2.6 | Diversity Analysis
The number of ASVs (ASV richness) observed within a given community was
used as a measure of α-diversity. While α-diversity indicates within
community diversity, β-diversity indicates differences in community
composition between communities. We analyzed two β-diversity metrics:
Euclidean distances from a centred log-ratio transformed ASV dataset
(Gloor, Macklaim, Pawlowsky-Glahn, & Egozcue, 2017) and weighted
UniFrac distance (Lozupone, Hamady, Kelley, & Knight, 2007) of a
rarefied ASV dataset (34,280 reads/sample; rarefaction curve: Figure S1,
Supporting information). Both β-diversity metrics weight differences in
the relative abundance of bacterial taxa between communities, but
weighted UniFrac measures simultaneously weight bacteria phylogenetic
relatedness. Finally, we also considered β-dispersion, calculated as the
distance from each sample to the sample-set centroid in Euclidean or
weighted UniFrac space (Anderson, Ellingsen, & McArdle, 2006).
We evaluated the ability of spatiotemporal (Julian date, longitude and
distance from the population’s midpoint), host physiology (using age and
parental status as proxies), and habitat class relative area to predict
patterns in the described microbiome diversity measures. Julian date,
longitude, and longitudinal distance from the population’s centre were
scaled to a mean of 0 and a standard deviation of 1 prior to analysis.
The average longitude of all observations from the 2014 field season was
used as a proxy for the population’s centre. Age was coded as continuous
data in 1-year increments, with a linear and 2nd order
polynomial fit considered in analyses, given a curvilinear relationship
between gut microbiome diversity and age among humans (Yatsunenko et
al., 2012). Parental status, shown to affect the microbiome in other
systems (Amato et al., 2014), was coded as a dichotomous variable based
on whether adult females were nursing a foal (<1 year old
offspring) during the 2014 field survey.
For univariate diversity measures (α-diversity and β-dispersion), we
used a multi-model inference approach implemented in the R package
‘MuMIn’ (Bartoń, 2009). A starting global general linear model was
parameterized with the spatiotemporal, physiological, and environmental
terms described above, without interactions. We determined parameter
estimates and significance from conditional AICc averaging of models
which had a Δ AICc < 3 (Burnham & Anderson, 2002; Grueber,
Nakagawa, Laws, & Jamieson, 2011). Patterns in β-diversity were
analyzed using a backwards selection approach from PERMANOVA outputs,
with the global model outputs reported in the Supporting information
(vegan R package, adonis2() function, parameter by = ‘margin’; Oksanen
et al., 2019). Additionally, we ran a Mantel test to test for a
correlation between spatial separation and β-diversity measures, and a
univariate PERMANOVA to test for an effect of social band membership.