Modeling Taxa-Abundance Distributions in Microbial Communities using Environmental Sequence Data

Abstract

We show that inferring the taxa-abundance distribution of a microbial community from small environmental samples alone is difficult. The difficulty stems from the disparity in scale between the number of genetic sequences that can be characterized and the number of individuals in communities that microbial ecologists aspire to describe. One solution is to calibrate and validate a mathematical model of microbial community assembly using the small samples and use the model to extrapolate to the taxa-abundance distribution for the population that is deemed to constitute a community. We demonstrate this approach by using a simple neutral community assembly model in which random immigrations, births, and deaths determine the relative abundance of taxa in a community. In doing so, we further develop a neutral theory to produce a taxa-abundance distribution for large communities that are typical of microbial communities. In addition, we highlight that the sampling uncertainties conspire to make the immigration rate calibrated on the basis of small samples very much higher than the true immigration rate. This scale dependence of model parameters is not unique to neutral theories; it is a generic problem in ecology that is particularly acute in microbial ecology. We argue that to overcome this, so that microbial ecologists can characterize large microbial communities from small samples, mathematical models that encapsulate sampling effects are required.

Assembly of seed-associated microbial communities within and across successive plant generations

“…This model estimates the rate of immigration (m) into the local community by comparing OTU frequency across multiple samples (Sloan et al 2007). The Sloan model predicts that abundant entities in the large community are widespread across samples because their dispersal could occur by chance, while rare members are more likely to be lost by ecological drift in the local community.…”

“…Goodness of fit of was assessed with root mean squared errors (RMSE) and compared with the fit of a binomial distribution model using the Akaike information criterion (AIC). The binomial distribution model is used for assessing the importance of random sampling on community structure in absence of drift and dispersal limitation (Sloan et al 2007). Calculations of 95% confidence intervals for the Sloan neutral community model were used to detect OTUs that were more and less frequent than expected.…”

“…To estimate the effect of dispersal and ecological drift on local microbial community structure, we compared the fit of the Sloan model to the fit of a binomial distribution model that is used for assessing the importance of random sampling on community structure (Sloan et al 2007). According to AIC, both models performed equally well (Table 2), therefore suggesting that dispersal, ecological drift and random sampling were involved in local assembly of seed-associated bacterial communities.…”

The rational exploration of microbial diversity

“…The microbial world is vast, with 10 30 organisms present on the Earth (Whitman et al, 1998), diverse (Curtis et al, 2002) and can only be observed through relatively tiny samples at discrete points in space and time (Sloan et al, 2007). Thus, it remains largely unexplored and is likely to remain so without quantitative statistical tools to estimate the magnitude of the task.…”

“…Without such an assessment, it is impossible to devise a rational strategy for the exploration of the microbial world, and until such a strategy is evinced, the systematic documentation of the microbial world will be impossible to plan and metagenomics studies will be conducted 'blind' (Curtis and Sloan, 2005). Taxa-abundance distributions (TADs) are central to this task (Curtis et al, 2002;Curtis and Sloan, 2005;Sloan et al, 2007), as estimates of richness alone do not give a realistic impression of the sequencing effort required to reveal the 'unseen' taxa . However, lack of data has confounded the best efforts to home in on plausible distributions.…”