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\begin{document}

\title{Diet in phenotypically divergent sympatric species of African weakly
electric fish (genus: Campylomormyrus) -- a hybrid capture/NGS
metabarcoding approach}



\author[1]{Rahma Amen}%
\author[2]{Katja Havenstein}%
\author[2]{Frank Kirschbaum}%
\author[3]{Ralph Tiedemann}%
\affil[1]{Universität Potsdam}%
\affil[2]{Affiliation not available}%
\affil[3]{Universitat Potsdam}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
Ecological speciation within the mormyrid genus Campylomormyrus resulted
in sympatric species exhibiting divergence in their feeding apparatus
and electric organ discharge (EOD). This study provides direct evidence
to support the suggested ecological speciation scenario that
Campylomormyrus radiation is caused by an adaptation to different food
sources. We performed diet assessment of sympatric Campylomormyrus
species with markedly different snout morphologies and EODs using hybrid
capture/NGS DNA metabarcoding of their stomach contents. Our approach
allowed for high taxonomic resolution of prey items, including benthic
invertebrates, allochthonous invertebrates, and vegetation. Comparisons
of the diet compositions using quantitative measures and diet overlap
indices revealed that all species are able to exploit multiple food
niches in their habitats, i.e. fauna at the bottom, the water surface,
and the water column. The major part of the diet is larvae of aquatic
insects, such as dipterans, coleopterans, and trichopterans, known to
occur in holes and interstitial spaces of the substrate. The results
showed that different snout morphologies and the associated divergence
in the EOD translate into different prey spectra. This suggests that the
diversification in EOD and the morphology of the feeding apparatus is
under functional adaptation.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

African weakly electric fish (Mormyridae) comprise a species rich group
of freshwater fish endemic to Africa with more than 200 described
species in 20 genera (Lavou\selectlanguage{ngerman}é et al. 2003). Within the
genus\emph{Campylomormyrus} , 15 species are described native to the
Congo River and its tributaries (Feulner et al. 2007). Each species
exhibits a species-specific electric organ discharge (EOD). Even closely
related species markedly differ in their waveform shape and/or pulse
duration (Tiedemann et al. 2010). Further, they exhibit species-specific
morphological traits in their feeding apparatus, i.e. the snout,
regarding the snout's length, thickness, and curvature (Feulner et al.
2008). This makes \emph{Campylomormyrus} a prime model system to study
the role of ecology in driving an adaptive radiation.

The adaptive radiation within the genus \emph{Campylomormyrus} has been
studied with regard to molecular genetics (Canitz et al. 2020; Lamanna
et al. 2016), electrophysiology (Feulner et al. 2006), morphometry
(Feulner et al. 2007; Lamanna et al. 2016), and behavior (Amen et al.
2020; Nagel et al.et al. 2018a,b). In combination, these studies suggest
an ecological speciation scenario that \emph{Campylomormyrus} radiation
is caused by an adaptation to exploit different microhabitats and/or
food sources, associated with diversification of the EOD. Indeed,
behavioral experiments using sympatric \emph{Campylomormyrus} species
revealed an association between differing snout morphologies and
preferences for certain types of substrate structure (Amen et al. 2020).
Specifically, in a choice experiment, the short snouted species
(\emph{C. tamandua} ) favored a sandy substrate, while the long snouted
species (\emph{C. rhynchophorus} ) preferred a stone substrate for
feeding.

While these trait-specific substrate preferences appear plausible from a
mechanical point of view (i.e., longer snouts allow for probing further
into interstitial between stones, Amen et al. 2020), there is currently
no information available as to whether different trunk shapes are
associated with different diets. Furthermore, it is still not known
whether the diversification of EOD serves as a prezygotic isolation
factor only (Nagel et al. 2018 a,b) or also is related to foraging
specialization (Feulner et al. 2009).

Information on diet composition of \emph{Campylomormyrus} is so far
limited to only two studies on single species: Roberts \& Stewart (1976)
reported briefly, based on field observations, some food items found in
the stomach of a single specimen assigned to \emph{C. rhynchophorus} .
At that time, phylogeny and species delimitation of
\emph{Campylomormyrus}was not well established. Nwani et al. (2008)
reported the diet composition of \emph{C. tamandua} based on
morphological determination. Hence, so far, to the best of our
knowledge, there have been no controlled studies which compare the diet
composition among\emph{Campylomormyrus} species under natural
conditions. Our study aims at contributing to fill that knowledge gap by
performing a dietary study for some species of \emph{Campylomormyrus}
with markedly different EOD and snout morphologies. Our purpose is not
only to document the dietary ranges and components for these species,
but also to infer whether species with specific morphological traits
prefer specific food items.

Direct observation of feeding in the natural habitat, i.e., the Congo
river, seems unfeasible and microscopic examination of gut contents may
yield incomplete results, as food items may be digested to various
degree, compromising their microscopic identification (Pompanon et al.
2012). Therefore, we used DNA-based dietary analysis using hybrid
capture and subsequent next generation sequencing (NGS) to analyze
stomach contents of wild-caught fish. NGS-based DNA metabarcoding has
been successfully used to investigate the DNA extracted from highly
degraded diet samples (Deagle et al. 2006; Jarman et al. 2004), however,
an initial PCR amplification necessitates DNA fragments of a certain
length to allow both for primer annealing and a large-enough
species-specific target sequence. As an alternative, DNA hybridization
capture (target enrichment) has been successfully applied to enrich
low-concentration and highly degraded DNA-fragments from environmental
DNA (Shokralla et al. 2012) or ancient sedimentary DNA (Krueger et al.
2022). Here, we apply a combined hybrid capture/NGS shotgun approach to
quantify diet composition in gut contents taken from wild-caught African
electric fish to contribute to our understanding of their adaptive
radiation.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Stomach samples
collection}

{\label{stomach-samples-collection}}

Stomach content samples (\(n=27\)) were collected from
five\emph{Campylomormyrus} species with markedly different EOD and snout
morphologies (\emph{C. alces} , \(n=2\);
\emph{C.compressirostris} ,\(n=10\); \emph{C.curvirostris} ,
\(n=2\);\emph{C. numenius} , \(n=1\); \emph{C.
tshokwe} , \(n=9\); see Lamanna et al. 2016 for phylogenetic
relationships, EODs, and snout characteristics). Additionally, stomach
content samples of\emph{Gnathonemus petersii} (\emph{G. petersii,}
\(n=3\)), the sister genus of \emph{Campylomormyrus} , were
used for comparison. Unlike\emph{Campylomormyrus} , \emph{Gnathonemus
petersii} has no snout, but a trunk-like protrusion on the head. The
fish specimens from which these stomach content samples are extracted
were collected during an expedition to the Republic of the Congo in fall
2012. All the analyzed stomach samples were extracted from sympatric
fishes live in the Congo River rapids south of Brazzavilla {[}S4°18.788\selectlanguage{english}'
E15deg13.790\selectlanguage{english}'{]}. Before stomach extracting, all fish individuals were
weighed and measured for total and standard length and were assigned
unique and museum identifiers (see Table S1.1 in Supplementary File S1).
After dissection, the stomach samples were stored in Queen's lysis
buffer solution (Seutin et al.1991). Before DNA extraction, the stomach
contents were visually checked and found to be heavily digested, with
very few visible tissue parts remaining. Handling of animals was
conducted at the University of Brazzaville in accordance with the
relevant national guidelines and regulations guidelines for the care and
use of animals for scientific purposes.

\subsection*{Lysis and DNA extraction}

{\label{lysis-and-dna-extraction}}

Each stomach content sample was homogenized using a Tissue Tip
Homogenizer before incubating with lysis reagents. The Qiagen DNeasy
Mericon Food Kit for extraction of high-quality DNA from digested foods
was used to carry out the DNA extraction from the homogenized samples.
Since the stomach content samples were almost digested, the DNA was
suspected to be highly fragmented. Therefore, the small fragment
protocol of this kit (200~mg) was used according to the manufacturer's
protocol. Initial attempts to directly amplify DNA with established DNA
barcoding primers were unsuccessful (Lamanna \& Tiedemann, unpubl.
results).

\subsection*{Libraries preparation, indexing and
amplification}

{\label{libraries-preparation-indexing-and-amplification}}

After DNA extraction, all samples underwent single-stranded DNA library
preparation using T4 DNA ligase following (Gansauge et al. 2017). The
procedure is optimized to highly degraded DNA fragments, as expected in
digested food from the stomach. An additional step of deoxyuracils
removal using Uracil DNA glycolase was included. Deamination of cytosin
to uracil is a typical damage pattern in degraded DNA and could cause
erroneous mispairing to adenin, resulting in genotyping errors.

After library preparation, the optimal number of Polymerase Chain
Reaction (PCR) cycles required for library indexing and amplification
was estimated by Quantitative Polymerase Chain Reaction (qPCR). The
rationale is to adjust the number of PCR cycles during the library
amplification to different DNA concentrations (Basler et al. 2017). The
reaction was performed by mixing 1ml of a 1:20 dilution of each library
with 3\selectlanguage{greek}µ\selectlanguage{english}l of nuclease free water, 5\selectlanguage{greek}µ\selectlanguage{english}l of 2 x SYBRTM green qPCR master mix
(ThermoFisher Scientic), 0.5\selectlanguage{greek}µ\selectlanguage{english}l of qPCR primer IS7, and 0.5\selectlanguage{greek}µ\selectlanguage{english}l of primer
IS8 (both 10\selectlanguage{greek}µ\selectlanguage{english}M; from Gansauge \& Meyer 2013). For each library three
reaction replicates were analyzed. The PCR reactions were performed with
7min at 95\selectlanguage{ngerman}°C, followed by 40 cycles of 10s at 95°C denaturation, 30s
annealing at 60°C, and 1min extension at 72°C. Considering the
differences in DNA concentration between the qPCR and the indexing PCR,
the optimal number of cycles for the indexing PCR was calculated
according to Basler et al. (2017).

The indexing primers P5 and P7 were chosen according to the library
protocol of Gansauge et al. (2017). The reaction mix for each sample
contained 44.8\selectlanguage{greek}µ\selectlanguage{english}l of nuclease free water, 8\selectlanguage{greek}µ\selectlanguage{english}l of 10 x AccuPrimeTM Pfx
reaction mix (ThermoFisher Scientic), 0.8\selectlanguage{greek}µ\selectlanguage{english}l of 2.5U AccuPrimeTM Pfx
polymerase, 3.2\selectlanguage{greek}µ\selectlanguage{english}l of P5 indexing primer, 3.2\selectlanguage{greek}µ\selectlanguage{english}l of P7 indexing primer
(both 10 \selectlanguage{greek}µ\selectlanguage{english}M) and 20\selectlanguage{greek}µ\selectlanguage{english}l of each library. Thermocycling started with 2min
at 95\selectlanguage{ngerman}°C, followed by the number of cycles calculated according to Basler
et al. (2017) consisting of 15s at 95°C denaturation, 30s annealing at
60°C and 1~min extension at 68°C. After indexing and amplification, all
libraries were purified using the MinElute PCR Purification Kit (QIAGEN)
following the manufacturer's instructions. The libraries were quantified
with a Qubit 3.0 fluorometer after purification and the distribution of
the DNA fragments lengths was measured using an Agilent 2100 bioanalyzer
system.

\subsection*{Probe design and DNA hybridization
capture}

{\label{probe-design-and-dna-hybridization-capture}}

For probe design, we selected target species based on literature
research on the potential taxa eaten by \emph{Campylomormyrus} and the
expected diversity of prey items in the Congo River region. We targeted
504 species across 6 animal taxa: Arthropoda, Annelida, Mollusca,
Nemertea, Nematoda, and Rotatoria. Additionally, we targeted 29 species
from 3 plant taxa: Streptophyta, Rhodophyta, and Chlorophyta. For these
species, mitochondrial COI gene (animals) resp. chloroplast rbcL gene
(plants) sequences were retrieved from the GenBank sequence database of
the National Center for Biotechnology Information (NCBI). When a species
of interest did not have sequence information available, sequence
information from another species of the same genus was included. If this
was not available, the taxon was excluded. The average sequence length
of all collected sequences was 700~bp. A complete list of taxonomic
names and NCBI accession numbers can be found in the Supplementary File
S2. The sequence capture probes were custom-made at Arbor Biosciences
(formerly MY croarray) and supplied in form of a MYBaits InSolution
Custom Target Capture Kit (designs with 1-20k probes). The probes were
designed to achieve a high overall probe count and an increased coverage
in high GCcontent regions. The final probe set contained 15284~probes,
each with a length of 80bp. The synthesized probes comprise short
overlapping fragments representing the whole probe template sequences of
COI resp. rbcL.

The hybridization capture enrichment reactions were performed according
to the manufacturer's instructions (Biosciences 2016). Following these
instructions, the recommended input of the library DNA was 100ng to
500ng. This was equivalent to 7\selectlanguage{greek}µ\selectlanguage{english}l of the library material with a DNA
concentration of 14 to 72ng/\selectlanguage{greek}µ\selectlanguage{english}l. All materials from the library were used
for two rounds of hybridization capture reactions, thereby increasing
specificity (Krueger et al. 2021). The captured and amplified libraries
of the first round of capture reaction were used as input libraries for
the second round. For both rounds of capture, the libraries were not
pooled since pooling might produce low quality results (Biosciences
2016). The second captured and amplified libraries were pooled and
underwent sequencing on an Illumina MiSeq instrument using a protocol
for 2x150bp paired end of double indexed libraries (Biosciences 2016).

\subsection*{Bioinformatics, data analysis, and
statistics}

{\label{bioinformatics-data-analysis-and-statistics}}

Before further analyzing the high throughput sequencing reads, which
were generated in FASTQ format, we performed a quality processing to
remove the adapter sequences and the low-quality reads, using Cutadapt
version 2.10 (Martin 2011). Quality cutoff, minimal sequence length, and
minimal overlap between sequence and adapter were set to 20, 30, and 3,
respectively. Then, we performed quality control checks to the filtered
sequences using FastQC (Andrews 2010) to ensure their suitability for
further analysis.

Taxonomic classification for the filtered sequences was done using
Kraken version 2.0.9 (Kraken2; Wood et al. 2019). Kraken compares the
reads of the metagenome to short sequences of length \(k\),
the so-called \(k\)mers, from a database that is associated
with the sequence information underlying the tree of life phylogeny. The
algorithm then places the read on the tree of life based on its
similarities to these \(k\)mers. Before applying Kraken2, a
custom marker database of available eukaryotic COI and RBCl sequences
from NCBI was built to map the reads. The results of Kraken2 were
visualized and inspected using the online tool Pavian metagenomics data
explorer (Breitwieser \& Salzberg, 2019).

The read counts were used to estimate the relative abundance of food
taxa in the diet of each sample. For any sample, these count data were
used to record both the occurrence (presence/absence) of a taxon and the
percentage of reads assigned to that taxon. The frequency of occurrence
(\%FOO) is used to quantify occurrence across samples. The relative read
abundance (RRA) is used as a proxy for relative biomass consumed.
Details on the calculations of these terms are given in the
Supplementary File S7.

The dietary niche width was assessed by calculating the Shannon
diversity index (alpha diversity) for each sample using the \emph{spaa}R
package (Zhang 2016). Further, we used two complementary metrics to
describe the diet overlap among species: Sch\selectlanguage{ngerman}öner index (Schoener 1970)
and Pianka index (Pianka 1974). Pairwise calculations of these indices
were performed using the \emph{spaa} R package (Zhang 2016). The
equations of the indices are given in the Supplementary File S7. We used
the \emph{EcoSimR} R package (Gotelli et al. 2015) to compare the
results with reference to 1000 permutations of a null model that holds
the same dietary niche width, while randomizing the values for the diet
items. We further calculated Bray--Curtis dissimilarity indices (beta
diversity) between each pair of samples using the\emph{vegdist}
-function in the \emph{vegan} R package (Oksanen et al. 2019).
Additionally, we performed nonmetric multidimensional scaling (NMDS)
using the function \emph{metaMDS} in the \emph{vegan} R package to
visualize the patterns of dietary dissimilarity among samples. We tested
for differences in diet composition among species by performing a
permutational multivariate analysis of variance (perMANOVA) with 999
permutations, using the \emph{adonis} -function in the \emph{vegan} R
package. To further test for the influence of our two divergent
phenotypic characters EOD and snout morphology on the food selection, we
grouped the stomach samples as follows: Based on the EOD, we formed two
groups: Long EOD (\textgreater{}2ms) and short EOD (\textless{}0.3ms;
note the almost 10fold difference in EOD length among the two groups).
The first group (\(n=12\)) included the samples of \emph{C.
alces} ,\emph{C. numenius} , and \emph{C. tshokwe} . The second group
(\(n=15\)) included the samples of \emph{C. compressirostris}
, \emph{C. curvirostris} , and \emph{G. petersii} . EOD categorization
was based on Lamanna et al. (2016). Further, the samples were grouped
based on the snout length into three groups: Long snout (\emph{C.
curvirostris} ,\emph{C. tshokwe} , and \emph{C. numenius} ,
\(n=12\), medium snout (\emph{C. compressirostris} ,
\(n=10\)), and short snout (\emph{C. alces} and \emph{G.
petersii} , \(n=5\)). For this classification, we took
advantage of a previous geometric morphometrics analysis, which
stratified the respective species according to snout length along
principal component 1 (Figure 3 in Lamanna et al. 2016). Differences in
diet among groups were assessed with a perMANOVA as above.

\section*{Results}

{\label{results}}

\subsection*{Sequencing results}

{\label{sequencing-results}}

The sequencing for all samples generated 6.9Gb raw sequencing data of
FASTQ formatted reads. The raw data have been deposited to the open
repository Zenodo (Amen et al. 2022). The total number of raw sequences
from the 27 stomach samples is 19531074, ranging from 19226 to 867093
per individual sample. Out of them, a total of 18357160 sequences were
obtained after quality filtering, ranging from 16703 to 828735 per
individual.

\subsection*{Taxonomic classification}

{\label{taxonomic-classification}}

The taxonomic classification of the filtered sequences against the
custom marker database using Kraken2 classified all the reads into seven
phylogenetic levels (domain, kingdom, phylum, class, order, family,
genus, and species) or unclassified reads. The percentage of the
unclassified reads reached 33.22~\% of the pooled sequences of all
samples. On the individual level, the percentage of unclassified reads
ranged from 8.38~\% to 71.67~\%. The high percentage of unclassified
reads is attributed to sequences outside the range of our custom marker
database (eukaryotic COI and rbcL only). When the entire nucleotide
database (www.ncbi.nlm.nih.gov/nucleotide/) is used instead of our
custom database, the percentage of unclassified reads dropped to
10.11~\% of the pooled sequences of all samples. We limited the analysis
to the taxonomic classifications using the eukaryote COI/rbcL custom
database, however, to keep the focus on the diet analysis (instead of
prokaryotes in the gut microbiomes).

The classified reads pooled for each species are visualized using the
metagenomics data explorer Pavian and depicted in the supplement
(Supplementary File S3). Comparing the distributions of the reads for
the food taxa classified at each phylogenetic level reveals that most of
the reads are assigned to few food taxa, while many of the classified
taxa are represented by only a few reads. We excluded records from
primates and birds, which were presumably contamination. Taxa
represented by less than 0.01~\% of reads were further excluded, as they
may constitute contamination/background noise (Alberdi et al. 2018). The
remaining identified prey items were assigned to 146 taxa of different
taxonomic level: 20 classes, 34 orders, 97 families, 105 genera, and 90
species (Tables S4.1-S4.6 Supplementary File S4).

\subsection*{Relative abundance}

{\label{relative-abundance}}

\emph{Campylomormyrus} had a broad spectrum of prey items. On the class
level, Insecta dominated by far. They were found in all samples,
representing more than 90\% of the total reads (Figure 1). Other classes
were also found in all samples but with less percentage of the total
reads, such as Clitellata, Arachnida, Malacostraca, and Hexanauplia. At
the order level, the most abundant prey items were Diptera, Coleoptera,
and Hymenoptera (all Insecta) class, all of which were in all samples
(Figure 1). Also, the Haplotaxida (Clitellata) and Araneae (Arachnida)
were found in all samples. Further insect orders such as Lepidoptera,
Trichoptera, Ephemeroptera, and Hemiptera, as well as Decapoda
(Crustacea) were found in more than 83~\% of the samples.

The Relative Read Abundances (RRA) of the primary food taxa for the
studied species are depicted in Figure 2. Most species share the same
food taxa, albeit in different proportions. The most dominant prey taxon
at class level is Insecta, followed by Clitellata. At the order level,
the most abundant orders are all insects, i.e., Diptera, Coleoptera,
Hymenoptera, and Lepidoptera.

The RRA data were used to assess the dietary niche width by calculating
the Shannon diversity index (Figure 3 and Table S5.1 in the
Supplementary File S5). The Shannon diversity index differed
considerably amongthe studied species, pointing towards different
dietary niche width. As an example, the lowest Shannon index values are
found for \emph{C. compressirostris} samples (1.62±0.02 SE), while the
highest are found for \emph{C. tshokwe} (2.21±0.03 SE).

\subsection*{Diet overlap}

{\label{diet-overlap}}

The diets of most of species significantly overlap at class and order
phylogenetic levels (Table 1): At these levels, Pianka index values
showed statistically significant niche overlap based on comparison with
1,000 null models (see the Supplementary File S8 for more details) and
Schöner index scored more than 0.6 for all species comparisons except
when compared to \emph{C. numenius} . At lower taxonomic level (family,
genus, species), there was much less overlap in the diet among species.
The degree of diet overlap is further confirmed by the Bray--Curtis
dissimilarity index (0: similar; 1: dissimilar), as shown in Table S5.2
in the Supplementary File S5.

As many reads could not be assigned to family, genus, or species level,
we performed further statistical analyses on read assignments at the
order level. A perMANOVA on the Bray--Curtis dissimilarity index data
derived from the RRA values at order level indicates significant dietary
differences among species (\emph{F} =3.596,\emph{r\textsuperscript{2}}
=0.275, \emph{p} \selectlanguage{english}[?]0.001), excluding the species with small sample size
(\emph{C. alces} , \emph{C. curvirostris} , and \emph{C. numenius} ). We
performed a post hoc pairwise perMANOVA using a Bonferroni correction of
the \emph{p} -values to find out which species drive these results.
\emph{C. compressirostris} versus \emph{C. tshokwe} and \emph{G.
petersii} versus \emph{C. tshokwe} were statistically significantly
different in their diet (\emph{p} \textless{}0.05), while \emph{C.
compressirostris} and \emph{G. petersii} did not show a significant
difference.

A perMANOVA on the EOD groups (long \emph{vs.} short EOD) indicated
significant dietary differences between the two groups (\emph{F}
=8.5,\emph{r\textsuperscript{2}} =0.254, \emph{p} \selectlanguage{english}[?]0.001, Bray--Curtis
dissimilarity index data derived from the RRA values at order level).
Similarly, for snout morphology (short \emph{vs.} medium
\emph{vs.}long), a perMANOVA on the Bray--Curtis dissimilarity index
data (derived from the RRA values at order level) showed significant
dietary differences among the three groups (\emph{F}
=2.676,\emph{r\textsuperscript{2}} =0.182, \emph{p} \selectlanguage{english}[?]0.001). The post
hoc pairwise perMANOVA using a Bonferroni correction of the \emph{p}
-values indicated that only the long snout versus the medium snout is
statistically significant (p\textless{}0.002).

The patterns of dietary difference among samples are visualized in
Figure 4 by ordinating the Bray--Curtis dissimilarity index values in
two dimensions using NMDS. The stress level for the NMDS was 0.133,
which indicates a good representation (Clarke 1993). The NMDS plot shows
the segregation of samples based on EOD and the degree of diet
overlap/dissimilarity based on species and snout length.

\section*{Discussion}

{\label{discussion}}

\subsection*{Diet profile}

{\label{diet-profile}}

The DNA metabarcoding method provided a clear taxonomic resolution of
the partially digested stomach content and potentially detected highly
diverse food taxa. This high taxonomic resolution is in line with those
of previous dietary studies based on DNA-based approaches (e.g.,
Harms-Tuohy et al. 2016; Pan et al. 2021; Rees et al. 2020; Sakaguchi et
al. 2017). The results of this study indicate that the diet
of\emph{Campylomormyrus} and \emph{Gnathonemus} species is composed
mainly of three types of prey items, i.e., benthic invertebrates,
allochthonous invertebrates, and macrophyte material. The most dominant
prey taxa found in the gut contents of these species belong to benthic
invertebrates, especially aquatic insects. In particular, dipterans
(Chironomidae, Simuliidae, Drosophilidae, and Tephritidae), coleopterans
(Zopheridae, Carabidae, Histeridae, and Scarabaeidae), trichopterans
(Hydropsychidae, Lycaenidae, and Elateridae), ephemeropterans
(Leptophlebiidae, Baetidae, and Ephemerellidae), and odonatans
(Coenagrionidae, Chlorocyphidae, and Chlorogomphidae) are important diet
constituents of all the species. The larvae of these insects live
usually in holes and interstitial spaces of the riverbed. Although the
DNA approach used here cannot tell the stage of the aquatic insects
found in the diet, it is reasonable to assume that the aquatic larvae,
rather than the terrestrial imagines, were targeted
by\emph{Campylomormyrus} and \emph{Gnathonemus} .

Beside aquatic insects, annelid worms (such as Glossoscolecidae,
Naididae, and Megascolecidae) were also found in the diet of
all\emph{Campylomormyrus} and \emph{Gnathonemus} species in this study.
Similar to the insects' larvae, the annelid worms hide in mud and among
aquatic vegetation in the substrate of the riverbed. Other benthic
invertebrates found were freshwater snails (Gastropoda, orders
Pachychilidae and Stylommatophora), and crustaceans (Malacostraca,
orders Decapoda, Copepoda, Cladocera, and Amphipoda).

The second group of food items found in the diet
of\emph{Campylomormyrus} and \emph{Gnathonemus} species is allochthonous
invertebrates. The most abundant prey taxa from this group are
Hymenoptera (including Formicidae, Mymaridae, and Braconidae) and
Lepidoptera (including Nymphalidae, Lycaenidae, and Hepialidae).
Additionally, Araneae (Arachnida) were frequently found in the diet.

The third group of food items is plants, including grasses, such as
Poaceae of the Poales order, and flowering plants, such as Fabaceae and
Asterales.

It must be noted that we cannot exclude some of these taxa having
derived from the diet of the primary prey (secondary predation; Sheppard
et al. 2005) or comprise small organisms and plant debris
unintentionally ingested during grasp suction. However, the stomach
contents of \emph{Campylomormyrus} and \emph{Gnathonemus} species found
in this study using a DNA metabarcoding approach are compatible
with\emph{Campylomormyrus} mainly (about 90~\%) feeding on aquatic
insects (Nwani et al., 2008; Roberts \& Stewart, 1976). A previous
study, based on morphological observation, reported that stomach
contents of some\emph{Campylomormyrus} species contain larvae of
chironomids, Povilla, trichopteran, ephemeropterans and odonates, dead
plant debris, and decomposing animal debris (Roberts \& Stewart, 1976).
This study reported also that the stomach content of a specimen of
\emph{C. rhynchophorus} had Chironomidae, Simuliidae, and trichopterans,
and a few small ephemeropterans. Another study on the stomach contents
of\emph{C. tamandua} using morphological observation reported similar
food taxa (Nwani et al., 2008). The few available dietary studies on
other fish species inhabiting the Congo River such as \emph{Schilbe
intermedius} (Dirat et al. 2019) and \emph{Distichodus antonii}
,\emph{D. affinis} and \emph{D. lusosso} (Zebe et al. 2010) showed
similar prey spectra.

\subsection*{Diet comparison among
species}

{\label{diet-comparison-among-species}}

The dietary compositions of the \emph{Campylomormyrus}
and\emph{Gnathonemus} species suggest that their feeding behavior is
opportunistic, hence depending on food availability and accessibility.
Based on our results, all species exploit diverse food niches in their
habitats. For instance, the prevalence of benthic invertebrates, such as
larvae of dipterans and coleopterans and annelid worms, in the diet of
all species suggests that these species exploit the bottom of the
riverbed, while the occurrence of allochthonous invertebrates, such as
Formicidae, Nymphalidae and Arachnida spiders, may indicate a certain
degree of surface feeding. Further, the diets also include food items
from the water column, such as copepods. Accordingly, these species may
exhibit high trophic flexibility and diverse feeding behaviors.

However, the RRA results revealed significant differences among the
dietary compositions among the species, potentially associated with EOD
and snout length. For example, the diet of some species, such as\emph{C.
compressirostris} , \emph{C. curvirostris} , and G.\emph{petersii} ,
contains more dipterans, while the diet of \emph{C. tshokwe} contains
more coleopterans. The diet of \emph{C. numenius} was exceptional, as it
contained large amount of grass. Unfortunately, this latter finding
remains anecdotal, as we had only one sample of this species available.

Species with long EOD had preferentially fed on other taxonomic groups
(i.e., coleopterans, ephemeropterans, spiders, annelids), compared to
the species with short EOD, where dipterans dominated in the diet (Fig.
S6.1 in the Supplementary File S6). Similar differences were found
according to snout length (Fig. S6.2 in the Supplementary File S6).
Note, however, that species with long EOD often also exhibit a long
snout (Fig. 4; cf. Lamanna et al. 2016).

\subsection*{Dietary analysis and the radiation
scenario}

{\label{dietary-analysis-and-the-radiation-scenario}}

Our study was motivated by potentially providing further support for the
hypothesis that radiation of \emph{Campylomormyrus} is caused by an
adaptation to di\selectlanguage{english}fferent food sources, associated with diversi\selectlanguage{english}fication of
EOD. The results proved that all the species tested in the current study
are able to exploit diverse niches, especially the bottom fauna,
regardless of their snout shape and EOD.

The current study still provides some evidence that different snout
morphologies and the associated divergence in the EOD translate into
different prey spectra. As a different morphology of the feeding
apparatus among the species may constitute a functional adaptation to
exploiting different substrates (Amen et al. 2020), the different prey
spectra could simply reflect differential availability of different prey
in the respective microhabitats. Unfortunately, no data on
substrate-specific benthofauna are available for the Congo River.
However, it has been argued that the duration of the EOD plays a crucial
role during food detection by determining the prey items that can be
detected best (Harlan Meyer 1982). In fact, prey detection via
electrolocation is a frequency-based process. Our results cannot
establish a causal link between EOD characteristics and prey spectra,
yet they are compatible with the hypothesis that the divergence in EOD
could be of adaptive value during feeding as well, beside its proven
function as a prezygotic isolation mechanism. In this case, EOD would be
a `magic trait' triggering both adaptation and reproductive isolation
(Feulner et al. 2009). One approach to further investigate a potential
dual function of the EOD divergence (feeding specialization and
reproductive isolation) would be to expand on the choice experiments
performed by Amen et al. (2020) by presenting a variety of food items.
This will though still be confounded by the association of long EOD with
long snout. Ultimately, one would like to know prey-specific detection
probabilities, relative to the physical properties of the different
discharges. To achieve such knowledge remains a challenge.

In general, the snout morphology of \emph{Campylomormyrus} may enhance
their grasp suction mode of feeding (Marrero \& Winemiller 1993). This
enhanced mode enables them to obtain the aquatic insects that burrow
into the different substrate structures. This functional foraging
specialization allow an efficient exploitation of the rich bottom fauna
of benthic invertebrates, especially aquatic insect larvae, which other
fishes may not reach. Such trophic specializations may have triggered
the observed radiation of \emph{Campylomormyrus} in the Congo river.

\section*{Acknowledgements}

{\label{acknowledgements}}

We thank Dr. Francesco Lamanna and Dr. Christiane Paul for participation
in sample collection in Congo/Brazzaville and Dr. Victor Mamonekene,
University of Brazzaville, for logistical support during field work.
Support from Katrin Kiemel in data analysis and manuscript revision is
also acknowledged. Financial support for this study was provided by the
University of Potsdam. Open Access funding is provided by the DEAL
project. R. Amen was funded by the Bridge Scholarship Program of the
University of Potsdam and from the Ministry of Higher Education of the
Arab Republic of Egypt.

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\par\null

\section*{Data Accessibility and Benefit-Sharing
Section}

{\label{data-accessibility-and-benefit-sharing-section}}

\textbf{Data Accessibility Statement}

Raw sequence reads are deposited in the open repository Zenodo (Amen et
al., 2022). The codes used to analyze the data and generate the results
can be found on GitHub repository DATMetaB:

https://github.com/RahmaAmen/DATMetaB

\textbf{Benefit-Sharing Statement}

Benefits from this research arise from the sharing of our data and
results on public databases as described above as well as the codes
developed to analyze the data.

\section*{Author Contributions}

{\label{author-contributions}}

RA participated in the design of the study, carried out the experiments,
analyzed the data and drafted the manuscript; KH participated in
carrying out the experiments and revised the manuscript; RT and FK
conceived the study, organized and participated in sample collection,
participated in the design of the study, revised the manuscript, and
coordinated the study. All authors have read and approved the manuscript
for publication.



\section*{Tables and Figures}

{\label{tables-and-figures}}

\textbf{Tables}

\textbf{Table 1:} Schöner index (bottom left) and Pianke index (top
right) for diet overlap (0: no diet overlap, 1: complete diet overlap)
of 5 species of Campylomormyrus and one species of its sister genus
Gnathonemus based on RRA data considering food taxa at di\selectlanguage{english}fferent
phylogenetic levels based on pooled samples\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
Species & \emph{C. alces} & \emph{C. compres.} & \emph{C. curvirost.} &
\emph{C. numenius} & \emph{C. tshokwe} & \emph{G.
petersii}\tabularnewline
\midrule
\endhead
Index\textsuperscript{*} at class/order level & Index\textsuperscript{*}
at class/order level & Index\textsuperscript{*} at class/order level &
Index\textsuperscript{*} at class/order level & Index\textsuperscript{*}
at class/order level & Index\textsuperscript{*} at class/order level &
Index\textsuperscript{*} at class/order level\tabularnewline
\emph{C. alces} & & \textbf{0.99}/\textbf{0.69} &
\textbf{0.99}/\textbf{0.74} & 0.51/0.31 & \textbf{0.99}/\textbf{0. 81} &
\textbf{0.99}/0.64\tabularnewline
\emph{C. compres.} & \textbf{0.93}/\textbf{0.60} & &
\textbf{0.99}/\textbf{0.98} & 0.50/0.31 & \textbf{0.99}/\textbf{0.74} &
\textbf{0.99}/\textbf{0.99}\tabularnewline
\emph{C. curvirost.} & \textbf{0.97}/\textbf{0.64} &
\textbf{0.92}/\textbf{0.80} & & 0.51/0.33 & \textbf{0.99}/\textbf{0.85}
& \textbf{0.99}/\textbf{0.97}\tabularnewline
\emph{C. numenius} & 0.37/0.37 & 0.36/0.36 & 0.37/0.35 & & 0.51/0.35 &
0.51/0.31\tabularnewline
\emph{C. tshokwe} & \textbf{0.97}/\textbf{0.69} &
\textbf{0.92}/\textbf{0.61} & \textbf{0.98}/\textbf{0.75} & 0.37/0.36 &
& \textbf{0.99}/\textbf{0.73}\tabularnewline
\emph{G. petersii} & \textbf{0.95}/0.56 & \textbf{0.93}/\textbf{0.88} &
\textbf{0.96}/\textbf{0.81} & 0.37/0.36 & \textbf{0.96}/\textbf{0.60}
&\tabularnewline
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level &
Index\textsuperscript{*} at family/genus level\tabularnewline
\emph{C. alces} & & \textbf{0.52}/\textbf{0.47} & 0.49/\textbf{0.42} &
0.12/0.20 & \textbf{0.58}/\textbf{0.57} & 0.39/0.01\tabularnewline
\emph{C. compres.} & 0.46/0.36 & & \textbf{0.93}/\textbf{0.40} &
0.19/0.21 & \textbf{0.63}/\textbf{0.49} &
\textbf{0.92}/0.11\tabularnewline
\emph{C. curvirost.} & 0.43/0.31 & \textbf{0.66}/0.32 & & 0.20/0.25 &
\textbf{0.70}/\textbf{0.74} & \textbf{0.93}/0.03\tabularnewline
\emph{C. numenius} & 0.14/0.11 & 0.18/0.10 & 0.19/0.09 & & 0.16/0.36 &
0.19/0.01\tabularnewline
\emph{C. tshokwe} & 0.52/0.48 & 0.44/0.34 & 0.59/0.48 & 0.18/0.13 & &
\textbf{0.51}/0.05\tabularnewline
\emph{G. petersii} & 0.24/0.03 & 0.55/0.09 & 0.50/0.04 & 0.16/0.01 &
0.26/0.04 &\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{*}Bold numbers indicate statistically significant niche
overlap (i.e., Pianke index is greater than that expected by chance
based on comparison with 1,000 null models, \selectlanguage{greek}α \selectlanguage{english}= 0.05, and Sch\selectlanguage{ngerman}öner index
is greater than 0.6).



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

\end{center}
\end{figure}

\par\null

Figure 1: Overall diet composition among all samples (5 species of
Campylomormyrus and one species of Gnathonemus) based on frequency of
occurrence (\%FOO, shown in blue bars) at class (upper panel) and order
(lower panel) levels. Taxa are ordered based on the reads percentage
abundance (shown in orange bars). Only primary prey items are
considered.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Relative read abundance (RRA) of food taxa from dietary
metabarcoding data sets of stomach contents of 5 species of
Campylomormyrus and one species of its sister genus Gnathomemus at class
level (upper panel) and order level (lower panel). Fish pictograms
provide a sketch on species-specific snout morphology.

\emph{}\selectlanguage{english}
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Figure 3: Boxplot of Shannon diversity indices of 5 species of
Campylomormyrus and one species of its sister genus Gnathonemus (based
on RRA at order level).

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\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

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Figure 4: Dietary niche partitioning within and among 5 species of
\emph{Campylomormyrus} and one species of its sister genus
\emph{Gnathomemus} by nMDS of RRA-based Bray--Curtis dissimilarity
indices of samples on the order level (adonis F = 3.04,
R\textsuperscript{2} = 0.243, p [?] .001). The symbols represent the
species and the colors represent snout length. The shaded convex hulls
indicate short vs. long EOD. The stress level of 0.133 is under the
cut-off value of 0.2 as recommended by Clarke (1993) to indicate an
interpretable ordination.

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