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 (Jørgensen 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 São 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.