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\begin{document}

\title{Development and validation of a DNA-based multi-species biomonitoring
toolkit using a High-Throughput qPCR platform: a case study of Irish
shellfish species}



\author[1]{Dennis van der Pouw Kraan}%
\author[1]{Conor Graham}%
\author[1]{Fiona Kavanagh}%
\author[1]{Luca Mirimin}%
\affil[1]{Atlantic Technological University - Galway City}%


\vspace{-1em}



  
  \date{May 26, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Biomonitoring of marine life has been enhanced in recent years by the
integration of innovative DNA-based approaches, which offer advantages
over more laborious conventional techniques (e.g. direct capture) and
greater taxonomic resolution especially in complex life cycles and early
life stages. However, tradeoffs between throughput, sensitivity and
quantitative measurements must be made when choosing between the
prevailing molecular methodologies (i.e. metabarcoding or qPCR/dPCR).
Thus, the aim of the present study was to demonstrate the utility of a
microfluidic-enabled High Throughput quantitative PCR platform (HT-qPCR)
for the rapid and cost-effective development and validation of a
DNA-based multi-species biomonitoring toolkit, using larvae of 24
commercially targeted bivalve and crustacean species as a case study.
The workflow was divided into three main phases: definition of target
taxa and establishment of reference databases (PHASE 1); in silico
selection/development and in vitro assessment of molecular assays (PHASE
2); and protocol optimization and field validation (PHASE 3). Of a total
of 85 assays in silico, 42 were eventually chosen and validated in
vitro. Genetic signal showed good correlation with direct visual counts
by microscopy, but also showed the ability to provide quantitative data
at the highest taxonomic resolution (species level) in a time- and
cost-effective fashion. This study developed a biomonitoring toolkit,
demonstrating the considerable advantages of this state-of-the-art
technology in boosting the development and application of panels of
molecular assays for the monitoring and management of natural resources
that can be applied to a range of monitoring programmes. Keywords: DNA,
High Throughput, qPCR, biomonitoring, shellfish%
\end{abstract}%



\sloppy


\textbf{Development and validation of a DNA-based multi-species
biomonitoring toolkit using a High Throughput qPCR platform: a case
study of Irish shellfish species.}

Dennis van der Pouw Kraan, Conor T. Graham, Fiona Kavanagh, Luca Mirimin

Marine and Freshwater Research Centre, Atlantic Technological University
(ATU), Dublin Road, Galway, Ireland

Correspondence: dennispouwkraan.research@gmail.com

\textbf{ABSTRACT}

Biomonitoring of marine life has been enhanced in recent years by the
integration of innovative DNA-based approaches, which offer advantages
over more laborious conventional techniques (e.g. direct capture) and
greater taxonomic resolution especially in complex life cycles and early
life stages. However, tradeoffs between throughput, sensitivity and
quantitative measurements must be made when choosing between the
prevailing molecular methodologies (i.e. metabarcoding or qPCR/dPCR).
Thus, the aim of the present study was to demonstrate the utility of a
microfluidic-enabled High Throughput quantitative PCR platform (HT-qPCR)
for the rapid and cost-effective development and validation of a
DNA-based multi-species biomonitoring toolkit, using larvae of 24
commercially targeted bivalve and crustacean species as a case study.
The workflow was divided into three main phases: definition of target
taxa and establishment of reference databases (PHASE 1); \emph{in
silico} selection/development and \emph{in vitro} assessment of
molecular assays (PHASE 2); and protocol optimization and field
validation (PHASE 3). Of a total of 85 assays \emph{in silico} , 42 were
eventually chosen and validated \emph{in vitro} . Genetic signal showed
good correlation with direct visual counts by microscopy, but also
showed the ability to provide quantitative data at the highest taxonomic
resolution (species level) in a time- and cost-effective fashion. This
study developed a biomonitoring toolkit, demonstrating the considerable
advantages of this state-of-the-art technology in boosting the
development and application of panels of molecular assays for the
monitoring and management of natural resources that can be applied to a
range of monitoring programmes.

\textbf{Keywords} : DNA, High Throughput, qPCR, biomonitoring, shellfish

\textbf{1\textbar{} INTRODUCTION}

Recent advances in molecular technology have boosted the biomonitoring
capabilities of DNA-based approaches, particularly in aquatic
environments . Such approaches rely on the detection and quantification
of DNA copies in an environmental sample, which is used as a method for
determining the presence and in some cases the relative abundance or
biomass of target species. Since DNA can be present in an environmental
sample as both organismal and/or extra-organismal DNA , DNA-based
approaches are capable of targeting a wide range of taxa, ranging from
micro- to macro-organism (e.g. , encompassing virtually all life-stages
from larvae to adults (e.g. . Even though many challenges still exist ,
the integration of molecular approaches to traditional monitoring
techniques such as direct capture, offers an unprecedented opportunity
to reduce the cost and increase the throughput of biomonitoring. The
development and application of powerful and time/cost-effective
biomonitoring tools would benefit many areas of research and applied
science, which are usually reliant on labour-intensive screening methods
such as microscopic inspection by expert taxonomists .

Generally, DNA-based species detection approaches fall under two main
categories: (i) multi-taxa detection, which uses ``universal'' markers
combined with High Throughput Sequencing (HTS) (also known as
metabarcoding) and (ii) targeted detection, which typically uses
species-specific quantitative real-time PCR (qPCR) assays . Approaches
from both categories rely on the availability of robust reference
databases such as curated repositories of DNA data from taxonomically
identified specimens, ; however while HTS-based methods are more readily
available, though require substantial downstream analytical steps,
qPCR-based methods require an extensive initial validation phase (i.e.
use reference data for assay design), though once established they can
be applied rapidly and cost-effectively. The effectiveness of different
methods has been compared both in terms of detectability, as well as
biomass of target organisms (, with species-specific qPCR methods
showing promising results.

The initial validation phase required to deem an eDNA qPCR assay
reliable can be laborious and demanding in terms of cost and time ,
however recent advances in real-time qPCR instrumentation have received
a boost in integration and throughput capability thanks to the use of
microfluidic and nanotechnologies . This includes platforms that can
enable qPCR sample/assay Medium- to High-Throughput . Thus,
High-Throughout qPCR (HT-qPCR) instruments can play a significant role
in speeding up the validation phase of eDNA qPCR assays as well as
co-screening samples for multiple targets simultaneously.

For the present study, we chose the monitoring of spawning and larval
occurrence/distribution of Irish shellfish species of ecological and
commercial importance as a case study. The Irish shellfish industry
produces approximately 29,000 tonnes with an estimated value in excess
of \textbackslash{}euro71 million per annum. The production mainly
consists of mussels (\emph{Mytilus spp} ) (\textbackslash{}euro17
million, 17.100 tonnes) and oysters (\emph{Magallana gigas, Ostrea
edulis} ) (\textbackslash{}euro51 million, 11.000 tonnes). Furthermore,
many smaller proportions of clam, crab, and lobster species are
harvested all around the country (\textbackslash{}euro3 million, 900
tonnes) (BIM, 2022). These fisheries are, like in my countries, of
national importance as they provide employment in rural maritime
communities where employment is especially required to allow people to
work and live in these areas. The production of many shellfish species
relies on natural processes of recruitment (i.e., larvae from wild
stocks), which can be difficult to predict due to seasonal and
interannual environmental variation. Furthermore, shellfish larvae
(particularly bivalve species) can be difficult to taxonomically
identify during early developmental stages . DNA-based approaches have
the potential to overcome such limitations hence improving the
biomonitoring capability of screening protocols, as demonstrated by an
increasing number of targeted single taxon qPCR studies, including for
oysters (\emph{Magallana gigas, Ostrea edulis} ) \emph{,} mussels
(\emph{Mytilus spp} ) , cockles (\emph{Cerastoderma edule} ) , king
scallop (\emph{Pecten maximus} ) , razor clams (\emph{Ensis spp.} )
\emph{,} common clams (\emph{Mya arenaria} ) , and crabs \emph{(Necora
puber, Cancer pagurus, Carcinas maenas)} . However, to the best of our
knowledge, except for a recent study targeting fish species ,
High-Throughput multi-species biomonitoring efforts in aquatic
ecosystems are limited to using metabarcoding approaches , which often
are non-specific and are characterized by uncertain quantitative
capability .

Thus, the aim of this study was to use an HT-qPCR platform to describe
the development and validation of a multi-species biomonitoring tool to
aid the screening of shellfish larvae of ecologic and economic
importance in Irish waters.

\textbf{2\textbar{} MATERIALS AND METHODS}

The following workflow was developed by considering prevailing qPCR
guidelines , as well as more recent adaptations to environmental DNA
screening , while integrating the use of a High-Throughput qPCR
platform. As described in detail below, the workflow was subdivided into
three main phases consisting of the definition of target taxa and
establishment of reference databases (PHASE 1); \emph{in
silico}selection and \emph{in vitro} assessment of molecular assays
(PHASE 2); and field testing and validation (PHASE 3) (\textbf{Figure 1}
).

\textbf{2.1. PHASE 1 - DEFINITION OF TARGET TAXA AND ESTABLISHMENT OF
REFERENCE DATABASES}

The first step was to identify the main purpose of the toolkit and the
research questions being addressed. In the case of this study, the goal
was to develop a screening tool capable of determining the presence of
bivalve and crustacean larvae in zooplankton samples, with particular
interest in the Irish shellfish fisheries and aquaculture industry.
Thus, a total of 24 species were chosen based on their ecological and
economic importance in the context of Irish coastal waters, including
seventeen bivalve species and decapod species (\textbf{Table 1} ). To
identify the most suitable genes for molecular species identification, a
literature review was conducted for previously published studies
including genetic markers designed for taxonomic resolution of selected
taxa. In order to identify the final target and off-target species
occurring in the geographic region of interest (Irish marine coastal
waters), a review of organisms of interest and other closely related
species (i.e. same Order) was carried out by consulting the World
Register of Marine Species (WoRMS) and Ireland's National Biodiversity
Data Centre databases (August 2022).

A dedicated reference DNA database was created by combining publicly
available barcode sequences with new data from locally sourced
organisms. Firstly, based on results obtained from the above review of
target and off-target species, previously published DNA sequence data of
selected genes from all relevant taxa was obtained from the Barcode Of
Life Database (BOLD) and GenBank database. Secondly, to fill any data
gaps and confirm target DNA sequences of local taxa, DNA barcode
sequence data was also obtained from taxonomically identified voucher
specimens (e.g. adult individuals obtained from local markets or
opportunistically). For this latter step, DNA was isolated from 50 mg of
muscle tissue using the Blood and Tissue DNEasy kit (Qiagen), and 658 bp
fragments of the COI gene were amplified and sequenced following the
protocol described in . Finally, the resulting dedicated reference DNA
database was organized in relevant gene-specific alignments created
using the mafft alignment plugin as implemented in Geneious Prime
2020.2.1 (Biomatters Ltd).

\textbf{2.2. PHASE 2 -- \emph{IN SILICO} SELECTION AND \emph{IN
VITRO}ASSESSMENT OF MOLECULAR ASSAYS}

\emph{Assay selection and design (in silico)}

The peer-reviewed scientific literature was queried for existing assays
(i.e. PCR/qPCR/dPCR molecular assays targeting relevant marker/taxon
combinations) by searching public repositories (i.e. Google Scholar, Web
of Science, Pubmed) using key words ``PCR'', ``qPCR'', ``dPCR'' in
combination with the common and scientific names of target species,
regardless of whether studies used DNA isolated directly from tissue of
the target species or environmental samples (e.g. plankton, sediment or
water). Subsequently, primer-pairs were aligned to the reference DNA
data and manually examined for mismatches with target and off-target
species. The same alignments were also used to identify suitable
conserved but taxon-specific DNA regions for the design of new primers.
The suitability of promising primer sets was tested using Oligoanalyzer
(IDT, Coralville, IA, USA), following the criteria detailed
in\textbf{Table 2} . Where possible, multiple markers per species were
designed/chosen to increase species-specific marker development success
rate. To further test for assay specificity, primer pairs that satisfied
the above criteria were also evaluated using a machine learning tool
that accurately predicts qPCR amplification (eDNAssay), as described in
The default settings were used and, as a model output, the lowest
assignment probability value (AMP value) was reported including the
mismatches with that specific target on each primer. Primer pairs that
fitted all relevant criteria and had desirable AMP value
(\textless{}0.5) (where possible) were retained for subsequent \emph{in
vitro} testing.

\emph{Assay efficiency, specificity, and sensitivity assessment (in
vitro)}

This step was facilitated by the use of a High Throughput qPCR (HT-qPCR)
platform (Biomark HD, Standard BioTools Inc, USA), which enabled the
time- and cost-efficient \emph{in vitro} assessment of a relatively
large number of makers (e.g. compared to conventional qPCR instruments).
For this step, template DNA (i.e. extracted from tissue of available
specimens) was normalized to 1.5 ng/\selectlanguage{greek}µ\selectlanguage{english}l per sample following
spectrophotometric quantification with a dsDNA BR (Broad Range) assay
kit using a Qubit 3.0 fluorometer (Invitrogen). In addition, to further
test specificity of \emph{Mytilus} and \emph{Ensis} species complexes,
synthetic double stranded DNA (GBLOCKS, Integrated DNA Technologies) of
the relevant target genes was obtained for \emph{Mytilus
galloprovincialis, Mytilus edulis, Mytilus trossulus, Ensis ensis, Ensis
siliqua, Ensis leei (} synonym \emph{Ensis directus), Ensis magnus
(synonym Ensis arcuatus)} and standardized to 10,000 copies/\selectlanguage{greek}µ\selectlanguage{english}l.
Additionally, to act as a universal positive control and generate
standard curves for all assays tested, a six points 10-fold dilution
series (ranging from 1,000,000 to 10 copies per \selectlanguage{greek}µ\selectlanguage{english}l) was created with an
oligonucleotide pool containing synthetic double-stranded DNA fragments
of all target genes/species combinations (OLIGOPOOLS, Integrated DNA
Technologies)\textbf{(} see supplementary material \textbf{Table S1)} .

The HT-qPCR runs were performed using IFC controller MX and HX (for
priming and loading Integrated Fluidic Controls, IFCs) and a BIOMARK HD
real-time PCR system (Standard Bio Tools Inc, USA), following
manufacturer's recommendations, including a pre-amplification step (as
per protocol QR 100-5876C2) and the actual qPCR runs using the 48.48 IFC
(as per protocol QR 100-9791Rev3) or 96.96 IFC configurations (as per
protocol QR 100-9792Rev3)(all protocols available at
https://www.standardbio.com/support/instrument-support/biomark-ep1-support).
Exceptions included the addition of of 1\selectlanguage{greek}µ\selectlanguage{english}g/\selectlanguage{greek}µ\selectlanguage{english}l BSA (Thermofisher) in the
pre-amplification reaction, and subsequent PCR reaction clean up by
addition of exonuclease at 4 U/reaction (New England Biolabs) and a
thermocycling profile including 37\selectlanguage{ngerman}°C for 30 minutes and 80°C for 15
minutes. Cleaned up products were then diluted 10-fold with TE
suspension buffer (Qiagen) prior to performing the actual qPCR run on
the relevant IFC controller and Biomark HD. Final thermal cycling
followed manufacturer's protocols and included a melt curve analysis
(i.e. ``GE Fast 48x48 PCR+Melt v2.pcl'' or ``GE Fast 96x96 PCR+Melt
v2.pcl''). Each configuration enables the simultaneous real-time qPCR
amplification (in parallel) of either 2304 (48 samples tested against 48
assays) or 9216 (96 samples tested against 96 assays) reactions. This
allowed the screening of all target species' template DNA in duplicate,
the standard dilutions points in six technical replicates for each
dilution point per run, including all assays (at least in duplicates) as
well as 3 Internal Amplification Controls (IACs) (see supplementary
material\textbf{Appendix S1} for a more detailed overview of the
protocols used). Following each run, qPCR data was inspected using the
Real-Time PCR analysis software version 4.5.2 (Standard BioTools Inc,
USA). Output data was exported and analysed in R-studio (Posit team,
version 2022.12.0+353).

To assess sensitivity of each assay, data generated by the six points
10-fold dilution series was used to estimate Limit Of Detection (LOD)
and Limit Of Quantification (LOQ) for each assay following a discrete
threshold as well as a curve-fitting modelling approach, as described in
Klymus et al. (2020). The discrete threshold approach for LOD identifies
the lowest concentration with 95\% positive replicates, while for LOQ
the lowest standard concentration that could be quantified with a CV
value below 0.35 was selected. These CVs were modeled for exponential
decay, linear, and polynomial models to select the lowest residual
standard error. When possible, a threshold of CV 0.35 was used on the
model to estimate the number of copies for modeled LOQ. Assay efficiency
was checked according to expected theoretical values (i.e. optimal
efficiency range 90-100\% and a regression line slope between \selectlanguage{english}-3.6 and
\selectlanguage{english}-3.3), whereas assay specificity was assessed by checking whether
positive amplification (i.e. beyond the LOD) was observed in the target
species and/or any off-target species, as well as by melt curve peaks
comparison with a difference of \textgreater{}1 degree Celsius selected
as the threshold for melt-curve specificity.

After calculating the efficiency, LOD and LOQ, two methods were used to
quantify the detected DNA Cq-values to copy numbers. The first method
which is the more conventional method, is based on the obtained slope
and intercept values from the standard curve. A second method was used
to recalibrate intercepts based on the efficiency values of all the
assays (see supplementary material \textbf{Appendix S2} for a more
detailed explanation). Assuming that efficiency was accurately
calculated over 5 similar concentrations for all markers in the HT-qPCR,
for the second quantification method, the intercepts were plotted
against the efficiency of the assays, which resulted in a significant
linear model. To improve the fit of the linear equation, outliers were
defined based on the standardised residuals of the linear model. More
specifically, any observation with a standardised residual equal to or
greater than 2 (in absolute value) was deemed to be an outlier and was
removed to improve the fit. The linear model was then used to calculate
an additional intercept that was used to convert Cq values into copy
numbers. The assays specificity was assessed by verifying the positive
reactions, grouped per sample/assay, using the melt curve and AMP
values. Results were displayed by creating a heatmap showing average
amplification strength in copies per/\selectlanguage{greek}µ\selectlanguage{english}l, with the corresponding AMP
values of the same PCR reaction. Matrices for the heatmaps were created
in R-studio with visualization done in Excel (Microsoft, Version 2208).

\textbf{2.3. PHASE 3 --FIELD TESTING AND VALIDATION}

\emph{Enzymatic inhibition}

DNA extracted from environmental samples is known to potentially include
co-extracted enzymatic inhibitors, which negatively affect the
polymerase in PCR and can lead to underestimation of original target DNA
copies as well as false negatives . Different strategies have been
proposed to mitigate the adverse effects of inhibitors, including the
dilution of template DNA used and/or the addition of inhibition-reducing
agents such as Bovine Serum Albumin (BSA). Thus, since a dilution-like
effect of template DNA is already expected when carrying out qPCR
reaction at nano-liter scales (as is the case for droplet digital PCR
(ddPCR) as well as the microfluidic-enabled HT-qPCR system used in the
present study), the inhibitor-resistant effects of adding BSA to the
intended HT-qPCR protocol was tested against two well-known inhibiting
substances (i.e. humic acid and EDTA) with the aid of Internal
Amplification Control (IAC) assays . For this step, 48.48 IFC
configurations were used (using protocol QR 100-9791Rev3), whereby the
qPCR amplification was assessed with or without the addition of BSA (at
1 \selectlanguage{greek}µ\selectlanguage{english}g/\selectlanguage{greek}µ\selectlanguage{english}l concentration). To test for potential effects on different
concentrations of target DNA, three distinct IACs were tested at 100000,
10000 and 1000 copies/\selectlanguage{greek}µ\selectlanguage{english}l, respectively, whereas to test for the effect
of BSA against different concentrations of inhibiting substances, five
concentrations of humic acid (2500, 1250, 625, 312.5, 156.25 \selectlanguage{greek}µ\selectlanguage{english}M) and
five of EDTA (40, 20, 10, 5, 2.5 ng/\selectlanguage{greek}µ\selectlanguage{english}l) were tested. All reactions were
carried out in triplicate, and concentration ranges of inhibiting
substances and evidence of inhibition in the form of delays or absence
of amplification in spiked samples (n = 30) vs non-spiked samples (n =
30) were chosen based on previous studies , with HT-qPCR conducted using
48.48 IFCs.

\emph{Field experiment}

To validate the panel of markers using actual samples, zooplankton
samples were collected on 21 October 2019 in waters around Howth Head
(see \textbf{Appendix S3} for further details), where mussel beds are
known to occur and expected to be spawning at the time of sampling.
Samples were collected at five stations in five replicates per station
(n = 25) by means of vertical tows using a 100 \selectlanguage{greek}μ\selectlanguage{english}m mesh plankton net with
a diameter of 40 cm. Depth and vertical profiles of temperature and
salinity were recorded using a YSI Castaway CTD probe (SonTek). Upon
collection, samples were equally split into two 50 ml tubes and
preserved in Lugol's iodine solution (for microscopical analysis), and
70\% ethanol (for DNA analysis), respectively. Microscopic analysis was
carried out using a Cobra Micro Zoom MZ1000 stereo microscope, where
larvae belonging to the class Bivalvia and Malacostraca were counted,
and their relative abundance (larvae/m\textsuperscript{3}) was
calculated by dividing the total number of larvae by the approximate
volume (m\textsuperscript{3}) of seawater sampled (estimated by
multiplying the net opening area by the depth). The 25 ethanol samples
were extracted with the Power soil Pro kit (Qiagen), using a customized
protocol for plankton samples (see \textbf{Appendix S4} for for a
detailed protocol). The HT-qPCR run was conducted using 96.96 IFCs
configuration and following relevant protocols as indicated above with
the inclusion of BSA (see also results section). For quantification the
conventional and modeled approach were used to calculate the total
copies detected by the HT-qPCR, the copies were averaged per site and
compared to the microscopic data. For statistical analysis, ANOVA with
Tukey tests were used to determine significant difference between sites
for both indirect and direct methods (microscopy and genetic analysis).

\textbf{RESULTS}

\textbf{3.1. PHASE 1 - DEFINITION OF TARGET TAXA AND ESTABLISHMENT OF
REFERENCE DATABASES}

Following a review of WoRMS and the Irish Biodiversity Data Centre
databases, a total of 151 bivalve and 147 decapod species were
determined to occur in Irish waters (\textbf{supplementary Table S2} ).
Among these species were 24 selected target species as well as 48
bivalve and 18 decapod closely related off-target species (i.e. same
Order). The most relevant and appropriate gene commonly targeted for
molecular species identification of these taxa was identified as the
mitochondrial Cytochrome Oxidase I (COI) with some studies targeting the
16S rRNA gene and the PAPM gene (nuclear) . The COI gene was selected as
the main gene of interest for this study as it is more widely studied
and better represented via BOLD with more sequences present. While
relevant DNA sequence data was available on public databases for all
target species, no genetic data were available for 10/48 bivalve and
5/18 decapod species (\textbf{supplementary Table S2} ). Of the gathered
tissue material, barcoding and relevant DNA sequence data were obtained
from 49 samples (encompassing 15 distinct species) (88\% successful
amplification/sequencing) (\textbf{supplementary} \textbf{Table S3} ).
Tissue DNA belonging to\emph{Aequipecten opercularis} (queen scallop)
and \emph{Pecten maximus}(king scallop) did not yield satisfactory
results. As expected,\emph{Mytilus edulis, Mytilus trossulus} and
\emph{Mytilus galloprovincialis} were not resolved to the species level
using the COI gene. Overall, once previously available and newly
generated DNA sequences were included, the dedicated reference database
achieved coverage of 80\% (for bivalves) and 70\% (for decapods) of
target species (\textbf{supplementary Table S2} ).

\textbf{3.2. PHASE 2 -- \emph{IN SILICO} SELECTION AND \emph{IN
VITRO}ASSESSMENT OF MOLECULAR ASSAYS}

In total 23 different primer pairs were sourced from the literature (14
publications) and 62 additional primer pairs were designed and selected
for further \emph{in silico} testing (total n = 85 primer pairs).
Following initial assessment of each primer pair, 42 assays were
selected for further \emph{in vitro} testing, including 34 assays
targeting mitochondrial DNA 16s (n = 3) and COI (n = 31) genes. To
gather more information on gene variation and increased taxonomic
resolution for \emph{Mytilus spp} and \emph{Magallana gigas} , three
cytoplasmic organelle 18s gene assays (n = 3) as well as nuclear genes
(n = 5) were added to the selected assays (overview of all assays
in\textbf{supplementary Table S4} ).

Results from the initial panel of 42 assays showed that average amplicon
length was 104 bp and average primer length was 22.5 bp. The average
theoretical annealing temperature was 56.2\selectlanguage{ngerman}°C, ranging from 52.7 to
61.7°C, with 17/83 primers outside of the 55-60°C range. Delta-G values
of the whole panel averaged -3.9, however, 49/83 primers had one or more
reported Delta-G value higher than -6.0 with 26/83 primers having a
higher value than -9.0. When blasting the primers, 62/83 were found to
match only with the target species. When taking both primers into
account, 5 out of the 42 primer pairs matched some non-European
off-target species (\textbf{supplementary Table S4} ). Two primer pairs
matched with European species: `'\emph{M. gigas} COI 2'' matched
with\emph{C. angulata} (not occurring in Irish coastal waters) and
`'\emph{O. edulis} 16S'' matched with different \emph{Ostrea} species
and was therefore considered as an \emph{Ostrea} specific marker. Using
the machine learning approach for predicting cross-amplification
(eDNAassay), 32 out of 42 assays were found to have off-target AMP
values below 0.5 including the highly specific nuclear \emph{Mytilus
spp} markers, hence indicating high specificity potential for 71\% of
selected assays \textbf{(supplementary Table S5)} . The three nuclear
assays targeting the \emph{Mytilus} complex were \emph{Mytilus}
-specific but had AMP values ranging from 0.5 to 0.675. The results from
the COI showed eight assays to have high AMP (\textgreater{}0.5) with
off-target species, including five \emph{Ensis} -specific assays
(possible cross-amplification with other \emph{Ensis/Pharidae} species;
0.5-0.9 AMP), the \emph{C. glaucum} assay (possible cross amplification
with\emph{Parvicardium exiquum} (0.8 AMP)), the \emph{Cancer pagurus}
assay (possible cross amplification with \emph{Liocarcinus depurator}
(0.6 AMP)) and the \emph{Homarus americanus} assay (possible cross
amplification with \emph{Eurynome spinosa} (0.6 AMP)). Despite the
potential for cross-amplification, all 42 assays were tested \emph{in
vitro} , as described below.

\emph{Assay efficiency and sensitivity}

The \emph{in vitro} assessment of assays was carried out with 2 HT-qPCR
runs (96.96 IFC configuration, total 9,216 reactions). The average SD
from both runs was 1.8\% indicating little variation between the two
separate runs (\textbf{supplementary Table S6} ). The 42 assays had an
average efficiency of 94.5\% (R\textsuperscript{2} ranging from
0.99-1.00), with all assays showing efficiency of 90-102.3\%, with the
exception of three assays which were found to be below
90\%:\emph{Msquinado} COI (87.8\%), \emph{Pmaximus} COI1 (84.3\%),
and\emph{Medulis} PAPM (77.9 \%) (\textbf{Table 3,}
\textbf{supplementary Table S6} ). Most assays showed high detection
probabilities with 39 out of 42 assays having a LOD of \textless{}10
copies/\selectlanguage{greek}µ\selectlanguage{english}L and the remaining three assays at 100 copies/\selectlanguage{greek}µ\selectlanguage{english}L, lowering to
10-34 copies/\selectlanguage{greek}µ\selectlanguage{english}L when using the modelled approach (\textbf{Table 3,}
\textbf{supplementary Table S6} ). As for the Limit of Quantification
(LOQ) analysis, the discrete results ranged from 10-1000 copies/\selectlanguage{greek}µ\selectlanguage{english}L,
whereas the modelled approach for the LOQ ranged from 10-387 copies/\selectlanguage{greek}µ\selectlanguage{english}L,
with 23 out of 42 assays obtaining a 4.4-fold increase in LOQ by the
curve-fitting approach over the discrete approach (\textbf{Table 3,}
\textbf{supplementary Table S6} ). The LOQ could not be determined for
15 assays as it is below 10 copies. There was no evident pattern between
lower efficiency, LOQ/LOD and the observed primer variables. Of 42
assays, 33 assays had a LOQ \textless{}50 copies/\selectlanguage{greek}µ\selectlanguage{english}L, four assays had a
LOQ between 50-387 copies/\selectlanguage{greek}µ\selectlanguage{english}L, and the five remaining assays had a LOQ
\textless{}61 copies/\selectlanguage{greek}µ\selectlanguage{english}L. When assessing the quantification based on the
Standard curve, it appeared that some assays with similar efficiency had
different Cq-values, even though the starting quantity was the same for
all the targets. After the removal of the outlier assays the efficiency
of different assays showed a linear relationship with the intercept.
This model \emph{(p \textless{} 0.005, R2 = 0.7)} was used to
recalibrate the intercept, which was then used as an alternative method
to accurately quantify the copy numbers \textbf{(supplementary Table S7
and supplementary Appendix S2)} .

\emph{Specificity}

Each species/assay combination (26 *42 = 1092 combinations) yielded at
least four qPCR data points (i.e. assay in duplicate tested against the
target in duplicate). When assessing assay performance, consistency in
amplification across all four qPCR reactions was considered. The overall
results are shown in \textbf{Figure 2} . In total, separating Bivalvia
and Malacostraca, AMP values were generated for 276 reaction
combinations. 12 of these reactions showed minor but positive
amplification when the AMP value was below 0.3. 3 out of 55 reactions
were positive with AMP values of 0.3-0.4 whilst 0 out of 5 reactions
with AMP-values of 0.5 amplified. Of the higher AMP-values,\emph{Mytilus
edulis} assay reacted positively on the obtained \emph{M. trossulus}
specimen from Scotland, however, the obtained oligo based on reference
data remained negative resulting in it being considered an artifact. The
\emph{M. galloprovincialis} assay amplified \emph{M. edulis} target
(100000 copies) \textless{}10 copies (AMP=0.7). Following removal of the
below 0.2 AMP value reactions (as they were considered artifacts), of
the 42 assays tested 38 showed satisfactory levels of specificity to the
intended target species, whereby they either amplified only the target
or produced a distinct melt temperature. The 4 remaining assays
amplified other targets with similar melt curves, indicating a lack of
specificity. Out of these 4 assays, 2 assays\emph{Hamericanus}
COI\emph{,} and \emph{Oedulis} 16S showed substantial amplification in
other closely related off targets (signal strength above 10 copies/\selectlanguage{greek}µ\selectlanguage{english}L).
The other 2 assays (\emph{M. galloprovincialis}(PAPM), \emph{Mytilus} 3
(18S) showed positive but weak amplification (signal strength below 10
copies/\selectlanguage{greek}µ\selectlanguage{english}L). Both \emph{Mytilus} and\emph{Ensis} species complexes could
be successfully resolved based on inspecting melt curves and
cross-referencing amplification (or lack thereof) of multiple assays.
Specifically, the \emph{Mytilus} markers on the PAPM gene showed a range
of melt curves, including 81\selectlanguage{ngerman}°C (\emph{Mytilus edulis} ), 79-80°C
(\emph{Mytilus trossulus} ), and 79°C (\emph{Mytilus galloprovincialis}
). Similarly, the \emph{Ensis} species (COI) marker generated distinct
melt curves at 75°C (\emph{Ensis siliqua} ) and 77°C (\emph{Ensis ensis}
), hence providing some level of resolution within these species
complex. Based on the highest AMP-values tested, detected and the in
vitro testing 29 out of 42 assays were found to have no risk of false
positives (\textbf{Table 3,supplementary Table S6} ).. A total of 6/42
assays were found to have a risk that could be eliminated by another
assay present on the panel. Furthermore, a total of 4/42 assays were
considered non-specific and therefore the detection of \emph{C. pagarus}
and \emph{C. glaucum}cannot be confirmed if the absence of
\emph{Liocarcinus depurator} and\emph{Parvicardium exiguum} is
uncertain. The remainder of the target species could be successfully
detected by the newly developed HT-qPCR panel (\textbf{Table 3,}
\textbf{supplementary Table S6} ).

\textbf{3.3. PHASE 3 -- FIELD TESTING AND VALIDATION}

\emph{Testing inhibition}

IPC, within the context of the described triplex assay, was tested
against two commonly encountered PCR inhibitors (\textbf{Figure 3)} .
The reactions without BSA showed almost complete inhibition at 40 ng/\selectlanguage{greek}μ\selectlanguage{english}l
humic acid, with only the 100.000 copies IPC amplifying with a 17.5
cycle delay. BSA added to the PCR reaction prevented full inhibition
with a minor delay being between 0.5-1 cycle for 40 ng/\selectlanguage{greek}μ\selectlanguage{english}l, displaying a
clear reduction in inhibition. As expected, BSA had less of an effect on
EDTA, which caused inhibition for 2500 and 1250 \selectlanguage{greek}µ\selectlanguage{english}M in all PCR reactions.
The remaining samples tested in this study were all non-inhibited.

\emph{Field validation}

Amplification of internal controls (IACs) showed no observable
difference in Cq values between field samples and no template controls
(data not shown), indicating no potential inhibition issues and
confirming the robustness of the protocol when applied to DNA from
actual environmental samples. Screening of collected samples using the
direct method (e.g. microscopy), revealed the presence of bivalve larvae
in all the sampling sites (averaging 308 larvae per
m\textsuperscript{3}of seawater processed per site) and of decapods in
all sampling sites except for site 3 (ranging from one to seven
individual larvae per site) (\textbf{Table 4} ). Across all samples
collected, genetic screening using the developed HT-qPCR panel (indirect
method) resulted in 13 positive molecular markers detecting the presence
of DNA from seven different species, with \emph{Mytilus edulis} being
detected in all five sites (\textbf{Figure 4} ). Overall, number of
larvae and estimated DNA copy numbers (i.e. cumulatively among all
markers) showed similar patterns across the five sites for both bivalve
and crustaceans, with sites 1, 2 and 5 showing significantly higher
larvae/DNA than sites 3 and 4 (p-values varying from 0.0005***-0.05*,
Tukey test)\textbf{(Table 4).} The copy numbers detected for
\emph{Mytilus spp} varied across the three genes used (nuclear PAPM,
mitochondrial COI, and cytoplasmic 18S), but were consistent over sites
with a nuclear: mitochondrial: cytoplasmic ratio of 1: 16: 78, showing
potential to follow expected trends in terms of DNA copies per cell
(i.e. 1 nucleus vs many organelles and mitochondria). \emph{Cerastoderma
edule} and\emph{aequipecten opercularis} were detected in all sites
except for Site 3. The copy numbers of these species varied over the
different sites, with higher prevalence of \emph{A. opercularis} in
Sites 2 and 5\emph{, C. edule} in Sites 1 and 2. \emph{Mya arenaria} was
only consistently present in all replicates of Site 1, but sporadically
detected in Sites 3 and 4. \emph{Ensis magnus/ Phaxas pellucidus} was
detected in lower presence with most presence in Site 5. The only
decapod DNA detected was \emph{Carcinus maenas} in Site 5, which was the
site with the highest decapod larvae count observed using the direct
method (\textbf{Table 4} ). The lack of decapods' DNA detection in Sites
1, 2 and 4 (where decapod larvae were actually observed), implies that
these results are either false negatives due to either being below the
limit of detection or simply not present in the DNA samples due to very
low numbers, and/or the decapod larvae in these samples are not species
included in the current molecular HT-qPCR panel.

\textbf{4 \textbar{} DISCUSSION}

The aim of this study was to avail of a state-of-the-art technology
(HT-qPCR) to facilitate the cost- and time-effective development and
application of a molecular screening toolkit in a case study to detect a
range of bivalve and decapod larvae in zooplankton samples. The overall
process was divided into three main phases consisting of the definition
of target taxa and establishment of reference databases (PHASE
1);\emph{in silico} selection and \emph{in vitro} assessment of
molecular assays (PHASE 2); and field testing and validation (PHASE 3).
While PHASE 1 is communal to any general protocol aiming at developing
taxon-specific molecular assays, PHASE 2 and 3 are enabled by the
HT-qPCR platform. Following initial marker selection, and \emph{in
silico} and \emph{in vitro} assessment, the resulting panel of assays
included a total of 42 marker/species combinations targeting 24 species
of relevance to Irish coastal marine ecosystems and of interest to the
local shellfish aquaculture industry. The molecular toolkit (indirect
method) performed well when validated on actual zooplankton samples by
comparison to a direct method (microscopy), which was used to determine
presence and abundance of bivalve and decapod larvae. The following
sections address considerations and resulting recommendations of this
new approach, which shows great potential in boosting the development of
biomonitoring and ecological assessment tools.

\emph{Technical and practical considerations regarding the assay panel
development}

This study showed that a HT-qPCR system substantially boosted the number
of assays that can be tested \emph{in vitro} (e.g. 96 assays in a single
run) resulting in great time-effectiveness and therefore, cost
efficiency proposed a 5-level validation scale for determining the
readiness of eDNA assays, ranging from ``Level 1 -- Incomplete'' to
``Level 5 -- Operational''. When available, a HT-qPCR system could
therefore play a crucial role in transitioning many molecular assays
from Level 2 (i.e. testing assays against non-target organisms) to Level
3 and 4 (validating assays in the field), because many assays can be
tested in parallel. Level 4 differs from Level 5 as it is missing
statistical modelling for detection probability, but both these two last
levels are considered valid to provide satisfactory interpretation of
presence/absence of target species. Furthermore, the possibility to
co-screen a given sample for many species in a single qPCR run would
also result in saving precious eDNA template, hence increasing the
potential number of targets that can be tested.

Efficiency was satisfactory even though assays were combined into
communal thermal cycling conditions, confirming robustness of marker
selection steps and reagents used. However, issues with standard for
quantification delay in pools of synthetic targets were unexpected. This
study experimenting using a second quantification method, which made
quantification more accurate but needs further investigation. In future
studies comparison between synthetic and pools of genomic DNA of known
quantity from target organisms could circumvent these problems of
quantification.

Using a machine learning tool (eDNAassay, Kronenberger et al., 2022)
during the \emph{in-silico} selection of markers proved to be very
useful in providing an initial screening of marker panels and in
particular, to assess potential biases due to data gaps in the reference
database used. For instance, 17 out of the 42 selected assays showed
higher affinity (i.e. higher AMP values) for species not necessarily
closely related to the target species, for example, \emph{Ensis} assays
with \emph{P. pellucidus,} which combined with the absence of reference
data from another closely related Pharidae species, \emph{Pharus
legumen} , could lead to potential false positives. This suggests that
the specificity of some assays may be lower than expected. Nonetheless,
when selected markers were tested \emph{in vitro} , the concordance
between expected and observed amplification was good, but in sporadic
instances low AMP values still reacted positive. To mitigate, in
uncertain situations, multiple markers are preferably selected for
various reasons i) to account for the possibility that intraspecific
variation prohibits amplification, ii) to increase the target
specificity in the case of markers not being fully diagnostic in
relation to other species, iii) to quantify the presence of targets more
accurately. In addition, multiple markers can also help
troubleshoot\emph{in vitro} experiments by identifying possible
contamination. This study identified 21 different primer pairs from
literature, eventually, only five were used with four being redeveloped
as adaptations on existing primers. Most primers from the literature
were either developed for a different purpose or did not fit in the
assay requirements. The development of a multi-species panel requires
strict rules to make multiple assays work under the same circumstances.
Protocol optimization showed that extraction protocol chosen and
inclusion of BSA are recommended for genetic screening of zooplankton
samples.

Assays developed using the HT-qPCR system are expected to be directly
transferable to conventional qPCR instruments ; however various aspects
especially related to quantification and sensitivity (e.g. LOD, LOQ)
should be re-estimated and confirmed when using a different instrument.
Nonetheless, as indicated above, the use of a HT-qPCR system enables the
rapid testing of multiple assays in parallel, which is a time-consuming
step in assay validation, prior to executing further downstream steps in
the laboratory.

\emph{Validation of the molecular toolkit using zooplankton samples}

This study showed good correlation between counts of decapod and bivalve
larvae using microscopy (direct method) and strength of genetic signal
(indirect method), with the latter providing greater taxonomic
resolution power to the species level. Even though previous studies
successfully found a similar correlation, it is unclear how the size of
larvae and different genetic markers affect signal strength . In this
study, the different assays in genes show different results in COI, 18S
and nuclear genes, meaning that gene selection is of great importance.

The samples collected for this study were carried out using a 100\selectlanguage{greek}µ\selectlanguage{english}m mesh
plankton net. Even though such mesh size is not expected to trap smaller
fractions of DNA that may be present in the environment such as gametes
or other cells released by adults, there was a risk that some target DNA
present in a given sample did not belong to larvae (e.g. eDNA adsorbed
to larger particles). While this aspect can be challenging to elucidate,
the variation of DNA signal in multiple field replicates (n = 5) and the
relative proximity of the locations sampled suggested that DNA of target
organisms was not ubiquitous and when detected showed differential
strength. While discerning fractions of eDNA in plankton samples is
beyond the scope of this study, these results emphasize the importance
of field replication as well as the usefulness of providing semi
quantitative data (e.g. beyond presence-absence. The possibility to
incorporate multiple markers/genes targeting the same taxon (species or
genus) makes HT-qPCR systems attractive, as this can prove useful in
resolving co-occurring closely related species and possibly provide more
reliable estimates of DNA copy numbers and hence relative abundance or
biomass. For instance, \emph{Mytilus} spp and \emph{Ensis} spp complexes
consist of very closely related species in which hybridization might
occur. However, single marker approaches, as developed by Inoue et al.
(1995) only provide an indication of a genotype but lack the resolution
power to clearly distinguish between species. Another benefit of HT-qPCR
tool is that some markers can rule out possible false positives. For
example, \emph{C. pagurus} and \emph{E. siliqua} are species that cannot
be quantified and due to a risk of false positives, including markers
for \emph{P. pellucidus} , \emph{P. exiguum} , \emph{L. depurator} could
potentially make the panel fully reliable for the species tested.

Finally, this toolkit proved useful in screening plankton samples for
the DNA of commercially important species and will be useful in the
future for better understanding spawning activities in support of local
aquaculture shellfish industry.

\emph{Recommendations for future work and applications}

This study has generated some valuable genetic resources, namely
species-specific assays for a range of important species
including\emph{Mytilus} spp. The toolkit could be used in future studies
if a HT-qPCR platform is accessible, or individual assays could be used
using conventional qPCR instruments. The latter would require some
further validation. The overall workflow presented here follows general
guidelines for the development of biomonitoring HT-qPCR toolkits,
encompassing applications such as monitoring of species of conservation
concern, surveillance of harmful or unwanted species (harmful algae or
invasive species), and supporting fisheries and aquaculture sectors.

Future work includes the continuous improvement of reference DNA
databases For instance, in this study, species of scallop (\emph{A.
opercularis} and \emph{P. maximus} ) were not successfully barcoded
using a general metazoan protocol , highlighting the need for refining
barcoding procedures. Besides taxonomic gaps, public repositories such
as GenBank are also known to be hampered by errors , hence flagging the
need for the establishment of dedicated and curated databases. On the
ecological aspect of this study, current developed methodology needs to
be implemented in national routine screening programmes, gathering data
over time for the different shellfish species, to generate baseline data
and understand distribution and spawning activities. In order to achieve
this, a better understanding is needed to translate copy numbers into
number of larvae of different species, using different genes Eventually
this could lead to fast and cost-effective data collection enabling
occupancy modelling or larval dispersal modelling while taking into
account life history of species (e.g. duration of planktonic stages)

\textbf{ACKNOWLEDGEMENTS}

The authors are grateful to Bord Iascaigh Mhara (BIM) for assistance
with the project, including sampling aboard the T Burke II survey boat
and financial support (Refs: RFT146818, RFT150321). Special thanks go to
Terence O'Carroll, Nicholas Chopin, Patricia Daly, and Joanne Gaffney.
Financial support was provided by Ireland's European Maritime and
Fisheries Fund (EMFF) Operational Program, co-funded by the Irish
Government and the EU, as well as from an Irish Research Council
postgraduate scholarship (Ref: GOIPG/2021/1712).

\textbf{REFERENCES}

\textbf{DATA ACCESSIBILITY AND BENEFIT SHARING SECTION}

\textbf{Data Accessibility Statement}

Raw data and R scripts are available upon request and safely stored on
hard drives, and eventually shared on GITHUB repositories. Raw sequence
reads are deposited to NCBI Nucleotide Database (XXXX) including the
related metadata can be found in XXX and provides unique sample
identifier tags that can be matched to both the deposited genetic data
and deposited metadata (for haplotypes, individual sample identifiers
and their corresponding haplotype). All samples and reagents are
carefully archived in Atlantic Technological Institute in the Marine and
Freshwater Research Centre.

\textbf{Benefit-Sharing Statement}

Benefits Generated: A research collaboration was developed with BIM and
ATU and the results of research have been shared with the provider
communities and the broader scientific community (see above). This
includes the screening tool being adopted and tested on samples
collected by local fisherman. In addition, benefits from this research
accrue from the shared paper with guidelines that can be followed in
future research and experimentation.

\textbf{Author Contributions}

Dennis van der Pouw Kraan: Conceptualisation, Investigation,
Methodology, Lab work, Validation, Writing - original draft, Formal
analysis.

Conor T. Graham: Conceptualisation, Writing - review \& editing,
Supervision.

Fiona Kavanagh: Conceptualisation, Writing - review \& editing,
Supervision.

Luca Mirimin: Conceptualisation, Investigation, Methodology, Validation,
Writing -- original draft/review \& editing, Supervision, Project
administration, Resources,

\textbf{Tables and Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1:} Outline and workflow of the methodological approach
used in the present study.

\textbf{Table 1: Target species (n = 24) identified as main targets in
this study. Tissue and/or DNA barcode data availability are also
displayed.}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{CLASS} & \textbf{ORDER/INFRAORDER} & \textbf{SPECIES SCIENTIFIC
NAME} & \textbf{SPECIES COMMON NAME} & \textbf{BARCODE
SOURCE}\tabularnewline
\midrule
\endhead
\textbf{Bivalvia} & \textbf{Mytilida} & \emph{Mytilus galloprovincialis}
(Adriatic sea, Italy) & Mediterranean mussel & In house\tabularnewline
& & \emph{Mytilus edulis} (Killary fjord, Ireland) & Blue mussel & In
house\tabularnewline
& & \emph{Mytilus trossulus} (Scotland) & Bay mussel &
Public\tabularnewline
& & \emph{Mytilus galloprovincialis-edulis hybrid genotype} (Ireland) &
Blue mussel & In house\tabularnewline
& & \emph{Mytilus trossulus-edulis hybrid genotype (}Ireland\emph{)} &
Blue mussel & Public\tabularnewline
& \textbf{Ostreida} & \emph{Ostrea edulis} & European Native oyster & In
house\tabularnewline
& & \emph{Magallana gigas} & Pacific oyster & In house\tabularnewline
& \textbf{Pectinida} & \emph{Pecten maximus} & King scallop &
Public\tabularnewline
& & \emph{Aequipecten opercularis} & Queen scallop &
Public\tabularnewline
& \textbf{Cardiida} & \emph{Cerastoderma edule} & Common cockle & In
house\tabularnewline
& & \emph{Cerastoderma glaucum} & the lagoon cockle &
Public\tabularnewline
& \textbf{Adapedonta} & \emph{Ensis ensis} & (Sword) Razor Clam & In
house\tabularnewline
& & \emph{Ensis siliqua} & Pod razor & In house\tabularnewline
& & \emph{Ensis leei (Ensis directus)} & Atlantic jackknife clam &
Public\tabularnewline
& & \emph{Ensis magnus (Ensis arcuatus)} & Sword razor clam &
Public\tabularnewline
& \textbf{Myida} & \emph{Mya arenaria} & Soft-shell clam &
Public\tabularnewline
& \textbf{Venerida} & \emph{Spisula solida} & Surf clam &
Public\tabularnewline
& & \emph{Mulinia lateralis} & dwarf surf clam & Public\tabularnewline
& & \emph{Rangia cuneata} & wedge clam & Public\tabularnewline
\textbf{Malacostraca} & \textbf{Brachyura} & \emph{Carcinus maenas} &
Littoral crab & In house\tabularnewline
& & \emph{Maja brachydactyla (Maja squinado)} & Spider crab & In
house\tabularnewline
& & \emph{Cancer pagurus} & Brown crab & In house\tabularnewline
& & \emph{Necora puber} & Velvet crab & In house\tabularnewline
& \textbf{Caridea} & \emph{Palaemon serratus} & Common prawn & In
house\tabularnewline
& \textbf{Astacidea} & \emph{Homarus gammarus} & European lobster & In
house\tabularnewline
& & \emph{Nephrops norvegicus} & Dublin Bay prawn & In
house\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{CRITERIUM \#} & \textbf{DESCRIPTION} & \textbf{OPTIMAL RANGE} &
\textbf{ACCEPTABLE RANGE}\tabularnewline
\midrule
\endhead
\textbf{I} & length fragment & \textless{} 100 bp &
\textless{}300bp\tabularnewline
& length primers & 17-25 bp & 17-30 bp\tabularnewline
\textbf{II} & Theoretical melt temperature & \textgreater{}55\selectlanguage{ngerman}°C -
\textless{}60°C & \textgreater{}50\selectlanguage{ngerman}°C - \textless{}65°C\tabularnewline
& range of variation in melt temperature for developed toolkit &
\textless{} 5 \selectlanguage{ngerman}°C & 5 \selectlanguage{ngerman}°C - 10 °C\tabularnewline
\textbf{III} & GC\% & 40-60 & 30-70\tabularnewline
\textbf{IV} & \selectlanguage{greek}Δ\selectlanguage{english}G hairpin, self-dimer, hetero dimer & \textless{} 6.00 &
\textless{} 9.00\tabularnewline
& bases suitable for binding with own primer sequence & \textless{}6 &
\textless{}10\tabularnewline
\textbf{V} & BLAST identity & \textgreater{}90\% &
\textgreater{}99\%\tabularnewline
\textbf{VI} & Mismatches both primer site closely related species
(Ireland) & \textgreater{}10 & \textgreater{}6\tabularnewline
& Mismatches each primer site (within first 5 basepairs on 3') &
\textgreater{}3 & \textgreater{}1\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2: Criteria used for checking suitability of existing and
new primer sets used in PHASE 2.}

\textbf{Table 3: Efficiency, sensitivity, and specificity of the newly
developed HT-qPCR panel comprising 42 assays.}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\textbf{N} & \textbf{Species name} & \textbf{Assay name} & \textbf{Eff
(\%)} & \textbf{LOD} & \textbf{LOQ} & \textbf{AMP \textless{} 0.5} &
\textbf{specific} & \textbf{RISK FALSE POSITVE}\tabularnewline
\midrule
\endhead
1 & \emph{A. opercularis} & \emph{Aopercularis}16S & 97.2 &
\textless{}10 & 11 & Y & Y & NO RISK\tabularnewline
2 & \emph{C. edule} & \emph{Cedule}COI1 & 95.1 & \textless{}10 & 43 & Y
& Y & NO RISK\tabularnewline
3 & \emph{} & \emph{Cedule}COI2 & 96.2 & \textless{}10 & 31 & Y & Y & NO
RISK\tabularnewline
4 & \emph{C. glaucum,} & \emph{Cglaucum}COI & 98.6 & \textless{}10 & 10
& N & Y & Parvicardium exiguum~ (0.8)\tabularnewline
5 & \emph{C. maenas} & \emph{Cmaenas\_dpk}COI1 & 97.1 & \textless{}10 &
15 & Y & Y & NO RISK\tabularnewline
6 & \emph{} & \emph{Cmeanas\_crane}COI2 & 92.7 & \textless{}10 & 10 & Y
& Y & NO RISK\tabularnewline
7 & \emph{C. pagurus} & \emph{Cpagurus}COI & 86.8 & \textless{}10 & 10 &
N & Y & Liocarcinus depurator (0.6)\tabularnewline
8 & \emph{Ensis spp.} & \emph{Ensisspp COI} & 92.8 & \textless{}10 & 32
& N & Y & HIGH RISK NOT SPECIFIC\tabularnewline
9 & \emph{E. ensis} & \emph{Eensis}COI1 & 96.2 & \textless{}10 & 24 & Y
& Y & NO RISK\tabularnewline
10 & \emph{} & \emph{Eensis}COI2 & 96.9 & \textless{}10 & 43 & N & Y &
Phaxas pellucidus (0.5)\tabularnewline
11 & \emph{E. siliqua} & \emph{Esiliqua}COI & 97.8 & \textless{}10 & 10
& N & N & E. magnus (0.5)\tabularnewline
12 & \emph{E. leei} & \emph{Eleei}COI1 & 96.3 & \textless{}10 & 10 & N &
Y & Phaxas pellucidus (0.9)\tabularnewline
13 & \emph{} & \emph{Eleei}COI2 & 95.0 & \textless{}10 & 37 & N & Y & NO
RISK\tabularnewline
14 & \emph{E. magnus} & \emph{Emagnus}COI & 102.3 & \textless{}10 & 15 &
N & Y & Phaxas pellucidus (0.5)\tabularnewline
15 & \emph{H. americanus} & \emph{Hamericanus}COI & 96.8 & \textless{}10
& 10 & N & N & Eurynome spinosa (0.6)\tabularnewline
16 & \emph{H. gammarus} & \emph{Hgammarus}COI0 & 91.3 & \textless{}10 &
112 & N & Y & \textbf{amplifies H. americanus}\tabularnewline
17 & \emph{} & \emph{Hgammarus}COI1 & 98.4 & \textless{}10 & 31 & Y & Y
& NO RISK\tabularnewline
18 & \emph{M squinado} & \emph{Msquinado} COI & 87.8 & \textless{}10 &
22 & Y & N & NO RISK\tabularnewline
19 & \emph{Mytilus spp.} & \emph{Mytilusspp\_}diasPAPM & 89.6 &
\textless{}10 & 49 & Y & Y & NO RISK\tabularnewline
20 & \emph{} & \emph{Mytilusspp\_}dpkPAPM & 92.1 & \textless{}10 & 15 &
Y & Y & NO RISK\tabularnewline
21 & \emph{} & \emph{Mytilusspp}18S2 & 94.1 & 34 & 387 & Y & Y & NO
RISK\tabularnewline
22 & \emph{} & \emph{Mytilusspp}18S3 & 100.1 & \textless{}10 & 12 & Y &
N & NO RISK\tabularnewline
23 & \emph{} & \emph{MytilussppCOI1} & 93.3 & \textless{}10 & 27 & Y & N
& NO RISK\tabularnewline
24 & \emph{} & \emph{MytilussppCOI2} & 95.7 & \textless{}10 & 10 & Y & N
& NO RISK\tabularnewline
25 & \emph{M. edulis} & \emph{MedulisPAPM} & 77.9 & \textless{}10 & 61 &
N & Y & NO RISK\tabularnewline
26 & \emph{M. galloprovincialis} & \emph{MgalloprovincialisPAPM} & 95.5
& \textless{}10 & 10 & N & N & M. edulis (0.7)\tabularnewline
27 & \emph{M.trossolus} & \emph{MtrossolusPAPM} & 97.5 & \textless{}10 &
10 & N & Y & NO RISK\tabularnewline
28 & \emph{M.arenaria} & \emph{MtarenariaCOI1} & 96.7 & \textless{}10 &
14 & Y & Y & NO RISK\tabularnewline
29 & \emph{} & \emph{MtarenariaCOI3} & 102.1 & \textless{}10 & 10 & Y &
Y & NO RISK\tabularnewline
30 & \emph{M. lateralis} & \emph{MlateralisCOI} & 98.1 & \textless{}10 &
10 & Y & Y & NO RISK\tabularnewline
31 & \emph{M. gigas} & \emph{Mgigas\_}sanchez16S & 93.8 & \textless{}10
& 55 & Y & Y & Crassostrea angulata (1.0)\tabularnewline
32 & \emph{} & \emph{Mgigas}18S & 91.3 & 14 & 59 & Y & Y & NO
RISK\tabularnewline
33 & \emph{} & \emph{Mgigas}COI1 & 97.0 & \textless{}10 & 49 & Y & Y &
NO RISK\tabularnewline
34 & \emph{} & \emph{Mgigas}COI2 & 93.6 & \textless{}10 & 24 & Y & Y &
Crassostrea angulata (1.0)\tabularnewline
35 & \emph{N. norvegicus} & \emph{Nnorvegicus}COI2 & 92.4 &
\textless{}10 & 10 & Y & Y & NO RISK\tabularnewline
36 & \emph{O. edulis} & \emph{Oedulis}16S & 95.3 & \textless{}10 & 29 &
N & N & M.gigas (0.7)\tabularnewline
37 & \emph{} & \emph{Oedulis}COI1 & 96.2 & \textless{}10 & 38 & Y & N &
NO RISK\tabularnewline
38 & \emph{P. maximus} & \emph{Pmaximus}COI1 & 84.3 & \textless{}10 & 10
& Y & Y & NO RISK\tabularnewline
39 & \emph{} & \emph{Pmaximus}COI3 & 90.2 & \textless{}10 & 10 & Y & Y &
NO RISK\tabularnewline
40 & \emph{P. serratus} & \emph{Pserratus}COI1 & 91.0 & \textless{}10 &
44 & Y & Y & NO RISK\tabularnewline
41 & \emph{} & \emph{Pserratus}COI2 & 98.4 & \textless{}10 & 10 & Y & Y
& NO RISK\tabularnewline
42 & \emph{R. cuneata} & \emph{Rcuneata}COI & 98.4 & \textless{}10 & 94
& Y & Y & NO RISK\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/640847/articles/655499-development-and-validation-of-a-dna-based-multi-species-biomonitoring-toolkit-using-a-high-throughput-qpcr-platform-a-case-study-of-irish-shellfish-species}

\textbf{Figure 2:} Assay performance displayed as strength of
amplification expressed as copy numbers (according to scale) (top
section of cell with colour and number) and calculated AMP value (bottom
section of cell in greyscale). The copy numbers used in the comparison
are the modelled copy numbers.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 3:} Inhibition experiment with 5 inhibitor concentrations
for EDTA (\textbf{black} ) and Humic acid (\textbf{grey} ). The two
vertical lines represent inhibition boundaries for each concentration.
BSA almost completely prevented inhibition for Humic Acid.

\textbf{Table 4} Number and estimated DNA copy number of bivalve and
decapod crustacean larvae in the five sampling sites (five field
replicate per site) using direct (microscopy) and indirect (genetic)
methods. *: presented as the sum of estimated DNA copies/\selectlanguage{greek}µ\selectlanguage{english}l over all
positive assays. \textsuperscript{\#}: values displayed
for\emph{Carcinus maenas} COI assay only, as it was the only decapod
species that amplified. Significant groups (A-B) and values
(\textgreater{}0.05-0.005*, \textgreater{}0.0005***) obtained using a
Tukey-test.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllll@{}}
\toprule
& \textbf{Bivalves} & \textbf{Bivalves} & \textbf{Bivalves} &
\textbf{Bivalves} & \textbf{Bivalves} & \textbf{Bivalves} &
\textbf{Bivalves} & \textbf{Bivalves} & \textbf{Decapods} &
\textbf{Decapods} & \textbf{Decapods}\tabularnewline
\midrule
\endhead
& \textbf{DIRECT METHOD (microscopy)} & \textbf{DIRECT METHOD
(microscopy)} & \textbf{DIRECT METHOD (microscopy)} & \textbf{INDIRECT
METHOD (genetics)*} & \textbf{INDIRECT METHOD (genetics)*} &
\textbf{INDIRECT METHOD (genetics)*} & \textbf{INDIRECT METHOD
(genetics)*} & \textbf{INDIRECT METHOD (genetics)*} & \textbf{DIRECT
METHOD (microscopy)} & \textbf{INDIRECT METHOD
(genetics)\textsuperscript{\#}} & \textbf{INDIRECT METHOD
(genetics)\textsuperscript{\#}}\tabularnewline
& \textbf{Number of larvae per m\textsuperscript{3}} & \textbf{SD
(across field reps)} & \textbf{Significance group (Tukey-test ANOVA)} &
\selectlanguage{greek}\textbf{DNA copies/µl run}\selectlanguage{english} & \textbf{SD (across field reps)} &
\selectlanguage{greek}\textbf{DNA copies/µl model}\selectlanguage{english} & \textbf{SD (across field reps)} &
\textbf{significance (Tukey-test ANOVA)} & \textbf{Number of decapoda
found in all replicates} & \selectlanguage{greek}\textbf{DNA copies/µl run}\selectlanguage{english} & \selectlanguage{greek}\textbf{DNA
copies/µl model}\selectlanguage{english}\tabularnewline
\textbf{Site 1} & 442 & 149 & A* & 119519 & 30914 & 145818 & 31949 & A*
& 3 & - & -\tabularnewline
\textbf{Site 2} & 592 & 275 & A*** & 91604 & 32293 & 126132 & 35644 &
A*** & 4 & - & -\tabularnewline
\textbf{Site 3} & 37 & 12 & B*** & 11364 & 7645 & 13817 & 9405 & B*** &
0 & - & -\tabularnewline
\textbf{Site 4} & 99 & 50 & B * & 34387 & 18403 & 45308 & 24089 & B * &
1 & - & -\tabularnewline
\textbf{Site 5} & 370 & 162 & A * & 106104 & 64140 & 133081 & 98351 & A
* & 7 & 2502 & 11044\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 4: Field sample analysis with both microscopic analysis
and the newly developed HT-qPCR panel.} number of copies detected in
each site for each assay using i) quantification based on LOD/LOQ runs
for assay optimisation, ii) quantification based on current run
st-curve, iii) quantification based on the modelled intercept described.

\selectlanguage{english}
\FloatBarrier
\end{document}

