of amplified markers - a user's guide. New Phytol. 2013;199(1):288\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.12243\u003c/span\u003e\u003cspan address=\"10.1111/nph.12243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang H, Liu ZY, Duan FF, Chen Y, Qiu KD, Xiong Q, Lin HT, Zhang J, Tan HB. Isolation, identification, and antibacterial evaluation of endophytic fungi from Gannan navel orange. Front Microbiol. 2023;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2023.1172629\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2023.1172629\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous PW, et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for. Proc Natl Acad Sci USA. 2012;109(16):6241\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1117018109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1117018109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson RH, Anslan S, Bahram M, Wurzbacher C, Baldrian P, Tedersoo L. Mycobiome diversity: high-throughput sequencing and identification of fungi. Nat Reviews Microbiol. 2019;17(2):95\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41579-018-0116-y\u003c/span\u003e\u003cspan address=\"10.1038/s41579-018-0116-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddique AB, Albrectsen BR, Ilbi H, Siddique AB. Optimization of protocol for construction of fungal ITS amplicon library for high-throughput illumina sequencing to study the mycobiome of aspen leaves. Appl Sci. 2022;12(3):1136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/app12031136\u003c/span\u003e\u003cspan address=\"10.3390/app12031136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiddique A, Menke L, Dinedurga M, Albrectsen BR. Molecular studies of rust on European aspen suggest an autochthonous relationship shaped by genotype. Front Plant Sci. 2023;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2023.1111001\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2023.1111001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViotti C, Chalot M, Kennedy PG, Maillard F, Santoni S, Blaudez D, Bertheau C. Primer pairs, PCR conditions, and peptide nucleic acid clamps affect fungal diversity assessment from plant root tissues. Mycology. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21501203.2023.2301003\u003c/span\u003e\u003cspan address=\"10.1080/21501203.2023.2301003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen NH, Williams LJ, Vincent JB, Stefanski A, Cavender-Bares J, Messier C, Paquette A, Gravel D, Reich PB, Kennedy PC. Ectomycorrhizal fungal diversity and saprotrophic fungal diversity are linked to different tree community attributes in a field-based tree experiment. Mol Ecol. 2016;25(16):4032\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.13719\u003c/span\u003e\u003cspan address=\"10.1111/mec.13719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026ouml;lme S, Abarenkov K, Nilsson RH, Lindahl BD, Clemmensen KE, Kauserud H, Nguyen N, Kjoller R, Bates ST, Baldrian P, et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers. 2021;105:1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13225-020-00466-2\u003c/span\u003e\u003cspan address=\"10.1007/s13225-020-00466-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrivonos DV, Konanov DN, Ilina EN, FunFun. ITS-based functional annotator of fungal communities. Ecol Evol. 2023;13(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.9874\u003c/span\u003e\u003cspan address=\"10.1002/ece3.9874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhanbary E, Kouchaksaraei MT, Mirabolfathy M, Sanavi SAMM, Rahaie M. Growth and physiological responses of \u003cem\u003eQuercus brantii\u003c/em\u003e seedlings inoculated with \u003cem\u003eBiscogniauxia mediterranea\u003c/em\u003e and \u003cem\u003eObolarina persica\u003c/em\u003e under drought stress. For Pathol. 2017;47(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/efp.12353\u003c/span\u003e\u003cspan address=\"10.1111/efp.12353\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaidoo S, Slippers B, Plett JM, Coles D, Oates CN. The road to resistance in forest trees. Front Plant Sci. 2019;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.00273\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.00273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSrikantaramas S, Asano T, Sudo H, Yamazaki M, Saito K. Camptothecin: therapeutic potential and Biotechnology. Curr Pharm Biotechnol. 2007;8:196\u0026ndash;202. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2174/138920107781387447\u003c/span\u003e\u003cspan address=\"10.2174/138920107781387447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArnold AE, Mej\u0026iacute;a LC, Kyllo D, Rojas EI, Maynard Z, Robbins N, Herre EA. Fungal endophytes limit pathogen damage in a tropical tree. Proc Natl Acad Sci USA. 2003;100(26):15649\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2533483100\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2533483100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaticov M, Abdelfattah A, Roslin T, Vacher C, Hamb\u0026auml;ck P, Blanchet FG, Lindahl BD, Tack AJM. Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak. New Phytol. 2021;231(5):1770\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.17434\u003c/span\u003e\u003cspan address=\"10.1111/nph.17434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlumenstein K, Albrectsen BR, Mart\u0026iacute;n JA, Hultberg M, Sieber TN, Helander M, Witzell J. Nutritional niche overlap potentiates the use of endophytes in biocontrol of a tree disease. Biocontrol. 2015;60(5):655\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10526-015-9668-1\u003c/span\u003e\u003cspan address=\"10.1007/s10526-015-9668-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbrectsen BR, Siddique AB, Decker VHG, Unterseher M, Robinson KM. Both plant genotype and herbivory shape aspen endophyte communities. Oecologia. 2018;187(2):535\u0026ndash;45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00442-018-4097-3\u003c/span\u003e\u003cspan address=\"10.1007/s00442-018-4097-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRandriamanana TR, Nissinen K, Ovaskainen A, Lavola A, Peltola H, Albrectsen BR, Julkunen-Tiitto R. Does fungal endophyte inoculation affect the responses of aspen seedlings to carbon dioxide enrichment? Fungal Ecol. 2018;33:24\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.funeco.2017.12.002\u003c/span\u003e\u003cspan address=\"10.1016/j.funeco.2017.12.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitzell J, Decker VHG, Agostinelli M, Romeralo C, Cleary M, Albrectsen BR. Aspen leaves as a chemical landscape for fungal endophyte diversity-effects of nitrogen addition. Front Microbiol. 2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2022.846208\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2022.846208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusby PE, Peay KG, Newcombe G. Common foliar fungi of \u003cem\u003ePopulus trichocarpa\u003c/em\u003e modify Melampsora rust disease severity. New Phytol. 2016;209(4):1681\u0026ndash;92. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nph.13742\u003c/span\u003e\u003cspan address=\"10.1111/nph.13742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu ZD, Ding HR, Shen KC, Bu FF, Newcombe G, Liu HX. Foliar endophytes in trees varying greatly in age. Eur J Plant Pathol. 2021;160(2):375\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10658-021-02250-7\u003c/span\u003e\u003cspan address=\"10.1007/s10658-021-02250-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMessal M, Vivas M, Kemler M, Begerow D, Brachmann A, Witfeld F, Naidoo S, Slippers B. Fungal communities of Eucalyptus grandis leaves are influenced by the insect pest. Front Microbiol. 2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2022.841621\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2022.841621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeay KGBM, Arnold AE. Not every fungus is everywhere: scaling to the biogeography of fungal\u0026ndash;plant interactions across roots, shoots and ecosystems. New Phytol. 2010;185(4):878\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2009.03158.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2009.03158.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandau F, Albrectsen BR, Julkunen-Tiitto R, Gundale MJ. Genotypic variability in \u003cem\u003ePopulus tremula\u003c/em\u003e L. affects how anthropogenic nitrogen enrichment influences litter decomposition. Plant Soil. 2017;410(1\u0026ndash;2):467\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11104-016-3033-8\u003c/span\u003e\u003cspan address=\"10.1007/s11104-016-3033-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandau F, Albrectsen BR, Robinson KM, Gundale MJ. European aspen with high compared to low constitutive tannin defenses grow taller in response to anthropogenic nitrogen enrichment. For Ecol Manag. 2021;487. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foreco.2021.118985\u003c/span\u003e\u003cspan address=\"10.1016/j.foreco.2021.118985\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson KM, Ingvarsson PK, Jansson S, Albrectsen BR. Genetic variation in functional traits influences arthropod community composition in aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e L). PLoS ONE. 2012;7(5):e37679. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0037679\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0037679\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38(3):276\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41587-020-0439-x\u003c/span\u003e\u003cspan address=\"10.1038/s41587-020-0439-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStraub D, Blackwell N, Langarica-Fuentes A, Peltzer A, Nahnsen S, Kleindienst S. Interpretations of environmental microbial community studies are biased by the selected 16S rRNA (Gene) amplicon sequencing pipeline. Front Microbiol. 2020;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2020.550420\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2020.550420\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ, Singh A, Wilkins MJ, Wrighton KC, Williams KH, Banfield JF. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature. 2015;523(7559):208\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature14486\u003c/span\u003e\u003cspan address=\"10.1038/nature14486\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrehenwinkel H, Wolf M, Lim JY, Rominger AJ, Simison WB, Gillespie RG. Estimating and mitigating amplification bias in qualitative and quantitative arthropod metabarcoding. Sci Rep. 2017;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-017-17333-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-017-17333-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDully V, Wilding TA, M\u0026uuml;hlhaus T, Stoeck T. Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning. Comput Struct Biotec. 2021;19:2256\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.csbj.2021.04.005\u003c/span\u003e\u003cspan address=\"10.1016/j.csbj.2021.04.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGotelli NJ, Colwell RK. Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecol Lett. 2001;4(4):379\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1461-0248.2001.00230.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1461-0248.2001.00230.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnterseher M, Schnittler M, Dormann C, Sickert A. Application of species richness estimators for the assessment of fungal diversity. Fems Microbiol Lett. 2008;282(2):205\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6968.2008.01128.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6968.2008.01128.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHill MO. Diversity and Evenness: a unifying notation and its consequences. Ecology. 1973;54(2):427\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1934352\u003c/span\u003e\u003cspan address=\"10.2307/1934352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray JR, Curtis JT. An ordination of the upland forest communities of Southern Wisconsin. Ecol Monogr. 1957;27(4):326\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2307/1942268\u003c/span\u003e\u003cspan address=\"10.2307/1942268\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14(6):927\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1654-1103.2003.tb02228.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1654-1103.2003.tb02228.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson MJ, Ellingsen KE, McArdle BH. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9(6):683\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1461-0248.2006.00926.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1461-0248.2006.00926.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaikkonen S. Forest structure and fungal endophytes. Fungal Biology Reviews. 2007;21(2\u0026ndash;3):67\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.fbr.2007.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.fbr.2007.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbrectsen BR, Witzell J. Disentangling functions of fungal endophytes in forest trees. In: Silva AP, Sol M, editors. Fungi: Types, Environmental Impact and Role in Disease. New York: Nova Science Publishers, Inc.; 2012. pp. 235\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbrectsen BR, Bj\u0026ouml;rk\u0026eacute;n L, Varad A, Hagner \u0026Aring;, Wedin M, Karlsson J, Jansson S. Endophytic fungi in European aspen \u003cem\u003e(Populus tremula\u003c/em\u003e) leaves-diversity, detection, and a suggested correlation with herbivory resistance. Fungal Divers. 2010;41(1):17\u0026ndash;28. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s13225-009-0011-y\u003c/span\u003e\u003cspan address=\"10.1007/s13225-009-0011-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamit LJ, Lau MK, Sthultz CM, Wooley SC, Whitham TG, Gehring CA. Tree genotype and genetically based growth traits structure twig endophyte communities. Am J Bot. 2014;101(3):467\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3732/ajb.1400034\u003c/span\u003e\u003cspan address=\"10.3732/ajb.1400034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026aacute;lint M, Tiffin P, Hallstr\u0026ouml;m B, O'Hara RB, Olson MS, Fankhauser JD, Piepenbring M, Schmitt I. Host Genotype Shapes the Foliar Fungal Microbiome of Balsam Poplar (Popular balsamifera). PLoS ONE. 2013;8(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0053987\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0053987\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarlinski L, Rudawska M, Kieliszewska-Rokicka B, Leski T. Relationship between genotype and soil environment during colonization of poplar roots by mycorrhizal and endophytic fungi. Mycorrhiza. 2010;20(5):315\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00572-009-0284-8\u003c/span\u003e\u003cspan address=\"10.1007/s00572-009-0284-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoeber S, Baum C, Weih M, Manzoni S, Fransson P. Site-dependent relationships between fungal community composition, plant genotypic diversity and environmental drivers in a salix biomass system. Front Fungal Biol. 2021;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/ffunb.2021.671270\u003c/span\u003e\u003cspan address=\"10.3389/ffunb.2021.671270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK\u0026uuml;ngas K, Bahram M, Poldmaa K. Host tree organ is the primary driver of endophytic fungal community structure in a hemiboreal forest. Fems Microbiol Ecol. 2020;96(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/femsec/fiz199\u003c/span\u003e\u003cspan address=\"10.1093/femsec/fiz199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgostinelli M, Cleary M, Mart\u0026iacute;n JA, Albrectsen BR, Witzell J. Pedunculate Oaks (\u003cem\u003eQuercus robur\u003c/em\u003e L.) differing in vitality as reservoirs for fungal biodiversity. Front Microbiol. 2018;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2018.01758\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2018.01758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusby PE, Zimmerman N, Weston DJ, Jawdy SS, Houbraken J, Newcombe G. Leaf endophytes and genotype affect severity of damage from the necrotrophic leaf pathogen, \u003cem\u003eDrepanopeziza populi\u003c/em\u003e. Ecosphere. 2013;4(10). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1890/ES13-00127.1\u003c/span\u003e\u003cspan address=\"10.1890/ES13-00127.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnterseher M, Siddique A, Brachmann A, Persoh D. diversity and composition of the leaf mycobiome of beech (\u003cem\u003eFagus sylvatica\u003c/em\u003e) are affected by local habitat conditions and leaf biochemistry. PLoS ONE. 2016;11(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0152878\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0152878\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelander M, Ahlholm J, Sieber TN, Hinneri S, Saikkonen K. Fragmented environment affects birch leaf endophytes. New Phytol. 2007;175(3):547\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2007.02110.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2007.02110.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers PC, Pinno BD, Sebesta J, Albrectsen BR, Li GQ, Ivanova N, Kusbach A, Kuuluvainen T, Landh\u0026auml;usser SM, Liu HY, et al. A global view of aspen: conservation science for widespread keystone systems. Glob Ecol Conserv. 2020;21:e00828. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gecco.2019.e00828\u003c/span\u003e\u003cspan address=\"10.1016/j.gecco.2019.e00828\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahram M, Polme S, Koljalg U, Tedersoo L. A single European aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e) tree individual may potentially harbour dozens of ITS genotypes and hundreds of species of ectomycorrhizal fungi. Fems Microbiol Ecol. 2011;75(2):313\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6941.2010.01000.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6941.2010.01000.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosoya T. Systematics, ecology, and application of Helotiales: Recent progress and future perspectives for research with special emphasis on activities within JapanHelotiales: Recent progress and future perspectives for research with special emphasis on activities within Japan. Mycoscience. 2021;62(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.47371/mycosci.2020.05.002\u003c/span\u003e\u003cspan address=\"10.47371/mycosci.2020.05.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCopot O, Tanase C. Substrate properties, forest structure and climate influences wood-inhabiting fungal diversity in broadleaved and mixed forests from Northeastern Romania. For Syst. 2020;29(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5424/fs/2020293-16728\u003c/span\u003e\u003cspan address=\"10.5424/fs/2020293-16728\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBailey JK, Deckert R, Schweitzer JA, Rehill BJ, Lindroth RL, Gehring C, Whitham TG. Host plant genetics affect hidden ecological players: links among \u003cem\u003ePopulus\u003c/em\u003e, condensed tannins, and fungal endophyte infection. Can J Bot. 2005;83(4):356\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1139/b05-008\u003c/span\u003e\u003cspan address=\"10.1139/b05-008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat TK, Singh B, Sharma OP. Microbial degradation of tannins - A current perspective. Biodegradation. 1998;9(5):343\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/a:1008397506963\u003c/span\u003e\u003cspan address=\"10.1023/a:1008397506963\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChowdhury J, Ferdous J, Lihavainen J, Albrectsen BR, Lundberg-Felten J. Fluorogenic properties of 4-dimethylaminocinnamaldehyde (DMACA) enable high resolution imaging of cell-wall-bound proanthocyanidins in plant root tissues. Front Plant Sci. 2023;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.1060804\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.1060804\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlumenstein K, Macaya-Sanz D, Mart\u0026iacute;n JA, Albrectsen BR, Witzell J. Phenotype MicroArrays as a complementary tool to next generation sequencing for characterization of tree endophytes. Front Microbiol. 2015;6:1033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmicb.2015.01033\u003c/span\u003e\u003cspan address=\"10.3389/fmicb.2015.01033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus TEC, Dahlgren RA, Zasoski RJ. Tannins in nutrient dynamics of forest ecosystems - a review. Plant Soil. 2003;256(1):41\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1023/A:1026206511084\u003c/span\u003e\u003cspan address=\"10.1023/A:1026206511084\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoanisse GD, Bradley RL, Preston CM, Munson AD. Soil enzyme inhibition by condensed litter tannins may drive ecosystem structure and processes: the case of Kalmia angustifolia. New Phytol. 2007;175(3):535\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2007.02113.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2007.02113.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu MX, Clarke LJ, Baker SC, Jordan GJ, Burridge CP. A practical guide to DNA metabarcoding for entomological ecologists. Ecol Entomol. 2020;45(3):373\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/een.12831\u003c/span\u003e\u003cspan address=\"10.1111/een.12831\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTedersoo L, Bahram M, Zinger L, Nilsson RH, Kennedy PG, Yang T, Anslan S, Mikryukov V. Best practices in metabarcoding of fungi: from experimental design to results. Mol Ecol. 2022;31(10):2769\u0026ndash;95. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mec.16460\u003c/span\u003e\u003cspan address=\"10.1111/mec.16460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBellemain E, Carlsen T, Brochmann C, Coissac E, Taberlet P, Kauserud H. ITS as an environmental DNA barcode for fungi: an approach reveals potential PCR biases. BMC Microbiol. 2010;10(189). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1471-2180-10-189\u003c/span\u003e\u003cspan address=\"10.1186/1471-2180-10-189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIhrmark K, B\u0026ouml;deker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, Strid Y, Stenlid J, Brandstr\u0026ouml;m-Durling M, Clemmensen KE, et al. New primers to amplify the fungal ITS2 region - evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82(3):666\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1574-6941.2012.01437.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1574-6941.2012.01437.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFurneaux B, Bahram M, Rosling A, Yorou NS, Ryberg M. Long- and short-read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities. Mol Ecol Resour. 2021;21(6):1833\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/1755-0998.13387\u003c/span\u003e\u003cspan address=\"10.1111/1755-0998.13387\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarg D, Patel N, Rawat A, Rosado AS. Cutting edge tools in the field of soil microbiology. Curr Res Microb Sci. 2024;100226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.crmicr.2024.100226\u003c/span\u003e\u003cspan address=\"10.1016/j.crmicr.2024.100226\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eDNA, Illumina sequencing, ITS2, mycobiome, microorganisms, bioinformatics, aspen. condensed tannins","lastPublishedDoi":"10.21203/rs.3.rs-4206868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4206868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePlant mycobiomes play a crucial role in plant health, growth, and adaptation to changing environments, making their diversity and dynamics essential for agricultural and environmental contexts, including conservation efforts, climate change mitigation, and potential biotechnological applications. Horizontally transferred mycobiomes are established in plant organs, yet the dynamics of their colonization and establishment remain unknown. New molecular technologies offer a deeper insight into the establishment and dynamics of plant-associated mycobiomes. In this study, we investigated leaf-associated mycobiomes in cloned replicates of aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e) with naturally varying phenolic profiles and a history of nitrogen fertilization.\u003c/p\u003e\u003ch2\u003eMain findings\u003c/h2\u003e \u003cp\u003eUsing ITS2 metabarcoding of 344 samples collected from a ca ten-year-old common garden with small aspen trees at various time points over two consecutive years, we identified 30,080,430 reads in our database, corresponding to an average of 87,448 reads per sample clustered into 581 amplicon sequence variants (ASVs). Analysis of ASV patterns revealed changes in both richness and abundance among genotypes and across the seasons, with no discernible effect of fertilization history. Additionally, the number of reads was negatively correlated with the ability of the genotypes to synthesize and store condensed tannins.\u003c/p\u003e","manuscriptTitle":"Genotype, Tannin Capacity, and Seasonality Influence the Structure and Function of Symptomless Fungal Communities in Aspen Leaves, Regardless of Historical Nitrogen Addition","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 12:36:21","doi":"10.21203/rs.3.rs-4206868/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fc0ca21-487d-434c-918a-3fc693ce23c6","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-05T09:14:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-09 12:36:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4206868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4206868","identity":"rs-4206868","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}