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\begin{document}

\title{Diet in phenotypically divergent sympatric species of African weakly
electric fish (genus: Campylomormyrus) -- a hybrid capture/HTS
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{June 16, 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 documents the overall
diet of the genus Campylomormyrus and examines the hypothesis that
Campylomormyrus radiation is caused by an adaptation to different food
sources. We performed diet assessment of five sympatric Campylomormyrus
species (C. alces, C. compressirostris, C. curvirostris, C. tshokwe, C.
numenius) and their sister taxon Gnathonemus petersii with markedly
different snout morphologies and EODs using hybrid capture/HTS 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. 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). They use the
EOD in social communication and pair formation as well as for object
location and foraging. Even closely related species markedly differ in
their pulse duration (up to 100-fold difference) and/or waveform shape
(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). The diversifications in these species-specific traits
make\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. 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). Currently, 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).

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 five species of \emph{Campylomormyrus}
with markedly different EODs 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 traits (EOD and snout
morphology) 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). High Throughput Sequencing (HTS)-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. A pilot study revealed that this approach is not
working for our samples (Lamanna \& Tiedemann, unpublished), indicative
of low DNA quality. This lowered DNA quality may be attributed to
logistical constraints during field work in Congo,
i.e.,\emph{Campylomormyrus} are known to be nocturnal feeders, while
fish catching activities were only possible at day-time, leaving time
for progressed digestion which may have prevented effective direct PCR
amplification with standard protocols. 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/HTS
shotgun approach to quantify diet composition in gut contents taken from
wild-caught African electric fish. We specifically test the hypothesis
that the diversification in EOD characteristics and snout shape
translates into differences in the diet compositions
of\emph{Campylomormyrus.} Thereby, we aim at contributing to our
understanding of the adaptive radiation in this genus.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Stomach samples
collection}

{\label{stomach-samples-collection}}

Stomach content samples (\(n=24\)) 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. tshokwe} , \(n=9\); \emph{C.
numenius} \(n=1\); 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{G. 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
fish living in the Congo River rapids south of Brazzaville {[}S4°18.788\selectlanguage{english}'
E15deg13.790\selectlanguage{english}'{]}. Fish were purchased from local anglers along the
northern shore line of the River Congo at Rapides de Kintambo downstream
of the Malebo Pool (Runge 2007) north of the island Ile de Singes, where
the river bed consists of rocks up to several meters in diameter and the
river is very turbid. 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
tissue buffer solution (20\% dimethyl sulfoxyde, 0.25\selectlanguage{english} M EDTA, saturated
with NaCl, pH 8.0; Seutin et al.1991) at 4degC. Before DNA extraction, the
stomach contents were visually checked and found to be heavily digested,
with very few visible tissue parts remaining.

We assembled 26 stomach contents samples, excluding the \emph{C.
numenius} sample due to its low read numbers (Sec. \selectlanguage{english}3.1), to three
categories, based on species, EOD duration, and snout length. For
species-specific analysis, only the two species with larger sample size,
i.e., \emph{C. compressirostris} (\(n=10\)) and \emph{C.
tshokwe}(\(n=9\)), were considered. Regarding EOD duration,
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 (\(\text{long\ EOD};\ n=11\)) included the
samples of\emph{C. alces} and \emph{C. tshokwe} . The second group
(\(\text{short\ EOD};\ n=15\)) included the samples of \emph{C. compressirostris}
, \emph{C. curvirostris} , and \emph{G. petersii} . EOD categorization
was based on Lamanna et al. (2016). Regarding snout length, we formed
three groups: Long snout (\emph{C. curvirostris} and\emph{C. tshokwe} ,
\(n=11\)), 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).

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}}

DNA extraction was performed in 2019, appr. 7 years after sampling. 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. DNA was
eluted in the kit's EB buffer (10 mM Tris-Cl, pH 8.5) and stored at
-20degC.

\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). In
this method, biotinylated adapter molecules are attached to the ends of
the DNA fragments via hybridization to a stretch of six random
nucleotides belonging to a splinter oligonucleotide complementary to the
adapter and nick closure with T4 DNA ligase. 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) and 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 and rbcL.

The hybridization capture enrichment reactions were performed according
to the manufacturer's instructions (Biosciences 2016). Using this
method, single-stranded DNA (ssDNA) fragments sharing sequence
similarity with predesigned ssDNA- or RNA-baits can be enriched.
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 (i.e., pooled
across replicates of the same specimen) 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, a \emph{k} -mer-based approach which provides a
fast taxonomic classification from the sequence data (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, as well as the
taxon's ancestors, based on its similarities to
these\(k\)-mers, in accordance with its annotation at the
lowest taxonomic level. During the taxonomic classification process
using Kraken2, specific weights are allocated to each node based on the
k-mer paths. This weight assignment improves the sensitivity of taxon
classification, ensuring more accurate and precise results.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 as a semi-quantitative estimate of 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 measures are given in the Supplementary File S3.

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, to describe the diet overlap among our
categories (species, EOD duration, snout length), we used two
complementary metrics: 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 S3. 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.
Discriminant Analysis of Principal Components (DAPC), using the
\emph{dapc} function in the\emph{adegenet} R package (Jombart, 2008),
was performed on the three categories, i.e. species, EOD duration, and
snout length, to identify dietary items that are significant
contributors to dietary differences among species/phenotypic groups. We
tested for differences in diet composition among the groups by
performing a permutational multivariate analysis of variance (perMANOVA)
with 999 permutations, using the\emph{adonis} -function in the
\emph{vegan} R package.

\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 19,531,074, ranging from 19,226 to
867,093 per individual sample (\(M\text{ean}=335,020\);\(SD=234,220\)).
Out of them, a total of 18,357,160 sequences were obtained after quality
filtering, ranging from 16,703 to 828,735 per individual
(\(M\text{ean}=314,421\); \(SD=222,465\); see Supplementary File S4 for
details).

Additionally, a series of negative (library and DNA extraction) and
positive controls, which were treated identically to other samples from
initial processing through library preparation, was included to further
filter the data and validate the obtained taxa. The positive control
sample was chosen as a stomach content sample of a fish (\emph{C.
compressirostris} ) raised and fed on a known food in the laboratory. Of
the total sequence pool generated, we detected 0.013~\% from library
controls and 0.019~\% from DNA extraction controls.

\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).

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. To determine which food taxa should be included in
subsequent diet analyses, we subtracted the number of sequences of each
taxa present in the negative controls from the sequence abundance of
that taxa in the samples, following Nguyen et al. (2015). Further, 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). This approach substantially decreased the
sequence yield of the \emph{C. numenius}sample, so this sample was
eliminated from the subsequent analyses.

The remaining identified prey items in each sample were assigned to
different taxonomic levels, i.e., to 20 classes, 34 orders, 90 families,
115 genera, and 127 species. For the species with larger sample sizes
(\emph{C. compressirostris} and \emph{C. tshokwe} ), the classified
reads are visualized using the metagenomics data explorer Pavian and
tabulated in Tables S5.1 and S5.2 (Supplementary File S5).

\subsection*{Relative abundance}

{\label{relative-abundance}}

\emph{Campylomormyrus} and\emph{G. petersii} 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 belong to the
class Insecta), 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 (excluding
taxa with \(\text{RRA}<0.01\%\)potentially stemming from secondary
predation,) for the three grouping categories are depicted in Figure 2.
Most groups share similar food taxa, albeit in different proportions.
The most dominant prey taxon are insects (Diptera, Coleoptera,
Hymenoptera, and Lepidoptera), annelid worms (Haplotaxida), spiders
(Araneae), crustaceans (Amphipoda, Diplostraca, and Decapoda), Copepoda
(Calanoida), and plants (Cupressaceae). The RRA for individual samples
are given in the supplement (Supplementary File S6).

The RRA data were used to assess the dietary niche width by calculating
the Shannon diversity index (Figure 3 and Table S7.1 in the
Supplementary File S7). The Shannon diversity index differed
considerably among the studied groups, pointing towards different
dietary niche widths across the two species \emph{C.
compressirostris}and \emph{C. tshokwe} species and relative to EOD
duration and snout length.

\subsection*{Diet overlap}

{\label{diet-overlap}}

The diets among groups of the three categories (species, EOD duration,
and snout length) 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 groups. 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 S7.2 in the Supplementary File S7.

As many reads could not be assigned to family, genus, or species level
(see Table S4.1 in Supplementary File S4), 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 \emph{C.
compressirostris} and \emph{C. tshokwe} (\emph{F} =
4.64,\emph{r\textsuperscript{2}} =0.215, \emph{p} \selectlanguage{english}[?]0.001, \emph{df} =1).

A perMANOVA on the EOD duration groups (long \emph{vs.} short EOD)
indicated significant dietary differences between the two groups
(\emph{F} =5.34, \emph{r\textsuperscript{2}} =0.182, \emph{p}
\selectlanguage{english}[?]0.001,\emph{df} =1, 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} =2.50, \emph{r\textsuperscript{2}} =0.182, \emph{p}
\selectlanguage{english}[?]0.01,\emph{df} =2). The post hoc pairwise perMANOVA using a Bonferroni
correction of the \emph{p} -values indicated that only the long snout
versus the medium snout and the long snout versus the short snout
differences are statistically significant (\emph{p} \selectlanguage{english}[?]0.001 and\emph{p}
\selectlanguage{english}[?]0.05, respectively).

DAPC on group-specific diet discriminated across species (only \emph{C.
compressirostris} and \emph{C. tshokwe} ), EOD duration, and snout
length (see Supplementary File S9). It inferred also the significant
contributors to dietary differences (1) among \emph{C.
compressirostris}and \emph{C. tshokwe} (Calanoida, Ephemeroptera, and
Diptera orders), (2) among fish with long and short EOD duration
(Calanoida, Diplostraca, Diptera, and Ephemeroptera), and (3) among long
and short snout (Ephemeroptera, Calanoida, and Hymenoptera;
Supplementary File S9).

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 0.152,
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). Our results 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} . Indeed, larvae of
aquatic insects were reported as food items found in the stomach of
mormyrid fish (Blake, 1977; Hyslop, 1986).

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. 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). Previous studies on the diet of
mormyrids using morphological observations did not report annelid worms.
The absence of this food taxon may be due to the progressed digestion
before the morphological examination.

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.
These food items are usually located in the habitat at the surface of
the water.

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 our 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 (Roberts \& Stewart, 1976),
reported that stomach contents of some \emph{Campylomormyrus} species
contain larvae of chironomids, \emph{Povilla} , trichopteran,
ephemeropterans and odonates, dead plant debris, and decomposing animal
debris. Roberts \& Stewart (1976) 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*{\texorpdfstring{Diet comparison among
\emph{Campylomormyrus}phenotypes}{Diet comparison among Campylomormyrusphenotypes}}

{\label{diet-comparison-among-campylomormyrus-phenotypes}}

The dietary compositions among \emph{Campylomormyrus}
and\emph{Gnathonemus} suggest that their feeding behavior is related to
phenotypical traits (EOD duration and snout morphology). Based on our
results, , different prey spectra were found in the stomach of the
different groups of \emph{Campylomormyrus} species, which may relate to
species exploiting 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 they 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 \emph{C. compressirostris} contains more dipterans,
while the diet of \emph{C. tshokwe} contains more coleopterans.

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.
Similar differences were found according to snout length. 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
the 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 also reflect differential availability of different prey
in the respective microhabitats. 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 also be
of adaptive value during feeding, 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.

The presence of distinct prey spectra among
\emph{Campylomormyrus}species with different snout morphologies implies
a potential association between snout morphology and feeding behavior.
Specifically, the species-specific snout morphology in
\emph{Campylomormyrus} may be related to variations in their grasp
suction mode of feeding, as previously described by Marrero and
Winemiller (1993). Enhanced grasp suction enables them to obtain the
aquatic insects that burrow into the different substrate structures.
This functional foraging specialization allows for an efficient
exploitation of the rich bottom fauna of benthic invertebrates,
especially aquatic insect larvae, which other fish may not reach. Such
trophic specializations may have triggered the observed radiation of
\emph{Campylomormyrus} in the Congo River.

\subsection*{Potential limitations of our dietary
analysis}

{\label{potential-limitations-of-our-dietary-analysis}}

\textbf{Sample sizes and quality of DNA.} Our study was limited by the
availability of stomach content samples we collected from the field,
i.e., the Congo River. Despite of a four weeks effort during our 2012
expedition, we did not yield equally high sample sizes for all target
species, such that the small number of individuals per species did not
allow for diet comparisons among the individual
\emph{Campylomormyrus}species, except for \emph{C. compressirostris} and
\emph{C. tshokwe} . Moreover, all species examined here are nocturnal,
so the stomach contents are expected to be highly digested, due to the
time between the last meal and the collection during day time. Our
samples had been stored for relatively long time (7 years) prior to DNA
extraction. We followed established protocols to store the samples
(Seutin et al. 1991) which had been successfully applied for degraded
samples (Epp et al. 2010; Stoof-Leichsenring et al. 2012). Note also
that \emph{Campylomormyrus} tissue samples taken and stored under the
same conditions (i.e., during Congo expeditions in 2004, 2006, and 2012)
yielded amplifiable DNA (entire cytochrome b gene, \textgreater{}1000bp)
when analyzed up to 10 years later (Lamanna et al. 2016). While we
cannot exclude that storage may have contributed to DNA degradation, our
positive control (a fresh sample taken from a \emph{C. compressirostris}
raised in our lab and fed with defined food) yielded a similar read
number (419,872) as the stomach samples taken in Congo and stored for
several years prior to analysis (\(M\text{ean}=335,020\);\(SD=234,220\)).
We hence conclude that progressed digestion may be the major cause of
the DNA degradation encountered in our study.

\textbf{Relative Read Abundance (RRA) and biomass correlation.} The
number of reads belonging to a particular food taxon is utilized as a
proxy for the biomass of this taxon to obtain semi-quantitative measures
on the relative amounts of different food taxa consumed
by\emph{Campylomormyrus} and \emph{Gnathonemus} species. This approach
is used in most of the dietary studies, and it implicitly assumes a
correlation between the number of sequences and the biomass. The logic
behind this assumption is that a large biomass of a particular food item
in the diet would translate into a large amount of DNA in the dietary
sample and, consequently, more recovered DNA sequences (reads). However,
potential biases may arise from technical (such as PCR efficiency,
primer choice, DNA extraction, etc.) and biological issues (such as
among-prey species variation in tissue cell density, survival of DNA
during digestion, and the state of digestion), obscuring the
quantitative signal. Despite of these caveats, RRA has been considered
valid as a proxy for abundance in eDNA (e.g., Kiemel et al. 2022) and
sedaDNA (e.g., Krueger et al. 2021) studies. We hence argue that
utilizing RRA as a semi-quantitative measure of prey biomass -- with all
caution -- provides relevant additional information relative to merely
reporting the presence of prey taxa, which implicitely overstates the
importance of rare food items. Moreover, technical biases may affect all
samples in a similar manner and hence not compromise our comparison of
relative diet composition among different EOD/snout phenotypes.

\textbf{Taxonomic resolution of dietary items.}The dietary differences
among the species and phenotypes are more visible in the low taxonomic
levels, i.e., at genus and species levels. However, the relatively low
percentages of reads assigned to the family, genus, or species level
(see Sec.~3.4) may compromise the confidence in the inferred prey
spectra at these taxonomic levels. Therefore, we limited the statistical
analysis of diet overlap and assessment to the order level (see
Sec.~3.4) which we argue could be more reliably inferred (as atested by
a lower percentage of unassigned reads). Notwithstanding this
limitation, the study confirmed significant differences in the diet
among EOD and snout phenotypes. It is plausible to expect that these
differences may be even more pronounced at the species/genus levels.

\section*{Conclusions}

{\label{conclusions}}

We analyzed stomach contents of wild-caught \emph{Campylomormyrus} fish
using hybrid capture and subsequent HTS DNA metabarcoding. Despite of
the highly degraded nature of the dietary material, this approach
provided a picture of the overall diet of this genus and revealed
dietary diversity, relative to divergent phenotypical traits (EOD and
snout). Although standard metabarcoding is technically less demanding
and may be preferred for analysis of stomach samples if DNA quality is
sufficient (Deagle et al. 2006; Jarman et al. 2004), hybrid capture
provides an alternative for samples of progressed digestion and
associated high DNA fragmentation. Generally, hybrid capture and
subsequent HTS has been found superior to direct PCR amplification, when
small fragment sizes are encountered, as in aDNA (e.g., Paijmans et al.
2016) and sedaDNA (e.g., Krueger et al. 2021). Our results confirmed
that \emph{Campylomormyrus} species can exploit multiple food niches in
their habitats (fauna at the bottom, at the water surface, and in the
water column) to feed on benthic invertebrates, allochthonous
invertebrates, and vegetation. alpha and beta dietary diversity were
significantly associated with differences in EOD duration and snout
length. Divergence in these traits may constitute functional adaptations
to exploiting different food sources and substrates and may underly the
radiation and sympatric coexistence of the numerous species of this
genus.

\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. R. Amen was funded by the Bridge Scholarship
Program of the University of Potsdam and by 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:} Pairwise diet overlap indices (Pianke index, O/
Schöner index, \selectlanguage{greek}α\selectlanguage{english}) of three groupings of Campylomormyrus and one species
of its sister genus Gnathonemus based on RRA data considering food taxa
at different phylogenetic levels (Class, Order, Family, Genus) based on
pooled samples. The comparisons are done based on EOD duration (short
EOD, \(n=15\), and long EOD,\(n=11\)) and snout
length (short snout, \(n=5\), medium
snout,\(n=10,\ \)and long snout, \(n=11\)).

Diet overlap among fish with different EOD duration {[}short EOD and
long EOD{]}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
Class & Class & Order & Order & Family & Family & Genus &
Genus\tabularnewline
\textbf{0.99}/\textbf{0.92} & \textbf{0.99}/\textbf{0.92} &
\textbf{0.74}/\textbf{0.63} & \textbf{0.74}/\textbf{0.63} &
\textbf{0.61}/0.46 & \textbf{0.61}/0.46 & \textbf{0.55}/0.36 &
\textbf{0.55}/0.36\tabularnewline
Diet overlap among fish with different snout length {[}short snout (S),
medium snout(M), and long snout (L){]} & Diet overlap among fish with
different snout length {[}short snout (S), medium snout(M), and long
snout (L){]} & Diet overlap among fish with different snout length
{[}short snout (S), medium snout(M), and long snout (L){]} & Diet
overlap among fish with different snout length {[}short snout (S),
medium snout(M), and long snout (L){]} & Diet overlap among fish with
different snout length {[}short snout (S), medium snout(M), and long
snout (L){]} & Diet overlap among fish with different snout length
{[}short snout (S), medium snout(M), and long snout (L){]} & Diet
overlap among fish with different snout length {[}short snout (S),
medium snout(M), and long snout (L){]} & Diet overlap among fish with
different snout length {[}short snout (S), medium snout(M), and long
snout (L){]}\tabularnewline
Class & Class & Class & Class & Order & Order & Order &
Order\tabularnewline
& M & M & L & & M & M & L\tabularnewline
S & \textbf{0.99}/\textbf{0.92} & \textbf{0.99}/\textbf{0.92} &
\textbf{0.99}/\textbf{0.94} & S & \textbf{0.94}/\textbf{0.89} &
\textbf{0.94}/\textbf{0.89} & \textbf{0.82}/\textbf{0.65}\tabularnewline
M & & & \textbf{0.99}/\textbf{0.91} & M & & &
\textbf{0.84}/\textbf{0.67}\tabularnewline
Family & Family & Family & Family & Genus & Genus & Genus &
Genus\tabularnewline
& M & M & L & & M & M & L\tabularnewline
S & \textbf{0.93}/0.60 & \textbf{0.93}/0.60 & 0.76/0.41 & S & 0.29/0.27
& 0.29/0.27 & 0.17/0.16\tabularnewline
M & & & 0.83/0.53 & M & & & \textbf{0.52}/0.38\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
(Campylomormyrus,\(n=24\), and Gnathonemus, n = 3) 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) of the total reads
(4,361,220 and 18,920,020 reads for class and order levels,
respectively). Only taxa which scored more than 0.01~\% of the total
reads after filtering are considered.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2:} Comparison of relative read abundance (RRA) of food
taxa from dietary metabarcoding data sets of stomach contents of
Campylomormyrus and its sister genus Gnathomemus at order level. Samples
are grouped based on species (only for C. compressirostris,
\(n=10\), and C. tshokwe, \(n=9\)), EOD duration
(short duration, \(n=11\), and long duration,
\(n=15\)), and snout length (short snout, \(n=5\),
medium snout, \(n=10\), and long snout, \(n=11\)).

\emph{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3:} Boxplot of Shannon diversity indices of food taxa
from dietary metabarcoding data sets of stomach contents of 25 samples
of Campylomormyrus and one sample of its sister genus Gnathomemus based
on RRA at order level for species, EOD duration, and snout length groups
(cf. Fig. 2 ).

\emph{}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4:} Dietary niche partitioning within and among
individual samples of\emph{Campylomormyrus} and its sister genus
\emph{Gnathomemus} by nMDS of RRA-based Bray--Curtis dissimilarity
indices of samples on the order level (adonis: comparison across EOD
phenotypes F = 5.34, R\textsuperscript{2} = 0.182, p [?] .002; across
snout phenotypes F = 2.50, R\textsuperscript{2} = 0.179, p [?] .002). The
symbols represent the species and the colors represent snout length. The
shaded convex hulls indicate short vs. long EOD. The stress level of
0.152 is under the cut-off value of 0.2 as recommended by Clarke (1993)
to indicate an interpretable ordination.

\selectlanguage{english}
\FloatBarrier
\end{document}

