Statistical Analyses
We used QIIME 2 version 2022.8 ; https://qiime2.org) to perform all
downstream statistical analyses. We first implemented thecore-metrics-phylogenetic function with sampling depth set to
2500 and a midpoint rooted phylogeny created with the alignmentand phylogeny functions in order to calculate multiple measures
of diversity. Alpha diversity metrics included observed features to
measure richness , Pielou’s evenness to measure species equitability ,
and the Shannon Diversity Index as a combined measure of richness and
evenness . Beta diversity metrics included Bray-Curtis Dissimilarity to
measure differences in microbial abundance and the Jaccard Index to
measure differences in species presence . We calculated these five
diversity measures in order to tease apart which aspects of diversity
(e.g. , bacterial presence vs. abundance) differed between our
groups of interest. We performed multivariate significance testing using
the longitudinal anova function for alpha diversity measures and
the diversity adonis function (multivariate PERMANOVA; for beta
diversity measures. These analyses contained the following variables:
island, species, their interaction, and sequencing plate. We visualized
beta diversity differences using principal coordinate analysis
implemented through the EMPeror plug-in . All figures were created using
the qiime2R and ggplot2 R packages.
We assigned taxonomy to representative ASVs by using a Naïve Bayes
classifier trained on Greengenes 13_8 reference sequences , as
described previously . We then characterized the taxonomic composition
of island fox and island spotted skunk gut microbiomes using thetaxa barplot function and identified differentially abundant taxa
within and between species using analysis of composition of microbiomes
(ANCOM; as implemented through the ancom function. ANCOM
overcomes statistical hurdles associated with compositional datasets by
calculating pairwise log-ratios between all taxa present within sampling
groups. It identifies which taxa consistently differ in abundance
between groups by calculating the test statistic, W, as the
number of times the null hypothesis of “no difference” is violated for
a particular taxon. As numerous differences are expected to arise
between host species, we performed this analysis at the taxonomic level
of class.