Statistical Analysis
We analyzed community alpha diversity using log (observed ASV richness)
and the Shannon Diversity Index. For modeling diversity, we used a
linear mixed model as implemented in the R package lme4 (Bateset al. 2007) and evaluated the importance of different variables,
taking into account the repeated sampling of some birds. We included
host age (SY or ASY), sex (male or female), year (2017 or 2018) and
sampling period (The Bahamas, first recapture in Michigan, and second
recapture in Michigan) as fixed effects and individual host as a random
effect. Using lmerTest (Kuznetsova et al. 2015), we
generated an ANOVA table from the linear model analysis, and
subsequently conducted a posteriori pairwise tests to compare the
three sampling periods. Additionally, we conducted a pairwise t-test to
assess differences between tagged and randomly caught birds within the
first recapture period of 2018. We tested for the influence of outliers,
which appeared to cause a deviation from normality in ASV richness
(Shapiro-Wilks test), by repeating the analyses with outliers omitted
and obtained very similar results. Finally, we tested for the effect of
individual-level random effects with a likelihood ratio test comparing
the model with and without individual ID as the random effect term, and
we found individuals did not consistently differ from each other.
To examine community differences in the microbiome (beta diversity), we
applied permutational multivariate analysis of variance (PERMANOVA) of
Bray-Curtis dissimilarity and unweighted UniFrac distances, calculated
among individual samples (Anderson 2014). For variables that showed
significant differences in the PERMANOVA analyses, we conducted ana posteriori test to assess differences in dispersion or
centroids using PERMDISP. We visualized beta diversity of significant
variables using non-metric multidimensional scaling (nMDS) ordination of
the Bray-Curtis measurements. Diversity calculations were implemented
using the R packages vegan and phyloseq (Oksanen et
al. 2007; McMurdie and Holmes 2013). Finally, to ask which taxa differ
in abundance across sampling periods, we implemented analysis of
composition of microbes (ANCOM) in QIIME2 (Mandal et al. 2105).
ANCOM utilizes the underlying structure of the microbiome data to
identify differentially abundant taxa between categories.