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\begin{document}

\title{Application of environmental DNA metabarcoding to identify fish
community characteristics in subtropical river systems}



\author[1]{Sai Wang}%
\author[1]{Dong-Hai Wu}%
\author[1]{En-Ni Wu}%
\author[1]{Nan-Lin Chen}%
\author[1]{Shi-Di Fan}%
\author[1]{Tuan-Tuan Wang}%
\author[2]{Zhong-Bing Chen}%
\author[1]{Yong-Duo Song}%
\author[3]{Yang Zhang}%
\author[4]{Zhuo-Luo a}%
\author[5]{Lei Xiang}%
\author[1]{Yu-Xi Yang}%
\author[1]{Rong-Mei Zi}%
\affil[1]{Hainan University}%
\affil[2]{Czech University of Life Sciences Prague}%
\affil[3]{Shenzhen Guanghuiyuan Environment Water Co., Ltd.}%
\affil[4]{China Water Resources Pearl River Planning Surveying \& Designing Co., Ltd.}%
\affil[5]{Jinan University}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Fish play vital roles in river ecosystems; however, traditional
investigations of fish usually cause certain ecological damage.
Extracting DNA from aquatic environments and identifying DNA sequences
offers an alternative, non-invasive approach for detecting fish species.
In this study, environmental DNA (eDNA), coupled with PCR and
next-generation sequencing, and electrofishing were used to compare
their effects in identifying fish community characteristics. In three
subtropical rivers of southern China, fish specimens and eDNA in water
were collected from headwaters to estuaries. Both eDNA OTU richness and
individual abundance (including number and biomass) could group 38
sampling sites into eight spatial zones with significant differences in
local fish community composition. Compared with the order-/family-level
grouping, the genus-/species-level grouping could more accurately
recognize the differences between upstream zones I - III, midstream
zones IV - V, and downstream zones VI -- VIII. From headwaters to
estuary, two environmental gradients significantly influenced the
longitudinal distribution of fish species, including the first gradient
composed of habitat and physical water parameters and the second
gradient composed of chemical water parameters. The high regression
coefficient of alpha diversity between eDNA and electrofishing methods
as well as the accurate recognition of dominant, alien, and biomarker
species at each spatial zone indicated that eDNA could characterize fish
community attributes at a similar level of traditional approach.
Generally, our results demonstrated that eDNA metabarcoding can be used
as an effective tool in revealing fish composition and diversity, which
is important for using the eDNA technique in aquatic field monitoring.%
\end{abstract}%



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\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/646453/articles/658361-application-of-environmental-dna-metabarcoding-to-identify-fish-community-characteristics-in-subtropical-river-systems}

\textbf{1 Introduction}

Large river systems are essential for providing critical foraging,
breeding ground, and nursery habitats for a variety of fauna and are
considered among the most productive ecosystems worldwide (Wang et al.,
2020a; Wang et al., 2021b). Fish, as consumers at high trophic levels in
river food webs, represent the sum of a wide range of complex trophic
interactions (Wang et al., 2018b). Linking the ecological indicators of
fish communities to human interference remains an important goal for
river managers (Zou et al., 2020). The distribution, composition and
diversity of fish communities are commonly used as proxies to assess an
ecosystem's health and integrity. To appropriately manage and protect
aquatic ecosystems, it is essential to develop effective and
eco-friendly monitoring approaches to collect field data and obtain
biological parameters (Kumar et al., 2022; Shu et al., 2021).

Traditional monitoring of fish diversity has depended largely on census
methods such as electrofishing, gill/hoop/seine netting, and
dredging/trawling (Wang et al., 2020a; Wang et al., 2019a). However,
those methods have always been limited by their low sampling
efficiencies, destructiveness to organisms, and strict reliance on
taxonomic expertise (Sakata et al., 2020; Zhang et al., 2020). The
application of environmental DNA (eDNA) metabarcoding for fish diversity
analysis has emerged and offers a new avenue for characterizing fish
diversity in river ecosystems (Pont et al., 2018). It provides
cost-effective, dependable, rapid and continuous investigations for
monitoring and assessing fish diversity, which is crucial for the timely
and effective management and conservation of river and estuary
ecosystems (Garlapati et al., 2019).

The metabarcoding approach coupled with the use of eDNA is a potentially
powerful tool for surveying and assessing aquatic diversity. Numerous
studies have demonstrated the utility of eDNA metabarcoding for
assessing fish diversity (Rourke et al., 2022). Researchers have
successfully applied eDNA metabarcoding to monitor fish diversity in
freshwater and seawater samples from different habitats, especially in
streams, reservoirs, estuaries and oceans (Civade et al., 2016; Stoeckle
et al., 2017; Yao et al., 2022; Zou et al., 2020). The results from
these studies have shown that eDNA metabarcoding is a sound
biomonitoring tool for use in the conservation and management of aquatic
ecosystems (Nguyen et al., 2020). Currently, the application of eDNA
metabarcoding to monitor and assess biodiversity is at the forefront of
the available methods used by ecologists and conservation scientists
(Beng and Corlett, 2020; Bernos et al., 2023).

Many studies have compared eDNA results to those from traditional
methods and have shown a correlation between the results from the two
approaches (Lacoursi\selectlanguage{ngerman}ère-Roussel et al., 2016; Port et al., 2016). In
some studies, eDNA analysis was superior for characterizing fish
biodiversity compared to traditional techniques such as electrofishing
and hoop netting (Nguyen et al., 2020; Pont et al., 2018). In other
studies, the results obtained from eDNA have been comparable to those of
traditional methods in which fishes are caught through visual dive
surveys and trawling (Port et al., 2016; Zou et al., 2020). Previous
studies have shown that eDNA metabarcoding retains a higher diversity of
taxa than the traditional method; however, the practical application of
eDNA in evaluating the composition and structure of fish communities has
been less explored, and whether eDNA could replace traditional
monitoring is still unknown.

The river systems of southern China, in a typical subtropical monsoon
climate zone, serve as reserves for biodiversity conservation. Due to
disproportionate use of coastal wetland resources and intense
anthropogenic activities (i.e., drainage, reclamation, and pollution),
subtropical river ecosystems (e.g., the Pearl River) have been badly
damaged, and their biodiversity and bioresources have seriously
declined. To investigate the distribution and composition of fish
communities in this area, eDNA metabarcoding studies combined with
electrofishing surveys were conducted. In addition, the diversity of
fluvial fishes observed by electrofishing with the characterization of
eDNA collected concurrently from rivers by metabarcoding was compared.
The objectives of our study include 1) using a metabarcoding protocol to
assess the eDNA-based composition and diversity of fish communities; 2)
analysing the response between eDNA OTU richness and fish amounts (e.g.,
individual number and biomass); and 3) exploring the application of eDNA
in assessing environmental influences on fish diversity.

\textbf{2 Materials and methods}

\textbf{2.1 Study area and sampling sites}

The Pearl River, the second largest river in China, is in the tropical
and subtropical areas of Southeast Asia and serves as a critical water
source for Guangdong Province and Hong Kong. The water sources of the
Pearl River are essential for supplying drinking water, generating
power, and performing irrigation in Guangdong Province. Thus, the
ecological health of the Pearl River is important for the sustainable
development of the Pearl River Delta. The Pearl River consists of
multiple tributaries -- the North, West, East, Liuxi, and Zeng Rivers --
which ultimately merge into the Pearl River Estuary in southern China.
The current survey originated from the Liuxi River and Zeng River (Fig.
1), which are regarded as the water sources with high cultural
significance for Guangzhou, the capital city of Guangdong Province in
southern China. The Liuxi River and Zeng River are the upstream source
waters for the Pearl River, including both upstream wadable and
downstream nonwadable habitats. Thus, they are important for the
comparison between the eDNA metabarcoding protocol and electrofishing
method.

Thirty-eight sampling sites were chosen from the headwaters of the Liuxi
(L1 -- L14) and Zeng Rivers (Z1 -- Z12) to the mainstream of the Pearl
River (P1 -- P12) and then to the Pearl River mouth connected with the
South China Sea (Fig. 1). The landforms within the drainage basin are
dominated by medium-low mountains and hills. The mean annual temperature
and precipitation are 21.6 °C and 2188 mm, respectively. Approximately
83.3\% of the runoff is discharged in the flood season (from April to
September). The zonal soil and vegetation are Udic Ferralisols and
southern subtropical evergreen broad-leaved forest, respectively. Forest
coverage is approximately 50\%. Arable land accounts for only 8.7\% of
the drainage area, with paddy fields accounting for most of this.
Environmental data, including physicochemical parameters of water
quality, habitat characteristics, and bacterial amounts (see Table S1 in
the Supplementary Material), were provided by two nationally accredited
(China Metrology Accreditation) third-party testing agencies (Hainan
Qingxiao Environmental Testing Co., Ltd. and Hainan Qianchao Ecological
Technology Co., Ltd.).

\textbf{2.2 Traditional method (electrofishing) for fish investigation}

Fish were collected from the headwaters to the estuary (Fig. 1) during
the rainy season (from June to July) in 2022; each site was sampled
three times over a rainy season following basic guidelines (Barbour et
al., 1999; Hauer and Lamberti, 2007). Electrofishing equipment consisted
of a 24-kW generator, a 12 V-160 A lithium battery, a silicon-controlled
inverter, and two continuously adjustable voltage and frequency
regulators. This equipment was used to effectively stun and collect fish
(individual weight \textless{} 10 kg) in a 2 m wide × 2 m long × 3.5 m
deep water column. Due to varying water levels, two electrofishing
operations were conducted as follows: 1) At wadeable sites, single-pass
backpack electrofishing was performed by two operators moving in a
zig-zag fashion. Electrofishing was adjusted at low voltage and mixed
frequency, and the walking speed was controlled to ensure a sampling
effort of approximately 8 m\textsuperscript{2}
min\textsuperscript{-1}over 30 minutes. 2) At nonwadeable sites,
personnel and equipment were loaded with a welded hull boat, and a
bamboo quant was used to propel the boat to eliminate noise disturbance
to fish. Electrofishing was adjusted at a high voltage and main
frequency, and the paddling speed was controlled to ensure a sampling
effort of approximately 6 m\textsuperscript{2} min\textsuperscript{-1}.
Due to the high water depth, a large scoop net was used by a sternward
auxiliary to collect the stunned benthic fish that floated slowly
upwards. Boat-electrofishing was conducted over a distance of 500 m,
spanning both riverbanks at a depth of 1-3 m (Flotemersch et al., 2006).
The number and biomass (weight mass) of fish individuals were collected
as traditional fishing data.

\textbf{2.3 Environmental DNA extraction and metabarcoding}

Within 24 hr of collection, all water samples were filtered using
Whatman glass microfiber filter papers (47 mm diameter, 1.2 \selectlanguage{greek}μ\selectlanguage{english}m pore
size). Prior to filtering each river sample, 500 ml of
ddH\textsubscript{2}O was filtered on a separate filter to act as
laboratory controls, followed by filtration of the river sample on new
filters using the same filtration apparatus. DNA from water was
extracted using the Qiagen DNeasy Tissue and Blood DNA extraction kit
following the manufacturer's protocol with minor modifications. Three
membranes (500 mL of water per membrane) for each sample were cut into
pieces, ground and mixed. Then, the sample was soaked in 600 \selectlanguage{greek}µ\selectlanguage{english}L of 2 \selectlanguage{ngerman}×
lysis buffer and 40 \selectlanguage{greek}µ\selectlanguage{english}L of proteinase K. Incubation with this mixture was
performed at 56 \selectlanguage{ngerman}°C for 2.5 hr. Finally, we washed the filters in the
mixture and performed elution in 200 \selectlanguage{greek}µ\selectlanguage{english}L of AE buffer. Filtration blanks
and negative controls were coextracted alongside the samples and were
subjected to the same protocol as the samples. The DNA concentration was
determined using the Qubit dsDNA HS Assay Kit and detected in a 1.0\%
agarose gel. No data or bands were observed for the filtration blanks or
negative controls.

Metabarcoding was performed in duplicate on each DNA extract with the
primers MiFish-F (5'GTCGGTAAAACTCGTGCCAGC-3') and MiFish-R
(5'-CATAGTGGGGTATCTAATCCCAGTTTG-3') (Miya et al., 2015), which target
the 12S rDNA region (amplifying an \textasciitilde{}180 bp region) of
the mitochondrial genome, to identify fish species. DNA amplifications
were performed in a two-step PCR protocol designed for the BGISEQ-500
platform. Three PCR replicates were performed for each sample. For each
set of replications, environmental samples, filtration blanks and
negative controls were included. The PCR assay volume was 50 \selectlanguage{greek}µ\selectlanguage{english}L,
including 0.3 \selectlanguage{greek}µ\selectlanguage{english}L of Takara Ex Taq (5 U/\selectlanguage{greek}µ\selectlanguage{english}L), 5 \selectlanguage{greek}µ\selectlanguage{english}L of 10\selectlanguage{ngerman}×Ex Taq buffer (20
mM Mg\textsuperscript{2+} plus), 4 \selectlanguage{greek}µ\selectlanguage{english}L of a dNTP mixture, 1 \selectlanguage{greek}µ\selectlanguage{english}L of the
forwards and reverse primers (10 \selectlanguage{greek}µ\selectlanguage{english}M), 1 \selectlanguage{greek}µ\selectlanguage{english}L of the DNA template
(environmental samples, filtration blanks and negative controls), and
molecular biology-grade water added to 50 \selectlanguage{greek}µ\selectlanguage{english}L. For all samples, the
first-step PCR was performed as follows: 94 \selectlanguage{ngerman}°C for 5 min, followed by 10
cycles of 94 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s, with a
final extension at 72 °C for 10 min. The first-step PCR products were
diluted 5 times with molecular biology grade water and used as the
templates for the second-step PCR. For subsequent sequencing on the
BGISEQ-500 platform, two or three random nucleotides were inserted into
the MiFish primers (to increase sequence diversity during sequencing).
The second-step PCR was carried out in a 50 \selectlanguage{greek}µ\selectlanguage{english}L reaction volume including
0.3 \selectlanguage{greek}µ\selectlanguage{english}L of Takara Ex Taq (5 U/\selectlanguage{greek}µ\selectlanguage{english}L), 5 \selectlanguage{greek}µ\selectlanguage{english}L of 10\selectlanguage{ngerman}× Ex Taq buffer (20 mM
Mg\textsuperscript{2+} plus), 4 \selectlanguage{greek}µ\selectlanguage{english}L of a dNTP Mixture, 1 \selectlanguage{greek}µ\selectlanguage{english}L of the
forwards and reverse primers with the BGISEQ-500 adapter (10 \selectlanguage{greek}µ\selectlanguage{english}M), 2 \selectlanguage{greek}µ\selectlanguage{english}L
of the template, and molecular biology-grade water added to 50 \selectlanguage{greek}µ\selectlanguage{english}L. The
PCR procedure was as follows: 94 \selectlanguage{ngerman}°C for 5 min, followed by 20 cycles of
94 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s, with a final
extension at 72 °C for 10 min. After the two-step PCR amplification, the
PCR products were detected in a 1.5\% agarose gel. None of the
filtration blanks or negative controls showed amplification.

\textbf{2.4 Library construction and sequencing}

The PCR products showing the target bands were mixed in equal amounts,
followed by electrophoresis in a 2\% agarose gel and gel cutting. The
PCR products were purified using a QIAquick Gel Extraction kit. Sixty
nanograms of the purified PCR products was denatured at 95 °C and
ligated with T4 DNA ligase at 37 °C to generate a single-stranded
circular DNA library. Library replicates and library blanks were
simultaneously included throughout the process. The concentrations and
fragment size distributions of the libraries were checked on an Agilent
2100 Bioanalyzer. All libraries were subsequently pooled in equal
amounts to generate DNA nanoballs (DNBs). Each DNB was pooled into 1
lane for sequencing. Sequencing was performed via 150 bp paired-end
sequencing on the BGISEQ-500 high-throughput platform.

To obtain clean reads, the raw data were filtered to eliminate adapter
contamination and reads of low quality. Then, paired-end reads were
combined with tags based on overlaps using FLASH (Mago\selectlanguage{polish}č \selectlanguage{english}and Salzberg,
2011). The tags were clustered into OTUs (operational taxonomic units)
using USEARCH with a 97\% threshold, and chimaera were filtered out
using UCHIME (Rognes et al., 2016). All tags were mapped to each
representative OTU sequence using USEARCH to obtain the OTU richness
table. The taxonomic assignment of OTU sequences was mapped to the NT
database downloaded from the National Center for Biotechnology
Information (NCBI) GenBank database using the Blastn tool. Sequences
were designated as belonging to a species if there was [?] 96\% sequence
identity to the NT and NCBI database barcode across the entire length of
the amplicon, if a sequence from at least one other species within the
same genus was available for comparison (and \textless{} 96\%
identical). An OTU was categorized into another OTU if a sequence could
not be assigned to a species. If a sequence could be assigned to several
species ([?] 99\% matching rate) and the species belonged to the same
genus, the taxonomic resolution collapsed to the genus level.

\textbf{2.5 Data processing and statistical analysis}

All statistical analyses were conducted in R 4.2.3 (R Core Team, 2021)
with the primary packages \emph{cluster} , \emph{factoextra}
,\emph{phyloseq} , \emph{vegan} , and \emph{ggplot2} . Nonmetric
multidimensional scaling (NMDS) was used to group the 38 sampling sites
into spatial zones with distinct differences in fish community
composition according to the relative OTU richness, fish individual
number and biomass. NMDS relies on the rank order of pairwise variable
dissimilarities (Euclidean distance in this study) and does not make any
underlying distributional assumptions of the data (Borcard et al.,
2011). Sampling sites were plotted in ordination space with the distance
between points positively related to the dissimilarity of output
parameters (i.e., sites with similar output parameters were plotted
closer to one another). The analysis of similarity (ANOSIM) test was
used to evaluate the dissimilarity matrix and test whether groups of
objects had significantly (\emph{P}\textless{} 0.05) different mean
dissimilarities.

Based on the OTU richness, the pairwise taxonomic Bray-Curtis
dissimilarity matrix between different samples was calculated using
the\emph{microeco} package (Liu et al., 2021). Environmental factors and
fish OTU richness that showed significant variations in their values
were used, and stepwise forwards selection was performed to linearly
reduce the correlated variables along the axes. A permutation limit
(with a \emph{P} value of 0.05) was used to determine which variables to
incorporate into the final model. The relationship between eDNA-based
and number-/biomass-based alpha diversity was estimated by linear
regression. Linear dependencies were explored by computing the variable
variance inflation factors to ensure no confounding colinearity. The
statistical significance of the axes derived from each analysis was
tested with a Monte Carlo test (999 permutations).

Linear discriminant analysis effect size (LEfSe) is an algorithm for
high-dimensional indicator discovery that identifies taxa by
characterizing the differences between two or more biological conditions
(Segata et al., 2011). LEfSe emphasizes both statistical significance
and biological relevance, allowing researchers to identify
discriminative features that are significantly different between
biological classes. The nonparametric factorial Kruskal-Wallis sum-rank
test was first used to detect features with significant differential
abundance with respect to the class of interest. Second, LEfSe uses
linear discriminant analysis to estimate the effect size of each
differentially abundant feature and rank the feature accordingly (Liu et
al., 2021).

Two-way hierarchical clustering analysis was performed using
the\emph{pheatmap} package. The package used clustering distances and
methods implemented in the \emph{dist} and \emph{hclust} functions in R.
The clustering analysis divided fish species with similar responses to
the environmental factors into a group. Statistically significant
cluster trees were identified using a bootstrap randomization technique
in which the nonzero values were resampled and used to generate
pseudovalues under the null hypothesis. The result was displayed as a
heatmap.

\textbf{3 Results}

\textbf{3.1 Spatial distribution pattern of fish communities along the
river}

There were 131 fish species belonging to 22 orders, 59 families, and 103
genera detected by the eDNA sampling protocol. There were 92 fish
species belonging to 16 orders, 48 families, and 79 genera detected by
electrofishing in situ. The fish species detected by eDNA across the 38
sampling sites contained all the fish species sampled by electrofishing,
indicating that the capacity of eDNA to find species was stronger than
that of electrofishing. NMDS results showed that, regardless of the data
types (i.e., novel eDNA OTU richness and traditional biological
counting), the relative percentage (\%) of OTU richness (Fig. 2A),
individual number (Fig. 2B), and biomass (Fig. 2C) could group the 38
sampling sites into eight spatial zones, including I (L1 - L2 and Z1 --
Z3), II (L3 - L6), III (L7 - L11 and Z4), IV (L12 - L14 and P1 - P2), V
(Z6 -- Z7 and P3 - P4), VI (Z8 - Z11), VII (P5 - P8), and VIII (Z5 and
P9 - P12). Along the river continuum, these zones were distributed in
sections with heterogeneous habitats, including headwaters, upper
stream, middle river, lower reaches, and river mouth. Our results
suggested that eDNA could indicate the spatial distribution pattern of
fish communities as well as the pattern reflected by electrofishing.

\textbf{3.2 The composition of fish communities at different taxonomic
levels}

The relative percentage of eDNA OTU richness showed that, at the order
level (Fig. 3A), Cypriniformes, Cichliformes, Siluriformes, and
Gobiiformes made up \textgreater{} 83\% of the fish communities in the
three rivers. At the family level (Fig. 3B), Cichlidae, Cultrinae,
Cyprinidae, and Labeoninae made up \textgreater{} 56\% of the fish
communities in Zones II - VIII, whereas Cyprinidae, Oxudercidae, and
Heteropteridae made up \textgreater{} 50\% of the fish communities in
Zone I. At the genus (Fig. 3C) and species levels (Fig. 3D), the
composition of fish communities was dispersed and determined by
different genera and species. Notably, except for zones I and II, where
native \emph{Oryzias curvinotus}and \emph{Hemiculter leucisculus} had
the highest OTU richness, respectively, exotic \emph{Coptodon zillii}
and \emph{Oreochromis aureus} had the highest OTU richness in the other
zones, indicating that the structure of fish communities in the middle
and lower reaches was dominated by alien species. This result indicated
that the eDNA metabarcoding protocol had advantages in discovering
dominant species and exotic species.

The cluster analysis between the eight spatial zones showed that the
order- and family-level grouping (Figs. 3A -- 3B) was insufficient to
distinguish the longitudinal differences in fish community composition
from headwaters to the estuary. Compared with the order- and
family-level grouping, the genus-level (Fig. 3C) grouping could more
clearly distinguish the differences between upstream zones I - IV and
downstream zones V - VIII. Moreover, the species-level grouping (Fig.
3D) was the most effective in distinguishing upstream zones I - III,
midstream zones IV - V, and downstream zones VI -- VIII. An interesting
finding was that at the family level, upstream zone II and downstream
zone VIII were clustered in the same group, which was caused by the
widespread species of Cichlidae, Cultrinae, and Oxudercidae that could
not be distinguished by family-level identification. In contrast,
compared with the family level, the higher resolution at the genus and
species levels could distinguish \emph{Coptodon} and \emph{Oreochromis}
of Cichlidae as well as \emph{Hemiculter} and \emph{Parabramis} of
Cultrinae, which were the key genera that distinguished fish
distribution in the upper and lower reaches.

\textbf{3.3 The diversity relationships between eDNA and electrofishing
sampling}

The linear regression results showed that the relationships between the
alpha diversity calculated by eDNA OTU richness and fish individual
number exhibited significant (\emph{P} \textless{} 0.05) positive
correlations (Fig. 4A). The regression equation is fitted as \emph{Y} =
0.9572[?]\emph{X} + 0.5425 (\emph{R} \selectlanguage{ngerman}² = 0.8218), where \emph{Y} is the
number-based diversity, \emph{X} is the eDNA-based diversity,
and\emph{R} -squared (\emph{R} \textsuperscript{2}) measures how close
the data points are to the fitted line. The linear regression between
the fish diversity calculated by eDNA OTU richness and by fish biomass
(i.e., wet weight) exhibited significant (\emph{P} \textless{} 0.05)
positive correlations. The regression equation is fitted as \emph{Y} =
0.4339\selectlanguage{english}[?]\emph{X} + 2.3043 (\emph{R} 2 = 0.4558), where \emph{Y} is the
biomass-based diversity and \emph{X} is the eDNA-based diversity (Fig.
4B). These results indicated that the eDNA metabarcoding protocol could
reflect the alpha diversity of local fish communities as well as the
diversity determined by traditional biological counting, and the
eDNA-based diversity had a higher matching degree with number-based
diversity than with biomass-based diversity. In addition,\emph{R}
\textsuperscript{2} showed significant (\emph{P} \textless{} 0.05)
intergroup differences between the spatial zones (Fig. 4C). The highest
\emph{R} \textsuperscript{2} values were observed in zones I \selectlanguage{english}- II and
VII -- VIII, which were significantly higher than those in zones IV \selectlanguage{english}- V.
This result indicated that the eDNA-based diversity had higher linear
regression with number-based diversity in headwater and the estuarial
zones.

\textbf{3.4 Biomarkers that determined site-specific fish communities}

The OTU-based LEfSe selected 21 families, 34 genera, and 37 species as
biomarkers (Fig. 4A). The number- and biomass-based LEfSe selected 15
families, 24 genera, and 27 species (Fig. 4B) as well as 22 families, 34
genera, and 37 species (Fig. 4C). No order-level biomarker was selected,
indicating that the spatial variation in fish communities at the order
level was not significant. The common biomarkers selected by LEfSe from
eDNA and electrofishing (see Table 1) were
Barbinae\textbar{}\emph{Acrossocheilus} \textbar{}\emph{A.
beijiangensis} , Nemacheilidae\textbar{}\emph{Schistura}
\textbar{}\emph{S. fasciolata} ,
Cyprinidae\textbar{}Nicholsicypris\textbar{}\emph{N. normalis} ,
Danioninae\textbar{}\emph{Zacco} \textbar{}\emph{Z. platypus} ,
Siluridae\emph{\textbar{}Silurus\textbar{}S. asotus} ,
Oxudercidae\textbar{}\emph{Rhinogobius} \textbar{}\emph{R. giurinusi}
and\emph{R. cliffordpopei} , Acheilognathinae\emph{\textbar{}Rhodeus} in
zone I; \emph{Culter} , \emph{Rhodeus} \textbar{}\emph{R. ocellatus}
,\emph{Hemiculter} \textbar{}\emph{H. leucisculus} ,\emph{Opsariichthys}
\textbar{}\emph{O. bidens} ,\emph{Paramisgurnus} \textbar{}\emph{P.
dabryanus} , \emph{Clarias fuscus}in zone II;
Gobioninae\textbar{}\emph{Squalidus} \textbar{}\emph{S. argentatus} ,
\emph{Coptodon} \textbar{}\emph{C. zillii} in zone III;\emph{Carassius}
\textbar{}\emph{C. auratus} ,\emph{Pseudorasbora} \textbar{}\emph{P.
parva} , Gastromyzontidae\textbar{}\emph{Vanmanenia} \textbar{}\emph{V.
pingchowensis} in zone IV; \emph{Osteochilus} \textbar{}\emph{O.
salsburyi} in zone V;
Hypophthalmichthyinae\textbar{}\emph{Hypophthalmichthys}
\textbar{}\emph{H. nobilis} and \emph{H. molitrix} ,
Mugilidae\textbar{}\emph{Liza} \textbar{}\emph{L. subviridis} ,
Cichlidae\textbar{}\emph{Oreochromis} \textbar{}\emph{O. aureus} ,
Scaridae\textbar{}\emph{Hipposcarus} \textbar{}\emph{H. harid} ,
Salmonidae\textbar{}\emph{Salmo} \textbar{}\emph{S} . \emph{salar}
,\emph{Squaliobarbus} \textbar{}\emph{S. curriculus} in zone VII;
Gobiidae\textbar{}\emph{Glossogobius} ,\emph{Konosirus} , \emph{Mugil}
\textbar{}\emph{M. cephalus} , \emph{Liza carinata} in zone VIII.
Notably, there were no common biomarkers observed in zone VI, indicating
a great difference in the composition of fish communities detected by
eDNA and electrofishing.

An interesting finding was that the eDNA-based biomarkers were similar
to the number-based biomarkers but greatly differed in biomass-based
biomarkers. Generally, the biomass-based biomarkers that were not
detected by eDNA were bottom-dwelling species with large body sizes,
such as Xenocyprinae\textbar{}\emph{Xenocypris} \textbar{}\emph{X.
argentea} , Leuciscinae\textbar{}\emph{Squaliobarbus} \textbar{}\emph{S.
curriculus} , Loricariidae\textbar{}\emph{Pterygoplichthys}
\textbar{}\emph{P. anisitsi} in zone IV, and
Ictaluridae\textbar{}\emph{Ictalurus} \textbar{}\emph{I. punctatus} ,
Sparidae\textbar{}\emph{Rhabdosargus} \textbar{}\emph{R. haffara} in
zone VII. Another finding was that the order-level biomarkers appeared
more in headwater zone I and downstream zones VII\selectlanguage{english}-VIII than in other
zones, indicating a habitat-specific distribution of these order-level
biomarkers. For example, in zone I,
Barbinae\textbar{}\emph{Acrossocheilus} ,
Danioninae\textbar{}\emph{Zacco} , Siluridae\textbar{}\emph{Silurus} ,
and Oxudercidae\textbar{}\emph{Rhinogo} \emph{bius}were typical
rheophilic fish living in rapids and riffles with widespread cobble
substrates. In zone VII,
Hypophthalmichthyinae\textbar{}\emph{Hypophthalmichthys} ,
Mugilidae\textbar{}\emph{Liza} , Cichlidae\textbar{}\emph{Oreochromis} ,
Scaridae\textbar{}\emph{Hipposcarus} , and
Salmonidae\textbar{}\emph{Salmo} preferred slow-flowing and open water
bodies. In zone VIII, the appearance of
Serrasalmidae\textbar{}\emph{Piaractus} ,
Clupeidae\textbar{}\emph{Konosirus} ,
Gobiidae\textbar{}\emph{Glossogobius} , and
Mugilidae\textbar{}\emph{Mugil} indicated brackish waters near the
estuary. Moreover, \emph{Coptodon zillii} and \emph{Labeo}
\emph{rohita}in zone III, \emph{Pterygoplichthys} \emph{pardalis} in
zone V,\emph{Oreochromis aureus} in zone VII, and \emph{Piaractus
brachypomus}in zone VIII were typical invasive species that could be
selected as biomarkers, indicating the sensitivity of eDNA in detecting
invasive species.

\textbf{3.5 Relationships between environmental factors and fish
community composition}

The correlation analysis between environmental factors (see Table S1 in
the Supplementary Material) and the composition of fish communities
showed that 44 (Fig. 6A), 41 (Fig. 6B), and 40 (Fig. 6C) species had
significant relationships with environmental factors according to the
eDNA, individual number, and biomass data types, respectively. In
addition, for eDNA-based fish community composition, at the order,
family, genus, and species levels, there were 5, 18, 41, and 44 units,
respectively, that significantly (\emph{P} \textless{} 0.05) correlated
with the environmental factors (see Fig. S1 in the Supplementary
Material). This result indicated that the species-level identification
of the fish community compositions had the strongest response to the
environmental changes, followed by genus-level identification.
Interestingly, regardless of input data types, there were two
environmental gradients that significantly influenced the longitudinal
distribution of fish communities. The first environmental gradient was
mostly composed of physical water parameters and habitat factors,
including elevation (m), dissolved oxygen (mg/L), flow velocity (m/s),
riffle area (\%), and vegetation cover (\%). The high values of these
factors indicated pristine habitats in the headwaters and upper reaches,
where there was less human disturbance. The second environmental
gradient was mostly composed of the chemical water parameters (e.g.,
high COD\textsubscript{Mn} {[}mg/L{]} and BOD\textsubscript{5}
{[}mg/L{]}), conductivity (\selectlanguage{greek}μ\selectlanguage{english}s/cm), bacteria levels (total number of
bacteria and coliform), and heavy metals (e.g., Hg, As, and Cu
{[}mg/L{]}). The high values of these factors indicated water pollution
and extraneous interference that commonly occurred in urban and
industrial areas of the lower reaches. These results indicated that
eDNA-based analysis of fish community composition could reflect the
longitudinal changes in environmental factors along the river, which was
similar to the pattern reflected by individual number and biomass.

\textbf{4 Discussion}

The application of nondestructive techniques to monitor and characterize
fish diversity has drawn increasing worldwide attention for assessing
aquatic ecosystem health. Given that it is difficult and costly to
investigate fish diversity in large rivers, finding a new monitoring
method with applicable technology has drawn great attention. Negative
impacts of increasing anthropogenic activities on aquatic organisms,
especially fish, have been verified in the Pearl River and other rivers
and reservoirs connected with it (Wang et al., 2020b; Wang et al.,
2019a; Wang et al., 2018a; Wang et al., 2019b; Wang et al., 2021a). To
protect and maintain the health of subtropical river systems, we
investigated the fish diversity along a river continuum using a
combination of electrofishing and eDNA metabarcoding. Our results
presented evidence that eDNA can be used to monitor fish diversity in
this kind of aquatic ecosystem.

\textbf{4.1 The eDNA-based distribution and composition of fish
communities in rivers}

The fish species detected by eDNA across the 38 sampling sites contained
all the fish species sampled by electrofishing, indicating that the
capacity of eDNA to find species was stronger than that of
electrofishing (also see Balasingham et al. (2018; Beng and Corlett
(2020). Generally, the extra species detected by eDNA were the species
that have been reported in the Pearl River system but were not
distributed in the designed sampling sites of this study. This indicated
that on the one hand, eDNA could trace the DNA released by fish species
that were difficult to sample by traditional methods; on the other hand,
the DNA of these hard-catch or rare fish species might be carried by
upstream or tributary waters and then collected at downstream or
mainstream sites. Therefore, eDNA in water reflected more information on
species distribution than the traditional method. However, eDNA contains
more extraneous species information than traditional in situ field
monitoring, which confuses site-specific assessments of ecological
health or biological integrity. In fact, the similar spatial
distribution pattern shown by NMDS and the high regression coefficient
between eDNA- and number-based diversity indicated that eDNA could
reveal the core information on the composition and diversity of fish
communities as comprehensively as those reflected by individual number
and biomass collected by traditional in situ investigation. Therefore,
we suggest that researchers and managers use eDNA as an ecological tool
to identify fish community structure and associated environmental
factors.

\textbf{4.2 Appropriate threshold of taxonomic classification for
evaluating fish diversity}

Compared with aquatic invertebrates, fish have more comprehensive DNA
information in the gene bank, and thus, using eDNA metabarcoding to
distinguish fish diversity has broad application prospects. Wang et al.
(2023) suggested that in complex habitats, such as fresh and brackish
converging lakes, analysing the invertebrate composition or diversity at
the phylum or class level (at least to family) is effective enough to
reflect the environmental properties. The great difference between eDNA
usefulness for fish and invertebrates is the structure of primary
taxonomic units, which is much simpler for fish than for invertebrates.
At the class or order level, different invertebrate assemblages had
totally different ecological properties (e.g., freshwater vs. brackish
or littoral vs. limnetic); however, for fishes, order-level
identification could not distinguish the habitat-specific ecological
properties. Thus, according to our results, we recommend that an
appropriate threshold for eDNA-based fish monitoring is at the genus or
species level, which could be further chosen according to the monitoring
or research targets. For example, if users focus on the influence of
environmental changes on fish community structure, genus-level
identification with eDNA is effective; if users focus on the fish
distribution, species-level identification might be favourable.

\textbf{4.3 Biomarkers that can distinguish the differences between fish
communities}

The LEfSe results showed that biomarkers selected by both eDNA and
electrofishing exhibited similarity in headwaters and lower reaches but
differences in middle reaches. eDNA could identify the fishes of
Danioninae, Siluridae, Nemacheilidae, Oxudercidae, Acheilognathinae, and
Barbinae, indicating that eDNA has a high degree of recognition for
rheophilic fish living in headwater habitats. With widespread rapids and
riffles, rushing waters in headwaters had a great perturbation effect on
DNA remaining in the environment, especially for those deposited in the
sediments. Thus, the eDNA samples collected in rapids (e.g., sites L1 --
L2 in the Liuxi River and Z1 -- Z3 in the Zeng River) contained more
species OTU information than the eDNA samples collected in slow-flowing
or limnetic waters of the middle and lower reaches.

An interesting finding was the difference between eDNA-based biomarkers
and biomass-based biomarkers. The biomarkers selected by LEfSe through
relative biomass preferred to screen out species with larger body sizes,
such as Xenocypris, Squaliobarbus, and \emph{Pterygoplichthys} in zone
IV and \emph{Ictalurus} and \emph{Rhabdosargus} in zone VII. This is a
problem that limits the promotion and application of eDNA in a wider
research area. As suggested by Rourke et al. (2022), key influencing
biotic factors on eDNA effects included the taxon examined as well as
their body size, distribution, reproduction, and migration.
Nevertheless, there is considerable evidence to support using eDNA as an
ancillary tool for assessing fish population abundance and/or biomass
across discrete spatiotemporal scales, following preliminary
investigations to determine species and context-specific factors that
influence the eDNA abundance/biomass relationship (Bylemans et al.,
2019; Doi et al., 2017). Advantages of eDNA monitoring relative to other
approaches include reduced costs, increased efficiencies, and nonlethal
sampling (Stewart, 2019).

\textbf{4.4 Use of fish diversity to evaluate the water quality}

Considering different monitoring targets, the use of eDNA-based fish
monitoring could be summarized into three goals: 1) water pollution that
may reduce fish diversity in water bodies, 2) evaluation of the effects
of serious environmental events on local aquatic communities, and 3)
tracing the target species such as indicator, protected, and endangered
fish in the river system. Our results demonstrated that the
environmental influence on fish distribution and composition analysed by
the eDNA method showed a similar tendency to that analysed by the
traditional method (e.g., individual number and biomass), suggesting
that eDNA is a useful tool for monitoring biological communities in the
field. In this study, DO, velocity, salinity, depth,
COD\textsubscript{Mn} (or BOD\textsubscript{5}), and the bacteria amount
in the water were the key environmental factors that influenced the fish
communities. In fact, given that fishes are not only the crucial
components of the local food web, but also the high-level predators that
exhibit top-down control effects, key environmental factors that
influence fish distribution and composition may also influence food web
properties (e.g., ominivory, connectivity and stability; see Wang et
al., 2018a; Wang et al., 2019b; Wang et al., 2021a). Our further
research will focus on the eDNA-based relationships between the
attributes of fish communities and those of invertebrate communities as
well as on the structure and functioning of aquatic food webs.

\textbf{5 Conclusions}

Our study used eDNA macrobarcoding technology to investigate the
distribution and composition of fish communities in subtropical river
systems. eDNA-based fish investigation was as effective as
electrofishing-based investigation in both composition and diversity
analysis. The relative OTU richness (\%) at the taxon-specific level
could effectively distinguish the dominance of fish communities. The
composition of fish communities in each spatial zone could be indicated
by local biomarkers, which could identify the specificity of the local
environment in each spatial zone. The response between environmental
factors and genus- and species-level OTU richness was effective enough
to indicate the ecological relationships between the environment and
fish communities. Macrobarcoding-based eDNA technology has great
potential to be used in field monitoring of fishes, and even extend to
invertebrates and the food web.

\textbf{ACKNOWLEDGEMENTS}

This research was financially supported by Key R\&D Program of Hainan
Province (Nos. ZDYF2022SHFZ034, ZDYF2022SHFZ032, and ZDYF2021SHFZ064),
National Key R\&D Program of China (No. 2022YFD2401301), Natural Science
Foundation of Hainan Province (Nos. 421QN196 and 421QN195), Open Project
of State Key Laboratory of Marine Resource Utilization in South China
Sea (Nos. MRUKF2023005 and MRUKF2023002), Hainan University Start-up
Funding for Scientific Research (Nos. KYQD{[}ZR{]}-21015 and
KYQD{[}ZR{]}-21033), the Program of Hainan Association for Science and
Technology Plans to Youth R\&D Innovation (No. QCXM201914), Guangdong
Water Conservancy Science and Technology Innovation Project (2020-26),
and Research Project of China Water Resources Pearl River Planning
Surveying \& Designing Company (201904).

\textbf{COMPETING INTERESTS}

The authors declare that they have no competing interests.

\textbf{DATA AVAILABILITY STATEMENT}

All data supporting this study are provided as supporting information
accompanying this manuscript.

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