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\begin{document}

\title{Biotic signals associated with benthic impacts of salmon farms from eDNA
metabarcoding of sediments}



\author[1]{Xiaoping He}%
\author[1]{Scott Gilmore}%
\author[1]{Terri  Sutherland}%
\author[2]{Mehrdad Hajibabaei}%
\author[1]{Kristina Miller}%
\author[1]{Kristen Westfall}%
\author[3]{Jan Pawlowski}%
\author[1]{Cathryn Abbott}%
\affil[1]{Fisheries and Oceans Canada}%
\affil[2]{University of Guelph Biodiversity Institute of Ontario}%
\affil[3]{University of Geneva}%


\vspace{-1em}



  
  \date{March 14, 2025}


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\selectlanguage{english}
\begin{abstract}
Environmental DNA (eDNA) metabarcoding can rapidly characterize the
composition and diversity of benthic communities. As such, it has high
potential utility for routine environmental assessments of benthic
impacts of marine finfish farming. In this study, 126 sediment grab
samples from 42 stations were collected along an organic enrichment
gradient at six salmon farms in British Columbia, Canada, and benthic
biotic community changes were assessed by both eDNA metabarcoding of
metazoans and macrofaunal polychaete surveys. The latter was done by
analyzing 11,466 individuals using a combination of morpho-taxonomy and
DNA barcoding. Study objectives were to: (1) compare biotic signals
associated with benthic impacts of salmon farming in the two data types;
and (2) identify potential eDNA indicators to facilitate eDNA-based
monitoring in Canada. Across both data types, alpha diversity parameters
were reduced in sediments near fish cage edge and were negatively
correlated with pore-water sulphide concentration. Presence/absence of
known indicator taxon Capitella generally agreed well between the two
methods despite that they differed in both the volume of sediment
sampled and the molecular marker used. In eDNA data, there was a strong
negative correlation between Nematoda OTU richness and pore-water
sulphide concentration, and multiple approaches were used to identify
OTUs related to organic enrichment statuses. We demonstrate that eDNA
metabarcoding generates biotic signals that could be leveraged for
environmental assessment of benthic impacts of fish farms in multiple
ways: both alpha diversity and Nematoda OTU richness could be used to
assess the spatial extent of impact, and OTUs related to organic
enrichment could be used to develop a local biotic index.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Benthic macrofauna are abundant in marine and freshwater ecosystems and
play vital roles in nutrient cycling and secondary production
(Snelgrove, 1997, 1998). For example, macrofauna decompose organic
inputs by feeding directly on organic matter, and they promote
metabolism of pollutants by bacteria through biological transport and by
increasing the oxygen supply in sediments (Pant, Negi, \& Kumar, 2017;
Sanz-L\selectlanguage{ngerman}ázaro \& Marín, 2011; Snelgrove, 1998). Because macrofauna are
important to ecosystem processes, can be identified by microscopy, and
have differing tolerances to enrichment gradients, they have been used
widely to assess environmental impacts associated with human activities,
including marine finfish farming (Keeley, Forrest, Crawford, \& Macleod,
2012; Sanz-Lázaro \& Marín, 2011), oil-drilling (Denoyelle, Jorissen,
Martin, Galgani, \& Mine, 2010), and mining (Josefson, Hansen, Asmund,
\& Johansen, 2008). Macrofauna assessments have also been used to
monitor environmental recovery from these activities (Daan, Booij,
Mulder, \& Weerlee, 1996; Machado et al., 2018; Zhulay, Reiss, \& Reiss,
2015).

To date, characterization of macrofauna communities has been done by
traditional morpho-taxonomic identification, which is very time- and
labour-consuming and relies heavily on highly specialized taxonomists.
These factors intrinsically limit the speed and magnitude of sample
throughput that can be achieved by traditional taxonomic approaches
(Bourlat et al., 2013; Danovaro et al., 2016; Goodwin et al., 2017; Ji
et al., 2013; Pawlowski et al., 2018). Furthermore, individuals that are
damaged during mechanical collection and sieving processes as well as
with poorly described life stages may be misidentified by visual
identification (Danovaro et al., 2016; Goodwin et al., 2017).
Exacerbating these challenges is that species that are difficult to
distinguish by morpho-taxonomy are commonly present in these communities
(Knowlton, 1993), and if improperly identified could lead to
underestimation of biodiversity and/or misleading interpretations about
indicator species used for environmental assessment (Dean, 2008; Nygren,
2014).

DNA barcoding alleviates the reliance on morphological characters for
taxonomic identification and can offer several benefits including higher
taxonomic resolution of closely-related or cryptic species and the
ability to verify morphologically-identified taxa (Nygren, 2014;
Westheide \& Schmidt, 2003; Witt, Threloff, \& Hebert, 2006). It can
also be used to characterize the taxonomic composition of opportunistic
indicator complexes (Silva et al., 2017). However, it is still
relatively time- and cost-intensive due to the one-by-one nature of the
workflow: samples need to be manually sorted into individual specimens
and the cost of Sanger DNA sequencing.

Environmental DNA (eDNA) metabarcoding is a high-throughput, rapid,
cost-effective, and relatively new method of characterizing species
composition in complex environmental samples (Shokralla, Spall, Gibson,
\& Hajibabaei, 2012; Taberlet, Bonin, Zinger, \& Coissac, 2018;
Taberlet, Coissac, Hajibabaei, \& Rieseberg, 2012). It has been proven
to be useful for detecting invasive (Klymus, Marshall, \& Stepien, 2017)
and rare species (Pikitch, 2018), assessing biodiversity for
conservation (Lacoursière\selectlanguage{english}-Roussel et al., 2018; Stat et al., 2019), and
monitoring environmental health status (Laroche et al., 2016; Pawlowski,
Esling, Lejzerowicz, Cedhagen, \& Wilding, 2014). Environmental DNA
metabarcoding has been used to characterize benthic macrofauna responses
to human activities associated with marine finfish aquaculture
(Lejzerowicz et al., 2015), offshore oil-drilling (Lanzen, Lekang,
Jonassen, Thompson, \& Troedsson, 2016), oil spills (Xie et al., 2018),
and chemical contamination (Xie et al., 2017). It has the potential to
supplement traditional morpho-taxonomic analysis of macrofauna from
sediments for environmental assessment and biomonitoring (Aylagas,
Borja, \& Rodriguez-Ezpeleta, 2014; Pawlowski et al., 2018), and to
increase the spatial and temporal scales at which biomonitoring can
realistically be done (Gibson et al., 2015). Available studies comparing
eDNA metabarcoding with traditional morpho-taxonomy have only used
visual morpho-taxonomy of macrofaunal individuals without the support of
DNA barcoding for resolving morphologically similar (or cryptic)
species. This makes it difficult to compare macrofauna inventories among
studies, particularly for taxonomic groups prone to cryptic speciation
including Capitellidae, a family that is especially relevant to benthic
biomonitoring. In the absence of an objective means of classifying
species based on morphology, there will inevitably be variation among
studies as to how taxa were classified when morpho-taxonomy alone is
used.

Benthic organic enrichment is a significant environmental issue
associated with marine finfish aquaculture (Forrest et al., 2007;
Wilson, Magill, \& Black, 2009). It can influence faunal diversity and
abundance, causing population shifts towards opportunistic taxa (Pearson
\& Rosenberg, 1978) thereby eliminating a diverse community and the
functions originally provided by it within the ecosystem (Snelgrove,
1997, 1998). As such, benthic impacts of finfish farming are subject to
regulatory control and routine monitoring in many countries (Wilson et
al., 2009). In New Zealand, Norway, and Scotland, biological indices
based on morpho-taxonomic macrofauna data are implemented for regulatory
purposes (Keeley et al., 2012; Maurer, Nguyen, Robertson, \& Gerlinger,
1999). To date, a regulatory biological index for evaluating benthic
impacts has not been implemented in Canada. Geochemical indicators (e.g.
sediment pore-water sulphide concentration) are used for routine
regulatory benthic soft sediment monitoring (Hargrave, 2010; Hargrave,
Holmer, \& Newcombe, 2008), and macrofauna are analyzed only under
certain benthic conditions (AAR, 2016; Wildish, Pohle, Hargrave,
Sutherland, \& Anderson, 2005). The addition of a biotic measure to
routine assessments of soft sediments to complement information obtained
by geochemistry would advance the state of the science of benthic
monitoring in Canada, providing regulators additional information for
decision-making. This could come in the form of eDNA-based data, either
by metabarcoding or assays targeting particular taxa once eDNA
indicators are identified.

Here we advance existing knowledge on the responses of benthic metazoans
to organic enrichment with the goal of testing the potential application
of eDNA metabarcoding to benthic impact assessments of fish farms. At
seven stations along a distance gradient at each of six salmon farms in
British Columbia, Canada, we collected sediment samples for eDNA
metabarcoding analysis of metazoans, geochemistry measurements, and
morpho-taxonomy combined with DNA barcoding analyses. For
morpho-taxonomy and DNA barcoding, we focused on Polychaeta as this was
the most abundant group of species observed and taxa therein are known
to be good indicators of benthic impacts (Dean, 2008; Tomassetti \&
Porrello, 2005), especially the \emph{Capitella capitata}complex (Dean,
2008; Wildish et al., 2005). The specific objectives of this study were
to: (1) compare biotic signals associated with benthic impacts of salmon
farming between morpho-taxonomic polychaete and eDNA metabarcoding data;
and (2) identify potential eDNA indicators that could form the basis for
development of eDNA-based monitoring methods in Canada. We analyzed
biotic signals associated with benthic impacts of fish farms in eDNA
metabarcoding data and macrofaunal polychaetes data, and used multiple
methods to analyze the relationship between operational taxonomic units
(OTUs) and organic enrichment.

\textbf{Materials and Methods}

\textbf{Sediment collection}

Sediment samples were collected in 2016 at six marine finfish farms
located in BC, Canada: Baxter Islet (BI, May 10), Bedwell Sound (BS,
April 13), Midsummer Island (MI, June 8), Mussel Rock (MR, April 12),
Plover Point (PP, May 11), and Venture Point (VP, June 14). Samples were
collected at seven stations along a transect in a dominant current
direction initiated from the fish cage edge. The seven stations were
intended to be 0 m, 15 m, 30 m, 60 m, 90 m, 125 m, and 500 m from cage
edge. Samples were collected from those distances from cage edge where
possible; the exception was the reference station at MI, which was 2260
m from cage edge as it was the closest reference station found with soft
(and thus `grab-able') sediments. Detailed site information was provided
in Table S1.

A total of three sediment grab samples were collected at each station
using a Van Veen Grab Sampler (0.1 m\textsuperscript{2}, 24L)
(Blomqvist, 1991; Gage \& Bett, 2005). Pore-water sulphide concentration
and redox potential for each grab were measured prior to the collection
of other environmental variables to avoid oxygen injection into the
sediment. Sediments were classified into four different sulphide
concentration categories according to the Canadian Aquaculture
Activities Regulations (AAR), as follows: anoxic (\textgreater{} 4,500
\selectlanguage{greek}µ\selectlanguage{english}M), hypoxic (1,300 -- 4,500 \selectlanguage{greek}µ\selectlanguage{english}M), oxic B (700 -- 1,300 \selectlanguage{greek}µ\selectlanguage{english}M), and oxic A
(\textless{} 700 \selectlanguage{greek}µ\selectlanguage{english}M) (AAR, 2016). How each station was sampled is
presented schematically in Figure S1 and summarized here. One grab
sample was devoted entirely to macrofauna analysis (details below); each
of the other two grabs were used to collect samples for geochemical
analyses (porosity and organic content) and three eDNA samples, which
were collected from the top 2 cm of sediment. The samples for
geochemical analyses were collected using spatulas and eDNA samples were
collected using modified 60 cc syringe-cores according to the sediment
and meiofaunal methods detailed in Sutherland \emph{et al} . (2007).
Pore-water sulphide concentration, redox potential, porosity and organic
content were measured following protocols used in Sutherland \emph{et
al} . (2007). To avoid cross-contamination during sample collection, the
grab sampler was rinsed in salt water and visually inspected for
cleanliness between grabs, samples were collected from the center of the
grab only, and syringes were cleaned using diluted bleach solution
(final concentration of 3\% sodium hypochlorite) between farms. In
summary, total numbers of the various sample types collected from a
total of 42 stations were: 42 benthic macrofauna samples; 84 samples
measured for porosity and organic content; 126 samples analyzed for
sediment pore-water sulphide concentration and redox potential; and 252
samples analyzed for eDNA metabarcoding.

\textbf{Collection and morphological sorting of macrofauna}

The macrofauna grab collected at each station was transferred to a
sealed plastic bin and transported to an onshore sieving station. A
portion of each sediment sample was transferred to a 1 mm mesh sieve
table and washed gently with running water. Once the sediment was washed
through the sieve, macrofauna were gently removed with tweezers and
transferred to a 500 ml container containing 95\% ethanol. This process
was carried out until the entire grab sample from each station was
sieved. In cases where excess debris (e.g. 'shellhash' or rocks)
remained on the sieve that might influence the search for macrofauna,
this debris was collected into containers with ethanol for further
sorting in the lab.

Macrofauna specimens from each grab were sorted into morphospecies. We
only analyzed vermiform macrofauna (e.g. Polychaeta), which comprised
the majority of total macrofauna abundance across the aquaculture sites.
All individuals of each morphospecies from each grab were separated for
counting and downstream DNA barcoding. This was done by one person to
maximize consistency. Morphospecies were photographed and defined when
first encountered to allow recognition across all farms and grabs.
Morphospecies were defined with the aid of taxonomic references (Banse
\& Hobson, 1974; Dutch et al., 2014; Hobson \& Banse, 1981) and/or were
identified by Leslie Harris from the Los Angeles County Museum via
specimen images uploaded to the iNaturalist platform
(\emph{https://www.inaturalist.org/}). After initial sorting of all
stations, each morphospecies was re-examined across all farms and
stations to confirm consistency of morphospecies assignment. Generally,
one or two specimens from each morphospecies at each station were picked
for DNA barcoding; however, in cases where morphospecies delineation was
less clear, additional specimens were picked for genetic confirmation.

\textbf{DNA barcoding analysis of macrofauna}

A total of 726 individuals were photographed. Primers amplifying
the\emph{cytochrome~c~oxidase I} (\emph{COI} ) gene were used for DNA
barcoding as this is the standard DNA barcoding marker (Hebert,
Cywinska, Ball, \& deWaard, 2003); it performs very well for species
level discrimination and has a well-populated reference database (the
Barcode of Life Data (BOLD) System) (Ratnasingham \& Hebert, 2007). A
piece of the tail-end was removed for DNA extraction, and the remaining
sample was placed in a 1.5 mL tube or larger container as needed. DNA
was extracted using DNeasy 96 Blood \& Tissue Kits (Qiagen). The COI
C\_VF1LFt1 and C\_VR1LRt1 primers were tried first (Ivanova, Zemlak,
Hanner, \& Hebert, 2007); samples that failed to amplify or sequence
with these primers were amplified with polyLCO and polyHCO primers
(Carr, Hardy, Brown, Macdonald, \& Hebert, 2011).

To ensure polychaete taxonomic data generated here can be compared to
other studies across spatial scales and over time, which is difficult
with complex taxonomies, the BOLD Barcode Index Number (BIN) system was
adopted to classify barcoded polychaetes (Ratnasingham \& Hebert, 2013).
When a barcode was too short for automatic inclusion into a BIN by BOLD,
it was manually applied to the appropriate BIN via the identification
portal of BOLD based on sequence similarity. There were sixteen
instances where one morphospecies produced sequences that fell into
multiple BINs. When this occurred, the sequenced specimens were examined
and then all individuals for that morphospecies were re-examined to
determine if they could be appropriately assigned to the identified
BINs. There were six occasions where there were 2-5 genetic groups
revealed by DNA barcoding that were not visually distinguishable upon
re-inspection of specimens, such that it was not possible to assign them
confidently into a BIN. In these cases, all specimens were 'lumped' into
what we refer to as a 'combined BIN' representing that morphospecies. In
a few instances there were individual specimens for which no sequence
was obtained but for which a taxonomic name could be confidently
assigned using traditional morpho-taxonomic methods. Two alpha diversity
parameters describing species richness, Shannon and Simpson indices,
were calculated for the macrofauna data using the\emph{vegan} R package
(Oksanen et al., 2017).

\textbf{eDNA metabarcoding}

Environmental DNA was extracted from approximate 0.25 g of sediment
using the DNeasy PowerSoil Kit (QIAGEN), with one DNA extraction control
for every 11 sediment samples. For metabarcoding, the V1 and V2 regions
of the \emph{18S rRNA} gene were amplified using primers (F04mod:
GCTTGWCTCAAAGATTAAGCC; R22mod: CCTGCTGCCTTCCTTDGA) (Blaxter et al.,
1998). We used this 18S rRNA marker rather than a COI marker for eDNA
metabarcoding because: (1) this marker has been used successfully to
characterize benthic metazoans (e.g. Fonseca et al., 2010; Sinniger et
al., 2016); and (2) it amplifies eukaryotes only, whereas amplicon
sequencing targeting eukaryotes using COI markers typically returns a
high percentage of off-target bacterial reads due to non-specific
amplification (Collins et al., 2019; Weigand \& Macher, 2018). To
multiplex, each synthesized primer starts with two or three Ns, followed
by an eight-base tag and the gene-specific primer sequences (He,
Sutherland, Pawlowski, \& Abbott, 2019). PCR protocol and program
followed He et al. (submitted). Three PCR replicates were used to
amplify each eDNA sample (using the same tagged primers) and replicates
were combined after PCR. PCR products from 21 eDNA samples, one DNA
extraction control and one PCR negative control were combined into a
mixture that was then purified using Agencourt AMPure XP beads (Beckman
Coulter) and quantified using SYBR Green I (Thermo Fisher Scientific)
with a Tecan Infinite M200PRO microplate reader. In total, 12 PCR
product mixtures were made. Then, 1 \selectlanguage{greek}µ\selectlanguage{english}g of purified PCR mixture was used
for library preparation using the TruSeq DNA PCR-Free Library Prep Kit
(Illumina). After library preparation and quantification, four libraries
were pooled for sequencing using one MiSeq Reagent Kit v3 (600-cycles)
(Illumina). In total, three MiSeq Reagent Kit v3 (600-cycles) were used
to sequence the 252 sediment samples.

\textbf{Bioinformatics and statistical analyses}

Bioinformatics analyses followed the pipeline used in He \emph{et al} .
(2019). Briefly, AdapterRemoval v2 was used to remove adapters in raw
sequences (Schubert, Lindgreen, \& Orlando, 2016). Sequences were
demultiplexed using the\emph{ngsfilter} command of obitools, and
sequences for each sample were split using the \emph{obisplit} command
(Boyer et al., 2016). Unpaired reads for each sample were removed using
modified fastqCombinePairedEnd.py
(\emph{https://github.com/enormandeau/Scripts/blob/master/fastqCombinePairedEnd.py}).
Paired sequences of each MiSeq run were imported into QIIME 2 separately
(Bolyen et al., 2018). Sequence quality control was conducted and the
amplicon sequence variant (ASV) tables were constructed using the DADA2
plugin of QIIME 2 (Callahan et al., 2016). Sequences from each MiSeq run
were processed separately by DADA2, and the three resulting ASV tables
were merged.

\emph{De novo} OTU clustering based on the ASV count table and sequences
was performed at 97\% identity using the \emph{vsearch} plugin of QIIME
2 (Rognes, Flouri, Nichols, Quince, \& Mah\selectlanguage{ngerman}é, 2016). The 97\% identity
was used as it has been shown to be a good cut-off for metazoans from a
mock community analysis (Fonseca et al., 2010). Although using ASVs can
retain more biological information (Callahan, McMurdie, \& Holmes,
2017), we did OTU clustering to: 1) allow a valid comparison between the
number of taxa detected using eDNA and morpho-taxonomy; 2) reduce the
number of features to make it easier to manually verify if they were
derived from benthic metazoan taxa or not; and 3) identify indicator
taxa at a higher level than the ASV level. In the raw OTU table, the
average number of reads in sediment samples was 168,454 while the
average number of reads in controls (DNA extraction and PCR negative
controls) was 23. Controls were then filtered out from the OTU table
using the \emph{feature-table} plugin. The OTUs with less than 10 reads
or present in only one sample were discarded to generate the
all-community OTU table (OTU\textsubscript{A}). Taxonomy assignment was
performed using the \emph{q2-feature-classifier} plugin of QIIME2 and
the Silva database v132 (Bokulich, Kaehler, et al., 2018; Quast et al.,
2013). Based on taxonomy assignment results (Table S2), non-metazoan
OTUs (i.e. not assigned to\emph{D\_3\_\_Metazoa (Animalia)} ) and two
OTUs (one Atlantic salmon OTU and one chicken OTU) that were considered
direct farm products were removed from the all-community OTU table to
generate the metazoan OTU table (OTU\textsubscript{M}). Further, OTUs
that were considered non-benthic metazoans (e.g. pelagic) (Table S3)
based on their role in oceanographic ecosystems (Sutherland, 1991;
Sutherland, Leonard, \& Taylor, 1992), including those identified in
Lanzén et al. (2016), were removed from the metazoan OTU table to
generate the benthic metazoan OTU table (OTU\textsubscript{BM}).
Downstream analyses used the OTU\textsubscript{BM} table unless stated
otherwise.

The feature sequences were used to generate a rooted phylogenetic tree
using the align-to-tree-mafft-fasttree plugin of QIIME2. For alpha
diversity analysis, rarefaction plotting was performed on the
OTU\textsubscript{BM} table (Figure S2). The OTU\textsubscript{BM} table
was then rarefied to 500 sequences per sample for estimation of two
alpha diversity richness parameters: Faith's Phylogenetic Diversity
(Faith's PD) (Faith, 1992) and Observed OTUs. Correlations between
various biotic parameters (i.e. alpha diversity, Nematoda and Polychaeta
OTU richness, relative abundance of \emph{Capitella capitata} ) and
pore-water sulphide concentrations were tested using Spearman's
correlation analyses as data transformation did not lead to normal
distributions.

Linear mixed effect models were used to independently describe the
relationships between distance from cage edge and two alpha diversity
parameters (Faith's PD and Observed OTUs) using the R
package\emph{lme4}(Bates, Mächler, Bolker, \& Walker, 2015). Distance
was set as a (continuous) fixed effect and farm was stated as a random
effect for each model using the\emph{lmer} function call
\emph{response\_variable \textasciitilde{} distance + (1\textbar{}farm)}
. Generalized linear mixed effect models based on Poisson distribution
were used to independently describe the relationships between distance
from cage edge and Nematoda OTU richness and Polychaeta OTU richness
using the package \emph{lme4} . Distance was set as a (continuous) fixed
effect and farm was stated as a random effect for each model using the
\emph{glmer} function call\emph{response\_variable \textasciitilde{}
distance + (1\textbar{}farm)} . Model fit assessed from QQ-plots of
residuals showed the data generally conformed to the assumed
distributions for all models, and only the Nematode OTU richness
deviated very slightly.

Three separate analyses were performed to identify OTUs associated with
benthic impacts of salmon aquaculture. First, a one-way ANOVA was
performed to analyze the difference in relative abundance of each OTU in
the OTU\textsubscript{BM} table among organic enrichment statuses using
the STAMP software, with \emph{P} values corrected using the
Benjamini-Hochberg (BH) method (Benjamini \& Hochberg, 1995; Parks,
Tyson, Hugenholtz, \& Beiko, 2014). Second, the OTU\textsubscript{BM}
table was transformed to a presence/absence table, and the Pearson's phi
coefficient of association was calculated to elucidate associations of
particular OTUs with sediments of a particular organic enrichment status
using the \emph{multipatt} function of the R package \emph{indicspecies}
with the \emph{func} set 'r.g', \emph{duleg}set 'TRUE' and \emph{nperm}
set '9999' (Cáceres \& Legendre, 2009). Again, these \emph{P} values
were corrected using the BH method. Third, supervised machine learning
was performed using the\emph{q2-sample-classifier} plugin of QIIME2
(Bokulich, Dillon, et al., 2018), with the \emph{random forest} method
and\emph{--p-n-estimators} set to 1000. For machine learning, the
OTU\textsubscript{BM} table and organic enrichment status based on
pore-water sulphide concentration for each sample were used to
investigate how accurately eDNA data could predict organic enrichment
status and the relative importance score of each OTU for this
prediction.

\textbf{Results}

\textbf{Morpho-taxonomy and DNA barcoding of Polychaeta}

A total of 11,466 macrofaunal individuals from 42 grabs across six farms
comprised 82 Polychaeta and three non-Polychaeta vermiform morphospecies
(Table S4). Combining morpho-taxonomy with BIN-based taxonomy derived
from DNA barcoding analyses lead to the identification of a total of 150
taxa plus 6 'combined BINs' (described earlier), all of which were
Polychaeta except five taxa. A total of 11,445 Polychaeta individuals
were counted, with 1279, 431, 3395, 763, 1029, and 4548 collected from
BI, BS, MI, MR, PP, and VP farms, respectively (Table 1). The number of
Polychaeta collected at each station varied from one (BS, 0 m) to 984
(VP, 30 m). All but two of the Polychaeta taxa sampled could be assigned
to at least the family level via barcode analysis, which determined
there were 32 Polychaeta families sampled. Numbers of individuals for
each Polychaeta family were summarized in Table S5. The most abundant
family was Capitellidae, with 7,929 individuals sampled (representing
69\% of all sampled polychaetes), 99\% of which (N = 7,843) were
assigned to the \emph{Capitella} genus. The second most abundant family
was Spionidae\emph{,} which had dramatically fewer individuals (N = 466;
4\% of total polychaetes sampled).

\textbf{Taxonomic composition of eDNA metabarcoding data}

There were 42.30 million eukaryote sequences in the
OTU\textsubscript{A}table, which clustered into 2,239 OTUs. The number
of eukaryotic reads per sample varied from 43,498 to 355,828, with an
average of 167,872. The most dominant taxon was Ochrophyta, represented
by 26.72 million reads and accounting for 63.2\% of total reads; the
majority of them (26.64 million) were assigned to diatoms (Diatomea).
Metazoa was the next most dominant taxon in the OTU\textsubscript{A}
table, with 12.57 million sequences accounting for 29.7\% of total reads
across samples. Within the OTU\textsubscript{A} table, there were 6,371
reads assigned to Atlantic salmon and 9,959 reads assigned to chicken (a
common ingredient in fish food). When chicken reads were detected, they
tended to be present at stations close to cage edge, with more reads
detected at cage edge (i.e. 0 m) than other stations (Table S6).
Atlantic salmon reads had a similar pattern at most farm sites.

Excluding non-metazoan reads and presumed farm contamination reads (i.e.
from salmon and chicken), there were 12.55 million metazoan reads in the
OTU\textsubscript{M} table, which clustered into 400 metazoan OTUs.
Metazoan sequences numbered 1.60, 1.51, 1.91, 1.17, 1.65, and 4.71
million at BI, BS, MI, MR, PP and VP farm sites, respectively, which
accounted for 20.5\%, 26.3\%, 30.6\%, 17.7\%, 16.8\%, 77.4\% of total
reads per farm, respectively (Table1). Most metazoan reads were
Calanoida (pelagic copepods), which had 9.11 million reads across
samples.

Benthic metazoans were represented by 3.42 million reads clustered into
367 OTUs (Table S7). The number of benthic metazoan sequences was
913,866, 163,707, 646,120, 253,574, 58,500, and 1,388,311 at BI, BS, MI,
MR, PP and VP, respectively. In the OTU\textsubscript{BM} table,
Nematoda was the most abundant phylum at BI, BS, MI and MR, accounting
for 30.9\%-62.5\% of reads, while Arthropoda and Annelida were most
abundant at PP and VP, respectively (Figure 1). At the order level, the
most abundant order of Polychaeta was Capitellida in both DNA barcoding
and eDNA metabarcoding data (Table S8).

\textbf{Alpha diversity}

For alpha diversity estimates based on macrofaunal polychaetes data and
eDNA benthic metazoan data, distant stations generally had higher alpha
diversity than stations close to cage edge for both datasets (Figure 2).
Alpha diversity richness parameters were negatively correlated with
pore-water sulphide concentration, with coefficients ranging from -0.62
to -0.48 (Figure 3, Figure S3). The LMM analyses showed positive linear
relationships between the two alpha diversity parameters generated from
eDNA data and distance from cage edge (Figure S4).

\textbf{Potential indicator taxa: Nematoda and Polychaeta}

There were 175 Nematoda OTUs and 38 Polychaeta OTUs in the
OTU\textsubscript{BM} table. Polychaeta are commonly used for
morpho-taxonomic analysis for benthic impacts of fish farming; however,
interestingly here we found a much stronger correlation between Nematoda
and pore-water sulphides than Polychaeta. The correlation between
Nematoda OTU richness and pore-water sulphide concentration was -0.86,
whereas that between Polychaeta OTU richness and pore-water sulphide
concentration was -0.38 (Figure 4). The GLMM analyses showed a positive
relationship between Nematoda OTU richness and distance from cage edge,
but no relationship was identified between Polychaeta OTU richness and
distance (Figure S4). Across all samples, Nematoda OTU richness
increased along distance from cage edge. Within each farm, the
correlation coefficients between Nematoda OTU richness and distance from
cage edge varied from 0.55 to 0.86 (Figure S5).

\emph{Capitella} polychaetes were found in both morphotaxonomic
macrofaunal data and eDNA metabarcoding data; they were present in 29/42
and 25/42 of sampled stations as revealed by macrofaunal and eDNA
metabarcoding analyses, respectively (Figure 5). Presence/absence
of\emph{Capitella} at each station of BS, MI and VP agreed between
macrofaunal and eDNA data. There was one station at MR (90 m)
where\emph{Capitella} was present in macrofaunal data but not eDNA data;
conversely, there was one station at BI (60 m) where \emph{Capitella}was
present in eDNA data but not macrofaunal data. Patterns at PP were
markedly different: macrofaunal analyses found \emph{Capitella} at six
stations, with a proportion in Polychaeta individuals ranging from
0.38\% to 82.5\% among stations, whereas \emph{Capitella} were present
only at two stations in eDNA data. \emph{Capitella} was not detected at
reference stations in either macrofaunal or eDNA datasets. The
correlation coefficient between pore-water sulphide concentration and
relative abundance of \emph{Capitella} in eDNA data was 0.28 (Figure 4)

\textbf{OTUs related to organic enrichment status}

ANOVA revealed six OTUs with significant differences in relative
abundances among organic enrichment statuses (Table 2). Of these, one
OTU showed highest relative abundance in oxic A sediments and one showed
highest relative abundance in oxic B sediments, and both of these two
OTUs were taxonomically assigned to a nematode species \emph{Sabatieria
punctata} . The other four OTUs showed higher relative abundances in
hypoxic and/or anoxic sediments.

Pearson's phi coefficient analyses revealed 35 OTUs correlated
significantly with organic enrichment status, with 26, eight, zero, and
one OTUs for oxic A, oxic B, hypoxic, and anoxic categories,
respectively (Table 3). Machine learning returned an overall prediction
accuracy for organic enrichment status based on pore-water sulphide
concentration of 0.77, with an accuracy of 0.91, 0.64, 0.40, and 0.85
for oxic A, oxic B, hypoxic, and anoxic samples, respectively. The three
OTUs with highest importance scores for prediction of organic enrichment
status were assigned to \emph{Sabatieria punctata} , \emph{Capitella
capitata} , and \emph{Gunnarea capensis} , respectively (Table 4); all
these three OTUs showed preference for oxic B sediments. The OTU (OTU
id: c94fc2ac2f0dc7c32b3decffc60bfc12) with highest importance score for
predicting organic enrichment status had highest relative abundance in
oxic B sediments.

\textbf{Discussion}

Benthic impacts of marine finfish farms have been studied for decades
(e.g. Hargrave, Duplisea, Pfeiffer, \& Wildish, 1993). To improve
benthic monitoring of fish farm impacts, novel approaches are being
developed including new biological indices (Keeley et al., 2012) and
more accurate measurements of pore-water sulphide concentration
(Cranford, Brager, \& Wong, 2017). Recently, eDNA metabarcoding based
methods (e.g. including machine learning and using metabarcoding data in
biotic indices) for benthic assessment have been proposed (Cordier et
al., 2017, 2018; Keeley, Wood, \& Pochon, 2018; Lejzerowicz et al.,
2015). In this study, we further determined eDNA metabarcoding to be a
useful complement to traditional macrofaunal benthic assessments of fish
farms by analyzing biotic signals associated with benthic impacts of
salmon farming in macrofaunal polychaetes data and eDNA metabarcoding
data.

Polychaetes are an abundant taxonomic group in benthic macrofauna (Dean,
2008; Tomassetti \& Porrello, 2005) and opportunistic polychaete complex
(OPC) are considered good indicator species for environmental monitoring
as they respond well to organic enrichment gradients (Pocklington \&
Wells, 1992). In this study, alpha diversity (richness) calculated using
genetically identified polychaete data was consistently reduced near
salmon cages compared to further out. This was consistent with previous
findings that most polychaetes are sensitive to organic enrichment
(Tomassetti \& Porrello, 2005), and species richness of benthic fauna
was reduced by marine finfish farming (Holmer, Wildish, \& Hargrave,
2005; Neofitou, Vafidis, \& Klaoudatos, 2010). The presence of
\emph{Capitella} at three stations close to cage edge (i.e. 0 m, 15 m,
and 30 m from cage edge) and its absence at reference stations at all
farm sites further supports that this genus is a good indicator for
organic enrichment caused by finfish farming (Pearson \& Rosenberg,
1978).

Consistent with results obtained using macrofaunal polychaete data,
evidence of benthic biodiversity being impacted by fish farming across
the six farms was also apparent using eDNA metabarcoding: there was a
negative correlation between alpha diversity parameters and sediment
pore-water sulphide concentration, and an increase in alpha diversity at
distant stations. This indicates that alpha diversity parameters
estimated from eDNA data can be used to analyze the spread (or
'footprint') of benthic impacts at marine finfish farm sites. While
other studies have shown eDNA metabarcoding generally provides similar
conclusions for environmental assessments to those provided by
morpho-taxonomy (Lanzén et al., 2016; Lejzerowicz et al., 2015;
Pawlowski et al., 2014, 2016; Pochon et al., 2015), the community
composition as revealed by these two methods can be very different
(Cowart et al., 2015; Kelly et al., 2017). This was indeed the case in
this study, as we detected a much higher number of Polychaeta species
using morpho-taxonomy combined with DNA barcoding (145 taxa) than using
eDNA metabarcoding (38 OTUs). The major reason causing this could be the
difference in amount of sediments used: eDNA metabarcoding was conducted
using ca. 1.5 g sediments per station (six 0.25 g samples) while
macrofaunal Polychaeta were collected from 24 liters of sediments per
station. Thus, it is not overly surprising that more species were
detected by picking specimen from sieved sediments, despite that eDNA
metabarcoding can detect much smaller species including meiofauna and
microfauna. Another important reason could be the primers biases (Zinger
et al., 2019): primers used for eDNA metabarcoding may not be very
efficient for Polychaeta and/or DNA from other taxonomic groups (e.g.
diatoms) were more competitive for PCR amplification. In addition, COI
evolves more quickly than 18S and thus is more effective at
differentiating species; this also likely contributed to the difference
(Tang et al., 2012). Last but not least, public databases not having
enough reference sequences for metazoans may have led to a low number of
Polychaeta taxa detected in our eDNA data; there were 148 OTUs assigned
as Eukaryota and not any further.

Both nematodes and polychaetes are known to be sensitive to organic
enrichment caused by fish farming (Dean, 2008; Mirto et al., 2014;
Mirto, La, Gambi, Danovaro, \& Mazzola, 2002; Tomassetti \& Porrello,
2005). In this study, nematode OTU number correlated much more strongly
with pore-water sulphide concentration than polychaete OTU number did,
and there were also many more nematode OTUs amplified than polychaete
OTUs. This is presumably due to the small size of nematodes (Heip,
Vincx, \& Vranken, 1985) and that they spend their entire life cycle
within sediments, whereas polychaetes are relatively larger-bodied, as
well as varying sulphide tolerances and habitat strategies (Fauchald \&
Rouse, 1997; Pocklington \& Wells, 1992). However, the proportion of
benthic metazoan reads in our metabarcoding data was not high, which may
have impacted the diversity of metazoan species detected. This is a
common problem when using a general (i.e. all eukaryotes) \emph{18S
rRNA} marker regardless of whether samples are collected in polluted
areas (e.g. Lanzén et al., 2016; Lejzerowicz et al., 2015) or
non-polluted areas (e.g. Sinniger et al., 2016). Increasing the
proportion of metazoan reads should improve the accuracy of eDNA-based
methods. Possibilities for achieving this include: treating sediments
prior to DNA extraction, such as elutriation by decantation (Brannock \&
Halanych, 2015) and meiofauna extraction (Fonseca et al., 2011); the use
of more metazoan-specific primers; and/or the use of a blocking primer
to inhibit amplification of non-target species during PCR (Vestheim \&
Jarman, 2008). Extracting DNA from a higher amount of sediments also may
increase number of species detected.

As already described, we assessed biotic responses to organic enrichment
using alpha diversity richness parameters derived from all benthic
metazoan reads and, more specifically, from two particular groups:
nematodes and polychaetes. In addition, we used eDNA data to investigate
responses to organic enrichment at the individual OTU level. Two OTUs
showed higher relative abundances in oxic sediments and both of them
were assigned to \emph{Sabatieria punctata} . A previous study found
the\emph{Sabatieria} genus had higher abundance near the fish cage than
at the reference station (Mirto et al., 2002). This discrepancy in
results between their findings and this study may be due to a different
sample collection period. We collected samples during peak fish
production while their monthly sample collection started 15 days after
fish were transferred to the cage; populations of benthic fauna can
increase when organic enrichment is slight and then decrease when
organic enrichment is severe (Pearson \& Rosenberg, 1978). We found OTUs
assigned to chicken and Atlantic salmon in eDNA data. Chicken feather is
used in fish feed diets and sediments were collected from Atlantic
salmon farms. We believe they are true reads, although their numbers
(9,959 for chicken and 6,371for salmon) were low compared to total
number of reads (42.3 million) obtained. Given that these taxa were
generally detected close to the net-pens, we feel that these occurrences
were directly associated with farming activities.

We did not calculate biotic indices for the sediment samples analyzed
here as none is available for this purpose in Canada. While
environmental assessments based on geochemistry are very fast to
perform, without biological information there is no way for benthic
monitoring programs to assess cumulative impacts of fish farming on
benthic communities (Lear, Dopheide, Ancion, \& Lewis, 2011). Given that
eDNA metabarcoding can provide a rapid estimation of species composition
and diversity and has high potential to complement current monitoring
programs, a few studies have proposed the use of eDNA metabarcoding for
benthic environmental health status assessment. For example, Aylagas et
al. (2014) used presence-absence of species based on metabarcoding data
to calculate a genetics-based AZTI's Marine Biotic Index (gAMBI).
However, gAMBI requires OTUs to be accurately assigned to species level,
and it uses only species in the AMBI database to calculate the biotic
index, potentially rendering most OTUs in a given dataset unusable.
Lejzerowicz et al. (2015) used eDNA and eRNA metabarcoding to calculate
the Infaunal Trophic Index and three AZTI Marine Biotic Indices, and
found that metabarcoding-based indices provided similar ecological
assessments of sediments to morphology-based assessments, although only
19.5\% of generated sequences were metazoan. Keeley et al. (2018)
assigned OTUs from three metabarcoding markers to different Eco-Group
categories, and then used OTU presence-absence data to calculate a few
biotic indices for benthic impact assessments of fish farms. Those
indices were shown to have strong linear relationships with the
Enrichment Stage index used for benthic assessments of fish farms in New
Zealand (Keeley et al., 2012, 2018). In our study, we categorized
sediments into four organic enrichment statuses based on sediment
pore-water sulphide concentration following the Canadian Aquaculture
Regulation (AAR, 2016) and used three methods to identify OTUs related
to organic enrichment. These OTUs are thus ideal candidates to test for
the development of biotic indices to aid benthic impact assessments in
British Columbia (and potentially similar environments elsewhere).
Alternatively, if further study can confirm the consistency of the
responses of these OTUs to organic enrichment status across a higher
number of study sites, one or several of them may be amenable for use as
a threshold-based bioindicator.

\textbf{Acknowledgements}

This work was funded by Fisheries and Oceans Canada's Program for
Aquaculture Regulatory Research, granted to Cathryn Abbott and Terri
Sutherland (\#PARR-2016-P-06). We thank Nathan Blasco, Kerra Shaw, and
Adrienne Paylor from DFO's Aquaculture Management Division in Campbell
River, as well as Geoff Lowe, Kara Aschenbrenner, and Guang Kun Zhang
for their support and help with sample collection. We thank Kara
Aschenbrenner, Louise-Marie Roux, Aaron Schuler, and Angela Schulze for
their assistance in the laboratory. We are also grateful to Leslie
Harris for taxonomic identification of polychaete specimens she provided
via iNaturalist (www.inaturalist.org).

\textbf{Data Accessibility}

MiSeq sequence data have been deposited in NCBI Sequence Read Archive
(SRA) database under the Accession PRJNA474384. DNA barcoding sequence
data have been deposited in BOLD database under the Accession CABIB
(http://www.boldsystems.org/index.php/MAS\_Management\_DataConsole?codes=CABIB).

\textbf{Author contributions}

XH conducted eDNA metabarcoding, data analysis, and paper writing. SG
collected samples, analyzed morpho-taxonomy and DNA barcoding data in
the BOLD database, and contributed to manuscript writing. CA, TS, and JP
co-conceived the study. CA led the funding proposal, supervised
laboratory aspects of the work, and contributed feedback on data
analyses. TS led field-planning and the collection of geochemistry data
as well as provided critical input to data analyses. MH provided
expertise and laboratory assistance related to the DNA barcoding aspects
of the project. KM provided expertise and laboratory assistance related
to eDNA metabarcoding. KW provided expertise related to statistical
analyses. All authors provided feedback on manuscript drafts.

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