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\begin{document}

\title{The interplay between host-specificity and habitat-filtering influences
sea cucumber microbiota across an environmental gradient of pollution}



\author[1]{Sheena Suet-Wah Chung}%
\author[1]{Khan Cheung}%
\author[1]{Bovern Arromrak}%
\author[1]{Zhenzhen Li}%
\author[1]{Simon Tse}%
\author[1]{Juan Diego Gaitán-Espitia}%
\affil[1]{The University of Hong Kong}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\selectlanguage{english}
\begin{abstract}
Environmental gradients can influence morpho-physiological and
life-history differences in natural populations. It is unclear, however,
to what extent such gradients can also modulate phenotypic differences
in other organismal characteristics such as the structure and function
of host-associated microbial communities. In this work, we addressed
this question by assessing intra-specific variation in the diversity,
structure and function of environmental-associated (sediment and water)
and animal-associated (skin and gut) microbiota along an environmental
gradient of pollution in one of the most urbanized coastal areas in the
world. Using the tropical sea cucumber Holothuria leucospilota, we
tested the interplay between deterministic (e.g., environmental/host
filtering) and stochastic (e.g., random microbial dispersal) processes
underpinning host-microbiome interactions and microbial assemblages.
Overall, our results indicate that microbial communities are complex and
vary in structure and function between the environment and the animal
hosts. However, these differences are modulated by the level of
pollution across the gradient with marked clines in alpha and beta
diversity. Yet, such clines and overall differences showed opposite
directions when comparing environmental- and animal-associated microbial
communities. In the sea cucumbers, intrinsic characteristics (e.g., body
compartments, biochemistry composition, immune systems), may underpin
the observed intra-individual differences in the associated microbiomes,
and their divergence from the environmental source. Such regulation
favours specific microbial functional pathways that may play an
important role in the survival and physiology of the animal host,
particularly in high polluted areas. These findings suggest that the
interplay between both, environmental and host filtering underpins
microbial community assembly in H. leucospilota along the pollution
gradient in Hong Kong.%
\end{abstract}%



\sloppy


\textbf{Running head} : Pollution gradient influences sea cucumber
microbiota

\textbf{The interplay between host-specificity and habitat-filtering
influences sea cucumber microbiota across an environmental gradient of
pollution}

Sheena Suet-Wah Chung\textsuperscript{1,2}, Khan
Cheung\textsuperscript{1}, Bovern Suchart Arromrak\textsuperscript{1},
Zhenzhen Li\textsuperscript{1}, Simon Tse\textsuperscript{1}, Juan Diego
Gait\selectlanguage{ngerman}án-Espitia\textsuperscript{1*}

\textsuperscript{1} The Swire Institute of Marine Science and School of
Biological Sciences, The University of Hong Kong, Hong Kong SAR, China

\textsuperscript{2} GEOMAR Helmholtz Centre for Ocean Research Kiel

\textbf{*Author for correspondence:}juadiegaitan@gmail.com

\textbf{Abstract}

Environmental gradients can influence morpho-physiological and
life-history differences in natural populations. It is unclear, however,
to what extent such gradients can also modulate phenotypic differences
in other organismal characteristics such as the structure and function
of host-associated microbial communities. In this work, we addressed
this question by assessing intra-specific variation in the diversity,
structure and function of environmental-associated (sediment and water)
and animal-associated (skin and gut) microbiota along an environmental
gradient of pollution in one of the most urbanized coastal areas in the
world. Using the tropical sea cucumber \emph{Holothuria leucospilota} ,
we tested the interplay between deterministic (e.g., environmental/host
filtering) and stochastic (e.g., random microbial dispersal) processes
underpinning host-microbiome interactions and microbial assemblages.
Overall, our results indicate that microbial communities are complex and
vary in structure and function between the environment and the animal
hosts. However, these differences are modulated by the level of
pollution across the gradient with marked clines in alpha and beta
diversity. Yet, such clines and overall differences showed opposite
directions when comparing environmental- and animal-associated microbial
communities. In the sea cucumbers, intrinsic characteristics (e.g., body
compartments, biochemistry composition, immune systems), may underpin
the observed intra-individual differences in the associated microbiomes,
and their divergence from the environmental source. Such regulation
favours specific microbial functional pathways that may play an
important role in the survival and physiology of the animal host,
particularly in high polluted areas. These findings suggest that the
interplay between both, environmental and host filtering underpins
microbial community assembly in \emph{H. leucospilota} along the
pollution gradient in Hong Kong.

\textbf{Keywords:} Microbial community, holothurian, nutrient pollution,
environmental gradient, host-microbiome

\textbf{Introduction}

Metazoans harbour diverse and dynamic microbial communities (microbiome)
that play essential roles in the ecology and function of their animal
host (Apprill, 2017). Symbiotic microorganisms influence host metabolic
processes (Egerton et al., 2018; Hakim et al., 2016; Hanning \&
Diaz-Sanchez, 2015), development (Carrier \& Reitzel, 2018), immune
responses (de Araujo et al., 2019; Krediet et al., 2013), reproduction
(Brucker \& Bordenstein, 2013; Sharon et al., 2010), behaviour (Sherwin
et al., 2019) and survival (Ke\selectlanguage{polish}š\selectlanguage{english}nerov\selectlanguage{ngerman}á et al., 2020). However, microbial
communities and the interactions with their hosts are not static and can
vary across time (e.g., host's development or seasonality, Ke\selectlanguage{polish}š\selectlanguage{english}nerov\selectlanguage{ngerman}á et
al., 2020; Shoemaker \& Moisander, 2017) and space (e.g., host's body
plan or geography, Griffiths et al., 2019; McKnight et al., 2020). Thus,
to understand the ecological dynamics of animal hosts in a changing
world, it is becoming more important to understand the mechanisms and
drivers underpinning the origin and regulation (functional and
structural) of their associated microbial communities (Kohl, 2020).

Host-associated microbiomes are shaped by a diversity of evolutionary
and ecological processes that can be explored through the framework used
in community ecology. Under this framework, the formation of microbial
communities is seen as the outcome of selective processes in which a
larger species pool is subjected to a set of biotic and abiotic filters
(Hardy et al., 2012). For instance, habitat/environmental filtering is
usually considered one of the dominant forces in structuring communities
consisting of habitat-specialized species (Cavender-Bares et al., 2009).
In this case, the habitat/environment is part of the selective processes
as it plays a dual role acting as a source for the hosts' microbiome and
also influencing the composition and dynamics of the established
microbial community (Johnke et al., 2020). As a result, host intra- and
interspecific variation in microbiome composition is expected to be
lower for organisms inhabiting similar environments in comparison to
their counterparts living in more divergent conditions (Johnke et al.,
2020). Hosts, however, can also influence associated microbiomes in
several ways, such as through selective feeding or filtering (Gao et
al., 2014; Theis et al., 2016). This may result from evolutionary
processes that produce patterns in which closely related host species
harbour similar microbiota even if they inhabit different environments
(San Juan et al., 2020; Sanders et al., 2015). In any case,
environmental and host filtering are aligned with the fundamental idea
that deterministic processes dictate the microbial community assembly in
natural populations (Näpflin \& Schmid-Hempel, 2018; J. Wang et al.,
2013; Weigel, 2020; Yan et al., 2016).

Previous studies have also suggested that stochastic processes can
influence microbial community assembly (Oliphant et al., 2019; Zhou et
al., 2013). These processes include random dispersal potential and
colonization chance of microbes (Zhou \& Ning, 2017), random arrival
sequence to host from the environment during dispersal (Burns et al.,
2017), changes in community abundance and survival due to random
speciation, extinction and ecological drift (Chase \& Myers, 2011).
Under this paradigm, species present in the microbial community would be
independent and unpredictable, with no specific occurrence pattern or
relation with the respective niche (Rosindell et al., 2012). This
provides us with a wider perspective when studying processes
underpinning microbial community assembly. What is worth mentioning is
that deterministic and stochastic processes are not mutually exclusive
(Bordenstein \& Theis, 2015). Both processes work together in
structuring microbial communities although a stronger contribution of a
particular process during the assembly may occur depending on the
investigated model and ecosystem (Yan et al., 2016).

In marine environments, the interplay between deterministic and
stochastic factors is an important driver of inter- and intra-specific
differences in organismal characteristics across heterogeneous
seascapes. In these systems, gradients of environmental conditions are
known to influence geographic variation in attributes such as physiology
(Gaitán-Espitia et al., 2014; Gaitán-Espitia, Villanueva, et al., 2017),
life-history (Pecuchet et al., 2018), zonation (Mangialajo et al.,
2012), behaviour (Cornwell et al., 2019), intra- and inter-specific
genetic diversity (Zhong et al., 2024), stress tolerance and phenotypic
plasticity (Gaitán-Espitia, Bacigalupe, et al., 2017) in natural
populations. As such, it would be expected that environmental gradients
would also influence variation and phenotypic differences in other
organismal characteristics, including the structure and function of
host-associated microbial communities. In fact, gradients in sea surface
temperature and salinity are known to influence \selectlanguage{english}symbiotic microbial
assemblages in benthic marine species (Capistrant-Fossa et al., 2021;
Ketchum et al., 2021; Osman et al., 2020) and coastal ecosystems
(Bolhuis et al., 2013; Z. Wang et al., 2019; Z\selectlanguage{ngerman}äncker et al., 2018; G.
Zhang et al., 2021). While these natural environmental gradients may
have a fundamental role in modulating ecological patterns of marine
microbiomes, anthropogenic-mediated gradients (e.g., nutrient pollution)
can potentially induce drastic changes in these patterns by altering
ecological dynamics, the origin, and regulation of host-associated
microbial communities (Degregori et al., 2021; Milan et al., 2018;
Stevick et al., 2021; Ziegler et al., 2019). \selectlanguage{english}

In this study, we aimed to assess to what extent deterministic and/or
stochastic processes along an anthropogenic-mediated gradient of
pollution influence intra-specific variation in diversity, structure,
and function of host-associated microbial communities in marine
organisms. Here, our model system was \emph{Holothuria leucospilota,} a
tropical sea cucumber that dominates shallow waters in Hong Kong and the
Indo-Pacific region. \emph{H. leucospilota} is a deposit-feeding species
that assimilates organic matter from surface sediments (including
bacteria, benthic phytoplankton, meiofauna, and organic detritus), which
serves as a constant environmental source for microbe acquisition (Gao
et al., 2014; Pag\selectlanguage{ngerman}án-Jiménez et al., 2019). However, \emph{H.
leucospilota} is characterised by the capacity to secrete secondary
metabolites from the skin, gonads, and guts, which have antibacterial
and antifouling properties (Darya et al., 2020), potentially allowing
them to regulate the influence of environmental microbial reservoirs.
Based on 16S amplicon sequencing, we examined both sea cucumber (skin
and intestine) and environment (sediment and water) microbiomes along a
pollution gradient in Hong Kong, one of the busiest ports and highly
urbanized areas in the world. If environmental filtering is the main
driver of microbial community assemblies along the pollution gradient,
then similar host and environmental microbiomes will be observed within
sites whilst higher microbiome variation would be expected across the
gradient. In this context, microbial dispersal patterns and ecological
drift might also play an important role in shaping inter-individual
microbiome variation within sites (Stothart et al., 2021). Conversely,
if host filtering and specificity are the main drivers of microbial
community assembly in \emph{H. leucospilota} , then low inter-individual
variation in sea cucumber microbiome composition would be expected along
the pollution gradient independently of the variation in the
environmental microbial communities. However, intra-individual variation
would be expected as sea cucumber skin and guts have different
antibacterial properties (Darya et al., 2020).

\textbf{2. Materials and Methods}

\emph{2.1 Sampling sites and environmental data}

In Hong Kong, the Tolo Harbour (TH; 22°43' N, 114° 22' W) is
characterised by a gradient of chemical pollution that has profound
implications for the diversity and distribution of benthic organisms,
from corals (Duprey et al., 2016) to microbial communities in the
sediments (J. Chen et al., 2019). For our experimental design, we first
assessed environmental parameters along the pollution gradient in the
Channel Water Control Zone of TH. This information was obtained from the
marine water quality database of the Hong Kong Environmental Protection
Department (EPD) (available from:
https://cd.epic.epd.gov.hk/EPICRIVER/marine/). Parameters such as
salinity, total phosphorus, total nitrogen, total Kjeldahl nitrogen,
nitrite, nitrate, ammonia, chlorophyll-a, dissolved oxygen, and 5-day
Biological Oxygen Demand (BOD) on the surface water across ten years
(2010-2020) were used for comparisons. Based on this information, we
established three experimental localities, including Starfish Bay (SFB)
(22° 26' 3.0'' N, 114° 14' 45.6'' E), Lai Chi Chong (LCC) (22° 27'
22.4'' N, 114° 17' 59.4'' E) and Tung Ping Chau (TPC) (22° 32' 34.1'' N,
114° 26' 6.5'' E) (Figure 1). A principal component analysis (PCA) was
conducted to visualize the differentiation of water quality between the
experimental sites along the pollution gradient in TH.

\emph{2.2 Animals and sample collection}

Samples from adult sea cucumbers \emph{Holothuria leucospilota} and
their surrounding environments (water and sediments) were collected in
December 2019 from each of the field sites. Sea cucumbers (5 animals per
site, separated at least by 10m) were isolated in Ziploc bags filled
with water from the collection site. In addition, surface sediment
samples of the top 3 cm (\emph{n} = 3 per site) were collected around
the sampled animals using sterile 15 mL falcon tubes. In parallel, 2L
seawater samples (\emph{n} = 3 per site) were also collected using
sterile plastic bottles wrapped with aluminium foil. All samples were
immediately stored in ice for transportation and processed immediately
in the laboratory at the University of Hong Kong.

Sea cucumbers were dissected to obtain microbial samples from the body
surface and guts. For this, animals were rinsed under sterile seawater
twice to remove particulates such as sand, algae and other loose organic
matter loosely attached to the surface. Then, a sterile cotton swab was
rolled over the whole outer body surface to collect microbial samples
from the skin. For the gut microbiome collection, the outer surface of
the sea cucumbers was first sterilized with 70 \% ethanol to reduce
contamination. After that, the ventral part of the animal was cut open
with a sterile scalpel. A 2 cm segment of the midgut including the
\selectlanguage{english}luminal epithelium was then collected from each animal. All skin and
gut samples were individually placed into sterile 1.5 mL microcentrifuge
tubes, snap-frozen in liquid nitrogen, and stored at -80  until
required for further analysis. For the seawater samples, a two-step
filtering process was conducted. First, samples were passed through
Millipore membrane filters of 0.45\selectlanguage{greek}µ\selectlanguage{english}m pore size to remove large
particles, and water was then filtered again using Millipore membrane
filters of 0.22 \selectlanguage{greek}µ\selectlanguage{english}m pore size. These last filters were transferred to 15
mL falcon tubes. Sediment samples were centrifuged (4,000 g x 5
minutes), and the supernatant of excess seawater was removed without
disturbing the sediment. Sediment and water samples were stored at -80 
until required for further analysis.

\emph{2.3 DNA extraction and 16s rRNA amplicon sequencing}

DNA from sediment and sea cucumber (skin and gut) samples was extracted
using the DNeasy PowerLyzer PowerSoil Kit (Qiagen, Germantown, MD). DNA
from seawater samples was extracted using the DNeasy PowerWater Kit
(Qiagen, Germantown, MD), following the manufacturer's instructions. In
both cases, the same batch of DNA extraction kits was used for all the
samples. Overall, we followed guidelines for sequence-based analyses of
microbial communities (Eisenhofer et al., 2019) to avoid any potential
contamination of samples originating from reagents or the laboratory
environment. As part of this, DNA extractions were performed in the
Marine Molecular Lab (HKU) under a sterilised laminar flow hood. After
extractions, DNA concentrations were verified using BioDrop (Biochrom,
UK), and DNA quality was checked via agarose gel electrophoresis. Total
genomic data was submitted to Novogene Bioinformatics Technology Co.,
Ltd., Beijing, China for amplicon sequencing. The V3-V4 hypervariable
region of the 16S ribosomal RNA gene was amplified with the primers 341F
(5'- CCTACGGGNGGCWGCAG-3') and 806R (5'- GGACTACNNGGGTATCTAAT -3') (Y.
Yu et al., 2005). PCR reactions were carried out with Phusion\selectlanguage{ngerman}®
High-Fidelity PCR Master Mix (New England Biolabs, US) following the
manufacturer's instructions. Amplicons from different samples were mixed
in equidensity ratios and purified with the Qiagen Gel Extraction Kit
(Qiagen, Germany). NEBNext® Ultra\selectlanguage{english} DNA Library Prep Kit for Illumina(r)
(New England Biolabs, US) was used to construct DNA libraries
(paired-end), following the manufacturer's recommendations. Index codes
were added, and the library quality was assessed on the Qubit(r) 2.0
Fluorometer (Thermo Scientific, US) and 2100 Bioanalyzer system
(Agilent, US). Amplicons from different samples were mixed in equimolar
amounts and sequenced on the Illumina NovaSeq 6000 platform with
sequencing strategy PE250. Two negative controls were included during
library preparation to check potential contamination. However, these
controls were not sequenced as they did not generate any product during
PCR amplification. Primary processing was done by removing barcodes,
adaptor sequences and indices.

\emph{2.4 Sequence and data processing}

Raw sequence data from seawater, sediments, skin, and intestinal
microbiota were processed with QIIME2 version 2021.11. After visualizing
interactive quality plots and checking the reads' quality, the DADA2
(Callahan et al., 2016) pipeline was applied to demultiplex and merge
pair-end reads. Quality control was performed by DADA2 on trimming,
sequence error elimination, detection, and removal of chimaeras
following these parameters: -p-trim-left-f 8 -p-trunc-len-f 225
-p-trunc-len-r 213. Then, a naive Bayes classifier was trained following
the RESCRIPt pipeline (Robeson II et al., 2020) using the non-redundant
SSU reference dataset at 99 \% identity of the full SILVA 138 release
(Quast et al., 2013) on the specific 16S rRNA V3-V4 amplicon region with
the pair of primers stated above. Amplicon sequence variants (ASVs)
classified as mitochondria, chloroplasts, archaea, eukaryotes, and
unassigned taxon were subsequently excluded. Singletons and ASVs with
less than 10 reads across all samples were removed (control of spurious
artefacts of the PCR amplification process and/or potential sequencing
errors). Samples were then \emph{clr} -transformed to retain the
compositional nature of microbiome datasets (Gloor et al., 2017) for
further downstream analysis. Raw sequences have been deposited at the
National Centre for Biotechnology Information (NCBI) under the project
accession number PRJNA731335.

\emph{2.5 Statistical analysis}

Statistical analysis was conducted on the ASV level of the\emph{clr}
-transformed 16S rRNA dataset. Prokaryotic community profiles were
constructed at the phylum level, and the relative abundance of major
phyla (\textgreater{} 0.1 \% of the total microbial community) in
environmental samples was compared between sites by Analysis of
Compositions of Microbiomes with Bias Correction (ANCOM-BC) (Lin \&
Peddada, 2020). Alpha diversity metrics including the abundance-based
coverage estimator (ACE), diversity (Shannon diversity index), and
evenness (Inverse Simpson's diversity index) were computed. A comparison
between each of the alpha diversity indices by sites and source was
performed by two-way ANOVA followed by a post-hoc Tukey honestly
significant difference (Tukey HSD) test after ensuring data normality
and homogeneity with tests as above.

To address the community variation between samples collected from
different sites and sources, we adopted a permutational multivariate
analysis of variance (PERMANOVA) analysis and Analysis of Similarity
(ANOSIM) with 999 permutations to compare their compositions. A distance
matrix based on the ASVs was first constructed with the
\emph{distance}function in the ``vegan'' R package (Oksanen et al.,
2022) with the Euclidean distance (Gloor et al., 2017). Then, the Adonis
method via the\emph{adonis} function with 999 permutations was used for
the comparison of communities. Pairwise PERMANOVA was carried out
with\emph{pairwise.adonis} with the ``pairwiseAdonis'' package (Arbizu,
2023). \selectlanguage{english}Permutation tests for homogeneity of multivariate dispersions
(PERMDISP) were conducted using \emph{betadisper} and \emph{permutest}to
verify significant PERMANOVA outcomes. Principal Component Analysis
(PCA) was used to visualize the beta diversity matrix in the
``phyloseq'' package (McMurdie \& Holmes, 2013).

\emph{2.6 Core analysis, differentially abundant taxa and functional
prediction}

Shared ASVs among sources of each site were visualized with the
``MicEco'' package. Core communities were defined to facilitate the
interpretation of host and environmental microbiota. ASVs being present
in at least 70 \% of samples were considered as core and rare ASVs were
those that were present in fewer than 30 \% of samples (Bj\selectlanguage{ngerman}örk et al.,
2018). All other ASVs were considered transient. Indicator ASVs were
identified with the \emph{multipatt} function with the ``indicspecies''
package (Cáceres et al., 2023). Differentially abundant ASVs between
sources were also identified with the ANOVA-Like Differential Gene
Expression Analysis (ALDEx2) with the ``ALDEx2'' package (Fernandes et
al., 2014).

Phylogenetic Investigation of Communities by Reconstruction of
Unobserved States (PICRUSt2) was used to predict physiological and
metabolic functions of the host and environment microbiota based on ASVs
generated from the QIIME2 DADA2 pipeline (Douglas et al., 2020; Langille
et al., 2013). This procedure predicts the relative abundance of
functional genes (expressed as Kegg Orthologs--KOs) in a 16S ASV
community from the phylogenetic conservation of these genes in all
currently sequenced and assembled prokaryotic genomes. Quality control
was implemented by computing weighted nearest sequenced taxon index
(NSTI) values of each ASV. NSTI evaluates the prediction accuracy of
PICRUSt because it reflects the average genetic distance (measured as
number of substitutions per site) between each ASV against a reference
genome (Douglas et al., 2020; Langille et al., 2013). NSTI values higher
than 2 were eliminated following the developer's guidelines (Douglas et
al., 2020). PERMANOVA with 999 permutations was adopted to compare
functional pathways between sources and sites. Potential differentially
abundant functional MetaCyc pathways between sources were analysed by
ALDEx2. Those that were significantly differentially abundant (p
\textless{} 0.01) were then visualized with the ``ComplexHeatmap''
package (Gu et al., 2016). All R packages mentioned were implemented in
RStudio ver. 1.2.5019. In order to support and facilitate scientific
reproducibility, all analyses performed were included in the script as
part of the supplementary materials.

\textbf{Results}

\emph{3.1 Environmental gradient based on water quality}

The averaged environmental parameters in the PCA analysis reflected a
water quality trend from TM6 (SFB) to MM5 (TPC) (Figure 1, Table S1).
The seven environmental parameters varied between sites. Nitrogen
(nitrite and nitrate) and salinity have a greater contribution to the
variation in TPC whilst ammonia and phosphorus to LCC. Overall, PC1
explained most of the variation observed (79.6 \%) among sites and this
was consistent with the geographic distribution, supporting the
occurrence of a pollution gradient along Tolo Harbour (Figure 1). Based
on the environmental data, sites were categorized into three relative
pollution levels along the Tolo Harbour Channel (SFB as highly polluted,
LCC as moderately polluted, and TPC as low polluted site).

\emph{3.2 Sequencing results}

A total of 2,117,314 sequences were obtained after quality control from
all 48 samples (sediment, seawater, sea cucumber skin, and guts) from
each of the three sites (SFB, LCC, and TPC) along the pollution
gradient. The effective reads ranged from 14,872 to 76,342 with an
average of 44,110 (± 17,337 SD) reads per sample. The maximum total
number of ASVs generated was approximately 300 (Figure S1) after
filtering unwanted taxa and annotating sequences to the genus level.

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 1.} Sampling sites and water stations (A). PCA analysis
for eight environmental parameters in the respective water station of
each site (B).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure S1.} Rarefaction curve of all samples. The horizontal
dashed line in grey represents the maximum number of observed taxa.

\emph{3.3 Microbial community composition in environmental and host
samples}

The taxonomic composition of microbial community abundance on the phylum
level of environmental samples showed \emph{Pseudomonadota}
(Class\emph{Alphaproteobacteria,} 56.7 \%), \emph{Actinobacteriota}
(23.6 \%),\emph{Bacteroidota} (11.4 \%), \emph{Firmicutes} (4.9 \%),
and\emph{Gammaproteobacteria} (1.3 \%), as the most abundant phyla in
seawater. For sediments, \emph{Actinobacteriota} (35.6
\%),\emph{Alphaproteobacteria} (32.9 \%), \emph{Bacteroidota} (10.9
\%),\emph{Gammaproteobacteria} (6.0 \%), and \emph{Chloroflexi} (4.0 \%)
were the main groups (Table S2). However, the microbial composition
varied between sites, with more accentuated differences in the
contribution of major phyla (\textgreater{} 0.1 \% of the total
microbial community) in more polluted sites. These differences in
microbial composition between seawater and sediments gradually declined
in moderated and less polluted sites, particularly
for\emph{Alphaproteobacteria} and \emph{Actinobacteriota} (Figure 2 and
3, Table S3), albeit some site-specific and source-specific departures
of the trend. In the moderately polluted site, \emph{Campilobacterota}
and\emph{Chloroflexi} increased their overall contribution to the
sediment microbiota while remaining invariable in the seawater (Figure
2). In the sea cucumber host, the microbiota was also dominated
by\emph{Alphaproteobacteria} , \emph{Bacteroidota}
,\emph{Actinobacteriota} , \emph{Firmicutes,}
and\emph{Gammaproteobacteria} (Table S2). This community composition
differed from the local environment and varied along the cline (Figure
2), with a major contribution of the family
\emph{Rhodobacteraceae}(\emph{Alphaproteobacteria} ; Figure S2) and
species from the associated genera \emph{Ruegeria} ,
\emph{Dinoroseobacter,} and \emph{Oceanicella}(Figure S3).

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 2.} Taxonomic plot showing the community composition of
the major phyla (class for Proteobacteria), which are those present in
more than 0.1 \% of the total microbial community. ``Others'' denote
phyla of less than 0.1 \% of the total microbial community.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure S2.} Taxonomic plot showing the community composition of
the major families, which are those present in more than 0.1 \% of the
total microbial community.

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure S3.} Taxonomic plot showing the community composition of
the major genera, which are those present in more than 0.1 \% of the
total microbial community.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 3.} Phyla that are statistically significant between
sites in sediment (asterisk annotation), seawater (caret annotation) or
both (in bold) samples.

\emph{3.4 Community structure along the pollution gradient}

A comparison of alpha diversity indices revealed statistically
significant differences between samples from skin and gut, as well as
between sites (Table 1, Table S4). Significant interaction was detected
between these two experimental factors. Seawater microbiota evidenced
lower species richness and diversity, while the other three sources
shared a similar richness and diversity regardless of sites (Figure S4).
Overall, environmental samples from the highly polluted site (SFB) were
higher in richness and diversity than the two other sites (Figure 4).
For the moderately polluted site (LCC), a higher richness and diversity
were observed in skin samples. Midgut microbiota is the richest and most
diverse community in samples from the low polluted site (TPC).

\textbf{Hosted file}

\verb`image7.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure S4.} Alpha diversity metrics boxplot of the site (A) and
source (B) respectively.

\textbf{Hosted file}

\verb`image8.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 4.} Alpha diversity metrics boxplot of sites by sources.

Overall, differences in microbial community structure were observed
between seawater, sediment, skin, and midgut samples collected from
different sites. Microbial communities from different sources were all
distinct from one another (Table 2), although skin and midgut samples
clustered closer together (Figure 5A). However, these similarities
between skin and midgut microbiota composition varied across sites
(Table 3). A deviation of seawater microbiota in Starfish Bay could be
observed along PC2, away from the two other sites (Figure 5A). Community
structure showed a high influence on the interaction between source and
site factors as can be seen in the clustering tendency of the samples
(Figure 5A). A general higher to lower dispersion trend in beta
diversity from highly polluted to low polluted sites was observed in
seawater and sediment samples. On the other hand, dispersion patterns in
hosts across the three sites did not align with that of the
environmental sources. In midgut samples, a general trend of higher beta
diversity dispersion in the least polluted site was observed (Figure 5B,
Table 3).

\emph{3.5 Environmental and sea cucumber core microbiota}

A majority of ASVs in microbiota communities (on average 73.59 \% \selectlanguage{ngerman}± 6.74
SD) were considered rare because of their low representation among
samples (present in less than 30\% of samples, as defined in Bjork et
al., 2018). On average, 13.64 \% (± 3.38 SD) of ASVs were considered as
core (Table 4). Around 20.54 \% of the core ASVs in the midgut or skin
were not found in either of the environmental samples, although a small
number of core ASVs were shared among the four sources (Figure 6). On
top of that, 13 ASVs belonging to the core midgut microbiome were absent
from sediment samples (Table S5). Indicator species also comprised on
average 13.97 \% (± 9.43 SD) of the microbial community (Table S6). The
contributions of unique ASVs from different sources were different
between the three sites. The highest contribution of unique ASVs in
Starfish Bay and Tung Ping Chau originated from midgut samples, while
more unique skin ASVs were found in Lai Chi Chong (Figure 6).

\textbf{Hosted file}

\verb`image9.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 5.} PCA plot (A) by Euclidean distance, eclipses in
dashed lines denote the multivariate 95 \% confidence interval by
source. Boxplot of distance to centroid (B).

\emph{3.6 Microbial taxa and functioning profile significantly
associated with host samples}

A comparison between sources in ALDEx2 analysis yielded 166
differentially abundant ASVs (Figure 7, Table S7). A majority of ASVs
were affiliated with \emph{Rhodobacteraceae} , \emph{Flavobacteriaceae}
,\emph{Rhizobiaceae} , \emph{Sphingomonadaceae,}
and\emph{Cyclobacteriaceae} . 126 of the differentially abundant ASVs
were also considered as bacteria indicator taxa. 334 pathways were
revealed as significantly differentiated profiles. Pathways associated
with the cofactor, carrier, and vitamin biosynthesis and degradation of
amino acid and carbohydrate generally occurred more abundant in midgut
samples. Whereas pathways involved in inorganic nutrient metabolism,
amine, and polyamine degradation, and aromatic compound degradation were
more likely to be found in seawater samples (Figure 8, Table S8).
Functional pathways distinguished between sources but not sites (Figure
S5, Table S9).

\textbf{Hosted file}

\verb`image10.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 6.} Heatmap showing the percentage of samples by source
containing the core ASVs (A). Only core taxa that appear in at least 80
\% of all samples were shown. Venn diagrams of the number of shared taxa
between the four sources in three sites (B).

\textbf{Hosted file}

\verb`image11.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 7.} Bubble plot showing the differentially abundant taxa
across sources. Only taxa with an overall relative abundance above 1 \%
were shown. Taxa in bold are also indicator species.

\textbf{Hosted file}

\verb`image12.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure 8.} Functional pathways that are differentially abundant
between sources. Only pathways with a relative abundance of over 70 \%
in one of the sources were shown in the above.

\textbf{Hosted file}

\verb`image13.emf`  available at \url{https://authorea.com/users/758839/articles/733027-the-interplay-between-host-specificity-and-habitat-filtering-influences-sea-cucumber-microbiota-across-an-environmental-gradient-of-pollution}

\textbf{Figure S5.} PCA plot of functional pathways by Euclidean
distance, eclipses in dashed lines denote the multivariate 95 \%
confidence interval by source.

\textbf{Discussion}

Environmental gradients can influence ecological interactions and
phenotypic characteristics (e.g., physiology, microbiome) of natural
populations. In this work, we investigated the extent to which an
environmental gradient of pollution in one of the most urbanized coastal
areas in the world, modulates the interactions of animal hosts and their
associated microbial communities. By assessing intra-specific variation
in the diversity, structure, and function of environmental and
animal-associated microbiota in the tropical sea
cucumber\emph{Holothuria leucospilota} , we tested the interplay between
deterministic (e.g., environmental/host filtering) and stochastic (e.g.,
random microbial dispersal) processes underpinning host-microbiome
interactions and microbial assemblages. Overall, our results indicate
that microbial communities are complex and vary in structure and
function between the environment and the animal hosts. However, these
differences are modulated by the level of pollution across the gradient
with marked clines in alpha and beta diversity. Yet, such clines and
overall differences showed opposite directions when comparing
environmental- and animal-associated microbial communities. These
findings suggest that the interplay between both, environmental and host
filtering underpins microbial community assembly in \emph{H.
leucospilota} along the pollution gradient in Hong Kong.

Excessive nutrient and metal loading, driven by rapid urban development,
is a major threat to coastal and marine ecosystems worldwide, leading to
profound changes in biodiversity, biochemical processes, and ecosystem
functioning (Duprey et al., 2016; Heery et al., 2018; Hong et al., 2021;
Johnston et al., 2015; Mayer-Pinto et al., 2015, 2018; Woodward et al.,
2012). In Hong Kong waters, at a larger scale, this chemical pollution
is influenced by the Pearl River (Geeraert et al., 2021), generating a
strong west-east gradient in nitrogen (west: nitrated dominated; east:
dissolved organic nitrogen dominated) and heavy metals, with levels
exceeding thresholds for sediment toxicity (Geeraert et al., 2021; Hong
et al., 2021). At smaller scales, other independent west-east pollution
gradients can be found in some areas of Hong Kong (e.g., Tolo Harbour)
influenced by the high sewage loading, the tidal hydrodynamics, and the
seascape structure (Figure 1). Both, large- and small-scale gradients of
pollution in Hong Kong have been linked with recent faunal changes in
benthic species (Hong et al., 2021; Nicholson et al., 2011), and the
alteration of the spatial distribution and loss of foundational species
such as hard corals (Duprey et al., 2016). Our study revealed that
environmental microbial communities are also influenced by the
geographic trend in pollution that exists along the Tolo Harbour
(west-east), a potential consequence of clinal differences in nutrient
availability, especially for nitrogen and phosphorus. Similar
correlations between microbial composition in sediments and seawater
have been documented along pollution gradients in other marine regions
as a function of geographic clines in phosphorus (Stevick et al., 2021),
heavy metals, and nitrogen availability (J. Chen et al., 2019; L. Chen
et al., 2019; Di Cesare et al., 2020). In our study, the west-to-east
gradient of pollution showed negative clines in the contribution of some
dominant alphaproteobacterial groups such as the \emph{PS1 Clade,
Balneola} , and \emph{Acholeplasma,} while positive trends
in\emph{AEGEAN-169, Candidatus actinomarina,} and \emph{Ilumatobacter} .
Such geographic differences could be attributed to shifts in the
microbial ecological niche and some degree of local adaptation. In fact,
it has been shown that microbial communities in more polluted sites can
exhibit higher capacity to reduce intracellular levels of heavy metals,
hydrocarbons, and other environmental contaminants compared to less
polluted areas (J. Chen et al., 2019; Dell'Anno et al., 2020). For
instance, \emph{Balneola} sp. is a competitive organic-degrading
bacteria that is known to proliferate in coastal areas with high levels
of N enrichment (Y.-F. Xu et al., 2022). In our study, this group
exhibited an increased abundance in the highly polluted site (SFB) and
decreased in the medium-low pollution areas (LCC--TPC) of the Tolo
Harbour. This trend was also observed in \emph{Acholeplasma} , a group
that typically dominates coastal areas characterized by high chemical
oxygen demand driven by wastewater discharges (G. Zhang et al., 2021). A
contrasting pattern was found for \emph{Candidatus actinomarina} , a
bacteria that has the capacity to proliferate in oligotrophic marine
waters (López-Pérez et al., 2020), thanks to physiological adaptations
that facilitate efficient nutrient acquisition and processing
(Giovannoni et al., 2014; Lauro et al., 2009).

Despite the general geographic trend in microbial communities, we found
micro-habitat differences in the functional profiles within the more
polluted site. In the inner part of the Harbour (west), significant
group dispersion was observed in environmental microbiota compared to
the outer parts (east: less polluted sites). The high to low variation
in bacterial community composition along the cline was particularly
evident between microbial communities in seawater and sediments, a
pattern that may be explained by differences in their enzymatic
capabilities (broader in sediments) and strategies to access organic
matter that has already been degraded during passage through the water
column (Teske et al., 2011). In seawater, the lower microbial diversity
was mainly dominated by \emph{Alphaproteobacteria} followed
by\emph{Actinobacteria} and \emph{Bacteroidota} . However, such
dominance declined with the level of pollution along the Tolo harbour.
These findings are partially aligned with previous studies along a
eutrophication gradient in the South China Sea (Li et al., 2020; J.
Zhang et al., 2019). \emph{Actinobacteria} in particular, has been well
documented as a dominant group in eutrophic environments (Gong et al.,
2019; Yun et al., 2017), in which these organisms are suggested to play
a wide range of ecological functions such as the decomposition of
organic matter (Puttaswamygowda et al., 2019). In sediments, on the
other hand, the higher diversity was dominated by\emph{Actinobacteriota}
, \emph{Alphaproteobacteria,} and\emph{Bacteroidota.} Of these, only
\emph{Actinobacteriota} showed major changes along the cline, together
with other groups such as\emph{Gammaproteobacteria} (copiotrophs and
involved in nitrate metabolism, Herlambang et al., 2021; Newton et al.,
2011) and\emph{Chloroflexi} . Such dominance and clinal trend in the
sediments contrast with the profiles observed in the seawater,
highlighting the differential ecological influence of physicochemical
conditions in these environments (i.e., water column and benthos). This
is particularly true for nutrient load and chemical pollution, as
environmental differences in these factors are known to influence
variation in microbial abundance across marine gradients (Conte et al.,
2018; Jiang et al., 2013).

The sea cucumber\emph{-} associated microbiome does not fully reflect
the microbial composition of the environment along the pollution
gradient. Similar to the sediments, the diversity of microbial
communities in the guts and skin of the sea cucumbers was higher than in
the surrounding seawater. However, contrary to the sediments, sea
cucumber microbiome diversity was higher in moderated (for skin) and
less polluted (for guts) sites. These clinal differences were also
reflected in the overall community composition where sea cucumber
microbiota was dominated by\emph{Alphaproteobacteria, Bacteroidota,
Actinobacteria, Firmicutes, Gammaproteobacteria,} and
\emph{Cyanobacteria.} While the initial three phyla also exhibited
dominance in environmental samples, there were evident differences in
their composition between the host and environmental samples. These
findings are in line with previous studies, highlighting the role of
host filtering as a ``modulatory tool'' shaping the microbial
composition from the external microbial source pool (Gao et al., 2022;
Weigel, 2020). In fact, these phyla have been documented as dominant
groups in the guts of diverse species of sea cucumbers (Feng et al.,
2021; Pagán-Jiménez et al., 2019; H. Zhang et al., 2019), suggesting a
conserved filtering mechanism, likely mediated by secondary metabolites
(Darya et al., 2020) or immune factors (Dolmatova et al., 2004; Gowda et
al., 2008; X. Wang et al., 2017). Such mechanism promotes/favours
specific sets of microbes with potential beneficial effects for the
health and survival of the sea cucumber host (Zhao et al., 2024), or
that display neutral and/or transient characteristics (Q. Wang et al.,
2018). However, other mechanisms, uncouple from the sea cucumber host,
seem to have an additional influence on the microbiome of\emph{H.
leucospilota} as can be seen by the clinal differences in some microbial
groups across the pollution gradient. These mechanisms are most likely
associated with the regulatory influence of environmental factors and
their role in shaping microbial assemblies (Deng et al., 2022; H. Xu et
al., 2019). In our study, sea cucumbers in the high polluted site
harbour microbiomes dominated by families such as\emph{Rhodobacteraceae}
, \emph{Flavobacteriaceae} , \emph{Rhizobiaceae}
,\emph{Sphingomonadaceae} and \emph{Cyclobacteriaceae} . The dominance
of these microbial groups in coastal polluted areas has been associated
with their physiological tolerances (e.g., to heavy metals) and
capacities to degrade organic pollutants (e.g., members of Rhizobiaceae;
Teng et al., 2015). The abundance of these microbes exhibited a
decreasing trend along the west-east pollution gradient, further
cementing the notion of its adaptation to more heavily polluted regions
compared to less contaminated areas. Overall, the distinctions observed
between the environmental and host samples suggest that, although the
environment has an influence on the sea cucumber microbiota, the host
filtering capacity plays a major role in regulating the composition and
abundance of their associated microbial communities. This filtering
capacity, however, may vary along geographic ranges depending on the
magnitude of the environmental gradient, in this case, the level of
pollution.

Intra-individual differences in sea cucumber microbiome reflect
tissue-specific control of microbial communities. Group dispersion was
lower in the gut microbiome across the cline while the dispersion of the
skin microbiome was higher, particularly in the more polluted site. This
observation can be attributed to the extent of exposure to the external
environment, where the skin directly interacts with both seawater and
sediments. Due to the high variability in the environment and the direct
exposure of the skin, there is a higher variation in microbial
composition for the skin samples. Contrary, the gut is an enclosed
system with strong control by the host on their microbial assemblages
(Lachnit et al., 2019; Weigel, 2020), resulting in lower variation.
Apart from the differential dispersion between the skin and gut samples,
the predominant microbial groups evidenced distinct structural and
functional profiles (e.g., degradation pathways associated with the
skin, while both biosynthesis and degradation pathways elevated in the
gut). Such intra-individual differences in the microbiome between body
parts are potentially associated with their unique characteristics
(e.g., biochemistry, nutrient and oxygen content), and regulatory
mechanisms (e.g., secondary metabolites, Pagán-Jiménez et al., 2019; B.
Wang et al., 2015; Y. Wang et al., 2023), as well as the influence of
the external environmental (Sylvain et al., 2020). For example,
biochemical characteristics in gastrointestinal systems in diverse
metazoans, including echinoderms, are suggested to favour members
of\emph{Bacteroidota} (the second most dominant phyla in the sea
cucumber gut in our study), supporting a symbiotic interaction with
their host (Balakirev et al., 2008; Thomas et al., 2011). This phylum is
composed of diverse physiological types that exist from strictly
anaerobic bacteria like \emph{Bacterodetes} sp. (present in the gut
samples of this study), to facultative anaerobes such as
\emph{Lutibacter} sp. (a dominant genus found in \emph{H. leucospilota}
,), and strictly aerobic bacteria ---\emph{Flavobacteria} (Choi \& Cho,
2006; Lee et al., 2012; Thomas et al., 2011). It has been increasingly
recognised that members of \emph{Bacteroidota,} are an integral part of
their host metabolism due to their specialised capacity for degradation
of high molecular weight organic matter such as protein and
carbohydrates, as well as organic pollutants (Mayer et al., 2016; Thomas
et al., 2011; H. Zhang et al., 2019). In our study, such function (e.g.,
carbon degradation) was found significantly expressed in the gut of the
sea cucumber, suggesting the beneficial and important existence of
\emph{Bacteroidota} members (e.g., \emph{Flabacteriaceae} which was in
high abundance compared to skin and environmental samples), for the
breakdown complex molecules, as well as for biosynthesis activity (less
expressed in the skin microbiome functional profile, Kirchman, 2002).
Our results here support the hypothesis that the interplay between
host-selective mechanisms and inherent host conditions modulate
contrasting intra-host microbiome composition (skin and gut) in the sea
cucumber \emph{H. leucospilota} .

The Tolo Harbour, like many urbanised and industrialised estuaries
around the globe, has been radically altered by historical and ongoing
anthropogenic activities. Such alterations have impacted local
biodiversity and the overall ecosystem functioning (L. Chen et al.,
2019; Fleddum et al., 2011; Lei et al., 2018). At the organismal level,
urbanization and pollution are known to influence the physiology,
behaviour and life history of diverse marine animals (Elizabeth Alter et
al., 2021; Morroni et al., 2023; Weis, 2014). These effects can also be
observed in the complex assembly of animal-associated microbial
communities and the functions they provide to their hosts (Fu et al.,
2023; Pei et al., 2022; Q. Wang et al., 2018; Wei et al., 2022).
Alterations in host-associated microbiomes driven by urbanization and
pollution are typically characterized by two types of outcomes: 1) host
showing resilience due to the presence and enhancement of beneficial
microbial members (Fragoso ados Santos et al., 2015; Palladino et al.,
2023) or 2) dysbiosis as a result of host dysregulation (Huang et al.,
2020; Lachnit et al., 2019). Our study is likely to indicate the former,
as we detected multiple dominant microbes with a beneficial role, such
as \emph{Rhodobacteriaceae} (keystone species in sea cucumber intestinal
system, J. Yu et al., 2023; H. Zhang et al., 2019)
and\emph{Rhizobiaceae} (potentially aiding in pollutant breakdown, Teng
et al., 2015) in sea cucumber within the highly polluted region.
However, further studies are needed to test this hypothesis,
disentangling the microbial contribution to the host's survival and
tolerance to marine pollution.

\textbf{Conclusion}

Marine chemical pollution is an important driver modulating the
structure and function of microbial communities. Spatial clines in the
intensity and magnitude of this driver can result in different patterns
of environmental filtering, even across short geographic scales.
However, for microbial communities associated with marine animal hosts
(e.g., the sea cucumber \emph{H. leucospilota} ), there are additional
mechanisms influencing their composition and abundance. Such mechanisms
are underpinned by intrinsic characteristics of their host (e.g., body
compartments, biochemistry composition, immune systems), resulting in
intra-individual differences in associated microbiomes, and their
divergence from the environmental source. These findings support the
hypothesis of an intrinsic capacity of the host to regulate its
microbiome. Such regulation favours specific microbial functional
pathways that may play an important role in the survival and physiology
of the animal host, particularly in high polluted areas. Despite the
observed differences in the environment and sea cucumber hosts, there
was a small component of the microbial community (core microbiome) that
was constant across the pollution cline and the animal body parts,
suggesting that other mechanisms are also involved in the control of
microbial communities in these animals.

\textbf{Acknowledgements}

The authors thank the staff of the School of Biological Sciences for
their technical support and advice as well as the late Ms Tsz Chun So
for sample collection in the field. The computations were performed
using research computing facilities offered by Information Technology
Services, at the University of Hong Kong. This study was partially
supported by the Research Grants Council of Hong Kong (GRF 17113221) and
by the NSFC/RGC Joint Research Scheme (Grant No. N\_HKU791/22)

\textbf{Data Accessibility}

DNA sequences used for this study are available at the NCBI Sequence
Read Archive accession SRR14599054 to SRR14599073 and Bioproject
PRJNA731335. All data and scripts can be downloaded from Dryad
(https://doi.org/10.5061/dryad.qv9s4mwnk)

\textbf{Author Contributions}

JDGE conceived the study. SS-WC, ST, ZL, and KC conducted the fieldwork.
ST and SS-WC processed the samples. SS-WC analysed the data with support
from JDGE, KC and BA. SS-WC and JDGE wrote the first draft of the
manuscript with feedback from KC and BA. All authors reviewed the
manuscript and contributed to the final version.

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\textbf{List of tables}

\textbf{Table 1} . Two-way ANOVA of alpha diversity indices of microbial
communities between different sites and sources. P-values in bold denote
statistical significance at \emph{p} \textless{} 0.05. ACE:
abundance-based coverage estimator, Inverse Simpson: Inverse Simpson's
diversity index, Shannon: Shannon diversity index.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Groups & & ACE & ACE & Inverse Simpson & Inverse Simpson & Shannon &
Shannon\tabularnewline
& df & \emph{F} & \emph{p}-value & \emph{F} & \emph{p}-value & \emph{F}
& \emph{p}-value\tabularnewline
Source & 3 & 14.84 & \textbf{\textless{} 0.0001} & 3.78 & \textbf{0.019}
& 30.84 & \textbf{\textless{} 0.0001}\tabularnewline
Site & 2 & 5.43 & \textbf{0.0087} & 11.31 & \textbf{0.00015} & 5.83 &
\textbf{0.0064}\tabularnewline
Source \selectlanguage{ngerman}× Site & 6 & 5.19 & \textbf{0.00062} & 3.18 & \textbf{0.013} &
4.89 & \textbf{0.00096}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} . Comparison of beta diversity of microbial communities
at the amplicon sequence variant (ASV) level between different sites and
sources. P-values in bold denote statistical significance at
\emph{p}\textless{} 0.05. PERMANOVA: permutational multivariate analysis
of variance, ANOSIM: analysis of similarities, PERMDISP: test for
homogeneity of multivariate dispersions.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Groups & & PERMANOVA & PERMANOVA & ANOSIM & ANOSIM & \selectlanguage{english}PERMDISP &
\selectlanguage{english}PERMDISP\tabularnewline
& df & \emph{F} & \emph{p}-value & \emph{R} & \emph{p}-value & \emph{F}
& \emph{p}-value\tabularnewline
Site & 2 & 1.71 & \textbf{0.038} & -0.003 & 0.443 & 0.383 &
0.69\tabularnewline
Source & 3 & 10.88 & \textbf{0.001} & 0.75 & \textbf{0.001} & 20.18 &
\textbf{0.001}\tabularnewline
Site \selectlanguage{ngerman}× Source & 6 & 1.85 & \textbf{0.003} & - & - & - & -\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3} . Pairwise comparisons of beta diversity by source.
P-values in bold denote statistical significance at \emph{p} \textless{}
0.05. PERMANOVA: Permutational multivariate analysis of variance,
PERMDISP: Test for homogeneity of multivariate dispersions.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Pairwise comparisons between source & PERMANOVA & PERMANOVA &
PERMDISP\tabularnewline
& \emph{F} & \emph{p}-value & \emph{p}-value\tabularnewline
Midgut vs Skin & 4.58 & \textbf{0.001} & 0.442\tabularnewline
Midgut vs Sediment & 9.89 & \textbf{0.001} & 0.135\tabularnewline
Midgut vs Seawater & 16.05 & \textbf{0.001} &
\textbf{0.002}\tabularnewline
Skin vs Sediment & 8.96 & \textbf{0.001} & 0.691\tabularnewline
Skin vs Seawater & 12.69 & \textbf{0.001} &
\textbf{0.003}\tabularnewline
Sediment vs Seawater & 7.17 & \textbf{0.001} &
\textbf{0.001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4} . ASVs belong to core, transient, rare community, and
indicator species in samples of the four different sources.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
& & Core (\%) & Transient (\%) & Rare (\%) & Indicator
(\%)\tabularnewline
Environment & Environment & & & &\tabularnewline
& Seawater & 8.61 & 8.26 & 83.13 & 8.33\tabularnewline
& Sediment & 15.43 & 16.99 & 67.58 & 26.96\tabularnewline
Host & Host & & & &\tabularnewline
& Skin & 15.79 & 13.64 & 70.57 & 14.71\tabularnewline
& Midgut & 14.71 & 12.2 & 73.09 & 5.88\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
\FloatBarrier
\end{document}

