Abstract
Many microbial ecology experiments use sequencing data to measure a community’s
response to an experimental treatment. In a common experimental design, two units, one
control and one experimental, are sampled before and after the treatment is applied to the
experimental unit. The four resulting samples contain information about the dynamics of
organisms that respond to the treatment, but there are no analytical methods designed to
extract exactly this type of information from this configuration of samples. Here we present
an analytical method specifically designed to visualize and generate hypotheses about
microbial community dynamics in experiments that have paired samples and few or no replicates.
The method is based on the Poisson lognormal distribution, long studied in macroecology,
which we found accurately models the abundance distribution of taxa counts from
16S rRNA surveys. To demonstrate the method’s validity and potential, we analyzed an
experiment that measured the effect of crude oil on ocean microbial communities in microcosm.
Our method identified known oil degraders as well as two clades, Maricurvus and
Rhodobacteraceae, that responded to amendment with oil but do not include known oil
degraders. Our approach is sensitive to organisms that increased in abundance only in the
experimental unit but less sensitive to organisms that increased in both control and experimental
units, thus mitigating the role of “bottle effects”.
