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Do Insect Host Diet and Taxonomy Influence Their Gut Bacterial Communities?
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D.R. Colman, E.C. Toolson, C.D. Takacs-Vesbach
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Department of Biology, University of New Mexico, 167 Castetter Hall MSC02 2020, 1
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University of New Mexico, Albuquerque, NM 87131-0001
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Keywords: Insect Microbial Community, Intestinal Microbiota, Gut Ecology, Host-microbe
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Interactions
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Corresponding Author: Cristina D. Takacs-Vesbach
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Cristina D. Takacs-Vesbach
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UNM Biology Department
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MSC03 2020
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1 University of New Mexico
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Albuquerque, NM 87131-0001
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Fax: (505) 277 - 3418
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cvesbach@unm.edu
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Abstract
Many insects contain diverse gut microbial communities. While a majority of studies
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have focused on a single or small group of species, comparative studies of phylogenetically
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diverse hosts are necessary to understand the association of intestinal communities and their
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hosts. In this study, we tested the hypotheses that 1) host diet and 2) host phylogeny influences
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insect intestinal community composition. We used published 16S rRNA gene sequence data for
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58 insect species in addition to four beetle species sampled from the Sevilleta National Wildlife
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Refuge to test these hypotheses. Overall, gut bacterial species richness in these insects was lower
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than what has been reported for mammals and other vertebrates. Xylophagous termites harbored
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the richest bacterial gut flora (116.8 species level OTUs / sample + 67.2), while bees and wasps
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harbored the least rich bacterial communities (12.7 species level OTUs/sample + 5.8). We found
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evidence to support our hypotheses that host diet and taxonomy influence insect gut bacterial
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communities (p < 0.001 for both). However, host orders and diet guilds were variable in
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community membership conformity. Hymenopteran, termite and lepidopteran bacterial
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communities were significantly similar within individuals from those orders (p < 0.01). Bacterial
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communities from xylophagous insects (live tree and decayed wood groups), detritivores,
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herbivores and pollenivores were all similar within their guilds (p < 0.01). Our analysis suggests
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insects with a diet reliant on cellulolysis maintain more similar bacterial communities than other
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guilds. Differences in community variation among dietary guilds and insect orders indicates that
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neither are universally important in determining gut microbial compositions. Our analysis
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provides a baseline comparison of insect gut bacterial communities from which to test further
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hypotheses concerning proximate and ultimate causes of these associations.
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Introduction
Intestinal tracts harbor rich communities of microorganisms (Dillon & Dillon 2004;
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Hongoh 2010; Ley et al. 2006). A single insect gut can harbor 107-109 prokaryotic cells mL-1 of
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gut fluid (Broderick et al. 2004; Egert et al. 2005; Hongoh et al. 2006a). Intestinal microbes can
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participate in many relationships with their hosts, though many are nutritional commensals or
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symbionts. Nutritional symbionts can aid nitrogen cycling in Tetraponera ants among others,
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lignocellulose metabolism in termites, and may facilitate granivory in carabid beetles (Breznak
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& Brune 1994; Lundgren & Lehman 2010; van Borm et al. 2002). While many studies have
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described insect gut microbial communities, a synthesis explaining the dynamics and causes of
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variation between and among different insect groups is still lacking. A further understanding of
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variation between insect hosts could potentially reveal insights into the proximate and ultimate
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causes of these associations. In addition, understanding relationship dynamics broadly across
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insects may aid efforts in biocontrol of insect pests, which is a focus of much insect gut
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microbiology (Broderick et al. 2004; Geib et al. 2009; Lundgren et al. 2007; Vasanthakumar et
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al. 2006).
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There are likely many factors influencing insect gut communities. Diet, pH, host-
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specificity (e.g. coevolutionary effects), life stage, and host environment can all influence
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community structure (Behar et al. 2008; Hongoh et al. 2005; Mohr & Tebbe 2006; Santo
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Domingo et al. 1998; Schmitt-Wagner et al. 2003). These different factors are not necessarily
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exclusive of one another, but there is strong evidence that the diet and taxonomy of the host can
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strongly affect an organism’s gut microbial community.
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Experimental evidence suggests that altering a host’s diet can not only change the
metabolic functioning of gut communities, but could also change the community structure
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(Broderick et al. 2004; Kane & Breznak 1991; Santo Domingo et al. 1998). One study of the
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fungus-growing termite Macrotermes gilvus indicated a strong effect of host diet on intraspecies
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gut community variation (Hongoh et al. 2006b). Termites, as a whole, have different domain
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level gut microbiota compositions which is coincident with dietary differences (Brauman et al.
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2001). In addition, mammals of similar diet share more similar gut communities, though the
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similarity of communities could also be explained by host taxonomy (Ley et al. 2008).
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Of the few studies that have compared disparate hosts by diet, there has been little
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evidence to exclude effects resulting from coevolutionary processes. While domain level
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microbial compositions are coincident with dietary differences in termites, they also reflect an
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influence of their host’s taxonomy (Brauman et al. 2001). Additionally, termite gut communities
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have stronger host-specificity than would be expected if diet were solely influencing community
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structure (Hongoh et al. 2005). A similar result was reported for mammalian gut microbiota,
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where similarities were significantly associated with host taxonomy and the clustering of gut
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communities reflected the hosts’ phylogeny (Ley et al. 2008). Diet may be inexorably linked to
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host taxonomy due to diet driven evolution of hosts. Thus, the relative influence of diet and
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taxonomy on host gut communities is yet unclear.
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Our study compared the bacterial community composition (based on 16S rRNA genes)
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associated with the intestinal tracts of a wide diversity of insects to test two hypotheses: 1) host
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diet and 2) host phylogeny is related to community composition. We found a wide variation in
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the bacterial species level richness as well as bacterial community similarity within diet guilds
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and host orders. The results suggest that gut microbial community dynamics may be dependent
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on the association with the host as well as the nature of the host’s diet.
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Materials & Methods
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Our study is largely a meta-analysis of published reports comprising 86 bacterial
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communities from 62 insect species representing seven taxonomic orders and nine diet types.
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We also included new data from four coleopteran species that represented three diet types to
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specifically test the effect of diet type within taxonomic order. Published bacterial community
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16S rRNA datasets were downloaded from Genbank. Datasets were selected to meet three
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criteria: 1) comparable methods were used to obtain 16S rRNA gene sequence data (clone library
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based studies), 2) the entire community dataset was publicly available and 3) sufficient host
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dietary information was given in the publishing paper. If a study dissected intestines by hindgut,
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midgut or lumen, the data were combined to represent the entire intestinal tract of the organism
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so that datasets were comparable across all published samples. Community hosts were classified
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as belonging to one of nine general feeding strategies: detritivorous, filter-feeding,
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hematophagous/nectarivorous, herbivorous (foliage and roots), omnivorous, pollenivorous,
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predacious, live tree xylophagy (phloem, sapwood, bark), and dead or decaying wood
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xylophagy.
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The four coleopteran species we sampled to augment the study were collected from the
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Nunn Flats area of the Sevilleta National Wildlife Refuge, New Mexico, USA (34° 24´ 24.8' N,
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106° 36´ 20.5' W) in 2008. The Nunn Flats area is classified as a Chihuahan desert grass
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dominated ecosystem, and thus all insects collected were from the same ecosystem type. Three
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adult individuals were collected for each of the four species (n=12), which were chosen based on
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known or presumptive feeding strategies and presence at the time of sampling. Individual
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samples were immediately stored on ice in the field and frozen at -80oC upon return to the
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laboratory.
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Samples were thawed and dissected using sterile instruments and techniques. Cuts were
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made along the elytra of the individual and the entire intestinal tract (fore, mid and hindgut) was
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removed and stored in a sucrose lysis buffer (Giovannoni et al. 1990). Within 48 h, total
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community DNA was extracted from the intestinal sample by using a variation of the CTAB
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(Cetyl trimethylammonium bromide) extraction method with phenol/chloroform purification
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(Mitchell & Takacs-Vesbach 2008), modified to include a 30 second bead-beating step with 3
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mm glass beads to homogenize the sample tissue. The remaining beetle specimens were
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preserved in 95% ethanol for taxonomic identification.
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Beetles were identified by comparison with voucher specimens deposited in the
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entomology collection at the Museum of Southwestern Biology. Two of the four species,
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Epicauta longicollis & Megetra cancellata, are foliovores of the Meloidae family (Cartron et al.
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2008; Toolson, pers. commun.). The third beetle, Calosoma peregrinator, is a predacious
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member of the family Carabidae (Burgess & Collins 1917). The fourth species, Gonasida
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inferna, is a beetle of the tribe Pimeliinae with generalist feeding strategies like other members
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of the Tenebrionidae family (Sanchez-Pinero & Gomez 1995; Thomas 1984).
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Genetic Analysis
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Bacterial 16S rRNA gene sequences were amplified from DNA extracted from the Nunn
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Flats beetles in triplicate PCRs using the universal bacterial-specific primers, 8F forward primer:
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5’GTTTACCTTGTTACGACTT 3’ (Liu et al. 1997) and 1391R reverse primer: 5’
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GACGGGCGGTGTGTRCA 3’ (Lane et al. 1985). PCR was performed in 50 µl reactions
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containing 5 µl 10X buffer (Promega Buffer B w/ 1.5 mM MgCl2), 12.5 mM of each dNTP
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(BioLine USA Inc.), 20 pmol of both the forward and reverse primers, 2.5 units of Taq
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polymerase (Promega), 3 µl of 2% (w/v) bovine serum albumin and approximately 150 ng of
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DNA. The PCR thermal cycling program consisted of 30 s at 94oC, 30 s at 50oC, and 90 s at
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72oC for a total of 30 cycles and was performed on an ABI GeneAmp 2700 (Applied Biosystems
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Inc.). Replicate PCR products were combined and the 16S rRNA gene amplicons were spin
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purified using a DNA purification kit (Mo Bio Laboratories). Amplified 16S rRNA genes were
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ligated and cloned using a TOPO TA cloning kit (Invitrogen corp.). Ninety-six cloned inserts
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from each individual beetle were randomly chosen for sequencing using the BigDye terminator
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cycle sequencing kit (PE Applied Biosystems) with the 8F forward primer on an ABI 3130x
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genetic analyzer (PE Applied Biosystems). Community 16S rRNA gene libraries for each insect
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species were named as follows: MEG (M. cancellata), EPI (E. longicollis), GON (G. inferna)
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and CAL (C. peregrinator).
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DNA sequences were edited using CodonCode Aligner (CodonCode Corporation). DNA
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sequences with less than 400 Phred20 bases were not used for further analysis. The remaining
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DNA sequences were aligned using the NAST alignment Tool (DeSantis et al. 2006) of
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Greengenes (http://greengenes.lbl.gov). The 16S rRNA gene sequences were checked for
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evidence of chimeric properties using the Bellerophon V 3.0 tool (Huber et al. 2004), and
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suspected chimeras were not included in further analysis. Taxonomic classification of sequences
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was conducted with the Bayesian classifier function in mothur (Schloss et al. 2009). Sequences
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with closest identity matches to plant chloroplasts were excluded, as they were likely remnants
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of undigested plant tissue, and were not useful in describing the intestinal communities.
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Community Richness & Composition
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Published 16S rRNA gene sequences were also aligned with the NAST alignment tool
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and classified in mothur. The published 16S rRNA gene dataset was combined with the 16S
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rRNA gene sequences originally reported here for further analysis. Taxonomic diversity was
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assessed with operational taxonomic unit (OTU) analysis in mothur (Schloss et al. 2009). A
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report of the fully aligned 16S rRNA gene dataset was used to find a region of the alignment
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covering 400 base pairs that was common to the greatest percentage of sequences. Sequences
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with less than 300 base pairs in the region encompassed by E. coli base positions 221-621 were
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screened from the analysis to ensure every sequence comparison utilized at least 200 base pairs.
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Less than 10% of sequences were filtered from the dataset, with most belonging to two
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individual studies. Samples with > 50% of 16S rRNA gene sequences filtered out were not used
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further in OTU analysis. A distance matrix was created in mothur with the remaining 16S rRNA
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gene sequences, which were then clustered into OTUs. Bacterial taxonomic diversity is reported
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as the number of observed OTUs with 3%, 10%, and 20% nucleotide dissimilarity, which
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correspond to commonly used criteria for species, family/class and phylum level, respectively, of
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the 16S rRNA gene (Schloss & Handelsman 2004).
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Comparison of Community Membership in Relation to Host Diet & Host Phylogeny
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The 16S rRNA gene sequence datasets were imported into ARB (Ludwig et al. 2004) and
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then parsimony added to a phylogenetic tree consisting of the entire aligned Greengenes 16S
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rRNA gene database
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(http://greengenes.lbl.gov/Download/Sequence_Data/Arb_databases/greengenes236469.arb.gz,
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downloaded on March 25th, 2009). The tree was exported for clustering and principal coordinate
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analysis (PCoA) in Unifrac using the Unifrac metric of phylogenetic similarity between samples
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(Lozupone & Knight 2005). The Unifrac metric distance matrix was used to assess hypotheses
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concerning the source of variation in the distance matrix (e.g. host diet and host taxonomy) using
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permutational MANOVA (Anderson 2001) as implemented in vegan (Oksanen et al. 2011). In
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addition, perMANOVA was used to test the significance of similarity among gut communities
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from hosts of the same order or diet category.
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Congruence of Host Phylogeny and Community Clustering
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A cladogram was constructed to represent the phylogeny of the gut community insect
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hosts used in this analysis. Because genetic data were not available for every host, a cladogram
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was created to represent simple bifurcations of hosts, as has already been established by
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published phylogenies (Fig. S1). Recent phylogenies were used to reconstruct an overall
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topology based on the following studies of taxa: all of Insecta (Kjer 2004), Isoptera (Legendre et
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al. 2008), Coleoptera (Hunt et al. 2007), Carabidae (Maddison et al. 1999), Harpalinae (Ober &
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Maddison 2008), Cerambycidae (Marvaldi et al. 2009), Lepidoptera (Kristensen et al. 2007),
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Diptera (Yeates & Wiegmann 1999), Culicidae (Harbach 2007), Apoidea (Danforth et al. 2006),
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Apidae (Cardinal et al. 2010) and the Apis genus (Arias & Sheppard 2005). Hosts were excluded
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from this cladogram when taxonomic information was not included in the publishing paper (e.g.
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species name) that could discern clade relationships to other taxa.
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TreeMap v 1.0 (Page 1994) was used to test the significance of topological congruence
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between the Unifrac clustering of gut community samples and the phylogeny of their respective
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hosts (Fig. S1). TreeMap tests the presence of congruence by the use of component analysis to
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map possible evolutionary histories of a host tree and a hypothesized host-dependent tree.
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Cospeciation, duplication, and host-switching are used to explain patterns of congruence
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between host-parasite pairs. The host dependent tree was randomized over 1000 iterations to give
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a distribution of cospeciation events that were assessed at each iteration. If the intestinal
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community clustergram is congruent with the host phylogeny, then the majority of the
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randomizations will have had less cospeciation events than what can be deduced from the actual
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host-host dependent tree reconstruction (Page 1996).
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Nucleotide Sequence Accession Numbers
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Partial 16S rRNA gene sequences for the beetle species determined in this study were
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submitted to Genbank under the accession numbers: HM920248-HM921042.
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Results
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We curated a dataset of 13,295 16S rRNA gene sequences, including the 795 DNA
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sequences we recovered from the four beetle species reported here, to compare gut communities
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among 62 insect species. The dataset represents the combination of 84 gut community samples
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from 36 published reports. Insect species belonged to 27 different families in seven different
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orders and included diverse dietary strategies that were consistent with nine general categories of
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diet (Table 1). The 795 16S rRNA gene sequences reported here were obtained from 12 beetles
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collected from the Sevilleta National Wildlife Refuge: three libraries each from M. cancellata, E.
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longicollis, G. inferna and C. peregrinator. Eight bacterial phyla were detected among the four
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species. The majority of the bacteria detected were of the phylum Firmicutes (56.98%) or the γ
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subdivision of Proteobacteria (29.94%). The herbivore M. cancellata harbored 16S rRNA gene
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sequences with the broadest taxonomic range (six of the eight phyla detected), whereas the
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omnivore G. inferna only contained two different phyla.
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Community Richness & Composition
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Twenty-five phyla of bacteria were represented in the curated dataset and there were
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2,202 species level OTUs. The average level of species level OTUs differed by an order of
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magnitude when the DNA sequences were grouped by host diet and taxa (12.7 + 5.8 – 116.8 +
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67.2 OTUs/sample) and within group variation was also high in many of the categories (Fig. 1).
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The lowest level of species level richness was consistently found among members of the order
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Hymenoptera (bees and wasps, 12.7 + 5.8 OTUs/sample), whereas the highest average species
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level richness was found among the Isoptera (termites, 98.1 + 62.7 OTUs/sample).
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Dead/decaying wood xylophagous termites had average levels of species richness (116.8 + 67.2
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OTUs/sample) over two times as high as detritivores (52.7 + 31.9 OTUs/sample), which were the
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next richest of the diet guilds. Live wood xylophagous beetles contained the second lowest
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richness (15.6 + 10.8 OTUs/sample), which was second to exclusively hymenopteran
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pollenivores (12.8 + 6.0 OTUs/sample).
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The taxonomic composition of OTU richness differed among host diet and orders. The
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richness of termite and detritivore gut communities is evident in the widespread, deep
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phylogenetic diversity they contain (Fig. 2). Several bacterial taxa were found almost exclusively
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in termites, including the phyla Spirochaetes and Synergistetes as well as the orders
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Deltaproteobacteria, Clostridiales, and Bacteroidales. While the Alphaproteobacteria and
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Betaproteobacteria are present in many of the samples studied here, they appear most prevalent
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in the hymenopteran samples that clustered together. The Bacillales/Lactobacillales and
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Gammaproteobacteria were prevalent in the other insect samples outside of the hymenopteran or
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termite/detritivore clusters.
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Comparison of Community Membership in Relation to Host Diet & Host Phylogeny
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Based on the Unifrac distance metric, gut communities clustered by host diet and host
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taxonomy (Fig. 2) and both variables contributed significantly to gut community composition (p
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< 0.001). However, similarities within groups varied by diet and host order (Table 2). All
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hymenopteran gut samples clustered exclusively together, regardless of their diet. The similarity
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of hymenopteran samples to one another was supported statistically (p < 0.001). Within the
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hymenoptera cluster, seven of eight samples from the genera Apis and Bombus clustered
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together, which is consistent with their hosts’ monophyly. Termite communities also clustered
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together: 15 of 17 termite samples clustered closely and contained similar communities (p <
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0.001). Lower wood-feeding termites (Rhinotermitidae) clustered separately from higher wood-
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feeding termites (Termitidae). Bacterial communities of higher termites that are principally
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detritivorous formed groups with other communities from non-termite detritivores and one root-
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feeding herbivore, Melolontha melolontha. Higher wood-feeding termites, Microcerotermes sp.
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and Nasutitermes sp., also clustered together exclusive of other members of the Termitidae
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family that are detritivorous. The topology of the gut community clustergram was not congruent
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with the overall phylogeny of the insect hosts (p >> 0.05).
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Bacterial communities from the orders Coleoptera and Diptera did not cluster by order
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and varied within their respective groups (p > 0.05 for both orders). Diet was related to
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community composition for some of the samples within these two orders. Live tree xylophagous
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coleopteran bacterial communities were similar to each other (p < 0.01). In addition,
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perMANOVA supported the clustering of detritivores (p < 0.001), which included the
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detritivorous termites, two detritus feeding dipterans, and the beetle Pachnoda ephippiata.
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Communities from herbivorous hosts, which included members of Lepidoptera, Coleoptera and
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Heteroptera were also similar to each other (p < 0.05). Similarities in communities from
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predatory, omnivorous or hemataphagous/nectarivorous hosts were not supported (p > 0.05).
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Communities from these dietary types also did not cluster together (Fig. 2).
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PCoA plots using Unifrac distances recapitulated the uniqueness of hymenopterans and
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termites among the insects analyzed here (Fig. 3b). The clustering of detritivores was also
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supported, with all but two house fly samples forming a loose cluster. Of the four carabid beetle
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gut communities used in this study, PCoA indicated close clustering of the two primarily
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carnivorous species, Calosoma peregrinator and Poecilus chalcites, which were separated from
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the two primarily granivorous carabids, Anisodactylus sanctaecrucis and Harpalus
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pensylvanicus. Across all predators, clustering was again not evident.
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Discussion
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The results reported here demonstrate a clear influence of host diet and host taxonomy on
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insect gut bacterial communities. Of the different host orders analyzed, hymenopterans, termites,
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and to a lesser extent, lepidopterans were significantly similar within their groups (Table 2; Fig.
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2). Our analyses confirm that bees and wasps do harbor gut bacterial communities that may be
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unique among insects in levels of richness and community membership. The original analysis of
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nearly all of the hymenopterans used here suggested a high degree of bacterial similarity between
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the genera Apis and Bombus, which our cluster analysis supports (Martinson et al. 2011). It was
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also suggested that A. mellifera gut communities may be unique among bees, which is supported
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here by the clustering of three A. mellifera samples from two independent studies (Babendreier et
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al. 2007; Martinson et al. 2011). The Apis and Bombus clustering, together with the clustering of
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all hymenopterans regardless of diet, provide further evidence of strong host specificity of
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bacterial communities throughout the evolution of wasps and bees. The behaviors, increased
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antimicrobial defenses and other immune functions that evolved for sociality may contribute to
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the uniqueness of Apinae bee gut communities among other wasps and bees (Martinson et al.
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2011; Mohr & Tebbe 2006; Stow et al. 2007). While Martinson et al. (2011) found no evidence
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to suggest the presence of ancestrally derived lineages in A. mellifera, our analysis demonstrates
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that there is some characteristic, aside from diet, of bees and wasps that has maintained their
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highly unique, and simple microbiota throughout their evolution.
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Termite gut communities were also highly similar to one another, although the effect of
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diet was more apparent than in bees and wasps. Congenerics generally clustered exclusively
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together, supporting the hypothesis of coevolution between closely related termite taxa and their
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microbial communities (Hongoh et al. 2005). In addition, the lower wood-feeding termites
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(Rhinotermitidae) clustered separately from the higher termites (Termitidae). Within the
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Termitidae clusters, wood-feeding higher termites (Microcerotermes sp. and Nasutitermes sp.)
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clustered exclusive from the rest of the detritivorous higher termites suggesting a dietary
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influence nested within host evolution derived effects. These results are in agreement with a
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distinction between Rhinotermitidae and Termitidae domain level gut microbiota compositions,
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as well as a further distinction between the wood-feeding and detritivorous higher termites
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(Brauman et al. 2001). We note that the xylophagous termite gut communities are significantly
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similar to each other, while the detritivorous termites share significant similarities with other
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detritivorous insects from Coleoptera and Diptera. This likely reflects the maintenance of a
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specialized microbiota that is necessary for efficient lignocellulose metabolism, and thus survival
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in xylophagous termites (Breznak & Brune 1994). The clustering of the Scarabaeidae beetle
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Melolontha melolontha, a root-feeding herbivore, with detritivores may reflect host-specificity of
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gut communities in Scarabaeidae family beetles, which are primarily detritivorous (Egert et al.
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2005).
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Bacterial communities from coleopterans and dipterans were not consistent among their
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respective orders. The variation in community membership of these two groups can be partially
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explained by a diet-dependent effect. For example, the xylophagous coleopterans were
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significantly similar to each other within that dietary guild. This may suggest that this specialized
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dietary niche either requires a distinct microbiota for nutritional supplementing of the host, or
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that the metabolites present as a result of this diet selects for a similar assemblage of microbial
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community. It’s unlikely that live tree xylophagous feeding requires a distinct microbiota as may
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be the case with decayed wood feeding termites because there is more variation in gut
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community composition of these hosts (Fig. 3a). Previous evidence also indicates loose
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affiliations between live tree xylophagous beetles and their microbiota at the intra and
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interspecific levels of host comparison (Geib et al. 2009; Grunwald et al. 2010; Schloss et al.
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2006). The average level of diversity in live tree xylophagous beetles was lower than that of
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decayed wood feeding termites (Fig. 1b), which also indicates disparate host-microbiota
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dynamics between the two groups. This difference in community dynamics may be partially
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explained by the mechanisms of cellulose digestion, which differs between beetles & termites.
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Cerambycid beetles depend on ingested fungal enzymes to degrade cellulose (Kukor et al. 1988),
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whereas lower wood-feeding termites employ cellulolytic protists (Cleveland 1924) and higher
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wood-feeding termites may rely on bacterial cellulolysis (Warnecke et al. 2007). The three
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approaches to cellulolysis likely have dramatic effects on metabolites produced during this
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process and thus on gut microbial community dynamics. Alternatively, feeding on live trees
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exposes the host and its internal microbiota to tree physiological responses (particularly
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chemical) (Hanover 1975; Morewood et al. 2004) which may further shape gut community
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dynamics in live tree xylophagous beetles and not dead wood xylophagous termites.
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In many cases it is difficult to separate the effects related to host diet from the effects
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derived from the host’s taxonomy. Animal speciation and divergence is often related to the
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filling of a new dietary niche, as is evident in phytophagous insects (Farrell 1998). In addition to
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the already discussed contrast between diet-variable hymenopterans and higher termites, several
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closely related insects with varying diet were analyzed and provide insight into dietary
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influences on gut communities. Two carnivorous carabid beetles clustered together (Fig. 3a), to
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the exclusion of the two omnivorous carabid beetles analyzed. This is particularly striking
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because one of the predatory species (P. chalcites) is more closely related to the omnivorous
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carabids than to the other predatory species (Maddison et al. 1999). Of the three fungus-growing
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termite samples from M. gilvus, the gut community from newly moulted workers appears to
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deviate from other detritivores and termites, and even from samples of more mature M. gilvus
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workers, which is concordant with a previous report (Hongoh et al. 2006b). This result may be
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an effect of diet, since the older M. gilvus consume more cellulosic substances compared to the
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newly moulted workers (Hongoh et al. 2006b). In contrast, the adult and larvae samples from
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Agrilus planipennis clustered together despite their different diets (foliage and cambium/phloem
346
feeding respectively) (Vasanthakumar et al. 2008).
347
The variation in gut community composition within hosts of similar diet guild may be
348
partially explained by the availability of metabolic niches within the gut due to the host’s food
349
source and/or the level of symbiosis between the gut community and the host. The lack of
350
similarity in predatory, omnivorous, and hematophagous/nectarivorous insect microbiota may be
351
due to the lack of complexity in ingested foods. In contrast to organisms that ingest materials that
352
are more refractory to decomposition, such as those that consume cellulose, these three types of
353
nutrition may require less metabolic specialization for degradation. Predatory diets consist
354
mostly of protein, and blood/nectar diets have large inputs of simple carbohydrates and/or
355
protein, which are degraded by a wide range of microorganisms. Alternatively, there may not be
356
a need for nutritional symbiosis with a distinct gut microbiota in these insect hosts due to the
357
ease of nutrient assimilation associated with these diets. Either of these explanations would be in
358
agreement with high community variation among most members of these diet guilds. Lastly,
16
359
some community variation may be explained by functional redundancy that is influenced by gut
360
colonization history. It has been demonstrated through gut community transplants between
361
taxonomically disparate hosts (zebrafish and mice) that transplant communities will resemble the
362
endemic community in function, while still being phylogenetically similar to its original host’s
363
natural community (Rawls et al. 2006), which suggests functional redundancy across disparate
364
gut microbiota. This mechanism may be driving variation in some of the diet guilds whose
365
communities were not significantly similar within their respective groups and may be more
366
prevalent in hosts that ingest materials degraded by a wide range of microorganisms.
367
We have reported evidence here that diet and host-specificity shape gut communities in
368
insects to varying extents depending on the nature of the diet and association with the host over
369
evolutionary time scales. While our analysis focused on the bacterial portion of gut communities,
370
it is perceivable that analyzing fungal, archaeal, and protozoan components may give additional
371
insights, as they are all known to be variously important contributors to gut dynamics (Breznak
372
& Brune 1994; Egert et al. 2003; Grunwald et al. 2010; Hongoh et al. 2006b). Our analysis also
373
contrasts gut richness in insects with that of extensively studied mammals, which appear to be
374
much more rich than what is generally reported here for insects (Ley et al. 2008; Ley et al.
375
2006). One limit to our metaanalysis is that the data were comprised from 37 separate studies,
376
and thus the coverage reported on the communities, and methods used to identify the microbiota
377
was likely different among the studies. We do however note that this analysis was robust enough
378
to confirm properties of insect gut communities that have been suggested previously.
379
In conclusion, the goal of this study was to investigate the emergent properties of insect
380
gut communities from the wealth of data that have been published in the previous 10 years. We
381
found evidence to support our hypotheses: that (i) host diet and (ii) host taxonomic status both
17
382
influence gut bacterial communities. The importance of each factor was not uniform across all
383
insect groups and each factor may be more important to some groups (e.g. taxonomy in
384
bees/wasps and diet in xylophagous insects). Our study contrasts with a previous report for
385
mammals that suggested ancestrally derived lineages were the only important factor involved in
386
shaping the microbiota in all mammals (Ley et al. 2008). In addition, the effect of diet on
387
microbiota composition appears to be more variable in insects than in mammals. The
388
mechanisms for these differences are beyond the scope of this analysis, but further exploration
389
may yield interesting insights into the fundamental nature of metazoan-microbiota dynamics.
390
This study also provides a baseline comparison of insect gut bacterial communities, which may
391
inform efforts in pest management. Our synthesis of published insect gut microbiota data also
392
furthers the study of insect gut microbiology, which has been suggested as necessary for progress
393
in this field (Hongoh et al. 2006b; Kaltenpoth 2011).
394
395
18
396
397
Acknowledgements
We would like to thank Dr. Sandra Brantley at the Museum of Southwest Biology for
398
assistance in insect identification and vouchering. We would also like to thank the 2008 Sevilleta
399
LTER REU participants, Jennifer Johnson, the Sevilleta National Wildlife Refuge administrative
400
team, and the U.S. Fish and Wildlife staff at Sevilleta for making this study possible. The
401
Takacs-Vesbach lab, in particular, Justine Hall, provided laboratory assistance and David Van
402
Horn provided editorial comments and assistance in figure preparation. A portion of the DNA
403
sequencing for this project was performed at the Genome Sequencing Center at Washington
404
University School of Medicine in St. Louis. Additional sequencing was performed at the
405
Molecular Biology Facility at the University of New Mexico, which is supported by NIH Grant
406
Number 1P20RR18754 from the Institute Development Award (IDeA) Program of the National
407
Center for Research Resources. This research was supported by the Undergraduate Nurturing
408
Opportunities program (NSF-DEB 0731350), the Sevilleta LTER REU program (NSF-DBI
409
0755059), and the Louis Stokes Alliance for Minority Participation Bridge to the Doctorate
410
program (NSF-EHR 0832947).
411
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Tables
642
Table 1. Study and diet information for insect species used in the metaanalysis.
Species
Family
Order
Reference
Diet
Label
Sequenc
es
Aedes aegypti
Culicidae
Diptera
Hematophagous/Nectarivorous
AA
57
Agapostemon
virescens
Agrilus
planipennis
(adult and
larvae)
Anisodactylus
sanctaecrucis
Anopheles
stephensi
(female, male
and larvae)
Anoplophora
glabripennis
Anoplophora
glabripennis
Halictidae
Hymenoptera
Pollenivorous
AV
273
Buprestidae
Coleoptera
(Gusmao et al.
2010)
(Martinson et al.
2011)
(Vasanthakumar
et al. 2008)
Herbivorous
Xylophagous : Live Trees
APa
APl
163
188
Carabidae
Coleoptera
Omnivorous
AS
3
Culicidae
Diptera
(Lundgren et al.
2007)
(Rani et al.
2009)
Hematophagous/Nectarivorous
Hematophagous/Nectarivorous
Filter Feeding
ASTf
ASTm
ASTl
138
141
95
Cerambycidae
Coleoptera
Xylophagous : Live Trees
AG
100
Cerambycidae
Coleoptera
(Schloss et al.
2006)
(Geib et al.
2009)
Apis
andreniformis
Apis dorsata
Apidae
Hymenoptera
Xylophagous : Live Trees
Artificial diet
Callery Pear
Horsechestunut
Pin Oak
Silver Maple
Sugar Maple
Sycamore Maple
Pollenivorous
AGgart
AGgcp
AGghc
AGgpo
AGgvm
AGggm
AGgym
AAN
99
126
27
122
147
196
75
78
Apidae
Hymenoptera
Pollenivorous
AD
72
Apis mellifera
Apidae
Hymenoptera
Pollenivorous
AM
38
Apis mellifera
Apidae
Hymenoptera
Pollenivorous
AMm
271
Apis mellifera
hive-wide
sampling
Bombus
impatiens
Bombus
sonorus
Bombus sp.
Apidae
Hymenoptera
Pollenivorous
AMmh
267
Apidae
Hymenoptera
Pollenivorous
BI
71
Apidae
Hymenoptera
Pollenivorous
BSO
78
Apidae
Hymenoptera
Pollenivorous
BS
80
Calliopsis
subalpinus
Calosoma
peregrinator
Caupolicana
yarrowi
Chalybion
Andrenidae
Hymenoptera
Pollenivorous
CS
282
Carabidae
Coleoptera
(Martinson et al.
2011)
(Martinson et al.
2011)
(Martinson et al.
2011)
(Martinson et al.
2011)
Present study
Predacious
CAL
248
Colletidae
Hymenoptera
Pollenivorous
CY
256
Sphecidae
Hymenoptera
(Martinson et al.
2011)
(Martinson et al.
Predacious
CC
204
(Martinson et al.
2011)
(Martinson et al.
2011)
(Babendreier et
al. 2007)
(Martinson et al.
2011)
(Martinson et al.
2011)
31
californicum
2011)
Colletes
inaequalis
Coptotermes
formosanus
Coptotermes
formosanus
Cubitermes
orthognathus
Culex
quinquefasciat
us
Dendroctonus
frontalis
(adult and
larvae)
Dendroctonus
valens
(adult and
larvae)
Diadasia
opuntia
Epicauta
longicollis
Gonasida
inferna
Halictus
patellatus
Harpalus
pensylvanicus
Hepialus
gonggaensis
Hesperapis
cockerelli
Hoplitis
biscutellae
Ips pini
Colletidae
Hymenoptera
(Martinson et al.
2011)
(Shinzato et al.
2005)
(Husseneder et
al. 2010)
(Schmitt-Wagner
et al. 2003)
(Pidiyar et al.
2004)
Pollenivorous
CI
301
Rhinotermitid
ae
Rhinotermitid
ae
Termitidae
Isoptera
Xylophagous : Dead Wood
CFs
51
Xylophagous : Dead Wood
CF
206
Detritivorous
CO
102
Culicidae
Diptera
Hematophagous/Nectarivorous
CQ
168
Curculionidae
Coleoptera
(Vasanthakumar
et al. 2006)
Xylophagous : Live Trees
DFa
DFl
99
91
Curculionidae
Coleoptera
(MoralesJimenez et al.
2009)
Xylophagous : Live Trees
DVa
DVl
32
8
Apidae
Hymenoptera
Pollenivorous
DO
347
Meloidae
Coleoptera
(Martinson et al.
2011)
Present study
Herbivorous
EPI
177
Tenebrionidae
Coleoptera
Present study
Omnivorous
GON
230
Halictidae
Hymenoptera
Pollenivorous
HPA
305
Carabidae
Coleoptera
Omnivorous
HP
6
Hepialidae
Lepidoptera
(Martinson et al.
2011)
(Lundgren et al.
2007)
(Yu et al. 2008)
Herbivorous
HG
35
Dasypodaidae
Hymenoptera
Pollenivorous
HC
349
Megachilidae
Hymenoptera
Pollenivorous
HB
182
Curculionidae
Coleoptera
Xylophagous : Live Trees
IP
77
Leptura rubra
Cerambycidae
Coleoptera
Xylophagous : Live Trees
LR
65
Lymantria
dispar
Lymantriidae
Lepidoptera
(Martinson et al.
2011)
(Martinson et al.
2011)
(Delalibera et al.
2007)
(Grunwald et al.
2010)
(Broderick et al.
2004)
Macrotermes
gilvus
(newly
moulted, old
and young
workers)
Macrotermes
michaelseni
Megachile
odontostoma
Termitidae
Isoptera
(Hongoh et al.
2006b)
LDart
LDasp
LDlar
LDwo
LDwil
MGnw
MGow
MGyw
10
11
16
7
10
26
88
82
Termitidae
Isoptera
Detritivorous
MMI
47
Megachilidae
Hymenoptera
(Mackenzie et al.
2007)
(Martinson et al.
2011)
Pollenivorous
MO
338
Isoptera
Isoptera
Herbivorous
Artificial Diet
Aspen Trees
Larch Trees
White Oak Trees
Willow Trees
Detritivorous
32
Megetra
cancellata
Melolontha
melolontha
Microceroter
mes sp. M1
Microceroter
mes sp. M2
Musca
domestica
(adult and
larvae)
Myrmeleon
mobilis
Nasutitermes
sp.
Nasutitermes
takasagoensis
Nezara
viridula
Odontotermes
formosanus
Pachnoda
ephippiata
Pachnoda
ephippiata
Paragia
vespiformis
Philanthus
gibbosus
Pieris rapae
Meloidae
Coleoptera
Present study
Herbivorous
MEG
140
Scarabaeidae
Coleoptera
Herbivorous
MME
164
Termitidae
Isoptera
Xylophagous : Dead Wood
MS1
217
Termitidae
Isoptera
Xylophagous : Dead Wood
MS2
128
Muscidae
Diptera
(Egert et al.
2005)
(Hongoh et al.
2005)
(Hongoh et al.
2005)
(Su et al. 2010)
Detritivorous
MDa
MDl
4
11
Myrmeleontid
ae
Termitidae
Neuroptera
Predacious
MM
34
Xylophagous : Dead Wood
NS
1252
Termitidae
Isoptera
Xylophagous : Dead Wood
NT
130
Pentatomidae
Heteroptera
Herbivorous
NV
21
Termitidae
Isoptera
Detritivorous
OF
56
Scarabaeidae
Coleoptera
Detritivorous
PEe
108
Scarabaeidae
Coleoptera
Detritivorous
PEa
87
Masaridae
Hymenoptera
Pollenivorous
PV
247
Crabronidae
Hymenoptera
Predacious
PG
360
Pieridae
Lepidoptera
Herbivorous
PR
1207
Plagionotus
arcuatus
Poecilus
chalcites
Rediviva
saetigera
Reticulitermes
santonensis
Reticulitermes
sp. R1
Reticulitermes
speratus
Reticulitermes
speratus
Rhagium
inquisitor
Saperda
vestita
Termes comis
Cerambycidae
Coleoptera
Xylophagous : Live Trees
PA
44
Carabidae
Coleoptera
Predacious
PC
45
Melittidae
Hymenoptera
Pollenivorous
RSA
333
Rhinotermitid
ae
Rhinotermitid
ae
Rhinotermitid
ae
Rhinotermitid
ae
Cerambycidae
Isoptera
Xylophagous : Dead Wood
RST
111
Xylophagous : Dead Wood
RSP
50
Xylophagous : Dead Wood
RS
270
Xylophagous : Dead Wood
RSh
108
Xylophagous : Live Trees
RI
85
Cerambycidae
Coleoptera
Xylophagous : Live Trees
SV
80
Termitidae
Isoptera
Detritivorous
TC
57
Tetropium
castaneum
Tipula
abdominalis
Cerambycidae
Coleoptera
Xylophagous : Live Trees
TCA
74
Tipulidae
Diptera
(Dunn & Stabb
2005)
(Warnecke et al.
2007)
(Hongoh et al.
2006a)
(Hirose et al.
2006)
(Shinzato et al.
2007)
(Egert et al.
2003)
(Andert et al.
2010)
(Martinson et al.
2011)
(Martinson et al.
2011)
(Robinson et al.
2010)
(Grunwald et al.
2010)
(Lehman et al.
2009)
(Martinson et al.
2011)
(Yang et al.
2005)
(Hongoh et al.
2005)
(Hongoh et al.
2003)
(Hongoh et al.
2005)
(Grunwald et al.
2010)
(Schloss et al.
2006)
(Thongaram et
al. 2005)
(Grunwald et al.
2010)
(Cook et al.
2007)
Detritivorous
TA
206
Isoptera
Isoptera
Isoptera
Isoptera
Coleoptera
33
Xylocopa
californica
Apidae
Hymenoptera
(Martinson et al.
2011)
Pollenivorous
XC
305
643
644
Table 2. Results of permutational multivariate analysis of variance (perMANOVA) by host diet
645
and orderab
Category
n
r2
Pr (>F)
Diet
86
0.48308
0.000999
Detritivorous
12
0.08562
0.000999
Hematophagous/Nectarivorous
4
0.00548
0.7712
Herbivorous
12
0.03608
0.02897
Omnivorous
3
0.01337
0.3137
Pollenivorous
20
0.1392
0.000999
Predacious
5
0.0142
0.2957
Xylophagous (primarily
10
0.21221
0.000999
19
0.05558
0.002997
86
0.46941
0.000999
Coleoptera
30
0.02416
0.1129
Diptera
8
0.00701
0.6324
Hymenoptera
22
0.16947
0.000999
Isoptera
17
0.3177
0.000999
Lepidoptera
7
0.06357
0.001998
dead/decaying woods)
Xylophagous (primarily live
trees)
Order
646
a
647
b
Groups with only one representative were excluded from this analysis
Pseudo F statistics are based on 1000 permutations
648
34
649
Figure Legends
650
Fig. 1. Average levels of bacterial OTU richness by host diet (1a) and order (1b). Bars indicate
651
standard deviation within groups. Only diet (1a) and order (1b) groups with more than one gut
652
community sample are shown.
653
Fig. 2. Clustergram of insect gut bacterial communities based on the Unifrac distance metric
654
with relative abundance of family/class level OTUs for each host. Clustergram leaves are colored
655
according to host diet. Scale indicates the relative abundance of OTUs within each individual
656
host. Hosts with a majority of 16S rRNA gene sequences aligned outside of E. coli base positions
657
221-621 were not used in OTU analysis.
658
Fig. 3. Insect gut bacterial communities ordinated by principal coordinates analysis (PCoA).
659
Samples are colored by host diet guild (2a) and host taxonomic order (2b). Asterisks in 2a denote
660
carabid beetles.
661
Fig. S1. TreeMap tanglegram showing the host cladogram on left, based on published
662
phylogenies, and the Unifrac gut community clustering on right. Red lines indicate community-
663
host relationships. Black dots at nodes are hypothesized ‘cospeciation’ events as inferred in the
664
intial TreeMap reconstruction.
35
665
Figures
666
Figure 1a.
667
36
668
Figure 1b.
669
670
37
671
Figure 2.
672
673
38
674
Figure 3a.
*
*
*
*
675
676
Figure 3b.
677
678
39
679
Figure S1
HG
DFl
AA
LD
HG
PR
TCA
AGgcp
BI
DVa
BSO
AGghc
MDl
AAN
AD
AM
DO
HP
NV
LDart
LDwil
LDlar
XC
LDwo
MM
HB
AS
MO
MDa
CS
DFa
AV
DVl
PA
HPA
SV
AGgar
CI
RI
CY
AG
RSA
IP
AGgpo
HC
LDasp
CC
AGggm
PG
AGgym
ASTl
PV
CQ
AST
ASTf
ASTm
AA
GON
CQ
EPI
CAL
MD
MEG
TA
PC
MGnw
GON
MMI
EPI
PR
MEG
AGgvm
LR
PA
APa
TCA
APl
AD
LR
AAN
RI
BI
AG
BSO
AM
SV
AMm
IP
AMmh
AV
DF
MO
DV
HC
AP
CS
PG
MME
XC
PEa
CC
CI
AS
HPA
HP
PV
PC
RSA
DO
CAL
CY
MM
HB
TA
NV
CF
TC
CFs
RS
MS1
MS2
RSh
RST
NS
CO
NT
TC
MME
CO
OF
MGow
OF
MG
MMI
MGyw
PEa
PEe
NS
CF
NT
RS
680
RST
MS1
MS2
Mon May 09 17:36:51 2011
40