INTRODUCTION
Marine ecosystems deliver many important services to society such as
climate regulation, coastal protection, food provision and recreation
(Barbier, 2017; Duncan, Thompson, & Pettorelli, 2015). To maintain
ecosystem services, good ecosystem health and proper ecosystem
functioning are crucial (Barbier, 2017) and both are inextricably linked
to marine biodiversity (Goodwin et al., 2017; Solan et al., 2004).
Several legal and policy frameworks have been developed with
conservation of marine ecosystem health as main objective (Goodwin et
al., 2017; Halpern et al., 2015) and which include monitoring of benthic
diversity. The characterization of benthic diversity relies mostly on
morphological taxonomy, which is slow and expensive because of manual
sorting and visual identification of taxa in the samples. The
development of innovative, cost-effective monitoring tools that allow a
quick screening of benthic diversity in large marine areas is therefore
needed (Borja et al., 2016; Elliott et al., 2018).
DNA metabarcoding is a promising tool for monitoring benthic
environments (Aylagas, Borja, Irigoien, & Rodriguez-Ezpeleta, 2016;
Aylagas, Borja, Muxika, & Rodriguez-Ezpeleta, 2018; Elbrecht, Vamos,
Meissner, Aroviita, & Leese, 2017; Leray & Knowlton, 2015). Instead of
identifying all specimens morphologically, DNA is extracted from the
total community, a short fragment of the genome is amplified through
PCR, the resulting library is sequenced using high throughput sequencing
and finally, the resulting sequences are bioinformatically processed
(Pawlowski et al., 2018). Each of these steps can introduce bias and
errors (Alberdi, Aizpurua, Gilbert, & Bohmann, 2018), and optimization
of the methodological aspects of the protocol are needed to achieve
reliable, comparable and repeatable data across studies. Different
marker genes can yield very different diversity estimates of
invertebrate communities and combining different markers increases the
detection of all species (Alberdi et al., 2018; Marquina, Esparza-Salas,
Roslin, & Ronquist, 2019). For metabarcoding metazoan communities, the
mitochondrial COI gene is the preferred marker gene because of the
availability of large reference databases and the presence of sufficient
genetic variation to allow species level identification (Andujar,
Arribas, Yu, Vogler, & Emerson, 2018). The final detection of a
particular species in the sample greatly depends on its biomass and on
the primers used in the DNA metabarcoding protocol (Elbrecht, Peinert,
& Leese, 2017). The PCR step is one of the most influential steps for
the detection of species using DNA metabarcoding, where primer
mismatches, GC content, template switching, stochasticity and polymerase
errors can affect the number of sequence variants found in the dataset
(Kebschull & Zador, 2015). Different primer sets are available for the
COI gene which also leads to differences in diversity (Braukmann et al.,
2019; Elbrecht & Leese, 2017; Lobo, Shokralla, Costa, Hajibabaei, &
Costa, 2017). Consequently, it is important to investigate primer
performance for (bio)geographic areas and taxonomic groups that have not
been studied before to obtain accurate results with DNA metabarcoding.
Next to primer choice, the DNA source used in metabarcoding studies can
affect whether or not a species is detected. DNA can be extracted from
bulk specimens that have been separated from the sediment by sieving,
decantation and manual sorting (Aylagas, Mendibil, Borja, &
Rodriguez-Ezpeleta, 2016) or from the ethanol preservative in which the
sieved macrobenthos sample was preserved (Zizka, Leese, Peinert, &
Geiger, 2019). Bulk DNA samples, can further be sorted in different size
fractions to enhance detection of smaller sized animals (Elbrecht,
Peinert, et al., 2017; Leray & Knowlton, 2015) which comes at an extra
cost of time to process samples. Extracting DNA from the ethanol
fixative avoids the time consuming step of having to sort specimens and
has the added advantage that voucher specimens are still present after
the analyses. However, non-target species such as bacteria and fungi can
be encountered more when analyzing the ethanol preservative compared to
bulk samples (Gauthier et al., 2020). A comparison between bulk and
ethanol samples for freshwater invertebrates showed that the two
approaches yield very different community compositions (Marquina,
Esparza-Salas, et al., 2019; Zizka, Leese, et al., 2019). Nevertheless,
DNA metabarcoding of the ethanol fraction has also shown considerable
overlap with morphology based analyses of freshwater communities
illustrating its potential for monitoring studies (Martins et al.,
2019). In view of the above mentioned advantages when using the ethanol
preservative, understanding the different results between bulk DNA and
eDNA from the ethanol preservative is essential to determine their
applicability for monitoring studies. For instance, it has been shown
that small and weakly sclerotized freshwater insects species are
overrepresented in the ethanol preservative while large and strongly
sclerotized species are overrepresented in the bulk DNA (Marquina,
Esparza-Salas, et al., 2019). Consequently, morphological traits of
species may be important to explain their detection in DNA metabarcoding
studies but this link has hitherto not been investigated for marine
macrobenthos.
In this study, we sampled four distinct macrobenthos communities in the
North Sea and identified them using traditional morpho-taxonomy before
molecular processing. We generated 104 COI reference sequences from
macrobenthos species in the Belgian part of the North Sea to allow
taxonomic assignment of the metabarcode datasets. Our first aim was to
find the best primer set to characterise marine macrobenthos communities
through DNA metabarcoding. We tested five primer sets from the
literature and defined the best primer set as the set that detected the
highest number of morphological species and that was able to
differentiate macrobenthic communities. Second, we investigated whether
morphological and metabarcode analyses detected the same species. We
expected to detect more diversity in the metabarcode datasets compared
to the morphological dataset as specimens are not lost during the
sorting process. We also expected to find a high proportion of species
shared between the two approaches. Third, we evaluated whether eDNA from
the ethanol preservative could be used for monitoring by comparing its
alpha and beta diversity patterns with that of bulk DNA. Based on the
results in freshwater communities, we expected to find beta diversity
patterns that differentiate the four communities in the bulk DNA and
eDNA from the ethanol preservative and to find additional
non-macrobenthic taxa in the eDNA from the ethanol preservative. Fourth,
we investigated whether morphological and ecological traits of the
macrobenthos species could explain their probability of detection in the
bulk DNA and eDNA from the ethanol preservative. Based on the results of
insects in freshwater environments (Marquina, Esparza-Salas, et al.,
2019), we expected to find a higher probability of detecting small and
weakly sclerotized species in the ethanol preservative.