Salinity and host drive Ulva‐associated bacterial communities across the Atlantic–Baltic Sea gradient

Abstract

The green seaweed Ulva is a model system to study seaweed–bacteria interactions, but the impact of environmental drivers on the dynamics of these interactions is little understood. In this study, we investigated the stability and variability of the seaweed‐associated bacteria across the Atlantic–Baltic Sea salinity gradient. We characterized the bacterial communities of 15 Ulva sensu lato species along 2,000 km of coastline in a total of 481 samples. Our results demonstrate that the Ulva‐associated bacterial composition was strongly structured by both salinity and host species (together explaining between 34% and 91% of the variation in the abundance of the different bacterial genera). The largest shift in the bacterial consortia coincided with the horohalinicum (5–8 PSU, known as the transition zone from freshwater to marine conditions). Low‐salinity communities especially contained high relative abundances of Luteolibacter, Cyanobium, Pirellula, Lacihabitans and an uncultured Spirosomaceae, whereas high‐salinity communities were predominantly enriched in Litorimonas, Leucothrix, Sulfurovum, Algibacter and Dokdonia. We identified a small taxonomic core community (consisting of Paracoccus, Sulfitobacter and an uncultured Rhodobacteraceae), which together contributed to 14% of the reads per sample, on average. Additional core taxa followed a gradient model, as more core taxa were shared between neighbouring salinity ranges than between ranges at opposite ends of the Atlantic–Baltic Sea gradient. Our results contradict earlier statements that Ulva‐associated bacterial communities are taxonomically highly variable across individuals and largely stochastically defined. Characteristic bacterial communities associated with distinct salinity regions may therefore facilitate the host's adaptation across the environmental gradient.

Full text

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Molecular Ecology. 2023;32:6260–6277.wileyonlinelibrary.com/journal/mec
Received: 1 December 2021
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Revised: 21 February 2022
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Accepted: 30 March 2022
DOI : 10.1111/me c.16 462
ORIGINAL ARTICLE
Salinity and host drive Ulva- associated bacterial communities
across the Atlantic– Baltic Sea gradient
Luna M. van der Loos1,2 | Sofie D’hondt1 | Aschwin H. Engelen3 | Henrik Pavia4 |
Gunilla B. Toth4 | Anne Willems2 | Florian Weinberger5 | Olivier De Clerck1 |
Sophie Steinhagen4
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2022 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.
1Phycology Research Group, Department
of Biolog y, Ghent University, Ghent,
Belgium
2Laboratory of Microbiology, Department
Biochemistr y and Microbiology, Ghent
University, Ghent, Belgium
3Marine Microbial Ecology &
Biotechnology, CCMAR, University of
Algar ve, Faro, Portugal
4Department of Marine Sciences- Tjärnö,
University of Gothenburg, Strömstad,
Sweden
5GEOMAR Helmholtz Centre for Ocean
Research Kiel, Kiel, Germany
Correspondence
Luna M. van der Loos, Phycology Research
Group, Department of Biology, Ghent
University, Ghent, Belgium.
Email: luna.vanderloos@ugent.be
Sophie Steinhagen, Department of
Marine Sciences- Tjärnö, University of
Gothenburg, Strömstad, Sweden.
Email: sophie.steinhagen@gu.se
Funding information
Portugese nation fund from Foundation
for Science and Technology, Grant/Award
Number: UIDB/04326/2020; Fonds
Wetenschappelijk Onderzoek, Grant/
Award Number: 3F020119; Formas
national research program for food,
Grant/Award Number: 2020 - 03119;
European Marine Biological Resource
Centre Belgium, Grant /Award Number:
FWO project I0 01621N
Handling Editor: Henrik Krehenwinkel
Abstract
The green seaweed Ulva is a model system to study seaweed– bacteria interactions,
but the impact of environmental drivers on the dynamics of these interactions is little
understood. In this study, we investigated the stability and variability of the seaweed-
associated bacteria across the Atlantic– Baltic Sea salinity gradient. We characterized
the bacterial communities of 15 Ulva sensu lato species along 2,000 km of coastline in a
total of 481 samples. Our results demonstrate that the Ulva- associated bacterial com-
position was strongly structured by both salinity and host species (together explaining
between 34% and 91% of the variation in the abundance of the different bacterial
genera). The largest shift in the bacterial consortia coincided with the horohalinicum
(5– 8 PSU, known as the transition zone from freshwater to marine conditions). Low-
salinity communities especially contained high relative abundances of Luteolibacter,
Cyanobium, Pirellula, Lacihabitans and an uncultured Spirosomaceae, whereas
high- salinity communities were predominantly enriched in Litorimonas, Leucothrix,
Sulfurovum, Algibacter and Dokdonia. We identified a small taxonomic core community
(consisting of Paracoccus, Sulfitobacter and an uncultured Rhodobacteraceae), which
together contributed to 14% of the reads per sample, on average. Additional core
taxa followed a gradient model, as more core taxa were shared between neighbouring
salinity ranges than between ranges at opposite ends of the Atlantic– Baltic Sea gradi-
ent. Our results contradict earlier statements that Ulva- associated bacterial commu-
nities are taxonomically highly variable across individuals and largely stochastically
defined. Characteristic bacterial communities associated with distinct salinity regions
may therefore facilitate the host's adaptation across the environmental gradient.
KEYWORDS
bacterial communities, Baltic Sea, microbiome, salinity gradient, Ulva

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1 | INTRODUC TION
Bacteria are of vital importance to marine multicellular organisms and
often play a crucial role throughout their host's life (Bordenstein & Theis,
2015; McFall- Ngai et al., 2013). Seaweeds— important primary produc-
ers in coastal ecosystems worldwide— likewise depend on their associ-
ated microbiota for optimal functioning, including nutrient exchange,
defence mechanisms and reproduction (Egan et al., 2013; Weinberger
et al., 2007). The algal host and its associated microbiome are often re-
ferred to as a holobiont: a single ecological unit (Egan et al., 2013). The
members of these ecological units are connected through complex in-
teractions on multiple levels (Pita et al., 2018). The dynamics of the sea-
weed holobiont, however, are little understood— especially with regard
to environmental drivers (Egan et al., 2013; van der Loos et al., 2019).
The green seaweed Ulva is a model to study algae– bacteria in-
teractions (Califano et al., 2020; Kessler et al., 2018; Wichard et al.,
2015). Ulva relies on specific bacterial partners to obtain its typi-
cal morphology (e.g., a blade that is two cells thick or a tube that is
one cell thick). In the absence of these specific bacteria, Ulva merely
grows as a loose aggregation of cells without rhizoids or proper cell
wall development. In addition to morphogenesis, bacteria are known
to promote Ulva growth (Gemin et al., 2019), induce settlement of
zoospores (Joint et al., 2000; Patel et al., 2003) and affect the bio-
chemical composition of the seaweed (Polikovsky et al., 2020).
As with other seaweeds, the entire spectrum of interactions be-
tween Ulva, its associated microbiome and the environment remains
largely unknown. Studies so far have only addressed variation in Ulva-
associated bacterial diversity across small and larger geographical
scales (see, e.g., Burke et al., 2011; Roth- Schulze et al., 2018; Tujula
et al., 2010), but not across environmental gradients. In the absence of
an explicit environmental gradient, neutral or stochastic processes are
more likely to drive microbial community structure, thus causing high
among- individual variation (Adair & Douglas, 2017). In the presence
of an environmental gradient, deterministic mechanisms (i.e., envi-
ronmental selection) could govern variation in microbial composition
(Adair & Douglas, 2017; Martiny et al., 2006). Indeed, previous studies
of Ulva- associated bacteria with samples taken from one or a few lo-
cations have highlighted high levels of intra- individual variation (Burke
et al., 2011; Roth- Schulze et al., 2018). Other studies, however, found
distinct differences among sampling habitats and Ulva host species
(Comba González et al., 2021; van der Loos et al., 2021).
Closely related to questions on the variability of the Ulva micro-
biome across environmental gradients, is the question on its stability
(the “core” microbiome). Identifying stable key microbes is important
to define “healthy” microbial communities and— especially with regard
to spatial and temporal distribution— gain insight into ecological func-
tions (Risely, 2020). Bonthond et al. (2020), for example, identified var-
ious prokaryotic and eukaryotic core taxa associated with the red alga
Gracilaria vermiculophylla on a global scale in both native and introduced
populations. This implies that Gracilaria's core taxa either have a world-
wide distribution, or have been co- introduced with their host during
the invasion process. The bacterial communities of the introduced
Mediterranean Caulerpa taxifolia likewise showed high similarity to the
communities of the native populations in eastern Australia (Arnaud-
Haond et al., 2017; Meusnier et al., 2001). Core microbes may even
facilitate successful introductions (Bonthond et al., 2021). Bacteria
probably play an important role in acclimatization and adaptation of
Ulva to environmental changes, as has been demonstrated in the fil-
amentous brown alga Ectocarpus, which depends on bacterial com-
munities for acclimatization to salinity changes (Dittami et al., 2016).
Incorporating an environmental gradient can, therefore, provide infor-
mation on the stochastic vs. deterministic mechanisms controlling the
variability and stability of microbial composition in general.
A study on the global, environmental distribution of bacterial di-
versity marked salinity as the most important driver of bacterial com-
munity composition, surpassing the effects of temperature and pH
(Lozupone & Knight, 2007). Salinity gradients are often studied in
estuaries, but estuarine environments are dynamic and the constant
mixing of water bodies causes unstable gradients. The Baltic Sea is the
world's largest inland brackish sea and one of the most widely stud-
ied coastal areas. This area represents a relatively young (8,000 years),
semi- enclosed postglacial sea that stands out by a successive transi-
tion from fully marine conditions of the North Sea (Northeast Atlantic)
towards a near freshwater state in its innermost parts (Reusch et al.,
2018). The lack of tides, as well as the freshwater influx on one side
of the gradient combined with limited exchange with North Sea water,
allow for stable salinity regions over a large geographical distance. In
addition, water retention time in the brackish central Baltic is high
(between 3 and 30 years), especially compared to the more dynamic
estuaries formed at river mouths (Herlemann et al., 2011). This makes
the Baltic Sea an excellent area to study salinity gradients.
The steepest salinity change in the Baltic Sea can be observed at
the Danish Straits (Johannesson et al., 2020), and species diversity
and distribution are strongly defined by the prevailing salinity regime
(Ojaveer et al., 2010). Marine species diversity generally decreases
with decreasing salinity, while simultaneously freshwater species in-
crease in number and abundance (Ojaveer et al., 2010). Consequently,
only few marine species successfully establish along this entire en-
vironmental gradient (Johannesson et al., 2020). Although salinity
does not affect bacterial species richness in seawater- and sediment-
associated communities in the Baltic, it is a strong driving force be-
hind bacterial community structure and composition (Herlemann
et al., 2011; Klier et al., 2018). Work on bacterial communities in the
Baltic region has been limited to bacterioplankton, bacteriobenthos
and bacteria as components of animal diets (Herlemann et al., 2011;
Klier et al., 2018; Skrodenytė- Arbačiauskienė et al., 2021), while host-
associated bacteria have rarely been investigated across the entire
salinity gradient. The question therefore remains how host- associated
bacterial communities are influenced by a gradual environmental tran-
sition, and whether the host itself or the prevailing salinity conditions
have a larger effect on the associated microbiomes.
This study aims to (i) characterize the dynamics of seaweed-
associated bacterial communities as a function of both host and a
stable salinity gradient, and (ii) assess whether we can define a tax-
onomic core community across the Atlantic– Baltic Sea gradient. We
sampled 481 Ulva sensu lato individuals along 2,000 km of coastline,

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spanning the full 3.5– 36 PSU salinity gradient in the Baltic Sea and ad-
jacent areas. To examine to what extent the ecological dynamics of the
Ulva- associated bacterial communities are driven by ecological factors
and host species, we generated full- length 16S rRNA gene amplicon
sequences with Oxford Nanopore Technologies. Previous studies on
Ulva- associated bacteria indicated that between- site effects were
more important than between- species effects, probably due to the
high morphological similarity and close phylogenetic relatedness be-
tween Ulva species (van der Loos et al., 2021). We therefore hypoth-
esize that Ulva- associated bacterial community composition in the
Baltic Sea is primarily established under the influence of the prevailing
salinity gradient and secondarily affected by host species.
2 | MATERIALS AND METHODS
2.1 | Study area, field collection and sample
preparation
Samples of Ulva sensu lato individuals (n = 481, including Ulva, Blidingia
and Kornmannia) used in the present study were collected along the
full salinity gradient present in the Baltic Sea and adjacent areas such
as the Kattegat, Skagerrak and the eastern North Sea (Figure 1). Ulva
species are commonly found in the Baltic Sea and on the Northeast
Atlantic coast, and are known for their high tolerance towards fluctua-
tions in salinity (Rybak, 2018). Under high nutrient conditions, some
species are known to cause nuisance blooms (Smetacek & Zingone,
2013). Ulva species are difficult to identify based on their simple mor-
phological characteristics due to the high plasticity within species and
high morphological similarities among species. Over 10 species of Ulva
have previously been identified based on genetic markers in the Baltic
area (Steinhagen et al., 2019). Many of these species occur in sym-
patry and can be found in a wide variety of habitats (Leskinen et al.,
2004; Steinhagen et al., 2019). Ulva has an isomorphic diplohaplontic
life cycle. Morphologically, the gametophytic and sporophytic phases
cannot be reliably distinguished (Wichard, 2015). The life stage of the
individuals sampled in this study was therefore not checked.
In total, 146 sampling sites, of which 63 in Denmark, 53 in Sweden, 25
in Norway and five in Germany, were visited during summer 2020 (June–
September; see also Table S1). The salinity ranged from 3.5 to 36 PSU
and is presented in the figures in this study either on a continuous scale
(0– 36) or in salinity zones defined according to the Venice classification
system (0.5– 5 = oligohaline, 5– 8 = horohalinicum, 8– 18 = mesohaline,
18– 30 = polyhaline, and 30– 36 = euhaline) (Alves et al., 2009; Bleich
et al., 2011; Hu et al., 2016). In addition, both water temperature (°C) and
oxygen levels (mg L−1) were measured at each site (Figures S1 and S2).
A variety of habitats, such as rock pools, harbours, estuaries,
fjords, drain channels as well as exposed and sheltered coastal areas,
FIGURE 1 Geographical distribution
of all 146 sampling sites in the Baltic Sea
and adjacent areas (eastern North Sea,
Skagerrak and Kattegat) where samples
were collected. The colour of the sites
corresponds to the measured salinity.
Major rivers are shown in blue.

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were visited. The different substrates (organic and inorganic) of the
attached thalli were recorded. Sampling was performed in the supra-
and midlittoral zones using waders, which allowed for sampling to a
depth of ~1.5 m below mean sea level. Additional samplings of the
mid- and infralittor al zones of chosen sites were conducted via snor-
kelling. At each site, representative specimens of each morphotype
and all observed populations were collected from the supralittoral to
the sublittoral (in horizontal transects of ~50 m depe ndi ng on sit e ac-
cessibility), including drifting and epiphytic green algae. All sampling
work in the respective countries was conducted by a single person
to ensure repeatability among sites. Sterilized disposable gloves and
sterilized equipment were used throughout the sampling procedure
to minimize contamination. After rinsing each individual with ~30–
50 ml sterile water to remove dir t, a cotton swab sample for microbi-
ome analyses was generated by rubbing for 30 s on the tissue.
Furthermore, to enable DNA barcoding of the host, clean and
epiphyte- free tissue samples (~1 cm2) of the respective individuals
were collected. All samples were stored in a portable freezer (−20°C)
until transfer to −80°C in the laboratory.
2.2 | Molecular identification of the algae host
Genomic DNA was extracted from lyophilized host tissue using
the Invisorb Spin Plant Mini Kit (Stratec) following the manufac-
turer's protocol and stored at −80°C. The tufA gene was used for
species identification of the host. PCR (polymerase chain reaction)
amplicons were successfully generated for 461 samples following
Steinhagen et al. (2019). The PCR products were first assessed by
gel electrophoresis and subsequently purified using the QIAquick
PCR Purification Kit (Qiagen). Sanger sequencing of the purified am-
plicons was performed by Eurofins Genomics. Forward and reverse
sequence reads were assembled in sequencher (version 4.1.4, Gene
Codes Corporation). Using the blast function in GenBank, first iden-
tifications via the specimens’ tufA sequences were made. To better
resolve species identities, a set of peer- reviewed and annotated ref-
erence sequences downloaded from GenBank were used in subse-
quent phylogenetic analyses. Host species were identified according
to the latest taxonomic revisions by Hughey et al. (2021). A multiple
sequence alignment was constructed using mafft (Katoh et al. , 2002).
An optimal substitution model (GTR+G+I) was determined using mr-
modeltest software version 2.2 (Nylander, 2004). Subsequently, a
maximum- likelihood analysis was performed using raxml (version 8;
Stamatakis, 2014) with 1,000 bootstrap iterations. All sequences are
publicly available in GenBank (see Table S1 for accession numbers).
2.3 | Molecular characterization of the microbial
communities
Bacterial communities were characterized with Oxford Nanopore
sequencing follow in g va n der Loos et al. (2021). In short , total micro-
bial DNA was extracted with the Qiagen DNeasy mini kit following
the manufacturer's protocol, with the addition of a bead beating step
before lysis using zirconium oxide beads (RETCH Mixer mill MM400;
5 min at 30 Hz). The full- length 16S rRNA gene was amplified using
the primers 27F_BCtail- FW (TTTCTGT TGGTGCTGATATT GC_
AGAGT TTGATCMTGGCTCAG) and 1492R_BCtail- RV (ACTTGCC
TGTC GC TC TATCTTC_CGGT TACCTTGTTACGACTT), each con-
taining a 5′ extension allowing for subsequent barcoding by PCR.
16S rDNA PCRs were performed using the Phire Tissue direct PCR
Master Mix (Thermo Fisher) and amplicons for each sample were
barcoded using the Oxford Nanopore “PCR Barcoding Expansion
Pack 1- 96 (EXP- PBC096)”. A total of 481 Ulva- associated samples
were processed in nine PCRs and the final libraries were prepared
with the ligation- based sequencing kit SQK- LSK109 according to
the manufacturer's protocol (Oxford Nanopore Technologies). The
libraries were subsequently sequenced in six separate sequenc-
ing runs on a MinION (with R10.3 flow cells, Oxford Nanopore
Technologies) for 72 hr each. Six negative extraction samples were
included in this study, as well as nine negative PCR controls, and four
positive controls (ATCC microbial standard MSA- 1002). In addition,
two randomly chosen samples (DK043 from Denmark and NO118
from Norway) were included in all PCRs and divided over the six
sequencing runs to verif y comparability across PCRs and sequenc-
ing runs.
The resulting raw FAST5 reads were basecalled and demul-
tiplexed with guppy (version 5.0.7, sup model, Oxford Nanopore
Technologies). Data quality and length were first visually inspected
with nanoplot (De Coster et al., 2018). Subsequently, high- quality
reads were obtained using chimaera removal with yacrd (Marijon
et al., 2020), and by filtering the data set on quality (Q- s c o r e >8) and
length (1,000– 2,000 bp) with nanofilt (De Coster et al., 2018). The
resulting 23,955,915 high- quality reads were used to assign taxon-
omy at the genus level with kraken2 in combination with the SILVA
16S database (138.1 release) (Lu & Salzberg, 2020; Quast et al.,
2013). In the presented results and figures, we use the nomenclature
as implemented in the SILVA database. The sequences are archived
at SRA (BioProject PRJNA781821).
After taxonomic assignment, all chloroplast reads (3% of the
high- quality reads) were removed from the data set. In addition, rare
taxa were discarded (optimal settings based on the positive controls
retained operational taxonomic units [OTUs] found more than 70
times in at least 20% of the samples) to protect against OTUs with
small mean and trivially large coefficients of variation. Finally, deseq2
was used to account for sequencing depth with a variance stabilizing
transformation (Love et al., 2 014).
2.4 | Statistical analyses
To assess genus- level dif ferenc es in ba cte rial comp osition , Bray– Curtis
dissimilarities were calculated and visualized with an NMDS ordination
(Bray & Curtis, 1957). Smooth surface lines were fitted to the ordina-
tion with the ordisurf function (vegan package) based on the correlation
with salinity. The effect of salinity, host species, temperature, oxygen

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levels and habitat (substrate from which the host was collected, being
either rock, sand, concrete, epiphytic/epizoic, metal, plastic, wood/
rubber/rope or drift samples) on community composition was tested
using the envfit function of the vegan package with 9,999 permuta-
tions (model included all factors, with p < .05 considered significant)
(Oksanen et al., 2020). Multivariate comparisons with 9,999 permuta-
tions were made with pairwise adonis (Martinez Arbizu, 2020) among
all salinity zones and among all host species. A Mantel test was subse-
quently used to evaluate the correlation between the bacterial com-
munity dissimilarity matrix (at the genus level) and the phylogenetic
host species distance matrix. Alpha diversity was calculated as the ob-
served genus richness, as well as by using the Shannon Index, Simpson
Index and Chao1 Index (Jost, 2007; Willis, 2019). Differences in alpha
diversity with salinity were assessed using a generalized linear mixed
model based on a negative binomial family (p < .05 considered signifi-
cant). The model included salinity, host species and habitat, as well
as the interaction between salinity and host. All categorical variables
(host and habitat) were included as random effects.
Significantly differential abundant bacterial genera (p < .01,
Benjamini– Hochberg corrected) were identified with deseq2 (model
included salinity, host species and habitat, as well as the interaction
between salinity and host) (Love et al., 2014). The amount of ex-
plained variation in abundance was quantified using the lme4 (Bates
et al., 2015) and variancepartition (Hoffman & Schadt, 2016) pack-
ages with a generalized linear mixed model fitted to a negative bi-
nomial family (model included salinity, host species and habitat, as
well as the interaction between salinity and host, and all categorical
variables were included as random effects).
There are many different ways to define and calculate the core
microbiome of a given data set (Risely, 2020; Shade & Handelsman,
2012). Both core composition and size differ with relative abun-
dance and prevalence (the number of samples in which the taxa
were encountered) threshold settings, and as such defining a “core”
microbiome remains relatively arbitrary. Here, the variation in core
size (number of core taxa) was calculated for a range of different
relative abundances (0.1%– 100%) and prevalences (50%– 90%) using
the microbiome R package (Lahti & Shetty, 2017).
All statistical tests were performed in R (R Core Team, 2020)
and data were visualized using the ggplot2 (Wickham, 2016), metac-
oder (Foster et al., 2017) and phyloseq (McMurdie & Holmes, 2013)
packages.
3 | RESULTS
3.1 | Taxonomic identification of host species
A total of 461 Ulva sensu lato samples were processed for species
discrimination and identification based on tufA sequence data. The
full data set was subject to phylogenetic analyses to allow for robust
identification of host species (see Table S1). The phylogenetic analy-
ses separated the investigated specimens into 15 well- delimited
taxonomic entities, including members of the genera Blidingia,
Kornmannia and Ulva. Eight entities of the genus Ulva could be re-
solved based on peer- reviewed reference sequences provided by
GenBank. Five entities (represented by a total of 25 samples) could
not be resolved to species level due to the absence of any similar
GenBank entries.
More specifically, the taxa were identified as Blidingia minima
(Nägli ex Kütz.) Kylin; see also Steinhagen et al. (2021) (n = 25 sam-
ples), Kornmannia leptoderma (Kjellmann) Bliding (n = 14), Ulva au stra-
lis Aresc hou g (n = 2), Ulva comp ressa Linna eus (n = 48), Ulva fenes trata
Postels & Ruprecht (n = 36), Ulva intestinalis Linnaeus (n = 116), Ulva
lacinulata (Kützing) Wittrock (n = 32), Ulva linza Linnaeus (n = 128),
Ulva prolifera O.F. Müller (n = 7), Ulva tor ta (Mertens) Trevisan
(n = 28), and unidentified Ulva sp. 1 (n = 1), Ulva sp. 2 (n = 15), Ulva
sp. 3 (n = 2), Ulva sp. 4 (n = 4) and Ulva sp. 5 (n = 3).
Distinct distribution patterns across the salinity gradient were
recorded for the host species. Corroborating previous studies fo-
cusing on different taxa, most of the green algal species investigated
were absent east of the Danish Straits. Ulva intestinalis and U. linza
showed the widest distribution and were present across the whole
salinity gradient (present from 3.5 to >30 PSU). For details on the
species distribution see Table S2.
3.2 | Bacterial alpha diversity associated with Ulva
sensu lato
After filtering out rare taxa (using optimal settings based on the
positive controls), we identified 96 bacterial genera, belonging
to 28 families and 24 orders, associated with Ulva, Blidingia and
Kornmannia. Highly abundant orders across all Ulva sensu lato spe-
cies included the Rhodobacterales, Sphingomonadales, Rhizobiales
and Flavobacteriales. Alpha diversity did not change with salinity
when calculated as either observed richness (p =.09, z = 1.71; nega-
tive binomial model), or a Shannon Index (p = .55, z = 0.59; negative
binomial model), Simpson Index (p = . 89, z = 0.14; negative binomial
model) or Chao1 Index (p = .27, z = 1.11; negative binomial model).
3.3 | Effect of environment and host species on
bacterial community
Bacterial community composition differed significantly with salinity
(p < .001, R2 = .48) and hos t species (p < .0001, R2 = .34). While pair-
wise comparisons showed that the oligohaline (0– 5 PSU) and horo-
halinicum (5– 8 PSU) shared similar bacterial communities (p = .816,
F = 1.77; pairwise Adonis test), pairwise contrasts among all other
salinity zones showed significant differences in bacterial communi-
ties (with p < .001 for all comparisons; pairwise Adonis test, Table
S3). Pairwise comparisons among all host species indicated that,
amongst others, U. linza and U. intestinalis were associated with dif-
ferent bacterial communities (p = .01, F = 25.97; pairwise Adonis
test), as well as U. compressa vs. U. fenestrata (p = .01, F = 6.09; pair-
wise Adonis test) and U. compressa vs. U. lacinulata (p =.02, F = 5.03;

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pairwise Adonis test). On the contrary, similar bacterial communities
were shared between U. compressa vs. U. tor ta (p = .24, F = 3.66;
pairwise Adonis test), and U. prolifera vs. U. torta (p = 1.0 0, F = 2.23;
pairwise Adonis test). See Table S4 for full statistics.
NMDS plots likewise showed a clear ordination influenced by the
salinity gr adient as well as host species (Figure 2). This salinity effect
was not only observed along the larger Atlantic– Baltic Sea gradi-
ent, but also on local scales (e.g., caused by freshwater river input).
Sample sites located south in the Oslofjord (Norway, Skagerrak
Strait) near the mouth of the Glomma river, for example, have a lower
salinity compared to the predominantly higher surrounding salinity
levels (Figure 1). Bacterial community composition in these sites was
generally more similar to samples collected in distant, low- salinity
sites in the Baltic Sea than to samples collected at neighbouring sites
in the Skagerrak (Figure S3).
Both habitat (p < .0001, R2 = .09) and temperature (p < .001,
R2 = .05) were found to be significant as well, but with very low ex-
planatory values. Several outliers in the NMDS plot, however, can be
explained by habitat. For example, the bacterial communities of two
U. intestinalis samples collected in high- salinity rock pools located 2
and 10 m away from the main waterbody were more similar to lower
salinity communities (Figure 2). The salinity of such rock pools is ex-
pected to vary considerably with rainfall and evaporation. Samples
collected from green tides (mass accumulation events, n = 8), be-
longing to U. compressa, U. lacinulata and U. intestinalis, were dis-
tinctly different from the general host species patterns (Figure 2).
Oxygen levels did not have a significant effect on bacterial commu-
nity composition (p = .69, R2 ≈ 0).
3.4 | Differentially abundant bacteria
The largest shift in bacterial community composition was ob-
served passing the horohalinicum (salinity 5– 8 PSU; Figure 3a).
This shift in community composition was attributed mostly to large
differences in abundance, rather than presence/absence pat-
terns. Lower salinity communities were enriched in Cyanobiaceae
(p < .0001), Rubritaleaceae (p = .0002), Sphingomonadaceae
(p = .0002) and Spirosomaceae (p < .0001) (contrasts between
0– 5 PSU and 30– 36 PSU, all p- values Benjamini– ochberg corrected;
Figure 3a). High- salinity communities were characterized by high rel-
ative abundances of amongst others Alteromonadaceae (p < .0001),
Granulosicoccaceae (p = .001), Hyphomonadaceae (p < .0001),
Sulfurovaceae (p < .0001) and Thiotrichaceae (p < .0001) (contrasts
between 0– 5 PSU and 30– 36 PSU, all p- values Benjamini– Hochberg
corrected; Figure 3a). These differences become more pronounced
when comparing oligohaline communities with increasingly higher
salinity communities (i.e., the differences between the euhaline and
oligohaline form a starker contrast than the differences between the
mesohaline and oligohaline).
A total of 70 bacterial genera were differentially abundant with
changing salinity levels (with p < .01, Benjamini– Hochberg corrected;
see Table S5 for an overview of all log2fold change and p- values).
Low- salinity communities especially contained high relative abun-
dances of Luteolibacter (Rubritaleaceae), Cyanobium (Cyanobiaceae),
Pirellula (Pirellulaceae), Lacihabitans (Spirosomaceae) and an un-
cultured Spirosomaceae (Figure 3b). High- salinity communities
were predominantly enriched in Litorimonas (Hyphomonadaceae),
Leucothrix (Thiotrichaceae), Sulfurovum (Sulfurovaceae), Algibacter
and Dokdonia (both Flavobacteriaceae) (Figure 3b; Figure S4).
As U. intestinalis and U. linza co- occurred over the entire salinity
gradient from the North Sea to the Baltic Sea (Table S2), they pro-
vided a good opportunity to assess differences in host species. Both
Ulva species contained high relative abundances of Luteolibacter
and Lacihabitans in low- salinity sites, but low- salinity communities
of U. intestinalis were further characterized by Pirellula, Rhizobium
and an uncultured Spirosomaceae, whereas U. linza communities
mainly contained Cyanobium, Flavobacterium and Pseudorhodobacter
(Figure 3c,d). Likewise in high- salinity environments, both host spe-
cies had high abundances of Algibacter, but U. intestinalis had sig-
nificantly more Litorimonas, Sulfurovum, Rubritalea and an uncultured
Flavobacteriaceae with increasing salinity. U. linza, on the other
hand, typically contained more Leucothrix, Glaciecola, Dokdonia and
Alteromonas in high- salinity environments (Figure 3c,d).
When comparing the bacterial communities of Ulva species
(Ulvaceae) with the more distantly related Kornmannia leptoderma
(Kornmanniaceae), Ulva harboured significantly higher abundances
of Algitalea, Marinagarivorans and Algibacter compared to K. lepto-
derma, whereas the latter typically contained more Cellulophaga,
Sulfurovum and Altererythrobacter (p < .01, Benjamini– Hochberg cor-
rected). Compared to Blidingia minima (Kornmanniaceae), Ulva was
enriched in Rubritalea, Algitalea and Roseitalea, while Phormidesmis,
Roseibacillus and Jannaschia were associated with Blidingia (p < .01,
Benjamini– Hochberg corrected).
Despite host species having a clear effect on the associated bac-
teria, the correlation between host phylogeny and bacterial commu-
nity composition was very weak (Mantel test, p = .004, r = .03).
3.5 | Variance partitioning
Salinity, host species and habitat together explained 34%– 91% of
the variation in the abundance of the bacterial genera (Figure 4;
Figure S5). In concordance with the differential abundance analy-
ses (based on log2fold change), the variation was best explained
for Lacihabitans (91% of the variation explained), Leucothrix (86%),
Algitalea (84%), Dokdonia (84%), Luteolibacter (83%) and Algibacter
(81%). For most genera, the interaction between salinity and host
species explained the highest proportion, followed by the single
effects of salinity and host species (Figure 4). Salinity explained
much of the variation for Litorimonas (39%) and Cyanobium (29%),
whereas host species explained a high proportion of the varia-
tion in Mesorhizobium (49%, especially abundant in Ulva linza),
Roseitalea (47%, less abundant in Blidingia and Kornmannia),
Fuerstia (44%, especially abundant in U. compressa, U. fenes-
trata and U. lacinulata), Ensifer (43%, enriched in Kornmannia),

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Marinagarivorans (40%, enriched in Kornmannia) and Jannaschia
(31%, enriched in Blidingia).
Habitat explained little of the variation in most genera, except
for Roseivivax (61%) and Olleya (21%) (Figure 4). Additional deseq2
analyses indicated Roseivivax was especially abundant on algae col-
lected from sandy habitats and Olleya on algae growing on metal.
Although relatively few samples in green tide events were collected
(n = 8), patterns could be distinguished. For example, the green tide
FIGURE 2 NMDS plots (stress =0.01, k = 4) of Ulva sensu lato associated bacterial community composition (based on Bray– Curtis
dissimilarities and genus- level identifications). The first panel shows the full data set (n = 481 samples). The remaining panels are split by
host species (Ulva compressa, U. fenestrata, U. intestinalis, U. lacinulata, U. linza, U. prolifera, U. torta, Ulva sp. 2, Ulva sp. 4, Ulva sp. 5, Blidingia
minima and Kornmannia leptoderma). Note that separate plots for U. australis, Ulva sp. 1, and Ulva sp. 3 are not shown due to the few data
points collected for these species. Colours represent salinity and symbols represent the habitat of the host species. The contour lines
(smooth surface lines) are fitted to the ordination based on the correlation with salinity.
FIGURE 3 Overview of the significantly differentially abundant bacterial families and genera associated with Ulva sensu lato across
Atlantic– Baltic salinity ranges. (a) Pairwise comparisons of phylogenetic heat trees depicting the 28 bacterial families associated with Ulva,
Blidingia and Kornmannia. The larger, grey tree on the lower left functions as a taxonomic key for the smaller unlabelled trees. The smaller
trees provide contrasts between five salinity zones: 0– 5 PSU (oligohaline), 5– 8 PSU (horohalinicum), 8– 18 PSU (mesohaline), 18– 30 PSU
(polyhaline) and 30– 36 PSU (euhaline). The colour (brown to green) of the nodes and edges corresponds to the log2fold change (only
significant differences are coloured, p <.05, Benjamini– Hochberg corrected). Taxa coloured brown are enriched in the salinity zones in
columns, whereas taxa coloured green are enriched in salinity zones in rows. For example, Rubritaleaceae, Spirosomaceae and Cyanobiaceae
are enriched in the oligohaline (brown) compared to most of the higher salinity zones (green). (b– d) Bar graphs of the top 10 differentially
abundant genera between high and low salinity, based on (b) the full data set when controlled for host species, (c) Ulva intestinalis samples
only and (d) Ulva linza samples only. The log2fold change is expressed on the y- axis and genus on the x- axis. Colours of the bars correspond
to family level (similar colours as used in the phylogenetic heat tree).

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(a)
(b)
(c) (d)

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van de r LOOS et aL .
in Gryt on the Baltic coast of Sweden (salinity = 7.0 PSU), consisted
of Ulva intestinalis (n = 2 samples). These Gryt green tide algal mi-
crobiomes were mainly characterized by the abundant presence of
Rhodopirellula and Rubripirellula (both Planctomycetota) compared
to the bacterial communities of non- green tide U. intestinalis spec-
imens collected at the same site or neighbouring sites (n = 3 sam-
ples) (Figure S6). The green tide in Frederikshavn in Denmark
(salinity = 30.0 PSU) was caused by Ulva lacinulata. Compared to U.
lacinulata specimens growing attached in the same harbour (n = 2),
the green tide communities (n = 3) were enriched in Thiothrix,
Limibaculum, Pseudophaeobacter, Octadecabacter and Sulfitobacter
(all Proteobacteria) (Figure S6).
3.6 | Bacterial core
The number of core taxa and members of the core bacterial com-
munity varied tremendously depending on the threshold settings
of relative abundance and prevalence (percentage of samples in
which the taxon occurs) (Figure 5). When setting the limits to ≥0.1%
FIGURE 4 Variance partitioning, showing the amount of variance in abundance of Ulva sensu lato associated bacterial genera explained
(%) by the interaction between salinity and host species (salinity:host species), host species, salinity and habitat. This is based on a
generalized linear mixed model (negative binomial family). Only genera for which >70% of the variation was explained are shown. For a
graph containing all genera, see Figure S5
2505075 100
Lacihabitans
Leucothrix
Algitalea
Dokdonia
Luteolibacter
Algibacter
Roseivivax
Roseitalea
Rhodobacter
Cyanobium
Olleya
uncultured Sphingomonadaceae
uncultured Rubinisphaeraceae
Polaribacter
Mesorhizobium
Alteromonas
Rubripirellula
Rhizobium
Sulfurovum
Pseudorhodobacter
Cellulophaga
uncultured Spirosomaceae
Jannaschia
Ensifer
Fuerstia
Pirellula
Taeseokella
Methylotenera
Rubritalia
Thiothrix
uncultured Saprospiraceae
Variance explained (%)
Phycispaera
Glaciecola
Paracoccus
Sphingorhabdus
Roseibacillus
Litorimonas
Sva0996 marine group
Granulosicoccus
uncultured Flavobacteriaceae
Portibacter
salinity:host species
host species
salinity
habitat
residuals

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van der LOOS et a L.
abundance and ≥50% prevalence, >60 genera were considered
part of the core of Ulva sensu lato along the entire salinity gradi-
ent. However, with strict thresholds of ≥1% relative abundance and
≥90% prevalence, only two genera were defined as core taxa: an un-
cultured Rhodobacteraceae and Sulfitobacter. When the prevalence
threshold was lowered to ≥80%, Paracoccus became part of the core
bacterial community as well, and when the prevalence was set to
≥70%, an uncultured Rhizobiaceae, Yoonia- Loktanella and an uncul-
tured Saprospiraceae became additional members.
Across the salinity gradient, a shift in core community compo-
sition occurred. Five taxa were considered core across all species
in the oligohaline region (0– 5 PSU) and four taxa in the horohalini-
cum (5– 8 PSU) with ≥75% prevalence and ≥1% relative abundance
(Figure 6a). In addition to the three taxa considered core across
the entire salinity gradient (Sulfitobacter, Paracoccus and an uncul-
tured Rhodobacteraceae), these low salinity ranges also shared
Luteolibacter as a core genus. The mesohaline samples (8– 18 PSU)
contained five core taxa, the polyhaline samples (18– 30 PSU) six
core taxa and the euhaline samples (30– 36 PSU) five core taxa.
These higher salinity regions all shared an uncultured Rhizobiaceae
in their core. In addition, the mesohaline and polyhaline core both
included Yoonia- Loktonella, and the polyhaline and euhaline shared
an uncultured Saprospiraceae (Figure 6a).
Core membership not only shifted with salinity. The differ-
ent host species were also associated with distinct core con-
sortia (Figure 6b). Ulva intestinalis and U. linza shared the same
geographical range, but the U. intestinalis core was larger (seven
taxa) and included amongst others Yoonia- Loktanella, an uncultured
Sphingomonadaceae, Erythrobacter and Roseovarius, while the U.
linza core was smaller (four taxa) and included only an uncultured
Saprospiraceae in addition to the three main core members. Ulva
fenestrata, a more typical marine species, was the only host with
Granulosicoccus and Blastopirellula in its core. Blidingia minima
shared a large proportion of its core with U. intestinalis, but addi-
tionally included Jannaschia and Altererythrobacter. Kornmannia lep-
toderma in parti cul ar ha d hig h rel ati ve ab und anc es of the co re taxon
Altererythrobacter as well.
4 | DISCUSSION
Seaweeds and associated bacteria show interdependent and complex
dynamics. Here, we tested the stability of Ulva- associated bacterial
communities across a stable salinity gradient in the Atlantic– Baltic
Sea. In addition, we made use of the rich diversity of Ulva species in
the study area, with some species covering the entire salinity gradi-
ent, to characterize species- specific responses vs. environmentally
driven variation.
4.1 | Salinity- driven seaweed– bacterial
interactions
The Baltic Sea is characterized by its strong and stable salin-
ity gradient. Salinity has been identified as the most important
structuring factor on seawater and sediment microbial consortia
FIGURE 5 Bacterial core size (number
of genera) of Ulva sensu lato across
the entire Baltic salinity gradient with
different relative abundance (based on
read counts) and prevalence (based on the
number of samples in which the taxa was
encountered) thresholds.
0.1 110
90
80
70
60
50
100
Relative abundance (%)
Prevalence (%
)
Core size (number of taxa)
0
20
40
60

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FIGURE 6 Mean relative abundance of bacterial core taxa of Ulva sensu lato, split by (a) salinity and (b) host species. The circle size
corresponds to relative abundance, and the dark grey shade indicates whether the taxon is part of the core community (based on >1%
relative abundance and >75% prevalence).
Salinity region
5 - 8
0 - 5
8 - 18
18 - 30
30 - 36
Relative abundance
not part of core
Core composition
(≥1% relative abundance and ≥75% prevalence)
part of core
Luteolibacter
Rhodobacter
Paracoccus
Sulfitobacter
uncultured Rhodobacteracea
e
Paracoccus
Erythrobacter
Roseovarius
Granulosicoccus
Blastopirellula
Jannaschia
Altererythrobacter
Sulfitobacter
uncultured Rhodobacteraceae
Yoonia-Loktanella
Yoonia-Loktanella
uncultured Rhizobiaceae
uncultured Rhizobiaceae
uncultured Sphingomonadaceae
uncultured Saprospiraceae
uncultured Saprospiraceae
(oligohaline)
(horohalinicum)
(mesohaline)
(polyhaline)
(euhaline)
Host species
Blidingia minima
Kornmannia leptoderma
Ulva compressa
Ulva fenestrata
Ulva intestinalis
Ulva linza
1%
5%
10%
(a)
(b)

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(Dupont et al., 2014; Herlemann et al., 2 011). This agrees with our
study, which showed that Ulva- associated bacterial composition is
strongly structured primarily by salinity and secondarily by host
species.
The largest shift in the bacterial consortia of Ulva sensu lato was
observed passing the horohalinicum (5– 8 PSU) from low salinity to
higher salinity. The brackish to full marine transition has also been
termed the “critical salinity region”, as this is the salinity range where
many chemical, physical and biological processes abruptly change
(Telesh & Khlebovich, 2010 ). For example, the ion Ca/Cl ratio is sta-
ble down to 7 PSU, but below this salinity level the ratio drastically
changes. Similar nonlinear dynamics are observed for, among others,
silicon concentration, suspended matter concentration and the sta-
bility of phosphorus compounds. In the Baltic Sea, the horohalini-
cum coincides with the Darss Sill (situated east of the Danish Straits;
Figure 1). This probably explains why most of the Ulva species’ dis-
tribution is limited to the North Sea and Danish Straits. The distri-
bution of bacteria associated with Ulva sensu lato is clearly affected
as well and this study provides the first report of a host- associated
bacterial community changing drastically in the horohalinicum.
The effect of salinity on seaweed bacteria has rarely been inves-
tigated in laboratory experiments. Two exceptions include the long-
term mesocosm studies conducted on the red seaweed Gracilaria
vermiculophylla collected along the Nor th Sea coast of Germany
(as Agarophyton vermiculophyllum, Saha et al., 2020), and the brown
seaweed Fucus vesiculosus collected in the Kiel fjord in the western
Baltic (Stratil et al., 20 14). Gracilaria vermiculophylla is non- native in
Europe and has also been introduced in the Baltic Sea where it oc-
curs across a wide salinity range. Both the bacterial communities of
Gracilaria and Fucus were strongly impacted by salinity. Despite the
occurrence of some shared bacterial taxa in Ulva and Gracilaria mi-
crobiomes, for example Dokdonia in higher salinity communities, the
bacterial microbiomes of respective seaweeds are very different.
Comparisons between Ulva- and Fucus- associated bacteria likewise
result in very few shared taxa. Although, in our study, salinity over-
all had a greater effect than host species, possibly the evolutionary
distances between the genera Ulva, Gracilaria and Fucus are large
enough to overrule salinity (Lachnit et al., 2009).
Our results may suggest that some of the typical low- salinity
bacteria in Ulva microbiomes (e.g., Lacihabitans, Luteolibacter and
Cyanobium) could facilitate acclimatization of the host to low salinity
in the Baltic. The effect of specific bacterial taxa on host tolerance
to lower salinities has so far only been tested in the brown algal
genus Ectocarpus (Dittami et al., 2016). In Ectocarpus, two bacterial
OTUs— Haliea and an uncultured Sphingomonadales— were linked to
increased host performance when the algae were first cultivated in
seawater medium and subsequently in freshwater medium (Dittami
et al., 2016). Axenic (bacteria- free) cultures of an Ectocarpus strain
originally isolated from a freshwater environment did not survive in
freshwater medium, nor did they sur vive the change from seawater
to freshwater medium. Acclimatization to freshwater medium was
only possible if the axenic strain was inoculated with medium con-
taining bacteria of the nonaxenic cultures (Dittami et al., 2016). To
be able to experimentally test whether characteristic low- salinity
consortia in Ulva bacterial communities likewise stimulate host ac-
climatization to freshwater, isolation and cultivation of the associ-
ated bacteria and subsequent experimental work with axenic Ulva
are required.
4.2 | Disentangling the effects of spatial
distance and salinity
In essence, all environmental gradients in the Baltic are spatial gra-
dients in a northeast– southwest direction. The samples in this study
were collected on a 2,000- km transect. The shortest route across
water from the most inland sampling site (Skepssmalen, Sweden) to
the most outer sampling site (Egersund, Norway) was over 1,670 km.
The spatial effect is statistically hard to separate from the environ-
mental salinity gradient. However, 13 samples from five different
sampling sites near the mouth of the Glomma river in the Oslofjord
(Skagerrak; Figure 1) provide a good test case. The Glomma is
Norway's longest and most voluminous river. Sampling at the sites
close to the Glomma river mouth took place in early July, imme-
diately after its discharge flow peak in May– June (Frigstad et al.,
2020). Measured salinity at these sites was 5.1– 13.6 PSU, which cor-
responds to prevailing central and northern Baltic salinity ranges,
whilst the surrounding sites in the Skagerrak are characterized by
salinity levels >20 PSU. As bacterial community composition at the
sites influenced by the Glomma discharge was in general more similar
to central– northern Baltic microbiomes >1,000 km away, than to the
Skagerrak sites only 20– 50 km further south or west, salinity seems
to overrule spatial distance (Figure S3). The effect of host species is
visible here as well. Regarding Kornmannia leptoderma, for example,
the samples at the mouth of the Glomma river represent the lowest
salinity levels in which the species was found in this study, but also
the most northern records in our data set. The samples were found
at least 300 km more to the north than other K. leptoderma samples
collected in the Danish Straits (salinity ~25 PSU), but are more similar
to samples collected in the Baltic Proper (salinity ~7 PSU) that are
geographically even further away. For seaweeds to recruit similar
bacterial communities in specific environmental conditions despite
large spatial distances requires the bacteria to be widely dispersed
in the environment. This agrees with the Baas- Becking hypothesis:
“everything is everywhere but the environment selects” (Martiny
et al., 2006).
4.3 | The Baltic Sea and its multiple
environmental gradients
Ulva sensu latoharbours distinct bacterial communities across the
different salinity regions along the Atlantic– Baltic Sea transect.
However, the Baltic is not only characterized by a pronounced
salinity gradient. Although less dramatic than the salinity gradi-
ent, the Baltic Sea also accommodates both a horizontal oxygen

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gradient (with the Kattegat and North Sea area being in general
oxygen- rich and the remainder of the Baltic Sea oxygen- poor)
and a vertical oxygen gradient (oxygen depletion at >50 m depth)
(Villnäs & Norkko, 2011). In add ition , the Balt ic ex per ience s strong
seasonal dynamics and is subjected to anthropogenic pressure
(e.g., eutrophication, pollution, fisheries, shipping) (Ojaveer et al.,
2010). Whilst both seawater surface temperature and oxygen lev-
els were measured in this study and explained limited variance of
the bacterial community composition, the sample distribution was
not designed to capture the full dynamics associated with these
environmental factors.
The Balti c temper ature gradient co varies to so me ex ten t with sa-
linity. In our study, lower temperatures were measured on the North
Sea coast of Norway, and higher temperatures in the Baltic Proper
(Figure S1). As sampling was only carried out in summer, yearly sea-
sonal fluctuations were not represented. In addition, sampling was
not restricted to a specific time of the day, hence small variations
in the measured temperature between sites may have been caused
by daily fluctuations. In spring, sea surface temperatures usually in-
crease earlier in the year in the south and west areas of the Baltic
Sea compared to the north and east areas (Mück & Heubel, 2018).
Bacterial community composition of seawater therefore not only
changes with salinity as a primary factor, but secondarily also with
seasons (Andersson et al., 2010; Herlemann et al., 2016). In fact,
these bacterioplankton community shifts display repeated patterns
between years (Lindh et al., 2015). It is likely that across the entire
Baltic, Ulva- associated bacterial communities also experience sea-
sonal dynamics, as has been shown before in local populations in the
Kiel fjord (Lachnit et al., 2011), as well as in the Caribbean (Comba
González et al., 2021).
Nutrients were not measured during this study but may drive
some of the unexplained variation. The Atlantic– Baltic Sea salinity
gradient is caused by freshwater input from subarctic rivers on one
side of the gradient and limited water exchange with the marine
water body of the North Sea on the opposite side of the gradient
(Seidel et al., 2017 ). Freshwater river discharge not only affects sa-
linity, but simultaneously increases nutrient influx, including nitrate,
phosphate and dissolved organic carbon (Frigstad et al., 2020; Korth
et al., 2012). Southern, high- salinity areas in the Baltic are character-
ized by high levels of autochthonous DOM (dissolved organic mat-
ter derived from, for example, phytoplankton primary production),
whereas northern, low- salinity areas are richer in allochthonous
DOM of terrestrial origin discharged by rivers (Rowe et al., 2018).
This increased nutrient load is a major stimulator of bacterioplank-
ton growth and pelagic productivity (Stepanauskas et al., 2002), and
pelagic bacterial growth efficiency is highest in the low- salinity re-
gions (Rowe et al., 2018). The community composition of bacteria
living in association with Ulva may be impacted by pre vaili ng nu tri ent
conditions as well, but not necessarily following the same patterns
as bacterioplankton, as the Ulva host probably provides its microbial
partners (and other associates) with carbon and nutrients (Hudson
et al., 2019).
4.4 | Green tides: Ulva on the drift
Green tides are mass accumulations of unattached green seaweeds
and are often caused by Ulva spp. They have profound negative ef-
fects on the environment, including reduced biodiversity, and smoth-
ering of the sea bed and its inhabitants (Wan et al., 20 17; Ye et al.,
2011). Decomposition of the Ulva biomass results in anoxic condi-
tions and the release of gaseous sulphur compounds. In the Baltic
Sea, where oxygen levels have already deteriorated over the past
decades due to eutrophication, these anoxic conditions in particular
pose a problem (Reusch et al., 2018). In the formation of green tides
too, eutrophication plays a major role (Smetacek & Zingone, 2013).
The microbial communities of green tide- forming ulvoid species
have rarely been sequenced, but mass growth events of seaweeds
are likely to induce a change in both the seaweed and environmen-
tal microbiome. Qu et al. (2020), for example, demonstrated that
sulphate- reducing bacteria and heterotrophic bacteria increased in
abundance in sediment directly under an Ulva prolifera bloom in the
Yellow Sea, especially towards the end of the bloom. In surface sea-
water samples, the abundance of heterotrophic diazotrophic bacte-
ria increased likewise during U. prolifera blooms (Zhang et al., 2015).
Diazotrophic bacteria are involved in N2- fixation. Hence, altered mi-
crobial communities during green tide events may affect the sulphur
and nitrogen cycles (Aires et al., 2019).
In the current study, green tides were encountered at Skive in
Denmark (caused by Ulva compressa and U. lacinulata), Frederikshavn
in the north of Denmark (caused by U. lacinulata) and Gryt in Sweden
(caused by monostromatic U. intestinalis). Several of these samples
were visible as distinct outliers in the NMDS plot (Figure 2). In
Frederikshavn, particularly high relative abundances of Thiothrix
and Sulfitobacter were observed in green tide samples compared
to attached thalli growing in the same harbour. These are sulphur-
oxidizing bacteria (SOB) and Sulfitobacter is additionally known to
promote Ulva growth (Grueneb e r g et al ., 2016; Krishnani et al., 2010).
Growth- promoting bacteria produce metabolites and chemical com-
pounds such as thallusin that induce cell division and thallus differ-
entiation, including rhizoid formation and the proper development
of cell walls, in Ulva (Alsufyani et al., 2020). In turn, Ulva can attract
growth- promoting bacteria through the release of the chemoattrac-
tant dimethylsulfoniopropionate (DMSP). In some cases, SOB form
visible mats on the sediment or on degrading organic material, such
as seaweed tissue (Fenchel et al., 2012). Depending on the green
tide phase, autotrophic as well as mixotrophic SOB could take ad-
vantage of degrading Ulva tissue for carbon and sulphur sources.
In Gryt, green tide samples were enriched in Rhodopirellula
and Rubripirellula compared to non- green tide U. intestinalis
thalli in neighbouring sites. Both bacterial genera belong to the
Planctomycetota. These bacteria are adapted to life in marine bio-
films, as they have a holdfast that accommodates surface coloni-
zation, they can reproduce by budding and can quickly adapt to
environmental changes (Kallscheuer et al., 2020). In addition, they
have large genomes that often encode a large number of sulfatase

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genes. The genome of Rhodopirellula baltica, for example, contains
110 sulfatases (Wegner et al., 2013). Sulfatases enable the deg-
radation of sulphated polysaccharides such as ulvan in the Ulva
cell wall. Planctomycetota are known to be abundant on algal sur-
faces and their high abundance in green tide samples may simply
be caused by the large accumulation of biomass (Bondoso et al.,
2017; Wiegand et al., 2021).
Drif ting se aweeds are not always necess arily green ti des. Al l foli-
ose U. compressa samples in this study were collected as unattached
specimens, most without the occurrence of mass accumulations.
Interestingly, all tubular shaped U. compressa were found growing at-
tached to substrates, such as rock and concrete. The bacterial com-
munities of foliose vs. tubular U. compressa were distinctly different.
However, all foliose samples were colle cted at low salinit y (<20 PSU)
and the tube- shaped samples at high salinity (>20 PSU). It is there-
fore not possible with the current data set to resolve whether dif-
ferences in the U. compressa bacterial communities fundamentally
differ with morphology or salinity.
4.5 | Ulva core bacterial communities along an
environmental gradient
Although the Ulva- associated bacterial communities varied with sa-
linity and host species, a small, stable consortium can be identified
as well. The term “core microbiome” was initially used to describe a
set of microbes or genes shared by the majority of host specimens
in a given habitat (Turnbaugh et al., 2007 ). This is often referred to
as a “common core”, but the core concept has since been used in a
broader context. Core microbiome members are, for example, hy-
pothesized to play a key role in ecosystem functioning or may sig-
nificantly affect host fitness and resilience to disturbance (Shade &
Handelsman, 2012). Such “functional cores” are based on commonly
occurring functional genes rather than taxonomic units (Risely,
2020). Whether based on taxonomy or function, the nature of a core
can vary from being “substantial” (the majority of individuals/sam-
ples share a large proportion of the microbial consortia), to “mini-
mal” (all individuals/samples only share a few core members) or even
“nonexistent” (no taxa or genes in common across the majority of
individuals/samples) (Hamady & Knight, 2009). In addition, there are
“gradient” core models (in which individuals close to each other on a
gradient share more microbial components than individuals at oppo-
site ends of the gradient) and “subpopulation” core models (distinct
subpopulations of host species each have their own, unique core)
(Hamady & Knight, 2009).
Following the different core models described in Hamady and
Knight (2009), we can define Ulva- associated bacterial communities
across the Baltic as having a “minimal” taxonomic core (only three
taxa are shared across the entire gradient), with in addition a “gradi-
ent” core (more taxa are shared between neighbouring salinity ranges
than bet ween ranges at opp os ite ends of the Atlan tic– Baltic Sea gr a-
dient). Depending on the chosen prevalence and relative abundance
settings, the minimal core consisted of Sulfitobacter, Paracoccus and
an uncultured Rhodobacteraceae. Together they made up on aver-
age 14% of the reads per sample. Sulfitobacter and Paracoccus are
known growth- promoting and morphogenesis- inducing bacteria of
Ulva (Ghaderiardakani et al., 2017, 2019), and are thus unsurprising
core members. Possible beneficial interactions with the uncultured
Rhodobacteraceae are unknown, but several of the reads assigned
to this family did have a strong match to sequences in the NCBI data-
base extracted from an Ulva prolifera– seawater interface (GenBank
accession no. JF769698.1).
Some gradient core taxa were clearly defined by differences in
relative abundance. Luteolibacter, for example, was highly abundant
at low salinity (7%– 9% relative abundance) and scarcely present at
higher salinity (<1% relative abundance), which was also demon-
strated by the differential abundance analyses. Other gradient core
taxa were defined predominantly by prevalence. An uncultured
Saprospiraceae, for example, was quite abundant across the entire
salinity gradient (5%– 9% relative abundance), but was only part of
the bacterial core in higher salinities due to low prevalence at lower
salinity levels. These varying prevalence levels might be due to dif-
ferences among host species, as the uncultured Saprospiraceae was
a highly abundant core member of U. linza, U. compressa and U. fenes-
trata, but did not have a high prevalence and abundance in U. intes ti-
nalis, Blidingia minima and Kornmannia leptoderma.
At host species level, the core consortia were slightly larger,
vary in g from four to nine co re members. Interes tingly, the core com-
munity of U. intestinalis was more similar to the core of B. minima
than to U. linza, despite U. intestinalis and U. linza sharing a similar
geographical range. The same pattern was observed in the NMDS
plot, in which U. intestinalis and B. minima samples were clustered
to the left of the plot, while the U. linza cluster was located to the
right of the plot. The Mantel test showed that overall bacterial com-
munity composition did not differ with host phylogeny, indicating
that differences in bacterial communities between host species
were caused by intrinsic factors (e.g., biochemical composition and
defence mechanism).
As the definition of a core community is flexible, the decision
on which taxa should be considered core members and whether
a core community exists at all remains arbitrary. Some studies
define core taxa purely based on prevalence (Aires et al., 2015),
others use both relative abundance and prevalence (Ainsworth
et al., 2015), and others use models (Bonthond et al., 2020; Shade
& Stopnisek, 2019). The threshold settings used vary tremen-
dously as well, and rarely have biological justifications, so the re-
sulting core depends on the authors (Risely, 2020). In Ulva, the
taxonomic variability has often been deemed too large to contain
a core consortium. A study on U. australis, for example, demon-
strated only six bacterial species were consistently present in all
samples (n = 6), and while these did make up on average 15.6%
relative abundance per sample, a core was considered nonexistent
(Burke et al., 2011). These results are relatively similar to our data
set, with three genera contributing up to 14% of the reads per
sample. The larger the data set and the wider the geographical
scale investigated, the less likely it becomes to define a large core

6274
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van de r LOOS et aL .
microbiome (Turnbaugh et al., 2007). Roth- Schulze et al. (2018)
investigated the bacterial communities of three Ulva species in
Spain and Australia, but found only one common OTU represent-
ing only 0.33% of the total number of sequences. By contrast,
>70% of the functional genes were shared across the microbi-
omes of all three Ulva species independent of biogeography, and
the remaining 30% could possibly be linked to environmental ad-
aptation. The large biogeographical scales in the aforementioned
study, however, were not associated with obvious environmental
gradients, and bacterial communities may therefore seem to be
influenced mostly by stochastic processes. The results from our
study, on the other hand, indicate a large deterministic effect
of the environment. Future studies investigating the functional
repertoire of bacterial communities across the Atlantic– Baltic
gradient could show whether the Ulva- associated functional core
likewise follows a gradient model, and if such functional patterns
could be linked to the taxonomic core.
5 | CONCLUSIONS
Salinity and host play a major role in Ulva- associated bacterial
community structure. Pronounced differences between low- and
high- salinity communities manifest themselves through defined
patterns in differential abundance rather than presence/absence
patt erns of certain bacteria. Deviations from the predom inant pat-
tern at a distinct salinity can often be ascribed to microhabitats
(e.g., high- salinity rock pools, green tides, river mouths) that dif-
fer from the prevailing conditions on surrounding sites. We iden-
tified a small taxonomic core consortium with in addition a few
gradient core members that change across the salinity gradient.
Future studies with experimental work could focus on causal rela-
tions hip s betwe en bac teria and hos t tolera nce towa rds fl uctuatin g
salinity, as well as functional analyses across the entire Atlantic–
Baltic salinity gradient.
ACKNOWLEDGEMENTS
The research leading to the results presented in this publication was
carried out wit h inf r a s t r u c ture funded by th e FWO Ph D Fellowsh ip
fundamental research (3F020119), the EMBRC Belgium— FWO
project I001621N, the Formas national research programme for
food (grant no. 2020- 03119), and Portuguese national funds from
FCT— Foundation for Science and Technology through project
UIDB/04326/2020 and contract CEECINST/00114/2018. We
would like to thank Samanta Hoffmann for her assistance during
field work.
AUTHOR CONTRIBUTIONS
S.S., G.B.T and H.P. designed the study. S.S. and F.W collected the
sample. L.M.L., S.D. and S.S. carried out the molecular work. L.M.L.
performed bioinformatics and statistical analyses. L.M.L and S.S.
wrote the original draft. O.D.C, A.W., G.B.T., H.P., F.W. and A.H.E.
edited the manuscript.
CONFLICT OF INTEREST
The authors declare no competing interests.
DATA AVA ILAB ILITY STATE MEN T
Raw sequence reads are deposited in the SRA (BioProject
PRJNA781821). Related metadata are also stored in SRA (BioProject
PRJNA781821) and can be found in Table S1. The tufA sequences
generated in this study to identify host species are deposited in
GenBank and accession numbers can be found in Table S1.
ORCID
Luna M. van der Loos https://orcid.org/0000-0003-3686-2844
Aschwin H. Engelen https://orcid.org/0000-0002-9579-9606
Henrik Pavia https://orcid.org/0000-0001-7834-6026
Gunilla B. Toth https://orcid.org/0000-0002-1225-7773
Anne Willems https://orcid.org/0000-0002-8421-2881
Florian Weinberger https://orcid.org/0000-0003-3366-6880
Olivier De Clerck https://orcid.org/0000-0002-3699-8402
Sophie Steinhagen https://orcid.org/0000-0001-8410-9932
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How to cite this article: van der Loos, L. M., D’hondt,
S.,Engelen, A. H.,Pavia, H.,Toth, G. B.,Willems, A.,Weinberger,
F.,De Clerck, O., & Steinhagen, S. (2023). Salinity and host
drive Ulva- associated bacterial communities across the
Atlantic– Baltic Sea gradient. Molecular Ecology, 32, 6260–
6277. https://doi.org/10.1111/mec.16 462