DOI: 10.5061/dryad.612jm648m
title: Data from: Primer sets evaluation and sampling method assessment for the monitoring of fish communities in the North-western part of the Mediterranean Sea through eDNA metabarcoding

description: Environmental DNA (eDNA) metabarcoding appears to be a promising tool for
 surveying fish communities. However, the effectiveness of this method
 relies on primer set performance and on a robust sampling strategy. While
 some studies have evaluated the efficiency of several primers for fish
 detection, it has not yet been assessed in situ for the Mediterranean Sea.
 In addition, mainly surface waters were sampled and no filter porosity
 testing was performed. In this pilot study, our aim was to evaluate the
 ability of six primer sets, targeting 12S rRNA (AcMDB07; MiFish; Tele04)
 or 16S rRNA (Fish16S; Fish16SFD; Vert16S) loci, to detect fish species in
 the Mediterranean Sea using a metabarcoding approach. We also assessed the
 influence of sampling depth and filter pore size (0.45 µm versus 5 µm
 filters). To achieve this, we developed a novel sampling strategy allowing
 simultaneous surface and bottom filtration of large water volumes along
 on-site the same transect. We found that 16S rRNA primer sets enabled more
 fish taxa to be detected across each taxonomic level. The best combination
 was Fish16S/Vert16S/AcMDB07, which recovered 95% of the 97 fish species
 detected in our study. There were highly significant differences in
 species composition between surface and bottom samples. Filters of 0.45 µm
 led to the detection of significantly more fish species. Therefore, to
 maximize fish detection in the studied area, we recommend to filter both
 surface and bottom waters through 0.45 µm filters and to use a combination
 of these three primer sets.
description: <strong><em>Sampling
 strategy<br></em></strong> This study
 was conducted over a two-day period (03/05/2022 - 04/05/2022) in the
 Marine Protected Area (MPA) of Cap Roux, located in the North-western part
 of the Mediterranean Sea, between Cannes and Saint-Raphael, France.
 Established in 2003, it covers an area of 450 ha spanning from the
 shoreline to the 100 m isobath. All types of fishing are prohibited but
 permanent surveillance is lacking. The habitats within the MPA consist of
 typical ecosystems of the Mediterranean Sea such as <em>Posidonia
 oceanica</em> meadows, rocky and coralligenous reefs. We have chosen
 this study area due to its high species richness, making it an ideal
 sampling location to test our metabarcoding strategy.
 For this study, we have developed a novel sampling strategy
 allowing the simultaneous filtration of surface and bottom water. Two
 transects of approximately 1.3 km in length were designed, inside the MPA,
 crossing several habitats to allow the detection of a broad range of
 species . Along these transects, we collected two samples concomitantly.
 The first one consisted of 30 L of surface water filtered one meter below
 the surface with a diaphragm pump (Argaly; flow: 1.0 L/min) attached to a
 boat. The second sample consisted of 30 L of bottom water filtered one
 meter above the substrate with a custom-made waterproof pump (flow: 1.0
 L/min) fixed on a Diver Propulsion Vehicle (DPV) driven by a scuba diver .
 Both pumps were started at the same moment. To enable the simultaneous
 pumping of water from both the surface and the bottom along the same
 transect, a second diver carried a buoy, enabling the boat to closely
 follow the divers, adapting its speed to their pace
 .<strong><em> </em></strong>The scuba divers
 navigated effectively underwater by identifying the capes and determining
 the appropriate time to spend in each habitat. Each transect was
 duplicated, resulting in a total of eight samples. Both pumps (i.e.,
 surface and bottom) were connected to a filtration capsule (eDNA water
 filter, Waterra; 600 cm²; Polyethersulfone), allowing immediate filtration
 of large water volumes. Two pore sizes, 0.45 µm and 5 µm, were tested.
 Since field sampling occurred during the plankton bloom period, we were
 expecting potential filter clogging. Thus, we wanted to try water
 filtration through a large mesh size (i.e., 5 µm) to assess if this
 porosity would perform better than conventional 0.45 µm filters in our
 area. After the filtration step, 50 mL of Longmire
 buffer solution (Longmire et al., 1997), an effective solution to allow
 long term conservation of eDNA samples, was directly injected into the
 capsules which were shaken by hand. eDNA capsules were always handled with
 gloves to avoid contamination. Upon returning to the laboratory, the
 capsules were vigorously agitated again for 1 minute. The 50 mL extract
 was finally stored at room temperature in the dark until DNA
 extraction. In addition, we collected two water samples
 in the aquarium of the Monaco Oceanographic Museum (MOM)<em>
 </em>(Table S2). Sampling in an aquarium was performed because we
 had prior knowledge of the species composition, enabling us to assess the
 performance of our metabarcoding strategy by comparing the number of
 detected species with the known list of fish species. Moreover, since fish
 composition is different between the aquarium and field samples (i.e.,
 some species absent from the field samples might be found in the
 aquarium), it gave us more information on the taxonomic coverage of each
 primer set. For each capsule, 30 L of water was filtered from the surface
 across three different tanks containing only Mediterranean fish species.
 One sample was collected with a 0.45 µm pore size capsule and the other
 with a 5 µm capsule. Following the filtration step, the capsules were
 treated in the same manner as the Cap Roux field samples.
 <strong><em>Metabarcoding analysis: Extraction, PCR,
 sequencing</em></strong> All the
 metabarcoding laboratory steps were performed by Argaly
 (Sainte-Hélène-du-Lac, France), using the following protocol: DNA
 extraction from the 10 samples was carried out in a laboratory dedicated
 to handle eDNA water samples following the NucleoSpin Soil kit protocol
 (Macherey Nagel) with the following modifications: the 50 mL falcon tubes
 were centrifuged for 1 h at 12,000 <em>g</em>. The pellets
 were then resuspended in ATL buffer and proteinase K, and placed for 2 h
 at 56 °C to lyse cells and cell debris. The extraction procedure was
 continued according to the manufacturer's protocol and the resulting
 DNA extracts were eluted in a final volume of 100 μL of elution
 buffer. Subsequently DNA from each sample was amplified
 in 12 replicates for each primer set. Each PCR replicate was uniquely
 identified by a combination of two eight-base tags appended to the PCR
 primer at the 5’ end. These tags were used during bioinformatics analysis
 to assign sequences to the corresponding replicate. Following
 amplification, all samples were purified with the MinElute purification
 kit (Qiagen). Library constructions and sequencing were then performed by
 Fasteris (Geneva, Switzerland). The libraries were prepared according to
 the Metafast protocol (analysis), designed to minimize sequencing
 artefacts. The libraries were then sequenced in several Illumina MiSeq
 runs with paired-end reads of 2 x 150 bp or 2 x 250 bp depending on the
 amplicon’s length. Various quality controls were
 conducted at each step of the protocol to identify potential
 contamination, ensuring an accurate interpretation of the results. For
 each PCR replicate, the following controls were performed: a negative
 extraction control, a negative PCR control, a positive control and eight
 bioinformatic controls. The positive control corresponded to a DNA sample
 from fish stomach contents diluted to 1/10th previously sequenced by
 Argaly. The success of the amplifications and purifications was confirmed
 on a 2% agarose gel (E-Gel Power Snap, Invitrogen).
 <strong><em>Metabarcoding analysis:
 Bioinformatics</em></strong> Argaly
 conducted the bioinformatic steps, using the following procedure: the raw
 sequence data for each primer, were analysed using the suite of OBITools
 programs (https://pythonhosted.org/OBITools/welcome.html; Boyer et al.,
 2016) and the SumaClust clustering tool (Mercier et al., 2013), which are
 specifically designed for analysing metabarcoding data. More specifically,
 the paired sequences were first assembled (“illuminapairedend” command),
 then only the sequences with an alignment score &gt;= 40 (i.e.,
 corresponding to an overlap of at least 10 bases) were assigned to the
 corresponding amplification replicate, thanks to the tags inserted in the
 5' of the primers (“ngsfilter” command). The resulting dataset was
 dereplicated (“obiuniq” command), then filtered (“obigrep” command) to
 remove low quality sequences (i.e., containing at least one N), sequences
 whose length does not belong to the length range observed in silico for
 the target group, and singletons (i.e.,<em> </em>sequences
 observed only once in the dataset). SumaClust was then used to group
 sequences sharing 97% identity into clusters. The abundances of sequences
 belonging to each cluster were summed for each PCR replicate. The cluster
 head, representing the most abundant sequence in the cluster, was chosen
 as the representative sequence, and clusters appearing less than 10 times
 in a sample were deleted. A taxonomic assignment of the cluster heads was
 then performed with the “ecotag” command, to obtain a list of MOTUs
 (Molecular Operational Taxonomic Units). The reference sequences used for
 this taxonomic assignment were obtained by performing an in-silico PCR on
 the public sequence database GenBank (v.249) with the ecoPCR program
 (Ficetola et al., 2010). This in silico PCR was conducted using the PCR
 primers associated with each marker allowing a maximum of three mismatches
 per primer and retaining only sequences assigned at least at the family
 level. The R package “metabaR” (Zinger et al., 2021)
 was then used to remove artefactual sequences from the resulting dataset
 that are present in low abundance in the metabarcoding data, but which may
 influence the ecological conclusions that can be drawn from them
 (Calderón‐Sanou et al., 2020). This included removing (1) MOTUs with
 sequence similarity to any sequence in the reference database below 0.95,
 as they are potential chimeras; (2) MOTUs whose frequency over the entire
 dataset is maximum in at least one negative control ("max"
 method of the “contaslayer” function), because they are potential
 contaminants; and (3) MOTUs with a relative frequency &lt; 0.03%
 within a PCR replicate (“tagjumpslayer” function), because they are
 potentially artefacts generated during sequencing library construction
 (i.e., "tag jumps"; Schnell et al., 2015). PCR replicates with a
 sequencing coverage &lt; 1000 sequences were also removed and then the
 remaining PCR replicates were aggregated by sample using the
 “aggregate_pcrs” function. Finally, MOTUs observed less than 10 times in a
 sample were recoded as absent in that sample. After
 receiving the results from Argaly, manual verification and modification of
 the taxonomic assignations were performed. Non-fish taxa or freshwater
 fish sequences were deleted and marine fish sequences were reviewed based
 on biogeographic data according to criteria used by Aglieri et al.
 (2021). <strong>Bioinformatic pipeline code:
 OBIToolsScript_Fish16S</strong>
 <pre>#!/bin/bash<br>#<br><br># Read
 assembly<br>illuminapairedend -r AOYG-107_R2.fastq AOYG-107_R1.fastq
 &gt; AOYG-107.fastq<br><br># Selection of sequences with
 an alignment score &gt; 40<br>obiannotate -S
 goodAli:'"Alignment" if score&gt;40.00 else
 "Bad"' AOYG-107.fastq | obisplit -t goodAli -p
 AOYG-107_<br><br># Sequence assignment to the right PCR
 replicate based on the descriptor file AOYG-107.ngs<br>ngsfilter -t
 AOYG-107.ngs -u unidentified_AOYG-107.fastq AOYG-107_Alignment.fastq
 &gt; AOYG-107_ngs.fastq<br><br># Sequence
 dereplication<br>obiuniq -m sample AOYG-107_ngs.fastq &gt;
 AOYG-107_uniq.fasta<br><br># Discarding of sequence observed
 only once, containing ambiguous nucleotides, and/or whose size is outside
 the range defined in silico <br>obigrep -s '^[ACGT]+$'
 -p'count&gt;1' -p 'seq_length&gt;11' -p
 'seq_length&lt;95' AOYG-107_uniq.fasta &gt;
 AOYG-107_filt.fasta<br><br># Clustering (and identification of
 clusters)<br>sumaclust -n -t 0.97 AOYG-107_filt.fasta &gt;
 AOYG-107_suma.fasta<br><br># Gathering of sequences by
 clusters and addition of their counts<br>obiselect -c cluster
 --merge=sample -f "cluster_center==True" AOYG-107_suma.fasta
 &gt; AOYG-107_centers.fasta<br><br># Selection of clusters
 with at least 10 reads in at least one PCR replicate<br>obigrep -p
 'max(merged_sample.values()) &gt;= 10'
 AOYG-107_centers.fasta &gt;
 AOYG-107_centers10.fasta<br><br># Taxonomic assignment using
 the reference database db_Fish16S_genbank249.fasta and the GenBank
 taxonomy v249<br>ecotag -d
 /bioinfo/Databases/Genbank/Genbank_249.0_light/genbank249 -R
 db_Fish16S_genbank249.fasta AOYG-107_centers10.fasta &gt;
 AOYG-107_tag.fasta<br><br># File annotation and preparation
 for exportation<br>obiannotate -d
 /bioinfo/Databases/Genbank/Genbank_249.0_light/genbank249
 --with-taxon-at-rank=class \<br> --with-taxon-at-rank=order
 --with-taxon-at-rank=phylum --with-taxon-at-rank=kingdom
 AOYG-107_tag.fasta  \<br> | obiannotate -k species_name -k
 genus_name -k family_name -k order_name -k class_name -k phylum_name
  \<br> -k kingdom_name -k species -k genus -k family -k order -k
 class -k phylum -k kingdom -k scientific_name \<br>  -k species_list
 -k taxid -k best_identity -k scientific_name_by_db -k merged_sample -k
 count | \<br>  obisort -r -k count  | obiannotate --seq-rank |
 obiannotate --set-identifier='"'Fish16S'_%05d" %
 seq_rank' \<br>  | obiannotate --delete-tag=seq_rank  | obitab
 -o -d &gt; AOYG-107.obitab</pre>
description: # Primer sets evaluation and sampling method assessment for the monitoring
 of fish communities in the North-western part of the Mediterranean Sea
 through eDNA metabarcoding
 [https://doi.org/10.5061/dryad.612jm648m](https://doi.org/10.5061/dryad.612jm648m) Raw sequence data for each primer, were analysed using the suite of OBITools programs ([https://pythonhosted.org/OBITools/welcome.html](https://pythonhosted.org/OBITools/welcome.html); Boyer et al., 2016) and the SumaClust clustering tool (Mercier et al., 2013). The script is provided with the data description (methods paragraph) ## Description of the data and file structure Raw data description: Library AOYG-150 contains sequences obtained using AcMDB07 primer set Library AOYG-160 contains sequences obtained using Fish16FD primer set Library AOYG-147 contains sequences obtained using Fish16S primer set Library AOYG-158 contains sequences obtained using Tele03 primer set Library AOYG-159 contains sequences obtained using Tele04 primer set Library AOYG-149 contains sequences obtained using Vert16S primer set Each library zip file contains two files: one for R1 reads and the other for R2 reads A text file for each primer set describes the tag combinations associated with each PCR replicate (ngs filter parameters) PCR replicates corresponding to controls: BLNKXXX correspond to bioinformatics blanks and allow tag jumps to be filtered CEXTXXX correspond to extraction controls CPCRXXX correspond to PCR controls CPOSXXX correspond to positive controls