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 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) 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 Sabatieria punctata . A previous study found theSabatieria 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.