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\subsection{16S rDNA amplicon library construction and sequencing}  DNA was extracted from all samples with the MoBio Power Soil kit, according to the manufacturer's instructions, with a final elution of 50uL. The 16S rRNA genes were targeted for amplification via PCR, using primer constructs that consisted of the "universal" bacterial primers 515F and 806R, both modified by the addition of Illumina adaptor and an 8bp barcode sequence. (All primer sequences used for this study are shown in table XXX). The PCR amplification protocol included a 3 minute denaturation step at 94degC, 30 cycles of (30sec at 94degC, 45sec at 55degC, 90sec at 72degC), and a final 10min extension step of 72degC. PCR reactions were visualized on an agarose gel and purified using AMPure Beads (Agilent Technologies, Inc.) Purified PCR products were quantified with the Qubit® 2.0 Fluorometer (Life Technologies) HS dsDNA assay. All samples were pooled in eqiumolar ratios prior to sequencing on the Illumina MiSeq sequencer.   \subsection{Sequence Processing}  The pooled amplicon libraries were de-multiplexed using a custom script (available in a GitHub repository at https://github.com/gjospin/scripts/blob/master/Demul\_trim\_prep.pl). This script allows for 1 base pair difference per barcode to accommodate for sequencing errors. Paired reads are aligned and a consensus is computed using FLASH \cite{Magoc_2011} with maximum overlap of 120 and a minimum overlap of 70 (other parameters are left as default). FLASH output is reformatted (as separate FASTA files,) with appropriate naming conventions for QIIME v. 1.8.0 \cite{Caporaso_2010}.The resulting consensus sequences were analyzed using the QIIME pipeline.  \subsection{Microbial Diversity Analyses}  Unless stated otherwise, all microbial diversity analyses were performed using python scripts included in the QIIME v. 1.8.0 analytical pipeline \cite{Caporaso_2010}. An annotated IPython notebook is provided \href{here}{linktext}.  \subsubsection{OTU assignment and quality control}  Sequences for which the forward and reverse reads had enough overlap to be aligned to create a single consensus sequence were clustered against the greengenes (gg\_13\_8\_otus) OTUs clustered at 97\% similarity. similarity using the pick\_open\_reference\_otus.py workflow.  All reads that failed to hit the reference database were clustered \emph{de novo} novo}. An OTU table in the .biom format \cite{McDonald_2012} was then constructed and used as the starting point for all downstream analyses (although see "PICRUSt metagenome prediction" for the exception in which closed-reference OTU assignment was performed.)   OTUs that were classified as mitochondrial, chloroplast, or "Unassigned" at the Domain level were filtered from the OTU table using the filter\_taxa\_from\_otu\_table.py script. Chimera Slayer \cite{Haas_2011} as implemented in the identify_chimeric_seqs.py script was used to identify putative chimeric sequences, which were then filtered from both the .biom table and from the set of representative sequences used to represent each OTU. A new phylogenetic tree, with chimeric OTUs removed, was produced with the make_phylogeny.py script. Because we wish to compare the microbiomes with and without the inclusion of the intracellular bacterium, Wolbachia, the filter\_taxa\_from\_otu\_table.py script was also used to produce an additional .biom table with OTUs classified as Wolbachia removed.  \subsubsection{Microbial taxonomic diversity within and between samples.}  The core\_diversity\_analyses.py script is a workflow that implements a large suite of \textit{alpha} and \textit{beta} diversity analyses, including taxonomic composition, diversity and richness estimates, rarefaction curves, both phylogenetic (UniFrac) and distance-based (Bray-Curtis) clustering of samples in Principlal Coordinates Analyses, as well as several hypothesis tests about the differences between groups of samples and the individual microbial taxa that contribute to those differences. Only analyses that are relevant to the discussion will be highlighted here.  \subsubsection{Statistical Analyses}  \subsubsection{PICRUSt metagenome prediction}