Data from: Wildlife fecal microbiota exhibit community stability across a semi-controlled longitudinal non-invasive sampling experiment

Wildlife microbiome studies are being used to assess microbial links with
animal health and habitat. The gold standard of sampling microbiomes
directly from captured animals is ideal for limiting potential abiotic
influences on microbiome composition, yet fails to leverage the many
benefits of non-invasive sampling. Application of microbiome-based
monitoring for rare, endangered, or elusive species creates a need to
non-invasively collect scat samples shed into the environment. Since
controlling sample age is not always possible, the potential influence of
time-associated abiotic factors was assessed. To accomplish this, we
analyzed partial 16S rRNA genes of fecal metagenomic DNA sampled
non-invasively from Rocky Mountain elk (Cervus canadensis) near
Yellowstone National Park. We sampled pellet piles from four different
elk, then aged them in a natural forest plot for 1, 3, 7, and 14 days,
with triplicate samples at each time point (i.e., a blocked, repeat
measures (longitudinal) study design). We compared microbiomes of each elk
through time with point estimates of diversity, bootstrapped hierarchical
clustering of samples, and a version of ANOVA–simultaneous components
analysis (ASCA) with PCA (LiMM-PCA) to assess the variance contributions
of time, individual and sample replication. Our results showed community
stability through days 0, 1, 3 and 7, with a modest but detectable change
in abundance in only 2 genera (Bacteroides and Sporobacter) at day 14. The
total variance explained by time in our LiMM-PCA model across the entire
2-week period was not statistically significant (p>0.195) and the
overall effect size was small (<10% variance) compared to the
variance explained by the individual animal (p<0.0005; 21% var.).
We conclude that non-invasive sampling of elk scat collected within one
week during winter/early spring provides a reliable approach to
characterize microbiome composition in a 16S rDNA survey and that sampled
individuals can be directly compared across unknown time points with
minimal bias. Further, point estimates of microbiome diversity were not
mechanistically affected by sample age. Our assessment of samples using
bootstrap hierarchical clustering produced clustering by animal (branches)
but not by sample age (nodes). These results support greater use of
non-invasive microbiome sampling to assess ecological patterns in animal
systems.