# Maternal effects shape the seed mycobiome in Quercus petraea

Tania Fort, Charlie Pauvert, Amy Zanne, Otso Ovaskainen, Thomas Caignard, Matthieu Barret, Stéphane Compant, Arndt Hampe, Sylvain S. Delzon, Corinne Vacher

► To cite this version:

Tania Fort, Charlie Pauvert, Amy Zanne, Otso Ovaskainen, Thomas Caignard, et al. *Maternal effects shape the seed mycobiome in Quercus petraea*. New Phytologist, In press, pp.1-15.
10.1111/nph.17153. *hal-03140262*

HAL Id: hal-03140262

https://hal.inrae.fr/hal-03140262v1

Submitted on 6 Oct 2025

**HAL** is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Maternal effects shape the seed mycobiome in *Quercus petraea*

Tania Fort¹, Charlie Pauvert¹, Amy E. Zanne², Otso Ovaskainen³,⁴, Thomas Caignard¹, Matthieu Barret⁵, Stéphane Compant⁶, Arndt Hampe¹, Sylvain Delzon⁷ and Corinne Vacher¹*

To cite this article: Fort et al. 2020. Maternal effects shape the seed mycobiome in *Quercus petraea*. *New Phytologist*, DOI: 10.1111/nph.17153

1. INRAE, Univ. Bordeaux, BIOGECO, 33615 Pessac, France

2. George Washington University, Department of Biological Sciences, 800 22nd St., Washington DC 20052, USA

3. Organismal and Evolutionary Biology Research Programme, P.O. Box 65, 00014 University of Helsinki, Finland.

4. Center for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, 7491 Trondheim, Norway

5. Univ Angers, Institut Agro, INRAE, IRHS, SFR 4207 QuaSaV, 49000 Angers, France

6. AIT Austrian Institute of Technology GmbH, Center for Health & Bioresources, Bioresources Unit, Konrad Lorenz Straße 24, 3430 Tulln, Austria

7. Univ. Bordeaux, INRAE, BIOGECO, Pessac France.

Tel: +33(0)5 40 00 88 99

E-mail: corinne.vacher@inrae.fr

Address: UMR BIOGECO, Université de Bordeaux, Allée Geoffroy St-Hilaire, Bât. B2, 33615 Pessac, France

<footnote>*Author for correspondence: Corinne Vacher</footnote>
## Summary

* The tree seed mycobiome has received little attention despite its potential role in forest regeneration and health. The aim of our study was to analyze the processes shaping the composition of seed fungal communities in natural forests as seeds transition from the mother plant to the ground for establishment.

* We used metabarcoding approaches and confocal microscopy to analyze the fungal communities of seeds collected in the canopy and on the ground in four natural populations of sessile oak (*Quercus petraea*). Ecological processes shaping the seed mycobiome were inferred using joint species distribution models.

* Fungi were present in seed internal tissues, including the embryo. The seed mycobiome differed among oak populations and trees within the same population. Its composition was largely influenced by the mother, with weak significant environmental influences. The models also revealed several likely interactions among fungal pathogens and mycoparasites.

* Our results demonstrate that maternal effects, environmental filtering and biotic interactions all shape the seed mycobiome of sessile oak. They provide a starting point for future research aimed at understanding how maternal genes and environments interact to control the vertical transmission of fungal species that could then influence seed dispersal and germination, and seedling recruitment.

## Keywords

Endophyte; environmental filtering; joint species distribution models; maternal effect; microbial network; seed; *Quercus petraea* (sessile oak); vertical transmission
Introduction

In forests, seeds and seedlings are the stages that usually incur the highest mortality rates (Petit & Hampe, 2006). Seed predators, seedling herbivores, fungal pathogens and harsh environments (Moles & Westoby, 2004; Mangan et al., 2010; Bagchi et al., 2014; Bever et al., 2015) are all known sources of mortality, with pathogens often a strong determinant of seed survival and seedling establishment. For example, the fungal pathogen *Ciboria batschiana*, the causal agent of acorn black rot, can damage up to 80% of oak seeds when conditions are wet (Prochazkova et al., 2005). Seeds are also associated with many microbes with positive effects including endophytic and epiphytic microorganisms that foster the growth of seedlings and protect them against natural enemies (Nelson, 2004; Links et al., 2014; Leroy et al., 2019). Understanding how pathogens and other microorganisms are acquired by trees, transmitted to their seeds, and influence seed germination and seedling development, is crucial for improving our ability to predict and manage the regeneration of forest ecosystems.

Up to now, seed transmission of microorganisms has received far more attention in crops than in
natural and managed forest ecosystems. In crops, microorganisms can be transmitted by the
mother plant to its seeds during the floral and early seed development stages, through vascular
tissues or contact between vegetative and reproductive organs (Maude, 1996). They can also be
transmitted via the pollen of the father plant, insect vectors, or bioaerosols (Frank et al., 2017;
Escobar Rodríguez et al., 2018; Prado et al., 2020). Once seeds fall on the ground, epiphytic
microbial communities coalesce with microbial communities of litter and upper soil (Rillig et al.,
2015; Castledine et al., 2020). Germinating seeds release molecules that attract soil microbes,
surrounding themselves with a microbiologically active soil area called the spermosphere
(Nelson, 2004; Schiltz et al., 2015). The emergence of the plant radicle creates cracks in the seed
tegument, enabling microbes to colonize internal tissues (Nelson et al., 2018). These events lead
to intense biotic interactions among microorganisms (Nelson, 2004) and drastic changes in seed
microbiota composition and function (Ofek et al., 2011; Yang et al., 2017; Torres-Cortés et al.,
2018). Recent studies suggest that seed colonization by soil microorganisms represents the
microbial acquisition that is most influential for seedling growth and health (Nelson et al., 2018).
The microorganisms that are directly transferred from the vascular system of the mother plant to the offspring through seeds are termed vertically-transmitted (Truyens et al., 2015), while those recruited from external sources are termed horizontally-acquired. Vertical transmission of microorganisms from mother to offspring is particularly important for plant population dynamics and evolution because it can provide progenies with microbial genes and functions that promote adaptation to local biotic and abiotic conditions (Schardl et al., 2004). The microbial communities associated with the mother plant aboveground tissues can directly affect the phenotype and fitness of seedlings, by being transmitted to seedlings through seeds (Truyens et al., 2015), or later through the litter (Christian et al., 2017). They might also affect offspring indirectly by influencing seed provisioning and epigenome (Vivas et al., 2015). Such effects of the maternal microbiota on the phenotype and fitness of the offspring are part of maternal effects. Maternal effects are often defined as the “influences of the mother plant on the phenotype of her offspring via mechanisms other than the genetic information carried on chromosomes” (Weiner et al., 1997; see also Roach et al., 1987; Räsänen et al., 2007). Other authors have broadened this definition by including genetic effects into maternal effects. For instance, Wolf et al. (2009) defined maternal effects as “the causal influence of the maternal genotype or phenotype on the offspring phenotype”. We use this broad definition in the present article.

Maternally-transmitted microorganisms can influence the dynamics of seed and seedling microbial communities. These latter are shaped, like all ecological communities, by four fundamental processes: selection by the abiotic environment (environmental filtering) and biotic interactions (biotic filtering), dispersal, ecological drift and evolutionary diversification (Vellend, 2010; Nemergut et al., 2013; Ovaskainen et al., 2017; Zhou & Ning, 2017). Some processes, such as the response to selection, are deterministic and depend on microbial functional traits (Torres-Cortès et al., 2018). Other processes are partly or purely stochastic (Zhou & Ning, 2017; Rezki et al., 2018) and generate divergences among communities occupying identical environments (Chase & Myers, 2011). Microbial community dispersal to the seed is a stepwise process because seeds are mobile so microorganisms can be recruited from a sequence of species pools. They can first be recruited from the mother plant's aboveground organs (but this is not the case in all plant species, as demonstrated by Sarmiento et al., 2017). Next seeds can be colonized from bioaerosols (both while on the mother plant and once on the ground), as well as litter and soil pools. The microbiota initially transmitted by the mother plant, when it is present, is expected
to have a large influence on seed community dynamics. Indeed, subtle differences in its
composition can result in large differences in community composition among seeds, because
initial differences can be amplified over time and space via microbial population growth and
interactions (a phenomenon known as priority effects; Fukami, 2015).

The aim of the present study was to analyze the processes shaping the composition of seed fungal
communities in natural forests as seeds transition from the mother plant to the ground for
establishment. Little is known about fungal communities of tree seeds, and in particular about the
vertical transmission of fungi from mother trees to their offspring; this transmission could be of
importance to the adaptation of forest ecosystems to environmental changes and should be
considered in forest tree planting (Vivas et al., 2020). In previous work, both fungal pathogens
and common endophytes were detected in the seeds of some tree species (Ganley & Newcombe
2006; Qi et al., 2012; Sarmiento et al., 2017; de la Bastide et al., 2019); however, the relative
contribution of vertically- and horizontally-transmitted species to the assembly of the whole
fungal community of seeds is not yet known in forest ecosystems. The extent to which fungal
species transmitted by the mother tree persist after seed fall is not known either, although this is a
prerequisite for their effect on offspring fitness. We therefore tested the following hypotheses in a
major tree species of European temperate forests, sessile oak (Quercus petraea): (H1) seed
fungal communities are shaped by maternal effects and environmental filtering, (H2) similarity of
seed fungal communities to the canopy environment decreases after seed fall because of
horizontal acquisition of fungi from the ground, (H3) the lifestyle of fungal species influences
their response to variations in the seed environment, and (H4) biotic interactions at the seed scale
are involved in seed protection against fungal pathogens. To test these hypotheses, we analyzed
the seed mycobiome in four unmanaged oak populations located in the Pyrenees mountains
(France). We collected oak seeds (i.e., acorns) in the canopy and on the ground beneath
individual trees and used Hierarchical Models of Species Communities (HMSC; Ovaskainen et
al., 2017) to quantify the ecological processes shaping seed fungal communities at each spatial
level (oak population, mother tree and seed) and to generate hypotheses regarding microbial
biotic interactions.

Materials and Methods
Sampling design

Samples were collected on October 21st and 22nd 2015 in four intensively studied populations of sessile oak (Quercus petraea, Vitasse et al., 2011, Dantec et al., 2015) growing naturally on mountain slopes in the Pyrenees, in the Southwest of France (Fig. 1 and Table 1). Two populations (Ade and Bager) are at an elevation of ~400 m a.s.l. and the other two (Gèdre-Bas and Gabas) at ~1200 m a.s.l. The populations are ~40 km apart (Fig. 1) and differ genetically (Alberto et al., 2010). They consist of mature oaks ~100 years old with a trunk diameter at breast height of ~45 cm, forming monospecific stands (Table 1). These populations were previously part of a long-term phenological monitoring (Vitasse et al., 2009). The sampling date was chosen as close as possible to the fruiting peak at both elevations (reproduction is monitored weekly in those four populations, see Caignard et al., 2017).

In each population, we selected three adult trees that had branches accessible for collection with a slingshot. For each tree, we collected four types of samples: (1) 4 acorns from the canopy, taken from a branch brought down with the slingshot and carefully collected from a part of the branch that had not touched the ground; (2) 4 acorns lying on the ground surface (on litter or mosses indicating that they were freshly fallen acorns of the year) beneath the tree crown within a distance of 2 m from the trunk, (3) the biotic microenvironment subtending canopy acorns, defined as all tree tissues present in a cylinder of 4 cm diameter and 6 cm length around the acorn (including the acorn cupule, the cupules of other acorns, the twig to which the acorn was attached, the leaf petioles and the base of leaves); (4) the biotic microenvironment subtending ground acorns, defined as all substrates beneath the acorn within a cylinder of 4 cm diameter and 1 cm depth (including dead oak leaves, dead leaves of other plant species, acorn caps, twigs, pieces of bark, granules of soil, mosses, lichens or herbs). Each sample was collected aseptically, using new latex gloves and scissors cleaned with 96% ethanol to minimize contamination. Samples were stored in individual plastic vials in a cooler with ice until they could be stored at -80°C. We collected 8 acorns per tree (4 in the canopy and 4 on the ground) plus each of their biotic microenvironments, for a total of 12 mother trees located in 4 populations (4 populations x 3 trees x 4 sample types x 4 replicates = 192 samples). These 192 environmental samples were not surface-sterilized and are hereafter referred to as Set#1.
Within the same sampling campaign, we harvested 10 additional acorns that were surface-sterilized and dissected to characterize the endophytic fungal communities associated with acorn internal tissues. These acorns were harvested from 5 trees (2 in Gèdre-Bas, 2 in Gabas and 1 in Ade). For each tree, 2 acorns were harvested, one in the canopy and one on the ground. We removed microbial DNA from the acorn surface by combining a classical seed disinfection protocol and a DNA decontamination protocol (Methods S1). After rinsing and drying the acorns on sterilized filter papers, the fruit walls and the embryos were detached using a nutcracker and pliers and stored separately at -80°C. The scissors and nutcracker were sterilized by autoclaving before dissection. Between samples, they were soaked in 70% alcohol and flame sterilized to limit cross-contaminations. The autoclaved filter papers on which dissections were performed were also changed between samples. Dissections resulted in 20 samples, corresponding to 5 trees x 2 acorns x 2 tissue types (fruit wall and embryo). These 20 samples of seed internal tissues are hereafter referred to as Set#2.

To complement the results obtained, 16 additional *Quercus petraea* acorns (Set#3) were collected in autumn 2017 from the ground of the oak forest of Bellebat (44°43'36.4"N 0°13'22.5"W, Southwest of France) to visualize fungal colonization inside acorns using confocal microscopy. These acorns were stored at -20°C before surface-sterilization as described above, cut in half with secators, and fixed overnight at 4°C in a paraformaldehyde solution (4% w/v in PBS, pH 7.2).

## Metabarcoding of fungal communities

Samples of Set#1 and Set#2 were processed in December 2016 (see Methods S2 for protocol details). After DNA extraction with DNeasy Plant Mini Kits (Qiagen, USA), metabarcoding approaches targeting the ITS region of the nuclear ribosomal internal transcribed spacer, which is considered the universal barcode marker for fungi (Schoch et al., 2012), were used to analyze the fungal communities (epiphytic and endophytic, without distinction) of Set#1 and the endophytic communities of Set#2. We inferred Amplicon sequence variants (ASVs) using DADA2 (Callahan et al., 2016) because we showed that DADA2 outperforms clustering-based methods on a fungal mock community (Pauvert et al., 2019). Positive and negative controls were used to remove contaminants, by following the method proposed by Galan et al. (2016) and subsequently validated using mock communities (Galan et al., 2018).

## SNP genotyping and maternity exclusion analyses
All acorns collected on the ground were genotyped to confirm that they originated from the tree growing above them. Genotyping was performed using 39 polymorphic single nucleotide polymorphism (SNP) markers (Gerzabek *et al.*, 2017). DNA was diluted to a final concentration of 15 to 20 ng/µL and sequenced using the iPLEX Gold Genotyping kit (Agena, San Diego, CA, USA) at the Genome Transcriptome Facility of Bordeaux (PGTB, Bordeaux, France) according to the manufacturer's instructions (see Chancerel *et al.*, 2013 for more details). Two samples of aboveground tissues of each tree were genotyped and compared to estimate the typing error rate of false calls during genotyping. Loci with poor performance during the clustering procedure (call rates <60%) were excluded, resulting in a final set of 28 loci. Acorn genotypes were compared to the genotype of their putative mother tree. Considering the low error rate of these SNPs (Gerzabek *et al.*, 2017), we took a deliberately conservative approach and assumed that if a given tree and acorn did not share alleles for at least one locus, the mother-offspring relationship was not confirmed. These acorns were removed from all further analyses.

## Confocal microscopy

The acorns of *Set#3* were rinsed three times with PBS after fixation, immersed in 15 mL PBS containing 50 µg ml⁻¹ of wheat germ agglutinin (WGA)-AlexaFluor488 conjugate (Life Technologies, USA), incubated 2 hours at 37°C, and rinsed again three times with PBS. The samples were observed under a confocal microscope (Olympus Fluoview FV1000 with multiline laser FV5-LAMAR-2 and HeNe(G) laser FV10-LAHEG230-2). Pictures were taken with X, Y, Z coordinates at 405, 488, 594 nm and with 10X, 20X, 40X or 60X objectives. Images were merged (RGB) using Image J software (Schneider *et al.* 2012). Pictures were created using Z Project Stacks (Campisano *et al.*, 2014), cropped, and the light/contrast balance was improved (Glassner *et al.*, 2015). Images presented in this publication correspond to the average colonization level observed.

## Statistical analyses

### Test of H1: Seed fungal communities are shaped by maternal effects and environmental filtering

All statistical analyses were performed with R (R Core Team, 2019) using the samples of *Set#1*. To test hypothesis H1, we analyzed the effects of the mother tree (*Mother*), the local environment
represented by the tree population (*Population*), and the microenvironment determined by the seed position in the canopy *versus* on the ground (*Seed position*) on acorn fungal community richness and composition.

Fungal richness was defined as the total number of ASVs per acorn sample and was modelled using generalized linear models (GLMs) with a negative binomial distribution and a log-link function using the MASS package v7.3.51.4 (Venables & Ripley, 2002). The model had *Mother*, *Seed position* and their interaction nested within *Population*, and the natural logarithm of the total number of sequences per sample as an offset to take into account variation in fungal richness triggered by variation in sequencing depth (*Sequencing depth*). The offset term represents a variable with a known effect, that is commonly introduced in count-data models with a predefined coefficient (here, 1) to account for variations in sampling effort (here, sequencing depth) (Kotze *et al.*, 2012; Chiquet *et al.*, 2018), including in microbiome data models (Xia, Sun & Chen, 2018).

Variations in fungal community composition were analyzed by using permutational multivariate analyses of variance (PERMANOVAs) with 9999 permutations, performed with the *adonis* function of the vegan package v2.4.6 (Oksanen *et al.*, 2019). Compositional dissimilarities among acorn samples were estimated using quantitative and binary versions of the Jaccard index (Jaccard, 1901) and visualized with principal coordinate analyses (PCoA) using the phyloseq package v1.22.3 (McMurdie & Holmes, 2013). The models had *Mother*, *Seed position* and their interaction nested within *Population*, and the natural logarithm of *Sequencing depth* as the first effect. Finally, to gain insight into the mechanisms underlying the effect of *Mother* on acorn fungal community composition, we investigated whether the mother tree genotype influenced community composition in the canopy, and whether fungal community assembly in the canopy was more deterministic than in ground materials (Methods S3).

**Test of H2: Similarity of seed fungal communities to the canopy environment decreases after seed fall because of horizontal acquisition of fungi from the ground**

To test H2, we examined the significance of the interaction between *Mother* and *Seed position* in PERMANOVA models of acorn fungal community composition. Separate models for acorns in the canopy and acorns on the ground were fitted when the interaction was significant. In addition, we investigated whether changes in fungal community composition after acorn fall were due to
either the substitution of canopy-associated fungal species by ground-associated fungal species or gain of ground-associated fungal species without loss of canopy-associated fungal species, by partitioning Jaccard binary dissimilarities among acorns of the same mother tree using the betapart package v1.5.1 (Baselga & Orme, 2012). The proportion of fungal species of acorns on the ground also found in acorns in the canopy was calculated for each mother tree.

**Test of H3: The lifestyle of fungal species influences their response to variations in the seed environment**

Hierarchical Models of Species Communities (HMSC; Ovaskainen *et al.*, 2017) were used to quantify maternal effects and environmental filtering (H1 and H2), and to test H3. In contrast to previous analyses, these models account for the biotic microenvironment of acorns (i.e. the fungal community associated with the materials surrounding each acorn) and the putative lifestyle of fungal species. They also assume that the relative effects of maternal effects and environmental filtering might differ among fungal ASVs. The models were fitted on both ASV presence-absence data and on sequence count data conditional on presence (Methods S4).

The models assumed that variation in fungal community composition among acorns (i.e. the **Y** matrix in the HMSC framework) was accounted for by four ecological predictors (*Mother mycobiota*, *Microenv mycobiota*, *Seed position* and *Site elevation*) introduced as fixed effects in the **X** matrix. The **Y** matrix represented ASV sequence counts in all acorn samples, out of which we included only the ASVs that were present in five or more acorns. *Mother mycobiota* and *Microenv mycobiota* represented fungal communities in the canopy of mother trees and in the microenvironment of acorns, respectively. They were ASV-specific predictors and thus the **X** matrix was different for every ASV of the **Y** matrix. *Mother mycobiota* was calculated for all acorn samples as the average relative abundance of the focal ASV in the twigs and leaves of mother trees, and was included at the tree level to model vertical transmission of fungi from the mother tree to its acorns. *Microenv mycobiota* was calculated as the residuals of the regression of the relative abundance of the focal ASV in the microenvironment of each acorn over its relative abundance in the canopy of the mother tree and was included at the sample level to model horizontal acquisition of fungi from the materials surrounding each acorn. *Site elevation* and *Seed position* represented filtering of fungal communities by climate and microclimate, respectively. *Site elevation* was included at the site level to model selection exerted by site-level abiotic
factors, such as average air temperature, on acorn fungal communities. *Seed position* (canopy vs. ground) was included at the sample level to model selection exerted by microclimate, such as higher humidity on the ground, on acorn fungal communities. *Mother mycobiota* and *Microenv mycobiota* were included in interaction with *Seed position* to test the hypothesis that their contribution to fungal communities differ between acorns in the canopy and acorns on the ground (H2). We also introduced the log-transformed sequencing depth of each sample (*Sequencing depth*) as a fixed effect in the **X** matrix, to take into account methodological biases influencing ASV sequence counts. Random effects at each hierarchical level (oak population, mother tree and seed) were also introduced to model variations in ASV sequence counts that can neither be attributed to the four ecological predictors nor sequencing depth.

To test H3, we included available knowledge on fungal lifestyle in the **T** matrix. For each ASV, we included the trophic mode (saprotroph and/or plant pathogen) and the degree of specialization toward acorns of each ASV. The putative trophic mode of each ASV was determined using the FUNGuild database (Nguyen *et al.*, 2016) as a first approach. The information provided by FUNGuild was verified and completed by an extensive literature search. ASVs degree of specialization toward acorns was defined as the log-ratio of ASV relative abundance in acorns versus other sample types (i.e. branches, leaves, litter and upper soil), calculated using DESeq2 (Love *et al.*, 2014).

**Test of H4: Biotic interactions at the seed scale are involved in seed protection against fungal pathogens.**

To test H4, we interpreted the significant residual correlations among fungal ASV sequence
counts at the acorn level in HMSC models as hypothetical biotic interactions among fungal
strains (see Ovaskainen *et al.*, 2017). Only associations with at least 95% posterior probability
were considered significant. We focused on the hypotheses of interactions among the fungal
ASVs assigned at the species level and investigated whether they were known in the literature
and if they corresponded to antagonistic interactions that might regulate oak pathogens.

Results

A thousand fungal ASVs were detected in and on acorns
The bioinformatic pipeline retained 21.8% of the raw MiSeq reads that corresponded to quality reads that could be assembled and assigned to a fungal phylum (Methods S2). Genotyping analysis revealed that 9 of the 87 acorns collected did not originate from the putative mother tree. These samples were removed. The final ASV table was composed of 184 samples (165 of *Set#1* and 19 of *Set#2*) and 4257 fungal ASVs, representing 1891194 sequences. The ASV table presented large variations in the total number of quality reads per sample (Table S1), with the lowest median numbers obtained for acorns in the canopy and internal tissues of surface-sterilized acorns. We however kept all samples in the statistical analyses because they contained quality fungal reads that provide unique information on acorn fungal communities. In total, 954 fungal ASVs were detected in acorn samples.

**All acorn tissues were colonized by fungi, with ascomycetes predominating**

Confocal microscopy analyses of the *Set#3* samples revealed that all internal tissues from acorns collected on the ground were colonized by fungi. The fruit wall and the seed coat were the most colonized tissues, with a dense colonization under the endocarp. We also detected fungi in the embryo (Fig. 2).

Metabarcoding analyses revealed that ascomycetes dominated the fungal community. The *Set#1* samples were dominated by ascomycetes, with 91.1% and 89.4% of the whole fungal community (including both epiphytes and endophytes) of ground and canopy acorns, respectively. Dothideomycetes, Leotiomycetes and Sordariomycetes were the three main classes of ascomycetes present in the whole fungal community (Fig. S1). Several putative plant pathogens and mycoparasites were among the most abundant species (Table 2). The *Set#2* were also dominated by ascomycetes, with 79.1% and 83% of the endophytic fungal community of the seed coat and embryo, respectively (Fig. S1). The endophytic community of acorns differed from the fungal community as a whole because of the higher proportion of Microbotryomycetes (Fig. S1 and Table S2), a class of basidiomycetes containing endophytes (also known as anther smut fungi) that manipulate plant reproduction during their pathogenic stage (Antonovics *et al.*, 2018).

**Seed fungal communities were shaped by maternal effects and environmental filtering, with maternal effects predominating (H1)**
In acorns that were not surface-sterilized (Set#1), fungal community richness (Table 3) and composition (Table 4 and Fig. 3) differed significantly among oak populations and mother trees within the same population, confirming the hypothesis that both maternal effects and environmental filtering shape acorn fungal communities (H1). When the qualitative Jaccard index was used to assess compositional dissimilarity among samples, the mother tree explained a larger part of variance in community composition than the population (11.6% vs 8.7%; Table 4), suggesting that maternal effects better explained fungal ASV presence-absence than environmental filtering. These results were consistent with those of the HMSC model of ASV presence-absence (Table 5). According to this model, elevation was a minor direct driver of fungal community composition (2% of the explained variance). In contrast, the average relative abundance of a fungal ASV in the tissues of a mother tree (*Mother mycobiota*) was the second most important predictor of the occurrence of this ASV in an acorn from this tree, in interaction with the acorn position (39% of the explained variance). *Mother mycobiota* was the unique predictor of occurrence for several ASVs belonging to the orders Helotiales, Venturiales and Xylariales (Fig. 4).

Genetic distance among mother trees did not account for variations in fungal community composition in acorns, twigs or leaves (Table S3). Community assembly was however significantly less stochastic in twigs and leaves than in ground materials (Table S4), suggesting that selective forces are exerted on aboveground fungal communities by mother trees. Community stochasticity level, measured with the Normalized Stochasticity Ratio (NST; Ning *et al.*, 2019), increased from mother tree aboveground tissues (53% on average), to acorns in the canopy (70.05%) and then to acorns on the ground (80.45%) and ground materials (80.95%) (Table S4).

## Similarity of seed fungal communities with the canopy environment decreased after seed fall because of horizontal acquisition of fungi from the ground (H2)

In acorns that were not surface-sterilized (Set#1), the fungal community differed significantly in richness (Table 3) and composition (Table 4) between acorns in the canopy and on the ground. Fungal richness increased and composition shifted toward that of ground materials after acorn fall (Fig. 5 and Table S5). Acorns on the ground shared a higher proportion of fungal ASVs with ground materials than acorns in the canopy (65 vs 56% on average, Table S5), confirming the
horizontal acquisition of fungi from the ground. Turnover accounted on average for 75% of Jaccard beta-diversity (Table S6), indicating that the horizontally acquired fungi replaced those originating from the canopy rather than adding to the community, in accordance with our hypothesis (H2). The HMSC presence-absence model confirmed the large influence of horizontal acquisition on acorn fungal communities (Table 5). The relative abundance of a fungal ASV in the microenvironment of an acorn (*Microenv mycobiota*), in interaction with the acorn position, was generally the best predictor of ASV occurrence (47% of the explained variance), especially for the Capnodiales, Dothideales and Taphrinales orders (Fig. 4A).

Despite the turnover in fungal community composition after acorn fall, some effects of the mother on acorn fungal communities persisted after acorn fall. The statistical interaction between the mother tree and the seed position was not significant for fungal richness (Table 3) or community composition measured with the qualitative Jaccard index (Table 4), indicating that mother tree effects in ASV richness and occurrence were similar for ground acorns and canopy acorns. The analysis of fungal ASVs shared between ground and canopy acorns (Table S5) confirmed that some maternal species were retained after acorn fall. Overall, acorns on the ground shared 10 to 40% of their fungal community with acorns in the canopy of the same mother tree, and 21 to 50% with mother tree leaves and twigs. On average, 38% of fungal ASVs of acorns on the ground were present in both acorns in the canopy and mother tree tissues. Fungal species most often retained after acorn fall were *Taphrina* sp., *Cladosporium delicatulum*, a mycoparasite of *Taphrina* sp. (Baharvandi & Zafari, 2015), and the ubiquitous *Epicoccum nigrum*. In contrast to the mother tree effects in ASV richness and occurrence, the mother tree effects in ASV relative abundance disappeared after acorn fall. The interaction between the mother tree and the seed position was significant for community composition measured with the quantitative Jaccard index (Table S7) and separate analyses on canopy and ground acorns revealed that mother tree effects were only significant for canopy acorns (Table S7).

**The response of seed fungal species to elevation and seed fall depended on their lifestyle (H3)**

The HMSC models showed that fungal lifestyle influenced the response of fungal ASVs to environmental variation, in accordance with our hypothesis (H3). High elevation selected for saprotroph species and seed specialists, increasing their proportion in acorn fungal communities
(Table S8). Acorn fall favored pathogen species and saprotrophs. Their proportion increased after acorn fall whereas their abundance (conditional on presence) was not altered (Table S8). These findings suggest that vertically-transmitted pathogens did not increase in abundance after acorn fall and that acorns were colonized by both pathogen and saprotroph species from the ground.

## Fungal pathogens were associated with their mycoparasites in the seed (H4)

The confocal microscopy analyses of Set#3 samples had shown that fungal colonization was very dense on internal surfaces of the fruit wall of fallen acorns (Fig. 2e), indicating that fungal colonizers might enter into contact and compete for space and eventually resources. We expected such competitive interactions to generate negative associations among ASV sequence counts. In contrast to this expectation, residual co-occurrence patterns of the HMSC models at the acorn level revealed only positive associations between fungal ASVs. Among the associated fungal ASVs, 14 could be assigned at the species level (Fig. 6). Six of them are described as plant pathogens and two of them, *Mycosphaerella tassiana* and *Taphrina carpini*, have already been found in association with oak (Table S9). Four species are described as mycoparasites, including *Cladosporium delicatulum*. The positive association between *Taphrina carpini* and *Cladosporium delicatulum* (Fig. 6), which were both found in the embryo and the fruit wall (Table S2), might therefore represent a vertically-transmitted host-parasite interaction (i.e., between a fungal pathogen and its mycoparasite). These findings suggest that biotic interactions structure fungal communities of acorns and might play a role in oak disease regulation, in accordance with our hypothesis (H4).

## Discussion

The seed mycobiome of trees has received little attention despite its likely role in forest regeneration and resilience to biotic and abiotic stresses (Vivas et al., 2015; 2020). To fill this gap, we analyzed fungal communities of seeds in four unmanaged populations of sessile oak (*Quercus petraea*) and showed that the fungal mycobiome varied among mother trees and environments.

## Seed mycobiome composition and its influence on forest health

Our confocal microscopy analyses revealed a dense fungal colonization on internal surfaces of fruit walls of fallen acorns and confirmed the presence of fungi within embryos of *Quercus*
*petraea*. The endophytic fungal communities of oak seeds contained ubiquitous fungal species, such as *Epicoccum nigrum* (Andrews & Harris, 2000), and endophytic yeasts previously described in the reproductive organs of other plant species, such as *Curvibasidium cygneicollum* (Sampaio *et al.*, 2004; Mašínová *et al.*, 2017; Antonovics *et al.*, 2018). Seeds also contained fungal pathogens and antagonists of the pathogens. For instance, we detected two foliar pathogens of Fagaceae tree species, *Mycosphaerella tassiana* and *Taphrina carpini* (Bacigálová, 1991; Schubert *et al.*, 2007), and a mycoparasite of *Taphrina* species, *Cladosporium delicatulum* (Baharvandi & Zafari 2015; Chaibub *et al.*, 2016). Our network analyses revealed a positive association between *Taphrina carpini* and *Cladosporium delicatulum*, in line with the mycoparasitic interaction described in the literature between these two species. Both the fungal pathogen and its mycoparasite were detected as seed endophytes, suggesting that mother trees not only transmit fungal pathogens to their progeny but also antagonists of these pathogens.

These findings could have important implications for tree breeding and seed production. It would be interesting to identify which maternal genes and environments favor the vertical transmission of mycoparasites, to produce seeds that contain these natural biocontrol agents and thereby foster the resistance and resilience of planted forests to fungal diseases. Such selective breeding might have more sustainable effects than the direct application of mycoparasites (e.g., in cacao orchards; Krauss *et al.*, 2010; 2013) and could be useful for larger, wood-producing plantations.

**Maternal effects in seed mycobiome composition and forest adaptation to environmental change**

A major result of our study is that seed fungal communities differed significantly among trees within the same oak population. Our study uniquely demonstrates, using two statistical approaches, that the mother tree influences seed mycobiome composition, which is a component of the offspring community phenotype (Whitham *et al.*, 2006). Such influence can be considered a maternal effect (*sensu* Wolf *et al.*, 2009). Both approaches revealed a major effect of the mother on the identity of the fungal species associated with seeds. The PERMANOVA model accounted for 36.7% of the variance in fungal ASV presence-absence, with the mother tree accounting for the largest part of the explained variance (31.6%). HMSC accounted for 80% of the variance in ASV presence-absence, with the average fungal community composition in the crown of the mother tree directly accounting for 39% of the explained variance. In our study, the mother tree
effect was partially due to selection of fungal species in aboveground mother tree tissues. Twigs and leaves harbored the most deterministic fungal communities, in comparison to seeds and ground materials. Community assembly was less deterministic in seeds than in twigs and leaves, but it was not purely stochastic, suggesting that seed fungal communities assembled from a set of fungal species selected by the mother that were then shaped by stochastic colonization events. These events might have increased divergence in seed fungal community composition among mother trees through priority effects (Fukami, 2015). It would be interesting to tease apart the mother tree genetic effect, by investigating whether aboveground fungal communities were genetically determined (Cordier et al., 2012a; Hunter et al., 2015; Sapkota et al., 2015). We did not find support for a genetic effect in this study, but our analyses were only based on a few neutral genetic markers. Future studies should consider additional markers. Another interesting next step would be to investigate whether the maternal effects continue into the seedling stage, as found by Vivas et al., (2017) on *Eucalyptus* seedlings.

Our analyses also indicated a significant, but weak, effect of environmental filtering on seed fungal communities in *Quercus petraea*. We found that the richness and composition of seed fungal communities varied significantly among the four oak populations, which were located in 2 watersheds, with within-watershed locations separated by up to 800 m in elevation. However, the HMSCs revealed that the direct influence of elevation on the seed mycobiome was small. Elevation accounted for only 2% of the explained variance in patterns of fungal ASV presence-absence, and 6% of the variance in ASV abundance. In contrast, the fungal community composition in the materials directly surrounding the seed (on the mother: leaves and twigs, on the soil: litter and upper soil) accounted for 47% and 23% of the explained variance, respectively. These findings suggest that the biotic microenvironments surrounding seeds, including mother tree tissues, are stronger drivers of seed mycobiome assembly than environmental filtering by abiotic conditions. The analysis of a larger number of sites along elevation gradients would however be needed to confirm the weak direct effect of elevation on the seed mycobiome.

Numerous studies have shown that the fungal communities thriving in the aboveground parts (leaves and twigs) of forest trees vary in composition along elevation gradients, including the gradient of the present study (Cordier et al., 2012b; Coince et al., 2014; Vacher et al., 2016). Our results suggest that some of these locally-adapted fungal species can be transmitted by mother trees to seeds, but that not all mother trees transmit all fungal species. Each mother tree appears
to exert a strong additional selection on the seed mycobiome, that could determine the mother tree's own fitness by influencing seedling growth and survival. At the tree population level, these variations in mycobiome transmission could increase the phenotypic variability in the offspring and favor population adaptation to environmental change (Vannier et al., 2015; Vujanovic et al., 2019). In turn, the selection of mother trees transmitting the most beneficial mycobiome could shape the local fungal community, linking the evolution of tree populations with the dynamics of forest biodiversity. Studies of plant species with shorter generation times than oaks will however be needed to experimentally confirm this eco-evolutionary scenario (Post & Palkovacs, 2009).

## Seed mycobiome transition from canopy to soil and forest regeneration

Finally, our analyses showed that seed fall corresponds to a major transition in the seed mycobiome of sessile oak. Fungal community richness significantly increased and composition shifted toward that of ground materials after seed fall, confirming that seeds on the ground are rapidly colonized by the fungal species present in the surrounding microenvironment (Crist, 2009, Truyens et al., 2015; Klaedtke et al., 2016; Qin et al., 2016). For instance, the species *Gnomoniopsis paraclavulata*, that was previously found in association with oak litter (U'Ren et al., 2016), drastically increased in abundance after seed fall. Our analyses also suggested that seed fall triggers a replacement of canopy-inherited species by ground-derived species, rather than an addition of species associated to ground materials. This replacement was however only partial, as 35% of the fungal ASV on fallen seeds were on average also found in the mother tree aboveground tissues, explaining why some maternal effects in seed mycobiome composition persisted after seed fall.

To better understand the processes driving the success of forest regeneration, future research will have to analyze the network of microbial interactions that builds up at the aboveground-belowground interface after seed fall. Such studies should experimentally test if maternally-transmitted species shape these networks through priority effects (Fukami, 2015) and link network dynamics with seed and seedling health and survival. The influence of vertically-transmitted fungal species on seed dispersal could also be investigated, as previous research on oak trees showed a significant relationship between a fungus-like pathogen associated with seeds and the abundance of several oak-dependent bird species, including seed dispersers (Monahan & Koenig, 2006).
**Conclusions**

Our study revealed that diversified fungal communities colonize seeds of sessile oak, including their internal tissues. For what we believe is the first recorded observance in *Quercus petraea*, we found fungi in the embryo. The seed fungal communities were shaped by maternal effects, environmental filtering and biotic interactions. Maternal effects were stronger drivers of seed mycobiome composition than environmental filtering by abiotic conditions. Biotic interactions included several host-parasite interactions between fungal pathogens and mycoparasites, one of which was likely to be vertically-transmitted. Such vertical transmission of biotic interactions from the mother tree to its progeny through seeds could have important implications for forest health, dynamics and evolution. Future experiments should analyze (1) how the maternal genes and the environment interact to determine the identity and function of vertically-transmitted fungi, (2) if these vertically-transmitted fungal species influence, through priority effects, the dynamics of microbial networks developing during seed germination, and (3) the relationship between network dynamics and seedling health. This research will allow us to integrate seed microbial ecology into programs of tree breeding and seed production, as well as predictive models of forest dynamics and evolution.

**Acknowledgements**

We thank Albin Lobo (University of Copenhagen, Denmark) and Jean-Marc Louvet (Biogeco, INRA-University of Bordeaux, France) for their assistance during sampling campaigns. We are also very grateful to Martine Martin-Clotté, Céline Lalanne, Patrick Leger, Adline Delcamp (Biogeco, INRA-University of Bordeaux, France) and Jessica Vallance (SAVE, INRA-Bordeaux Sciences Agro, France) for providing materials and reagents for processing the samples and advice on molecular biology protocols. We thank Samuel Pennarun and all members of the Genotoul sequencing facility (Get-PlaGe, Toulouse, France) for performing MiSeq sequencing, and the Genotoul bioinformatics facility (Bioinfo Genotoul, Toulouse, France) for providing computing and storage resources. We also thank Erwan Guichoux and all members of the Genome Transcriptome facility of Bordeaux (PGTB, Cestas, France) for performing the SNP genotyping. Thanks to Franck Deniel and Gaëtan Burgaud (ESIAB, Université de Bretagne Occidentale, France) for giving us marine fungal strains used as positive controls. Very constructive comments from three anonymous reviewers helped improve the manuscript. We
would like to acknowledge George Washington University for its contribution to the funding of
molecular biology analyses. We also thank the INRA Meta-Omics of Microbial Ecosystems
(MEM) metaprogramme (Learn-biocontrol project), the INRA Ecosystem Services (EcoServ)
metaprogramme (IBISC project), the LABEX COTE (MICROMIC project; ANR-10-LABX-45),
the LABEX CEBA (ANR-10-LABX-25-01) and the Aquitaine Region (Athene project, n°2016-
1R20301-00007218) for additional financial support throughout the project. TF's PhD grant was
funded by the University of Bordeaux. OO was supported by the Academy of Finland (grant
309581), the Erkko foundation, and the Research Council of Norway (SFF-III grant 223257).

Author contributions

C.V., A.E.Z. and S.D. designed the research; T.F., T.C. and C.V. conducted fieldwork; T.F., C.P. and S.C. performed experiments; T.F., C.P., O.O. and C.V. analyzed and interpreted the data; M.B. and A.H. contributed with protocols and analysis tools; T.F. and C.V. wrote the manuscript, with input from all authors.

Data availability

All raw sequences obtained from the sequencing of seeds of sessile oak and their biotic
microenvironment are available from the National Center for Biotechnology Information
Sequence Read Archive (http://www.ncbi.nlm.nih.gov/sra) under the accession number
PRJNA551388. The code, ASV tables and metadata (including ASV richness, sequencing depth,
site and sample descriptions, and genotypic data) are available as an archive at
https://doi.org/10.15454/0CNFWS.

References

**Ahumada-Rudolph R, Cajas-Madriaga D, Rudolph A, Reinoso R, Torres C, Silva M, Becerra J. 2014.**
Variation of sterols and fatty acids as an adaptive response to changes in temperature, salinity and pH of a marine
fungus *Epicoccum nigrum* isolated from the Patagonian Fjords. *Revista de biología marina y oceanografía* **49**: 293-
305.

Alberto F, Niort J, Derory J, Lepais O, Vitalis R, Galop D, Kremer, A. 2010. Population differentiation of sessile oak at the altitudinal front of migration in the French Pyrenees. *Molecular Ecology* **19**: 2626-2639.

Amanelah Baharvandi H, Zafari D. 2015. Identification of *Cladosporium delicatulum* as a mycoparasite of *Taphrina pruni*. *Archives of Phytopathology and Plant Protection* **48**: 688–697.

Andrews JH, Harris RF. 2000. The ecology and biogeography of microorganisms on plant surfaces. *Annual Review of Phytopathology* **38**: 145–180.
Antonovics J, Abbate JL, Bruns EL, Fields PD, Forrester NJ, Gilbert KJ, Hood ME, Park T, Taylor DR. 2018. Effect of the anther- smut fungus *Microbotryum* on the juvenile growth of its host *Silene latifolia*. *American journal of botany* **105**: 1088-1095.

Bacigálová K. 1991. New localities of *Taphrina carpini* (Rostr.) Johans, on *Carpinus betulus* in Slovakia. *Czech Mycology* **46**: 296-302.

Bagchi R, Gallery RE, Gripenberg S, Gurr SJ, Narayan L, Addis CE, Freckleton RP, Lewis OT. 2014. Pathogens and insect herbivores drive rainforest plant diversity and composition. *Nature* **506**: 85-88.

Baharvandi H, Zafari D. 2015. Identification of *Cladosporium delicatulum* as a mycoparasite of *Taphrina pruni*. *Archives of Phytopathology and Plant Protection* **48**: 688-697.

Baselga A, Orme CDL. 2012. Betapart: An R package for the study of beta diversity. *Methods in Ecology and Evolution* **3**: 808-812.

Bever JD, Mangan SA, Alexander HM. 2015. Maintenance of plant species diversity by pathogens. *Annual review of ecology, evolution, and systematics* **46**: 305-325.

Caignard T, Kremer A, Firmat C, Nicolas M, Venner S, Delzon S. 2017. Increasing spring temperatures favor oak seed production in temperate areas. *Scientific Reports* **7**: 1-8.

Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. *Nature Methods* **13**: 581-583.

Campisano A, Ometto L, Compant S, Pancher M, Antonielli L, Yousaf S, Varotto C, Anfora G, Pertot I, Sessitsch A, et al. 2014. Interkingdom transfer of the acne-causing agent, propionibacterium acnes, from human to grapevine. *Molecular Biology and Evolution* **31**: 1059-1065.

Castledine M, Sierocinski P, Padfield D, Buckling A. 2020. Community coalescence: an eco-evolutionary perspective. *Philosophical Transactions of the Royal Society B* **375**: 20190252.

Chaibub AA, de Carvalho JCB, de Sousa Silva C, Collevatti RG, Gonçalves FJ, de Carvalho Barros Côrtes MV, de Filippi MCC, de Faria FP, Lopes DCB, de Araújo LG. 2016. Defence responses in rice plants in prior and simultaneous applications of *Cladosporium* sp. during leaf blast suppression. *Environmental Science and Pollution Research* **23**: 21554-21564.

Chancerel E, Lamy JB, Lesur I, Noirot C, Kropp C, Ehrenmann F, Boury C, Provost G Le, Label P, Lalanne C, et al. 2013. High-density linkage mapping in a pine tree reveals a genomic region associated with inbreeding depression and provides clues to the extent and distribution of meiotic recombination. *BMC Biology* **11**: 50.

Chase JM, Myers JA. 2011. Disentangling the importance of ecological niches from stochastic processes across scales. *Philosophical Transactions of the Royal Society B* **366**: 2351-2363.

Cheewangkoon R, Groenewald JZ, Summerell BA, Hyde KD, To-Anun C, Crous PW. 2009. Myrtaceae, a cache of fungal biodiversity. *Persoonia: Molecular Phylogeny and Evolution of Fungi* **23**: 55-85.

Chiquet J, Mariadassou M, Robin S. 2018. Variational inference for sparse network reconstruction from count data. *arXiv preprint:1806.03120*.

Christian N, Herre EA, Mejia LC, Clay K. 2017. Exposure to the leaf litter microbiome of healthy adults protects seedlings from pathogen damage. *Proceedings of the Royal Society B: Biological Sciences* **284**: 20170641.
Coince A, Cordier T, Lengellé J, Defossez E, Vacher C, Robin C, Buée M, Marçais B. 2014. Leaf and root-associated fungal assemblages do not follow similar elevational diversity patterns. *PLoS ONE* 9: e100668.

Cordier T, Robin C, Capdevielle X, Desprez-Loustau ML, Vacher C. 2012a. Spatial variability of phyllosphere fungal assemblages: genetic distance predominates over geographic distance in a European beech stand (*Fagus sylvatica*). *Fungal Ecology* 5: 509-520.

Cordier T, Robin C, Capdevielle X, Fabreguettes O, Desprez-Loustau ML, Vacher C. 2012b. The composition of phyllosphere fungal assemblages of European beech (*Fagus sylvatica*) varies significantly along an elevation gradient. *New Phytologist* 196: 510-519.

Crous PW, Schumacher RK, Wingfield MJ, Akulov A, Denman S, Roux J, Braun U, Burgess TI, Carnegie AJ, Váczy KZ, et al. 2018. New and interesting fungi. *Fungal Systematics and Evolution* 1: 169.

Dantec CF, Ducasse H, Capdevielle X, Fabreguettes O, Delzon S, Desprez-Loustau ML. 2015. Escape of spring frost and disease through phenological variations in oak populations along elevation gradients. *Journal of Ecology* 103: 1044-1056.

de la Bastide PY, LeBlanc J, Kong L, Finston T, May EM, Reich R, Hintz WE, von Aderkas P. 2019. Fungal colonizers and seed loss in lodgepole pine orchards of British Columbia. *Botany* 97: 23-33.

Duarte S, Bärlocher F, Trabulo J, Cássio F. 2015. Stream-dwelling fungal decomposer communities along a gradient of eutrophication unraveled by 454 pyrosequencing. *Fungal Diversity* 70: 127-148.

Escobar Rodriguez C, Mitter B, Barret M, Sessitsch A, Compant S. 2018. Commentary: seed bacterial inhabitants and their routes of colonization. *Plant and Soil* 422: 129-134.

Frank A, Saldierna Guzmán J, Shay J. 2017. Transmission of bacterial endophytes. *Microorganisms* 5: 70.

Fukami T. 2015. Historical contingency in community assembly: integrating niches, species pools, and priority effects. *Annual Review of Ecology, Evolution, and Systematics* 46: 1-23.

Galan M, Razzauti M, Bard E, Bernard M, Brouat C, Charbonnel N, Dehne-Garcia A, Loiseau A, Tatard C, Tamisier L, et al. 2016. 16S rRNA amplicon sequencing for epidemiological surveys of bacteria in wildlife. *mSystems* 1: e00032-16.

Galan M, Pons JB, Tournayre O, Pierre E, Leuchtmann M, Pontier D, Charbonnel N. 2018. Metabarcoding for the parallel identification of several hundred predators and their prey: Application to bat species diet analysis. *Molecular ecology resources* 18: 474-489.

Ganley RJ, Newcombe G. 2006. Fungal endophytes in seeds and needles of *Pinus monticola*. *Mycological research* 110: 318-327.

Gerzabek G, Oddou-Muratorio S, Hampe A. 2017. Temporal change and determinants of maternal reproductive success in an expanding oak forest stand. *Journal of Ecology* 105: 39-48.

Glassner H, Zchori-Fein E, Compant S, Sessitsch A, Katzir N, Portnoy V, Yaron S. 2015. Characterization of endophytic bacteria from cucurbit fruits with potential benefits to agriculture in melons (*Cucumis melo* L.). *FEMS Microbiology Ecology* 91: 1-13.

Hunter PJ, Pink DA, Bending GD. 2015. Cultivar-level genotype differences influence diversity and composition of lettuce (Lactuca sp.) phyllosphere fungal communities. *Fungal Ecology* 17: 183-186.

Inácio J, Rodrigues MG, Sobral P, Fonseca Á. 2004. Characterisation and classification of phylloplane yeasts
from Portugal related to the genus *Taphrina* and description of five novel *Lalaria* species. *FEMS Yeast Research* **4**: 541-555.

Jaccard P. 1901. Étude comparative de la distribution florale dans une portion des Alpes et du Jura. *Bulletin de la Société Vaudoise des Sciences Naturelles* **37**: 547-579.

Jakuschkin B, Fievet V, Schwaller L, Fort T, Robin C, Vacher C. 2016. Deciphering the pathobiome: intra- and interkingdom interactions involving the pathogen *Erysiphe alphitoides*. *Microbial Ecology* **72**: 870-880.

Klaedtke S, Jacques MA, Raggi L, Préveaux A, Bonneau S, Negri V, Chable V, Barret M. 2016. Terroir is a key driver of seed-associated microbial assemblages. *Environmental microbiology* **18**: 1792-1804.

Krauss U, Hidalgo E, Bateman R, Adonijah V, Arroyo C, García J, Crozier J, Brown NA, Martijn ten Hoopen G, Holmes KA. 2010. Improving the formulation and timing of application of endophytic biocontrol and chemical agents against frosty pod rot (*Moniliophthora roreri*) in cocoa (*Theobroma cacao*). *Biological control* **54**: 230-240.

Krauss U, Ten Hoopen M, Rees R, Stirrup T, Argyle T, George A, Arroyo C, Corrales E, Casanoves F. (2013). Mycoparasitism by *Clonostachys byssicola* and Clonostachys rosea on Trichoderma spp. from cocoa (*Theobroma cacao*) and implication for the design of mixed biocontrol agents. *Biological Control* **67**: 317-327.

Kotze DJ, O'Hara RB, Lehvävirta S. 2012. Dealing with varying detection probability, unequal sample sizes and clumped distributions in count data. *PLoS One* 7:e40923.

Leroy C, Maes AQ, Louisanna E, Séjalon-Delmas N. 2019. How significant are endophytic fungi in bromeliad seeds and seedlings? Effects on germination, survival and performance of two epiphytic plant species. *Fungal Ecology* **39**: 296-306.

Links MG, Demeke T, Gräfenhan T, Hill JE, Hemmingsen SM, Dumonceaux TJ. 2014. Simultaneous profiling of seed-associated bacteria and fungi reveals antagonistic interactions between microorganisms within a shared epiphytic microbiome on Triticum and Brassica seeds. *New Phytologist* **202**: 542-553.

Love MI, Huber W, Anders S, Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-Seq data with DESeq2. *Genome biology* **15**: 521-550.

Mangan SA, Schnitzer SA, Herre EA, Mack KM, Valencia MC, Sanchez EI, Bever JD. 2010. Negative plant-soil feedback predicts tree-species relative abundance in a tropical forest. *Nature* **466**: 752-755.

Mašínová T, Bahnmann BD, Větrovský T, Tomšovský M, Merunková K, Baldrian P. 2017. Drivers of yeast community composition in the litter and soil of a temperate forest. *FEMS microbiology ecology* **93**: 1-10.

Maude RB. 1996. *Seedborne Diseases and Their Control: Principles and Practice*.

McMurdie P & Holmes S. 2013. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. *PLoS ONE* 8(4): e61217.

Moles AT, Westoby M. 2004. What do seedlings die from and what are the implications for evolution of seed size? *Oikos* **106**: 193-199.

Monahan WB, Koenig WD. 2006. Estimating the potential effects of sudden oak death on oak-dependent birds. *Biological Conservation* **127**: 146-157.

Mouquet, N., & Loreau, M. 2003. Community patterns in source-sink metacommunities. *The American Naturalist*, **162**(5), 544-557.
Nalim FA, Samuels GJ, Wijesundera RL, Geiser DM. 2011. New species from the *Fusarium solani* species complex derived from perithecia and soil in the old world tropics. *Mycologia* **103**: 1302–1330.

Nelson EB. 2004. Microbial dynamics and interactions in the spermosphere. *Annual Review of Phytopathology* **42**: 271–309.

Nelson EB, Simoneau P, Barret M, Mitter B, Compant S. 2018. The soil, the seed, the microbes and the plant. *Plant Soil* **422**: 1–5.

Nemergut DR, Schmidt SK, Fukami T, O'Neill SP, Bilinski TM, Stanish LF, Knelman JE, Darcy JL, Lynch RC, Wickey P, et al. 2013. Patterns and processes of microbial community assembly. *Microbiology and Molecular Biology Reviews* **77**: 342–356.

Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, Schilling JS, Kennedy PG. 2016. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. *Fungal Ecology* **20**: 241–248.

Ning D, Deng Y, Tiedje JM, Zhou J. 2019. A general framework for quantitatively assessing ecological stochasticity. *Proceedings of the National Academy of Sciences* **116**: 16892-16898.

Ofek M, Hadar Y, Minz D. 2011. Colonization of cucumber seeds by bacteria during germination. *Environmental Microbiology* **13**: 2794–2807.

Oksanen J, Blanchet G, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin P, O'Hara R. B, L. Simpson G, Solymos P, Stevens H, Szoecs E and Wagner H. 2019. vegan: Community Ecology Package. R package version 2.5-6. https://CRAN.R-project.org/package=vegan.

Ovaskainen O, Tikhonov G, Norberg A, Guillaume Blanchet F, Duan L, Dunson D, Roslin T, Abrego N. 2017. How to make more out of community data? A conceptual framework and its implementation as models and software. *Ecology Letters* **20**: 561–576.

Pauvert C, Buée M, Laval V, Edel-Hermann V, Fauchery L, Gautier A, Lesur I, Vallance J, Vacher C. 2019. Bioinformatics matters: the accuracy of plant and soil fungal community data is highly dependent on the metabarcoding pipeline. *Fungal ecology* **41**: 23-33.

Petit RJ, Hampe A. 2006. Some evolutionary consequences of being a tree. *Annual Reviews in Ecology Evolution and Systematics* **37**: 187-214.

Post DM, Palkovacs EP. 2009. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. *Philosophical Transactions of the Royal Society B: Biological Sciences* **364**: 1629-1640.

Prado A, Marolleau B, Vaissière BE, Barret M, Torres-Cortes G. 2020. Insect pollination: an ecological process involved in the assembly of the seed microbiota. *Scientific reports* **10**: 1-11.

Prochazkova Z, Sikorova A, Peskova V. 2005. Preliminary observations on the occurrence of *Ciboria batschiana* (Zopf) Buchwald in the Czech Republic. *Working Papers of the Finnish Forest Research Institute* **11**: 13–18.

Qi F, Jing T, Zhan Y. 2012. Characterization of endophytic fungi from *Acer ginnala* Maxim. in an artificial plantation: media effect and tissue-dependent variation. *PLoS One* **7**: e46785.

Qin Y, Pan X, Yuan Z. 2016. Seed endophytic microbiota in a coastal plant and phytobeneficial properties of the fungus *Cladosporium cladosporioides*. *Fungal Ecology* **24**: 53–60.
Quaedvlieg W, Verkley GJM, Shin HD, Barreto RW, Alfenas AC, Swart WJ, Groenewald JZ, Crous PW.
2013. Sizing up *Septoria*. *Studies in Mycology* **75**: 307–390.

R Core Team. 2020. The R Stats Package. R package (version: 3.5. 3).

Räsänen K, Kruuk LEB. 2007. Maternal effects and evolution at ecological time- scales. *Functional Ecology* 21: 408-421.

Reyes-Estebanez M. 2011. Antimicrobial and nematicidal screening of anamorphic fungi isolated from plant debris of tropical areas in Mexico. *African Journal of Microbiology Research* 5: 1083–1089.

Rezki S, Campion C, Simoneau P, Jacques MA, Shade A, Barret M. 2018. Assembly of seed-associated microbial communities within and across successive plant generations. *Plant and Soil* **422**: 67–79.

Rillig MC, Antonovics J, Caruso T, Lehmann A, Powell JR, Veresoglou SD, Verbruggen E. 2015. Interchange of entire communities: microbial community coalescence. *Trends in Ecology and Evolution* **30**: 470–476.

Roach DA, Wulff RD. 1987. Maternal effects in plants. *Annual review of ecology and systematics* **18**: 209-235.

Rodríguez CE, Antonielli L, Mitter B, Trognitz F, Sessitsch A. 2020. Heritability and functional importance of the *Setaria viridis* bacterial seed microbiome. *Phytobiomes Journal*, 4(1), 40-52.

Sampaio JP, Golubev WI, Fell JW, Gadanhão M, Golubev NW. 2004. *Curvibasidium cygneicollum* gen. nov., sp. nov. and *Curvibasidium pallidicorallinum* sp. nov., novel taxa in the microbotryomycetidae (Urediniomycetes), and their relationship with *Rhodotorula fujisanensis* and *Rhodotorula nothofagi*. *International Journal of Systematic and Evolutionary Microbiology* **54**: 1401–1407.

Sapkota R, Knorr K, Jørgensen LN, O'Hanlon KA, Nicolaisen, M. 2015. Host genotype is an important determinant of the cereal phyllosphere mycobiome. *New Phytologist* **207**: 1134-1144.

Sarmiento C, Zalamea PC, Dalling JW, Davis AS, Stump SM, U'Ren JM, Arnold AE. 2017. Soilborne fungi have host affinity and host-specific effects on seed germination and survival in a lowland tropical forest. *Proceedings of the National Academy of Sciences* **114**: 11458-11463.

Schardl CL, Leuchtmann A, Spiering MJ. 2004. Symbioses of grasses with seedborne fungal endophytes. *Annual Review of Plant Biology* **55**: 315-340.

Schiltz S, Gaillard I, Pawlicki-Jullian N, Thiombiano B, Mesnard F, Gontier E. 2015. A review: what is the spermosphere and how can it be studied? *Journal of Applied Microbiology* **119**: 1467–1481.

Schneider C, Rasband W, Eliceiri K. 2012. NIH Image to ImageJ: 25 years of image analysis. *Nature Methods* **9**: 671–675.

Schoch CL, Seifert KA, Huhndorf S, Robert V, Spouge JL, Levesque CA, Chen W, Bolchacova E, Voigt K, Crous PW, et al. 2012. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. *Proceedings of the National Academy of Sciences* **109**: 6241–6246.

Schubert K, Groenewald JZ, Braun U, Dijksterhuis J, Starink M, Hill CF, Zalar P, De Hoog GS, Crous PW.
2007. Biodiversity in the *Cladosporium herbarum* complex (*Davidiellaceae*, *Capnodiales*), with standardisation of methods for *Cladosporium* taxonomy and diagnostics. *Studies in Mycology* **58**: 105–156.

Sogonov M V., Castlebury LA, Rossman AY, Mejía LC, White JF. 2008. Leaf-inhabiting genera of the Gnomoniaceae, Diaporthales. *Studies in Mycology* **62**: 1–77.
Torres-Cortés G, Bonneau S, Bouchez O, Genthon C, Briand M, Jacques M-A, Barret M. 2018. Functional microbial features driving community assembly during seed germination and emergence. *Frontiers in Plant Science* 9: 902.

Tosi L, Beccari G, Rondoni G, Covarelli L, Ricci C. 2015. Natural occurrence of *Fusarium proliferatum* on chestnut in Italy and its potential entomopathogenicity against the Asian chestnut gall wasp Dryocosmuskuriphilus. *Journal of pest science* 88: 369-381.

Truyens S, Weyens N, Cuypers A, Vangronsveld J. 2015. Bacterial seed endophytes: genera, vertical transmission and interaction with plants. *Environmental Microbiology Reports* 7: 40–50.

U'Ren JM, Arnold AE. 2016. Diversity, taxonomic composition, and functional aspects of fungal communities in living, senesced, and fallen leaves at five sites across North America. *PeerJ* 4: e2768.

Vacher C, Cordier T, Vallance J. 2016. Phyllosphere fungal communities differentiate more thoroughly than bacterial communities along an elevation gradient. *Microbial ecology* 72: 1-3.

Vannier N, Mony C, Bittebière AK, Vandenkoornhuyse P. 2015. Epigenetic mechanisms and microbiota as a toolbox for plant phenotypic adjustment to environment. *Frontiers in plant science* 6: 1159.

Vellend M. 2010. Conceptual synthesis in community ecology. *The quarterly review of biology* 85: 183–206.

Venables WN, Ripley BD. 2002. Modern Applied Statistics with S, Fourth edition. Springer, New York. ISBN 0-387-95457-0, http://www.stats.ox.ac.uk/pub/MASS4.

Vitasse Y, Porté AJ, Kremer A, Michalet R, Delzon S. 2009. Responses of canopy duration to temperature changes in four temperate tree species: relative contributions of spring and autumn leaf phenology. *Oecologia* 161: 187-198.

Vitasse Y, François C, Delpierre N, Dufrêne E, Kremer A, Chuine I, Delzon S. 2011. Assessing the effects of climate change on the phenology of European temperate trees. *Agricultural and Forest Meteorology* 151: 969-980.

Vivas M, Kemler M, Slippers B. 2015. Maternal effects on tree phenotypes: considering the microbiome. *Trends in Plant Science* 20: 541–544.

Vivas M, Kemler M, Mphahlele MM, Wingfield MJ, Slippers B. 2017. Maternal effects on phenotype, resistance and the structuring of fungal communities in *Eucalyptus grandis*. *Environmental and Experimental Botany* 140: 120–127.

Vivas M, Wingfield MJ, Slippers B. 2020. Maternal effects should be considered in the establishment of forestry plantations. *Forest Ecology and Management* 460: 117909.

Vujanovic V, Islam MN, Daida P. 2019. Transgenerational role of seed mycobiome–an endosymbiotic fungal composition as a prerequisite to stress resilience and adaptive phenotypes in Triticum. *Scientific Reports* 9: 1-13.

Weiner J, Martinez S, Muller-Scharer H, Stoll P, Schmid B. 1997. How important are environmental maternal effects in plants? A study with Centaurea maculosa. *Journal of Ecology* 85:133-142.

Wolf JB, Wade MJ. 2009. What are maternal effects (and what are they not)? *Philosophical Transactions of the Royal Society B: Biological Sciences* 364: 1107-1115.

Whitham TG, Bailey JK, Schweitzer JA, Shuster SM, Bangert RK, LeRoy CJ, Lonsdorf EV, Allan GJ, DiFazio SP, Potts, BM. 2006. A framework for community and ecosystem genetics: from genes to ecosystems.
*Nature Reviews Genetics* 7: 510-523.
Xia Y, Sun J, Chen DG. 2018. Statistical analysis of microbiome data with R. Springer, Singapore. pp. 1-505.

Yang L, Danzberger J, Schöler A, Schröder P, Schloter M, Radl V. 2017. Dominant Groups of Potentially Active Bacteria Shared by Barley Seeds become Less Abundant in Root Associated Microbiome. *Frontiers in Plant Science* 8: 1-12.

Zhou J, Ning D. 2017. Stochastic community assembly: does it matter in microbial ecology? *Microbiology and Molecular Biology Reviews* 81: 2-17.
**Supporting information**

**Table S1** Number of fungal reads per sample after all quality filters.

**Table S2** Most abundant fungal species found as endophytes in seeds of sessile oak.

**Table S3** Mantel correlations between the genetic distance among mother trees and compositional dissimilarities among fungal communities.

**Table S4** Contribution of stochastic processes to community assembly.

**Table S5** Number and percentage of fungal ASVs of seeds shared with ground materials and canopy tissues.

**Table S6** Nestedness and turnover components of compositional dissimilarities among seed fungal communities.

**Table S7** Permutational multivariate analyses of variance of compositional dissimilarities among seed fungal communities.

**Table S8** Response of fungal ASVs to site elevation and seed position depending on their lifestyle according to HMSC models.

**Table S9** Fungal ASVs positively associated with another ASV at the seed level according to HMSC models.

**Fig. S1** Taxonomic composition of fungal communities of seeds of sessile oak.

**Methods S1** Protocol used to remove microbial DNA from the seed surface.

**Methods S2** Protocols and pipelines used for the metabarcoding of fungal communities.

**Methods S3** Methods used to investigate the mechanisms underlying maternal effects.

**Methods S4** Methods used to fit the HMSC models.
# Tables

<table><caption>Table 1. Location and description of the oak populations.</caption><thead><tr><th>Population</th><th>Elevation a.s.l. (m)</th><th>GPS coordinates</th><th>Exposition</th><th>Average (cm)</th><th>DBH</th><th>Average (yrs)</th><th>age</th></tr></thead><tbody><tr><td>Ade</td><td>427</td><td>43°08′N, 00°00′W</td><td>South</td><td>44.82 ± 6.36</td><td></td><td></td><td>99.92</td></tr><tr><td>Gèdre-bas</td><td>1235</td><td>42°47′N, 00°01′E</td><td>South</td><td>43.66 ± 15.78</td><td></td><td></td><td>134.71</td></tr><tr><td>Bager</td><td>422</td><td>43°07′N, 00°32′W</td><td>North West</td><td>46.73 ± 20.52</td><td></td><td></td><td>99.92</td></tr><tr><td>Gabas</td><td>1194</td><td>42°53′N, 00°25′W</td><td>South</td><td>40.58 ± 15.54</td><td></td><td></td><td>134.71</td></tr></tbody></table>

Average diameter at breast height (DBH) of mature trees was estimated in Vitasse et al. (2009). Average age was estimated by S. Delzon (personal com.).
<table><caption>Table 2. Most abundant fungal species associated with seeds of sessile oak and their microenvironment.</caption><thead><tr><th rowspan="2">Fungal species name and phylum (CL >80%)</th><th colspan="5">Average relative abundance per sample type (%)</th><th rowspan="2">Lifestyle</th><th rowspan="2">Reference(s)</th></tr><tr><th>All seeds (N=78)</th><th>Seeds in the canopy (N=40)</th><th>Canopy samples (twigs, leaves) (N=40)</th><th>Seeds on the ground (N=38)</th><th>Ground samples (litter, upper soil) (N=47)</th></tr></thead><tbody><tr><td>Gnomoniopsis paraclavulata (A)</td><td>13.4</td><td>5.3</td><td>1.5</td><td>21.9</td><td>0</td><td>Pathogen isolated in leaves, buds, cupules and shoots of Castanea sativa. Commonly isolated from overwintered leaves of Quercus sp.</td><td>Tosi et al. (2015); Sogonov et al. (2008)</td></tr><tr><td>Stromatoseptoria castaneicola (A)</td><td>2.7</td><td>5.2</td><td>0.9</td><td>0</td><td>0.2</td><td>Causes leaf spots on Castanea sativa.</td><td>Quaedvlieg et al. (2013)</td></tr><tr><td>Taphrina carpini (A)</td><td>2.4</td><td>3.4</td><td>0.7</td><td>1.4</td><td>0.3</td><td>Common pathogen encountered on Fagaceae leaves.</td><td>Bacigálová (1991); Inácio et al. (2004); Cordier et al. (2012b)</td></tr><tr><td>Epicoccum nigrum (A)</td><td>2.3</td><td>0.4</td><td>0.1</td><td>4.2</td><td>0.2</td><td>Ubiquitous fungus found in soil, leaves and seeds described as primary saprotroph and plant pathogen.</td><td>Ahumada-Rudolph et al. (2014)</td></tr><tr><td>Mycosphaerella tassiana (A)</td><td>1.7</td><td>1.5</td><td>0.1</td><td>1.9</td><td>0.3</td><td>Common pathogen found in the phyllosphere including that of oak.</td><td>Schubert et al. (2007); Jakuschkin et al. (2016)</td></tr><tr><td>Curvibasidium cygneicollum (B)</td><td>1.5</td><td>1.2</td><td>0.2</td><td>1.9</td><td>0.1</td><td>Endophyte of fruits, leaves, trunk and soil behaving as a phytopathogen or a mycoparasite. The species is insensitive to the mycocins produced by Filobasidium and Cystofilobasidium.</td><td>Sampaio et al. (2004); Mašínová et al. (2017)</td></tr><tr><td>Cylindrium elongatum (A)</td><td>1.5</td><td>1.4</td><td>0.1</td><td>1.5</td><td>4.3</td><td>Bacterial and fungal antagonist found on oak leaves.</td><td>Reyes-Estebanez (2011); Duarte et al. (2015)</td></tr><tr><td>Polyscytalum algarvense (A)</td><td>1.5</td><td>1.9</td><td>0</td><td>1</td><td>0</td><td>Necrotroph fungi found on Eucalyptus leaves.</td><td>Cheewangkoon et al. (2009); Crous (2018)</td></tr><tr><td>Fusarium pseudensiforme (A)</td><td>1.3</td><td>1.4</td><td>0</td><td>1.2</td><td>0</td><td>Found on bark of trees.</td><td>Nalim et al. (2011)</td></tr></tbody></table>
<table><tr><td>Cladosporium delicatulum (A)</td><td>1.3</td><td>1.3</td><td>0.2</td><td>1.3</td><td>0.3</td><td>Found in cereal seeds, mycoparasite of Taphrina spp and Magnaporthe oryzae</td><td>Amanelah Baharvandi & Zafari (2015); Chaibub et al. (2016)</td></tr></table>

Seeds were either collected in the tree canopy or on the ground. Materials from the seed microenvironment (twigs and leaves, or litter and upper soil) were also collected. The fungal community of all 4 sample types was analyzed using a metabarcoding approach. Only Amplicon sequence variants (ASV) assigned to Ascomycota (A) or Basidiomycota (B) with the UNITE database were kept. Average relative abundances of all ASVs were computed for each sample type, after merging ASVs assigned to the same fungal species. Only ASVs assigned to species with a confidence level (CL) >80% are shown in the table.
<table><caption>Table 3. Generalized linear model (GLM) of fungal community richness of seeds of sessile oak.</caption><thead><tr><th></th><th>d.f.</th><th>chi-square</th><th>P-value</th></tr></thead><tbody><tr><td>Population</td><td>3</td><td>20.765</td><td>&lt;0.001</td></tr><tr><td>Mother (Population)</td><td>7</td><td>14.173</td><td>&lt;.05</td></tr><tr><td>Seed position (Population)</td><td>4</td><td>23.678</td><td>&lt;0.001</td></tr><tr><td>Mother x Seed position (Population)</td><td>7</td><td>11.121</td><td>0.13</td></tr></tbody></table>

Fungal richness was defined as the total number of ASVs per seed sample. The model had *Mother*, *Seed position* and their interaction nested within *Population*, and the natural logarithm of the total number of sequences per sample as an offset.
<table><caption>Table 4. Permutational multivariate analysis of variance (PERMANOVA) of compositional dissimilarities among fungal communities of seeds of sessile oak.</caption><thead><tr><th></th><th>d.f.</th><th>SS</th><th>MS</th><th>F-value</th><th>P-value</th><th>R<sup>2</sup></th></tr></thead><tbody><tr><td>Sequencing depth</td><td>1</td><td>0.551</td><td>0.550</td><td>1.35</td><td>&lt;0.01</td><td>0.015</td></tr><tr><td>Population</td><td>3</td><td>3.026</td><td>1.008</td><td>2.47</td><td>&lt;0.001</td><td>0.087</td></tr><tr><td>Mother (Population)</td><td>8</td><td>4.060</td><td>0.507</td><td>1.24</td><td>&lt;0.001</td><td>0.116</td></tr><tr><td>Seed position (Population)</td><td>4</td><td>2.240</td><td>0.559</td><td>1.37</td><td>&lt;0.001</td><td>0.064</td></tr><tr><td>Mother x Seed position (Population)</td><td>7</td><td>2.844</td><td>0.406</td><td>0.99</td><td>0.52</td><td>0.081</td></tr><tr><td>Residuals</td><td>54</td><td>22.019</td><td>0.407</td><td></td><td></td><td>0.633</td></tr><tr><td>Total</td><td>77</td><td>34.739</td><td></td><td></td><td></td><td>1.000</td></tr></tbody></table>

Dissimilarities among seed fungal communities were estimated using the Jaccard binary distance. The model had the mother tree (*Mother*), the seed position (*Seed position*, in the canopy or on the ground) and their interaction nested within the tree population (*Population*), and the natural logarithm of *Sequencing depth* as the first effect. d.f, degree of freedom, SS, Sum of square, MS, mean sum of square.
<table><caption>Table 5. Partitioning of the variance in fungal community composition of seeds of sessile oak.</caption><thead><tr><th rowspan="2">Type</th><th rowspan="2">Level</th><th rowspan="2">Predictor Name</th><th colspan="2">Explained variance (%)</th></tr><tr><th>PA model</th><th>SC model</th></tr></thead><tbody><tr><td rowspan="3">Fixed</td><td>Seed</td><td><i>Sequencing depth</i></td><td>1</td><td>17</td></tr><tr><td>Seed</td><td><i>Seed position (P)</i></td><td>4</td><td>8</td></tr><tr><td>Seed</td><td><i>Microenv. mycobiota * P</i></td><td>47</td><td>23</td></tr><tr><td rowspan="2">Fixed</td><td>Tree</td><td><i>Mother mycobiota * P</i></td><td>39</td><td>25</td></tr><tr><td>Population</td><td><i>Elevation</i></td><td>2</td><td>6</td></tr><tr><td rowspan="3">Random</td><td>Seed</td><td><i>Seed</i></td><td>3</td><td>9</td></tr><tr><td>Tree</td><td><i>Tree</i></td><td>1</td><td>6</td></tr><tr><td>Population</td><td><i>Population</i></td><td>2</td><td>6</td></tr></tbody></table>

Four fixed effects were included in the HMSC models to explain variations in the presence-absence (PA) or the sequence count (SC) of a focal fungal ASV among seeds: *Sequencing depth* (total number of sequences per sample), *Seed position* (canopy or ground), *Microenv. mycobiota* (relative abundance of the focal ASV in the seed biotic microenvironment), *Mother mycobiota* (average relative abundance of the focal ASV in the mother tree aboveground tissues), and *Elevation*. Random effects were included at each spatial scale (seed, tree and population). Results of variance partitioning are given as percentages (%) of total explained variance.
## Figure legends

**Figure 1. Map of the oak populations sampled in the Pyrenees mountains (France).** Four populations (Bager, Ade, Gabas, Gèdre-Bas) were sampled. Three trees (indicated by symbols and numbers) were sampled in each population. The geographical distances between populations are indicated.

**Figure 2. Endophytic fungi in cross sections of acorns of sessile oak collected on the ground and surface-sterilized.** Fungi (green fluorescent) were revealed by confocal microscopy and WGA-ALEXA fluor488 staining. (A) Fruit wall and seed coat. (B) Zoom-in view of panel A. (C) Embryo and cotyledon. (D) Zoom-in view of panel C. (E) Internal surface of the endocarp.

**Figure 3. Compositional dissimilarities among fungal communities of seeds collected (A) in the canopy and (B) on the ground in four populations of sessile oak.** Dissimilarities among seeds were estimated using binary Jaccard distance and represented with a PCoA plot. Fungal community composition differed significantly among tree populations and among mother trees (Table 4). Seeds belonging to the same mother tree were represented with the same color and connected with lines to their group centroid.

**Figure 4. Partitioning of the variance in the composition of seed fungal communities of sessile oak.** Four fixed effects were included in the HMSC models to explain variations in (A) the presence-absence or (B) the sequence count of a focal fungal ASV among seed samples: *Sequencing depth* (total number of sequences per sample), *Seed position* (*P*, canopy or ground), *Microenv mycobiota* (relative abundance of the focal ASV in the seed microenvironment), *Mother mycobiota* (relative abundance of the focal ASV in the mother tree aboveground tissues), and *Site elevation*. *Mother mycobiota* and *Microenv mycobiota* were introduced in interaction with *Seed position* (*P*). Random effects were included at each spatial scale (seed, tree and population). Results of variance partitioning are given as percentages (%) of total explained variance. ASV are ranked by fungal phylum, class and order (Dothi: Dothideomycetes, Euro: Eurotiomyecetes, Leo: Leotiomyecetes, Sorda: Sordariomycetes, Tap: Taphrinomycetes, Ag: Agaricostilbmoyecetes, Mi: Microbotryomycetes, Tremel: Tremellomycetes). Only ASVs assigned at the order level are shown.

**Figure 5. Fungal community composition and richness of seeds of sessile oak collected in the canopy and on the ground.** (A) PCoA plot of compositional dissimilarities among fungal communities associated with seeds, canopy (leaves and twigs) and ground materials (litter and upper soil). Dissimilarities among samples were estimated using binary Jaccard distance. Fungal community composition differed significantly among the four sample types (PERMANOVA on Jaccard qualitative; $F = 4.8, P<0.001$; PERMANOVA on Jaccard quantitative; $F=4.3, P<0.001$). (B) Richness (log-transformed) of seed fungal communities, defined as the number of ASVs per sample. Richness was significantly higher in seeds on the ground (Table 3).

**Figure 6. Network of fungal associations estimated by HMSC models.** Associations were all positive. Network nodes correspond to fungal ASVs assigned to the species level and their color correspond to putative functions according to FUNGuild and the literature (Table S9). Network links indicate associations with at least 95% posterior probability estimated by the presence-absence model (thin black plain line) or by both the presence-absence model
and the sequence count model (thick black plain line). The positive association between *Taphrina carpini* and *Cladosporium delicatulum* is indicated in red because it is described in the literature as a host-parasite interaction (Table S9).
<caption>Figure 1</caption>