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\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 using the pick\_open\_reference\_otus.py workflow. All reads that failed to hit the reference database were clustered \emph{de 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.) analyses.  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 QIIME workflow script 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 in the Results/Discussion.  \subsubsection{Statistical Analyses}  Is there a difference between the space flies and the ground flies? Is that true for all genotypes? Is that true for all sources (poop, guts, bodies?) Is there a difference between genotypes? Is there a difference between guts and poop? Bodies and guts? Bodies and poop? Are there any taxa unique to the space samples? to the ground samples?  \subsubsection{PICRUSt metagenome prediction}