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\begin{document}

\title{Qualitative and quantitative DNA metabarcoding assessment of Neotropical
ichthyoplankton communities is marker-dependent}



\author[1]{Daniel Teixeira}%
\author[2]{Heron Hilário}%
\author[3]{Gilmar Santos}%
\author[2]{Daniel Carvalho}%
\affil[1]{UFMG}%
\affil[2]{Pontifícia Universidade Católica de Minas Gerais}%
\affil[3]{Pontificia Universidade Catolica de Minas Gerais}%


\vspace{-1em}



  
  \date{March 13, 2025}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The study of ichthyoplankton is paramount to understanding fish
assemblages' reproductive dynamics. DNA metabarcoding has been applied
as a rapid, cost-effective, and accurate taxonomy tool, allowing the
identification of multiple individuals simultaneously. However, there
remain significant challenges when using DNA metabarcoding, such as
molecular marker choice according to the taxonomic resolution and length
of the fragment to be sequenced, primer bias, incomplete reference
databases, and qualitative inferences incongruences. Here, 30
ichthyoplankton pools collected from a Neotropical River were identified
at a molecular level using DNA metabarcoding to compare the resolution,
sensibility, specificity and relative read abundance (RRA) recovery of
three molecular markers: the standard COI fragment (650pb, with each
strand analyzed individually) and two short 12S rRNA genes markers (?
200bp - NeoFish and MiFish markers). The combined use of the three
markers increased the genera detection rates by 25\% to 87.5\%, allowing
an increased taxonomic coverage and robust taxonomic identification of
complex neotropical ichthyoplankton communities. RRA is
marker-dependent, indicating caution is still needed whilst inferring
species abundance based on DNA metabarcoding data when using
PCR-dependent protocols.%
\end{abstract}%



\sloppy


\textbf{Qualitative and quantitative DNA metabarcoding assessment of
Neotropical ichthyoplankton communities is marker-dependent}

Running Title: \emph{Qualitative and quantitative DNA metabarcode}

Daniel Fonseca Teixeira\textsuperscript{1, 2}, Heron Oliveira
Hil\selectlanguage{ngerman}ário\textsuperscript{1}, Gilmar Bastos Santos\textsuperscript{1},
Daniel Cardoso Carvalho\textsuperscript{1*}

1- Post-Graduate Program in Vertebrate Biology, Pontifical Catholic
University of Minas Gerais, PUC Minas, Belo Horizonte 30535-610, Brazil;
danielcarvalho@pucminas.br (D.C.C.); heronoh@gmail.com (H.O.H);
gilmarsantos4@hotmail.com (G.B.S.)

2- Post-Graduate Program in Genetics, Federal University of Minas
Gerais, Belo Horizonte 31270-901, Brazil; danielfonsecat@gmail.com
(D.F.T.);

*Corresponding author: Daniel Cardoso Carvalho
(danielcarvalho@pucminas.br)

\textbf{Abstract}

The study of ichthyoplankton is paramount to understanding fish
assemblages' reproductive dynamics. DNA metabarcoding has been applied
as a rapid, cost-effective, and accurate taxonomy tool, allowing the
identification of multiple individuals simultaneously. However, there
remain significant challenges when using DNA metabarcoding, such as
molecular marker choice according to the taxonomic resolution and length
of the fragment to be sequenced, primer bias, incomplete reference
databases, and qualitative inferences incongruences. Here, 30
ichthyoplankton pools collected from a Neotropical River were identified
at a molecular level using DNA metabarcoding to compare the resolution,
sensibility, specificity and relative read abundance (RRA) recovery of
three molecular markers: the standard COI fragment (650pb, with each
strand analyzed individually) and two short 12S rRNA genes markers (\selectlanguage{english}[?]
200bp - NeoFish and MiFish markers). The combined use of the three
markers increased the genera detection rates by 25\% to 87.5\%, allowing
an increased taxonomic coverage and robust taxonomic identification of
complex neotropical ichthyoplankton communities. RRA is
marker-dependent, indicating caution is still needed whilst inferring
species abundance based on DNA metabarcoding data when using
PCR-dependent protocols.

\textbf{Keywords: Molecular taxonomy, COI, 12S rRNA, High-throughput DNA
sequencing 1. Introduction}

The study of ichthyoplankton composition, abundance and distribution is
pivotal for understanding the reproductive dynamics of local fish
assemblages (Mariac et al., 2018). The analysis of these parameters
allows the identification of spawning sites, nursery areas where
recruitment occurs, migration routes, temporal and spatial pattern
variations and differences in the reproduction patterns of migratory and
nonmigratory fish (Baumgartner et al., 2004; Bialetzki et al., 2005;
Reynalte-Tataje et al., 2012). This information is instrumental in
elucidating the influence of anthropogenic environmental alterations on
fish reproduction and in the definition of effective management actions
for species conservation and, consequently, fishing stock maintenance
(da Silva et al., 2015; Silva et al., 2017).

Traditionally, ichthyoplankton taxonomy has applied the regressive
development sequence technique, based on the morphological comparison of
younger larvae with previously identified juveniles (Ahlstrom and Moser,
1976; Nakatani et al., 2001). However, due to the absence of
morphological diagnostic characters during the egg stage, some authors
exclude them from the studies and resort to identifying exclusively
larvae, which in the initial stages is also a difficult task
(Baumgartner et al., 2008; Reynalte-Tataje et al., 2012). Moreover, the
accuracy of the traditional morphological identification can diverge
between taxonomists and laboratories, according to their experience and
specialty (Ko et al., 2013). These limitations can compromise surveying
essential information to conserve the areas of interest (Nobile et al.,
2019).

Studies have employed molecular techniques to strengthen the precision
and reliability of ichthyoplankton taxonomy. Comparative investigations
have demonstrated that molecular taxonomy using DNA barcoding is more
efficient than traditional morphological taxonomy, identifying the eggs
and larvae to lower taxonomic levels and correcting erroneous
morphological identifications (Becker et al., 2015; Ko et al., 2013).
Using DNA barcoding, (Frantine-Silva et al., 2015) identified over 99\%
of 536 ichthyoplankton samples at species levels, including eggs, which
accounted for 30\% of the observed species richness. Morphologically,
(Becker et al., 2015) identified eggs only as migratory or nonmigratory,
when possible, while DNA barcoding allowed the identification of eggs
(plus damaged larvae) to species level, and highlighted imprecisions in
the morphological taxonomy even with such broader analysis. Nonetheless,
despite its great taxonomic precision, DNA barcoding relies on
individual processing and sequencing of each organism, and can become
expensive and laborious for large scale inventories (Taberlet et al.,
2012; Yu et al., 2012), such as ichthyoplankton studies (Mariac et al.,
2018; Nobile et al., 2019).

The DNA metabarcoding approach, using High-Throughput Sequencing (HTS),
has gained prominence for its ability to allow massive biodiversity
access and transform ecology (Yu et al., 2012). The method combines DNA
barcode-based taxonomy with HTS to simultaneously identify hundreds to
thousands of organisms. DNA metabarcoding analyses are economical,
quick, broad, minimally dependent on taxonomic expertise, and its data
remain available for further verification (Taberlet et al., 2012; Yu et
al., 2012). This approach has allowed the reconstruction of ancestral
communities (Jorgensen et al., 2012), biodiversity monitoring (Andersen
et al., 2012), and detection of larger operational taxonomic units in a
fraction of the time spent in conventional studies based on morphology
and DNA barcoding (Fonseca et al., 2010). This approach has also shown
high efficiency in ecological ichthyoplankton studies, allowing precise
and reliable identification of fish egg and larva bulk samples
(Kimmerling et al., 2018; Mariac et al., 2018).

Different from environmental samples (for example, soil and water), in
which genetic material is often degraded, bulk samples usually provide
genomic DNA of better quality, allowing the amplification of markers
with more extensive sequences (Taberlet et al., 2012). However, the HTS
platforms accessible to most research laboratories have limited
sequencing lengths of up to 600 base pairs (bp). This hampers the usage
of markers previously standardized for DNA barcoding, such as the 650 bp
fragment of the mitochondrial cytochrome c oxidase subunit I (COI) gene
commonly used for fish (Ward, 2009). Additionally, the variability in
COI sequences hinders the design of internal minibarcode primers, taking
some researchers to pass this gene over in favor of more conserved ones
for metabarcoding (Deagle et al., 2014). Among these conserved genes,
mitochondrial 12S rRNA has been highlighted as a good alternative for
fish metabarcoding (Milan et al., 2020; Miya et al., 2020; Sales et al.,
2021).

Besides marker selection, another challenge in DNA metabarcoding is
quantitative analysis. Some factors can bias the number of read copies
obtained for each individual or species, such as the number of
mitochondria per cell, different-sized individuals in the same sample,
and amplification bias (Carvalho, 2022; Fonseca, 2018). Nonetheless,
some studies have shown a positive correlation between the number of
eggs or larvae in mock samples and the number of reads obtained for each
taxon using DNA metabarcoding with an amplification step (Duke and
Burton, 2021; Nobile et al., 2019).

This study used DNA metabarcoding to analyze the composition of
ichthyoplankton sampled at the Neotropical megadiverse Sao Francisco
River Basin, in Brazil. Additionally, the sensibility, specificity, and
taxonomic resolution of two 12S markers were tested and compared with
the traditional COI fragment used for DNA barcoding. The results
obtained here will contribute to an improved method for ecological
studies focusing on the ichthyofauna reproductive dynamics, and to
design management and conservation strategies for the maintenance of
fish reproduction locally.

\textbf{2. Materials and Methods}

\emph{Sample collection}

The Sao Francisco River Basin harbors at least 205 fish species (Alves
et al., 2007), making it an excellent challenge for testing the
sensibility and resolution of different markers within such complex
Neotropical ichthyofauna. Thirty ichthyoplankton samples were used,
denoted SF01 to SF30, 22 composed of eggs and eight composed of larvae.
The larvae samples were assembled with similar-sized fragments of all
specimens to minimize bias due to larvae size and relative read
abundance estimative of input DNA from each pool.

\emph{Genomic DNA extraction}

Genomic DNA was extracted from samples containing a pool of fish eggs
and larvae fixed in ethanol. To ensure the complete evaporation of the
alcohol, initially the excess was removed through pipetting, and then
the microtubes were kept open for three hours at 55degC. We then added
600\selectlanguage{greek}µ\selectlanguage{english}l of TNES buffer to each sample and ground the bulk with a plastic
pestle until only minuscule tissue fragments were left. Next, 20\selectlanguage{greek}µ\selectlanguage{english}l of
proteinase K (20mg/ml) was added to each microtube. The samples were
kept at 55\selectlanguage{ngerman}°C until complete tissue digestion. Finally, the genomic DNA
was extracted using a low-cost saline protocol adapted from (Aljanabi
and Martinez, 1997).

The samples were quantified using a Qubit 4 Fluorometer (Thermo Fisher)
with a 1x dsDNA HS Assay Kit (Thermo Fisher) to verify the success of
the DNA extraction. The samples were then diluted to 100ng/\selectlanguage{greek}µ\selectlanguage{english}l.

\emph{DNA amplification}

The DNA was amplified using via polymerase chain reaction (PCR). For the
12S rRNA gene, NeoFish (Milan et al., 2020) and MiFish (Miya et al.,
2015) markers were amplified, and the fragment of the COI gene was
amplified using a cocktail of primers targeting the standard COI
fragment (Ward et al., 2005). Each sample was amplified in triplicate
using the primer with a specific barcode tag for demultiplexing. The PCR
reaction solution had a final volume of 20\selectlanguage{greek}µ\selectlanguage{english}l, containing: 8.34\selectlanguage{greek}µ\selectlanguage{english}l of
ultrapure water, 0.16\selectlanguage{greek}µ\selectlanguage{english}l of BSA (100\selectlanguage{greek}µ\selectlanguage{english}g/ml), 10\selectlanguage{greek}µ\selectlanguage{english}l of AmpliTaq Gold 360
Master Mix (Thermo Fisher), 0.25\selectlanguage{greek}µ\selectlanguage{english}l of each primer, and 1,0\selectlanguage{greek}µ\selectlanguage{english}l of DNA
template. An additional 1,0\selectlanguage{greek}µ\selectlanguage{english}l of ultrapure water was added for the
negative control samples instead of the DNA template. For the positive
control sample, 1.0\selectlanguage{greek}µ\selectlanguage{english}l of template DNA from a saltwater fish
species,\emph{Prionace glauca} , was added.

PCR conditions consisted of initial denaturation for 10 min at 95\selectlanguage{ngerman}°C,
followed by 35 cycles of denaturation for 1 min at 95°C, primers
annealing for 30 sec at 56°C (COI), 60°C (MiFish) or 63°C (NeoFish) and
extension for 30 sec at 72°C, with a final extension for 7 min at 72°C.

The PCR results were checked using 1.8\% agarose gel electrophoresis.
Both 12S markers presented bands of the expected size for all samples,
including the positive control, whereas for COI, the sample SF08 failed
to produce any bands but was also included in the sequencing step. The
negative control samples did not produce any bands but were also
sequenced with the other samples.

\emph{Library preparation and DNA sequencing}

According to the manufacturer's protocol, one library for each marker
was prepared using the Collibri\selectlanguage{english} PCR-free Kit (Thermo Fisher). The
libraries were quantified at the start of the preparation, after each
major step, and at the end, by fluorometry. The 12S libraries were
sequenced in a MiniSeq\selectlanguage{english} Sequencing System (Illumina) using a MiniSeq Mid
Output Kit (300-cycle), and the COI library was sequenced in a MiSeq\selectlanguage{english}
Sequencing System (Illumina) using a MiSeq Reagent Kit v3 (600-cycle).

\emph{Bioinformatic analyses}

The bioinformatics analyses were carried out using the metabarcoding
analysis package DADA2 (Callahan et al., 2016) and the Phyloseq package
(McMurdie and Holmes, 2013). A pipeline in R v4.4.0 (R Core Team, 2021)
was used for read quality control, removal of adaptors (Cutadapt,
Martin, 2011), removal of sequencing errors and chimeric reads, reads
merge and for obtaining the Amplicon Sequencing Variants (ASVs)
distribution visualization and for taxonomic assignment. The taxonomic
assignment was conducted in two rounds. First, using the DADA2 RPD
classifier against a custom 12S database based on the database developed
by Milan et al. (2020) for both 12S markers, containing 252 DNA
sequences, with 181 specifically from Sao Francisco Basin. Secondly,
using local BLASTn (Camacho et al., 2008) against the NCBI nucleotide
database (Sayers, 2022; NCBInt). Both 98\% and 99\% percentual identity
thresholds were applied for identifications at the species level for COI
and 12S, respectively. The RRA (relative read abundance) was determined
by dividing the absolute counts of each ASV by the sum of the absolute
counts of all ASVs in a sample.

To compare species identifications between markers, Venn Diagrams were
built using the web application Lucidchart (https://lucid.app/). To
examine the potential difference between marker choice on sample
composition, a Permutational Multivariate Analysis of Variance
(PERMANOVA) and principal coordinate analysis (PCoA) were performed with
1000 permutations, applying the Jaccard and Bray-Curtis dissimilarity
indexes using the function `adonis' (vegan 2.5--2 R package).

Due to the maximum 600 bp length limitation of the sequencing technology
available, the forward R1 and reverse R2 COI reads could not be merged
by overlap to reconstruct the barcoding amplicon, as each strand covers
a different region of the COI gene with possible distinct variations for
each taxon. Therefore, reads R1 and R2 were analyzed separately, and
each sample's taxonomic assignment results were combined.

The ASVs found in the negative controls were removed from all other
samples. Additionally, considering that the high throughput could
amplify contaminations not detected by negative controls, and the risk
of false positives, but also aiming not to exclude underrepresented
taxa, only ASVs with more than 0.01\% of relative read abundance (RRA)
in each sample were considered.

\textbf{3. Results}

All primer sets produced successful sequencing results for most samples.
However, one sample (SF08) for COI did not produce any amplification,
even after further DNA purification, quantification, and a new PCR
adjustment. Nevertheless, SF08 was successfully amplified and sequenced
using the 12S markers Mifish and Neofish. A low number of reads were
observed for SF04 and SF08, resulting in only one and three reads,
respectively, despite the latter not presenting any problems in the
amplification process, resulting in 93.33\% (two failed samples out of
30) sequencing success. Notably, both 12S markers resulted in 100\%
amplification and sequencing success, with at least 38,697 reads in a
sample (SF27) for MiFish, and 37,228 reads (SF10) for NeoFish.

The sequencing effort resulted in 4,505,309 reads for all markers after
quality filtering. The number of reads showed considerable differences
among markers and samples. COI produced 584,309 reads, averaging 19,477
reads per sample, ranging from one (SF04) to 29,485 (SF11). MiFish
presented 1,919,545 total reads, averaging 63,985, with a minimum of
38,697 (SF27) and maximum of 83,734 (SF23). Sequencing with NeoFish
resulted in 2,001,455 reads, with an average number of 66,715 per
sample, varying from 37,228 (SF10) to 96,285 (SF23). After BLASTn
searches, 1,699 COI reads remained without taxonomic assignment, and 69
were assigned to Bacteria. On the other hand, all MiFish and NeoFish
reads were assigned to fish taxa.

ASVs were assigned to 26 fish taxa, from which 22 were identified at the
species level, two at the genus level and three at the subfamily level.
The 12S marker NeoFish was able to detect the highest number of orders,
families, genera, and the same number of species as MiFish. In contrast,
COI detected fewer species, genera, and families than the other markers
and the same number of orders as MiFish (Table S1, Figs. 1, S1).

The combined use of the three markers increased the genera detection
rates by 25\% to 87.5\% when considering an initial analysis with only
NeoFish or COI, respectively (Fig. 2). The improvement in species
recovery rates with the use of all three markers combined ranged from
31.25\% to 61.54\% when considering an initial analysis with either 12S
gene markers or COI, respectively (Fig. 2).

The COI marker detected 16 taxa belonging to 13 species, eight genera,
six families, three orders and one class (Fig. S1). Besides the 13 taxa
identified at the species level, one was identified at the genus level,
one at the subfamily level and another at the family level (Table S1).
Among the 13 species, three were detected exclusively by the COI gene
(\emph{Bergiaria westermanni} , \emph{Leporinus friderici}
and\emph{Prochilodus lineatus} ). The species \emph{B} .
\emph{westermanni}was present in 12 samples, with an average RRA of
2.29\%, ranging from 0.13\% to 6.16\%. The anostomid \emph{L} .
\emph{friderici} was detected in eight samples, with the RRA ranging
from 0.13\% to 32.64\%, and an average of 7.81\%. Lastly, \emph{P} .
\emph{lineatus} was present in three samples and had an average RRA of
3.60\%, ranging between 1.20\% to 7.12\% (Fig. 3).

With the 12S gene markers, 21 taxa were detected, including 18 species,
14 genera, 11 families, four orders and one class (Table S1). One of
these taxa was identified at the genus level (\emph{Characidium} sp.)
and two at the subfamily level (Doradinae and Stevardiinae). Of the 18
identifications at species level, eight were exclusively detected with
the 12S gene, all indigenous to the Sao Francisco
Basin:\emph{Cetopsorhamdia iheringi} , \emph{Megaleporinus elongatus}
,\emph{M} . \emph{reinhardti} , \emph{Pachyurus squamipennis}
,\emph{Planaltina myersi} , \emph{Pseudoplatystoma corruscans}
,\emph{Steindachnerina elegans} and \emph{Sternopygus macrurus} (Fig. 1,
Table S1).

Four of the eight species detected exclusively with the 12S markers were
detected by both markers, but with some variation in samples and
abundance. For example, the species \emph{C} . \emph{iheringi} was
detected by MiFish and NeoFish in sample SF15, with 0.07\% and 0.02\%
RRA, respectively (Fig. 3).

Overall, RRA and taxon detection was not consistent between each marker
(Fig. 3). For instance, the most abundant taxon \emph{P} .
\emph{pohli}had a total of 1.348.589 (70.79\% of the total) for MiFish
and 424.499 (73.02\%) reads recovered for Neofish. Notably, the highest
RRA detected for NeoFish was \emph{M} . \emph{elongatus} , with 236.332
(20.90\% of the total) reads. Additionally, in some samples (e.g., SF05,
SF13, SF14 and SF15) where both MiFish and COI detected multiple taxa
for Pimelodidae, NeoFish was not able to identify any taxa for this
family (Fig. 3).

The PERMANOVA evidenced significant differentiation of fish communities
using distinct molecular markers (Table 1). The influence of primer
choice on taxa recovered in each sample was significant for both
presence-absence (Jaccard) and RRA (Bray-Curtis) analyses (Table 1).
While there is considerable overlap between both 12S markers considering
only presence-absence of each taxon in each sample, as can be seen in
the PcoA plot (Fig. 4a), the analysis taking taxa abundance into account
revealed a slight overlap between MiFish and COI (Fig. 4b).

\textbf{4. Discussion}

DNA metabarcoding has become an essential tool for species inventory and
monitoring. However, its use in identifying ichthyoplankton is still
incipient in the Neotropics, with several methodological challenges and
biases still needing to be tackled (Carvalho, 2023). Considering the
crescent demand for innovative techniques to unravel the complex
reproductive dynamics of fish communities for both research and
practical applications, there is an underlying need for the continuous
refinement of this methodology. The COI gene has been commonly used as
the marker of choice (Mariac et al., 2018; Nobile et al., 2019) because
of its well-established primers and complete reference sequences
libraries encouraged by the global initiative Fish Barcode of Life.
Here, each molecular marker recovers a distinct community structure when
considering both quantitative and qualitative analysis.

Using a marker of choice still raises concerns since using several
markers is still expensive when using HTS and because each marker has
distinct amplification biases and taxonomic resolution (Deagle et al.,
2014). The high interspecific variability of the COI gene, when compared
to other mitochondrial genes (Hebert et al., 2003), can help
differentiate closely related species. However, the same high
variability creates the need to use universal degenerate primers with
lower specificity than those designed for more conserved genes. Also, it
hinders the design of internal minibarcodes for COI (Deagle et al.,
2014). In the present study, while COI presented 93.33\% sequencing
success and some of its sequences remained unassigned or were assigned
to Bacteria, both 12S markers were successfully sequenced for all
samples, and all their sequences were assigned to fish taxa.
Additionally, the technology used for sequencing limits the total
fragment size to 600bp, precluding the merging of both COI strands from
forming the full-sized barcode, therefore, each strand was analyzed
independently. The loss of resolution power caused by this could explain
why COI detected fewer species, genera, and families than the 12S
markers and why two of the three exclusive species level identifications
were assigned to nonnative fishes closely related to species from Sao
Francisco.

Minibarcode markers for the 12S gene have been developed and applied to
environmental DNA metabarcoding studies (Milan et al., 2020; Miya et
al., 2020; Sales et al., 2021) and, more recently, to ichthyoplankton
studies as well (Jiang et al., 2022; Van Nynatten et al., 2023). One of
the main concerns when using these markers is the conserved nature of
the gene, which can impact their ability to differentiate closely
related species, especially in diverse regions. However, the current
study shows that both MiFish and NeoFish were able to successfully
identify and distinguish multiple congeneric species, such
as\emph{Leporinus piau} and \emph{L} . \emph{taeniatus}
,\emph{Megaleporinus elongatus} and \emph{M} . \emph{reinhardti}
,\emph{Pimelodus fur} , \emph{P} . \emph{maculatus} and \emph{P}
.\emph{pohli} , and \emph{Prochilodus argenteus} and \emph{P}
.\emph{costatus} . Moreover, the 12S markers have a higher species
detection sensibility than COI, considering that the exclusive fishes
they retrieved were underrepresented, with low RRA. This could result
from low-efficiency primer binding by COI, which can lead to a lack of
amplification (Zhang et al., 2020).

Database completeness is another variable that directly impacts species
detection, as a lack of reference sequences for a given species may
hamper accurate taxonomic assignment (Collins et al., 2019). This aspect
has affected both COI and 12S markers in this study. For instance,
while\emph{Pachyurus squamipennis} is not represented by any COI
reference sequence in the public databases and was exclusively detected
by 12S, the only native species retrieved solely by COI, \emph{Bergiaria
westermanni} , does not have any 12S representative sequence in neither
the public nor our custom library. These limitations highlight the
importance of continuous sequencing efforts to broaden reference
sequence databases, especially for megadiverse regions.

Considering that each marker has advantages and limitations, some
studies suggest combining multiple primer sets to increase taxonomic
coverage (Liu and Zhang 2021; Zhang et al., 2020). In a metabarcoding
study using multiplexed markers to identify zooplankton mock
communities, Zhang et al. (2018) demonstrated that a multi-maker
approach can improve species detection and allow the cross-validation of
taxa detected by each marker. Our results support this conclusion, as
using the three markers combined increased the genera detection by up to
87.5\% and species detection by up to 61.54\%. Therefore, employing
multiple markers, whenever feasible, reduces the likelihood of
overlooking species or incorrectly classifying them due to the absence
or mislabeling of sequences in the reference database (Locatelli et al.,
2020).

Discrepancies between markers were observed in the quantitative analysis
using the RRA estimative. Although some studies with mock samples of
eggs (Duke and Burton, 2020) and larvae (Nobile et al., 2019) yielded a
positive correlation between input organisms and output reads for each
species, the results from this study support the idea that amplification
bias is one of the main pitfalls for quantitative metabarcoding
analyses, as already reported (Carvalho, 2023; Fonseca, 2018). While
MiFish and COI presented RRA resemblance for samples in which both
detected similar taxa, low-efficiency primer binding to Siluriformes and
especially Pimelodidae sequences resulted in completely different
abundance patterns for NeoFish.

In conclusion, using multiple markers from two distinct genes and
lengths allowed an increased taxonomic coverage and robust taxonomic
classification of complex neotropical ichthyoplankton communities.
Finally, precaution is still needed when inferring species abundance
based on DNA metabarcoding data when using PCR-dependent protocols since
it is marker dependent. Nonetheless, ichthyoplankton metabarcoding
offers superior resolution and feasible scalability compared to
traditional techniques, and provides qualitative information, which is
paramount for characterizing reproducing species and definition of
conservation strategies.

\textbf{Acknowledgements}

We are grateful to Amanda Jessica Silva Santos for bench work
assistance, Guilherme Moreira Santos and Gustavo Ribeiro Rosa for
helping with taxonomic assignment curation and Izabela Santos Mendes for
contributing to statistical analysis. This work has been financially
supported by FAPEMIG, CAPES, FIP-PUC Minas and ANEEL/CEMIG (P\&D 0624).
We thank CNPq for providing a research fellowship (312102/2022-4) to
DCC.

\textbf{Data availability statement}

Raw Illumina sequences and corresponding metadata are deposited in the
Zenodo repository (DOI 10.5281/zenodo.8038835).

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\textbf{Figure caption}

Figure 1. Venn diagrams recovered by each molecular marker MiFish,
NeoFish and COI considering distinct taxonomic levels: (a) order, (b)
family/(subfamily), and (c) genus/species. Species marked with an
asterisk (*) have not been reported for the Sao Francisco River Basin.

Figure 2. Improvement in detected taxa when using a single marker for
the COI gene, combining COI and Mifish and combining COI, Mifish and
Neofish.

Figure 3. Relative read abundance (RRA) for each taxon, in each sample,
with each marker.

Figure 4. PCoA plots of 30 ichthyoplankton fish communities detected
using three different molecular markers: COI, Mifish and Neofish.
Analyses were conducted using (a) Jaccard coefficient considering the
presence/absence of fish tax and (b) the Bray-Curtis coefficient, which
uses a matrix of abundance based on each taxon.

\textbf{Table caption}

Table 1. Summary of PERMANOVA results (R2-effect sizes and significance
level)

showing the effect of marker choice on taxa recovered. Df= Degree of
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