Fine-scale differences in eukaryotic communities inside and outside salmon aquaculture cages revealed by eDNA metabarcoding

Abstract

Aquaculture impacts on marine benthic ecosystems are widely recognized and monitored. However, little is known about the community changes occurring in the water masses surrounding aquaculture sites. In the present study, we studied the eukaryotic communities inside and outside salmonid aquaculture cages through time to assess the community changes in the neighbouring waters of the farm. Water samples were taken biweekly over five months during the production phase from inside the cages and from nearby points located North and South of the salmon farm. Eukaryotic communities were analyzed by eDNA metabarcoding of the partial COI Leray-XT fragment. The results showed that eukaryotic communities inside the cages were significantly different from those in the outside environment, with communities inside the cages having higher diversity values and more indicator species associated with them. This is likely explained by the appearance of fouling species that colonize the artificial structures, but also by other species that are attracted to the cages by other means. Moreover, these effects were highly localized inside the cages, as the communities identified outside the cages, both North and South, had very similar eukaryotic composition at each point in time. Overall, the eukaryotic communities, both inside and outside the cages, showed similar temporal fluctuations through the summer months, with diversity peaks occurring at the end of July, beginning of September, and in the beginning of November, with the latter showing the highest Shannon diversity and richness values. Hence, our study suggests that seasonality, together with salmonid aquaculture, are the main drivers of eukaryotic community structure in surface waters surrounding the farm.

Full text

Fine-scale differences in
eukaryotic communities inside
and outside salmon aquaculture
cages revealed by eDNA
metabarcoding
Marta Turon
1
, Magnus Nygaard
1
, Gledis Guri
1
,
2
,
Owen S. Wangensteen
1
and Kim Præbel
1
*
1
Norwegian College of Fishery Science, UiT The Arctic University of Norway, Tromsø, Norway,
2
Norwegian Institute of Marine Research, Tromsø, Norway
Aquaculture impacts on marine benthic ecosystems are widely recognized and
monitored. However, little is known about the community changes occurring in
the water masses surrounding aquaculture sites. In the present study, we
studied the eukaryotic communities inside and outside salmonid aquaculture
cages through time to assess the community changes in the neighbouring
waters of the farm. Water samples were taken biweekly over five months during
the production phase from inside the cages and from nearby points located
North and South of the salmon farm. Eukaryotic communities were analyzed by
eDNA metabarcoding of the partial COI Leray-XT fragment. The results showed
that eukaryotic communities inside the cages were significantly different from
those in the outside environment, with communities inside the cages having
higher diversity values and more indicator species associated with them. This is
likely explained by the appearance of fouling species that colonize the artificial
structures, but also by other species that are attracted to the cages by other
means. Moreover, these effects were highly localized inside the cages, as the
communities identified outside the cages, both North and South, had very
similar eukaryotic composition at each point in time. Overall, the eukaryotic
communities, both inside and outside the cages, showed similar temporal
fluctuations through the summer months, with diversity peaks occurring at
the end of July, beginning of September, and in the beginning of November,
with the latter showing the highest Shannon diversity and richness values.
Hence, our study suggests that seasonality, together with salmonid
aquaculture, are the main drivers of eukaryotic community structure in
surface waters surrounding the farm.
KEYWORDS
eukaryotic communities, eDNA, aquaculture, metabarcoding, surface water,
biodiversity, salmonid net pins, COI
OPEN ACCESS
EDITED BY
Georgina Valentine Wood,
University of Western Australia, Australia
REVIEWED BY
Simo Njabulo Maduna,
Norwegian Institute of Bioeconomy
Research (NIBIO), Norway
Liz Alter,
California State University, United States
*CORRESPONDENCE
Kim Præbel,
kim.praebel@uit.no
SPECIALTY SECTION
This article was submitted to
Evolutionary and Population Genetics,
a section of the journal
Frontiers in Genetics
RECEIVED 30 May 2022
ACCEPTED 25 July 2022
PUBLISHED 26 August 2022
CITATION
Turon M, Nygaard M, Guri G,
Wangensteen OS and Præbel K (2022),
Fine-scale differences in eukaryotic
communities inside and outside salmon
aquaculture cages revealed by
eDNA metabarcoding.
Front. Genet. 13:957251.
doi: 10.3389/fgene.2022.957251
COPYRIGHT
© 2022 Turon, Nygaard, Guri,
Wangensteen and Præbel. This is an
open-access article distributed under
the terms of the Creative Commons
Attribution License (CC BY). The use,
distribution or reproduction in other
forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the
original publication in this journal is
cited, in accordance with accepted
academic practice. No use, distribution
or reproduction is permitted which does
not comply with these terms.
Frontiers in Genetics frontiersin.org01
TYPE Original Research
PUBLISHED 26 August 2022
DOI 10.3389/fgene.2022.957251

Introduction
Human activities are one of the main threats to the stability of
marine ecosystems, producing structural and functional changes
in marine habitats that hamper the ecosystems’capacity to
provide goods and services (Halpern et al., 2008;Claudet and
Fraschetti, 2010). Therefore, a proper assessment on the
distribution and intensity of human activities, and the
appraisal of their impacts on marine ecosystems, is of crucial
importance for the sustainable use of the ocean biodiversity
(Halpern et al., 2008,2012). Among the variety of human
stressors, aquaculture represents one of the main threats for
coastal marine environments (De Silva, 2012) and their
associated ecosystems (Sarà et al., 2011;Taranger et al., 2015),
as it is a rapidly growing industry that contributes up to 46% of
the total global fish output (FAO, 2018). Environmental impact
assessments regulated by national and international directives
(e.g., Marine Strategic Framework Directive, MSFD in the EU)
are required to maintain a healthy trade-off between ecosystem
services and exploitation and to protect, conserve and enhance
marine ecosystems.
Aquaculture impacts on the natural environment are widely
recognized and monitored (Holmer et al., 2005,2008;Kalantzi
and Karakassis, 2006). Impacts include the release of particulate
matter into the environment, which usually leads to significant
ecological changes, such as shifts in macrofaunal communities,
decrease in species diversity or complete removal of native
infauna (Holmer et al., 2008;Keeley et al., 2014;Stoeck et al.,
2018a). Most monitoring programs focus on the benthic impacts,
which are easier to measure than in the water column, and they
traditionally include inventories of benthic macroinvertebrates
and detection of presence/absence of specific species identified by
morphological taxonomy, which are used as indicators of
ecosystem health (Aylagas et al., 2014;Keeley et al., 2014). A
variety of benthic indices, such as AZTI’s Marine Biotic Index
(AMBI) (Borja et al., 2000), have been developed to classify the
degree of impact of a certain area and prompt the consequent
restoration measures (Diaz et al., 2004;Pinto et al., 2009).
However, little is known about the small-scale changes
occurring at the surface waters of aquaculture sites, where
interactions with wild populations, spreading of diseases and
release of parasites from farms are also of environmental concern
(Holmer et al., 2008). Moreover, such effects are mainly subjected
to distance from the aquaculture cages and water current
direction and velocity (Hamoutene et al., 2016). This includes
the need for knowledge about the hydrodynamic patterns within
and around aquaculture cages to properly understand the spatial
and temporal variability of the environmental parameters to
perform the correct assessment of the environmental impact
of a aquaculture site (Klebert et al., 2013;Gansel et al., 2014).
Environmental DNA (eDNA) metabarcoding has
revolutionized the way in which biomonitoring is performed,
from single-species detection to community studies and
environmental impact assessments (Bohmann et al., 2014;
Thomsen and Willerslev, 2015;Pawlowski et al., 2021). eDNA
is the combined genetic material of trace and community DNA
that can be extracted from an environmental sample, such as
water (Rodriguez-Ezpeleta et al., 2021). Analysis of eDNA can
overcome the difficulties associated with traditional monitoring
techniques such as the correct identification of cryptic species,
the need for taxonomic expertise, the lack of standardized
samplings or the invasive nature of some survey techniques
(Thomsen and Willerslev, 2015). Moreover, eDNA data allows
biomonitoring to be performed at higher temporal and spatial
scales than traditional surveys (Gibson et al., 2015). Several
studies using eDNA metabarcoding have already been
performed to characterize benthic macrofaunal responses to
aquaculture pressures and introduce its use to develop a
genetic based marine biotic index for benthos (gAMBI)
(Aylagas et al., 2014;Lejzerowicz et al., 2015;Pawlowski et al.,
2018;He et al., 2020). Moreover, the application of high-
throughput sequencing has facilitated the potential use of
bacterial communities as bioindicators of aquaculture impact,
as they rapidly respond to environmental changes (Stoeck et al.,
2018a;Borja, 2018;Verhoeven et al., 2018;Armstrong and
Verhoeven, 2020).
The implementation of eDNA-based technologies in routine
biomonitoring is still hampered by the lack of consensus on
whether it should only be applied to conventional bioindicators
or also include new taxonomy-free bioindicators (Pawlowski
et al., 2021), which use environmental genomics based
profiling on communities and independently generated
ecological status or known disturbance gradients (Cordier
et al., 2021). Examples of new bioindicators have been found
when considering all the operational taxonomical unit (OTU)
profiles along known impact gradients such as eutrophication
(Apothéloz-Perret-Gentil et al., 2017), oil spills (Bik et al., 2012),
or aquaculture sites (Stoeck et al., 2018a). Cordier et al. (2021)
proposed four general implementation strategies of
environmental genomics for monitoring which include DNA-
based taxonomic identification of known taxa, de novo
bioindicator analysis, structural community metrics and
functional community metrics. The visual identification of
known bioindicators is only possible for macrofaunal species
found in the sediments, where the majority of surveys are
performed (Aylagas et al., 2014;He et al., 2020). However,
taxonomy-free discovery of new bioindicators in the water
column in the vicinity of aquaculture sites is likely to occur
when planktonic communities are analyzed with eDNA
methods, considering hydrodynamics and temporal patterns
(Lanzén et al., 2021).
In the present study we aimed to assess the dynamics of
eukaryotic communities inside an aquaculture farm through time
using eDNA metabarcoding and contrasted the community
composition with that obtained for the surface waters
surrounding the aquaculture farm. To accomplish this aim, we
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1) compared eukaryotic communities from filtered surface water
taken inside salmonid cages and from nearby points around the
aquaculture facility, 2) evaluated the use of eDNA metabarcoding
for impact assessment, and 3) assessed temporal variability, by
taking biweekly samples over a 5-month period.
Materials and methods
Study site and water collection
This study took place at a commercial salmon aquaculture
farm (“Uløybukt”, locality number: 10726, position: 69°51.605′,
20°42.838′) located in the southern part of Skervøy municipality,
Troms, Northern Norway. The farm was located in Rotsundet in
a coastal area (Supplementary Figure S1) and is characterised by a
sub-Arctic climate and water regime. Although high latitude
Norwegian coastal areas are defined as ice-free, they have strong
seasonality due to high variability of light intensity throughout
the year (Wiedmann et al., 2016). The sampling was performed
every 2 week from 20th July 2017 to 7th November 2017),
accounting for a total of nine sampling dates. The farm had a
total of 10 circular net-pins (diameter 140 m, depth ca. 40 m)
composed by PE-plastic and nets of nylon/polyester and the
distance between the net-pins were approximately 40 m. The
depth at the locality were 50–95 m and the farm were located at a
bottom consisting of a mix of sand, stones, and mud. Atlantic
salmon smolts were stocked in the farm in April/May 2017 and it
was estimated that farm had 800.000–1.000.000 fish during the
sampling. At the beginning of the sampling the fish were
distributed in seven net pins, whereas they were distributed in
up to nine net-pins at the end of the sampling due to increased
biomass. The fish were fed, based on appetite, throughout the
day. In this study we defined outside environment as control and
collected these samples at a distance 200 m from the cages North
and South of the aquaculture farm, whereas samples taken inside
the cage were considered as the treatment. In each sampling
event, 2.5 L of water were collected at 1.5 m depth using a Niskin
bottle (model 1010-2.5 L, GeneralOceanics) from five different
spatial replicate points (A–E) inside three cages (M1, M2, M3)
containing fish. Moreover, 2.5 L of water were also collected in
five spatial replicates (A–E) in the outside environment at fixed
points (200 m distance assessed with a GPS chart plotter) at the
northern (N) and southern (S) side of the middle of the salmon
farm at 1.5 m depth. All sampling equipment was sterilized with
10% bleach solution and 70% EtOH before each sampling event
and thoroughly rinsed with seawater from the sampling point
area before each use. All samples were collected and processed
while wearing newly donned protective equipment such as nitrile
gloves to prevent risk of contamination between samples or from
outside sources.
Upon collection of the seawater samples, a filtering station
was set up on site, and each sample bag was filtered through a
0.22 μm Sterivex filter units (Merck KGaA, Darmstadt,
Germany) using a sterile 50/60 ml syringe from BD Plastipak.
A total of 0.5 L of water were pressed through each of the five
filters from each location to ensure a standard volume between
samples. After drying the filters by pumping air through them,
the filters were placed in prelabeled sterile 50 ml Falcon tubes
(Thermo Fisher Scientific, Waltham, MA, United States), and
prelabeled bags for transport to UiT The Arctic University of
Norway (UiT) and long-term storage at −80°C in an eDNA
dedicated freezer. The syringes were changed, and operators’
hands were meticulously sterilized, between each sample using
5% bleach solution and a MilliQ Ultrapure deionized water rinse
to limit contamination. A control blank was run on each
sampling day to quantify contamination during the filtering
process by filtering 0.5 L of the remaining MilliQ rinse water
through a filter and drying the filter in the same manner as the
previous samples.
DNA extraction
The Sterivex filters used for water sampling underwent DNA
extraction in over-pressured eDNA clean-labs using trace eDNA
extraction protocols specifically designed to prevent
contamination from all airborne DNA present within UiTs
facilities or present on the lab user’s skin, hair, or breath.
These protocols relied on vigilant care for cleanliness within
and outside of the eDNA laboratories and avoidance of potential
contaminant sources at UiT and personal life during the weeks of
eDNA extraction lab use. Airborne DNA contamination risks
were mitigated through use of a pressure positive eDNA
extraction rooms and airlocked changing and sample
preparation rooms. eDNA extraction protocols were
meticulously followed for the modified use of DNEasy Blood
and Tissue®(Qiagen, Hilden, Germany) kits. In short, a total of
500 μl of lysis buffer were added to each Sterivex filter, sealed
with sterile caps at both ends, and incubated 24 h on a rotary
wheel placed in a 56°C incubator oven to ensure full lysis of the
particulates captured within the filter membrane. The lysed
solution was then centrifuged out of the filter casing and into
2 ml Eppendorf tubes and the rest of the extraction followed the
standard protocol recommended by the extraction kit handbook
(Qiagen 2020). Subsequently each sample was eluted in 75 μl
elution buffer, of which 20 μl was aliquoted for library
preparation and sequencing.
PCR amplification and sequencing
A multiplexing approach was used for sequencing the
320 samples on an Illumina MiSeq next-generation sequencer
(Illumina, San Diego, CA, United States). The partial COI Leray-
XT fragment (313 bp) was amplified using the mlCOIintF-XT/
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jgHCO2198 primer pair (Wangensteen et al., 2018). Samples
included 19 PCR blanks as well as field blanks for each sampling
event. 8-base tags were used to uniquely label each sample as in
Wangensteen et al. (2018). PCR amplifications were conducted
in 20 μl reactions containing 2 μl of DNA template, 10 μlof
AmpliTaq Gold Master mix, 0.16 μl of Bovine Serum Albumin
(20 μg/μl), 1 μl of each forward and reverse primer (5 μM) and
5.84 μl of H2O. The temperature profile was as follows: 95°C for
10 min; 35 cycles × (94°C/1 min, 45°C/1 min, 72°C/1 min); 72°C/
5 min. Only one PCR replicate was run per sample. The success
of PCR amplifications was checked by gel electrophoresis in 1%
agarose and PCR products were then pooled together into two
multiplex sample pools. MinElute PCR purification columns
(Qiagen) were used to concentrate the pooled DNA and to
remove fragments below 70 bp. Library preparation was
performed with the NEXTflex PCR-free library preparation kit
(BIOO Scientific) and the exact library concentration was
measured in a qPCR machine (ThermoFisher), using the
NEBNext Library Quant Kit (New England BioLabs). Finally,
pools were sequenced along with 1% PhiX on an Illumina MiSeq
platform using v3 chemistry (2 × 250 bp).
Metabarcoding pipeline
The OBITools v1.01.22 software suite (Boyer et al., 2016)
was used for the initial steps of the bioinformatic analyses.
Paired-end reads were aligned using illuminapairedend and
only sequences with alignment quality score >40 were kept.
Demultiplexingwasdoneusingthesampletagswithngsfilter,
which also removed primer sequences. Aligned reads with
length of 299–320 bp and without ambiguous positions were
selected using obigrep and then dereplicated with obiuniq.
Chimeric sequences were removed using the uchime-denovo
algorithm implemented in vsearch v1.10.1 (Rognes et al.,
2016). Clustering of sequences into molecular operational
taxonomic units (MOTUs) was performed using SWARM
2.0 (Mahé et al., 2015)withadvalue of 13 (Bakker et al.,
2019). Taxonomic assignment of the representative sequence
of each MOTU was done with the ecotag algorithm (Boyer
et al., 2016) using a local database of Leray fragment sequences
(available from https://github.com/uit-metabarcoding/
DUFA). COI sequences for the MOTUs of interest
(abundances >0.5% of the total reads) were manually
checked for better match by BLAST search against the
NCBI GenBank and BOLD databases, and best IDs were
changed to reflect a higher percent match if one was found.
MOTU best IDs were then reassigned to an appropriate
taxonomic rank based on percent match to the assigned
species. Sequences assigned to bacteria or to the root of the
Tree of Life, contamination of terrestrial origin, and MOTUs
that were present in the control samples with more than 10%
of their total read abundance were removed.
Data analysis
Statistical analyses were performed in R version 3.1.3 (https://
www.R-project.org/) with the vegan package [version 2.5–6;
(Oksanen et al., 2019)] and graphic visualisations were done
with ggplot2 package (Wickham, 2016). Reads were first
transformed to relative abundances to build a Bray-Curtis
dissimilarity matrix, which was used to assess the variance in
community composition using Permutational Multivariate
Analyses of Variance (PERMANOVA). Samples were
categorized as a function of Type (cage, outside), and Date
(9 levels) and the univariate effects of these factors on the
community composition were tested using adonis function
with 999 permutations. Additionally, PERMDISP analysis
(betadisper function) was performed for significant factors to
determine if their effect was due to different multivariate mean or
to different heterogeneity of the groups. Non-metric
multidimensional scaling (nMDS) representation with Bray-
Curtis dissimilarities was performed with the metaMDS
function with 500 iterations. Shannon diversity and MOTU
richness per sample were calculated in vegan (Oksanen, J.
et al., 2019) after rarefaction to the lowest total number of
reads per sample, to account for differences in sample
sequencing depth. Then, two-way analysis of variance
(ANOVA) was performed to detect significant differences
between Date and Type in alpha diversity values.
An indicator species analysis (Dufrêne and Legendre, 1997)
was performed in R using the labdsv package (Roberts, 2016)to
detect potential associations of certain eukaryotic phyla to each
type of environment (cage or outside). Those with Indval
values >0.5 (p-value <0.05 in all cases) were selected as
indicator phyla. The same analysis was performed to look for
MOTUs associated to type of environment and to specific
sampling dates. We retained the top 50 significant MOTUs
with highest Indval values (in all cases these values are >
0.5 and with p-value <0.05) as indicator MOTUs in each case.
Upset plots from the UpSetR package (Conway et al., 2017)were
used to visualize the number of shared MOTUs between cage and
outside environments for each sampling date and Treemaps from
treemapify package (Wilkins, 2021) were created to visualize the
overall eukaryotic composition of the sampling location considering
read abundance and MOTU richness.
Results
The overall eukaryotic composition
After quality check, dereplication, chimera removal and manual
filtering, we obtained a total of 6,985,791 reads assigned to
3471 eukaryotic MOTUs. After removal of MOTUs present in
blank samples (58 MOTUs), and low sample reads
(17 samples, <2000 reads), we obtained a final dataset of
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6,847,336 reads assigned to 2984 eukaryotic MOTUs in 282 samples,
which corresponded to 35 different Phyla. The average mean reads per
sample was 22,915. Of the total MOTUs, 2906 were rare, with <0.05%
of the reads per MOTU, whereas only 17 MOTUs were considered
abundant, with >0.5% of the reads per MOTU.
Dinoflagellates, Viridiplantae, Arthropoda and Haptophyta
dominated the communities in terms of read abundance
(Figure 1A), whereas the most MOTU-rich groups could not
be classified at Phylum level and were represented by unclassified
members of Metazoa or Eukarya (Figure 1B). However, those
unclassified MOTUs represented a smaller proportion when
considering their read abundance (Figure 1A). The following
MOTU-rich phyla were Haptophyta, Dinoflagellata and
Bacillariophyta (Figure 1B).
Within the most abundant MOTUs (Supplementary Table
S1), an unassigned dinoflagellate (class Dinophyceae) was the
most abundant in the whole dataset, followed by MOTUs
classified as Bathycoccus prasinos,Oithona similis, and
Emiliania huxleyi.
Beta diversity
The non-metric multidimensional scaling (nMDS)
representation based on relative read abundance clearly
showed that samples appeared ordered along the first
dimension following the sampling date, while the second
dimension separated samples from inside and outside the
cages (Figure 2). Moreover, the nMDS ordination indicated
that the eukaryotic communities from inside and outside the
cages gradually differed less as the time went on and the sampling
time approached the winter.
FIGURE 1
Treemap of the overall eukaryotic composition at phylum level considering (A) read abundances and (B) MOTU richness.
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Initially we indicated that there were no significant
differences between 1) the three cages and 2) between the
North and South points of the outside environment
(Supplementary Table S2), which were not significant
(p-value >0.05 respectively, Supplementary Tables S2.1, S2.2).
Additionally, PERMANOVA with date and environment (inside
and outside) as factors found significant differences and
significant interaction between the factors (p-value <
0.001 respectively, Supplementary Table S2.3). PERMDISP
analyses substantiated also differences in dispersion in these
groups of samples (Supplementary Table S2). The R
2
value
corresponding to the date factor (0.641) was greater than the
one corresponding to environment type (0.079) or the
interaction (0.061) (Supplementary Table S2.3).
Community changes through time
In general terms we observe a shift in the predominance of
dinoflagellates, especially during September, to Viridiplantae
throughout October sampling dates (Figure 3). The
dominance of dinoflagellates is more evident outside the
cages, especially in the dates comprised between 19th August
to 12th September, when they represent more than half of the
total community abundance. However, inside the cages, this
dominance is shared with other groups such as Arthropoda or
unassigned members of Metazoa. Viridiplantae are almost absent
inside the cage environment and in low abundance outside the
cages during August and September and they clearly peak at the
beginning of October, accounting for more than 50% of the
relative abundance in both cage and outside-cage environments.
Eukaryotic communities were distinctly different between
the environments inside and outside the cages in the first
sampling dates (July) and they tend to have a more similar
community composition by the last sampling date in November
(Figure 3). During July, the cages are dominated by Arthropoda,
which represent a small proportion in the outer environment,
whereas Viridiplantae are the prevailing members of the outside-
cages environment. Also noticeable is the presence of Annelida
and Echinodermata inside the cages and their absence in the
outer environment during July. Through time, both
environments are mainly differentiated by the presence of
certain phyla inside the cages, particularly Arthropoda the
most evident during the first sampling dates and the increased
FIGURE 2
nMDS based on Bray-Curtis distances of sea water eukaryotic communities colored by sampling date. Shape corresponds to the environment
inside the cages (circles) and North (triangle) or South (square) of the salmon facility.
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abundance of Chordata and Cnidaria by the last samplings.
Nevertheless, by the end of the sampling period, the
eukaryotic composition of both environments resembles each
other, dominated by Viridiplantae, Dinoflagellata, unassigned
Eukarya and unassigned Metazoa, Arthropoda and Cnidaria,
with the distinct presence of Chordata inside the cages due to the
salmon presence.
Peaks of certain phyla at given dates are also noteworthy,
such as the high abundance of Haptophyta during August in both
environments, or the presence of Cnidaria and Ochrophyta
inside the cages during September (Figure 3).
Alpha diversity
Alpha diversity of eukaryotic communities associated to the
aquaculture environment showed fluctuating values through
time, with peaks diversity in July, beginning of September and
highest values in November (Figure 4). The Shannon diversity
and richness were always higher inside the cages than outside the
cages, although both environments followed a similar fluctuating
diversity pattern through time. This pattern showed a decrease in
diversity after July diversity peak, followed by a peak at the
beginning of September, reaching the lowest values in mid-
September, whereafter an increasing trend was observed
towards the highest diversity values in November (Figure 4).
Shannon diversity values ranged from 0.93 (12th September)
to 2.90 (7th November), whereas richness values ranged from
17.13 (19th August) to 114.50 (7th November). Significant
differences in Shannon diversity were observed between dates
(Anova: F-value = 112,03, p-value <0.0001), and environmental
type (cage and outside, Anova: F-value = 33.75, p-value <0.0001)
and for the interaction of both factors (Anova: F-value = 4,05,
p-value <0.001). Similarly, significant differences were observed
FIGURE 3
Composition at phylum level of all the samples. Cage samples on top and Outside samples in the bottom: ordered by sampling date Each color
represents a different phylum.
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for richness values through time (Anova: F-value = 69,3,
p-value <0.0001) and environmental type (cage and outside,
Anova: F-value = 43,9, p-value <0.0001) and for the interaction
of both factors (Anova: F-value = 3,65, p-value <0.001). No
significant differences were observed between different cages
(M1, M2, M3) for Shannon diversity (Anova: F-value = 1.57,
p-value >0.05) or for richness values (Anova: F-value = 0.38,
p-value >0.05).
Indicator species
Sampling time was the main variable affecting eukaryotic
community structure (Figure 2). Therefore, eukaryotic
communities found in the surface waters of aquaculture
environment were differentiated between sampling dates, with
certain taxonomic groups only present or enriched at given dates.
The indicator species analysis for the Date factor shows which
eukaryotic MOTUs are driving the main differences between
sampling dates (Figure 5). First (20th July) and last (07th
November) sampling have the highest number of indicator
MOTUs, implying that they have the most differentiated
communities, which is also coincident with the highest alpha
diversity values for those dates (Figure 4). Relevant indicator
MOTUs for July include MOTUs assigned to different species of
the diatom Pseudo-nitzschia, the mussel Modiolus modiolus, the
salmon louse Lepeophtheirus salmonis, and the diatom
Cylindrotheca closterium (Figure 5).The majority of indicator
MOTUs for November were not taxonomically assigned to a
specific species, except for a MOTU classified as the
coccolithophore Emiliania huxleyi. Other indicator MOTUs
that could be taxonomically classified at species level include
the tube-forming worm Hydroides elegans, a harbor fouling
invasive species, and the mollusk Antalis entalis, both highly
abundant at the end of October (Figure 5).
Significant differences in composition were also observed
between the communities inside the aquaculture cages and the
outer environment (Figure 2). The indicator species analysis at
the phylum level (Indval >0.5, p-value <0.05) showed that
Ochrophyta, Cnidaria, Chordata, Arthropoda, Annelida, and
FIGURE 4
Boxplot showing Shannon diversity index (A) and Richness (B) of eukaryotic communities per sampling dates, inside (first) and outside (second)
the cages.
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Amoebozoa were indicator phyla of the inside-cages
environment, whereas Viridiplantae, Oomycetes, Fungi and
Dinoflagellata were characteristic of the outer environment
(Figure 6A). However, the differences in relative read
abundance between cage and outside environments is much
more evident for the indicator phyla of the cage environment,
indicating that they have a more differentiated eukaryotic
community (Figure 6A). Similarly, many MOTUs were found
to be indicators of the cage environment, with highly different
abundances compared to the outer cage environment
(Figure 6B). Among those, relevant MOTUs belong to the
fishes Salmo salar (Atlantic salmon) and Pollachius virens
(Saithe), the brown algae Pylaiella littoralis,Hecatonema
maculans,Ectocarpus siliculosus, and Ectocarpus fasciculatus, a
rotifer belonging to the Ploima order, the himatismenid amoeba
Parvamoeba rugata, the copepod Oithona similis, the diatom
Grammonema striatula, the harmful diatom Cylindrotheca
closterium and the lion’s mane jellyfish Cyanea capillata
(Figure 6B).
The analysis of the most abundant MOTUs (relative
abundance >1%) confirms that most of the MOTUs follow
the same temporal trend inside and outside the cages, peaking at
the same dates, with the relevant exceptions of the MOTUs
assigned to Salmo salar,Oithona similis,Cyanea capillata and the
ploimid rotifer, which peak within the cages but are detected at
low abundances in the outer environment (Figure 7). These
results are consistent with the indicator species analysis at
MOTU level for the environmental type, which found that
those species were specifically associated to the cage
environment (Figure 6B).
Shared molecular operational taxonomic
units between cage and outside
environments through time
The Upset plot shows the number of shared MOTUs between
the environment inside and outside the cages for each sampling
date, as well as the total number of shared MOTUs for all the
samples collected inside cages and for all the samples collected
outside them through all sampling dates (Supplementary Figure
S2). The results show that the number of shared MOTUs increase
with time, being the last sampling date (7th November), the one
with the highest number (671) of shared MOTUs between the
cages and the outside environment. On the other hand, the initial
sampling dates had fewer MOTUs in common, with values
ranging from 261 to 387. Only 129 and 100 MOTUs were
consistently found in all sampling dates inside and outside the
cages, respectively (Supplementary Figure S2).
Discussion
Differentiated communities between
inside and outside the cages
In this study, we have assessed the eukaryotic communities
inside and outside salmonid aquaculture cages through a period
of 5 months, using eDNA metabarcoding of water samples.
Results show that the communities inside the cages
significantly differ from the outside environment at distances
FIGURE 5
Indicator MOTUs (p-value <0.05) of each sampling date. Size
of the circles correspond to the relative abundances of each
MOTU in each sample type.
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of the order of 200 m, having eukaryotic communities with
higher alpha diversity values and more indicator species
associated with them. That is likely explained by the existence
of artificial structures, such as the salmon cages, which allow for
the settlement of new species. This colonization of organisms on
submerged surfaces is known as biofouling, and it has several
negative effects on farming equipment, water quality and fish
health (Braithwaite and McEvoy, 2004;de Nys and Guenther,
2009;Guenther et al., 2010). Published information on fouling
community in the Norwegian aquaculture industry is scarce and
it has mostly been analyzed by visual census (Kvenseth, 1996;
Guenther et al., 2009,2010). Many of these fouling species have a
rapid growth and present specific forms of attachment to the net
which escape the naked-eye, hindering its complete removal
during in situ washing of the nets (Guenther et al., 2009).
While molecular methods are far from being a solution to the
problem, they can offer a rapid and accurate assessment of the
fouling communities, making it easier to identify the most
problematic groups so that specific treatments can be applied.
It is important to note that in the present study we did not
FIGURE 6
Indicator (A) phyla (p-value <0.05) and (B) MOTUs (p-value <0.05) of Cage (colored in yellow) or Outside environment (colored in Blue). Size of
the circles correspond to the relative abundances of each phylum (A) or MOTU (B) in each sample type.
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specifically target the biofouling community, but rather the
eukaryotic communities that are associated with the whole
cage environment, giving a broader overview of the
community changes. Among the indicator species that we
found associated with the cages we can differentiate benthic
and pelagic species. It is likely that the benthic species found,
such as certain brown algae species, constitute the fouling
community, while pelagic species represent other organisms
associatedwiththecagesbyothermeans.Thisisthecase,for
example, for the saithe (Pollachius virens),which swims close
to the facilities and are common visitors and inhabitants
inside the salmon cages (Otterå and Skilbrei, 2014). Species
of rotifers and copepods were also found specifically
associated with the cages, which may represent part of the
salmon diet, although specific analysis of salmon feed
components is required to confirm this hypothesis. Another
species of interest is the lion’s mane jellyfish. It is likely that its
polyp phase (scyphistoma) is associated with the cage
structure and the peak in its eDNA is detected when
strobilation occurs. In fact, jellyfish blooms have been
widely reported in aquaculture facilities (Bosch-Belmar
et al., 2017,2019), causing significant gill damage to the
Atlantic salmon (Baxter et al., 2011) and therefore, its
monitoring is of great importance for preventing significant
losses for aquaculture. Pelagia noctiluca and Aurelia aurita
have been identified as the main agents causing mass fish
deaths at salmon facilities in northern Europe (Doyle et al.,
2008;Marcos-López et al., 2016). Our findings suggest that
Cyanea capillata could also represent a threat for the
aquaculture industry in Northern Norway.
FIGURE 7
Temporal dynamics of the most abundant MOTUs (relab >1%) in the cage (yellow) and outside (blue) environment.
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In our indicator species analysis for dates, we also find
relevant species that peak at specific times but without
significant differences between the cages and the outside
environment. Potential fouling species were found to have
specific temporal peaks, such as the mussel Modiolus modiolus
in July. Among those, it is important to note the detection of
salmon lice, a major threat for the health of Atlantic salmon (Pike
and Wadsworth, 1999). Lice counting and anti-lice treatments,
such as the use of chemical bathing, cleaner fish, or mechanical
removal, are routinely performed in the salmon facilities
(Overton et al., 2019). Available data for salmon lice counts in
our sampling facility show that it peaks in October (https://www.
barentswatch.no/fiskehelse/fishhealthogram/10726/2017/44).
However, our eDNA results show a significant abundance of
salmon lice in July, coincident with the rise in the sea water
temperature. This fact suggests that eDNA can be an efficient
method to detect this parasite in the surface waters before it peaks
inside the aquaculture cages later in the season, allowing for a
better application of specific preventive anti-lice treatments at the
right time. The higher alpha diversity values inside the cages can
also be explained by the intermediate disturbance hypothesis,
which states that species diversity is maximized at intermediate
levels of disturbance because species that thrive at both early and
late successional stages can coexist (Dial and Roughgarden,
1998). In that regard, the placement of aquaculture cages
represents a disturbance to the natural ecosystem which allow
for the settlement of r-selected species, such as seaweeds, which
quickly colonize and dominate new environments. At the initial
phases of disturbance, K-selected species that dominate the stable
environments can thrive with the new colonizing r-selected species
and thus, diversity is maximized. However, it is not only the
colonization of new structures by itself that might imply the
presence of more species, but also the changes in the primary
productivity in the area, the rapid transfer of nutrients up the
food web, or the attraction of wild fish communities to the
floating structures (Holmer et al., 2008). Although during our
5 months sampling period, diversity inside the cages was always
higher than outside the cages, a longer-term study following the
whole production cycle is needed to assess if continued disturbance
to the ecosystem leads to a final decrease in alpha diversity values.
Several studies have reported decreases in alpha diversity metrics for
certain taxonomic groups under anthropogenic impact (Pawlowski
et al., 2014,2016;Stoeck et al., 2018a,2018b;Laroche et al., 2018),
although increases in bacterial diversity and metabolic activity have
also been detected in marine sediments (Galand et al., 2016;Pérez-
Valera et al., 2017). Therefore, assumptions based on alpha diversity
metrics should be considered carefully (Cordier et al., 2021), as
higher diversity does not always imply a healthier ecosystem (Shade,
2017) and the introduction of invasive species is likely to occur with
the placement of new structures into the natural environment.
Interestingly, the effect of the salmonid cages on the surface
eukaryotic communities is highly localized, with the outside
communities located North and South of the facility having
highly similar eukaryotic compositions and differentiated from
those inside the salmon cages. Such fine-scale differences in
eukaryotic composition are likely to occur, as demonstrated
by Antich et al. (2021) that found that only 7.5% of benthic
MOTUs were retrieved in the water immediately adjacent to the
benthos and that the number of shared MOTUs between water
and benthos decreased as they moved apart from the benthic
habitat (20 m.). In that sense, it is expectable to observe such
differences in the eukaryotic communities only 200 m outside the
aquaculture cages. However, it is relevant to point out that eDNA
from the farm (i.e., trace levels of salmon DNA and other cage
indicator species) can be still detected at these outer points, albeit
at lower abundances.
A proper understanding on water movements is needed
to understand the dynamics of eDNA around aquaculture
facilitiesandshouldbeconsideredinfuturestudiesassessing
community metrics in the water column. Recent papers on
hydrodynamic interactions on net panel and aquaculture fish
cages (Klebert et al., 2013;Gansel et al., 2014)providesome
insights into the flow dispersion around salmon facilities,
being biological effects of fish, fish movements, and fouling,
the major forces modulating the natural currents in these
areas, influencing the redistribution of waste and nutrient-
depleted water (Klebertetal.,2013). Indeed, the effect of fish
swimming inside the cages generates currents that redirect
the water flow (Chacon-Torres et al., 1988;Johansson et al.,
2007;Klebert et al., 2013)withfish biomass, swimming
behavior and schooling pattern of fish having differential
effects on flow direction (Gansel et al., 2014). In the results of
Gansel et al. (2014) the main fish biomass was found between
2 and 5 m depth, where fish were circling in the cage
producing a rotational flow. Future studies that aim at the
implementation of eDNA methods for aquaculture
monitoring need a clear understanding of the
hydrodynamics around salmon cages, considering different
sampling depths and changes in the fish biomass, among
others. Moreover, the time frame in which these studies are
performed is also relevant, as we observe a homogenization
oftheoutsideandcageeukaryoticcommunitiesasthetime
goes on. At the initial sampling points, closer to the
placement of the cages and the introduction of fish,
eukaryotic composition between the two sampled
environments is highly differentiated with low number of
shared MOTUs between them. This can be explained by the
creation of a completely new environment that produces a
peak in diversity with the introduction of new species
associated with the cages. Over time, communities inside
and outside the cages tend to homogenize, which can indicate
a localized effect of the farm to the outer environment.
However, the temperature drop towards winter months
can also explain the higher similarity between different
environments as homogenization of eukaryotic
communities occurs during winter months. A longer study
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following a whole annual cycle would be needed to confirm
whether homogenization of both environments is occurring
or not.
General community composition and
temporal variation
Although eukaryotic communities outside the cages
significantly differ from those inside the cages, the temporal
environmental patterns were the main driver of community
composition for both environments, which have the same
succession of the main phyla and similar diversity peaks
through time. Dinoflagellates were the most abundant phylum
from July to September and they were replaced by Viridiplantae
from October to November. Dinoflagellates were also found to be
the most abundant and diverse group in a study on protist
diversity and seasonal dynamics in the Southern Norwegian
coastal waters (Gran-Stadniczeñko et al., 2019) and they are
considered to be one of the most important primary producers in
the ocean (Not et al., 2012). Although the two most abundant
dinoflagellate MOTUs could not be identified at species level due
to gaps in COI reference databases, Heterocapsa rotundata was
the third most abundant dinoflagellate MOTU. This species is a
mixotrophic dinoflagellate that can ingest picoplankton and it is
known to form large blooms in temperate estuaries during wet
winters (Millette et al., 2017). It has been reported in a range of
environments all over the word and tends to dominate the
phytoplankton community for part of the year in some areas
such as South Korea (Seong et al., 2006), Australia (Balzano et al.,
2015), and Chesapeake Bay, United States (Millette et al., 2015).
It is hypothesized that the mixotrophy of H. rotundata can give to
this species an advantage over other phytoplankton species
(Millette et al., 2017), allowing it to bloom under certain
conditions. Among Chlorophyta (Viridiplantae), Bathycoccus
prasinos, and the two picoflagellates Micromonas commoda
and Micromonas pusilla were the most abundant MOTUs.
These two latter species have recently been separated (Simon
et al., 2017) and M. pusilla was shown to dominate the eukaryotic
picoplankton in North Atlantic coastal and Arctic waters (Not
et al., 2004). Similarly to our findings, M. commoda dominated
the community in Oslofjorden (Gran-Stadniczeñko et al., 2019),
and it is the second most dominant Chlorophyta species in Uløy
bay. The third most abundant phylum was Arthropoda, with the
small copepod Oithona similis being the most abundant MOTU.
This result is consistent with the peak in abundance of this small
copepod from June to December found in a Sub-Arctic fjord
(Coguiec et al., 2021), which coincides with our sampling period.
According to these authors, the autumn bloom (starting mid-
September in Sub-Arctic waters) coincided with highest copepod
diversity but also with a steep decline in zooplankton biomass
driven by the decrease in abundance of the large Calanus species,
which created a free niche in upper water layers that benefit small
copepods, such as Oithona similis (Coguiec et al., 2021). Other
Atlantic/boreal copepod species such as C. helgolandicus,P.
elongatus,T. longicornis, and P. elongatus, were also detected
in Uløy bay and found to be restricted to the Autumn bloom in a
Sub-Arctic fjord, when strong south-west winds prevail in
Tromsø area, forcing water of Atlantic origin into the fjord
system (Coguiec et al., 2021). Finally, Haptophyta was the
fourth most abundant phylum, with Emiliania huxleyi being
the most abundant MOTU. This species was also found to be the
most abundant haptophyte OTU in the Skagerrak strait,
Southern Norway (Egge et al., 2015;Gran-Stadniczeñko et al.,
2019).
Temporal variation in eukaryotic communities in terms of
taxonomic composition and alpha diversity was also detected in
our study, with peaks of certain species at specific sampling dates.
Although communities inside the cages always presented higher
diversity values than the outside environment, they both followed
the same temporal fluctuations. It is well-known that plankton
communities in Arctic and sub-Arctic marine waters present
very strong seasonal changes in diversity and biomass due to the
strong seasonality in solar radiation, snow and ice melt, river run-
off and wind mixing, which produce stratification and mixing of
water masses that govern nutrient availability (Coguiec et al.,
2021). Year-round seasonal dynamics has been studied for
zooplankton communities in a sub-Arctic fjord (Coguiec
et al., 2021) and for protist diversity in Oslofjorden (Gran-
Stadniczeñko et al., 2019), combining both molecular and
morphological methods. Certain patterns found in those
studies corresponding to our sampling period are comparable
to the ones found in Uløy bay, such as the peak in diversity in
November (Gran-Stadniczeñko et al., 2019) or the peak of O.
similis in Autumn (Coguiec et al., 2021). Although our study did
not attempt to study annual seasonality due to the restricted time
frame, we did detect changes in community composition and
dominant MOTUs through the sampling period that are
equivalent to the spring-summer period (May–August) and
the autumn-winter period (September–December) described
for the zooplankton communities in a sub-Arctic fjord
(Coguiec et al., 2021).
Use of environmental DNA metabarcoding
for eukaryotic monitoring in the water
column
In the present study we have analyzed eukaryotic
communities using eDNA metabarcoding of the COI
fragment present in the surface waters inside and in the
vicinity of salmonid aquaculture cages. To our knowledge,
this is the first time that planktonic communities associated
with aquaculture have been assessed by molecular methods,
which provides a new perspective for aquaculture monitoring.
Up to date, most of the molecular studies trying to monitor the
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Turon et al. 10.3389/fgene.2022.957251

impacts of aquaculture have focused on sediment samples,
trying to standardize the traditionally used AMBI values
(Aylagas et al., 2014,2016) or generating new bioindicators
(Cordier et al., 2017;Armstrong and Verhoeven, 2020;Frühe
et al., 2020;He et al., 2020). However, molecular studies
focusing on the planktonic communities have been largely
neglected and the need for new protocols assessing the degree
of change imposed by aquaculture on water quality and
plankton dynamics have already been emphasized (Holmer
et al., 2008). Our results show that eDNA methods can detect
not only possible pathogens, but also members of the fouling
communities and differential community composition
between the cages and the outside environment. In the
present paper we have used two of the proposed novel
approaches to monitor ecosystems in Cordier et al. (2021);
the taxonomy-free discovery of new bioindicators and
structural community metrics. For the former one, we have
used the indicator value approach to detect groups of MOTUs
specifically associated with the cage environment, which
significantly differ from the outside environment. Although
promising, there are still several limitations to this approach,
such as the gaps in the reference database that prevent the
proper taxonomic identification of several indicator MOTUs,
or the impossibility to assess the life stage of the indicator
organisms. Specific assessment of species that may be
identified by eDNA but are present in salmon feed is also
an important step for future studies utilising this approach. In
terms of community metrics, we have assessed differences in
alpha diversity between the cages and the outside
environment, which give a hint at the possible effects of
aquaculture impacts. However, variation of alpha diversity
alone is insufficient as a widely applicable indicator of
disturbance (Cordier et al., 2021) and longer studies to
evaluate diversity patterns under continued disturbance and
detailed knowledge on the natural variability in the area are
needed to extract significant conclusions. Finally, more
detailed knowledge regarding the hydrodynamics around
the salmon cages is of crucial importance to understand the
eDNA dispersion flow in the water column and reveal the
extent of the aquaculture impacts. Moreover, we acknowledge
that our study only evaluated the eukaryotic communities in
the surface waters at the close proximity of the salmon farm,
butmoreeffectsarelikelytobedetectedwhenaddressing
sediment communities or planktonic communities from
further points.
Conclusion
Analysis of eukaryotic communities inside and outside
salmonid aquaculture cages through time revealed significant
differences between both environments, with the cages having
higher diversity values and more specific species associated
with them. The placement of the cages creates structure that
allows for the settlement of certain species that otherwise
would not be found in the water column, explaining the higher
diversity found within the salmon facilities. Interestingly, the
effect of the cages on the eukaryotic communities of the
surface waters surrounding the facilities was highly
localized, with the communities located North and South of
the cages having the same eukaryotic composition and being
differentiated from the communities inside the cages. These
results suggest that small-scale spatial changes in eukaryotic
communities can be revealed by eDNA metabarcoding and
provide additional rationale for the use of this method in
impact assessment. Overall, the temporal pattern was the
main driver of eukaryotic community structure, regardless
of the environment studied (inside or outside the cages), with
significant differences in alpha and beta diversity at given
sampling times.
Data availability statement
The raw sequence reads for this study can be found in the
Sequence Read Archive (SRA-NCBI) repository, Bioproject
number: PRJNA839741.
Author contributions
KP conceived and funded the study, MN, MT, OW, GG, and
KP designed the study, MN and KP conducted the fieldwork,
MN, OW, and KP did the labwork, MT and OW did the
bioinformatic analyses, MT did the statistical analyses with
support from MN, GG, OW, and KP. MT wrote the
manuscript draft with contributions from all co-authors and
all authors contributed to the revisions and approved the
submitted version.
Funding
UiT –a strategic eDNA grant (KP) and RFFNORD (project
number: 285272) (KP). UiT is thanked for financial support
for GG.
Acknowledgments
We thank Arnøy Laks A/S for providing access and allowing
us to sample at their facility in Uløybukt. We also thank Niklas
Högstedt and Geir Nygaard for assistance during sampling. KP
thank UiT for a strategic eDNA grant and RFFNORD for the
grant (project number: 285272) that financed this study. UiT is
thanked for financial support for GG.
Frontiers in Genetics frontiersin.org14
Turon et al. 10.3389/fgene.2022.957251

Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fgene.
2022.957251/full#supplementary-material
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