# Drivers of seasonal dynamics in Ulva spp. associated microbiota and surface metabolome: The interplay between environment and host physiology

Sauvann Paulino, Cyril Noël, Laura Rieusset, Laure Taupin, Gwenaelle Le Blay, Nathalie Bourgougnon

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

## ► To cite this version:

Sauvann Paulino, Cyril Noël, Laura Rieusset, Laure Taupin, Gwenaelle Le Blay, et al. Drivers of seasonal dynamics in Ulva spp. associated microbiota and surface metabolome: The interplay between environment and host physiology. Current Research in Microbial Sciences, 2025, 9, pp.100439. 10.1016/j.crmicr.2025.100439. hal-05215199

HAL Id: hal-05215199

https://hal.univ-brest.fr/hal-05215199v1

Submitted on 20 Aug 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published 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_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Drivers of seasonal dynamics in *Ulva* spp. associated microbiota and surface
metabolome: The interplay between environment and host physiology

Sauvann Paulino<sup>a,b,●</sup>, Cyril Noël<sup>c</sup>, Laura Rieusset<sup>a</sup>, Laure Taupin<sup>a</sup>, Gwenaelle Le Blay<sup>b,*</sup>,
Nathalie Bourgoungon<sup>a</sup>

ARTICLE INFO

Keywords:

Epiphytic bacteria
Surface metabolome
Temporal dynamic
Seasonal drivers

A B S T R A C T

Marine ecosystems are increasingly affected by climate change and eutrophication, placing considerable stress on macroalgae and potentially disrupting their mutualistic interactions with surface-associated microorganisms, especially bacteria. This study explored the temporal dynamics of epiphytic microbial communities (16S rRNA) and the surface metabolome (LC-MS) of *Ulva* spp. from southern Brittany (France) over a 17-month period. The data obtained were analyzed through multivariate techniques to uncover correlations between bacterial community structure and surface metabolites. Biochemical and environmental parameters were also examined to identify the factors driving temporal variations in the macroalgal surface landscape. The analysis revealed distinct bacterial communities on *Ulva* spp. surface, compared to those in surrounding seawater or on rocky substrate. Key environmental factors, including salinity, nitrate concentration, and precipitation, as well as the physiological traits of *Ulva* spp. (such as uronic acid content, lipid levels, and moisture), were identified as primary drivers of microbiota dynamics. A stable core microbial community, primarily composed of *Alphaproteobacteria*, *Flavobacteriia*, *Oligoflexia*, and *Saprospira*, was also observed within the *Ulva* spp. microbiota. Finally, correlations between Amplicon Sequence Variants (ASVs) and surface metabolites underscored the importance of an integrated multi-omics approach to enhance our understanding of the complex dynamics between eukaryotic hosts and their associated microbiota.

1. Introduction

Macroalgae play a pivotal role in marine ecosystems by producing oxygen, providing shelter for diverse marine species, and serving as a food source for a wide range of herbivores (Pfister et al., 2019; García-Poza et al., 2022). However, macroalgal-based ecosystems are facing growing threats from human activities, such as coastal development, pollution, and industrial practices, as well as from climate change-related factors, including rising temperatures, ocean acidification, sea-level rise, and the spread of invasive species (Filbee-Dexter et al., 2020; Pacheco et al., 2020; García-Poza et al., 2022). Over time, these disturbances may drastically alter the biogeographic distribution of macroalgae and compromise the critical ecosystem services they provide.

Growing interest has been directed toward understanding the

interactions between macroalgae and their associated microorganisms. As with all submerged surfaces in seawater, macroalgae are colonized by diverse microbial communities. The composition of these communities is influenced by factors such as the section and age of the thallus, the season, the location, and the algal species (Jensen et al., 1996; Green and Bohannan, 2006; Trias et al., 2012; Egan et al., 2013; Goecke et al., 2013; Nemergut et al., 2013). The concept of the holobiont, originally developed for corals (Rosenberg et al., 2007; Barott et al., 2011), is highly relevant for macroalgae, as surface-associated microbial communities play vital roles in various aspects of the algal life cycle (Singh and Reddy, 2014; Egan et al., 2013) and are functionally integrated with their eukaryotic host.

Environmental stressors, however, can destabilize these symbiotic relationships, resulting in a loss of epiphytic bacteria and a breakdown of the holobiont, a phenomenon known as holobiont break-up (De Fouw

<footnote><sup>a</sup> UBS, Laboratoire de Biotechnologie et Chimie Marines, EMR CNRS 6076, Vannes/Lorient, France</footnote>

<footnote><sup>b</sup> Univ Brest, CNRS, IRD, Ifremer, LEMAR, F-29290 Plouzané, France</footnote>

<footnote><sup>c</sup> IFREMER-IRSI-Service de Bioinformatique (SeBiMER), Plouzané, France</footnote>

<footnote>* Corresponding author at: UMR-CNRS 6539- Laboratoire des Sciences de l'Environnement Marin (LEMAR), Institut Universitaire et Europeen de la Mer (IUEM), Rue Dumont d'Urville, 29280 Plouzane, France.

E-mail address: gwenaelle.leblay@univ-brest.fr (G. Le Blay).</footnote>
et al., 2016; Egan et al., 2017). This disruption often leads to dysbiosis, characterized by microbial imbalance, which can negatively impact algal health and ecosystem stability (Campbell, 2011; Fernandes et al., 2012; Zozaya-Valdes et al., 2015).

Ulva, a genus of green macroalgae, has emerged as a model for studying macroalgae-bacteria interactions. Its associated bacterial community is known to play essential roles in reproduction, growth, and defense. For example, bacteria associated with Ulva promote reproduction by attracting zoospores for adhesion (Joint et al., 2002; 2007), support growth through the release of morphogenetic factors such as thallusin (Matsuo et al., 2005; Spoerner et al., 2012; Alsfuyani et al., 2020), and contribute to defense due to their antibacterial properties (Ismail et al., 2018). Furthermore, Ulva mutabilis has become a model organism for exploring macroalgal growth and developing a reductionist model of bacteria-induced morphogenesis (Wichard, 2023). Unlike many macroalgae, Ulva can benefit from anthropogenic environmental stressors, proliferating under eutrophic conditions and causing green tides in coastal regions worldwide (Ye et al., 2011).

Fluctuations in Ulva's microbiota across environmental gradients raise important questions about the stability of its bacterial community over time and under varying environmental conditions. Understanding this stability is crucial in the context of global change, as it informs efforts to define what constitutes a "healthy" microbial community (Risely, 2020) and improves the sustainability of macroalgal aquaculture (Califano et al., 2020). However, studies on Ulva's core microbiota remain inconclusive. While some have reported bacterial taxa that persist across spatial scales (Tujula et al., 2010; Liu et al., 2023; Van der Loos et al., 2023), longitudinal gradients (Van der Loos et al., 2023), or temporal monitoring (Comba González et al., 2021), others emphasize bacterial assemblages based on functional roles (Burke, Steinberg, et al., 2011; Roth-Schulze et al., 2018).

Moreover, although seasonal and environmental variations in Ulva's surface-associated bacterial communities have been documented (Lachnit et al., 2011; Comba González et al., 2021; Van der Loos et al., 2023), no in situ study has simultaneously monitored the microbiota and metabolome of Ulva over time. Such an approach, while studied under experimental conditions as part of reductionist model systems, remains largely unexplored in natural settings.

The aim of this study was to investigate the temporal variations in the epibacterial community and surface metabolome of Ulva spp. along a northwestern Atlantic shore in France, while accounting for environmental and physiological parameters. Metabarcoding analyses were conducted to characterize the specificity of the bacterial communities on the Ulva spp. surface and identify portions of the community that remained relatively stable over time. To evaluate this specificity, bacterial communities from surrounding seawater and biofilm on rocky substrates near the algal specimens were also sampled. Due to the limited taxonomic resolution inherent to short-read amplicon sequencing, particularly at the ASV or species level, we chose to focus our analysis at the genus level—a more reliable and interpretable taxonomic scale for ecological inference. This approach aligns with previous studies on algal-bacterial interactions and is especially relevant for formulating functional hypotheses, as many traits of interest—such as biofilm formation (Wahl et al., 2012) or algal polysaccharide degradation (Martin et al., 2014) are conserved at the genus level. Environmental parameters were also thoroughly assessed to identify their contributions to temporal variation in microbiota composition. Adopting this taxonomic and analytical framework allowed us to investigate microbiota-metabolome-environment relationships in a robust and comparable manner across both spatial and temporal gradients.

By combining meta-omics approaches, this study advances our knowledge of the complexity and stability of macroalgal-microbial interactions in the context of global environmental change.

## 2. Experimental procedures

### 2.1. Sampling

The surface microbiota of the algae, the rocky substrate and the seawater were monitored from February 2021 to June 2022 every other month. The sampling site was located in Kerleven beach along three stations in the southern coast of Finistère (France): station 1 (47°53′36.69″N; 3°58′16.9″W) with a direct access to the beach, and stations 2 (47°53′24.95″N; 3°57′24.95″W) and 3 (47°53′40″N; 3°57′22.78″W), two infralittoral stations only discovered during low tides with high coefficients (>80). Ulva spp. thalli were collected at high-coefficient low tide by carefully detaching from rocky substrate with sterile gloves and immediately stored in sterile bottles filled with surrounding seawater. For each station, three different individuals collected nearby were considered as three biological replicates. One individual per station was also swabbed on site in June 2022 to demonstrate that sample transport did not impact the microbiota structure. In addition to algal samples, surrounding water (1 L in triplicate) was collected in sterile bottles and rocky substrates were swabbed, in triplicate. The rocky substrate swabs (Ozyme, France) were immediately put into DNA/RNA Shield Collection Tubes (Zymo Research, Irvine, CA, USA) containing an extraction and stabilizing buffer and placed at -20 °C upon arrival at the laboratory. From the point of collection, samples were transported in cool boxes to maintain the seawater temperature.

Samples were processed in the laboratory within 2 h. Upon arrival at the laboratory, Ulva spp. samples were rinsed three times with sterile artificial seawater (ASW, Sea salt 30 g.L⁻¹, Sigma-Aldrich, St. Louis, USA). To ensure multi-omics cross-comparison, each thallus was separated into two equal parts for metabarcoding and metabolomic analyses.

#### 2.1.1. Characterization of the algal host

Ulva specimens collected between February 2021 and February 2022 were sent to Pr. de Clerck (Research group phycology, Ghent University) for molecular identification based on the *tufA* gene sequencing, according to the protocol described by Van der Loos et al., 2023. Moreover, biochemical analyses were carried out to characterize the composition of the algae. Furthermore, the surface area of each alga was measured using the image J software, and the moisture content was determined by the ratio of wet and dry weight after 36 h in a laboratory oven (60 °C).

#### 2.1.2. Characterization of the environment

Seawater parameters (salinity, pH, fluorescence, depth, temperature, turbidity, dissolved oxygen) were measured with a multiparameter probe (YSI 6920 V2-2, A xylem brand). These parameters were collected in December 2021 and June 2022 to characterize the sampling stations in two contrasting seasons (Supplementary Table 3).

Temporal monitoring of environmental data over 17 months was facilitated by datasets obtained from Previmer models (ECO-MARS3D, https://marc.ifremer.fr) and Météo France (Quimper station n°29,216,001, 47°58′22″N - 4°09′38″W). These datasets included atmospheric variables such as atmospheric temperature, solar radiation, irradiation duration, precipitation, relative humidity, and wind speed. In addition, surface seawater parameters were characterized, including temperature and salinity, as well as proxies for primary production, such as chlorophyll a, nitrates, and phosphates.

### 2.2. Surface metabolome extraction and analyses

Extraction of surface metabolomes were standardized to 45 mm diameter by using a cookie cutter. Each algal fragment was dipped in 2 mL of methanol or 2 mL of n-hexane for 20 s. This extraction procedure was performed independently on three replicate algal fragments per station, and the extracts for each solvent were then pooled. A total of 81 extracts from the surface metabolome of Ulva spp. were collected (9 per
sampling date). In order to collect only surface metabolites, particular
care was taken not to dip the algal fragments edges in solvents. Simi-
larly, preliminary tests were done to identify dipping times that would
not damage the algal cell wall. Surface extracts were concentrated by
evaporating the solvent with a vacuum concentrator (Genevac™,
United-Kingdom). They were then stored in sealed vials at -20 °C until
analysis.

For LC-MS analyses, samples were prepared by solubilizing the dried
extracts in a 200 µL mixture of acetonitrile (ACN) and water (H₂O)
(65:35, v/v) acidified with formic acid (0.1 %). Additionally, one
experimental blank was prepared and injected at the beginning and at
the end of the injection sequence. The chemical profiling of the surface
extracts was obtained using an LC-ESI-MS (Dionex, Ultimate 3000
Bruker, Germany) coupled with a QToF Impact II mass spectrometer
(Bruker Daltonik GmbH, Germany) in positive ionization mode. Sepa-
rations were carried out with an analytical reverse-phase column. A 10
µL extract sample was injected for each analysis, at a flow rate of 0.5 mL
min⁻¹ with a column temperature of 30 °C. In order to optimize the
surface extract signal, two injection heights were defined in order to
analyze the ACN phase and the aqueous phase separately. The ACN
phase proved to be the most convincing in terms of providing defined
peaks. The elution gradient was adopted from Othmani et al. (2016). The
gradient set up was as follows: (i) an initial isocratic stage of 10 min with
ACN/H2O (65:35, v/v); (ii) a linear gradient passing from 65 % to 100 %
ACN in 20 min; (iii) an isocratic stage of 30 min with 100 % ACN, fin-
ishing by a return to initial conditions (0.1 min) and equilibration of the
column (9.9 min). Regarding the mass spectrometer, operating condi-
tions were set as follow: drying temperature: 350 °C, capillary voltage: 4
kV, nebulizer pressure: 3.45 bar, drying gas: helium at a flow rate of 12 L
min⁻¹ (Othmani et al. 2016). Mass spectra acquisition was set at 0.5 Hz
from *m/z* 50 to 1000.

## 2.3. Pre-processing of metabolomic data

Eighty-one LC/MS data were exported in .mzXML format and pre-
processed by MZmine 4.3.0 for peaks searching. The parameters used
for the analyses were inspired by those used by Remy et al. (2019).
Blanks were analyzed to define a minimum noise threshold. Mass
detection was performed using a centroid algorithm with a noise level
threshold set at 5000. The retention time considered was set between 3
and 21 min. Chromatogram formation (ADAP chromatogram builder)
was established using a minimum scan group size of 4, a group intensity
threshold of 3000, a minimum intensity of the highest data of 4000 and
an *m/z* tolerance of 0.05 (or 20 ppm). The ADAP wavelet deconvolution
algorithm was used with the following parameters: S/N threshold of 8,
minimum peak height = 4000, area coefficient/threshold = 2, peak
duration range = 0.001-2 min, wavelet range = 0.001 - 1 min. The list of
identified peaks was aligned using the joint aligner algorithm with a *m/z*
tolerance of 0.01 (or 50 ppm) and a retention time tolerance of 0.5 min,
weight for *m/z*= 1 for retention time = 1. The results were exported in *.
csv format and included all the features detected, referenced by their
*m/z* and retention time (rt, minutes), as well as their intensity. The final
matrix corresponded to a total of 358 characteristic features (*m/z*; rt)
present in at least 10 among 81 samples as well as their respective in-
tensity, transformed by a log-10, for the 81 samples investigated. The
metabolomics data were first analyzed by Principal Component Analysis
(PCA) to determine whether there was a separation of *Ulva* spp. surface
extracts according to the month of sampling and environmental factors,
and then integrated into the multi-omics analysis as response variables.

## 2.4. DNA extraction, 16S rRNA gene amplification and sequencing

Upon arrival at the laboratory, each thallus was thoroughly rinsed
with sterile artificial seawater to remove non-surface-associated bacte-
ria. The surface of each of the six fragments composing a statistical in-
dividual was then gently swabbed over an area of approximately 10–12

cm² under sterile conditions inside a microbiological safety cabinet (MSC). In parallel, rocky biofilm samples were swabbed directly in situ over the same surface area (~10–12 cm²). Concurrently, one litre of seawater (in triplicate) was filtered through polycarbonate membranes (0.22 µm, Whatman), which were immediately placed in cryotubes and flash frozen in liquid nitrogen before storage at –80 °C. DNA from epiphytic bacteria was extracted from the swabs according to the ZymoBIOMICS DNA/RNA Mini Kit (Zymo Research) protocol. Rocky substrate and seawater DNA were extracted using a PCI extraction as described in Offret et al. (2020). DNA concentrations from the algal surface, seawater and rocky substrate were quantified using spectro-fluorometry with the Quantifluor kit (Invitrogen, Massachusetts, USA) and a NanoDrop™ Spectrophotometer (Ozyme, France). Bacterial diversity was investigated by targeting the V3-V4 region of the 16S rRNA gene, using the NOCHL primers developed by Thomas et al. (2020) to minimize plastid amplification. This set of primers consisted of the forward primer S-d-Bact-0341-b-S-17 (CCTACGGNGGCWGCAG), and the reverse primer S-d-Bact-0785-a-A-21 (GACTACHVGGGTATC-TAATCC), and amplified a 478 bp fragment (Thomas et al., 2020). The amplicons were paired-end sequenced (2 × 250 bp) on an Illumina MiSeq platform (Genome Québec, Canada). Sequences assessed in this study have been submitted to European Nucleotide Archive (ENA) under the accession number PRJEB85979.

## 2.5. Metabarcoding data processing and statistical analyses

Raw data were analyzed using the SAMBA v3.0.1 workflow (Noël et al., 2021; https://github.com/ifremer-bioinformatics/samba) developed by the SeBiMER (Ifremer's Bioinformatics Core Facility), a Standardized and Automatized MetaBarcoding Analysis workflow using DADA2 (Callahan et al., 2016) and QIIME2 (Bolyen et al., 2019) with default parameters unless otherwise indicated. A first step of raw data integrity checking was performed after sequencing with the SAMBA checking process. Then, sequencing primers were trimmed from reads and reads without primers were removed. Afterward, DADA2 was used to filter poor quality reads, correct sequencing errors as well as overlap paired reads, infer Amplicon Sequence Variants (ASVs) and remove chimeras. A step of ASV clustering was added using the dbOTU3 algorithm (Olesen et al., 2017) relying on both distribution of ASVs and phylogenetic distance, to correct the diversity overestimation generated by the ASV inference from DADA2. Finally, ASV taxonomy was assigned using a Naïve Bayes classifier against the SSU SILVA (Version 138) database (Pruesse et al. (2007); https://www.arb-silva.de). Consequently, the names assigned to the sequences correspond to the classification based on the SILVA v138.1 database (Quast et al., 2012). In total, 8593,157 reads were generated with 38,023 average per sample obtained through the sequencing of 84 algal surface samples, 81 seawater samples and 63 rocky substrate samples corresponding to 13, 619 ASVs. Rarefaction curves showed a sufficient sequencing effort to describe the bacterial diversity among the different habitats (Supplementary Figure 1). A total of 211 samples with an abundance of at least 0.01 % of the total abundance were retained for further analyses (Alberdi et al., 2018). Alpha-diversity metrics (Chao1, Shannon and invSimpson indexes, Pielou evenness) were calculated using the Phylloseq R package and tested statistically with a T-test followed by a post hoc test (Bonferroni multiple-significance-test correction).

A core taxa was identified at the genus level by grouping ASVs
assigned to the same genus. A genus was considered part of the core
microbiota if it was detected in at least one replicate per sampling sta-
tion (3 stations) for each of the 9 sampling months (see Paix et al., 2019).
Prior to beta-diversity analysis, data were normalized using the ‘cum-
NormStatFast’ function with metagenomeSeq R package (Paulson,
2013) performing a cumulative-sum scaling (CSS). Dissimilarity
matrices using the Weighted-UniFrac distances were represented in a
two-dimensional space by Non-metric multidimensional scalings
(nMDS). The similarity differences observed between the three habitats
and between each month within the nMDS, was statistically tested with a PERMANOVA test (999 permutations) using the Vegan R package. Then, environmental and biochemical variables were overlaid onto the Bray Curtis based-distance nMDS plots without disrupting the configuration of the original ordinations using function 'envfit' from the Vegan R package. A R² measure of fit and a “significance” value based on the probability that random permutations of the environmental variables (permutations = 999) would yield a higher degree of fit than the true environmental variables were also generated. Finally, predictive cellular functions, which were likely associated with a marker gene based on its sequence similarity with a reference genome, were inferred using the PICRUSt2 tool (Douglas et al., 2020).

## 2.6. Integration of metabolomic, metabarcoding, and predicted functions dataset

Metabarcoding and predicted functions datasets were filtered to remove features for which the sum of counts were below a certain threshold (removing ASVs/features for which the sum of counts are below 1 % of the total sum of all counts, see Arumugam et al. (2011). A Log-transformation was then performed on those two datasets; the metabolomic dataset had already been filtered and Log-transformed in previous metabolomic treatment. A correlation analysis between the three datasets was performed with the multiblock sPLS-DA method (referred to as DIABLO), used to perform a PLS Discriminant Analysis (PLS-DA) on more than two datasets, from MixOmics R package (Lê Cao et al., 2008). The purpose of this multivariate analysis was to perform an N-block integration of different omic datasets to identify the most correlated ASVs, metabolites and predicted functions involved in the discrimination of each month. A correlation circle plot was output to exhibit selected features on a circle and the links between or within omic features, which presented strong positive or negative correlations.

# 3. Results

## 3.1. Characterization of the algal host and the environment

Molecular identification based on *tufA* gene sequencing revealed the presence of four distinct species within the *Ulva* genus across stations 1, 2, and 3 between February 2021 and February 2022 (**Supplementary Figure 2**). *Ulva australis* emerged as the dominant species, consistently detected throughout the sampling period (**Supplementary Table 1**). Additionally, a representative of the genus *Ulvaria* was identified at station 2 in February 2021. Alpha diversity indices showed no significant differences in microbial richness and evenness among the various *Ulva* species and *Ulvaria* (**Supplementary Figure 3**). However, the macroalgal species factor had a significant effect on microbiota composition, as revealed by the *envfit* analysis (*p* < 0.05), indicating that host identity contributed to shaping epiphytic bacterial communities (Fig. 1).

Despite this taxonomic heterogeneity, we retained all specimens in the microbial analyses, as they represented valuable replicates spanning environmental gradients and contributed to the overall diversity of the epiphytic community. This inclusive approach enabled a more comprehensive assessment of microbiota dynamics in situ. Biochemical analyses of specimens collected between June 2021 and June 2022 revealed significant monthly variations in the overall physiological state of the macroalgae (ANOVA, *p* < 0.05) (**Supplementary Figure 4 and Table 2**). As anticipated, lipid content was significantly higher during winter (December/February 2021), while a notable decrease in total protein content was observed in April/June 2022. Neutral sugar levels increased markedly during the summer months (June 2021 and 2022, August 2021), whereas uronic acid content peaked primarily in April 2022 (post hoc test, Tukey's HSD, *p* < 0.05). In contrast, no clear patterns were observed in pigment assays. These biochemical data were subsequently integrated into analyses aimed at identifying the driving factors behind the temporal dynamics of bacterial communities associated with

<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Non-metric multidimensional scaling of Bray-Curtis distance computed from CSS-transformed ASV abundances for the Ulva-associated bacterial community across all stations (S1, S2, S3) and replicates, sampled between February 2021 and February 2022 (Stress: 0.19). Host species were included as explanatory variables using ‘envfit’ with centroid positions displayed.</figcaption></figure>
the surface of *Ulva* spp.

Results for the sampling site parameters are described in the Supplementary Materials, including the environmental data obtained from the Previmer models and Météo France, which were used in subsequent analyses (Supplementary Tables 3 and 4).

## 3.2. Structure and diversity of epiphytic bacterial communities

Alpha-diversity was measured using Chao1, InvSimpson, Shannon and Pielou evenness indexes (Supplementary Figure 5). When considering the bacterial communities associated with the three different habitats, all indexes showed significantly higher values (*p*< 0.05) for the biofilm samples collected on rocky substrates in comparison with the seawater and algal samples, which were not different from each other. Moreover, Pielou evenness index was above 0.7 for the three habitats (0.92 for rocky substrate), indicating an even distribution of ASVs within each bacterial community. No significant difference was observed between the sampling dates nor the sampling station within each habitat.

Beta-diversity was analyzed using the weighted Unifrac distance matrix. As expected, the resulting NMDS plot (Fig. 2) showed a distinct clustering pattern between bacterial communities from surrounding seawater, rocky substrates and *Ulva* spp. surfaces. A greater proximity was observed between samples from the rocky substrate biofilm and the algal surface. In terms of dispersion, the seawater samples appeared less dispersed than those from the rocky substrate and the algal surface (betadisper, *p*< 0.05).

The structure of the bacterial communities in the three habitats showed similarities at high taxonomic levels. At the class level, *Alphaproteobacteria* (>29 %), *Bacteroidia* (>26 %) and *Gammaproteobacteria* (>15 %) were the predominant groups (Supplementary Figure 6). At the genus level, the structure of the bacterial communities was much more distinct between the three habitats (Fig. 3).

Within the algal samples, the genus *Granulosicoccus* and two unknown genera affiliated to the *Rhodobacteraceae* and *Saprospiraceae* families occupied a relative abundance over 8 % (with a relative abundance of 18.7 % for the unknown genus affiliated within the *Rhodobacteraceae*). In seawater samples, bacterial communities were dominated by *Amylibacter* and *Planktomarina* genera

(*Alphaproteobacteria*). In accordance with the high Pielou index, no genus dominated the rocky substrates samples, with almost 816 genera having a relative abundance of <1.74 %.

Within the bacterial community associated with the algae, almost 50 % of the relative bacterial abundance was distributed among 10 genera. A bacterial community that remained relatively constant over time, that we named "core" was then identified. The 24 genera that make up this core community were present at all sampling stations for each of the nine sampling dates (Fig. 4).

These 24 genera belonged to the *Alphaproteobacteria*, *Flavobacteriia*, *Oligoflexia* and *Saprospiria* classes. However, the relative abundances of genera belonging to this core community associated with the algal surface varied over time. Indeed, the relative abundance of certain genera highly increased on certain dates. This was the case for the *Dokdonia*, which was particularly present in June 2022 (48 % of relative abundance) and to a lesser extent in August 2021 (21 %). Similarly, the uncultured *Rhizobiaceae* was predominant in summer, particularly between June 2021 and October 2021, with a relative abundance of almost 16 % in August 2021, as well as in June 2022. Similarities from one year to the next were also highlighted, notably for April 2021 and April 2022. The relative abundance of the uncultured *Rhodobacteraceae* occupied a large proportion of the stable algal community, reaching 39.4 % in April 2021 and 31.6 % in April 2022. In addition, the genus *Litorimonas* was, to a lesser extent, more present in April 2021 (12.4 %) and April 2022 (15 %). The core bacterial community constituted the majority on all nine sampling dates, with its proportion ranging from 59 % in February 2021 to 92.8 % in April 2021 and April 2022. The lower proportion observed in February 2021 was probably attributable to the high abundance of the genus *Paraglaciecola* (data not shown, not included in the core community) on that particular date.

Although we defined the core microbiota at the genus level, applying the same criteria at the ASV level revealed only a very limited number of shared ASVs across samples, reflecting the high taxonomic and compositional variability at finer resolution (Supplementary Figure 7). Notably, 30 % of the total ASVs (1491) were affiliated with the 24 genera constituting the constant fraction of algal-associated bacteria, while the remaining 70 % were distributed among the 526 other genera, highlighting the diversity and transience of non-core taxa.

A constant bacterial community over time was also found in the

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Non-metric multidimensional scaling of Bray Curtis distance computed from CSS-transformed ASVs abundances for the bacterial community of *Ulva* spp. (green), rocky substrate (red) and seawater (blue) sampled between February 2021 and June 2022 (Stress: 0.14).</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Relative abundance of majoritarian bacterial ASVs summarized at the genus taxonomic rank in associated communities of Ulva spp., seawater and rocky substrate.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Relative abundance of ASVs belonging to the core Ulva spp. bacterial community between February 2021 and June 2022, summarized at the genera taxonomic rank.</figcaption></figure>

seawater samples (Supplementary Figure 8). This constant community was made up of 47 genera, 6 of which were in common with those associated with the algal surface (Granulosicoccus, Hellea, Peredibacter, Polaribacter, Sulfitobacter, Yoonia). These 47 genera belonged to the Alphaproteobacteria, Gammaproteobacteria, Flavobacteriia, Acidimicrobiia, Cytophagia, Espilonproteobacteria, Fusobacteriia, Oligoflexia and Saprospiria classes. Within Alphaproteobacteria, the dominant class in seawater samples, Roseobacteraceae was the majority family.

A comprehensive investigation was conducted on various environmental factors to explore the structuring of the bacterial communities colonizing the surface of the algae, the surrounding seawater and the rocky substrate to which the algae were attached. Although the study did not focus on the spatial distinction of the sampling stations, a divergence in the factors structuring the bacterial communities was observed between station 1 as compared to stations 2 and 3. Only samples from the two infralittoral pools (stations 2 and 3) were
structured by the tested environmental drivers. Samples from station 1, located along the beach, were subjected to high anthropogenic influences (walk fishing, bathing), and appeared to be structured by unidentified drivers. Thus, this station was not included in the beta-diversity analysis.

Bacterial communities present in the surrounding seawater could be clustered in three distinct groups: 'summer' (June 2021, August 2021, June 2022), 'mid-season' (April 2021, October 2021, April 2022) and 'winter' (February 2021, December 2021, February 2022) communities [permutational multivariate analysis of variance (PERMANOVA); *p* < 0.05] (Fig. 5).

The distinction between the clusters was influenced by various environmental factors. The 'summer' community was mainly driven by wind speed (*p* < 0.05, R² = 48 %), nitrates (*p* < 0.001, R² = 48 %) and phosphate concentrations (*p* < 0.05, R² = 49 %). In summer, wind intensity was significantly weaker, while concentrations of nitrates and phosphates increased at the surface of the oceans. The intermediate community was structured by solar radiations (*p* < 0.1, R² = 31 %), atmospheric temperatures (*p* ≤ 0.05, R² = 34 %) and sea surface temperatures (*p* < 0.05, R² = 41 %). Finally, 'winter' community was mainly impacted by surface salinity (*p* < 0.01, R² = 54 %), which slightly decreased in winter. No environmental drivers were highlighted to explain the dynamic of the rocky substrate bacterial communities.

Epiphytic bacterial communities also varied according to both environmental parameters and the physiological state of the algae. For the epiphytic communities, the 'summer' community was correlated with an increase of seawater nitrates concentrations (*p* < 0.05, R² = 42 %) and the 'winter' community by the slight decrease of surface salinity (*p* < 0.05, R² = 27 %) (Fig. 6). Moreover, precipitation amounts (*p* ≤ 0.01, R² = 44 %), which reached a maximum in October 2021, were found to drive the 'fall' community. The samples collected from one year to the next, in particular those from April 2021 and April 2022, were close.

A link was also established between the physiological state of the algae over the course of the year and its surface bacterial community (Fig. 7). The 'summer' bacterial community appeared to be correlated with increased uronic acid concentrations (*p* < 0.05, R² = 56 %) and

algal humidity (*p* < 0.01, R² = 57 %), alongside a decrease in the algal lipid contents (*p* < 0.01, R² = 67 %). In contrast, the 'winter' bacterial community was related to the increase in lipid content in algal tissues and the decrease in uronic acid concentrations.

### 3.3. Variation of the algal surface metabolites

The surface extracts of *Ulva* spp. were analyzed from February 2021 to June 2022, resulting in the identification of 358 distinct features, each detected in at least 10 of the 81 samples analyzed. Two months, October 2021 and February 2021, displayed minimal features probably due to a problem during sample preparation, were therefore excluded from the analysis. First, principal component analysis performed on all the 358 features and 81 samples, clustered biological replicates from the same sampling month, thus demonstrating the consistency of the data (Fig. 8a). Second, multivariate statistical analysis performed on discriminant compounds and abiotic parameters highlight relationship between environmental factors and four discriminant features (Fig. 8b).

Features X16 (m/z: 114.09; retention time (min): 6.099); X73 (m/z: 451.17; retention time (min): 6.79); X78 (m/z: 80.95; retention time (min): 3.91) and X42 (m/z: 193.13; retention time (min): 8.44 were positively correlated (correlation > 0.75) with irradiation time, solar radiation, chlorophyll a concentration, atmospheric/seawater temperatures, salinity and negatively correlated with precipitation, nitrate and phosphate concentrations, wind speed and relative humidity. Among these five compounds, X16 was putatively identified as 5,5-dimethyl-1-pyrroline N-oxide (DMPO), while X42 was probably a chlorine molecule based on isotopic distribution.

### 3.4. Focus on sampling dates of interest

April 2021 and 2022 were very similar both in terms of surface bacterial structuration and chemical surface landscape. First, the stable bacterial community represented the majority (92.8 %) of total bacteria in April 2021 and 2022. Within this core community, *Rhodobacteraceae* occupied the largest part (39.4 % in April 2021 and 31.6 % in April 2022) of the surface bacterial community, leading to a high degree of

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Non-metric multidimensional scaling of Bray Curtis distance computed from CSS-transformed ASVs abundances for the bacterial community in seawater sampled between February 2021 and June 2022 (Stress 0.15). Environmental parameters were added using envfit. The red arrows represent a statistically significant relationship (*p* > 0.05) measured through the envfit test.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Non-metric multidimensional scaling of Bray Curtis distance computed from CSS-transformed ASVs abundances for the bacterial community of Ulva spp. sampled between February 2021 and June 2022 (Stress: 0.07). Environmental parameters were added using envfit. The red arrows represent a statistically significant relationship (*p* > 0.05) measured through the envfit test.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Non-metric multidimensional scaling of Bray Curtis distance computed from CSS-transformed ASVs abundances for the bacterial community of Ulva spp. sampled between June 2021 and February 2022 (Stress: 0.05). Biochemical parameters were added using envfit. The red arrows represent a statistically significant relationship (*p* > 0.05) measured through the envfit test.</figcaption></figure>

similarity in April from one year to the next. Secondly, chemical surface profiles were very close, meaning that a large amount of the same metabolites were screened in April 2021 and April 2022 for algae sampled at the same locations. Finally, bacterial functions predicted on the basis of phylogeny (Picrust2), indicated similar functions in April 2021 and April 2022. Predictions were based on several gene family databases supported by the Kyoto Encyclopedia of Genes and Genomes11 (KEGG) orthologs (KOs) and Enzyme Commission numbers (EC numbers). Bacterial functions included production of phenylacetyl-CoA 1,2 epoxydase (pvalue < 0.001), proline dehydrogenase (pvalue < 0.05) and cyclic

pyranopterin phosphate synthase (pvalue < 0.001). Metabolites synthesis (pvalue < 0.05) and Nicotinamide Adenine Dinucleotide (NAD) salvage pathway I (pvalue < 0.1) were also identified.

Furthermore, June 2022 also presented a clearly distinct profile compared to the other dates. First, the surface extracts profile differed notably from the other periods, especially summer 2021 (June and August). Twelve metabolites, consistently detected across all extracts, were produced in significantly higher amounts in June 2022 compared to other months. These include metabolites identified using analytical standards, such as DiMethylSulfonioPropionate (DMSP), proline
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Principal component analysis of the seasonal structuring of the Ulva spp. surface metabolome (a); multivariate statistical analysis on discriminant features (orange) and abiotic parameters (b).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Heatmap of mean normalized and centered seasonal features intensity; p value < 0.05, Kruskall-Wallis test). Abbreviations: DMSP: dimethylsulfoniopropionate; DMPO: 5,5-Dimethyl-1-pyrroline N-oxide; LDAO: Lauryldimethylamine oxide.</figcaption></figure>

betaine, and Lauryldimethylamine Oxide (LDAO), as well as putatively identified metabolites, including 5,5-Dimethyl-1-Pyrroline N-Oxide (DMPO) (Fig. 9).

Among these 12 metabolites tested, 11 showed a significant difference from one month to the next (pvalue < 0.05, Kruskall Wallis test). This differentiation in the chemical profile on the algae surface in June 2022 was also mirrored in the composition of the associated bacterial community. Bacteria belonging to the genus *Dokdonia* were the most abundant, accounting for a total relative abundance of 22 % in June 2022. This relative abundance increased to 48 % when considering only the core bacterial community. Bacterial functions predicted also revealed the expression of peroxiredoxins in June 2022. On the other hand, considering the environmental data collected in June 2022, a maximum of solar radiation was recorded over the month (2062.833 Joules/cm², Météo France - Quimper, 47°58'22 "N - 4°09'38 "W). Moreover, Pearson's correlation tests indicated that solar radiation was

significantly correlated with the increase of *Dokdonia* abundance (Coefficient = 0.56; *p* ≤ 0.05). This could therefore be a potential environmental stress detected in June 2022, with a response from both the microbiota and the metabolome on the surface of *Ulva* spp.

### 3.5. Integration of surface metabolome, surface microbiota and putative bacterial functions datasets

Each dataset variable, encompassing surface metabolome, surface microbiota and putative bacterial functions, was integrated, and their correlations were assessed. The resulting vectors were plotted inside a unit circle, with each vector's position corresponding to its correlation with the components (Supplementary Figure 9).

Stronger associations are depicted by vectors extending further away from the origin. In total, 31 ASVs grouped into 18 different genera, 115 metabolite peaks and 58 putative bacterial functions correlated
positively (correlation ≥ 0.7). The bacterial genera represented included *Amphiplicatus*, *Bdellovibrio*, *Dokdonia*, *Hellea*, *Jannaschia*, *Peridibacter* as well as several genera affiliated with the *Micavibrationaceae*, *Rhizobiaceae*, *Rhodobacteraceae* and *Sphingomonadaceae* families. At the phylum level, nearly 80 % of these bacteria were assigned to *Pseudomonadota* with the great majority belonging to the *Alphaproteobacteria* class (52 %). The phyla *Bdellovibrio* (9 %); *Bacteroidota* (6 %) and *Planctomycetota* (6 %) were also found. As for the metabolic peaks, only a putative identification based on mass-to-charge ratio (m/z) and retention time (min) was carried out. Several peaks appeared to be identified as phenolic compounds (flavonoids), terpenoids and lipids. Finally, the putative positively correlated bacterial functions were essentially synthesis functions (amino acids, nucleotides, sugars, lipids, vitamins) as well as energy production. The biosynthesis of pigments, particularly flavin, was also noted.

## 4. Discussion

The surface microbiota of macroalgae, including green macroalgae, is increasingly studied within the framework of the holobiont concept (Wichard, 2023). However, to the best of our knowledge, the intricate relationships between the green macroalgae *Ulva* spp. and its epiphytic bacterial communities, as well as the environmental factors influencing these interactions, remain poorly understood. In this study, the surface microbiota and metabolome of a pool of macroalgae from the *Ulva* genus were monitored over an 18-month period. A succession of four *Ulva* species was also observed at the sampling site.

### 4.1. *Ulva* spp. harboured a specific surface microbiota

The macroalgal epibacterial community structure was compared to bacterial communities from rocky substrate biofilm and seawater to assess host-specificity. A clear distinction emerged between habitats, as observed for *Ulva australis* (Bare Island, Australia) surface microbiota versus seawater planktonic bacteria (Burke et al., 2011) and for *Ulva linza* surface microbiota versus rocky biofilm (Guo et al., 2022). Over 1.5 years, seawater bacterial planktonic communities showed less dispersion than those on algal surfaces and rocky biofilms. Algal epibacterial communities were closer to rocky biofilms, likely due to shared biofilm-forming bacteria, distinct from planktonic bacteria (Flemming et al., 2016; Alsufyani et al., 2017; Li et al., 2019). Despite proximity between bacterial composition with predominance of *Alphaproteobacteria*, *Bacteroidia* and *Gammaproteobacteria*, differences were observed at lower taxonomic levels. The algal surface microbiota was dominated by unknown *Rhodobacteraceae* and *Saprospiraceae*, as already described in *Ulva australis* (Burke et al., 2011). Other genera described in this study (Granolusicoccus, Littorimonas, Hellea and Rubidimonas) were also present in *Ulva lactuca* surface (Comba González et al., 2021). Seawater bacterial community was dominated by *Planktomarina* and *Amylibacter*, previously identified as part of the seawater core taxa community (Ul-Hasan et al., 2019; Paix et al., 2020; Chen et al., 2022). Rocky biofilms, in contrast, lacked a dominant genus (Pielou index ≈ 1) and had greater alpha-diversity as previously described in Guo et al., 2022. Both algal and rocky surfaces faced similar environmental pressures but showed distinct taxa due to active host selection. This could result from algal exudates like DMSP, which inhibits *Cytophaga* spp. fouling (Saha et al., 2012; Ingle et al., 2022) or acts as a chemotactic signal for *Roseovarius* sp. MS2, promoting *Ulva* development (Kessler et al., 2018). Structural compounds like sulfated xyloarabinogalactans and elvanes in algae (Abdul Malik et al., 2020) may also affect host selection and community specificity.

These patterns highlight host-specificity in the structure of *Ulva*-associated surface microbiota. However, it is important to acknowledge that our sampling included multiple *Ulva* species, which could not be distinguished in the field due to their morphological similarity. While this reflects the natural diversity of *Ulva* populations in situ, the

temporal succession of *Ulva* species—previously documented, particularly in the context of green tides (Bermejo et al., 2019; Bermejo et al., 2019) introduces a degree of taxonomic heterogeneity that may influence microbiota composition and adds complexity to the interpretation of host-specific microbial patterns.

### 4.2. Demonstration of a constant bacterial community associated with the algal surface

On all sampling dates and stations, 24 genera, representing at least 50 % of total relative bacterial abundance, were consistently detected. This led to the investigation of a constant bacterial community over a 1.5-year period. Taxonomic similarities were noted with other studies on *Ulva* spp. associated bacteria. Comba González et al. (2021) identified a consistent core microbiota on *Ulva lactuca* over two years using 16S rRNA sequencing. They mentioned several taxonomic affiliations in common (order level) with the present study (*Flavobacteriales*, *Caulobacterales*, *Chromatiales*, *Cytophagales* and *Rhodobacterales*). At the genus level, Van der Loos et al. (2023) found a small core microbiota, including *Sulfitobacter* and uncultured *Rhodobacteraceae*, across 15 distinct *Ulva* taxa along 2000 km of Baltic coastline. More recently, Liu et al. (2023) identified a year-round core microbiota in *Ulva* spp. with 14 genera, half of which overlap with those in this study (e.g., *Algitalea*, *Hellea*, *Lewinella*, *Sulfitobacter*, *Rhodobacteraceae* unc., *Saprospiraceae* unc., *Microtrichaceae* unc.), including *Maribacter*, *Rubidimonas*, and *Yoonia*- Loktanella as dominant taxa. Other known *Ulva* epiphytic bacteria found in this study's core microbiota include *Algitalea* (Yoon et al., 2015), *Hellea* (Tujula et al., 2010; Guo et al., 2022), *Littorimonas*, and *Dokdonia*, associated with high-salinity *Ulva* environments (Van der Loos et al., 2023). The influence of specific environmental factors (salinity, temperature) on the bacterial communities associated with *Ulva* has already been proven, and a consensus is emerging on the dynamic nature of the composition of the *Ulva* microbiota, from both a taxonomic and a functional point of view (Ghaderiardakani et al., 2020; Van der Loos et al., 2023; Wichard, 2023). However, the existence of a taxonomic core microbiota remains debated, with studies supporting (Lachnit et al., 2009; Tujula et al., 2010, 2011; Comba González et al., 2021) or rejecting (e.g., Burke et al., 2011) this concept. Several studies have demonstrated the presence of macroalgae-specific bacterial communities, along with temporally adapted epiphytic bacterial abundances over time (Lachnit et al., 2009, 2011; Comba González et al., 2021; Guo et al., 2022). These findings suggest a functional role for these bacterial communities in association with macroalgae. Strains belonging to the genera *Maribacter* (*Maribacter* sp. MS6) and *Roseovarius* (*Roseovarius* sp. MS2), have been identified as essential to the morphogenesis of *Ulva mutabilis*, forming a tripartite community with the alga (Spoerner et al., 2012; Grueneberg et al., 2016; Ghaderiardakani et al., 2017). Other key *Ulva* functions (e.g., reproduction, defense) depend on genera such as *Maribacter*, *Sulfitobacter*, and *Polaribacter* (Manley and Barbero, 2001; Joint et al., 2002), found in this study's core microbiota. *Maribacter* produces the morphogenesis factor thallusin (Matsuo et al., 2003); *Sulfitobacter* supports *Ulva mutabilis* growth (Grueneberg et al., 2016); *Polaribacter* promotes *Ulva fenestrata* growth (Nedashkovskaya et al., 2013). Chemical signaling between the host and microorganisms reveals the intricate specificity of the microbiota-host relationship and underscores the functional role of the microbiota. Host-microbiota interactions involve chemical molecules like N-acylhomoserine lactones (AHLs), biofilm communication molecules that attract *Ulva* zoospores; however quorum sensing disruption through autoinducer inactivation (AiiA) proteins can nullify this effect (Tait et al., 2005; Joint et al., 2007). The hypothesis of a core microbiota with a global functional role (Burke et al., 2011) may also be supported by the consistently high abundance (59 %–92.8 %) of the constant bacterial community associated with *Ulva* spp. in this study. The 24 genera in the core microbiota include 1491 ASVs, representing 30 % of total ASVs but only 4 % of genera identified. Recent studies have applied ecological models from plants
and animals to host-associated microbial communities (Roth-Schulze et al., 2016). Burke et al. (2011) found high taxonomic variability in *Ulva australis* epibiotic communities but strong functional similarities. Roth-Schulze et al. (2018) later identified a shared gene pool across *Ulva* species and biogeographies, potentially reflecting random colonization by a "functional guild." This aligns with the competitive lottery model (Sale, 1978), suggesting core microbiota genera perform essential functions for algae. Changes in bacterial abundance, both core and variable, likely aid *Ulva*'s environmental adaptability.

### 4.3. Environmental parameters and host physiology as drivers to explain variations of Ulva spp. epiphytic bacteria

This study identified salinity, nitrate concentration, and precipitation as the main environmental drivers influencing microbial community shifts. Seasonal clustering of bacterial communities was observed, with distinct compositions in spring, summer, fall, and winter. These dynamics are consistent with previous findings for brown (Michelou et al., 2013; Paix et al., 2019), red (Lachnit et al., 2011; Lu et al., 2023), and green macroalgae (Comba González et al., 2021; Lu et al., 2023). For instance, Lachnit et al. (2011) reported recurring bacterial patterns in *Ulva intestinalis* between summer and winter, while Comba González et al. (2021) linked changes in *Ulva lactuca* microbiota to rising seawater temperatures. A similar pattern was observed in a three-years study on *Gracilaria vermiculophylla* where the epibiotic microbiota exhibited predictable, seasonally driven taxonomic and functional shifts, with a seasonal core microbiota reappearing at specific times and a permanent core persisting year-round regardless of season or location (Mudlaff et al., 2025).

In our study, temperature-related patterns were also evident. A higher abundance of *Paraglaciecola*—a genus common in cold waters (Luhtanen et al., 2018) was observed in February (8.4 °C), reinforcing the role of seawater temperature. Similarly, salinity influenced bacterial composition: low-salinity samples were dominated by *Luteolibacter*, *Cyanobium*, *Pirellula*, and *Lacihabitans*, whereas high-salinity samples were enriched in *Litorimonas*, *Leucothrix*, *Sulfurovum*, *Algibacter*, and *Dokdonia* (Van der Loos et al., 2023). The microbiota in our study most closely resembled the high-salinity assemblage.

Seasonal changes also affected the abundance of both core and rare taxa, likely reflecting bacterial adaptations to changing environmental conditions. Cold-adapted genera such as *Paraglaciecola* and *Psychromonas*, typically found in polar or deep-sea environments (Lauro et al., 2013; Bech et al., 2017), peaked in February 2021. *Paraglaciecola* is also known to degrade ulvan from *Ulva* surfaces (Tanaka et al., 2022). The denitrifier *Truepera* showed high relative abundance during nitrate peaks in winter (Feb. 2021/2022, Dec. 2021), while *Sphingorhabdus*—a UV-resistant genus (Coppola et al., 2023) peaked in summer, especially June 2022, likely in response to elevated temperature and solar radiation. Bacterial communities likely aid macroalgae in adapting to environmental stresses.

This study also explored the role of *Ulva* spp. physiology, including neutral sugars, uronic acids, lipids, proteins, and pigments, in shaping microbial communities. Variations in uronic acid content, lipid levels, and algal humidity appeared to be correlated with shifts in bacterial community structure. Uronic acids, a major cell wall component, were higher in summer and significantly correlated with bacterial responses (*p* < 0.05, R² = 56 %). An increase in uronic acids in summer could mean that the cell wall of *Ulva* spp. is thicker, which would be an advantage for bacteria with the enzymatic machinery to use them. According to the literature, *Flavobacterium* and *Alteromonas* genera (both represented in *Ulva* spp. microbiota in this study) can use uronic acids in their metabolism (Karapally and Dietrich, 1970; Coste et al., 2015; Inoue et al., 2015). However, we acknowledge that this correlation does not imply a direct causal relationship. It remains unclear whether these physiological traits shape the microbial community or whether specific bacterial groups may themselves influence host physiology—for instance by

degrading uronic acids or modulating surface properties.

### 4.4. Evidence of seasonal co-variation between Ulva spp. epiphytic bacteria and surface metabolome

The surface metabolome of *Ulva* spp. showed a clustering pattern aligned with the seasonal behavior of algal epiphytic bacteria. Seasonality in antifouling/antibacterial activities has been previously observed in green macroalgae extracts, particularly in *Ulva rigida*, where antibacterial and antioxidant activities peak in spring and summer due to higher temperature and fouling pressure (Hellio et al., 2004; Trigui et al., 2013). Non-targeted metabolomics has enhanced the understanding of the seasonal variations in the chemical composition of algae. For instance, monthly variations of the metabolome over a 13-month survey were demonstrated in 4 species of the brown macroalgae *Lobophora*, these variations being positively correlated with sea surface temperature and salinity (Gaubert et al., 2019). Chemical surface landscape in brown macroalgae *Fucus* also exhibits seasonal differences between spring/summer and autumn/winter, months when temperature and light are the main environmental drivers (Rickert et al., 2016). In the current study, factors such as irradiation time, solar radiation, chlorophyll-a, temperature and salinity were identified as key drivers of *Ulva* spp. surface metabolome. These were correlated with several metabolites, including potential chlorine molecules. In the current study, an integrative analysis of the surface metabolome, microbiota, and predictive bacterial function datasets revealed a positive correlation (correlation ≥ 0.7) between 31 amplicon sequence variants (ASVs) classified into 18 genera, 115 metabolite peaks, and 58 bacterial functions. These findings suggest that over a period of one and a half years of sampling, certain bacterial taxa exhibited strong associations with the presence of specific chemical compounds on the surface of *Ulva* spp., further supporting the connection previously established between epiphytic bacteria and the surface metabolome.

June 2022, a distinct chemical profile emerged, with increased levels of DMSP and proline-betaine, which are known to function as protective molecules against stresses (salinity, oxidative, temperature) in macroalgae (Sunda et al., 2002; Carrión et al., 2023). Both DMSP and proline-betaine have previously been identified as discriminative metabolites in the seasonal monitoring of the surface metabolome of the brown alga *Taonia atomaria* (Paix et al., 2019). These compounds are notably elevated during the summer months in brown macroalgae (Paix et al., 2019; Carvalho et al., 2022). Seasonal shifts in bacterial communities were also observed, with *Dokdonia* reaching its peak abundance in June 2022, coinciding with high solar radiation levels (Pearson's correlation coefficient = 0.56; $p \le 0.05$) and the presence of antioxidant metabolites. *Dokdonia* is representative of the *Flavobacteriia* class and some of them contain proteorhodopsin (PRs), which are membrane proteins with retinal protein as light absorbing molecules (González et al., 2011). Those bacterial light-dependent proton pumps allow bacteria to use light as a source of energy (photoheterotrophy) for maintenance as well as active cell growth (Gómez-Consarnau et al., 2007). This correlation supports the hypothesis that solar radiation influences the surface microbiota and is reinforced by the presence of peroxiredoxins in bacterial functions which protect against cellular oxidative damages (Dubbs and Mongkolsuk, 2007). Another notable example of the link between microbiota dynamics and the surface metabolome was observed in April 2021 and April 2022, as both months exhibited similar surface microbiota compositions and metabolome profiles. Indeed, the core microbiota accounted for 92.8 %, with *Rhodobacteraceae* representing 35.3 % of total relative abundance in April 2021 and 30.7 % in April 2022. *Rhodobacteraceae*, known for their adaptation to an epiphytic lifestyle (Dogs et al., 2017) and temporal shifts in abundance (Comba González et al., 2021), contain members with a biosynthetic pathway for vitamin B12 (Sañudo-Wilhelmy et al., 2014), which may influence algal growth (Fries, 1993; Dogs et al., 2017). The peak abundance of *Rhodobacteraceae* coincided with *Ulva* growth in
March/April, as observed in previous studies (Rosenberg and Ramus, 1982; Coffaro and Sfriso, 1997). These findings highlight the potential link between microbiota and host physiological needs, emphasizing metabolic complementarity.

In conclusion, the multi-omics approach is particularly relevant for studying holobiont dynamics, suggesting that surface-associated bacteria of *Ulva* spp. are distinct from those in the surrounding environment. Changes in the relative abundances of the bacterial community were observed, affecting both the core microbiota and rare taxa. Environmental factors, as well as host physiology, were identified as potential drivers that partially explain the seasonal dynamics of *Ulva* spp. associated bacteria. Seasonal variations were also detected in the surface metabolome, with covariations observed between surface bacteria and the metabolome, indicating potential responses to environmental stresses. A deeper understanding of the coupled dynamics of surface microbiota and metabolome over time will enhance our knowledge of the functioning of the macroalgal holobiont in response to environmental variations, which is particularly relevant in the context of climate change.

## Author contributions

**Sauvann Paulino:** conceptualization, formal analysis, investigation, methodology, visualization, writing – original draft. **Cyril Noel:** formal analysis, methodology, visualization. **Laura Rieusset:** formal analysis, visualization. **Laure Taupin:** formal analysis, investigation. **Gwenaelle Le Blay:** conceptualization, funding acquisition, supervision, writing – original draft. **Nathalie Bourgougnon:** conceptualization, funding acquisition, supervision, writing – original draft.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgment

This work was supported by ISblue project, Interdisciplinary graduate school for the 720blue planet (ANR-17-EURE-0015). Sauvann Paulino was supported by "Université de Bretagne sud" and "Allocation de Recherche Doctorale" grants for her Ph.D. thesis.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.crmicr.2025.100439.

## Data availability

Sequences data were deposited and are publicly available in the European Nucleotide Archive (ENA) under the BioProject PRJEB85979, accession number. https://www.ebi.ac.uk/ena/browser/view/PRJEB85979

## References

* Abdul Malik, S.A., Bedoux, G., Garcia Maldonado, J.Q., Freile-Pelegrín, Y., Robledo, D., Bourgougnon, N., 2020. Defence on surface: macroalgae and their surface-associated microbiome. Advances in Botanical Research. Elsevier, pp. 327–368.

* Alberdi, A., Aizpurua, O., Gilbert, M.T.P., Bohmann, K., 2018. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol 9, 134–147.

* Alsyfani, T., Califano, G., Deicke, M., Grueneberg, J., Weiss, A., Engelen, A.H., et al., 2020. Macroalgal-bacterial interactions: identification and role of thallusin in morphogenesis of the seaweed Ulva (Chlorophyta). Journal of Experimental Botany 71, 3340–3349.

* Alsyfani, T., Weiss, A., Wichard, T., 2017. Time Course Exo-Metabolomic Profiling in the Green Marine Macroalga Ulva (Chlorophyta) for Identification of Growth Phase-Dependent Biomarkers. Marine Drugs 15, 14.

* Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D.R., et al., 2011. Enterotypes of the human gut microbiome. Nature 473, 174–180.

* Barott, K.L., Rodriguez-Brito, B., Janouskovec, J., Marhaver, K.L., Smith, J.E., Keeling, P., Rohwer, F.L., 2011. Microbial diversity associated with four functional groups of benthic reef algae and the reef-building coral Montastraea annularis. Environmental Microbiology 13, 1192–1204.

* Bech, P.K., Schultz-Johansen, M., Glaring, M.A., Barbeyron, T., Czjzek, M., Stougaard, P., 2017. Paraglaciecola hydrolytica sp. nov., a bacterium with hydrolytic activity against multiple seaweed-derived polysaccharides. International Journal of Systematic and Evolutionary Microbiology 67, 2242–2247.

* Bermejo, R., Heesch, S., Mac Monagail, M., O'Donnell, M., Daly, E., Wilkes, R.J., Morrison, L., 2019. Spatial and temporal variability of biomass and composition of green tides in Ireland. Harmful Algae 81, 94–105.

* Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Galith, G.A., et al., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857.

* Burke, C., Steinberg, P., Rusch, D., Kjelleberg, S., Thomas, T., 2011. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci USA 108, 14288–14293.

* Califano, G., Kwantes, M., Abreu, M.H., Costa, R., Wichard, T., 2020. Cultivating the Macroalgal Holobiont: Effects of Integrated Multi-Trophic Aquaculture on the Microbiome of Ulva rigida (Chlorophyta). Front Mar Sci 7, 52.

* Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583.

* Campbell, A., 2011. The ecology of bacterially mediated bleaching in a chemically defended seaweed.

* Carrión, O., Li, C.Y., Peng, M., Wang, J., Pohnert, G., Azizah, M., et al., 2023. DMSOP-cleaving enzymes are diverse and widely distributed in marine microorganisms. Nat Microbiol 8, 2326–2337.

* Chen, J., Zang, Y., Yang, Z., Qu, T., 2022. Composition and Functional Diversity of Epiphytic Bacterial and Fungal Communities on Marine Macrophytes in an Intertidal Zone. Front Microbiol 13.

* Coffaro, G., Sfriso, A., 1997. Simulation model of Ulva rigida growth in shallow water of the Lagoon of Venice. Ecological Modelling 102, 55–66.

* Comba González, N.B., Niño Corredor, A.N., López Kleine, L., Montoya Castano, D., 2021. Temporal changes of the epiphytic bacteria community from the marine macroalga Ulva lactuca. Current Microbiology 78, 534–543.

* Coppola, D., Lauritano, C., Zazo, G., Nuzzo, G., Fontana, A., Ianora, A., et al., 2023. Biodiversity of UV-Resistant Bacteria in Antarctic Aquatic Environments. JMSE 11, 968.

* Coste, O., Malta, E., López, J.C., Fernández-Díaz, C., 2015. Production of sulfated oligosaccharides from the seaweed Ulva sp. using a new ulvan-degrading enzymatic bacterial crude extract. Algal Research 10, 224–231.

* Dogs, M., Wemheuer, B., Wolter, L., Bergen, N., Daniel, R., Simon, M., Brinkhoff, T., 2017. Rhodobacteraceae on the marine brown alga Fucus spiralis are abundant and show physiological adaptation to an epiphytic lifestyle. Systematic and Applied Microbiology 40, 370–382.

* Douglas, G.M., Maffei, V.J., Zaneveld, J.R., Yurgel, S.N., Brown, J.R., Taylor, C.M., et al., 2020. PICRUST2 for prediction of metagenome functions. Nat Biotechnol 38, 685–688.

* Dubbs, J.M., Mongkolsuk, S., 2007. Peroxiredoxins in Bacterial Antioxidant Defense. In: L., Flohé, J.R., Harris (Eds.), Peroxiredoxin Systems. Subcellular Biochemistry. Springer Netherlands, Dordrecht, pp. 143–193.

* Egan, S., Harder, T., Burke, C., Steinberg, P., Kjelleberg, S., Thomas, T., 2013. The seaweed holobiont: understanding seaweed-bacteria interactions. FEMS Microbiol Rev 37, 462–476.

* Egan, S., Kumar, V., Nappi, J., Gardiner, M., 2017. Microbial Diversity and Symbiotic Interactions with Macroalgae. Algal and Cyanobacteria Symbioses. WORLD SCIENTIFIC (EUROPE), pp. 493–546.

* Fernandes, N., Steinberg, P., Rusch, D., Kjelleberg, S., Thomas, T., 2012. Community structure and functional gene profile of bacteria on healthy and diseased thalli of the red seaweed Delisea pulchra. PloS one 7, e50854.

* Filbee-Dexter, K., Wernberg, T., Grace, S.P., Thormar, J., Fredriksen, S., Narvaez, C.N., et al., 2020. Marine heatwaves and the collapse of marginal North Atlantic kelp forests. Sci Rep 10, 13388.

* Flemming, H.C., Wingender, J., Szewzyk, U., Steinberg, P., Rice, S.A., Kjelleberg, S., 2016. Biofilms: an emergent form of bacterial life. Nat Rev Microbiol 14, 563–575.

* García-Poza, S., Pacheco, D., Cotas, J., Marques, J.C., Pereira, L., Gonçalves, A.M.M., 2022. Marine macroalgae as a feasible and complete resource to address and promote Sustainable Development Goals (SDGs). Integrated Environmental Assessment and Management 18, 1148–1161.

* Gaubert, J., Payri, C.E., Vieira, C., Solanki, H., Thomas, O.P., 2019. High metabolic variation for seaweeds in response to environmental changes: a case study of the brown algae Lobophora in coral reefs. Sci Rep 9, 993.

* Ghaderiardakani, F., Coates, J.C., Wichard, T., 2017. Bacteria-induced morphogenesis of Ulva intestinalis and Ulva mutabilis (Chlorophyta): a contribution to the lottery theory. FEMS Microbiology Ecology 93.

* Ghaderiardakani, F., Quartino, M.L., Wichard, T., 2020. Microbiome-Dependent Adaptation of Seaweeds Under Environmental Stresses: a Perspective. Front Mar Sci 7, 575228.
Goecke, F., Labes, A., Wiese, J., Imhoff, J.F., 2013. Phylogenetic analysis and antibiotic activity of bacteria isolated from the surface of two co-occurring macroalgae from the Baltic Sea. European Journal of Pycnology 48, 47–60.

Gómez-Consarnau, L., González, J.M., Coll-Lladó, M., Gourdon, P., Pascher, T., Neutze, R., et al., 2007. Light stimulates growth of proteorhodopsin-containing marine Flavobacteria. Nature 445, 210–213.

González, J.M., Pinhassi, J., Fernández-Gómez, B., Coll-Lladó, M., González- Velázquez, M., Puigbo, P., Pedrós-Alió, C., 2011. Genomics of the proteorhodopsin-containing marine flavobacterium Dokdonia sp. strain MED134 Applied and Environmental Microbiology 77 (24), 8676–8686.

Green, J., Bohannan, B.J.M., 2006. Spatial scaling of microbial biodiversity. Trends in Ecology & Evolution 21, 501–507.

Grueneberg, J., Engelen, A.H., Costa, R., Wichard, T., 2016. Macroalgal Morphogenesis Induced by Waterborne Compounds and Bacteria in Coastal Seawater. PLoS ONE 11, e0146307.

Guo, Z., Wang, L., Jiang, Z., Liang, Z., 2022. Comparison studies of epiphytic microbial communities on four macroalgae and their rocky substrates. Marine Pollution Bulletin 176, 113435.

Hellio, C., Marechal, J.P., Véron, B., Bremer, G., Clare, A.S., Le Gal, Y., 2004. Seasonal Variation of Antifouling Activities of Marine Algae from the Brittany Coast (France). Marine Biotechnology 6, 67–82.

Inoue, A., Nishiya, R., Mochizuki, S., Ojima, T., 2015. Identification of a 4-Deoxy-l- erythro-5-hexoseulose Uronic Acid Reductase, FLRed, in an Alginolytic Bacterium Flavobacterium sp. Strain UMI-01. Marine Drugs 13, 493–508.

Ismail, A., Ktari, L., Ahmed, M., Bolhuis, H., Bouhaouala-Zahar, B., Stal, L.J., et al., 2018. Heterotrophic bacteria associated with the green alga Ulva rigida: identification and antimicrobial potential. J Appl Biol 30, 2883–2899.

Jensen, P.R., Kauffman, C.A., Fenical, W., 1996. High recovery of culturable bacteria from the surfaces of marine algae. Mar Biol 126, 1–7.

Joint, I., Tait, K., Callow, M.E., Callow, J.A., Milton, D., Williams, P., Cámara, M., 2002. Cell-to-Cell Communication Across the Prokaryote-Eukaryote Boundary. Science 298, 1207, 1207–

Joint, I., Tait, K., Wheeler, G., 2007. Cross-kingdom signalling: exploitation of bacterial quorum sensing molecules by the green seaweed Ulva. Phil Trans R Soc B 362, 1223–1233.

Karapally, J.C., Dietrich, C.P., 1970. A uronic acid isomerase in Flavobacterium heparinum. Can J Biochem 48, 164–169.

Kessler, R.W., Weiss, A., Kuegler, S., Hermes, C., Wichard, T., 2018. Macroalgal–bacterial interactions: Role of dimethylsulfoniumpropionate in microbial gardening by Ulva (Chlorophyta). Molecular Ecology 27, 1808–1819.

Lachnit, T., Blümel, M., Imhoff, J.F., Wahl, M., 2009. Specific epibacterial communities on macroalgae: phylogeny matters more than habitat. Aquatic Biology 5, 181–186.

Lachnit, T., Meske, D., Wahl, M., Harder, T., Schmitz, R., 2011. Epibacterial community patterns on marine macroalgae are host-specific but temporally variable. Environmental Microbiology 13, 655–665.

Lauro, F.M., Stratton, T.K., Chastain, R.A., Ferriera, S., Johnson, J., Goldberg, S.M.D., et al., 2013. Complete Genome Sequence of the Deep-Sea Bacterium Psychromonas Strain CNPT3. Genome Announc 1, e00304–e00313.

Lé Cao, K.A., Rossouw, D., Robert-Gniet, C., Besse, P., 2008. A Sparse PLS for Variable Selection when Integrating Omics Data. Statistical Applications in Genetics and Molecular Biology 7.

Li, J., Wang, T., Yu, S., Bai, J., Qin, S., 2019. Community characteristics and ecological roles of bacterial biofilms associated with various algal settlements on coastal reefs. Journal of Environmental Management 250, 109459.

Lu, D.C., Wang, F.Q., Amann, R.I., Teeling, H., Du, Z.J., 2023. Epiphytic common core bacteria in the microbiomes of co-located green (Ulva), brown (Saccharina) and red (Grzeloupia, Gelidium) macroalgae. Microbiol 11 (1), 126.

Luhtanen, A.M., Eronen-Rasimus, E., Oksanen, H.M., Tison, J.L., Delille, B., Dieckmann, G.S., et al., 2018. The first known virus isolates from Antarctic sea ice have complex infection patterns. FEMS Microbiology Ecology 94.

Manley, S.L., Barbero, P.E., 2001. Physiological constraints on bromoforin (CBR 3) production by Ulva lactuca (Chlorophyta). Limnology & Oceanography 46, 1392–1399.

Martin, M., Portetelle, D., Michel, G., Vandenbol, M., 2014. Microorganisms living on macroalgae: diversity, interactions, and biotechnological applications. Applied microbiology and biotechnology 98, 2917–2935.

Matsuo, Y., Suzuki, M., Kasai, H., Shizuri, Y., Harayama, S., 2003. Isolation and phylogenetic characterization of bacteria capable of inducing differentiation in the green alga Monostroma oxyspermum. Environmental Microbiology 5, 25–35.

Matsuo, Y., Imagawa, H., Nishizawa, M., Shizuri, Y., 2005. Isolation of an Algal Morphogenesis Inducer from a Marine Bacterium. Science 307, 1598, 1598–.

Michelou, V.K., Caporaso, J.G., Knight, R., Palumbi, S.R., 2013. The Ecology of Microbial Communities Associated with Macrocystis pyrifera. PLoS ONE 8, e67480.

Mudlaff, C.M., Weinberger, F., Düsedau, L., Ghotbi, M., Künzel, S., Bonthond, G., 2025. Seasonal Cycles in a Seaweed Holobiont: a Multiyear Time Series Reveals Repetitive Microbial Shifts and Core Taxa. Environmental Microbiology 27 (3), e70062.

Nedashkovskaya, O.I., Kukhlevskiy, A.D., Zhukova, N.V., 2013. Polaribacter reichenbachii sp. nov.: a New Marine Bacterium Associated with the Green Alga Ulva fenestra. Curr Microbiol 66, 16–21.

Nemergut, D.R., Schmidt, S.K., Fukami, T., O'Neill, S.P., Bilinski, T.M., Stanish, L.F., et al., 2013. Patterns and Processes of Microbial Community Assembly. Microbiology and Molecular Biology Reviews 77, 342–356.

Noël, C., Quintrie, L., Cormier, A., Leroi, L., Durand, P., 2021. SAMBA: Standardized and Automated Metabarcoding Analyses workflow. WorkflowHub.

Offret, C., Paulino, S., Gauthier, O., Château, K., Bidault, A., Corporeau, C., Blay, G.L., 2020. The marine intertidal zone shapes oyster and clam digestive bacterial microbiota. FEMS Microbiology Ecology 96 (8), fiaa078.

Olesen, S.W., Duvallet, C., Alm, E.J., 2017. dbOTU3: a new implementation of distribution-based OTU calling. PLoS One 12 (5), e0176335.

Othmani, A., Briand, J.F., Ayé, M., Molmeret, M., Culioli, G., 2016. Surface metabolites of the brown alga Taonia atomaria have the ability to regulate epibiosis. Biofouling 32, 801–813.

Pacheco, D., Araújo, G.S., Cotas, J., Gaspar, R., Neto, J.M., Pereira, L., 2020. Invasive seaweeds in the Iberian Peninsula: a contribution for food supply. Marine drugs 18, 560.

Paix, B., Carroti, N., Barry-Martinet, R., Greff, S., Misson, B., Briand, J.F., Culioli, G., 2020. A Multi-Omics Analysis Suggests Links Between the Differentiated Surface Metabolome and Epiphytic Microbiota Along the Thallus of a Mediterranean Seaweed Holobiont. Front Microbiol 11, 494.

Paix, B., Othmani, A., Debros, D., Culioli, G., Briand, J., 2019. Temporal covenation of epibacterial community and surface metabolome in the Mediterranean seaweed holobiont Taonia atomaria. Environmental Microbiology 21, 3346–3363.

Pfister, C.A., Altabet, M.A., Weigel, B.L., 2019. Kelp beds and their local effects on seawater chemistry, productivity, and microbial communities. Ecology 100 (10), e02798.

Pruesse, E., Quast, C., Knittel, K., Fuchs, B.M., Ludwig, W., Peplies, J., Göckner, F.O., 2007. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Research 35, 7188–7196.

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schwer, T., Yarza, P., Göckner, F.O., 2012. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research 41 (D1), D590–D596.

Remy, S., Solis, D., Silland, P., Neyts, J., Roussi, F., Touboul, D., Litaudon, M., 2019. Isolation of phenanthrenes and identification of phorbol ester derivatives as potential anti-CHIKV agents using FBMN and NAP from Sagotia racemosa. Phytochemistry 167, 112101.

Rickert, E., Wahl, M., Link, H., Richter, H., Pohnert, G., 2016. Seasonal Variations in Surface Metabolite Composition of Fucus vesiculosus and Fucus serratus from the Baltic Sea. PLoS ONE 11, e0168196.

Risely, A., 2020. Applying the core microbiome to understand host-microbe systems. Journal of Animal Ecology 89, 1549–1558.

Rosenberg, E., Koren, O., Reshef, L., Efrony, R., Zilber-Rosenberg, I., 2007. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol 5, 355–362.

Rosenberg, C., Ramus, J., 1982. Ecological growth strategies in the seaweeds Gracilaria foliifera (Rhodophyceae) and Ulva sp. (Chlorophyceae): Soluble nitrogen and reserve carbohydrates. Mar Biol 66, 251–259.

Roth-Schulze, A.J., Pintado, J., Zozaya-Valdés, E., Cremades, J., Ruiz, P., Kjelleberg, S., Thomas, T., 2018. Functional biogeography and host specificity of bacterial communities associated with the Marine Green Alga Ulva spp. Molecular Ecology 27, 1952–1965.

Saha, M., Rempt, M., Gebser, B., Gueneberg, J., Pohnert, G., Weinberger, F., 2012. Dimethylsulphonopropionate (DMSP) and proline from the surface of the brown alga Fucus vesiculosus inhibit bacterial attachment. Biofouling 28, 593–604.

Sale, P.F., 1978. Coexistence of coral reef fishes ? a lottery for living space. Environ Biol Fish 3, 85–102.

Singh, R.P., Reddy, C.R.K., 2014. Seaweed-microbial interactions: key functions of associated bacteria. FEMS Microbiol Ecol 88, 213–230.

Spoerner, M., Wichard, T., Bachhuber, T., Stratmann, J., Oertel, W., 2012. Growth and Thallus Morphogenesis of Ulva mutabilis (Chlorophyta) Depends on A Combination of Two Bacterial Species Excreting Regulatory Factors. Journal of Pycnology 48, 1433–1447.

Steinhagen, S., Karez, R., Weinberger, F., 2019. Cryptic, alien and lost species: molecular diversity of Ulva sensu lato along the German coasts of the North and Baltic Seas. European Journal of Pycnology 54, 466–483.

Sunda, W., Kieber, D.J., Kiene, R.P., Huntsman, S., 2002. An antioxidant function for DMSP and DMS in marine algae. Nature 418, 317–320.

Tait, K., Joint, I., Daykin, M., Milton, D.L., Williams, P., Cámara, M., 2005. Disruption of quorum sensing in seawater abolishes attraction of zoospores of the green alga Ulva to bacterial biofilms. Environmental Microbiology 7, 229–240.

Tanaka, R., Kurishiba, Y., Miyake, H., Shibata, T., 2022. Isolation, Diversity and Characterization of Ulvan-Degrading Bacteria Isolated from Marine Environments. Molecules 27, 3420.

Thomas, F., Dittami, S.M., Brunet, M., Le Duff, N., Tanguy, G., Leblanc, C., Gobet, A., 2020. Evaluation of a new primer combination to minimize plastid contamination in 16S rDNA metabarcoding analyses of alga-associated bacterial communities. Environ Microbiol Rep 12, 30–37.

Trias, R., García-Lledó, A., Sánchez, N., López-Jurado, J.L., Hallin, S., Bañeras, L., 2012. Abundance and Composition of Epiphytic Bacterial and Archaeal Ammonia Oxidizers of Marine Red and Brown Macroalgae. Applied and Environmental Microbiology 78, 318–325.

Trigui, M., Gasmi, L., Zouari, I., Tounsi, S., 2013. Seasonal variation in phenolic composition, antibacterial and antioxidant activities of Ulva rigida (Chlorophyta) and assessment of antiacetylcholinesterase potential. J Appl Pycnol 25, 319–328.

Tujula, N.A., Crocetti, G.R., Burke, C., Thomas, T., Holmström, C., Kjelleberg, S., 2010. Variability and abundance of the epiphytic bacterial community associated with a green marine Ulvacean alga. ISME J 4, 301–311.

Ul-Hasan, S., Bowers, R.M., Figueroa-Montiel, A., Licea-Navarro, A.F., Beman, J.M., Woyke, T., Nobile, C.J., 2019. Community ecology across bacteria, archaea and
microbial eukaryotes in the sediment and seawater of coastal Puerto Nuevo. PLoS ONE 14, e0212355.

Van Der Loos, L.M., D'hondt, S., Engelen, A.H., Pavia, H., Toth, G.B., Willems, A., et al., 2023. Salinity and host drive Ulva -associated bacterial communities across the Atlantic-Baltic Sea gradient. Molecular Ecology 32, 6260-6277.

Wichard, T., 2023. From model organism to application: Bacteria-induced growth and development of the green seaweed Ulva and the potential of microbe leveraging in algal aquaculture. Seminars in Cell & Developmental Biology 134, 69-78.

Ye, N., Zhang, X., Mao, Y., Liang, C., Xu, D., Zou, J., et al., 2011. Green tides' are overwhelming the coastline of our blue planet: taking the world's largest example. Ecol Res 26, 477-485.

Yoon, J., Adachi, K., Kasai, H., 2015. Isolation and characterization of a novel marine Bacteroidetes as Algitalea ulvae gen. nov., sp. nov., isolated from the green alga Ulva pertusa. Antonie van Leeuwenhoek 108, 505-513.