RT Journal Article
SR Electronic
T1 The power and pitfalls of Dirichlet-multinomial mixture models for ecological count data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 045468
DO 10.1101/045468
A1 John D. O'Brien
A1 Nicholas Record
YR 2016
UL http://biorxiv.org/content/early/2016/03/24/045468.abstract
AB The Dirichlet-multinomial mixture model (DMM) and its extensions provide powerful new tools for understanding the ecological dynamics underlying taxa abundance. However, their capacity to effectiveness in capturing features of empirical data is not well understood. In this work, we use posterior predictive distributions (PPDs) to explore the performance of an infinite dimensional version of the DMM in three case studies, including two amplicon metagenomic time series. We avoid concentrating on fluctuations within individual taxa and instead focus on consortial-level dynamics with straight-forward methods for visualizing the output of the model that emphasizes this perspective. In each study, the DMM appears to perform well in organizing the data as a framwork for biological interpretation. Using the PPDs, we also observe several exceptions where the data appear to significantly depart from the model in ways that give useful ecological insight. We summarize the conclusions as a set of considerations for field researchers: problems with samples and taxa; relevant scales of ecological fluctuation; additional niches as outgroups; and possible violations of niche neutrality.