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\begin{document}

\title{Diet components associated with specific bacterial taxa shape overall
gut community compositions in omnivorous African viverrids}



\author[1]{Malou Storm}%
\author[1]{Emilia Langkjær}%
\author[2]{Phumlile Simelane}%
\author[3]{Jason Denlinger}%
\author[3]{Celina Dias}%
\author[3]{Ana da Conceição}%
\author[2]{Ara Monadjem}%
\author[4]{Kristine Bohmann}%
\author[1]{Michael Poulsen}%
\author[1]{Kasun Bodawatta}%
\affil[1]{University of Copenhagen}%
\affil[2]{University of Eswatini}%
\affil[3]{Gorongosa National Park}%
\affil[4]{University of Copenhagen Faculty of Health and Medical Sciences}%


\vspace{-1em}



  
  \date{June 16, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Gut bacterial communities provide flexibility to hosts during dietary
changes. Despite the increasing number of studies exploring the
associations between broader dietary guilds of mammalian hosts and their
gut bacteria, it is generally unclear how diversity and variability in
consumed diets link to gut bacteria in wild non-primate mammals. Here we
contribute to filling this gap by exploring consumed diets and gut
bacterial community compositions with metabarcoding of faecal samples
for two African mammals, Civettictis civetta and Genetta spp., from the
family Viverridae. For each individual sample, we characterised
bacterial communities and identified dietary taxa by sequencing
vertebrate, invertebrate, and plant markers. This led us to establish
diet compositions that diverged from what has previously been found from
visual identification methods. Specifically, while the two genera have
been categorised into the same dietary guild, we detected more animal
dietary items than plant items in C. Civetta, while in Genetta spp. we
observed an opposite pattern. We further found that individuals with
similar diets have similar gut bacterial communities in both species.
This association tended to be driven by specific associations of dietary
items to specific gut bacterial taxa, rather than entire communities,
implying diet-driven selection for specific gut microbes in individual
wild hosts. Our findings underline the importance of molecular tools for
improving characterisations of wild mammalian diets and highlight the
opportunities for simultaneously disentangling links between diets and
gut symbionts. Such insights can inform robustness and flexibility in
host-microbe symbioses to dietary change associated with seasonal and
habitat change.%
\end{abstract}%



\sloppy


\textbf{Diet components associated with specific bacterial taxa shape
overall gut community compositions in omnivorous African viverrids}

Short title: Diet and microbiome links in wild viverrids

Malou B. Storm\textsuperscript{1,2\#}, Emilia M. R.
Langkj\selectlanguage{ngerman}ær\textsuperscript{1,2*\#}, Phumlile Simelane\textsuperscript{3},
Jason Denlinger\textsuperscript{4}, Celine Alfredo
Dias\textsuperscript{4}, Ana Gledis da Conceição\textsuperscript{4}, Ara
Monadjem\textsuperscript{3,5}, Kristine Bohmann\textsuperscript{2},
Michael Poulsen\textsuperscript{1}, and Kasun H.
Bodawatta\textsuperscript{2*}

\textsuperscript{1}Section for Ecology and Evolution, Department of
Biology, University of Copenhagen, Copenhagen, Denmark

\textsuperscript{2}Section for Molecular Ecology and Evolution, Globe
Institute, University of Copenhagen, Copenhagen, Denmark

\textsuperscript{3}Department of Biological Sciences, University of
Eswatini, Kwaluseni, Eswatini

\textsuperscript{4}Gorongosa National Park, Mozambique

\textsuperscript{5}Mammal Research Institute, Department of Zoology and
Entomology, University of Pretoria, Hatfield, Pretoria, South Africa

\textsuperscript{*}Correspondence: Emilia M. R. Langkjær,
email:\emph{emiliamrl93@gmail.com}; Kasun H. Bodawatta, email:
bodawata@sund.ku.dk

\textsuperscript{\#}Contributed equally

\textbf{Abstract}

Gut bacterial communities provide flexibility to hosts during dietary
changes. Despite the increasing number of studies exploring the
associations between broader dietary guilds of mammalian hosts and their
gut bacteria, it is generally unclear how diversity and variability in
consumed diets link to gut bacteria in wild non-primate mammals. Here we
contribute to filling this gap by exploring consumed diets and gut
bacterial community compositions with metabarcoding of faecal samples
for two African mammals, \emph{Civettictis civetta} and
\emph{Genetta}spp., from the family Viverridae. For each individual
sample, we characterised bacterial communities and identified dietary
taxa by sequencing vertebrate, invertebrate, and plant markers. This led
us to establish diet compositions that diverged from what has previously
been found from visual identification methods. Specifically, while the
two genera have been categorised into the same dietary guild, we
detected more animal dietary items than plant items in \emph{C. Civetta}
, while in \emph{Genetta} spp. we observed an opposite pattern. We
further found that individuals with similar diets have similar gut
bacterial communities in both species. This association tended to be
driven by specific associations of dietary items to specific gut
bacterial taxa, rather than entire communities, implying diet-driven
selection for specific gut microbes in individual wild hosts. Our
findings underline the importance of molecular tools for improving
characterisations of wild mammalian diets and highlight the
opportunities for simultaneously disentangling links between diets and
gut symbionts. Such insights can inform robustness and flexibility in
host-microbe symbioses to dietary change associated with seasonal and
habitat change.

\textbf{Keywords}

Gut microbiome, metabarcoding, omnivores, southern Africa,
Viverridae,\emph{Civettictis} , \emph{Genetta}

\textbf{Introduction}

Animals harbour a diversity of symbiotic bacteria in their digestive
tracts that facilitate a variety of functions, particularly related to
digestion and nutrition (Ley, Lozupone, Hamady, Knight, \& Gordon, 2008;
McFall-Ngai et al., 2013). Consequently, associations between host diets
and their gut bacterial community composition have been observed across
the animal kingdom (Bodawatta, Koane, et al., 2021; Nishida \& Ochman,
2018; Song et al., 2020). This is particularly evident in mammals, where
host phylogeny, physiology and dietary guild structure gut communities
(Amato et al., 2019; Ley, Hamady, et al., 2008; McCord et al., 2014;
Muegge et al., 2011; Song et al., 2020; Youngblut et al., 2019),
ultimately resulting in some degree of specificity in complex gut
microbial communities (Mallott \& Amato, 2021).

Diets of wild mammals, however, fluctuate due to seasonal variations in
food item availability (De Barba et al., 2014), and most mammals do not
specialise in a single diet component (Spencer, Newsome, \& Dickman,
2017). This implies that dietary changes are likely to lead to changes
in gut bacterial communities. Studies on humans and captive mammals have
demonstrated the importance of dietary macronutrients and compositions
for gut microbiome structure (Amato et al., 2015; Coelho et al., 2018;
Frankel, Mallott, Hopper, Ross, \& Amato, 2019; Ley, Hamady, et al.,
2008) and ability in microbial communities to track dietary changes of
individual hosts (David et al., 2014). Similar patterns have been
observed in wild mammals, where gut bacterial communities change with
seasonal and environmental dietary shifts (Bjork et al., 2022;
Kartzinel, Hsing, Musili, Brown, \& Pringle, 2019; Li et al., 2016; Ren
et al., 2017) and individual variation in diet consumption (Mallott,
Amato, Garber, \& Malhi, 2018). However, most of this work stems from
wild primates (Barelli et al., 2020; Bjork et al., 2022; Mallott et al.,
2018; Sharma et al., 2020), with a large gap in knowledge from other
wild mammalian clades.

To establish the link between variation in diet and microbiome structure
in animals, characterization of both diets and microbiomes are needed on
the same focal host individuals (Baiz, Benavides, Miller, Wood, \&
Toews, 2023; Bodawatta et al., 2022; Mallott et al., 2018).
Traditionally, diets have been primarily characterized by visual
observations of feeding events or morphological identification of diet
remains from gut content, faeces, or regurgitated samples (Breuer, 2008;
Emmons, 1987; Mallott et al., 2018). These techniques provide limited
taxonomic resolution and thus tend to underestimate the dietary breadth
of a species (Neilsen, Clare, Hayden, Brett, \& Kratina, 2017; Soininen
et al., 2009; Valentini et al., 2009). DNA metabarcoding has improved
this by enabling the identification of prey taxa that are not evident
morphologically (Bodawatta et al., 2022; Deagle, Kirkwood, \& Jarman,
2009; Pompanon et al., 2011; Soininen et al., 2009) and reducing
human-related identification errors (De Barba et al., 2014). Results
from diet metabarcoding approaches need to be evaluated with caution, as
these methods can detect non-voluntarily consumed diet items (e.g.,
flower mites on consumed vegetation) and/or items consumed by the prey
itself (secondary consumption) (Bowser, Diamond, \& Addison, 2013;
Neilsen et al., 2017). Despite these caveats, the taxonomic resolution
that can be obtained through metabarcoding enables deeper insights into
associations between host diets and gut bacteria (Bodawatta et al.,
2022). Coupling metabarcoding of diet and gut bacteria from the same
individual remains absent for non-primate wild mammals, particularly in
omnivores.

To help fill this knowledge gap, we investigated diet and gut bacterial
composition of two closely related mammalian genera from the family
Viverridae: \emph{Civettictis} (\emph{Civettictis civetta,} The African
Civet) and \emph{Genetta} (\emph{Genetta} spp.) (Figure 1a-d) that
belong to the order Carnivora, but feed on a diversity of plant and
animal taxa. We characterised the dietary intake of individuals from
three populations in Southern Africa (Figure 1e) through metabarcoding
of faecal samples with primer sets targeting invertebrates, vertebrates
and plants, and gut bacteria through amplicon sequencing of the v4
region of the bacterial 16S rRNA gene. We expected host genus-specific
and geographical differences in diets, as previous visual diet
identification studies detected varying diets between the genera and
across geographic regions (Amiard, Kruger, Mullers, \& Schipper, 2015;
Amroun, Bensidhoum, Delattre, \& Gaubert, 2014). Further, if bacterial
communities would be strongly associated with diets, we expected similar
host genus- and region-specific differences in gut microbiome
composition, so that individuals with similar diets would harbour
compositionally similar gut communities. Finally, if gut bacteria are
associated with individual variation in diet intake, we expected links
between particular dietary items and specific bacterial taxa.

\textbf{Materials and Methods}

\emph{Study organisms}

The family Viverridae (Viverrids; Order: Carnivora) consists of small,
nocturnal carnivorous mammals (Carvalho, Matolengwa, Camps, Gaubert, \&
Do Linh San, 2016). Of these, those that inhabit southern Africa include
the African civet (\emph{Civettictis civetta} ) and several species in
the genus \emph{Genetta} (e.g., \emph{G. maculata} and \emph{G. genetta}
) (Figure 1) (Carvalho et al., 2016; Roux, Zemouhe, Blomsterberg, \& Do
Linh San, 2016; Swanepoel, Power, Camacho, \& Amiard, 2016; Veron, 2007;
Widdows, Roberts, Maddock, \& Carvalho, 2016). Previous research found
that \emph{Genetta} spp. feed mainly on rodents (Roberts, White, \&
Somers, 2007; Rosalino \& Santos-Reis, 2002), while \emph{C. civetta} ,
despite having a varied diet, feeds mostly on various fruits (Amiard et
al., 2015; Mullu \& Balakrishnan, 2015). These species use latrines as
defecation sites and return to these regularly (Abiyu, Teketay, Glatzel,
\& Gratzer, 2015; Mullu \& Balakrishnan, 2015; Sánchez, Rodrigues,
Ortuño, \& Herrero, 2008), making it possible to monitor activity in the
latrine, while ensuring collection of fresh faeces (Figure 1a-d).

\emph{Study sites and sample collection}

Faecal samples were collected at three locations from \emph{C.
Civetta}and \emph{Genetta} spp. in southern Africa (Figure 1d). Samples
were collected at Lajuma, a part of the Soutpansberg Mountain range
within the UNESCO Vhembe Biosphere Reserve (23°02´S, 29°26´E), South
Africa (Willems \& Hill, 2009), Mbuluzi Game Reserve (26°05´-26°22´S
31°45´-32°10´E) in the Mlawula-Mbuluzi reserve network in the
north-eastern Eswatini (Monadjem \& Reside, 2008), and Gorongosa
National Park in central Mozambique (18°45´58'' S 34°30´00'' E),
comprising 4,000 km\textsuperscript{2} of protected area (Correia,
Timoteo, Rodriguez-Echeverria, Mazars-Simon, \& Heleno, 2017; Rodrigues
et al., 2017). Three additional samples were collected by collaborators
in Medike Mountain Reserve and Blouberg Nature Reserve, South Africa,
close to Lajuma. All these study areas only have one
\emph{Civettictis}species (\emph{C. Civetta} ), but there are at least
two known species of the genus \emph{Genetta} . Our sample sites in
Eswatini and Mozambique are known to be inhabited by \emph{G. maculate}
, while sample sites in South Africa are inhabited by both \emph{G.
maculata} and \emph{G. genetta} (Kingdon \& Hoffmann, 2013).

Latrines were located and monitored every 2-3 days, from the start of
September to mid-December 2017, resulting in 119 fresh faecal pellets:
77 in South Africa, 22 in Mozambique and 20 in Eswatini (Table S1). To
avoid environmental contamination, no sample was collected after
rainfall and faecal material was collected from the core of the pellets
(avoiding the material that has been in contact with the environment)
(King, Read, Traugott, \& Symondson, 2008). Ethanol and flame-sterilised
forceps were used for sampling, and latex gloves were used during
collection. Each sample was divided into three sub-samples, which were
placed in separate 2 ml Eppendorf tubes containing
RNAlater\textsuperscript{®}, composed of 25 mM Sodium Citrate, 10 mM
EDTA, 70 g ammonium sulphate/100 ml solution (pH 5.2) and stored at 5°C
(Nsubuga et al., 2004) (Table S2). To create DNA reference sequences for
host species determination, approximately 1×0.5 cm skin samples were
acquired from the skin collection at the Natural History Museum of
Denmark, from specimens of \emph{C. civetta} , \emph{Genetta tigrina}
and\emph{Genetta genetta} and the Mongoose species \emph{Cynictis
penicillata, Helogale parvula, Bdeogale crassicauda, Galerella
sanguinea} , and \emph{Mungos mungo} (Table S3)\emph{.}

\emph{DNA extractions}

The QIAamp® PowerFecal® DNA kit (Hilden, Germany) was used for DNA
extraction from the 119 faecal samples and the Qiagen DNeasy® blood and
tissue kit (Hilden, Germany) was used for extractions from skin samples.
Both protocols were used following the manufacturer's guidelines with
the following alterations, applied to both kits. Proteinase K (5\selectlanguage{greek}μ\selectlanguage{english}L per
reaction) was added with the lysis buffer, and lysis time was increased
to appx. 22 hours. The Elution buffer was replaced with a Tris-EDTA
buffer with TWEEN\selectlanguage{ngerman}®20 (TET-buffer). Fifty \selectlanguage{greek}μ\selectlanguage{english}L of TET-buffer was added to
the Spin Column, followed by 15 min incubation at 37 \selectlanguage{ngerman}°C. This step was
repeated one time before centrifuging, resulting in a total elution
volume of 100 \selectlanguage{greek}μ\selectlanguage{english}L. One negative extraction control was included per five
biological samples to screen for contamination.

\emph{Metabarcoding for diet analysis}

We used SYBR Green ROX quantitative PCRs (qPCR) to screen for inhibition
within our DNA extracts and to identify optimal number of PCR cycles
required for each of the five dietary primer sets (Table 1), following
(Murray, Coghlan, \& Bunce, 2015; Schnell, Bohmann, \& Gilbert, 2015).
Specifically, qPCRs were conducted on a subset of sample DNA extracts
and the negative extraction controls using the following primers
targeting vertebrates; 16S mamF and 16S mamR (90 bp excluding primers)
(Taylor, 1996) and RiazF and RiazR (97 bp excluding primers) (Riaz et
al., 2011), invertebrates; Coleop\_16Sc and Coleop\_16Sd (105 bp
excluding primers) (Epp et al., 2012) and ZBJ-ArtF1c and ZBJ-ArtR2c (157
bp excluding primers) (Zeale, Butlin, Barker, Lees, \& Jones, 2011) and
plants; Trac01 and ITS 7A (267 bp excluding primers) (Taberlet, Coissac,
Hajibabaei, \& Rieseberg, 2012) (Table 1). To screen for inhibition,
qPCRs were run in a dilution series of the sample DNA extracts -- neat,
1:5 and 1:10 dilutions. The 25 \selectlanguage{greek}µ\selectlanguage{english}L reactions included 1 \selectlanguage{greek}µ\selectlanguage{english}L DNA template
and 1x AmpliTaq Gold\textsuperscript{TM} PCR buffer (Applied Biosystems,
Waltham, MA, USA), 2.5 mM MgCl\textsubscript{2}, 0.2 mM dNTPs, 1 unit
Taq Gold\textsuperscript{TM} (Applied Biosystems, Waltham, MA, USA),
0.5mg/mL BSA, 0.6 \selectlanguage{greek}µ\selectlanguage{english}M forward primer, 0.6 \selectlanguage{greek}µ\selectlanguage{english}M reverse primer and 1 \selectlanguage{greek}µ\selectlanguage{english}L SYBR
green mix (1:4 SYBR green (S7563) (Invitrogen) to ROX reference dye
(12223-012) (Invitrogen, with 200 parts high grade DMSO)). qPCRs were
carried out with conditions specified in Table 1, followed by a melting
curve and no extension step. Based on the qPCR screening, the use of
1:10 diluted template and 40 cycles was chosen for the following
metabarcoding PCR amplifications. For these, the reagent mix was the
same as for the qPCR, but excluding the SYBR Green ROX. Extraction and
PCR negative controls were included in the metabarcoding PCRs. PCRs were
carried out using the tagged PCR approach, where primers carry 5'
nucleotide tags, which allow for pooling after a single PCR step
(Binladen et al., 2007; Bohmann et al., 2022). We used un-matching tags,
meaning that forward and reverse tags did not match (e.g., F1-R2,
F2-R3). For each sample, negative and positive controls (16S
mam:\emph{Canis lupus} , Riaz: \emph{Canis lupus} ,
Coleop\_16S:\emph{Tenebrio molitor} , ZBJ-Art: \emph{Tenebrio molitor} ,
Trac01:\emph{Galanthus} spp.), three PCR replicates with unique tag
combinations were produced. No nucleotide primer-tag combination was
repeated in the replicates of the same samples nor in the same amplicon
pool. All faecal samples were amplified using all five primer sets with
their respective PCR parameters (Table 1), whereas skin samples were
only amplified using the Mam16S primer set. PCR products were visualised
on 2\% agarose gels. Samples with no bands were reamplified and
discarded if no band appeared after trials. Amplified samples were
pooled approximately equimolar, according to band intensity (5 \selectlanguage{greek}µ\selectlanguage{english}L for
bright, 10 \selectlanguage{greek}µ\selectlanguage{english}L for medium and 15 \selectlanguage{greek}µ\selectlanguage{english}L for weak). Fifteen \selectlanguage{greek}µ\selectlanguage{english}L of each
negative control with no band were added to the library pools. However,
if a band were present, they were pooled according to the band strength
equal to how the samples were pooled. PCR products were pooled into 10
pools, two pools per each primer set (no identical tag-combinations were
present in the same pool). Amplicon pools were bead-purified, using
SPRI-beads (Rohland \& Reich, 2012) and libraries were built using the
PCR-free Tagsteady library protocol (Caroe \& Bohmann, 2020) and
quantified using NEBNext\selectlanguage{ngerman}® Library Quant Kit for Illumina® (New England
BioLabs). Libraries were sequenced with 250 bp paired-end read length
and 7-8\% PhiX on an Illumina MiSeq platform aiming for 25,000 reads per
PCR replicate.

\emph{16S rRNA amplicon sequencing for gut microbiome characterisation}

To characterise gut bacterial composition, DNA extracts were first
screened for bacterial DNA by conducting PCR amplifications with the
primers V4.SA504 and V4.SB711 that target the V4 region of the 16S rRNA
gene (250 bp) (Kozich, Westcott, Baxter, Highlander, \& Schloss, 2013).
Untagged PCRs were performed in 20 \selectlanguage{greek}µ\selectlanguage{english}L reactions including 1 \selectlanguage{greek}µ\selectlanguage{english}L of
template DNA, 8.5 \selectlanguage{greek}µ\selectlanguage{english}L of REDTaq ReadyMix (Sigma-Aldrich, USA), 8.5 \selectlanguage{greek}µ\selectlanguage{english}L of
sterile distilled water, 1 \selectlanguage{greek}µ\selectlanguage{english}L each from 10 \selectlanguage{greek}µ\selectlanguage{english}M forward and reverse
primers. PCRs were carried out using conditions specified in Table 1
(Otani et al., 2016). Amplification success was checked on a 1.5\%
agarose gel. Of the 119 sample extracts, 112 amplified successfully and
their DNA extracts were sent for MiSeq amplicon sequencing at an
Illumina platform (250 bp paired-end) at the Microbiome Core at the
University of Michigan, USA, prepared using the approach described in
(Kozich et al., 2013).

\emph{Bioinformatics and data analysis}

Analyses of diet data (i.e., adaptor and quality trim and merging of
paired-end reads) were carried out using AdaptorRemoval version 2
(Schubert, Lindgreen, \& Orlando, 2016). A modified version of the DAMe
pipeline (Yang et al., 2021) was used to sort sequences. Sequences were
sorted according to primer sequence and assigned tag combinations from
PCRs. After sorting, sequences were filtered and only sequences that
were present in minimum two out of three PCR replicates per sample were
kept. Further, minimum sequencing length was set for 80 bp for Mam16S F
and R, 250 bp for Trac01 and ITS 7A, 80 bp for Riaz F and R, 140 for
ZBJ-ArtF1c and ZBJ-ArtR2c and 90 bp for Coleop\_16Sc and Coleop\_16Sd.
Sequences were clustered using Sumaclust with a similarity cut-off of
0.97, and a contingency table of operational taxonomic units (OTUs) were
created for each of the five primer sets. Then the reads of each OTU
were normalised by scaling to 50,000, creating five different OTU
tables. Taxonomy was assigned to OTUs (Altschul et al., 1997) by
blasting them to the NCBI GenBank database using blastN (accessed April
2020), with a 97\% similarity match. The LULU algorithm was used to
remove erroneous OTUs (Froslev et al., 2017). We blasted the sequences
against each other with Percent query coverage per hsp set to 80 and
Percent identity set to 84. Hereafter, LULU was run with the following
settings: minimum\_ratio\_type = ''min'', minimum\_ratio = 1,
minimum\_match = 84, minimum\_relative\_cooccurence = 0.95. The output
was imported into Megan version 6.12.3, with standard LCA parameter
settings, except for minimum percentage identity which was set to 90\%
(Huson, Auch, Qi, \& Schuster, 2007). OTUs assigned to humans, fungi,
bacteria, nematodes and mites were removed from the data sets. Any OTUs
present in any of the negative controls were removed from the dataset.
These removed OTUs included genus \emph{Macrotermes} (one blank) from
Coleop\_16S primers; order Sarcoptiformes (one blank) from ZBJ-Art
primers; \emph{Homo sapiens} (six blanks), order Carnivora (one blank),
and family Felidae (one blank) from Mam16S primers; \emph{Homo
sapiens}(nine blanks), and family Felidae (one blank) from Riaz primers;
genus\emph{Morus} (two blanks), \emph{Thyrsodium puberulum} (two
blanks), and genus \emph{Solanum} (one blank) from Trac01 primers.
Furthermore, 28 samples with host identity that were unknown or not
assigned to \emph{C. civetta} or the genus \emph{Genetta} were omitted
from the analyses. Furthermore, one sample was removed due to an error
in the original 16S rRNA bacterial sequencing file and 10 were removed
as no dietary OTUs were classified, leaving 80 samples for downstream
analysis.

Bacterial 16S rRNA sequences were processed using the DADA2 pipeline
(Callahan et al., 2016) in RStudio v.4.0.3 (Team, 2022). The reads were
filtered, using the filterAndTrim command (truncLen=c(225,160), maxN=0,
truncQ=2, rm.phix=TRUE, maxEE=2). After merging the paired reads,
amplicon sequence variants (ASVs) at 100\% similarity were generated and
chimaeras were removed using the removeBimeraDenovo command. ASVs were
assigned to taxonomy using the Silva v.138.1 reference database ( (Quast
et al., 2013); accessed on January 4\textsuperscript{th}, 2022).

Species accumulation curves (Figure S1) were generated to assess if the
sample depth was sufficient to cover expected diversity (Wynne, Sommer,
\& Howarth, 2018), using the vegan package (Oksanen et al., 2022).
Frequency of occurrence were calculated in order to investigate the
frequency of different dietary items among the individuals. Analyses
based on taxonomic identification were limited to order level, as the
majority of OTUs were only classified to order or higher taxonomy. Data
from \emph{C. civetta} and \emph{Genetta} spp. were treated separately.
All statistical analyses and visualisations were done in RStudio v.4.0.3
(Team, 2022). Alluvial plots to visualise diet proportions were
generated using the ggalluvial (Brunson \& Read, 2023) and ggplot2
(Wickham, 2016) packages.

To investigate the influence of host species and sample location and
their interaction on gut microbiomes and consumed diets we conducted
Permutational Multivariate Analysis of Variance (PERMANOVA) using the
adonis2 function in the vegan package (Oksanen et al., 2022). We used
Bray-Curtis dissimilarity distance matrix for the gut bacterial
communities and Jaccard (unweighted) dissimilarity distance matrix for
the diet with 999 permutations. Same distance matrixes were used for
computing Principal Coordinate Analysis (PCoA) using the phyloseq
(McMurdie \& Holmes, 2013) and ggplot2 (Wickham, 2016) packages and the
outcome of PCoAs were visualized using the scatterplot3d package (Ligges
\& M\selectlanguage{ngerman}ächler, 2003). The vegan package was used to compute species
richness for both gut microbiome and diet and was tested for normality
using a Shapiro-Wilk test of normality and tested for homogeneity using
a Bartlett test of homogeneity of variance. As the data was normally
distributed but not homogeneous, a Welch one-way test was used to test
for difference in species richness between different categories.

To test whether diet richness is correlated with the gut bacteria
richness we performed correlation using the cor\_test
(method=''spearman'', exact=FALSE) function from R standard statistics.
To investigate the association between the overall bacterial community
and diet similarity we conducted mantel tests using Jaccard
dissimilarity distance matrices with 10,000 permutations in the vegan
v2.5.7 package (Oksanen et al., 2022). A heatmap of the Jaccard
dissimilarity with a trendline was plotted using ggplot2 v3.3.5
(Wickham, 2016). Finally, to explore whether different consumed diet
items are associated with specific bacterial taxa we conducted Pearson's
correlations between relative abundance of bacterial taxa at the
genus-level and the proportion of different dietary items at order-level
in individual diets using the cal\_cor function in the microeco package
(Liu, Cui, Li, \& Yao, 2021). These analyses were done individually for
the plants, vertebrates and invertebrates and significance of
correlations were adjusted using the false discovery rates.

\textbf{Results}

\emph{Host identification}

The 16S rRNA marker did not show any sequence variation between skin
samples from \emph{Genetta genetta} and \emph{Genetta tigrina} (Table
S3). This meant that we could not distinguish between
\emph{Genetta}species, and we therefore hereafter refer to all samples
from the genus as being from \emph{Genetta} spp.

\emph{Diet composition differed between species and among geographic
locations}

We obtained diet information from 62 samples from \emph{Genetta} spp.
and 18 from \emph{C. civetta} , which includes 45 \emph{Genetta} spp.
and nine \emph{C. civetta} samples from South Africa, three
\emph{Genetta}spp. and nine \emph{C. civetta} from Mozambique, and 14
\emph{Genetta}spp. samples from Eswatini (Table S1). The diets of both
genera included vertebrates, invertebrates and plants. For
\emph{Genetta} spp., we identified 253 taxonomically assigned OTUs (diet
components), spanning 44 taxonomic orders, 65 families, 88 genera and 48
species. We identified far fewer OTUs from \emph{C. civetta} samples,
with only 105 taxonomically assigned OTUs that spanned 32 orders, 40
families, 30 genera and 11 species (Table S4 -- For individual OTU
tables for 5 primer pairs see Table S5-S9). All the diet orders that
were detected were known to the respective areas from where samples were
collected. Species accumulation curves indicated that more samples would
likely provide a better representation of diet compositions (Figure S1).

\emph{Genetta} spp. predominantly consumed a plant-based diet (53.0\% of
OTUs) (Figure 2a), while \emph{C. civetta} predominantly consumed
invertebrates and vertebrates (59.0\% of OTUs) (Figure 2b). As measured
by the number of different OTUs, members of the order Rosales were most
frequent among the plants consumed by \emph{Genetta} spp., but their
diets also included insects (predominantly Lepidoptera) and mammals
(predominantly Rodents) (Figure 2a). \emph{C. civetta} consumed more
insects (predominantly Diptera) followed by plants (predominantly
Rosales) and mammals (predominantly primates, probably carcasses
(Wadley, 2020)). For both \emph{Genetta} spp. and \emph{C. civetta} ,
the most frequent diet component was the genus \emph{Ficus} within the
order Rosales, which was present in 38.0\% of \emph{Genetta} spp. and
19.0\% of \emph{C. civetta} samples (Table S4).

For both host genera, we observed differences in consumed diets between
the sampling regions (two locations for \emph{C. civetta} and three
locations for \emph{Genetta} spp.). For \emph{Genetta} spp. in South
Africa, the diet contained many different plant orders and rodents
(Table S4). In contrast, the \emph{Genetta} spp. diet from Eswatini had
a higher proportion of plants, which were less frequent in in Mozambique
(Table S4). The diet of \emph{C. civetta} contained both vertebrates and
invertebrates, the latter most frequently being insects in both South
Africa and Mozambique (Table S4). \emph{C. civetta} diets differed in
plant orders by locations, with the highest number (11 orders) being in
samples from Mozambique (Table S4).

Overall, diet item richness was significantly higher in \emph{C.
civetta} than \emph{Genetta} spp. (ANOVA: F\textsubscript{1} = 5.978, p
= 0.0167); however, it varied by sampling locations (Figure 3a). Diet
composition (measured with Jaccard distances) also differed
significantly between \emph{C. civetta} and \emph{Genetta} spp.
(PERMANOVA\textsubscript{Host}: F = 1.776, R\textsuperscript{2} =
0.0216, p \textless{} 0.0001; Figure 3b), and there was a significant
effect of location (PERMANOVA\textsubscript{Location}: F = 2.109,
R\textsuperscript{2} = 0.0513, p \textless{} 0.0001; Figure 3b). We also
observed a significant interaction between host genus and sampling
location (PERMANOVA\textsubscript{Host×Loaction}: F = 1.288,
R\textsuperscript{2} = 0.0157, p = 0.0062), indicating that the
influence of host species on diet composition varies geographically.
This aligns with the OTU-level dietary differences we observed for both
genera across sampling locations (Table S4).

\emph{Diverse gut bacterial communities of viverrids differed between
host species and locations}

We obtained 17 successfully sequenced gut bacterial community samples
from \emph{C. civetta} and 60 from \emph{Genetta} spp. From \emph{C.
civetta} we acquired 1,510,564 bacterial sequences (average ± SD: 88,856
± 10,812) that belonged to 3,508 ASVs (Table S10). \emph{Genetta} spp.
samples contained 4,509,590 sequences belonging to 5,141 ASVs. Overall,
we identified 38 bacterial phyla, dominated by Firmicutes (45.0\%),
followed by Proteobacteria (23.6\%), Bacteroidetes (12.6\%), and
Fusobacteria (11.9\%). Less than 0.1\% of the sequences could not be
classified to a phylum (Table S11). Species accumulation curves
indicated that the number of sequences per sample was insufficient to
fully capture gut bacteria diversity (Figure S1).

ASV richness was significantly higher in \emph{C. civetta}
than\emph{Genetta} spp. (Welch one-way test: F\textsubscript{1} = 58.61,
p \textless{} 0.0001; Figure 3c). Similar to diet composition, both host
genera (PERMANOVA\textsubscript{Host}, F\textsubscript{1} = 5.194,
R\textsuperscript{2} = 0.0606, p \textless{} 0.0001) and location
(PERMANOVA\textsubscript{Host}, F\textsubscript{2} = 3.512,
R\textsuperscript{2} = 0.0815, p \textless{} 0.001) had significant
effects on microbiome structure (Figure 3d). We also observed a
significant interaction between host genera and sampling location
(PERMANOVA\textsubscript{Host}, F\textsubscript{4} = 1.998,
R\textsuperscript{2} = 0.0232, p = 0.0107), indicating that the
structure of microbiomes differed between genera depending on their
sampling location.

\emph{Diet items and gut bacterial taxa were strongly associated with
one-another}

For 17 \emph{C. civetta} individuals and 60 \emph{Genetta} spp.
individuals we had both diet and microbiome data. In \emph{C. civetta}we
did not find a correlation between diet item richness and ASV richness
(Pearson's correlation, \selectlanguage{greek}ρ \selectlanguage{english}= 0.0450, p = 0.864). In contrast, we observed
a significant correlation between diet item and ASV richness
in\emph{Genetta} spp. (\selectlanguage{greek}ρ \selectlanguage{english}= 0.3100, p = 0.0145). However, this
correlation tended to be sampling location specific as we did not see a
strong correlation in South African (\selectlanguage{greek}ρ \selectlanguage{english}= 0.0950, p = 0.5450), but only
in Eswatinian (\selectlanguage{greek}ρ \selectlanguage{english}= 0.8500, p = 0.0001) individuals. Mantel tests
indicated significant positive correlations between diet similarity and
gut bacterial community similarity in both \emph{C. civetta} (Mantel r =
0.4548, p = 0.0002) and \emph{Genetta} spp. (Mantel r = 0.4858, p
\textless{} 0.0001; Figure 4). \emph{C. civetta} sample SA197 was an
outlier with extremely low diet item and gut microbe sharing with other
individuals, so we removed it from this analysis. Overall, this suggests
that the composition of the consumed diet is associated with the
composition of the bacterial communities in both host genera.

The overall associations between diet and bacterial community
similarities tended to be driven by associations between specific
bacterial taxa and the proportion of dietary items in individual diets.
We observed specific sets of bacterial genera to be associated with
proportions of specific order-level dietary items in both \emph{C.
civetta} and \emph{Genetta} spp. However, the taxonomic identity of
bacterial taxa associated with taxonomically-similar diet items differed
between the two host genera. All significant correlations were positive.
In \emph{C. civetta,} there were 144 significant correlations between
different bacterial genera and invertebrate orders (Figure 5, Table
S12), for which most were with Hemiptera (57) and Coleoptera (38).
In\emph{Genetta} spp., there were 91 significant correlations of which
Hymenoptera accounted for the majority (47) (Figure 5, Table S12). We
found 276 significant correlations between vertebrate orders and
bacterial genera in \emph{C. civetta} , dominated by associations with
Artiodactyla (113), Rodentia (47), and Galliformes (60) (Table S12).
In\emph{Genetta} spp., the 172 significant correlations were most
frequently with Coliiformes (48) and Macroscelidea (41). Lastly, there
were 408 and 339 significant correlations between plant orders and
bacterial genera in \emph{C. civetta} and \emph{Genetta} spp.,
respectively. These interactions imply that diet variation is a driver
of individual variation in gut communities in wild viverrids.

\textbf{Discussion}

Through characterizing both consumed diets and faecal bacterial
communities we investigated associations between diet and gut
microbiomes of two genera of wild Southern African omnivorous mammals.
As predicted, we observed compositionally different diet and gut
bacterial communities between the two host genera. These differences
were also impacted by sampling localities, in support that geography
influences diet selection in wild viverrids with likely direct impacts
on gut bacterial communities. This aligns with previous findings from
wild rodents (Wang, Zhang, Li, \& Yi, 2022) and primates (Zhao et al.,
2018). We did not observe strong relationships between dietary item and
gut bacterial ASV richness for either genus, but we did detect strong
associations between compositional similarity of consumed diets and the
similarity of gut bacterial communities. These overall associations
appear to be driven by correlations between specific diet and microbial
taxa that also differ by host genus. However, the latter should be
interpreted with care, given the unknown nature of specific functions of
these bacterial taxa and the uncertainty of whether diet-associated taxa
are transient microbes or symbionts. Nevertheless, taken together our
results imply that composition/breadth of diets are strongly linked to
gut bacterial communities in wild omnivorous mammals, aligning with
previous findings from tropical birds (Bodawatta et al., 2022) and
potentially revealing a major source of individual variation in gut
microbiomes in wild mammals.

Mammals with similar diets tend to have similar gut microbiomes (Delsuc
et al., 2014; Ley, Hamady, et al., 2008; Ley, Lozupone, et al., 2008;
Muegge et al., 2011). However, although \emph{C. civetta}
and\emph{Genetta} spp. are closely related hosts (Veron, 2007) that
previously have been assigned to similar dietary niches, we found clear
differences in both diet and gut bacterial community composition and
diversity. This is consistent with work in other mammals where
microbiome structure has often been attributed to the combination of
host genetics and diet variation (Ley, Hamady, et al., 2008; Ley,
Lozupone, et al., 2008; Mallott et al., 2018; Suzuki et al., 2019).
Overall, gut bacterial and diet richness were higher in \emph{C.
civetta} than \emph{Genetta} spp. and the former had more comparable
proportions of plant and animal dietary taxa, indicating that more a
diverse omnivorous diet may lead to a more diverse gut microbiome that
could aid in the more complex digestion. The specific associations we
observed between diet orders and gut bacterial genera also support this
assertion, as more diverse diets led to an increased number of specific
diet-bacteria associations. Similar diet-specific associations of gut
microbes have also been found in wild primates (Mallott et al., 2018)
and in diet manipulation work in both mammals (David et al., 2014) and
birds (Bodawatta, Freiberga, et al., 2021).

Metabarcoding provided novel insights into the dietary patterns
of\emph{C. Civetta} and \emph{Genetta} spp., where we identified four
times as many diet taxa than previous morphological studies have
obtained (Amiard et al., 2015; Amroun et al., 2014; Bekele, Afework, \&
Balakrishnan, 2008; Daniel, Eniang, Bekelle, Balakrishnan, \& Egwali,
2008; S\selectlanguage{ngerman}ánchez et al., 2008). This implies that our current knowledge of
dietary niches of these genera is likely underestimated. Although there
were dietary overlaps between \emph{C. civetta} and \emph{Genetta} spp.,
their overall compositions were significantly different between the two
and between locations. \emph{Genetta} spp. had overall more diverse
diets and more plant taxa than previous visual observations and
morphological identification have indicated (Breuer, 2008; Emmons,
1987). As \emph{Genetta} spp. have previously been characterised as
carnivores (Roberts et al., 2007; Rosalino \& Santos-Reis, 2002), this
questions the applicability of using dietary niche classifications based
on visual identification. Similarly, the diet of \emph{C. civetta} was
dominated by animal taxa, contradicting a previous study that suggested
that it feeds mostly on grass and fruits (Mullu \& Balakrishnan, 2015).
However, identification of a diverse array of plant, vertebrate, and
invertebrate taxa in both genera imply that they belong to a broader
omnivorous dietary guild (Admasu, Thirgood, Bekele, \& Laurenson, 2004),
underlining the importance of metabarcoding to decipher dietary niches
of wild mammals.

Although developments in technologies have opened up new approaches in
molecular biology for studying diets (Deiner et al., 2017; Pompanon et
al., 2011), metabarcoding is not without flaws. It can provide
information on the presence of an organism, but it does not reliably
provide information on relative abundances (Neilsen et al., 2017).
Therefore, we were only able to consider the frequency of a given diet
taxon within or among samples. Furthermore, there is a risk of false
positive detection of secondary diet items, i.e., organisms that were
not consumed by the focal individual but by its prey, or as a component
on prey items ingested for other reasons than nutrition (Bowser et al.,
2013; Neilsen et al., 2017) or merely DNA contamination from the
environment such as the air (Lynggaard et al., 2022). Like most
ecological survey methods, metabarcoding also carries a risk of false
negatives (Serrana, Miyake, Gamboa, \& Watanabe, 2019). False negatives
can be introduced through primer bias, as two primers may provide vastly
different taxonomic profiles and it is therefore important to choose
primers with care and using multiple primer sets can minimize the
effects of primer bias (Alberdi, Aizpurua, Gilbert, \& Bohmann, 2017).
Insufficient sampling depth was revealed in the species accumulation
curves (Figure S1), indicating the differences in sample sizes between
the two host species could potentially have an effect on our
conclusions. However, this may not have impacted our interpretations as
we observed higher richness in the gut microbiomes and the consumed
diets of \emph{C. civetta} , despite due to small sample size compared
to\emph{Genetta} spp.

\textbf{Conclusions}

Characterisation of animal diets has been revolutionised with
metabarcoding techniques, providing deep insights into variation and
breadth of dietary niches that otherwise could not be identified with
traditional morphological methods. Our findings provide new insights
into the more diverse and variable dietary consumption of
omnivorous\emph{C. civetta} and \emph{Genetta} spp. than previously
appreciated. Despite being sympatric, we detected genus-specific dietary
consumption, indicating non-random and only partially overlapping
dietary niches. This new knowledge on the feeding ecology of the focal
genera is directly applicable to improve conservation strategies and
preserve optimal habitats (and hence diet items) for these species
(Chuang \& Lee, 1997; Neilsen et al., 2017). Diet is one of the major
drivers of mammalian gut microbial communities, but our results indicate
that even closely related species under the same broad dietary guild
classification would experience varying levels of associations between
consumed diets and gut microbiomes. This implies that the effect of diet
on mammalian microbiomes might be overestimated, when using broad
dietary guilds. To improve our understanding of the magnitude of effects
of diet on long- and short-term gut bacterial associations with
mammalian hosts, and diet contributions to microbiome variation, we need
to understand realised dietary niches and consider geographic origins of
individuals and species.

\textbf{Acknowledgements}

We are grateful to fellow researchers at Gorongosa National Park, the
Savannah Research Center in Mbuluzi Game Reserve and Lajuma Research
Centre. We thank Melissa Petford and Ryan Van Huyssteen for the
additional sampling at Medike Mountain Reserve and Blouberg Nature
Reserve. We thank the eDNA group and the UCPH Social and Symbiotic
Evolution Group for feedback and fruitful discussions. The work was
supported by an EvoGenomics MSc thesis grant, the Oticon foundation, by
a Danish Microbiology Society (DMS) travel grant and the Lemvigh-Müller
foundation to MS and EL, and a Carlsberg Foundation Infrastructure grant
(CF17-0597) to MP.

\textbf{Data accessibilities}

Sequence data is published in Erda.dk repository with DOI
https://doi.org/10.17894/ucph.db688f38-95b7-4b62-902b-86857a76c21e and R
scripts and bioinformatic pipelines are available
from\emph{https://github.com/emiliamrl/Viverridae\_diet-gutmicrobiome}.

\textbf{Author Contributions}

The study was designed by MS, EL, AM, KB, and MP. The fieldwork and
sampling was carried out by MS, EL, PS, JD, CD, and AM and laboratory
work were done by MS and EL. Analyses were performed by MS, EL, KHB.
Supervision by KB, AM, and MP. First draft of the manuscript written by
MS and EL, with subsequent contributions from all authors.

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\textbf{Tables and Figures with captions}

\textbf{Table 1.} Primers and overview of consisting taxa, target gene,
marker length, forward and reverse sequences, and suitable qPCR and PCR
parameters for each primer\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Primer name}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Target taxa}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Target marker}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Forward primer sequence}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{Reverse primer sequence}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{qPCR/PCR temperature profile}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{No. of Cycles}\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textbf{References}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F: 16S mamF R: 16S mamR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Mammalia\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16S rDNA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
CGGTTGGGGTGACCTCGGA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GCTGTTATCCCTAGGGTAACT\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95C\selectlanguage{ngerman}° - 10 min 95C° - 12 sec 59C° - 30 sec 70C° - 25 sec 72C° - 7
min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Taylor, 1996)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F: Trac01 R: ITS-7A\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Tracheophyta\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ITS1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GATATCCRTTGCCGAGAGTC\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
GAAGGAGAAGTCGTAACAAGG\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95C\selectlanguage{ngerman}° - 10 min 95C° - 30 sec 56C° - 30 sec 72C° - 30 sec 72C°- 7
min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Taberlet et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F: RiazF R: RiazR\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Vertebrata\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12S rDNA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
TTAGATACCCCACTATGC\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
TAGAACAGGCTCCTCTAG\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95C\selectlanguage{ngerman}° - 10 min 94C° - 30 sec 51C° - 30 sec 72C° - 60 sec 72C° - 5
min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Riaz et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F: ZBJ-ArtF1c R: ZBJ-ArtR2c\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Arthropoda\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COI\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AGATATTGGAACWTTATATTTTATTTTTGG\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
WACTAATCAATTWCCAAATCCTCC\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95C\selectlanguage{ngerman}° - 10 min 95C° - 15 sec 52C° - 30 sec 72C° - 30 sec 72C° - 7
min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Zeale, 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
F: Coleop\_16Sc R: Coleop\_16Sd\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Coleoptera\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
16S rDNA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
TGCAAAGGTAGCATAATMATTAG\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
TCCATAGGGTCTTCTCGTC\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
95C\selectlanguage{ngerman}° - 10 min 95C° - 15 sec 55C° - 30 sec 72C° - 30 sec 72C°- 7
min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Epp et al., 2012)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
V4.SA504 V4.SB711\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Bacteria\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
V4 region of the 16S rRNA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
AATGATACGGCGACCACCGAGATCTACACCTGCGTGTTATGGTAATTGTGTGCCAGCMGCCGCGGTAA\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
CAAGCAGAAGACGGCATACGAGATTCAGCGTTAGTCAGTCAGCCGGACTACHVGGGTWTCTAAT\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
94C\selectlanguage{ngerman}° - 4 min 94C° 30 sec 56C° - 30 sec 72C° - 30 sec 72C° - 4 min\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
(Otani et al., 2016)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure 1}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
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\textbf{Figure 1.} \textbf{Focal genera, sample collection and maps of
sampling locations.} Images of a) \emph{Civettictis civetta,}
b)\emph{Genetta} sp\emph{.,} c) Latrine of \emph{C. civetta,} d) Latrine
of \emph{Genetta} sp. e) Sampling locations in South Africa (Lajuma
Research Centre), Mozambique (Gorongosa National Park), and Eswatini
(Mbuluzi Game Reserve).

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/741013/articles/908816-diet-components-associated-with-specific-bacterial-taxa-shape-overall-gut-community-compositions-in-omnivorous-african-viverrids}

\textbf{Figure 2.} \textbf{Proportions of dietary taxa (to the order
level) detected in \emph{C. civetta} and \emph{Genetta} spp.} Alluvial
plots visualising the relative frequency of taxa in the diet of
a)\emph{C. civetta} and b) \emph{Genetta} spp. Dietary orders only found
in one host genus are indicated with asterisks.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/741013/articles/908816-diet-components-associated-with-specific-bacterial-taxa-shape-overall-gut-community-compositions-in-omnivorous-african-viverrids}

\textbf{Figure 3.} \textbf{Diet and gut bacterial diversity and
composition in \emph{C. civetta} and \emph{Genetta} spp.} Box plots
demonstrate the diet OUT richness (a) and ASV richness (b) in the two
host species. Colours indicate the sampling localities and * indicating
significance level p \textless{} 0.05 and *** p \textless{} 0.001. 3D
PCoA ordination plots demonstrate the composition of b) diet (based on
Jaccard dissimilarity distance) and d) gut microbiomes (based on
Bray-Curtis dissimilarity distances) for \emph{C. civetta} (circles)
and\emph{Genetta} spp. (triangles) at the three collection locations.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/741013/articles/908816-diet-components-associated-with-specific-bacterial-taxa-shape-overall-gut-community-compositions-in-omnivorous-african-viverrids}

\textbf{Figure 4. Association between diet composition with microbiome
composition.} Relationship between the gut bacterial and consumed diet
dissimilarity (measured with Jaccard dissimilarity distances) in
a)\emph{C. civetta} and b) \emph{Genetta} spp. Mantel correlations for
each genus are given within the panels. The grey area around each
trendline is the 95\% confidence interval (note this trendline is only
for visualisation and the statistics were conducted using mantel tests).
Coloured squares in the heatmap show the comparison of two samples with
their dissimilarity value for both the gut bacteria (x-axis) and the
diet (y-axis). The legends show the number of comparisons between
samples for a given square in the plot.

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/741013/articles/908816-diet-components-associated-with-specific-bacterial-taxa-shape-overall-gut-community-compositions-in-omnivorous-african-viverrids}

\textbf{Figure 5. Associations between proportions of invertebrate
dietary items (order level) and relative abundance of bacterial taxa
(genus level) in a) \emph{C. civetta} and b) \emph{Genetta} spp.} Text
colour of bacterial genera represent the phylum they belong to
(Firmicutes, Bacteroidota, Proteobacteria, Actinobacteriota,
Myxococcota, Verrucomicrobiota, Chloroflexi, Fibrobacterota,
Acidobacteriota, Planctomycetota, Cyanobacteria, Deinococcota and
Desulfobacterota). Figure only depicts Pearson's correlations with p
\textless{} 0.0001 (***). Full list and correlations between vertebrate
and plant dietary items and bacterial genera in Table S12.\selectlanguage{english}
\begin{figure}[H]
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\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4}

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\includegraphics[width=0.70\columnwidth]{figures/Figure-5/Figure-5}

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