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\begin{document}

\title{Ichthyoplankton DNA metabarcoding: challenges and perspectives}



\author[1]{Daniel Carvalho}%
\affil[1]{Pontifícia Universidade Católica de Minas Gerais}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
DNA metabarcoding has been widely used to access and monitor species.
However, several challenges remain open for its mainstream application
in ecological studies, particularly when dealing with a quantitative
approach. In a from the Cover article in this issue of Molecular
Ecology, C\selectlanguage{ngerman}édric et al. (2021) report species-level ichthyoplankton
dynamics for 97 fish species from two Amazon river basins using a clever
quantitative metabarcoding approach employing a probe capture method.
They clearly show that most species spawned during the rainy season when
the floods started, but interestingly, species from the same genus
reproduced in distinct periods (i.e., inverse phenology).
Opportunistically, Cédric et al. (2021) reported that during an intense
hydrological anomaly, several species had a sharp reduction in spawning
activity, demonstrating a quick response to environmental cues. This is
an interesting result since the speed at which fish species can react to
environmental changes, during the spawning period, is largely unknown.
Thus, this study brings remarkable insights into basic life history
information that is imperative for proposing strategies that could lead
to a realistic framework for sustainable fisheries management practices
and conservation, fundamental for an under-studied and threatened realm,
such as the Amazon River basin.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\emph{Article type: News and Views Perspectives}

\textbf{Ichthyoplankton DNA metabarcoding: challenges and perspectives}

Daniel Cardoso Carvalho

Conservation Genetics Lab, Postgraduate Program in Vertebrate Biology,
Pontif\selectlanguage{ngerman}ícal Catholic University of Minas Gerais (PUC Minas), Belo
Horizonte, Brazil. e-mail: carvalho.lgc@gmail.com

\textbf{ORCID}

https://orcid.org/0000-0003-4517-5069

\textbf{Funding information}

CNPq Fellowship Award number 306155/2018-4.

\textbf{Keywords}

Environmental genomics, Biomonitoring 2.0, DNA barcode, High-throughput
DNA sequencing, Neotropical fish,

\textbf{DNA metabarcoding has been widely used to access and monitor
species. However, several challenges remain open for its mainstream
application in ecological studies, particularly when dealing with a
quantitative approach. In a from the Cover article in this issue of
Molecular Ecology, Cédric et al. (2021) report species-level
ichthyoplankton dynamics for 97 fish species from two Amazon river
basins using a clever quantitative metabarcoding approach employing a
probe capture method. They clearly show that most species spawned during
the rainy season when the floods started, but interestingly, species
from the same genus reproduced in distinct periods (i.e., inverse
phenology). Opportunistically, Cédric et al. (2021) reported that during
an intense hydrological anomaly, several species had a sharp reduction
in spawning activity, demonstrating a quick response to environmental
cues. This is an interesting result since the speed at which fish
species can react to environmental changes, during the spawning period,
is largely unknown. Thus, this study brings remarkable insights into
basic life history information that is imperative for proposing
strategies that could lead to a realistic framework for sustainable
fisheries management practices and conservation, fundamental for an
under-studied and threatened realm, such as the Amazon River basin} .

The use of novel molecular tools to access and monitor biodiversity has
been revolutionized by the introduction of high-throughput sequencing
(HTS) platforms that allow the analysis not only of individual specimens
but of whole communities (Leese et al. 2018). The use of HTS coupled
with DNA barcoding enables the identification of multiple species from
environmental bulk samples, commonly termed DNA metabarcoding. Such DNA
based next generation biomonitoring, or ``Biomonitoring 2.0'' (Baird and
Hajibabaei, 2012), has a tremendous potential to advance the traditional
protocols applied to assess and monitor the environment (Leese et al.
2018), ensuring a rapid and cost-efficient biodiversity assessment. This
is particularly important when dealing with a large number of organisms,
as is common with ichthyoplankton samples.

Despite the large potential of DNA metabarcoding for analyzing bulk
samples, technical issues that may hinder full mainstream Biomonitoring
2.0 practices are still under debate, particularly regarding
quantitative analysis (Lamb et al. 2018, Deagle et al. 2018). The
potential quantitative value of DNA metabarcoding has been questioned,
mainly due to the difficulty of obtaining sound and unbiased data.
However, it is controversial whether or not the proportions of reads
generated from DNA metabarcoding studies correspond to the real
proportions of species in a community (Lamb et al. 2018). Constraints
affecting the quantitative performance of metabarcoding are due to bias
introduced during the four main stages of its workflow: (1) sample
collection and processing, (2) choice of target gene, (3) HTS DNA
sequencing and (4) bioinformatics (Figure 1). Numerous approaches have
been implemented to mitigate bias during each stage in order to avoid
constraints that hinder accurate quali-quantitative DNA metabarcoding
analysis (Thomas et al. 2016, Deagle et al. 2018; Ratcliffe et al.
2021).

Mariac et al. (2021) addresses well the bias that may arise from
ichthyoplankton sampling and the unequal body size of fish larvae that
may lead to differences in cell and DNA quantity from each
species/specimen per bulk sample. Since, when dealing with
ichthyoplankton, it is possible to estimate the number of larvae per
second drifting through the river using the total larval flow (TLF), so
the authors applied TFL as a correction factor for species abundance.
Also, the authors were able to size fractionated specimens and extract
DNA from individuals of similar size to minimize bias due to different
initial amounts of DNA from each larva. Similarly, Ratcliffe et al.
(2021) used a standardized amount of tissue for each species to improve
quantification accuracy. But instead, they used PCR and conserved primer
binding sites to amplify the mitochondrial 12S gene and avoid bias due
to differential amplification rates. Further adjustment of read counts
(i.e. relative read abundance estimates - RRA) implemented in their
bioinformatics pipeline was capable of generating more accurate DNA
metabarcoding quantitative estimations. Thomas et al. (2016), also using
a PCR DNA metabarcoding approach, dealt with read abundance biases by
applying the relative correction factor (RCF) obtained from HTS
sequencing of 50/50 mixtures of target and control species. By applying
the RCF, they were able to correct the majority of species-specific
biases from control and field samples and improve the relationship
between RRA and mass percentage of each taxon.

Most DNA metabarcoding bias may be previously diagnosed and adjusted
using mock communities composed of known species composition. The
analyses of such mock samples are paramount to validate DNA
metabarcoding accuracy to detect and quantify species, and therefore,
are recommended to be conducted prior to analyzing bulk samples of
unknown species composition obtained from field samples (Duke \& Burton,
2020). Following that, before analyzing ichthyoplankton sampled from the
environment, Mariac et al. (2018) previously demonstrated that
metabarcoding by capture using a single COI probe was able to identify
and quantify fish species in ichthyoplankton swarms from the Amazon
realm using mock communities.

Nonetheless, powerful ecological inferences from metabarcoding studies
are strongly dependent on reference DNA databases that may allow the
identification of reads to the lowest taxonomic level possible. While
the COI gene database is one of the most complete for molecular
assignment of fish species, it is still incomplete for several groups,
particularly in high biodiversity realms such as the Amazon. Moreover,
the use of multiple markers could increase sensitivity of species
detection due to mismatches between probes/primers (Duke \& Burton,
2020). With the decrease of DNA sequencing costs, a possible good
cost/benefit solution would be ''genome skimming'' of voucher tissue
samples and assembling whole mitogenomes for the development of a
broader reference database. Such a mitogenome database would allow the
analysis of multiple markers and primers development for metabarcoding
communities from specific river basins (Milan et al. 2020). However,
sequencing several mitogenomes per species to generate intraspecific
diversity, important for detecting highly conserved primer sites, might
severely increase costs. Despite this, examples of Neotropical fish
species with multiple mitogenomes are already available (Santos et al.
2021).

In perspective, DNA metabarcoding (PCR free or not) may allow accurate
estimation of ecological information, since there is a great amount of
evidence that adjustments can overcome known constraints (Figure 1).
Thus, with the continuous refinements of DNA metabarcoding methodology,
I foresee its mainstream use to monitor and assess biodiversity allowing
rapid and relatively inexpensive processing of complex bulk and
environmental samples for several taxa and highly diverse realms.

\textbf{Figure Caption}

\textbf{Figure 1.} DNA metabarcoding constraints and proposed solutions.

\par\null

\textbf{References}

\par\null

Baird DJ, Hajibabaei M (2012) Biomonitoring 2.0: a new paradigm in
ecosystem assessment made possible by next-generation DNA sequencing.
\emph{Molecular Ecology} ,\textbf{21} , 2039-2044.

Duke EM, Burton RS (2020) Efficacy of metabarcoding for identification
of fish eggs evaluated with mock communities. \emph{Ecology and
evolution} ,\textbf{10} (7), 3463-3476.

Deagle BE, Thomas AC, McInnes JC \emph{et al.} (2018) Counting with DNA
in metabarcoding studies: How should we convert sequence reads to
dietary data?\emph{Molecular Ecology} , \textbf{28} (2), 391-406.

Lamb PD, Hunter E, Pinnegar JK, \emph{et al.} (2019) How quantitative is
metabarcoding: A meta\selectlanguage{english}-analytical approach. \emph{Molecular Ecology} ,
\textbf{28} (2), 420-430.

Leese F, Bouchez A, Abarenkov K, \emph{et al.} (2018) Chapter Two -- Why
We Need Sustainable Networks Bridging Countries, Disciplines, Cultures
and Generations for Aquatic Biomonitoring 2.0: A Perspective Derived
From the DNAqua-Net COST Action, in: Bohan DA, Dumbrell AJ, Woodward G,
Jackson M (Eds.),\emph{Advances in Ecological Research, Next Generation
Biomonitoring: Part 1} . Academic Press, pp. 63--99.

Mariac C, Renno JF, Carmen Garcia-Davila \emph{et al.} (2021)
Species-level ichthyoplankton dynamics for 97 fishes in two major river
basins of the Amazon using quantitative metabarcoding. Molecular
ecology, 30, XXX-XXX.

Mariac C, Vigouroux Y, Duponchelle F \emph{et al.} (2018) Metabarcoding
by capture using a single COI probe (MCSP) to identify and quantify fish
species in ichthyoplankton swarms. \emph{PLoS One} , 13(9), e0202976.

Milan DT, Mendes IS, Damasceno JS \emph{et al.} (2020) New 12S
metabarcoding primers for enhanced Neotropical freshwater fish
biodiversity assessment.\emph{Scientific reports} , \textbf{10} , 17966.

Ratcliffe FC, Uren Webster TM, Rodriguez\selectlanguage{english}-Barreto D \emph{et al.} (2021)
Quantitative assessment of fish larvae community composition in spawning
areas using metabarcoding of bulk samples. \emph{Ecological
Applications} ,\textbf{31} (3), e02284.

Santos RP, Melo BF, Yazbeck GM \emph{et al.} (2021). Diversification of
\emph{Prochilodus}in the eastern Brazilian Shield: Evidence from
complete mitochondrial genomes (Teleostei, Prochilodontidae).
\emph{Journal of Zoological Systematics and Evolutionary Research} ,
\textbf{59} (5), 1053-1063.

Thomas AC, Deagle BE, Eveson JP, Harsch CH, \& Trites AW (2016)
Quantitative DNA metabarcoding: improved estimates of species
proportional biomass using correction factors derived from control
material. \emph{Molecular ecology resources} , \textbf{16} (3), 714-726.\selectlanguage{english}
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