# Are plant traits drivers of endophytic communities in seasonally flooded tropical forests?

Marion Boisseaux, Valérie Troispoux, Alice Bordes, Jocelyn Cazal, Saint-omer Cazal, Sabrina Coste, Clément Stahl, Heidy Schimann

► To cite this version:

Marion Boisseaux, Valérie Troispoux, Alice Bordes, Jocelyn Cazal, Saint-omer Cazal, et al.. Are plant traits drivers of endophytic communities in seasonally flooded tropical forests?. American Journal of Botany, 2024, The Role of Plant-Microbe Interactions in Plant Community Dynamics of Tropical and Subtropical Ecosystems, 111 (12), pp.e16366. 10.1002/ajb2.16366. hal-04649310

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## RESEARCH ARTICLE

Short title: Plant traits: Drivers of endophytic communities in seasonally flooded tropical forests?

## SPECIAL ISSUE ARTICLE

### Are plant traits drivers of endophytic communities in seasonally flooded tropical forests?

Marion Boisseaux¹, Valérie Troispoux¹, Alice Bordes, Jocelyn Cazal¹, Saint-Omer Cazal¹, Sabrina Coste¹, Clément Stahl¹, and Heidy Schimann²

Manuscript received 27 September 2023; revision accepted 13 May 2024

¹UMR EcoFoG, Agroparistech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane. Campus Agronomique, 97310 Kourou, France

²INRAE UMR BIOGECO 1202, Domaine de l'Hermitage 33612 Cestas ORCID : 0000-0002-9139-920X

### Corresponding Author

Marion Boisseaux, UMR EcoFoG, Agroparistech, Cirad, CNRS, INRAE, Université des Antilles, Université de la Guyane. Campus Agronomique, 97310 Kourou, France

Email: marionboisseaux@gmail.com

### ORCID ID:

0000-0003-4998-8956 Marion Boisseaux

0000-0002-1118-1367 Valérie Troispoux

0009-0001-9565-4693 Alice Bordes

0000-0003-3948-4375 Sabrina Coste

0000-0002-9139-920X Heidy Schimann

0000-0001-5411-1169 Clément Stahl

**Citation:** Boisseaux, M., V. Troispoux, A. Bordes, J. Cazal, S.-O. Cazal, S. Coste, C. Stahl, and H. Schimann. 2024. Are plant traits drivers of endophytic communities in seasonally flooded tropical forests? *American Journal of Botany* 111: XXXX.
DOI: XXXX

This article is part of the *American Journal of Botany* special issue: “The role of plant–microbe
interactions in plant community dynamics of tropical and subtropical ecosystems.”
**Abstract**

**Premise:** In the Amazon basin, seasonally flooded (SF) forests offer varying water constraints, providing an excellent way to investigate the role of habitat selection on microbial communities within plants. However, variations in the microbial community among host plants cannot solely be attributed to environmental factors, and how plant traits contribute to microbial assemblages remains an open question.

**Methods:** We described leaf- and root-associated microbial communities using ITS2 and 16S high-throughput sequencing and investigated the stochastic-deterministic balance shaping these community assemblies using two null models. Plant ecophysiological functioning was evaluated by focusing on 10 leaf and root traits in 72 seedlings, belonging to seven tropical SF tree species in French Guiana. We then analyzed how root and leaf traits drove the assembly of endophytic communities.

**Results:** While both stochastic and deterministic processes governed the endophyte assembly in the leaves and roots, stochasticity prevailed. Discrepancies were found between fungi and bacteria, highlighting that these microorganisms have distinct ecological strategies within plants. Traits, especially leaf traits, host species and spatial predictors better explained diversity than composition, but they were modest predictors overall.

**Conclusions:** This study widens our knowledge about tree species in SF forests, a habitat sensitive to climate change, through the combined analyses of their associated microbial communities with functional traits. We emphasize the need to investigate other plant traits to better disentangle the drivers of the relationship between seedlings and their associated microbiomes, ultimately enhancing their adaptive capacities to climate change.

**KEYWORDS**

determinism, high-throughput sequencing, leaf, microbial communities, root, stochasticity, traits, tropical tree species

The factors that drive the diversity and composition in local communities have been a central unresolved question in ecology (Vellend et al., 2014; Loke et al., 2023). Deterministic
and stochastic processes are mutually non-exclusive and have now been broadly recognized to rule community assembly (Hutchinson, 1961; Hubbell, 2001). Deterministic processes suggest that environmental filtering and biotic interactions largely determine the species diversity and composition of a community. In contrast, Chase and Myers (2011) disentangled, from deterministic processes, the relative importance of stochastic processes such as chance colonization, random extinction, and ecological drift. This theoretical framework can be applied to all members of the communities, such as endophytes, the microbes harbored within plants (Nemergut et al., 2013), which include all microorganisms that colonize internal tissues of plants (Hardoim et al., 2015), including archaea, algae, protists, and viruses (Lwindow and Brandl, 2003; Dlamini et al., 2022), but primarily bacteria and fungi (including yeasts) (Rosado et al., 2018; Stone et al., 2018; Chaudhry et al., 2021; Grabka et al., 2022). Simultaneous analyses of fungi and bacteria in plants are needed to understand whether these microbial groups adhere to similar assembly processes.

The assembly processes of bacterial and fungal endophytic communities have attracted attention due to their intimate relationships and influences on plant health, growth, and resilience to environmental changes (Trivedi et al., 2020; Zhuang et al., 2020; Xiong et al., 2021a; Li and Gao, 2023). Fungal communities within tropical or desert plants were found to be more driven by deterministic processes compared to bacteria (Yan et al., 2022; Schimann et al., 2023; Taniguchi et al., 2023). These observations could be explained by the size plasticity hypothesis, which suggests that larger multicellular organisms, such as fungi, have less adaptable metabolic rates and are more strongly influenced by environmental conditions than smaller unicellular organisms such as bacteria (Farjalla et al., 2012). The differences in life history strategy between bacteria and fungi may result in varying assembly patterns within plants. Furthermore, within the host plant, studies have shown that endophytic assemblages could also vary among tissues (Haruna et al., 2018).

Both leaves and roots are important microbial habitats (Vorholt, 2012), respectively, the phyllosphere (Morris, 2002) and the rhizosphere (York et al., 2016). Plant tissues shelter microorganisms from detrimental climatic stresses (e.g., UV-rays, rain, temperature, and wind) and provide carbohydrates and water, buffering the effect of chemical and physical changes in the soil or air (Rosenblueth and Martínez-Romero, 2006; Goldmann et al., 2016). The composition of microbial communities in the rhizosphere and phyllosphere have been assumed
to be initially determined by stochastic processes (Sloan et al., 2006; Dini-Andreote et al., 2015; Trivedi et al., 2020). The identity and abundance of the species that first colonize a plants’ organs (i.e., priority effect) may affect the colonization success of species that arrive later and thus affect the assemblage of the plant-associated microbiome (Trivedi et al., 2020). As succession continues, deterministic processes become more pronounced (Dini-Andreote et al., 2015). The surrounding biotic and abiotic environmental factors and the plant also participate in the selection and dispersal of microbes. The host can affect the endophytic community by controlling the space and nutrient pool available in tissues or by producing antimicrobial molecules, thereby favoring particular microbial species. The microbial communities clearly differ between the roots and leaves (Coleman-Derr et al., 2016; de Souza et al., 2016; Hamonts et al., 2018) and are regulated by host-specific factors and deterministic and stochastic processes within each organ (Trivedi et al., 2020; Li and Gao, 2023). Many studies have described the importance of deterministic processes in shaping the root endosphere, strongly influenced by the soil, root exudates from the host plant metabolism, and different root tissues (Guo et al., 2021; Wang et al., 2023). However, no consensus has been reached since stochastic processes have also been found to be dominant in root assembly processes (Liu et al., 2021; Bell et al., 2022; Sonam et al., 2023).

The functional traits that provide information on the plant's ecological strategies (Violle et al., 2007) can also affect the endophytic community (Kembel et al., 2014; Tellez et al., 2022, Yan et al., 2022). Leaf traits of plants can be classified according to the leaf economics spectrum (LES), which links leaf functional traits to evolutionary life history strategies of plants, opposing conservative (higher leaf mass per area [LMA], longer leaf life span [LLS], lower leaf nitrogen [N] content) against acquisitive strategies (lower LMA, shorter LLS, higher N content) (Wright et al., 2004). Lajoie and Kembel (2020) showed that more-conservative tree strategies with higher LMA and lower phosphorous concentrations were associated with higher turnover in bacterial communities, suggesting that thicker leaves filter resource-use strategies in microbes. Leaf traits also modulated fungal communities, which were more abundant and diverse in thin, softer leaves (Kembel et al., 2014; Tellez et al., 2022). Phyllosphere communities are also particularly influenced by leaf traits related to water and resource capture because they create microclimatic gradients that determine the development of microorganisms (Vacher et al., 2016). Therefore, both fungi and bacteria can be influenced by the plant's trait values.
Additionally, the different morphologies of roots affect the establishment of endophytes, and roots can also produce different exudates and alter the soil environment (Berg and Smalla, 2009; Sasse et al., 2018; Herms et al., 2022). The recent developments in the field of root ecology has led to the definition of the root economic space (RES) (Bergmann et al., 2020) to describe natural variations in root diversity. The “conservation” axis separates species with high root tissue density (RTD) and species with high root N concentration (Weemstra et al., 2016). The “collaboration” axis organizes species based on how much they rely on mycorrhizal fungi for resource acquisition (Brundrett et al., 2002; Brundrett and Tedersoo, 2018). While thicker roots have been theorized to provide space for mycorrhizal fungi (Gu et al., 2014; Kong et al., 2016), they also provide suitable niches for a broad range of other microorganisms such as bacteria and other types of fungi (Amend et al., 2019). The underground organs developed specific molecular mechanisms that are highly selective in their interactions with microorganisms. While promoting the establishment of beneficial symbionts, roots can also secrete specific chemicals such as coumarin, which inhibit soil pathogens but not growth-promoting bacteria (Bai et al., 2022).

Plant traits might therefore help us bridge functional plant ecology and microbial ecology, shedding light on endophytic assemblage patterns. Confined within different sites or plant compartments such as the leaves and the roots, the associated endophytes provide an excellent model to test community ecology theories. Studies have shown variations of microbial communities among species within leaves (Kembel et al., 2014; Kembel and Mueller, 2014; Yao et al., 2019, 2020; Donald et al., 2020; Bowsher et al., 2021; Cambon et al., 2022) or in roots (Petrolli et al., 2021; Leroy et al., 2022), none have looked at both bacterial and fungal endophytes in roots and leaves of tropical trees. Within this framework, we are interested in understanding the various drivers that shape bacterial and fungal endophytic communities inside the leaves and roots of tropical plants. Assuming that leaves and roots act as filters for endophytes based on their trait values, we questioned the potential dominance of deterministic processes governing bacterial and fungal communities in the present study.

We investigated a particular habitat in French Guiana, seasonally flooded forest (SF), which harbors a distinct tree species composition, shorter canopies and lower aboveground biomass compared to terra firme forests (Ferry et al., 2010; Baraloto et al., 2011). SF forests are characterized by waterlogged soils, which are seasonally under water (Baraloto et al., 2011;
Costa et al., 2023). These conditions create stressful hypoxic to anoxic conditions, and the reduced oxygen supply is a major constraint to the plant and associated microbiota (Martínez-Arias et al., 2022). A global analysis of LES traits in wetlands (Pan et al., 2020) revealed that plants tend to cluster at the acquisitive end of the LES compared to non-wetland plants. A recent study also revealed stronger leaf trait coordination in SF forests for adult trees compared to terra firme forests (M. Boisseaux et al., unpublished manuscript). In response to hypoxic stress, plants have developed various metabolic (Gupta et al., 2020), anatomical, and morphological adaptations (Colmer et al., 2009; Bailey-Serres et al., 2012) to enhance gas exchange in their tissues (Parolin, 2001; Colmer and Pedersen, 2008). For example, wetland species tend to have lower LMA (Mommer et al., 2006; Pierce et al., 2012), indicating their tolerance to flooding (Douma et al., 2012), and roots in SF forests are highly ramified and have a high specific root length (SRL) and smaller root cortex than in terra firme forests (Weemstra et al. (2023).

Especially for root endophytes, the seasonal flooding causes constraining conditions such as low oxygen and high reactive oxygen species, which accumulate in plant tissues, especially upon return to aerobic conditions after flooding (Colmer et al., 2009). Denitrification caused by prolonged soil flooding may also decrease nitrate availability, thus reducing leaf N (Mommer et al., 2006) and affecting microbial communities. Studies on this specific habitat would provide better understanding of the synergies between the plant's microbiota and the plant's functional traits, especially related to water strategies (Rosado et al., 2018; Cambon et al., 2022) inherent to the SF forests.

We specifically tested three hypotheses: (H1) The endophytic community assemblage would be governed predominantly by deterministic processes because of the influence of the SF environment and the tree host species identity. (H2) The processes regulating bacterial and fungal endophytic assemblages will differ because of differences in their life history traits and ecological strategies; more stochastic processes will govern bacterial communities according to the size plasticity hypothesis. (H3) Given the distinct microbial communities in plant compartments, each plant organ (leaves and roots) serves as a distinct habitat filter for their respective endophytic communities. Since water and nutrient conditions vary so greatly in SF forest soils, more selective pressures are likely to be stronger on roots. We therefore hypothesized that the contribution of deterministic processes increases from the leaves to the roots due to higher selection in the roots.
Even less is known how environmental conditions of SF forests mediate LES or RES trait
interrelationships for tropical tree seedlings and ultimately impact the assemblage of endophytic
communities. To address our hypotheses, we studied 72 tree seedlings belonging to seven
tropical tree species strongly associated with SF forests in French Guiana. We measured leaf and
root functional traits of seedlings and analyzed their foliar and root microbial associated
communities (bacteria and fungi) using DNA metabarcoding.

<h1>MATERIALS AND METHODS

<h2>Seedling collection

Seedlings were collected at the Paracou field station (5°18′N, 52°53′W) in French Guiana. This site has an average annual rainfall of 3102 mm and a mean air temperature of 25.7°C (Aguilos et al., 2019). The landscape is a patchwork of hills and includes contrasting forest habitats such as terra firme and SF. The SF habitats are in the valleys and have relatively more fertile and sandier soils compared to the terra firme forests. The physical and chemical properties of the 0–10 cm horizon in our study is typical of SF soils (Baraloto et al., 2021, Appendix S1). The water table is never below 60 cm depth, remaining at the soil surface or sometimes exceeding it for at least two consecutive months each year during the rainy season (Ferry et al., 2010). Seed germination was monitored for several months to ensure that the collected seedlings were from the current year. In total, 72 1-year-old seedlings were collected during the rainy season of 2021, with 10 to 11 individuals for each of the following seven tropical species (Appendix S2): *Eperua falcata* Aubl. (Fabaceae), *Iryanthera hostmannii* (Benth.) Warb. (Myristicaceae), *Jacaranda copaia* subsp. *copaia* Aubl. (Bignoniaceae, *Pterocarpus officinalis* Jacq. (Fabaceae), *Symphonia globulifera* L.f. (Clusiaceae), *Tachigali melinonii* (Harms) Zarucchi & Herend (Fabaceae), *Virola surinamensis* (Rol. ex Rottb.) Warb (Myristicaceae). Except for *J. copaia* and *T. melinonii*, all are SF specialists (Fougnies et al., 2007; Baraloto et al., 2007, 2021). The GPS position for each seedling is in Appendix S3. Once collected, whole seedlings were placed by species in plastic bags with wet paper and stored in a cooler until brought to the laboratory to measure traits and analyze the microbial communities (Appendix S4).

<h2>Ecophysiological traits: whole plant, leaf, and root

We assessed four whole-plant traits, six leaf traits, and three root traits.
For each seedling, whole-plant traits included number of leaves, basal diameter (mm), height (cm), and ratio of root to shoot dry mass (oven-dried at 60°C at least 72 h) (Appendix S5).

Leaf functional traits included total leaf area (Total_LA, $cm^2$), specific leaf area (SLA, $cm^2 g^{-1}$), leaf thickness (LT, mm), leaf saturated water content (LSWC, $g g^{-1}$), leaf minimum conductance ($g_{min}$, mmol m$^{-2}$ s$^{-1}$), and stomatal density (SD, mm$^{-2}$). Total leaf area was determined using scanned images of fresh leaves with ImageJ software (Schneider et al., 2012). Specific leaf area was calculated based on dry mass weighed with digital balance at a 0.0001 g precision (Mettler Toledo, Greifensee, Switzerland) and analyzing scanned images of fresh leaves with ImageJ software. Leaf thickness was measured using a micrometer (Digit Outside Micrometre 193-101, Mitutoyo, Kawasaki, Japan). The average leaf thickness was calculated from multiple measurements taken at different points on a single leaf per seedling. We calculated LSWC based on saturated wet and dry mass (Barrs and Weatherley, 1962). For saturated mass, detached leaves were wrapped in a wet paper towel for 24 h in the dark and at low temperature (4°C). We determined $g_{min}$ from the consecutive loss in mass of desiccating leaves with cut petioles that had been sealed with nail polish (Sack, Scoffoni, and PrometheusWiki contributors, 2011). We weighed the leaves at regular intervals, twice at 15-min intervals and then at 30-min intervals for 3-4 h to obtain leaf-drying curves. Leaves of tropical species lose water very rapidly, so the given time intervals were sufficient to assess minimum conductance (Levionnois et al., 2021). At each weighing, air temperature and humidity were recorded using a digital thermo-hygrometer (Fisherbrand Traceable Relative Humidity/Temperature Meters, Hampton, NH, USA). The slope of the curve of mass loss versus time was estimated using only the linear part of the regression (>0.95), suggesting maximal stomatal closure (Billon et al., 2020). For stomatal density, we used transparent nail polish to make surface replicas of three randomly selected middle sections of the adaxial side of each fresh leaf per seedling, then photographed the replicas at 200× or 400× magnification with a light microscope (Olympus BX 51, Tokyo, Japan); with camera (Kern ODC 832, 5.1 MP, Balingen, Germany) to calculate stomatal density using the python interface labelStoma (Casado-Garcia et al., 2020), which was calibrated for our species (M. Boisseaux et al., unpublished manuscript).

Root systems were washed of soil and scanned using the Epson Perfection V 800 scanner (resolution 800 dpi; Epson, Long Beach, CA, USA). Fresh and oven-dried roots during 72 h at 60°C were weighed with the digital balance (0.0001 g precision). Root traits included specific
root length (SRL, root length per unit dry mass, cm g⁻¹), root tissue density (RTD, root dry mass per unit volume; g cm⁻³) and root diameter (RD, mm) were calculated using WinRHIZO 2016 (Regent Instruments, Quebec City, Quebec, Canada).

<h2>Microbial communities sampling

For characterizing endophytic communities inside plant tissues, leaves and roots were surface sterilized (Compant et al., 2020) successively in baths of 75% v/v ethanol (30 s), ultrapure water (30 s), 9% v/v bleach (30 s) and ultrapure water (30 s × 2), and in CTAB (30 s) buffer (2% cetyl trimethylammonium bromide, 1% w/v polyvinylpyrrolidone, 100 mM Tris-HCl, 1.4 M NaCl, 20 mM EDTA). Leaf and roots samples were then transferred to 2-mL UV-sterilized plastic tubes and filled with the CTAB buffer. The CTAB buffer was also used for subsequent DNA extraction as the sterilization control. The samples were ground using the Mixer mill MM 200 (Retsch GmbH, Germany). For each plant individual, up to three replicates of 0.2 g for each sample were weighed for DNA extraction, according to the amount available. Negative sterilization and extraction controls (ultrapure water) were also added. DNA was extracted using a CTAB method (Carrell and Frank, 2014); 1400 µL of CTAB solution and 0.6 g of tissue or Whatman paper were incubated for 2 h at 60°C, then homogenized with glass beads for 3 min. Proteins were removed with the two-step addition of 600 µL of chloroform, centrifuging for 10 min at 16,000 × g, and isolating the top aqueous phase in a sterile tube. Nucleic acids were precipitated with 120 µL of cold 3 M sodium acetate and 1 mL of cold isopropanol, then the sample was frozen at –20°C for 12 h and centrifuged for 30 min at 16,000 × g. The supernatant was discarded, 700 µL of 70% ethanol was added, and the sample centrifuged for 10 min. The air-dried pellet was resuspended with 30 µL of DNA resuspension fluid (1.0 M Tris-HCL and 0.1 M EDTA) and stored at –20°C until used to characterize the bacterial and fungal communities.

For bacterial communities, the V5-V6 region of the bacterial 16S rDNA gene was amplified using the chloroplast-excluding forward primer 799F (Chelius and Triplett, 2001) and the reverse primer 1115R (Reysenbach and Pace, 1995).

For characterizing the fungal community, the ITS2 region of the rRNA gene was amplified using the ITS86F (Turenne et al., 1999) forward and the ITS4 (White et al., 1990) reverse primers as recommended by (Beeck et al., 2014). Each forward and reverse primers was tagged at the 5' end with a combination of two different eight-nucleotide labels. The sterilization
and extraction negative controls were chosen randomly among all sterilization and extraction
negative controls. Additionally, PCR negative controls (6 µL of ultra-pure water) and PCR
positive controls (6 µL of 10 ng µL⁻¹ DNA, *Pseudomonas* sp. for bacteria and *Lentinus crinitus*
for fungi) were also added and amplified like all other samples. The 50-µL PCR mixture
consisted of 10 µL of the Blend Master Mix 5X, 4 µL of each tagged primer (5 µM), 6 µL of
DNA diluted accordingly, 24 µL of ultra-pure water and 2 µL of 10 mg mL⁻¹ BSA. The PCRs
were run in a Tetrad2 thermocycler (BioRad, Hercules, CA, USA) with initial denaturation at
94°C for 15 min; 30 cycles of 30 s at 94°C, 30 s at 58°C and 30 s at 72°C; and final extension at
72°C for 7 min. Quality of PCR products were verified by electrophoresis using a 1-h migration
at 130 V in 1.8% agarose gels with GelRed Nucleic Acid Gel Stain, 10,000X (Biotium, Fremont,
CA, USA). Amplicons were then purified with magnetic beads (CleanNGS beads, CleanNA,
Waddinxveen, Netherlands) using the manufacturer's instructions, quantified with a
fluorescence-based Quant-it PicoGreen dsDNA assay kit (Thermo Fisher Scientific, Waltham,
MA, USA). Samples were then pooled in equimolar conditions. Two libraries were built (for
each primer) using the Fasteris MetaFast protocol (FASTERIS SA, Plan-les-Ouates,
Switzerland) and sequenced using one MiSeq Illumina run and paired-end sequencing
technology (Metafast protocol, FASTERIS SA). For controlling for potential contaminants and
false positive sequences caused by tag-switching events (Esling et al., 2015), the molecular
experimental design comprised sterilization, extractions and PCR negative controls, that were
systematically sequenced, as well as unused tag combinations.

<h2>Bioinformatic analyses

Sequencing reads were curated using obitools (Boyer et al., 2016) and sumaclust
(Mercier et al., 2013) for the analysis of both markers (16S and ITS2). The snakemake pipeline
template is publicly hosted on github (A.-S. Benoiston [2022], AnneSoBen/obitools_workerflow
v1.0.2, available at GitHub.https://doi.org/10.5281/zenodo.6676577). The pipeline consists of the
following steps: (1) merging—forward and reverse reads are aligned to create a single consensus
sequence; (2) demultiplexing—each sequence is assigned to its sample, and primers are
removed; (3) dereplication—only one representative sequence is kept, and counts of the number
of reads are recorded for strictly identical sequences; (4) quality filtering—sequences that are too
short or contain ambiguous bases are removed. The minimum sequence length accepted was 80
bp for ITS2 (Beek et al., 2014) and 100 bp for 16S (Redford et al., 2010). The similarity
threshold for clustering was 97% for ITS2 and 98% for 16S. Workflows were run on the cluster
of the genotoul bioinformatics platform (Bioinfo Genotoul,
https://doi.org/10.15454/1.5572369328961167E12). Further processing based on controls were
used to filter spurious OTUs and contaminants using the metabar R package (Zinger et al.,
2020). Taxonomic assignment was done using SILVA for 16S (Quast et al., 2012) (versions
1.9.10/1.4.9; SILVA: r138.1, last updated 14 March 2023) and UNITE for ITS2 version
16.10.2022 (Abarenkov et al., 2021). Finally, we checked the taxonomic assignments and kept
only sequences assigned as bacteria and fungi. For the 72 seedling samples from among seven
tree species, we generated a total of 1,992,461 sequencing reads that were affiliated with fungi
only and 1,648,273 sequencing reads for samples that were affiliated with bacteria only (see
more details in Appendix S6). Rarefaction curves are provided in Appendix S7. Appendix S8
summarizes the taxonomic assignment for the bacteria and fungi across all taxonomic levels.

The data set was then partitioned among four subsets: leaf bacteria, leaf fungi, root bacteria and root fungi. Each subset was then analyzed separately.

<h2>Data analyses

<h3>Endophyte community composition and diversity in leaf and root compartments</h3>

The diversity of endophytic communities was estimated for each seedling in each
compartment by computing the exponential of the Shannon diversity index (Jost et al., 2010),
which corresponds to the Hill numbers at q = 1. Hill numbers have been shown to provide more
robust estimates of diversity, with q representing the order of diversity (Calderón-Sanou et al.,
2020; Haegeman et al., 2013). To stay in the framework of the Hill numbers, we assessed the
composition of communities for each seedling in each leaf and root compartment at q = 1 (Jost et
al., 2010; Chao et al., 2014) using the package HillR (Chao et al., 2014) and the functions
hill_taxa and hill_taxa_parti_pairwise at q = 1. We compared compositions among the leaf and
root compartments, and among host species within each compartments using a PERMANOVA
test with the adonis2 function in the R package vegan (version 2.6-2; Oksanen et al., 2022) with
999 permutations, followed by an analysis of multivariate homogeneity of dispersions using the
betadisper function in vegan. We conducted an ANOVA to test for significant differences in
dispersions between compartments and among host species using the anova function in the R
package stats version 4.2.1, followed by Tukey's honestly significant difference (HSD) test to
identify significantly different dispersion between compartments and among host species, using
the tukeyHSD function in stats. Additionally, we assessed the significance of observed
differences in dispersion using a permutation test with the permutest function in vegan (version
2.6-2, Oksanen et al., 2022). We then drew box plots using the ggplot2 package (version 3.4.2,
Wickham 2016) of the distances to the centroid for each compartment and host species. We
removed *T. melinonii* from the analyses of root fungi due to insufficient microbial samples.

<h3>Community assembly processes of SF seedling endophytes</h3>

To disentangle the relative contribution of deterministic and stochastic processes on
community assemblages (H1), we implemented two null models. First, we used the abundance-
based β-null modeling approach (Dini-Andreote et al., 2015; Tucker et al., 2016) to calculate the
deviation between the observed β-diversity and the null-expected β-diversity of randomly
assembled pairs of communities. We used the R code proposed by Luan et al. (2020) to generate
a null scenario by random resampling OTUs and reads in the community matrix. The total
abundances of each OTU were used as probabilities of selecting an OTU. Null expectations of
community dissimilarities for each sample pair were obtained based on average Bray-Curtis
dissimilarities of the simulated communities. The null deviation value (NDV) was then defined
as the difference between the observed and the averaged null dissimilarities. An NDV close to 0
indicates communities that are closer to the chance expectation (i.e., neutral processes), whereas
values close to 1 represent communities that are less similar than expected by chance (i.e., niche
processes). Second, we calculated the normalized stochasticity ratio (NST) based on the Bray-
Curtis abundance dissimilarity matrix using the tNST and nst boot functions in the R package
NST as recommended by Ning et al. (2019). Null taxa abundances in each sample were
calculated as a random draw (1000 times) of the observed number of individuals. Since the null
dissimilarity values were not independent, bootstrapping was carried out as a random draw of
samples (100 times). The microbial community assembly is completely deterministic when NST
is 0 and completely stochastic when NST is 1. Averaged NDVs and NSTs were compared with a
Welch two-sample t-test between organs.

<h4>Trait analyses</h4>

We examined trait variation in all individuals among the seven species of SF seedlings.
All traits were centered and scaled to unit variance before multivariate analyses. Minimal leaf
conductance (g<sub>min</sub>), stomatal density (SD) and root diameter (RD) had less than 7% missing
values. Missing values for those traits were imputed using the R package mice version 3.15.0 (Buuren et Groothuis-Oudshoorn, 2011) at the individual level. We used PCAs to evaluate the patterns of covariation between individual traits: one on leaf traits only and a second on root traits only. Each time, we plotted all individuals and variables on the two first PCA dimensions and showed the 85% confidence ellipses for all seven species. Interspecific differences were examined using ANOVA models followed by Tukey's honestly significant difference (HSD) tests on Box-Cox-transformed variables to improve normal distribution of residuals.

<h4>Drivers of the diversity and composition of endophytes</h4>

To identify the drivers of the diversity of endophytic communities (H2), we built linear models that included the Shannon diversity index as the response variable. The predictors were leaf and root traits and geographic position of the seedlings. To investigate the potential influence of geographic distance on the structure of microbial communities, we used principal coordinates of a neighborhood matrix (MEM) (Dray et al., 2006). This method involved creating spatial variables that captured various scales of heterogeneity. Using the R package adespatial, we computed spatial variables from a geographic distance matrix and tested associated Moran's *I* values. We retained the first four significant positive eigenvalues proportional to Moran's *I*. Each MEM eigenvector represented a different scale of spatial heterogeneity, with MEM1 capturing broad-scale spatial variation and the highest-numbered MEM reflecting fine-scale spatial variation. These MEM eigenvectors were subsequently employed as predictor variables in subsequent analyses. We checked the potential collinearity among variables with a variance inflation factor (VIF) with the vif function from the R package car (version 3.1-0, Fox and Weisberg 2019). To minimize model overfitting, we then conducted a stepwise selection based on the Akaike information criterion (AIC) using the stepAIC function in the R package MASS (version 7.3-57, Venables and Ripley, 2002). The normality of the distribution of the residuals for all models was verified using Shapiro's test.

The relationships between the composition of endophytes communities and their environments were estimated by performing a distance-based redundancy analysis of β-diversity matrices assessed at *q* = 1 (Jost et al., 2010; Chao et al., 2014) using the package HillR (Chao et al., 2014) and the function hill_taxa_parti_pairwise at *q* = 1. These β-diversity matrices were the response variables using the dbrda function in vegan for both bacterial and fungal communities (Oksanen et al., 2022). We used host species, leaf and root traits, and geographic position of the
seedlings (significant MEM) as the predictors. We checked for potential collinearity among variables with a VIF. To avoid model overfitting and to enhance predictive power (Bauman et al., 2018), we selected the most parsimonious model with a stepwise removal of the least contributing variable based on a significance level of 0.05 and an adjusted R² using the ordiR2step function in vegan.

We used R version 4.2.1 for all statistical analyses (R Core Team, 2020).

# <h1>RESULTS

## <h2>Endophyte community composition

### <h3>Fungi

By phylum, 83% of leaf fungi belonged to *Ascomycota* and 10% to *Basidiomycota*. For root fungi, 72% belonged to *Ascomycota*, 21% to *Basidiomycota*, and 3% to *Glomeromycota* (for more details, see Appendix S9). Within leaves, the most dominant orders across all fungal groups were *Botryosphaeriales* (13%), *Xylariales* (12%), and unknown orders from *Ascomycota* (7%) and *Hypocreales* (7%). Overall, the leaves of all host species shared the same orders, but the abundance of *Chaetosphaeriales* was particularly high in the leaves of *P. officinalis* (for more details, see Appendix S10). The most abundant fungal genus identified in leaves was *Phyllosticta* (10%; Appendix S11). Within roots, the most dominant orders across all fungal groups all belonged to *Ascomycota*: unidentified orders (16%), *Venturiales* (7%), *Sordariales* (6%), *Hypocreales* (6%), and *Helotiales* (6%). The remaining orders were present at low frequencies. The fungal orders present in roots seemed to vary more among the host species than those in leaves did (for more details, see Appendix S10). The most abundant identified fungal genus in the roots was *Melanconiella* (4%; Appendix S11).

### <h3>Bacteria

Abundant bacterial phyla in leaves included *Actinobacteriota* (52%), *Proteobacteria* (31%), *Firmicutes* (7%), and *Chloroflexi* (5%); phyla *Actinobacteriota* (43%), *Proteobacteria* (27%), *Firmicutes* (14%), and *Myxococcota* (6%) were abundant in the roots. Overall, bacterial phyla were similar between leaves and roots, except for the higher abundance of *Bacteriodota* in leaves (1% in leaves; 0.5% in roots) and *Myxococcota* in roots (0.8% in leaves; 6% in roots) (for more details, see Appendix S12). Within leaves, the most dominant orders across all bacterial community groups were *Corynebacteriales* (18%), *Rhizobiales* (16%), and *Frankiales* (9%).
(non-nitrogen-fixing). Overall, the leaves of all host species shared the same orders (for more
details, see Appendix S13). The most abundant bacterial genera identified in the leaves were
*Mycobacterium* (17%) and *Pseudonocardia* (5%; Appendix S14). Within the roots, the most
dominant orders across all bacterial groups were *Frankiales* (27%), *Rhizobiales* (13%) (non-
nitrogen-fixing), and *Bacillales* (8%). The roots among host species had the same fungal orders,
but *Burkholderiales* was especially high in the roots of *T. melinonii*. The most-abundant bacterial
genera identified in the roots were *Acidothermus* (27%) and *Bacillus* (6%) (Appendix S14).

<h3>Diversity of endophytic communities</h3>

The diversity (Hill number $^qD$, $q = 1$) of fungi and bacteria was evaluated for all seven
species in the leaf and root compartments (Appendix S15).

<h3>Fungi</h3>

Higher values of fungal diversity were found in the leaves ($^1D_{mean\_fungi\_leaf}$ = 76.1 effective species) than in the roots ($^1D_{mean\_fungi\_root}$ = 41.7 effective species) (Appendix S15A, B). There were significant interspecific differences of fungal diversity in the roots ($P$ = 0.03) but not in leaves. Highest values of fungal diversity in leaves were obtained for *P. officinalis* ($^1D_{mean\_fungal\_leaf}$ = 93.3 effective species), and lowest values in leaves were obtained for *E. falcata* ($^1D_{mean\_fungi\_leaf}$ = 63.2 effective species). Highest values of fungal diversity in roots were obtained for *T. melinonii* ($^1D_{mean\_fungi\_root}$ = 58.6 effective species), and lowest values in roots were obtained for *E. falcata* ($^1D_{mean\_fungi\_root}$ = 30.4 effective species). For fungi, microbial composition (Figure 1) differed significantly between leaves and roots (Figure 1A, B). The PERMANOVA identified differences between leaf and root fungal communities (PERMANOVA: $F_{1,127}$ = 14.133, $P$ < 0.001). Homogeneity of dispersions (permutest: $F_{1,127}$ = 89.308, $P$ = 0.01) identified significant community changes, with a larger dispersion for the root fungal communities. There were no effects of host plant species on the composition of leaf fungal communities (PERMANOVA: $F_6,64$ = 1.133, $P$ = 0.052) (Figure 1C), nor on root fungal communities (PERMANOVA: $F_{5,50}$ = 1.062, $P$ = 0.1) (Figure 1E). Across host plant species, dispersion did not differ for leaf fungal communities (permutest: $F_{6,64}$ = 0.644, $P$ = 0.8) (Figure 1D) or for root fungal communities (permutest: $F_{5,50}$ = 0.789, $P$ = 0.6) (Figure 1F).

<h3>Bacteria</h3>

For bacteria (Appendix S15C, D), we did not find significant differences among host
species. Root communities tended to be more variable than the leaf communities depending on
the host species. Higher values for bacterial diversity were found in the leaves (¹D<sub>mean_bacteria_leaf</sub> = 65.4 effective species) than in the roots (¹D<sub>mean_bacteria_root</sub> = 61.9 effective species). Highest values of bacteria diversity in leaves were obtained for *V. surinamensis* (¹D<sub>mean_bacteria_leaf</sub> = 73.4 effective species) and lowest values in leaves were obtained for *T. melinonii* (¹D<sub>mean_bacteria_leaf</sub> = 53.9 effective species). Highest values of bacteria diversity in roots were obtained for *P. officinalis* (¹D<sub>mean_bacteria_root</sub> = 71.5 effective species), and lowest values in roots were obtained for *I. hostmannii* (¹D<sub>mean_bacteria_root</sub> = 49.5 effective species). Bacterial composition (Figure 2) differed significantly between the leaves and the roots (Figure 2A, B). The PERMANOVA identified composition differences between leaf and root bacterial communities (PERMANOVA: *F*<sub>1, 130</sub> = 38.162, *P* < 0.001). Homogeneity of dispersions (permutest: *F*<sub>1, 130</sub> = 4.626, *P* = 0.07) did not identify significant community changes. Leaf bacterial communities did not differ significantly across host plant species (PERMANOVA: *F*<sub>6, 62</sub> = 1.076, *P* = 0.1) (Figure 2C). However, the dispersion of leaf bacterial communities differed across plant host species (permutest: *F*<sub>6, 62</sub> = 2.839, *P* = 0.03) (Figure 2D). Specifically, leaf bacterial communities in *V. surinamensis* were less variable than leaf bacterial communities in *T. melinonii* (Tukey's HSD; 95% CI = -0.19 to -0.01, *P* = 0.01). There were host plant species effects on the composition of root bacterial communities (PERMANOVA: *F*<sub>6, 56</sub> = 2.036, *P* < 0.001) (Figure 2E), but no differences in dispersion of root bacteria communities among host plant species (permutest: *F*<sub>6, 56</sub> = 0.953, *P* = 0.4) (Figure 2F).

<h2>Community assembly processes of SF seedling endophytes

The results of the abundance-based *β*-null model showed that both communities found
either in the leaves or roots differed from neutral predictions. The NDV values did not differ
between bacterial leaf and root communities (bacterial NDV<sub>leaf</sub> = 0.49; bacterial NDV<sub>root</sub> = 0.50;
t = -1.8, df = 103, *P* = 0.24; Figure 3A). For fungi, the deviation from the random expectation
was significantly larger for root fungal communities than for leaf fungal communities (fungal
NDV<sub>leaf</sub> = 0.65; fungal NDV<sub>root</sub> = 0.76; t = -18.01, df = 106, *P* < 2.2e-16; Figure 3A). The NST
revealed that the endophytic communities were mainly dominated by stochastic processes
(Figure 3B). While assembly processes did not differ between leaves and roots for bacterial
communities (bacterial NST<sub>leaf</sub> = 0.85; bacterial NST<sub>root</sub> = 0.85; t = -0.2, df = 173.7, *P* = 0.8;
Figure 3B), fungal assembly processes did (fungal NST<sub>leaf</sub> = 0.66; fungal NST<sub>root</sub> = 0.90; t = -
37.4, df = 153.9, *P* = < 2.2e-16; Figure 3B).
## <h2>Functional trait space in seedlings

The nine functional traits varied among the seven SF tropical tree species (Appendix S16). Leaf traits such as $g_{min}$, LSWC, and LT varied more than Total_LA, SLA, and SD. Among host species, LSWC varied greatly, with seedlings of *V. surinamensis* having the greatest leaf water capacity and *E. falcata*, *I. hostmannii*, and *T. melinonii* the lowest. For root traits, *J. copaia* and *S. globulifera* had the highest SRL. *T. melinonii* and *V. surinamensis* had opposite trends for RD and RTD: *T. melinonii* had higher RTD and lower RD, while *V. surinamensis* had lower RTD and higher RD.

The PCA on leaf traits (Figure 4A) and root traits (Figure 4B) showed that root traits explained more of the variability (93%) compared to leaf traits (56%). Considering leaf traits, LSWC (36%), $g_{min}$ (26%) and Total_LA (22%) contributed to the first axis, while SLA (55%) and LT (39%) contributed the most to the second axis (Appendix S17). Stomatal density (SD) contributed the most (79%) to the third axis and Total_LA the most (68%) to the fourth axis. For root traits, RD contributed the most to the first (50%) and third (50%) dimension but not the second dimension. For the second axis, RTD contributed to 74% of the variation (Appendix S17).

## <h2>Drivers of the diversity and composition of endophytes

We performed linear regressions (for diversity) and built distance-based linear models (for composition).

## <h3>Fungi

The diversity of fungal endophytes (Table 1) was explained by other variables than those explaining the diversity of bacterial endophytes. The model selected total leaf area, one spatial predictor, and host species identity, which collectively, explained 44% of leaf fungal community diversity. Root tissue density and one spatial predictor collectively explained 8% of root fungal diversity. Up to 2% of the leaf fungal endophyte composition could be explained by host species identity and $g_{min}$ (Appendix S18). The root fungal endophyte composition could not be explained by host species or any of the measured traits or spatial predictors (Appendix S18).

## <h3>Bacteria

The model selection identified LSWC, one spatial predictor, and host species identity as contributing to leaf bacterial diversity (Table 2). Collectively, these variables explained 60% of leaf bacterial diversity. For root bacterial diversity, RTD, RD, and two spatial predictors were
retained by the model selection and collectively, explained 34% of the root bacterial diversity.
The composition of the leaf bacterial endophytes was best explained by the null model, and up to
8% of the root bacterial endophytic composition could be explained by host species identity and
broad-scale spatial predictors (Appendix S18).

<h1>DISCUSSION

To our knowledge, this study is the first on the endophytic microbiota of SF seedlings.
Contrasting ecological processes drive the assembly of endophytic communities in tree seedlings
of a tropical seasonally flooded forest. Discrepancies were found between fungal and bacterial
endophytes in terms of diversity, community composition, and the factors influencing their
assembly, highlighting that these microorganisms have distinct ecological responses within
plants. Leaves and roots acted as selective forces, differently shaping their endophytic
communities by contributing to much of their diversity and composition, but the contribution of
leaf and root functional traits was relatively low.

<h2>Interplay of stochastic and niche-based processes

In line with our first hypothesis, we anticipated that the unique environmental conditions
present in SF forests and our study of the microorganisms residing within plants would enable us
to detect significant selective pressure on both fungal and bacterial communities. However, our
findings suggested that while both stochastic and deterministic processes governed the
endophytic assemblage in the leaves and roots of SF seedlings, stochasticity prevailed (Figure
3B). Stochastic processes encompass random changes due to birth, death, immigration and
emigration (Zhou and Ning, 2017), which certainly have structured both fungal and bacterial
communities and have been shown to be important drivers of microbial assembly (Nemergut et
al., 2013; Evans et al., 2017). Factors such as dispersal limitation, mass effects, priority effects,
or heterogeneous selection can also structure the community, resulting in a greater level of
stochasticity (Evans et al., 2017; Boyle et al., 2021). Interestingly, niche-based processes were
also at play in shaping endophytic assembly (Figure 3A). Selection could result from intrinsic
differences among bacteria and fungi (growth rates, survival, or reproduction), the influence of
abiotic conditions (environmental filtering) or biotic interactions (among microorganisms and
between microorganisms and the host).
In support of the second hypothesis, we found differences between bacterial and fungal
communities in terms of diversity (Appendix S15), composition (Figures 1, 2), and assembly
processes (Figure 3). Our results showed that the balance between stochasticity and determinism
differed between the bacteria and fungi communities (Figure 3B) similar to the contrasting
assembly patterns between bacterial and fungal communities in various terrestrial ecosystems
(Zinger et al., 2019; Luan et al., 2020; Bahram et al., 2021), termite mounds (Chen et al., 2021),
phyllosphere (Yan et al., 2022), and roots (Zhuang et al., 2020). These differences can be
explained by inherent structural and functional differences between multicellular fungi and
unicellular bacteria. For example, filamentous fungi can deal better with heterogeneous habitats
and conditions because their hyphae can transfer water and nutrients, whereas bacteria are more
sensitive to pH and nutrient availability. However, in our study, stochasticity did not entirely
govern the abundance and composition of the bacterial communities, because stochastic and
deterministic processes were equally at play, regardless of the plant compartment (Figure 3).

Thus, our results do support studies such as Liu et al. (2021) that found that stochastic processes
shape the assemblage of the bacterial communities across different plant compartments.

However, factors such as soil type, plant host identity, as highlighted by Bulgarelli et al. (2013),
are also important and can potentially account for a large portion of deterministic patterns
(Figure 3). Concerning fungal assembly, our results align with those of Gao et al. (2020); the
contributing processes differed according to the plant compartment (Figure 3), illustrating that
the organs also help shape microbial communities.

<h2>Plant organs shape microbial communities

Our results revealed significant variation in the endophytic community composition
between the leaves and roots (Figures 1, 2), highlighting that they provide different ecological
niches for microbes (Bodenhausen et al., 2013; Robinson et al., 2016; Fang et al., 2019; Given et
al., 2020). Interestingly, the leaves hosted a greater diversity of bacteria and fungi compared to
the roots (Appendix S12). *Corynebacteriales* and actinobacteria *Mycobacterium* and
*Pseudonocardia* were especially abundant in leaves and are known to be associated with plants
undergoing flooding (Wang et al., 2016; Francioli et al., 2022) (Appendices S13, S14). One
might have expected roots to host more microbes in terms of total number of taxa because the
soil and rhizosphere generally have high microbial diversity (Li et al., 2021; Sun et al., 2021).

However, root hypoxia or anoxia in SF forests could strongly affect microbial diversity. Indeed,
we found that the roots were dominated by genera known to be growth-promoting bacteria and involved in biogeochemical cycling of elements such as nitrogen (*Frankiales*, *Rhizobiales*, and *Bacillales*). *Acidothermus*, a genus of thermophilic, acidophilic bacteria belonging to *Frankiales*, could contribute to the plant's adjustment to low soil pH (Appendices S1, S14) (Liu et al., 2019). In environments with low oxygen, high reactive oxygen species (Colmer et al., 2009), and low nutrients (Martínez-Arias et al., 2022), roots of SF seedlings may provide stronger selection pressure than leaves on the endophytes.

Our study provided partial support for the third hypothesis; the NDV results estimated more selection in roots than leaves, but only for fungal communities (Figure 3A). Roots have molecular and architectural properties that drive their interactions with microorganisms (Kawa and Brady, 2022). For example, root exudates can “screen” microbes from bulk soil (Sasse et al., 2018; Bai et al., 2022). Each type of root cells has its own unique molecular signature that selects for very specific microorganisms, even between beneficial and pathogenic microbes (Kawa and Brady, 2022). Selection within roots might be greater for fungal endophytes than bacterial due to the physicochemical properties of the soil in this SF habitat, thus constituting an environmental filter that favors more determinism (Wang et al., 2022; Philippot et al., 2023). However, the NST results (Figure 3B) also showed that stochasticity processes shape root fungal communities, contradicting previous findings that fungal community assemblies were mainly driven by deterministic processes (Yan et al., 2022; Schimann et al., 2023; Taniguchi et al., 2023). The greater fungal species diversity in the roots than in leaves also suggests stochastic processes at work (Figure 1B). Plant endophytes can also be impacted by the plant's developmental stage. For example, fungal communities might initially be more influenced by stochastic processes during the early growth stages of the plant, then eventually be more strongly driven by deterministic processes as the plant matures (Gao et al., 2020; Xiong et al., 2021b; Wang et al., 2022).

<h2>Plant traits poorly explain microbial communities

Linking functional traits and plant endophytes can offer insights into how plant filtering effects can shape the richness (i.e., effective number of species) (Appendix S15) and composition expressed in terms of effective number of distinct communities (Figures 1, 2) microbial communities found. Mean values for leaf and root traits varied among the species of SF seedlings (Appendix S16), indicating functional differences among the tree species (Figure 4). While leaf traits explained 55% of the trait variation, root traits explained 93%, highlighting
the importance of root traits in evaluating plant strategies. Weak root trait convergence in SF soils (Weemstra et al., 2023), suggests that the tree species have diverse root strategies when the trees are subjected to strong environmental filtering (M. Boisseaux et al., unpublished manuscript).

Major drivers for leaf microbial diversity were host species identity, broad-scale spatial variations, and leaf saturated water content (LSWC) for bacterial communities. These drivers collectively explained 60% (bacteria) and 44% (fungi) of the variation in composition (Tables 1, 2). These results illustrate that the leaves that can hold more water harbor a greater diversity of bacteria, aligning with previous research on water-flow dynamics in leaves (Grinberg et al., 2019; Doan et al., 2020). Stomatal density did not emerge as a significant driver of leaf bacterial diversity, even though stomata are entry points for endophyte colonization. Perhaps leaf veins, considered as a promising leaf trait because they transport nutrients and water, need more attention because Yan et al. (2022) showed that they can influence the abundance and diversity of phyllosphere bacteria. Similar to previous findings (Godsoe et al., 2022; Liu et al., 2021; Harris et al., 2023), the composition of endophytic microorganisms (Appendix S15) was poorly explained by plant traits. Leaf minimal conductance ($g_{min}$) was the only trait to explain fungal community composition in leaves. This trait is related to residual passive water loss through a permeable cuticle and/or leaky stomata (Duursma et al., 2019), which are also entry points for some fungi. Future studies could investigate whether the anatomy and chemical composition of the cuticle influence the fungal community composition (Arya et al., 2021).

Root traits partly explain root endophyte diversity (34% for bacteria and 8% for fungi), primarily driven by spatial predictors, RTD (for bacteria and fungi), and RD (for bacteria). Spatial heterogeneity within the reservoir of recitable soil microbes could explain part of the variation (Arnault et al., 2023), even though seedlings were sampled in the same SF forest habitat. Curiously, host species identity was not a key factor for endophytic community diversity in roots, because it was never retained by the model selections (Tables 1, 2), emphasizing that other unmeasured physiological root traits (e.g., root porosity, root phosphatase activity) could be at play and masking the effect of host species (Pan et al., 2022; Yaffar et al., 2022). However, host plant species partly explained variations in the composition of bacterial root communities (Figure 2E; Appendix S18), but not for fungal communities in either leaves or roots. Most surprisingly, root fungal composition could not be explained by the measured root traits that
pertained to the RES (Bergmann et al., 2020). The RES was initially designed to assess root trait relationships with arbuscular mycorrhizal (AM) fungi (Sweeney et al., 2021). Root studies have indeed mostly focused on AM fungi for obvious reasons concerning their mutualistic relationships with plants, but roots also host many other fungi. Members of *Ascomycota* were found to be dominant in the fungal communities inhabiting SF seedlings, similar to the results of other studies (Bonito et al., 2014; Hardoim et al., 2015). The dark septate root endophyte *Melanconiella* (Diaporthales) was the most abundant fungus colonizing SF roots (Appendix S11) and is known to be involved in host resistance (Farias et al., 2020; dos Reis et al., 2022), even under flooding conditions (Kohout et al., 2012; Martínez-Arias et al., 2022). Inherent to the choice of our PCR primer for fungal ITS, if we had targeted AM fungi, RES traits most likely could have better explained fungal composition. We hypothesize that for non-AM fungi, microbe-microbe interactions (Hassani et al., 2018), priority effects (Boyle et al., 2021), and stochastic processes might be more important. Indeed, the null model analysis (Figure 3) complements the identification of the drivers of composition well, indicating that stochasticity prevails in endophytic communities.

Finally, the influence of plant traits could be obscured by functional redundancy within microbial communities (Allison and Martiny, 2008). For fungi and bacteria to colonize and persist in plant species inhabiting the particular environment of SF forests, they must be resilient to the changing environmental conditions in plant tissues, which also leads to difficulties in explaining their composition (Allison and Martiny, 2008; Liu et al., 2019). Although the harshness of the environment correlates with the dominance of deterministic processes, microbial communities driven by stochasticity, with increasing potential redundancy, could be better equipped to handle disturbances (De Vrieze et al., 2020).

<h2>Limits to our study

This study provides a snapshot of plant-associated microbial communities, but how the endophytic communities vary temporally remains a question. Subject to seasonal flooding, SF tree species are colonized and recolonized by microorganisms before and after flooded conditions (Martínez-Arias et al., 2022). The sampling time is a critical factor in the experimental design; for example, root exudation changes with plant growth and environmental conditions (Preece et al., 2018), impacting the microbes recruited in the rhizosphere (Berg and Smalla, 2009). In our study of tree seedlings, the low light environment in the understory of the
tropical forest impacts functional traits (Funk et al., 2020) and microbial assembly processes (Bai et al., 2022), but how leaf and root endophytic communities resemble those of adult trees remains unexplored.

Additionally, microbial communities consist of much more than just fungi and bacteria; diverse microbes such as archaea, nematodes, yeasts, protists, and viruses are also present secreting and exchanging various metabolites that allow for their coexistence and create new niches (Yamagishi et al., 2021). How these other microbes influence plant endophytic assemblages and plant performance has yet to be revealed. Complementing the study of endophytes, work to simultaenously characterize soil microbes and plant epiphytes (Schimann et al., 2023) could enhance our understanding of habitat filtering because the bulk soil is a common reservoir for plant microbiota belowground and aboveground (Bai et al., 2015; Zarraonaindia et al., 2015).

Although we focused here on the role of plant functional traits as niche variables that shape microbial communities, we are aware that plant-microbiome interactions are not unidirectional. Phyllosphere and rhizosphere microbes can affect plant growth and health. For example, species of *Bacillus* (in our study, 2% of the bacteria in the leaves, 6% in the roots) can promote plant fitness (Kashyap et al., 2019), and species of *Colletotrichum* (3% of fungi in the leaves) are economically serious phytopathogens (Dean et al., 2012) and can make leaves less appealing to leaf-cutting ants (Devi et al., 2020). Endophytes could also help tree species to resist waterlogging stress by colonizing the porous aerenchyma tissue that is characteristic of plant species in SF forests and by enhancing the oxygen status of the tissue, especially in the roots. *Rhizophagus irregularis* (*Glomeromycota*) found in our study (0.3%), increases the water uptake capacity in flooded tomato plants (Calvo-Polanco et al., 2014).

<h1>CONCLUSIONS

Overall, our findings contribute to the understanding of endophyte diversity and community composition in SF tree seedlings. We demonstrated distinct patterns in the fungal and bacterial communities between the leaf and root compartments. The endophytic communities were governed by both stochastic and deterministic processes. However, numerous studies have shown that endophytic community assembly patterns vary considerably; some have found greater stochastic effects, while others have found greater deterministic effects. This diversity of
findings may be attributed to factors such as study site, the host species, developmental stage,
and organ of the host plant, and the choice of models to dissect assembly processes.

Additionally, host plant traits only partly explained the diversity of the endophytic communities,
which raises questions about which traits to select for study. Future research on other avenues
such as root and leaf metabolism (e.g., chemical composition of tissues, defense metabolites,
phytohormones; Eichmann et al., 2021) should lead to a more comprehensive understanding of
the drivers that shape endophytic communities in different plant compartments and whether or
not these communities will be resilient to environmental changes.

<h1>ACKNOWLEDGMENTS

The authors thank the University of Guyane, CNES and CTG for a Ph.D. grant to M. Boisseaux.
The authors are grateful for financial support from the Ceba annual project DRYER, part of an
"Investissement d'Avenir"" grant managed by the Agence Nationale de la Recherche and the
Center for the Study of Biodiversity in Amazonia (CEBA, ref. ANR-10-LABX-25-01). The
internationally recognized certificate of compliance with information from the permit is available
in the Access and Benefit-sharing Clearing-House with the ABS-CH Unique Identifier (UID)
ABSCH-IRCC-FR-260359-1. The reference number of the permit is the TREL2206915S/563,
issued 09 May 2022 by the French Ministry of Ecological Transition. The authors are grateful to
Pascal Petronelli and the CIRAD inventory team for their work on tree inventories and species
identification. Special thanks go to Luna Saunier, Léa Lebert, Paul Mischler, Clarisse Pettier,
Thomas Gaquière, Sylvain Schmitt for their tremendous help during seedling samplings. The
authors also thank CIRAD-US Analyses (TA B-49/01 34398 Montpellier Cedex 5, France) for
the soil analyses. The authors thank staff of the GenoToul bioinformatics platform Toulouse
Occitanie for help and computing and storage resources (Bioinfo Genotoul,
https://doi.org/10.15454/1.5572369328961167E12). For Open Access, the authors received a
CC-BY public copyright license for all versions of this article. We thank reviewer Erika
Buscardo, an anonymous reviewer, Associate Editor Meghna Krishnadas, and the copy editor
Beth E. Hazen for their valuable comments on the manuscript.

<h1>AUTHOR CONTRIBUTIONS
M.B.: investigation, conceptualization, methodology, validation, data curation, formal analysis, visualization, project administrator, funding acquisition, writing original draft, review, and editing. V.T.: investigation, methodology. A.B.: investigation. J.C.: investigation. S.-O.C.: investigation. S.C.: conceptualization, investigation, supervision, writing original draft, review and editing. H.S.: conceptualization, supervision, writing original draft, review, and editing. C. Stahl: conceptualization, methodology, investigation, supervision, writing original draft, review, and editing. All authors gave final approval for publication.

## <H1>CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflict of interest.

## <h1>DATA AVAILABILITY STATEMENT

Data and software commands necessary to reproduce this study are available at
https://github.com/mbthese/DRYER_natura: Plant traits as drivers of endophytic communities in
seasonally flooded forests? The raw sequence data for the 16S rRNA and ITS2 amplicons for
this study and the supporting metadata have been deposited in the Cirad Dataverse repository
(doi:10.18167/DVN1/D93AUT).

## <h1>SUPPORTING INFORMATION

Additional Supporting Information may be found online in the supporting Information section at
the end of the article.

**Appendix S1.** Results of analyses of composite samples from the 0–10-cm horizon of seasonally flooded soils at eight locations of Paracou station in French Guiana.

**Appendix S2.** Phylogeny of the seven selected tree species.

**Appendix S3.** Map and description of collection location for each seedling and dates of soil sampling.

**Appendix S4.** Experimental design.

**Appendix S5.** Seedling sizes by species.

**Appendix S6.** Description of bacterial and fungal endophyte data sets.

**Appendix S7.** Rarefaction curves for the fungal and bacterial communities in leaves and roots of seedlings from the seven tree species in the seasonally flooded forest.
**Appendix S8.** Taxonomic assignment.

**Appendix S9.** Major fungal phyla in leaves and roots of the seven tropical tree seedlings.

**Appendix S10.** Top 20 fungal orders in leaves and roots of the seven tropical tree seedlings.

**Appendix S11.** Top 20 fungal genera in leaves and roots of the seven tropical tree seedlings.

**Appendix S12.** Major bacterial phyla in leaves and roots of the seven tropical tree seedlings.

**Appendix S13.** Top 20 bacterial orders in leaves and roots of the seven tropical tree seedlings.

**Appendix S14.** Top 20 bacterial genera in leaves and roots of the seven tropical tree seedlings.

**Appendix S15.** Diversity, calculated using Hill numbers with $q = 1$, of (A, B) fungal and (C, D) bacterial communities in the (A, C) leaves and (B, D) roots of seven tropical tree seedlings.

**Appendix S16.** Trait variations in 72 seedlings from among seven species of seasonally flooded tropical forest. Box plots show variations in raw trait variations. Box-Cox-transformed variables were tested for significant differences among species using Anova models followed by Tukey's HSD tests. Lowercase letters indicate significant differences (*P* < 0.05). Traits included minimum leaf conductance (gmin, mmol m⁻² s⁻¹), leaf saturated water content (LSWC, g g⁻¹), specific leaf area (SLA, cm² g⁻¹), leaf thickness (LT, mm), total leaf area (Total_LA, cm²), stomatal density (SD, mm⁻²), specific root length (SRL, cm g⁻¹), root diameter (RD, mm), and root tissue density (RTD, g cm⁻³).

**Appendix S17.** Total contribution of variables to PCA axes for leaf and root PCA. Dimension (Dim), leaf area (Total_LA, cm²), leaf saturated water content (LSWC, g g⁻¹), leaf thickness (LT, mm), minimum leaf conductance (gmin, mmol m⁻² s⁻¹), root diameter (RD, mm), root tissue density (RTD, g cm⁻³), specific leaf area (SLA, cm² g⁻¹), specific root length (SRL, cm g⁻¹) and stomatal density (SD, mm⁻²).

**Appendix S18.** Drivers of community composition. Fungal (left) and bacterial (right) endophyte community structure within leaves (top) and roots (bottom) explained by host species, leaf and root traits, and spatial predictors (MEM). The figure shows the results from the stepwise selection of variables using dbrda function in vegan. Traits were BoxCox transformed and standardized. The x-axis shows the adjusted R² for each individual tested variable and the cumulative adjusted R² for all variables. Traits include leaf area (Total_LA, cm²), leaf saturated water content (LSWC, g g⁻¹), leaf thickness (LT, mm), minimum leaf conductance (gmin, mmol m⁻² s⁻¹), root diameter (RD, mm), root tissue density (RTD, g cm⁻³), specific leaf area (SLA, cm² g⁻¹), specific root length (SRL, cm g⁻¹) and stomatal density (SD, mm⁻²).
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<table><caption>**Table 1.** Drivers of fungal diversity within leaves and roots of seasonally flooded seedlings belonging to seven species. Linear models were built with the Box–Cox-transformed Shannon diversity index as the response variable. Predictors were AIC-stepwise-selected variables that included leaf and root traits, host species, and host geographic position (MEM). The leaf trait retained by the model was total leaf area (Total_LA, cm²). The root trait retained by the model was root tissue density (RTD, g cm⁻³). The normality of the distribution of the residuals for all models was verified using Shapiro's test. Bold *P*-values indicate statistically significant (*P* < 0.05) predictor variables.</caption><thead><tr><th>Fungi</th><th>Estimate</th><th>SE</th><th>t</th><th>P</th></tr></thead><tbody><tr><td>Leaf</td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td>8.16</td><td>1.40</td><td>5.828</td><td>0.000</td></tr></tbody></table>
<table><caption>**Table 2.** Drivers of bacterial diversity within leaves and roots of seasonally flooded seedlings belonging to seven species. Linear models were built with the Shannon index as the response variable. Predictors were AIC-stepwise-selected variables that included leaf and root traits, host species, and host geographic position (MEM). The leaf trait retained by the model was leaf saturated water content (LSWC, g g⁻¹). Root traits retained by the model were root diameter (RD,</caption><tr><td>Total_LA</td><td>-1.04</td><td>0.61</td><td>-1.690</td><td>0.096</td></tr><tr><td>Iryanthera<br>hostmannii</td><td>4.77</td><td>1.95</td><td>2.443</td><td><strong>0.017</strong></td></tr><tr><td>Jacaranda copaia</td><td>8.05</td><td>1.88</td><td>4.270</td><td><strong>0.000</strong></td></tr><tr><td>Pterocarpus<br>officinalis</td><td>3.47</td><td>1.86</td><td>1.872</td><td>0.066</td></tr><tr><td>Symphonia<br>globulifera</td><td>2.15</td><td>2.16</td><td>0.999</td><td>0.322</td></tr><tr><td>Tachigali<br>melinonii</td><td>6.53</td><td>1.96</td><td>3.331</td><td><strong>0.001</strong></td></tr><tr><td>Virola<br>surinamensis</td><td>10.11</td><td>1.90</td><td>5.333</td><td><strong>0.000</strong></td></tr><tr><td>MEM1</td><td>-0.91</td><td>0.57</td><td>-1.601</td><td>0.114</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.44</td><td></td><td></td><td></td></tr><tr><td>P</td><td><strong>&lt;0.001</strong></td><td></td><td></td><td></td></tr><tr><td><strong>Root</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td>5.56</td><td>0.15</td><td>36.004</td><td><strong>0.000</strong></td></tr><tr><td>RTD</td><td>-0.40</td><td>0.17</td><td>-2.385</td><td><strong>0.021</strong></td></tr><tr><td>MEM3</td><td>-0.32</td><td>0.17</td><td>-1.916</td><td>0.061</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.082</td><td></td><td></td><td></td></tr><tr><td>P</td><td><strong>&lt;0.037</strong></td><td></td><td></td><td></td></tr></table>
mm) and root tissue density (RTD, g cm⁻³). The normality of the distribution of the residuals for all models was verified using Shapiro's test. Bold *P*-values indicate statistically significant (*P* < 0.05) predictor variables. Note that here the intercept is higher than in Table 1 because linear models were carried out on non-transformed diversity as it already followed a normal distribution.

<table><thead><tr><th>Bacteria</th><th>Estimate</th><th>SE</th><th>t</th><th>P</th></tr></thead><tbody><tr><td><strong>Leaf</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td>45.15</td><td>8.02</td><td>5.627</td><td><strong>0.000</strong></td></tr><tr><td>LSWC</td><td>12.83</td><td>5.69</td><td>2.254</td><td><strong>0.028</strong></td></tr><tr><td>Iryanthera<br>hostmannii</td><td>18.32</td><td>7.81</td><td>2.347</td><td><strong>0.022</strong></td></tr><tr><td>Jacaranda copaia</td><td>35.68</td><td>10.26</td><td>3.478</td><td><strong>0.001</strong></td></tr><tr><td>Pterocarpus<br>officinalis</td><td>-0.69</td><td>11.55</td><td>-0.060</td><td>0.953</td></tr><tr><td>Symphonia<br>globulifera</td><td>30.91</td><td>13.59</td><td>2.274</td><td><strong>0.027</strong></td></tr><tr><td>Tachigali<br>melinonii</td><td>32.11</td><td>8.21</td><td>3.910</td><td><strong>0.000</strong></td></tr><tr><td>Virola<br>surinamensi</td><td>27.41</td><td>16.96</td><td>1.616</td><td>0.111</td></tr><tr><td>MEM1</td><td>5.69</td><td>2.55</td><td>2.227</td><td><strong>0.030</strong></td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.60</td><td></td><td></td><td></td></tr><tr><td><strong>P</strong></td><td><strong>&lt;0.001</strong></td><td></td><td></td><td></td></tr><tr><td><strong>Root</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>(Intercept)</td><td>61.92</td><td>2.27</td><td>27.329</td><td>0.000</td></tr><tr><td>RTD</td><td>-4.59</td><td>3.16</td><td>-1.454</td><td>0.151</td></tr><tr><td>RD</td><td>-7.35</td><td>3.20</td><td>-2.300</td><td>0.025</td></tr><tr><td>MEM1</td><td>11.73</td><td>2.38</td><td>4.930</td><td>0.000</td></tr></tbody></table>
<table><tr><td>MEM2</td><td>-7.56</td><td>2.36</td><td>3.205</td><td>0.002</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.34</td><td></td><td></td><td></td></tr><tr><td>P</td><td>&lt;0.001</td><td></td><td></td><td></td></tr></table>

Figure legends

<figure><img src="image_2.png" /></figure>

Figure 1 Analyses of composition of fungal communities from roots and from leaves of seven seasonally flooded tropical tree species using Hill numbers ($q = 1$). Results of PCoA at (A) the organ level and at the plant host species level within (C) leaves and (E) roots. Permutation test on the distances to the centroid for each corresponding PCoA to detect significant dispersion differences between compartments and among host species (B, D, F). *Tachigali melinonii* was removed from the root analysis because of the low sample size.
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

Figure 2 Composition of bacterial communities belonging to seven seasonally flooded tropical tree species using Hill numbers ($q = 1$). Results of PCoA at (A) the organ level, at the plant host species level within (C) leaves and (E) roots. Permutation test on the distances to the centroid for each corresponding PCoA to detect significant dispersion differences between compartments and among host species (B, D, F).
A. Niche or neutral processes

<figure><img src="image_10.png" /></figure>

B. Stochastic or deterministic assembly

<figure><img src="image_11.png" /><figcaption>Figure 3. (A) β-Null deviation values (NDV) and (B) normalized stochasticity ratio (NST) for leaf- and root-associated fungi and bacteria from seedlings of seven seasonally flooded tree species. Results of the Welch two-sample *t*-test between communities from roots and leaves are shown. NDV values close to 0 indicate higher influence of neutral processes; those close to +1 indicate higher influence of niche processes structuring community assembly. NST values greater than 0.5 indicate a more stochastic assembly; those less than 0.5 indicate a more deterministic assembly.</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>Figure 4 PCA of functional plant traits for 72 individual seedlings belonging to seven seasonally flooded tree species. PCA for (A) leaf traits only: minimum leaf conductance (g<sub>min</sub>, mmol m<sup>-2</sup> s<sup>-1</sup>), leaf saturated water content (LSWC, g g<sup>-1</sup>), specific leaf area (SLA; cm<sup>2</sup> g<sup>-1</sup>), leaf thickness (LT, mm), total leaf area (Total_LA, cm<sup>2</sup>), and stomatal density (SD, mm<sup>-2</sup>). PCA for (B) root traits only: specific root length (SRL, cm g<sup>-1</sup>), root diameter (RD, mm), and root tissue density (RTD, g cm<sup>-3</sup>). All traits were centered and scaled to unit variance. All individuals and variables were plotted on the two first PCA dimensions. Confidence intervals ellipses were computed at 85% around host species.</figcaption></figure>