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\begin{document}

\title{Host phylogeny and ecological associations best explain Wolbachia host
shifts in scale insects Running title: Wolbachia host shifts}



\author[1]{Ehsan Sanaei}%
\author[2]{Greg Albery}%
\author[3]{Yun Kit Yeoh}%
\author[4]{Yen-Po Lin}%
\author[1]{Lyn Cook}%
\author[1]{Jan Engelstädter}%
\affil[1]{The University of Queensland}%
\affil[2]{Georgetown University}%
\affil[3]{The Chinese University of Hong Kong}%
\affil[4]{National Chiayi University}%


\vspace{-1em}



  
  \date{May 15, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Wolbachia are among the most prevalent and widespread endosymbiotic
bacteria on earth. Wolbachia's success in infecting an enormous number
of arthropod species is attributed to two features: the range of
phenotypes they induce in their hosts, and their ability to switch
between host species. Whilst much progress has been made in elucidating
their induced phenotypes, our understanding of Wolbachia host shifting
is still very limited: we lack answers to even fundamental questions
concerning Wolbachia's routes of transfer and the importance of factors
influencing host shifts. Here, we investigate the diversity and
host-shift patterns of Wolbachia in scale insects, a group of arthropods
with intimate associations with other insects that make them well-suited
to studying host shifts. Using Illumina multi-target amplicon sequencing
of Wolbachia-infected scale insects and their direct associates we
determined the identity of all Wolbachia strains. We then fitted a
Generalised Additive Mixed Model (GAMM) to our data to estimate the
influence of host phylogeny and the geographic distribution on Wolbachia
strain sharing among scale insect species. The model predicts no
significant contribution of host geography but strong effects of host
phylogeny, with high rates of Wolbachia sharing among closely related
species and a sudden drop-off in sharing with increasing phylogenetic
distance. We also detected the same Wolbachia strain in scale insects
and several intimately associated species (ants, wasps, beetles, and
flies). This indicates putative host shifts and potential routes of
transfers via these associates and highlights the importance of
ecological connectivity in Wolbachia host-shifting.%
\end{abstract}%



\sloppy


\section*{Introduction}

{\label{introduction}}

\emph{Wolbachia} is one of the best-known groups of heritable
endosymbionts, widely distributed in arthropods and some nematodes
(Hertig 1936; Sironi et al. 1995; Werren 1997). These bacteria form one
of the most abundant and diverse groups of symbionts on earth: an
estimated 40-60\% of arthropod species are infected with
\emph{Wolbachia} strains (Zug \& Hammerstein 2012; Weinert et al. 2015).
Their ability to induce reproduction manipulations (Rousset et al. 1992;
Werren et al. 2008) and their application in vector born disease control
(Kambris et al. 2009; Hoffmann et al. 2015; Ross et al. 2019) are key
aspects of \emph{Wolbachia} that have been studied extensively in the
last two decades.

Similar to many other symbionts, the current distribution of
\emph{Wolbachia} results from three major processes: co-diversification
with the host clade, shifting between host species, and symbiont loss
(Thompson 1987; Charleston \& Perkins 2006). Although co-speciation is
common among \emph{Wolbachia} strains belonging to supergroups C and D
in their nematode hosts (Bandi et al. 1998; Fenn \& Blaxter 2004) and
certain strains of supergroup F infecting bed bugs (Balv\selectlanguage{ngerman}ín et al. 2018),
many studies have failed to find evidence of codiversification between
\emph{Wolbachia} strains of supergroups A/B and arthropods (e.g., in fig
wasps (Shoemaker et al. 2002), ants (Frost et al. 2010), butterflies
(Ahmed et al. 2016), bees (Gerth et al. 2013), and collembolans (Ma et
al. 2017)). In the absence of co-diversification, host-shifting is the
alternative hypothesis to explain the current distribution of
\emph{Wolbachia} (reviewed in Sanaei et al. (2021a)).\emph{Wolbachia}
shift hosts when a given strain infects a novel arthropod species,
mostly through horizontal transfer (Boyle et al. 1993; Heath et al.
1999) and possibly occasionally through hybridisation (Turelli et al.
2018; Cooper et al. 2019). The possibility of host-shift events in
\emph{Wolbachia} has been confirmed through numerous transinfection
studies when a strain is artificially introduced to an uninfected
species (reviewed in Hughes \& Rasgon, 2014), and the existence of
``super spreader strains'' that infect host species which are
phylogenetically distantly related (e.g., ST41 strain type in
Lepidoptera (Ilinsky \& Kosterin 2017)). Physical transfer
of\emph{Wolbachia} from donor to recipient species is the first step of
host-shifting, achieved via various ''routes of transfer'' and usually
facilitated by a biological vector or a suitable environmental medium
(Vavre et al. 2003; Riegler et al. 2004). Routes of transfer reported so
far include prey-predator interactions (Le Clec'h et al. 2013),
host-parasite interactions (Cook \& Butcher 1999; Vavre et al. 1999;
Ahmed et al. 2015) and sharing a common food resource (Li et al. 2017).

Host phylogeny and ecological connectivity are thought to be the two
main factors determining \emph{Wolbachia} host shifting. As
phylogenetically closely related species are similar in many respects,
including their intercellular environment and immunology (Perlman \&
Jaenike 2003), it is expected that a given symbiont will shift more
easily between them than between distantly related species (Charleston
\& Robertson 2002). This assumption, referred to as the ``phylogenetic
distance effect'' (PDE) (Longdon et al. 2011; Engelstädter \& Fortuna
2019), may partly explain host shifts of \emph{Wolbachia} across closely
related species. In spite of limited case studies which indicated the
presence of PDE in part of the host phylogeny (e.g., in fig wasps
(Shoemaker et al. 2002), fungus growing ants (Frost et al. 2010), bees
(Gerth et al. 2013) and Collembolans (Ma et al. 2017)), the influence of
PDE on \emph{Wolbachia}host shifting is not clear. Overlapping
geographic distributions of host species is another possible explanatory
factor. Sharing a common habitat and consequently potential ecological
interactions may lead to several direct and indirect physical contacts
between a given donor and recipient host and, therefore, also increase
the probability of\emph{Wolbachia} host shifting. Indeed, several case
studies documented host-shift events between host species that share the
same habitat, e.g., in a rice field community (Kittayapong et al. 2003)
and a mushroom habitat (Stahlhut et al. 2010)).

Here, we use scale insects as a model system to gain a better
understanding of \emph{Wolbachia} host shifting. With more than 8200
described species and 24 families, the superfamily of scale insects
(Coccoidea) is globally distributed (Gullan \& Cook 2007; García Morales
et al. 2016). Like many other members of the suborder Sternorrhyncha,
such as aphids, whiteflies and psyllids, scale insects exclusively feed
on plants and some are considered serious agricultural pests (Kondo et
al. 2008). Scale insects have been recorded in ecological associations
with a range of arthropods species. In particular, many are usually
observed in close interactions with ants through trophallaxis (where the
honeydew produced by the scale insects is consumed by ants) (Hölldobler
et al. 1990; Buckley \& Gullan 1991; Gullan et al. 1993). Despite
several similarities with other hemipterans, Sanaei et al. (2021b) found
that most species are predicted to have low to
intermediate\emph{Wolbachia} prevalence, in contrast to a u-shaped
distribution (most species have a very low or very high prevalence)
predicted for other groups (Hilgenboecker et al. 2008). Also, a positive
correlation between \emph{Wolbachia} infection in scale insects and
their associate ants indirectly points to a plausible route of transfer
(Sanaei et al. 2021b). These preliminary results provided a broad view
of the\emph{Wolbachia} infection dynamic in scale insects and thus
inspired us to investigate \emph{Wolbachia} strain diversity and
consequently host-shifting in scale insects.

Studying of \emph{Wolbachia} host-shifting requires using and developing
new methodologies. To overcome technical problems associated with Sanger
sequencing (see Discussion), we adopted Illumina multi-target amplicon
sequencing techniques to determine the \emph{Wolbachia} strains in scale
insects and their associate species. Using this effective methodology,
we revealed the strain diversity and composition (including both single
and multiple infections) in scale insects. Using phylogenetic trees of
both scale insects and their \emph{Wolbachia} strains, and the
geographic distribution range of each scale insect species, we assessed
which factors (phylogeny or geography) best explain host-shifting
events. Finally, by determining \emph{Wolbachia} strains in individual
scale insects and directly associated individuals of other species, we
identified plausible routes of horizontal transfer.

\section*{Materials and Methods}

{\label{materials-and-methods}}

\subsection*{Sampling}

{\label{sampling}}

In a previous study, we screened 689 scale insect specimens
for\emph{Wolbachia} (Sanaei et al. 2021b). Among samples with
positive\emph{Wolbachia} infection, we selected those whose MLST genes
(Multilocus Sequence Typing, (Baldo et al. 2006)) were successfully
amplified. This included 59 specimens belonging to 29 species and four
scale insect families (Monophlebidae, Pseudococcidae, Coccidae and
Eriococcidae). Of 29 species, 15 are represented by single samples, two
are represented by two samples from the same population, and 12 species
are represented by multiple samples from more than one population. For
full details we refer to File S2. 16 of these scale insects were
collected together with a directly associated insect (including ants,
wasps, flies, beetles, moths), and these were also included. The tight
ecological connection between the scale insect and an associate was
established either by direct observation (e.g., ant-scale insect
interactions) or by rearing both members of the pair in the laboratory
conditions (e.g., rearing parasitoids from the scale insect sample).
Based on observation, wasps and flies are mostly parasites and moth
caterpillars are predators of scale insects. We selected 16 infected
pairs: five scale insect-ant, seven scale insect-wasp, two scale
insect-beetle, two scale insect-fly, and one scale insect-moth pair. We
were unable to determine the species for any of the associates, except
for the ants (\emph{Technomyrmex albipes} ) and one of the beetles
(\emph{Neopocadius pilistriatus} ); however, we determined their COI
barcode.

\subsection*{PCR and sequencing}

{\label{pcr-and-sequencing}}

To be able to detect all \emph{Wolbachia} strains even in multiple
infected host individuals, we implemented an approach of Illumina
multi-target amplicon sequencing. For this, we used 16S and MLST genes,
which included five housekeeping genes (coxA, fbpA, ftsZ, gatb, hcpA),
as well as the \emph{wsp} (\emph{Wolbachia} Surface Protein) gene (Baldo
et al. 2006). Despite some limitation in using MLST (Bleidorn \& Gerth
2018), it is still a reliable source in strain determination and
evolutionary history analysis (Wang et al. 2020). For the host genes, we
targeted Cytochrome Oxidase I (COI), 18S and 28S Ribosomal RNA genes.
The host genes were used later to confirm both scale insect and
associate species identity and to build the host phylogeny. As a
requirement for our Illumina sequencing platform, some of the primers
were re-designed to yield products shorter than 500bp length (Table S1).
We also added Illumina specific overhang adapters at the start (5`) of
the forward and reverse primers (GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG and
TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG, respectively). PCR configurations
were applied as originally suggested for each gene (Baldo et al. 2006).

We pooled the amplicons for all ten genes (seven \emph{Wolbachia} and
three host genes) per sample and sent them to the Australian Centre of
Ecogenomics, ACE (The University of Queensland, Australia). At ACE,
library preparation was performed by dual indexing workflow elaborated
by Teh el al., (2021). At first, PCR products were amplified by NEBNext®
Ultra\selectlanguage{english} II Q5(r) Mastermix (New England Biolabs \#M0544). Generated PCR
amplicons then were purified using Agencourt AMPure XP beads (Beckman
Coulter). These products were then indexed with unique 8bp barcodes
using the Illumina Nextera XT 384 sample Index Kit A-D (Illumina
FC-131-1002) with NEBNext(r) Ultra\selectlanguage{english} II Q5(r) Mastermix. All the PCRs were
conducted in the standard condition. Indexed amplicons were pooled
together in equimolar concentrations and sequenced on Illumina MiSeq
Sequencing System using paired end sequencing with V3 300bp chemistry.
As part of the workflow, the following controls were applied: 1.
Negative amplification control from a like processed reagent control to
monitor for contamination in library construction 2. Single well empty
chamber controls within processing plates to monitor for cross
contamination within the library preparation 3. Negative index positions
between runs to monitor for run-to-run bleed through designated as in
line controls. Passing QC of resulting sequence is determined as 10,000
raw reads per sample prior to data processing and passing Quality
Control metrics in line with Illumina supplied reagent metrics of
overall Q30 for 600bp reads of \textgreater{}70\%.

\subsection*{Wolbachia strain
determination}

{\label{wolbachia-strain-determination}}

To determine the identity of the \emph{Wolbachia} strain in our sample,
we developed an R-based (R Core Team 2013) bioinformatics pipeline based
on the DADA2 pipeline (Callahan et al. 2016), which includes a series of
quality controls, trimming and mapping to the references. In addition,
we blasted all generated OTUs against Genbank
(https://www.ncbi.nlm.nih.gov/genbank/) and the \emph{Wolbachia} MLST
database (https://pubmlst.org/organisms/\emph{Wolbachia}-spp). Strain
determination was first conducted on single infected samples. In the
next step, these strains as well as registered strains in the MLST
database were utilized as references to determine all the strains in
multiple infected samples.

Despite our methodology being powerful enough to identify co-infecting
strains, it is limited in its ability to detect intra-host
recombination. The details of the pipeline are explained in File S1
(including Figure S1) and the R scripts are available in File S4.

\emph{Wolbachia} strains are commonly determined by their MLST allele.
Based on data available on the MLST database, any genetic variation from
one or more MLST alleles of a given strain (which can be a single
nucleotide base) is defined as a distinct strain (Baldo et al. 2006).
This definition of \emph{Wolbachia}strains is controversial (Bleidorn \&
Gerth 2018). Based on MLST genes, the lowest estimated and suggested
pairwise distance among\emph{Wolbachia} strains is 0.001 (Ilinsky \&
Kosterin 2017). Therefore, to avoid promoting subtle variations in MLST
as a new strain, we grouped strains with up to five bases difference
across all seven genes (a total of \textasciitilde{}3065bp) into
''strain groups.'' (e.g., grouping\emph{w} Api1.1 and \emph{w} Api1.2
into \emph{w} Api1). To construct\emph{Wolbachia} phylogeny, conduct
host-parasite congruency tests, detect potential host shift events and
finally run the GAMM model, we always used strain groups instead of
strains.

\subsection*{Reconstruction of
phylogenies}

{\label{reconstruction-of-phylogenies}}

\emph{Wolbachia} ~and host genes were aligned in Geneious (Version
11.0.5, Biomatters) using the MAFFT algorithm (Katoh et al. 2002). Each
gene was then trimmed to have identical lengths across samples.
PartitionFinder2 (Lanfear et al. 2017) was used to find the best-fit
partitioning scheme and substitution model for phylogeny estimation
using default parameters. The results were then used as an input for
estimating the Maximum Likelihood tree using RAxML (Stamatakis 2014)
with ''Rapid Bootstrapping and Search for the Best scoring ML'' and 1000
bootstrap replicates. As recombination is common among
\emph{Wolbachia}genes, the branch lengths of the~\emph{Wolbachia}
~phylogenetic tree were corrected with ClonalFrameML (Didelot \& Wilson
2015) to account for recombination events.

The MLST profile of all registered strains in the \emph{Wolbachia} MLST
depository (https://pubmlst.org/organisms/\emph{Wolbachia} -spp) was
downloaded (on 5\textsuperscript{th} November 2020). As most of the
original \emph{Wolbachia} MLST gene fragments were longer than the gene
fragments in our study, the imported database was trimmed in Geneious to
match the current study. The phylogenetic tree of all strains, including
the reported strains in the MLST database and those from the current
study, was estimated as above. This tree was used to determine the
position of strains from scale insects within the
various\emph{Wolbachia} supergroups.

The phylogenetic trees of all hosts and~\emph{Wolbachia} ~strains, as
well as the \emph{Wolbachia} -host association network, were plotted in
R by using the phytools package (Revell 2012). A 3D interactive
bipartite graph (File S5) was also created using the bipartiteD3 R
package (Terry 2019). To test the phylogenetic congruence between
\emph{Wolbachia} and their hosts, we ran two tests. First, we performed
a Parafit test (Legendre et al. 2002), which assesses the genetic
distance similarity of host and parasite phylogenies. To this end, we
used the parafit function implemented within the ape R-package (Paradis
\& Schliep 2019) with the lingoes correction method for negative
eigenvalues and 100,000 permutations. Second, we adopted the Procrustean
Approach (known as PACo) which assesses the similarity between host and
parasite trees by estimation of Euclidean embeddings derived from
distance matrices (Balbuena et al. 2013). This test, which is
implemented in PACo R package (Balbuena et al. 2013), was performed with
100,000 permutations. These two tests provide statistics to assess the
independence of phylogenies by either rejecting or accepting the null
hypothesis that the similarity between the trees is not higher than
expected by chance. All R scripts developed and used in this study are
provided in File S3.

\subsection*{Factors determining host
shifts}

{\label{factors-determining-host-shifts}}

An expanded version of a Generalised Additive Mixed Model (GAMM),
originally developed for viral sharing across mammal species (Albery et
al. 2020), was applied by using the mgcv package in R (Wood 2011). This
GAMM allowed us to model a non-linear fit between our explanatory and
response variables and allowed us to more readily account for their
uneven distributions. Specifically, this model examined the probability
of a given pair of scale insect species sharing one or
more\emph{Wolbachia} symbionts, as a function of their phylogenetic and
geographic similarity, with a logistic link function:

\emph{Wolbachia} (0/1) \textasciitilde{} phylogenetic distance + home
range overlap + geographic distance

Phylogenetic distance was inferred from the Australian scale insect
phylogenetic tree as explained above. To quantify habitat sharing
between scale insect species, we constructed each species' geographic
range using their observed locations. For all species with 5 or more
samples, we constructed a minimum convex polygon (MCP) in R. The
coordinates for the MCP (File S4) were collected from various sources,
mainly including the LGC collection (Cook Lab, School of Biological
Science, The University of Queensland), ScaleNet (Garcia Morales et al.
2016), the Atlas of Living Australia website (https://www.ala.org.au/),
and several published articles (File S4). For each pair of species, we
calculated the overlap of these polygons as a proportion of both
species' total range size. We also derived Euclidean distances between
species' sampling locations by calculating pairwise distances between
species' centroids. Species with fewer than 5 geographic observations
were not included in the model. We also excluded \emph{Coccus
formicarii} which was collected from Taiwan and therefore difficult to
fit in the model. A total number of 22 species were included in the GAMM
model.

We fitted phylogenetic distance, home range overlap, and geographic
distance as explanatory variables, and we fitted paired species'
identities as multi-membership random effects to account for variation
in richness and sampling frequency between species (Albery et al. 2020).
To quantify their impact on model fit we examined the change in deviance
information criterion (DIC), where a change in 2 DIC was taken to
represent an improved model. To avoid fitting too many variables in the
model, we sequentially added each pairwise term, retaining the one that
most improved model fit, and then repeating the process with the
remaining variables, until no remaining variables improved the model.
The R scripts are available in File S3.

\section*{Results}

{\label{results}}

The output of our bioinformatics pipeline successfully recovered 94.03\%
of all \emph{Wolbachia}amplicons and yielded read count numbers between
4658 and 36548 per sample (summed over all seven \emph{Wolbachia} genes;
for details see File S1 and S2). This high coverage enabled us to
identify a diversity of \emph{Wolbachia} strains within and across
hosts. Among 75\emph{Wolbachia} -positive samples (59 scale insect
samples and 16 associates), 68\% were infected with a single
\emph{Wolbachia} strain and 32\% were infected with more than
one~\emph{Wolbachia} ~strain (20 double and 4 triple infected). Among
nine scale insect species with samples from more than one population,
intra-species infection polymorphism was observed in \emph{Cryptes
baccatus, Eriococcus confusus, Icerya purchasi, Parasaissetia nigra} and
\emph{Sphaerococcus ferrugineus} (File S2). Among 29 scale insect
species that were screened in this study, 6 species were always found
co-infected (File S2). For example, two screened samples of\emph{Akermes
scrobiculatus} were coinfected with strains belonging to Supergroup A
(\emph{w} Ake1) and Supergroup B (\emph{w} Ake2) and two samples of
\emph{Cystococcus echiniformis} were coinfected with strains belonging
to Supergroup A (\emph{w} Sph1) and Supergroup F (\emph{w} Cys1).

A total of 63 strains were identified and clustered into 31 strain
groups belonging to three \emph{Wolbachia} -supergroups (Figures 1, S2
and File S4). Most of the strains belong to Supergroups A (38) and B
(21), but we also identified three strains groups from Supergroup
F:\emph{w} Cys1 and~\emph{w} Sph5 (respectively infecting
\emph{Cystococcus echiniformis} ~and \emph{Sphaerococcus ferrugineus} ),
and \emph{w} Sph3 (infecting two specimens of \emph{S} .
\emph{ferrugineus} ). Based on the MLST database, these are the first
Supergroup F strains reported in Australia. Although \emph{w} Cys1 is
placed within Supergroup F, it forms a unique clade compared to other
reported Supergroup F strains (Figure S2).

The most diverse and abundant~\emph{Wolbachia} ~strain group in our
dataset is~\emph{w} Sph1, which includes 12 closely related strains and
was detected in 23 samples belonging to eight scale insect, four wasp
and one ant species (Figure 2, S3 and File S2, S4). Similar strains
which are grouped within \emph{w} Sph1 were reported before in several
Australian ant species (MLST ST = 54, 19, 478 and 112) (Russell 2012).
Based on the MLST database, it appears that this strain group has a
cosmopolitan distribution (Oceania, North America, Europe, Asia and
South Africa) and has already been reported in various insect groups
(e.g., ST 19 in Coleoptera, Hymenoptera, Lepidoptera and Orthoptera). By
contrast, some of the scale insect species are infected with
unique\emph{Wolbachia} strains that were not observed in other scale
insects or reported in any other insects (by searching both MLST and
GenBank on October 1\textsuperscript{st}2021), including infection of
\emph{Apiomorpha variabilis} with\emph{w} Aphi1 and co-infection of
\emph{Coccus hesperidium} with\emph{w} Coc1 and \emph{w} Coc2.

ML trees based on a 947 bp alignment of scale insect genes (including
COI, 28S and 18S), and based on 3065 bp aligned~\emph{Wolbachia} genes
(including MLST, wsp, and 16S) are shown in Figure 2. In addition, an
interactive figure of \emph{Wolbachia} sharing among all host species,
including associates, is provided in File S5. Evidence of numerous host
shifting events can be seen in these figures. Both the Parafit test
(ParaFitGlobal = 0.0008, \emph{P} -value = 0.27) and the PACo test
(m\textsuperscript{2}\textsubscript{XY} = 49.3897, \emph{P} -value =
0.2056) were non-significant. Therefore, there is no evidence of
phylogenetic congruence between \emph{Wolbachia} and their scale insect
hosts, in the sense that non-independent evolution of the two groups is
not supported by these tests.

Our \emph{Wolbachia} sharing models revealed that incorporating
phylogenetic distance substantially improved model fit (change in DIC =
-5.88), and had a significant effect in the model (P\textless{}0.0001).
The effect was highly non-linear, with high sharing probabilities at
high relatedness that quickly dropped to near zero at greater
phylogenetic distances (Figure 3). In contrast, incorporating geographic
home range overlap slightly improved model fit (change in DIC = -2.76),
but had no significant effect in the model (P=0.199). Inspecting the
shape of the effect was not revealing. Furthermore, there was no
significant effect of geographic distance between sampling locations
(change in DIC \textgreater{} -2). Therefore, we do not interpret this
effect as representing strong support for geographic effects
on\emph{Wolbachia} sharing.

In searching for \emph{Wolbachia} strains in pairs consisting of a scale
insect and its direct associate, we found the same
\emph{Wolbachia}strain groups in one out of five ants, one out of two
flies, and three out of seven wasps. In the ant-Eriococcus sp1 pair that
is co-infected with \emph{w} Sph4 and\emph{w} Apio2 strain groups, both
the ant and the scale insect are infected with the same \emph{w} Sph4
strain (\emph{w} Sph4.4), but different \emph{w} Apio2 strains (\emph{w}
Apio2.4 in the ant and\emph{w} Apio2.5 in the scale insect, which differ
by 4bp in gatB gene). The three wasp-scale insect pairs with similar
\emph{Wolbachia} strain groups have a single infection (wasp-
\emph{Ascelis schraderi} pair with\emph{w} Sph1.11, wasp-
\emph{Apiomorpha floralis} pair with \emph{w} Api 2.1 and
wasp-\emph{Lachnodius eucalypti} pair with \emph{w} Lac). The
fly-\emph{Icerya seychellarum} pair share identical strains of\emph{w}
Cal2.2. We did not find the same strain group in the single tested
moth-scale insect and two beetle-scale insect pairs. As in scale insects
overall, the most common strain group shared between scale insects and
associates is the \emph{w} Sph1 strain group (File S2).

\section*{Discussion}

{\label{discussion}}

\subsection*{Amplicon sequencing as a powerful method of Wolbachia strain
determination}

{\label{amplicon-sequencing-as-a-powerful-method-of-wolbachia-strain-determination}}

Strain determination is a key step in studying
\emph{Wolbachia}distribution and host-shifting among a given host group
that needs to be performed using an efficient method. Given that
infection with more than one \emph{Wolbachia} strain is common in
various arthropod groups (Werren et al. 1995; Perrot-Minnot et al. 1996;
Hiroki et al. 2004; Narita et al. 2007; Hou et al. 2020), strain
determination methods should be able to distinguish and identify strains
in both singly and multiply-infected samples. The traditional method of
using Sanger sequencing is not effective in dealing with co-infected
arthropod samples, and improvements such as using different primers and
cloning (Schuster 2008; Vo \& Jedlicka 2014)) are costlier and more
labour-intensive, and also have limitations (Van Borm et al. 2003;
Schuler et al. 2011). High-throughput whole genome sequencing (WGS)
would seem to be the most accurate available methodology for strain
identification, but this approach has its own difficulties (Bleidorn \&
Gerth 2018). First, given that \emph{Wolbachia} is not culturable, it is
challenging to obtain genetic material enriched for
\emph{Wolbachia}relative to host DNA, possibly resulting in low
sequencing depth. Second, even with high sequencing depth, assembling
\emph{Wolbachia}genomes can be difficult due to a high density of mobile
elements (Wang et al. 2019) and thus only draft genomes can be
recovered. Finally, the still relatively high costs of WGS make this
approach less applicable in large \emph{Wolbachia} surveys. Due to these
limitations, only 33\emph{Wolbachia} annotated whole genomes have been
publicly available on GenBank so far (as of October 2021). As suggested
by Bleidorn and Gerth (2018), instead of whole genomes, sequencing and
assembling a\emph{Wolbachia} draft genome is sufficient for strain
determination. However, many more draft genomes should be publicly
available first to provide a reliable reference bank for strain
determination. Although a draft genome can indeed be mapped to the
selective marker amplicons (e.g. MLST), generating such data for large
surveys is still time- and cost-intensive. To overcome these technical
obstacles, we suggest Illumina multi-target amplicon sequencing as a
middle-ground, efficient and affordable method that can be applied to
large surveys and is also capable of dealing with multiple infections.
In particular, the five\emph{Wolbachia} MLST genes along with \emph{wsp}
and 16S used in our study appear to be well suited to distinguish
between strains, as has also been shown in a recent comparative study of
available whole genomes of \emph{Wolbachia} (Wang et al. 2020).

\subsection*{Wolbachia diversity in scale
insects}

{\label{wolbachia-diversity-in-scale-insects}}

This study revealed that a substantial portion of tested scale insects
are infected with more than one strain of \emph{Wolbachia} (27\% double
and 5\% triple infected). We also found \emph{Wolbachia} multiple
infections in associate species (including wasps and ants), indicating
co-infection might be a common phenomenon in most of these insect
groups. However, it is important to caution that detecting a given
\emph{Wolbachia} strain in a given host is not conclusive evidence of a
stable infection, and laboratory assays should be conducted to ascertain
\emph{Wolbachia} maternal transmission and establishment within the host
population (Chrostek et al. 2017). Moreover, in the case of parasitoids
and predators, a detected strain may derive from their undigested prey
rather than the screened insect itself (Ross et al. 2020).
Unfortunately, laboratory rearing of collected samples is not feasible
for large \emph{Wolbachia}surveys such as the current study. Therefore,
any interpretation from this type of data should be treated with
caution.

Based on the MLST database (as of 31st August 2021), 24 strains with
complete MLST gene sequences had previously been reported from the
Australian fauna (https://pubmlst.org/organisms/wolbachia-spp). Here, we
report 63 new strains (belonging to 31 strain groups) for Australia,
including the first three Supergroup F strains in Australasia. Apart
from two strains (\emph{w} Sph4.1 = ST 289, and\emph{w} Cal = ST 357),
none of the strains in the current study were 100\% identical to any
registered in the MLST database. As our sequenced regions were slightly
(\textasciitilde{}5\%) smaller than the MLST amplicons available on MLST
online database, there is a possibility that the two strains that were
identical to the MLST profiles were different in the remaining part of
the gene fragments. We found \emph{w} Sph1 to be the most common and
widely distributed strain group in Australia (detected in seven scale
insects, four wasps and one ant species). Based on the phylogenetic tree
of all reported strains in the MLST database and the current study
strains, there are six registered strains (STs) within the \emph{w} Sph1
strain group (Figure S2). These strains seem to be globally distributed
across various insect orders. For example, one of the strains in this
group, registered as ST=19, has been reported in 16 different host
species belonging to four insect orders. This broad host range may be an
indicator of an extraordinary host-shifting ability of \emph{w} Sph1.
Mostly based on the number of infected host species, several
\emph{Wolbachia} strains have been reported with a similar ability
(e.g., HVR-2 in ants (Tolley et al. 2019), ST41 in Lepidoptera (Ilinsky
\& Kosterin 2017), and \emph{w} Hypera in weevils (Sanaei et al. 2019)).
Among all the superspreaders, \emph{w} Ri is one of the best-studied
\emph{Wolbachia} strain groups that has rapidly (within 14,000 years)
naturally infected five \emph{Drosophila} species (Turelli et al. 2018).
\emph{w} Ri can also be introduced to mosquitoes by transinfection,
corroborating this strain's potential to infect new host species (Fraser
et al. 2017). Compared to \emph{w} Ri, it seems that\emph{w} Sph1 has
been reported in a higher number of host species that are taxonomically
more diversified (belonging to various insect orders). Although the
\emph{w} Ri group has an extensive genomic diversity (Ishmael et al.
2009; Turelli et al. 2018), low variation has been observed within its
MLST profiles (https://pubmlst.org/organisms/wolbachia-spp). Four
strains have been reported in the \emph{w} Ri group and only one strain
(ST=17) has been reported in more than one species of \emph{Drosophila}
(based on MLST website as of 31st August 2021). Therefore, \emph{w} Sph1
might have a higher diversity than \emph{w} Ri and may therefore have a
potential to be artificially introduced to other insects for human
applications (e.g., controlling vector born disease). However,
transinfection studies are necessary to ascertain the host-shifting
ability of \emph{w} Sph1 in laboratory conditions.

\subsection*{Phylogenetic distance effect can explain
host-shifting}

{\label{phylogenetic-distance-effect-can-explain-host-shifting}}

As is typical of \emph{Wolbachia} infection in an arthropod family,
non-independence between the scale insect and
\emph{Wolbachia}phylogenetic trees were not statistically supported,
i.e. no signal of congruence was detected between them. Instead, the
current distribution of \emph{Wolbachia} in scale insects was most
likely shaped by host-shifting. Among many potential factors determining
host shifts, it seems that host phylogeny and geographic distributions
are two major players (Sanaei et al. 2021a). Combining data from 25
transinfection studies, Russell et al. (2009) showed that there is a
positive correlation between host phylogenetic relatedness and success
of the\emph{Wolbachia} transinfection. In addition, by focusing only on
a part of the host phylogenetic tree, several studies uncovered a
pattern of host-shifting among closely related species (Haine et al.
2005; Guz et al. 2012; Turelli et al. 2018). On the other hand, the
observation of identical \emph{Wolbachia} strains in species that live
in the same area points to a role of geography in host-shifting
(Kittayapong et al. 2003; Stahlhut et al. 2010; Morrow et al. 2014;
Gupta et al. 2021). The relative contributions of the host phylogenetic
and geographic distance effect on \emph{Wolbachia} host shifts are
poorly understood. Here, we tried to evaluate these two factors in
\emph{Wolbachia} host shifting by using a powerful statistical method.
The results of our GAMM indicate that host shifts in scale insects can
be mainly explained by the phylogenetic distance effect (host shifting
is more feasible between closely related species compared to distantly
related) (Figure 3). This result is in line with numerous examples of
finding the same\emph{Wolbachia} strain group in congeneric species
(e.g.,\emph{wHypera1} in the genus \emph{Hypera} (Coleoptera) (Sanaei et
al. 2019), \emph{w} Lev in the genus \emph{Lutzomyia} (Diptera) (Vivero
et al. 2017), ST19 in the genus \emph{Bicyclus} (Lepidoptera) (Duplouy
\& Brattstrom 2018)).

Horizontal transfer of parasites/symbionts among closely related species
can generate a phylogenetic signal similar to host-parasite
co-speciation (De Vienne et al. 2007). However, there is indirect
evidence advocating\emph{Wolbachia} sharing patterns in scale insects
that can be explained best by recent host-shifting. In contrast to
horizontal transmission which occurs rapidly, co-speciation happens in
an evolutionary timeframe which allows \emph{Wolbachia} genes to be
mutated. By investigating\emph{Wolbachia} infection in \emph{Nasonia}
species complex, it is estimated that \emph{Wolbachia} MLST genes
mutation rate is one third of their host nuclear genes (from nine single
copy nuclear regions) (Raychoudhury et al. 2009). Although this ratio
can be slightly different among various host species and
\emph{Wolbachia} strains (see also Conner et al. (2017)), it can be
adopted as a tool to distinguish co-diversifications from recent host
shifting. Given that the lowest pairwise distance between host species
nuclear genes that we have in our dataset is 2\%, in the case of
\emph{Wolbachia} co-speciation, at least 17 bp differences (out of 2608
bp) should be observed between two closely related strains. We infer
host-shift events based on sharing either identical strains or identical
strain groups (which includes strains with up to only 5bp differences
across all \emph{Wolbachia}amplicons) (File S1). In addition, in 73\% of
determined host-shift events in scale insects, shared \emph{Wolbachia}
strains have identical wsp which is less conserved compared to the MLST
genes.

Interestingly, the significant impact of host phylogenetic distance on
\emph{Wolbachia} strain group sharing was not mirrored by statistically
significant tests for host and\emph{Wolbachia} phylogenetic tree
non-independence (Parafit and Paco), as might have been expected under
host shifting with a PDE. We speculate that these tests for tree
independence are not sufficiently powerful to detect minor departures
from tree independence as caused by host shifting under a PDE. It would
be useful to verify this using computer simulations of host shifting
with PDE (e.g., de Vienne et al. (2007); Engelstadter \& Fortuna
(2019)).

Hybridisation between closely related species may lead to introgression
of\emph{Wolbachia} into a new species by vertical transmission. This has
been demonstrated in the~\emph{Nasonia} ~species complex (Raychoudhury
et al. 2009) and some species of~\emph{Drosophila} (Turelli et al. 2018;
Cooper et al. 2019). Introgression~can be considered a special case of
host shifting (referred to as~`hybridisation-mediated host shifts' by
Sanaei et al., (2021a), but also has similarities to cospeciation. Since
this type of host shifting can only occur between closely related
species, it is expected to produce a PDE signal.~We believe, however,
that hybridization is unlikely to explain the observed patterns of
strain group sharing in our dataset. There are only two con-generic
species in our dataset that share the same \emph{Wolbachia} strain
(\emph{Cystococcus pomiformis} /\emph{C. echiniformis}
and\emph{Eriococcus sp1} /\emph{E. sp2} ), and hybridisation between
species from different genera seems unlikely. Given that
\emph{Wolbachia} and mitochondria are co-transmitted, introgression of
\emph{Wolbachia} would lead to mtDNA hitchhiking and hence be expected
to leave a signature of similar mtDNA sequences (Jiggins 2003; Cooper et
al. 2019; Miyata et al. 2020). By contrast, none of our congeneric
species have very similar COI sequences. (The difference between
\emph{C. pomiformis and C. echiniformis} is 6.5\%, and the difference
between \emph{E. sp1 and E. sp2} is 3\%).

\emph{No signal of geographic home range on Wolbachia strain sharing}

As a first step in the host shift process, \emph{Wolbachia} need to
physically reach the recipient host species, which requires direct or
indirect species interactions. Therefore, it is expected that one should
observe host shifting among species with an overlap in geographic
distribution. However, our model indicates that the geographic home
range of scale insect species has no significant contribution
to\emph{Wolbachia} sharing (Figure 3). This finding may firstly relate
to the age of infection. Estimation of the \emph{Wolbachia} infection
age and consequently the intervals of host-shift events is
controversial, with vastly different estimates across different case
studies being reported (from a few thousand (Turelli et al. 2018; Cooper
et al. 2019) to nine million years (Bailly-Bechet et al. 2017), see also
Sanaei et al. (2021a)). If the changes in the host geographic
distribution occur faster than \emph{Wolbachia} host shift events, the
current geographic distribution may not be able to explain host-shift
events (and thus, we would need to reconstruct the historical home
range). In addition, the geographic distribution of a given species is
not necessarily representative of the realised niche of that species,
including ecological connectivity (Pulliam 2000; Kearney 2006; Peterson
\& Soberon 2012). Therefore, two species may have the same geographic
distribution but have no direct or even indirect physical interactions
(e.g., via sharing foods or other resources). In that case, host
ecological niche may be a better tool to explain \emph{Wolbachia}
host-shifting. However, ecological niches are technically harder to
measure, especially when trying to account for ecological interactions.

\subsection*{Role of scale insect associate species in Wolbachia host
shifts}

{\label{role-of-scale-insect-associate-species-in-wolbachia-host-shifts}}

Another possible reason why host geographic distance effect has such a
weak effect on \emph{Wolbachia}sharing is host shifting via ecological
vectors. Such vectors can carry on the infection, either temporarily or
permanently, and transmit it to a recipient species at a distant
geographic location from the donor species. From prey-predator
(Johanowicz \& Hoy 1996; Le Clec'h et al. 2013) to host-parasitoid
(Vavre et al. 1999; Kageyama et al. 2010; Tzuri et al. 2020) and
trophallaxis interactions (Ramalho \& Moreau 2020), there are several
direct and indirect ecological pathways which can be routes of
\emph{Wolbachia} transfer. Intimacy of direct physical interaction
between ants and scale insects may provide a route of microbial
exchange, as seen in other hemipteran groups (Pringle \& Moreau 2017;
Ivens et al. 2018). Moreover, Gruwell et al., (2007) found plausible
routes of \emph{Cardinium} horizontal transmission between armoured
scale insects and their associate parasitoids.\emph{Wolbachia} may also
utilize these ecological routes to spread within scale insect
communities.

In a previous study, a positive correlation between
\emph{Wolbachia}infection in scale insects and their associates
indicated that ants may play a role in host-shifting (Sanaei et al.
2021b). Here, we found that only one out of five case of infected
ant-scale insect pairs shares the same \emph{Wolbachia} strain. While we
do not have enough statistical power to test which route of transfer is
the most common and in which directions these transfers take place, our
data support the hypothesis that the associates tested in the current
study play a role in host shifting. Although positive correlations were
not previously observed between infection of scale insects and their
associates (Sanaei et al. 2021b), here we observed sharing similar
\emph{Wolbachia} strains between pairs of scale insect and not only ants
but also wasps, and flies (File S3). In addition, infection by the
super-spreader stain ``\emph{w} Sph1'' of several species of scale
insects, ants, and wasps is another source of evidence for a substantial
contribution of associate species in \emph{Wolbachia} host-shifting in
scale insects.

This study provided the first insight into \emph{Wolbachia} strain
diversity in scale insects, revealed a high portion of co-infected
samples and detected \emph{w} Sph1 as one the most common strain
of\emph{Wolbachia} in scale insects. We also found that the host
phylogenetic distance effect plays a critical role in host shifting in
scale insects. In future studies, the methodology suggested by this
study could be applied to a larger data set to detect the factors
influencing host-shifting on the global perspective.

\section*{Acknowledgements}

{\label{acknowledgements}}

We are grateful to Sylvain Charlat, Benjamin Normark and Anne Duplouy
for reading and reviewing the manuscript. We thank Juan Antonio Balbuena
and Xavier Didelot for their technical advice on, respectively, PACo and
ClonalFrame analysis. We also thank John Dwyer and Andrew Letten for
providing constructive feedbacks on the statistical models. Penelope
Mills kindly allowed us to access her scale insect database which is
much appreciated.

\section*{Conflict of interest}

{\label{conflict-of-interest}}

There is no conflict of interest.

\textbf{Data Accessibility Statement}

All the raw sequences are available on public databases (GenBank
BioSample: SAMN21874044-SAMN21874118 and SRA: SRR16095177-SRR16095251)
as described in File S1. In addition, we provided all generated metadata
in the form of supplementary materials.

\section*{Authors Contribution}

{\label{authors-contribution}}

ES, YPL and LGC collected scale insects, their associates as well as the
information of the geographic distribution of species and performed DNA
extractions. ES designed the primers, performed the PCRs and prepared
the samples for Illumina multi-target amplicon sequencing. ES, YKY and
JE developed the bioinformatics pipeline. ES built the phylogeny and
conducted the analysis regarding strain determination. GFA developed the
mathematical model. ES wrote the manuscript under the supervision of LGC
and JE.

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\textbf{Figure legends}

Figure 1. ML phylogenetic tree of the strain groups based on
concatenated sequences of seven \emph{Wolbachia} genes (16S, MLST and
wsp). Black circles are nodes with bootstrap value \textgreater{} 95\%.

Figure 2. Phylogenetic tree of \emph{Wolbachia} strain groups (top) and
their scale insect hosts (left), with the black squares in the matrix
indicating which host species are infected with which
\emph{Wolbachia}strain group(s). Colors in the phylogenies represent
supergroups in\emph{Wolbachia} and host families, respectively. Host
species are represented by codes (for full species names please refer to
File S2).

Figure 3. The effect of host phylogenetic distance (left) and home range
overlap (right) on~\emph{Wolbachia} ~sharing probability. Points
represent pairs of host species that either share (1) or do not share
(0) the same\emph{Wolbachia} strain; the thick blue line represents the
mean predicted effect from our~\emph{Wolbachia} ~sharing GAMMs. The
light blue ribbon represents the 95\% confidence intervals of that
effect. Phylogenetic distance is the sum of the branch lengths, i.e. the
estimated numbers of substitutions derived from the Maximum Likelihood
tree. Sharing decreased with both phylogenetic and geographic distance,
but the phylogenetic effect was significant and much steeper.\selectlanguage{english}
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